TY - JOUR AU - Yang, Liuyang AU - Zhang, Xinzhang AU - Li, Zhenhui AU - Wang, Jian AU - Zhang, Yiwen AU - Shan, Liyu AU - Shi, Xin AU - Si, Yapeng AU - Wang, Shuailong AU - Li, Lin AU - Wu, Ping AU - Xu, Ning AU - Liu, Lizhu AU - Yang, Junfeng AU - Leng, Jinjun AU - Yang, Maolin AU - Zhang, Zhuorui AU - Wang, Junfeng AU - Dong, Xingxiang AU - Yang, Guangjun AU - Yan, Ruiying AU - Li, Wei AU - Liu, Zhimin AU - Li, Wenliang PY - 2025/4/24 TI - Localization and Classification of Adrenal Masses in Multiphase Computed Tomography: Retrospective Study JO - J Med Internet Res SP - e65937 VL - 27 KW - MA-YOLO model KW - multi-class adrenal masses KW - multi-phase CT images KW - localization KW - classification N2 - Background: The incidence of adrenal incidentalomas is increasing annually, and most types of adrenal masses require surgical intervention. Accurate classification of common adrenal masses based on tumor computed tomography (CT) images by radiologists or clinicians requires extensive experience and is often challenging, which increases the workload of radiologists and leads to unnecessary adrenal surgeries. There is an urgent need for a fully automated, noninvasive, and precise approach for the identification and accurate classification of common adrenal masses. Objective: This study aims to enhance diagnostic efficiency and transform the current clinical practice of preoperative diagnosis of adrenal masses. Methods: This study is a retrospective analysis that includes patients with adrenal masses who underwent adrenalectomy from January 1, 2021, to May 31, 2023, at Center 1 (internal dataset), and from January 1, 2016, to May 31, 2023, at Center 2 (external dataset). The images include unenhanced, arterial, and venous phases, with 21,649 images used for the training set, 2406 images used for the validation set, and 12,857 images used for the external test set. We invited 3 experienced radiologists to precisely annotate the images, and these annotations served as references. We developed a deep learning?based adrenal mass detection model, Multi-Attention YOLO (MA-YOLO), which can automatically localize and classify 6 common types of adrenal masses. In order to scientifically evaluate the model performance, we used a variety of evaluation metrics, in addition, we compared the improvement in diagnostic efficacy of 6 doctors after incorporating model assistance. Results: A total of 516 patients were included. In the external test set, the MA-YOLO model achieved an intersection over union of 0.838, 0.885, and 0.890 for the localization of 6 types of adrenal masses in unenhanced, arterial, and venous phase CT images, respectively. The corresponding mean average precision for classification was 0.885, 0.913, and 0.915, respectively. Additionally, with the assistance of this model, the classification diagnostic performance of 6 radiologists and clinicians for adrenal masses improved. Except for adrenal cysts, at least 1 physician significantly improved diagnostic performance for the other 5 types of tumors. Notably, in the categories of adrenal adenoma (for senior clinician: P=.04, junior radiologist: P=.01, and senior radiologist: P=.01) and adrenal cortical carcinoma (junior clinician: P=.02, junior radiologist: P=.01, and intermediate radiologist: P=.001), half of the physicians showed significant improvements after using the model for assistance. Conclusions: The MA-YOLO model demonstrates the ability to achieve efficient, accurate, and noninvasive preoperative localization and classification of common adrenal masses in CT examinations, showing promising potential for future applications. UR - https://www.jmir.org/2025/1/e65937 UR - http://dx.doi.org/10.2196/65937 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65937 ER - TY - JOUR AU - Faccin, Mauro AU - Geenen, Caspar AU - Happaerts, Michiel AU - Ombelet, Sien AU - Migambi, Patrick AU - André, Emmanuel PY - 2025/4/24 TI - Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study JO - JMIR Public Health Surveill SP - e68355 VL - 11 KW - tuberculosis KW - Rwanda KW - satellite image KW - TB KW - PCR testing KW - PCR KW - questionnaire KW - satellite KW - active case-finding KW - diagnostic KW - urban KW - Africa KW - TB screening KW - ACF KW - polymerase chain reaction N2 - Background: Early diagnosis and treatment initiation for tuberculosis (TB) not only improve individual patient outcomes but also reduce circulation within communities. Active case-finding (ACF), a cornerstone of TB control programs, aims to achieve this by targeting symptom screening and laboratory testing for individuals at high risk of infection. However, its efficiency is dependent on the ability to accurately identify such high-risk individuals and communities. The socioeconomic determinants of TB include difficulties in accessing health care and high within-household contact rates. These two determinants are common in the poorest neighborhoods of many sub-Saharan cities, where household crowding and lack of health-care access often coincide with malnutrition and HIV infection, further contributing to the TB burden. Objective: In this study, we propose a new approach to enhance the efficacy of ACF with focused interventions that target subpopulations at high risk. In particular, we focus on densely inhabited urban areas, where the proximity of individuals represents a proxy for poorer neighborhoods with enhanced contact rates. Methods: To this end, we used satellite imagery of the city of Kigali, Rwanda, and computer-vision algorithms to identify areas with a high density of small residential buildings. We subsequently screened 10,423 people living in these areas for TB exposure and symptoms and referred patients with a higher risk score for polymerase chain reaction testing. Results: We found autocorrelation in questionnaire scores for adjacent areas up to 782 meters. We removed the effects of this autocorrelation by aggregating the results based on H3 hexagons with a long diagonal of 1062 meters. Out of 324 people with high questionnaire scores, 202 underwent polymerase chain reaction testing, and 9 people had positive test results. We observed a weak but statistically significant correlation (r=0.28; P=.04) between the mean questionnaire score and the mean urban density of each hexagonal area. Conclusions: Nine previously undiagnosed individuals had positive test results through this screening program. This limited number may be due to low TB incidence in Kigali, Rwanda, during the study period. However, our results suggest that analyzing satellite imagery may allow the identification of urban areas where inhabitants are at higher risk of TB. These findings could be used to efficiently guide targeted ACF interventions. UR - https://publichealth.jmir.org/2025/1/e68355 UR - http://dx.doi.org/10.2196/68355 ID - info:doi/10.2196/68355 ER - TY - JOUR AU - Sakaguchi, Kota AU - Sakama, Reiko AU - Watari, Takashi PY - 2025/4/24 TI - Evaluating ChatGPT in Qualitative Thematic Analysis With Human Researchers in the Japanese Clinical Context and Its Cultural Interpretation Challenges: Comparative Qualitative Study JO - J Med Internet Res SP - e71521 VL - 27 KW - ChatGPT KW - large language models KW - qualitative research KW - sacred moment(s) KW - thematic analysis N2 - Background: Qualitative research is crucial for understanding the values and beliefs underlying individual experiences, emotions, and behaviors, particularly in social sciences and health care. Traditionally reliant on manual analysis by experienced researchers, this methodology requires significant time and effort. The advent of artificial intelligence (AI) technology, especially large language models such as ChatGPT (OpenAI), holds promise for enhancing qualitative data analysis. However, existing studies have predominantly focused on AI?s application to English-language datasets, leaving its applicability to non-English languages, particularly structurally and contextually complex languages such as Japanese, insufficiently explored. Objective: This study aims to evaluate the feasibility, strengths, and limitations of ChatGPT-4 in analyzing qualitative Japanese interview data by directly comparing its performance with that of experienced human researchers. Methods: A comparative qualitative study was conducted to assess the performance of ChatGPT-4 and human researchers in analyzing transcribed Japanese semistructured interviews. The analysis focused on thematic agreement rates, interpretative depth, and ChatGPT?s ability to process culturally nuanced concepts, particularly for descriptive and socio-culturally embedded themes. This study analyzed transcripts from 30 semistructured interviews conducted between February and March 2024 in an urban community hospital (Hospital A) and a rural university hospital (Hospital B) in Japan. Interviews centered on the theme of ?sacred moments? and involved health care providers and patients. Transcripts were digitized using NVivo (version 14; Lumivero) and analyzed using ChatGPT-4 with iterative prompts for thematic analysis. The results were compared with a reflexive thematic analysis performed by human researchers. Furthermore, to assess the adaptability and consistency of ChatGPT in qualitative analysis, Charmaz?s grounded theory and Pope?s five-step framework approach were applied. Results: ChatGPT-4 demonstrated high thematic agreement rates (>80%) with human researchers for descriptive themes such as ?personal experience of a sacred moment? and ?building relationships.? However, its performance declined for themes requiring deeper cultural and emotional interpretation, such as ?difficult to answer, no experience of sacred moments? and ?fate.? For these themes, agreement rates were approximately 30%, revealing significant limitations in ChatGPT?s ability to process context-dependent linguistic structures and implicit emotional expressions in Japanese. Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool in qualitative research, particularly for efficiently identifying descriptive themes within Japanese-language datasets. However, its limited capacity to interpret cultural and emotional nuances highlights the continued necessity of human expertise in qualitative analysis. These findings emphasize the complementary role of AI-assisted qualitative research and underscore the importance of further advancements in AI models tailored to non-English linguistic and cultural contexts. Future research should explore strategies to enhance AI?s interpretability, expand multilingual training datasets, and assess the applicability of emerging AI models in diverse cultural settings. In addition, ethical and legal considerations in AI-driven qualitative analysis require continued scrutiny. UR - https://www.jmir.org/2025/1/e71521 UR - http://dx.doi.org/10.2196/71521 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/71521 ER - TY - JOUR AU - Zhang, Yahan AU - Chun, Yi AU - Tu, Liping AU - Xu, Jiatuo PY - 2025/4/22 TI - Authors? Reply: The Anemia Risk Warning Model Based on a Noninvasive Method: Key Insights and Clarifications JO - JMIR Med Inform SP - e74333 VL - 13 KW - anemia KW - hemoglobin KW - spectroscopy KW - machine learning KW - risk warning model KW - Shapley Additive Explanation UR - https://medinform.jmir.org/2025/1/e74333 UR - http://dx.doi.org/10.2196/74333 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/74333 ER - TY - JOUR AU - Wei, Jiaqi AU - Zheng, Nana AU - Wu, Depei PY - 2025/4/22 TI - The Anemia Risk Warning Model Based on a Noninvasive Method: Key Insights and Clarifications JO - JMIR Med Inform SP - e73297 VL - 13 KW - anemia KW - hemoglobin KW - spectroscopy KW - machine learning KW - risk warning model KW - Shapley Additive Explanation UR - https://medinform.jmir.org/2025/1/e73297 UR - http://dx.doi.org/10.2196/73297 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/73297 ER - TY - JOUR AU - Billot, Maxime AU - Ounajim, Amine AU - Moens, Maarten AU - Goudman, Lisa AU - Deneuville, Jean-Philippe AU - Roulaud, Manuel AU - Nivole, Kévin AU - Many, Mathilde AU - Baron, Sandrine AU - Lorgeoux, Bertille AU - Bouche, Bénédicte AU - Lampert, Lucie AU - David, Romain AU - Rigoard, Philippe PY - 2025/4/16 TI - The Added Value of Digital Body Chart Pain Surface Assessment as an Objective Biomarker: Multicohort Study JO - J Med Internet Res SP - e62786 VL - 27 KW - chronic pain KW - neuropathic pain KW - mechanical pain KW - assessment tool KW - digital body chart KW - pain assessment KW - pain treatment KW - digital tool KW - quality of life KW - financial burdens KW - machine learning KW - pain management KW - digital health biomarker KW - pain typology KW - neuropathic KW - nociceptive N2 - Background: Although it has been well-documented that pain intensity alone is not sufficient to assess chronic pain, the objective pain surface encapsulated in a digital tool might present a major interest in the objective assessment of pain. Objective: This study aims to determine the potential added value of pain surface measurement by determining the correlation between pain surface and pain intensity in chronic pain patients. Methods: Two databases from observational prospective and retrospective longitudinal studies including patients with chronic pain were used in this research. Pain intensity was assessed by the Numeric Pain Rating Scale. Pain surface (cm²) and pain typology (neuropathic vs mechanical components) were measured by a specific pain mapping digital tool (PRISMap, Poitiers University Hospital). Patients were asked to draw their pain surface on a computerized tactile interface in a predetermined body (adapted from the patient?s BMI). A color code was used to represent pain intensity (very intense, intense, moderate, and low). Simple linear regression was used to assess the proportion of variance in pain surface explained by pain intensity. Results: The final analysis included 637 patients with chronic pain. The percentage of variance of the pain surface explained by pain intensity was 1.24% (R²=0.0124; 95% CI 0.11%-6.3%). In addition, 424 (66.6%) patients used more than 1 intensity or color, among whom 218 (34.2%) used 2 intensities or colors, 155 (24.3%) used 3 intensities or colors, and 51 (8%) used 4 intensities or colors. Conclusions: This study showed that pain intensity and pain surface provide complementary and distinct information that would help to improve pain assessment. Two-thirds of the cohort used 2 or more intensities to describe their pain. Combining pain intensity and pain surface should be strongly considered as a means of improving daily practice assessment of patients with chronic pain in primary and secondary care. Trial Registration: ClinicalTrials.gov NCT02964130; https://clinicaltrials.gov/study/NCT02964130?term=PREDIBACK&rank=2 UR - https://www.jmir.org/2025/1/e62786 UR - http://dx.doi.org/10.2196/62786 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62786 ER - TY - JOUR AU - Su, Zhengyuan AU - Jiang, Huadong AU - Yang, Ying AU - Hou, Xiangqing AU - Su, Yanli AU - Yang, Li PY - 2025/4/14 TI - Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach JO - J Med Internet Res SP - e67772 VL - 27 KW - suicide KW - crisis hotline KW - acoustic feature KW - machine learning KW - acoustics KW - suicide risk KW - artificial intelligence KW - feasibility KW - prediction models KW - hotline callers KW - voice N2 - Background: Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is constrained by factors such as lack of nonverbal communication cues, anonymity, time limits, and single-occasion intervention. Therefore, it is necessary to develop approaches, including acoustic features, for identifying the suicide risk among hotline callers early and quickly. Given the complicated features of sound, adopting artificial intelligence models to analyze callers? acoustic features is promising. Objective: In this study, we investigated the feasibility of using acoustic features to predict suicide risk in crisis hotline callers. We also adopted a machine learning approach to analyze the complex acoustic features of hotline callers, with the aim of developing suicide risk prediction models. Methods: We collected 525 suicide-related calls from the records of a psychological assistance hotline in a province in northwest China. Callers were categorized as low or high risk based on suicidal ideation, suicidal plans, and history of suicide attempts, with risk assessments verified by a team of 18 clinical psychology raters. A total of 164 clearly categorized risk recordings were analyzed, including 102 low-risk and 62 high-risk calls. We extracted 273 audio segments, each exceeding 2 seconds in duration, which were labeled by raters as containing suicide-related expressions for subsequent model training and evaluation. Basic acoustic features (eg, Mel Frequency Cepstral Coefficients, formant frequencies, jitter, shimmer) and high-level statistical function (HSF) features (using OpenSMILE [Open-Source Speech and Music Interpretation by Large-Space Extraction] with the ComParE 2016 configuration) were extracted. Four supervised machine learning algorithms (logistic regression, support vector machine, random forest, and extreme gradient boosting) were trained and evaluated using grouped 5-fold cross-validation and a test set, with performance metrics, including accuracy, F1-score, recall, and false negative rate. Results: The development of machine learning models utilizing HSF acoustic features has been demonstrated to enhance recognition performance compared to models based solely on basic acoustic features. The random forest classifier, developed with HSFs, achieved the best performance in detecting the suicide risk among the models evaluated (accuracy=0.75, F1-score=0.70, recall=0.76, false negative rate=0.24). Conclusions: The results of our study demonstrate the potential of developing artificial intelligence?based early warning systems using acoustic features for identifying the suicide risk among crisis hotline callers. Our work also has implications for employing acoustic features to identify suicide risk in salient voice contexts. UR - https://www.jmir.org/2025/1/e67772 UR - http://dx.doi.org/10.2196/67772 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67772 ER - TY - JOUR AU - Cho, Minseo AU - Park, Doeun AU - Choo, Myounglee AU - Han, Hyun Doug AU - Kim, Jinwoo PY - 2025/4/9 TI - Adolescent Self-Reflection Process Through Self-Recording on Multiple Health Metrics: Qualitative Study JO - J Med Internet Res SP - e62962 VL - 27 KW - self-recording KW - self-tracking KW - self-regulation KW - personal informatics KW - digital health KW - qualitative study KW - grounded theory KW - adolescents KW - teenagers KW - adolescent health KW - self-reflection KW - health metrics KW - behavior change KW - self-awareness KW - decision-making KW - mental health KW - behavioral health KW - health management KW - semi-structured interview N2 - Background: Self-recording is an effective behavior change technology that has long been used in diverse health contexts. Recent technological advancements have broadened its applications. While previous studies have explored its role and benefits in enhancing self-awareness and informed decision-making, relatively little attention has been given to its potential to address the multidimensional nature of health with various health metrics. Objective: This study investigates the process of self-recording in adolescent health, recognizing the connections between lifestyle behaviors and mental health. Specifically, we aim to incorporate both behavioral and emotional health metrics into the self-recording process. Grounded in self-regulation theory, we explore how adolescents record lifestyle behaviors and emotions, and how they inform and implement health management strategies. Methods: We conducted a qualitative study using the grounded theory methodology. Data were collected through individual semistructured interviews with 17 adolescents, who recorded their emotions and behaviors over 4 weeks using a prototype application. Analysis followed iterative phases of coding, constant comparison, and theme saturation. This process revealed how adolescents engage in self-recording for behaviors and emotions, as well as their failures and potential system support strategies. We further examined the relevance of the identified themes to theoretical constructs in self-regulation theory. Results: Under self-regulation theory, we gained insights into how adolescents manage their health through self-recording. The findings suggested variability in the self-recording process, in relation to specific health metrics of lifestyle behaviors and emotions. Adolescents focused on evaluating behaviors for management purposes while exploring the causes underlying emotional experiences. Throughout the health management, which involved modifying behavior or distancing from triggering factors, they monitored progress and outcomes, demonstrating a self-experimental approach. Uncertainty emerged as a barrier throughout the self-regulation process, suggesting that self-recording systems for adolescents should prioritize strategies to address these uncertainties. In addition, the self-recording system demonstrated interventional effects in aiding future planning and fostering a sense of relatedness among users. Conclusions: This study offers a theoretical framework for adolescents? self-recording process on diverse health metrics. By integrating self-regulation theory, we suggest a stepwise process from recording lifestyle behaviors and emotions to health management behaviors. Through exploring potential features and health-supportive effects, our findings contribute to the development of digital self-recording systems that address various health metrics in adolescent health. UR - https://www.jmir.org/2025/1/e62962 UR - http://dx.doi.org/10.2196/62962 UR - http://www.ncbi.nlm.nih.gov/pubmed/40202781 ID - info:doi/10.2196/62962 ER - TY - JOUR AU - Jin, Yudi AU - Zhao, Min AU - Su, Tong AU - Fan, Yanjia AU - Ouyang, Zubin AU - Lv, Fajin PY - 2025/4/8 TI - Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study JO - J Med Internet Res SP - e69864 VL - 27 KW - breast cancer KW - neoadjuvant chemotherapy KW - pathological complete response KW - survival risk KW - random survival forest N2 - Background: Breast cancer is one of the most common malignancies among women worldwide. Patients who do not achieve a pathological complete response (pCR) or a clinical complete response (cCR) post?neoadjuvant chemotherapy (NAC) typically have a worse prognosis compared to those who do achieve these responses. Objective: This study aimed to develop and validate a random survival forest (RSF) model to predict survival risk in patients with breast cancer who do not achieve a pCR or cCR post-NAC. Methods: We analyzed patients with no pCR/cCR post-NAC treated at the First Affiliated Hospital of Chongqing Medical University from January 2019 to 2023, with external validation in Duke University and Surveillance, Epidemiology, and End Results (SEER) cohorts. RSF and Cox regression models were compared using the time-dependent area under the curve (AUC), the concordance index (C-index), and risk stratification. Results: The study cohort included 306 patients with breast cancer, with most aged 40-60 years (204/306, 66.7%). The majority had invasive ductal carcinoma (290/306, 94.8%), with estrogen receptor (ER)+ (182/306, 59.5%), progesterone receptor (PR)? (179/306, 58.5%), and human epidermal growth factor receptor 2 (HER2)+ (94/306, 30.7%) profiles. Most patients presented with T2 (185/306, 60.5%), N1 (142/306, 46.4%), and M0 (295/306, 96.4%) staging (TNM meaning ?tumor, node, metastasis?), with 17.6% (54/306) experiencing disease progression during a median follow-up of 25.9 months (IQR 17.2-36.3). External validation using Duke (N=94) and SEER (N=2760) cohorts confirmed consistent patterns in age (40-60 years: 59/94, 63%, vs 1480/2760, 53.6%), HER2+ rates (26/94, 28%, vs 935/2760, 33.9%), and invasive ductal carcinoma prevalence (89/94, 95%, vs 2506/2760, 90.8%). In the internal cohort, the RSF achieved significantly higher time-dependent AUCs compared to Cox regression at 1-year (0.811 vs 0.763), 3-year (0.834 vs 0.783), and 5-year (0.810 vs 0.771) intervals (overall C-index: 0.803, 95% CI 0.747-0.859, vs 0.736, 95% CI 0.673-0.799). External validation confirmed robust generalizability: the Duke cohort showed 1-, 3-, and 5-year AUCs of 0.912, 0.803, and 0.776, respectively, while the SEER cohort maintained consistent performance with AUCs of 0.771, 0.729, and 0.702, respectively. Risk stratification using the RSF identified 25.8% (79/306) high-risk patients and a significantly reduced survival time (P<.001). Notably, the RSF maintained improved net benefits across decision thresholds in decision curve analysis (DCA); similar results were observed in external studies. The RSF model also showed promising performance across different molecular subtypes in all datasets. Based on the RSF predicted scores, patients were stratified into high- and low-risk groups, with notably poorer survival outcomes observed in the high-risk group compared to the low-risk group. Conclusions: The RSF model, based solely on clinicopathological variables, provides a promising tool for identifying high-risk patients with breast cancer post-NAC. This approach may facilitate personalized treatment strategies and improve patient management in clinical practice. UR - https://www.jmir.org/2025/1/e69864 UR - http://dx.doi.org/10.2196/69864 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69864 ER - TY - JOUR AU - Lim, De Ming AU - Connie, Tee AU - Goh, Ong Michael Kah AU - Saedon, ?Izzati Nor PY - 2025/4/8 TI - Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study JO - JMIR Aging SP - e65629 VL - 8 KW - model-based features KW - gait analysis KW - Parkinson disease KW - computer vision KW - support vector machine N2 - Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that affects motor coordination, leading to gait abnormalities. Early detection of PD is crucial for effective management and treatment. Traditional diagnostic methods often require invasive procedures or are performed when the disease has significantly progressed. Therefore, there is a need for noninvasive techniques that can identify early motor symptoms, particularly those related to gait. Objective: The study aimed to develop a noninvasive approach for the early detection of PD by analyzing model-based gait features. The primary focus is on identifying subtle gait abnormalities associated with PD using kinematic characteristics. Methods: Data were collected through controlled video recordings of participants performing the timed up and go (TUG) assessment, with particular emphasis on the turning phase. The kinematic features analyzed include shoulder distance, step length, stride length, knee and hip angles, leg and arm symmetry, and trunk angles. These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns. Results: The analysis of kinematic features during the turning phase of the TUG assessment revealed that individuals with PD exhibited subtle gait abnormalities, such as freezing of gait, reduced step length, and asymmetrical movements. The model-based features proved effective in differentiating between normal and PD-affected gait, demonstrating the potential of this approach in early detection. Conclusions: This study presents a promising noninvasive method for the early detection of PD by analyzing specific gait features during the turning phase of the TUG assessment. The findings suggest that this approach could serve as a sensitive and accurate tool for diagnosing and monitoring PD, potentially leading to earlier intervention and improved patient outcomes. UR - https://aging.jmir.org/2025/1/e65629 UR - http://dx.doi.org/10.2196/65629 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65629 ER - TY - JOUR AU - Jeremic, Danko AU - Navarro-Lopez, D. Juan AU - Jimenez-Diaz, Lydia PY - 2025/4/7 TI - Clinical Benefits and Risks of Antiamyloid Antibodies in Sporadic Alzheimer Disease: Systematic Review and Network Meta-Analysis With a Web Application JO - J Med Internet Res SP - e68454 VL - 27 KW - Alzheimer disease KW - antibodies KW - donanemab KW - aducanumab KW - lecanemab N2 - Background: Despite the increasing approval of antiamyloid antibodies for Alzheimer disease (AD), their clinical relevance and risk-benefit profile remain uncertain. The heterogeneity of AD and the limited availability of long-term clinical data make it difficult to establish a clear rationale for selecting one treatment over another. Objective: The aim of this work was to assess and compare the efficacy and safety of antiamyloid antibodies through an interactive online meta-analytic approach by performing conventional pair-wise meta-analyses and frequentist and Bayesian network meta-analyses of phase II and III clinical trial results. To achieve this, we developed AlzMeta.app 2.0, a freely accessible web application that enables researchers and clinicians to evaluate the relative and absolute risks and benefits of these therapies in real time, incorporating different prior choices and assumptions of baseline risks of disease progression and adverse events. Methods: We adhered to PRISMA-NMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for reporting of systematic reviews with network meta-analysis) and GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) guidelines for reporting and rating the certainty of evidence. Clinical trial reports (until September 30, 2024) were retrieved from PubMed, Google Scholar, and clinical trial databases (including ClinicalTrials.gov). Studies with <20 sporadic AD patients and a modified Jadad score <3 were excluded. Risk of bias was assessed with the RoB-2 tool. Relative risks and benefits have been expressed as risk ratios and standardized mean differences, with confidence, credible, and prediction intervals calculated for all outcomes. For significant results, the intervention effects were ranked in frequentist and Bayesian frameworks, and their clinical relevance was determined by the absolute risk per 1000 people and number needed to treat (NNT) for a wide range of control responses. Results: Among 7 treatments tested in 21,236 patients (26 studies with low risk of bias or with some concerns), donanemab was the best-ranked treatment on cognitive and functional measures, and it was almost 2 times more effective than aducanumab and lecanemab and significantly more beneficial than other treatments on the global (cognitive and functional) Clinical Dementia Rating Scale-Sum of Boxes (NNT=10, 95% CI 8-16). Special caution is required regarding cerebral edema and microbleeding due to the clinically relevant risks of edema for donanemab (NNT=8, 95% CI 5-16), aducanumab (NNT=10, 95% CI 6-17), and lecanemab (NNT=14, 95% CI 7-31), which may outweigh the benefits. Conclusions: Our results showed that donanemab is more effective and has a safety profile similar to aducanumab and lecanemab, highlighting the need for treatment options with improved safety. Potential bias may have been introduced in the included trials due to unblinding caused by frequent cerebral edema and microbleeds, as well as the impact of the COVID-19 pandemic. UR - https://www.jmir.org/2025/1/e68454 UR - http://dx.doi.org/10.2196/68454 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68454 ER - TY - JOUR AU - Lee, Heonyi AU - Kim, Yi-Jun AU - Kim, Jin-Hong AU - Kim, Soo-Kyung AU - Jeong, Tae-Dong PY - 2025/3/31 TI - Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study JO - J Med Internet Res SP - e63983 VL - 27 KW - algorithm KW - machine learning KW - therapeutic drug monitoring KW - vancomycin KW - area under curve KW - pharmacokinetics KW - vancomycin dosing N2 - Background: Vancomycin is commonly dosed using standard weight?based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive model that personalizes initial dosing based on patient-specific pharmacokinetic factors prior to administration may enhance target attainment and minimize the need for subsequent dose adjustments. Objective: This study aimed to develop and evaluate a machine learning (ML)?based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under the curve to minimum inhibitory concentration, thereby optimizing the initial vancomycin dosage. Methods: A retrospective cohort study was conducted using hospitalized patients who received intravenous vancomycin and underwent pharmacokinetic TDM consultation (n=415). The cohort was randomly divided into training and testing datasets in a 7:3 ratio, and multiple ML techniques were used to develop an algorithm for optimizing initial vancomycin dosing. The optimal algorithm, referred to as the OPTIVAN algorithm, was selected and validated using an external cohort (n=268). We evaluated the performance of 4 ML models: gradient boosting machine, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB). Additionally, a web-based clinical support tool was developed to facilitate real-time vancomycin TDM application in clinical practice. Results: The SVM algorithm demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.832 (95% CI 0.753-0.900) for the training dataset and 0.720 (95% CI 0.654-0.783) for the external validation dataset. The gradient boosting machine followed closely with AUROC scores of 0.802 (95% CI 0.667-0.857) for the training dataset and 0.689 (95% CI 0.596-0.733) for the validation dataset. In contrast, both XGB and RF exhibited relatively lower performance. XGB achieved AUROC values of 0.769 (95% CI 0.671-0.853) for the training set and 0.707 (95% CI 0.644-0.772) for the validation set, while RF recorded AUROC scores of 0.759 (95% CI 0.656-0.846) for the test dataset and 0.693 (95% CI 0.625-0.757) for the external validation set. The SVM model incorporated 7 covariates: age, BMI, glucose, blood urea nitrogen, estimated glomerular filtration rate, hematocrit, and daily dose per body weight. Subgroup analyses demonstrated consistent performance across different patient categories, such as renal function, sex, and BMI. A web-based TDM analysis tool was developed using the OPTIVAN algorithm. Conclusions: The OPTIVAN algorithm represents a significant advancement in personalized initial vancomycin dosing, addressing the limitations of current TDM practices. By optimizing the initial dose, this algorithm may reduce the need for subsequent dosage adjustments. The algorithm?s web-based app is easy to use, making it a practical tool for clinicians. This study highlights the potential of ML to enhance the effectiveness of vancomycin treatment. UR - https://www.jmir.org/2025/1/e63983 UR - http://dx.doi.org/10.2196/63983 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63983 ER - TY - JOUR AU - Wu, Tong AU - Wang, Yuting AU - Cui, Xiaoli AU - Xue, Peng AU - Qiao, Youlin PY - 2025/3/31 TI - AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study JO - JMIR Cancer SP - e69672 VL - 11 KW - artificial intelligence KW - AI KW - cervical cancer screening KW - transformation zone KW - diagnosis and early treatment KW - lightweight neural network N2 - Background: In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary diagnostic tool for identifying cervical lesions and guiding biopsies. The transformation zone (TZ) is where a stratified squamous epithelium develops from the metaplasia of simple columnar epithelium and is the most common site of precancerous lesions. However, inexperienced colposcopists may find it challenging to accurately identify the type and location of the TZ during a colposcopy examination. Objective: This study aims to present an artificial intelligence (AI) method for identifying the TZ to enhance colposcopy examination and evaluate its potential clinical application. Methods: The study retrospectively collected data from 3616 women who underwent colposcopy at 6 tertiary hospitals in China between 2019 and 2021. A dataset from 4 hospitals was collected for model conduction. An independent dataset was collected from the other 2 geographic hospitals to validate model performance. There is no overlap between the training and validation datasets. Anonymized digital records, including each colposcopy image, baseline clinical characteristics, colposcopic findings, and pathological outcomes, were collected. The classification model was proposed as a lightweight neural network with multiscale feature enhancement capabilities and designed to classify the 3 types of TZ. The pretrained FastSAM model was first implemented to identify the location of the new squamocolumnar junction for segmenting the TZ. Overall accuracy, average precision, and recall were evaluated for the classification and segmentation models. The classification performance on the external validation was assessed by sensitivity and specificity. Results: The optimal TZ classification model performed with 83.97% classification accuracy on the test set, which achieved average precision of 91.84%, 89.06%, and 95.62% for types 1, 2, and 3, respectively. The recall and mean average precision of the TZ segmentation model were 0.78 and 0.75, respectively. The proposed model demonstrated outstanding performance in predicting 3 types of the TZ, achieving the sensitivity with 95% CIs for TZ1, TZ2, and TZ3 of 0.78 (0.74-0.81), 0.81 (0.78-0.82), and 0.8 (0.74-0.87), respectively, with specificity with 95% CIs of 0.94 (0.92-0.96), 0.83 (0.81-0.86), and 0.91 (0.89-0.92), based on a comprehensive external dataset of 1335 cases from 2 of the 6 hospitals. Conclusions: Our proposed AI-based identification system classified the type of cervical TZs and delineated their location on multicenter, colposcopic, high-resolution images. The findings of this study have shown its potential to predict TZ types and specific regions accurately. It was developed as a valuable assistant to encourage precise colposcopic examination in clinical practice. UR - https://cancer.jmir.org/2025/1/e69672 UR - http://dx.doi.org/10.2196/69672 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69672 ER - TY - JOUR AU - Brown, Jeffrey AU - Mitchell, Zachary AU - Jiang, Albert Yu AU - Archdeacon, Ryan PY - 2025/3/28 TI - Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation JO - JMIR Form Res SP - e67861 VL - 9 KW - snore detection KW - snore tracking KW - machine learning KW - SleepWatch KW - Bodymatter KW - neural net KW - mobile device KW - smartphone KW - smartphone application KW - mobile health KW - sleep monitoring KW - sleep tracking KW - sleep apnea N2 - Background: High-quality sleep is essential for both physical and mental well-being. Insufficient or poor-quality sleep is linked to numerous health issues, including cardiometabolic diseases, mental health disorders, and increased mortality. Snoring?a prevalent condition?can disrupt sleep and is associated with disease states, including coronary artery disease and obstructive sleep apnea. Objective: The SleepWatch smartphone app (Bodymatter, Inc) aims to monitor and improve sleep quality and has snore detection capabilities that were built through a machine-learning process trained on over 60,000 acoustic events. This study evaluated the accuracy of the SleepWatch snore detection algorithm in a simulated real-world setting. Methods: The snore detection algorithm was tested by using 36 simulated snoring audio files derived from 18 participants. Each file simulated a snoring index between 30 and 600 snores per hour. Additionally, 9 files with nonsnoring sounds were tested to evaluate the algorithm?s capacity to avoid false positives. Sensitivity, specificity, and accuracy were calculated for each test, and results were compared by using Bland-Altman plots and Spearman correlation to assess the statistical association between detected and actual snores. Results: The SleepWatch algorithm showed an average sensitivity of 86.3% (SD 16.6%), an average specificity of 99.5% (SD 10.8%), and an average accuracy of 95.2% (SD 5.6%) across the snoring tests. The positive predictive value and negative predictive value were 98.9% (SD 2.6%) and 93.8% (SD 14.4%) respectively. The algorithm performed exceptionally well in avoiding false positives, with a specificity of 97.1% (SD 3.5%) for nonsnoring files. Inclusive of all snoring and nonsnore tests, the aggregated accuracy for all trials in this bench study was 95.6% (SD 5.3%). The Bland-Altman analysis indicated a mean bias of ?29.8 (SD 41.7) snores per hour, and the Spearman correlation analysis revealed a strong positive correlation (rs=0.974; P<.001) between detected and actual snore rates. Conclusions: The SleepWatch snore detection algorithm demonstrates high accuracy and compares favorably with other snore detection apps. Aside from its broader use in sleep monitoring, SleepWatch demonstrates potential as a tool for identifying individuals at risk for sleep-disordered breathing, including obstructive sleep apnea, on the basis of the snoring index. UR - https://formative.jmir.org/2025/1/e67861 UR - http://dx.doi.org/10.2196/67861 ID - info:doi/10.2196/67861 ER - TY - JOUR AU - Yang, Hao AU - Li, Jiaxi AU - Zhang, Chi AU - Sierra, Pazos Alejandro AU - Shen, Bairong PY - 2025/3/27 TI - Large Language Model?Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study JO - J Med Internet Res SP - e65537 VL - 27 KW - sepsis KW - knowledge graph KW - large language models KW - prompt engineering KW - real-world KW - GPT-4.0 N2 - Background: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing substantial challenges for traditional knowledge graph construction methods. The integration of large language models (LLMs) with real-world data offers a promising avenue to address these challenges and enhance the understanding and management of sepsis. Objective: This study aims to develop a comprehensive sepsis knowledge graph by leveraging the capabilities of LLMs, specifically GPT-4.0, in conjunction with multicenter clinical databases. The goal is to improve the understanding of sepsis and provide actionable insights for clinical decision-making. We also established a multicenter sepsis database (MSD) to support this effort. Methods: We collected clinical guidelines, public databases, and real-world data from 3 major hospitals in Western China, encompassing 10,544 patients diagnosed with sepsis. Using GPT-4.0, we used advanced prompt engineering techniques for entity recognition and relationship extraction, which facilitated the construction of a nuanced sepsis knowledge graph. Results: We established a sepsis database with 10,544 patient records, including 8497 from West China Hospital, 690 from Shangjin Hospital, and 357 from Tianfu Hospital. The sepsis knowledge graph comprises of 1894 nodes and 2021 distinct relationships, encompassing nine entity concepts (diseases, symptoms, biomarkers, imaging examinations, etc) and 8 semantic relationships (complications, recommended medications, laboratory tests, etc). GPT-4.0 demonstrated superior performance in entity recognition and relationship extraction, achieving an F1-score of 76.76 on a sepsis-specific dataset, outperforming other models such as Qwen2 (43.77) and Llama3 (48.39). On the CMeEE dataset, GPT-4.0 achieved an F1-score of 65.42 using few-shot learning, surpassing traditional models such as BERT-CRF (62.11) and Med-BERT (60.66). Building upon this, we compiled a comprehensive sepsis knowledge graph, comprising of 1894 nodes and 2021 distinct relationships. Conclusions: This study represents a pioneering effort in using LLMs, particularly GPT-4.0, to construct a comprehensive sepsis knowledge graph. The innovative application of prompt engineering, combined with the integration of multicenter real-world data, has significantly enhanced the efficiency and accuracy of knowledge graph construction. The resulting knowledge graph provides a robust framework for understanding sepsis, supporting clinical decision-making, and facilitating further research. The success of this approach underscores the potential of LLMs in medical research and sets a new benchmark for future studies in sepsis and other complex medical conditions. UR - https://www.jmir.org/2025/1/e65537 UR - http://dx.doi.org/10.2196/65537 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65537 ER - TY - JOUR AU - Hasegawa, Tatsuki AU - Kizaki, Hayato AU - Ikegami, Keisho AU - Imai, Shungo AU - Yanagisawa, Yuki AU - Yada, Shuntaro AU - Aramaki, Eiji AU - Hori, Satoko PY - 2025/3/27 TI - Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation JO - JMIR Med Inform SP - e65371 VL - 13 KW - systematic review KW - natural language processing KW - guideline updates KW - bidirectional encoder representations from transformer KW - screening model KW - literature KW - efficiency KW - updating systematic reviews KW - language model N2 - Background: A challenge in updating systematic reviews is the workload in screening the articles. Many screening models using natural language processing technology have been implemented to scrutinize articles based on titles and abstracts. While these approaches show promise, traditional models typically treat abstracts as uniform text. We hypothesize that selective training on specific abstract components could enhance model performance for systematic review screening. Objective: We evaluated the efficacy of a novel screening model that selects specific components from abstracts to improve performance and developed an automatic systematic review update model using an abstract component classifier to categorize abstracts based on their components. Methods: A screening model was created based on the included and excluded articles in the existing systematic review and used as the scheme for the automatic update of the systematic review. A prior publication was selected for the systematic review, and articles included or excluded in the articles screening process were used as training data. The titles and abstracts were classified into 5 categories (Title, Introduction, Methods, Results, and Conclusion). Thirty-one component-composition datasets were created by combining 5 component datasets. We implemented 31 screening models using the component-composition datasets and compared their performances. Comparisons were conducted using 3 pretrained models: Bidirectional Encoder Representations from Transformer (BERT), BioLinkBERT, and BioM- Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA). Moreover, to automate the component selection of abstracts, we developed the Abstract Component Classifier Model and created component datasets using this classifier model classification. Using the component datasets classified using the Abstract Component Classifier Model, we created 10 component-composition datasets used by the top 10 screening models with the highest performance when implementing screening models using the component datasets that were classified manually. Ten screening models were implemented using these datasets, and their performances were compared with those of models developed using manually classified component-composition datasets. The primary evaluation metric was the F10-Score weighted by the recall. Results: A total of 256 included articles and 1261 excluded articles were extracted from the selected systematic review. In the screening models implemented using manually classified datasets, the performance of some surpassed that of models trained on all components (BERT: 9 models, BioLinkBERT: 6 models, and BioM-ELECTRA: 21 models). In models implemented using datasets classified by the Abstract Component Classifier Model, the performances of some models (BERT: 7 models and BioM-ELECTRA: 9 models) surpassed that of the models trained on all components. These models achieved an 88.6% reduction in manual screening workload while maintaining high recall (0.93). Conclusions: Component selection from the title and abstract can improve the performance of screening models and substantially reduce the manual screening workload in systematic review updates. Future research should focus on validating this approach across different systematic review domains. UR - https://medinform.jmir.org/2025/1/e65371 UR - http://dx.doi.org/10.2196/65371 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65371 ER - TY - JOUR AU - Ackerhans, Sophia AU - Wehkamp, Kai AU - Petzina, Rainer AU - Dumitrescu, Daniel AU - Schultz, Carsten PY - 2025/3/26 TI - Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features JO - JMIR Form Res SP - e64266 VL - 9 KW - artificial intelligence KW - clinical decision support systems KW - explainable artificial intelligence KW - professional identity threat KW - health care KW - physicians KW - perceptions KW - professional identity N2 - Background: Artificial intelligence (AI)?based systems in medicine like clinical decision support systems (CDSSs) have shown promising results in health care, sometimes outperforming human specialists. However, the integration of AI may challenge medical professionals? identities and lead to limited trust in technology, resulting in health care professionals rejecting AI-based systems. Objective: This study aims to explore the impact of AI process design features on physicians? trust in the AI solution and on perceived threats to their professional identity. These design features involve the explainability of AI-based CDSS decision outcomes, the integration depth of the AI-generated advice into the clinical workflow, and the physician?s accountability for the AI system-induced medical decisions. Methods: We conducted a 3-factorial web-based between-subject scenario-based experiment with 292 medical students in their medical training and experienced physicians across different specialties. The participants were presented with an AI-based CDSS for sepsis prediction and prevention for use in a hospital. Each participant was given a scenario in which the 3 design features of the AI-based CDSS were manipulated in a 2×2×2 factorial design. SPSS PROCESS (IBM Corp) macro was used for hypothesis testing. Results: The results suggest that the explainability of the AI-based CDSS was positively associated with both trust in the AI system (?=.508; P<.001) and professional identity threat perceptions (?=.351; P=.02). Trust in the AI system was found to be negatively related to professional identity threat perceptions (?=?.138; P=.047), indicating a partially mediated effect on professional identity threat through trust. Deep integration of AI-generated advice into the clinical workflow was positively associated with trust in the system (?=.262; P=.009). The accountability of the AI-based decisions, that is, the system required a signature, was found to be positively associated with professional identity threat perceptions among the respondents (?=.339; P=.004). Conclusions: Our research highlights the role of process design features of AI systems used in medicine in shaping professional identity perceptions, mediated through increased trust in AI. An explainable AI-based CDSS and an AI-generated system advice, which is deeply integrated into the clinical workflow, reinforce trust, thereby mitigating perceived professional identity threats. However, explainable AI and individual accountability of the system directly exacerbate threat perceptions. Our findings illustrate the complex nature of the behavioral patterns of AI in health care and have broader implications for supporting the implementation of AI-based CDSSs in a context where AI systems may impact professional identity. UR - https://formative.jmir.org/2025/1/e64266 UR - http://dx.doi.org/10.2196/64266 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64266 ER - TY - JOUR AU - Oh, Mi-Young AU - Kim, Hee-Soo AU - Jung, Mi Young AU - Lee, Hyung-Chul AU - Lee, Seung-Bo AU - Lee, Mi Seung PY - 2025/3/19 TI - Machine Learning?Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study JO - J Med Internet Res SP - e58021 VL - 27 KW - machine learning KW - explainability KW - score KW - computation scoring system KW - Nonlinear computation KW - application KW - perioperative stroke KW - perioperative KW - stroke KW - efficiency KW - ML-based models KW - patient KW - noncardiac surgery KW - noncardiac KW - surgery KW - effectiveness KW - risk tool KW - risk KW - tool KW - real-world data N2 - Background: Machine learning (ML) has the potential to enhance performance by capturing nonlinear interactions. However, ML-based models have some limitations in terms of interpretability. Objective: This study aimed to develop and validate a more comprehensible and efficient ML-based scoring system using SHapley Additive exPlanations (SHAP) values. Methods: We developed and validated the Explainable Automated nonlinear Computation scoring system for Health (EACH) framework score. We developed a CatBoost-based prediction model, identified key features, and automatically detected the top 5 steepest slope change points based on SHAP plots. Subsequently, we developed a scoring system (EACH) and normalized the score. Finally, the EACH score was used to predict perioperative stroke. We developed the EACH score using data from the Seoul National University Hospital cohort and validated it using data from the Boramae Medical Center, which was geographically and temporally different from the development set. Results: When applied for perioperative stroke prediction among 38,737 patients undergoing noncardiac surgery, the EACH score achieved an area under the curve (AUC) of 0.829 (95% CI 0.753-0.892). In the external validation, the EACH score demonstrated superior predictive performance with an AUC of 0.784 (95% CI 0.694-0.871) compared with a traditional score (AUC=0.528, 95% CI 0.457-0.619) and another ML-based scoring generator (AUC=0.564, 95% CI 0.516-0.612). Conclusions: The EACH score is a more precise, explainable ML-based risk tool, proven effective in real-world data. The EACH score outperformed traditional scoring system and other prediction models based on different ML techniques in predicting perioperative stroke. UR - https://www.jmir.org/2025/1/e58021 UR - http://dx.doi.org/10.2196/58021 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58021 ER - TY - JOUR AU - MacKinnon, Ross Kinnon AU - Khan, Naail AU - Newman, M. Katherine AU - Gould, Ariel Wren AU - Marshall, Gin AU - Salway, Travis AU - Pullen Sansfaçon, Annie AU - Kia, Hannah AU - Lam, SH June PY - 2025/3/17 TI - Introducing Novel Methods to Identify Fraudulent Responses (Sampling With Sisyphus): Web-Based LGBTQ2S+ Mixed-Methods Study JO - J Med Internet Res SP - e63252 VL - 27 KW - sampling KW - bots KW - transgender KW - nonbinary KW - detransition KW - lesbian, gay, bisexual, and transgender KW - mobile phone N2 - Background: The myth of Sisyphus teaches about resilience in the face of life challenges. Detransition after an initial gender transition is an emerging experience that requires sensitive and community-driven research. However, there are significant complexities and costs that researchers must confront to collect reliable data to better understand this phenomenon, including the lack of a uniform definition and challenges with recruitment. Objective: This paper presents the sampling and recruitment methods of a new study on detransition-related phenomena among lesbian, gay, bisexual, transgender, queer, and 2-spirit (LGBTQ2S+) populations. It introduces a novel protocol for identifying and removing bot, scam, and ineligible responses from survey datasets and presents preliminary descriptive sociodemographic results of the sample. This analysis does not present gender-affirming health care outcomes. Methods: To attract a large and heterogeneous sample, 3 different study flyers were created in English, French, and Spanish. Between December 1, 2023, and May 1, 2024, these flyers were distributed to >615 sexual and gender minority organizations and gender care providers in the United States and Canada, and paid advertisements totaling >CAD $7400 (US $5551) were promoted on 5 different social media platforms. Although many social media promotions were rejected or removed, the advertisements reached >7.7 million accounts. Study website visitors were directed from 35 different traffic sources, with the top 5 being Facebook (3,577,520/7,777,218, 46%), direct link (2,255,393/7,777,218, 29%), Reddit (1,011,038/7,777,218, 13%), Instagram (466,633/7,777,218, 6%), and X (formerly known as Twitter; 233,317/7,777,218, 3%). A systematic protocol was developed to identify scam, nonsense, and ineligible responses and to conduct web-based Zoom video platform screening with select participants. Results: Of the 1377 completed survey responses, 957 (69.5%) were deemed eligible and included in the analytic dataset after applying the exclusion protocol and conducting 113 virtual screenings. The mean age of the sample was 25.87 (SD 7.77; median 24, IQR 21-29 years). A majority of the participants were White (Canadian, American, or of European descent; 748/950, 78.7%), living in the United States (704/957, 73.6%), and assigned female at birth (754/953, 79.1%). Many participants reported having a sexual minority identity, with more than half the sample (543/955, 56.8%) indicating plurisexual orientations, such as bisexual or pansexual identities. A minority of participants (108/955, 11.3%) identified as straight or heterosexual. When asked about their gender-diverse identities after stopping or reversing gender transition, 33.2% (318/957) reported being nonbinary, 43.2% (413/957) transgender, and 40.5% (388/957) identified as detransitioned. Conclusions: Despite challenges encountered during the study promotion and data collection phases, a heterogeneous sample of >950 eligible participants was obtained, presenting opportunities for future analyses to better understand these LGBTQ2S+ experiences. This study is among the first to introduce an innovative strategy to sample a hard-to-reach and equity-deserving group, and to present an approach to remove fraudulent responses. UR - https://www.jmir.org/2025/1/e63252 UR - http://dx.doi.org/10.2196/63252 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63252 ER - TY - JOUR AU - Olsavszky, Victor AU - Bazari, Mutaz AU - Dai, Ben Taieb AU - Olsavszky, Ana AU - Finkelmeier, Fabian AU - Friedrich-Rust, Mireen AU - Zeuzem, Stefan AU - Herrmann, Eva AU - Leipe, Jan AU - Michael, Alexander Florian AU - Westernhagen, von Hans AU - Ballo, Olivier PY - 2025/3/14 TI - Digital Translation Platform (Translatly) to Overcome Communication Barriers in Clinical Care: Pilot Study JO - JMIR Form Res SP - e63095 VL - 9 KW - language barriers KW - health care communication KW - medical app KW - real-time translation KW - medical translation N2 - Background: Language barriers in health care can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients? relatives for interpretation, compromising the quality of care. Objective: This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating (1) how health care professionals overcome language barriers and their acceptance of an on-demand video telephony platform, (2) the feasibility of the platform during medical consultations, and (3) identifying potential challenges for future development. Methods: The study included ethnographic interviews with health care professionals and an observational pilot to assess the use of the Translatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system?s backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that health care professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer nonprofessional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari, and Arabic. Results: Ethnographic research conducted by interviewing health care professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools such as Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5/16, 31%) and emergency (4/16, 25%). Challenges were identified around translator availability, with 23 (59%) total requests (N=39) going unanswered, which was further evidenced by user feedback. Conclusions: This pilot study demonstrates the feasibility of the Translatly platform in real-world health care settings. It shows the potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development. UR - https://formative.jmir.org/2025/1/e63095 UR - http://dx.doi.org/10.2196/63095 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63095 ER - TY - JOUR AU - Tzeng, Jing-Tong AU - Li, Jeng-Lin AU - Chen, Huan-Yu AU - Huang, Chu-Hsiang AU - Chen, Chi-Hsin AU - Fan, Cheng-Yi AU - Huang, Pei-Chuan Edward AU - Lee, Chi-Chun PY - 2025/3/13 TI - Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning?Based Audio Enhancement: Algorithm Development and Validation JO - JMIR AI SP - e67239 VL - 4 KW - respiratory sound KW - lung sound KW - audio enhancement KW - noise robustness KW - clinical applicability KW - artificial intelligence KW - AI N2 - Background: Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. In addition, predicting signals with only background noise could undermine user trust in the system. Objective: This study aimed to investigate the feasibility and effectiveness of incorporating a deep learning?based audio enhancement preprocessing step into automatic respiratory sound classification systems to improve robustness and clinical applicability. Methods: We conducted extensive experiments using various audio enhancement model architectures, including time-domain and time-frequency?domain approaches, in combination with multiple classification models to evaluate the effectiveness of the audio enhancement module in an automatic respiratory sound classification system. The classification performance was compared against the baseline noise injection data augmentation method. These experiments were carried out on 2 datasets: the International Conference in Biomedical and Health Informatics (ICBHI) respiratory sound dataset, which contains 5.5 hours of recordings, and the Formosa Archive of Breath Sound dataset, which comprises 14.6 hours of recordings. Furthermore, a physician validation study involving 7 senior physicians was conducted to assess the clinical utility of the system. Results: The integration of the audio enhancement module resulted in a 21.88% increase with P<.001 in the ICBHI classification score on the ICBHI dataset and a 4.1% improvement with P<.001 on the Formosa Archive of Breath Sound dataset in multi-class noisy scenarios. Quantitative analysis from the physician validation study revealed improvements in efficiency, diagnostic confidence, and trust during model-assisted diagnosis, with workflows that integrated enhanced audio leading to an 11.61% increase in diagnostic sensitivity and facilitating high-confidence diagnoses. Conclusions: Incorporating an audio enhancement algorithm significantly enhances the robustness and clinical utility of automatic respiratory sound classification systems, improving performance in noisy environments and fostering greater trust among medical professionals. UR - https://ai.jmir.org/2025/1/e67239 UR - http://dx.doi.org/10.2196/67239 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67239 ER - TY - JOUR AU - Dai, Zhang-Yi AU - Wang, Fu-Qiang AU - Shen, Cheng AU - Ji, Yan-Li AU - Li, Zhi-Yang AU - Wang, Yun AU - Pu, Qiang PY - 2025/3/11 TI - Accuracy of Large Language Models for Literature Screening in Thoracic Surgery: Diagnostic Study JO - J Med Internet Res SP - e67488 VL - 27 KW - accuracy KW - large language models KW - meta-analysis KW - literature screening KW - thoracic surgery N2 - Background: Systematic reviews and meta-analyses rely on labor-intensive literature screening. While machine learning offers potential automation, its accuracy remains suboptimal. This raises the question of whether emerging large language models (LLMs) can provide a more accurate and efficient approach. Objective: This paper evaluates the sensitivity, specificity, and summary receiver operating characteristic (SROC) curve of LLM-assisted literature screening. Methods: We conducted a diagnostic study comparing the accuracy of LLM-assisted screening versus manual literature screening across 6 thoracic surgery meta-analyses. Manual screening by 2 investigators served as the reference standard. LLM-assisted screening was performed using ChatGPT-4o (OpenAI) and Claude-3.5 (Anthropic) sonnet, with discrepancies resolved by Gemini-1.5 pro (Google). In addition, 2 open-source, machine learning?based screening tools, ASReview (Utrecht University) and Abstrackr (Center for Evidence Synthesis in Health, Brown University School of Public Health), were also evaluated. We calculated sensitivity, specificity, and 95% CIs for the title and abstract, as well as full-text screening, generating pooled estimates and SROC curves. LLM prompts were revised based on a post hoc error analysis. Results: LLM-assisted full-text screening demonstrated high pooled sensitivity (0.87, 95% CI 0.77-0.99) and specificity (0.96, 95% CI 0.91-0.98), with the area under the curve (AUC) of 0.96 (95% CI 0.94-0.97). Title and abstract screening achieved a pooled sensitivity of 0.73 (95% CI 0.57-0.85) and specificity of 0.99 (95% CI 0.97-0.99), with an AUC of 0.97 (95% CI 0.96-0.99). Post hoc revisions improved sensitivity to 0.98 (95% CI 0.74-1.00) while maintaining high specificity (0.98, 95% CI 0.94-0.99). In comparison, the pooled sensitivity and specificity of ASReview tool-assisted screening were 0.58 (95% CI 0.53-0.64) and 0.97 (95% CI 0.91-0.99), respectively, with an AUC of 0.66 (95% CI 0.62-0.70). The pooled sensitivity and specificity of Abstrackr tool-assisted screening were 0.48 (95% CI 0.35-0.62) and 0.96 (95% CI 0.88-0.99), respectively, with an AUC of 0.78 (95% CI 0.74-0.82). A post hoc meta-analysis revealed comparable effect sizes between LLM-assisted and conventional screening. Conclusions: LLMs hold significant potential for streamlining literature screening in systematic reviews, reducing workload without sacrificing quality. Importantly, LLMs outperformed traditional machine learning-based tools (ASReview and Abstrackr) in both sensitivity and AUC values, suggesting that LLMs offer a more accurate and efficient approach to literature screening. UR - https://www.jmir.org/2025/1/e67488 UR - http://dx.doi.org/10.2196/67488 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67488 ER - TY - JOUR AU - Han, Chuanliang AU - Zhang, Zhizhen AU - Lin, Yuchen AU - Huang, Shaojia AU - Mao, Jidong AU - Xiang, Weiwen AU - Wang, Fang AU - Liang, Yuping AU - Chen, Wufang AU - Zhao, Xixi PY - 2025/3/10 TI - Monitoring Sleep Quality Through Low ?-Band Activity in the Prefrontal Cortex Using a Portable Electroencephalogram Device: Longitudinal Study JO - J Med Internet Res SP - e67188 VL - 27 KW - EEG KW - electroencephalogram KW - alpha oscillation KW - prefrontal cortex KW - sleep KW - portable device N2 - Background: The pursuit of sleep quality has become an important aspect of people?s global quest for overall health. However, the objective neurobiological features corresponding to subjective perceptions of sleep quality remain poorly understood. Although previous studies have investigated the relationship between electroencephalogram (EEG) and sleep, the lack of longitudinal follow-up studies raises doubts about the reproducibility of their findings. Objective: Currently, there is a gap in research regarding the stable associations between EEG data and sleep quality assessed through multiple data collection sessions, which could help identify potential neurobiological targets related to sleep quality. Methods: In this study, we used a portable EEG device to collect resting-state prefrontal cortex EEG data over a 3-month follow-up period from 42 participants (27 in the first month, 25 in the second month, and 40 in the third month). Each month, participants? sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) to estimate their recent sleep quality. Results: We found that there is a significant and consistent positive correlation between low ? band activity in the prefrontal cortex and PSQI scores (r=0.45, P<.001). More importantly, this correlation remained consistent across all 3-month follow-up recordings (P<.05), regardless of whether we considered the same cohort or expanded the sample size. Furthermore, we discovered that the periodic component of the low ? band primarily contributed to this significant association with PSQI. Conclusions: These findings represent the first identification of a stable and reliable neurobiological target related to sleep quality through multiple follow-up sessions. Our results provide a solid foundation for future applications of portable EEG devices in monitoring sleep quality and screening for sleep disorders in a broad population. UR - https://www.jmir.org/2025/1/e67188 UR - http://dx.doi.org/10.2196/67188 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67188 ER - TY - JOUR AU - Fekete, Tibor János AU - Gy?rffy, Balázs PY - 2025/3/6 TI - MetaAnalysisOnline.com: Web-Based Tool for the Rapid Meta-Analysis of Clinical and Epidemiological Studies JO - J Med Internet Res SP - e64016 VL - 27 KW - statistics KW - pharmacology KW - treatment KW - epidemiology KW - fixed effect model KW - random effect model KW - hazard rate KW - response rate KW - clinical trial KW - funnel plot KW - z score plot N2 - Background: A meta-analysis is a quantitative, formal study design in epidemiology and clinical medicine that systematically integrates and quantitatively synthesizes findings from multiple independent studies. This approach not only enhances statistical power but also enables the exploration of effects across diverse populations and helps resolve controversies arising from conflicting studies. Objective: This study aims to develop and implement a user-friendly tool for conducting meta-analyses, addressing the need for an accessible platform that simplifies the complex statistical procedures required for evidence synthesis while maintaining methodological rigor. Methods: The platform available at MetaAnalysisOnline.com enables comprehensive meta-analyses through an intuitive web interface, requiring no programming expertise or command-line operations. The system accommodates diverse data types including binary (total and event numbers), continuous (mean and SD), and time-to-event data (hazard rates with CIs), while implementing both fixed-effect and random-effect models using established statistical approaches such as DerSimonian-Laird, Mantel-Haenszel, and inverse variance methods for effect size estimation and heterogeneity assessment. Results: In addition to statistical tests, graphical representations including the forest plot, the funnel plot, and the z score plot can be drawn. A forest plot is highly effective in illustrating heterogeneity and pooled results. The risk of publication bias can be revealed by a funnel plot. A z score plot provides a visual assessment of whether more research is needed to establish a reliable conclusion. All the discussed models and visualization options are integrated into the registration-free web-based portal. Leveraging MetaAnalysisOnline.com's capabilities, we examined treatment-related adverse events in patients with cancer receiving perioperative anti?PD-1 immunotherapy through a systematic review encompassing 10 studies with 8099 total participants. Meta-analysis revealed that anti?PD-1 therapy doubled the risk of adverse events (risk ratio 2.15, 95% CI 1.39-3.32), with significant between-study heterogeneity (I2=95%) and publication bias detected through the Egger test (P=.02). While these findings suggest increased toxicity associated with anti?PD-1 treatment, the z score analysis indicated that additional studies are needed for definitive conclusions. Conclusions: In summary, the web-based tool aims to bridge the void for clinical and life science researchers by offering a user-friendly alternative for the swift and reproducible meta-analysis of clinical and epidemiological trials. UR - https://www.jmir.org/2025/1/e64016 UR - http://dx.doi.org/10.2196/64016 UR - http://www.ncbi.nlm.nih.gov/pubmed/39928123 ID - info:doi/10.2196/64016 ER - TY - JOUR AU - El Kababji, Samer AU - Mitsakakis, Nicholas AU - Jonker, Elizabeth AU - Beltran-Bless, Ana-Alicia AU - Pond, Gregory AU - Vandermeer, Lisa AU - Radhakrishnan, Dhenuka AU - Mosquera, Lucy AU - Paterson, Alexander AU - Shepherd, Lois AU - Chen, Bingshu AU - Barlow, William AU - Gralow, Julie AU - Savard, Marie-France AU - Fesl, Christian AU - Hlauschek, Dominik AU - Balic, Marija AU - Rinnerthaler, Gabriel AU - Greil, Richard AU - Gnant, Michael AU - Clemons, Mark AU - El Emam, Khaled PY - 2025/3/5 TI - Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study JO - J Med Internet Res SP - e66821 VL - 27 KW - generative models KW - study accrual KW - recruitment KW - clinical trial replication KW - oncology KW - validation KW - simulated patient KW - simulation KW - retrospective KW - dataset KW - patient KW - artificial intelligence KW - machine learning N2 - Background: Insufficient patient accrual is a major challenge in clinical trials and can result in underpowered studies, as well as exposing study participants to toxicity and additional costs, with limited scientific benefit. Real-world data can provide external controls, but insufficient accrual affects all arms of a study, not just controls. Studies that used generative models to simulate more patients were limited in the accrual scenarios considered, replicability criteria, number of generative models, and number of clinical trials evaluated. Objective: This study aimed to perform a comprehensive evaluation on the extent generative models can be used to simulate additional patients to compensate for insufficient accrual in clinical trials. Methods: We performed a retrospective analysis using 10 datasets from 9 fully accrued, completed, and published cancer trials. For each trial, we removed the latest recruited patients (from 10% to 50%), trained a generative model on the remaining patients, and simulated additional patients to replace the removed ones using the generative model to augment the available data. We then replicated the published analysis on this augmented dataset to determine if the findings remained the same. Four different generative models were evaluated: sequential synthesis with decision trees, Bayesian network, generative adversarial network, and a variational autoencoder. These generative models were compared to sampling with replacement (ie, bootstrap) as a simple alternative. Replication of the published analyses used 4 metrics: decision agreement, estimate agreement, standardized difference, and CI overlap. Results: Sequential synthesis performed well on the 4 replication metrics for the removal of up to 40% of the last recruited patients (decision agreement: 88% to 100% across datasets, estimate agreement: 100%, cannot reject standardized difference null hypothesis: 100%, and CI overlap: 0.8-0.92). Sampling with replacement was the next most effective approach, with decision agreement varying from 78% to 89% across all datasets. There was no evidence of a monotonic relationship in the estimated effect size with recruitment order across these studies. This suggests that patients recruited earlier in a trial were not systematically different than those recruited later, at least partially explaining why generative models trained on early data can effectively simulate patients recruited later in a trial. The fidelity of the generated data relative to the training data on the Hellinger distance was high in all cases. Conclusions: For an oncology study with insufficient accrual with as few as 60% of target recruitment, sequential synthesis can enable the simulation of the full dataset had the study continued accruing patients and can be an alternative to drawing conclusions from an underpowered study. These results provide evidence demonstrating the potential for generative models to rescue poorly accruing clinical trials, but additional studies are needed to confirm these findings and to generalize them for other diseases. UR - https://www.jmir.org/2025/1/e66821 UR - http://dx.doi.org/10.2196/66821 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053790 ID - info:doi/10.2196/66821 ER - TY - JOUR AU - Kuipers, M. Ellen A. AU - Timmerman, G. Josien AU - van Det, J. Marc AU - Vollenbroek-Hutten, R. Miriam M. PY - 2025/3/5 TI - Feasibility and Links Between Emotions, Physical States, and Eating Behavior in Patients After Metabolic Bariatric Surgery: Experience Sampling Study JO - JMIR Form Res SP - e60486 VL - 9 KW - feasibility KW - experience sampling methodology KW - metabolic bariatric surgery KW - eating behavior KW - positive and negative affect KW - physical states KW - contextual factors KW - mobile phone N2 - Background: Lifestyle modification is essential to achieve and maintain successful outcomes after metabolic bariatric surgery (MBS). Emotions, physical states, and contextual factors are considered important determinants of maladaptive eating behavior, emphasizing their significance in understanding and addressing weight management. In this context, experience sampling methodology (ESM) offers promise for measuring lifestyle and behavior in the patient?s natural environment. Nevertheless, there is limited research on its feasibility and association among emotions and problematic eating behavior within the population after MBS. Objective: This study aimed to examine the feasibility of ESM in the population after MBS regarding emotions, physical states, contextual factors, and problematic eating behavior, and to explore the temporal association among these variables. Methods: An experience sampling study was conducted in which participants rated their current affect (positive and negative), physical states (disgust, boredom, fatigue, and hunger), contextual factors (where, with whom, and doing what), and problematic eating behavior (ie, grazing, dietary relapse, craving, and binge eating) via smartphone-based ESM questionnaires at 6 semirandom times daily for 14 consecutive days. Feasibility was operationalized as the study?s participation rate and completion rate, compliance in answering ESM questionnaires, and response rates per day. At the end of the study period, patients reflected on the feasibility of ESM in semistructured interviews. Generalized estimation equations were conducted to examine the temporal association between emotions, physical states, contextual factors, and problematic eating behavior. Results: In total, 25 out of 242 participants consented to participate, resulting in a study participation rate of 10.3%. The completion rate was 83%. Overall compliance was 57.4% (1072/1868), varying from 13% (11/84) to 89% (75/84) per participant. Total response rates per day decreased from 65% (90/138) to 52% (67/130) over the 14-day study period. According to the interviews, ESM was considered feasible and of added value. Temporal associations were found for hunger and craving (odds ratio 1.04, 95% CI 1.00-1.07; P=.03), and for positive affect and grazing (odds ratio 1.61, 95% CI 1.03-2.51; P=.04). Conclusions: In this exploratory study, patients after MBS were not amenable to participate. Only a small number of patients were willing to participate. However, those who participated found it feasible and expressed satisfaction with it. Temporal associations were identified between hunger and craving, as well as between positive affect and grazing. However, no clear patterns were observed among emotions, physical states, context, and problematic eating behaviors. UR - https://formative.jmir.org/2025/1/e60486 UR - http://dx.doi.org/10.2196/60486 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053719 ID - info:doi/10.2196/60486 ER - TY - JOUR AU - Pearkao, Chatkhane AU - Apiratwarakul, Korakot AU - Wicharit, Lerkiat AU - Potisopha, Wiphawadee AU - Jaitieng, Arunnee AU - Homvisetvongsa, Sukuman AU - Namwaing, Puthachad AU - Pudtuan, Peerapon PY - 2025/3/4 TI - Development of a Mobile App Game for Practicing Lung Exercises: Feasibility Study JO - JMIR Rehabil Assist Technol SP - e63512 VL - 12 KW - mobile app game KW - practice lung exercises KW - feasibility study KW - mobile phone KW - pulmo device KW - app N2 - Background: Chest injuries are a leading cause of death and disability, accounting for 10% of hospital admissions and 25% of injury-related deaths. About two-thirds of patients with thoracic injuries experience complications such as blood or air in the pleural space, causing lung deflation and poor gas exchange. Proper breathing management, using tools like incentive spirometers, improves lung function and recovery. However, there is a gap in mobile-based gaming apps designed for lung exercise, which could benefit both the general population and patients recovering from lung injuries. Objective: This research aimed to develop and evaluate a mobile app game for practicing lung exercises, accompanied by a prototype device called the Pulmo device. Methods: The study involved a sample group of 110 participants from the general public. It followed a research and development methodology comprising 4 steps. The research instruments included a mobile app game, a prototype lung exercise device, and questionnaires to assess users? satisfaction and the feasibility of both the app and the device. Results: The findings revealed that the participants demonstrated a high level of overall satisfaction with both the mobile app game and the prototype lung exercise device (mean 4.4, SD 0.4). The feasibility for the mobile app game and the prototype lung exercise device connected to the game was evaluated. The results indicated that the sample group perceived the overall feasibility to be at a high level (mean 4.4, SD 0.5). Conclusions: The research results reflected that the sample group believed the mobile app game for practicing lung exercises and the prototype device developed in this project have a high potential for practical application in promoting lung rehabilitation through gameplay. The mobile app game and the Pulmo device prototype received positive user feedback, indicating potential practical use; however, further validation is required among patients in need of pulmonary rehabilitation. UR - https://rehab.jmir.org/2025/1/e63512 UR - http://dx.doi.org/10.2196/63512 ID - info:doi/10.2196/63512 ER - TY - JOUR AU - Lu, Shao-Chi AU - Chen, Guang-Yuan AU - Liu, An-Sheng AU - Sun, Jen-Tang AU - Gao, Jun-Wan AU - Huang, Chien-Hua AU - Tsai, Chu-Lin AU - Fu, Li-Chen PY - 2025/2/28 TI - Deep Learning?Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study JO - J Med Internet Res SP - e67576 VL - 27 KW - cardiac arrest KW - emergency department KW - deep learning KW - computer vision KW - electrocardiogram N2 - Background: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)?based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging. Traditional EDCA prediction methods primarily rely on structured vital signs or electrocardiogram (ECG) signals, which require additional preprocessing or specialized devices. This study introduces a novel approach using image-based 12-lead ECG data obtained at ED triage, leveraging the inherent richness of visual ECG patterns to enhance prediction and integration into clinical workflows. Objective: This study aims to address the challenge of early detection of EDCA by developing an innovative deep learning model, the ECG-Image-Aware Network (EIANet), which uses 12-lead ECG images for early prediction of EDCA. By focusing on readily available triage ECG images, this research seeks to create a practical and accessible solution that seamlessly integrates into real-world ED workflows. Methods: For adult patients with EDCA (cases), 12-lead ECG images at ED triage were obtained from 2 independent data sets: National Taiwan University Hospital (NTUH) and Far Eastern Memorial Hospital (FEMH). Control ECGs were randomly selected from adult ED patients without cardiac arrest during the same study period. In EIANet, ECG images were first converted to binary form, followed by noise reduction, connected component analysis, and morphological opening. A spatial attention module was incorporated into the ResNet50 architecture to enhance feature extraction, and a custom binary recall loss (BRLoss) was used to balance precision and recall, addressing slight data set imbalance. The model was developed and internally validated on the NTUH-ECG data set and was externally validated on an independent FEMH-ECG data set. The model performance was evaluated using the F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). Results: There were 571 case ECGs and 826 control ECGs in the NTUH data set and 378 case ECGs and 713 control ECGs in the FEMH data set. The novel EIANet model achieved an F1-score of 0.805, AUROC of 0.896, and AUPRC of 0.842 on the NTUH-ECG data set with a 40% positive sample ratio. It achieved an F1-score of 0.650, AUROC of 0.803, and AUPRC of 0.678 on the FEMH-ECG data set with a 34.6% positive sample ratio. The feature map showed that the region of interest in the ECG was the ST segment. Conclusions: EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering an effective solution for early detection of high-risk cases in emergency settings. This approach may enhance the ability of health care professionals to make timely decisions, with the potential to improve patient outcomes by enabling earlier interventions for EDCA. UR - https://www.jmir.org/2025/1/e67576 UR - http://dx.doi.org/10.2196/67576 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053733 ID - info:doi/10.2196/67576 ER - TY - JOUR AU - Sakazaki, Hiroyuki AU - Noda, Masao AU - Dobashi, Yumi AU - Kuroda, Tatsuaki AU - Tsunoda, Reiko AU - Fushiki, Hiroaki PY - 2025/2/27 TI - Monitoring Nystagmus in a Patient With Vertigo Using a Commercial Mini-Infrared Camera and 3D Printer: Cost-Effectiveness Evaluation and Case Report JO - JMIR Form Res SP - e70015 VL - 9 KW - dizziness KW - vertigo KW - smartphone KW - BPPV KW - telemedicine KW - 3D-printer N2 - Background: Observing eye movements during episodic vertigo attacks is crucial for accurately diagnosing vestibular disorders. In clinical practice, many cases lack observable symptoms or clear findings during outpatient examinations, leading to diagnostic challenges. An accurate diagnosis is essential for timely treatment, as conditions such as benign paroxysmal positional vertigo (BPPV), Ménière?s disease, and vestibular migraine require different therapeutic approaches. Objective: This study aimed to develop and evaluate a cost-effective diagnostic tool that integrates a mini-infrared camera with 3D-printed goggles, enabling at-home recording of nystagmus during vertigo attacks. Methods: A commercially available mini-infrared camera (US $25) was combined with 3D-printed goggles (US $13) to create a system for recording eye movements in dark conditions. A case study was conducted on a male patient in his 40s who experienced recurrent episodic vertigo. Results: Initial outpatient evaluations, including oculomotor and vestibular tests using infrared Frenzel glasses, revealed no spontaneous or positional nystagmus. However, with the proposed system, the patient successfully recorded geotropic direction-changing positional nystagmus during a vertigo attack at home. The nystagmus was beating distinctly stronger on the left side down with 2.0 beats/second than the right side down with 1.2 beats/second. Based on the recorded videos, a diagnosis of lateral semicircular canal-type BPPV was made. Treatment with the Gufoni maneuver effectively alleviated the patient?s symptoms, confirming the diagnosis. The affordability and practicality of the device make it particularly suitable for telemedicine and emergency care applications, enabling patients in remote or underserved areas to receive accurate diagnoses. Conclusions: The proposed system demonstrates the feasibility and utility of using affordable, accessible technology for diagnosing vestibular disorders outside of clinical settings. By addressing key challenges, such as the absence of symptoms during clinical visits and the high costs associated with traditional diagnostic tools, this device offers a practical solution for real-time monitoring and accurate diagnosis. Its potential applications extend to telemedicine, emergency settings, and resource-limited environments. Future iterations that incorporate higher-resolution imaging and automated analysis could further enhance its diagnostic capabilities and usability across diverse patient populations. UR - https://formative.jmir.org/2025/1/e70015 UR - http://dx.doi.org/10.2196/70015 ID - info:doi/10.2196/70015 ER - TY - JOUR AU - Choo, Seungheon AU - Yoo, Suyoung AU - Endo, Kumiko AU - Truong, Bao AU - Son, Hi Meong PY - 2025/2/27 TI - Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study JO - JMIR Nursing SP - e63058 VL - 8 KW - artificial intelligence KW - patient education KW - therapy KW - computer-assisted KW - computer KW - understandable KW - accurate KW - understandability KW - automation KW - chatbots KW - bots KW - conversational agents KW - emotions KW - emotional KW - depression KW - depressive KW - anxiety KW - anxious KW - nervous KW - nervousness KW - empathy KW - empathetic KW - communication KW - interactions KW - frustrated KW - frustration KW - relationships N2 - Background: The health care sector faces a projected shortfall of 10 million workers by 2030. Artificial intelligence (AI) automation in areas such as patient education and initial therapy screening presents a strategic response to mitigate this shortage and reallocate medical staff to higher-priority tasks. However, current methods of evaluating early-stage health care AI chatbots are highly limited due to safety concerns and the amount of time and effort that goes into evaluating them. Objective: This study introduces a novel 3-bot method for efficiently testing and validating early-stage AI health care provider chatbots. To extensively test AI provider chatbots without involving real patients or researchers, various AI patient bots and an evaluator bot were developed. Methods: Provider bots interacted with AI patient bots embodying frustrated, anxious, or depressed personas. An evaluator bot reviewed interaction transcripts based on specific criteria. Human experts then reviewed each interaction transcript, and the evaluator bot?s results were compared to human evaluation results to ensure accuracy. Results: The patient-education bot?s evaluations by the AI evaluator and the human evaluator were nearly identical, with minimal variance, limiting the opportunity for further analysis. The screening bot?s evaluations also yielded similar results between the AI evaluator and human evaluator. Statistical analysis confirmed the reliability and accuracy of the AI evaluations. Conclusions: The innovative evaluation method ensures a safe, adaptable, and effective means to test and refine early versions of health care provider chatbots without risking patient safety or investing excessive researcher time and effort. Our patient-education evaluator bots could have benefitted from larger evaluation criteria, as we had extremely similar results from the AI and human evaluators, which could have arisen because of the small number of evaluation criteria. We were limited in the amount of prompting we could input into each bot due to the practical consideration that response time increases with larger and larger prompts. In the future, using techniques such as retrieval augmented generation will allow the system to receive more information and become more specific and accurate in evaluating the chatbots. This evaluation method will allow for rapid testing and validation of health care chatbots to automate basic medical tasks, freeing providers to address more complex tasks. UR - https://nursing.jmir.org/2025/1/e63058 UR - http://dx.doi.org/10.2196/63058 ID - info:doi/10.2196/63058 ER - TY - JOUR AU - Schenzel, A. Holly AU - Palmer, K. Allyson AU - Shah, B. Neel AU - Lawson, K. Donna AU - Fischer, M. Karen AU - Lapid, I. Maria AU - DeFoster, E. Ruth PY - 2025/2/26 TI - Weighted Blankets for Agitation in Hospitalized Patients with Dementia: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e57264 VL - 14 KW - dementia KW - hospitalized dementia patients KW - agitation KW - aggression KW - behaviors KW - sleep KW - weighted blankets KW - nonpharmacologic strategy KW - pilot study KW - inpatients KW - occupational therapy N2 - Background: There are limited therapies approved for the treatment of aggression and agitation in patients with dementia. While antipsychotics and benzodiazepines are commonly used, these medications have been associated with significant side effects and US Food and Drug Administration (FDA) boxed warnings. Weighted blankets have been associated with decreased anxiety and improved sleep. Weighted blankets are potentially a nonpharmacologic option to reduce agitation in hospitalized patients with dementia. Objective: The aim of this study is to investigate the effect of weighted blankets on aggression and agitation in hospitalized patients with dementia. Methods: A pilot study will be conducted on a total of 30 hospitalized patients with a documented clinical diagnosis of dementia and ongoing agitated behaviors admitted to a medicine or psychiatry service. Patients will be randomly allocated to receive either a weighted blanket for 3 nights or continued usual care. The primary outcome is the change in the observational version of the Cohen-Mansfield Agitation Inventory (CMAI-O) over the course of the 3-night study period. The secondary outcomes are changes in Edmonton Symptom Assessment System Revised (ESAS-r) and Clinical Global Impression (CGI) scores, hours of sleep, use of antipsychotics and benzodiazepines, and incidence of delirium. Identical study assessments will be completed for both the usual care and the weighted blanket study groups. At 5 study time points (baseline, postnight 1, postnight 2, postnight 3, and a final assessment 48-72 h after the last use of the weighted blanket), patients will be assessed with the CMAI-O, ESAS-r, and CGI tools. All assessments will be completed by the bedside nurse or patient care assistant caring for the patient each day. Within 2 to 4 weeks post discharge from the hospital, study coordinators will contact the patient?s legally authorized representative (LAR) to assess for continued use of the weighted blanket. Results: Enrollment of participants began on April 23, 2023. As of November 2024, a total of 24 participants have been enrolled in the study. Baseline characteristics of enrolled participants will be analyzed and reported upon completion of enrollment. We anticipate completing data collection by March 2026. Conclusions: The study will determine the effect of weighted blankets on agitation in hospitalized patients with dementia. Insights into the effect of weighted blankets on sleep will also be gained. The results of this study will be relevant in the setting of increasing numbers of older adults with dementia exhibiting agitation, leading to increased hospitalizations, caregiver burden, and health care costs. Trial Registration: ClinicalTrials.gov NCT03643991; http://clinicaltrials.gov/ct2/show/NCT03643991 International Registered Report Identifier (IRRID): DERR1-10.2196/57264 UR - https://www.researchprotocols.org/2025/1/e57264 UR - http://dx.doi.org/10.2196/57264 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57264 ER - TY - JOUR AU - Campagner, Andrea AU - Agnello, Luisa AU - Carobene, Anna AU - Padoan, Andrea AU - Del Ben, Fabio AU - Locatelli, Massimo AU - Plebani, Mario AU - Ognibene, Agostino AU - Lorubbio, Maria AU - De Vecchi, Elena AU - Cortegiani, Andrea AU - Piva, Elisa AU - Poz, Donatella AU - Curcio, Francesco AU - Cabitza, Federico AU - Ciaccio, Marcello PY - 2025/2/26 TI - Complete Blood Count and Monocyte Distribution Width?Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study JO - J Med Internet Res SP - e55492 VL - 27 KW - sepsis KW - medical machine learning KW - external validation KW - complete blood count KW - controllable AI KW - machine learning KW - artificial intelligence KW - development study KW - validation study KW - organ KW - organ dysfunction KW - detection KW - clinical signs KW - clinical symptoms KW - biomarker KW - diagnostic KW - machine learning model KW - sepsis detection KW - early detection KW - data distribution N2 - Background: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patient outcome. Laboratory medicine can play a crucial role by providing biomarkers whose alteration can be detected before the onset of clinical signs and symptoms. In particular, the relevance of monocyte distribution width (MDW) as a sepsis biomarker has emerged in the previous decade. However, despite encouraging results, MDW has poor sensitivity and positive predictive value when compared to other biomarkers. Objective: This study aims to investigate the use of machine learning (ML) to overcome the limitations mentioned earlier by combining different parameters and therefore improving sepsis detection. However, making ML models function in clinical practice may be problematic, as their performance may suffer when deployed in contexts other than the research environment. In fact, even widely used commercially available models have been demonstrated to generalize poorly in out-of-distribution scenarios. Methods: In this multicentric study, we developed ML models whose intended use is the early detection of sepsis on the basis of MDW and complete blood count parameters. In total, data from 6 patient cohorts (encompassing 5344 patients) collected at 5 different Italian hospitals were used to train and externally validate ML models. The models were trained on a patient cohort encompassing patients enrolled at the emergency department, and it was externally validated on 5 different cohorts encompassing patients enrolled at both the emergency department and the intensive care unit. The cohorts were selected to exhibit a variety of data distribution shifts compared to the training set, including label, covariate, and missing data shifts, enabling a conservative validation of the developed models. To improve generalizability and robustness to different types of distribution shifts, the developed ML models combine traditional methodologies with advanced techniques inspired by controllable artificial intelligence (AI), namely cautious classification, which gives the ML models the ability to abstain from making predictions, and explainable AI, which provides health operators with useful information about the models? functioning. Results: The developed models achieved good performance on the internal validation (area under the receiver operating characteristic curve between 0.91 and 0.98), as well as consistent generalization performance across the external validation datasets (area under the receiver operating characteristic curve between 0.75 and 0.95), outperforming baseline biomarkers and state-of-the-art ML models for sepsis detection. Controllable AI techniques were further able to improve performance and were used to derive an interpretable set of diagnostic rules. Conclusions: Our findings demonstrate how controllable AI approaches based on complete blood count and MDW may be used for the early detection of sepsis while also demonstrating how the proposed methodology can be used to develop ML models that are more resistant to different types of data distribution shifts. UR - https://www.jmir.org/2025/1/e55492 UR - http://dx.doi.org/10.2196/55492 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55492 ER - TY - JOUR AU - Hadar-Shoval, Dorit AU - Lvovsky, Maya AU - Asraf, Kfir AU - Shimoni, Yoav AU - Elyoseph, Zohar PY - 2025/2/24 TI - The Feasibility of Large Language Models in Verbal Comprehension Assessment: Mixed Methods Feasibility Study JO - JMIR Form Res SP - e68347 VL - 9 KW - large language models KW - verbal comprehension assessment KW - artificial intelligence KW - AI in psychodiagnostics KW - personalized intelligence tests KW - verbal comprehension index KW - Wechsler Adult Intelligence Scale KW - WAIS-III KW - psychological test validity KW - ethics in computerized cognitive assessment N2 - Background: Cognitive assessment is an important component of applied psychology, but limited access and high costs make these evaluations challenging. Objective: This study aimed to examine the feasibility of using large language models (LLMs) to create personalized artificial intelligence?based verbal comprehension tests (AI-BVCTs) for assessing verbal intelligence, in contrast with traditional assessment methods based on standardized norms. Methods: We used a within-participants design, comparing scores obtained from AI-BVCTs with those from the Wechsler Adult Intelligence Scale (WAIS-III) verbal comprehension index (VCI). In total, 8 Hebrew-speaking participants completed both the VCI and AI-BVCT, the latter being generated using the LLM Claude. Results: The concordance correlation coefficient (CCC) demonstrated strong agreement between AI-BVCT and VCI scores (Claude: CCC=.75, 90% CI 0.266-0.933; GPT-4: CCC=.73, 90% CI 0.170-0.935). Pearson correlations further supported these findings, showing strong associations between VCI and AI-BVCT scores (Claude: r=.84, P<.001; GPT-4: r=.77, P=.02). No statistically significant differences were found between AI-BVCT and VCI scores (P>.05). Conclusions: These findings support the potential of LLMs to assess verbal intelligence. The study attests to the promise of AI-based cognitive tests in increasing the accessibility and affordability of assessment processes, enabling personalized testing. The research also raises ethical concerns regarding privacy and overreliance on AI in clinical work. Further research with larger and more diverse samples is needed to establish the validity and reliability of this approach and develop more accurate scoring procedures. UR - https://formative.jmir.org/2025/1/e68347 UR - http://dx.doi.org/10.2196/68347 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68347 ER - TY - JOUR AU - Lewis, C. Callum AU - Taba, Melody AU - Allen, B. Tiffany AU - Caldwell, H.Y Patrina AU - Skinner, Rachel S. AU - Kang, Melissa AU - Henderson, Hamish AU - Bray, Liam AU - Borthwick, Madeleine AU - Collin, Philippa AU - McCaffery, Kirsten AU - Scott, M. Karen PY - 2025/2/20 TI - Authors? Reply: ?Adolescent Cocreation in Digital Health: From Passive Subjects to Active Stakeholders? JO - J Med Internet Res SP - e71897 VL - 27 KW - adolescent health KW - digital health literacy KW - adolescents KW - online health information KW - co-design KW - health education KW - eHealth literacy KW - social media UR - https://www.jmir.org/2025/1/e71897 UR - http://dx.doi.org/10.2196/71897 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/71897 ER - TY - JOUR AU - Yang, Alina PY - 2025/2/20 TI - Adolescent Cocreation in Digital Health: From Passive Subjects to Active Stakeholders JO - J Med Internet Res SP - e70020 VL - 27 KW - adolescent health KW - digital health literacy KW - adolescents KW - online health information KW - co-design KW - health education KW - eHealth literacy KW - social media UR - https://www.jmir.org/2025/1/e70020 UR - http://dx.doi.org/10.2196/70020 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/70020 ER - TY - JOUR AU - Mainer-Pearson, Graham AU - Stewart, Kurtis AU - Williams, Kim AU - Pawlovich, John AU - Graham, Scott AU - Riches, Linda AU - Cressman, Sonya AU - Ho, Kendall PY - 2025/2/19 TI - Estimating Patient and Family Costs and CO2 Emissions for Telehealth and In-Person Health Care Appointments in British Columbia, Canada: Geospatial Mixed Methods Study JO - J Med Internet Res SP - e56766 VL - 27 KW - virtual care KW - economic evaluation KW - patient costs KW - lost productivity KW - informal caregiving KW - out-of-pocket costs KW - environmental costs KW - geospatial KW - patient KW - family KW - CO2 KW - emission costs KW - health care KW - Canada KW - virtual service KW - emergency department KW - hospitalization KW - physician visit KW - consultation KW - sensitivity analysis KW - patient-paid KW - telehealth N2 - Background: Patients inevitably incur some cost for accessing health care, even in universal systems such as Canada. The COVID-19 pandemic dramatically shifted health care delivery from in-person to telehealth services, also shifting the proportion of costs offset by patients and their families by reducing the need to travel to in-person appointments. Objective: This study aimed to develop a method for estimating the costs patients and their families incur and CO2 emissions attributed to travel needed for emergency department (ED) visits, hospitalizations, and physician appointments. Methods: We present a method to evaluate the costs associated with in-person and telehealth care appointments from the perspective of patients, their families, and the environment. We used ED locations, road distances, and duration of appointment to account for costs paid by patients (ie, lost productivity, informal caregiving, and out-of-pocket expenses) attributed to travel to receive medical care. Costs to the environment were evaluated by calculating the amount of CO2 emitted per medical visit. Using our costs calculated per visit, we apply our method to calculate total patient costs for a simulated population over 1 year. Results: Our method estimates that patients in British Columbia pay up to $300 (2023 CAD, CAD $1=US $0.86) on average to attend an in-person ED visit, depending on where they live; $166 may be attributed to lost productivity, $83 to informal caregiving, and $50 to out-of-pocket expenses. These estimates are higher than most observed cost estimates. In addition, avoiding in-person care diverts up to 13 kg of CO2 per medical visit, depending on the distance and frequency of travel to appointments. This translates to up to $0.70 in carbon costs per visit, or cumulatively $44,120 per year in British Columbia, conventionally not included in patient cost estimates. Conclusions: We present a novel method for estimating patient-incurred costs and CO2 emissions from accessing health care and apply it to estimate that every year, patients in British Columbia pay upwards of 30 million dollars to access health care services, primarily for medical travel. Our method adds to the economic evaluation literature by providing a more comprehensive and context-modifiable calculation of patient costs that will allow for more informed decision-making regarding health care services. UR - https://www.jmir.org/2025/1/e56766 UR - http://dx.doi.org/10.2196/56766 UR - http://www.ncbi.nlm.nih.gov/pubmed/39969971 ID - info:doi/10.2196/56766 ER - TY - JOUR AU - Zhang, Yahan AU - Chun, Yi AU - Fu, Hongyuan AU - Jiao, Wen AU - Bao, Jizhang AU - Jiang, Tao AU - Cui, Longtao AU - Hu, Xiaojuan AU - Cui, Ji AU - Qiu, Xipeng AU - Tu, Liping AU - Xu, Jiatuo PY - 2025/2/14 TI - A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study JO - JMIR Med Inform SP - e64204 VL - 13 KW - anemia KW - hemoglobin KW - spectroscopy KW - machine learning KW - risk warning model KW - Shapley additive explanation N2 - Background: Anemia is a global public health issue causing symptoms such as fatigue, weakness, and cognitive decline. Furthermore, anemia is associated with various diseases and increases the risk of postoperative complications and mortality. Frequent invasive blood tests for diagnosis also pose additional discomfort and risks to patients. Objective: This study aims to assess the facial spectral characteristics of patients with anemia and to develop a predictive model for anemia risk using machine learning approaches. Methods: Between August 2022 and September 2023, we collected facial image data from 78 anemic patients who met the inclusion criteria from the Hematology Department of Shanghai Hospital of Traditional Chinese Medicine. Between March 2023 and September 2023, we collected data from 78 healthy adult participants from Shanghai Jiading Community Health Center and Shanghai Gaohang Community Health Center. A comprehensive statistical analysis was performed to evaluate differences in spectral characteristics between the anemic patients and healthy controls. Then, we used 10 different machine learning algorithms to create a predictive model for anemia. The least absolute shrinkage and selection operator was used to analyze the predictors. We integrated multiple machine learning classification models to identify the optimal model and developed Shapley additive explanations (SHAP) for personalized risk assessment. Results: The study identified significant differences in facial spectral features between anemic patients and healthy controls. The support vector machine classifier outperformed other classification models, achieving an accuracy of 0.875 (95% CI 0.825-0.925) for distinguishing between the anemia and healthy control groups. In the SHAP interpretation of the model, forehead-570 nm, right cheek-520 nm, right zygomatic-570 nm, jaw-570 nm, and left cheek-610 nm were the features with the highest contributions. Conclusions: Facial spectral data demonstrated clinical significance in anemia diagnosis, and the early warning model for anemia risk constructed based on spectral information demonstrated a high accuracy rate. UR - https://medinform.jmir.org/2025/1/e64204 UR - http://dx.doi.org/10.2196/64204 ID - info:doi/10.2196/64204 ER - TY - JOUR AU - Kottlors, Jonathan AU - Hahnfeldt, Robert AU - Görtz, Lukas AU - Iuga, Andra-Iza AU - Fervers, Philipp AU - Bremm, Johannes AU - Zopfs, David AU - Laukamp, R. Kai AU - Onur, A. Oezguer AU - Lennartz, Simon AU - Schönfeld, Michael AU - Maintz, David AU - Kabbasch, Christoph AU - Persigehl, Thorsten AU - Schlamann, Marc PY - 2025/2/13 TI - Large Language Models?Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study JO - J Med Internet Res SP - e48328 VL - 27 KW - artificial intelligence KW - radiology KW - report KW - large language model KW - text-based augmented supporting system KW - mechanical thrombectomy KW - GPT KW - stroke KW - decision-making KW - thrombectomy KW - imaging KW - model KW - machine learning KW - ischemia N2 - Background: The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts of text, enabling humanlike and semantical responses to text-based inputs and requests. Foreshadowing numerous possible applications in various fields, the potential of such tools for medical data integration and clinical decision-making is not yet clear. Objective: In this study, we investigate the potential of LLMs in report-based medical decision-making on the example of acute ischemic stroke (AIS), where clinical and image-based information may indicate an immediate need for mechanical thrombectomy (MT). The purpose was to elucidate the feasibility of integrating radiology report data and other clinical information in the context of therapy decision-making using LLMs. Methods: A hundred patients with AIS were retrospectively included, for which 50% (50/100) was indicated for MT, whereas the other 50% (50/100) was not. The LLM was provided with the computed tomography report, information on neurological symptoms and onset, and patients? age. The performance of the AI decision-making model was compared with an expert consensus regarding the binary determination of MT indication, for which sensitivity, specificity, and accuracy were calculated. Results: The AI model had an overall accuracy of 88%, with a specificity of 96% and a sensitivity of 80%. The area under the curve for the report-based MT decision was 0.92. Conclusions: The LLM achieved promising accuracy in determining the eligibility of patients with AIS for MT based on radiology reports and clinical information. Our results underscore the potential of LLMs for radiological and medical data integration. This investigation should serve as a stimulus for further clinical applications of LLMs, in which this AI should be used as an augmented supporting system for human decision-making. UR - https://www.jmir.org/2025/1/e48328 UR - http://dx.doi.org/10.2196/48328 UR - http://www.ncbi.nlm.nih.gov/pubmed/39946168 ID - info:doi/10.2196/48328 ER - TY - JOUR AU - Bhak, Youngmin AU - Lee, Ho Yu AU - Kim, Joonhyung AU - Lee, Kiwon AU - Lee, Daehwan AU - Jang, Chan Eun AU - Jang, Eunjeong AU - Lee, Seungkyu Christopher AU - Kang, Seok Eun AU - Park, Sehee AU - Han, Wook Hyun AU - Nam, Min Sang PY - 2025/2/7 TI - Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach JO - JMIR Med Inform SP - e55825 VL - 13 KW - multimodal deep learning KW - chronic kidney disease KW - fundus image KW - saliency map KW - urine dipstick N2 - Background: Chronic kidney disease (CKD) is a prevalent condition with significant global health implications. Early detection and management are critical to prevent disease progression and complications. Deep learning (DL) models using retinal images have emerged as potential noninvasive screening tools for CKD, though their performance may be limited, especially in identifying individuals with proteinuria and in specific subgroups. Objective: We aim to evaluate the efficacy of integrating retinal images and urine dipstick data into DL models for enhanced CKD diagnosis. Methods: The 3 models were developed and validated: eGFR-RIDL (estimated glomerular filtration rate?retinal image deep learning), eGFR-UDLR (logistic regression using urine dipstick data), and eGFR-MMDL (multimodal deep learning combining retinal images and urine dipstick data). All models were trained to predict an eGFR<60 mL/min/1.73 m², a key indicator of CKD, calculated using the 2009 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. This study used a multicenter dataset of participants aged 20?79 years, including a development set (65,082 people) and an external validation set (58,284 people). Wide Residual Networks were used for DL, and saliency maps were used to visualize model attention. Sensitivity analyses assessed the impact of numerical variables. Results: eGFR-MMDL outperformed eGFR-RIDL in both the test and external validation sets, with area under the curves of 0.94 versus 0.90 and 0.88 versus 0.77 (P<.001 for both, DeLong test). eGFR-UDLR outperformed eGFR-RIDL and was comparable to eGFR-MMDL, particularly in the external validation. However, in the subgroup analysis, eGFR-MMDL showed improvement across all subgroups, while eGFR-UDLR demonstrated no such gains. This suggested that the enhanced performance of eGFR-MMDL was not due to urine data alone, but rather from the synergistic integration of both retinal images and urine data. The eGFR-MMDL model demonstrated the best performance in individuals younger than 65 years or those with proteinuria. Age and proteinuria were identified as critical factors influencing model performance. Saliency maps indicated that urine data and retinal images provide complementary information, with urine offering insights into retinal abnormalities and retinal images, particularly the arcade vessels, being key for predicting kidney function. Conclusions: The MMDL model integrating retinal images and urine dipstick data show significant promise for noninvasive CKD screening, outperforming the retinal image?only model. However, routine blood tests are still recommended for individuals aged 65 years and older due to the model?s limited performance in this age group. UR - https://medinform.jmir.org/2025/1/e55825 UR - http://dx.doi.org/10.2196/55825 ID - info:doi/10.2196/55825 ER - TY - JOUR AU - Borhany, Hojjat PY - 2025/2/4 TI - Converting Organic Municipal Solid Waste Into Volatile Fatty Acids and Biogas: Experimental Pilot and Batch Studies With Statistical Analysis JO - JMIRx Med SP - e50458 VL - 6 KW - multistep fermentation KW - specific methane production KW - anaerobic digestion KW - kinetics study KW - biochar KW - first-order KW - modified Gompertz KW - mass balance KW - waste management KW - environment sustainability N2 - Background: Italy can augment its profit from biorefinery products by altering the operation of digesters or different designs to obtain more precious bioproducts like volatile fatty acids (VFAs) than biogas from organic municipal solid waste. In this context, recognizing the process stability and outputs through operational interventions and its technical and economic feasibility is a critical issue. Hence, this study involves an anaerobic digester in Treviso in northern Italy. Objective: This research compares a novel line, consisting of pretreatment, acidogenic fermentation, and anaerobic digestion, with single-step anaerobic digestion regarding financial profit and surplus energy. Therefore, a mass flow model was created and refined based on the outputs from the experimental and numerical studies. These studies examine the influence of hydraulic retention time (HRT), pretreatment, biochar addition, and fine-tuned feedstock/inoculum (FS/IN) ratio on bioproducts and operational parameters. Methods: VFA concentration, VFA weight ratio distribution, and biogas yield were quantified by gas chromatography. A t test was then conducted to analyze the significance of dissimilar HRTs in changing the VFA content. Further, a feasible biochar dosage was identified for an assumed FS/IN ratio with an adequately long HRT using the first-order rate model. Accordingly, the parameters for a mass flow model were adopted for 70,000 population equivalents to determine the payback period and surplus energy for two scenarios. We also explored the effectiveness of amendments in improving the process kinetics. Results: Both HRTs were identical concerning the ratio of VFA/soluble chemical oxygen demand (0.88 kg/kg) and VFA weight ratio distribution: mainly, acetic acid (40%), butyric acid (24%), and caproic acid (17%). However, a significantly higher mean VFA content was confirmed for an HRT of 4.5 days than the quantity for an HRT of 3 days (30.77, SD 2.82 vs 27.66, SD 2.45 g?soluble chemical oxygen demand/L), using a t test (t8=?2.68; P=.03; CI=95%). In this research, 83% of the fermented volatile solids were converted into biogas to obtain a specific methane (CH4) production of 0.133 CH4-Nm3/kg?volatile solids. While biochar addition improved only the maximum methane content by 20% (86% volumetric basis [v/v]), the FS/IN ratio of 0.3 volatile solid basis with thermal plus fermentative pretreatment improved the hydrolysis rate substantially (0.57 vs 0.07, 1/d). Furthermore, the biochar dosage of 0.12 g-biochar/g?volatile solids with an HRT of 20 days was identified as a feasible solution. Principally, the payback period for our novel line would be almost 2 years with surplus energy of 2251 megajoules [MJ] per day compared to 45 years and 21,567 MJ per day for single-step anaerobic digestion. Conclusions: This research elaborates on the advantage of the refined novel line over the single-step anaerobic digestion and confirms its financial and technical feasibility. Further, changing the HRT and other amendments significantly raised the VFA concentration and the process kinetics and stability. UR - https://xmed.jmir.org/2025/1/e50458 UR - http://dx.doi.org/10.2196/50458 ID - info:doi/10.2196/50458 ER - TY - JOUR AU - Baker, Venetia AU - Mulwa, Sarah AU - Khanyile, David AU - Arnold, Georgia AU - Cousens, Simon AU - Cawood, Cherie AU - Birdthistle, Isolde PY - 2025/1/31 TI - Evaluating Reaction Videos of Young People Watching Edutainment Media (MTV Shuga): Qualitative Observational Study JO - JMIR Form Res SP - e55275 VL - 9 KW - mass media KW - edutainment KW - adolescents KW - sexual health KW - HIV prevention KW - participatory research N2 - Background: Mass media campaigns, particularly edutainment, are critical in disseminating sexual health information to young people. However, there is limited understanding of the authentic viewing experience or how viewing contexts influence engagement with media campaigns. Reaction videos, a popular format in web-based culture in which users film themselves reacting to television shows, can be adapted as a research method for immediate and unfiltered insights into young people?s engagement with edutainment media. Objective: We explored how physical and social context influences young people?s engagement with MTV Shuga, a dramatic television series based on sexual health and relationships among individuals aged 15 to 25 years. We trialed reaction videos as a novel research method to investigate how young people in South Africa experience the show, including sexual health themes and messages, in their viewing environments. Methods: In Eastern Cape, in 2020, purposively selected participants aged 18 to 24 years of an evaluation study were invited to take part in further research to video record themselves watching MTV Shuga episodes with their COVID-19 social bubble. To guide the analysis of the visual and audio data, we created a framework to examine the physical setting, group composition, social dynamics, coinciding activities, and viewers? spoken and unspoken reactions to the show. We identified patterns within and across groups to generate themes about the nature and role of viewing contexts. We also reflected on the utility of the method and analytical framework. Results: In total, 8 participants recorded themselves watching MTV Shuga episodes in family or friendship groups. Viewings occurred around a laptop in the home (living room or bedroom) and outside (garden or vehicle). In same-age groups, viewers appeared relaxed, engaging with the content through discussion, comments, empathy, and laughter. Intergenerational groups experienced discomfort, with older relatives? presence causing embarrassment and younger siblings? distractions interrupting the engagement. Scenes featuring physical intimacy prompted some viewers to hide their eyes or leave the room. While some would prefer watching MTV Shuga alone to avoid the self-consciousness experienced in group settings, others valued the social experience and the lively discussions it spurred. This illustrates varied preferences for consuming edutainment and the factors influencing these preferences. Conclusions: The use of reaction videos for research captured real-time verbal and nonverbal reactions, physical environments, and social dynamics that other methods cannot easily measure. They revealed how group composition, dynamics, settings, and storylines can maximize engagement with MTV Shuga to enhance HIV prevention education. The presence of parents and the camera may alter young people?s behavior, limiting the authenticity of their viewing experience. Still, reaction videos offer a unique opportunity to understand audience engagement with media interventions and promote participatory digital research with young people. UR - https://formative.jmir.org/2025/1/e55275 UR - http://dx.doi.org/10.2196/55275 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55275 ER - TY - JOUR AU - Januraga, Putu Pande AU - Lukitosari, Endang AU - Luhukay, Lanny AU - Hasby, Rizky AU - Sutrisna, Aang PY - 2025/1/30 TI - Mapping Key Populations to Develop Improved HIV and AIDS Interventions: Multiphase Cross-Sectional Observational Mapping Study Using a District and City Approach JO - JMIR Public Health Surveill SP - e56820 VL - 11 KW - Indonesia KW - key population KW - mapping KW - pandemic KW - HIV KW - AIDS KW - hotspot N2 - Background: Indonesia?s vast archipelago and substantial population size present unique challenges in addressing its multifaceted HIV epidemic, with 90% of its 514 districts and cities reporting cases. Identifying key populations (KPs) is essential for effectively targeting interventions and allocating resources to address the changing dynamics of the epidemic. Objective: We examine the 2022 mapping of Indonesia?s KPs to develop improved HIV and AIDS interventions. Methods: In 2022, a district-based mapping of KPs was conducted across 201 districts and cities chosen for their HIV program intensity. This multiphase process included participatory workshops for hotspot identification, followed by direct hotspot observation, then followed by a second direct observation in selected hotspots for quality control. Data from 49,346 informants (KPs) were collected and analyzed. The results from individual hotspots were aggregated at the district or city level, and a formula was used to estimate the population size. Results: The mapping initiative identified 18,339 hotspots across 201 districts and cities, revealing substantial disparities in hotspot distribution. Of the 18,339 hotspots, 16,964 (92.5%) were observed, of which 1822 (10.74%) underwent a second review to enhance data accuracy. The findings mostly aligned with local stakeholders? estimates, but showed a lower median. Interviews indicated a shift in KP dynamics, with a median decline in hotspot attendance since the pandemic, and there was notable variation in mapping results across district categories. In ?comprehensive? areas, the average results for men who have sex with men (MSM), people who inject drugs, transgender women, and female sex workers (FSWs) were 1008 (median 694, IQR 317-1367), 224 (median 114, IQR 59-202), 196 (median 167, IQR 81-265), and 775 (median 573, IQR 352-1131), respectively. ?Medium? areas had lower averages: MSM at 381 (median 199, IQR 91-454), people who inject drugs at 51 (median 54, IQR 15-63), transgender women at 101 (median 55, IQR 29-127), and FSWs at 304 (median 231, IQR 118-425). ?Basic? areas showed the lowest averages: MSM at 161 (median 73, IQR 49-285), people who inject drugs at 7 (median 7, IQR 7-7), transgender women at 59 (median 26, IQR 12-60), and FSWs at 161 (median 131, IQR 59-188). Comparisons with ongoing outreach programs revealed substantial differences: the mapped MSM population was >50% lower than program coverage; the estimates for people who inject drugs were twice as high as the program coverage. Conclusions: The mapping results highlight significant variations in hotspots and KPs across districts and cities and underscore the necessity of adaptive HIV prevention strategies. The findings informed programmatic decisions, such as reallocating resources to underserved districts and recalibrating outreach strategies to better match KP dynamics. Developing strategies beyond identified hotspots, integrating mapping data into planning, and adopting a longitudinal approach to understand KP behavior over time are critical for effective HIV and AIDS prevention and control. UR - https://publichealth.jmir.org/2025/1/e56820 UR - http://dx.doi.org/10.2196/56820 UR - http://www.ncbi.nlm.nih.gov/pubmed/39883483 ID - info:doi/10.2196/56820 ER - TY - JOUR AU - Sankesara, Heet AU - Denyer, Hayley AU - Sun, Shaoxiong AU - Deng, Qigang AU - Ranjan, Yatharth AU - Conde, Pauline AU - Rashid, Zulqarnain AU - Asherson, Philip AU - Bilbow, Andrea AU - Groom, J. Madeleine AU - Hollis, Chris AU - Dobson, B. Richard J. AU - Folarin, Amos AU - Kuntsi, Jonna PY - 2025/1/29 TI - Identifying Digital Markers of Attention-Deficit/Hyperactivity Disorder (ADHD) in a Remote Monitoring Setting: Prospective Observational Study JO - JMIR Form Res SP - e54531 VL - 9 KW - ADHD KW - smartphones KW - wearable devices KW - mobile health KW - mHealth KW - remote monitoring KW - surveillance KW - digital markers KW - attention-deficit/hyperactivity disorder KW - behavioral data KW - real world KW - adult KW - adolescent KW - participants KW - digital signals KW - restlessness KW - severity KW - predicting outcomes N2 - Background: The symptoms and associated characteristics of attention-deficit/hyperactivity disorder (ADHD) are typically assessed in person at a clinic or in a research lab. Mobile health offers a new approach to obtaining additional passively and continuously measured real-world behavioral data. Using our new ADHD remote technology (ART) system, based on the Remote Assessment of Disease and Relapses (RADAR)?base platform, we explore novel digital markers for their potential to identify behavioral patterns associated with ADHD. The RADAR-base Passive App and wearable device collect sensor data in the background, while the Active App involves participants completing clinical symptom questionnaires. Objective: The main aim of this study was to investigate whether adults and adolescents with ADHD differ from individuals without ADHD on 10 digital signals that we hypothesize capture lapses in attention, restlessness, or impulsive behaviors. Methods: We collected data over 10 weeks from 20 individuals with ADHD and 20 comparison participants without ADHD between the ages of 16 and 39 years. We focus on features derived from (1) Active App (mean and SD of questionnaire notification response latency and of the time interval between questionnaires), (2) Passive App (daily mean and SD of response time to social and communication app notifications, the SD in ambient light during phone use, total phone use time, and total number of new apps added), and (3) a wearable device (Fitbit) (daily steps taken while active on the phone). Linear mixed models and t tests were employed to assess the group differences for repeatedly measured and time-aggregated variables, respectively. Effect sizes (d) convey the magnitude of differences. Results: Group differences were significant for 5 of the 10 variables. The participants with ADHD were (1) slower (P=.047, d=1.05) and more variable (P=.01, d=0.84) in their speed of responding to the notifications to complete the questionnaires, (2) had a higher SD in the time interval between questionnaires (P=.04, d=1.13), (3) had higher daily mean response time to social and communication app notifications (P=.03, d=0.7), and (4) had a greater change in ambient (background) light when they were actively using the smartphone (P=.008, d=0.86). Moderate to high effect sizes with nonsignificant P values were additionally observed for the mean of time intervals between questionnaires (P=.06, d=0.82), daily SD in responding to social and communication app notifications (P=.05, d=0.64), and steps taken while active on the phone (P=.09, d=0.61). The groups did not differ in the total phone use time (P=.11, d=0.54) and the number of new apps downloaded (P=.24, d=0.18). Conclusions: In a novel exploration of digital markers of ADHD, we identified candidate digital signals of restlessness, inconsistent attention, and difficulties completing tasks. Larger future studies are needed to replicate these findings and to assess the potential of such objective digital signals for tracking ADHD severity or predicting outcomes. UR - https://formative.jmir.org/2025/1/e54531 UR - http://dx.doi.org/10.2196/54531 ID - info:doi/10.2196/54531 ER - TY - JOUR AU - Gu, Anqi AU - Chan, Lam Cheng AU - Xu, Xiaolei AU - Dexter, P. Joseph AU - Becker, Benjamin AU - Zhao, Zhiying PY - 2025/1/29 TI - Real-Time fMRI Neurofeedback Modulation of Dopaminergic Midbrain Activity in Young Adults With Elevated Internet Gaming Disorder Risk: Randomized Controlled Trial JO - J Med Internet Res SP - e64687 VL - 27 KW - real-time functional magnetic resonance imaging neurofeedback KW - internet gaming disorder KW - craving KW - reward processing KW - ventral tegmental area UR - https://www.jmir.org/2025/1/e64687 UR - http://dx.doi.org/10.2196/64687 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64687 ER - TY - JOUR AU - Kezbers, M. Krista AU - Robertson, C. Michael AU - Hébert, T. Emily AU - Montgomery, Audrey AU - Businelle, S. Michael PY - 2025/1/28 TI - Detecting Deception and Ensuring Data Integrity in a Nationwide mHealth Randomized Controlled Trial: Factorial Design Survey Study JO - J Med Internet Res SP - e66384 VL - 27 KW - ecological momentary assessment KW - enrollment KW - fraud KW - mHealth KW - randomized controlled trial KW - recruitment KW - deception KW - data integrity KW - behavior KW - social KW - RCT KW - factorial design KW - mobile phone N2 - Background: Social behavioral research studies have increasingly shifted to remote recruitment and enrollment procedures. This shifting landscape necessitates evolving best practices to help mitigate the negative impacts of deceptive attempts (eg, fake profiles and bots) at enrolling in behavioral research. Objective: This study aimed to develop and implement robust deception detection procedures during the enrollment period of a remotely conducted randomized controlled trial. Methods: A 32-group (2×2×2×2×2) factorial design study was conducted from November 2021 to September 2022 to identify mobile health (mHealth) survey design features associated with the highest completion rates of smartphone-based ecological momentary assessments (n=485). Participants were required to be at least 18 years old, live in the United States, and own an Android smartphone that was compatible with the Insight app that was used in the study. Recruitment was conducted remotely through Facebook advertisements, a 5-minute REDCap (Research Electronic Data Capture) prescreener, and a screening and enrollment phone call. The research team created and implemented a 12-step checklist (eg, address verification and texting a copy of picture identification) to identify and prevent potentially deceptive attempts to enroll in the study. Descriptive statistics were calculated to understand the prevalence of various types of deceptive attempts at study enrollment. Results: Facebook advertisements resulted in 5236 initiations of the REDCap prescreener. A digital deception detection procedure was implemented for those who were deemed pre-eligible (n=1928). This procedure resulted in 26% (501/1928) of prescreeners being flagged as potentially deceptive. Completing multiple prescreeners (301/501, 60.1%) and providing invalid addresses (156/501, 31.1%) were the most common reasons prescreeners were flagged. An additional 1% (18/1928) of prescreeners were flagged as potentially deceptive during the subsequent study screening and enrollment phone call. Reasons for exclusion at the screening and enrollment phone call level included having an invalid phone type (6/18, 33.3%), completing multiple prescreeners (6/18, 33.3%), and providing an invalid address (5/18, 27.7%). This resulted in 1409 individuals being eligible after all deception checks were completed. Postenrollment social security number checks revealed that 3 (0.6%) fully enrolled participants out of 485 provided erroneous social security numbers during the screening process. Conclusions: Implementation of a deception detection procedure in a remotely conducted randomized controlled trial resulted in a substantial proportion of cases being flagged as potentially engaging in deceptive attempts at study enrollment. The results of the deception detection procedures in this study confirmed the need for vigilance in conducting remote behavioral research in order to maintain data integrity. Implementing systematic deception detection procedures may support study administration, data quality, and participant safety in remotely conducted behavioral research. Trial Registration: ClinicalTrials.gov NCT05194228; https://clinicaltrials.gov/study/NCT05194228 UR - https://www.jmir.org/2025/1/e66384 UR - http://dx.doi.org/10.2196/66384 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/66384 ER - TY - JOUR AU - Oh, Sejun AU - Lee, SangHeon PY - 2025/1/28 TI - Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review JO - J Med Internet Res SP - e54790 VL - 27 KW - cloud-based KW - health KW - rehabilitation KW - rehabilomics KW - strategies N2 - Background: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other ?-omics? fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation. Objective: This scoping review aimed to investigate the implementation of rehabilomics strategies through cloud-based rehabilitation and summarize the current state of knowledge within the research domain. This analysis aims to understand the impact of cloud platforms on the field of rehabilomics and provide insights into future research directions. Methods: In this scoping review, we systematically searched major academic databases, including CINAHL, Embase, Google Scholar, PubMed, MEDLINE, ScienceDirect, Scopus, and Web of Science to identify relevant studies and apply predefined inclusion criteria to select appropriate studies. Subsequently, we analyzed 28 selected papers to identify trends and insights regarding cloud-based rehabilitation and rehabilomics within this study?s landscape. Results: This study reports the various applications and outcomes of implementing rehabilomics strategies through cloud-based rehabilitation. In particular, a comprehensive analysis was conducted on 28 studies, including 16 (57%) focused on personalized rehabilitation and 12 (43%) on data security and privacy. The distribution of articles among the 28 studies based on specific keywords included 3 (11%) on the cloud, 4 (14%) on platforms, 4 (14%) on hospitals and rehabilitation centers, 5 (18%) on telehealth, 5 (18%) on home and community, and 7 (25%) on disease and disability. Cloud platforms offer new possibilities for data sharing and collaboration in rehabilomics research, underpinning a patient-centered approach and enhancing the development of personalized therapeutic strategies. Conclusions: This scoping review highlights the potential significance of cloud-based rehabilomics strategies in the field of rehabilitation. The use of cloud platforms is expected to strengthen patient-centered data management and collaboration, contributing to the advancement of innovative strategies and therapeutic developments in rehabilomics. UR - https://www.jmir.org/2025/1/e54790 UR - http://dx.doi.org/10.2196/54790 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54790 ER - TY - JOUR AU - Subramanian, Ajan AU - Cao, Rui AU - Naeini, Kasaeyan Emad AU - Aqajari, Hossein Seyed Amir AU - Hughes, D. Thomas AU - Calderon, Michael-David AU - Zheng, Kai AU - Dutt, Nikil AU - Liljeberg, Pasi AU - Salanterä, Sanna AU - Nelson, M. Ariana AU - Rahmani, M. Amir PY - 2025/1/27 TI - Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach JO - JMIR Form Res SP - e67969 VL - 9 KW - pain intensity recognition KW - multimodal information fusion KW - signal processing KW - weak supervision KW - health care KW - pain intensity KW - pain recognition KW - machine learning approach KW - acute pain KW - pain assessment KW - behavioral pain KW - pain measurement KW - pain monitoring KW - multimodal machine learning?based framework KW - machine learning?based framework KW - electrocardiogram KW - electromyogram KW - electrodermal activity KW - self-reported pain level KW - clinical pain management N2 - Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. Objective: This study aimed to develop and evaluate a multimodal machine learning?based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals. Methods: The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3). Results: The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy. Conclusions: This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings. UR - https://formative.jmir.org/2025/1/e67969 UR - http://dx.doi.org/10.2196/67969 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67969 ER - TY - JOUR AU - Liu, Weiqi AU - Wu, You AU - Zheng, Zhuozhao AU - Bittle, Mark AU - Yu, Wei AU - Kharrazi, Hadi PY - 2025/1/27 TI - Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis JO - J Med Internet Res SP - e64649 VL - 27 KW - artificial intelligence KW - diagnostic accuracy KW - lung nodule KW - radiology KW - AI system N2 - Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation. Objective: This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance. Methods: We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests. Results: The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (?21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (?21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (?21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (?21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (?21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%. Conclusions: The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems. UR - https://www.jmir.org/2025/1/e64649 UR - http://dx.doi.org/10.2196/64649 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64649 ER - TY - JOUR AU - Martinez, Stanford AU - Ramirez-Tamayo, Carolina AU - Akhter Faruqui, Hasib Syed AU - Clark, Kal AU - Alaeddini, Adel AU - Czarnek, Nicholas AU - Aggarwal, Aarushi AU - Emamzadeh, Sahra AU - Mock, R. Jeffrey AU - Golob, J. Edward PY - 2025/1/22 TI - Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study JO - JMIR Form Res SP - e53928 VL - 9 KW - machine learning KW - eye-tracking KW - experience level determination KW - radiology education KW - search pattern feature extraction KW - search pattern KW - radiology KW - classification KW - gaze KW - fixation KW - education KW - experience KW - spatio-temporal KW - image KW - x-ray KW - eye movement N2 - Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. This discrepancy can interfere with quality improvement interventions and negatively impact patient care. Objective: The objective of this study is to provide an alternative method for distinguishing between radiologists by means of captured eye-tracking data such that the raw gaze (or processed fixation data) can be used to discriminate users based on subconscious behavior in visual inspection. Methods: We present a novel discretized feature encoding based on spatiotemporal binning of fixation data for efficient geometric alignment and temporal ordering of eye movement when reading chest x-rays. The encoded features of the eye-fixation data are used by machine learning classifiers to discriminate between faculty and trainee radiologists. A clinical trial case study was conducted using metrics such as the area under the curve, accuracy, F1-score, sensitivity, and specificity to evaluate the discriminability between the 2 groups regarding their level of experience. The classification performance was then compared with state-of-the-art methodologies. In addition, a repeatability experiment using a separate dataset, experimental protocol, and eye tracker was performed with 8 participants to evaluate the robustness of the proposed approach. Results: The numerical results from both experiments demonstrate that classifiers using the proposed feature encoding methods outperform the current state-of-the-art in differentiating between radiologists in terms of experience level. An average performance gain of 6.9% is observed compared with traditional features while classifying experience levels of radiologists. This gain in accuracy is also substantial across different eye tracker?collected datasets, with improvements of 6.41% using the Tobii eye tracker and 7.29% using the EyeLink eye tracker. These results signify the potential impact of the proposed method for identifying radiologists? level of expertise and those who would benefit from additional training. Conclusions: The effectiveness of the proposed spatiotemporal discretization approach, validated across diverse datasets and various classification metrics, underscores its potential for objective evaluation, informing targeted interventions and training strategies in radiology. This research advances reliable assessment tools, addressing challenges in perception-related errors to enhance patient care outcomes. UR - https://formative.jmir.org/2025/1/e53928 UR - http://dx.doi.org/10.2196/53928 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53928 ER - TY - JOUR AU - Tang, Dongmei AU - Peng, Yuzhu AU - Gu, Dantong AU - Wu, Yongzhen AU - Li, Huawei PY - 2025/1/17 TI - Digital Frequency Customized Relieving Sound for Chronic Subjective Tinnitus Management: Prospective Controlled Study JO - J Med Internet Res SP - e60150 VL - 27 KW - tinnitus KW - digital frequency customized relieving sound KW - unmodified music KW - sound therapy KW - prospective study KW - mobile phone N2 - Background: Tinnitus is a major health issue, but currently no tinnitus elimination treatments exist for chronic subjective tinnitus. Acoustic therapy, especially personalized acoustic therapy, plays an increasingly important role in tinnitus treatment. With the application of smartphones, personalized acoustic stimulation combined with smartphone apps will be more conducive to the individualized treatment and management of patients with tinnitus. Objective: The aim of this study was to evaluate the efficacy of a new personalized approach known as the digital frequency customized relieving sound (DFCRS) for tinnitus treatment and to explore the factors that may influence its therapeutic effect. Methods: Patients with subjective tinnitus were enrolled in this study from July 14, 2020, to May 24, 2021, in the tinnitus specialist clinic of Eye and ENT Hospital, Fudan University, Shanghai, China. In this nonrandomized concurrent controlled trial, a total of 107 participants were assigned to listen to personalized DFCRS through our developed app, while the other 77 participants who did not want to download and use the app were assigned to listen to unmodified music (UM). All the recruits were instructed to listen to DFCRS or UM for at least 2 hours a day and complete follow-up assessments at baseline, 1, 2, and 3 months. Multidimensional assessment scales, that is, Tinnitus Handicapped Inventory (THI), Hospital Anxiety and Depression Scale (HADS), Athens Insomnia Scale (AIS), Fear of Tinnitus Questionnaire (FTQ), and Tinnitus Catastrophizing Scale (TCS) were used to evaluate the severity of tinnitus and the quality of life. Linear mixed models were used to test for changes in the THI scores across 3 months of acoustic treatment between group (DFCRS or UM treatment) and time. A multiclass logistic model was built with a stepwise function to determine the influence of the different covariates on the effects of acoustic treatment. Results: The results of the multidimensional assessment scales after 3 months of treatment showed that DFCRS-treated patients had significant tinnitus relief compared to those in the UM group. Linear mixed models revealed a significant reduction in the THI scores over time (P<.001), with the DFCRS group showing significantly greater improvement than the UM group (P<.001). At 3 months, 92.5% (99/107) of the patients undergoing DFCRS reported tinnitus relief or disappearance, and longer daily treatment time was associated with better outcomes (P=.007). Multiclass logistic regression confirmed that longer treatment time (odds ratio [OR] 13.07-64.78; P<.001) and more severe tinnitus at baseline (OR 10.46-83.71; P<.001) predicted better treatment response. All secondary outcomes (HADS, AIS, FTQ, TCS) showed significant improvements over time (P<.001). Conclusions: Our study suggests that DFCRS is a new promising and noninvasive therapy for chronic tinnitus, and it can be delivered through a mobile app to bring more convenience to patients with tinnitus. UR - https://www.jmir.org/2025/1/e60150 UR - http://dx.doi.org/10.2196/60150 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60150 ER - TY - JOUR AU - Liu, Chaofeng AU - Liu, Yan AU - Yi, Chunyan AU - Xie, Tao AU - Tian, Jingjun AU - Deng, Peishen AU - Liu, Changyu AU - Shan, Yan AU - Dong, Hangyu AU - Xu, Yanhua PY - 2025/1/15 TI - Application of a 3D Fusion Model to Evaluate the Efficacy of Clear Aligner Therapy in Malocclusion Patients: Prospective Observational Study JO - J Med Internet Res SP - e67378 VL - 27 KW - clear aligners KW - CBCT KW - intraoral scanning KW - fusion model KW - artificial intelligence KW - efficacy evaluation KW - orthodontic treatment N2 - Background: Investigating the safe range of orthodontic tooth movement is essential for maintaining oral and maxillofacial stability posttreatment. Although clear aligners rely on pretreatment digital models, their effect on periodontal hard tissues remains uncertain. By integrating cone beam computed tomography?derived cervical and root data with crown data from digital intraoral scans, a 3D fusion model may enhance precision and safety. Objective: This study aims to construct a 3D fusion model based on artificial intelligence software that matches cone beam computed tomography and intraoral scanning data using the Andrews? Six Element standard. The model will be used to assess the 3D effects of clear aligners on tooth movement, to provide a reference for the design of pretreatment target positions. Methods: Between May 2022 and May 2024, a total of 320 patients who completed clear aligner therapy at our institution were screened; 136 patients (aged 13-35 years, fully erupted permanent dentition and periodontal pocket depth <3 mm) met the criteria. Baseline (?simulation?) and posttreatment (?fusion?) models were compared. Outcomes included upper core discrepancy (UCD), upper incisors anteroposterior discrepancy (UAP), lower Spee curve deep discrepancy (LSD), upper anterior teeth width discrepancy (UAW), upper canine width discrepancy (UCW), upper molar width discrepancy (UMW), and total scores. Subanalyses examined sex, age stage (adolescent vs adult), and treatment method (extraction vs nonextraction). Results: The study was funded in May 2022, with data collection beginning the same month and continuing until May 2024. Of 320 initial participants, 136 met the inclusion criteria. Data analysis is ongoing, and final results are expected by late 2024. Among the 136 participants, 90 (66%) were female, 46 (34%) were male, 64 (47%) were adolescents, 72 (53%) were adults, 38 (28%) underwent extraction, and 98 (72%) did not. Total scores did not differ significantly by sex (mean difference 0.01, 95% CI ?0.13 to 0.15; P=.85), age stage (mean difference 0.03, 95% CI ?0.10 to 0.17; P=.60), or treatment method (mean difference 0.07, 95% CI ?0.22 to 0.07; P=.32). No significant differences were found in UCD (mean difference 0.001, 95% CI ?0.02 to 0.01; P=.90) or UAP (mean difference 0.01, 95% CI ?0.03 to 0.00; P=.06) by treatment method. However, adolescents exhibited smaller differences in UCD, UAW, UCW, and UMW yet larger differences in UAP and LSD (df=134; P<.001). Extraction cases showed smaller LSD, UAW, and UCW but larger UMW differences compared with nonextraction (df=134; P<.001). Conclusions: The 3D fusion model provides a reliable clinical reference for target position design and treatment outcome evaluation in clear aligner systems. The construction and application of a 3D fusion model in clear aligner orthodontics represent a significant leap forward, offering substantial clinical benefits while establishing a new standard for precision, personalization, and evidence-based treatment planning in the field. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400094304, https://www.chictr.org.cn/hvshowproject.html?id=266090&v=1.0 UR - https://www.jmir.org/2025/1/e67378 UR - http://dx.doi.org/10.2196/67378 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67378 ER - TY - JOUR AU - Inagaki, Keigo AU - Tsuriya, Daisuke AU - Hashimoto, Takuya AU - Nakamura, Katsumasa PY - 2025/1/14 TI - Verification of the Reliability of an Automated Urine Test Strip Colorimetric Program Using Colorimetric Analysis: Survey Study JO - JMIR Form Res SP - e62772 VL - 9 KW - urine test strip KW - reliability KW - automatic urine analyzer KW - quasi-experimental study KW - colorimetric analysis KW - colorimetric KW - urinalysis KW - urinary KW - urine KW - evaluation KW - mobile phone N2 - Background: One method for noninvasive and simple urinary microalbumin testing is urine test strips. However, when visually assessing urine test strips, accurate assessment may be difficult due to environmental influences?such as lighting color and intensity?and the physical and psychological influences of the assessor. These complicate the formation of an objective assessment. Objectives: This study developed an ?automated urine test strip colorimetric program? (hereinafter referred to as ?this program?) to objectively assess urine test strips. Using this program may allow urine tests to be conducted at home. In this study, urine samples from hospitalized or outpatient patients were randomly obtained, and the reliability of this program was verified by comparing the agreement rate between this program and an automatic urine analyzer (US-3500 [Eiken Chemical Co, Ltd] and LABOSPECT 006 [Hitachi High-Tech Co, Ltd]). Furthermore, the sensitivity and specificity of the urine albumin test were investigated, and its applicability to screening for microalbuminuria was verified. Methods: A urine test strip was placed in a photography box with constant light intensity and color temperature conditions. The image taken with a smartphone camera on top of the photography box was judged by this program. This program used Accelerated KAZE to perform image-matching processing to reduce the effect of misalignment during photography. It also calculated and judged the item with the smallest color difference between the color chart and the urine test strip using the CIEDE2000 color difference formula. The agreement rate of the results of this program was investigated using the results of an automatic urine analyzer as the gold standard. Results: Compared with the judgments of an automatic urine analyzer, the average agreement rate for 12 items (protein, glucose, urobilinogen, bilirubin, ketone bodies, specific gravity, occult blood, pH, white blood cells, nitrite, creatinine, and albumin) was 78.6%. Furthermore, the average agreement rate of the 12 items within ±1 rank was 95.4%. The results showed a sensitivity of 100% and a specificity of 58.6% in determining albumin in urine, which is important for determining the stage of diabetic nephropathy. Finally, the area under the curve (0.907) derived from the receiver operating characteristic curve was satisfactory. Conclusions: The program developed by the authors can determine urine test strips without requiring calibration in a certain shooting environment. If this program can be used at home to perform urinary microalbumin tests, the early detection and treatment of diabetic nephropathy may prevent the condition from becoming severe. UR - https://formative.jmir.org/2025/1/e62772 UR - http://dx.doi.org/10.2196/62772 ID - info:doi/10.2196/62772 ER - TY - JOUR AU - Kaufman, Jaycee AU - Jeon, Jouhyun AU - Oreskovic, Jessica AU - Thommandram, Anirudh AU - Fossat, Yan PY - 2025/1/9 TI - Longitudinal Changes in Pitch-Related Acoustic Characteristics of the Voice Throughout the Menstrual Cycle: Observational Study JO - JMIR Form Res SP - e65448 VL - 9 KW - menstrual cycle KW - women's health KW - voice KW - acoustic analysis KW - longitudinal observational study KW - fertility tracking KW - fertility KW - reproductive health KW - feasibility KW - voice recording KW - vocal pitch KW - follicular KW - luteal phase KW - fertility status KW - mobile phone N2 - Background: Identifying subtle changes in the menstrual cycle is crucial for effective fertility tracking and understanding reproductive health. Objective: The aim of the study is to explore how fundamental frequency features vary between menstrual phases using daily voice recordings. Methods: This study analyzed smartphone-collected voice recordings from 16 naturally cycling female participants, collected every day for 1 full menstrual cycle. Fundamental frequency features (mean, SD, 5th percentile, and 95th percentile) were extracted from each voice recording. Ovulation was estimated using luteinizing hormone urine tests taken every morning. The analysis included comparisons of these features between the follicular and luteal phases and the application of changepoint detection algorithms to assess changes and pinpoint the day in which the shifts in vocal pitch occur. Results: The fundamental frequency SD was 9.0% (SD 2.9%) lower in the luteal phase compared to the follicular phase (95% CI 3.4%?14.7%; P=.002), and the 5th percentile of the fundamental frequency was 8.8% (SD 3.6%) higher (95% CI 1.7%?16.0%; P=.01). No significant differences were found between phases in mean fundamental frequency or the 95th percentile of the fundamental frequency (P=.65 and P=.07). Changepoint detection, applied separately to each feature, identified the point in time when vocal frequency behaviors shifted. For the fundamental frequency SD and 5th percentile, 81% (n=13) of participants exhibited shifts within the fertile window (P=.03). In comparison, only 63% (n=10; P=.24) and 50% (n=8; P=.50) of participants had shifts in the fertile window for the mean and 95th percentile of the fundamental frequency, respectively. Conclusions: These findings indicate that subtle variations in vocal pitch may reflect changes associated with the menstrual cycle, suggesting the potential for developing a noninvasive and convenient method for monitoring reproductive health. Changepoint detection may provide a promising avenue for future work in longitudinal fertility analysis. UR - https://formative.jmir.org/2025/1/e65448 UR - http://dx.doi.org/10.2196/65448 ID - info:doi/10.2196/65448 ER - TY - JOUR AU - Yang, Xiaomeng AU - Li, Zeyan AU - Lei, Lei AU - Shi, Xiaoyu AU - Zhang, Dingming AU - Zhou, Fei AU - Li, Wenjing AU - Xu, Tianyou AU - Liu, Xinyu AU - Wang, Songyun AU - Yuan, Quan AU - Yang, Jian AU - Wang, Xinyu AU - Zhong, Yanfei AU - Yu, Lilei PY - 2025/1/7 TI - Noninvasive Oral Hyperspectral Imaging?Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study JO - J Med Internet Res SP - e67256 VL - 27 KW - heart failure with preserved ejection fraction KW - HFpEF KW - hyperspectral imaging KW - HSI KW - diagnostic model KW - digital health KW - Shapley Additive Explanations KW - SHAP KW - machine learning KW - artificial intelligence KW - AI KW - cardiovascular disease KW - predictive modeling KW - oral health N2 - Background: Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics. Objective: The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms. Methods: Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People?s Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance. Results: Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model?s capacity to accurately identify participants with HFpEF. Conclusions: This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care. Trial Registration: China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133 UR - https://www.jmir.org/2025/1/e67256 UR - http://dx.doi.org/10.2196/67256 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67256 ER - TY - JOUR AU - Bae, Won Sang AU - Chung, Tammy AU - Zhang, Tongze AU - Dey, K. Anind AU - Islam, Rahul PY - 2025/1/2 TI - Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study JO - JMIR AI SP - e52270 VL - 4 KW - digital phenotyping KW - smart devices KW - intoxication KW - smartphone-based sensors KW - wearables KW - mHealth KW - marijuana KW - cannabis KW - data collection KW - passive sensing KW - Fitbit KW - machine learning KW - eXtreme Gradient Boosting Machine classifier KW - XGBoost KW - algorithmic decision-making process KW - explainable artificial intelligence KW - XAI KW - artificial intelligence KW - JITAI KW - decision support KW - just-in-time adaptive interventions KW - experience sampling N2 - Background: Acute marijuana intoxication can impair motor skills and cognitive functions such as attention and information processing. However, traditional tests, like blood, urine, and saliva, fail to accurately detect acute marijuana intoxication in real time. Objective: This study aims to explore whether integrating smartphone-based sensors with readily accessible wearable activity trackers, like Fitbit, can enhance the detection of acute marijuana intoxication in naturalistic settings. No previous research has investigated the effectiveness of passive sensing technologies for enhancing algorithm accuracy or enhancing the interpretability of digital phenotyping through explainable artificial intelligence in real-life scenarios. This approach aims to provide insights into how individuals interact with digital devices during algorithmic decision-making, particularly for detecting moderate to intensive marijuana intoxication in real-world contexts. Methods: Sensor data from smartphones and Fitbits, along with self-reported marijuana use, were collected from 33 young adults over a 30-day period using the experience sampling method. Participants rated their level of intoxication on a scale from 1 to 10 within 15 minutes of consuming marijuana and during 3 daily semirandom prompts. The ratings were categorized as not intoxicated (0), low (1-3), and moderate to intense intoxication (4-10). The study analyzed the performance of models using mobile phone data only, Fitbit data only, and a combination of both (MobiFit) in detecting acute marijuana intoxication. Results: The eXtreme Gradient Boosting Machine classifier showed that the MobiFit model, which combines mobile phone and wearable device data, achieved 99% accuracy (area under the curve=0.99; F1-score=0.85) in detecting acute marijuana intoxication in natural environments. The F1-score indicated significant improvements in sensitivity and specificity for the combined MobiFit model compared to using mobile or Fitbit data alone. Explainable artificial intelligence revealed that moderate to intense self-reported marijuana intoxication was associated with specific smartphone and Fitbit metrics, including elevated minimum heart rate, reduced macromovement, and increased noise energy around participants. Conclusions: This study demonstrates the potential of using smartphone sensors and wearable devices for interpretable, transparent, and unobtrusive monitoring of acute marijuana intoxication in daily life. Advanced algorithmic decision-making provides valuable insight into behavioral, physiological, and environmental factors that could support timely interventions to reduce marijuana-related harm. Future real-world applications of these algorithms should be evaluated in collaboration with clinical experts to enhance their practicality and effectiveness. UR - https://ai.jmir.org/2025/1/e52270 UR - http://dx.doi.org/10.2196/52270 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52270 ER - TY - JOUR AU - Jiang, Xiangkui AU - Wang, Bingquan PY - 2024/12/31 TI - Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study JO - JMIR Med Inform SP - e58812 VL - 12 KW - prediction model KW - heart failure KW - hospital readmission KW - machine learning KW - cardiology KW - admissions KW - hospitalization N2 - Background: Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding clinical decision-making and optimizing patient care. However, the effectiveness of existing models tailored specifically to the Chinese population is still limited. Objective: This study aimed to formulate a predictive model for assessing the likelihood of readmission among patients diagnosed with heart failure. Methods: In this study, we analyzed data from 1948 patients with heart failure in a hospital in Sichuan Province between 2016 and 2019. By applying 3 variable selection strategies, 29 relevant variables were identified. Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. Results: The graph convolutional network model showed the highest prediction accuracy with an area under the receiver operating characteristic curve of 0.831, accuracy of 75%, sensitivity of 52.12%, and specificity of 90.25%. Conclusions: The model crafted in this study proves its effectiveness in forecasting the likelihood of readmission among patients with heart failure, thus serving as a crucial reference for clinical decision-making. UR - https://medinform.jmir.org/2024/1/e58812 UR - http://dx.doi.org/10.2196/58812 ID - info:doi/10.2196/58812 ER - TY - JOUR AU - Zhao, Yiran AU - Arora, Jatin AU - Tao, Yujie AU - Miller, B. Dave AU - Adams, T. Alexander AU - Choudhury, Tanzeem PY - 2024/12/31 TI - Translation Effectiveness of Offset Heart Rate Biofeedback as a Mindless Intervention for Alcohol Craving Among Risky Drinkers: Controlled Experiment JO - JMIR Form Res SP - e54438 VL - 8 KW - wearable device KW - alcohol craving KW - risky drinking KW - digital intervention KW - entrainment KW - offset heart rate biofeedback KW - mindless intervention N2 - Background: Digital and wearable intervention systems promise to improve how people manage their behavioral health conditions by making interventions available when the user can best benefit from them. However, existing interventions are obtrusive because they require attention and motivation to engage in, limiting the effectiveness of such systems in demanding contexts, such as when the user experiences alcohol craving. Mindless interventions, developed by the human-computer interaction community, offer an opportunity to intervene unobtrusively. Offset heart rate biofeedback is an iconic type of mindless intervention powered by entrainment and can mitigate the physiological and psychological response to stressors. Objective: This work aimed to characterize the translational effectiveness of offset heart rate biofeedback on cue-elicit alcohol craving among risky drinkers. Methods: We conducted an out-of-lab, between-group, controlled experiment with 26 participants who performed harmful or hazardous drinking. The control group served as negative control and received no intervention, while the experimental group received offset heart rate biofeedback during alcohol exposure and recovery. We elicited alcohol cravings through a series of alcohol cues, including performing mental imagery, viewing alcohol images, and sniffing alcohol. We measured the physiological response to alcohol (ie, heart rate variability), self-reported craving, and self-reported anxiety. We constructed linear mixed-effects models to understand the effect of intervention during alcohol exposure and alcohol recovery after exposure. Following the linear mixed effect model, we conducted pair-wise comparisons for measures between the control and experimental groups. Results: We found that offset heart rate biofeedback significantly reduced the increase in heart rate variability (P=.01 and P=.052) and self-reported craving (P=.04 and P=.02) in response to alcohol cues. Participants? anxiety was not affected by either the alcohol cues or the offset heart rate biofeedback. Conclusions: Offset heart rate biofeedback has the potential to immediately and unobtrusively mitigate cue-elicit alcohol craving among risky drinkers. The results of this study opened new opportunities for digital and wearable interventions to mitigate alcohol craving, either as wellness apps for risky drinkers or as digital prescriptions and integration with sensing systems for people with alcohol dependency. UR - https://formative.jmir.org/2024/1/e54438 UR - http://dx.doi.org/10.2196/54438 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54438 ER - TY - JOUR AU - Zhao, Ziwei AU - Zhang, Weiyi AU - Chen, Xiaolan AU - Song, Fan AU - Gunasegaram, James AU - Huang, Wenyong AU - Shi, Danli AU - He, Mingguang AU - Liu, Na PY - 2024/12/30 TI - Slit Lamp Report Generation and Question Answering: Development and Validation of a Multimodal Transformer Model with Large Language Model Integration JO - J Med Internet Res SP - e54047 VL - 26 KW - large language model KW - slit lamp KW - medical report generation KW - question answering N2 - Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited. Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2. We term this entire process slit lamp?GPT. Methods: Our research used a dataset of 25,051 slit lamp images from 3409 participants, paired with their corresponding physician-created medical reports. We used these data, split into training, validation, and test sets, to finetune the Bootstrapping Language-Image Pre-training framework toward report generation. The generated text reports and human-posed questions were then input into Llama2 for subsequent QA. We evaluated performance using qualitative metrics (including BLEU [bilingual evaluation understudy], CIDEr [consensus-based image description evaluation], ROUGE-L [Recall-Oriented Understudy for Gisting Evaluation?Longest Common Subsequence], SPICE [Semantic Propositional Image Caption Evaluation], accuracy, sensitivity, specificity, precision, and F1-score) and the subjective assessments of two experienced ophthalmologists on a 1-3 scale (1 referring to high quality). Results: We identified 50 conditions related to diseases or postoperative complications through keyword matching in initial reports. The refined slit lamp?GPT model demonstrated BLEU scores (1-4) of 0.67, 0.66, 0.65, and 0.65, respectively, with a CIDEr score of 3.24, a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score of 0.61, and a Semantic Propositional Image Caption Evaluation score of 0.37. The most frequently identified conditions were cataracts (22.95%), age-related cataracts (22.03%), and conjunctival concretion (13.13%). Disease classification metrics demonstrated an overall accuracy of 0.82 and an F1-score of 0.64, with high accuracies (?0.9) observed for intraocular lens, conjunctivitis, and chronic conjunctivitis, and high F1-scores (?0.9) observed for cataract and age-related cataract. For both report generation and QA components, the two evaluating ophthalmologists reached substantial agreement, with ? scores between 0.71 and 0.84. In assessing 100 generated reports, they awarded scores of 1.36 for both completeness and correctness; 64% (64/100) were considered ?entirely good,? and 93% (93/100) were ?acceptable.? In the evaluation of 300 generated answers to questions, the scores were 1.33 for completeness, 1.14 for correctness, and 1.15 for possible harm, with 66.3% (199/300) rated as ?entirely good? and 91.3% (274/300) as ?acceptable.? Conclusions: This study introduces the slit lamp?GPT model for report generation and subsequent QA, highlighting the potential of large language models to assist ophthalmologists and patients. UR - https://www.jmir.org/2024/1/e54047 UR - http://dx.doi.org/10.2196/54047 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54047 ER - TY - JOUR AU - Handra, Julia AU - James, Hannah AU - Mbilinyi, Ashery AU - Moller-Hansen, Ashley AU - O'Riley, Callum AU - Andrade, Jason AU - Deyell, Marc AU - Hague, Cameron AU - Hawkins, Nathaniel AU - Ho, Kendall AU - Hu, Ricky AU - Leipsic, Jonathon AU - Tam, Roger PY - 2024/12/30 TI - The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review JO - JMIR Cardio SP - e60697 VL - 8 KW - machine learning KW - cardiac fibrosis KW - electrocardiogram KW - ECG KW - detection KW - ML KW - cardiovascular disease KW - review N2 - Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility. Electrocardiograms (ECGs) are widely available and cost-effective for monitoring cardiac electrical activity. While ECG-based methods for inferring fibrosis exist, they are not commonly used due to accuracy limitations and the need for cardiac expertise. However, the ECG shows promise as a target for machine learning (ML) applications in fibrosis detection. Objective: This study aims to synthesize and critically evaluate the current state of ECG-based ML approaches for cardiac fibrosis detection. Methods: We conducted a scoping review of research in ECG-based ML applications to identify cardiac fibrosis. Comprehensive searches were performed in PubMed, IEEE Xplore, Scopus, Web of Science, and DBLP databases, including publications up to October 2024. Studies were included if they applied ML techniques to detect cardiac fibrosis using ECG or vectorcardiogram data and provided sufficient methodological details and outcome metrics. Two reviewers independently assessed eligibility and extracted data on the ML models used, their performance metrics, study designs, and limitations. Results: We identified 11 studies evaluating ML approaches for detecting cardiac fibrosis using ECG data. These studies used various ML techniques, including classical (8/11, 73%), ensemble (3/11, 27%), and deep learning models (4/11, 36%). Support vector machines were the most used classical model (6/11, 55%), with the best-performing models of each study achieving accuracies of 77% to 93%. Among deep learning approaches, convolutional neural networks showed promising results, with one study reporting an area under the receiver operating characteristic curve (AUC) of 0.89 when combined with clinical features. Notably, a large-scale convolutional neural network study (n=14,052) achieved an AUC of 0.84 for detecting cardiac fibrosis, outperforming cardiologists (AUC 0.63-0.66). However, many studies had limited sample sizes and lacked external validation, potentially impacting the generalizability of the findings. Variability in reporting methods may affect the reproducibility and applicability of these ML-based approaches. Conclusions: ML-augmented ECG analysis shows promise for accessible and cost-effective detection of cardiac fibrosis. However, there are common limitations with respect to study design and insufficient external validation, raising concerns about the generalizability and clinical applicability of the findings. Inconsistencies in methodologies and incomplete reporting further impede cross-study comparisons. Future work may benefit from using prospective study designs, larger and more clinically and demographically diverse datasets, advanced ML models, and rigorous external validation. Addressing these challenges could pave the way for the clinical implementation of ML-based ECG detection of cardiac fibrosis to improve patient outcomes and health care resource allocation. UR - https://cardio.jmir.org/2024/1/e60697 UR - http://dx.doi.org/10.2196/60697 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60697 ER - TY - JOUR AU - Wu, Sixuan AU - Song, Kefan AU - Cobb, Jason AU - Adams, T. Alexander PY - 2024/12/23 TI - Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation JO - JMIR Biomed Eng SP - e62770 VL - 9 KW - mobile health KW - mHealth KW - ubiquitous health KW - smartphone KW - chip KW - microscope KW - microfluidics KW - cells counting, body fluid analysis, blood test, urinalysis, computer vision, machine learning KW - fluid KW - cell KW - cellular KW - concentration N2 - Background: Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms. Objective: The objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips? Methods: To answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement. Results: In the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F6, 99=6144.45, P<.001; Bottleneck: F6, 99=3475.78, P<.001) showed the height difference had a significant impact on the cell velocity, which implied gravity could drive the flow. The video sharpness analysis demonstrated that video quality followed an exponential decay with increasing height differences (video quality=100e?k×Height) and a bottleneck design could effectively preserve video quality (Straight: R2=0.95, k=4.33; Bottleneck: R2=0.91, k=0.59). Samples from the 13 cell concentrations were used for cell counting and cell concentration estimation analysis. The accuracy of cell counting (n=35, 60-second samples, R2=0.96, mean absolute error=1.10, mean squared error=2.24, root mean squared error=1.50) and cell concentration regression (n=39, 150-second samples, R2=0.99, mean absolute error=0.24, mean squared error=0.11, root mean squared error=0.33 on a logarithmic scale, mean average percentage error=0.25) were evaluated using 5-fold cross-validation by comparing the algorithmic estimation to ground truth. Conclusions: In conclusion, we demonstrated the importance of the flow velocity in a microfluidic system, and we proposed SmartFlow, a low-cost system for computer vision?based cellular analysis. The proposed system could count the cells and estimate cell concentrations in the samples. UR - https://biomedeng.jmir.org/2024/1/e62770 UR - http://dx.doi.org/10.2196/62770 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62770 ER - TY - JOUR AU - Cochran, M. Jeffrey PY - 2024/12/20 TI - Developing a Sleep Algorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study JO - JMIR Ment Health SP - e62959 VL - 11 KW - actigraphy KW - machine learning KW - accelerometer KW - sleep-wake cycles KW - sleep monitoring KW - sleep quality KW - sleep disorder KW - polysomnography KW - wearable sensor KW - electrocardiogram N2 - Background: Sleep-wake patterns are important behavioral biomarkers for patients with serious mental illness (SMI), providing insight into their well-being. The gold standard for monitoring sleep is polysomnography (PSG), which requires a sleep lab facility; however, advances in wearable sensor technology allow for real-world sleep-wake monitoring. Objective: The goal of this study was to develop a PSG-validated sleep algorithm using accelerometer (ACC) and electrocardiogram (ECG) data from a wearable patch to accurately quantify sleep in a real-world setting. Methods: In this noninterventional, nonsignificant-risk, abbreviated investigational device exemption, single-site study, participants wore the reusable wearable sensor version 2 (RW2) patch. The RW2 patch is part of a digital medicine system (aripiprazole with sensor) designed to provide objective records of medication ingestion for patients with schizophrenia, bipolar I disorder, and major depressive disorder. This study developed a sleep algorithm from patch data and did not contain any study-related or digitized medication. Patch-acquired ACC and ECG data were compared against PSG data to build machine learning classification models to distinguish periods of wake from sleep. The PSG data provided sleep stage classifications at 30-second intervals, which were combined into 5-minute windows and labeled as sleep or wake based on the majority of sleep stages within the window. ACC and ECG features were derived for each 5-minute window. The algorithm that most accurately predicted sleep parameters against PSG data was compared to commercially available wearable devices to further benchmark model performance. Results: Of 80 participants enrolled, 60 had at least 1 night of analyzable ACC and ECG data (25 healthy volunteers and 35 participants with diagnosed SMI). Overall, 10,574 valid 5-minute windows were identified (5854 from participants with SMI), and 84% (n=8830) were classified as greater than half sleep. Of the 3 models tested, the conditional random field algorithm provided the most robust sleep-wake classification. Performance was comparable to the middle 50% of commercial devices evaluated in a recent publication, providing a sleep detection performance of 0.93 (sensitivity) and wake detection performance of 0.60 (specificity) at a prediction probability threshold of 0.75. The conditional random field algorithm retained this performance for individual sleep parameters, including total sleep time, sleep efficiency, and wake after sleep onset (within the middle 50% to top 25% of the assessed devices). The only parameter where the model performance was lower was sleep onset latency (within the bottom 25% of all comparator devices). Conclusions: Using industry-best practices, we developed a sleep algorithm for use with the RW2 patch that can accurately detect sleep and wake windows compared to PSG-labeled sleep data. This algorithm may be used for a more complete understanding of well-being for patients with SMI in a real-world setting, without the need for PSG and a sleep lab. UR - https://mental.jmir.org/2024/1/e62959 UR - http://dx.doi.org/10.2196/62959 ID - info:doi/10.2196/62959 ER - TY - JOUR AU - Kuo, Nai-Yu AU - Tsai, Hsin-Jung AU - Tsai, Shih-Jen AU - Yang, C. Albert PY - 2024/12/19 TI - Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study JO - J Med Internet Res SP - e51615 VL - 26 KW - sleep apnea KW - machine learning KW - questionnaire KW - oxygen saturation KW - polysomnography KW - screening KW - sleep disorder KW - insomnia KW - utilization KW - dataset KW - training KW - diagnostic N2 - Background: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time consuming and labor intensive, thus limiting diagnostic efficiency. Objective: This study aims to develop 2 sequential machine learning models to efficiently screen and differentiate OSA. Methods: We used 2 datasets comprising 8444 cases from the Sleep Heart Health Study (SHHS) and 1229 cases from Taipei Veterans General Hospital (TVGH). The Questionnaire Model (Model-Questionnaire) was designed to distinguish OSA from primary insomnia using demographic information and Pittsburgh Sleep Quality Index questionnaires, while the Saturation Model (Model-Saturation) categorized OSA severity based on multiple blood oxygen saturation parameters. The performance of the sequential machine learning models in screening and assessing the severity of OSA was evaluated using an independent test set derived from TVGH. Results: The Model-Questionnaire achieved an F1-score of 0.86, incorporating demographic data and the Pittsburgh Sleep Quality Index. Model-Saturation training by the SHHS dataset displayed an F1-score of 0.82 when using the power spectrum of blood oxygen saturation signals and reached the highest F1-score of 0.85 when considering all saturation-related parameters. Model-saturation training by the TVGH dataset displayed an F1-score of 0.82. The independent test set showed stable results for Model-Questionnaire and Model-Saturation training by the TVGH dataset, but with a slightly decreased F1-score (0.78) in Model-Saturation training by the SHHS dataset. Despite reduced model accuracy across different datasets, precision remained at 0.89 for screening moderate to severe OSA. Conclusions: Although a composite model using multiple saturation parameters exhibits higher accuracy, optimizing this model by identifying key factors is essential. Both models demonstrated adequate at-home screening capabilities for sleep disorders, particularly for patients unsuitable for in-laboratory sleep studies. UR - https://www.jmir.org/2024/1/e51615 UR - http://dx.doi.org/10.2196/51615 UR - http://www.ncbi.nlm.nih.gov/pubmed/39699950 ID - info:doi/10.2196/51615 ER - TY - JOUR AU - Parekh, Pranav AU - Oyeleke, Richard AU - Vishwanath, Tejas PY - 2024/12/18 TI - The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study JO - JMIR Dermatol SP - e59839 VL - 7 KW - machine learning KW - ML KW - computer vision KW - neural networks KW - explainable AI KW - XAI KW - computer graphics KW - red spot analysis KW - mixed reality KW - MR KW - artificial intelligence KW - visualization N2 - Background: Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be malignant and quickly grow beneath the skin. While biopsy slides provide clear information on lesion depth, it is an emerging domain to find quick and noninvasive methods to estimate depth, particularly based on 2D images. Objective: This study proposes a novel methodology for the depth estimation and visualization of skin lesions. Current diagnostic methods are approximate in determining how much a lesion may have proliferated within the skin. Using color gradients and depth maps, this method will give us a definite estimate and visualization procedure for lesions and other skin issues. We aim to generate 3D holograms of the lesion depth such that dermatologists can better diagnose melanoma. Methods: We started by performing classification using a convolutional neural network (CNN), followed by using explainable artificial intelligence to localize the image features responsible for the CNN output. We used the gradient class activation map approach to perform localization of the lesion from the rest of the image. We applied computer graphics for depth estimation and developing the 3D structure of the lesion. We used the depth from defocus method for depth estimation from single images and Gabor filters for volumetric representation of the depth map. Our novel method, called red spot analysis, measures the degree of infection based on how a conical hologram is constructed. We collaborated with a dermatologist to analyze the 3D hologram output and received feedback on how this method can be introduced to clinical implementation. Results: The neural model plus the explainable artificial intelligence algorithm achieved an accuracy of 86% in classifying the lesions correctly as benign or malignant. For the entire pipeline, we mapped the benign and malignant cases to their conical representations. We received exceedingly positive feedback while pitching this idea at the King Edward Memorial Institute in India. Dermatologists considered this a potentially useful tool in the depth estimation of lesions. We received a number of ideas for evaluating the technique before it can be introduced to the clinical scene. Conclusions: When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin, and that the malignant cases have deeper conical sections when compared with the benign cases. This proves that the qualitative results map with the initial classification performed by the neural model. The positive feedback provided by the dermatologist suggests that the qualitative conclusion of the method is sufficient. UR - https://derma.jmir.org/2024/1/e59839 UR - http://dx.doi.org/10.2196/59839 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59839 ER - TY - JOUR AU - Jeanmougin, Pauline AU - Larramendy, Stéphanie AU - Fournier, Jean-Pascal AU - Gaultier, Aurélie AU - Rat, Cédric PY - 2024/12/18 TI - Effect of a Feedback Visit and a Clinical Decision Support System Based on Antibiotic Prescription Audit in Primary Care: Multiarm Cluster-Randomized Controlled Trial JO - J Med Internet Res SP - e60535 VL - 26 KW - antibacterial agents KW - feedback KW - clinical decision support system KW - prescriptions KW - primary health care KW - clinical decision KW - antibiotic prescription KW - antimicrobial KW - antibiotic stewardship KW - interventions KW - health insurance KW - systematic antibiotic prescriptions N2 - Background: While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is needed to optimize future interventions. Objective: This study aimed to evaluate 2 multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care. Methods: An open-label, cluster-randomized controlled trial of 2501 general practitioners (GPs) working in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention, consisting of a visit by a health insurance representative who gave prescription feedback and provided a leaflet for treating cystitis and tonsillitis; and a clinical decision support system (CDSS)?based intervention, consisting of a visit with prescription feedback and a CDSS demonstration on antibiotic prescribing. The control group received no intervention. Data on systemic antibiotic dispensing was obtained from the National Health Insurance System (Système National d?Information Inter-Régimes de l?Assurance Maladie) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using a 2-level hierarchical analysis of covariance adjusted for annual antibiotic prescription volume at baseline. Results: Overall, 2501 GPs were randomized (n=1099, 43.9% women). At 12 months, the mean volume of systemic antibiotics per GP decreased by 219.2 (SD 61.4; 95% CI ?339.5 to ?98.8; P<.001) defined daily doses in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systemic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (?109.7, SD 62.4; 95% CI ?232.0 to 12.5 defined daily doses; P=.08). Conclusions: A visit by a health insurance representative combining feedback and a CDSS demonstration resulted in a 4.4% (-219.2/4930) reduction in the total volume of systemic antibiotic prescriptions in 12 months. Trial Registration: ClinicalTrials.gov NCT04028830; https://clinicaltrials.gov/study/NCT04028830 UR - https://www.jmir.org/2024/1/e60535 UR - http://dx.doi.org/10.2196/60535 UR - http://www.ncbi.nlm.nih.gov/pubmed/39693139 ID - info:doi/10.2196/60535 ER - TY - JOUR AU - Bernanke, Alyssa AU - Hasley, Rebecca AU - Sabetfakhri, Niki AU - de Wit, Harriet AU - Smith, M. Bridget AU - Wang, Lei AU - Brenner, A. Lisa AU - Hanlon, Colleen AU - Philip, S. Noah AU - Ajilore, Olusola AU - Herrold, Amy AU - Aaronson, Alexandra PY - 2024/12/13 TI - Frontal Pole Neuromodulation for Impulsivity and Suicidality in Veterans With Mild Traumatic Brain Injury and Common Co-Occurring Mental Health Conditions: Protocol for a Pilot Randomized Controlled Trial JO - JMIR Res Protoc SP - e58206 VL - 13 KW - mild traumatic brain injury KW - transcranial magnetic stimulation KW - intermittent theta burst stimulation KW - suicidality KW - suicidal ideation KW - impulsivity KW - neuromodulation KW - social and occupational functioning N2 - Background: Suicide remains a leading cause of death among veterans in the United States, and mild traumatic brain injury (mTBI) increases the risk of suicidal ideation (SI) and suicide attempts (SAs). mTBI worsens impulsivity and contributes to poor social and occupational functioning, which further increases the risk of SI and SAs. Repetitive transcranial magnetic stimulation is a neuromodulatory treatment approach that induces neuroplasticity, potentially repairing neurodamage. Intermittent theta burst stimulation (iTBS) is a second-generation form of transcranial magnetic stimulation that is safe, shorter in duration, displays a minimal side effect profile and is a promising treatment approach for impulsivity in mTBI. Our novel proposed treatment protocol uses frontal pole iTBS to target the ventromedial prefrontal cortex, which may reduce impulsivity by strengthening functional connectivity between the limbic system and frontal cortex, allowing for improved top-down control of impulsive reactions, including SI and SAs. Objective: The objectives of this study are to (1) develop an iTBS intervention for veterans with mTBI, impulsivity, and SI; (2) assess the feasibility and tolerability of the intervention; and (3) gather preliminary clinical outcome data on SI, impulsivity, and functions that will guide future studies. Methods: This is a pilot, double-blinded, randomized controlled trial. In developing this protocol, we referenced the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidelines. We will enroll 56 participants (28 active iTBS and 28 sham iTBS). The iTBS intervention will be performed daily, 5 days a week, for 2 weeks. We will collect 10 validated, psychometric, quantitative outcome measures before, during, and after the intervention. Measures included will assess functioning, impulsivity, suicidality, posttraumatic stress disorder, and depressive symptoms. We will collect qualitative data through semistructured interviews to elicit feedback on the participants? experiences and symptoms. We will perform quantitative and qualitative analyses to (1) assess the feasibility, tolerability, and acceptability of the treatment; (2) gather advanced neuroimaging data to assess neural changes elicited by treatment; and (3) assess improvements in outcome measures of impulsivity and suicidality in veterans with mTBI. Results: This study protocol was approved by the Edward Hines, Jr. VA Hospital Institutional Review Board (Hines IRB number 14-003). This novel treatment is a 5-year research project (April 1, 2023, to March 31, 2028) funded by the Veterans Administration Rehabilitation Research and Development service (CDA2 award IK2 RX002938). Study results will be disseminated at or before the project?s end date in March 2028. Conclusions: We will provide preliminary evidence of the safety, feasibility, and acceptability of a novel frontal pole iTBS treatment for mTBI, impulsivity, SI and SAs, and functional deficits. Trial Registration: ClinicalTrials.gov NCT05647044; https://clinicaltrials.gov/study/NCT05647044 International Registered Report Identifier (IRRID): PRR1-10.2196/58206 UR - https://www.researchprotocols.org/2024/1/e58206 UR - http://dx.doi.org/10.2196/58206 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58206 ER - TY - JOUR AU - Fernandez, Diana Isabel AU - Yang, Yu-Ching AU - Chang, Wonkyung AU - Kautz, Amber AU - Farchaus Stein, Karen PY - 2024/12/13 TI - Developing Components of an Integrated mHealth Dietary Intervention for Mexican Immigrant Farmworkers: Feasibility Usability Study of a Food Photography Protocol for Dietary Assessment JO - JMIR Form Res SP - e54664 VL - 8 KW - Mexican immigrant farmworker KW - diet-related noncommunicable diseases KW - mHealth KW - dietary assessment KW - image-based KW - healthcare disparities KW - minority KW - feasibility study KW - food photography KW - rural health KW - health literacy KW - culutural adaptation KW - women KW - technology acceptance KW - mobile health N2 - Background: Rural-urban disparities in access to health services and the burden of diet-related noncommunicable diseases are exacerbated among Mexican immigrant farmworkers due to work demands, social and geographical isolation, literacy issues, and limited access to culturally and language-competent health services. Although mobile health (mHealth) tools have the potential to overcome structural barriers to health services access, efficacious mHealth interventions to promote healthy eating have not considered issues of low literacy and health literacy, and food preferences and norms in the Mexican immigrant farmworker population. To address this critical gap, we conducted a series of preliminary studies among Mexican immigrant farmworkers with the long-term goal of developing a culture- and literacy-specific smartphone app integrating dietary assessment through food photography, diet analyses, and a non?text-based dietary intervention. Objective: This study aimed to report adherence and reactivity to a 14-day food photography dietary assessment protocol, in which Mexican immigrant farmworker women were instructed to take photos of all foods and beverages consumed. Methods: We developed a secure mobile app with an intuitive graphical user interface to collect food images. Adult Mexican immigrant farmworker women were recruited and oriented to the photography protocol. Adherence and reactivity were examined by calculating the mean number of food photos per day over time, differences between the first and second week, and differences between weekdays and weekends. The type of foods and meals photographed were compared with reported intake in three 24-hour dietary recalls. Results: In total, 16 Mexican farmworker women took a total of 1475 photos in 14 days, with a mean of 6.6 (SD 2.3) photos per day per participant. On average, participants took 1 fewer photo per day in week 2 compared with week 1 (mean 7.1, SD 2.5 in week 1 vs mean 6.1, SD 2.6 in week 2; P=.03), and there was a decrease of 0.6 photos on weekdays versus weekends (mean 6.4, SD 2.5 on weekdays vs mean 7, SD 2.7 on weekends; P=.50). Of individual food items, 71% (352/495) of foods in the photos matched foods in the recalls. Of all missing food items (n=138) and meals (n=36) in the photos, beverages (74/138, 54%), tortillas (15/138, 11%), snacks 16/36, 44%), and dinners (10/36, 28%) were the most frequently missed. Most of the meals not photographed (27/36, 75%) were in the second week of the protocol. Conclusions: Dietary assessment through food photography is feasible among Mexican immigrant farmworker women. For future protocols, substantive adjustments will be introduced to reduce the frequency of missing foods and meals. Our preliminary studies are a step in the right direction to extend the benefits of mHealth technologies to a hard-to-reach group and contribute to the prevention and control of diet-related noncommunicable diseases. UR - https://formative.jmir.org/2024/1/e54664 UR - http://dx.doi.org/10.2196/54664 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54664 ER - TY - JOUR AU - Lee, Kiseong AU - Chung, Yoongi AU - Kim, Ji-Su PY - 2024/12/12 TI - Research Trends on Metabolic Syndrome in Digital Health Care Using Topic Modeling: Systematic Search of Abstracts JO - J Med Internet Res SP - e53873 VL - 26 KW - metabolic syndrome KW - digital health care KW - topic modeling KW - text network analysis KW - research trends KW - prevention KW - management KW - telemedicine KW - wearable KW - devices KW - apps KW - applications KW - methodological KW - cardiovascular disease N2 - Background: Metabolic syndrome (MetS) is a prevalent health condition that affects 20%-40% of the global population. Lifestyle modification is essential for the prevention and management of MetS. Digital health care, which incorporates technologies like wearable devices, mobile apps, and telemedicine, is increasingly becoming integral to health care systems. By analyzing existing research trends in the application of digital health care for MetS management, this study identifies gaps in current knowledge and suggests avenues for future research. Objective: This study aimed to identify core keywords, topics, and research trends concerning the use of digital health care in the management of MetS. Methods: A systematic search of abstracts from peer-reviewed papers was conducted across 6 academic databases. Following eligibility screening, 162 abstracts were selected for further analysis. The methodological approach included text preprocessing, text network analysis, and topic modeling using the BERTopic algorithm. Results: Analysis of the 162 selected abstracts yielded a keyword network comprising 1047 nodes and 34,377 edges. The top 5 core keywords were identified as ?MetS,? ?use,? ?patient,? ?health,? and ?intervention.? We identified 12 unique topics, with topic 1 focusing on the use of telehealth for self-management of diabetes. The diversity of the 12 topics reflected various aspects of digital health care, including telehealth for diabetes management, electronic health records for MetS complications, and wearable devices for monitoring metabolic status. Research trends showed an expanding field of precision medicine driven by the demand for tailored interventions and the significant impact of the COVID-19 pandemic. Conclusions: By analyzing past research trends and extracting data from scholarly databases, this study has provided valuable insights that can guide future investigations in the field of digital health care and MetS management. UR - https://www.jmir.org/2024/1/e53873 UR - http://dx.doi.org/10.2196/53873 UR - http://www.ncbi.nlm.nih.gov/pubmed/39666378 ID - info:doi/10.2196/53873 ER - TY - JOUR AU - Knobel, J. Samuel E. AU - Oberson, Raphael AU - Räber, Jonas AU - Schütz, Narayan AU - Egloff, Niklaus AU - Botros, Angela AU - Gerber, M. Stephan AU - Nef, Tobias AU - Heydrich, Lukas PY - 2024/12/11 TI - Evaluation of a New Mobile Virtual Reality Setup to Alter Pain Perception: Pilot Development and Usability Study in Healthy Participants JO - JMIR Serious Games SP - e52340 VL - 12 KW - immersive virtual reality KW - embodiment KW - pain management KW - chronic pain KW - full-body illusion KW - cardiovisual illusion KW - pain KW - virtual reality KW - pilot study KW - development KW - mobile virtual reality KW - mobile KW - virtual environment KW - usability KW - heart rate KW - mobile phone N2 - Background: Chronic pain presents a significant treatment challenge, often leading to frustration for both patients and therapists due to the limitations of traditional methods. Research has shown that synchronous visuo-tactile stimulation, as used in the rubber hand experiment, can induce a sense of ownership over a fake body part and reduces pain perception when ownership of the fake body part is reported. The effect of the rubber hand experiment can be extended to the full body, for example, during the full-body illusion, using both visuo-tactile and cardiovisual signals. Objective: This study first aimed to evaluate the usability and accuracy of a novel, mobile virtual reality (VR) setup that displays participants? heartbeats as a flashing silhouette on a virtual avatar, a technique known as the cardiovisual full-body illusion. The second part of the study investigated the effects of synchronous cardiovisual stimulation on pain perception and ownership in 20 healthy participants as compared with asynchronous stimulation (control condition). Methods: The setup comprised a head-mounted display (HMD) and a heart rate measurement device. A smartphone-based HMD (Samsung Galaxy S8+) was selected for its mobility, and heart rates were measured using smartwatches with photoplethysmography (PPG). The accuracy of 2 smartwatch positions was compared with a 5-point electrocardiogram (ECG) standard in terms of their accuracy (number and percent of missed beats). Each participant underwent two 5-minute sessions of synchronous cardiovisual stimulation and two 5-minute sessions of asynchronous cardiovisual stimulation (total of 4 sessions), followed by pain assessments. Usability, symptoms of cybersickness, and ownership of the virtual body were measured using established questionnaires (System Usability Scale, Simulator Sickness Questionnaire, Ownership Questionnaire). Pain perception was assessed using advanced algometric methods (Algopeg and Somedic algometer). Results: Results demonstrated high usability scores (mean 4.42, SD 0.56; out of 5), indicating ease of use and acceptance, with minimal side effects (mean 1.18, SD 0.46; out of a possible 4 points on the Simulator Sickness Questionnaire). The PPG device showed high heart rate measurement precision, which improved with optimized filtering and peak detection algorithms. However, compared with previous work, no significant effects on body ownership and pain perception were observed between the synchronous and asynchronous conditions. These findings are discussed in the context of existing literature on VR interventions for chronic pain. Conclusions: In conclusion, while the new VR setup showed high usability and minimal side effects, it did not significantly affect ownership or pain perception. This highlights the need for further research to refine VR-based interventions for chronic pain management, considering factors like visual realism and perspective. UR - https://games.jmir.org/2024/1/e52340 UR - http://dx.doi.org/10.2196/52340 ID - info:doi/10.2196/52340 ER - TY - JOUR AU - Fobelets, Kristel AU - Mohanty, Nikita AU - Thielemans, Mara AU - Thielemans, Lieze AU - Lake-Thompson, Gillian AU - Liu, Meijing AU - Jopling, Kate AU - Yang, Kai PY - 2024/12/10 TI - User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey JO - JMIR Biomed Eng SP - e58166 VL - 9 KW - health technology KW - wearability of knitted sensors KW - focus groups KW - asthma observation KW - medical device KW - wearable device KW - medical instrument KW - medical equipment KW - medical tool KW - sensor KW - physiological sensor KW - focus group KW - breathing KW - respiratory KW - respirology KW - lung KW - monitoring KW - monitor KW - health monitoring N2 - Background: Long-term unobtrusive monitoring of breathing patterns can potentially give a more realistic insight into the respiratory health of people with asthma or chronic obstructive pulmonary disease than brief tests performed in medical environments. However, it is uncertain whether users would be willing to wear these sensor garments long term. Objective: Our objective was to explore whether users would wear ordinary looking knitted garments with unobtrusive knitted-in breathing sensors long term to monitor their lung health and under what conditions. Methods: Multiple knitted breathing sensor garments, developed and fabricated by the research team, were presented during a demonstration. Participants were encouraged to touch and feel the garments and ask questions. This was followed by two semistructured, independently led focus groups with a total of 16 adults, of whom 4 had asthma. The focus group conversations were recorded and transcribed. Thematic analysis was carried out by three independent researchers in 3 phases consisting of familiarization with the data, independent coding, and overarching theme definition. Participants also completed a web-based questionnaire to probe opinion about wearability and functionality of the garments. Quantitative analysis of the sensors? performance was mapped to participants? garment preference to support the feasibility of the technology for long-term wear. Results: Key points extracted from the qualitative data were (1) garments are more likely to be worn if medically prescribed, (2) a cotton vest worn as underwear was preferred, and (3) a breathing crisis warning system was seen as a promising application. The qualitative analysis showed a preference for a loose-fitting garment style with short sleeves (13/16 participants), 11 out of 16 would also wear snug fitting garments and none of the participants would wear tight-fitting garments over a long period of time. In total, 10 out of 16 participants would wear the snug fitting knitted garment for the whole day and 13 out of 16 would be happy to wear it only during the night if not too hot. The sensitivity demands on the knitted wearable sensors can be aligned with most users? garment preferences (snug fit). Conclusions: There is an overall positive opinion about wearing a knitted sensor garment over a long period of time for monitoring respiratory health. The knit cannot be tight but a snugly fitted vest as underwear in a breathable material is acceptable for most participants. These requirements can be fulfilled with the proposed garments. Participants with asthma supported using it as a sensor garment connected to an asthma attack alert system. UR - https://biomedeng.jmir.org/2024/1/e58166 UR - http://dx.doi.org/10.2196/58166 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58166 ER - TY - JOUR AU - Lad, Meher AU - Taylor, John-Paul AU - Griffiths, David Timothy PY - 2024/12/9 TI - Reliable Web-Based Auditory Cognitive Testing: Observational Study JO - J Med Internet Res SP - e58444 VL - 26 KW - auditory testing KW - hearing loss KW - cognitive testing KW - auditory KW - observational study KW - older adult KW - hearing KW - questionnaire KW - auditory cognitive testing KW - in person KW - web-based setting KW - auditory memory KW - Pearson KW - female KW - women KW - audiology N2 - Background: Web-based experimentation, accelerated by the COVID-19 pandemic, has enabled large-scale participant recruitment and data collection. Auditory testing on the web has shown promise but faces challenges such as uncontrolled environments and verifying headphone use. Prior studies have successfully replicated auditory experiments but often involved younger participants, limiting the generalizability to older adults with varying hearing abilities. This study explores the feasibility of conducting reliable auditory cognitive testing using a web-based platform, especially among older adults. Objective: This study aims to determine whether demographic factors such as age and hearing status influence participation in web-based auditory cognitive experiments and to assess the reproducibility of auditory cognitive measures?specifically speech-in-noise perception and auditory memory (AuM)?between in-person and web-based settings. Additionally, this study aims to examine the relationship between musical sophistication, measured by the Goldsmiths Musical Sophistication Index (GMSI), and auditory cognitive measures across different testing environments. Methods: A total of 153 participants aged 50 to 86 years were recruited from local registries and memory clinics; 58 of these returned for web-based, follow-up assessments. An additional 89 participants from the PREVENT cohort were included in the web-based study, forming a combined sample. Participants completed speech-in-noise perception tasks (Digits-in-Noise and Speech-in-Babble), AuM tests for frequency and amplitude modulation rate, and the GMSI questionnaire. In-person testing was conducted in a soundproof room with standardized equipment, while web-based tests required participants to use headphones in a quiet room via a web-based app. The reproducibility of auditory measures was evaluated using Pearson and intraclass correlation coefficients, and statistical analyses assessed relationships between variables across settings. Results: Older participants and those with severe hearing loss were underrepresented in the web-based follow-up. The GMSI questionnaire demonstrated the highest reproducibility (r=0.82), while auditory cognitive tasks showed moderate reproducibility (Digits-in-Noise and Speech-in-Babble r=0.55 AuM tests for frequency r=0.75 and amplitude modulation rate r=0.44). There were no significant differences in the correlation between age and auditory measures across in-person and web-based settings (all P>.05). The study replicated previously reported associations between AuM and GMSI scores, as well as sentence-in-noise perception, indicating consistency across testing environments. Conclusions: Web-based auditory cognitive testing is feasible and yields results comparable to in-person testing, especially for questionnaire-based measures like the GMSI. While auditory tasks demonstrated moderate reproducibility, the consistent replication of key associations suggests that web-based testing is a viable alternative for auditory cognition research. However, the underrepresentation of older adults and those with severe hearing loss highlights a need to address barriers to web-based participation. Future work should explore methods to enhance inclusivity, such as remote guided testing, and address factors like digital literacy and equipment standards to improve the representativeness and quality of web-based auditory research. UR - https://www.jmir.org/2024/1/e58444 UR - http://dx.doi.org/10.2196/58444 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58444 ER - TY - JOUR AU - Miladinovi?, Aleksandar AU - Quaia, Christian AU - Kresevic, Simone AU - Aj?evi?, Milo? AU - Diplotti, Laura AU - Michieletto, Paola AU - Accardo, Agostino AU - Pensiero, Stefano PY - 2024/12/9 TI - High-Resolution Eye-Tracking System for Accurate Measurement of Short-Latency Ocular Following Responses: Development and Observational Study JO - JMIR Pediatr Parent SP - e64353 VL - 7 KW - ocular following response KW - stereopsis KW - video-oculography KW - ocular KW - tracker KW - vision KW - pediatric KW - children KW - youth KW - infrared KW - algorithm KW - eye tracking N2 - Background: Ocular following responses (OFRs)?small-amplitude, short-latency reflexive eye movements?have been used to study visual motion processing, with potential diagnostic applications. However, they are difficult to record with commercial, video-based eye trackers, especially in children. Objective: We aimed to design and develop a noninvasive eye tracker specialized for measuring OFRs, trading off lower temporal resolution and a smaller range for higher spatial resolution. Methods: We developed a high-resolution eye-tracking system based on a high-resolution camera operating in the near-infrared spectral range, coupled with infrared illuminators and a dedicated postprocessing pipeline, optimized to measure OFRs in children. To assess its performance, we: (1) evaluated our algorithm for compensating small head movements in both artificial and real-world settings, (2) compared OFRs measured simultaneously by our system and a reference scleral search coil eye-tracking system, and (3) tested the system?s ability to measure OFRs in a clinical setting with children. Results: The simultaneous measurement by our system and a reference system showed that our system achieved an in vivo resolution of approximately 0.06°, which is sufficient for recording OFRs. Head motion compensation was successfully tested, showing a displacement error of less than 5 ?m. Finally, robust OFRs were detected in 16 children during recording sessions lasting less than 5 minutes. Conclusions: Our high-resolution, noninvasive eye-tracking system successfully detected OFRs with minimal need for subject cooperation. The system effectively addresses the limits of other OFR measurement methods and offers a versatile solution suitable for clinical applications, particularly in children, where eye tracking is more challenging. The system could potentially be suitable for diagnostic applications, particularly in pediatric populations where early detection of visual disorders like stereodeficiencies is critical. UR - https://pediatrics.jmir.org/2024/1/e64353 UR - http://dx.doi.org/10.2196/64353 ID - info:doi/10.2196/64353 ER - TY - JOUR AU - Lee, Ting-Yi AU - Chen, Ching-Hsuan AU - Chen, I-Ming AU - Chen, Hsi-Chung AU - Liu, Chih-Min AU - Wu, Shu-I AU - Hsiao, Kate Chuhsing AU - Kuo, Po-Hsiu PY - 2024/12/6 TI - Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study JO - J Med Internet Res SP - e55635 VL - 26 KW - ecological momentary assessment KW - digital phenotyping KW - GPS mobility KW - bipolar disorder KW - major depressive disorder KW - GPS KW - global positioning system KW - mood disorders KW - assessment KW - depression KW - anxiety KW - digital phenotype KW - smartphone app KW - technology KW - behavioral changes KW - patient KW - monitoring N2 - Background: Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring. Objective: This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology. Methods: Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints. Results: Our findings revealed a significant negative correlation between normalized entropy (r=?0.353; P=.04) and weekly depressed mood as well as between location variance (r=?0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (?=0.256; P=.03), depressed mood (?=0.235; P=.01), and irritability (?=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (?=?0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (?=?0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns. Conclusions: This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients? conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home. UR - https://www.jmir.org/2024/1/e55635 UR - http://dx.doi.org/10.2196/55635 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55635 ER - TY - JOUR AU - Chen, Hongbo AU - Alfred, Myrtede AU - Brown, D. Andrew AU - Atinga, Angela AU - Cohen, Eldan PY - 2024/12/5 TI - Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study JO - JMIR Form Res SP - e59045 VL - 8 KW - explainable artificial intelligence KW - deep learning KW - chest x-ray KW - thoracic pathology KW - fairness KW - interpretability N2 - Background: While deep learning classifiers have shown remarkable results in detecting chest X-ray (CXR) pathologies, their adoption in clinical settings is often hampered by the lack of transparency. To bridge this gap, this study introduces the neural prototype tree (NPT), an interpretable image classifier that combines the diagnostic capability of deep learning models and the interpretability of the decision tree for CXR pathology detection. Objective: This study aimed to investigate the utility of the NPT classifier in 3 dimensions, including performance, interpretability, and fairness, and subsequently examined the complex interaction between these dimensions. We highlight both local and global explanations of the NPT classifier and discuss its potential utility in clinical settings. Methods: This study used CXRs from the publicly available Chest X-ray 14, CheXpert, and MIMIC-CXR datasets. We trained 6 separate classifiers for each CXR pathology in all datasets, 1 baseline residual neural network (ResNet)?152, and 5 NPT classifiers with varying levels of interpretability. Performance, interpretability, and fairness were measured using the area under the receiver operating characteristic curve (ROC AUC), interpretation complexity (IC), and mean true positive rate (TPR) disparity, respectively. Linear regression analyses were performed to investigate the relationship between IC and ROC AUC, as well as between IC and mean TPR disparity. Results: The performance of the NPT classifier improved as the IC level increased, surpassing that of ResNet-152 at IC level 15 for the Chest X-ray 14 dataset and IC level 31 for the CheXpert and MIMIC-CXR datasets. The NPT classifier at IC level 1 exhibited the highest degree of unfairness, as indicated by the mean TPR disparity. The magnitude of unfairness, as measured by the mean TPR disparity, was more pronounced in groups differentiated by age (chest X-ray 14 0.112, SD 0.015; CheXpert 0.097, SD 0.010; MIMIC 0.093, SD 0.017) compared to sex (chest X-ray 14 0.054 SD 0.012; CheXpert 0.062, SD 0.008; MIMIC 0.066, SD 0.013). A significant positive relationship between interpretability (ie, IC level) and performance (ie, ROC AUC) was observed across all CXR pathologies (P<.001). Furthermore, linear regression analysis revealed a significant negative relationship between interpretability and fairness (ie, mean TPR disparity) across age and sex subgroups (P<.001). Conclusions: By illuminating the intricate relationship between performance, interpretability, and fairness of the NPT classifier, this research offers insightful perspectives that could guide future developments in effective, interpretable, and equitable deep learning classifiers for CXR pathology detection. UR - https://formative.jmir.org/2024/1/e59045 UR - http://dx.doi.org/10.2196/59045 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59045 ER - TY - JOUR AU - de Boer, Kathleen AU - Mackelprang, L. Jessica AU - Nedeljkovic, Maja AU - Meyer, Denny AU - Iyer, Ravi PY - 2024/12/2 TI - Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis JO - JMIR Res Protoc SP - e54966 VL - 13 KW - family violence KW - artificial intelligence KW - natural language processing KW - voice signal characteristics KW - public health KW - behaviors KW - research literature KW - policy KW - prevalence KW - detection KW - social policy KW - prevention KW - machine learning KW - mental health KW - suicide risk KW - psychological distress N2 - Background: Despite the implementation of prevention strategies, family violence continues to be a prevalent issue worldwide. Current strategies to reduce family violence have demonstrated mixed success and innovative approaches are needed urgently to prevent the occurrence of family violence. Incorporating artificial intelligence (AI) into prevention strategies is gaining research attention, particularly the use of textual or voice signal data to detect individuals at risk of perpetrating family violence. However, no review to date has collated extant research regarding how accurate AI is at identifying individuals who are at risk of perpetrating family violence. Objective: The primary aim of this systematic review and meta-analysis is to assess the accuracy of AI models in differentiating between individuals at risk of engaging in family violence versus those who are not using textual or voice signal data. Methods: The following databases will be searched from conception to the search date: IEEE Xplore, PubMed, PsycINFO, EBSCOhost (Psychology and Behavioral Sciences collection), and Computers and Applied Sciences Complete. ProQuest Dissertations and Theses A&I will also be used to search the grey literature. Studies will be included if they report on human adults and use machine learning to differentiate between low and high risk of family violence perpetration. Studies may use voice signal data or linguistic (textual) data and must report levels of accuracy in determining risk. In the data screening and full-text review and quality analysis phases, 2 researchers will review the search results and discrepancies and decisions will be resolved through masked review of a third researcher. Results will be reported in a narrative synthesis. In addition, a random effects meta-analysis will be conducted using the area under the receiver operating curve reported in the included studies, assuming sufficient eligible studies are identified. Methodological quality of included studies will be assessed using the risk of bias tool in nonrandomized studies of interventions. Results: As of October 2024, the search has not commenced. The review will document the state of the research concerning the accuracy of AI models in detecting the risk of family violence perpetration using textual or voice signal data. Results will be presented in the form of a narrative synthesis. Results of the meta-analysis will be summarized in tabular form and using a forest plot. Conclusions: The findings from this study will clarify the state of the literature on the accuracy of machine learning models to identify individuals who are at high risk of perpetuating family violence. Findings may be used to inform the development of AI and machine learning models that can be used to support possible prevention strategies. Trial Registration: PROSPERO CRD42023481174; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481174 International Registered Report Identifier (IRRID): PRR1-10.2196/54966 UR - https://www.researchprotocols.org/2024/1/e54966 UR - http://dx.doi.org/10.2196/54966 UR - http://www.ncbi.nlm.nih.gov/pubmed/39621402 ID - info:doi/10.2196/54966 ER - TY - JOUR AU - Liu, Zhongling AU - Li, Jinkai AU - Zhang, Yuanyuan AU - Wu, Dan AU - Huo, Yanyan AU - Yang, Jianxin AU - Zhang, Musen AU - Dong, Chuanfei AU - Jiang, Luhui AU - Sun, Ruohan AU - Zhou, Ruoyin AU - Li, Fei AU - Yu, Xiaodan AU - Zhu, Daqian AU - Guo, Yao AU - Chen, Jinjin PY - 2024/11/29 TI - Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study JO - JMIR Mhealth Uhealth SP - e58927 VL - 12 KW - attention deficit disorder with hyperactivity KW - eye-tracking KW - auxiliary diagnosis KW - digital biomarker KW - antisaccade KW - machine learning N2 - Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in school-aged children. The lack of objective biomarkers for ADHD often results in missed diagnoses or misdiagnoses, which lead to inappropriate or delayed interventions. Eye-tracking technology provides an objective method to assess children?s neuropsychological behavior. Objective: The aim of this study was to develop an objective and reliable auxiliary diagnostic system for ADHD using eye-tracking technology. This system would be valuable for screening for ADHD in schools and communities and may help identify objective biomarkers for the clinical diagnosis of ADHD. Methods: We conducted a case-control study of children with ADHD and typically developing (TD) children. We designed an eye-tracking assessment paradigm based on the core cognitive deficits of ADHD and extracted various digital biomarkers that represented participant behaviors. These biomarkers and developmental patterns were compared between the ADHD and TD groups. Machine learning (ML) was implemented to validate the ability of the extracted eye-tracking biomarkers to predict ADHD. The performance of the ML models was evaluated using 5-fold cross-validation. Results: We recruited 216 participants, of whom 94 (43.5%) were children with ADHD and 122 (56.5%) were TD children. The ADHD group showed significantly poorer performance (for accuracy and completion time) than the TD group in the prosaccade, antisaccade, and delayed saccade tasks. In addition, there were substantial group differences in digital biomarkers, such as pupil diameter fluctuation, regularity of gaze trajectory, and fixations on unrelated areas. Although the accuracy and task completion speed of the ADHD group increased over time, their eye-movement patterns remained irregular. The TD group with children aged 5 to 6 years outperformed the ADHD group with children aged 9 to 10 years, and this difference remained relatively stable over time, which indicated that the ADHD group followed a unique developmental pattern. The ML model was effective in discriminating the groups, achieving an area under the curve of 0.965 and an accuracy of 0.908. Conclusions: The eye-tracking biomarkers proposed in this study effectively identified differences in various aspects of eye-movement patterns between the ADHD and TD groups. In addition, the ML model constructed using these digital biomarkers achieved high accuracy and reliability in identifying ADHD. Our system can facilitate early screening for ADHD in schools and communities and provide clinicians with objective biomarkers as a reference. UR - https://mhealth.jmir.org/2024/1/e58927 UR - http://dx.doi.org/10.2196/58927 UR - http://www.ncbi.nlm.nih.gov/pubmed/39477792 ID - info:doi/10.2196/58927 ER - TY - JOUR AU - Barnes, Keely AU - Sveistrup, Heidi AU - Bayley, Mark AU - Egan, Mary AU - Bilodeau, Martin AU - Rathbone, Michel AU - Taljaard, Monica AU - Karimijashni, Motahareh AU - Marshall, Shawn PY - 2024/11/27 TI - Investigation of Study Procedures to Estimate Sensitivity and Reliability of a Virtual Physical Assessment Developed for Workplace Concussions: Method-Comparison Feasibility Study JO - JMIR Neurotech SP - e57661 VL - 3 KW - brain injury KW - virtual KW - assessment KW - remote KW - evaluation KW - concussion KW - adult KW - clinician review KW - in-person KW - comparison KW - sensitivity KW - reliability KW - acceptability survey KW - feasibility study KW - psychometric properties KW - vestibular/ocular motor screening KW - VOMS KW - workplace KW - clinician KW - hospital KW - rehabilitation center KW - brain KW - neurology KW - neuroscience KW - neurotechnology KW - technology KW - digital intervention KW - digital health KW - psychometrics KW - physical assessment KW - clinical assessment KW - workplace safety KW - mobile phone N2 - Background: Remote approaches to workplace concussion assessment have demonstrated value to end users. The feasibility of administering physical concussion assessment measures in a remote context has been minimally explored, and there is limited information on important psychometric properties of physical assessment measures used in remote contexts. Objective: The objectives of this feasibility study were to determine recruitment capability for a future larger-scale study aimed at determining sensitivity and reliability of the remote assessment, time required to complete study assessments, and acceptability of remote assessment to people with brain injuries and clinicians; document preliminary results of the sensitivity of the remote assessment when compared to the in-person assessment; and estimate the preliminary interrater and intrarater reliability of the remote assessments to inform procedures of a future larger-scale study that is adequately powered to reliably estimate these parameters of interest. Methods: People living with acquired brain injury attended 2 assessments (1 in-person and 1 remote) in a randomized order. The measures administered in these assessments included the finger-to-nose test; balance testing; and the Vestibular/Ocular Motor Screening (VOMS) tool, including documentation of change in symptoms and distance for near point convergence, saccades, cervical spine range of motion, and evaluation of effort. Both assessments occurred at the Ottawa Hospital Rehabilitation Center. After the assessments, a clinician different from the person who completed the original assessments then viewed and documented findings independently on the recordings of the remote assessment. The same second clinician viewed the recording again approximately 1 month following the initial observation. Results: The rate of recruitment was 61% (20/33) of people approached, with a total of 20 patient-participants included in the feasibility study. A total of 3 clinicians participated as assessors. The length of time required to complete the in-person and remote assessment procedures averaged 9 and 13 minutes, respectively. The majority of clinicians and patient-participants agreed or strongly agreed that they were confident in the findings on both in-person and remote assessments. Feedback obtained revolved around technology (eg, screen size), lighting, and fatigue of participants in the second assessment. Preliminary estimates of sensitivity of the remote assessment ranged from poor (finger-to-nose testing: 0.0) to excellent (near point convergence: 1.0). Preliminary estimates of reliability of the remote assessment ranged from poor (balance testing, saccades, and range of motion: ?=0.38?0.49) to excellent (VOMS change in symptoms: ?=1.0). Conclusions: The results of this feasibility study indicate that our study procedures are feasible and acceptable to participants. Certain measures show promising psychometric properties (reliability and sensitivity); however, wide CIs due to the small sample size limit the ability to draw definitive conclusions. A planned follow-up study will expand on this work and include a sufficiently large sample to estimate these important properties with acceptable precision. International Registered Report Identifier (IRRID): RR2-10.2196/57663 UR - https://neuro.jmir.org/2024/1/e57661 UR - http://dx.doi.org/10.2196/57661 ID - info:doi/10.2196/57661 ER - TY - JOUR AU - Van De Sijpe, Greet AU - Gijsen, Matthias AU - Van der Linden, Lorenz AU - Strouven, Stephanie AU - Simons, Eline AU - Martens, Emily AU - Persan, Nele AU - Grootaert, Veerle AU - Foulon, Veerle AU - Casteels, Minne AU - Verelst, Sandra AU - Vanbrabant, Peter AU - De Winter, Sabrina AU - Spriet, Isabel PY - 2024/11/27 TI - A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study JO - J Med Internet Res SP - e55185 VL - 26 KW - medication reconciliation KW - medication discrepancy KW - emergency department KW - prediction model KW - risk stratification KW - MED-REC predictor KW - MED-REC KW - predictor KW - patient KW - medication KW - hospital KW - software-implemented prediction model KW - software KW - geographic validation KW - geographic N2 - Background: Many patients do not receive a comprehensive medication reconciliation, mostly owing to limited resources. We hence need an approach to identify those patients at the emergency department (ED) who are at increased risk for clinically relevant discrepancies. Objective: The aim of our study was to develop and externally validate a prediction model to identify patients at risk for at least 1 clinically relevant medication discrepancy upon ED presentation. Methods: A prospective, multicenter, observational study was conducted at the University Hospitals Leuven and General Hospital Sint-Jan Brugge-Oostende AV, Belgium. Medication histories were obtained from patients admitted to the ED between November 2017 and May 2022, and clinically relevant medication discrepancies were identified. Three distinct datasets were created for model development, temporal external validation, and geographic external validation. Multivariable logistic regression with backward stepwise selection was used to select the final model. The presence of at least 1 clinically relevant discrepancy was the dependent variable. The model was evaluated by measuring calibration, discrimination, classification, and net benefit. Results: We included 824, 350, and 119 patients in the development, temporal validation, and geographic validation dataset, respectively. The final model contained 8 predictors, for example, age, residence before admission, number of drugs, and number of drugs of certain drug classes based on Anatomical Therapeutic Chemical coding. Temporal validation showed excellent calibration with a slope of 1.09 and an intercept of 0.18. Discrimination was moderate with a c-index (concordance index) of 0.67 (95% CI 0.61-0.73). In the geographic validation dataset, the calibration slope and intercept were 1.35 and 0.83, respectively, and the c-index was 0.68 (95% CI 0.58-0.78). The model showed net benefit over a range of clinically reasonable threshold probabilities and outperformed other selection criteria. Conclusions: Our software-implemented prediction model shows moderate performance, outperforming random or typical selection criteria for medication reconciliation. Depending on available resources, the probability threshold can be customized to increase either the specificity or the sensitivity of the model. UR - https://www.jmir.org/2024/1/e55185 UR - http://dx.doi.org/10.2196/55185 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55185 ER - TY - JOUR AU - Oh, Soyeon Sarah AU - Kang, Bada AU - Hong, Dahye AU - Kim, Ivy Jennifer AU - Jeong, Hyewon AU - Song, Jinyeop AU - Jeon, Minkyu PY - 2024/11/22 TI - A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation JO - JMIR Med Inform SP - e59396 VL - 12 KW - mild cognitive impairment KW - machine learning algorithms KW - sociodemographic factors KW - gerontology KW - geriatrics KW - older people KW - aging KW - MCI KW - dementia KW - Alzheimer KW - cognitive KW - machine learning KW - prediction KW - algorithm N2 - Background: Mild cognitive impairment (MCI) poses significant challenges in early diagnosis and timely intervention. Underdiagnosis, coupled with the economic and social burden of dementia, necessitates more precise detection methods. Machine learning (ML) algorithms show promise in managing complex data for MCI and dementia prediction. Objective: This study assessed the predictive accuracy of ML models in identifying the onset of MCI and dementia using the Korean Longitudinal Study of Aging (KLoSA) dataset. Methods: This study used data from the KLoSA, a comprehensive biennial survey that tracks the demographic, health, and socioeconomic aspects of middle-aged and older Korean adults from 2018 to 2020. Among the 6171 initial households, 4975 eligible older adult participants aged 60 years or older were selected after excluding individuals based on age and missing data. The identification of MCI and dementia relied on self-reported diagnoses, with sociodemographic and health-related variables serving as key covariates. The dataset was categorized into training and test sets to predict MCI and dementia by using multiple models, including logistic regression, light gradient-boosting machine, XGBoost (extreme gradient boosting), CatBoost, random forest, gradient boosting, AdaBoost, support vector classifier, and k-nearest neighbors, and the training and test sets were used to evaluate predictive performance. The performance was assessed using the area under the receiver operating characteristic curve (AUC). Class imbalances were addressed via weights. Shapley additive explanation values were used to determine the contribution of each feature to the prediction rate. Results: Among the 4975 participants, the best model for predicting MCI onset was random forest, with a median AUC of 0.6729 (IQR 0.3883-0.8152), followed by k-nearest neighbors with a median AUC of 0.5576 (IQR 0.4555-0.6761) and support vector classifier with a median AUC of 0.5067 (IQR 0.3755-0.6389). For dementia onset prediction, the best model was XGBoost, achieving a median AUC of 0.8185 (IQR 0.8085-0.8285), closely followed by light gradient-boosting machine with a median AUC of 0.8069 (IQR 0.7969-0.8169) and AdaBoost with a median AUC of 0.8007 (IQR 0.7907-0.8107). The Shapley values highlighted pain in everyday life, being widowed, living alone, exercising, and living with a partner as the strongest predictors of MCI. For dementia, the most predictive features were other contributing factors, education at the high school level, education at the middle school level, exercising, and monthly social engagement. Conclusions: ML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults. UR - https://medinform.jmir.org/2024/1/e59396 UR - http://dx.doi.org/10.2196/59396 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59396 ER - TY - JOUR AU - Schmollinger, Martin AU - Gerstner, Jessica AU - Stricker, Eric AU - Muench, Alexander AU - Breckwoldt, Benjamin AU - Sigle, Manuel AU - Rosenberger, Peter AU - Wunderlich, Robert PY - 2024/11/21 TI - Evaluation of an App-Based Mobile Triage System for Mass Casualty Incidents: Within-Subjects Experimental Study JO - J Med Internet Res SP - e65728 VL - 26 KW - disaster medicine KW - mass casualty incidents KW - digitalization KW - triage KW - Germany KW - mobile triage app N2 - Background: Digitalization in disaster medicine holds significant potential to accelerate rescue operations and ultimately save lives. Mass casualty incidents demand rapid and accurate information management to coordinate effective responses. Currently, first responders manually record triage results on patient cards, and brief information is communicated to the command post via radio communication. Although this process is widely used in practice, it involves several time-consuming and error-prone tasks. To address these issues, we designed, implemented, and evaluated an app-based mobile triage system. This system allows users to document responder details, triage categories, injury patterns, GPS locations, and other important information, which can then be transmitted automatically to the incident commanders. Objective: This study aims to design and evaluate an app-based mobile system as a triage and coordination tool for emergency and disaster medicine, comparing its effectiveness with the conventional paper-based system. Methods: A total of 38 emergency medicine personnel participated in a within-subject experimental study, completing 2 triage sessions with 30 patient cards each: one session using the app-based mobile system and the other using the paper-based tool. The accuracy of the triages and the time taken for each session were measured. Additionally, we implemented the User Experience Questionnaire along with other items to assess participants? subjective ratings of the 2 triage tools. Results: Our 2 (triage tool) × 2 (tool order) mixed multivariate analysis of variance revealed a significant main effect for the triage tool (P<.001). Post hoc analyses indicated that participants were significantly faster (P<.001) and more accurate (P=.005) in assigning patients to the correct triage category when using the app-based mobile system compared with the paper-based tool. Additionally, analyses showed significantly better subjective ratings for the app-based mobile system compared with the paper-based tool, in terms of both school grading (P<.001) and across all 6 scales of the User Experience Questionnaire (all P<.001). Of the 38 participants, 36 (95%) preferred the app-based mobile system. There was no significant main effect for tool order (P=.24) or session order (P=.06) in our model. Conclusions: Our findings demonstrate that the app-based mobile system not only matches the performance of the conventional paper-based tool but may even surpass it in terms of efficiency and usability. This advancement could further enhance the potential of digitalization to optimize processes in disaster medicine, ultimately leading to the possibility of saving more lives. UR - https://www.jmir.org/2024/1/e65728 UR - http://dx.doi.org/10.2196/65728 UR - http://www.ncbi.nlm.nih.gov/pubmed/39474975 ID - info:doi/10.2196/65728 ER - TY - JOUR AU - Pak, Lam Sharon Hoi AU - Wu, Chanchan AU - Choi, Ying Kitty Wai AU - Choi, Hang Edmond Pui PY - 2024/11/19 TI - Measuring Technology-Facilitated Sexual Violence and Abuse in the Chinese Context: Development Study and Content Validity Analysis JO - JMIR Form Res SP - e65199 VL - 8 KW - technology-facilitated sexual violence and abuse KW - TFSVA KW - image-based sexual abuse KW - sexual abuse KW - content validity KW - measurement KW - questionnaire KW - China N2 - Background: Technology-facilitated sexual violence and abuse (TFSVA) encompasses a range of behaviors where digital technologies are used to enable both virtual and in-person sexual violence. Given that TFSVA is an emerging and continually evolving form of sexual abuse, it has been challenging to establish a universally accepted definition or to develop standardized measures for its assessment. Objective: This study aimed to address the significant gap in research on TFSVA within the Chinese context. Specifically, it sought to develop a TFSVA measurement tool with robust content validity, tailored for use in subsequent epidemiological studies within the Chinese context. Methods: The first step in developing the measurement approach for TFSVA victimization and perpetration was to conduct a thorough literature review of existing empirical research on TFSVA and relevant measurement tools. After the initial generation of items, all the items were reviewed by an expert panel to assess the face validity. The measurement items were further reviewed by potential research participants, who were recruited through snowball sampling via online platforms. The assessment results were quantified by computing the content validity index (CVI). The participants were asked to rate each scale item in terms of its relevance, appropriateness, and clarity regarding the topic. Results: The questionnaire was reviewed by 24 lay experts, with a mean age of 27.96 years. They represented different genders and sexual orientations. The final questionnaire contained a total of 89 items. Three key domains were identified to construct the questionnaire, which included image-based sexual abuse, nonimage-based TFSVA, and online-initiated physical sexual violence. The overall scale CVI values of relevance, appropriateness, and clarity for the scale were 0.90, 0.96, and 0.97, respectively, which indicated high content validity for all the instrument items. To ensure the measurement accurately reflects the experiences of diverse demographic groups, the content validity was further analyzed by gender and sexual orientation. This analysis revealed variations in item validity among participants from different genders and sexual orientations. For instance, heterosexual male respondents showed a particularly low CVI for relevance of 0.20 in the items related to nudity, including ?male?s chest/nipples are visible? and ?the person is sexually suggestive.? This underscored the importance of an inclusive approach when developing a measurement for TFSVA. Conclusions: This study greatly advances the assessment of TFSVA by examining the content validity of our newly developed measurement. The findings revealed that our measurement tool demonstrated adequate content validity, thereby providing a strong foundation for assessing TFSVA within the Chinese context. Implementing this tool is anticipated to enhance our understanding of TFSVA and aid in the development of effective interventions to combat this form of abuse. UR - https://formative.jmir.org/2024/1/e65199 UR - http://dx.doi.org/10.2196/65199 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65199 ER - TY - JOUR AU - Slade, Christopher AU - Benzo, M. Roberto AU - Washington, Peter PY - 2024/11/18 TI - Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study JO - J Med Internet Res SP - e55694 VL - 26 KW - mobile health sensing KW - mHealth KW - active data collection KW - passive data collection KW - ecological momentary assessment KW - mobile data KW - mobile phone KW - machine learning KW - real-world setting KW - mixed method KW - college KW - student KW - user data KW - data consistency N2 - Background: Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist. Objective: We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions? Methods: We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection. Results: For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F1,144=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (?21=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks. Conclusions: We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning. UR - https://www.jmir.org/2024/1/e55694 UR - http://dx.doi.org/10.2196/55694 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55694 ER - TY - JOUR AU - Wang, Renwu AU - Xu, Huimin AU - Zhang, Xupin PY - 2024/11/15 TI - Impact of Image Content on Medical Crowdfunding Success: A Machine Learning Approach JO - J Med Internet Res SP - e58617 VL - 26 KW - medical crowdfunding KW - visual analytics KW - machine learning KW - image content KW - crowdfunding success N2 - Background: As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience. Objective: This paper aims to explore the relationship between images and crowdfunding success in cancer-related crowdfunding projects. Methods: We used the Alibaba Cloud platform to detect individual features in images. In addition, we used the Recognize Anything Model to label images and obtain content tags. Furthermore, the discourse atomic topic model was used to generate image topics. After obtaining the image features and image content topics, we built regression models to investigate the factors that influence the results of crowdfunding success. Results: Images with a higher proportion of young people (?=0.0753; P<.001), a larger number of people (?=0.00822; P<.001), and a larger proportion of smiling faces (?=0.0446; P<.001) had a higher success rate. Image content related to good things and patient health also contributed to crowdfunding success (?=0.082, P<.001; and ?=0.036, P<.001, respectively). In addition, the interaction between image topics and image characteristics had a significant effect on the final fundraising outcome. For example, when smiling faces are considered in conjunction with the image topics, using more smiling faces in the rest and play theme increased the amount of money raised (?=0.0152; P<.001). We also examined causality through a counterfactual analysis, which confirmed the influence of the variables on crowdfunding success, consistent with the results of our regression models. Conclusions: In the realm of web-based medical crowdfunding, the importance of uploaded images cannot be overstated. Image characteristics, including the number of people depicted and the presence of youth, significantly improve fundraising results. In addition, the thematic choice of images in cancer crowdfunding efforts has a profound impact. Images that evoke beauty and resonate with health issues are more likely to result in increased donations. However, it is critical to recognize that reinforcing character traits in images of different themes has different effects on the success of crowdfunding campaigns. UR - https://www.jmir.org/2024/1/e58617 UR - http://dx.doi.org/10.2196/58617 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58617 ER - TY - JOUR AU - Chung, Jane AU - Pretzer-Aboff, Ingrid AU - Parsons, Pamela AU - Falls, Katherine AU - Bulut, Eyuphan PY - 2024/11/12 TI - Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study JO - JMIR Res Protoc SP - e53447 VL - 13 KW - Wi-Fi sensing KW - dementia KW - mild cognitive impairment KW - older adults KW - health disparities KW - in-home activities KW - mobility KW - machine learning N2 - Background: Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. Objective: This study aims to examine the feasibility of developing a novel channel state information?based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. Methods: This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. Results: The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing?based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults? Wi-Fi sensing technology acceptance. Conclusions: This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. International Registered Report Identifier (IRRID): DERR1-10.2196/53447 UR - https://www.researchprotocols.org/2024/1/e53447 UR - http://dx.doi.org/10.2196/53447 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53447 ER - TY - JOUR AU - Agnello, Marie Danielle AU - Balaskas, George AU - Steiner, Artur AU - Chastin, Sebastien PY - 2024/11/11 TI - Methods Used in Co-Creation Within the Health CASCADE Co-Creation Database and Gray Literature: Systematic Methods Overview JO - Interact J Med Res SP - e59772 VL - 13 KW - co-creation KW - coproduction KW - co-design KW - methods KW - participatory KW - inventory KW - text mining KW - methodology KW - research methods KW - CASCADE N2 - Background: Co-creation is increasingly recognized for its potential to generate innovative solutions, particularly in addressing complex and wicked problems in public health. Despite this growing recognition, there are no standards or recommendations for method use in co-creation, leading to confusion and inconsistency. While some studies have examined specific methods, a comprehensive overview is lacking, limiting the collective understanding and ability to make informed decisions about the most appropriate methods for different contexts and research objectives. Objective: This study aimed to systematically compile and analyze methods used in co-creation to enhance transparency and deepen understanding of how co-creation is practiced. Methods: To enhance transparency and deepen understanding of how co-creation is practiced, this study systematically inventoried and analyzed methods used in co-creation. We conducted a systematic methods overview, applying 2 parallel processes: one within the peer-reviewed Health CASCADE Co-Creation Database and another within gray literature. An artificial intelligence?assisted recursive search strategy, coupled with a 2-step screening process, ensured that we captured relevant methods. We then extracted method names and conducted textual, comparative, and bibliometric analyses to assess the content, relationship between methods, fields of research, and the methodological underpinnings of the included sources. Results: We examined a total of 2627 academic papers and gray literature sources, with the literature primarily drawn from health sciences, medical research, and health services research. The dominant methodologies identified were co-creation, co-design, coproduction, participatory research methodologies, and public and patient involvement. From these sources, we extracted and analyzed 956 co-creation methods, noting that only 10% (n=97) of the methods overlap between academic and gray literature. Notably, 91.3% (230/252) of the methods in academic literature co-occurred, often involving combinations of multiple qualitative methods. The most frequently used methods in academic literature included surveys, focus groups, photo voice, and group discussion, whereas gray literature highlighted methods such as world café, focus groups, role-playing, and persona. Conclusions: This study presents the first systematic overview of co-creation methods, providing a clear understanding of the diverse methods currently in use. Our findings reveal a significant methodological gap between researchers and practitioners, offering insights into the relative prevalence and combinations of methods. By shedding light on these methods, this study helps bridge the gap and supports researchers in making informed decisions about which methods to apply in their work. Additionally, it offers a foundation for further investigation into method use in co-creation. This systematic investigation is a valuable resource for anyone engaging in co-creation or similar participatory methodologies, helping to navigate the diverse landscape of methods. UR - https://www.i-jmr.org/2024/1/e59772 UR - http://dx.doi.org/10.2196/59772 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59772 ER - TY - JOUR AU - Calderon Ramirez, Lucrecia Claudia AU - Farmer, Yanick AU - Downar, James AU - Frolic, Andrea AU - Opatrny, Lucie AU - Poirier, Diane AU - Bravo, Gina AU - L'Espérance, Audrey AU - Gaucher, Nathalie AU - Payot, Antoine AU - Dahine, Joseph AU - Tanuseputro, Peter AU - Rousseau, Louis-Martin AU - Dumez, Vincent AU - Descôteaux, Annie AU - Dallaire, Clara AU - Laporte, Karell AU - Bouthillier, Marie-Eve PY - 2024/11/11 TI - Assessing the Quality of an Online Democratic Deliberation on COVID-19 Pandemic Triage Protocols for Access to Critical Care in an Extreme Pandemic Context: Mixed Methods Study JO - J Particip Med SP - e54841 VL - 16 KW - quality assessment KW - online democratic deliberation KW - COVID-19 triage or prioritization KW - critical care KW - clinical ethics N2 - Background: Online democratic deliberation (ODD) may foster public engagement in new health strategies by providing opportunities for knowledge exchange between experts, policy makers, and the public. It can favor decision-making by generating new points of view and solutions to existing problems. Deliberation experts recommend gathering feedback from participants to optimize future implementation. However, this online modality has not been frequently evaluated. Objective: This study aims to (1) assess the quality of an ODD held in Quebec and Ontario, Canada, on the topic of COVID-19 triage protocols for access to critical care in an extreme pandemic context and (2) determine its transformative aspect according to the perceptions of participants. Methods: We conducted a simultaneous ODD in Quebec and Ontario on May 28 and June 4, 2022, with a diversified target audience not working in the health care system. We used a thematic analysis for the transcripts of the deliberation and the written comments of the participants related to the quality of the process. Participants responded to a postdeliberation questionnaire to assess the quality of the ODD and identify changes in their perspectives on COVID-19 pandemic triage protocols after the deliberation exercise. Descriptive statistics were used. An index was calculated to determine equality of participation. Results: The ODD involved 47 diverse participants from the public (n=20, 43% from Quebec and n=27, 57% from Ontario). Five themes emerged: (1) process appreciation, (2) learning experience, (3) reflecting on the common good, (4) technological aspects, and (5) transformative aspects. A total of 46 participants responded to the questionnaire. Participants considered the quality of the ODD satisfactory in terms of process, information shared, reasoning, and videoconferencing. A total of 4 (80%) of 5 participants reported at least 1 change of perspective on some of the criteria and values discussed. Most participants reported that the online modality was accessible and user-friendly. We found low polarization when calculating equal participation. Improvements identified were measures to replace participants when unable to connect and optimization of time during discussions. Conclusions: Overall, the participants perceived the quality of ODD as satisfactory. Some participants self-reported a change of opinion after deliberation. The online modality may be an acceptable alternative for democratic deliberation but with some organizational adaptations. UR - https://jopm.jmir.org/2024/1/e54841 UR - http://dx.doi.org/10.2196/54841 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54841 ER - TY - JOUR AU - Lin, Yu-Chun AU - Yan, Huang-Ting AU - Lin, Chih-Hsueh AU - Chang, Hen-Hong PY - 2024/11/8 TI - Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study JO - J Med Internet Res SP - e58466 VL - 26 KW - frailty phenotypes KW - older adults KW - successful aging KW - vocal biomarkers KW - frailty KW - phenotype KW - vocal biomarker KW - cross-sectional KW - gerontology KW - geriatrics KW - older adult KW - Taiwan KW - energy-based KW - hybrid-based KW - sarcopenia N2 - Background: Researchers have developed a variety of indices to assess frailty. Recent research indicates that the human voice reflects frailty status. Frailty phenotypes are seldom discussed in the literature on the aging voice. Objective: This study aims to examine potential phenotypes of frail older adults and determine their correlation with vocal biomarkers. Methods: Participants aged ?60 years who visited the geriatric outpatient clinic of a teaching hospital in central Taiwan between 2020 and 2021 were recruited. We identified 4 frailty phenotypes: energy-based frailty, sarcopenia-based frailty, hybrid-based frailty?energy, and hybrid-based frailty?sarcopenia. Participants were asked to pronounce a sustained vowel ?/a/? for approximately 1 second. The speech signals were digitized and analyzed. Four voice parameters?the average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4)?were used for analyzing changes in voice. Logistic regression was used to elucidate the prediction model. Results: Among 277 older adults, an increase in A1 values was associated with a lower likelihood of energy-based frailty (odds ratio [OR] 0.81, 95% CI 0.68-0.96), whereas an increase in A2 values resulted in a higher likelihood of sarcopenia-based frailty (OR 1.34, 95% CI 1.18-1.52). Respondents with larger A3 and A4 values had a higher likelihood of hybrid-based frailty?sarcopenia (OR 1.03, 95% CI 1.002-1.06) and hybrid-based frailty?energy (OR 1.43, 95% CI 1.02-2.01), respectively. Conclusions: Vocal biomarkers might be potentially useful in estimating frailty phenotypes. Clinicians can use 2 crucial acoustic parameters, namely A1 and A2, to diagnose a frailty phenotype that is associated with insufficient energy or reduced muscle function. The assessment of A3 and A4 involves a complex frailty phenotype. UR - https://www.jmir.org/2024/1/e58466 UR - http://dx.doi.org/10.2196/58466 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58466 ER - TY - JOUR AU - Tagi, Masato AU - Hamada, Yasuhiro AU - Shan, Xiao AU - Ozaki, Kazumi AU - Kubota, Masanori AU - Amano, Sosuke AU - Sakaue, Hiroshi AU - Suzuki, Yoshiko AU - Konishi, Takeshi AU - Hirose, Jun PY - 2024/11/5 TI - A Food Intake Estimation System Using an Artificial Intelligence?Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study JO - JMIR Form Res SP - e55218 VL - 8 KW - artificial intelligence KW - machine learning KW - system development KW - food intake KW - dietary intake KW - dietary assessment KW - food consumption KW - image visual estimation KW - AI estimation KW - direct visual estimation N2 - Background: Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients? food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake. Objective: This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI?s estimation was compared with that of visual estimation for liquid foods served to hospitalized patients. Methods: The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method. Results: The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R2 value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (?=0.89-0.97), protein (?=0.94-0.97), fat (?=0.91-0.94), and carbohydrate (?=0.89-0.97). Conclusions: The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue. UR - https://formative.jmir.org/2024/1/e55218 UR - http://dx.doi.org/10.2196/55218 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55218 ER - TY - JOUR AU - Davis, Kevin AU - Curry, Laurel AU - Bradfield, Brian AU - Stupplebeen, A. David AU - Williams, J. Rebecca AU - Soria, Sandra AU - Lautsch, Julie PY - 2024/11/5 TI - The Validity of Impressions as a Media Dose Metric in a Tobacco Public Education Campaign Evaluation: Observational Study JO - J Med Internet Res SP - e55311 VL - 26 KW - communication KW - public education KW - tobacco KW - media KW - public health N2 - Background: Evaluation research increasingly needs alternatives to target or gross rating points to comprehensively measure total exposure to modern multichannel public education campaigns that use multiple channels, including TV, radio, digital video, and paid social media, among others. Ratings data typically only capture delivery of broadcast media (TV and radio) and excludes other channels. Studies are needed to validate objective cross-channel metrics such as impressions against self-reported exposure to campaign messages. Objective: This study aimed to examine whether higher a volume of total media campaign impressions is predictive of individual-level self-reported campaign exposure in California. Methods: We analyzed over 3 years of advertisement impressions from the California Tobacco Prevention Program?s statewide tobacco education campaigns from August 2019 through December 2022. Impressions data varied across designated market areas (DMAs) and across time. These data were merged to individual respondents from 45 waves of panel survey data of Californians aged 18-55 years (N=151,649). Impressions were merged to respondents based on respondents? DMAs and time of survey completion. We used logistic regression to estimate the odds of respondents? campaign recall as a function of cumulative and past 3-month impressions delivered to each respondent?s DMA. Results: Cumulative impressions were positively and significantly associated with recall of each of the Flavors Hook Kids (odds ratio [OR] 1.15, P<.001), Dark Balloons and Apartment (OR 1.20, P<.001), We Are Not Profit (OR 1.36, P<.001), Tell Your Story (E-cigarette, or Vaping, product use Associated Lung Injury; OR 1.06, P<.05), and Thrown Away and Little Big Lies (OR 1.05, P<.01) campaigns. Impressions delivered in the past 3 months were associated with recall of the Flavors Hook Kids (OR 1.13, P<.001), Dark Balloons and Apartment (OR 1.08, P<.001), We Are Not Profit (OR 1.14, P<.001), and Thrown Away and Little Big Lies (OR 1.04, P<.001) campaigns. Past 3-month impressions were not significantly associated with Tell Your Story campaign recall. Overall, magnitudes of these associations were greater for cumulative impressions. We visualize recall based on postestimation predicted values from our multivariate logistic regression models. Conclusions: Variation in cumulative impressions for California Tobacco Prevention Program?s long-term multichannel tobacco education campaign is predictive of increased self-reported campaign recall, suggesting that impressions may be a valid proxy for potential campaign exposure. The use of impressions for purposes of evaluating public education campaigns may help address current methodological limitations arising from the fragmented nature of modern multichannel media campaigns. UR - https://www.jmir.org/2024/1/e55311 UR - http://dx.doi.org/10.2196/55311 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55311 ER - TY - JOUR AU - Riad, Rachid AU - Denais, Martin AU - de Gennes, Marc AU - Lesage, Adrien AU - Oustric, Vincent AU - Cao, Nga Xuan AU - Mouchabac, Stéphane AU - Bourla, Alexis PY - 2024/10/31 TI - Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study JO - J Med Internet Res SP - e58572 VL - 26 KW - speech analysis KW - voice detection KW - voice analysis KW - speech biomarkers KW - speech-based systems KW - computer-aided diagnosis KW - mental health symptom detection KW - machine learning KW - mental health KW - fatigue KW - anxiety KW - depression N2 - Background: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess the limitations of speech-based systems, such as uncertainty, or fairness for a safe clinical deployment. Objective: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population. Methods: We included 865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically modeled speech with pretrained deep learning models that were pretrained on a large, open, and free database, and we selected the best one on the validation set. Based on the best speech modeling approach, clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, and education) were evaluated. We used a train-validation-test split for all evaluations: to develop our models, select the best ones, and assess the generalizability of held-out data. Results: The best model was Whisper M with a max pooling and oversampling method. Our methods achieved good detection performance for all symptoms, depression (Patient Health Questionnaire-9: area under the curve [AUC]=0.76; F1-score=0.49 and Beck Depression Inventory: AUC=0.78; F1-score=0.65), anxiety (Generalized Anxiety Disorder 7-item scale: AUC=0.77; F1-score=0.50), insomnia (Athens Insomnia Scale: AUC=0.73; F1-score=0.62), and fatigue (Multidimensional Fatigue Inventory total score: AUC=0.68; F1-score=0.88). The system performed well when it needed to abstain from making predictions, as demonstrated by low abstention rates in depression detection with the Beck Depression Inventory and fatigue, with risk-coverage AUCs below 0.4. Individual symptom scores were accurately predicted (correlations were all significant with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average disparity ratio [DR] 0.86, SD 0.13), to a lesser extent for education level (average DR 0.47, SD 0.30), and worse for age groups (average DR 0.33, SD 0.30). Conclusions: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems. UR - https://www.jmir.org/2024/1/e58572 UR - http://dx.doi.org/10.2196/58572 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58572 ER - TY - JOUR AU - Kallio, Johanna AU - Kinnula, Atte AU - Mäkelä, Satu-Marja AU - Järvinen, Sari AU - Räsänen, Pauli AU - Hosio, Simo AU - Bordallo López, Miguel PY - 2024/10/31 TI - Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis JO - J Med Internet Res SP - e50461 VL - 26 KW - field trial KW - behavioral research KW - sensor data KW - machine learning KW - pervasive technology KW - stakeholder engagement KW - qualitative coding KW - mobile phone N2 - Background: Pervasive technologies are used to investigate various phenomena outside the laboratory setting, providing valuable insights into real-world human behavior and interaction with the environment. However, conducting longitudinal field trials in natural settings remains challenging due to factors such as low recruitment success and high dropout rates due to participation burden or data quality issues with wireless sensing in changing environments. Objective: This study gathers insights and lessons from 3 real-world longitudinal field studies assessing human behavior and derives factors that impacted their research success. We aim to categorize challenges, observe how they were managed, and offer recommendations for designing and conducting studies involving human participants and pervasive technology in natural settings. Methods: We developed a qualitative coding framework to categorize and address the unique challenges encountered in real-life studies related to influential factor identification, stakeholder management, data harvesting and management, and analysis and interpretation. We applied inductive reasoning to identify issues and related mitigation actions in 3 separate field studies carried out between 2018 and 2022. These 3 field studies relied on gathering annotated sensor data. The topics involved stress and environmental assessment in an office and a school, collecting self-reports and wrist device and environmental sensor data from 27 participants for 3.5 to 7 months; work activity recognition at a construction site, collecting observations and wearable sensor data from 15 participants for 3 months; and stress recognition in location-independent knowledge work, collecting self-reports and computer use data from 57 participants for 2 to 5 months. Our key extension for the coding framework used a stakeholder identification method to identify the type and role of the involved stakeholder groups, evaluating the nature and degree of their involvement and influence on the field trial success. Results: Our analysis identifies 17 key lessons related to planning, implementing, and managing a longitudinal, sensor-based field study on human behavior. The findings highlight the importance of recognizing different stakeholder groups, including those not directly involved but whose areas of responsibility are impacted by the study and therefore have the power to influence it. In general, customizing communication strategies to engage stakeholders on their terms and addressing their concerns and expectations is essential, while planning for dropouts, offering incentives for participants, conducting field tests to identify problems, and using tools for quality assurance are relevant for successful outcomes. Conclusions: Our findings suggest that field trial implementation should include additional effort to clarify the expectations of stakeholders and to communicate with them throughout the process. Our framework provides a structured approach that can be adopted by other researchers in the field, facilitating robust and comparable studies across different contexts. Constantly managing the possible challenges will lead to better success in longitudinal field trials and developing future technology-based solutions. UR - https://www.jmir.org/2024/1/e50461 UR - http://dx.doi.org/10.2196/50461 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50461 ER - TY - JOUR AU - Park, Jin-Hyuck PY - 2024/10/30 TI - Discriminant Power of Smartphone-Derived Keystroke Dynamics for Mild Cognitive Impairment Compared to a Neuropsychological Screening Test: Cross-Sectional Study JO - J Med Internet Res SP - e59247 VL - 26 KW - digital biomarker KW - motor function KW - digital device KW - neuropsychological screening KW - screening tools KW - cognitive assessment KW - mild cognitive impairment KW - keystroke dynamics N2 - Background: Conventional neuropsychological screening tools for mild cognitive impairment (MCI) face challenges in terms of accuracy and practicality. Digital health solutions, such as unobtrusively capturing smartphone interaction data, offer a promising alternative. However, the potential of digital biomarkers as a surrogate for MCI screening remains unclear, with few comparisons between smartphone interactions and existing screening tools. Objective: This study aimed to investigate the effectiveness of smartphone-derived keystroke dynamics, captured via the Neurokeys keyboard app, in distinguishing patients with MCI from healthy controls (HCs). This study also compared the discriminant performance of these digital biomarkers against the Korean version of the Montreal Cognitive Assessment (MoCA-K), which is widely used for MCI detection in clinical settings. Methods: A total of 64 HCs and 47 patients with MCI were recruited. Over a 1-month period, participants generated 3530 typing sessions, with 2740 (77.6%) analyzed for this study. Keystroke metrics, including hold time and flight time, were extracted. Receiver operating characteristics analysis was used to assess the sensitivity and specificity of keystroke dynamics in discriminating between HCs and patients with MCI. This study also explored the correlation between keystroke dynamics and MoCA-K scores. Results: Patients with MCI had significantly higher keystroke latency than HCs (P<.001). In particular, latency between key presses resulted in the highest sensitivity (97.9%) and specificity (96.9%). In addition, keystroke dynamics were significantly correlated with the MoCA-K (hold time: r=?.468; P<.001; flight time: r=?.497; P<.001), further supporting the validity of these digital biomarkers. Conclusions: These findings highlight the potential of smartphone-derived keystroke dynamics as an effective and ecologically valid tool for screening MCI. With higher sensitivity and specificity than the MoCA-K, particularly in measuring flight time, keystroke dynamics can serve as a noninvasive, scalable, and continuous method for early cognitive impairment detection. This novel approach could revolutionize MCI screening, offering a practical alternative to traditional tools in everyday settings. Trial Registration: Thai Clinical Trials Registry TCTR20220415002; https://www.thaiclinicaltrials.org/show/TCTR20220415002 UR - https://www.jmir.org/2024/1/e59247 UR - http://dx.doi.org/10.2196/59247 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59247 ER - TY - JOUR AU - Lee, Heather Younga AU - Zhang, Yingzhe AU - Kennedy, J. Chris AU - Mallard, T. Travis AU - Liu, Zhaowen AU - Vu, Linh Phuong AU - Feng, Anne Yen-Chen AU - Ge, Tian AU - Petukhova, V. Maria AU - Kessler, C. Ronald AU - Nock, K. Matthew AU - Smoller, W. Jordan PY - 2024/10/23 TI - Enhancing Suicide Risk Prediction With Polygenic Scores in Psychiatric Emergency Settings: Prospective Study JO - JMIR Bioinform Biotech SP - e58357 VL - 5 KW - polygenic risk score KW - suicide risk prediction KW - suicide attempt KW - predictive algorithms KW - genomics KW - genotypes KW - electronic health record KW - machine learning N2 - Background: Despite growing interest in the clinical translation of polygenic risk scores (PRSs), it remains uncertain to what extent genomic information can enhance the prediction of psychiatric outcomes beyond the data collected during clinical visits alone. Objective: This study aimed to assess the clinical utility of incorporating PRSs into a suicide risk prediction model trained on electronic health records (EHRs) and patient-reported surveys among patients admitted to the emergency department. Methods: Study participants were recruited from the psychiatric emergency department at Massachusetts General Hospital. There were 333 adult patients of European ancestry who had high-quality genotype data available through their participation in the Mass General Brigham Biobank. Multiple neuropsychiatric PRSs were added to a previously validated suicide prediction model in a prospective cohort enrolled between February 4, 2015, and March 13, 2017. Data analysis was performed from July 11, 2022, to August 31, 2023. Suicide attempt was defined using diagnostic codes from longitudinal EHRs combined with 6-month follow-up surveys. The clinical risk score for suicide attempt was calculated from an ensemble model trained using an EHR-based suicide risk score and a brief survey, and it was subsequently used to define the baseline model. We generated PRSs for depression, bipolar disorder, schizophrenia, suicide attempt, and externalizing traits using a Bayesian polygenic scoring method for European ancestry participants. Model performance was evaluated using area under the receiver operator curve (AUC), area under the precision-recall curve, and positive predictive values. Results: Of the 333 patients (n=178, 53.5% male; mean age 36.8, SD 13.6 years; n=333, 100% non-Hispanic and n=324, 97.3% self-reported White), 28 (8.4%) had a suicide attempt within 6 months. Adding either the schizophrenia PRS or all PRSs to the baseline model resulted in the numerically highest discrimination (AUC 0.86, 95% CI 0.73-0.99) compared to the baseline model (AUC 0.84, 95% Cl 0.70-0.98). However, the improvement in model performance was not statistically significant. Conclusions: In this study, incorporating genomic information into clinical prediction models for suicide attempt did not improve patient risk stratification. Larger studies that include more diverse participants are required to validate whether the inclusion of psychiatric PRSs in clinical prediction models can enhance the stratification of patients at risk of suicide attempts. UR - https://bioinform.jmir.org/2024/1/e58357 UR - http://dx.doi.org/10.2196/58357 UR - http://www.ncbi.nlm.nih.gov/pubmed/39442166 ID - info:doi/10.2196/58357 ER - TY - JOUR AU - Manion, J. Frank AU - Du, Jingcheng AU - Wang, Dong AU - He, Long AU - Lin, Bin AU - Wang, Jingqi AU - Wang, Siwei AU - Eckels, David AU - Cervenka, Jan AU - Fiduccia, C. Peter AU - Cossrow, Nicole AU - Yao, Lixia PY - 2024/10/23 TI - Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing?Assisted Intelligent Systematic Literature Review System JO - JMIR Med Inform SP - e54653 VL - 12 KW - machine learning KW - deep learning KW - natural language processing KW - systematic literature review KW - artificial intelligence KW - software development KW - data extraction KW - epidemiology N2 - Background: Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task. Objective: This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data. Methods: We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning?based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus?associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines. Results: Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks). Conclusions: Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously. UR - https://medinform.jmir.org/2024/1/e54653 UR - http://dx.doi.org/10.2196/54653 ID - info:doi/10.2196/54653 ER - TY - JOUR AU - Renne, Lorenzo Salvatore AU - Cammelli, Manuela AU - Santori, Ilaria AU - Tassan-Mangina, Marta AU - Samà, Laura AU - Ruspi, Laura AU - Sicoli, Federico AU - Colombo, Piergiuseppe AU - Terracciano, Maria Luigi AU - Quagliuolo, Vittorio AU - Cananzi, Maria Ferdinando Carlo PY - 2024/10/22 TI - True Mitotic Count Prediction in Gastrointestinal Stromal Tumors: Bayesian Network Model and PROMETheus (Preoperative Mitosis Estimator Tool) Application Development JO - J Med Internet Res SP - e50023 VL - 26 KW - GIST mitosis KW - risk classification KW - mHealth KW - mobile health KW - neoadjuvant therapy KW - patient stratification KW - Gastrointestinal Stroma KW - preoperative risk N2 - Background: Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy. Objective: The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs. Methods: Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed. Results: Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model?s accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model. Conclusions: The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs. UR - https://www.jmir.org/2024/1/e50023 UR - http://dx.doi.org/10.2196/50023 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50023 ER - TY - JOUR AU - Mollalo, Abolfazl AU - Hamidi, Bashir AU - Lenert, A. Leslie AU - Alekseyenko, V. Alexander PY - 2024/10/15 TI - Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review JO - JMIR Med Inform SP - e56343 VL - 12 KW - clinical phenotypes KW - electronic health records KW - geocoding KW - geographic information systems KW - patient phenotypes KW - spatial analysis N2 - Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes. Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains. Results: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited. Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support. UR - https://medinform.jmir.org/2024/1/e56343 UR - http://dx.doi.org/10.2196/56343 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56343 ER - TY - JOUR AU - Singh, Sanidhya AU - Bennett, Romney Miles AU - Chen, Chen AU - Shin, Sooyoon AU - Ghanbari, Hamid AU - Nelson, W. Benjamin PY - 2024/10/10 TI - Impact of Skin Pigmentation on Pulse Oximetry Blood Oxygenation and Wearable Pulse Rate Accuracy: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e62769 VL - 26 KW - photoplethysmography KW - pulse oximetry KW - arterial blood gas KW - skin tone KW - skin pigmentation KW - bias KW - digital technology N2 - Background: Photoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO2) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities. Objective: This systematic review and meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO2 and PR according to skin pigmentation. Methods: We identified 23 pulse oximetry studies (n=59,684; 197,353 paired SpO2-arterial blood observations) and 4 wearable PR studies (n=176; 140,771 paired PPG-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO2 accuracy root-mean-square values to the regulatory threshold of 3% and PR 95% limits of agreement values to +5 or ?5 beats per minute (bpm), according to the standards of the American National Standards Institute, Association for the Advancement of Medical Instrumentation, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% CI. Results: For SpO2, accuracy root-mean-square values were 3.96%, 4.71%, and 4.15%, and pooled mean biases were 0.70% (95% CI 0.17%-1.22%), 0.27% (95% CI ?0.64% to 1.19%), and 1.27% (95% CI 0.58%-1.95%) for light, medium, and dark pigmentation, respectively. For PR, 95% limits of agreement values were from ?16.02 to 13.54, from ?18.62 to 16.84, and from ?33.69 to 32.54, and pooled mean biases were ?1.24 (95% CI ?5.31 to 2.83) bpm, ?0.89 (95% CI ?3.70 to 1.93) bpm, and ?0.57 (95% CI ?9.44 to 8.29) bpm for light, medium, and dark pigmentation, respectively. Conclusions: SpO2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching U.S. Food and Drug Administration guidance and industry standard thresholds. Pulse oximeters significantly overestimate SpO2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation. UR - https://www.jmir.org/2024/1/e62769 UR - http://dx.doi.org/10.2196/62769 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62769 ER - TY - JOUR AU - Rizvi, L. Shireen AU - Ruork, K. Allison AU - Yin, Qingqing AU - Yeager, April AU - Taylor, E. Madison AU - Kleiman, M. Evan PY - 2024/10/9 TI - Using Biosensor Devices and Ecological Momentary Assessment to Measure Emotion Regulation Processes: Pilot Observational Study With Dialectical Behavior Therapy JO - JMIR Ment Health SP - e60035 VL - 11 KW - wearable device KW - ecological momentary assessment KW - emotion regulation KW - psychotherapy mechanisms KW - dialectical behavior therapy KW - wearable KW - wristwatch KW - novel technology KW - psychological KW - treatment KW - pilot study KW - adult KW - personality disorder KW - mental health KW - mobile phone KW - EMA KW - observational study N2 - Background: Novel technologies, such as ecological momentary assessment (EMA) and wearable biosensor wristwatches, are increasingly being used to assess outcomes and mechanisms of change in psychological treatments. However, there is still a dearth of information on the feasibility and acceptability of these technologies and whether they can be reliably used to measure variables of interest. Objective: Our objectives were to assess the feasibility and acceptability of incorporating these technologies into dialectical behavior therapy and conduct a pilot evaluation of whether these technologies can be used to assess emotion regulation processes and associated problems over the course of treatment. Methods: A total of 20 adults with borderline personality disorder were enrolled in a 6-month course of dialectical behavior therapy. For 1 week out of every treatment month, participants were asked to complete EMA 6 times a day and to wear a biosensor watch. Each EMA assessment included measures of several negative affect and suicidal thinking, among other items. We used multilevel correlations to assess the contemporaneous association between electrodermal activity and 11 negative emotional states reported via EMA. A multilevel regression was conducted in which changes in composite ratings of suicidal thinking were regressed onto changes in negative affect. Results: On average, participants completed 54.39% (SD 33.1%) of all EMA (range 4.7%?92.4%). They also wore the device for an average of 9.52 (SD 6.47) hours per day and for 92.6% of all days. Importantly, no associations were found between emotional state and electrodermal activity, whether examining a composite of all high-arousal negative emotions or individual emotional states (within-person r ranged from ?0.026 to ?0.109). Smaller changes in negative affect composite scores were associated with greater suicidal thinking ratings at the subsequent timepoint, beyond the effect of suicidal thinking at the initial timepoint. Conclusions: Results indicated moderate overall compliance with EMA and wearing the watch; however, there was no concurrence between EMA and wristwatch data on emotions. This pilot study raises questions about the reliability and validity of these technologies incorporated into treatment studies to evaluate emotion regulation mechanisms. UR - https://mental.jmir.org/2024/1/e60035 UR - http://dx.doi.org/10.2196/60035 ID - info:doi/10.2196/60035 ER - TY - JOUR AU - Meskó, Bertalan AU - Kristóf, Tamás AU - Dhunnoo, Pranavsingh AU - Árvai, Nóra AU - Katonai, Gellért PY - 2024/10/9 TI - Exploring the Need for Medical Futures Studies: Insights From a Scoping Review of Health Care Foresight JO - J Med Internet Res SP - e57148 VL - 26 KW - foresight KW - futures studies KW - health care future KW - medical futures KW - technology foresight N2 - Background: The historical development and contemporary instances of futures studies, an interdisciplinary field that focuses on exploring and formulating alternative futures, exemplify the increasing significance of using futures methods in shaping the health care domain. Despite the wide array of these methodologies, there have been limited endeavors to employ them within the medical community thus far. Objective: We undertook the first scoping review to date about the application of futures methodologies and published foresight projects in health care. Methods: Through the use of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) method, we identified 59 studies that were subsequently categorized into the following 5 distinct themes: national strategies (n=19), strategic health care foresight (n=15), health care policy and workforce dynamics (n=6), pandemic preparedness and response (n=7), and specialized medical domains (n=12). Results: Our scoping review revealed that the application of futures methods and foresight has been successfully demonstrated in a wide range of fields, including national strategies, policy formulation, global threat preparedness, and technological advancements. The results of our review indicate that a total of 8 futures methods have already been used in medicine and health care, while there are more than 50 futures methods available. It may underscore the notion that the field is unexploited. Furthermore, the absence of structured methodologies and principles for employing foresight and futures techniques in the health care domain warrants the creation of medical futures studies as a separate scientific subfield within the broad domains of health care, medicine, and life sciences. This subfield would focus on the analysis of emerging technological trends, the evaluation of policy implications, and the proactive anticipation and mitigation of potential challenges. Conclusions: Futures studies can significantly enhance medical science by addressing a crucial deficiency in the promotion of democratic participation, facilitating interdisciplinary dialogue, and shaping alternative futures. To further contribute to the development of a new research community in medical futures studies, it is recommended to establish a specialized scientific journal. Additionally, appointing dedicated futurists in decision-making and national strategy, and incorporating futures methods into the medical curriculum could be beneficial. UR - https://www.jmir.org/2024/1/e57148 UR - http://dx.doi.org/10.2196/57148 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57148 ER - TY - JOUR AU - Ang, Gregory AU - Tan, Seng Chuen AU - Teerawattananon, Yot AU - Müller-Riemenschneider, Falk AU - Chen, Cynthia PY - 2024/10/4 TI - A Nationwide Physical Activity Intervention for 654,500 Adults in Singapore: Cost-Utility Analysis JO - JMIR Public Health Surveill SP - e46178 VL - 10 KW - physical activity KW - mHealth KW - mobile health KW - nationwide program KW - Markov model KW - diabetes KW - hypertension KW - prevention KW - modeling study KW - productivity KW - cost KW - mortality KW - cost-effectiveness N2 - Background: Increasing physical inactivity is a primary risk factor for diabetes and hypertension, contributing to rising health care expenditure and productivity losses. As Singapore?s aging population grows, there is an increased disease burden on Singapore?s health systems. Large-scale physical activity interventions could potentially reduce the disease burden but face challenges with the uncertainty of long-term health impact and high implementation costs, hindering their adoption. Objective: We examined the cost-effectiveness of the Singapore National Steps Challenge (NSC), an annual nationwide mobile health (mHealth) intervention to increase physical activity, from both the health care provider perspective, which only considers the direct costs, and the societal perspective, which considers both the direct and indirect costs. Methods: We used a Markov model to assess the long-term impact of increased physical activity from the NSC on adults aged 17 years and older. A Monte Carlo simulation with 1000 samples was conducted to compare two situations: the NSC conducted yearly for 10 years against a no-intervention situation with no NSC. The model projected inpatient and outpatient costs and mortality arising from diabetes and hypertension, as well as their complications. Health outcomes were expressed in terms of the quality-adjusted life-years (QALYs) gained. All future costs and QALYs were discounted at 3% per annum. Sensitivity analyses were done to test the robustness of our model results. Results: We estimated that conducting the NSC yearly for 10 years with a mean cohort size of 654,500 participants was projected to prevent 6200 diabetes cases (95% credible interval 3700 to 9100), 10,500 hypertension cases (95% credible interval 6550 to 15,200), and 4930 deaths (95% credible interval 3260 to 6930). This led to a reduction in health care costs of SGD (Singapore dollar) 448 million (95% credible interval SGD 132 million to SGD 1.09 billion; SGD 1=US $0.73 for the year 2019). There would be 78,800 (95% credible interval 55,700 to 102,000) QALYs gained. Using a willingness-to-pay threshold of SGD 10,000 per QALY gained, the NSC would be cost-saving. When indirect costs were included, the NSC was estimated to reduce societal costs by SGD 1.41 billion (95% credible interval SGD 353 million to SGD 3.80 billion). The model was most sensitive to changes in the inpatient cost of treatment for diabetes complications, time horizon, and program compliance. Conclusions: In this modeling study, increasing physical activity by conducting a yearly nationwide physical activity intervention was cost-saving, preventing diabetes and hypertension and reducing mortality from these diseases. Our results provide important information for decision-making in countries that may consider introducing similar large-scale physical activity programs. UR - https://publichealth.jmir.org/2024/1/e46178 UR - http://dx.doi.org/10.2196/46178 ID - info:doi/10.2196/46178 ER - TY - JOUR AU - Agmon, Shunit AU - Singer, Uriel AU - Radinsky, Kira PY - 2024/10/2 TI - Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study JO - JMIR AI SP - e49546 VL - 3 KW - natural language processing KW - NLP KW - BERT KW - word embeddings KW - statistical models KW - bias KW - algorithms KW - gender N2 - Background: Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are models that enable representing words as vectors and are the building block of most natural language processing systems. If word embeddings are trained on clinical trial abstracts, predictive models that use the embeddings will exhibit gender performance gaps. Objective: We aim to capture temporal trends in clinical trials through temporal distribution matching on contextual word embeddings (specifically, BERT) and explore its effect on the bias manifested in downstream tasks. Methods: We present TeDi-BERT, a method to harness the temporal trend of increasing women?s inclusion in clinical trials to train contextual word embeddings. We implement temporal distribution matching through an adversarial classifier, trying to distinguish old from new clinical trial abstracts based on their embeddings. The temporal distribution matching acts as a form of domain adaptation from older to more recent clinical trials. We evaluate our model on 2 clinical tasks: prediction of unplanned readmission to the intensive care unit and hospital length of stay prediction. We also conduct an algorithmic analysis of the proposed method. Results: In readmission prediction, TeDi-BERT achieved area under the receiver operating characteristic curve of 0.64 for female patients versus the baseline of 0.62 (P<.001), and 0.66 for male patients versus the baseline of 0.64 (P<.001). In the length of stay regression, TeDi-BERT achieved a mean absolute error of 4.56 (95% CI 4.44-4.68) for female patients versus 4.62 (95% CI 4.50-4.74, P<.001) and 4.54 (95% CI 4.44-4.65) for male patients versus 4.6 (95% CI 4.50-4.71, P<.001). Conclusions: In both clinical tasks, TeDi-BERT improved performance for female patients, as expected; but it also improved performance for male patients. Our results show that accuracy for one gender does not need to be exchanged for bias reduction, but rather that good science improves clinical results for all. Contextual word embedding models trained to capture temporal trends can help mitigate the effects of bias that changes over time in the training data. UR - https://ai.jmir.org/2024/1/e49546 UR - http://dx.doi.org/10.2196/49546 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49546 ER - TY - JOUR AU - Ming, Antao AU - Clemens, Vera AU - Lorek, Elisabeth AU - Wall, Janina AU - Alhajjar, Ahmad AU - Galazky, Imke AU - Baum, Anne-Katrin AU - Li, Yang AU - Li, Meng AU - Stober, Sebastian AU - Mertens, David Nils AU - Mertens, Rene Peter PY - 2024/10/1 TI - Game-Based Assessment of Peripheral Neuropathy Combining Sensor-Equipped Insoles, Video Games, and AI: Proof-of-Concept Study JO - J Med Internet Res SP - e52323 VL - 26 KW - diabetes mellitus KW - metabolic syndrome KW - peripheral neuropathy KW - sensor-equipped insoles KW - video games KW - machine learning KW - feature extraction N2 - Background: Detecting peripheral neuropathy (PNP) is crucial in preventing complications such as foot ulceration. Clinical examinations for PNP are infrequently provided to patients at high risk due to restrictions on facilities, care providers, or time. A gamified health assessment approach combining wearable sensors holds the potential to address these challenges and provide individuals with instantaneous feedback on their health status. Objective: We aimed to develop and evaluate an application that assesses PNP through video games controlled by pressure sensor?equipped insoles. Methods: In the proof-of-concept exploratory cohort study, a complete game-based framework that allowed the study participant to play 4 video games solely by modulating plantar pressure values was established in an outpatient clinic setting. Foot plantar pressures were measured by the sensor-equipped insole and transferred via Bluetooth to an Android tablet for game control in real time. Game results and sensor data were delivered to the study server for visualization and analysis. Each session lasted about 15 minutes. In total, 299 patients with diabetes mellitus and 30 with metabolic syndrome were tested using the game application. Patients? game performance was initially assessed by hypothesis-driven key capabilities that consisted of reaction time, sensation, skillfulness, balance, endurance, and muscle strength. Subsequently, specific game features were extracted from gaming data sets and compared with nerve conduction study findings, neuropathy symptoms, or disability scores. Multiple machine learning algorithms were applied to 70% (n=122) of acquired data to train predictive models for PNP, while the remaining data were held out for final model evaluation. Results: Overall, clinically evident PNP was present in 247 of 329 (75.1%) participants, with 88 (26.7%) individuals showing asymmetric nerve deficits. In a subcohort (n=37) undergoing nerve conduction study as the gold standard, sensory and motor nerve conduction velocities and nerve amplitudes in lower extremities significantly correlated with 79 game features (|R|>0.4, highest R value +0.65; P<.001; adjusted R2=0.36). Within another subcohort (n=173) with normal cognition and matched covariates (age, sex, BMI, etc), hypothesis-driven key capabilities and specific game features were significantly correlated with the presence of PNP. Predictive models using selected game features achieved 76.1% (left) and 81.7% (right foot) accuracy for PNP detection. Multiclass models yielded an area under the receiver operating characteristic curve of 0.76 (left foot) and 0.72 (right foot) for assessing nerve damage patterns (small, large, or mixed nerve fiber damage). Conclusions: The game-based application presents a promising avenue for PNP screening and classification. Evaluation in expanded cohorts may iteratively optimize artificial intelligence model efficacy. The integration of engaging motivational elements and automated data interpretation will support acceptance as a telemedical application. UR - https://www.jmir.org/2024/1/e52323 UR - http://dx.doi.org/10.2196/52323 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52323 ER - TY - JOUR AU - Franc, Micheal Jeffrey AU - Hertelendy, Julius Attila AU - Cheng, Lenard AU - Hata, Ryan AU - Verde, Manuela PY - 2024/9/30 TI - Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study JO - J Med Internet Res SP - e55648 VL - 26 KW - disaster medicine KW - large language models KW - triage KW - disaster KW - emergency KW - disasters KW - emergencies KW - LLM KW - LLMs KW - GPT KW - ChatGPT KW - language model KW - language models KW - NLP KW - natural language processing KW - artificial intelligence KW - repeatability KW - reproducibility KW - accuracy KW - accurate KW - reproducible KW - repeatable N2 - Background: The release of ChatGPT (OpenAI) in November 2022 drastically reduced the barrier to using artificial intelligence by allowing a simple web-based text interface to a large language model (LLM). One use case where ChatGPT could be useful is in triaging patients at the site of a disaster using the Simple Triage and Rapid Treatment (START) protocol. However, LLMs experience several common errors including hallucinations (also called confabulations) and prompt dependency. Objective: This study addresses the research problem: ?Can ChatGPT adequately triage simulated disaster patients using the START protocol?? by measuring three outcomes: repeatability, reproducibility, and accuracy. Methods: Nine prompts were developed by 5 disaster medicine physicians. A Python script queried ChatGPT Version 4 for each prompt combined with 391 validated simulated patient vignettes. Ten repetitions of each combination were performed for a total of 35,190 simulated triages. A reference standard START triage code for each simulated case was assigned by 2 disaster medicine specialists (JMF and MV), with a third specialist (LC) added if the first two did not agree. Results were evaluated using a gage repeatability and reproducibility study (gage R and R). Repeatability was defined as variation due to repeated use of the same prompt. Reproducibility was defined as variation due to the use of different prompts on the same patient vignette. Accuracy was defined as agreement with the reference standard. Results: Although 35,102 (99.7%) queries returned a valid START score, there was considerable variability. Repeatability (use of the same prompt repeatedly) was 14% of the overall variation. Reproducibility (use of different prompts) was 4.1% of the overall variation. The accuracy of ChatGPT for START was 63.9% with a 32.9% overtriage rate and a 3.1% undertriage rate. Accuracy varied by prompt with a maximum of 71.8% and a minimum of 46.7%. Conclusions: This study indicates that ChatGPT version 4 is insufficient to triage simulated disaster patients via the START protocol. It demonstrated suboptimal repeatability and reproducibility. The overall accuracy of triage was only 63.9%. Health care professionals are advised to exercise caution while using commercial LLMs for vital medical determinations, given that these tools may commonly produce inaccurate data, colloquially referred to as hallucinations or confabulations. Artificial intelligence?guided tools should undergo rigorous statistical evaluation?using methods such as gage R and R?before implementation into clinical settings. UR - https://www.jmir.org/2024/1/e55648 UR - http://dx.doi.org/10.2196/55648 UR - http://www.ncbi.nlm.nih.gov/pubmed/39348189 ID - info:doi/10.2196/55648 ER - TY - JOUR AU - Zhang, Daiwen AU - Ma, Zixuan AU - Gong, Ru AU - Lian, Liangliang AU - Li, Yanzhuo AU - He, Zhenghui AU - Han, Yuhan AU - Hui, Jiyuan AU - Huang, Jialin AU - Jiang, Jiyao AU - Weng, Weiji AU - Feng, Junfeng PY - 2024/9/26 TI - Using Natural Language Processing (GPT-4) for Computed Tomography Image Analysis of Cerebral Hemorrhages in Radiology: Retrospective Analysis JO - J Med Internet Res SP - e58741 VL - 26 KW - GPT-4 KW - natural language processing KW - NLP KW - artificial intelligence KW - AI KW - cerebral hemorrhage KW - computed tomography KW - CT N2 - Background: Cerebral hemorrhage is a critical medical condition that necessitates a rapid and precise diagnosis for timely medical intervention, including emergency operation. Computed tomography (CT) is essential for identifying cerebral hemorrhage, but its effectiveness is limited by the availability of experienced radiologists, especially in resource-constrained regions or when shorthanded during holidays or at night. Despite advancements in artificial intelligence?driven diagnostic tools, most require technical expertise. This poses a challenge for widespread adoption in radiological imaging. The introduction of advanced natural language processing (NLP) models such as GPT-4, which can annotate and analyze images without extensive algorithmic training, offers a potential solution. Objective: This study investigates GPT-4?s capability to identify and annotate cerebral hemorrhages in cranial CT scans. It represents a novel application of NLP models in radiological imaging. Methods: In this retrospective analysis, we collected 208 CT scans with 6 types of cerebral hemorrhages at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, between January and September 2023. All CT images were mixed together and sequentially numbered, so each CT image had its own corresponding number. A random sequence from 1 to 208 was generated, and all CT images were inputted into GPT-4 for analysis in the order of the random sequence. The outputs were subsequently examined using Photoshop and evaluated by experienced radiologists on a 4-point scale to assess identification completeness, accuracy, and success. Results: The overall identification completeness percentage for the 6 types of cerebral hemorrhages was 72.6% (SD 18.6%). Specifically, GPT-4 achieved higher identification completeness in epidural and intraparenchymal hemorrhages (89.0%, SD 19.1% and 86.9%, SD 17.7%, respectively), yet its identification completeness percentage in chronic subdural hemorrhages was very low (37.3%, SD 37.5%). The misidentification percentages for complex hemorrhages (54.0%, SD 28.0%), epidural hemorrhages (50.2%, SD 22.7%), and subarachnoid hemorrhages (50.5%, SD 29.2%) were relatively high, whereas they were relatively low for acute subdural hemorrhages (32.6%, SD 26.3%), chronic subdural hemorrhages (40.3%, SD 27.2%), and intraparenchymal hemorrhages (26.2%, SD 23.8%). The identification completeness percentages in both massive and minor bleeding showed no significant difference (P=.06). However, the misidentification percentage in recognizing massive bleeding was significantly lower than that for minor bleeding (P=.04). The identification completeness percentages and misidentification percentages for cerebral hemorrhages at different locations showed no significant differences (all P>.05). Lastly, radiologists showed relative acceptance regarding identification completeness (3.60, SD 0.54), accuracy (3.30, SD 0.65), and success (3.38, SD 0.64). Conclusions: GPT-4, a standout among NLP models, exhibits both promising capabilities and certain limitations in the realm of radiological imaging, particularly when it comes to identifying cerebral hemorrhages in CT scans. This opens up new directions and insights for the future development of NLP models in radiology. Trial Registration: ClinicalTrials.gov NCT06230419; https://clinicaltrials.gov/study/NCT06230419 UR - https://www.jmir.org/2024/1/e58741 UR - http://dx.doi.org/10.2196/58741 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58741 ER - TY - JOUR AU - AlSaad, Rawan AU - Abd-alrazaq, Alaa AU - Boughorbel, Sabri AU - Ahmed, Arfan AU - Renault, Max-Antoine AU - Damseh, Rafat AU - Sheikh, Javaid PY - 2024/9/25 TI - Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook JO - J Med Internet Res SP - e59505 VL - 26 KW - artificial intelligence KW - large language models KW - multimodal large language models KW - multimodality KW - multimodal generative artificial intelligence KW - multimodal generative AI KW - generative artificial intelligence KW - generative AI KW - health care UR - https://www.jmir.org/2024/1/e59505 UR - http://dx.doi.org/10.2196/59505 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59505 ER - TY - JOUR AU - Bergh, Eric AU - Rennie, Kimberly AU - Espinoza, Jimmy AU - Johnson, Anthony AU - Papanna, Ramesha PY - 2024/9/11 TI - Use of Web-Based Surveys to Collect Long-Term Pediatric Outcomes in Patients With Twin-Twin Transfusion Syndrome Treated With Fetoscopic Laser Photocoagulation: Observational Study JO - JMIR Pediatr Parent SP - e60039 VL - 7 KW - automation KW - REDCap KW - data collection KW - reporting KW - response rate KW - response rates KW - survey KW - surveys KW - questionnaire KW - questionnaires KW - fetal medicine KW - pediatric outcomes KW - long-term outcomes KW - photocoagulation KW - twin KW - twins KW - blood KW - pregnant KW - pregnancy KW - pediatric KW - pediatrics KW - infant KW - infants KW - infancy KW - baby KW - babies KW - neonate KW - neonates KW - neonatal KW - newborn KW - newborns KW - maternal KW - in utero KW - TTTS KW - fetus KW - fetal KW - twin-twin transfusion syndrome N2 - Background: In the United States, patients with monochorionic diamniotic twins who undergo in utero fetoscopic laser photocoagulation (FLP) for twin-twin transfusion syndrome (TTTS) may travel great distances for care. After delivery, many parents cannot return to study sites for formal pediatric evaluation due to geographic location and cost. Objective: The aim of this study was to collect long-term pediatric outcomes in patients who underwent FLP for TTTS. Methods: We assessed the feasibility of using a web-based survey designed in REDCap (Research Electronic Data Capture; Vanderbilt University) to collect parent-reported outcomes in children treated for TTTS at a single center during 2011?2019. Patients with ?1 neonatal survivor were invited via email to complete 5 possible questionnaires: the child status questionnaire (CSQ); fetal center questionnaire (FCQ); Ages & Stages Questionnaires, Third Edition (ASQ-3); Modified Checklist for Autism in Toddlers, Revised With Follow-Up (M-CHAT-R/F); and thank you questionnaire (TYQ). The R programming language (R Foundation for Statistical Computing) was used to automate survey distribution, scoring, and creation of customized reports. The survey was performed in 2019 and repeated after 12 months in the same study population in 2020. Results: A total of 389 patients in 26 different states and 2 international locations had an email address on file and received an invitation in 2019 to complete the survey (median pediatric age 48.9, IQR 1.0?93.6 months). Among surveyed mothers in 2019, the overall response rate was 37.3% (145/389), and the questionnaire completion rate was 98% (145/148), 87.8% (130/148), 71.1% (81/100), 86.4% (19/22), and 74.3% (110/148) for the CSQ, FCQ, ASQ-3, M-CHAT-R/F, and TYQ, respectively. In 2020, the overall response rate was 57.8% (56/97), and the questionnaire completion rate was 96.4% (54/56), 91.1% (51/56), 86.1% (31/36), 91.7% (11/12), and 80.4% (45/56) for the CSQ, FCQ, ASQ-3, M-CHAT-R/F, and TYQ, respectively. Conclusions: This is the first study to use both REDCap and computer automation to aid in the dissemination, collection, and reporting of surveys to collect long-term pediatric outcomes in the field of fetal medicine. UR - https://pediatrics.jmir.org/2024/1/e60039 UR - http://dx.doi.org/10.2196/60039 ID - info:doi/10.2196/60039 ER - TY - JOUR AU - Kiani, Parmiss AU - Dolling-Boreham, Roberta AU - Hameed, Saif Mohamed AU - Masino, Caterina AU - Fecso, Andras AU - Okrainec, Allan AU - Madani, Amin PY - 2024/9/10 TI - Usability, Ergonomics, and Educational Value of a Novel Telestration Tool for Surgical Coaching: Usability Study JO - JMIR Hum Factors SP - e57243 VL - 11 KW - augmented reality KW - AR KW - surgical training KW - telestration KW - tele-stration KW - surgical training technology KW - minimally invasive surgery KW - surgery KW - surgeon KW - surgeons KW - surgical KW - surgical coaching KW - surgical teaching KW - telemonitoring KW - telemonitor KW - tele-monitoring KW - tele-monitor KW - usability KW - usable KW - usableness KW - usefulness KW - utility KW - digital health KW - digital technology KW - digital intervention KW - digital interventions N2 - Background: Telementoring studies found technical challenges in achieving accurate and stable annotations during live surgery using commercially available telestration software intraoperatively. To address the gap, a wireless handheld telestration device was developed to facilitate dynamic user interaction with live video streams. Objective: This study aims to find the perceived usability, ergonomics, and educational value of a first-generation handheld wireless telestration platform. Methods: A prototype was developed with four core hand-held functions: (1) free-hand annotation, (2) cursor navigation, (3) overlay and manipulation (rotation) of ghost (avatar) instrumentation, and (4) hand-held video feed navigation on a remote monitor. This device uses a proprietary augmented reality platform. Surgeons and trainees were invited to test the core functions of the platform by performing standardized tasks. Usability and ergonomics were evaluated with a validated system usability scale and a 5-point Likert scale survey, which also evaluated the perceived educational value of the device. Results: In total, 10 people (9 surgeons and 1 senior resident; 5 male and 5 female) participated. Participants strongly agreed or agreed (SA/A) that it was easy to perform annotations (SA/A 9, 90% and neutral 0, 0%), video feed navigation (SA/A 8, 80% and neutral 1, 10%), and manipulation of ghost (avatar) instruments on the monitor (SA/A 6, 60% and neutral 3, 30%). Regarding ergonomics, 40% (4) of participants agreed or strongly agreed (neutral 4, 40%) that the device was physically comfortable to use and hold. These results are consistent with open-ended comments on the device?s size and weight. The average system usability scale was 70 (SD 12.5; median 75, IQR 63-84) indicating an above average usability score. Participants responded favorably to the device?s perceived educational value, particularly for postoperative coaching (agree 6, 60%, strongly agree 4, 40%). Conclusions: This study presents the preliminary usability results of a novel first-generation telestration tool customized for use in surgical coaching. Favorable usability and perceived educational value were reported. Future iterations of the device should focus on incorporating user feedback and additional studies should be conducted to evaluate its effectiveness for improving surgical education. Ultimately, such tools can be incorporated into pedagogical models of surgical coaching to optimize feedback and training. UR - https://humanfactors.jmir.org/2024/1/e57243 UR - http://dx.doi.org/10.2196/57243 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57243 ER - TY - JOUR AU - Zhang, Zhenwen AU - Zhu, Jianghong AU - Guo, Zhihua AU - Zhang, Yu AU - Li, Zepeng AU - Hu, Bin PY - 2024/9/4 TI - Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis JO - JMIR Ment Health SP - e58259 VL - 11 KW - depression KW - social media KW - natural language processing KW - deep learning KW - mental health KW - statistical analysis KW - linguistic analysis KW - Sina Weibo KW - risk prediction KW - mood analysis N2 - Background: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis. Objective: This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. Methods: First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count. Results: We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1-score of 84.32% on the test set without employing sampling techniques. However, by applying our proposed retrieval-based sampling strategy, we observed significant improvements in performance: an accuracy of 95.46%, precision of 95.30%, recall of 95.70%, and F1-score of 95.43%. These outstanding results clearly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This breakthrough provides new insights for large-scale depression detection through social media. Through language behavior analysis, we discovered that individuals with depression are more likely to use negation words (the value of ?swear? is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, our analysis revealed that individuals with depression tend to use negative emotional vocabulary in their expressions (?NegEmo?: 0.022306; ?Anx?: 0.003829; ?Anger?: 0.004327; ?Sad?: 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings toward life, themselves, or their surrounding environment. Conclusions: The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users. UR - https://mental.jmir.org/2024/1/e58259 UR - http://dx.doi.org/10.2196/58259 ID - info:doi/10.2196/58259 ER - TY - JOUR AU - Copland, R. Rachel AU - Hanke, Sten AU - Rogers, Amy AU - Mpaltadoros, Lampros AU - Lazarou, Ioulietta AU - Zeltsi, Alexandra AU - Nikolopoulos, Spiros AU - MacDonald, M. Thomas AU - Mackenzie, S. Isla PY - 2024/9/3 TI - The Digital Platform and Its Emerging Role in Decentralized Clinical Trials JO - J Med Internet Res SP - e47882 VL - 26 KW - decentralized clinical trials KW - digital platform KW - digitalization KW - clinical trials KW - mobile phone UR - https://www.jmir.org/2024/1/e47882 UR - http://dx.doi.org/10.2196/47882 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/47882 ER - TY - JOUR AU - Lee, Haedeun AU - Oh, Bumjo AU - Kim, Seung-Chan PY - 2024/8/26 TI - Recognition of Forward Head Posture Through 3D Human Pose Estimation With a Graph Convolutional Network: Development and Feasibility Study JO - JMIR Form Res SP - e55476 VL - 8 KW - posture correction KW - injury prediction KW - human pose estimation KW - forward head posture KW - machine learning KW - graph convolutional networks KW - posture KW - graph neural network KW - graph KW - pose KW - postural KW - deep learning KW - neural network KW - neural networks KW - upper KW - algorithms N2 - Background: Prolonged improper posture can lead to forward head posture (FHP), causing headaches, impaired respiratory function, and fatigue. This is especially relevant in sedentary scenarios, where individuals often maintain static postures for extended periods?a significant part of daily life for many. The development of a system capable of detecting FHP is crucial, as it would not only alert users to correct their posture but also serve the broader goal of contributing to public health by preventing the progression of chronic injuries associated with this condition. However, despite significant advancements in estimating human poses from standard 2D images, most computational pose models do not include measurements of the craniovertebral angle, which involves the C7 vertebra, crucial for diagnosing FHP. Objective: Accurate diagnosis of FHP typically requires dedicated devices, such as clinical postural assessments or specialized imaging equipment, but their use is impractical for continuous, real-time monitoring in everyday settings. Therefore, developing an accessible, efficient method for regular posture assessment that can be easily integrated into daily activities, providing real-time feedback, and promoting corrective action, is necessary. Methods: The system sequentially estimates 2D and 3D human anatomical key points from a provided 2D image, using the Detectron2D and VideoPose3D algorithms, respectively. It then uses a graph convolutional network (GCN), explicitly crafted to analyze the spatial configuration and alignment of the upper body?s anatomical key points in 3D space. This GCN aims to implicitly learn the intricate relationship between the estimated 3D key points and the correct posture, specifically to identify FHP. Results: The test accuracy was 78.27% when inputs included all joints corresponding to the upper body key points. The GCN model demonstrated slightly superior balanced performance across classes with an F1-score (macro) of 77.54%, compared to the baseline feedforward neural network (FFNN) model?s 75.88%. Specifically, the GCN model showed a more balanced precision and recall between the classes, suggesting its potential for better generalization in FHP detection across diverse postures. Meanwhile, the baseline FFNN model demonstrates a higher precision for FHP cases but at the cost of lower recall, indicating that while it is more accurate in confirming FHP when detected, it misses a significant number of actual FHP instances. This assertion is further substantiated by the examination of the latent feature space using t-distributed stochastic neighbor embedding, where the GCN model presented an isotropic distribution, unlike the FFNN model, which showed an anisotropic distribution. Conclusions: Based on 2D image input using 3D human pose estimation joint inputs, it was found that it is possible to learn FHP-related features using the proposed GCN-based network to develop a posture correction system. We conclude the paper by addressing the limitations of our current system and proposing potential avenues for future work in this area. UR - https://formative.jmir.org/2024/1/e55476 UR - http://dx.doi.org/10.2196/55476 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55476 ER - TY - JOUR AU - Burns, James AU - Chen, Kelly AU - Flathers, Matthew AU - Currey, Danielle AU - Macrynikola, Natalia AU - Vaidyam, Aditya AU - Langholm, Carsten AU - Barnett, Ian AU - Byun, Soo) Andrew (Jin AU - Lane, Erlend AU - Torous, John PY - 2024/8/23 TI - Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial JO - J Med Internet Res SP - e58502 VL - 26 KW - digital phenotyping KW - mental health KW - data visualization KW - data analysis KW - smartphones KW - smartphone KW - Cortex KW - open-source KW - data processing KW - mindLAMP KW - app KW - apps KW - data set KW - clinical KW - real world KW - methodology KW - mobile phone UR - https://www.jmir.org/2024/1/e58502 UR - http://dx.doi.org/10.2196/58502 UR - http://www.ncbi.nlm.nih.gov/pubmed/39178032 ID - info:doi/10.2196/58502 ER - TY - JOUR AU - Li, Danis Kevin AU - Fernandez, M. Adrian AU - Schwartz, Rachel AU - Rios, Natalie AU - Carlisle, Nathaniel Marvin AU - Amend, M. Gregory AU - Patel, V. Hiren AU - Breyer, N. Benjamin PY - 2024/8/21 TI - Comparing GPT-4 and Human Researchers in Health Care Data Analysis: Qualitative Description Study JO - J Med Internet Res SP - e56500 VL - 26 KW - artificial intelligence KW - ChatGPT KW - large language models KW - qualitative analysis KW - content analysis KW - buried penis KW - qualitative interviews KW - qualitative description KW - urology N2 - Background: Large language models including GPT-4 (OpenAI) have opened new avenues in health care and qualitative research. Traditional qualitative methods are time-consuming and require expertise to capture nuance. Although large language models have demonstrated enhanced contextual understanding and inferencing compared with traditional natural language processing, their performance in qualitative analysis versus that of humans remains unexplored. Objective: We evaluated the effectiveness of GPT-4 versus human researchers in qualitative analysis of interviews with patients with adult-acquired buried penis (AABP). Methods: Qualitative data were obtained from semistructured interviews with 20 patients with AABP. Human analysis involved a structured 3-stage process?initial observations, line-by-line coding, and consensus discussions to refine themes. In contrast, artificial intelligence (AI) analysis with GPT-4 underwent two phases: (1) a naïve phase, where GPT-4 outputs were independently evaluated by a blinded reviewer to identify themes and subthemes and (2) a comparison phase, where AI-generated themes were compared with human-identified themes to assess agreement. We used a general qualitative description approach. Results: The study population (N=20) comprised predominantly White (17/20, 85%), married (12/20, 60%), heterosexual (19/20, 95%) men, with a mean age of 58.8 years and BMI of 41.1 kg/m2. Human qualitative analysis identified ?urinary issues? in 95% (19/20) and GPT-4 in 75% (15/20) of interviews, with the subtheme ?spray or stream? noted in 60% (12/20) and 35% (7/20), respectively. ?Sexual issues? were prominent (19/20, 95% humans vs 16/20, 80% GPT-4), although humans identified a wider range of subthemes, including ?pain with sex or masturbation? (7/20, 35%) and ?difficulty with sex or masturbation? (4/20, 20%). Both analyses similarly highlighted ?mental health issues? (11/20, 55%, both), although humans coded ?depression? more frequently (10/20, 50% humans vs 4/20, 20% GPT-4). Humans frequently cited ?issues using public restrooms? (12/20, 60%) as impacting social life, whereas GPT-4 emphasized ?struggles with romantic relationships? (9/20, 45%). ?Hygiene issues? were consistently recognized (14/20, 70% humans vs 13/20, 65% GPT-4). Humans uniquely identified ?contributing factors? as a theme in all interviews. There was moderate agreement between human and GPT-4 coding (?=0.401). Reliability assessments of GPT-4?s analyses showed consistent coding for themes including ?body image struggles,? ?chronic pain? (10/10, 100%), and ?depression? (9/10, 90%). Other themes like ?motivation for surgery? and ?weight challenges? were reliably coded (8/10, 80%), while less frequent themes were variably identified across multiple iterations. Conclusions: Large language models including GPT-4 can effectively identify key themes in analyzing qualitative health care data, showing moderate agreement with human analysis. While human analysis provided a richer diversity of subthemes, the consistency of AI suggests its use as a complementary tool in qualitative research. With AI rapidly advancing, future studies should iterate analyses and circumvent token limitations by segmenting data, furthering the breadth and depth of large language model?driven qualitative analyses. UR - https://www.jmir.org/2024/1/e56500 UR - http://dx.doi.org/10.2196/56500 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56500 ER - TY - JOUR AU - Greene, Barry AU - Tobyne, Sean AU - Jannati, Ali AU - McManus, Killian AU - Gomes Osman, Joyce AU - Banks, Russell AU - Kher, Ranjit AU - Showalter, John AU - Bates, David AU - Pascual-Leone, Alvaro PY - 2024/8/19 TI - The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation JO - J Med Internet Res SP - e49794 VL - 26 KW - cognitive function KW - dual task KW - inertial sensors KW - mHealth KW - tablet KW - MCI KW - Alzheimer KW - dementia KW - motor KW - older adults KW - cognitive impairment KW - balance? N2 - Background: Dual task paradigms are thought to offer a quantitative means to assess cognitive reserve and the brain?s capacity to allocate resources in the face of competing cognitive demands. The most common dual task paradigms examine the interplay between gait or balance control and cognitive function. However, gait and balance tasks can be physically challenging for older adults and may pose a risk of falls. Objective: We introduce a novel, digital dual-task assessment that combines a motor-control task (the ?ball balancing? test), which challenges an individual to maintain a virtual ball within a designated zone, with a concurrent cognitive task (the backward digit span task [BDST]). Methods: The task was administered on a touchscreen tablet, performance was measured using the inertial sensors embedded in the tablet, conducted under both single- and dual-task conditions. The clinical use of the task was evaluated on a sample of 375 older adult participants (n=210 female; aged 73.0, SD 6.5 years). Results: All older adults, including those with mild cognitive impairment (MCI) and Alzheimer disease?related dementia (ADRD), and those with poor balance and gait problems due to diabetes, osteoarthritis, peripheral neuropathy, and other causes, were able to complete the task comfortably and safely while seated. As expected, task performance significantly decreased under dual task conditions compared to single task conditions. We show that performance was significantly associated with cognitive impairment; significant differences were found among healthy participants, those with MCI, and those with ADRD. Task results were significantly associated with functional impairment, independent of diagnosis, degree of cognitive impairment (as indicated by the Mini Mental State Examination [MMSE] score), and age. Finally, we found that cognitive status could be classified with >70% accuracy using a range of classifier models trained on 3 different cognitive function outcome variables (consensus clinical judgment, Rey Auditory Verbal Learning Test [RAVLT], and MMSE). Conclusions: Our results suggest that the dual task ball balancing test could be used as a digital cognitive assessment of cognitive reserve. The portability, simplicity, and intuitiveness of the task suggest that it may be suitable for unsupervised home assessment of cognitive function. UR - https://www.jmir.org/2024/1/e49794 UR - http://dx.doi.org/10.2196/49794 UR - http://www.ncbi.nlm.nih.gov/pubmed/39158963 ID - info:doi/10.2196/49794 ER - TY - JOUR AU - Matsui, Kentaro AU - Utsumi, Tomohiro AU - Aoki, Yumi AU - Maruki, Taku AU - Takeshima, Masahiro AU - Takaesu, Yoshikazu PY - 2024/8/16 TI - Human-Comparable Sensitivity of Large Language Models in Identifying Eligible Studies Through Title and Abstract Screening: 3-Layer Strategy Using GPT-3.5 and GPT-4 for Systematic Reviews JO - J Med Internet Res SP - e52758 VL - 26 KW - systematic review KW - screening KW - GPT-3.5 KW - GPT-4 KW - language model KW - information science KW - library science KW - artificial intelligence KW - prompt engineering KW - meta-analysis N2 - Background: The screening process for systematic reviews is resource-intensive. Although previous machine learning solutions have reported reductions in workload, they risked excluding relevant papers. Objective: We evaluated the performance of a 3-layer screening method using GPT-3.5 and GPT-4 to streamline the title and abstract-screening process for systematic reviews. Our goal is to develop a screening method that maximizes sensitivity for identifying relevant records. Methods: We conducted screenings on 2 of our previous systematic reviews related to the treatment of bipolar disorder, with 1381 records from the first review and 3146 from the second. Screenings were conducted using GPT-3.5 (gpt-3.5-turbo-0125) and GPT-4 (gpt-4-0125-preview) across three layers: (1) research design, (2) target patients, and (3) interventions and controls. The 3-layer screening was conducted using prompts tailored to each study. During this process, information extraction according to each study?s inclusion criteria and optimization for screening were carried out using a GPT-4?based flow without manual adjustments. Records were evaluated at each layer, and those meeting the inclusion criteria at all layers were subsequently judged as included. Results: On each layer, both GPT-3.5 and GPT-4 were able to process about 110 records per minute, and the total time required for screening the first and second studies was approximately 1 hour and 2 hours, respectively. In the first study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.900/0.709 and 0.806/0.996, respectively. Both screenings by GPT-3.5 and GPT-4 judged all 6 records used for the meta-analysis as included. In the second study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.958/0.116 and 0.875/0.855, respectively. The sensitivities for the relevant records align with those of human evaluators: 0.867-1.000 for the first study and 0.776-0.979 for the second study. Both screenings by GPT-3.5 and GPT-4 judged all 9 records used for the meta-analysis as included. After accounting for justifiably excluded records by GPT-4, the sensitivities/specificities of the GPT-4 screening were 0.962/0.996 in the first study and 0.943/0.855 in the second study. Further investigation indicated that the cases incorrectly excluded by GPT-3.5 were due to a lack of domain knowledge, while the cases incorrectly excluded by GPT-4 were due to misinterpretations of the inclusion criteria. Conclusions: Our 3-layer screening method with GPT-4 demonstrated acceptable level of sensitivity and specificity that supports its practical application in systematic review screenings. Future research should aim to generalize this approach and explore its effectiveness in diverse settings, both medical and nonmedical, to fully establish its use and operational feasibility. UR - https://www.jmir.org/2024/1/e52758 UR - http://dx.doi.org/10.2196/52758 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52758 ER - TY - JOUR AU - Chiang, Byron AU - Law, Wa Yik AU - Yip, Fai Paul Siu PY - 2024/8/7 TI - Using Discrete-Event Simulation to Model Web-Based Crisis Counseling Service Operation: Evaluation Study JO - JMIR Form Res SP - e46823 VL - 8 KW - discrete-event simulation KW - community operational research KW - queuing KW - web-based counseling KW - service management KW - repeat users N2 - Background: According to the Organisation for Economic Co-operation and Development, its member states experienced worsening mental health during the COVID-19 pandemic, leading to an increase of 60% to 1000% in digital counseling access. Hong Kong, too, witnessed a surge in demand for crisis intervention services during the pandemic, attracting both nonrepeat and repeat service users during the process. As a result of the continuing demand, platforms offering short-term emotional support are facing an efficiency challenge in managing caller responses. Objective: This aim of this paper was to assess the queuing performance of a 24-hour text-based web-based crisis counseling platform using a Python-based discrete-event simulation (DES) model. The model evaluates the staff combinations needed to meet demand and informs service priority decisions. It is able to account for unbalanced and overlapping shifts, unequal simultaneous serving capacities among custom worker types, time-dependent user arrivals, and the influence of user type (nonrepeat users vs repeat users) and suicide risk on service durations. Methods: Use and queue statistics by user type and staffing conditions were tabulated from past counseling platform database records. After calculating the data distributions, key parameters were incorporated into the DES model to determine the supply-demand equilibrium and identify potential service bottlenecks. An unobserved-components time-series model was fitted to make 30-day forecasts of the arrival rate, with the results piped back to the DES model to estimate the number of workers needed to staff each work shift, as well as the number of repeat service users encountered during a service operation. Results: The results showed a marked increase (from 3401/9202, 36.96% to 5042/9199, 54.81%) in the overall conversion rate after the strategic deployment of human resources according to the values set in the simulations, with an 85% chance of queuing users receiving counseling service within 10 minutes and releasing an extra 39.57% (3631/9175) capacity to serve nonrepeat users at potential risk. Conclusions: By exploiting scientifically informed data models with DES, nonprofit web-based counseling platforms, even those with limited resources, can optimize service capacity strategically to manage service bottlenecks and increase service uptake. UR - https://formative.jmir.org/2024/1/e46823 UR - http://dx.doi.org/10.2196/46823 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/46823 ER - TY - JOUR AU - Lewis, C. Callum AU - Taba, Melody AU - Allen, B. Tiffany AU - Caldwell, HY Patrina AU - Skinner, Rachel S. AU - Kang, Melissa AU - Henderson, Hamish AU - Bray, Liam AU - Borthwick, Madeleine AU - Collin, Philippa AU - McCaffery, Kirsten AU - Scott, M. Karen PY - 2024/8/7 TI - Developing an Educational Resource Aimed at Improving Adolescent Digital Health Literacy: Using Co-Design as Research Methodology JO - J Med Internet Res SP - e49453 VL - 26 KW - Adolescent health KW - digital health literacy KW - adolescents KW - online health information KW - co-design KW - health education KW - eHealth literacy KW - social media N2 - Background: Adolescence is a key developmental period that affects lifelong health and is impacted by adolescents regularly engaging with digital health information. Adolescents need digital health literacy (DHL) to effectively evaluate the quality and credibility of such information, and to navigate an increasingly complex digital health environment. Few educational resources exist to improve DHL, and few have involved adolescents during design. The co-design approach may hold utility through developing interventions with participants as design partners. Objective: This project aimed to explore the co-design approach in developing an educational resource to improve adolescents? DHL. Methods: Adolescents (12-17 years old) attended 4 interactive co-design workshops (June 2021-April 2022). Participant perspectives were gathered on DHL and the design of educational resources to improve it. Data generated were analyzed through content analysis to inform educational resource development. Results: In total, 27 participants from diverse backgrounds attended the workshops. Insight was gained into participants? relationship with digital health information, including acceptance of its benefits and relevance, coupled with awareness of misinformation issues, revealing areas of DHL need. Participants provided suggestions for educational resource development that incorporated the most useful aspects of digital formats to develop skills across these domains. The following 4 themes were derived from participant perspectives: ease of access to digital health information, personal and social factors that impacted use, impacts of the plethora of digital information, and anonymity offered by digital sources. Initial participant evaluation of the developed educational resource was largely positive, including useful suggestions for improvement. Conclusions: Co-design elicited and translated authentic adolescent perspectives and design ideas into a functional educational resource. Insight into adolescents? DHL needs generated targeted educational resource content, with engaging formats, designs, and storylines. Co-design holds promise as an important and empowering tool for developing interventions to improve adolescents? DHL. UR - https://www.jmir.org/2024/1/e49453 UR - http://dx.doi.org/10.2196/49453 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49453 ER - TY - JOUR AU - McBee, C. Joseph AU - Han, Y. Daniel AU - Liu, Li AU - Ma, Leah AU - Adjeroh, A. Donald AU - Xu, Dong AU - Hu, Gangqing PY - 2024/8/7 TI - Assessing ChatGPT?s Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study JO - JMIR Med Educ SP - e51157 VL - 10 KW - ChatGPT KW - chatbots KW - multirole-playing KW - interdisciplinary inquiry KW - medical education KW - sports medicine N2 - Background: ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. Objective: The aim of this study was to evaluate ChatGPT?s competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. Methods: We developed a model termed PanelGPT to assess ChatGPT?s competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. Results: By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. Conclusions: ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education. UR - https://mededu.jmir.org/2024/1/e51157 UR - http://dx.doi.org/10.2196/51157 UR - http://www.ncbi.nlm.nih.gov/pubmed/39042885 ID - info:doi/10.2196/51157 ER - TY - JOUR AU - Cho, HyunYi AU - Li, Wenbo AU - Lopez, Rachel PY - 2024/8/6 TI - A Multidimensional Approach for Evaluating Reality in Social Media: Mixed Methods Study JO - J Med Internet Res SP - e52058 VL - 26 KW - fake KW - fact KW - misinformation KW - reality KW - social media KW - scale KW - measure KW - instrument KW - user-centric KW - tailoring KW - digital media literacy N2 - Background: Misinformation is a threat to public health. The effective countering of misinformation may require moving beyond the binary classification of fake versus fact to capture the range of schemas that users employ to evaluate social media content. A more comprehensive understanding of user evaluation schemas is necessary. Objective: The goal of this research was to advance the current understanding of user evaluations of social media information and to develop and validate a measurement instrument for assessing social media realism. Methods: This research involved a sequence of 2 studies. First, we used qualitative focus groups (n=48). Second, building on the first study, we surveyed a national sample of social media users (n=442). The focus group data were analyzed using the constant comparison approach. The survey data were analyzed using confirmatory factor analyses and ordinary least squares regression. Results: The findings showed that social media reality evaluation involves 5 dimensions: falsity, naturality, authenticity, resonance, and social assurance. These dimensions were differentially mapped onto patterns of social media use. Authenticity was strongly associated with the existing global measure of social media realism (P<.001). Naturality, or the willingness to accept artificiality and engineered aspects of social media representations, was linked to hedonic enjoyment (P<.001). Resonance predicted reflective thinking (P<.001), while social assurance was strongly related to addictive use (P<.001). Falsity, the general belief that much of what is on social media is not real, showed a positive association with both frequency (P<.001) and engagement with (P=.003) social media. These results provide preliminary validity data for a social media reality measure that encompasses multiple evaluation schemas for social media content. Conclusions: The identification of divergent schemas expands the current focus beyond fake versus fact, while the goals, contexts, and outcomes of social media use associated with these schemas can guide future digital media literacy efforts. Specifically, the social media reality measure can be used to develop tailored digital media literacy interventions for addressing diverse public health issues. UR - https://www.jmir.org/2024/1/e52058 UR - http://dx.doi.org/10.2196/52058 UR - http://www.ncbi.nlm.nih.gov/pubmed/39106092 ID - info:doi/10.2196/52058 ER - TY - JOUR AU - Zhang, Jinxi AU - Li, Zhen AU - Liu, Yu AU - Li, Jian AU - Qiu, Hualong AU - Li, Mohan AU - Hou, Guohui AU - Zhou, Zhixiong PY - 2024/8/5 TI - An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design JO - J Med Internet Res SP - e56750 VL - 26 KW - fall detection KW - deep learning KW - self-attention KW - accelerometer KW - gyroscope KW - human health KW - wearable sensors KW - Sisfall KW - MobiFall N2 - Background: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors?based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning?based FDSs using manual feature extraction, and deep learning (DL)?based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. Objective: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Methods: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. Results: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). Conclusions: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation. UR - https://www.jmir.org/2024/1/e56750 UR - http://dx.doi.org/10.2196/56750 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56750 ER - TY - JOUR AU - Pellemans, Mathijs AU - Salmi, Salim AU - Mérelle, Saskia AU - Janssen, Wilco AU - van der Mei, Rob PY - 2024/8/1 TI - Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial JO - J Med Internet Res SP - e53562 VL - 26 KW - motivational interviewing KW - behavioral coding KW - suicide prevention KW - artificial intelligence KW - effectiveness KW - counseling KW - support tool KW - online help KW - mental health N2 - Background: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. Objective: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. Methods: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. Results: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen ?=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, ?=0.65) and evocative and nonevocative language (AUC=0.92, ?=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, ?=0.55). The model?s interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. Conclusions: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback. UR - https://www.jmir.org/2024/1/e53562 UR - http://dx.doi.org/10.2196/53562 UR - http://www.ncbi.nlm.nih.gov/pubmed/39088244 ID - info:doi/10.2196/53562 ER - TY - JOUR AU - Chan, Lisa Jennifer AU - Tsay, Sarah AU - Sambara, Sraavya AU - Welch, B. Sarah PY - 2024/8/1 TI - Understanding the Use of Mobility Data in Disasters: Exploratory Qualitative Study of COVID-19 User Feedback JO - JMIR Hum Factors SP - e52257 VL - 11 KW - mobility data KW - disasters KW - surveillance KW - COVID-19 KW - qualitative KW - user feedback KW - policy making KW - emergency KW - pandemic KW - disaster response KW - data usage KW - situational awareness KW - data translation KW - big data N2 - Background: Human mobility data have been used as a potential novel data source to guide policies and response planning during the COVID-19 global pandemic. The COVID-19 Mobility Data Network (CMDN) facilitated the use of human mobility data around the world. Both researchers and policy makers assumed that mobility data would provide insights to help policy makers and response planners. However, evidence that human mobility data were operationally useful and provided added value for public health response planners remains largely unknown. Objective: This exploratory study focuses on advancing the understanding of the use of human mobility data during the early phase of the COVID-19 pandemic. The study explored how researchers and practitioners around the world used these data in response planning and policy making, focusing on processing data and human factors enabling or hindering use of the data. Methods: Our project was based on phenomenology and used an inductive approach to thematic analysis. Transcripts were open-coded to create the codebook that was then applied by 2 team members who blind-coded all transcripts. Consensus coding was used for coding discrepancies. Results: Interviews were conducted with 45 individuals during the early period of the COVID-19 pandemic. Although some teams used mobility data for response planning, few were able to describe their uses in policy making, and there were no standardized ways that teams used mobility data. Mobility data played a larger role in providing situational awareness for government partners, helping to understand where people were moving in relation to the spread of COVID-19 variants and reactions to stay-at-home orders. Interviewees who felt they were more successful using mobility data often cited an individual who was able to answer general questions about mobility data; provide interactive feedback on results; and enable a 2-way communication exchange about data, meaning, value, and potential use. Conclusions: Human mobility data were used as a novel data source in the COVID-19 pandemic by a network of academic researchers and practitioners using privacy-preserving and anonymized mobility data. This study reflects the processes in analyzing and communicating human mobility data, as well as how these data were used in response planning and how the data were intended for use in policy making. The study reveals several valuable use cases. Ultimately, the role of a data translator was crucial in understanding the complexities of this novel data source. With this role, teams were able to adapt workflows, visualizations, and reports to align with end users and decision makers while communicating this information meaningfully to address the goals of responders and policy makers. UR - https://humanfactors.jmir.org/2024/1/e52257 UR - http://dx.doi.org/10.2196/52257 UR - http://www.ncbi.nlm.nih.gov/pubmed/39088256 ID - info:doi/10.2196/52257 ER - TY - JOUR AU - Brobbin, Eileen AU - Deluca, Paolo AU - Parkin, Stephen AU - Drummond, Colin PY - 2024/7/31 TI - Use of Transdermal Alcohol Sensors in Conjunction With Contingency Management to Reduce Alcohol Consumption in People With Alcohol Dependence Attending Alcohol Treatment Services: Protocol for a Pilot Feasibility Randomized Controlled Trial JO - JMIR Res Protoc SP - e57653 VL - 13 KW - accuracy KW - addiction KW - alcohol KW - alcohol monitoring KW - alcohol treatment KW - contingency management KW - transdermal alcohol sensors KW - wearables KW - mobile phone KW - transdermal KW - TAS KW - wearable technology KW - alcohol use disorders KW - AUD KW - RCT KW - randomized controlled trial KW - abstinence KW - community-based KW - residential rehabilitation KW - consumption KW - alcohol consumption KW - low-risk consumption N2 - Background: Wearable technology for objective, continuous, and reliable alcohol monitoring has been developed. These are known as transdermal alcohol sensors (TASs). They can be worn on the wrist or ankle with the sensor pressed against the skin and can measure sweat vapors being emitted from the skin, to record transdermal alcohol concentration (TAC). Previous studies have investigated the accuracy and acceptability of the available TAS brands, but there has been little research into their use in people with alcohol use disorders (AUD). Objective: This feasibility randomized controlled trial aims to explore the feasibility, strengths, and limitations of using a TAS to monitor alcohol consumption in individuals in treatment for AUD with or without contingency management (CM) to promote abstinence or low-level alcohol consumption. Methods: The target sample size is 30 (15 randomized to each group). Participants will be recruited through poster adverts at alcohol services. Both groups (control and CM) will wear the TAS (BACtrack Skyn) for 2 weeks in the context of their usual treatment, meeting with the researcher every other weekday. In the last meeting, the participants will complete a postwear survey on their experience of wearing the TAS. The CM group will also receive small financial incentives for low or no alcohol consumption, as measured by the TAS. On days where the TAC peak is below a set threshold (<115.660 g/L), CM group participants will be rewarded with a £5 (US $6.38) voucher. There are financial bonuses if this target is achieved on consecutive days. The researcher will monitor TAC for each day of the study at each research visit and allocate financial incentives to participants according to a set reinforcement schedule. Results: The first participant was enrolled in June 2023, and the last in December 2023. Data analysis is underway and is estimated to be completed by June 2024. A total of 32 participants were enrolled. Conclusions: Most TAS brands have had limited application in clinical settings, and most studies have included healthy adults rather than people with AUD. TAS has the potential to enhance treatment outcomes in clinical alcohol treatment. The accuracy, acceptability, and feasibility of TAS for people with AUD in clinical settings need to be investigated. This is the first study to use TAS in specialized alcohol services with diagnosed AUD individuals currently receiving treatment from a south London alcohol service. Trial Registration: ISRCTN Registry ISRCTN46845361; https://www.isrctn.com/ISRCTN46845361 International Registered Report Identifier (IRRID): DERR1-10.2196/57653 UR - https://www.researchprotocols.org/2024/1/e57653 UR - http://dx.doi.org/10.2196/57653 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57653 ER - TY - JOUR AU - Akhter-Khan, C. Samia AU - Tao, Qiushan AU - Ang, Alvin Ting Fang AU - Karjadi, Cody AU - Itchapurapu, Swetha Indira AU - Libon, J. David AU - Alosco, Michael AU - Mez, Jesse AU - Qiu, Qiao Wei AU - Au, Rhoda PY - 2024/7/29 TI - Cerebral Microbleeds in Different Brain Regions and Their Associations With the Digital Clock-Drawing Test: Secondary Analysis of the Framingham Heart Study JO - J Med Internet Res SP - e45780 VL - 26 KW - cerebral microbleeds KW - CMB KW - digital clock-drawing test KW - DCT KW - Alzheimer disease KW - dementia KW - early screening KW - Boston Process Approach KW - cerebral microbleed KW - neuroimaging KW - cerebrovascular diseases KW - aging KW - MRI KW - magnetic resonance imaging KW - clock-drawing test KW - cognitive function N2 - Background: Cerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible. Objective: This study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB. Methods: In this study, we analyzed data from participants in the Framingham Heart Study offspring cohort who underwent both brain magnetic resonance imaging scans (Siemens 1.5T, Siemens Healthcare Private Limited; T2*-GRE weighted sequences) for CMB diagnosis and the DCT as a predictor. Additionally, paper-based clock-drawing tests were also collected during the DCT. Individuals with a history of dementia or stroke were excluded. Robust multivariable linear regression models were used to examine the association between DCT facet scores with CMB prevalence, adjusting for relevant covariates. Receiver operating characteristic (ROC) curve analyses were used to evaluate DCT facet scores as predictors of CMB prevalence. Sensitivity analyses were conducted by further including participants with stroke and dementia. Results: The study sample consisted of 1020 (n=585, 57.35% female) individuals aged 45 years and older (mean 72, SD 7.9 years). Among them, 64 (6.27%) participants exhibited CMB, comprising 46 with lobar-only, 11 with deep-only, and 7 with mixed (lobar+deep) CMB. Individuals with CMB tended to be older and had a higher prevalence of mild cognitive impairment and higher white matter hyperintensities compared to those without CMB (P<.05). While CMB were not associated with the paper-based clock-drawing test, participants with CMB had a lower overall DCT score (CMB: mean 68, SD 23 vs non-CMB: mean 76, SD 20; P=.009) in the univariate comparison. In the robust multiple regression model adjusted for covariates, deep CMB were significantly associated with lower scores on the drawing efficiency (?=?0.65, 95% CI ?1.15 to ?0.15; P=.01) and simple motor (?=?0.86, 95% CI ?1.43 to ?0.30; P=.003) domains of the command DCT. In the ROC curve analysis, DCT facets discriminated between no CMB and the CMB subtypes. The area under the ROC curve was 0.76 (95% CI 0.69-0.83) for lobar CMB, 0.88 (95% CI 0.78-0.98) for deep CMB, and 0.98 (95% CI 0.96-1.00) for mixed CMB, where the area under the ROC curve value nearing 1 indicated an accurate model. Conclusions: The study indicates a significant association between CMB, especially deep and mixed types, and reduced performance in drawing efficiency and motor skills as assessed by the DCT. This highlights the potential of the DCT for early detection of CMB and their subtypes, providing a reliable alternative for cognitive assessment and making it a valuable tool for primary care screening before neuroimaging referral. UR - https://www.jmir.org/2024/1/e45780 UR - http://dx.doi.org/10.2196/45780 UR - http://www.ncbi.nlm.nih.gov/pubmed/39073857 ID - info:doi/10.2196/45780 ER - TY - JOUR AU - Bijker, Rimke AU - Merkouris, S. Stephanie AU - Dowling, A. Nicki AU - Rodda, N. Simone PY - 2024/7/25 TI - ChatGPT for Automated Qualitative Research: Content Analysis JO - J Med Internet Res SP - e59050 VL - 26 KW - ChatGPT KW - natural language processing KW - qualitative content analysis KW - Theoretical Domains Framework N2 - Background: Data analysis approaches such as qualitative content analysis are notoriously time and labor intensive because of the time to detect, assess, and code a large amount of data. Tools such as ChatGPT may have tremendous potential in automating at least some of the analysis. Objective: The aim of this study was to explore the utility of ChatGPT in conducting qualitative content analysis through the analysis of forum posts from people sharing their experiences on reducing their sugar consumption. Methods: Inductive and deductive content analysis were performed on 537 forum posts to detect mechanisms of behavior change. Thorough prompt engineering provided appropriate instructions for ChatGPT to execute data analysis tasks. Data identification involved extracting change mechanisms from a subset of forum posts. The precision of the extracted data was assessed through comparison with human coding. On the basis of the identified change mechanisms, coding schemes were developed with ChatGPT using data-driven (inductive) and theory-driven (deductive) content analysis approaches. The deductive approach was informed by the Theoretical Domains Framework using both an unconstrained coding scheme and a structured coding matrix. In total, 10 coding schemes were created from a subset of data and then applied to the full data set in 10 new conversations, resulting in 100 conversations each for inductive and unconstrained deductive analysis. A total of 10 further conversations coded the full data set into the structured coding matrix. Intercoder agreement was evaluated across and within coding schemes. ChatGPT output was also evaluated by the researchers to assess whether it reflected prompt instructions. Results: The precision of detecting change mechanisms in the data subset ranged from 66% to 88%. Overall ? scores for intercoder agreement ranged from 0.72 to 0.82 across inductive coding schemes and from 0.58 to 0.73 across unconstrained coding schemes and structured coding matrix. Coding into the best-performing coding scheme resulted in category-specific ? scores ranging from 0.67 to 0.95 for the inductive approach and from 0.13 to 0.87 for the deductive approaches. ChatGPT largely followed prompt instructions in producing a description of each coding scheme, although the wording for the inductively developed coding schemes was lengthier than specified. Conclusions: ChatGPT appears fairly reliable in assisting with qualitative analysis. ChatGPT performed better in developing an inductive coding scheme that emerged from the data than adapting an existing framework into an unconstrained coding scheme or coding directly into a structured matrix. The potential for ChatGPT to act as a second coder also appears promising, with almost perfect agreement in at least 1 coding scheme. The findings suggest that ChatGPT could prove useful as a tool to assist in each phase of qualitative content analysis, but multiple iterations are required to determine the reliability of each stage of analysis. UR - https://www.jmir.org/2024/1/e59050 UR - http://dx.doi.org/10.2196/59050 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59050 ER - TY - JOUR AU - Avnat, Eden AU - Samin, Michael AU - Ben Joya, Daniel AU - Schneider, Eyal AU - Yanko, Elia AU - Eshel, Dafna AU - Ovadia, Shahar AU - Lev, Yossi AU - Souroujon, Daniel PY - 2024/7/16 TI - The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study JO - J Med Internet Res SP - e49570 VL - 26 KW - clinical intake tool KW - evidence-based medicine KW - big data KW - digital health KW - symptoms KW - prevalence N2 - Background: Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help health care providers make informed decisions. The growing demand for personalized medicine, along with the big data revolution, has rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate diagnosis, while contributing to the grounding of medical care. Objective: This work aims to examine whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, and thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground the real prevalence of symptoms in different disorders thereby expanding medical knowledge and further supporting medical diagnoses made by physicians. Methods: Between August 1, 2022, and January 15, 2023, patients who used the services of a digital health care (DH) provider in the United States were first assessed by the Kahun EBCIT. Kahun platform gathered and analyzed the information from the sessions. This study estimated the prevalence of patients? symptoms in medical disorders using 2 data sets. The first data set analyzed symptom prevalence, as determined by Kahun?s knowledge engine. The second data set analyzed symptom prevalence, relying solely on data from the DH patients gathered by Kahun. The variance difference between these 2 prevalence data sets helped us assess Kahun?s ability to incorporate new data while integrating existing knowledge. To analyze the comprehensiveness of Kahun?s knowledge engine, we compared how well it covers weighted data for the symptoms and disorders found in the 2019 National Ambulatory Medical Care Survey (NMCAS). To assess Kahun?s diagnosis accuracy, physicians independently diagnosed 250 of Kahun-DH?s sessions. Their diagnoses were compared with Kahun?s diagnoses. Results: In this study, 2550 patients used Kahun to complete a full assessment. Kahun proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6496 conditions were presented to the caregiver. Kahun covered 94% (526,157,569/562,150,572) of the weighted symptoms and 91% (1,582,637,476/173,4783,244) of the weighted disorders in the 2019 NMCAS. In 90% (224/250) of the sessions, both physicians and Kahun suggested at least one identical disorder, with a 72% (367/507) total accuracy rate. Kahun?s engine yielded 519 prevalences while the Kahun-DH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both data sets. Conclusions: ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnoses. Using this credible database, the potential prevalence of symptoms in different disorders was discovered or grounded. This highlights the ability of ECBITs to refine the understanding of relationships between disorders and symptoms, which further supports physicians in medical diagnosis. UR - https://www.jmir.org/2024/1/e49570 UR - http://dx.doi.org/10.2196/49570 UR - http://www.ncbi.nlm.nih.gov/pubmed/39012659 ID - info:doi/10.2196/49570 ER - TY - JOUR AU - Zondag, M. Anna G. AU - Hollestelle, J. Marieke AU - van der Graaf, Rieke AU - Nathoe, M. Hendrik AU - van Solinge, W. Wouter AU - Bots, L. Michiel AU - Vernooij, M. Robin W. AU - Haitjema, Saskia AU - PY - 2024/7/11 TI - Comparison of the Response to an Electronic Versus a Traditional Informed Consent Procedure in Terms of Clinical Patient Characteristics: Observational Study JO - J Med Internet Res SP - e54867 VL - 26 KW - informed consent KW - learning health care system KW - e-consent KW - cardiovascular risk management KW - digital health KW - research ethics N2 - Background: Electronic informed consent (eIC) is increasingly used in clinical research due to several benefits including increased enrollment and improved efficiency. Within a learning health care system, a pilot was conducted with an eIC for linking data from electronic health records with national registries, general practitioners, and other hospitals. Objective: We evaluated the eIC pilot by comparing the response to the eIC with the former traditional paper-based informed consent (IC). We assessed whether the use of eIC resulted in a different study population by comparing the clinical patient characteristics between the response categories of the eIC and former face-to-face IC procedure. Methods: All patients with increased cardiovascular risk visiting the University Medical Center Utrecht, the Netherlands, were eligible for the learning health care system. From November 2021 to August 2022, an eIC was piloted at the cardiology outpatient clinic. Prior to the pilot, a traditional face-to-face paper-based IC approach was used. Responses (ie, consent, no consent, or nonresponse) were assessed and compared between the eIC and face-to-face IC cohorts. Clinical characteristics of consenting and nonresponding patients were compared between and within the eIC and the face-to-face cohorts using multivariable regression analyses. Results: A total of 2254 patients were included in the face-to-face IC cohort and 885 patients in the eIC cohort. Full consent was more often obtained in the eIC than in the face-to-face cohort (415/885, 46.9% vs 876/2254, 38.9%, respectively). Apart from lower mean hemoglobin in the full consent group of the eIC cohort (8.5 vs 8.8; P=.0021), the characteristics of the full consenting patients did not differ between the eIC and face-to-face IC cohorts. In the eIC cohort, only age differed between the full consent and the nonresponse group (median 60 vs 56; P=.0002, respectively), whereas in the face-to-face IC cohort, the full consent group seemed healthier (ie, higher hemoglobin, lower glycated hemoglobin [HbA1c], lower C-reactive protein levels) than the nonresponse group. Conclusions: More patients provided full consent using an eIC. In addition, the study population remained broadly similar. The face-to-face IC approach seemed to result in a healthier study population (ie, full consenting patients) than the patients without IC, while in the eIC cohort, the characteristics between consent groups were comparable. Thus, an eIC may lead to a better representation of the target population, increasing the generalizability of results. UR - https://www.jmir.org/2024/1/e54867 UR - http://dx.doi.org/10.2196/54867 UR - http://www.ncbi.nlm.nih.gov/pubmed/38990640 ID - info:doi/10.2196/54867 ER - TY - JOUR AU - Sato, Daisuke AU - Ikarashi, Koyuki AU - Nakajima, Fumiko AU - Fujimoto, Tomomi PY - 2024/7/5 TI - Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping: Validity and Effectiveness Study JO - JMIR Form Res SP - e55834 VL - 8 KW - menstrual cycle KW - ovulation KW - biphasic temperature shift KW - estimation method KW - women N2 - Background: Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However, issues related to its low accuracy are still under discussion. Objective: This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual cycle. We investigated whether core body temperature (CBT) estimation can improve the accuracy of temperature biphasic shift discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the CBT estimation method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts. Methods: In experiment 1, healthy women aged between 18 and 40 years had their true CBT measured using an ingestible thermometer and their CBT estimated from skin temperature and ambient temperature measured during sleep in both the follicular and luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations in temperature between the 2 phases, and the repeated measures correlation between the true and estimated CBT. Experiment 2 followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches (estimated CBT and traditional oral basal body temperature [BBT]) for identifying ovulatory cycles. This was performed using urine luteinizing hormone (LH) as the reference standard. Menstrual cycles were categorized based on the results of the LH tests, and a temperature shift was identified using a specific criterion called the ?three-over-six rule.? This rule and the nested design of the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity. Results: The main findings showed that CBT estimated from skin temperature and ambient temperature during sleep was consistently lower than directly measured CBT in both the follicular and luteal phases of the menstrual cycle. Despite this, the pattern of temperature variation between these phases was comparable for both the estimated and true CBT measurements, suggesting that the estimated CBT accurately reflected the cyclical variations in the true CBT. Significantly, the CBT estimation method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral BBT measurements, highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the CBT provides a practical and noninvasive method for monitoring CBT, which is essential for identifying biphasic shifts in the BBT throughout the menstrual cycle. Conclusions: This study demonstrated that the estimated CBT derived from skin temperature and ambient temperature during sleep accurately captures variations in true CBT and is more accurate in determining the presence or absence of ovulation than traditional oral BBT measurements. This method holds promise for improving reproductive health monitoring and understanding of menstrual cycle dynamics. UR - https://formative.jmir.org/2024/1/e55834 UR - http://dx.doi.org/10.2196/55834 UR - http://www.ncbi.nlm.nih.gov/pubmed/38967967 ID - info:doi/10.2196/55834 ER - TY - JOUR AU - Duggan, M. Nicole AU - Jin, Mike AU - Duran Mendicuti, Alejandra Maria AU - Hallisey, Stephen AU - Bernier, Denie AU - Selame, A. Lauren AU - Asgari-Targhi, Ameneh AU - Fischetti, E. Chanel AU - Lucassen, Ruben AU - Samir, E. Anthony AU - Duhaime, Erik AU - Kapur, Tina AU - Goldsmith, J. Andrew PY - 2024/7/4 TI - Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis JO - J Med Internet Res SP - e51397 VL - 26 KW - crowdsource KW - crowdsourced KW - crowdsourcing KW - machine learning KW - artificial intelligence KW - point-of-care ultrasound KW - POCUS KW - lung ultrasound KW - B-lines KW - gamification KW - gamify KW - gamified KW - label KW - labels KW - labeling KW - classification KW - lung KW - pulmonary KW - respiratory KW - ultrasound KW - imaging KW - medical image KW - diagnostic KW - diagnose KW - diagnosis KW - data science N2 - Background: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. Objective: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. Methods: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. Results: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts? opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. Conclusions: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems. UR - https://www.jmir.org/2024/1/e51397 UR - http://dx.doi.org/10.2196/51397 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51397 ER - TY - JOUR AU - Maekawa, Eduardo AU - Grua, Martino Eoin AU - Nakamura, Akemi Carina AU - Scazufca, Marcia AU - Araya, Ricardo AU - Peters, Tim AU - van de Ven, Pepijn PY - 2024/7/4 TI - Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation JO - JMIR Ment Health SP - e52045 VL - 11 KW - Bayesian network KW - target depressive symptomatology KW - probabilistic machine learning KW - stochastic gradient descent KW - patient screening KW - depressive symptom KW - machine learning model KW - machine learning KW - survey KW - prediction KW - socioeconomic data sets KW - utilization KW - depression KW - mental health KW - digital mental health KW - artificial intelligence KW - AI KW - prediction modeling KW - patient KW - mood KW - anxiety KW - mood disorders KW - mood disorder KW - eHealth KW - mobile health KW - mHealth KW - telehealth N2 - Background: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. Objective: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. Methods: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ?10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. Results: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. Conclusions: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS. UR - https://mental.jmir.org/2024/1/e52045 UR - http://dx.doi.org/10.2196/52045 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52045 ER - TY - JOUR AU - Wong, Chia-En AU - Chen, Pei-Wen AU - Hsu, Heng-Jui AU - Cheng, Shao-Yang AU - Fan, Chen-Che AU - Chen, Yen-Chang AU - Chiu, Yi-Pei AU - Lee, Jung-Shun AU - Liang, Sheng-Fu PY - 2024/7/4 TI - Collaborative Human?Computer Vision Operative Video Analysis Algorithm for Analyzing Surgical Fluency and Surgical Interruptions in Endonasal Endoscopic Pituitary Surgery: Cohort Study JO - J Med Internet Res SP - e56127 VL - 26 KW - algorithm KW - computer vision KW - endonasal endoscopic approach KW - pituitary KW - transsphenoidal surgery N2 - Background: The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is time-consuming. The application of a computer vision (CV) algorithm could potentially reduce the time required for operative video review and facilitate the training of surgeons to overcome the learning curve of EEA. Objective: This study aimed to evaluate the performance of a CV-based video analysis system, based on OpenCV algorithm, to detect surgical interruptions and analyze surgical fluency in EEA. The accuracy of the CV-based video analysis was investigated, and the time required for operative video review using CV-based analysis was compared to that of manual review. Methods: The dominant color of each frame in the EEA video was determined using OpenCV. We developed an algorithm to identify events of surgical interruption if the alterations in the dominant color pixels reached certain thresholds. The thresholds were determined by training the current algorithm using EEA videos. The accuracy of the CV analysis was determined by manual review, and the time spent was reported. Results: A total of 46 EEA operative videos were analyzed, with 93.6%, 95.1%, and 93.3% accuracies in the training, test 1, and test 2 data sets, respectively. Compared with manual review, CV-based analysis reduced the time required for operative video review by 86% (manual review: 166.8 and CV analysis: 22.6 minutes; P<.001). The application of a human-computer collaborative strategy increased the overall accuracy to 98.5%, with a 74% reduction in the review time (manual review: 166.8 and human-CV collaboration: 43.4 minutes; P<.001). Analysis of the different surgical phases showed that the sellar phase had the lowest frequency (nasal phase: 14.9, sphenoidal phase: 15.9, and sellar phase: 4.9 interruptions/10 minutes; P<.001) and duration (nasal phase: 67.4, sphenoidal phase: 77.9, and sellar phase: 31.1 seconds/10 minutes; P<.001) of surgical interruptions. A comparison of the early and late EEA videos showed that increased surgical experience was associated with a decreased number (early: 4.9 and late: 2.9 interruptions/10 minutes; P=.03) and duration (early: 41.1 and late: 19.8 seconds/10 minutes; P=.02) of surgical interruptions during the sellar phase. Conclusions: CV-based analysis had a 93% to 98% accuracy in detecting the number, frequency, and duration of surgical interruptions occurring during EEA. Moreover, CV-based analysis reduced the time required to analyze the surgical fluency in EEA videos compared to manual review. The application of CV can facilitate the training of surgeons to overcome the learning curve of endoscopic skull base surgery. Trial Registration: ClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/study/NCT06156020 UR - https://www.jmir.org/2024/1/e56127 UR - http://dx.doi.org/10.2196/56127 UR - http://www.ncbi.nlm.nih.gov/pubmed/38963694 ID - info:doi/10.2196/56127 ER - TY - JOUR AU - Herman Bernardim Andrade, Gabriel AU - Yada, Shuntaro AU - Aramaki, Eiji PY - 2024/7/2 TI - Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study JO - JMIR Med Inform SP - e59680 VL - 12 KW - natural language processing KW - named entity recognition KW - information extraction KW - text annotation KW - entity boundaries KW - lenient annotation KW - case reports KW - annotation KW - case study KW - medical case report KW - efficiency KW - model KW - model performance KW - dataset KW - Japan KW - Japanese KW - entity KW - clinical domain KW - clinical N2 - Background: Named entity recognition (NER) is a fundamental task in natural language processing. However, it is typically preceded by named entity annotation, which poses several challenges, especially in the clinical domain. For instance, determining entity boundaries is one of the most common sources of disagreements between annotators due to questions such as whether modifiers or peripheral words should be annotated. If unresolved, these can induce inconsistency in the produced corpora, yet, on the other hand, strict guidelines or adjudication sessions can further prolong an already slow and convoluted process. Objective: The aim of this study is to address these challenges by evaluating 2 novel annotation methodologies, lenient span and point annotation, aiming to mitigate the difficulty of precisely determining entity boundaries. Methods: We evaluate their effects through an annotation case study on a Japanese medical case report data set. We compare annotation time, annotator agreement, and the quality of the produced labeling and assess the impact on the performance of an NER system trained on the annotated corpus. Results: We saw significant improvements in the labeling process efficiency, with up to a 25% reduction in overall annotation time and even a 10% improvement in annotator agreement compared to the traditional boundary-strict approach. However, even the best-achieved NER model presented some drop in performance compared to the traditional annotation methodology. Conclusions: Our findings demonstrate a balance between annotation speed and model performance. Although disregarding boundary information affects model performance to some extent, this is counterbalanced by significant reductions in the annotator?s workload and notable improvements in the speed of the annotation process. These benefits may prove valuable in various applications, offering an attractive compromise for developers and researchers. UR - https://medinform.jmir.org/2024/1/e59680 UR - http://dx.doi.org/10.2196/59680 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59680 ER - TY - JOUR AU - Courtney, Elizabeth Kelly AU - Liu, Weichen AU - Andrade, Gianna AU - Schulze, Jurgen AU - Doran, Neal PY - 2024/6/27 TI - Attentional Bias, Pupillometry, and Spontaneous Blink Rate: Eye Characteristic Assessment Within a Translatable Nicotine Cue Virtual Reality Paradigm JO - JMIR Serious Games SP - e54220 VL - 12 KW - nicotine KW - craving KW - cue exposure KW - virtual reality KW - attentional bias KW - pupillometry KW - spontaneous blink rate KW - eye-tracking KW - tobacco KW - VR KW - development KW - addiction KW - eye KW - pupil KW - biomarker KW - biomarkers KW - tobacco product N2 - Background: Incentive salience processes are important for the development and maintenance of addiction. Eye characteristics such as gaze fixation time, pupil diameter, and spontaneous eyeblink rate (EBR) are theorized to reflect incentive salience and may serve as useful biomarkers. However, conventional cue exposure paradigms have limitations that may impede accurate assessment of these markers. Objective: This study sought to evaluate the validity of these eye-tracking metrics as indicators of incentive salience within a virtual reality (VR) environment replicating real-world situations of nicotine and tobacco product (NTP) use. Methods: NTP users from the community were recruited and grouped by NTP use patterns: nondaily (n=33) and daily (n=75) use. Participants underwent the NTP cue VR paradigm and completed measures of nicotine craving, NTP use history, and VR-related assessments. Eye-gaze fixation time (attentional bias) and pupillometry in response to NTP versus control cues and EBR during the active and neutral VR scenes were recorded and analyzed using ANOVA and analysis of covariance models. Results: Greater subjective craving, as measured by the Tobacco Craving Questionnaire?Short Form, following active versus neutral scenes was observed (F1,106=47.95; P<.001). Greater mean eye-gaze fixation time (F1,106=48.34; P<.001) and pupil diameter (F1,102=5.99; P=.02) in response to NTP versus control cues were also detected. Evidence of NTP use group effects was observed in fixation time and pupillometry analyses, as well as correlations between these metrics, NTP use history, and nicotine craving. No significant associations were observed with EBR. Conclusions: This study provides additional evidence for attentional bias, as measured via eye-gaze fixation time, and pupillometry as useful biomarkers of incentive salience, and partially supports theories suggesting that incentive salience diminishes as nicotine dependence severity increases. UR - https://games.jmir.org/2024/1/e54220 UR - http://dx.doi.org/10.2196/54220 ID - info:doi/10.2196/54220 ER - TY - JOUR AU - Saskovets, Marina AU - Liang, Zilu AU - Piumarta, Ian AU - Saponkova, Irina PY - 2024/6/27 TI - Effects of Sound Interventions on the Mental Stress Response in Adults: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e54030 VL - 13 KW - mental stress KW - anxiety KW - sound therapy KW - music therapy KW - voice-guided relaxation KW - voice-guided meditation KW - prosody KW - paralanguage KW - expressive sounds KW - psychoacoustics N2 - Background: Sound therapy methods have seen a surge in popularity, with a predominant focus on music among all types of sound stimulation. There is substantial evidence documenting the integrative impact of music therapy on psycho-emotional and physiological outcomes, rendering it beneficial for addressing stress-related conditions such as pain syndromes, depression, and anxiety. Despite these advancements, the therapeutic aspects of sound, as well as the mechanisms underlying its efficacy, remain incompletely understood. Existing research on music as a holistic cultural phenomenon often overlooks crucial aspects of sound therapy mechanisms, particularly those related to speech acoustics or the so-called ?music of speech.? Objective: This study aims to provide an overview of empirical research on sound interventions to elucidate the mechanism underlying their positive effects. Specifically, we will focus on identifying therapeutic factors and mechanisms of change associated with sound interventions. Our analysis will compare the most prevalent types of sound interventions reported in clinical studies and experiments. Moreover, we will explore the therapeutic effects of sound beyond music, encompassing natural human speech and intermediate forms such as traditional poetry performances. Methods: This review adheres to the methodological guidance of the Joanna Briggs Institute and follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist for reporting review studies, which is adapted from the Arksey and O?Malley framework. Our search strategy encompasses PubMed, Web of Science, Scopus, and PsycINFO or EBSCOhost, covering literature from 1990 to the present. Among the different study types, randomized controlled trials, clinical trials, laboratory experiments, and field experiments were included. Results: Data collection began in October 2022. We found a total of 2027 items. Our initial search uncovered an asymmetry in the distribution of studies, with a larger number focused on music therapy compared with those exploring prosody in spoken interventions such as guided meditation or hypnosis. We extracted and selected papers using Rayyan software (Rayyan) and identified 41 eligible papers after title and abstract screening. The completion of the scoping review is anticipated by October 2024, with key steps comprising the analysis of findings by May 2024, drafting and revising the study by July 2024, and submitting the paper for publication in October 2024. Conclusions: In the next step, we will conduct a quality evaluation of the papers and then chart and group the therapeutic factors extracted from them. This process aims to unveil conceptual gaps in existing studies. Gray literature sources, such as Google Scholar, ClinicalTrials.gov, nonindexed conferences, and reference list searches of retrieved studies, will be added to our search strategy to increase the number of relevant papers that we cover. International Registered Report Identifier (IRRID): DERR1-10.2196/54030 UR - https://www.researchprotocols.org/2024/1/e54030 UR - http://dx.doi.org/10.2196/54030 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54030 ER - TY - JOUR AU - Huecker, Martin AU - Schutzman, Craig AU - French, Joshua AU - El-Kersh, Karim AU - Ghafghazi, Shahab AU - Desai, Ravi AU - Frick, Daniel AU - Thomas, Jeremy Jarred PY - 2024/6/26 TI - Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study JO - JMIR Cardio SP - e57111 VL - 8 KW - ejection fraction KW - stroke volume KW - auscultation KW - digital health KW - telehealth KW - acoustic recording KW - acoustic recordings KW - acoustic KW - mHealth KW - mobile health KW - mobile phone KW - mobile phones KW - heart failure KW - heart KW - cardiac KW - cardiology KW - health care costs KW - audio KW - echocardiographic KW - echocardiogram KW - ultrasonography KW - echocardiography KW - accuracy KW - monitoring KW - telemonitoring KW - recording KW - recordings KW - ejection KW - machine learning KW - algorithm KW - algorithms N2 - Background: Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. Objective: This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. Methods: This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. Results: Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ?40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. Conclusions: This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist. UR - https://cardio.jmir.org/2024/1/e57111 UR - http://dx.doi.org/10.2196/57111 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57111 ER - TY - JOUR AU - Luo, Xufei AU - Chen, Fengxian AU - Zhu, Di AU - Wang, Ling AU - Wang, Zijun AU - Liu, Hui AU - Lyu, Meng AU - Wang, Ye AU - Wang, Qi AU - Chen, Yaolong PY - 2024/6/25 TI - Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses JO - J Med Internet Res SP - e56780 VL - 26 KW - large language model KW - ChatGPT KW - systematic review KW - chatbot KW - meta-analysis UR - https://www.jmir.org/2024/1/e56780 UR - http://dx.doi.org/10.2196/56780 UR - http://www.ncbi.nlm.nih.gov/pubmed/38819655 ID - info:doi/10.2196/56780 ER - TY - JOUR AU - González-Colom, Rubèn AU - Mitra, Kangkana AU - Vela, Emili AU - Gezsi, Andras AU - Paajanen, Teemu AU - Gál, Zsófia AU - Hullam, Gabor AU - Mäkinen, Hannu AU - Nagy, Tamas AU - Kuokkanen, Mikko AU - Piera-Jiménez, Jordi AU - Roca, Josep AU - Antal, Peter AU - Juhasz, Gabriella AU - Cano, Isaac PY - 2024/6/24 TI - Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study JO - J Med Internet Res SP - e53162 VL - 26 KW - health risk assessment KW - multimorbidity KW - disease trajectories KW - major depressive disorder N2 - Background: Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions. Objective: This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions. Methods: In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland. Results: The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases. Conclusions: The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD. UR - https://www.jmir.org/2024/1/e53162 UR - http://dx.doi.org/10.2196/53162 UR - http://www.ncbi.nlm.nih.gov/pubmed/38913991 ID - info:doi/10.2196/53162 ER - TY - JOUR AU - Qsous, Ghaith AU - Ramaraj, Prashanth AU - Avtaar Singh, Singh Sanjeet AU - Herd, Philip AU - Sooraj, Runveer Nayandra AU - Will, Brodie Malcolm PY - 2024/6/21 TI - Treating Spontaneous Pneumothorax Using an Innovative Surgical Technique Called Capnodissection Pleurectomy: Case Report JO - Interact J Med Res SP - e54497 VL - 13 KW - capnodissection KW - pleurectomy KW - VATS KW - video-assisted thorascopic surgery KW - novel technique KW - thoracic surgery KW - surgical innovation KW - pneumothorax KW - spontaneous pneumothorax KW - pleurodesis KW - management KW - bullectomy KW - bullae KW - young patient KW - lung diseases KW - chronic obstructive pulmonary disease KW - COPD KW - surgical treatment KW - male KW - capnothorax UR - https://www.i-jmr.org/2024/1/e54497 UR - http://dx.doi.org/10.2196/54497 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54497 ER - TY - JOUR AU - Bhattacharjee, Parinita AU - McClarty, M. Leigh AU - Kimani, Joshua AU - Isac, Shajy AU - Wanjiru Kabuti, Rhoda AU - Kinyua, Antony AU - Karakaja Okoyana, Jaffred AU - Njeri Ndukuyu, Virjinia AU - Musyoki, Helgar AU - Kiplagat, Anthony AU - Arimi, Peter AU - Shaw, Souradet AU - Emmanuel, Faran AU - Gandhi, Monica AU - Becker, Marissa AU - Blanchard, James PY - 2024/6/19 TI - Assessing Outcomes in HIV Prevention and Treatment Programs With Female Sex Workers and Men Who Have Sex With Men: Expanded Polling Booth Survey Protocol JO - JMIR Public Health Surveill SP - e54313 VL - 10 KW - female sex workers KW - men who have sex with men KW - Kenya KW - polling booth survey KW - program science KW - HIV prevention KW - outcome assessment N2 - Background: Assessing HIV outcomes in key population prevention programs is a crucial component of the program cycle, as it facilitates improved planning and monitoring of anticipated results. The Joint United Nations Programme on HIV and AIDS recommends using simple, rapid methods to routinely measure granular and differentiated program outcomes for key populations. Following a program science approach, Partners for Health and Development in Africa, in partnership with the Nairobi County Government and the University of Manitoba, aims to conduct an outcome assessment using a novel, expanded polling booth survey (ePBS) method with female sex workers and men who have sex with men in Nairobi County, Kenya. Objective: This study aims to (1) estimate the incidence and prevalence of HIV; (2) assess biomedical, behavioral, and structural outcomes; and (3) understand barriers contributing to gaps in access and use of available prevention and treatment services among female sex workers and men who have sex with men in Nairobi. Methods: The novel ePBS approach employs complementary data collection methods, expanding upon the traditional polling booth survey (PBS) method by incorporating additional quantitative, qualitative, and biological data collection components and an improved sampling methodology. Quantitative methods will include (1) PBS, a group interview method in which individuals provide responses through a ballot box in an unlinked and anonymous way, and (2) a behavioral and biological survey (BBS), including a face-to-face individual interview and collection of linked biological samples. Qualitative methods will include focus group discussions. The ePBS study uses a 2-stage, population- and location-based random sampling approach involving the random selection of locations from which random participants are selected at a predetermined time on a randomly selected day. PBS data will be analyzed at the group level, and BBS data will be analyzed at an individual level. Qualitative data will be analyzed thematically. Results: Data were collected from April to May 2023. The study has enrolled 759 female sex workers (response rate: 759/769, 98.6%) and 398 men who have sex with men (response rate: 398/420, 94.7%). Data cleaning and analyses are ongoing, with a focus on assessing gaps in program coverage and inequities in program outcomes. Conclusions: The study will generate valuable HIV outcome data to inform program improvement and policy development for Nairobi County?s key population HIV prevention program. This study served as a pilot for the novel ePBS method, which combines PBS, BBS, and focus group discussions to enhance its programmatic utility. The ePBS method holds the potential to fill an acknowledged gap for a rapid, low-cost, and simple method to routinely measure HIV outcomes within programs and inform incremental program improvements through embedded learning processes. UR - https://publichealth.jmir.org/2024/1/e54313 UR - http://dx.doi.org/10.2196/54313 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54313 ER - TY - JOUR AU - Wu, Mingli AU - Chen, Lulu AU - Wang, Yamin AU - Li, Yunpeng AU - An, Yuqi AU - Wu, Ruonan AU - Zhang, Yuhan AU - Gao, Jing AU - Su, Kaiqi AU - Feng, Xiaodong PY - 2024/6/17 TI - The Effect of Acupuncture on Brain Iron Deposition and Body Iron Metabolism in Vascular Cognitive Impairment: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e56484 VL - 13 KW - acupuncture KW - vascular cognitive impairment KW - iron metabolism KW - mechanisms explored KW - clinical trial KW - needling technique KW - dry needling KW - acupunctures KW - activities of daily living KW - iron KW - prevalence KW - cerebrovascular diseases KW - vascular dementia KW - vascular KW - traditional Chinese method KW - Chinese methods N2 - Background: Vascular cognitive impairment (VCI) persistently impairs cognition and the ability to perform activities of daily living, seriously compromising patients? quality of life. Previous studies have reported that disorders of serum iron metabolism and iron deposition in the brain can lead to inflammation, abnormal protein aggregation and degeneration, and massive neuronal apoptosis in the central nervous system, which in turn leads to a progressive decline in cognitive processes. Our previous clinical studies have found acupuncture to be a safe and effective intervention for treating VCI, but the specific mechanisms require further exploration. Objective: The objective of the trial is to evaluate the clinical efficacy of Tongdu Xingshen acupuncture and to investigate whether it can improve VCI by regulating brain iron deposition and body iron metabolism. Methods: In total, 42 patients with VCI and 21 healthy individuals will participate in this clinical trial. The 42 patients with VCI will be randomized into acupuncture and control groups, while the 21 healthy individuals will be in the healthy control group. Both the control and acupuncture groups will receive conventional medical treatment and cognitive rehabilitation training. In addition, the acupuncture group will receive electroacupuncture treatment with Tongdu Xingshen for 30 minutes each time, 6 times a week for 4 weeks. Meanwhile, the healthy control group will not receive any intervention. All 3 groups will undergo baseline assessments of brain iron deposition, serum iron metabolism, and neuropsychological tests after enrollment. The acupuncture and control groups will be evaluated again at the end of 4 weeks of treatment, as described earlier. By comparing neuropsychological test scores between groups, we will examine the efficacy of Tongdu Xingshen acupuncture in treating VCI. Additionally, we will test the correlations between neuropsychological test scores, brain iron deposition, and body iron metabolism indexes to explore the possible mechanisms of Tongdu Xingshen acupuncture in treating VCI. Results: Participants are currently being recruited. The first participant was enrolled in June 2023, which marked the official start of the experiment. As of the submission of the paper, there were 23 participants. The recruitment process is expected to continue until June 2025, at which point the processing and analysis of data will begin. As of May 15, 2024, up to 30 people have been enrolled in this clinical trial. Conclusions: This study will provide data on the effects of Tongdu Xingshen acupuncture on cerebral iron deposition as well as somatic iron metabolism in patients with VCI. These results will help to prove whether Tongdu Xingshen acupuncture can improve VCI by regulating brain iron deposition and body iron metabolism, which will provide the clinical and theoretical basis for the wide application of acupuncture therapy in VCI rehabilitation. Trial Registration: China Clinical Registration Agency ChiCTR2300072188; https://tinyurl.com/5fcydtkv International Registered Report Identifier (IRRID): PRR1-10.2196/56484 UR - https://www.researchprotocols.org/2024/1/e56484 UR - http://dx.doi.org/10.2196/56484 UR - http://www.ncbi.nlm.nih.gov/pubmed/38885500 ID - info:doi/10.2196/56484 ER - TY - JOUR AU - Chen, Hung-Hsun AU - Lin, Chen AU - Chang, Hsiang-Chih AU - Chang, Jen-Ho AU - Chuang, Hai-Hua AU - Lin, Yu-Hsuan PY - 2024/6/17 TI - Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities JO - J Med Internet Res SP - e56144 VL - 26 KW - digital phenotyping KW - human-smartphone interaction KW - labor or leisure KW - machine learning KW - mental activity KW - physical activity N2 - Background: Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. Objective: This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). Methods: A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the ?Staff Hours? app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data?s nested structure. Results: Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night?s midpoint of sleep (?=?0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA?s activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. Conclusions: This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies. UR - https://www.jmir.org/2024/1/e56144 UR - http://dx.doi.org/10.2196/56144 UR - http://www.ncbi.nlm.nih.gov/pubmed/38885499 ID - info:doi/10.2196/56144 ER - TY - JOUR AU - Hartvigsen, Benedikte AU - Jakobsen, Kronberg Kathrine AU - Benfield, Thomas AU - Gredal, Tobias Niels AU - Ersbøll, Kjær Annette AU - Grønlund, Waldemar Mathias AU - Bundgaard, Henning AU - Andersen, Porsborg Mikkel AU - Steenhard, Nina AU - von Buchwald, Christian AU - Todsen, Tobias PY - 2024/6/12 TI - Molecular Detection of SARS-CoV-2 From Throat Swabs Performed With or Without Specimen Collection From the Tonsils: Protocol for a Multicenter Randomized Controlled Trial JO - JMIR Res Protoc SP - e47446 VL - 13 KW - SARS-CoV-2 KW - COVID-19 KW - pandemic KW - oropharyngeal sampling KW - diagnostic accuracy KW - PCR KW - polymerase chain reaction KW - PCR analysis KW - swab KW - diagnostic KW - oropharyngeal KW - virology KW - testing KW - detection KW - molecular biology KW - microbiology KW - laboratory KW - palatine tonsil KW - COVID-19 detection KW - COVID-19 testing KW - tonsil KW - swabs KW - oropharyngeal swabs KW - oropharyngeal swab KW - nasal swab KW - nasal swabs KW - molecular detection KW - tool KW - diagnostic technique KW - diagnostic procedure KW - clinical laboratory techniques N2 - Background: Testing for SARS-CoV-2 is essential to provide early COVID-19 treatment for people at high risk of severe illness and to limit the spread of infection in society. Proper upper respiratory specimen collection is the most critical step in the diagnosis of the SARS-CoV-2 virus in public settings, and throat swabs were the preferred specimens used for mass testing in many countries during the COVID-19 pandemic. However, there is still a discussion about whether throat swabs have a high enough sensitivity for SARS-CoV-2 diagnostic testing, as previous studies have reported a large variability in the sensitivity from 52% to 100%. Many previous studies exploring the diagnostic accuracy of throat swabs lack a detailed description of the sampling technique, which makes it difficult to compare the different diagnostic accuracy results. Some studies perform a throat swab by only collecting specimens from the posterior oropharyngeal wall, while others also include a swab of the palatine tonsils for SARS-CoV-2 testing. However, studies suggest that the palatine tonsils could have a tissue tropism for SARS-CoV-2 that may improve the SARS-CoV-2 detection during sampling. This may explain the variation of sensitivity reported, but no clinical studies have yet explored the differences in sensitivity and patient discomfort whether the palatine tonsils are included during the throat swab or not. Objective: The objective of this study is to examine the sensitivity and patient discomfort of a throat swab including the palatine tonsils compared to only swabbing the posterior oropharyngeal wall in molecular testing for SARS-CoV-2. Methods: We will conduct a randomized controlled study to compare the molecular detection rate of SARS-CoV-2 by a throat swab performed from the posterior oropharyngeal wall and the palatine tonsils (intervention group) or the posterior oropharyngeal wall only (control group). Participants will be randomized in a 1:1 ratio. All participants fill out a baseline questionnaire upon enrollment in the trial, examining their reason for being tested, symptoms, and previous tonsillectomy. A follow-up questionnaire will be sent to participants to explore the development of symptoms after testing. Results: A total of 2315 participants were enrolled in this study between November 10, 2022, and December 22, 2022. The results from the follow-up questionnaire are expected to be completed at the beginning of 2024. Conclusions: This randomized clinical trial will provide us with information about whether throat swabs including specimens from the palatine tonsils will improve the diagnostic sensitivity for SARS-CoV-2 molecular detection. These results can, therefore, be used to improve future testing recommendations and provide additional information about tissue tropism for SARS-CoV-2. Trial Registration: ClinicalTrials.gov NCT05611203; https://clinicaltrials.gov/study/NCT05611203 International Registered Report Identifier (IRRID): DERR1-10.2196/47446 UR - https://www.researchprotocols.org/2024/1/e47446 UR - http://dx.doi.org/10.2196/47446 UR - http://www.ncbi.nlm.nih.gov/pubmed/38865190 ID - info:doi/10.2196/47446 ER - TY - JOUR AU - Chandhiruthil Sathyan, Anjana AU - Yadav, Pramod AU - Gupta, Prashant AU - Mahapathra, Kumar Arun AU - Galib, Ruknuddin PY - 2024/6/10 TI - In Silico Approaches to Polyherbal Synergy: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e56646 VL - 13 KW - polyherbal formulation KW - Ayurveda system KW - Ayurveda KW - Ayurvedic medicine KW - Ayurvedic treatment KW - herbal KW - herbal drug KW - pharmacodynamic KW - pharmacology KW - computer-aided drug design KW - in silico methodology KW - scoping review N2 - Background: According to the World Health Organization, more than 80% of the world?s population relies on traditional medicine. Traditional medicine is typically based on the use of single herbal drugs or polyherbal formulations (PHFs) to manage diseases. However, the probable mode of action of these formulations is not well studied or documented. Over the past few decades, computational methods have been used to study the molecular mechanism of phytochemicals in single herbal drugs. However, the in silico methods applied to study PHFs remain unclear. Objective: The aim of this protocol is to develop a search strategy for a scoping review to map the in silico approaches applied in understanding the activity of PHFs used as traditional medicines worldwide. Methods: The scoping review will be conducted based on the methodology developed by Arksey and O?Malley and the recommendations of the Joanna Briggs Institute (JBI). A set of predetermined keywords will be used to identify the relevant studies from five databases: PubMed, Embase, Science Direct, Web of Science, and Google Scholar. Two independent reviewers will conduct the search to yield a list of relevant studies based on the inclusion and exclusion criteria. Mendeley version 1.19.8 will be used to remove duplicate citations, and title and abstract screening will be performed with Rayyan software. The JBI System for the Unified Management, Assessment, and Review of Information tool will be used for data extraction. The scoping review will be reported based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: Based on the core areas of the scoping review, a 3-step search strategy was developed. The initial search produced 3865 studies. After applying filters, 875 studies were short-listed for further review. Keywords were further refined to yield more relevant studies on the topic. Conclusions: The findings are expected to determine the extent of the knowledge gap in the applications of computational methods in PHFs for any traditional medicine across the world. The study can provide answers to open research questions related to the phytochemical identification of PHFs, criteria for target identification, strategies applied for in silico studies, software used, and challenges in adopting in silico methods for understanding the mechanisms of action of PHFs. This study can thus provide a better understanding of the application and types of in silico methods for investigating PHFs. International Registered Report Identifier (IRRID): PRR1-10.2196/56646 UR - https://www.researchprotocols.org/2024/1/e56646 UR - http://dx.doi.org/10.2196/56646 UR - http://www.ncbi.nlm.nih.gov/pubmed/38857494 ID - info:doi/10.2196/56646 ER - TY - JOUR AU - Mastrototaro, J. John AU - Leabman, Michael AU - Shumate, Joe AU - Tompkins, L. Kim PY - 2024/6/5 TI - Performance of a Wearable Ring in Controlled Hypoxia: A Prospective Observational Study JO - JMIR Form Res SP - e54256 VL - 8 KW - pulse oximetry KW - SpO2 KW - pulse oximeter KW - hypoxia KW - hypoxemia KW - clinical trial KW - accuracy KW - digital health KW - wearable KW - smart ring KW - ISO 80601-2-61 KW - racial bias N2 - Background: Over recent years, technological advances in wearables have allowed for continuous home monitoring of heart rate and oxygen saturation. These devices have primarily been used for sports and general wellness and may not be suitable for medical decision-making, especially in saturations below 90% and in patients with dark skin color. Wearable clinical-grade saturation of peripheral oxygen (SpO2) monitoring can be of great value to patients with chronic diseases, enabling them and their clinicians to better manage their condition with reliable real-time and trend data. Objective: This study aimed to determine the SpO2 accuracy of a wearable ring pulse oximeter compared with arterial oxygen saturation (SaO2) in a controlled hypoxia study based on the International Organization for Standardization (ISO) 80601-2-61:2019 standard over the range of 70%-100% SaO2 in volunteers with a broad range of skin color (Fitzpatrick I to VI) during nonmotion conditions. In parallel, accuracy was compared with a calibrated clinical-grade reference pulse oximeter (Masimo Radical-7). Acceptable medical device accuracy was defined as a maximum of 4% root mean square error (RMSE) per the ISO 80601-2-61 standard and a maximum of 3.5% RMSE per the US Food and Drug Administration guidance. Methods: We performed a single-center, blinded hypoxia study of the test device in 11 healthy volunteers at the Hypoxia Research Laboratory, University of California at San Francisco, under the direction of Philip Bickler, MD, PhD, and John Feiner, MD. Each volunteer was connected to a breathing apparatus for the administration of a hypoxic gas mixture. To facilitate frequent blood gas sampling, a radial arterial cannula was placed on either wrist of each participant. One test device was placed on the index finger and another test device was placed on the fingertip. SaO2 analysis was performed using an ABL-90 multi-wavelength oximeter. Results: For the 11 participants included in the analysis, there were 236, 258, and 313 SaO2-SpO2 data pairs for the test device placed on the finger, the test device placed on the fingertip, and the reference device, respectively. The RMSE of the test device for all participants was 2.1% for either finger or fingertip placement, while the Masimo Radical-7 reference pulse oximeter RMSE was 2.8%, exceeding the standard (4% or less) and the Food and Drug Administration guidance (3.5% or less). Accuracy of SaO2-SpO2 paired data from the 4 participants with dark skin in the study was separately analyzed for both test device placements and the reference device. The test and reference devices exceeded the minimum accuracy requirements for a medical device with RMSE at 1.8% (finger) and 1.6% (fingertip) and for the reference device at 2.9%. Conclusions: The wearable ring meets an acceptable standard of accuracy for clinical-grade SpO2 under nonmotion conditions without regard to skin color. Trial Registration: ClinicalTrials.gov NCT05920278; https://clinicaltrials.gov/study/NCT05920278 UR - https://formative.jmir.org/2024/1/e54256 UR - http://dx.doi.org/10.2196/54256 UR - http://www.ncbi.nlm.nih.gov/pubmed/38838332 ID - info:doi/10.2196/54256 ER - TY - JOUR AU - Chuang, Hai-Hua AU - Lin, Chen AU - Lee, Li-Ang AU - Chang, Hsiang-Chih AU - She, Guan-Jie AU - Lin, Yu-Hsuan PY - 2024/6/5 TI - Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study JO - J Med Internet Res SP - e50149 VL - 26 KW - actigraphy KW - body composition KW - circadian rhythm KW - human-smartphone interaction KW - interdaily stability KW - obesity N2 - Background: This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. Objective: We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. Methods: We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. Results: Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. Conclusions: Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted. UR - https://www.jmir.org/2024/1/e50149 UR - http://dx.doi.org/10.2196/50149 UR - http://www.ncbi.nlm.nih.gov/pubmed/38838328 ID - info:doi/10.2196/50149 ER - TY - JOUR AU - Altweck, Laura AU - Schmidt, Silke AU - Tomczyk, Samuel PY - 2024/5/31 TI - Daily Time-Use Patterns and Quality of Life in Parents: Protocol for a Pilot Quasi-Experimental, Nonrandomized Controlled Trial Using Ecological Momentary Assessment JO - JMIR Res Protoc SP - e54728 VL - 13 KW - time-use KW - well-being KW - parents KW - ecological momentary assessment KW - feasibility KW - health-related quality of life KW - ambulatory assessment KW - work-family conflict KW - gender roles KW - mixed-methods KW - sex differences KW - stress N2 - Background: The gender gap in time use and its impact on health and well-being are still prevalent. Women work longer hours than men when considering both paid and unpaid (eg, childcare and chores) work, and this gender disparity is particularly visible among parents. Less is known about factors that could potentially mediate or moderate this relationship (eg, work-family conflict and gender role beliefs). Ecological momentary assessment (EMA) allows for the documentation of changes in momentary internal states, such as time use, stress, or mood. It has shown particular validity to measure shorter-term activities (eg, unpaid work) and is thus useful to address gender differences. Objective: The feasibility of the daily EMA surveys in a parent sample will be examined. The associations between time use, well-being, and stress will be examined, along with potential moderating and mediating factors such as gender, gender role beliefs, and work-family conflict. Finally, the act of monitoring one?s own time use, well-being, and stress will be examined in relation to, for example, the quality of life. Methods: We conducted a quasi-experimental, nonrandomized controlled trial with 3 data collection methods, namely, online questionnaires, EMA surveys, and qualitative interviews. The intervention group (n=64) will participate in the online questionnaires and EMA surveys, and a subsample of the intervention group (n=6-17) will also be invited to participate in qualitative interviews. Over a period of 1 week, participants in the intervention group will answer daily EMA surveys (4 times per day). In contrast, the control group (n=17) will only participate in the online questionnaires at baseline and after 1 week. The following constructs were surveyed: sociodemographic background (eg, age, gender, and household composition; baseline questionnaire); mediators and moderators (eg, gender role beliefs and work-family conflict; baseline and follow-up questionnaires); well-being, quality of life, and trait mindfulness (baseline and follow-up questionnaires); momentary activity and well-being, as well as state mindfulness (EMA); and feasibility (baseline and follow-up questionnaires as well as interviews). We anticipate that participants will regard the daily EMA as feasible. Particular daily time-use patterns (eg, high paid and unpaid workload) are expected to be related to lower well-being, higher stress, and health-related quality of life. These associations are expected to be moderated and mediated by factors such as gender, gender role beliefs, work-family conflict, and social support. Participants in the intervention group are expected to show higher values of mindfulness, well-being, health-related quality of life, and lower stress. Results: Patient recruitment started in November 2023 and ended in mid April 2024. Data analysis commenced in mid April 2024. Conclusions: This study aims to provide valuable insights into the feasibility of using EMAs and the potential benefits of activity tracking in various aspects of daily life. Trial Registration: Open Science Framework 8qj3d; https://osf.io/8qj3d International Registered Report Identifier (IRRID): PRR1-10.2196/54728 UR - https://www.researchprotocols.org/2024/1/e54728 UR - http://dx.doi.org/10.2196/54728 UR - http://www.ncbi.nlm.nih.gov/pubmed/38820576 ID - info:doi/10.2196/54728 ER - TY - JOUR AU - Wang, Rui AU - Liu, Guangtian AU - Jing, Liwei AU - Zhang, Jing AU - Li, Chenyang AU - Gong, Lichao PY - 2024/5/31 TI - Finite Element Analysis of Pelvic Floor Biomechanical Models to Elucidate the Mechanism for Improving Urination and Defecation Dysfunction in Older Adults: Protocol for a Model Development and Validation Study JO - JMIR Res Protoc SP - e56333 VL - 13 KW - elderly KW - older adults KW - pelvic cavity KW - finite element analysis KW - biomechanical model KW - protocol KW - urination KW - incontinence KW - aging KW - bowel dysfunction N2 - Background: The population is constantly aging, and most older adults will experience many potential physiological changes as they age, leading to functional decline. Urinary and bowel dysfunction is the most common obstacle in older people. At present, the analysis of pelvic floor histological changes related to aging has not been fully elucidated, and the mechanism of improving intestinal control ability in older people is still unclear. Objective: The purpose of this study is to describe how the finite element method will be used to understand the mechanical characteristics of and physiological changes in the pelvic cavity during the rehabilitation process, providing theoretical support for the mechanism for improving urination and defecation dysfunction in older individuals. Methods: We will collect magnetic resonance imaging (MRI) and computed tomography (CT) data of the pelvic cavity of one male and one female volunteer older than 60 years and use the finite element method to construct a 3D computer simulation model of the pelvic cavity. By simulating different physiological states, such as the Valsalva maneuver and bowel movement, we will verify the accuracy of the constructed model, investigate the effects of different neuromuscular functional changes, and quantify the impact proportions of the pelvic floor muscle group, core muscle group, and sacral nerve. Results: At present, we have registered the study in the Chinese Clinical Trial Registry and collected MRI and CT data for an older male and an older female patient. Next, the construction and analysis of the finite element model will be accomplished according to the study plan. We expect to complete the construction and analysis of the finite element model by July 2024 and publish the research results by October 2025. Conclusions: Our study will build finite element models of the pelvic floor of older men and older women, and we shall elucidate the relationship between the muscles of the pelvic floor, back, abdomen, and hips and the ability of older adults to control bowel movements. The results of this study will provide theoretical support for elucidating the mechanism for improving urination and defecation dysfunction through rehabilitation. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400080749; https://www.chictr.org.cn/showproj.html?proj=193428 International Registered Report Identifier (IRRID): DERR1-10.2196/56333 UR - https://www.researchprotocols.org/2024/1/e56333 UR - http://dx.doi.org/10.2196/56333 UR - http://www.ncbi.nlm.nih.gov/pubmed/38820582 ID - info:doi/10.2196/56333 ER - TY - JOUR AU - Lee, Vien V. AU - van der Lubbe, C. Stephanie C. AU - Goh, Hoon Lay AU - Valderas, Maria Jose PY - 2024/5/31 TI - Harnessing ChatGPT for Thematic Analysis: Are We Ready? JO - J Med Internet Res SP - e54974 VL - 26 KW - ChatGPT KW - thematic analysis KW - natural language processing KW - NLP KW - medical research KW - qualitative research KW - qualitative data KW - technology KW - viewpoint KW - efficiency UR - https://www.jmir.org/2024/1/e54974 UR - http://dx.doi.org/10.2196/54974 UR - http://www.ncbi.nlm.nih.gov/pubmed/38819896 ID - info:doi/10.2196/54974 ER - TY - JOUR AU - Wang, Guanyi AU - Chen, Chen AU - Jiang, Ziyu AU - Li, Gang AU - Wu, Can AU - Li, Sheng PY - 2024/5/28 TI - Efficient Use of Biological Data in the Web 3.0 Era by Applying Nonfungible Token Technology JO - J Med Internet Res SP - e46160 VL - 26 KW - NFTs KW - biobanks KW - blockchains KW - health care KW - medical big data KW - sustainability KW - blockchain platform KW - platform KW - tracing KW - virtual KW - biomedical data KW - transformation KW - development KW - promoted UR - https://www.jmir.org/2024/1/e46160 UR - http://dx.doi.org/10.2196/46160 UR - http://www.ncbi.nlm.nih.gov/pubmed/38805706 ID - info:doi/10.2196/46160 ER - TY - JOUR AU - Mittal, Ajay AU - Elkaldi, Yasmine AU - Shih, Susana AU - Nathu, Riken AU - Segal, Mark PY - 2024/5/27 TI - Mobile Electrocardiograms in the Detection of Subclinical Atrial Fibrillation in High-Risk Outpatient Populations: Protocol for an Observational Study JO - JMIR Res Protoc SP - e52647 VL - 13 KW - mobile ECG KW - digital health KW - cardiology KW - ECG KW - electrocardiogram KW - atrial fibrillation KW - outpatient KW - randomized KW - controlled trial KW - controlled trials KW - smartphone KW - mobile health KW - app KW - apps KW - feasibility KW - effectiveness KW - KardiaMobile single-lead ECGs KW - mobile phone N2 - Background: Single-lead, smartphone-based mobile electrocardiograms (ECGs) have the potential to provide a noninvasive, rapid, and cost-effective means of screening for atrial fibrillation (AFib) in outpatient settings. AFib has been associated with various comorbid diseases that prompt further investigation and screening methodologies for at-risk populations. A simple 30-second sinus rhythm strip from the KardiaMobile ECG (AliveCor) can provide an effective screen for cardiac rhythm abnormalities. Objective: The aim of this study is to demonstrate the feasibility of performing Kardia-enabled ECG recordings routinely in outpatient settings in high-risk populations and its potential use in uncovering previous undiagnosed cases of AFib. Specific aim 1 is to determine the feasibility and accuracy of performing routine cardiac rhythm sampling in patients deemed at high risk for AFib. Specific aim 2 is to determine whether routine rhythm sampling in outpatient clinics with high-risk patients can be used cost-effectively in an outpatient clinic without increasing the time it takes for the patient to be seen by a physician. Methods: Participants were recruited across 6 clinic sites across the University of Florida Health Network: University of Florida Health Nephrology, Sleep Center, Ophthalmology, Urology, Neurology, and Pre-Surgical. Participants, aged 18-99 years, who agreed to partake in the study were given a consent form and completed a questionnaire regarding their past medical history and risk factors for cardiovascular disease. Single-lead, 30-second ECGs were taken by the KardiaMobile ECG device. If patients are found to have newly diagnosed AFib, the attending physician is notified, and a 12-lead ECG or standard ECG equivalent will be ordered. Results: As of March 1, 2024, a total of 2339 participants have been enrolled. Of the data collected thus far, the KardiaMobile rhythm strip reported 381 abnormal readings, which are pending analysis from a cardiologist. A total of 78 readings were labeled as possible AFib, 159 readings were labeled unclassified, and 49 were unreadable. Of note, the average age of participants was 61 (SD 10.25) years, and the average self-reported weight was 194 (SD 14.26) pounds. Additionally, 1572 (67.25%) participants report not regularly seeing a cardiologist. Regarding feasibility, the average length of enrolling a patient into the study was 3:30 (SD 0.5) minutes after informed consent was completed, and medical staff across clinic sites (n=25) reported 9 of 10 level of satisfaction with the impact of the screening on clinic flow. Conclusions: Preliminary data show promise regarding the feasibility of using KardiaMobile ECGs for the screening of AFib and prevention of cardiological disease in vulnerable outpatient populations. The use of a single-lead mobile ECG strip can serve as a low-cost, effective AFib screen for implementation across free clinics attempting to provide increased health care accessibility. International Registered Report Identifier (IRRID): DERR1-10.2196/52647 UR - https://www.researchprotocols.org/2024/1/e52647 UR - http://dx.doi.org/10.2196/52647 UR - http://www.ncbi.nlm.nih.gov/pubmed/38801762 ID - info:doi/10.2196/52647 ER - TY - JOUR AU - Lin, Chien-Chung AU - Shen, Jian-Hong AU - Chen, Shu-Fang AU - Chen, Hung-Ming AU - Huang, Hung-Meng PY - 2024/5/24 TI - Developing a Cost-Effective Surgical Scheduling System Applying Lean Thinking and Toyota?s Methods for Surgery-Related Big Data for Improved Data Use in Hospitals: User-Centered Design Approach JO - JMIR Form Res SP - e52185 VL - 8 KW - algorithm KW - process KW - computational thinking KW - continuous improvement KW - customer needs KW - lean principles KW - problem solving KW - Toyota Production System KW - value stream map KW - need KW - needs KW - operating room N2 - Background: Surgical scheduling is pivotal in managing daily surgical sequences, impacting patient experience and hospital resources significantly. With operating rooms costing approximately US $36 per minute, efficient scheduling is vital. However, global practices in surgical scheduling vary, largely due to challenges in predicting individual surgeon times for diverse patient conditions. Inspired by the Toyota Production System?s efficiency in addressing similar logistical challenges, we applied its principles as detailed in the book ?Lean Thinking? by Womack and Jones, which identifies processes that do not meet customer needs as wasteful. This insight is critical in health care, where waste can compromise patient safety and medical quality. Objective: This study aims to use lean thinking and Toyota methods to develop a more efficient surgical scheduling system that better aligns with user needs without additional financial burdens. Methods: We implemented the 5 principles of the Toyota system: specifying value, identifying the value stream, enabling flow, establishing pull, and pursuing perfection. Value was defined in terms of meeting the customer?s needs, which in this context involved developing a responsive and efficient scheduling system. Our approach included 2 subsystems: one handling presurgery patient data and another for intraoperative and postoperative data. We identified inefficiencies in the presurgery data subsystem and responded by creating a comprehensive value stream map of the surgical process. We developed 2 Excel (Microsoft Corporation) macros using Visual Basic for Applications. The first calculated average surgery times from intra- or postoperative historic data, while the second estimated surgery durations and generated concise, visually engaging scheduling reports from presurgery data. We assessed the effectiveness of the new system by comparing task completion times and user satisfaction between the old and new systems. Results: The implementation of the revised scheduling system significantly reduced the overall scheduling time from 301 seconds to 261 seconds (P=.02), with significant time reductions in the revised process from 99 seconds to 62 seconds (P<.001). Despite these improvements, approximately 21% of nurses preferred the older system for its familiarity. The new system protects patient data privacy and streamlines schedule dissemination through a secure LINE group (LY Corp), ensuring seamless flow. The design of the system allows for real-time updates and has been effectively monitoring surgical durations daily for over 3 years. The ?pull? principle was demonstrated when an unplanned software issue prompted immediate, user-led troubleshooting, enhancing system reliability. Continuous improvement efforts are ongoing, except for the preoperative patient confirmation step, which requires further enhancement to ensure optimal patient safety. Conclusions: Lean principles and Toyota?s methods, combined with computer programming, can revitalize surgical scheduling processes. They offer effective solutions for surgical scheduling challenges and enable the creation of a novel surgical scheduling system without incurring additional costs. UR - https://formative.jmir.org/2024/1/e52185 UR - http://dx.doi.org/10.2196/52185 UR - http://www.ncbi.nlm.nih.gov/pubmed/38787610 ID - info:doi/10.2196/52185 ER - TY - JOUR AU - Harris, Daniel AU - Delcher, Chris PY - 2024/5/21 TI - Geospatial Imprecision With Constraints for Precision Public Health: Algorithm Development and Validation JO - Online J Public Health Inform SP - e54958 VL - 16 KW - social determinants of health KW - geocoding KW - privacy KW - poverty KW - obfuscation KW - security KW - confidentiality KW - low income KW - geography KW - geographic KW - location KW - locations KW - spatial KW - geospatial KW - precision N2 - Background: Location and environmental social determinants of health are increasingly important factors in both an individual?s health and the monitoring of community-level public health issues. Objective: We aimed to measure the extent to which location obfuscation techniques, designed to protect an individual?s privacy, can unintentionally shift geographical coordinates into neighborhoods with significantly different socioeconomic demographics, which limits the precision of findings for public health stakeholders. Methods: Point obfuscation techniques intentionally blur geographic coordinates to conceal the original location. The pinwheel obfuscation method is an existing technique in which a point is moved along a pinwheel-like path given a randomly chosen angle and a maximum radius; we evaluate the impact of this technique using 2 data sets by comparing the demographics of the original point and the resulting shifted point by cross-referencing data from the United States Census Bureau. Results: Using poverty measures showed that points from regions of low poverty may be shifted to regions of high poverty; similarly, points in regions with high poverty may be shifted into regions of low poverty. We varied the maximum allowable obfuscation radius; the mean difference in poverty rate before and after obfuscation ranged from 6.5% to 11.7%. Additionally, obfuscation inadvertently caused false hot spots for deaths by suicide in Cook County, Illinois. Conclusions: Privacy concerns require patient locations to be imprecise to protect against risk of identification; precision public health requires accuracy. We propose a modified obfuscation technique that is constrained to generate a new point within a specified census-designated region to preserve both privacy and analytical accuracy by avoiding demographic shifts. UR - https://ojphi.jmir.org/2024/1/e54958 UR - http://dx.doi.org/10.2196/54958 UR - http://www.ncbi.nlm.nih.gov/pubmed/38772021 ID - info:doi/10.2196/54958 ER - TY - JOUR AU - Ray, Jessica AU - Finn, Benjamin Emily AU - Tyrrell, Hollyce AU - Aloe, F. Carlin AU - Perrin, M. Eliana AU - Wood, T. Charles AU - Miner, S. Dean AU - Grout, Randall AU - Michel, J. Jeremy AU - Damschroder, J. Laura AU - Sharifi, Mona PY - 2024/5/21 TI - User-Centered Framework for Implementation of Technology (UFIT): Development of an Integrated Framework for Designing Clinical Decision Support Tools Packaged With Tailored Implementation Strategies JO - J Med Internet Res SP - e51952 VL - 26 KW - user-centered design KW - implementation science KW - clinical decision support KW - human factors KW - implementation KW - decision support KW - develop KW - development KW - framework KW - frameworks KW - design KW - user-centered KW - digital health KW - health technology KW - health technologies KW - need KW - needs KW - tailor KW - tailoring KW - guidance KW - guideline KW - guidelines KW - pediatric KW - pediatrics KW - child KW - children KW - obese KW - obesity KW - weight KW - overweight KW - primary care N2 - Background: Electronic health record?based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. Objective: This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. Methods: In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. Results: We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. Conclusions: UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. Trial Registration: ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011 UR - https://www.jmir.org/2024/1/e51952 UR - http://dx.doi.org/10.2196/51952 UR - http://www.ncbi.nlm.nih.gov/pubmed/38771622 ID - info:doi/10.2196/51952 ER - TY - JOUR AU - Xie, Puguang AU - Wang, Hao AU - Xiao, Jun AU - Xu, Fan AU - Liu, Jingyang AU - Chen, Zihang AU - Zhao, Weijie AU - Hou, Siyu AU - Wu, Dongdong AU - Ma, Yu AU - Xiao, Jingjing PY - 2024/5/10 TI - Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study JO - J Med Internet Res SP - e49848 VL - 26 KW - acute myocardial infarction KW - mortality KW - deep learning KW - explainable model KW - prediction N2 - Background: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. Objective: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. Methods: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. Results: A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. Conclusions: The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI. UR - https://www.jmir.org/2024/1/e49848 UR - http://dx.doi.org/10.2196/49848 UR - http://www.ncbi.nlm.nih.gov/pubmed/38728685 ID - info:doi/10.2196/49848 ER - TY - JOUR AU - Yue, Qi-Xuan AU - Ding, Ruo-Fan AU - Chen, Wei-Hao AU - Wu, Lv-Ying AU - Liu, Ke AU - Ji, Zhi-Liang PY - 2024/5/3 TI - Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study JO - J Med Internet Res SP - e48572 VL - 26 KW - clinical drug toxicity KW - adverse drug reaction KW - ADR severity KW - ADR frequency KW - mathematical model N2 - Background: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. Objective: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. Methods: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. Results: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. Conclusions: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation. UR - https://www.jmir.org/2024/1/e48572 UR - http://dx.doi.org/10.2196/48572 UR - http://www.ncbi.nlm.nih.gov/pubmed/38700923 ID - info:doi/10.2196/48572 ER - TY - JOUR AU - Cangussu, Izabel Anna AU - Lucarini, Beatriz AU - Melo, Freitas Igor de AU - Diniz, Araújo Paula AU - Mancini, Marisa AU - Viana, Mattos Bernardo de AU - Romano-Silva, Aurélio Marco AU - Miranda, de Débora Marques PY - 2024/4/30 TI - Motor Effects of Intervention With Transcranial Direct Current Stimulation for Physiotherapy Treatment in Children With Cerebral Palsy: Protocol for a Randomized Clinical Trial JO - JMIR Res Protoc SP - e52922 VL - 13 KW - cerebral palsy KW - tDCS KW - motor KW - development KW - randomized clinical trial KW - RCT KW - clinical trial KW - randomized KW - transcranial direct current stimulation KW - stimulation KW - children KW - child KW - brain stimulation KW - physical therapy KW - quality of life KW - researchers KW - researcher KW - neurological injuries KW - injury KW - injuries KW - gait KW - patient KW - patients N2 - Background: Children diagnosed with cerebral palsy (CP) often experience various limitations, particularly in gross motor function and activities of daily living. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has been used to improve movement, gross motor function, and activities of daily living. Objective: This study aims to evaluate the potential additional effects of physiotherapy combined with tDCS in children with CP in comparison with physiotherapy only. Methods: This is a 2-arm randomized controlled trial that will compare the effects of tDCS as an adjunctive treatment during rehabilitation sessions to rehabilitation without tDCS. Children with CP classified by the Gross Motor Function Classification System as levels I and II will be randomly assigned to either the sham + rehabilitation group or the tDCS + rehabilitation group. The primary outcome will be the motor skills assessed using the Gross Motor Function Measure domain E scores, and the secondary outcome will be the measurement scores of the children?s quality of life. The intervention will consist of a 10-day stimulation protocol with tDCS spread over 2 weeks, with stimulation or sham tDCS administered for 20 minutes at a frequency of 1 Hz, in combination with physiotherapy. Physical therapy exercises will be conducted in a circuit based on each child?s baseline Gross Motor Function Measure results. The participants? changes will be evaluated and compared in both groups. Intervenient features will be tested. Results: Data collection is ongoing and is expected to be completed by January 2025. A homogeneous sample and clear outcomes may be a highlight of this protocol, which may allow us to understand the potential use of tDCS and for whom it should or should not be used. Conclusions: A study with good evidence and clear outcomes in children with CP might open an avenue for the potential best use of neurostimulation. Trial Registration: Brazilian Registry of Clinical Trials RBR-104h4s4y; https://tinyurl.com/47r3x2e4 International Registered Report Identifier (IRRID): PRR1-10.2196/52922 UR - https://www.researchprotocols.org/2024/1/e52922 UR - http://dx.doi.org/10.2196/52922 UR - http://www.ncbi.nlm.nih.gov/pubmed/38687586 ID - info:doi/10.2196/52922 ER - TY - JOUR AU - Rantakokko, Merja AU - Matikainen-Tervola, Emmi AU - Aartolahti, Eeva AU - Sihvonen, Sanna AU - Chichaeva, Julija AU - Finni, Taija AU - Cronin, Neil PY - 2024/4/29 TI - Gait Features in Different Environments Contributing to Participation in Outdoor Activities in Old Age (GaitAge): Protocol for an Observational Cross-Sectional Study JO - JMIR Res Protoc SP - e52898 VL - 13 KW - walking KW - aging KW - environment KW - biomechanics KW - kinematics KW - spatiotemporal KW - gait KW - GaitAge KW - observational cross-sectional study KW - gerontology KW - geriatric KW - geriatrics KW - older adult KW - older adults KW - elder KW - elderly KW - older person KW - older people KW - ageing KW - health disparities KW - health disparity KW - assessment KW - assessments KW - physical test KW - physical tests KW - interview KW - interviews KW - biomechanic KW - activities KW - outdoor KW - activity KW - movement analysis KW - analysis of walk KW - posture KW - free living N2 - Background: The ability to walk is a key issue for independent old age. Optimizing older peoples? opportunities for an autonomous and active life and reducing health disparities requires a better understanding of how to support independent mobility in older people. With increasing age, changes in gait parameters such as step length and cadence are common and have been shown to increase the risk of mobility decline. However, gait assessments are typically based on laboratory measures, even though walking in a laboratory environment may be significantly different from walking in outdoor environments. Objective: This project will study alterations in biomechanical features of gait by comparing walking on a treadmill in a laboratory, level outdoor, and hilly outdoor environments. In addition, we will study the possible contribution of changes in gait between these environments to outdoor mobility among older people. Methods: Participants of the study were recruited through senior organizations of Central Finland and the University of the Third Age, Jyväskylä. Inclusion criteria were community-dwelling, aged 70 years and older, able to walk at least 1 km without assistive devices, able to communicate, and living in central Finland. Exclusion criteria were the use of mobility devices, severe sensory deficit (vision and hearing), memory impairment (Mini-Mental State Examination ?23), and neurological conditions (eg, stroke, Parkinson disease, and multiple sclerosis). The study protocol included 2 research visits. First, indoor measurements were conducted, including interviews (participation, health, and demographics), physical performance tests (short physical performance battery and Timed Up and Go), and motion analysis on a treadmill in the laboratory (3D Vicon and next-generation inertial measurement units [NGIMUs]). Second, outdoor walking tests were conducted, including walking on level (sports track) and hilly (uphill and downhill) terrain, while movement was monitored via NGIMUs, pressure insoles, heart rate, and video data. Results: A total of 40 people (n=26, 65% women; mean age 76.3, SD 5.45 years) met the inclusion criteria and took part in the study. Data collection took place between May and September 2022. The first result is expected to be published in the spring of 2024. Conclusions: This multidisciplinary study will provide new scientific knowledge about how gait biomechanics are altered in varied environments, and how this influences opportunities to participate in outdoor activities for older people. International Registered Report Identifier (IRRID): RR1-10.2196/52898 UR - https://www.researchprotocols.org/2024/1/e52898 UR - http://dx.doi.org/10.2196/52898 UR - http://www.ncbi.nlm.nih.gov/pubmed/38684085 ID - info:doi/10.2196/52898 ER - TY - JOUR AU - Khalilnejad, Arash AU - Sun, Ruo-Ting AU - Kompala, Tejaswi AU - Painter, Stefanie AU - James, Roberta AU - Wang, Yajuan PY - 2024/4/26 TI - Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning JO - JMIR Form Res SP - e54373 VL - 8 KW - diabetes KW - diabetic KW - DM KW - diabetes mellitus KW - type 2 diabetes KW - type 1 diabetes KW - self-monitoring KW - predictive model KW - predictive models KW - predictive analytics KW - predictive system KW - practical model KW - practical models KW - ML KW - machine learning KW - AI KW - artificial intelligence KW - algorithm KW - algorithms KW - behavior KW - behaviour KW - telehealth KW - tele-health KW - chronic condition KW - chronic conditions KW - chronic disease KW - chronic diseases KW - chronic illness KW - chronic illnesses N2 - Background: The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. Objective: This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. Methods: Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants? program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant?s program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy. Results: The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. Conclusions: This study explored the Livongo for Diabetes RDMP participants? temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant?s diabetes management. UR - https://formative.jmir.org/2024/1/e54373 UR - http://dx.doi.org/10.2196/54373 UR - http://www.ncbi.nlm.nih.gov/pubmed/38669074 ID - info:doi/10.2196/54373 ER - TY - JOUR AU - Abu Attieh, Hammam AU - Neves, Telmo Diogo AU - Guedes, Mariana AU - Mirandola, Massimo AU - Dellacasa, Chiara AU - Rossi, Elisa AU - Prasser, Fabian PY - 2024/4/23 TI - A Scalable Pseudonymization Tool for Rapid Deployment in Large Biomedical Research Networks: Development and Evaluation Study JO - JMIR Med Inform SP - e49646 VL - 12 KW - biomedical research KW - research network KW - data sharing KW - data protection KW - privacy KW - pseudonymization N2 - Background: The SARS-CoV-2 pandemic has demonstrated once again that rapid collaborative research is essential for the future of biomedicine. Large research networks are needed to collect, share, and reuse data and biosamples to generate collaborative evidence. However, setting up such networks is often complex and time-consuming, as common tools and policies are needed to ensure interoperability and the required flows of data and samples, especially for handling personal data and the associated data protection issues. In biomedical research, pseudonymization detaches directly identifying details from biomedical data and biosamples and connects them using secure identifiers, the so-called pseudonyms. This protects privacy by design but allows the necessary linkage and reidentification. Objective: Although pseudonymization is used in almost every biomedical study, there are currently no pseudonymization tools that can be rapidly deployed across many institutions. Moreover, using centralized services is often not possible, for example, when data are reused and consent for this type of data processing is lacking. We present the ORCHESTRA Pseudonymization Tool (OPT), developed under the umbrella of the ORCHESTRA consortium, which faced exactly these challenges when it came to rapidly establishing a large-scale research network in the context of the rapid pandemic response in Europe. Methods: To overcome challenges caused by the heterogeneity of IT infrastructures across institutions, the OPT was developed based on programmable runtime environments available at practically every institution: office suites. The software is highly configurable and provides many features, from subject and biosample registration to record linkage and the printing of machine-readable codes for labeling biosample tubes. Special care has been taken to ensure that the algorithms implemented are efficient so that the OPT can be used to pseudonymize large data sets, which we demonstrate through a comprehensive evaluation. Results: The OPT is available for Microsoft Office and LibreOffice, so it can be deployed on Windows, Linux, and MacOS. It provides multiuser support and is configurable to meet the needs of different types of research projects. Within the ORCHESTRA research network, the OPT has been successfully deployed at 13 institutions in 11 countries in Europe and beyond. As of June 2023, the software manages data about more than 30,000 subjects and 15,000 biosamples. Over 10,000 labels have been printed. The results of our experimental evaluation show that the OPT offers practical response times for all major functionalities, pseudonymizing 100,000 subjects in 10 seconds using Microsoft Excel and in 54 seconds using LibreOffice. Conclusions: Innovative solutions are needed to make the process of establishing large research networks more efficient. The OPT, which leverages the runtime environment of common office suites, can be used to rapidly deploy pseudonymization and biosample management capabilities across research networks. The tool is highly configurable and available as open-source software. UR - https://medinform.jmir.org/2024/1/e49646 UR - http://dx.doi.org/10.2196/49646 ID - info:doi/10.2196/49646 ER - TY - JOUR AU - Kernberg, Annessa AU - Gold, A. Jeffrey AU - Mohan, Vishnu PY - 2024/4/22 TI - Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study JO - J Med Internet Res SP - e54419 VL - 26 KW - generative AI KW - generative artificial intelligence KW - ChatGPT KW - simulation KW - large language model KW - clinical documentation KW - quality KW - accuracy KW - reproducibility KW - publicly available KW - medical note KW - medical notes KW - generation KW - medical documentation KW - documentation KW - documentations KW - AI KW - artificial intelligence KW - transcript KW - transcripts KW - ChatGPT-4 N2 - Background: Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)?powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. Objective: This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model?s performance across different categories. Methods: We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. Results: Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the ?Objective? section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). Conclusions: Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model?s effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time. UR - https://www.jmir.org/2024/1/e54419 UR - http://dx.doi.org/10.2196/54419 UR - http://www.ncbi.nlm.nih.gov/pubmed/38648636 ID - info:doi/10.2196/54419 ER - TY - JOUR AU - Pham, Cecilia AU - Govender, Romi AU - Tehami, Salik AU - Chavez, Summer AU - Adepoju, E. Omolola AU - Liaw, Winston PY - 2024/4/22 TI - ChatGPT?s Performance in Cardiac Arrest and Bradycardia Simulations Using the American Heart Association's Advanced Cardiovascular Life Support Guidelines: Exploratory Study JO - J Med Internet Res SP - e55037 VL - 26 KW - ChatGPT KW - artificial intelligence KW - AI KW - large language model KW - LLM KW - cardiac arrest KW - bradycardia KW - simulation KW - advanced cardiovascular life support KW - ACLS KW - bradycardia simulations KW - America KW - American KW - heart association KW - cardiac KW - life support KW - exploratory study KW - heart KW - heart attack KW - clinical decision support KW - diagnostics KW - algorithms N2 - Background: ChatGPT is the most advanced large language model to date, with prior iterations having passed medical licensing examinations, providing clinical decision support, and improved diagnostics. Although limited, past studies of ChatGPT?s performance found that artificial intelligence could pass the American Heart Association?s advanced cardiovascular life support (ACLS) examinations with modifications. ChatGPT?s accuracy has not been studied in more complex clinical scenarios. As heart disease and cardiac arrest remain leading causes of morbidity and mortality in the United States, finding technologies that help increase adherence to ACLS algorithms, which improves survival outcomes, is critical. Objective: This study aims to examine the accuracy of ChatGPT in following ACLS guidelines for bradycardia and cardiac arrest. Methods: We evaluated the accuracy of ChatGPT?s responses to 2 simulations based on the 2020 American Heart Association ACLS guidelines with 3 primary outcomes of interest: the mean individual step accuracy, the accuracy score per simulation attempt, and the accuracy score for each algorithm. For each simulation step, ChatGPT was scored for correctness (1 point) or incorrectness (0 points). Each simulation was conducted 20 times. Results: ChatGPT?s median accuracy for each step was 85% (IQR 40%-100%) for cardiac arrest and 30% (IQR 13%-81%) for bradycardia. ChatGPT?s median accuracy over 20 simulation attempts for cardiac arrest was 69% (IQR 67%-74%) and for bradycardia was 42% (IQR 33%-50%). We found that ChatGPT?s outputs varied despite consistent input, the same actions were persistently missed, repetitive overemphasis hindered guidance, and erroneous medication information was presented. Conclusions: This study highlights the need for consistent and reliable guidance to prevent potential medical errors and optimize the application of ChatGPT to enhance its reliability and effectiveness in clinical practice. UR - https://www.jmir.org/2024/1/e55037 UR - http://dx.doi.org/10.2196/55037 UR - http://www.ncbi.nlm.nih.gov/pubmed/38648098 ID - info:doi/10.2196/55037 ER - TY - JOUR AU - Karimian Sichani, Elnaz AU - Smith, Aaron AU - El Emam, Khaled AU - Mosquera, Lucy PY - 2024/4/22 TI - Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation JO - JMIR Form Res SP - e53241 VL - 8 KW - synthetic data KW - tensor decomposition KW - data sharing KW - data utility KW - data privacy KW - electronic health record KW - longitudinal KW - model development KW - model validation KW - generative models N2 - Background: Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients? privacy while properly reflecting the data. Objective: This study aims to develop and validate a model that generates valuable synthetic longitudinal health data while protecting the privacy of the patients whose data are collected. Methods: We investigated the best model for generating synthetic health data, with a focus on longitudinal observations. We developed a generative model that relies on the generalized canonical polyadic (GCP) tensor decomposition. This model also involves sampling from a latent factor matrix of GCP decomposition, which contains patient factors, using sequential decision trees, copula, and Hamiltonian Monte Carlo methods. We applied the proposed model to samples from the MIMIC-III (version 1.4) data set. Numerous analyses and experiments were conducted with different data structures and scenarios. We assessed the similarity between our synthetic data and the real data by conducting utility assessments. These assessments evaluate the structure and general patterns present in the data, such as dependency structure, descriptive statistics, and marginal distributions. Regarding privacy disclosure, our model preserves privacy by preventing the direct sharing of patient information and eliminating the one-to-one link between the observed and model tensor records. This was achieved by simulating and modeling a latent factor matrix of GCP decomposition associated with patients. Results: The findings show that our model is a promising method for generating synthetic longitudinal health data that is similar enough to real data. It can preserve the utility and privacy of the original data while also handling various data structures and scenarios. In certain experiments, all simulation methods used in the model produced the same high level of performance. Our model is also capable of addressing the challenge of sampling patients from electronic health records. This means that we can simulate a variety of patients in the synthetic data set, which may differ in number from the patients in the original data. Conclusions: We have presented a generative model for producing synthetic longitudinal health data. The model is formulated by applying the GCP tensor decomposition. We have provided 3 approaches for the synthesis and simulation of a latent factor matrix following the process of factorization. In brief, we have reduced the challenge of synthesizing massive longitudinal health data to synthesizing a nonlongitudinal and significantly smaller data set. UR - https://formative.jmir.org/2024/1/e53241 UR - http://dx.doi.org/10.2196/53241 UR - http://www.ncbi.nlm.nih.gov/pubmed/38648097 ID - info:doi/10.2196/53241 ER - TY - JOUR AU - Siepe, Sebastian Björn AU - Sander, Christian AU - Schultze, Martin AU - Kliem, Andreas AU - Ludwig, Sascha AU - Hegerl, Ulrich AU - Reich, Hanna PY - 2024/4/18 TI - Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data JO - JMIR Ment Health SP - e50136 VL - 11 KW - depression KW - time series analysis KW - network analysis KW - experience sampling KW - idiography KW - time varying KW - mobile phone N2 - Background: As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed. Objective: To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time. Methods: Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models. Results: The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants. Conclusions: Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes. UR - https://mental.jmir.org/2024/1/e50136 UR - http://dx.doi.org/10.2196/50136 UR - http://www.ncbi.nlm.nih.gov/pubmed/38635978 ID - info:doi/10.2196/50136 ER - TY - JOUR AU - Park, Bogyeom AU - Kim, Yuwon AU - Park, Jinseok AU - Choi, Hojin AU - Kim, Seong-Eun AU - Ryu, Hokyoung AU - Seo, Kyoungwon PY - 2024/4/17 TI - Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study JO - J Med Internet Res SP - e54538 VL - 26 KW - magnetic resonance imaging KW - MRI KW - virtual reality KW - VR KW - early detection KW - mild cognitive impairment KW - multimodal learning KW - hand movement KW - eye movement N2 - Background: Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. Objective: We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. Methods: The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. Results: The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). Conclusions: The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers. UR - https://www.jmir.org/2024/1/e54538 UR - http://dx.doi.org/10.2196/54538 UR - http://www.ncbi.nlm.nih.gov/pubmed/38631021 ID - info:doi/10.2196/54538 ER - TY - JOUR AU - Herrmann-Werner, Anne AU - Festl-Wietek, Teresa AU - Holderried, Friederike AU - Herschbach, Lea AU - Griewatz, Jan AU - Masters, Ken AU - Zipfel, Stephan AU - Mahling, Moritz PY - 2024/4/16 TI - Authors? Reply: ?Evaluating GPT-4?s Cognitive Functions Through the Bloom Taxonomy: Insights and Clarifications? JO - J Med Internet Res SP - e57778 VL - 26 KW - answer KW - artificial intelligence KW - assessment KW - Bloom?s taxonomy KW - ChatGPT KW - classification KW - error KW - exam KW - examination KW - generative KW - GPT-4 KW - Generative Pre-trained Transformer 4 KW - language model KW - learning outcome KW - LLM KW - MCQ KW - medical education KW - medical exam KW - multiple-choice question KW - natural language processing KW - NLP KW - psychosomatic KW - question KW - response KW - taxonomy UR - https://www.jmir.org/2024/1/e57778 UR - http://dx.doi.org/10.2196/57778 UR - http://www.ncbi.nlm.nih.gov/pubmed/38625723 ID - info:doi/10.2196/57778 ER - TY - JOUR AU - Huang, Kuan-Ju PY - 2024/4/16 TI - Evaluating GPT-4?s Cognitive Functions Through the Bloom Taxonomy: Insights and Clarifications JO - J Med Internet Res SP - e56997 VL - 26 KW - artificial intelligence KW - ChatGPT KW - Bloom taxonomy KW - AI KW - cognition UR - https://www.jmir.org/2024/1/e56997 UR - http://dx.doi.org/10.2196/56997 UR - http://www.ncbi.nlm.nih.gov/pubmed/38625725 ID - info:doi/10.2196/56997 ER - TY - JOUR AU - Lee, Lachlan AU - Hall, Rosemary AU - Stanley, James AU - Krebs, Jeremy PY - 2024/4/15 TI - Tailored Prompting to Improve Adherence to Image-Based Dietary Assessment: Mixed Methods Study JO - JMIR Mhealth Uhealth SP - e52074 VL - 12 KW - dietary assessment KW - diet KW - dietary KW - nutrition KW - mobile phone apps KW - image-based dietary assessment KW - nutritional epidemiology KW - mHealth KW - mobile health KW - app KW - apps KW - applications KW - image KW - RCT KW - randomized KW - controlled trial KW - controlled trials KW - cross-over KW - images KW - photo KW - photographs KW - photos KW - photograph KW - assessment KW - prompt KW - prompts KW - nudge KW - nudges KW - food KW - meal KW - meals KW - consumption KW - behaviour change KW - behavior change N2 - Background: Accurately assessing an individual?s diet is vital in the management of personal nutrition and in the study of the effect of diet on health. Despite its importance, the tools available for dietary assessment remain either too imprecise, expensive, or burdensome for clinical or research use. Image-based methods offer a potential new tool to improve the reliability and accessibility of dietary assessment. Though promising, image-based methods are sensitive to adherence, as images cannot be captured from meals that have already been consumed. Adherence to image-based methods may be improved with appropriately timed prompting via text message. Objective: This study aimed to quantitatively examine the effect of prompt timing on adherence to an image-based dietary record and qualitatively explore the participant experience of dietary assessment in order to inform the design of a novel image-based dietary assessment tool. Methods: This study used a randomized crossover design to examine the intraindividual effect of 3 prompt settings on the number of images captured in an image-based dietary record. The prompt settings were control, where no prompts were sent; standard, where prompts were sent at 7:15 AM, 11:15 AM, and 5:15 PM for every participant; and tailored, where prompt timing was tailored to habitual meal times for each participant. Participants completed a text-based dietary record at baseline to determine the timing of tailored prompts. Participants were randomized to 1 of 6 study sequences, each with a unique order of the 3 prompt settings, with each 3-day image-based dietary record separated by a washout period of at least 7 days. The qualitative component comprised semistructured interviews and questionnaires exploring the experience of dietary assessment. Results: A total of 37 people were recruited, and 30 participants (11 male, 19 female; mean age 30, SD 10.8 years), completed all image-based dietary records. The image rate increased by 0.83 images per day in the standard setting compared to control (P=.23) and increased by 1.78 images per day in the tailored setting compared to control (P?.001). We found that 13/21 (62%) of participants preferred to use the image-based dietary record versus the text-based dietary record but reported method-specific challenges with each method, particularly the inability to record via an image after a meal had been consumed. Conclusions: Tailored prompting improves adherence to image-based dietary assessment. Future image-based dietary assessment tools should use tailored prompting and offer both image-based and written input options to improve record completeness. UR - https://mhealth.jmir.org/2024/1/e52074 UR - http://dx.doi.org/10.2196/52074 ID - info:doi/10.2196/52074 ER - TY - JOUR AU - Otaka, Eri AU - Osawa, Aiko AU - Kato, Kenji AU - Obayashi, Yota AU - Uehara, Shintaro AU - Kamiya, Masaki AU - Mizuno, Katsuhiro AU - Hashide, Shusei AU - Kondo, Izumi PY - 2024/4/11 TI - Positive Emotional Responses to Socially Assistive Robots in People With Dementia: Pilot Study JO - JMIR Aging SP - e52443 VL - 7 KW - dementia care KW - robotics KW - emotion KW - facial expression KW - expression intensity KW - long-term care KW - sensory modality KW - gerontology KW - gerontechnology N2 - Background: Interventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been sufficiently elucidated. Objective: This study aimed to quantitatively examine the immediate emotional responses of people with dementia to stimuli presented by socially assistive robots using facial expression analysis in order to determine whether they elicited positive emotions. Methods: This pilot study adopted a single-arm interventional design. Socially assistive robots were presented to nursing home residents in a three-step procedure: (1) the robot was placed in front of participants (visual stimulus), (2) the robot was manipulated to produce sound (visual and auditory stimuli), and (3) participants held the robot in their hands (visual, auditory, and tactile stimuli). Expression intensity values for ?happy,? ?sad,? ?angry,? ?surprised,? ?scared,? and ?disgusted? were calculated continuously using facial expression analysis with FaceReader. Additionally, self-reported feelings were assessed using a 5-point Likert scale. In addition to the comparison between the subjective and objective emotional assessments, expression intensity values were compared across the aforementioned 3 stimuli patterns within each session. Finally, the expression intensity value for ?happy? was compared between the different types of robots. Results: A total of 29 participants (mean age 88.7, SD 6.2 years; n=27 female; Japanese version of Mini-Mental State Examination mean score 18.2, SD 5.1) were recruited. The expression intensity value for ?happy? was the largest in both the subjective and objective assessments and increased significantly when all sensory modalities (visual, auditory, and tactile) were presented (median expression intensity 0.21, IQR 0.09-0.35) compared to the other 2 patterns (visual alone: median expression intensity 0.10, IQR 0.03-0.22; P<.001; visual and auditory: median expression intensity 0.10, IQR 0.04-0.23; P<.001). The comparison of different types of robots revealed a significant increase when all stimuli were presented by doll-type and animal-type robots, but not humanoid-type robots. Conclusions: By quantifying the emotional responses of people with dementia, this study highlighted that socially assistive robots may be more effective in eliciting positive emotions when multiple sensory stimuli, including tactile stimuli, are involved. More studies, including randomized controlled trials, are required to further explore the effectiveness of using socially assistive robots in dementia care. Trial Registration: UMIN Clinical Trials Registry UMIN000046256; https://tinyurl.com/yw37auan UR - https://aging.jmir.org/2024/1/e52443 UR - http://dx.doi.org/10.2196/52443 ID - info:doi/10.2196/52443 ER - TY - JOUR AU - Rieckmann, Andreas AU - Nielsen, Sebastian AU - Dworzynski, Piotr AU - Amini, Heresh AU - Mogensen, Wengel Søren AU - Silva, Bartolomeu Isaquel AU - Chang, Y. Angela AU - Arah, A. Onyebuchi AU - Samek, Wojciech AU - Rod, Hulvej Naja AU - Ekstrøm, Thorn Claus AU - Benn, Stabell Christine AU - Aaby, Peter AU - Fisker, Bærent Ane PY - 2024/4/9 TI - Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study JO - JMIR Public Health Surveill SP - e48060 VL - 10 KW - child mortality KW - causal discovery KW - Guinea-Bissau KW - inductive-deductive KW - machine learning KW - targeted preventive and risk-mitigating interventions N2 - Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. Methods: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. Results: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. Conclusions: The study?s results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups. UR - https://publichealth.jmir.org/2024/1/e48060 UR - http://dx.doi.org/10.2196/48060 UR - http://www.ncbi.nlm.nih.gov/pubmed/38592761 ID - info:doi/10.2196/48060 ER - TY - JOUR AU - Sivarajkumar, Sonish AU - Gao, Fengyi AU - Denny, Parker AU - Aldhahwani, Bayan AU - Visweswaran, Shyam AU - Bove, Allyn AU - Wang, Yanshan PY - 2024/4/3 TI - Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study JO - JMIR Med Inform SP - e52289 VL - 12 KW - natural language processing KW - electronic health records KW - rehabilitation KW - physical exercise KW - ChatGPT KW - artificial intelligence KW - stroke KW - physical rehabilitation KW - rehabilitation therapy KW - exercise KW - machine learning N2 - Background: The rehabilitation of a patient who had a stroke requires precise, personalized treatment plans. Natural language processing (NLP) offers the potential to extract valuable exercise information from clinical notes, aiding in the development of more effective rehabilitation strategies. Objective: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of patients who had a stroke treated at the University of Pittsburgh Medical Center. Methods: A cohort of 13,605 patients diagnosed with stroke was identified, and their clinical notes containing rehabilitation therapy notes were retrieved. A comprehensive clinical ontology was created to represent various aspects of physical rehabilitation exercises. State-of-the-art NLP algorithms were then developed and compared, including rule-based, machine learning?based algorithms (support vector machine, logistic regression, gradient boosting, and AdaBoost) and large language model (LLM)?based algorithms (ChatGPT [OpenAI]). The study focused on key performance metrics, particularly F1-scores, to evaluate algorithm effectiveness. Results: The analysis was conducted on a data set comprising 23,724 notes with detailed demographic and clinical characteristics. The rule-based NLP algorithm demonstrated superior performance in most areas, particularly in detecting the ?Right Side? location with an F1-score of 0.975, outperforming gradient boosting by 0.063. Gradient boosting excelled in ?Lower Extremity? location detection (F1-score: 0.978), surpassing rule-based NLP by 0.023. It also showed notable performance in the ?Passive Range of Motion? detection with an F1-score of 0.970, a 0.032 improvement over rule-based NLP. The rule-based algorithm efficiently handled ?Duration,? ?Sets,? and ?Reps? with F1-scores up to 0.65. LLM-based NLP, particularly ChatGPT with few-shot prompts, achieved high recall but generally lower precision and F1-scores. However, it notably excelled in ?Backward Plane? motion detection, achieving an F1-score of 0.846, surpassing the rule-based algorithm?s 0.720. Conclusions: The study successfully developed and evaluated multiple NLP algorithms, revealing the strengths and weaknesses of each in extracting physical rehabilitation exercise information from clinical notes. The detailed ontology and the robust performance of the rule-based and gradient boosting algorithms demonstrate significant potential for enhancing precision rehabilitation. These findings contribute to the ongoing efforts to integrate advanced NLP techniques into health care, moving toward predictive models that can recommend personalized rehabilitation treatments for optimal patient outcomes. UR - https://medinform.jmir.org/2024/1/e52289 UR - http://dx.doi.org/10.2196/52289 UR - http://www.ncbi.nlm.nih.gov/pubmed/38568736 ID - info:doi/10.2196/52289 ER - TY - JOUR AU - Silva, Rui AU - Morouço, Pedro AU - Lains, Jorge AU - Amorim, Paula AU - Alves, Nuno AU - Veloso, Prieto António PY - 2024/4/2 TI - Innovative Design and Development of Personalized Ankle-Foot Orthoses for Survivors of Stroke With Equinovarus Foot: Protocol for a Feasibility and Comparative Trial JO - JMIR Res Protoc SP - e52365 VL - 13 KW - 3D printing KW - 3D scanner KW - ankle foot orthosis KW - biomechanical analysis KW - equinovarus foot N2 - Background: Ankle-foot orthoses (AFOs) are vital in gait rehabilitation for patients with stroke. However, many conventional AFO designs may not offer the required precision for optimized patient outcomes. With the advent of 3D scanning and printing technology, there is potential for more individualized AFO solutions, aiming to enhance the rehabilitative process. Objective: This nonrandomized trial seeks to introduce and validate a novel system for AFO design tailored to patients with stroke. By leveraging the capabilities of 3D scanning and bespoke software solutions, the aim is to produce orthoses that might surpass conventional designs in terms of biomechanical effectiveness and patient satisfaction. Methods: A distinctive 3D scanner, complemented by specialized software, will be developed to accurately capture the biomechanical data of leg movements during gait in patients with stroke. The acquired data will subsequently guide the creation of patient-specific AFO designs. These personalized orthoses will be provided to participants, and their efficacy will be compared with traditional AFO models. The qualitative dimensions of this experience will be evaluated using the Quebec User Evaluation of Satisfaction With Assistive Technology (QUEST) assessment tool. Feedback from health care professionals and the participants will be considered throughout the trial to ensure a rounded understanding of the system?s implications. Results: Spatial-temporal parameters will be statistically compared using paired t tests to determine significant differences between walking with the personalized orthosis, the existing orthosis, and barefoot conditions. Significant differences will be identified based on P values, with P<.05 indicating statistical significance. The Statistical Parametric Mapping method will be applied to graphically compare kinematic and kinetic data across the entire gait cycle. QUEST responses will undergo statistical analysis to evaluate patient satisfaction, with scores ranging from 1 (not satisfied) to 5 (very satisfied). Satisfaction scores will be presented as mean and SD values. Significant variations in satisfaction levels between the personalized and existing orthosis will be assessed using a Wilcoxon signed rank test. The anticipation is that the AFOs crafted through this innovative system will either match or outperform existing orthoses in use, with higher patient satisfaction rates. Conclusions: Embracing the synergy of technology and biomechanics may hold the key to revolutionizing orthotic design, with the potential to set new standards in patient-centered orthotic solutions. However, as with all innovations, a balanced approach, considering both the technological possibilities and individual patient needs, will be paramount to achieving optimal outcomes. International Registered Report Identifier (IRRID): PRR1-10.2196/52365 UR - https://www.researchprotocols.org/2024/1/e52365 UR - http://dx.doi.org/10.2196/52365 UR - http://www.ncbi.nlm.nih.gov/pubmed/38564249 ID - info:doi/10.2196/52365 ER - TY - JOUR AU - Castro Ribeiro, Thais AU - García Pagès, Esther AU - Ballester, Laura AU - Vilagut, Gemma AU - García Mieres, Helena AU - Suárez Aragonès, Víctor AU - Amigo, Franco AU - Bailón, Raquel AU - Mortier, Philippe AU - Pérez Sola, Víctor AU - Serrano-Blanco, Antoni AU - Alonso, Jordi AU - Aguiló, Jordi PY - 2024/3/29 TI - Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study JO - JMIR Res Protoc SP - e51298 VL - 13 KW - mental health KW - mental well-being KW - mobile health KW - mHealth KW - remote monitoring KW - physiological variables KW - experimental protocol KW - depression KW - anxiety N2 - Background: Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual?s well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. Objective: This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. Methods: This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. Results: Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. Conclusions: This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. Trial Registration: OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH International Registered Report Identifier (IRRID): DERR1-10.2196/51298 UR - https://www.researchprotocols.org/2024/1/e51298 UR - http://dx.doi.org/10.2196/51298 UR - http://www.ncbi.nlm.nih.gov/pubmed/38551647 ID - info:doi/10.2196/51298 ER - TY - JOUR AU - Sheng, Yiyang AU - Bond, Raymond AU - Jaiswal, Rajesh AU - Dinsmore, John AU - Doyle, Julie PY - 2024/3/28 TI - Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial JO - J Med Internet Res SP - e46287 VL - 26 KW - aging KW - digital health KW - multimorbidity KW - chronic disease KW - engagement KW - k-means clustering N2 - Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change. Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months. Methods: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered. Results: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants? outcomes (eg, reduce symptom exacerbation and increase physical activity). Conclusions: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity. UR - https://www.jmir.org/2024/1/e46287 UR - http://dx.doi.org/10.2196/46287 UR - http://www.ncbi.nlm.nih.gov/pubmed/38546724 ID - info:doi/10.2196/46287 ER - TY - JOUR AU - Kilshaw, E. Robyn AU - Boggins, Abigail AU - Everett, Olivia AU - Butner, Emma AU - Leifker, R. Feea AU - Baucom, W. Brian R. PY - 2024/3/27 TI - Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study JO - JMIR Res Protoc SP - e53857 VL - 13 KW - audio data KW - computational psychiatry KW - data repository KW - digital phenotyping KW - machine learning KW - passive sensor data N2 - Background: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. Objective: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. Methods: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). Results: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. Conclusions: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field?s move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. International Registered Report Identifier (IRRID): DERR1-10.2196/53857 UR - https://www.researchprotocols.org/2024/1/e53857 UR - http://dx.doi.org/10.2196/53857 UR - http://www.ncbi.nlm.nih.gov/pubmed/38536220 ID - info:doi/10.2196/53857 ER - TY - JOUR AU - Nguyen, Duy-Anh AU - Li, Minyi AU - Lambert, Gavin AU - Kowalczyk, Ryszard AU - McDonald, Rachael AU - Vo, Bao Quoc PY - 2024/3/25 TI - Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach JO - JMIR Form Res SP - e52482 VL - 8 KW - question answering KW - machine reading comprehension KW - context extraction KW - covid19 KW - health care N2 - Background: Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context. Objective: This study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately. Methods: Existing approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model?s performance. Results: We demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F1-score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases. Conclusions: The proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts. UR - https://formative.jmir.org/2024/1/e52482 UR - http://dx.doi.org/10.2196/52482 UR - http://www.ncbi.nlm.nih.gov/pubmed/38526545 ID - info:doi/10.2196/52482 ER - TY - JOUR AU - Kulangareth, Valsan Nikhil AU - Kaufman, Jaycee AU - Oreskovic, Jessica AU - Fossat, Yan PY - 2024/3/21 TI - Investigation of Deepfake Voice Detection Using Speech Pause Patterns: Algorithm Development and Validation JO - JMIR Biomed Eng SP - e56245 VL - 9 KW - voice KW - vocal biomarkers KW - deepfakes KW - artificial intelligence KW - vocal KW - sound KW - sounds KW - speech KW - audio KW - deepfake KW - cloning KW - text to speech KW - cloned KW - deep learning KW - machine learning KW - model-naive N2 - Background: The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of ?deepfake? technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information. Objective: To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices. We propose that the presence or absence of certain perceptual features, such as pauses in speech, can effectively distinguish between cloned and authentic audio. Methods: A total of 49 adult participants representing diverse ethnic backgrounds and accents were recruited. Each participant contributed voice samples for the training of up to 3 distinct voice cloning text-to-speech models and 3 control paragraphs. Subsequently, the cloning models generated synthetic versions of the control paragraphs, resulting in a data set consisting of up to 9 cloned audio samples and 3 control samples per participant. We analyzed the speech pauses caused by biological actions such as respiration, swallowing, and cognitive processes. Five audio features corresponding to speech pause profiles were calculated. Differences between authentic and cloned audio for these features were assessed, and 5 classical machine learning algorithms were implemented using these features to create a prediction model. The generalization capability of the optimal model was evaluated through testing on unseen data, incorporating a model-naive generator, a model-naive paragraph, and model-naive participants. Results: Cloned audio exhibited significantly increased time between pauses (P<.001), decreased variation in speech segment length (P=.003), increased overall proportion of time speaking (P=.04), and decreased rates of micro- and macropauses in speech (both P=.01). Five machine learning models were implemented using these features, with the AdaBoost model demonstrating the highest performance, achieving a 5-fold cross-validation balanced accuracy of 0.81 (SD 0.05). Other models included support vector machine (balanced accuracy 0.79, SD 0.03), random forest (balanced accuracy 0.78, SD 0.04), logistic regression, and decision tree (balanced accuracies 0.76, SD 0.10 and 0.72, SD 0.06). When evaluating the optimal AdaBoost model, it achieved an overall test accuracy of 0.79 when predicting unseen data. Conclusions: The incorporation of perceptual, biological features into machine learning models demonstrates promising results in distinguishing between authentic human voices and cloned audio. UR - https://biomedeng.jmir.org/2024/1/e56245 UR - http://dx.doi.org/10.2196/56245 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875685 ID - info:doi/10.2196/56245 ER - TY - JOUR AU - Yim, Dobin AU - Khuntia, Jiban AU - Parameswaran, Vijaya AU - Meyers, Arlen PY - 2024/3/20 TI - Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review JO - JMIR Med Inform SP - e52073 VL - 12 KW - generative artificial intelligence tools and applications KW - GenAI KW - service KW - clinical KW - health care KW - transformation KW - digital N2 - Background: Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients? families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. Objective: This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. Methods: We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. Results: Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. Conclusions: GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service?level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI. UR - https://medinform.jmir.org/2024/1/e52073 UR - http://dx.doi.org/10.2196/52073 UR - http://www.ncbi.nlm.nih.gov/pubmed/38506918 ID - info:doi/10.2196/52073 ER - TY - JOUR AU - Killian, A. Jackson AU - Jain, Manish AU - Jia, Yugang AU - Amar, Jonathan AU - Huang, Erich AU - Tambe, Milind PY - 2024/3/15 TI - New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program: Simulation Study JO - JMIR Diabetes SP - e52688 VL - 9 KW - chronic disease KW - type-2 diabetes KW - T2D KW - restless multiarmed bandits KW - multi-armed bandit KW - multi-armed bandits KW - machine learning KW - resource allocation KW - digital health KW - equity N2 - Background: Digital health programs provide individualized support to patients with chronic diseases and their effectiveness is measured by the extent to which patients achieve target individual clinical outcomes and the program?s ability to sustain patient engagement. However, patient dropout and inequitable intervention delivery strategies, which may unintentionally penalize certain patient subgroups, represent challenges to maximizing effectiveness. Therefore, methodologies that optimize the balance between success factors (achievement of target clinical outcomes and sustained engagement) equitably would be desirable, particularly when there are resource constraints. Objective: Our objectives were to propose a model for digital health program resource management that accounts jointly for the interaction between individual clinical outcomes and patient engagement, ensures equitable allocation as well as allows for capacity planning, and conducts extensive simulations using publicly available data on type 2 diabetes, a chronic disease. Methods: We propose a restless multiarmed bandit (RMAB) model to plan interventions that jointly optimize long-term engagement and individual clinical outcomes (in this case measured as the achievement of target healthy glucose levels). To mitigate the tendency of RMAB to achieve good aggregate performance by exacerbating disparities between groups, we propose new equitable objectives for RMAB and apply bilevel optimization algorithms to solve them. We formulated a model for the joint evolution of patient engagement and individual clinical outcome trajectory to capture the key dynamics of interest in digital chronic disease management programs. Results: In simulation exercises, our optimized intervention policies lead to up to 10% more patients reaching healthy glucose levels after 12 months, with a 10% reduction in dropout compared to standard-of-care baselines. Further, our new equitable policies reduce the mean absolute difference of engagement and health outcomes across 6 demographic groups by up to 85% compared to the state-of-the-art. Conclusions: Planning digital health interventions with individual clinical outcome objectives and long-term engagement dynamics as considerations can be both feasible and effective. We propose using an RMAB sequential decision-making framework, which may offer additional capabilities in capacity planning as well. The integration of an equitable RMAB algorithm further enhances the potential for reaching equitable solutions. This approach provides program designers with the flexibility to switch between different priorities and balance trade-offs across various objectives according to their preferences. UR - https://diabetes.jmir.org/2024/1/e52688 UR - http://dx.doi.org/10.2196/52688 UR - http://www.ncbi.nlm.nih.gov/pubmed/38488828 ID - info:doi/10.2196/52688 ER - TY - JOUR AU - Reiter, Vittoria Alisa Maria AU - Pantel, Tori Jean AU - Danyel, Magdalena AU - Horn, Denise AU - Ott, Claus-Eric AU - Mensah, Atta Martin PY - 2024/3/13 TI - Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study JO - J Med Internet Res SP - e42904 VL - 26 KW - facial phenotyping KW - DeepGestalt KW - facial recognition KW - Face2Gene KW - medical genetics KW - diagnostic accuracy KW - genetic syndrome KW - machine learning KW - GestaltMatcher KW - D-Score KW - genetics N2 - Background: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. Objective: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. Methods: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. Results: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score?s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. Conclusions: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face. UR - https://www.jmir.org/2024/1/e42904 UR - http://dx.doi.org/10.2196/42904 UR - http://www.ncbi.nlm.nih.gov/pubmed/38477981 ID - info:doi/10.2196/42904 ER - TY - JOUR AU - Fahimi, Mansour AU - Hair, C. Elizabeth AU - Do, K. Elizabeth AU - Kreslake, M. Jennifer AU - Yan, Xiaolu AU - Chan, Elisa AU - Barlas, M. Frances AU - Giles, Abigail AU - Osborn, Larry PY - 2024/3/7 TI - Improving the Efficiency of Inferences From Hybrid Samples for Effective Health Surveillance Surveys: Comprehensive Review of Quantitative Methods JO - JMIR Public Health Surveill SP - e48186 VL - 10 KW - hybrid samples KW - composite estimation KW - optimal composition factor KW - unequal weighting effect KW - composite weighting KW - weighting KW - surveillance KW - sample survey KW - data collection KW - risk factor N2 - Background: Increasingly, survey researchers rely on hybrid samples to improve coverage and increase the number of respondents by combining independent samples. For instance, it is possible to combine 2 probability samples with one relying on telephone and another on mail. More commonly, however, researchers are now supplementing probability samples with those from online panels that are less costly. Setting aside ad hoc approaches that are void of rigor, traditionally, the method of composite estimation has been used to blend results from different sample surveys. This means individual point estimates from different surveys are pooled together, 1 estimate at a time. Given that for a typical study many estimates must be produced, this piecemeal approach is computationally burdensome and subject to the inferential limitations of the individual surveys that are used in this process. Objective: In this paper, we will provide a comprehensive review of the traditional method of composite estimation. Subsequently, the method of composite weighting is introduced, which is significantly more efficient, both computationally and inferentially when pooling data from multiple surveys. With the growing interest in hybrid sampling alternatives, we hope to offer an accessible methodology for improving the efficiency of inferences from such sample surveys without sacrificing rigor. Methods: Specifically, we will illustrate why the many ad hoc procedures for blending survey data from multiple surveys are void of scientific integrity and subject to misleading inferences. Moreover, we will demonstrate how the traditional approach of composite estimation fails to offer a pragmatic and scalable solution in practice. By relying on theoretical and empirical justifications, in contrast, we will show how our proposed methodology of composite weighting is both scientifically sound and inferentially and computationally superior to the old method of composite estimation. Results: Using data from 3 large surveys that have relied on hybrid samples composed of probability-based and supplemental sample components from online panels, we illustrate that our proposed method of composite weighting is superior to the traditional method of composite estimation in 2 distinct ways. Computationally, it is vastly less demanding and hence more accessible for practitioners. Inferentially, it produces more efficient estimates with higher levels of external validity when pooling data from multiple surveys. Conclusions: The new realities of the digital age have brought about a number of resilient challenges for survey researchers, which in turn have exposed some of the inefficiencies associated with the traditional methods this community has relied upon for decades. The resilience of such challenges suggests that piecemeal approaches that may have limited applicability or restricted accessibility will prove to be inadequate and transient. It is from this perspective that our proposed method of composite weighting has aimed to introduce a durable and accessible solution for hybrid sample surveys. UR - https://publichealth.jmir.org/2024/1/e48186 UR - http://dx.doi.org/10.2196/48186 UR - http://www.ncbi.nlm.nih.gov/pubmed/38451620 ID - info:doi/10.2196/48186 ER - TY - JOUR AU - Guo, Yunyong AU - Ganti, Sudhakar AU - Wu, Yi PY - 2024/3/6 TI - Enhancing Energy Efficiency in Telehealth Internet of Things Systems Through Fog and Cloud Computing Integration: Simulation Study JO - JMIR Biomed Eng SP - e50175 VL - 9 KW - cloud computing KW - energy-efficient KW - fog computing KW - Internet of Things KW - IoT KW - telehealth N2 - Background: The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency. Objective: This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing?based platform, aiming to enhance energy efficiency in telehealth IoT systems. Methods: The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model?s effectiveness in reducing energy consumption and enhancing data processing efficiency. Results: Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings. Conclusions: The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts. UR - https://biomedeng.jmir.org/2024/1/e50175 UR - http://dx.doi.org/10.2196/50175 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875671 ID - info:doi/10.2196/50175 ER - TY - JOUR AU - Virto, Naiara AU - Río, Xabier AU - Angulo-Garay, Garazi AU - García Molina, Rafael AU - Avendaño Céspedes, Almudena AU - Cortés Zamora, Belen Elisa AU - Gómez Jiménez, Elena AU - Alcantud Córcoles, Ruben AU - Rodriguez Mañas, Leocadio AU - Costa-Grille, Alba AU - Matheu, Ander AU - Marcos-Pérez, Diego AU - Lazcano, Uxue AU - Vergara, Itziar AU - Arjona, Laura AU - Saeteros, Morelva AU - Lopez-de-Ipiña, Diego AU - Coca, Aitor AU - Abizanda Soler, Pedro AU - Sanabria, J. Sergio PY - 2024/2/23 TI - Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study JO - JMIR Res Protoc SP - e50325 VL - 13 KW - muscle KW - ultrasound KW - blood-based biomarkers KW - sarcopenia KW - frailty KW - older adults N2 - Background: Frailty resulting from the loss of muscle quality can potentially be delayed through early detection and physical exercise interventions. There is a demand for cost-effective tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessments. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional, and microstructural muscle properties, while biological assays derived from blood samples are associated with functional information. Objective: This study aims to assess multiparametric combinations of ultrasound and blood-based biomarkers to offer a cross-sectional evaluation of the patient frailty phenotype and to track changes in muscle quality associated with supervised exercise programs. Methods: This prospective observational multicenter study will include patients aged 70 years and older who are capable of providing informed consent. We aim to recruit 100 patients from hospital environments and 100 from primary care facilities. Each patient will undergo at least two examinations (baseline and follow-up), totaling a minimum of 400 examinations. In hospital environments, 50 patients will be measured before/after a 16-week individualized and supervised exercise program, while another 50 patients will be followed up after the same period without intervention. Primary care patients will undergo a 1-year follow-up evaluation. The primary objective is to compare cross-sectional evaluations of physical performance, functional capacity, body composition, and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based assays. We will analyze ultrasound raw data obtained with a point-of-care device, along with a set of biomarkers previously associated with frailty, using quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Additionally, we will examine the sensitivity of these biomarkers to detect short-term muscle quality changes and functional improvement after a supervised exercise intervention compared with usual care. Results: At the time of manuscript submission, the enrollment of volunteers is ongoing. Recruitment started on March 1, 2022, and ends on June 30, 2024. Conclusions: The outlined study protocol will integrate portable technologies, using quantitative muscle ultrasound and blood biomarkers, to facilitate an objective cross-sectional assessment of muscle quality in both hospital and primary care settings. The primary objective is to generate data that can be used to explore associations between biomarker combinations and the cross-sectional clinical assessment of frailty and sarcopenia. Additionally, the study aims to investigate musculoskeletal changes following multicomponent physical exercise programs. Trial Registration: ClinicalTrials.gov NCT05294757; https://clinicaltrials.gov/ct2/show/NCT05294757 International Registered Report Identifier (IRRID): DERR1-10.2196/50325 UR - https://www.researchprotocols.org/2024/1/e50325 UR - http://dx.doi.org/10.2196/50325 UR - http://www.ncbi.nlm.nih.gov/pubmed/38393761 ID - info:doi/10.2196/50325 ER - TY - JOUR AU - Tegegne, Kassaw Teketo AU - Tran, Ly-Duyen AU - Nourse, Rebecca AU - Gurrin, Cathal AU - Maddison, Ralph PY - 2024/2/21 TI - Daily Activity Lifelogs of People With Heart Failure: Observational Study JO - JMIR Form Res SP - e51248 VL - 8 KW - heart failure KW - self-management KW - lifelogs KW - daily activity KW - wearable camera KW - E-Myscéal KW - activities of daily living KW - ADL KW - intervention KW - self-report method KW - wearable KW - chronic condition N2 - Background: Globally, heart failure (HF) affects more than 64 million people, and attempts to reduce its social and economic burden are a public health priority. Interventions to support people with HF to self-manage have been shown to reduce hospitalizations, improve quality of life, and reduce mortality rates. Understanding how people self-manage is imperative to improve future interventions; however, most approaches to date, have used self-report methods to achieve this. Wearable cameras provide a unique tool to understand the lived experiences of people with HF and the daily activities they undertake, which could lead to more effective interventions. However, their potential for understanding chronic conditions such as HF is unclear. Objective: This study aimed to determine the potential utility of wearable cameras to better understand the activities of daily living in people living with HF. Methods: The ?Seeing is Believing (SIB)? study involved 30 patients with HF who wore wearable cameras for a maximum of 30 days. We used the E-Myscéal web-based lifelog retrieval system to process and analyze the wearable camera image data set. Search terms for 7 daily activities (physical activity, gardening, shopping, screen time, drinking, eating, and medication intake) were developed and used for image retrieval. Sensitivity analysis was conducted to compare the number of images retrieved using different search terms. Temporal patterns in daily activities were examined, and differences before and after hospitalization were assessed. Results: E-Myscéal exhibited sensitivity to specific search terms, leading to significant variations in the number of images retrieved for each activity. The highest number of images returned were related to eating and drinking, with fewer images for physical activity, screen time, and taking medication. The majority of captured activities occurred before midday. Notably, temporal differences in daily activity patterns were observed for participants hospitalized during this study. The number of medication images increased after hospital discharge, while screen time images decreased. Conclusions: Wearable cameras offer valuable insights into daily activities and self-management in people living with HF. E-Myscéal efficiently retrieves relevant images, but search term sensitivity underscores the need for careful selection. UR - https://formative.jmir.org/2024/1/e51248 UR - http://dx.doi.org/10.2196/51248 UR - http://www.ncbi.nlm.nih.gov/pubmed/38381484 ID - info:doi/10.2196/51248 ER - TY - JOUR AU - O'Hara, Cathal AU - Gibney, R. Eileen PY - 2024/2/14 TI - Dietary Intake Assessment Using a Novel, Generic Meal?Based Recall and a 24-Hour Recall: Comparison Study JO - J Med Internet Res SP - e48817 VL - 26 KW - meal patterns KW - eating behaviors KW - eating occasions KW - nutrition assessment KW - dietary intake assessment KW - 24-hour recall KW - relative validity N2 - Background: Dietary intake assessment is an integral part of addressing suboptimal dietary intakes. Existing food-based methods are time-consuming and burdensome for users to report the individual foods consumed at each meal. However, ease of use is the most important feature for individuals choosing a nutrition or diet app. Intakes of whole meals can be reported in a manner that is less burdensome than reporting individual foods. No study has developed a method of dietary intake assessment where individuals report their dietary intakes as whole meals rather than individual foods. Objective: This study aims to develop a novel, meal-based method of dietary intake assessment and test its ability to estimate nutrient intakes compared with that of a web-based, 24-hour recall (24HR). Methods: Participants completed a web-based, generic meal?based recall. This involved, for each meal type (breakfast, light meal, main meal, snack, and beverage), choosing from a selection of meal images those that most represented their intakes during the previous day. Meal images were based on generic meals from a previous study that were representative of the actual meal intakes in Ireland. Participants also completed a web-based 24HR. Both methods were completed on the same day, 3 hours apart. In a crossover design, participants were randomized in terms of which method they completed first. Then, 2 weeks after the first dietary assessments, participants repeated the process in the reverse order. Estimates of mean daily nutrient intakes and the categorization of individuals according to nutrient-based guidelines (eg, low, adequate, and high) were compared between the 2 methods. P values of less than .05 were considered statistically significant. Results: In total, 161 participants completed the study. For the 23 nutrient variables compared, the median percentage difference between the 2 methods was 7.6% (IQR 2.6%-13.2%), with P values ranging from <.001 to .97, and out of 23 variables, effect sizes for the differences were small for 19 (83%) variables, moderate for 2 (9%) variables, and large for 2 (9%) variables. Correlation coefficients were statistically significant (P<.05) for 18 (78%) of the 23 variables. Statistically significant correlations ranged from 0.16 to 0.45, with median correlation of 0.32 (IQR 0.25-0.40). When participants were classified according to nutrient-based guidelines, the proportion of individuals who were classified into the same category ranged from 52.8% (85/161) to 84.5% (136/161). Conclusions: A generic meal?based method of dietary intake assessment provides estimates of nutrient intake comparable with those provided by a web-based 24HR but with varying levels of agreement among nutrients. Further studies are required to refine and improve the generic recall across a range of nutrients. Future studies will consider user experience including the potential feasibility of incorporating image recognition of whole meals into the generic recall. UR - https://www.jmir.org/2024/1/e48817 UR - http://dx.doi.org/10.2196/48817 UR - http://www.ncbi.nlm.nih.gov/pubmed/38354039 ID - info:doi/10.2196/48817 ER - TY - JOUR AU - Kim, Jina AU - Choi, Sung Yong AU - Lee, Joo Young AU - Yeo, Geun Seung AU - Kim, Won Kyung AU - Kim, Seo Min AU - Rahmati, Masoud AU - Yon, Keon Dong AU - Lee, Jinseok PY - 2024/2/6 TI - Limitations of the Cough Sound-Based COVID-19 Diagnosis Artificial Intelligence Model and its Future Direction: Longitudinal Observation Study JO - J Med Internet Res SP - e51640 VL - 26 KW - COVID-19 variants KW - cough sound KW - artificial intelligence KW - diagnosis KW - human lifestyle KW - SARS-CoV-2 KW - AI model KW - cough KW - sound-based KW - sounds app KW - development KW - COVID-19 KW - AI N2 - Background: The outbreak of SARS-CoV-2 in 2019 has necessitated the rapid and accurate detection of COVID-19 to manage patients effectively and implement public health measures. Artificial intelligence (AI) models analyzing cough sounds have emerged as promising tools for large-scale screening and early identification of potential cases. Objective: This study aimed to investigate the efficacy of using cough sounds as a diagnostic tool for COVID-19, considering the unique acoustic features that differentiate positive and negative cases. We investigated whether an AI model trained on cough sound recordings from specific periods, especially the early stages of the COVID-19 pandemic, were applicable to the ongoing situation with persistent variants. Methods: We used cough sound recordings from 3 data sets (Cambridge, Coswara, and Virufy) representing different stages of the pandemic and variants. Our AI model was trained using the Cambridge data set with subsequent evaluation against all data sets. The performance was analyzed based on the area under the receiver operating curve (AUC) across different data measurement periods and COVID-19 variants. Results: The AI model demonstrated a high AUC when tested with the Cambridge data set, indicative of its initial effectiveness. However, the performance varied significantly with other data sets, particularly in detecting later variants such as Delta and Omicron, with a marked decline in AUC observed for the latter. These results highlight the challenges in maintaining the efficacy of AI models against the backdrop of an evolving virus. Conclusions: While AI models analyzing cough sounds offer a promising noninvasive and rapid screening method for COVID-19, their effectiveness is challenged by the emergence of new virus variants. Ongoing research and adaptations in AI methodologies are crucial to address these limitations. The adaptability of AI models to evolve with the virus underscores their potential as a foundational technology for not only the current pandemic but also future outbreaks, contributing to a more agile and resilient global health infrastructure. UR - https://www.jmir.org/2024/1/e51640 UR - http://dx.doi.org/10.2196/51640 UR - http://www.ncbi.nlm.nih.gov/pubmed/38319694 ID - info:doi/10.2196/51640 ER - TY - JOUR AU - Spies, Erica AU - Andreu, Thomas AU - Hartung, Matthias AU - Park, Josephine AU - Kamudoni, Paul PY - 2024/2/2 TI - Exploring the Perspectives of Patients Living With Lupus: Retrospective Social Listening Study JO - JMIR Form Res SP - e52768 VL - 8 KW - systemic lupus erythematosus KW - SLE KW - cutaneous lupus erythematosus KW - CLE KW - quality of life KW - health-related quality of life KW - HRQoL KW - social media listening KW - lupus KW - rare KW - cutaneous KW - social media KW - infodemiology KW - infoveillance KW - social listening KW - natural language processing KW - machine learning KW - experience KW - experiences KW - tagged KW - tagging KW - visualization KW - visualizations KW - knowledge graph KW - chronic KW - autoimmune KW - inflammation KW - inflammatory KW - skin KW - dermatology KW - dermatological KW - forum KW - forums KW - blog KW - blogs N2 - Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease affecting various organs with a wide range of clinical manifestations. Cutaneous lupus erythematosus (CLE) can manifest as a feature of SLE or an independent skin ailment. Health-related quality of life (HRQoL) is frequently compromised in individuals living with lupus. Understanding patients? perspectives when living with a disease is crucial for effectively meeting their unmet needs. Social listening is a promising new method that can provide insights into the experiences of patients living with their disease (lupus) and leverage these insights to inform drug development strategies for addressing their unmet needs. Objective: The objective of this study is to explore the experience of patients living with SLE and CLE, including their disease and treatment experiences, HRQoL, and unmet needs, as discussed in web-based social media platforms such as blogs and forums. Methods: A retrospective exploratory social listening study was conducted across 13 publicly available English-language social media platforms from October 2019 to January 2022. Data were processed using natural language processing and knowledge graph tagging technology to clean, format, anonymize, and annotate them algorithmically before feeding them to Pharos, a Semalytix proprietary data visualization and analysis platform, for further analysis. Pharos was used to generate descriptive data statistics, providing insights into the magnitude of individual patient experience variables, their differences in the magnitude of variables, and the associations between algorithmically tagged variables. Results: A total of 45,554 posts from 3834 individuals who were algorithmically identified as patients with lupus were included in this study. Among them, 1925 (authoring 5636 posts) and 106 (authoring 243 posts) patients were identified as having SLE and CLE, respectively. Patients frequently mentioned various symptoms in relation to SLE and CLE including pain, fatigue, and rashes; pain and fatigue were identified as the main drivers of HRQoL impairment. The most affected aspects of HRQoL included ?mobility,? ?cognitive capabilities,? ?recreation and leisure,? and ?sleep and rest.? Existing pharmacological interventions poorly managed the most burdensome symptoms of lupus. Conversely, nonpharmacological treatments, such as exercise and meditation, were frequently associated with HRQoL improvement. Conclusions: Patients with lupus reported a complex interplay of symptoms and HRQoL aspects that negatively influenced one another. This study demonstrates that social listening is an effective method to gather insights into patients? experiences, preferences, and unmet needs, which can be considered during the drug development process to develop effective therapies and improve disease management. UR - https://formative.jmir.org/2024/1/e52768 UR - http://dx.doi.org/10.2196/52768 UR - http://www.ncbi.nlm.nih.gov/pubmed/38306157 ID - info:doi/10.2196/52768 ER - TY - JOUR AU - Fuller, Joshua AU - Abramov, Alexey AU - Mullin, Dana AU - Beck, James AU - Lemaitre, Philippe AU - Azizi, Elham PY - 2024/2/2 TI - A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study JO - JMIR Biomed Eng SP - e48497 VL - 9 KW - extracorporeal membrane oxygenation KW - ECMO KW - venovenous KW - VV KW - machine learning KW - supervised learning KW - dynamic data KW - time series KW - clinical decision support KW - artificial intelligence KW - AI KW - clinical AI KW - health informatics N2 - Background: Venovenous extracorporeal membrane oxygenation (VV-ECMO) is a therapy for patients with refractory respiratory failure. The decision to decannulate someone from extracorporeal membrane oxygenation (ECMO) often involves weaning trials and clinical intuition. To date, there are limited prognostication metrics to guide clinical decision?making to determine which patients will be successfully weaned and decannulated. Objective: This study aims to assist clinicians with the decision to decannulate a patient from ECMO, using Continuous Evaluation of VV-ECMO Outcomes (CEVVO), a deep learning?based model for predicting success of decannulation in patients supported on VV-ECMO. The running metric may be applied daily to categorize patients into high-risk and low-risk groups. Using these data, providers may consider initiating a weaning trial based on their expertise and CEVVO. Methods: Data were collected from 118 patients supported with VV-ECMO at the Columbia University Irving Medical Center. Using a long short-term memory?based network, CEVVO is the first model capable of integrating discrete clinical information with continuous data collected from an ECMO device. A total of 12 sets of 5-fold cross validations were conducted to assess the performance, which was measured using the area under the receiver operating characteristic curve (AUROC) and average precision (AP). To translate the predicted values into a clinically useful metric, the model results were calibrated and stratified into risk groups, ranging from 0 (high risk) to 3 (low risk). To further investigate the performance edge of CEVVO, 2 synthetic data sets were generated using Gaussian process regression. The first data set preserved the long-term dependency of the patient data set, whereas the second did not. Results: CEVVO demonstrated consistently superior classification performance compared with contemporary models (P<.001 and P=.04 compared with the next highest AUROC and AP). Although the model?s patient-by-patient predictive power may be too low to be integrated into a clinical setting (AUROC 95% CI 0.6822-0.7055; AP 95% CI 0.8515-0.8682), the patient risk classification system displayed greater potential. When measured at 72 hours, the high-risk group had a successful decannulation rate of 58% (7/12), whereas the low-risk group had a successful decannulation rate of 92% (11/12; P=.04). When measured at 96 hours, the high- and low-risk groups had a successful decannulation rate of 54% (6/11) and 100% (9/9), respectively (P=.01). We hypothesized that the improved performance of CEVVO was owing to its ability to efficiently capture transient temporal patterns. Indeed, CEVVO exhibited improved performance on synthetic data with inherent temporal dependencies (P<.001) compared with logistic regression and a dense neural network. Conclusions: The ability to interpret and integrate large data sets is paramount for creating accurate models capable of assisting clinicians in risk stratifying patients supported on VV-ECMO. Our framework may guide future incorporation of CEVVO into more comprehensive intensive care monitoring systems. UR - https://biomedeng.jmir.org/2024/1/e48497 UR - http://dx.doi.org/10.2196/48497 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875691 ID - info:doi/10.2196/48497 ER - TY - JOUR AU - Fu, Ziru AU - Hsu, Cheng Yu AU - Chan, S. Christian AU - Lau, Ming Chaak AU - Liu, Joyce AU - Yip, Fai Paul Siu PY - 2024/1/30 TI - Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study JO - J Med Internet Res SP - e51069 VL - 26 KW - Cantonese KW - ChatGPT KW - counseling KW - natural language processing KW - NLP KW - sentiment analysis N2 - Background: Sentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its colloquialism and multilingualism, make it different from other languages and pose additional challenges to sentiment analysis. Recent advances in models such as ChatGPT offer potential viable solutions. Objective: This study investigated the efficacy of GPT-3.5 and GPT-4 in Cantonese sentiment analysis in the context of web-based counseling and compared their performance with other mainstream methods, including lexicon-based methods and machine learning approaches. Methods: We analyzed transcripts from a web-based, text-based counseling service in Hong Kong, including a total of 131 individual counseling sessions and 6169 messages between counselors and help-seekers. First, a codebook was developed for human annotation. A simple prompt (?Is the sentiment of this Cantonese text positive, neutral, or negative? Respond with the sentiment label only.?) was then given to GPT-3.5 and GPT-4 to label each message?s sentiment. GPT-3.5 and GPT-4?s performance was compared with a lexicon-based method and 3 state-of-the-art models, including linear regression, support vector machines, and long short-term memory neural networks. Results: Our findings revealed ChatGPT?s remarkable accuracy in sentiment classification, with GPT-3.5 and GPT-4, respectively, achieving 92.1% (5682/6169) and 95.3% (5880/6169) accuracy in identifying positive, neutral, and negative sentiment, thereby outperforming the traditional lexicon-based method, which had an accuracy of 37.2% (2295/6169), and the 3 machine learning models, which had accuracies ranging from 66% (4072/6169) to 70.9% (4374/6169). Conclusions: Among many text analysis techniques, ChatGPT demonstrates superior accuracy and emerges as a promising tool for Cantonese sentiment analysis. This study also highlights ChatGPT?s applicability in real-world scenarios, such as monitoring the quality of text-based counseling services and detecting message-level sentiments in vivo. The insights derived from this study pave the way for further exploration into the capabilities of ChatGPT in the context of underresourced languages and specialized domains like psychotherapy and natural language processing. UR - https://www.jmir.org/2024/1/e51069 UR - http://dx.doi.org/10.2196/51069 UR - http://www.ncbi.nlm.nih.gov/pubmed/38289662 ID - info:doi/10.2196/51069 ER - TY - JOUR AU - Hildebrand, Lindsey AU - Huskey, Alisa AU - Dailey, Natalie AU - Jankowski, Samantha AU - Henderson-Arredondo, Kymberly AU - Trapani, Christopher AU - Patel, Imran Salma AU - Chen, Yu-Chin Allison AU - Chou, Ying-Hui AU - Killgore, S. William D. PY - 2024/1/26 TI - Transcranial Magnetic Stimulation of the Default Mode Network to Improve Sleep in Individuals With Insomnia Symptoms: Protocol for a Double-Blind Randomized Controlled Trial JO - JMIR Res Protoc SP - e51212 VL - 13 KW - continuous theta burst stimulation KW - transcranial magnetic stimulation KW - default mode network KW - sleep KW - insomnia KW - cTBS KW - randomized controlled trial N2 - Background: Cortical hyperarousal and ruminative thinking are common aspects of insomnia that have been linked with greater connectivity in the default mode network (DMN). Therefore, disrupting network activity within the DMN may reduce cortical and cognitive hyperarousal and facilitate better sleep. Objective: This trial aims to establish a novel, noninvasive method for treating insomnia through disruption of the DMN with repetitive transcranial magnetic stimulation, specifically with continuous theta burst stimulation (cTBS). This double-blind, pilot randomized controlled trial will assess the efficacy of repetitive transcranial magnetic stimulation as a novel, nonpharmacological approach to improve sleep through disruption of the DMN prior to sleep onset for individuals with insomnia. Primary outcome measures will include assessing changes in DMN functional connectivity before and after stimulation. Methods: A total of 20 participants between the ages of 18 to 50 years with reported sleep disturbances will be recruited as a part of the study. Participants will then conduct an in-person screening and follow-on enrollment visit. Eligible participants then conduct at-home actigraphic collection until their first in-residence overnight study visit. In a double-blind, counterbalanced, crossover study design, participants will receive a 40-second stimulation to the left inferior parietal lobule of the DMN during 2 separate overnight in-residence visits. Participants are randomized to the order in which they receive the active stimulation and sham stimulation. Study participants will undergo a prestimulation functional magnetic resonance imaging scan and a poststimulation functional magnetic resonance imaging scan prior to sleep for each overnight study visit. Sleep outcomes will be measured using clinical polysomnography. After their first in-residence study visit, participants conduct another at-home actigraphic collection before returning for their second in-residence overnight study visit. Results: Our study was funded in September 2020 by the Department of Defense (W81XWH2010173). We completed the enrollment of our target study population in the October 2022 and are currently working on neuroimaging processing and analysis. We aim to publish the results of our study by 2024. Primary neuroimaging outcome measures will be tested using independent components analysis, seed-to-voxel analyses, and region of interest to region of interest analyses. A repeated measures analysis of covariance (ANCOVA) will be used to assess the effects of active and sham stimulation on sleep variables. Additionally, we will correlate changes in functional connectivity to polysomnography-graded sleep. Conclusions: The presently proposed cTBS protocol is aimed at establishing the initial research outcomes of the effects of a single burst of cTBS on disrupting the network connectivity of the DMN to improve sleep. If effective, future work could determine the most effective stimulation sites and administration schedules to optimize this potential intervention for sleep problems. Trial Registration: ClinicalTrials.gov NCT04953559; https://clinicaltrials.gov/ct2/show/NCT04953559 International Registered Report Identifier (IRRID): DERR1-10.2196/51212 UR - https://www.researchprotocols.org/2024/1/e51212 UR - http://dx.doi.org/10.2196/51212 UR - http://www.ncbi.nlm.nih.gov/pubmed/38277210 ID - info:doi/10.2196/51212 ER - TY - JOUR AU - Sienko, Anna AU - Thirunavukarasu, James Arun AU - Kuzmich, Tanya AU - Allen, Louise PY - 2024/1/25 TI - An Initial Validation of Community-Based Air-Conduction Audiometry in Adults With Simulated Hearing Impairment Using a New Web App, DigiBel: Validation Study JO - JMIR Form Res SP - e51770 VL - 8 KW - audiology KW - audiometry KW - hearing test KW - eHealth KW - mobile application KW - automated audiometry KW - hearing loss KW - hearing impairment KW - web-app KW - web-apps KW - web-application KW - digital health KW - hearing KW - adult KW - adults KW - mobile health KW - mhealth KW - community-based KW - home-based KW - assistive technology KW - screening KW - usability KW - ears KW - ear N2 - Background: Approximately 80% of primary school children in the United States and Europe experience glue ear, which may impair hearing at a critical time for speech acquisition and social development. A web-based app, DigiBel, has been developed primarily to identify individuals with conductive hearing impairment who may benefit from the temporary use of bone-conduction assistive technology in the community. Objective: This preliminary study aims to determine the screening accuracy and usability of DigiBel self-assessed air-conduction (AC) pure tone audiometry in adult volunteers with simulated hearing impairment prior to formal clinical validation. Methods: Healthy adults, each with 1 ear plugged, underwent automated AC pure tone audiometry (reference test) and DigiBel audiometry in quiet community settings. Threshold measurements were compared across 6 tone frequencies and DigiBel test-retest reliability was calculated. The accuracy of DigiBel for detecting more than 20 dB of hearing impairment was assessed. A total of 30 adults (30 unplugged ears and 30 plugged ears) completed both audiometry tests. Results: DigiBel had 100% sensitivity (95% CI 87.23-100) and 72.73% (95% CI 54.48-86.70) specificity in detecting hearing impairment. Threshold mean bias was insignificant except at 4000 and 8000 Hz where a small but significant overestimation of threshold measurement was identified. All 24 participants completing feedback rated the DigiBel test as good or excellent and 21 (88%) participants agreed or strongly agreed that they would be able to do the test at home without help. Conclusions: This study supports the potential use of DigiBel as a screening tool for hearing impairment. The findings will be used to improve the software further prior to undertaking a formal clinical trial of AC and bone-conduction audiometry in individuals with suspected conductive hearing impairment. UR - https://formative.jmir.org/2024/1/e51770 UR - http://dx.doi.org/10.2196/51770 UR - http://www.ncbi.nlm.nih.gov/pubmed/38271088 ID - info:doi/10.2196/51770 ER - TY - JOUR AU - Ma, Shaoying AU - Jiang, Shuning AU - Yang, Olivia AU - Zhang, Xuanzhi AU - Fu, Yu AU - Zhang, Yusen AU - Kaareen, Aadeeba AU - Ling, Meng AU - Chen, Jian AU - Shang, Ce PY - 2024/1/24 TI - Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation JO - JMIR Form Res SP - e49031 VL - 8 KW - machine learning KW - natural language processing KW - tobacco control KW - sexual and gender diverse populations KW - lesbian KW - gay KW - bisexual KW - transgender KW - queer KW - LGBTQ+ KW - evidence synthesis N2 - Background: From 2016 to 2021, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations. Objective: In order to expedite evidence synthesis and research gap discoveries, this pilot study has the following three aims: (1) to compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) to develop natural language processing (NLP) algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) to compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations. Methods: We built a tobacco research domain?specific semantic database using data from 2993 paper abstracts from 4 leading tobacco-specific journals, with enrichment from other publicly available sources. We then trained an NLP model to extract named entities after learning patterns and relationships between words and their context in text, which further enriched the semantic database. Using this iterative process, we extracted and assessed studies relevant to LGBTQ+ tobacco control issues, further comparing our findings with an ongoing systematic review that also focuses on evidence synthesis for this demographic group. Results: In total, 33 studies were identified as relevant to sexual and gender diverse individuals? nicotine and tobacco product use. Consistent with the ongoing systematic review, the NLP results showed that there is a scarcity of studies assessing policy impact on this demographic using causal inference methods. In addition, the literature is dominated by US data. We found that the product drawing the most attention in the body of existing research is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults, consistent with the research needs identified by the US health agencies. Conclusions: Our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. While future research is needed to statistically test the NLP tool?s performance, there is potential for NLP tools to fundamentally transform the approach to evidence synthesis. UR - https://formative.jmir.org/2024/1/e49031 UR - http://dx.doi.org/10.2196/49031 UR - http://www.ncbi.nlm.nih.gov/pubmed/38265858 ID - info:doi/10.2196/49031 ER - TY - JOUR AU - Daryabeygi-Khotbehsara, Reza AU - Rawstorn, C. Jonathan AU - Dunstan, W. David AU - Shariful Islam, Mohammed Sheikh AU - Abdelrazek, Mohamed AU - Kouzani, Z. Abbas AU - Thummala, Poojith AU - McVicar, Jenna AU - Maddison, Ralph PY - 2024/1/24 TI - A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study JO - JMIR Form Res SP - e47157 VL - 8 KW - activity tracker KW - algorithms KW - deep neural network KW - machine learning KW - real-time data KW - Sedentary behaviOR Detector KW - sedentary behavior KW - SORD KW - standing KW - validation KW - walking KW - wearables N2 - Background: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. Objective: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. Methods: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. Results: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=?0.3% to 0.9%), 1.19% for standing (LoA=?1.5% to 3.42%), and ?4.71% for walking (LoA=?9.26% to ?0.16%). The mean biases between SORD and activPAL were ?3.45% for sitting and reclining (LoA=?11.59% to 4.68%), 7.45% for standing (LoA=?5.04% to 19.95%), and ?5.40% for walking (LoA=?11.44% to 0.64%). Conclusions: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior. UR - https://formative.jmir.org/2024/1/e47157 UR - http://dx.doi.org/10.2196/47157 UR - http://www.ncbi.nlm.nih.gov/pubmed/38265864 ID - info:doi/10.2196/47157 ER - TY - JOUR AU - Herrmann-Werner, Anne AU - Festl-Wietek, Teresa AU - Holderried, Friederike AU - Herschbach, Lea AU - Griewatz, Jan AU - Masters, Ken AU - Zipfel, Stephan AU - Mahling, Moritz PY - 2024/1/23 TI - Assessing ChatGPT?s Mastery of Bloom?s Taxonomy Using Psychosomatic Medicine Exam Questions: Mixed-Methods Study JO - J Med Internet Res SP - e52113 VL - 26 KW - answer KW - artificial intelligence KW - assessment KW - Bloom?s taxonomy KW - ChatGPT KW - classification KW - error KW - exam KW - examination KW - generative KW - GPT-4 KW - Generative Pre-trained Transformer 4 KW - language model KW - learning outcome KW - LLM KW - MCQ KW - medical education KW - medical exam KW - multiple-choice question KW - natural language processing KW - NLP KW - psychosomatic KW - question KW - response KW - taxonomy N2 - Background: Large language models such as GPT-4 (Generative Pre-trained Transformer 4) are being increasingly used in medicine and medical education. However, these models are prone to ?hallucinations? (ie, outputs that seem convincing while being factually incorrect). It is currently unknown how these errors by large language models relate to the different cognitive levels defined in Bloom?s taxonomy. Objective: This study aims to explore how GPT-4 performs in terms of Bloom?s taxonomy using psychosomatic medicine exam questions. Methods: We used a large data set of psychosomatic medicine multiple-choice questions (N=307) with real-world results derived from medical school exams. GPT-4 answered the multiple-choice questions using 2 distinct prompt versions: detailed and short. The answers were analyzed using a quantitative approach and a qualitative approach. Focusing on incorrectly answered questions, we categorized reasoning errors according to the hierarchical framework of Bloom?s taxonomy. Results: GPT-4?s performance in answering exam questions yielded a high success rate: 93% (284/307) for the detailed prompt and 91% (278/307) for the short prompt. Questions answered correctly by GPT-4 had a statistically significant higher difficulty than questions answered incorrectly (P=.002 for the detailed prompt and P<.001 for the short prompt). Independent of the prompt, GPT-4?s lowest exam performance was 78.9% (15/19), thereby always surpassing the ?pass? threshold. Our qualitative analysis of incorrect answers, based on Bloom?s taxonomy, showed that errors were primarily in the ?remember? (29/68) and ?understand? (23/68) cognitive levels; specific issues arose in recalling details, understanding conceptual relationships, and adhering to standardized guidelines. Conclusions: GPT-4 demonstrated a remarkable success rate when confronted with psychosomatic medicine multiple-choice exam questions, aligning with previous findings. When evaluated through Bloom?s taxonomy, our data revealed that GPT-4 occasionally ignored specific facts (remember), provided illogical reasoning (understand), or failed to apply concepts to a new situation (apply). These errors, which were confidently presented, could be attributed to inherent model biases and the tendency to generate outputs that maximize likelihood. UR - https://www.jmir.org/2024/1/e52113 UR - http://dx.doi.org/10.2196/52113 UR - http://www.ncbi.nlm.nih.gov/pubmed/38261378 ID - info:doi/10.2196/52113 ER - TY - JOUR AU - Morsa, Maxime AU - Perrin, Amélie AU - David, Valérie AU - Rault, Gilles AU - Le Roux, Enora AU - Alberti, Corinne AU - Gagnayre, Rémi AU - Pougheon Bertrand, Dominique PY - 2024/1/23 TI - Experiences Among Patients With Cystic Fibrosis in the MucoExocet Study of Using Connected Devices for the Management of Pulmonary Exacerbations: Grounded Theory Qualitative Research JO - JMIR Form Res SP - e38064 VL - 8 KW - cystic fibrosis KW - mobile health KW - mHealth KW - patient education KW - chronic disease KW - empowerment KW - devices KW - patients KW - detection KW - treatment KW - respiratory KW - education KW - monitoring KW - care N2 - Background: Early detection of pulmonary exacerbations (PEx) in patients with cystic fibrosis is important to quickly trigger treatment and reduce respiratory damage. An intervention was designed in the frame of the MucoExocet research study providing patients with cystic fibrosis with connected devices and educating them to detect and react to their early signs of PEx. Objective: This study aims to identify the contributions and conditions of home monitoring in relation to their care teams from the users? point of view to detect PEx early and treat it. This study focused on the patients? experiences as the first and main users of home monitoring. Methods: A qualitative study was conducted to explore patients? and professionals? experiences with the intervention. We interviewed patients who completed the 2-year study using semistructured guides and conducted focus groups with the care teams. All the interviews were recorded and transcribed verbatim. Their educational material was collected. A grounded analysis was conducted by 2 researchers. Results: A total of 20 patients completed the study. Three main categories emerged from the patients? verbatim transcripts and were also found in those of the professionals: (1) task technology fit, reflecting reliability, ease of use, accuracy of data, and support of the technology; (2) patient empowerment through technology, grouping patients? learnings, validation of their perception of exacerbation, assessment of treatment efficacy, awareness of healthy behaviors, and ability to react to PEx signs in relation to their care team; (3) use, reflecting a continuous or intermittent use, the perceived usefulness balanced with cumbersome measurements, routinization and personalization of the measurement process, and the way data are shared with the care team. Furthermore, 3 relationships were highlighted between the categories that reflect the necessary conditions for patient empowerment through the use of technology. Conclusions: We discuss a theorization of the process of patient empowerment through the use of connected devices and call for further research to verify or amend it in the context of other technologies, illnesses, and care organizations. Trial Registration: ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/ct2/show/results/NCT03304028 UR - https://formative.jmir.org/2024/1/e38064 UR - http://dx.doi.org/10.2196/38064 UR - http://www.ncbi.nlm.nih.gov/pubmed/38261372 ID - info:doi/10.2196/38064 ER - TY - JOUR AU - Adams, B. Leslie AU - Watts, Thomasina AU - DeVinney, Aubrey AU - Haroz, E. Emily AU - Thrul, Johannes AU - Stephens, Brooks Jasmin AU - Campbell, N. Mia AU - Antoine, Denis AU - Lê Cook, Benjamin AU - Joe, Sean AU - Thorpe Jr, J. Roland PY - 2024/1/22 TI - Acceptability and Feasibility of a Smartphone-Based Real-Time Assessment of Suicide Among Black Men: Mixed Methods Pilot Study JO - JMIR Form Res SP - e48992 VL - 8 KW - Black men KW - suicide KW - ecological momentary assessment KW - feasibility KW - acceptability KW - mixed methods KW - smartphone KW - real-time assessment KW - suicide prevention KW - user experience KW - behavior KW - implementation KW - intervention KW - mobile phone N2 - Background: Suicide rates in the United States have increased recently among Black men. To address this public health crisis, smartphone-based ecological momentary assessment (EMA) platforms are a promising way to collect dynamic, real-time data that can help improve suicide prevention efforts. Despite the promise of this methodology, little is known about its suitability in detecting experiences related to suicidal thoughts and behavior (STB) among Black men. Objective: This study aims to clarify the acceptability and feasibility of using smartphone-based EMA through a pilot study that assesses the user experience among Black men. Methods: We recruited Black men aged 18 years and older using the MyChart patient portal messaging (the patient-facing side of the Epic electronic medical record system) or outpatient provider referrals. Eligible participants self-identified as Black men with a previous history of STB and ownership of an Android or iOS smartphone. Eligible participants completed a 7-day smartphone-based EMA study. They received a prompt 4 times per day to complete a brief survey detailing their STB, as well as proximal risk factors, such as depression, social isolation, and feeling like a burden to others. At the conclusion of each day, participants also received a daily diary survey detailing their sleep quality and their daily experiences of everyday discrimination. Participants completed a semistructured exit interview of 60-90 minutes at the study?s conclusion. Results: In total, 10 participants completed 166 EMA surveys and 39 daily diary entries. A total of 4 of the 10 participants completed 75% (21/28) or more of the EMA surveys, while 9 (90%) out of 10 completed 25% (7/28) or more. The average completion rate of all surveys was 58% (20.3/35), with a minimum of 17% (6/35) and maximum of 100% (35/35). A total of 4 (40%) out of 10 participants completed daily diary entries for the full pilot study. No safety-related incidents were reported. On average, participants took 2.08 minutes to complete EMA prompts and 2.72 minutes for daily diary surveys. Our qualitative results generally affirm the acceptability and feasibility of the study procedures, but the participants noted difficulties with the technology and the redundancy of the survey questions. Emerging themes also addressed issues such as reduced EMA survey compliance and diminished mood related to deficit-framed questions related to suicide. Conclusions: Findings from this study will be used to clarify the suitability of EMA for Black men. Overall, our EMA pilot study demonstrated mixed feasibility and acceptability when delivered through smartphone-based apps to Black men. Specific recommendations are provided for managing safety within these study designs and for refinements in future intervention and implementation science research. International Registered Report Identifier (IRRID): RR2-10.2196/31241 UR - https://formative.jmir.org/2024/1/e48992 UR - http://dx.doi.org/10.2196/48992 UR - http://www.ncbi.nlm.nih.gov/pubmed/38252475 ID - info:doi/10.2196/48992 ER - TY - JOUR AU - van Bennekom, J. Martine AU - van Wingen, Guido AU - Bruin, Benjamin Willem AU - Luigjes, Judy AU - Denys, Damiaan PY - 2024/1/19 TI - Brain Activation During Virtual Reality Symptom Provocation in Obsessive-Compulsive Disorder: Proof-of-Concept Study JO - JMIR XR Spatial Comput SP - e47468 VL - 1 KW - virtual reality KW - obsessive-compulsive disorder KW - VR KW - symptom provocation KW - MRI KW - neuroimaging KW - OCD N2 - Background: Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by obsessions and compulsions. We previously showed that a virtual reality (VR) game can be used to provoke and measure anxiety and compulsions in patients with OCD. Here, we investigated whether this VR game activates brain regions associated with symptom provocation. Objective: In this study, we aim to investigate the neural regions that are activated in patients with OCD when they are interactively confronted with a symptom-provoking event and when they are performing compulsive actions in VR. Methods: In a proof-of-concept study, we investigated brain activation in response to the VR game in 9 patients with OCD and 9 healthy controls. Participants played the VR game while regional changes in blood oxygenation were measured using functional magnetic resonance imaging. We investigated brain activation in relation to OCD-related events and virtual compulsions in the VR game. Due to low statistical power because of the sample size, we also reported results at trend significance level with a threshold of P<.10. Additionally, we investigated correlations between OCD severity and brain activation. Results: We observed a trend for increased activation in the left amygdala (P=.07) upon confrontation with OCD-related events and for increased activation in the bilateral amygdala (P=.06 and P=.09) and right insula (P=.09) when performing virtual compulsive actions in patients with OCD compared to healthy controls, but this did not attain statistical significance. The amygdala and insula activation did not correlate with OCD severity. Conclusions: The findings of this proof-of-concept study indicate that VR elicits brain activation in line with previous provocation studies. Our findings need to be replicated in a study with a larger sample size. VR may be used as an innovative and unique method of interactive symptom provocation in future neuroimaging studies. Trial Registration: Netherlands Trial Register NTR6420; https://onderzoekmetmensen.nl/nl/trial/25755 UR - https://xr.jmir.org/2024/1/e47468 UR - http://dx.doi.org/10.2196/47468 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/47468 ER - TY - JOUR AU - Thangavel, Gomathi AU - Memedi, Mevludin AU - Hedström, Karin PY - 2024/1/17 TI - Information and Communication Technology for Managing Social Isolation and Loneliness Among People Living With Parkinson Disease: Qualitative Study of Barriers and Facilitators JO - J Med Internet Res SP - e48175 VL - 26 KW - social isolation KW - loneliness KW - Parkinson disease KW - ICT KW - information and communication technology N2 - Background: Parkinson disease (PD) is a complex, noncurable, and progressive neurological disease affecting different areas of the human nervous system. PD is associated with both motor and nonmotor symptoms, which negatively affect patients? quality of life and may cause changes in socialization such as intentional social withdrawal. This may further lead to social isolation and loneliness. The use of information and communication technology (ICT) plays an important role in managing social isolation and loneliness. Currently, there is a lack of research focusing on designing and developing ICT solutions that specifically address social isolation and loneliness among people living with PD. Objective: This study addresses this gap by investigating barriers and social needs in the context of social isolation, loneliness, and technology use among people living with PD. The insights gained can inform the development of effective ICT solutions, which can address social isolation and loneliness and improve the quality of life for people living with PD. Methods: A qualitative study with 2 phases of data collection were conducted. During the first phase, 9 health care professionals and 16 people living with PD were interviewed to understand how PD affects social life and technology use. During the second phase, 2 focus groups were conducted with 4 people living with PD in each group to gather insights into their needs and identify ways to manage social isolation and loneliness. Thematic analysis was used to analyze both data sets and identify key themes. Results: The results showed that the barriers experienced by people living with PD due to PD such as ?fatigue,? ?psychological conditions,? ?social stigma,? and ?medication side effects? affect their social life. People living with PD also experience difficulties using a keyboard and mouse, remembering passwords, and navigating complex applications due to their PD-related physical and cognitive limitations. To manage their social isolation and loneliness, people living with PD suggested having a simple and easy-to-use solution, allowing them to participate in a digital community based on their interests, communicate with others, and receive recommendations for social events. Conclusions: The new ICT solutions focusing on social isolation and loneliness among people living with PD should consider the barriers restricting user?s social activities and technology use. Given the wide range of needs and barriers experienced by people living with PD, it is more suitable to adopt user-centered design approaches that emphasize the active participation of end users in the design process. Importantly, any ICT solution designed for people living with PD should not encourage internet addiction, which will further contribute to the person?s withdrawal from society. UR - https://www.jmir.org/2024/1/e48175 UR - http://dx.doi.org/10.2196/48175 UR - http://www.ncbi.nlm.nih.gov/pubmed/38231548 ID - info:doi/10.2196/48175 ER - TY - JOUR AU - Bérubé, Caterina AU - Lehmann, Franziska Vera AU - Maritsch, Martin AU - Kraus, Mathias AU - Feuerriegel, Stefan AU - Wortmann, Felix AU - Züger, Thomas AU - Stettler, Christoph AU - Fleisch, Elgar AU - Kocaballi, Baki A. AU - Kowatsch, Tobias PY - 2024/1/9 TI - Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial JO - JMIR Hum Factors SP - e42823 VL - 11 KW - hypoglycemia KW - type-1 diabetes mellitus KW - in-vehicle voice assistant KW - voice interface KW - voice warning KW - digital health intervention KW - mobile phone N2 - Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology?a voice warning that can potentially be delivered via an in-vehicle voice assistant. Objective: This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. Methods: We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants? self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants? verbal feedback. Results: Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. Conclusions: This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions. UR - https://humanfactors.jmir.org/2024/1/e42823 UR - http://dx.doi.org/10.2196/42823 UR - http://www.ncbi.nlm.nih.gov/pubmed/38194257 ID - info:doi/10.2196/42823 ER - TY - JOUR AU - Yang, Shiming AU - Galvagno, Samuel AU - Badjatia, Neeraj AU - Stein, Deborah AU - Teeter, William AU - Scalea, Thomas AU - Shackelford, Stacy AU - Fang, Raymond AU - Miller, Catriona AU - Hu, Peter AU - PY - 2024/1/5 TI - A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study JO - JMIR Hum Factors SP - e46030 VL - 11 KW - clinical decision-making KW - health information technology KW - intensive care units KW - patient care prioritization KW - physiological monitoring KW - visualization KW - vital signs N2 - Background: Clinicians working in intensive care units (ICUs) are immersed in a cacophony of alarms and a relentless onslaught of data. Within this frenetic environment, clinicians make high-stakes decisions using many data sources and are often oversaturated with information of varying quality. Traditional bedside monitors only depict static vital signs data, and these data are not easily viewable remotely. Clinicians must rely on separate nursing charts?handwritten or electric?to review physiological patterns, including signs of potential clinical deterioration. An automated physiological data viewer has been developed to provide at-a-glance summaries and to assist with prioritizing care for multiple patients who are critically ill. Objective: This study aims to evaluate a novel vital signs viewer system in a level 1 trauma center by subjectively assessing the viewer?s utility in a high-volume ICU setting. Methods: ICU attendings were surveyed during morning rounds. Physicians were asked to conduct rounds normally, using data reported from nurse charts and briefs from fellows to inform their clinical decisions. After the physician finished their assessment and plan for the patient, they were asked to complete a questionnaire. Following completion of the questionnaire, the viewer was presented to ICU physicians on a tablet personal computer that displayed the patient?s physiologic data (ie, shock index, blood pressure, heart rate, temperature, respiratory rate, and pulse oximetry), summarized for up to 72 hours. After examining the viewer, ICU physicians completed a postview questionnaire. In both questionnaires, the physicians were asked questions regarding the patient?s stability, status, and need for a higher or lower level of care. A hierarchical clustering analysis was used to group participating ICU physicians and assess their general reception of the viewer. Results: A total of 908 anonymous surveys were collected from 28 ICU physicians from February 2015 to June 2017. Regarding physicians? perception of whether the viewer enhanced the ability to assess multiple patients in the ICU, 5% (45/908) strongly agreed, 56.6% (514/908) agreed, 35.3% (321/908) were neutral, 2.9% (26/908) disagreed, and 0.2% (2/908) strongly disagreed. Conclusions: Morning rounds in a trauma center ICU are conducted in a busy environment with many data sources. This study demonstrates that organized physiologic data and visual assessment can improve situation awareness, assist clinicians with recognizing changes in patient status, and prioritize care. UR - https://humanfactors.jmir.org/2024/1/e46030 UR - http://dx.doi.org/10.2196/46030 UR - http://www.ncbi.nlm.nih.gov/pubmed/38180791 ID - info:doi/10.2196/46030 ER - TY - JOUR AU - Henry, M. Lauren AU - Hansen, Eleanor AU - Chimoff, Justin AU - Pokstis, Kimberly AU - Kiderman, Miryam AU - Naim, Reut AU - Kossowsky, Joe AU - Byrne, E. Meghan AU - Lopez-Guzman, Silvia AU - Kircanski, Katharina AU - Pine, S. Daniel AU - Brotman, A. Melissa PY - 2024/1/4 TI - Selecting an Ecological Momentary Assessment Platform: Tutorial for Researchers JO - J Med Internet Res SP - e51125 VL - 26 KW - ecological momentary assessment KW - methodology KW - psychology and psychiatry KW - child and adolescent KW - in vivo and real time N2 - Background: Although ecological momentary assessment (EMA) has been applied in psychological research for decades, delivery methods have evolved with the proliferation of digital technology. Technological advances have engendered opportunities for enhanced accessibility, convenience, measurement precision, and integration with wearable sensors. Notwithstanding, researchers must navigate novel complexities in EMA research design and implementation. Objective: In this paper, we aimed to provide guidance on platform selection for clinical scientists launching EMA studies. Methods: Our team includes diverse specialties in child and adolescent behavioral and mental health with varying expertise on EMA platforms (eg, users and developers). We (2 research sites) evaluated EMA platforms with the goal of identifying the platform or platforms with the best fit for our research. We created a list of extant EMA platforms; conducted a web-based review; considered institutional security, privacy, and data management requirements; met with developers; and evaluated each of the candidate EMA platforms for 1 week. Results: We selected 2 different EMA platforms, rather than a single platform, for use at our 2 research sites. Our results underscore the importance of platform selection driven by individualized and prioritized laboratory needs; there is no single, ideal platform for EMA researchers. In addition, our project generated 11 considerations for researchers in selecting an EMA platform: (1) location; (2) developer involvement; (3) sample characteristics; (4) onboarding; (5) survey design features; (6) sampling scheme and scheduling; (7) viewing results; (8) dashboards; (9) security, privacy, and data management; (10) pricing and cost structure; and (11) future directions. Furthermore, our project yielded a suggested timeline for the EMA platform selection process. Conclusions: This study will guide scientists initiating studies using EMA, an in vivo, real-time research tool with tremendous promise for facilitating advances in psychological assessment and intervention. UR - https://www.jmir.org/2024/1/e51125 UR - http://dx.doi.org/10.2196/51125 UR - http://www.ncbi.nlm.nih.gov/pubmed/38175682 ID - info:doi/10.2196/51125 ER - TY - JOUR AU - Groulx, Mark AU - Freeman, Shannon AU - Gourlay, Keone AU - Hemingway, Dawn AU - Rossnagel, Emma AU - Chaudhury, Habib AU - Nouri, Mohammadjavad PY - 2024/1/3 TI - Monitoring and Evaluation of Dementia-Friendly Neighborhoods Using a Walkshed Approach: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e50548 VL - 13 KW - dementia-friendly KW - neighborhood KW - persons living with dementia KW - walkability KW - walkshed N2 - Background: The number of people in society living with dementia is growing. In Canada, most people who live with dementia live at home, often in a neighborhood setting. Neighborhood environments can be a source of independence, social engagement, and well-being. They can also contain barriers that limit physical activity, social engagement, and well-being. A dementia-friendly neighborhood includes assets that support persons living with dementia and their caregivers in multiple life domains, including those that support walking within the neighborhood environment. Objective: The objectives for this scoping review are twofold. First, focusing on walkshed analysis, we aim to extend scholarly understandings of methodological practices used in the monitoring and evaluation of dementia-friendly neighborhoods. Second, we aim to provide clear and practical guidance for those working in planning, design, and public health fields to assess the neighborhood context in support of evidence-based action to improve the lives of persons living with dementia. Methods: The study design follows Arksey and O?Malley?s scoping review framework and PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. We will conduct a search of peer-reviewed studies in 6 electronic databases to identify the use of Geographic Information System analysis to measure the walkshed of persons living with dementia in a community setting. As age is a primary risk factor associated with dementia, we will also include studies that focus more broadly on community-dwelling older adults aged 65 years and older. Data will be extracted, analyzed, and represented according to 3 domains. This includes study details, walkshed analysis methods, and criteria and indicators used to measure dementia-friendly neighborhoods. Results: The results of the study and the submission of a manuscript for peer review are expected in June 2024. The results of the review are expected to contribute to an understanding of methods for monitoring and evaluating dementia-friendly neighborhoods. Expected findings will include a detailed breakdown of current parameters and routines used to conduct walkshed analysis. Findings will also convey criteria that can be operationalized in a Geographic Information System as indicators to assess barriers and facilitators to walking in a neighborhood setting. Conclusions: As far as we are aware, the proposed scoping review will be the first to provide comprehensive methodological or technical guidance for conducting walkshed analysis specific to persons living with dementia. Both the scalability and objective nature of walkshed analysis are likely to be of direct interest to public health practitioners, planners, and allied professionals. Clearly documenting methods used in walkshed analysis can spur increased collaboration across these disciplines to enable an evidence-informed approach to improving neighborhood environments for persons living with dementia. International Registered Report Identifier (IRRID): PRR1-10.2196/50548 UR - https://www.researchprotocols.org/2024/1/e50548 UR - http://dx.doi.org/10.2196/50548 UR - http://www.ncbi.nlm.nih.gov/pubmed/38170573 ID - info:doi/10.2196/50548 ER - TY - JOUR AU - Chen, Hung-Hsun AU - Lu, Horng-Shing Henry AU - Weng, Wei-Hung AU - Lin, Yu-Hsuan PY - 2023/12/29 TI - Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study JO - J Med Internet Res SP - e48834 VL - 25 KW - human-smartphone interaction KW - digital phenotyping KW - work hours KW - machine learning KW - deep learning KW - probability in work mode KW - one-dimensional convolutional neural network KW - extreme gradient-boosted trees N2 - Background: Traditional methods for investigating work hours rely on an employee?s physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. Objective: In this study, we aimed to develop a novel approach called ?probability in work mode,? which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. Methods: To capture human-smartphone interactions and GPS locations, we used the ?Staff Hours? app, developed by our team, to passively and continuously record participants? screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. Results: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. Conclusions: Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being. UR - https://www.jmir.org/2023/1/e48834 UR - http://dx.doi.org/10.2196/48834 UR - http://www.ncbi.nlm.nih.gov/pubmed/38157232 ID - info:doi/10.2196/48834 ER - TY - JOUR AU - Breitinger, Scott AU - Gardea-Resendez, Manuel AU - Langholm, Carsten AU - Xiong, Ashley AU - Laivell, Joseph AU - Stoppel, Cynthia AU - Harper, Laura AU - Volety, Rama AU - Walker, Alex AU - D'Mello, Ryan AU - Byun, Soo Andrew Jin AU - Zandi, Peter AU - Goes, S. Fernando AU - Frye, Mark AU - Torous, John PY - 2023/12/29 TI - Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study JO - J Med Internet Res SP - e47006 VL - 25 KW - mood disorders KW - depression KW - bipolar disorder KW - digital health KW - digital phenotyping KW - mobile apps KW - patient-generated health data KW - wearable devices N2 - Background: In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. Objective: This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. Methods: We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. Results: We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. Conclusions: Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites. UR - https://www.jmir.org/2023/1/e47006 UR - http://dx.doi.org/10.2196/47006 UR - http://www.ncbi.nlm.nih.gov/pubmed/38157233 ID - info:doi/10.2196/47006 ER - TY - JOUR AU - Esposito, Giuseppina AU - Allarà, Ciro AU - Randon, Marco AU - Aiello, Marco AU - Salvatore, Marco AU - Aceto, Giuseppe AU - Pescapè, Antonio PY - 2023/12/21 TI - A Biobanking System for Diagnostic Images: Architecture Development, COVID-19?Related Use Cases, and Performance Evaluation JO - JMIR Form Res SP - e42505 VL - 7 KW - biobank KW - diagnostics KW - COVID-19 KW - network performance KW - eHealth N2 - Background: Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes. Objective: The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform. Methods: Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay. Results: Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist. Conclusions: This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system. UR - https://formative.jmir.org/2023/1/e42505 UR - http://dx.doi.org/10.2196/42505 UR - http://www.ncbi.nlm.nih.gov/pubmed/38064636 ID - info:doi/10.2196/42505 ER - TY - JOUR AU - Yamashita, Masashi AU - Kamiya, Kentaro AU - Hamazaki, Nobuaki AU - Uchida, Shota AU - Noda, Takumi AU - Maekawa, Emi AU - Ako, Junya PY - 2023/12/20 TI - Effects of Acute Phase Intensive Physical Activity (ACTIVE-PA) Monitoring and Education for Cardiac Patients: Pilot Study of a Randomized Controlled Trial JO - J Med Internet Res SP - e42235 VL - 25 KW - physical activity KW - monitoring KW - information and communication technology KW - cardiovascular disease KW - cardiovascular KW - cardiology KW - exercise KW - RCT KW - randomized KW - cardiac rehabilitation KW - fitness KW - accelerometer KW - physiotherapy KW - hospitalized KW - hospitalization KW - in-patient N2 - Background: Although physical activity (PA) decreases dramatically during hospitalization, an effective intervention method has not yet been established for this issue. We recently developed a multiperson PA monitoring system using information and communication technology (ICT) that can provide appropriate management and feedback about PA at the bedside or during rehabilitation. This ICT-based PA monitoring system can store accelerometer data on a tablet device within a few seconds and automatically display a graphical representation of activity trends during hospitalization. Objective: This randomized pilot study aims to estimate the feasibility and effect size of an educational PA intervention using our ICT monitoring system for in-hospital patients undergoing cardiac rehabilitation. Methods: A total of 41 patients (median age 70 years; 24 men) undergoing inpatient cardiac rehabilitation were randomly assigned to 2 groups as follows: wearing an accelerometer only (control) and using both an accelerometer and an ICT-based PA monitoring system. Patients assigned to the ICT group were instructed to gradually increase their step counts according to their conditions. Adherence to wearing the accelerometer was defined as having enough wear records for at least 2 days to allow for adequate analysis during the lending period. An analysis of covariance was performed to compare the change in average step count during hospitalization as a primary outcome and the 6-minute walking distance at discharge. Results: The median duration of wearing the accelerometer was 4 days in the ICT group and 6 days in the control group. Adherence was 100% (n=22) in the ICT group but 83% (n=20) in the control group. The ICT group was more active (mean difference=1370 steps, 95% CI 437-2303) and had longer 6-minute walking distances (mean difference=81.6 m, 95% CI 18.1-145.2) than the control group. Conclusions: Through this study, the possibility of introducing a multiperson PA monitoring system in a hospital and promoting PA during hospitalization was demonstrated. These findings support the rationale and feasibility of a future clinical trial to test the efficacy of this educational intervention in improving the PA and physical function of in-hospital patients. Trial Registration: University Hospital Medical Information Network UMIN000043312; http://tinyurl.com/m2bw8vkz UR - https://www.jmir.org/2023/1/e42235 UR - http://dx.doi.org/10.2196/42235 UR - http://www.ncbi.nlm.nih.gov/pubmed/38117552 ID - info:doi/10.2196/42235 ER - TY - JOUR AU - O'Hagan, Ross AU - Poplausky, Dina AU - Young, N. Jade AU - Gulati, Nicholas AU - Levoska, Melissa AU - Ungar, Benjamin AU - Ungar, Jonathan PY - 2023/12/14 TI - The Accuracy and Appropriateness of ChatGPT Responses on Nonmelanoma Skin Cancer Information Using Zero-Shot Chain of Thought Prompting JO - JMIR Dermatol SP - e49889 VL - 6 KW - ChatGPT KW - artificial intelligence KW - large language models KW - nonmelanoma skin KW - skin cancer KW - cell carcinoma KW - chatbot KW - dermatology KW - dermatologist KW - epidermis KW - dermis KW - oncology KW - cancer UR - https://derma.jmir.org/2023/1/e49889 UR - http://dx.doi.org/10.2196/49889 UR - http://www.ncbi.nlm.nih.gov/pubmed/38096013 ID - info:doi/10.2196/49889 ER - TY - JOUR AU - Wang, Tse-Lun AU - Wu, Hao-Yi AU - Wang, Wei-Yun AU - Chen, Chao-Wen AU - Chien, Wu-Chien AU - Chu, Chi-Ming AU - Wu, Yi-Syuan PY - 2023/12/14 TI - Assessment of Heart Rate Monitoring During Exercise With Smart Wristbands and a Heart Rhythm Patch: Validation and Comparison Study JO - JMIR Form Res SP - e52519 VL - 7 KW - running KW - wearable device KW - photoplethysmography KW - heart rhythm patch, smart wristband N2 - Background: The integration of wearable devices into fitness routines, particularly in military settings, necessitates a rigorous assessment of their accuracy. This study evaluates the precision of heart rate measurements by locally manufactured wristbands, increasingly used in military academies, to inform future device selection for military training activities. Objective: This research aims to assess the reliability of heart rate monitoring in chest straps versus wearable wristbands. Methods: Data on heart rate and acceleration were collected using the Q-Band Q-69 smart wristband (Mobile Action Technology Inc) and compared against the Zephyr Bioharness standard measuring device. The Lin concordance correlation coefficient, Pearson product moment correlation coefficient, and intraclass correlation coefficient were used for reliability analysis. Results: Participants from a Northern Taiwanese medical school were enrolled (January 1-June 31, 2021). The Q-Band Q-69 demonstrated that the mean absolute percentage error (MAPE) of women was observed to be 13.35 (SD 13.47). Comparatively, men exhibited a lower MAPE of 8.54 (SD 10.49). The walking state MAPE was 7.79 for women and 10.65 for men. The wristband?s accuracy generally remained below 10% MAPE in other activities. Pearson product moment correlation coefficient analysis indicated gender-based performance differences, with overall coefficients of 0.625 for women and 0.808 for men, varying across walking, running, and cooldown phases. Conclusions: This study highlights significant gender and activity-dependent variations in the accuracy of the MobileAction Q-Band Q-69 smart wristband. Reduced accuracy was notably observed during running. Occasional extreme errors point to the necessity of caution in relying on such devices for exercise monitoring. The findings emphasize the limitations and potential inaccuracies of wearable technology, especially in high-intensity physical activities. UR - https://formative.jmir.org/2023/1/e52519 UR - http://dx.doi.org/10.2196/52519 UR - http://www.ncbi.nlm.nih.gov/pubmed/38096010 ID - info:doi/10.2196/52519 ER - TY - JOUR AU - Jones, Bree AU - Michou, Stavroula AU - Chen, Tong AU - Moreno-Betancur, Margarita AU - Kilpatrick, Nicky AU - Burgner, David AU - Vannahme, Christoph AU - Silva, Mihiri PY - 2023/12/14 TI - Caries Detection in Primary Teeth Using Intraoral Scanners Featuring Fluorescence: Protocol for a Diagnostic Agreement Study JO - JMIR Res Protoc SP - e51578 VL - 12 KW - dental caries KW - diagnosis KW - oral KW - technology KW - dental KW - image interpretation KW - computer-assisted KW - imaging KW - 3D KW - quantitative light-induced fluorescence KW - diagnostic agreement KW - intra oral scanners KW - oral health KW - teeth KW - 3D model KW - color KW - fluorescence KW - intraoral scanner KW - device KW - dentistry N2 - Background: Digital methods that enable early caries identification can streamline data collection in research and optimize dental examinations for young children. Intraoral scanners are devices used for creating 3D models of teeth in dentistry and are being rapidly adopted into clinical workflows. Integrating fluorescence technology into scanner hardware can support early caries detection. However, the performance of caries detection methods using 3D models featuring color and fluorescence in primary teeth is unknown. Objective: This study aims to assess the diagnostic agreement between visual examination (VE), on-screen assessment of 3D models in approximate natural colors with and without fluorescence, and application of an automated caries scoring system to the 3D models with fluorescence for caries detection in primary teeth. Methods: The study sample will be drawn from eligible participants in a randomized controlled trial at the Royal Children?s Hospital, Melbourne, Australia, where a dental assessment was conducted, including VE using the International Caries Detection and Assessment System (ICDAS) and intraoral scan using the TRIOS 4 (3Shape TRIOS A/S). Participant clinical records will be collected, and all records meeting eligibility criteria will be subject to an on-screen assessment of 3D models by 4 dental practitioners. First, all primary tooth surfaces will be examined for caries based on 3D geometry and color, using a merged ICDAS index. Second, the on-screen assessment of 3D models will include fluorescence, where caries will be classified using a merged ICDAS index that has been modified to incorporate fluorescence criteria. After 4 weeks, all examiners will repeat the on-screen assessment for all 3D models. Finally, an automated caries scoring system will be used to classify caries on primary occlusal surfaces. The agreement in the total number of caries detected per person between methods will be assessed using a Bland-Altman analysis and intraclass correlation coefficients. At a tooth surface level, agreement between methods will be estimated using multilevel models to account for the clustering of dental data. Results: Automated caries scoring of 3D models was completed as of October 2023, with the publication of results expected by July 2024. On-screen assessment has commenced, with the expected completion of scoring and data analysis by March 2024. Results will be disseminated by the end of 2024. Conclusions: The study outcomes may inform new practices that use digital models to facilitate dental assessments. Novel approaches that enable remote dental examination without compromising the accuracy of VE have wide applications in the research environment, clinical practice, and the provision of teledentistry. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12622001237774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384632 International Registered Report Identifier (IRRID): DERR1-10.2196/51578 UR - https://www.researchprotocols.org/2023/1/e51578 UR - http://dx.doi.org/10.2196/51578 UR - http://www.ncbi.nlm.nih.gov/pubmed/38096003 ID - info:doi/10.2196/51578 ER - TY - JOUR AU - Kawai, Keita AU - Iwamoto, Kunihiro AU - Miyata, Seiko AU - Okada, Ippei AU - Fujishiro, Hiroshige AU - Noda, Akiko AU - Nakagome, Kazuyuki AU - Ozaki, Norio AU - Ikeda, Masashi PY - 2023/12/13 TI - Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study JO - J Med Internet Res SP - e51336 VL - 25 KW - consumer sleep-tracking device KW - polysomnography KW - portable sleep EEG monitor KW - electroencephalography KW - EEG KW - psychiatric disorders KW - sleep logs KW - sleep state misperception KW - sleep study KW - wearable KW - psychiatric disorder KW - sleep KW - disturbances KW - quality of sleep KW - Fitbit KW - mHealth KW - wearables KW - psychiatry KW - electroencephalogram N2 - Background: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. Objective: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. Methods: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. Results: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=?0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=?0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. Conclusions: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders. UR - https://www.jmir.org/2023/1/e51336 UR - http://dx.doi.org/10.2196/51336 UR - http://www.ncbi.nlm.nih.gov/pubmed/38090797 ID - info:doi/10.2196/51336 ER - TY - JOUR AU - Bufano, Pasquale AU - Laurino, Marco AU - Said, Sara AU - Tognetti, Alessandro AU - Menicucci, Danilo PY - 2023/12/13 TI - Digital Phenotyping for Monitoring Mental Disorders: Systematic Review JO - J Med Internet Res SP - e46778 VL - 25 KW - digital phenotyping KW - mobile KW - mental health KW - smartphone KW - mobile sensing KW - passive sensing KW - active sensing KW - digital phenotype KW - digital biomarker KW - mobile phone N2 - Background: The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. Objective: This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. Methods: Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. Results: We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. Conclusions: Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation). UR - https://www.jmir.org/2023/1/e46778 UR - http://dx.doi.org/10.2196/46778 UR - http://www.ncbi.nlm.nih.gov/pubmed/38090800 ID - info:doi/10.2196/46778 ER - TY - JOUR AU - Draucker, Claire AU - Carrión, Andrés AU - Ott, A. Mary AU - Knopf, Amelia PY - 2023/12/13 TI - Assessing Facilitator Fidelity to Principles of Public Deliberation: Tutorial JO - JMIR Form Res SP - e51202 VL - 7 KW - public deliberation KW - deliberative democracy KW - bioethics KW - engagement KW - theory KW - process KW - ethical conflict KW - ethical KW - ethics KW - coding KW - evaluation KW - tutorial KW - biomedical KW - HIV KW - HIV prevention KW - HIV research UR - https://formative.jmir.org/2023/1/e51202 UR - http://dx.doi.org/10.2196/51202 UR - http://www.ncbi.nlm.nih.gov/pubmed/38090788 ID - info:doi/10.2196/51202 ER - TY - JOUR AU - Zhang, Xupin AU - Wang, Jingjing AU - Lane, M. Jamil AU - Xu, Xin AU - Sörensen, Silvia PY - 2023/12/12 TI - Investigating Racial Disparities in Cancer Crowdfunding: A Comprehensive Study of Medical GoFundMe Campaigns JO - J Med Internet Res SP - e51089 VL - 25 KW - crowdfunding KW - racial discrimination KW - GoFundMe N2 - Background: In recent years, there has been growing concern about prejudice in crowdfunding; however, empirical research remains limited, particularly in the context of medical crowdfunding. This study addresses the pressing issue of racial disparities in medical crowdfunding, with a specific focus on cancer crowdfunding on the GoFundMe platform. Objective: This study aims to investigate racial disparities in cancer crowdfunding using average donation amount, number of donations, and success of the fundraising campaign as outcomes. Methods: Drawing from a substantial data set of 104,809 campaigns in the United States, we used DeepFace facial recognition technology to determine racial identities and used regression models to examine racial factors in crowdfunding performance. We also examined the moderating effect of the proportion of White residents on crowdfunding bias and used 2-tailed t tests to measure the influence of racial anonymity on crowdfunding success. Owing to the large sample size, we set the cutoff for significance at P<.001. Results: In the regression and supplementary analyses, the racial identity of the fundraiser significantly predicted average donations (P<.001), indicating that implicit bias may play a role in donor behavior. Gender (P=.04) and campaign description length (P=.62) did not significantly predict the average donation amounts. The race of the fundraiser was not significantly associated with the number of donations (P=.42). The success rate of cancer crowdfunding campaigns, although generally low (11.77%), showed a significant association with the race of the fundraiser (P<.001). After controlling for the covariates of the fundraiser gender, fundraiser age, local White proportion, length of campaign description, and fundraising goal, the average donation amount to White individuals was 17.68% higher than for Black individuals. Moreover, campaigns that did not disclose racial information demonstrated a marginally higher average donation amount (3.92%) than those identified as persons of color. Furthermore, the racial composition of the fundraiser?s county of residence was found to exert influence (P<.001); counties with a higher proportion of White residents exhibited reduced racial disparities in crowdfunding outcomes. Conclusions: This study contributes to a deeper understanding of racial disparities in cancer crowdfunding. It highlights the impact of racial identity, geographic context, and the potential for implicit bias in donor behavior. As web-based platforms evolve, addressing racial inequality and promoting fairness in health care financing remain critical goals. Insights from this research suggest strategies such as maintaining racial anonymity and ensuring that campaigns provide strong evidence of deservingness. Moreover, broader societal changes are necessary to eliminate the financial distress that drives individuals to seek crowdfunding support. UR - https://www.jmir.org/2023/1/e51089 UR - http://dx.doi.org/10.2196/51089 UR - http://www.ncbi.nlm.nih.gov/pubmed/38085562 ID - info:doi/10.2196/51089 ER - TY - JOUR AU - Lown, Mark AU - Smith, A. Kirsten AU - Muller, Ingrid AU - Woods, Catherine AU - Maund, Emma AU - Rogers, Kirsty AU - Becque, Taeko AU - Hayward, Gail AU - Moore, Michael AU - Little, Paul AU - Glogowska, Margaret AU - Hay, Alastair AU - Stuart, Beth AU - Mantzourani, Efi AU - Wilcox, R. Christopher AU - Thompson, Natalie AU - Francis, A. Nick PY - 2023/12/8 TI - Internet Tool to Support Self-Assessment and Self-Swabbing of Sore Throat: Development and Feasibility Study JO - J Med Internet Res SP - e39791 VL - 25 KW - sore throat KW - ear, neck, throat KW - pharyngitis KW - self-assessment KW - self-swabbing KW - primary care KW - throat KW - development KW - feasibility KW - web-based tool KW - tool KW - antibiotics KW - develop KW - self-assess KW - symptoms KW - diagnostic testing KW - acceptability KW - adult KW - children KW - social media KW - saliva KW - swab KW - inflammation KW - samples KW - support KW - clinical KW - antibiotic KW - web-based support tool KW - think-aloud KW - neck KW - tonsil KW - tongue KW - teeth KW - dental KW - dentist KW - tooth KW - laboratory KW - lab KW - oral KW - oral health KW - mouth KW - mobile phone N2 - Background: Sore throat is a common problem and a common reason for the overuse of antibiotics. A web-based tool that helps people assess their sore throat, through the use of clinical prediction rules, taking throat swabs or saliva samples, and taking throat photographs, has the potential to improve self-management and help identify those who are the most and least likely to benefit from antibiotics. Objective: We aimed to develop a web-based tool to help patients and parents or carers self-assess sore throat symptoms and take throat photographs, swabs, and saliva samples for diagnostic testing. We then explored the acceptability and feasibility of using the tool in adults and children with sore throats. Methods: We used the Person-Based Approach to develop a web-based tool and then recruited adults and children with sore throats who participated in this study by attending general practices or through social media advertising. Participants self-assessed the presence of FeverPAIN and Centor score criteria and attempted to photograph their throat and take throat swabs and saliva tests. Study processes were observed via video call, and participants were interviewed about their views on using the web-based tool. Self-assessed throat inflammation and pus were compared to clinician evaluation of patients? throat photographs. Results: A total of 45 participants (33 adults and 12 children) were recruited. Of these, 35 (78%) and 32 (71%) participants completed all scoring elements for FeverPAIN and Centor scores, respectively, and most (30/45, 67%) of them reported finding self-assessment relatively easy. No valid response was provided for swollen lymph nodes, throat inflammation, and pus on the throat by 11 (24%), 9 (20%), and 13 (29%) participants respectively. A total of 18 (40%) participants provided a throat photograph of adequate quality for clinical assessment. Patient assessment of inflammation had a sensitivity of 100% (3/3) and specificity of 47% (7/15) compared with the clinician-assessed photographs. For pus on the throat, the sensitivity was 100% (3/3) and the specificity was 71% (10/14). A total of 89% (40/45), 93% (42/45), 89% (40/45), and 80% (30/45) of participants provided analyzable bacterial swabs, viral swabs, saliva sponges, and saliva drool samples, respectively. Participants were generally happy and confident in providing samples, with saliva samples rated as slightly more acceptable than swab samples. Conclusions: Most adult and parent participants were able to use a web-based intervention to assess the clinical features of throat infections and generate scores using clinical prediction rules. However, some had difficulties assessing clinical signs, such as lymph nodes, throat pus, and inflammation, and scores were assessed as sensitive but not specific. Many participants had problems taking photographs of adequate quality, but most were able to take throat swabs and saliva samples. UR - https://www.jmir.org/2023/1/e39791 UR - http://dx.doi.org/10.2196/39791 UR - http://www.ncbi.nlm.nih.gov/pubmed/38064265 ID - info:doi/10.2196/39791 ER - TY - JOUR AU - Liu, Jiaxing AU - Gupta, Shalini AU - Chen, Aipeng AU - Wang, Chen-Kai AU - Mishra, Pratik AU - Dai, Hong-Jie AU - Wong, Shui-Yee Zoie AU - Jonnagaddala, Jitendra PY - 2023/12/6 TI - OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study JO - J Med Internet Res SP - e48145 VL - 25 KW - deidentification KW - scrubbing KW - anonymization KW - surrogate generation KW - unstructured EHRs KW - electronic health records KW - BERT KW - Bidirectional Encoder Representations from Transformers N2 - Background: Electronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health information (SHI) must be removed in several cases to protect patient privacy. Rule-based and machine learning?based methods have been shown to be effective in deidentification. However, very few studies investigated the combination of transformer-based language models and rules. Objective: The objective of this study is to develop a hybrid deidentification pipeline for Australian EHR text notes using rules and transformers. The study also aims to investigate the impact of pretrained word embedding and transformer-based language models. Methods: In this study, we present a hybrid deidentification pipeline called OpenDeID, which is developed using an Australian multicenter EHR-based corpus called OpenDeID Corpus. The OpenDeID corpus consists of 2100 pathology reports with 38,414 SHI entities from 1833 patients. The OpenDeID pipeline incorporates a hybrid approach of associative rules, supervised deep learning, and pretrained language models. Results: The OpenDeID achieved a best F1-score of 0.9659 by fine-tuning the Discharge Summary BioBERT model and incorporating various preprocessing and postprocessing rules. The OpenDeID pipeline has been deployed at a large tertiary teaching hospital and has processed over 8000 unstructured EHR text notes in real time. Conclusions: The OpenDeID pipeline is a hybrid deidentification pipeline to deidentify SHI entities in unstructured EHR text notes. The pipeline has been evaluated on a large multicenter corpus. External validation will be undertaken as part of our future work to evaluate the effectiveness of the OpenDeID pipeline. UR - https://www.jmir.org/2023/1/e48145 UR - http://dx.doi.org/10.2196/48145 UR - http://www.ncbi.nlm.nih.gov/pubmed/38055317 ID - info:doi/10.2196/48145 ER - TY - JOUR AU - Thirunavukarasu, James Arun PY - 2023/12/5 TI - How Can the Clinical Aptitude of AI Assistants Be Assayed? JO - J Med Internet Res SP - e51603 VL - 25 KW - artificial intelligence KW - AI KW - validation KW - clinical decision aid KW - artificial general intelligence KW - foundation models KW - large language models KW - LLM KW - language model KW - ChatGPT KW - chatbot KW - chatbots KW - conversational agent KW - conversational agents KW - pitfall KW - pitfalls KW - pain point KW - pain points KW - implementation KW - barrier KW - barriers KW - challenge KW - challenges UR - https://www.jmir.org/2023/1/e51603 UR - http://dx.doi.org/10.2196/51603 UR - http://www.ncbi.nlm.nih.gov/pubmed/38051572 ID - info:doi/10.2196/51603 ER - TY - JOUR AU - Ota, Hirofumi AU - Mukaino, Masahiko AU - Inoue, Yukari AU - Matsuura, Shoh AU - Yagi, Senju AU - Kanada, Yoshikiyo AU - Saitoh, Eiichi AU - Otaka, Yohei PY - 2023/12/5 TI - Movement Component Analysis of Reaching Strategies in Individuals With Stroke: Preliminary Study JO - JMIR Rehabil Assist Technol SP - e50571 VL - 10 KW - stroke KW - upper limb paresis KW - compensatory movements KW - three-dimensional motion analysis KW - reaching movement KW - rehabilitation KW - motion analysis KW - reaching KW - 3D KW - three dimensional KW - motion capture KW - motion KW - movement KW - limb KW - extremity KW - extremities KW - mobility KW - hemiparesis KW - paralysis KW - compensate KW - compensatory N2 - Background: Upper limb motor paresis is a major symptom of stroke, which limits activities of daily living and compromises the quality of life. Kinematic analysis offers an in-depth and objective means to evaluate poststroke upper limb paresis, with anticipation for its effective application in clinical settings. Objective: This study aims to compare the movement strategies of patients with hemiparesis due to stroke and healthy individuals in forward reach and hand-to-mouth reach, using a simple methodology designed to quantify the contribution of various movement components to the reaching action. Methods: A 3D motion analysis was conducted, using a simplified marker set (placed at the mandible, the seventh cervical vertebra, acromion, lateral epicondyle of the humerus, metacarpophalangeal [MP] joint of the index finger, and greater trochanter of the femur). For the forward reach task, we measured the distance the index finger?s MP joint traveled from its starting position to the forward target location on the anterior-posterior axis. For the hand-to-mouth reach task, the shortening of the vertical distance between the index finger MP joint and the position of the chin at the start of the measurement was measured. For both measurements, the contributions of relevant upper limb and trunk movements were calculated. Results: A total of 20 healthy individuals and 10 patients with stroke participated in this study. In the forward reach task, the contribution of shoulder or elbow flexion was significantly smaller in participants with stroke than in healthy participants (mean 52.5%, SD 24.5% vs mean 85.2%, SD 4.5%; P<.001), whereas the contribution of trunk flexion was significantly larger in stroke participants than in healthy participants (mean 34.0%, SD 28.5% vs mean 3.0%, SD 2.8%; P<.001). In the hand-to-mouth reach task, the contribution of shoulder or elbow flexion was significantly smaller in participants with stroke than in healthy participants (mean 71.8%, SD 23.7% vs mean 90.7%, SD 11.8%; P=.009), whereas shoulder girdle elevation and shoulder abduction were significantly larger in participants with stroke than in healthy participants (mean 10.5%, SD 5.7% vs mean 6.5%, SD 3.0%; P=.02 and mean 16.5%, SD 18.7% vs mean 3.0%, SD 10.4%; P=.02, respectively). Conclusions: Compared with healthy participants, participants with stroke achieved a significantly greater distance via trunk flexion in the forward reach task and shoulder abduction and shoulder girdle elevation in the hand-to-mouth reach task, both of these differences are regarded as compensatory movements. Understanding the characteristics of individual motor strategies, such as dependence on compensatory movements, may contribute to tailored goal setting in stroke rehabilitation. UR - https://rehab.jmir.org/2023/1/e50571 UR - http://dx.doi.org/10.2196/50571 UR - http://www.ncbi.nlm.nih.gov/pubmed/38051570 ID - info:doi/10.2196/50571 ER - TY - JOUR AU - Harrison, Madeleine AU - Palmer, Rebecca AU - Cooper, Cindy PY - 2023/12/5 TI - Identifying the Active Ingredients of a Computerized Speech and Language Therapy Intervention for Poststroke Aphasia: Multiple Methods Investigation Alongside a Randomized Controlled Trial JO - JMIR Rehabil Assist Technol SP - e47542 VL - 10 KW - aphasia KW - stroke KW - computer therapy KW - tele-rehabilitation KW - speech and language therapy KW - word finding KW - qualitative KW - language KW - language therapy KW - speech therapy KW - aphasia therapy KW - speech KW - interview KW - self managed KW - computer aphasia KW - persistent aphasia KW - rehabilitation KW - machines KW - technology KW - computer KW - online KW - online health KW - ehealth KW - digital health N2 - Background: Aphasia is a communication disorder affecting more than one-third of stroke survivors. Computerized Speech and Language Therapy (CSLT) is a complex intervention requiring computer software, speech and language therapists, volunteers, or therapy assistants, as well as self-managed practice from the person with aphasia. CSLT was found to improve word finding, a common symptom of aphasia, in a multicenter randomized controlled trial (Clinical and Cost Effectiveness of Computer Treatment for Aphasia Post Stroke [Big CACTUS]). Objective: This study provides a detailed description of the CSLT intervention delivered in the Big CACTUS trial and identified the active ingredients of the intervention directly associated with improved word finding for people with aphasia. Methods: We conducted a multiple methods study within the context of a randomized controlled trial. In study 1, qualitative interviews explored key informants? understanding of the CSLT intervention, how the components interacted, and how they could be measured. Qualitative data were transcribed verbatim and analyzed thematically. Qualitative findings informed the process measures collected as part of a process evaluation of the CSLT intervention delivered in the Big CACTUS trial. In study 2, quantitative analyses explored the relationship between intervention process measures (length of computer therapy access; therapists? knowledge of CSLT; degree of rationale for CSLT tailoring; and time spent using the software to practice cued confrontation naming, noncued naming, and using words in functional sentences) and change in word-finding ability over a 6-month intervention period. Results: Qualitative interviews were conducted with 7 CSLT approach experts. Thematic analysis identified four overarching components of the CSLT approach: (1) the StepByStep software (version 5; Steps Consulting Ltd), (2) therapy setup: tailoring and personalizing, (3) regular independent practice, and (4) support and monitoring. Quantitative analyses included process and outcome data from 83 participants randomized to the intervention arm of the Big CACTUS trial. The process measures found to be directly associated with improved word-finding ability were therapists providing a thorough rationale for tailoring the computerized therapy exercises and the amount of time the person with aphasia spent using the computer software to practice using words in functional sentences. Conclusions: The qualitative exploration of the CSLT approach provided a detailed description of the components, theories, and mechanisms underpinning the intervention and facilitated the identification of process measures to be collected in the Big CACTUS trial. Quantitative analysis furthered our understanding of which components of the intervention are associated with clinical improvement. To optimize the benefits of using the CSLT approach for word finding, therapists are advised to pay particular attention to the active ingredients of the intervention: tailoring the therapy exercises based on the individual?s specific language difficulties and encouraging people with aphasia to practice the exercises focused on saying words in functional sentences. Trial Registration: ISRCTN Registry ISRCTN68798818; https://www.isrctn.com/ISRCTN68798818 UR - https://rehab.jmir.org/2023/1/e47542 UR - http://dx.doi.org/10.2196/47542 UR - http://www.ncbi.nlm.nih.gov/pubmed/38051577 ID - info:doi/10.2196/47542 ER - TY - JOUR AU - Gu, Dongmei AU - Lv, Xiaozhen AU - Shi, Chuan AU - Zhang, Tianhong AU - Liu, Sha AU - Fan, Zili AU - Tu, Lihui AU - Zhang, Ming AU - Zhang, Nan AU - Chen, Liming AU - Wang, Zhijiang AU - Wang, Jing AU - Zhang, Ying AU - Li, Huizi AU - Wang, Luchun AU - Zhu, Jiahui AU - Zheng, Yaonan AU - Wang, Huali AU - Yu, Xin AU - PY - 2023/12/1 TI - A Stable and Scalable Digital Composite Neurocognitive Test for Early Dementia Screening Based on Machine Learning: Model Development and Validation Study JO - J Med Internet Res SP - e49147 VL - 25 KW - mild cognitive impairment KW - digital cognitive assessment KW - machine learning KW - neurocognitive test KW - cognitive screening KW - dementia N2 - Background: Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and dementia. Early identification of MCI is an essential step in dementia prevention. Objective: Based on machine learning (ML) methods, this study aimed to develop and validate a stable and scalable panel of cognitive tests for the early detection of MCI and dementia based on the Chinese Neuropsychological Consensus Battery (CNCB) in the Chinese Neuropsychological Normative Project (CN-NORM) cohort. Methods: CN-NORM was a nationwide, multicenter study conducted in China with 871 participants, including an MCI group (n=327, 37.5%), a dementia group (n=186, 21.4%), and a cognitively normal (CN) group (n=358, 41.1%). We used the following 4 algorithms to select candidate variables: the F-score according to the SelectKBest method, the area under the curve (AUC) from logistic regression (LR), P values from the logit method, and backward stepwise elimination. Different models were constructed after considering the administration duration and complexity of combinations of various tests. Receiver operating characteristic curve and AUC metrics were used to evaluate the discriminative ability of the models via stratified sampling cross-validation and LR and support vector classification (SVC) algorithms. This model was further validated in the Alzheimer?s Disease Neuroimaging Initiative phase 3 (ADNI-3) cohort (N=743), which included 416 (56%) CN subjects, 237 (31.9%) patients with MCI, and 90 (12.1%) patients with dementia. Results: Except for social cognition, all other domains in the CNCB differed between the MCI and CN groups (P<.008). In feature selection results regarding discrimination between the MCI and CN groups, the Hopkins Verbal Learning Test-5 minutes Recall had the best performance, with the highest mean AUC of up to 0.80 (SD 0.02) and an F-score of up to 258.70. The scalability of model 5 (Hopkins Verbal Learning Test-5 minutes Recall and Trail Making Test-B) was the lowest. Model 5 achieved a higher level of discrimination than the Hong Kong Brief Cognitive test score in distinguishing between the MCI and CN groups (P<.05). Model 5 also provided the highest sensitivity of up to 0.82 (range 0.72-0.92) and 0.83 (range 0.75-0.91) according to LR and SVC, respectively. This model yielded a similar robust discriminative performance in the ADNI-3 cohort regarding differentiation between the MCI and CN groups, with a mean AUC of up to 0.81 (SD 0) according to both LR and SVC algorithms. Conclusions: We developed a stable and scalable composite neurocognitive test based on ML that could differentiate not only between patients with MCI and controls but also between patients with different stages of cognitive impairment. This composite neurocognitive test is a feasible and practical digital biomarker that can potentially be used in large-scale cognitive screening and intervention studies. UR - https://www.jmir.org/2023/1/e49147 UR - http://dx.doi.org/10.2196/49147 UR - http://www.ncbi.nlm.nih.gov/pubmed/38039074 ID - info:doi/10.2196/49147 ER - TY - JOUR AU - Shea, A. Amanda AU - Thornburg, Jonathan AU - Vitzthum, J. Virginia PY - 2023/12/1 TI - Assessment of App-Based Versus Conventional Survey Modalities for Reproductive Health Research in India, South Africa, and the United States: Comparative Cross-Sectional Study JO - JMIR Form Res SP - e44705 VL - 7 KW - mobile health KW - mHealth KW - femtech KW - reproductive health KW - menstrual health KW - sexual health KW - survey modalities KW - menstrual tracking app KW - India KW - South Africa KW - United States KW - mobile phone N2 - Background: There is a widely acknowledged global need for more research on reproductive health (including contraception, menstrual health, sexuality, and maternal morbidities) and its impact on overall well-being. However, several factors?notably, high costs, considerable effort, and the sensitivity of these topics?impede the collection of the necessary data, especially in less accessible and lower-income populations. The burgeoning ownership of smartphones and growing use of menstrual tracking apps (MTAs) may present an opportunity to conduct reproductive health research with fewer impediments than those associated with conventional survey methods. Objective: The main objective was to ascertain the feasibility, potential usefulness, and limitations of conducting reproductive health research using a mainstream MTA. Methods: In each of the 3 countries, we evaluated questionnaire responses from (1) current users of an MTA (Clue) and (2) participants surveyed using conventional survey modalities (in-person interviews, SMS text messaging, and web-based questionnaires). We compared these responses with published data collected from large nationally representative benchmark samples (the United States Census and the Demographic and Health Surveys for South Africa and India). Results: Given a sufficiently large user base, app-distributed surveys were able to quickly capture large samples on par with other methods and at low cost, with the additional advantage of being able to deploy remotely and simultaneously across countries. In each country, neither the app nor the conventional modality sample emerged as a consistently closer match to the distributions of the demographic attributes and the patterns of contraceptive use reported for the respective benchmark sample. Despite efforts to obtain representative samples, the conventional modality samples sometimes over- and other times underrepresented some subgroups (eg, underrepresentation of married persons in the United States and overrepresentation of rural residents in India). In all 3 countries, app users were younger, more educated, more likely to be urban residents, and more likely to use nonhormonal rather than hormonal contraceptive methods compared with the respective national benchmark. App users, compared with the conventional modality samples, consistently reported being more comfortable discussing their menstrual periods with other persons (eg, family, friends, and health care providers), suggesting that MTA users may be more likely to respond truthfully to questions on sensitive or taboo health topics. The app samples? consistency across countries regarding users? demographic profiles, contraceptive choices, and personal attitudes toward menstruation supports the validity of making cross-country comparisons of survey findings for a given app?s users. Conclusions: MTAs such as Clue can provide a quick, scalable, and cost-effective method for collecting health data, including on sensitive topics, across a wide variety of settings and countries. With expanding global access to technology and the increasing use of these tools, consumer MTAs can be a viable survey modality to strengthen reproductive health research. UR - https://formative.jmir.org/2023/1/e44705 UR - http://dx.doi.org/10.2196/44705 UR - http://www.ncbi.nlm.nih.gov/pubmed/38039064 ID - info:doi/10.2196/44705 ER - TY - JOUR AU - Fruytier, A. Lonneke AU - Janssen, M. Daan AU - Campero Jurado, Israel AU - van de Sande, AJP Danny AU - Lorato, Ilde AU - Stuart, Shavini AU - Panditha, Pradeep AU - de Kok, Margreet AU - Kemps, MC Hareld PY - 2023/11/30 TI - The Utility of a Novel Electrocardiogram Patch Using Dry Electrodes Technology for Arrhythmia Detection During Exercise and Prolonged Monitoring: Proof-of-Concept Study JO - JMIR Form Res SP - e49346 VL - 7 KW - arrhythmia detection KW - coronary artery disease KW - ECG monitoring KW - electrocardiogram KW - exercise KW - patch KW - usability N2 - Background: Accurate detection of myocardial ischemia and arrhythmias during free-living exercise could play a pivotal role in screening and monitoring for the prevention of exercise-related cardiovascular events in high-risk populations. Although remote electrocardiogram (ECG) solutions are emerging rapidly, existing technology is neither designed nor validated for continuous use during vigorous exercise. Objective: In this proof-of-concept study, we evaluated the usability, signal quality, and accuracy for arrhythmia detection of a single-lead ECG patch platform featuring self-adhesive dry electrode technology in individuals with chronic coronary syndrome. This sensor was evaluated during exercise and for prolonged, continuous monitoring. Methods: We recruited a total of 6 consecutive patients with chronic coronary syndrome scheduled for an exercise stress test (EST) as part of routine cardiac follow-up. Traditional 12-lead ECG recording was combined with monitoring with the ECG patch. Following the EST, the participants continuously wore the sensor for 5 days. Intraclass correlation coefficients (ICC) and Wilcoxon signed rank tests were used to assess the utility of detecting arrhythmias with the patch by comparing the evaluations of 2 blinded assessors. Signal quality during EST and prolonged monitoring was evaluated by using a signal quality indicator. Additionally, connection time was calculated for prolonged ECG monitoring. The comfort and usability of the patch were evaluated by a web-based self-assessment questionnaire. Results: A total of 6 male patients with chronic coronary syndrome (mean age 69.8, SD 6.2 years) completed the study protocol. The patch was worn for a mean of 118.3 (SD 5.6) hours. The level of agreement between the patch and 12-lead ECG was excellent for the detection of premature atrial contractions and premature ventricular contractions during the whole test (ICC=0.998, ICC=1.000). No significant differences in the total number of premature atrial contractions and premature ventricular contractions were detected neither during the entire exercise test (P=.79 and P=.18, respectively) nor during the exercise and recovery stages separately (P=.41, P=.66, P=.18, and P=.66). A total of 1 episode of atrial fibrillation was detected by both methods. Total connection time during recording was between 88% and 100% for all participants. There were no reports of skin irritation, erythema, or pain while wearing the patch. Conclusions: This proof-of-concept study showed that this innovative ECG patch based on self-adhesive dry electrode technology can potentially be used for arrhythmia detection during vigorous exercise. The results suggest that the wearable patch is also usable for prolonged continuous ECG monitoring in free-living conditions and can therefore be of potential use in cardiac rehabilitation and tele-monitoring for the prevention of exercise-related cardiovascular events. Future efforts will focus on optimizing signal quality over time and conducting a larger-scale validation study focusing on both arrhythmia and ischemia detection. UR - https://formative.jmir.org/2023/1/e49346 UR - http://dx.doi.org/10.2196/49346 UR - http://www.ncbi.nlm.nih.gov/pubmed/38032699 ID - info:doi/10.2196/49346 ER - TY - JOUR AU - Yoon, Jeewoo AU - Han, Jinyoung AU - Ko, Junseo AU - Choi, Seong AU - Park, In Ji AU - Hwang, Seo Joon AU - Han, Mo Jeong AU - Hwang, Duck-Jin Daniel PY - 2023/11/29 TI - Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy JO - J Med Internet Res SP - e48142 VL - 25 KW - computer aided diagnosis KW - ophthalmology KW - deep learning KW - artificial intelligence KW - computer vision KW - imaging informatics KW - retinal disease KW - central serous chorioretinopathy KW - diagnostic study N2 - Background: Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)?based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. Objective: This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. Methods: For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. Results: The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. Conclusions: Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists. UR - https://www.jmir.org/2023/1/e48142 UR - http://dx.doi.org/10.2196/48142 UR - http://www.ncbi.nlm.nih.gov/pubmed/38019564 ID - info:doi/10.2196/48142 ER - TY - JOUR AU - Baron, Ruth AU - Hamdiui, Nora AU - Helms, B. Yannick AU - Crutzen, Rik AU - Götz, M. Hannelore AU - Stein, L. Mart PY - 2023/11/29 TI - Evaluating the Added Value of Digital Contact Tracing Support Tools for Citizens: Framework Development JO - JMIR Res Protoc SP - e44728 VL - 12 KW - contact tracing KW - digital tools KW - citizen involvement KW - COVID-19 KW - infectious disease outbreak KW - framework KW - mobile phone N2 - Background: The COVID-19 pandemic revealed that with high infection rates, health services conducting contact tracing (CT) could become overburdened, leading to limited or incomplete CT. Digital CT support (DCTS) tools are designed to mimic traditional CT, by transferring a part of or all the tasks of CT into the hands of citizens. Besides saving time for health services, these tools may help to increase the number of contacts retrieved during the contact identification process, quantity and quality of contact details, and speed of the contact notification process. The added value of DCTS tools for CT is currently unknown. Objective: To help determine whether DCTS tools could improve the effectiveness of CT, this study aims to develop a framework for the comprehensive assessment of these tools. Methods: A framework containing evaluation topics, research questions, accompanying study designs, and methods was developed based on consultations with CT experts from municipal public health services and national public health authorities, complemented with scientific literature. Results: These efforts resulted in a framework aiming to assist with the assessment of the following aspects of CT: speed; comprehensiveness; effectiveness with regard to contact notification; positive case detection; potential workload reduction of public health professionals; demographics related to adoption and reach; and user experiences of public health professionals, index cases, and contacts. Conclusions: This framework provides guidance for researchers and policy makers in designing their own evaluation studies, the findings of which can help determine how and the extent to which DCTS tools should be implemented as a CT strategy for future infectious disease outbreaks. UR - https://www.researchprotocols.org/2023/1/e44728 UR - http://dx.doi.org/10.2196/44728 UR - http://www.ncbi.nlm.nih.gov/pubmed/38019583 ID - info:doi/10.2196/44728 ER - TY - JOUR AU - Schopow, Nikolas AU - Osterhoff, Georg AU - Baur, David PY - 2023/11/28 TI - Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review JO - JMIR Med Inform SP - e48933 VL - 11 KW - natural language processing KW - clinical practice KW - systematic review KW - healthcare KW - health care KW - GPT-3 KW - GPT-4 KW - large language models KW - artificial intelligence KW - machine learning KW - clinical decision support systems KW - language model KW - NLP KW - ChatGPT KW - systematic KW - review methods KW - review methodology KW - text KW - unstructured KW - extract KW - extraction N2 - Background: This research integrates a comparative analysis of the performance of human researchers and OpenAI's ChatGPT in systematic review tasks and describes an assessment of the application of natural language processing (NLP) models in clinical practice through a review of 5 studies. Objective: This study aimed to evaluate the reliability between ChatGPT and human researchers in extracting key information from clinical articles, and to investigate the practical use of NLP in clinical settings as evidenced by selected studies. Methods: The study design comprised a systematic review of clinical articles executed independently by human researchers and ChatGPT. The level of agreement between and within raters for parameter extraction was assessed using the Fleiss and Cohen ? statistics. Results: The comparative analysis revealed a high degree of concordance between ChatGPT and human researchers for most parameters, with less agreement for study design, clinical task, and clinical implementation. The review identified 5 significant studies that demonstrated the diverse applications of NLP in clinical settings. These studies? findings highlight the potential of NLP to improve clinical efficiency and patient outcomes in various contexts, from enhancing allergy detection and classification to improving quality metrics in psychotherapy treatments for veterans with posttraumatic stress disorder. Conclusions: Our findings underscore the potential of NLP models, including ChatGPT, in performing systematic reviews and other clinical tasks. Despite certain limitations, NLP models present a promising avenue for enhancing health care efficiency and accuracy. Future studies must focus on broadening the range of clinical applications and exploring the ethical considerations of implementing NLP applications in health care settings. UR - https://medinform.jmir.org/2023/1/e48933 UR - http://dx.doi.org/10.2196/48933 UR - http://www.ncbi.nlm.nih.gov/pubmed/38015610 ID - info:doi/10.2196/48933 ER - TY - JOUR AU - Rønn, Camille AU - Wieland, Andreas AU - Lehrer, Christiane AU - Márton, Attila AU - LaRoche, Jason AU - Specker, Adrien AU - Leroy, Pascal AU - Fürstenau, Daniel PY - 2023/11/24 TI - Circular Business Model for Digital Health Solutions: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e47874 VL - 12 KW - business model KW - circular economy KW - digital health solution KW - digital health KW - digital tool KW - digital KW - healthcare KW - life cycle KW - MedTech device KW - monitoring device KW - technology N2 - Background: The circular economy reshapes the linear ?take, make, and dispose? approach and evolves around minimizing waste and recapturing resources in a closed-loop system. The health sector accounts for 4.6% of global greenhouse gas emissions and has, over the decades, been built to rely on single-use devices and deal with high volumes of medical waste. With the increase in the adoption of digital health solutions in the health care industry, leading the industry into a new paradigm of how we provide health care, a focus must be put on the amount of waste that will follow. Digital health solutions will shape health care through the use of technology and lead to improved patient care, but they will also make medical waste more complex to deal with due to the e-waste component. Therefore, a transformation of the health care industry to a circular economy is a crucial cornerstone in decreasing the impact on the environment. Objective: This study aims to address the lack of direction in the current literature on circular business models. It will consider micro, meso, and macro factors that would impact the operational validity of circular models using the digital health solutions ePaper label (medical packaging), smart wearable sensor (health monitoring devices), smart pill box (medication management), and endo-cutter (surgical equipment) as examples. Methods: The study will systematically perform a scoping review through a database and snowball search. We will analyze and classify the studies from a predetermined set of categories and then summarize them into an evidence map. Based on the review, the study will develop a 2D framework for businesses to follow or for future research to take a standpoint from. Results: Preliminarily, the review has analyzed 26 studies in total. The results are close to equally distributed among the micro (8/26, 31%), meso (10/26, 38%), and macro (8/26, 31%) levels. Circular economy studies emphasize several circular practices such as recycling (17/26, 65%), reusing (18/26, 69%), reducing (15/26, 58%), and remanufacturing (8/26, 31%). The value proposition in the examined business model is mostly dominated by stand-alone products (18/26, 69%) compared to product as a service (7/26, 27%), involving stakeholders such as health care professionals or hospitals (20/26, 77%), manufacturers (11/26, 42%), and consumers (9/26, 35%). All studies encompass societal (12/26, 46%), economic (23/26, 88%), and environmental (24/26, 92%) viewpoints. Conclusions: The study argues that each digital health solution would have to be accessed individually to find the optimal business model to follow. This is due to their differing life cycles and complexity. The manufacturer will need a layered value proposition, implementing several business models dependent on their respective product portfolios. The need to incorporate several business models implies an ecosystem perspective that is relevant to consider. International Registered Report Identifier (IRRID): DERR1-10.2196/47874 UR - https://www.researchprotocols.org/2023/1/e47874 UR - http://dx.doi.org/10.2196/47874 UR - http://www.ncbi.nlm.nih.gov/pubmed/37999949 ID - info:doi/10.2196/47874 ER - TY - JOUR AU - Jing, Fengshi AU - Ye, Yang AU - Zhou, Yi AU - Ni, Yuxin AU - Yan, Xumeng AU - Lu, Ying AU - Ong, Jason AU - Tucker, D. Joseph AU - Wu, Dan AU - Xiong, Yuan AU - Xu, Chen AU - He, Xi AU - Huang, Shanzi AU - Li, Xiaofeng AU - Jiang, Hongbo AU - Wang, Cheng AU - Dai, Wencan AU - Huang, Liqun AU - Mei, Wenhua AU - Cheng, Weibin AU - Zhang, Qingpeng AU - Tang, Weiming PY - 2023/11/23 TI - Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach JO - J Med Internet Res SP - e37719 VL - 25 KW - HIV self-testing KW - machine learning KW - MSM KW - men who have sex with men KW - secondary distribution KW - key influencers identification N2 - Background: HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as ?indexes?) applying for multiple kits and subsequently sharing them with people (defined as ?alters?) in their social networks. However, identifying key influencers is difficult. Objective: This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits. Methods: We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach. Results: We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI ?14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention?s efficiency by 17.7% (95% CI ?3.5% to 38.8%) compared to that of human identification. Conclusions: We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits. Trial Registration: Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001 UR - https://www.jmir.org/2023/1/e37719 UR - http://dx.doi.org/10.2196/37719 UR - http://www.ncbi.nlm.nih.gov/pubmed/37995110 ID - info:doi/10.2196/37719 ER - TY - JOUR AU - Watase, Teruhisa AU - Omiya, Yasuhiro AU - Tokuno, Shinichi PY - 2023/11/6 TI - Severity Classification Using Dynamic Time Warping?Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study JO - JMIR Biomed Eng SP - e50924 VL - 8 KW - voice biomarker KW - dynamic time warping KW - COVID-19 KW - smartphone KW - severity classification KW - biomarker KW - feasibility study KW - illness KW - monitoring KW - respiratory disease KW - accuracy KW - logistic model KW - tool KW - model N2 - Background: In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores. Objective: This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients? voices to distinguish moderate illness from mild illness at a significant level. Methods: We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (?<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy. Results: Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants? attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/. Conclusions: The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care. UR - https://biomedeng.jmir.org/2023/1/e50924 UR - http://dx.doi.org/10.2196/50924 UR - http://www.ncbi.nlm.nih.gov/pubmed/37982072 ID - info:doi/10.2196/50924 ER - TY - JOUR AU - Liang, Xueping AU - Zhao, Juan AU - Chen, Yan AU - Bandara, Eranga AU - Shetty, Sachin PY - 2023/10/30 TI - Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study JO - J Med Internet Res SP - e46547 VL - 25 KW - fairness KW - federated learning KW - bias KW - health care KW - blockchain KW - software KW - proof of concept KW - implementation KW - privacy N2 - Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients? privacy at each site. Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. Methods: We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. Results: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra?based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. Conclusions: Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain. UR - https://www.jmir.org/2023/1/e46547 UR - http://dx.doi.org/10.2196/46547 UR - http://www.ncbi.nlm.nih.gov/pubmed/37902833 ID - info:doi/10.2196/46547 ER - TY - JOUR AU - Waters, R. Austin AU - Turner, Cindy AU - Easterly, W. Caleb AU - Tovar, Ida AU - Mulvaney, Megan AU - Poquadeck, Matt AU - Johnston, Hailey AU - Ghazal, V. Lauren AU - Rains, A. Stephen AU - Cloyes, G. Kristin AU - Kirchhoff, C. Anne AU - Warner, L. Echo PY - 2023/10/30 TI - Exploring Online Crowdfunding for Cancer-Related Costs Among LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer, Plus) Cancer Survivors: Integration of Community-Engaged and Technology-Based Methodologies JO - JMIR Cancer SP - e51605 VL - 9 KW - community-engaged KW - LGBT KW - SGM KW - financial burden KW - crowdfunding KW - sexual monitory KW - sexual minorities KW - crowdfund KW - fund KW - funding KW - fundraising KW - fundraise KW - engagement KW - finance KW - financial KW - campaign KW - campaigns KW - web scraping KW - cancer KW - oncology KW - participatory KW - dictionary KW - term KW - terms KW - terminology KW - terminologies KW - classification KW - underrepresented KW - equity KW - inequity KW - inequities KW - cost KW - costs N2 - Background: Cancer survivors frequently experience cancer-related financial burdens. The extent to which Lesbian, Gay, Bisexual, Transgender, Queer, Plus (LGBTQ+) populations experience cancer-related cost-coping behaviors such as crowdfunding is largely unknown, owing to a lack of sexual orientation and gender identity data collection and social stigma. Web-scraping has previously been used to evaluate inequities in online crowdfunding, but these methods alone do not adequately engage populations facing inequities. Objective: We describe the methodological process of integrating technology-based and community-engaged methods to explore the financial burden of cancer among LGBTQ+ individuals via online crowdfunding. Methods: To center the LGBTQ+ community, we followed community engagement guidelines by forming a study advisory board (SAB) of LGBTQ+ cancer survivors, caregivers, and professionals who were involved in every step of the research. SAB member engagement was tracked through quarterly SAB meeting attendance and an engagement survey. We then used web-scraping methods to extract a data set of online crowdfunding campaigns. The study team followed an integrated technology-based and community-engaged process to develop and refine term dictionaries for analyses. Term dictionaries were developed and refined in order to identify crowdfunding campaigns that were cancer- and LGBTQ+-related. Results: Advisory board engagement was high according to metrics of meeting attendance, meeting participation, and anonymous board feedback. In collaboration with the SAB, the term dictionaries were iteratively edited and refined. The LGBTQ+ term dictionary was developed by the study team, while the cancer term dictionary was refined from an existing dictionary. The advisory board and analytic team members manually coded against the term dictionary and performed quality checks until high confidence in correct classification was achieved using pairwise agreement. Through each phase of manual coding and quality checks, the advisory board identified more misclassified campaigns than the analytic team alone. When refining the LGBTQ+ term dictionary, the analytic team identified 11.8% misclassification while the SAB identified 20.7% misclassification. Once each term dictionary was finalized, the LGBTQ+ term dictionary resulted in a 95% pairwise agreement, while the cancer term dictionary resulted in an 89.2% pairwise agreement. Conclusions: The classification tools developed by integrating community-engaged and technology-based methods were more accurate because of the equity-based approach of centering LGBTQ+ voices and their lived experiences. This exemplar suggests integrating community-engaged and technology-based methods to study inequities is highly feasible and has applications beyond LGBTQ+ financial burden research. UR - https://cancer.jmir.org/2023/1/e51605 UR - http://dx.doi.org/10.2196/51605 UR - http://www.ncbi.nlm.nih.gov/pubmed/37902829 ID - info:doi/10.2196/51605 ER - TY - JOUR AU - Underly, Robert AU - Dull, M. Gary AU - Nudi, Evan AU - Pionk, Timothy AU - Prevette, Kristen AU - Smith, Jeffrey PY - 2023/10/30 TI - Using a Novel Connected Device for the Collection of Puffing Topography Data for the Vuse Solo Electronic Nicotine Delivery System in a Real-World Setting: Prospective Ambulatory Clinical Study JO - JMIR Form Res SP - e49876 VL - 7 KW - topography KW - electronic cigarette KW - e-cigarette KW - electronic nicotine delivery system KW - ENDS KW - ambulatory puffing KW - use behavior KW - sessions KW - mobile phone N2 - Background: Over the last decade, the use of electronic nicotine delivery systems (ENDSs) has risen, whereas studies that describe how consumers use these products have been limited. Most studies related to ENDS use have involved study designs focused on use in a central location environment or attempted to measure use outcomes through subjective self-reported end points. The development of accurate and reliable tools to collect data in a naturalistic real-world environment is necessary to capture the complexities of ENDS use. Using connected devices in a real-world setting provides a convenient and objective approach to collecting behavioral outcomes with ENDS. Objective: The Product Use and Behavior instrument was developed and used to capture the use of the Vuse Solo ENDS in an ambulatory setting to best replicate real-world use behavior. This study aims to determine overall mean values for topography outcomes while also providing a definition for an ENDS use session. Methods: A prospective ambulatory clinical study was performed with the Product Use and Behavior instrument. Participants (n=75) were aged between 21 and 60 years, considered in good health, and were required to be established regular users of ENDSs. To better understand use behavior within the population, the sample was sorted into percentiles with bins based on daily puff counts. To frame these data in the relevant context, they were binned into low-, moderate-, and high-use categories (10th to 40th, 40th to 70th, and 70th to 100th percentiles, respectively), with the low-use group representing the nonintense category, the high-use group representing the intense category, and the moderate-use group being reflective of the average consumer. Results: Participants with higher daily use took substantially more puffs per use session (6.71 vs 4.40) and puffed more frequently (interpuff interval: 32.78 s vs 61.66 s) than participants in the low-use group. Puff duration remained consistent across the low-, moderate?, and high-use groups (2.10 s, 2.18 s, and 2.19 s, respectively). The moderate-use group had significantly shorter session lengths (P<.001) than the high- and low-use groups, which did not differ significantly from each other (P=.16). Conclusions: Using connected devices allows for a convenient and robust approach to the collection of behavioral outcomes related to product use in an ambulatory setting. By using the variables captured with these tools, it becomes possible to move away from predefined periods of use to better understand topography outcomes and define use sessions. The data presented here offer a possible method to define these sessions. These data also begin to frame international standards used for the analytical assessments of ENDSs in the correct context and begin to shed light on the differences between standardized testing regimens and actual use behavior. Trial Registration: Clinicaltrials.gov NCT04226404; https://clinicaltrials.gov/study/NCT04226404 UR - https://formative.jmir.org/2023/1/e49876 UR - http://dx.doi.org/10.2196/49876 UR - http://www.ncbi.nlm.nih.gov/pubmed/37902830 ID - info:doi/10.2196/49876 ER - TY - JOUR AU - Sükei, Emese AU - Romero-Medrano, Lorena AU - de Leon-Martinez, Santiago AU - Herrera López, Jesús AU - Campaña-Montes, José Juan AU - Olmos, M. Pablo AU - Baca-Garcia, Enrique AU - Artés, Antonio PY - 2023/10/30 TI - Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study JO - JMIR Form Res SP - e47167 VL - 7 KW - WHODAS KW - functional limitations KW - mobile sensing KW - passive ecological momentary assessment KW - predictive modeling KW - interpretable machine learning KW - machine learning KW - disability KW - clinical outcome N2 - Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients? functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning?based models for predicting patients? WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning?based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models? decisions?an important aspect in clinical practice. UR - https://formative.jmir.org/2023/1/e47167 UR - http://dx.doi.org/10.2196/47167 UR - http://www.ncbi.nlm.nih.gov/pubmed/37902823 ID - info:doi/10.2196/47167 ER - TY - JOUR AU - Keogh, Alison AU - Mc Ardle, Ríona AU - Diaconu, Gabriela Mara AU - Ammour, Nadir AU - Arnera, Valdo AU - Balzani, Federica AU - Brittain, Gavin AU - Buckley, Ellen AU - Buttery, Sara AU - Cantu, Alma AU - Corriol-Rohou, Solange AU - Delgado-Ortiz, Laura AU - Duysens, Jacques AU - Forman-Hardy, Tom AU - Gur-Arieh, Tova AU - Hamerlijnck, Dominique AU - Linnell, John AU - Leocani, Letizia AU - McQuillan, Tom AU - Neatrour, Isabel AU - Polhemus, Ashley AU - Remmele, Werner AU - Saraiva, Isabel AU - Scott, Kirsty AU - Sutton, Norman AU - van den Brande, Koen AU - Vereijken, Beatrix AU - Wohlrab, Martin AU - Rochester, Lynn AU - PY - 2023/10/27 TI - Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial JO - J Med Internet Res SP - e44206 VL - 25 KW - patient involvement KW - patient engagement KW - public-private partnership KW - research consortium KW - digital mobility outcomes KW - real-world mobility KW - digital mobility measures UR - https://www.jmir.org/2023/1/e44206 UR - http://dx.doi.org/10.2196/44206 UR - http://www.ncbi.nlm.nih.gov/pubmed/37889531 ID - info:doi/10.2196/44206 ER - TY - JOUR AU - Joo, Hyeon AU - Mathis, R. Michael AU - Tam, Marty AU - James, Cornelius AU - Han, Peijin AU - Mangrulkar, S. Rajesh AU - Friedman, P. Charles AU - Vydiswaran, Vinod V. G. PY - 2023/10/24 TI - Applying AI and Guidelines to Assist Medical Students in Recognizing Patients With Heart Failure: Protocol for a Randomized Trial JO - JMIR Res Protoc SP - e49842 VL - 12 KW - medical education KW - clinical decision support systems KW - artificial intelligence KW - machine learning KW - heart failure KW - evidence-based medicine KW - guidelines KW - digital health interventions N2 - Background: The integration of artificial intelligence (AI) into clinical practice is transforming both clinical practice and medical education. AI-based systems aim to improve the efficacy of clinical tasks, enhancing diagnostic accuracy and tailoring treatment delivery. As it becomes increasingly prevalent in health care for high-quality patient care, it is critical for health care providers to use the systems responsibly to mitigate bias, ensure effective outcomes, and provide safe clinical practices. In this study, the clinical task is the identification of heart failure (HF) prior to surgery with the intention of enhancing clinical decision-making skills. HF is a common and severe disease, but detection remains challenging due to its subtle manifestation, often concurrent with other medical conditions, and the absence of a simple and effective diagnostic test. While advanced HF algorithms have been developed, the use of these AI-based systems to enhance clinical decision-making in medical education remains understudied. Objective: This research protocol is to demonstrate our study design, systematic procedures for selecting surgical cases from electronic health records, and interventions. The primary objective of this study is to measure the effectiveness of interventions aimed at improving HF recognition before surgery, the second objective is to evaluate the impact of inaccurate AI recommendations, and the third objective is to explore the relationship between the inclination to accept AI recommendations and their accuracy. Methods: Our study used a 3 × 2 factorial design (intervention type × order of prepost sets) for this randomized trial with medical students. The student participants are asked to complete a 30-minute e-learning module that includes key information about the intervention and a 5-question quiz, and a 60-minute review of 20 surgical cases to determine the presence of HF. To mitigate selection bias in the pre- and posttests, we adopted a feature-based systematic sampling procedure. From a pool of 703 expert-reviewed surgical cases, 20 were selected based on features such as case complexity, model performance, and positive and negative labels. This study comprises three interventions: (1) a direct AI-based recommendation with a predicted HF score, (2) an indirect AI-based recommendation gauged through the area under the curve metric, and (3) an HF guideline-based intervention. Results: As of July 2023, 62 of the enrolled medical students have fulfilled this study?s participation, including the completion of a short quiz and the review of 20 surgical cases. The subject enrollment commenced in August 2022 and will end in December 2023, with the goal of recruiting 75 medical students in years 3 and 4 with clinical experience. Conclusions: We demonstrated a study protocol for the randomized trial, measuring the effectiveness of interventions using AI and HF guidelines among medical students to enhance HF recognition in preoperative care with electronic health record data. International Registered Report Identifier (IRRID): DERR1-10.2196/49842 UR - https://www.researchprotocols.org/2023/1/e49842 UR - http://dx.doi.org/10.2196/49842 UR - http://www.ncbi.nlm.nih.gov/pubmed/37874618 ID - info:doi/10.2196/49842 ER - TY - JOUR AU - Lei, Mingxing AU - Wu, Bing AU - Zhang, Zhicheng AU - Qin, Yong AU - Cao, Xuyong AU - Cao, Yuncen AU - Liu, Baoge AU - Su, Xiuyun AU - Liu, Yaosheng PY - 2023/10/23 TI - A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study JO - J Med Internet Res SP - e47590 VL - 25 KW - bone metastasis KW - early death KW - machine learning KW - prediction model KW - local interpretable model?agnostic explanation N2 - Background: Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. Objective: This study aimed to develop a machine learning?based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. Methods: This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches?logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine?were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. Results: In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was ?0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. Conclusions: This study develops a machine learning?based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death. UR - https://www.jmir.org/2023/1/e47590 UR - http://dx.doi.org/10.2196/47590 UR - http://www.ncbi.nlm.nih.gov/pubmed/37870889 ID - info:doi/10.2196/47590 ER - TY - JOUR AU - Pacheco, Alissa AU - van Schaik, A. Tempest AU - Paleyes, Nadzeya AU - Blacutt, Miguel AU - Vega, Julio AU - Schreier, R. Abigail AU - Zhang, Haiyan AU - Macpherson, Chelsea AU - Desai, Radhika AU - Jancke, Gavin AU - Quinn, Lori PY - 2023/10/23 TI - A Wearable Vibratory Device (The Emma Watch) to Address Action Tremor in Parkinson Disease: Pilot Feasibility Study JO - JMIR Biomed Eng SP - e40433 VL - 8 KW - Parkinson?s disease KW - action tremor KW - Emma Watch KW - vibration KW - haptic feedback KW - handwriting KW - drawing KW - spirals KW - hand function N2 - Background: Parkinson disease (PD) is a neurodegenerative disease that has a wide range of motor symptoms, such as tremor. Tremors are involuntary movements that occur in rhythmic oscillations and are typically categorized into rest tremor or action tremor. Action tremor occurs during voluntary movements and is a debilitating symptom of PD. As noninvasive interventions are limited, there is an ever-increasing need for an effective intervention for individuals experiencing action tremors. The Microsoft Emma Watch, a wristband with 5 vibrating motors, is a noninvasive, nonpharmaceutical intervention for tremor attenuation. Objective: This pilot study investigated the use of the Emma Watch device to attenuate action tremor in people with PD. Methods: The sample included 9 people with PD who were assessed on handwriting and hand function tasks performed on a digitized tablet. Tasks included drawing horizontal or vertical lines, tracing a star, spiral, writing ?elelelel? in cursive, and printing a standardized sentence. Each task was completed 3 times with the Emma Watch programmed at different vibration intensities, which were counterbalanced: high intensity, low intensity (sham), and no vibration. Digital analysis from the tablet captured kinematic, dynamic, and spatial attributes of drawing and writing samples to calculate mathematical indices that quantify upper limb motor function. APDM Opal sensors (APDM Wearable Technologies) placed on both wrists were used to calculate metrics of acceleration and jerk. A questionnaire was provided to each participant after using the Emma Watch to gain a better understanding of their perspectives of using the device. In addition, drawings were compared to determine whether there were any visual differences between intensities. Results: In total, 9 people with PD were tested: 4 males and 5 females with a mean age of 67 (SD 9.4) years. There were no differences between conditions in the outcomes of interest measured with the tablet (duration, mean velocity, number of peaks, pause time, and number of pauses). Visual differences were observed within a small subset of participants, some of whom reported perceived improvement. The majority of participants (8/9) reported the Emma Watch was comfortable, and no problems with the device were reported. Conclusions: There were visually depicted and subjectively reported improvements in handwriting for a small subset of individuals. This pilot study was limited by a small sample size, and this should be taken into consideration with the interpretation of the quantitative results. Combining vibratory devices, such as the Emma Watch, with task specific training, or personalizing the frequency to one?s individual tremor may be important steps to consider when evaluating the effect of vibratory devices on hand function or writing ability in future studies. While the Emma Watch may help attenuate action tremor, its efficacy in improving fine motor or handwriting skills as a stand-alone tool remains to be demonstrated. UR - https://biomedeng.jmir.org/2023/1/e40433 UR - http://dx.doi.org/10.2196/40433 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875672 ID - info:doi/10.2196/40433 ER - TY - JOUR AU - Caulley, Desmond AU - Alemu, Yared AU - Burson, Sedara AU - Cárdenas Bautista, Elizabeth AU - Abebe Tadesse, Girmaw AU - Kottmyer, Christopher AU - Aeschbach, Laurent AU - Cheungvivatpant, Bryan AU - Sezgin, Emre PY - 2023/10/23 TI - Objectively Quantifying Pediatric Psychiatric Severity Using Artificial Intelligence, Voice Recognition Technology, and Universal Emotions: Pilot Study for Artificial Intelligence-Enabled Innovation to Address Youth Mental Health Crisis JO - JMIR Res Protoc SP - e51912 VL - 12 KW - pediatric KW - trauma KW - voice AI KW - machine learning KW - mental health KW - predictive modeling KW - artificial intelligence KW - social determinants of health KW - speech-recognition KW - adverse childhood experiences KW - trauma and emotional distress KW - voice marker KW - speech biomarker KW - pediatrics KW - at-risk youth N2 - Background: Providing Psychotherapy, particularly for youth, is a pressing challenge in the health care system. Traditional methods are resource-intensive, and there is a need for objective benchmarks to guide therapeutic interventions. Automated emotion detection from speech, using artificial intelligence, presents an emerging approach to address these challenges. Speech can carry vital information about emotional states, which can be used to improve mental health care services, especially when the person is suffering. Objective: This study aims to develop and evaluate automated methods for detecting the intensity of emotions (anger, fear, sadness, and happiness) in audio recordings of patients? speech. We also demonstrate the viability of deploying the models. Our model was validated in a previous publication by Alemu et al with limited voice samples. This follow-up study used significantly more voice samples to validate the previous model. Methods: We used audio recordings of patients, specifically children with high adverse childhood experience (ACE) scores; the average ACE score was 5 or higher, at the highest risk for chronic disease and social or emotional problems; only 1 in 6 have a score of 4 or above. The patients? structured voice sample was collected by reading a fixed script. In total, 4 highly trained therapists classified audio segments based on a scoring process of 4 emotions and their intensity levels for each of the 4 different emotions. We experimented with various preprocessing methods, including denoising, voice-activity detection, and diarization. Additionally, we explored various model architectures, including convolutional neural networks (CNNs) and transformers. We trained emotion-specific transformer-based models and a generalized CNN-based model to predict emotion intensities. Results: The emotion-specific transformer-based model achieved a test-set precision and recall of 86% and 79%, respectively, for binary emotional intensity classification (high or low). In contrast, the CNN-based model, generalized to predict the intensity of 4 different emotions, achieved test-set precision and recall of 83% for each. Conclusions: Automated emotion detection from patients? speech using artificial intelligence models is found to be feasible, leading to a high level of accuracy. The transformer-based model exhibited better performance in emotion-specific detection, while the CNN-based model showed promise in generalized emotion detection. These models can serve as valuable decision-support tools for pediatricians and mental health providers to triage youth to appropriate levels of mental health care services. International Registered Report Identifier (IRRID): RR1-10.2196/51912 UR - https://www.researchprotocols.org/2023/1/e51912 UR - http://dx.doi.org/10.2196/51912 UR - http://www.ncbi.nlm.nih.gov/pubmed/37870890 ID - info:doi/10.2196/51912 ER - TY - JOUR AU - Kim, Young Se AU - Park, Jinseok AU - Choi, Hojin AU - Loeser, Martin AU - Ryu, Hokyoung AU - Seo, Kyoungwon PY - 2023/10/20 TI - Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study JO - J Med Internet Res SP - e48093 VL - 25 KW - Alzheimer disease KW - biomarkers KW - dementia KW - digital markers KW - eye movement KW - hand movement KW - machine learning KW - mild cognitive impairment KW - screening KW - virtual reality N2 - Background: With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a ?virtual kiosk test? for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. Objective: We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. Methods: A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test?developed by our group?where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. Results: In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=?3.44; P=.004), and a greater number of errors (t49=?3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. Conclusions: Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach. UR - https://www.jmir.org/2023/1/e48093 UR - http://dx.doi.org/10.2196/48093 UR - http://www.ncbi.nlm.nih.gov/pubmed/37862101 ID - info:doi/10.2196/48093 ER - TY - JOUR AU - Lou, Pei AU - Fang, An AU - Zhao, Wanqing AU - Yao, Kuanda AU - Yang, Yusheng AU - Hu, Jiahui PY - 2023/10/20 TI - Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph?Based Approach JO - J Med Internet Res SP - e45225 VL - 25 KW - coronavirus KW - heterogeneous data integration KW - knowledge graph embedding KW - drug repurposing KW - interpretable prediction KW - COVID-19 N2 - Background: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. Objective: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph?based approach. Methods: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. Results: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. Conclusions: We showed the effectiveness of a knowledge graph?based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications. UR - https://www.jmir.org/2023/1/e45225 UR - http://dx.doi.org/10.2196/45225 UR - http://www.ncbi.nlm.nih.gov/pubmed/37862061 ID - info:doi/10.2196/45225 ER - TY - JOUR AU - Chen, Chih-Chi AU - Wu, Cheng-Ta AU - Chen, C. Carl P. AU - Chung, Chia-Ying AU - Chen, Shann-Ching AU - Lee, S. Mel AU - Cheng, Chi-Tung AU - Liao, Chien-Hung PY - 2023/10/20 TI - Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study JO - JMIR Form Res SP - e42788 VL - 7 KW - osteoarthritis KW - orthopedic procedure KW - artificial intelligence KW - AI KW - deep learning KW - machine learning KW - orthopedic KW - pelvic KW - radiograph KW - predict KW - hip replacement KW - surgery KW - convolutional neural network KW - CNN KW - algorithm KW - surgical KW - medical image KW - medical imaging N2 - Background: Total hip replacement (THR) is considered the gold standard of treatment for refractory degenerative hip disorders. Identifying patients who should receive THR in the short term is important. Some conservative treatments, such as intra-articular injection administered a few months before THR, may result in higher odds of arthroplasty infection. Delayed THR after functional deterioration may result in poorer outcomes and longer waiting times for those who have been flagged as needing THR. Deep learning (DL) in medical imaging applications has recently obtained significant breakthroughs. However, the use of DL in practical wayfinding, such as short-term THR prediction, is still lacking. Objective: In this study, we will propose a DL-based assistant system for patients with pelvic radiographs to identify the need for THR within 3 months. Methods: We developed a convolutional neural network?based DL algorithm to analyze pelvic radiographs, predict the hip region of interest (ROI), and determine whether or not THR is required. The data set was collected from August 2008 to December 2017. The images included 3013 surgical hip ROIs that had undergone THR and 1630 nonsurgical hip ROIs. The images were split, using split-sample validation, into training (n=3903, 80%), validation (n=476, 10%), and testing (n=475, 10%) sets to evaluate the algorithm performance. Results: The algorithm, called SurgHipNet, yielded an area under the receiver operating characteristic curve of 0.994 (95% CI 0.990-0.998). The accuracy, sensitivity, specificity, and F1-score of the model were 0.977, 0.920, 0932, and 0.944, respectively. Conclusions: The proposed approach has demonstrated that SurgHipNet shows the ability and potential to provide efficient support in clinical decision-making; it can assist physicians in promptly determining the optimal timing for THR. UR - https://formative.jmir.org/2023/1/e42788 UR - http://dx.doi.org/10.2196/42788 UR - http://www.ncbi.nlm.nih.gov/pubmed/37862084 ID - info:doi/10.2196/42788 ER - TY - JOUR AU - Wong, Kang-An AU - Ang, Hou Bryan Chin AU - Gunasekeran, Visva Dinesh AU - Husain, Rahat AU - Boon, Joewee AU - Vikneson, Krishna AU - Tan, Qi Zyna Pei AU - Tan, Wei Gavin Siew AU - Wong, Yin Tien AU - Agrawal, Rupesh PY - 2023/10/19 TI - Remote Perimetry in a Virtual Reality Metaverse Environment for Out-of-Hospital Functional Eye Screening Compared Against the Gold Standard Humphrey Visual Fields Perimeter: Proof-of-Concept Pilot Study JO - J Med Internet Res SP - e45044 VL - 25 KW - eye KW - screening KW - glaucoma KW - virtual reality KW - metaverse KW - digital health KW - visual impairment KW - visually impaired KW - functional testing KW - ophthalmologic KW - ophthalmology KW - remote care KW - visual field KW - HVF KW - perimetry test N2 - Background: The growing global burden of visual impairment necessitates better population eye screening for early detection of eye diseases. However, accessibility to testing is often limited and centralized at in-hospital settings. Furthermore, many eye screening programs were disrupted by the COVID-19 pandemic, presenting an urgent need for out-of-hospital solutions. Objective: This study investigates the performance of a novel remote perimetry application designed in a virtual reality metaverse environment to enable functional testing in community-based and primary care settings. Methods: This was a prospective observational study investigating the performance of a novel remote perimetry solution in comparison with the gold standard Humphrey visual field (HVF) perimeter. Subjects received a comprehensive ophthalmologic assessment, HVF perimetry, and remote perimetry testing. The primary outcome measure was the agreement in the classification of overall perimetry result normality by the HVF (Swedish interactive threshold algorithm?fast) and testing with the novel algorithm. Secondary outcome measures included concordance of individual testing points and perimetry topographic maps. Results: We recruited 10 subjects with an average age of 59.6 (range 28-81) years. Of these, 7 (70%) were male and 3 (30%) were female. The agreement in the classification of overall perimetry results was high (9/10, 90%). The pointwise concordance in the automated classification of individual test points was 83.3% (8.2%; range 75%-100%). In addition, there was good perimetry topographic concordance with the HVF in all subjects. Conclusions: Remote perimetry in a metaverse environment had good concordance with gold standard perimetry using the HVF and could potentially avail functional eye screening in out-of-hospital settings. UR - https://www.jmir.org/2023/1/e45044 UR - http://dx.doi.org/10.2196/45044 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856179 ID - info:doi/10.2196/45044 ER - TY - JOUR AU - Tutunji, Rayyan AU - Kogias, Nikos AU - Kapteijns, Bob AU - Krentz, Martin AU - Krause, Florian AU - Vassena, Eliana AU - Hermans, J. Erno PY - 2023/10/19 TI - Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study JO - J Med Internet Res SP - e39995 VL - 25 KW - biosensor KW - devices KW - ecological momentary assessments KW - experience sampling KW - machine learning KW - mental disorder KW - mental health KW - monitoring KW - physiological KW - prevention KW - psychological KW - smartwatches KW - stress KW - wearables N2 - Background: Increasing efforts toward the prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated. Objective: We aimed to determine the utility of ecological momentary assessments (EMA) and physiological arousal measured through wearable devices in detecting ecologically relevant stress states. Methods: Using EMA combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (ie, a high-stakes examination week) on physiological arousal and affect compared to a control week without examinations in first-year medical and biomedical science students (51/83, 61.4% female). We first used generalized linear mixed-effects models with maximal fitting approaches to investigate the impact of examination periods on subjective stress exposure, mood, and physiological arousal. We then used machine learning models to investigate whether we could use EMA, wearable biosensors, or the combination of both to classify momentary data (ie, beeps) as belonging to examination or control weeks. We tested both individualized models using a leave-one-beep-out approach and group-based models using a leave-one-subject-out approach. Results: During stressful high-stakes examination (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal decreased on average during the examination week. Time-resolved analyses revealed peaks in physiological arousal associated with both momentary self-reported stress exposure and self-reported positive affect. Mediation models revealed that the decreased physiological arousal in the examination week was mediated by lower positive affect during the same period. We then used machine learning to show that while individualized EMA outperformed EPA in its ability to classify beeps as originating from examinations or from control weeks (1603/4793, 33.45% and 1648/4565, 36.11% error rates, respectively), a combination of EMA and EPA yields optimal classification (1363/4565, 29.87% error rate). Finally, when comparing individualized models to group-based models, we found that the individualized models significantly outperformed the group-based models across all 3 inputs (EMA, EPA, and the combination). Conclusions: This study underscores the potential of wearable biosensors for stress-related mental health monitoring. However, it emphasizes the necessity of psychological context in interpreting physiological arousal captured by these devices, as arousal can be related to both positive and negative contexts. Moreover, our findings support a personalized approach in which momentary stress is optimally detected when referenced against an individual?s own data. UR - https://www.jmir.org/2023/1/e39995 UR - http://dx.doi.org/10.2196/39995 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856180 ID - info:doi/10.2196/39995 ER - TY - JOUR AU - Solomon, Jeffrey AU - Dauber-Decker, Katherine AU - Richardson, Safiya AU - Levy, Sera AU - Khan, Sundas AU - Coleman, Benjamin AU - Persaud, Rupert AU - Chelico, John AU - King, D'Arcy AU - Spyropoulos, Alex AU - McGinn, Thomas PY - 2023/10/19 TI - Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study JO - JMIR Form Res SP - e44065 VL - 7 KW - clinical decision support system KW - cloud based KW - decision support KW - development KW - EHR KW - electronic health record KW - evidence-based medicine KW - health information technology KW - platform KW - user-centered design N2 - Background: Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. Objective: The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. Methods: We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. Results: The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells? criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform?s flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells? criteria tool will be reported in a future publication. Conclusions: The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics ?nudges? on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint. UR - https://formative.jmir.org/2023/1/e44065 UR - http://dx.doi.org/10.2196/44065 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856193 ID - info:doi/10.2196/44065 ER - TY - JOUR AU - Blatter, Ueli Tobias AU - Witte, Harald AU - Fasquelle-Lopez, Jules AU - Nakas, Theodoros Christos AU - Raisaro, Louis Jean AU - Leichtle, Benedikt Alexander PY - 2023/10/18 TI - The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application JO - J Med Internet Res SP - e47254 VL - 25 KW - personalized health KW - laboratory medicine KW - reference interval KW - research infrastructure KW - sensitive data KW - confidential data KW - data security KW - differential privacy KW - precision medicine N2 - Background: Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. Objective: This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group?specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. Methods: A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified ?big data? resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict ?no copy, no move? principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. Results: The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group?specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. Conclusions: With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine. UR - https://www.jmir.org/2023/1/e47254 UR - http://dx.doi.org/10.2196/47254 UR - http://www.ncbi.nlm.nih.gov/pubmed/37851984 ID - info:doi/10.2196/47254 ER - TY - JOUR AU - Contreras, Ivan AU - Navarro-Otano, Judith AU - Rodríguez-Pintó, Ignasi AU - Güemes, Amparo AU - Alves, Eduarda AU - Rios-Garcés, Roberto AU - Espinosa, Gerard AU - Alejaldre, Aida AU - Beneyto, Aleix AU - Ramkissoon, Mary Charrise AU - Vehi, Josep AU - Cervera, Ricard PY - 2023/10/13 TI - Optimizing Noninvasive Vagus Nerve Stimulation for Systemic Lupus Erythematosus: Protocol for a Multicenter Randomized Controlled Trial JO - JMIR Res Protoc SP - e48387 VL - 12 KW - vagus nerve stimulation KW - autonomic nervous system KW - computational models KW - systemic lupus erythematosus KW - vagus KW - vagal KW - nerve stimulation KW - noninvasive KW - RCT KW - randomized KW - lupus KW - inflammation KW - autoimmune KW - chronic KW - nerve KW - nerve damage KW - vagus nerve N2 - Background: Systemic lupus erythematosus is a chronic, multisystem, inflammatory disease of autoimmune etiology occurring predominantly in women. A major hurdle to the diagnosis, treatment, and therapeutic advancement of this disease is its heterogeneous nature, which presents as a wide range of symptoms such as fatigue, fever, musculoskeletal involvement, neuropsychiatric disorders, and cardiovascular involvement with varying severity. The current therapeutic approach to this disease includes the administration of immunomodulatory drugs that may produce unfavorable secondary effects. Objective: This study explores the known relationship between the autonomic nervous system and inflammatory pathways to improve patient outcomes by treating autonomic nervous system dysregulation in patients via noninvasive vagus nerve stimulation. In this study, data including biomarkers, physiological signals, patient outcomes, and patient quality of life are being collected and analyzed. After completion of the clinical trial, a computer model will be developed to identify the biomarkers and physiological signals related to lupus activity in order to understand how they change with different noninvasive vagus nerve stimulation frequency parameters. Finally, we propose building a decision support system with integrated noninvasive wearable technologies for continuous cardiovascular and peripheral physiological sensing for adaptive, patient-specific optimization of the noninvasive vagus nerve stimulation frequency parameters in real time. Methods: The protocol was designed to evaluate the efficacy and safety of transauricular vagus nerve stimulation in patients with systemic lupus erythematosus. This multicenter, national, randomized, double-blind, parallel-group, placebo-controlled study will recruit a minimum of 18 patients diagnosed with this disease. Evaluation and treatment of patients will be conducted in an outpatient clinic and will include 12 visits. Visit 1 consists of a screening session. Subsequent visits up to visit 6 involve mixing treatment and evaluation sessions. Finally, the remaining visits correspond with early and late posttreatment follow-ups. Results: On November 2022, data collection was initiated. Of the 10 participants scheduled for their initial appointment, 8 met the inclusion criteria, and 6 successfully completed the entire protocol. Patient enrollment and data collection are currently underway and are expected to be completed in December 2023. Conclusions: The results of this study will advance patient-tailored vagus nerve stimulation therapies, providing an adjunctive treatment solution for systemic lupus erythematosus that will foster adoption of technology and, thus, expand the population with systemic lupus erythematosus who can benefit from improved autonomic dysregulation, translating into reduced costs and better quality of life. Trial Registration: ClinicalTrials.gov NCT05704153; https://clinicaltrials.gov/study/NCT05704153 International Registered Report Identifier (IRRID): DERR1-10.2196/48387 UR - https://www.researchprotocols.org/2023/1/e48387 UR - http://dx.doi.org/10.2196/48387 UR - http://www.ncbi.nlm.nih.gov/pubmed/37831494 ID - info:doi/10.2196/48387 ER - TY - JOUR AU - Castellucci, Clara AU - Malorgio, Amos AU - Budowski, Dinah Alexandra AU - Akbas, Samira AU - Kolbe, Michaela AU - Grande, Bastian AU - Braun, Julia AU - Noethiger, B. Christoph AU - Spahn, R. Donat AU - Tscholl, Werner David AU - Roche, Raoul Tadzio PY - 2023/10/12 TI - Coagulation Management of Critically Bleeding Patients With Viscoelastic Testing Presented as a 3D-Animated Blood Clot (The Visual Clot): Randomized Controlled High-Fidelity Simulation Study JO - J Med Internet Res SP - e43895 VL - 25 KW - avatar technology KW - coagulation management KW - high-fidelity simulation KW - point-of-care testing KW - thrombelastography KW - user-centered design KW - Visual Clot N2 - Background: Guidelines recommend using viscoelastic coagulation tests to guide coagulation management, but interpreting the results remains challenging. Visual Clot, a 3D animated blood clot, facilitates interpretation through a user-centered and situation awareness?oriented design. Objective: This study aims to compare the effects of Visual Clot versus conventional viscoelastic test results (rotational thrombelastometry [ROTEM] temograms) on the coagulation management performance of anesthesia teams in critical bleeding situations. Methods: We conducted a prospective, randomized, high-fidelity simulation study in which anesthesia teams (consisting of a senior anesthesiologist, a resident anesthesiologist, and an anesthesia nurse) managed perioperative bleeding scenarios. Teams had either Visual Clot or ROTEM temograms available to perform targeted coagulation management. We analyzed the 15-minute simulations with post hoc video analysis. The primary outcome was correct targeted coagulation therapy. Secondary outcomes were time to targeted coagulation therapy, confidence, and workload. In addition, we have conducted a qualitative survey on user acceptance of Visual Clot. We used Poisson regression, Cox regression, and mixed logistic regression models, adjusted for various potential confounders, to analyze the data. Results: We analyzed 59 simulations. Teams using Visual Clot were more likely to deliver the overall targeted coagulation therapy correctly (rate ratio 1.56, 95% CI 1.00-2.47; P=.05) and administer the first targeted coagulation product faster (hazard ratio 2.58, 95% CI 1.37-4.85; P=.003). In addition, participants showed higher decision confidence with Visual Clot (odds ratio 3.60, 95% CI 1.49-8.71; P=.005). We found no difference in workload (coefficient ?0.03, 95% CI ?3.08 to 2.88; P=.99). Conclusions: Using Visual Clot led to a more accurate and faster-targeted coagulation therapy than using ROTEM temograms. We suggest that relevant viscoelastic test manufacturers consider augmenting their complex result presentation with intuitive, easy-to-understand visualization to ease users? burden from unnecessary cognitive load and enhance patient care. UR - https://www.jmir.org/2023/1/e43895 UR - http://dx.doi.org/10.2196/43895 UR - http://www.ncbi.nlm.nih.gov/pubmed/37824182 ID - info:doi/10.2196/43895 ER - TY - JOUR AU - Kim, Seong-Yeol AU - Song, Minji AU - Jo, Yunju AU - Jung, Youngjae AU - You, Heecheon AU - Ko, Myoung-Hwan AU - Kim, Gi-Wook PY - 2023/10/11 TI - Effect of Voice and Articulation Parameters of a Home-Based Serious Game for Speech Therapy in Children With Articulation Disorder: Prospective Single-Arm Clinical Trial JO - JMIR Serious Games SP - e49216 VL - 11 KW - articulation disorder KW - home-based therapy KW - serious game KW - children KW - speech KW - voice N2 - Background: Articulation disorder decreases the clarity of language and causes a decrease in children?s learning and social ability. The demand for non?face-to-face treatment is increasing owing to the limited number of therapists and geographical or economic constraints. Non?face-to-face speech therapy programs using serious games have been proposed as an alternative. Objective: The aim of this study is to investigate the efficacy of home therapy on logopedic and phoniatric abilities in children with articulation disorder using the Smart Speech game interface. Methods: This study is a prospective single-arm clinical trial. Children with articulation disorders, whose Urimal Test of Articulation and Phonology (U-TAP) was ?2 SDs or less and the Receptive and Expressive Vocabulary Test score was ?1 SD or more, were enrolled. A preliminary evaluation (E0) was conducted to check whether the children had articulation disorders, and for the next 4 weeks, they lived their usual lifestyle without other treatments. Prior to the beginning of the training, a pre-evaluation (E1) was performed, and the children trained at home for ?30 minutes per day, ?5 times a week, over 4 weeks (a total of 20 sessions). The Smart Speech program comprised oral exercise training, breathing training, and speech training; the difficulty and type of the training were configured differently according to the participants? articulation error, exercise, and vocal ability. After the training, postevaluation (E2) was performed using the same method. Finally, 8 weeks later, postevaluation (E3) was performed as a follow-up. A voice evaluation included parameters such as maximum phonation time (MPT), fundamental frequency (F0), jitter, peak air pressure (relative average perturbation), pitch, intensity, and voice onset time. Articulation parameters included a percentage of correct consonants (PCC; U-TAP word-unit PCC, U-TAP sentence-unit PCC, and three-position articulation test) and alternate motion evaluation (diadochokinesis, DDK). Data obtained during each evaluation (E1-E2-E3) were compared. Results: A total of 13 children with articulation disorders aged 4-10 years were enrolled in the study. In voice parameters, MPT, jitter, and pitch showed significant changes in repeated-measures ANOVA. However, only MPT showed significant changes during E1-E2 (P=.007) and E1-E3 (P=.004) in post hoc tests. Other voice parameters did not show significant changes. In articulation parameters, U-TAP, three-position articulation test (TA), and DDK showed significant changes in repeated-measures ANOVA. In post hoc tests, U-TAP (word, sentence) and TA showed significant changes during E1-E2 (P=.003, .04, and .01) and E1-E3 (P=.001, .03, and .003), and DDK showed significant changes during E1-E2 only (P=.03). Conclusions: Home-based serious games can be considered an alternative treatment method to improve language function. Trial Registration: Clinical Research Information Service KCT0006448; https://cris.nih.go.kr/cris/search/detailSearch.do/20119 UR - https://games.jmir.org/2023/1/e49216 UR - http://dx.doi.org/10.2196/49216 UR - http://www.ncbi.nlm.nih.gov/pubmed/37819707 ID - info:doi/10.2196/49216 ER - TY - JOUR AU - Jenci?t?, Gabriel? AU - Kasputyt?, Gabriel? AU - Bunevi?ien?, Inesa AU - Korobeinikova, Erika AU - Vaitiekus, Domas AU - In?i?ra, Arturas AU - Jaru?evi?ius, Laimonas AU - Bunevi?ius, Romas AU - Krik?tolaitis, Ri?ardas AU - Krilavi?ius, Tomas AU - Juozaityt?, Elona AU - Bunevi?ius, Adomas PY - 2023/10/10 TI - Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study JO - JMIR Res Protoc SP - e49096 VL - 12 KW - cancer KW - digital phenotyping KW - biomarkers KW - oncology KW - digital phenotype KW - biomarker KW - data collection KW - data generation KW - monitor KW - monitoring KW - predict KW - prediction KW - model KW - models KW - mobile phone N2 - Background: Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. Objective: We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. Methods: We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. Results: As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. Conclusions: This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. International Registered Report Identifier (IRRID): PRR1-10.2196/49096 UR - https://www.researchprotocols.org/2023/1/e49096 UR - http://dx.doi.org/10.2196/49096 UR - http://www.ncbi.nlm.nih.gov/pubmed/37815850 ID - info:doi/10.2196/49096 ER - TY - JOUR AU - Sangeorzan, Irina AU - Antonacci, Grazia AU - Martin, Anne AU - Grodzinski, Ben AU - Zipser, M. Carl AU - Murphy, J. Rory K. AU - Andriopoulou, Panoraia AU - Cook, E. Chad AU - Anderson, B. David AU - Guest, James AU - Furlan, C. Julio AU - Kotter, N. Mark R. AU - Boerger, F. Timothy AU - Sadler, Iwan AU - Roberts, A. Elizabeth AU - Wood, Helen AU - Fraser, Christine AU - Fehlings, G. Michael AU - Kumar, Vishal AU - Jung, Josephine AU - Milligan, James AU - Nouri, Aria AU - Martin, R. Allan AU - Blizzard, Tammy AU - Vialle, Roberto Luiz AU - Tetreault, Lindsay AU - Kalsi-Ryan, Sukhvinder AU - MacDowall, Anna AU - Martin-Moore, Esther AU - Burwood, Martin AU - Wood, Lianne AU - Lalkhen, Abdul AU - Ito, Manabu AU - Wilson, Nicky AU - Treanor, Caroline AU - Dugan, Sheila AU - Davies, M. Benjamin PY - 2023/10/9 TI - Toward Shared Decision-Making in Degenerative Cervical Myelopathy: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e46809 VL - 12 KW - degenerative cervical myelopathy KW - spine KW - spinal cord KW - chronic KW - aging KW - geriatric KW - patient engagement KW - shared decision-making KW - process mapping KW - core information set KW - decision-making KW - patient education KW - common data element KW - Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy KW - RECODE-DCM N2 - Background: Health care decisions are a critical determinant in the evolution of chronic illness. In shared decision-making (SDM), patients and clinicians work collaboratively to reach evidence-based health decisions that align with individual circumstances, values, and preferences. This personalized approach to clinical care likely has substantial benefits in the oversight of degenerative cervical myelopathy (DCM), a type of nontraumatic spinal cord injury. Its chronicity, heterogeneous clinical presentation, complex management, and variable disease course engenders an imperative for a patient-centric approach that accounts for each patient?s unique needs and priorities. Inadequate patient knowledge about the condition and an incomplete understanding of the critical decision points that arise during the course of care currently hinder the fruitful participation of health care providers and patients in SDM. This study protocol presents the rationale for deploying SDM for DCM and delineates the groundwork required to achieve this. Objective: The study?s primary outcome is the development of a comprehensive checklist to be implemented upon diagnosis that provides patients with essential information necessary to support their informed decision-making. This is known as a core information set (CIS). The secondary outcome is the creation of a detailed process map that provides a diagrammatic representation of the global care workflows and cognitive processes involved in DCM care. Characterizing the critical decision points along a patient?s journey will allow for an effective exploration of SDM tools for routine clinical practice to enhance patient-centered care and improve clinical outcomes. Methods: Both CISs and process maps are coproduced iteratively through a collaborative process involving the input and consensus of key stakeholders. This will be facilitated by Myelopathy.org, a global DCM charity, through its Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy community. To develop the CIS, a 3-round, web-based Delphi process will be used, starting with a baseline list of information items derived from a recent scoping review of educational materials in DCM, patient interviews, and a qualitative survey of professionals. A priori criteria for achieving consensus are specified. The process map will be developed iteratively using semistructured interviews with patients and professionals and validated by key stakeholders. Results: Recruitment for the Delphi consensus study began in April 2023. The pilot-testing of process map interview participants started simultaneously, with the formulation of an initial baseline map underway. Conclusions: This protocol marks the first attempt to provide a starting point for investigating SDM in DCM. The primary work centers on developing an educational tool for use in diagnosis to enable enhanced onward decision-making. The wider objective is to aid stakeholders in developing SDM tools by identifying critical decision junctures in DCM care. Through these approaches, we aim to provide an exhaustive launchpad for formulating SDM tools in the wider DCM community. International Registered Report Identifier (IRRID): DERR1-10.2196/46809 UR - https://www.researchprotocols.org/2023/1/e46809 UR - http://dx.doi.org/10.2196/46809 UR - http://www.ncbi.nlm.nih.gov/pubmed/37812472 ID - info:doi/10.2196/46809 ER - TY - JOUR AU - Lyon, Matthieu AU - Fehlmann, Alain Christophe AU - Augsburger, Marc AU - Schaller, Thomas AU - Zimmermann-Ivol, Catherine AU - Celi, Julien AU - Gartner, Andrea Birgit AU - Lorenzon, Nicolas AU - Sarasin, François AU - Suppan, Laurent PY - 2023/10/6 TI - Evaluation of a Portable Blood Gas Analyzer for Prehospital Triage in Carbon Monoxide Poisoning: Instrument Validation Study JO - JMIR Form Res SP - e48057 VL - 7 KW - carbon monoxide poisoning KW - carbon monoxide intoxication KW - prehospital triage KW - Avoximeter 4000 KW - CO-oximetry KW - blood gas KW - blood work KW - pulse oximeter KW - cohort study KW - carbon monoxide KW - poisoning KW - sensor KW - triage tool KW - triage KW - oximeter KW - pilot study KW - medical device N2 - Background: Carbon monoxide (CO) poisoning is an important cause of morbidity and mortality worldwide. Symptoms are mostly aspecific, making it hard to identify, and its diagnosis is usually made through blood gas analysis. However, the bulkiness of gas analyzers prevents them from being used at the scene of the incident, thereby leading to the unnecessary transport and admission of many patients. While multiple-wavelength pulse oximeters have been developed to discriminate carboxyhemoglobin (COHb) from oxyhemoglobin, their reliability is debatable, particularly in the hostile prehospital environment. Objective: The main objective of this pilot study was to assess whether the Avoximeter 4000, a transportable blood gas analyzer, could be considered for prehospital triage. Methods: This was a monocentric, prospective, pilot evaluation study. Blood samples were analyzed sequentially with 2 devices: the Avoximeter 4000 (experimental), which performs direct measurements on blood samples of about 50 µL by analyzing light absorption at 5 different wavelengths; and the ABL827 FLEX (control), which measures COHb levels through an optical system composed of a 128-wavelength spectrophotometer. The blood samples belonged to 2 different cohorts: the first (clinical cohort) was obtained in an emergency department and consisted of 68 samples drawn from patients admitted for reasons other than CO poisoning. These samples were used to determine whether the Avoximeter 4000 could properly exclude the diagnosis. The second (forensic) cohort was derived from the regional forensic center, which provided 12 samples from documented CO poisoning. Results: The mean COHb level in the clinical cohort was 1.7% (SD 1.8%; median 1.2%, IQR 0.7%-1.9%) with the ABL827 FLEX versus 3.5% (SD 2.3%; median 3.1%, IQR 2.2%-4.1%) with the Avoximeter 4000. Therefore, the Avoximeter 4000 overestimated COHb levels by a mean difference of 1.8% (95% CI 1.5%-2.1%). The consistency of COHb readings by the Avoximeter 4000 was excellent, with an intraclass correlation coefficient of 0.97 (95% CI 0.93-0.99) when the same blood sample was analyzed repeatedly. Using prespecified cutoffs (5% in nonsmokers and 10% in smokers), 3 patients (4%) had high COHb levels according to the Avoximeter 4000, while their values were within the normal range according to the ABL827 FLEX. Therefore, the specificity of the Avoximeter 4000 in this cohort was 95.6% (95% CI 87%-98.6%), and the overtriage rate would have been 4.4% (95% CI 1.4%-13%). Regarding the forensic samples, 10 of 12 (83%) samples were positive with both devices, while the 2 remaining samples were negative with both devices. Conclusions: The limited difference in COHb level measurements between the Avoximeter 4000 and the control device, which erred on the side of safety, and the relatively low overtriage rate warrant further exploration of this device as a prehospital triage tool. UR - https://formative.jmir.org/2023/1/e48057 UR - http://dx.doi.org/10.2196/48057 UR - http://www.ncbi.nlm.nih.gov/pubmed/37801355 ID - info:doi/10.2196/48057 ER - TY - JOUR AU - Zhang, Ying AU - Li, Xiaoying AU - Liu, Yi AU - Li, Aihua AU - Yang, Xuemei AU - Tang, Xiaoli PY - 2023/10/5 TI - A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification JO - JMIR Med Inform SP - e44892 VL - 11 KW - text classification KW - publication-level classifier KW - cancer literature KW - deep learning N2 - Background: Given the threat posed by cancer to human health, there is a rapid growth in the volume of data in the cancer field and interdisciplinary and collaborative research is becoming increasingly important for fine-grained classification. The low-resolution classifier of reported studies at the journal level fails to satisfy advanced searching demands, and a single label does not adequately characterize the literature originated from interdisciplinary research results. There is thus a need to establish a multilabel classifier with higher resolution to support literature retrieval for cancer research and reduce the burden of screening papers for clinical relevance. Objective: The primary objective of this research was to address the low-resolution issue of cancer literature classification due to the ambiguity of the existing journal-level classifier in order to support gaining high-relevance evidence for clinical consideration and all-sided results for literature retrieval. Methods: We trained a multilabel classifier with scalability for classifying the literature on cancer research directly at the publication level to assign proper content-derived labels based on the ?Bidirectional Encoder Representation from Transformers (BERT) + X? model and obtain the best option for X. First, a corpus of 70,599 cancer publications retrieved from the Dimensions database was divided into a training and a testing set in a ratio of 7:3. Second, using the classification terminology of International Cancer Research Partnership cancer types, we compared the performance of classifiers developed using BERT and 5 classical deep learning models, such as the text recurrent neural network (TextRNN) and FastText, followed by metrics analysis. Results: After comparing various combined deep learning models, we obtained a classifier based on the optimal combination ?BERT + TextRNN,? with a precision of 93.09%, a recall of 87.75%, and an F1-score of 90.34%. Moreover, we quantified the distinctive characteristics in the text structure and multilabel distribution in order to generalize the model to other fields with similar characteristics. Conclusions: The ?BERT + TextRNN? model was trained for high-resolution classification of cancer literature at the publication level to support accurate retrieval and academic statistics. The model automatically assigns 1 or more labels to each cancer paper, as required. Quantitative comparison verified that the ?BERT + TextRNN? model is the best fit for multilabel classification of cancer literature compared to other models. More data from diverse fields will be collected to testify the scalability and extensibility of the proposed model in the future. UR - https://medinform.jmir.org/2023/1/e44892 UR - http://dx.doi.org/10.2196/44892 UR - http://www.ncbi.nlm.nih.gov/pubmed/37796584 ID - info:doi/10.2196/44892 ER - TY - JOUR AU - Jordan, J. Evan AU - Shih, C. Patrick AU - Nelson, J. Erik AU - Carter, J. Stephen AU - Schootman, Mario AU - Prather, A. Aric AU - Yao, Xing AU - Peters, D. Chasie AU - Perry, E. Canaan S. PY - 2023/10/5 TI - Ecological Momentary Assessment of Midlife Adults? Daily Stress: Protocol for the Stress Reports in Variable Environments (STRIVE) App Study JO - JMIR Res Protoc SP - e51845 VL - 12 KW - activity trackers KW - built environment KW - ecological momentary assessment KW - heart rate monitoring KW - life stress KW - physical activity KW - spatial analysis KW - wearable technology N2 - Background: Daily stressors are associated with cognitive decline and increased risk of heart disease, depression, and other debilitating chronic illnesses in midlife adults. Daily stressors tend to occur at home or at work and are more frequent in urban versus rural settings. Conversely, spending time in natural environments such as parks or forests, or even viewing nature-themed images in a lab setting, is associated with lower levels of perceived stress and is hypothesized to be a strong stress ?buffer,? reducing perceived stress even after leaving the natural setting. However, many studies of daily stress have not captured environmental contexts and relied on end-of-day recall instead of in-the-moment data capture. With new technology, these limitations can be addressed to enhance knowledge of the daily stress experience. Objective: We propose to use our novel custom-built Stress Reports in Variable Environments (STRIVE) ecological momentary assessment mobile phone app to measure the experience of daily stress of midlife adults in free-living conditions. Using our app to capture data in real time will allow us to determine (1) where and when daily stress occurs for midlife adults, (2) whether midlife adults? daily stressors are linked to certain elements of the built and natural environment, and (3) how ecological momentary assessment measurement of daily stress is similar to and different from a modified version of the popular Daily Inventory of Stressful Events measurement tool that captures end-of-day stress reports (used in the Midlife in the United States [MIDUS] survey). Methods: We will enroll a total of 150 midlife adults living in greater Indianapolis, Indiana, in this study on a rolling basis for 3-week periods. As those in underrepresented minority groups and low-income areas have previously been found to experience greater levels of stress, we will use stratified sampling to ensure that half of our study sample is composed of underrepresented minorities (eg, Black, American Indian, Hispanic, or Native Pacific Islanders) and approximately one-third of our sample falls within low-, middle-, and high-income brackets. Results: This project is funded by the National Institute on Aging from December 2022 to November 2024. Participant enrollment began in August 2023 and is expected to finish in July 2024. Data will be spatiotemporally analyzed to determine where and when stress occurs for midlife adults. Pictures of stressful environments will be qualitatively analyzed to determine the common elements of stressful environments. Data collected by the STRIVE app will be compared with retrospective Daily Inventory of Stressful Events data. Conclusions: Completing this study will expand our understanding of midlife adults? experience of stress in free-living conditions and pave the way for data-driven individual and community-based intervention designs to promote health and well-being in midlife adults. International Registered Report Identifier (IRRID): DERR1-10.2196/51845 UR - https://www.researchprotocols.org/2023/1/e51845 UR - http://dx.doi.org/10.2196/51845 UR - http://www.ncbi.nlm.nih.gov/pubmed/37796561 ID - info:doi/10.2196/51845 ER - TY - JOUR AU - Homburg, Maarten AU - Meijer, Eline AU - Berends, Matthijs AU - Kupers, Thijmen AU - Olde Hartman, Tim AU - Muris, Jean AU - de Schepper, Evelien AU - Velek, Premysl AU - Kuiper, Jeroen AU - Berger, Marjolein AU - Peters, Lilian PY - 2023/10/4 TI - A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study JO - J Med Internet Res SP - e49944 VL - 25 KW - natural language processing KW - primary care KW - COVID-19 KW - EHR KW - electronic health records KW - public health KW - multidisciplinary KW - NLP KW - disease identification KW - BERT model KW - model development KW - prediction N2 - Background: Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general practitioners (GPs) serve as the first point of contact for health care, EHRs generated by these primary care providers contain a wealth of potentially valuable information. Nonetheless, the unstructured nature of free-text entries in EHRs poses challenges in identifying trends, detecting disease outbreaks, or accurately pinpointing COVID-19 cases. Objective: This study aims to develop and validate a BERT model for detecting COVID-19 consultations in general practice EHRs in the Netherlands. Methods: The BERT model was initially pretrained on Dutch language data and fine-tuned using a comprehensive EHR data set comprising confirmed COVID-19 GP consultations and non?COVID-19?related consultations. The data set was partitioned into a training and development set, and the model?s performance was evaluated on an independent test set that served as the primary measure of its effectiveness in COVID-19 detection. To validate the final model, its performance was assessed through 3 approaches. First, external validation was applied on an EHR data set from a different geographic region in the Netherlands. Second, validation was conducted using results of polymerase chain reaction (PCR) test data obtained from municipal health services. Lastly, correlation between predicted outcomes and COVID-19?related hospitalizations in the Netherlands was assessed, encompassing the period around the outbreak of the pandemic in the Netherlands, that is, the period before widespread testing. Results: The model development used 300,359 GP consultations. We developed a highly accurate model for COVID-19 consultations (accuracy 0.97, F1-score 0.90, precision 0.85, recall 0.85, specificity 0.99). External validations showed comparable high performance. Validation on PCR test data showed high recall but low precision and specificity. Validation using hospital data showed significant correlation between COVID-19 predictions of the model and COVID-19?related hospitalizations (F1-score 96.8; P<.001; R2=0.69). Most importantly, the model was able to predict COVID-19 cases weeks before the first confirmed case in the Netherlands. Conclusions: The developed BERT model was able to accurately identify COVID-19 cases among GP consultations even preceding confirmed cases. The validated efficacy of our BERT model highlights the potential of NLP models to identify disease outbreaks early, exemplifying the power of multidisciplinary efforts in harnessing technology for disease identification. Moreover, the implications of this study extend beyond COVID-19 and offer a blueprint for the early recognition of various illnesses, revealing that such models could revolutionize disease surveillance. UR - https://www.jmir.org/2023/1/e49944 UR - http://dx.doi.org/10.2196/49944 UR - http://www.ncbi.nlm.nih.gov/pubmed/37792444 ID - info:doi/10.2196/49944 ER - TY - JOUR AU - Hill, Adele AU - Joyner, H. Christopher AU - Keith-Jopp, Chloe AU - Yet, Barbaros AU - Tuncer Sakar, Ceren AU - Marsh, William AU - Morrissey, Dylan PY - 2023/10/3 TI - Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study JO - JMIR Form Res SP - e44187 VL - 7 KW - artificial intelligence KW - back pain KW - Bayesian network KW - expert consensus N2 - Background: Identifying and managing serious spinal pathology (SSP) such as cauda equina syndrome or spinal infection in patients presenting with low back pain is challenging. Traditional red flag questioning is increasingly criticized, and previous studies show that many clinicians lack confidence in managing patients presenting with red flags. Improving decision-making and reducing the variability of care for these patients is a key priority for clinicians and researchers. Objective: We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge. Methods: A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics. Results: The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable. Conclusions: The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP. International Registered Report Identifier (IRRID): RR2-10.2196/21804 UR - https://formative.jmir.org/2023/1/e44187 UR - http://dx.doi.org/10.2196/44187 UR - http://www.ncbi.nlm.nih.gov/pubmed/37788068 ID - info:doi/10.2196/44187 ER - TY - JOUR AU - Abdou, Abdelrahman AU - Krishnan, Sridhar AU - Mistry, Niraj PY - 2023/10/2 TI - Evaluating a Novel Infant Heart Rate Detector for Neonatal Resuscitation Efforts: Protocol for a Proof-of-Concept Study JO - JMIR Res Protoc SP - e45512 VL - 12 KW - newborn KW - electrocardiogram KW - ECG KW - dry electrode KW - heart rate KW - pediatric KW - resuscitation KW - infant KW - vital signs KW - neonatal N2 - Background: Over 10 million newborns worldwide undergo resuscitation at birth each year. Pediatricians may use electrocardiogram (ECG), pulse oximetry (PO), and stethoscope in determining heart rate (HR), as HR guides the need for and steps of resuscitation. HR must be obtained quickly and accurately. Unfortunately, the current diagnostic modalities are either too slow, obtaining HR in more than a minute, or inaccurate. With time constraints, a reliable robust heart rate detector (HRD) modality is required. This paper discusses a protocol for conducting a methods-based comparison study to determine the HR accuracy of a novel real-time HRD based on 3D-printed dry-electrode single-lead ECG signals for cost-effective and quick HR determination. The HRD?s HR results are compared to either clinical-grade ECG or PO monitors to ensure robustness and accuracy. Objective: The purpose of this study is to design and examine the feasibility of a proof-of-concept HRD that quickly obtains HR using biocompatible 3D-printed dry electrodes for single-lead neonatal ECG acquisition. This study uses a novel HRD and compares it to the gold-standard 3-lead clinical ECG or PO in a hospital setting. Methods: A cross-sectional study is planned to be conducted in the neonatal intensive care unit or postpartum unit of a large community teaching hospital in Toronto, Canada, from June 2023 to June 2024. In total, 50 newborns will be recruited for this study. The HRD and an ECG or PO monitor will be video recorded using a digital camera concurrently for 3 minutes for each newborn. Hardware-based signal processing and patent-pending embedded algorithm-based HR estimation techniques are applied directly to the raw collected single-lead ECG and displayed on the HRD in real time during video recordings. These data will be annotated and compared to the ECG or PO readings at the same points in time. Accuracy, F1-score, and other statistical metrics will be produced to determine the HRD?s feasibility in providing reliable HR. Results: The study is ongoing. The projected end date for data collection is around July 2024. Conclusions: The study will compare the novel patent-pending 3D-printed dry electrode?based HRD?s real-time HR estimation techniques with the state-of-the-art clinical-grade ECG or PO monitors for HR accuracy and examines how fast the HRD provides reliable HR. The study will further provide recommendations and important improvements that can be made to implement the HRD for clinical applications, especially in neonatal resuscitation efforts. This work can be seen as a stepping stone in the development of robust dry-electrode single-lead ECG devices for HR estimations in the pediatric population. International Registered Report Identifier (IRRID): DERR1-10.2196/45512 UR - https://www.researchprotocols.org/2023/1/e45512 UR - http://dx.doi.org/10.2196/45512 UR - http://www.ncbi.nlm.nih.gov/pubmed/37782528 ID - info:doi/10.2196/45512 ER - TY - JOUR AU - Billington, Olive Emma AU - Hasselaar, M. Charley AU - Kembel, Lorena AU - Myagishima, C. Rebecca AU - Arain, A. Mubashir PY - 2023/9/29 TI - Effectiveness and Cost of Using Facebook Recruitment to Elicit Canadian Women?s Perspectives on Bone Health and Osteoporosis: Cross-Sectional Survey Study JO - J Med Internet Res SP - e47970 VL - 25 KW - osteoporosis KW - bone health KW - bone mineral density KW - fracture KW - survey KW - Facebook KW - advertisement KW - recruitment KW - women?s health KW - social media KW - bone KW - perspective N2 - Background: Surveys can help health researchers better understand the public?s perspectives and needs regarding prevalent conditions such as osteoporosis, which affects more than two-thirds of postmenopausal women. However, recruitment of large cohorts for survey research can be time-consuming and expensive. With 2.9 billion active users across the globe and reasonable advertising costs, Facebook (Meta Platforms, Inc) has emerged as an effective recruitment tool for surveys, although previous studies have targeted young populations (<50 years of age) and none have focused on bone health. Objective: We assessed the effectiveness and cost of using Facebook to recruit Canadian women aged ?45 years to share their perspectives on bone health and osteoporosis via a web-based survey. Methods: We developed a 15-minute web-based survey with the goal of eliciting perspectives on bone health and osteoporosis. A Facebook advertisement was placed for 2 weeks in February 2022, during which time it was shown to women of age ?45 years who resided in Canada, inviting them to participate and offering a chance to win 1 of 5 CAD $100 gift cards (at the time of this study [February 14, 2022], a currency exchange rate of CAD $1=US $0.79 was applicable). Those who clicked on the advertisement were taken to an eligibility screening question on the survey home screen. Individuals who confirmed eligibility were automatically directed to the first survey question. All individuals who answered the first survey question were considered participants and included in the analyses. We determined the survey reach, click rate, cooperation rate, completion rate, cost per click, and cost per participant. Sociodemographic characteristics of respondents were compared with data from the 2021 Canadian Census. Results: The Facebook advertisement was shown to 34,086 unique Facebook users, resulting in 2033 link clicks (click rate: 6.0%). A total of 1320 individuals completed the eligibility screening question, 1195 started the survey itself (cooperation rate: 58.8%), and 966 completed the survey (completion rate: 47.5%). The cost of the advertising campaign was CAD $280.12, resulting in a cost per click of CAD $0.14 and a cost per participant of CAD $0.23. The 1195 participants ranged in age from 45-89 years (mean 65, SD 7 years), 921 (93.7%) were of White ethnicity, 854 (88.3%) had completed some postsecondary education, and 637 (65.8%) resided in urban areas. Responses were received from residents of all 10 Canadian provinces and 2 of 3 territories. When compared to 2021 Canadian Census data, postsecondary education and rural residence were overrepresented in our study population. Conclusions: Facebook advertising is an efficient, effective, and inexpensive way of recruiting large samples of older women for participation in web-based surveys for health research. However, it is important to recognize that this modality is a form of convenience sampling and the benefits of Facebook recruitment must be balanced with its limitations, which include selection bias and coverage error. UR - https://www.jmir.org/2023/1/e47970 UR - http://dx.doi.org/10.2196/47970 UR - http://www.ncbi.nlm.nih.gov/pubmed/37773625 ID - info:doi/10.2196/47970 ER - TY - JOUR AU - Miao, Hongyu AU - Li, Chengdong AU - Wang, Jing PY - 2023/9/26 TI - A Future of Smarter Digital Health Empowered by Generative Pretrained Transformer JO - J Med Internet Res SP - e49963 VL - 25 KW - generative pretrained model KW - artificial intelligence KW - digital health KW - generative pretrained transformer KW - ChatGPT KW - precision medicine KW - AI KW - privacy KW - ethics UR - https://www.jmir.org/2023/1/e49963 UR - http://dx.doi.org/10.2196/49963 UR - http://www.ncbi.nlm.nih.gov/pubmed/37751243 ID - info:doi/10.2196/49963 ER - TY - JOUR AU - Park, Junghwan AU - Kim, Meelim AU - El Mistiri, Mohamed AU - Kha, Rachael AU - Banerjee, Sarasij AU - Gotzian, Lisa AU - Chevance, Guillaume AU - Rivera, E. Daniel AU - Klasnja, Predrag AU - Hekler, Eric PY - 2023/9/26 TI - Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions JO - JMIR Res Protoc SP - e52161 VL - 12 KW - just-in-time adaptive intervention KW - JITAI KW - just-in-time KW - JIT KW - walking KW - physical activity KW - needs KW - opportunity KW - receptivity KW - mobile phone N2 - Background: Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. Objective: The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). Methods: We recruited physically inactive English-speaking adults aged ?25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person?s steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person?s baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. Results: As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. Conclusions: This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. Trial Registration: ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437 International Registered Report Identifier (IRRID): DERR1-10.2196/52161 UR - https://www.researchprotocols.org/2023/1/e52161 UR - http://dx.doi.org/10.2196/52161 UR - http://www.ncbi.nlm.nih.gov/pubmed/37751237 ID - info:doi/10.2196/52161 ER - TY - JOUR AU - Shaikh, Yahya AU - Gibbons, Christopher Michael PY - 2023/9/21 TI - Pathophysiologic Basis of Connected Health Systems JO - J Med Internet Res SP - e42405 VL - 25 KW - smart health KW - connected health KW - systematic methodology KW - pathophysiology KW - architecting connected health systems KW - design KW - community KW - clinic KW - environment KW - system KW - technology KW - digital therapeutic KW - therapeutic systems UR - https://www.jmir.org/2023/1/e42405 UR - http://dx.doi.org/10.2196/42405 UR - http://www.ncbi.nlm.nih.gov/pubmed/37733435 ID - info:doi/10.2196/42405 ER - TY - JOUR AU - Bannon, Sarah AU - Brewer, Julie AU - Ahmad, Nina AU - Cornelius, Talea AU - Jackson, Jonathan AU - Parker, A. Robert AU - Dams-O'Connor, Kristen AU - Dickerson, C. Bradford AU - Ritchie, Christine AU - Vranceanu, Ana-Maria PY - 2023/9/20 TI - A Live Video Dyadic Resiliency Intervention to Prevent Chronic Emotional Distress Early After Dementia Diagnoses: Protocol for a Dyadic Mixed Methods Study JO - JMIR Res Protoc SP - e45532 VL - 12 KW - dyad KW - dementia KW - emotional distress KW - intervention KW - diagnosis KW - telehealth N2 - Background: By 2030, approximately 75 million adults will be living with Alzheimer disease and related dementias (ADRDs). ADRDs produce cognitive, emotional, and behavioral changes for persons living with dementia that undermine independence and produce considerable stressors for persons living with dementia and their spousal care-partners?together called a ?dyad.? Clinically elevated emotional distress (ie, depression and anxiety symptoms) is common for both dyad members after ADRD diagnosis, which can become chronic and negatively impact relationship functioning, health, quality of life, and collaborative management of progressive symptoms. Objective: This study is part of a larger study that aims to develop, adapt, and establish the feasibility of Resilient Together for Alzheimer Disease and Related Dementias (RT-ADRD), a novel dyadic skills-based intervention aimed at preventing chronic emotional distress. This study aims to gather comprehensive information to develop the first iteration of RT-ADRD and inform a subsequent open pilot. Here, we describe the proposed study design and procedures. Methods: All procedures will be conducted virtually (via phone and Zoom) to minimize participant burden and gather information regarding feasibility and best practices surrounding virtual procedures for older adults. We will recruit dyads (up to n=20) from Mount Sinai Hospital (MSH) clinics within 1 month of ADRD diagnosis. Dyads will be self-referred or referred by their treating neurologists and complete screening to assess emotional distress and capacity to consent to participate in the study. Consenting dyads will then participate in a 60-minute qualitative interview using an interview guide designed to assess common challenges, unmet needs, and support preferences and to gather feedback on the proposed RT-ADRD intervention content and design. Each dyad member will then have the opportunity to participate in an optional individual interview to gather additional feedback. Finally, each dyad member will complete a brief quantitative survey remotely (by phone, tablet, or computer) via a secure platform to assess feasibility of assessment and gather preliminary data to explore associations between proposed mechanisms of change and secondary outcomes. We will conduct preliminary explorations of feasibility markers, including recruitment, screening, live video interviews, quantitative data collection, and mixed methods analyses. Results: This study has been approved by the MSH Institutional Review Board. We anticipate that the study will be completed by late 2023. Conclusions: We will use results from this study to develop the first live video telehealth dyadic resiliency intervention focused on the prevention of chronic emotional distress in couples shortly after ADRD diagnoses. Our study will allow us to gather comprehensive information from dyads on important factors to address in an early prevention-focused intervention and to explore feasibility of study procedures to inform future open pilot and pilot feasibility randomized control trial investigations of RT-ADRD. International Registered Report Identifier (IRRID): PRR1-10.2196/45532 UR - https://www.researchprotocols.org/2023/1/e45532 UR - http://dx.doi.org/10.2196/45532 UR - http://www.ncbi.nlm.nih.gov/pubmed/37728979 ID - info:doi/10.2196/45532 ER - TY - JOUR AU - Han, Yongjun AU - Wei, Jiangpeng AU - Wang, Weidong AU - Gao, Ruiqi AU - Shen, Ning AU - Song, Xiaofeng AU - Ni, Yang AU - Li, Yulong AU - Xu, Li-Di AU - Chen, Weizhi AU - Li, Xiaohua PY - 2023/9/20 TI - Multidimensional Analysis of a Cell-Free DNA Whole Methylome Sequencing Assay for Early Detection of Gastric Cancer: Protocol for an Observational Case-Control Study JO - JMIR Res Protoc SP - e48247 VL - 12 KW - gastric cancer KW - circulating cell-free DNA KW - early detection KW - methylation KW - fragmentation KW - chromosomal instability KW - whole methylome sequencing KW - multidimensional model N2 - Background: Commonly used noninvasive serological indicators serve as a step before endoscope diagnosis and help identify the high-risk gastric cancer (GC) population. However, they are associated with high false positives and high false negatives. Alternative noninvasive approaches, such as cancer-related features in cell-free DNA (cfDNA) fragments, have been gradually identified and play essential roles in early cancer detection. The integrated analysis of multiple cfDNA features has enhanced detection sensitivity compared to individual features. Objective: This study aimed to develop and validate an assay based on assessing genomic-scale methylation and fragmentation profiles of plasma cfDNA for early cancer detection, thereby facilitating the early diagnosis of GC. The primary objective is to evaluate the overall specificity and sensitivity of the assay in predicting GC within the entire cohort, and subsequently within each clinical stage of GC. The secondary objective involved investigating the specificity and sensitivity of the assay in combination with possible serological indicators. Methods: This is an observational case-control study. Blood samples will be prospectively collected before gastroscopy from 180 patients with GC and 180 nonmalignant control subjects (healthy or with benign gastric diseases). Cases and controls will be randomly divided into a training and a testing data set at a ratio of 2:1. Plasma cfDNA will be isolated and extracted, followed by bisulfite-free low-depth whole methylome sequencing. A multidimensional model named Thorough Epigenetic Marker Integration Solution (THEMIS) will be constructed in the training data set. The model includes features such as the methylated fragment ratio, chromosomal aneuploidy of featured fragments, fragment size index, and fragment end motif. The performance of the model in distinguishing between patients with cancer and noncancer controls will then be evaluated in the testing data set. Furthermore, GC-related biomarkers, such as pepsinogen, gastrin-17, and Helicobacter pylori, will be measured for each patient, and their predictive accuracy will be assessed both independently and in combination with the THEMIS model Results: Recruitment began in November 2022 and will be ended in April 2024. As of August 2022,250 patients have been enrolled. The final data analysis is anticipated to be completed by September 2024. Conclusions: This is the first registered case-control study designed to investigate a stacked ensemble model integrating several cfDNA features generated from a bisulfite-free whole methylome sequencing assay. These features include methylation patterns, fragmentation profiles, and chromosomal copy number changes, with the aim of identifying the GC population. This study will determine whether multidimensional analysis of cfDNA will prove to be an effective strategy for distinguishing patients with GC from nonmalignant individuals within the Chinese population. We anticipate the THEMIS model will complement the standard-of-care screening and aid in identifying high-risk patients for further diagnosis. Trial Registration: ClinicalTrial.gov NCT05668910; https://www.clinicaltrials.gov/study/NCT05668910 International Registered Report Identifier (IRRID): DERR1-10.2196/48247 UR - https://www.researchprotocols.org/2023/1/e48247 UR - http://dx.doi.org/10.2196/48247 UR - http://www.ncbi.nlm.nih.gov/pubmed/37728978 ID - info:doi/10.2196/48247 ER - TY - JOUR AU - Kwun, Ju-Seung AU - Lee, Hoon Jang AU - Park, Eun Bo AU - Park, Sung Jong AU - Kim, Jeong Hyeon AU - Kim, Sun-Hwa AU - Jeon, Ki-Hyun AU - Cho, Hyoung-won AU - Kang, Si-Hyuck AU - Lee, Wonjae AU - Youn, Tae-Jin AU - Chae, In-Ho AU - Yoon, Chang-Hwan PY - 2023/9/18 TI - Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study JO - J Med Internet Res SP - e45760 VL - 25 KW - arrhythmias KW - atrial fibrillation KW - wearable electronic device KW - patch electrocardiogram monitor KW - electrocardiogram KW - adult KW - AT-Patch KW - heart failure KW - mobile phone N2 - Background: While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation. Objective: We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ?75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates. Methods: In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ?19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ?2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists. Results: Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ?4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively. Conclusions: This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation. Trial Registration: ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268 UR - https://www.jmir.org/2023/1/e45760 UR - http://dx.doi.org/10.2196/45760 UR - http://www.ncbi.nlm.nih.gov/pubmed/37721791 ID - info:doi/10.2196/45760 ER - TY - JOUR AU - Verweij, Lynn AU - Metsemakers, M. Sanne J. J. P. AU - Ector, G. Geneviève I. C. AU - Rademaker, Peter AU - Bekker, L. Charlotte AU - van Vlijmen, Bas AU - van der Reijden, A. Bert AU - Blijlevens, A. Nicole M. AU - Hermens, G. Rosella P. M. PY - 2023/9/15 TI - Improvement, Implementation, and Evaluation of the CMyLife Digital Care Platform: Participatory Action Research Approach JO - J Med Internet Res SP - e45259 VL - 25 KW - eHealth KW - digital care platform KW - feasibility KW - patient experiences KW - usability KW - chronic myeloid leukemia KW - participatory action research KW - CMyLife N2 - Background: The evaluation of a continuously evolving eHealth tool in terms of improvement and implementation in daily practice is unclear. The CMyLife digital care platform provides patient-centered care by empowering patients with chronic myeloid leukemia, with a focus on making medication compliance insightful, discussable, and optimal, and achieving optimal control of the biomarker BCR-ABL1. Objective: The aim of this study was to investigate to what extent the participatory action research approach is suitable for the improvement and scientific evaluation of eHealth innovations in daily clinical practice (measured by user experiences) combined with the promotion of patient empowerment. Methods: The study used iterative cycles of planning, action, and reflection, whereby participants? experiences (patients, health care providers, the CMyLife team, and app suppliers) with the platform determined next actions. Co-design workshops were the foundation of this cyclic process. Moreover, patients filled in 2 sets of questionnaires for assessing experiences with CMyLife, the actual use of the platform, and the influence of the platform after 3 and at least 6 months. Data collected during the workshops were analyzed using content analysis, which is often used for making a practical guide to action. Descriptive statistics were used to characterize the study population in terms of information related to chronic myeloid leukemia and sociodemographics, and to describe experiences with the CMyLife digital care platform and the actual use of this platform. Results: The co-design workshops provided insights that contributed to the improvement, implementation, and evaluation of CMyLife and empowered patients with chronic myeloid leukemia (for example, simplification of language, and improvement of the user friendliness of functionalities). The results of the questionnaires indicated that (1) the platform improved information provision on chronic myeloid leukemia in 67% (33/49) of patients, (2) the use of the medication app improved medication compliance in 42% (16/38) of patients, (3) the use of the guideline app improved guideline adherence in 44% (11/25) of patients, and (4) the use of the platform caused patients to feel more empowered. Conclusions: A participatory action research approach is suited to scientifically evaluate digital care platforms in daily clinical practice in terms of improvement, implementation, and patient empowerment. Systematic iterative evaluation of users? needs and wishes is needed to keep care centered on patients and keep the innovation up-to-date and valuable for users. UR - https://www.jmir.org/2023/1/e45259 UR - http://dx.doi.org/10.2196/45259 UR - http://www.ncbi.nlm.nih.gov/pubmed/37713242 ID - info:doi/10.2196/45259 ER - TY - JOUR AU - Harrison, Conrad AU - Trickett, Ryan AU - Wormald, Justin AU - Dobbs, Thomas AU - Lis, Przemys?aw AU - Popov, Vesselin AU - Beard, J. David AU - Rodrigues, Jeremy PY - 2023/9/14 TI - Remote Symptom Monitoring With Ecological Momentary Computerized Adaptive Testing: Pilot Cohort Study of a Platform for Frequent, Low-Burden, and Personalized Patient-Reported Outcome Measures JO - J Med Internet Res SP - e47179 VL - 25 KW - patient-reported outcome measures KW - ecological momentary assessment KW - computerized adaptive testing KW - EMCAT KW - symptom monitoring KW - monitoring KW - assessment KW - smartphone app KW - trauma KW - arthritis KW - usability KW - mobile phone N2 - Background: Remote patient-reported outcome measure (PROM) data capture can provide useful insights into research and clinical practice and deeper insights can be gained by administering assessments more frequently, for example, in ecological momentary assessment. However, frequent data collection can be limited by the burden of multiple, lengthy questionnaires. This burden can be reduced with computerized adaptive testing (CAT) algorithms that select only the most relevant items from a PROM for an individual respondent. In this paper, we propose ?ecological momentary computerized adaptive testing? (EMCAT): the use of CAT algorithms to reduce PROM response burden and facilitate high-frequency data capture via a smartphone app. We develop and pilot a smartphone app for performing EMCAT using a popular hand surgery PROM. Objective: The aim of this study is to determine the feasibility of EMCAT as a system for remote PROM administration. Methods: We built the EMCAT web app using Concerto, an open-source CAT platform maintained by the Psychometrics Centre, University of Cambridge, and hosted it on an Amazon Web Service cloud server. The platform is compatible with any questionnaire that has been parameterized with item response theory or Rasch measurement theory. For this study, the PROM we chose was the patient evaluation measure, which is commonly used in hand surgery. CAT algorithms were built using item response theory models derived from UK Hand Registry data. In the pilot study, we enrolled 40 patients with hand trauma or thumb-base arthritis, across 2 sites, between July 13, 2022, and September 14, 2022. We monitored their symptoms with the patient evaluation measure, via EMCAT, over a 12-week period. Patients were assessed thrice weekly, once daily, or thrice daily. We additionally administered full-length PROM assessments at 0, 6, and 12 weeks, and the User Engagement Scale at 12 weeks. Results: The use of EMCAT significantly reduced the length of the PROM (median 2 vs 11 items) and the time taken to complete it (median 8.8 seconds vs 1 minute 14 seconds). Very similar scores were obtained when EMCAT was administered concurrently with the full-length PROM, with a mean error of <0.01 on a logit (z score) scale. The median response rate in the daily assessment group was 93%. The median perceived usability score of the User Engagement Scale was 4.0 (maximum possible score 5.0). Conclusions: EMCAT reduces the burden of PROM assessments, enabling acceptable high-frequency, remote PROM data capture. This has potential applications in both research and clinical practice. In research, EMCAT could be used to study temporal variations in symptom severity, for example, recovery trajectories after surgery. In clinical practice, EMCAT could be used to monitor patients remotely, prompting early intervention if a patient?s symptom trajectory causes clinical concern. Trial Registration: ISRCTN 19841416; https://www.isrctn.com/ISRCTN19841416 UR - https://www.jmir.org/2023/1/e47179 UR - http://dx.doi.org/10.2196/47179 UR - http://www.ncbi.nlm.nih.gov/pubmed/37707947 ID - info:doi/10.2196/47179 ER - TY - JOUR AU - Huang, Guoqing AU - Jin, Qiankai AU - Mao, Yushan PY - 2023/9/12 TI - Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study JO - J Med Internet Res SP - e46891 VL - 25 KW - nonalcoholic fatty liver disease KW - machine learning KW - independent risk factors KW - prediction model KW - model KW - fatty liver KW - prevention KW - liver KW - prognostic KW - China KW - development KW - validation KW - risk model KW - clinical applicability N2 - Background: Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. Objective: This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. Methods: The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)?recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. Results: The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. Conclusions: Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events. UR - https://www.jmir.org/2023/1/e46891 UR - http://dx.doi.org/10.2196/46891 UR - http://www.ncbi.nlm.nih.gov/pubmed/37698911 ID - info:doi/10.2196/46891 ER - TY - JOUR AU - Burns, L. Michael AU - Sinha, Anik AU - Hoffmann, Alexander AU - Wu, Zewen AU - Medina Inchauste, Tomas AU - Retsky, Aaron AU - Chesney, David AU - Kheterpal, Sachin AU - Shah, Nirav PY - 2023/9/7 TI - Development and Testing of a Data Capture Device for Use With Clinical Incentive Spirometers: Testing and Usability Study JO - JMIR Biomed Eng SP - e46653 VL - 8 KW - incentive KW - spirometry KW - Internet-of-Things KW - electronic health records KW - web-based intervention KW - medical device KW - medical tool KW - data collection KW - spirometry data KW - incentive spirometer KW - data analysis KW - algorithm KW - effectiveness N2 - Background: The incentive spirometer is a basic and common medical device from which electronic health care data cannot be directly collected. As a result, despite numerous studies investigating clinical use, there remains little consensus on optimal device use and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications. Objective: The aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer. Methods: An add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad. Results: In user testing, sensor locations were captured at an accuracy of 99% (SD 1.4%) for volume and 100% accuracy for flow. Median and average volumes were within 7.5% (SD 6%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100% sensitivity and a 99% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay. Conclusions: An effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care. UR - https://biomedeng.jmir.org/2023/1/e46653 UR - http://dx.doi.org/10.2196/46653 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875693 ID - info:doi/10.2196/46653 ER - TY - JOUR AU - Fernandes, J. Glenn AU - Choi, Arthur AU - Schauer, Michael Jacob AU - Pfammatter, F. Angela AU - Spring, J. Bonnie AU - Darwiche, Adnan AU - Alshurafa, I. Nabil PY - 2023/9/6 TI - An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study JO - J Med Internet Res SP - e42047 VL - 25 KW - explainable artificial intelligence KW - explainable AI KW - machine learning KW - ML KW - interpretable ML KW - random forest KW - decision-making KW - weight loss prediction KW - mobile phone N2 - Background: Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts. Objective: This study aimed to build and evaluate an ML model that can predict 6-month weight loss success (ie, ?7% weight loss) from 5 engagement and diet-related features collected over the initial 2 weeks of an intervention, to assess whether providing ML-based explanations increases weight management experts? agreement with ML model predictions, and to inform factors that influence the understanding and trust of ML models to advance explainability in early prediction of weight loss among weight management experts. Methods: We trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). We leveraged findings from existing explainability metrics to develop Prime Implicant Maintenance of Outcome (PRIMO), an interactive tool to understand predictions made by the RF model. We asked 14 weight management experts to predict hypothetical participants? weight loss success before and after using PRIMO. We compared PRIMO with 2 other explainability methods, one based on feature ranking and the other based on conditional probability. We used generalized linear mixed-effects models to evaluate participants? agreement with ML predictions and conducted likelihood ratio tests to examine the relationship between explainability methods and outcomes for nested models. We conducted guided interviews and thematic analysis to study the impact of our tool on experts? understanding and trust in the model. Results: Our RF model had 81% accuracy in the early prediction of weight loss success. Weight management experts were significantly more likely to agree with the model when using PRIMO (?2=7.9; P=.02) compared with the other 2 methods with odds ratios of 2.52 (95% CI 0.91-7.69) and 3.95 (95% CI 1.50-11.76). From our study, we inferred that our software not only influenced experts? understanding and trust but also impacted decision-making. Several themes were identified through interviews: preference for multiple explanation types, need to visualize uncertainty in explanations provided by PRIMO, and need for model performance metrics on similar participant test instances. Conclusions: Our results show the potential for weight management experts to agree with the ML-based early prediction of success in weight loss treatment programs, enabling timely and dynamic modification of intervention components to enhance intervention effectiveness. Our findings provide methods for advancing the understandability and trust of ML models among weight management experts. UR - https://www.jmir.org/2023/1/e42047 UR - http://dx.doi.org/10.2196/42047 UR - http://www.ncbi.nlm.nih.gov/pubmed/37672333 ID - info:doi/10.2196/42047 ER - TY - JOUR AU - Lau, Tiang Siew AU - Siah, Jiat Rosalind Chiew AU - Dzakirin Bin Rusli, Khairul AU - Loh, Liang Wen AU - Yap, Gwee John Yin AU - Ang, Emily AU - Lim, Ping Fui AU - Liaw, Ying Sok PY - 2023/8/30 TI - Design and Evaluation of Using Head-Mounted Virtual Reality for Learning Clinical Procedures: Mixed Methods Study JO - JMIR Serious Games SP - e46398 VL - 11 KW - user experience KW - acceptability KW - usability KW - virtual patient KW - clinical procedure KW - immersive KW - nursing student KW - virtual reality KW - education KW - performance N2 - Background: The capacity of health care professionals to perform clinical procedures safely and competently is crucial as it will directly impact patients? outcomes. Given the ability of head-mounted virtual reality to simulate the authentic clinical environment, this platform should be suitable for nurses to refine their clinical skills for knowledge and skills acquisition. However, research on head-mounted virtual reality in learning clinical procedures is limited. Objective: The objectives of this study were (1) to describe the design of a head-mounted virtual reality system and evaluate it for education on clinical procedures for nursing students and (2) to explore the experience of nursing students using head-mounted virtual reality for learning clinical procedures and the usability of the system. Methods: This usability study used a mixed method approach. The stages included developing 3D models of the necessary instruments and materials used in intravenous therapy and subcutaneous injection procedures performed by nurses, followed by developing the procedures using the Unreal Engine (Epic Games). Questionnaires on the perception of continuance intention and the System Usability Scale were used along with open-ended questions. Results: Twenty-nine nursing students took part in this questionnaire study after experiencing the immersive virtual reality (IVR) intervention. Participants reported largely favorable game perception and learning experience. Mean perception scores ranged from 3.21 to 4.38 of a maximum score of 5, while the mean system usability score was 53.53 of 100. The majority found that the IVR experience was engaging, and they were immersed in the game. The challenges encountered included unfamiliarity with the new learning format; technological constraints, such as using hand controllers; and physical discomfort. Conclusions: The conception of IVR for learning clinical procedures through deliberate practice to enhance nurses? knowledge and skills is promising. However, refinement of the prototypes is required to improve user experience and learning. Future research can explore other ways to use IVR for better education and health care purposes. UR - https://games.jmir.org/2023/1/e46398 UR - http://dx.doi.org/10.2196/46398 UR - http://www.ncbi.nlm.nih.gov/pubmed/37647108 ID - info:doi/10.2196/46398 ER - TY - JOUR AU - Lee, C. Rico S. AU - Albertella, Lucy AU - Christensen, Erynn AU - Suo, Chao AU - Segrave, A. Rebecca AU - Brydevall, Maja AU - Kirkham, Rebecca AU - Liu, Chang AU - Fontenelle, F. Leonardo AU - Chamberlain, R. Samuel AU - Rotaru, Kristian AU - Yücel, Murat PY - 2023/8/25 TI - A Novel, Expert-Endorsed, Neurocognitive Digital Assessment Tool for Addictive Disorders: Development and Validation Study JO - J Med Internet Res SP - e44414 VL - 25 KW - cognitive neuroscience KW - addictive behaviors KW - mental health KW - gaming KW - gamified KW - gamification KW - development KW - assessment KW - software KW - addiction KW - mental disorder KW - neurocognition KW - neurocognitive KW - brain health KW - gamified task KW - psychometric KW - game developer KW - game development KW - validation KW - validate KW - validity N2 - Background: Many people with harmful addictive behaviors may not meet formal diagnostic thresholds for a disorder. A dimensional approach, by contrast, including clinical and community samples, is potentially key to early detection, prevention, and intervention. Importantly, while neurocognitive dysfunction underpins addictive behaviors, established assessment tools for neurocognitive assessment are lengthy and unengaging, difficult to administer at scale, and not suited to clinical or community needs. The BrainPark Assessment of Cognition (BrainPAC) Project sought to develop and validate an engaging and user-friendly digital assessment tool purpose-built to comprehensively assess the main consensus-driven constructs underpinning addictive behaviors. Objective: The purpose of this study was to psychometrically validate a gamified battery of consensus-based neurocognitive tasks against standard laboratory paradigms, ascertain test-retest reliability, and determine their sensitivity to addictive behaviors (eg, alcohol use) and other risk factors (eg, trait impulsivity). Methods: Gold standard laboratory paradigms were selected to measure key neurocognitive constructs (Balloon Analogue Risk Task [BART], Stop Signal Task [SST], Delay Discounting Task [DDT], Value-Modulated Attentional Capture [VMAC] Task, and Sequential Decision-Making Task [SDT]), as endorsed by an international panel of addiction experts; namely, response selection and inhibition, reward valuation, action selection, reward learning, expectancy and reward prediction error, habit, and compulsivity. Working with game developers, BrainPAC tasks were developed and validated in 3 successive cohorts (total N=600) and a separate test-retest cohort (N=50) via Mechanical Turk using a cross-sectional design. Results: BrainPAC tasks were significantly correlated with the original laboratory paradigms on most metrics (r=0.18-0.63, P<.05). With the exception of the DDT k function and VMAC total points, all other task metrics across the 5 tasks did not differ between the gamified and nongamified versions (P>.05). Out of 5 tasks, 4 demonstrated adequate to excellent test-retest reliability (intraclass correlation coefficient 0.72-0.91, P<.001; except SDT). Gamified metrics were significantly associated with addictive behaviors on behavioral inventories, though largely independent of trait-based scales known to predict addiction risk. Conclusions: A purpose-built battery of digitally gamified tasks is sufficiently valid for the scalable assessment of key neurocognitive processes underpinning addictive behaviors. This validation provides evidence that a novel approach, purported to enhance task engagement, in the assessment of addiction-related neurocognition is feasible and empirically defensible. These findings have significant implications for risk detection and the successful deployment of next-generation assessment tools for substance use or misuse and other mental disorders characterized by neurocognitive anomalies related to motivation and self-regulation. Future development and validation of the BrainPAC tool should consider further enhancing convergence with established measures as well as collecting population-representative data to use clinically as normative comparisons. UR - https://www.jmir.org/2023/1/e44414 UR - http://dx.doi.org/10.2196/44414 UR - http://www.ncbi.nlm.nih.gov/pubmed/37624635 ID - info:doi/10.2196/44414 ER - TY - JOUR AU - Wilson, L. Shannon AU - Crosley-Lyons, Rachel AU - Junk, Jordan AU - Hasanaj, Kristina AU - Larouche, L. Miranda AU - Hollingshead, Kevin AU - Gu, Haiwei AU - Whisner, Corrie AU - Sears, D. Dorothy AU - Buman, P. Matthew PY - 2023/8/23 TI - Effects of Increased Standing and Light-Intensity Physical Activity to Improve Postprandial Glucose in Sedentary Office Workers: Protocol for a Randomized Crossover Trial JO - JMIR Res Protoc SP - e45133 VL - 12 KW - sitting bouts KW - digital health intervention KW - standing desk KW - endothelial function KW - glycemic control KW - sleep KW - blood pressure KW - insulin KW - mHealth N2 - Background: Prolonged bouts of sedentary time, independent from the time spent in engaging in physical activity, significantly increases cardiometabolic risk. Nonetheless, the modern workforce spends large, uninterrupted portions of the day seated at a desk. Previous research suggests?via improved cardiometabolic biomarkers?that this risk might be attenuated by simply disrupting sedentary time with brief breaks of standing or moving. However, this evidence is derived from acute, highly controlled laboratory experiments and thus has low external validity. Objective: This study aims to investigate if similar or prolonged cardiometabolic changes are observed after a prolonged (2-week) practice of increased brief standing and moving behaviors in real-world office settings. Methods: This randomized crossover trial, called the WorkWell Study, will compare the efficacy of two 2-week pilot intervention conditions designed to interrupt sitting time in sedentary office workers (N=15) to a control condition. The intervention conditions use a novel smartphone app to deliver real-time prompts to increase standing (STAND) or moving (MOVE) by an additional 6 minutes each hour during work. Our primary aim is to assess intervention-associated improvements to daily postprandial glucose using continuous glucose monitors. Our secondary aim is to determine whether the interventions successfully evoke substantive positional changes and light-intensity physical activity (LPA). Other outcomes include the feasibility and acceptability of the intervention conditions, fasting blood glucose concentration, femoral artery flow-mediated dilation (f-FMD), and systolic and diastolic blood pressure. Results: The trial is ongoing at the time of submission. Conclusions: This study is a novel, randomized crossover trial designed to extend a laboratory-based controlled study design into the free-living environment. By using digital health technologies to monitor and prompt participants in real time, we will be able to rigorously test the effects of breaking up sedentary behavior over a longer period of time than is seen in traditional laboratory-based studies. Our innovative approach will leverage the strengths of highly controlled laboratory and free-living experiments to achieve maximal internal and external validity. The research team?s multidisciplinary expertise allows for a broad range of biological measures to be sampled, providing robust results that will extend knowledge of both the acute and chronic real-life effects of increased standing and LPA in sedentary office workers. The WorkWell Study uses a rigorous transdisciplinary protocol that will contribute to a more comprehensive picture of the beneficial effects of breaking up sitting behavior. Trial Registration: ClinicalTrials.gov NCT04269070; https://clinicaltrials.gov/study/NCT04269070 International Registered Report Identifier (IRRID): DERR1-10.2196/45133 UR - https://www.researchprotocols.org/2023/1/e45133 UR - http://dx.doi.org/10.2196/45133 UR - http://www.ncbi.nlm.nih.gov/pubmed/37610800 ID - info:doi/10.2196/45133 ER - TY - JOUR AU - Hsu, Hsing-Yu AU - Hsu, Kai-Cheng AU - Hou, Shih-Yen AU - Wu, Ching-Lung AU - Hsieh, Yow-Wen AU - Cheng, Yih-Dih PY - 2023/8/21 TI - Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT Performance Evaluation JO - JMIR Med Educ SP - e48433 VL - 9 KW - ChatGPT KW - large language model KW - natural language processing KW - real-world medication consultation questions KW - NLP KW - drug-herb interactions KW - pharmacist KW - LLM KW - language models KW - chat generative pre-trained transformer N2 - Background: Since OpenAI released ChatGPT, with its strong capability in handling natural tasks and its user-friendly interface, it has garnered significant attention. Objective: A prospective analysis is required to evaluate the accuracy and appropriateness of medication consultation responses generated by ChatGPT. Methods: A prospective cross-sectional study was conducted by the pharmacy department of a medical center in Taiwan. The test data set comprised retrospective medication consultation questions collected from February 1, 2023, to February 28, 2023, along with common questions about drug-herb interactions. Two distinct sets of questions were tested: real-world medication consultation questions and common questions about interactions between traditional Chinese and Western medicines. We used the conventional double-review mechanism. The appropriateness of each response from ChatGPT was assessed by 2 experienced pharmacists. In the event of a discrepancy between the assessments, a third pharmacist stepped in to make the final decision. Results: Of 293 real-world medication consultation questions, a random selection of 80 was used to evaluate ChatGPT?s performance. ChatGPT exhibited a higher appropriateness rate in responding to public medication consultation questions compared to those asked by health care providers in a hospital setting (31/51, 61% vs 20/51, 39%; P=.01). Conclusions: The findings from this study suggest that ChatGPT could potentially be used for answering basic medication consultation questions. Our analysis of the erroneous information allowed us to identify potential medical risks associated with certain questions; this problem deserves our close attention. UR - https://mededu.jmir.org/2023/1/e48433 UR - http://dx.doi.org/10.2196/48433 UR - http://www.ncbi.nlm.nih.gov/pubmed/37561097 ID - info:doi/10.2196/48433 ER - TY - JOUR AU - Liu, Jen-Hsuan AU - Shih, Chih-Yuan AU - Huang, Hsien-Liang AU - Peng, Jen-Kuei AU - Cheng, Shao-Yi AU - Tsai, Jaw-Shiun AU - Lai, Feipei PY - 2023/8/18 TI - Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study JO - J Med Internet Res SP - e47366 VL - 25 KW - artificial intelligence KW - end-of-life care KW - machine learning KW - palliative care KW - survival prediction KW - terminal cancer KW - wearable device N2 - Background: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. Objective: This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. Methods: This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning?based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. Results: From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. Conclusions: We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. Trial Registration: ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907 UR - https://www.jmir.org/2023/1/e47366 UR - http://dx.doi.org/10.2196/47366 UR - http://www.ncbi.nlm.nih.gov/pubmed/37594793 ID - info:doi/10.2196/47366 ER - TY - JOUR AU - Bilu, Yonatan AU - Amit, Guy AU - Sudry, Tamar AU - Akiva, Pinchas AU - Avgil Tsadok, Meytal AU - Zimmerman, R. Deena AU - Baruch, Ravit AU - Sadaka, Yair PY - 2023/8/18 TI - A Developmental Surveillance Score for Quantitative Monitoring of Early Childhood Milestone Attainment: Algorithm Development and Validation JO - JMIR Public Health Surveill SP - e47315 VL - 9 KW - child development KW - risk scores KW - scoring methods KW - language delay KW - motor skills delay KW - developmental KW - surveillance KW - developmental delays KW - developmental milestones KW - young children KW - intervention KW - child N2 - Background: Developmental surveillance, conducted routinely worldwide, is fundamental for timely identification of children at risk of developmental delays. It is typically executed by assessing age-appropriate milestone attainment and applying clinical judgment during health supervision visits. Unlike developmental screening and evaluation tools, surveillance typically lacks standardized quantitative measures, and consequently, its interpretation is often qualitative and subjective. Objective: Herein, we suggested a novel method for aggregating developmental surveillance assessments into a single score that coherently depicts and monitors child development. We described the procedure for calculating the score and demonstrated its ability to effectively capture known population-level associations. Additionally, we showed that the score can be used to describe longitudinal patterns of development that may facilitate tracking and classifying developmental trajectories of children. Methods: We described the Developmental Surveillance Score (DSS), a simple-to-use tool that quantifies the age-dependent severity level of a failure at attaining developmental milestones based on the recently introduced Israeli developmental surveillance program. We evaluated the DSS using a nationwide cohort of >1 million Israeli children from birth to 36 months of age, assessed between July 1, 2014, and September 1, 2021. We measured the score?s ability to capture known associations between developmental delays and characteristics of the mother and child. Additionally, we computed series of the DSS in consecutive visits to describe a child?s longitudinal development and applied cluster analysis to identify distinct patterns of these developmental trajectories. Results: The analyzed cohort included 1,130,005 children. The evaluation of the DSS on subpopulations of the cohort, stratified by known risk factors of developmental delays, revealed expected relations between developmental delay and characteristics of the child and mother, including demographics and obstetrics-related variables. On average, the score was worse for preterm children compared to full-term children and for male children compared to female children, and it was correspondingly worse for lower levels of maternal education. The trajectories of scores in 6 consecutive visits were available for 294,000 children. The clustering of these trajectories revealed 3 main types of developmental patterns that are consistent with clinical experience: children who successfully attain milestones, children who initially tend to fail but improve over time, and children whose failures tend to increase over time. Conclusions: The suggested score is straightforward to compute in its basic form and can be easily implemented as a web-based tool in its more elaborate form. It highlights known and novel relations between developmental delay and characteristics of the mother and child, demonstrating its potential usefulness for surveillance and research. Additionally, it can monitor the developmental trajectory of a child and characterize it. Future work is needed to calibrate the score vis-a-vis other screening tools, validate it worldwide, and integrate it into the clinical workflow of developmental surveillance. UR - https://publichealth.jmir.org/2023/1/e47315 UR - http://dx.doi.org/10.2196/47315 UR - http://www.ncbi.nlm.nih.gov/pubmed/37489583 ID - info:doi/10.2196/47315 ER - TY - JOUR AU - Chen, Yifei AU - Li, Xiaoying AU - Li, Aihua AU - Li, Yongjie AU - Yang, Xuemei AU - Lin, Ziluo AU - Yu, Shirui AU - Tang, Xiaoli PY - 2023/8/18 TI - A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study JO - JMIR Form Res SP - e47434 VL - 7 KW - multisource literature KW - institution name normalization KW - deep learning KW - bidirectional encoder representations from transformers KW - BERT N2 - Background: The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors? writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning?based institution name normalization model can increase the accuracy of institution name normalization at the semantic level. Objective: This study aimed to train a deep learning?based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization. Methods: In this study, an institution name normalization?oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus. Results: It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names. Conclusions: The deep learning?based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value. UR - https://formative.jmir.org/2023/1/e47434 UR - http://dx.doi.org/10.2196/47434 UR - http://www.ncbi.nlm.nih.gov/pubmed/37594844 ID - info:doi/10.2196/47434 ER - TY - JOUR AU - Yi, Min AU - Cao, Yuebin AU - Wang, Lin AU - Gu, Yaowen AU - Zheng, Xueqian AU - Wang, Jiangjun AU - Chen, Wei AU - Wei, Liangyu AU - Zhou, Yujin AU - Shi, Chenyi AU - Cao, Yanlin PY - 2023/8/17 TI - Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study JO - J Med Internet Res SP - e46854 VL - 25 KW - medical workers KW - medical disputes KW - hospital legal construction KW - machine learning KW - multicenter analysis N2 - Background: Medical disputes are a global public health issue that is receiving increasing attention. However, studies investigating the relationship between hospital legal construction and medical disputes are scarce. The development of a multicenter model incorporating machine learning (ML) techniques for the individualized prediction of medical disputes would be beneficial for medical workers. Objective: This study aimed to identify predictors related to medical disputes from the perspective of hospital legal construction and the use of ML techniques to build models for predicting the risk of medical disputes. Methods: This study enrolled 38,053 medical workers from 130 tertiary hospitals in Hunan province, China. The participants were randomly divided into a training cohort (34,286/38,053, 90.1%) and an internal validation cohort (3767/38,053, 9.9%). Medical workers from 87 tertiary hospitals in Beijing were included in an external validation cohort (26,285/26,285, 100%). This study used logistic regression and 5 ML techniques: decision tree, random forest, support vector machine, gradient boosting decision tree (GBDT), and deep neural network. In total, 12 metrics, including discrimination and calibration, were used for performance evaluation. A scoring system was developed to select the optimal model. Shapley additive explanations was used to generate the importance coefficients for characteristics. To promote the clinical practice of our proposed optimal model, reclassification of patients was performed, and a web-based app for medical dispute prediction was created, which can be easily accessed by the public. Results: Medical disputes occurred among 46.06% (17,527/38,053) of the medical workers in Hunan province, China. Among the 26 clinical characteristics, multivariate analysis demonstrated that 18 characteristics were significantly associated with medical disputes, and these characteristics were used for ML model development. Among the ML techniques, GBDT was identified as the optimal model, demonstrating the lowest Brier score (0.205), highest area under the receiver operating characteristic curve (0.738, 95% CI 0.722-0.754), and the largest discrimination slope (0.172) and Youden index (1.355). In addition, it achieved the highest metrics score (63 points), followed by deep neural network (46 points) and random forest (45 points), in the internal validation set. In the external validation set, GBDT still performed comparably, achieving the second highest metrics score (52 points). The high-risk group had more than twice the odds of experiencing medical disputes compared with the low-risk group. Conclusions: We established a prediction model to stratify medical workers into different risk groups for encountering medical disputes. Among the 5 ML models, GBDT demonstrated the optimal comprehensive performance and was used to construct the web-based app. Our proposed model can serve as a useful tool for identifying medical workers at high risk of medical disputes. We believe that preventive strategies should be implemented for the high-risk group. UR - https://www.jmir.org/2023/1/e46854 UR - http://dx.doi.org/10.2196/46854 UR - http://www.ncbi.nlm.nih.gov/pubmed/37590041 ID - info:doi/10.2196/46854 ER - TY - JOUR AU - Dalko, Katharina AU - Kraft, Bernhard AU - Jahn, Patrick AU - Schildmann, Jan AU - Hofstetter, Sebastian PY - 2023/8/15 TI - Cocreation of Assistive Technologies for Patients With Long COVID: Qualitative Analysis of a Literature Review on the Challenges of Patient Involvement in Health and Nursing Sciences JO - J Med Internet Res SP - e46297 VL - 25 KW - cocreation KW - participatory development KW - transdisciplinary research KW - technological development KW - long COVID syndrome KW - mobile phone N2 - Background: Digital assistive technologies have the potential to address the pressing need for adequate therapy options for patients with long COVID (also known as post?COVID-19 condition) by enabling the implementation of individual and independent rehabilitation programs. However, the involvement of the target patient group is necessary to develop digital devices that are closely aligned to the needs of this particular patient group. Objective: Participatory design approaches, such as cocreation, may be a solution for achieving usability and user acceptance. However, there are currently no set methods for implementing cocreative development processes incorporating patients. This study addresses the following research questions: what are the tasks and challenges associated with the involvement of patient groups? What lessons can be learned regarding the adequate involvement of patients with long COVID? Methods: First, a literature review based on a 3-stage snowball process was conducted to identify the tasks and challenges emerging in the context of the cocreation of digital assistive devices and services with patient groups. Second, a qualitative analysis was conducted in an attempt to extract relevant findings and criteria from the identified studies. Third, using the method of theory adaptation, this paper presents recommendations for the further development of the existing concepts of cocreation in relation to patients with long COVID. Results: The challenges of an active involvement of patients in cocreative development in health care include hierarchical barriers and differences in the levels of specific knowledge between professionals and patients. In the case of long COVID, patients themselves are still inexperienced in dealing with their symptoms and are hardly organized into established groups. This amplifies general hurdles and leads to questions of group identity, power structure, and knowledge creation, which are not sufficiently addressed by the current methods of cocreation. Conclusions: The adaptation of transdisciplinary methods to cocreative development approaches focusing on collaborative and inclusive communication can address the recurring challenges of actively integrating patients with long COVID into development processes. UR - https://www.jmir.org/2023/1/e46297 UR - http://dx.doi.org/10.2196/46297 UR - http://www.ncbi.nlm.nih.gov/pubmed/37581906 ID - info:doi/10.2196/46297 ER - TY - JOUR AU - Bayshtok, Gabriella AU - Tiosano, Shmuel AU - Furer, Ariel PY - 2023/8/15 TI - Use of Wearable Devices for Peak Oxygen Consumption Measurement in Clinical Cardiology: Case Report and Literature Review JO - Interact J Med Res SP - e45504 VL - 12 KW - cardiac fitness KW - cardiac patient KW - cardiorespiratory fitness KW - CRF KW - clinical cardiology KW - oxygen consumption KW - peak VO2 KW - smartwatch KW - wearable device N2 - Background: Oxygen consumption is an important index to evaluate in cardiac patients, particularly those with heart failure, and is measured in the setting of advanced cardiopulmonary exercise testing. However, technological advances now allow for the estimation of this parameter in many consumer and medical-grade wearable devices, making it available for the medical provider at the initial evaluation of patients. We report a case of an apparently healthy male aged 40 years who presented for evaluation due to an Apple Watch (Apple Inc) notification of low cardiac fitness. This alert triggered a thorough workup, revealing a diagnosis of familial nonischemic cardiomyopathy with severely reduced left ventricular systolic function. While the use of wearable devices for the measurement of oxygen consumption and related parameters is promising, further studies are needed for validation. Objective: The aim of this report is to investigate the potential utility of wearable devices as a screening and risk stratification tool for cardiac fitness for the general population and those with increased cardiovascular risk, particularly through the measurement of peak oxygen consumption (VO2). We discuss the possible advantages of measuring oxygen consumption using wearables and propose its integration into routine patient evaluation and follow-up processes. With the current evidence and limitations, we encourage researchers and clinicians to explore bringing wearable devices into clinical practice. Methods: The case was identified at Sheba Medical Center, and the patient?s cardiac fitness was monitored through an Apple Watch Series 6. The patient underwent a comprehensive cardiac workup following his presentation. Subsequently, we searched the literature for articles relating to the clinical utility of peak VO2 monitoring and available wearable devices. Results: The Apple Watch data provided by the patient demonstrated reduced peak VO2, a surrogate index for cardiac fitness, which improved after treatment initiation. A cardiological workup confirmed familial nonischemic cardiomyopathy with severely reduced left ventricular systolic function. A review of the literature revealed the potential clinical benefit of peak VO2 monitoring in both cardiac and noncardiac scenarios. Additionally, several devices on the market were identified that could allow for accurate oxygen consumption measurement; however, future studies and approval by the Food and Drug Administration (FDA) are still necessary. Conclusions: This case report highlights the potential utility of peak VO2 measurements by wearable devices for early identification and screening of cardiac fitness for the general population and those at increased risk of cardiovascular disease. The integration of wearable devices into routine patient evaluation may allow for earlier presentation in the diagnostic workflow. Cardiac fitness can be serially measured using the wearable device, allowing for close monitoring of functional capacity parameters. Devices need to be used with caution, and further studies are warranted. UR - https://www.i-jmr.org/2023/1/e45504 UR - http://dx.doi.org/10.2196/45504 UR - http://www.ncbi.nlm.nih.gov/pubmed/37581915 ID - info:doi/10.2196/45504 ER - TY - JOUR AU - Njovu, Kiiza Israel AU - Nalumaga, Petra Pauline AU - Ampaire, Lucas AU - Nuwagira, Edwin AU - Mwesigye, James AU - Musinguzi, Benson AU - Kassaza, Kennedy AU - Taseera, Kabanda AU - Kiguli Mukasa, James AU - Bazira, Joel AU - Iramiot, Stanley Jacob AU - Baguma, Andrew AU - Bongomin, Felix AU - Kwizera, Richard AU - Achan, Beatrice AU - Cox, J. Michael AU - King, S. Jason AU - May, Robin AU - Ballou, R. Elizabeth AU - Itabangi, Herbert PY - 2023/8/15 TI - Investigating Metabolic and Molecular Ecological Evolution of Opportunistic Pulmonary Fungal Coinfections: Protocol for a Laboratory-Based Cross-Sectional Study JO - JMIR Res Protoc SP - e48014 VL - 12 KW - pulmonary mycoses KW - fungal-bacterial coinfection KW - metabolic KW - evolutionary KW - opportunistic infections KW - cross-kingdom interaction KW - tuberculosis N2 - Background: Fungal-bacterial cocolonization and coinfections pose an emerging challenge among patients suspected of having pulmonary tuberculosis (PTB); however, the underlying pathogenic mechanisms and microbiome interactions are poorly understood. Understanding how environmental microbes, such as fungi and bacteria, coevolve and develop traits to evade host immune responses and resist treatment is critical to controlling opportunistic pulmonary fungal coinfections. In this project, we propose to study the coexistence of fungal and bacterial microbial communities during chronic pulmonary diseases, with a keen interest in underpinning fungal etiological evolution and the predominating interactions that may exist between fungi and bacteria. Objective: This is a protocol for a study aimed at investigating the metabolic and molecular ecological evolution of opportunistic pulmonary fungal coinfections through determining and characterizing the burden, etiological profiles, microbial communities, and interactions established between fungi and bacteria as implicated among patients with presumptive PTB. Methods: This will be a laboratory-based cross-sectional study, with a sample size of 406 participants. From each participant, 2 sputa samples (one on-spot and one early morning) will be collected. These samples will then be analyzed for both fungal and bacterial etiology using conventional metabolic and molecular (intergenic transcribed spacer and 16S ribosomal DNA?based polymerase chain reaction) approaches. We will also attempt to design a genome-scale metabolic model for pulmonary microbial communities to analyze the composition of the entire microbiome (ie, fungi and bacteria) and investigate host-microbial interactions under different patient conditions. This analysis will be based on the interplays of genes (identified by metagenomics) and inferred from amplicon data and metabolites (identified by metabolomics) by analyzing the full data set and using specific computational tools. We will also collect baseline data, including demographic and clinical history, using a patient-reported questionnaire. Altogether, this approach will contribute to a diagnostic-based observational study. The primary outcome will be the overall fungal and bacterial diagnostic profile of the study participants. Other diagnostic factors associated with the etiological profile, such as incidence and prevalence, will also be analyzed using univariate and multivariate schemes. Odds ratios with 95% CIs will be presented with a statistical significance set at P<.05. Results: The study has been approved by the Mbarara University Research Ethic Committee (MUREC1/7-07/09/20) and the Uganda National Council of Science and Technology (HS1233ES). Following careful scrutiny, the protocol was designed to enable patient enrollment, which began in March 2022 at Mbarara University Teaching Hospital. Data collection is ongoing and is expected to be completed by August 2023, and manuscripts will be submitted for publication thereafter. Conclusions: Through this protocol, we will explore the metabolic and molecular ecological evolution of opportunistic pulmonary fungal coinfections among patients with presumptive PTB. Establishing key fungal-bacterial cross-kingdom synergistic relationships is crucial for instituting fungal bacterial coinfecting etiology. Trial Registration: ISRCTN Registry ISRCTN33572982; https://tinyurl.com/caa2nw69 International Registered Report Identifier (IRRID): DERR1-10.2196/48014 UR - https://www.researchprotocols.org/2023/1/e48014 UR - http://dx.doi.org/10.2196/48014 UR - http://www.ncbi.nlm.nih.gov/pubmed/37581914 ID - info:doi/10.2196/48014 ER - TY - JOUR AU - Hermsen, Sander AU - Verbiest, Vera AU - Buijs, Marije AU - Wentink, Eva PY - 2023/8/11 TI - Perceived Use Cases, Barriers, and Requirements for a Smart Health-Tracking Toilet Seat: Qualitative Focus Group Study JO - JMIR Hum Factors SP - e44850 VL - 10 KW - digital health KW - internet of things KW - human factors KW - health tracking KW - device KW - automated KW - biomarker KW - personal health KW - personal hygiene KW - hygiene KW - data KW - privacy KW - innovation KW - mobile phone N2 - Background: Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive. Objective: This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience? Methods: Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences. Results: Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants. Conclusions: This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted. UR - https://humanfactors.jmir.org/2023/1/e44850 UR - http://dx.doi.org/10.2196/44850 UR - http://www.ncbi.nlm.nih.gov/pubmed/37566450 ID - info:doi/10.2196/44850 ER - TY - JOUR AU - Raggatt, Michelle AU - Wright, C. Cassandra J. AU - Sacks-Davis, Rachel AU - Dietze, M. Paul AU - Hellard, E. Margaret AU - Hocking, S. Jane AU - Lim, C. Megan S. PY - 2023/8/11 TI - Identifying the Most Effective Recruitment Strategy Using Financial Reimbursements for a Web-Based Peer Network Study With Young People Aged 16-18 Years: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e44813 VL - 12 KW - young adult KW - incentive reimbursement KW - research subject KW - study participant KW - financial KW - research subject recruitment KW - social network KW - peer network KW - web-based network KW - randomized KW - friend KW - recruit KW - incentive KW - reimburse KW - reward KW - incentivized KW - youth KW - adolescent KW - teenage KW - recruitment KW - reinforcing factor KW - enabling factor KW - disambiguation KW - intrinsic incentive KW - extrinsic incentive KW - motivation KW - reward system KW - positive reinforcement KW - compensation KW - monetary KW - remuneration KW - remunerative incentive KW - financial incentive KW - bonus KW - stipend KW - donation N2 - Background: Peers are an important determinant of health and well-being during late adolescence; however, there is limited quantitative research examining peer influence. Previous peer network research with adolescents faced methodological limitations and difficulties recruiting young people. Objective: This study aims to determine whether a web-based peer network survey is effective at recruiting adolescent peer networks by comparing 2 strategies for reimbursement. Methods: This study will use a 2-group randomized trial design to test the effectiveness of reimbursements for peer referral in a web-based cross-sectional peer network survey. Young people aged 16-18 years recruited through Instagram, Snapchat, and a survey panel will be randomized to receive either scaled group reimbursement (the experimental group) or fixed individual reimbursement (the control group). All participants will receive a reimbursement of Aus $5 (US $3.70) for their own survey completion. In the experimental group (scaled group reimbursement), all participants within a peer network will receive an additional Aus $5 (US $3.70) voucher for each referred participant who completes the study, up to a maximum total value of Aus $30 (US $22.20) per participant. In the control group (fixed individual reimbursement), participants will only be reimbursed for their own survey completion. Participants? peer networks are assessed during the survey by asking about their close friends. A unique survey link will be generated to share with the participant?s nominated friends for the recruitment of secondary participants. Outcomes are the proportion of a participant?s peer network and the number of referred peers who complete the survey. The required sample size is 306 primary participants. Using a multilevel logistic regression model, we will assess the effect of the reimbursement intervention on the proportion of primary participants? close friends who complete the survey. The secondary aim is to determine participant characteristics that are associated with successfully recruiting close friends. Young people aged 16-18 years were involved in the development of the study design through focus groups and interviews (n=26). Results: Participant recruitment commenced in 2022. Conclusions: A longitudinal web-based social network study could provide important data on how social networks and their influence change over time. This trial aims to determine whether scaled group reimbursement can increase the number of peers referred. The outcomes of this trial will improve the recruitment of young people to web-based network studies of sensitive health issues. International Registered Report Identifier (IRRID): DERR1-10.2196/44813 UR - https://www.researchprotocols.org/2023/1/e44813 UR - http://dx.doi.org/10.2196/44813 UR - http://www.ncbi.nlm.nih.gov/pubmed/37566448 ID - info:doi/10.2196/44813 ER - TY - JOUR AU - Kim, Hyunsoo AU - Jang, Jin Seong AU - Lee, Dong Hee AU - Ko, Hoon Jae AU - Lim, Young Jee PY - 2023/8/7 TI - Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study JO - JMIR Form Res SP - e47325 VL - 7 KW - analysis KW - auto-mapping KW - monitoring KW - healthcare KW - health-monitoring KW - online KW - piezo-resistance sensor KW - pressure mat KW - real-time KW - sensing mats KW - smart home technology KW - smart home KW - spatial map KW - technology KW - textile N2 - Background: The rise in single-person households has resulted in social problems like loneliness and isolation, commonly known as ?death by loneliness.? Various factors contribute to this increase, including a desire for independent living and communication challenges within families due to societal changes. Older individuals living alone are particularly susceptible to loneliness and isolation due to limited family communication and a lack of social activities. Addressing these issues is crucial, and proactive solutions are needed. It is important to explore diverse measures to tackle the challenges of single-person households and prevent deaths due to loneliness in our society. Objective: Non?face-to-face health care service systems have gained widespread interest owing to the rapid development of smart home technology. Particularly, a health monitoring system must be developed to manage patients? health status and send alerts for dangerous situations based on their activity. Therefore, in this study, we present a novel health monitoring system based on the auto-mapping method, which uses real-time position sensing mats. Methods: The smart floor mats are operated as piezo-resistive devices, which are composed of a carbon nanotube?based conductive textile, electrodes, main processor circuit, and a mat. The developed smart floor system acquires real-time position information using a multiconnection method between the modules based on the auto-mapping algorithm, which automatically creates a spatial map. The auto-mapping algorithm allows the user to freely set various activity areas through floor mapping. Then, the monitoring system was evaluated in a room with an area of 41.3 m2, which is embedded with the manufactured floor mats and monitoring application. Results: This monitoring system automatically acquires information on the total number, location, and direction of the mats and creates a spatial map. The position sensing mats can be easily configured with a simple structure by using a carbon nanotube?based piezo-resistive textile. The mats detect the activity in real time and record location information since they are connected through auto-mapping technology. Conclusions: This system allows for the analysis of patients? behavior patterns and the management of health care on the web by providing important basic information for activity patterns in the monitoring system. The proposed smart floor system can serve as the foundation for smart home applications in the future, which include health care, intelligent automation, and home security, owing to its advantages of low cost, large area, and high reliability. UR - https://formative.jmir.org/2023/1/e47325 UR - http://dx.doi.org/10.2196/47325 UR - http://www.ncbi.nlm.nih.gov/pubmed/37548993 ID - info:doi/10.2196/47325 ER - TY - JOUR AU - Mueller, Christian AU - Herrmann, Patrick AU - Cichos, Stephan AU - Remes, Bernhard AU - Junker, Erwin AU - Hastenteufel, Tobias AU - Mundhenke, Markus PY - 2023/8/4 TI - Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis JO - J Med Internet Res SP - e47958 VL - 25 KW - digital transformation KW - automated data transfer KW - electronic medical record KW - electronic data capture KW - EDC KW - data transfer KW - electronic health record KW - EHR KW - digital transfer KW - barrier KW - clinical practice KW - EHR2EDC KW - health care system N2 - Background: Data transfer between electronic health records (EHRs) at the point of care and electronic data capture (EDC) systems for clinical research is still mainly carried out manually, which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHRs to EDC systems (EHR2EDC) would enable more accurate and efficient data capture but has so far encountered technological barriers primarily related to data format and the technological environment: in Germany, health care data are collected at the point of care in a variety of often individualized practice management systems (PMSs), most of them not interoperable. Data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports. Objective: We aimed to develop a model for automated data transfer as part of an observational study that allows data of sufficient quality to be captured at the point of care, extracted from various PMSs, and automatically transferred to electronic case report forms in EDC systems. This required addressing aspects of data security, as well as the lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research. Methods: The SaniQ software platform (Qurasoft GmbH) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical?hosted PMSs. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system (Alcedis GmbH) for data collection and management. EHR2EDC synchronization occurs automatically overnight, and real-time updates can be initiated manually following each data entry in the EHR. The electronic case report form (eCRF) contains 13 forms with 274 variables. Of these, 5 forms with 185 variables contain 67 automatically transferable variables (67/274, 24% of all variables and 67/185, 36% of eligible variables). Results: This model for automated data transfer bridges the current gap between clinical practice data capture at the point of care and the data sets required by regulatory agencies; it also enables automated EHR2EDC data transfer in compliance with the General Data Protection Regulation (GDPR). It addresses feasibility, connectivity, and system compatibility of currently used PMSs in health care and clinical research and is therefore directly applicable. Conclusions: This use case demonstrates that secure, consistent, and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry-wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data. UR - https://www.jmir.org/2023/1/e47958 UR - http://dx.doi.org/10.2196/47958 UR - http://www.ncbi.nlm.nih.gov/pubmed/37540555 ID - info:doi/10.2196/47958 ER - TY - JOUR AU - Nagino, Ken AU - Okumura, Yuichi AU - Akasaki, Yasutsugu AU - Fujio, Kenta AU - Huang, Tianxiang AU - Sung, Jaemyoung AU - Midorikawa-Inomata, Akie AU - Fujimoto, Keiichi AU - Eguchi, Atsuko AU - Hurramhon, Shokirova AU - Yee, Alan AU - Miura, Maria AU - Ohno, Mizu AU - Hirosawa, Kunihiko AU - Morooka, Yuki AU - Murakami, Akira AU - Kobayashi, Hiroyuki AU - Inomata, Takenori PY - 2023/8/3 TI - Smartphone App?Based and Paper-Based Patient-Reported Outcomes Using a Disease-Specific Questionnaire for Dry Eye Disease: Randomized Crossover Equivalence Study JO - J Med Internet Res SP - e42638 VL - 25 KW - dry eye syndrome KW - mobile app KW - equivalence trial KW - Ocular Surface Disease Index KW - patient-reported outcome measures KW - mobile health KW - reliability KW - validity KW - telemedicine KW - precision medicine N2 - Background: Using traditional patient-reported outcomes (PROs), such as paper-based questionnaires, is cumbersome in the era of web-based medical consultation and telemedicine. Electronic PROs may reduce the burden on patients if implemented widely. Considering promising reports of DryEyeRhythm, our in-house mHealth smartphone app for investigating dry eye disease (DED) and the electronic and paper-based Ocular Surface Disease Index (OSDI) should be evaluated and compared to determine their equivalency. Objective: The purpose of this study is to assess the equivalence between smartphone app?based and paper-based questionnaires for DED. Methods: This prospective, nonblinded, randomized crossover study enrolled 34 participants between April 2022 and June 2022 at a university hospital in Japan. The participants were allocated randomly into 2 groups in a 1:1 ratio. The paper-app group initially responded to the paper-based Japanese version of the OSDI (J-OSDI), followed by the app-based J-OSDI. The app-paper group responded to similar questionnaires but in reverse order. We performed an equivalence test based on minimal clinically important differences to assess the equivalence of the J-OSDI total scores between the 2 platforms (paper-based vs app-based). A 95% CI of the mean difference between the J-OSDI total scores within the ±7.0 range between the 2 platforms indicated equivalence. The internal consistency and agreement of the app-based J-OSDI were assessed with Cronbach ? coefficients and intraclass correlation coefficient values. Results: A total of 33 participants were included in this study. The total scores for the app- and paper-based J-OSDI indicated satisfactory equivalence per our study definition (mean difference 1.8, 95% CI ?1.4 to 5.0). Moreover, the app-based J-OSDI total score demonstrated good internal consistency and agreement (Cronbach ?=.958; intraclass correlation=0.919; 95% CI 0.842 to 0.959) and was significantly correlated with its paper-based counterpart (Pearson correlation=0.932, P<.001). Conclusions: This study demonstrated the equivalence of PROs between the app- and paper-based J-OSDI. Implementing the app-based J-OSDI in various scenarios, including telehealth, may have implications for the early diagnosis of DED and longitudinal monitoring of PROs. UR - https://www.jmir.org/2023/1/e42638 UR - http://dx.doi.org/10.2196/42638 UR - http://www.ncbi.nlm.nih.gov/pubmed/37535409 ID - info:doi/10.2196/42638 ER - TY - JOUR AU - Patnaik, Rajashree AU - Jannati, Shirin AU - Sivani, Mohan Bala AU - Rizzo, Manfredi AU - Naidoo, Nerissa AU - Banerjee, Yajnavalka PY - 2023/7/28 TI - Efficient Generation of Chondrocytes From Bone Marrow?Derived Mesenchymal Stem Cells in a 3D Culture System: Protocol for a Practical Model for Assessing Anti-Inflammatory Therapies JO - JMIR Res Protoc SP - e42964 VL - 12 KW - chondrocytes KW - bone marrow?derived mesenchymal stem cell KW - BMSC KW - tumor necrosis factor-? KW - TNF-? KW - vitamin D KW - curcumin KW - resveratrol KW - enzyme-linked immunosorbent assay KW - ELISA KW - inflammation KW - anti-inflammation KW - proinflammation KW - 3D culture system N2 - Background: Chondrocytes are the primary cells responsible for maintaining cartilage integrity and function. Their role in cartilage homeostasis and response to inflammation is crucial for understanding the progression and potential therapeutic interventions for various cartilage-related disorders. Developing an accessible and cost-effective model to generate viable chondrocytes and to assess their response to different bioactive compounds can significantly advance our knowledge of cartilage biology and contribute to the discovery of novel therapeutic approaches. Objective: We developed a novel, streamlined protocol for generating chondrocytes from bone marrow?derived mesenchymal stem cells (BMSCs) in a 3D culture system that offers significant implications for the study of cartilage biology and the discovery of potential therapeutic interventions for cartilage-related and associated disorders. Methods: We developed a streamlined protocol for generating chondrocytes from BMSCs in a 3D culture system using an ?in-tube? culture approach. This simple pellet-based 3D culture system allows for cell aggregation and spheroid formation, facilitating cell-cell and cell?extracellular matrix interactions that better mimic the in vivo cellular environment compared with 2D monolayer cultures. A proinflammatory chondrocyte model was created by treating the chondrocytes with lipopolysaccharide and was subsequently used to evaluate the anti-inflammatory effects of vitamin D, curcumin, and resveratrol. Results: The established protocol successfully generated a large quantity of viable chondrocytes, characterized by alcian blue and toluidine blue staining, and demonstrated versatility in assessing the anti-inflammatory effects of various bioactive compounds. The chondrocytes exhibited reduced inflammation, as evidenced by the decreased tumor necrosis factor-? levels, in response to vitamin D, curcumin, and resveratrol treatment. Conclusions: Our novel protocol offers an accessible and cost-effective approach for generating chondrocytes from BMSCs and for evaluating potential therapeutic leads in the context of inflammatory chondrocyte?related diseases. Although our approach has several advantages, further investigation is required to address its limitations, such as the potential differences between chondrocytes generated using our protocol and those derived from other established methods, and to refine the model for broader applicability and clinical translation. UR - https://www.researchprotocols.org/2023/1/e42964 UR - http://dx.doi.org/10.2196/42964 UR - http://www.ncbi.nlm.nih.gov/pubmed/37505889 ID - info:doi/10.2196/42964 ER - TY - JOUR AU - Woelfle, Tim AU - Bourguignon, Lucie AU - Lorscheider, Johannes AU - Kappos, Ludwig AU - Naegelin, Yvonne AU - Jutzeler, Ruth Catherine PY - 2023/7/27 TI - Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective JO - J Med Internet Res SP - e44428 VL - 25 KW - multiple sclerosis KW - digital biomarkers KW - digital health technologies KW - digital mobility outcomes KW - wearables KW - sensors KW - inertial motion unit KW - accelerometry KW - actigraphy KW - review N2 - Background: Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. Objective: This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. Methods: We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms ?multiple sclerosis? and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Results: Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. Conclusions: Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. Trial Registration: PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249 UR - https://www.jmir.org/2023/1/e44428 UR - http://dx.doi.org/10.2196/44428 UR - http://www.ncbi.nlm.nih.gov/pubmed/37498655 ID - info:doi/10.2196/44428 ER - TY - JOUR AU - Argyridou, Elina AU - Nifakos, Sokratis AU - Laoudias, Christos AU - Panda, Sakshyam AU - Panaousis, Emmanouil AU - Chandramouli, Krishna AU - Navarro-Llobet, Diana AU - Mora Zamorano, Juan AU - Papachristou, Panagiotis AU - Bonacina, Stefano PY - 2023/7/27 TI - Cyber Hygiene Methodology for Raising Cybersecurity and Data Privacy Awareness in Health Care Organizations: Concept Study JO - J Med Internet Res SP - e41294 VL - 25 KW - cyber hygiene KW - cybersecurity KW - awareness KW - training KW - health care KW - risk management KW - mobile phone N2 - Background: Cyber threats are increasing across all business sectors, with health care being a prominent domain. In response to the ever-increasing threats, health care organizations (HOs) are enhancing the technical measures with the use of cybersecurity controls and other advanced solutions for further protection. Despite the need for technical controls, humans are evidently the weakest link in the cybersecurity posture of HOs. This suggests that addressing the human aspects of cybersecurity is a key step toward managing cyber-physical risks. In practice, HOs are required to apply general cybersecurity and data privacy guidelines that focus on human factors. However, there is limited literature on the methodologies and procedures that can assist in successfully mapping these guidelines to specific controls (interventions), including awareness activities and training programs, with a measurable impact on personnel. To this end, tools and structured methodologies for assisting higher management in selecting the minimum number of required controls that will be most effective on the health care workforce are highly desirable. Objective: This study aimed to introduce a cyber hygiene (CH) methodology that uses a unique survey-based risk assessment approach for raising the cybersecurity and data privacy awareness of different employee groups in HOs. The main objective was to identify the most effective strategy for managing cybersecurity and data privacy risks and recommend targeted human-centric controls that are tailored to organization-specific needs. Methods: The CH methodology relied on a cross-sectional, exploratory survey study followed by a proposed risk-based survey data analysis approach. First, survey data were collected from 4 different employee groups across 3 European HOs, covering 7 categories of cybersecurity and data privacy risks. Next, survey data were transcribed and fitted into a proposed risk-based approach matrix that translated risk levels to strategies for managing the risks. Results: A list of human-centric controls and implementation levels was created. These controls were associated with risk categories, mapped to risk strategies for managing the risks related to all employee groups. Our mapping empowered the computation and subsequent recommendation of subsets of human-centric controls to implement the identified strategy for managing the overall risk of the HOs. An indicative example demonstrated the application of the CH methodology in a simple scenario. Finally, by applying the CH methodology in the health care sector, we obtained results in the form of risk markings; identified strategies to manage the risks; and recommended controls for each of the 3 HOs, each employee group, and each risk category. Conclusions: The proposed CH methodology improves the CH perception and behavior of personnel in the health care sector and provides risk strategies together with a list of recommended human-centric controls for managing a wide range of cybersecurity and data privacy risks related to health care employees. UR - https://www.jmir.org/2023/1/e41294 UR - http://dx.doi.org/10.2196/41294 UR - http://www.ncbi.nlm.nih.gov/pubmed/37498644 ID - info:doi/10.2196/41294 ER - TY - JOUR AU - Duarte, Miguel AU - Pereira-Rodrigues, Pedro AU - Ferreira-Santos, Daniela PY - 2023/7/26 TI - The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review JO - J Med Internet Res SP - e47735 VL - 25 KW - obstructive sleep apnea KW - diagnosis KW - digital tools KW - smartphone KW - wearables KW - sensor KW - polysomnography KW - systematic review KW - mobile phone N2 - Background: Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard. Objective: This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. Methods: We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures. Results: We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)?30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI?30. It uses the Sonomat?a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events. Conclusions: These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings. Trial Registration: PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748 UR - https://www.jmir.org/2023/1/e47735 UR - http://dx.doi.org/10.2196/47735 UR - http://www.ncbi.nlm.nih.gov/pubmed/37494079 ID - info:doi/10.2196/47735 ER - TY - JOUR AU - Kuo, Ming-Hao AU - Lin, You-Jin AU - Huang, Wun-Wei AU - Chiang, Kwo-Tsao AU - Tu, Min-Yu AU - Chu, Chi-Ming AU - Lai, Chung-Yu PY - 2023/7/26 TI - G Tolerance Prediction Model Using Mobile Device?Measured Cardiac Force Index for Military Aircrew: Observational Study JO - JMIR Mhealth Uhealth SP - e48812 VL - 11 KW - G force KW - baroreflex KW - anti-G straining maneuver KW - G tolerance KW - cardiac force index KW - anti-G suit KW - relaxed G tolerance KW - straining G tolerance KW - cardiac force ratio N2 - Background: During flight, G force compels blood to stay in leg muscles and reduces blood flow to the heart. Cardiovascular responses activated by the autonomic nerve system and strengthened by anti-G straining maneuvers can alleviate the challenges faced during G loading. To our knowledge, no definite cardiac information measured using a mobile health device exists for analyzing G tolerance. However, our previous study developed the cardiac force index (CFI) for analyzing the G tolerance of military aircrew. Objective: This study used the CFI to verify participants? cardiac performance when walking and obtained a formula for predicting an individual?s G tolerance during centrifuge training. Methods: Participants from an air force aircrew undertook high-G training from January 2020 to December 2022. Their heart rate (HR) in beats per minute and activity level per second were recorded using the wearable BioHarness 3.0 device. The CFI was computed using the following formula: weight × activity / HR during resting or walking. Relaxed G tolerance (RGT) and straining G tolerance (SGT) were assessed at a slowly increasing rate of G loading (0.1 G/s) during training. Other demographic factors were included in the multivariate regression to generate a model for predicting G tolerance from the CFI. Results: A total of 213 eligible trainees from a military aircrew were recruited. The average age was 25.61 (SD 3.66) years, and 13.1% (28/213) of the participants were women. The mean resting CFI and walking CFI (WCFI) were 0.016 (SD 0.001) and 0.141 (SD 0.037) kg × G/beats per minute, respectively. The models for predicting RGT and SGT were as follows: RGT = 0.066 × age + 0.043 × (WCFI × 100) ? 0.037 × height + 0.015 × systolic blood pressure ? 0.010 × HR + 7.724 and SGT = 0.103 × (WCFI × 100) ? 0.069 × height + 0.018 × systolic blood pressure + 15.899. Thus, the WCFI is a positive factor for predicting the RGT and SGT before centrifuge training. Conclusions: The WCFI is a vital component of the formula for estimating G tolerance prior to training. The WCFI can be used to monitor physiological conditions against G stress. UR - https://mhealth.jmir.org/2023/1/e48812 UR - http://dx.doi.org/10.2196/48812 UR - http://www.ncbi.nlm.nih.gov/pubmed/37494088 ID - info:doi/10.2196/48812 ER - TY - JOUR AU - Foxon, Floe PY - 2023/7/21 TI - The Loch Ness Monster: If It?s Real, Could It Be an Eel? JO - JMIRx Bio SP - e49063 VL - 1 KW - European eel KW - Anguilla anguilla KW - probability distribution KW - Loch Ness KW - folk zoology KW - unknown animals KW - cryptozoology N2 - Background: Previous studies have estimated the size, mass, and population of hypothetical unknown animals in a large oligotrophic freshwater loch in Scotland based on biomass and other observational considerations. The ?eel hypothesis? proposes that the anthrozoological phenomenon at Loch Ness can be explained in part by observations of large specimens of European eel (Anguilla anguilla), as these animals are most compatible with morphological, behavioral, and environmental considerations. Objective: This study expands upon the ?eel hypothesis? and related literature by estimating the probability of observing eels at least as large as have been proposed, using catch data from Loch Ness and other freshwater bodies in Europe. Methods: Skew normal and generalized extreme value distributions were fitted to eel body length distributions to estimate cumulative distribution functions from which probabilities were obtained. Results: The chances of finding a large eel in Loch Ness are around 1 in 50,000 for a 1-meter specimen, which is reasonable given the loch?s fish stock and suggests some sightings of smaller unknown animals may be accounted for by large eels. However, the probability of finding a specimen upward of 6 meters is essentially zero; therefore, eels probably do not account for sightings of larger animals. Conclusions: The existence of exceedingly large eels in the loch is not likely based on purely statistical considerations. (Reviewed by the Plan P #PeerRef Community). UR - https://bio.jmirx.org/2023/1/e49063 UR - http://dx.doi.org/10.2196/49063 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49063 ER - TY - JOUR AU - Woods, Andrew AU - Kramer, T. Skyler AU - Xu, Dong AU - Jiang, Wei PY - 2023/7/18 TI - Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development JO - JMIR Bioinform Biotech SP - e44700 VL - 4 KW - secure multiparty computation KW - single nucleotide polymorphism KW - Variant Call Format KW - Jaccard similarity N2 - Background: While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective: In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods: Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results: We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions: Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security. UR - https://bioinform.jmir.org/2023/1/e44700 UR - http://dx.doi.org/10.2196/44700 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/44700 ER - TY - JOUR AU - Yang, Dan AU - Su, Zihan AU - Mu, Runqing AU - Diao, Yingying AU - Zhang, Xin AU - Liu, Yusi AU - Wang, Shuo AU - Wang, Xu AU - Zhao, Lei AU - Wang, Hongyi AU - Zhao, Min PY - 2023/7/17 TI - Effects of Using Different Indirect Techniques on the Calculation of Reference Intervals: Observational Study JO - J Med Internet Res SP - e45651 VL - 25 KW - comparative study KW - data transformation KW - indirect method KW - outliers KW - reference interval KW - clinical decision-making KW - complete blood count KW - red blood cells KW - white blood cells KW - platelets KW - laboratory KW - clinical N2 - Background: Reference intervals (RIs) play an important role in clinical decision-making. However, due to the time, labor, and financial costs involved in establishing RIs using direct means, the use of indirect methods, based on big data previously obtained from clinical laboratories, is getting increasing attention. Different indirect techniques combined with different data transformation methods and outlier removal might cause differences in the calculation of RIs. However, there are few systematic evaluations of this. Objective: This study used data derived from direct methods as reference standards and evaluated the accuracy of combinations of different data transformation, outlier removal, and indirect techniques in establishing complete blood count (CBC) RIs for large-scale data. Methods: The CBC data of populations aged ?18 years undergoing physical examination from January 2010 to December 2011 were retrieved from the First Affiliated Hospital of China Medical University in northern China. After exclusion of repeated individuals, we performed parametric, nonparametric, Hoffmann, Bhattacharya, and truncation points and Kolmogorov?Smirnov distance (kosmic) indirect methods, combined with log or BoxCox transformation, and Reed?Dixon, Tukey, and iterative mean (3SD) outlier removal methods in order to derive the RIs of 8 CBC parameters and compared the results with those directly and previously established. Furthermore, bias ratios (BRs) were calculated to assess which combination of indirect technique, data transformation pattern, and outlier removal method is preferrable. Results: Raw data showed that the degrees of skewness of the white blood cell (WBC) count, platelet (PLT) count, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular volume (MCV) were much more obvious than those of other CBC parameters. After log or BoxCox transformation combined with Tukey or iterative mean (3SD) processing, the distribution types of these data were close to Gaussian distribution. Tukey-based outlier removal yielded the maximum number of outliers. The lower-limit bias of WBC (male), PLT (male), hemoglobin (HGB; male), MCH (male/female), and MCV (female) was greater than that of the corresponding upper limit for more than half of 30 indirect methods. Computational indirect choices of CBC parameters for males and females were inconsistent. The RIs of MCHC established by the direct method for females were narrow. For this, the kosmic method was markedly superior, which contrasted with the RI calculation of CBC parameters with high |BR| qualification rates for males. Among the top 10 methodologies for the WBC count, PLT count, HGB, MCV, and MCHC with a high-BR qualification rate among males, the Bhattacharya, Hoffmann, and parametric methods were superior to the other 2 indirect methods. Conclusions: Compared to results derived by the direct method, outlier removal methods and indirect techniques markedly influence the final RIs, whereas data transformation has negligible effects, except for obviously skewed data. Specifically, the outlier removal efficiency of Tukey and iterative mean (3SD) methods is almost equivalent. Furthermore, the choice of indirect techniques depends more on the characteristics of the studied analyte itself. This study provides scientific evidence for clinical laboratories to use their previous data sets to establish RIs. UR - https://www.jmir.org/2023/1/e45651 UR - http://dx.doi.org/10.2196/45651 UR - http://www.ncbi.nlm.nih.gov/pubmed/37459170 ID - info:doi/10.2196/45651 ER - TY - JOUR AU - Chen, Kay-Yut AU - Lang, Yan AU - Zhou, Yuan AU - Kosmari, Ludmila AU - Daniel, Kathryn AU - Gurses, Ayse AU - Xiao, Yan PY - 2023/7/13 TI - Assessing Interventions on Crowdsourcing Platforms to Nudge Patients for Engagement Behaviors in Primary Care Settings: Randomized Controlled Trial JO - J Med Internet Res SP - e41431 VL - 25 KW - Amazon Mechanical Turk KW - behavioral interventions KW - crowdsourcing KW - medication safety KW - Mturk KW - patient engagement KW - primary care N2 - Background: Engaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics?based interventions offer potential solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives. Objective: The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics?based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting. Methods: We conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants? baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants? willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality. Results: A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants? willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions. Conclusions: In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement. UR - https://www.jmir.org/2023/1/e41431 UR - http://dx.doi.org/10.2196/41431 UR - http://www.ncbi.nlm.nih.gov/pubmed/37440308 ID - info:doi/10.2196/41431 ER - TY - JOUR AU - Dagenais, Frédéric AU - Neville, Catriona AU - Desmet, Liesbet AU - Martineau, Sarah PY - 2023/7/7 TI - Measuring the Potential Effects of Mirror Therapy Added to the Gold Standard Facial Neuromuscular Retraining in Patients With Chronic Peripheral Facial Palsy: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e47709 VL - 12 KW - facial rehabilitation KW - neuromuscular retraining KW - mirror therapy KW - facial paralysis KW - synkinesis KW - palsy KW - rehabilitation KW - neuromuscular KW - paralysis KW - palsies KW - physical therapy KW - physiotherapy N2 - Background: Facial neuromuscular retraining (fNMR) is a noninvasive physical therapy widely used to treat peripheral facial palsies. It consists of different intervention methods that aim to reduce the debilitating sequelae of the disease. Recently, the use of mirror therapy in the acute facial palsy and postsurgical rehabilitation contexts has shown promising results, suggesting its use as an adjunct to fNMR in treating patients with later stages of paralysis, such as the paretic, early, or chronic synkinetic. Objective: The main aim of this study is to compare the efficacy of an added mirror therapy component with fNMR in patients with peripheral facial palsy (PFP) sequelae in 3 different stages. The specific objectives of this study are to measure the effects of combined therapy compared to fNMR alone on (1) participants? facial symmetry and synkinesis, (2) quality of life and psychological aspects of the participants, (3) motivation and treatment adherence, and (4) different stages of facial palsies. Methods: This study is a randomized controlled trial that compares the effect of fNMR combined with mirror therapy (experimental group: n=45) with fNMR alone (control group: n=45) in 90 patients with peripheral facial palsy presenting with sequelae 3-12 months after onset. Both groups will receive 6 months of rehabilitation training. Facial symmetry and synkinesis; participants? quality of life; and their psychological factors, motivation, and compliance will be assessed at baseline (T0), 3 months (T1), 6 months (T2), and 12 months (T3) postintervention. Outcome measures are (1) changes in facial symmetry and synkinesis assessed with facial grading tools, (2) quality of life changes with patient questionnaires, and (3) therapy motivation with a standardized scale, as well as adherence to treatment with metadata. Changes in facial symmetry and synkinesis will be judged by 3 assessors blinded to group assignment. Mixed models and Kruskal-Wallis, chi-square, and multilevel analyses will be conducted according to the appropriate variable type. Results: Inclusion will start in 2024 and is anticipated to be completed in 2027. The 12-month follow-up will be completed with the last patient in 2028. We expect patients included in this study to experience improvement in facial symmetry, synkinesis, and quality of life, regardless of group assignments. A potential benefit of mirror therapy for facial symmetry and synkinesis could be noted for patients in the paretic phase. We hypothesize better motivation and adherence to treatment for the mirror therapy group. Conclusions: The results of this trial may provide new guidelines for PFP rehabilitation with patients dealing with long-term sequelae. It also fills the need for robust evidence-based data in behavioral facial rehabilitation. International Registered Report Identifier (IRRID): PRR1-10.2196/47709 UR - https://www.researchprotocols.org/2023/1/e47709 UR - http://dx.doi.org/10.2196/47709 UR - http://www.ncbi.nlm.nih.gov/pubmed/37418307 ID - info:doi/10.2196/47709 ER - TY - JOUR AU - Bottani, Eleonora AU - Bellini, Valentina AU - Mordonini, Monica AU - Pellegrino, Mattia AU - Lombardo, Gianfranco AU - Franchi, Beatrice AU - Craca, Michelangelo AU - Bignami, Elena PY - 2023/7/5 TI - Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study JO - JMIR Res Protoc SP - e45477 VL - 12 KW - internet of things KW - artificial intelligence KW - machine learning KW - perioperative organization KW - operating rooms N2 - Background: Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital. Objective: This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources. Methods: Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number. Results: The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization?s information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient?s specific profile. Simulation can also be applied to reproduce the system?s functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block. Conclusions: This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system. Trial Registration: ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621 International Registered Report Identifier (IRRID): DERR1-10.2196/45477 UR - https://www.researchprotocols.org/2023/1/e45477 UR - http://dx.doi.org/10.2196/45477 UR - http://www.ncbi.nlm.nih.gov/pubmed/37405821 ID - info:doi/10.2196/45477 ER - TY - JOUR AU - Nelson, Walter AU - Khanna, Nityan AU - Ibrahim, Mohamed AU - Fyfe, Justin AU - Geiger, Maxwell AU - Edwards, Keith AU - Petch, Jeremy PY - 2023/6/29 TI - Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and Validation JO - JMIR Form Res SP - e44331 VL - 7 KW - medical record linkage KW - electronic health records KW - medical record systems KW - computerized KW - machine learning KW - quality of care KW - health care system KW - open-source software KW - Bayesian optimization KW - pilot KW - data linkage KW - master patient index KW - master index KW - record link KW - matching algorithm KW - FEBRL N2 - Background: To provide quality care, modern health care systems must match and link data about the same patient from multiple sources, a function often served by master patient index (MPI) software. Record linkage in the MPI is typically performed manually by health care providers, guided by automated matching algorithms. These matching algorithms must be configured in advance, such as by setting the weights of patient attributes, usually by someone with knowledge of both the matching algorithm and the patient population being served. Objective: We aimed to develop and evaluate a machine learning?based software tool, which automatically configures a patient matching algorithm by learning from pairs of patient records previously linked by humans already present in the database. Methods: We built a free and open-source software tool to optimize record linkage algorithm parameters based on historical record linkages. The tool uses Bayesian optimization to identify the set of configuration parameters that lead to optimal matching performance in a given patient population, by learning from prior record linkages by humans. The tool is written assuming only the existence of a minimal HTTP application programming interface (API), and so is agnostic to the choice of MPI software, record linkage algorithm, and patient population. As a proof of concept, we integrated our tool with SantéMPI, an open-source MPI. We validated the tool using several synthetic patient populations in SantéMPI by comparing the performance of the optimized configuration in held-out data to SantéMPI?s default matching configuration using sensitivity and specificity. Results: The machine learning?optimized configurations correctly detect over 90% of true record linkages as definite matches in all data sets, with 100% specificity and positive predictive value in all data sets, whereas the baseline detects none. In the largest data set examined, the baseline matching configuration detects possible record linkages with a sensitivity of 90.2% (95% CI 88.4%-92.0%) and specificity of 100%. By comparison, the machine learning?optimized matching configuration attains a sensitivity of 100%, with a decreased specificity of 95.9% (95% CI 95.9%-96.0%). We report significant gains in sensitivity in all data sets examined, at the cost of only marginally decreased specificity. The configuration optimization tool, data, and data set generator have been made freely available. Conclusions: Our machine learning software tool can be used to significantly improve the performance of existing record linkage algorithms, without knowledge of the algorithm being used or specific details of the patient population being served. UR - https://formative.jmir.org/2023/1/e44331 UR - http://dx.doi.org/10.2196/44331 UR - http://www.ncbi.nlm.nih.gov/pubmed/37384382 ID - info:doi/10.2196/44331 ER - TY - JOUR AU - Fu, Zhiying AU - Liu, Xiaohong AU - Zhao, Shuhua AU - Yuan, Yannan AU - Jiang, Min PY - 2023/6/27 TI - Reducing Clinical Trial Monitoring Resources and Costs With Remote Monitoring: Retrospective Study Comparing On-Site Versus Hybrid Monitoring JO - J Med Internet Res SP - e42175 VL - 25 KW - clinical trial KW - management KW - on-site monitoring KW - hybrid monitoring model KW - remote monitoring KW - hybrid KW - monitoring KW - cost KW - economic KW - trial monitoring KW - research quality KW - scientific research KW - trials methodology KW - trial management KW - research management N2 - Background: Clinical research associates (CRAs) monitor the progress of a trial, verify the data collected, and ensure that the trial is carried out and reported in accordance with the trial protocol, standard operating procedures, and relevant laws and regulations. In response to monitoring challenges during the COVID-19 pandemic, Peking University Cancer Hospital launched a remote monitoring system and established a monitoring model, combining on-site and remote monitoring of clinical trials. Considering the increasing digitization of clinical trials, it is important to determine the optimal monitoring model for the general benefit of centers conducting clinical trials worldwide. Objective: We sought to summarize our practical experience of a hybrid model of remote and on-site monitoring of clinical trials and provide guidance for clinical trial monitoring management. Methods: We evaluated 201 trials conducted by our hospital that used on-site monitoring alone or a hybrid monitoring model, of which 91 trials used on-site monitoring alone (arm A) and 110 used a hybrid model of remote and on-site monitoring (arm B). We reviewed trial monitoring reports from June 20, 2021, to June 20, 2022, and used a customized questionnaire to collect and compare the following information: monitoring cost of trials in the 2 models as a sum of the CRAs? transportation (eg, taxi fare and air fare), accommodation, and meal costs; differences in monitoring frequency; the number of monitored documents; and monitoring duration. Results: From June 20, 2021, to June 20, 2022, a total of 320 CRAs representing 201 sponsors used the remote monitoring system for source data review and the verification of data from 3299 patients in 320 trials. Arm A trials were monitored 728 times and arm B trials were monitored 849 times. The hybrid model in arm B had 52.9% (449/849) remote visits and 48.1% (409/849) on-site visits. The number of patients? visits that could be reviewed in the hybrid monitoring model increased by 34% (4.70/13.80; P=.004) compared with that in the traditional model, whereas the duration of monitoring decreased by 13.8% (3.96/28.61; P=.03) and the total cost of monitoring decreased by 46.2% (CNY ¥188.74/408.80; P<.001). These differences were shown by nonparametric testing to be statistically significant (P<.05). Conclusions: The hybrid monitoring model can ensure timely detection of monitoring issues, improve monitoring efficiency, and reduce the cost of clinical trials and should therefore be applied more broadly in future clinical studies. UR - https://www.jmir.org/2023/1/e42175 UR - http://dx.doi.org/10.2196/42175 UR - http://www.ncbi.nlm.nih.gov/pubmed/37368468 ID - info:doi/10.2196/42175 ER - TY - JOUR AU - Reid, C. Sean AU - Wang, Vania AU - Assaf, D. Ryan AU - Kaloper, Sofia AU - Murray, T. Alan AU - Shoptaw, Steven AU - Gorbach, Pamina AU - Cassels, Susan PY - 2023/6/22 TI - Novel Location-Based Survey Using Cognitive Interviews to Assess Geographic Networks and Hotspots of Sex and Drug Use: Implementation and Validation Study JO - JMIR Form Res SP - e45188 VL - 7 KW - networks KW - sexual network geography KW - activity space KW - HIV KW - survey design KW - risk hotspots KW - cognitive interviews KW - health interventions KW - mobile phone N2 - Background: The Ending the HIV Epidemic initiative in the United States relies on HIV hotspots to identify where to geographically target new resources, expertise, and technology. However, interventions targeted at places with high HIV transmission and infection risk, not just places with high HIV incidence, may be more effective at reducing HIV incidence and achieving health equity. Objective: We described the implementation and validation of a web-based activity space survey on HIV risk behaviors. The survey was intended to collect geographic information that will be used to map risk behavior hotspots as well as the geography of sexual networks in Los Angeles County. Methods: The survey design team developed a series of geospatial questions that follow a 3-level structure that becomes more geographically precise as participants move through the levels. The survey was validated through 9 cognitive interviews and iteratively updated based on participant feedback until the saturation of topics and technical issues was reached. Results: In total, 4 themes were identified through the cognitive interviews: functionality of geospatial questions, representation and accessibility, privacy, and length and understanding of the survey. The ease of use for the geospatial questions was critical as many participants were not familiar with mapping software. The inclusion of well-known places, landmarks, and road networks was critical for ease of use. The addition of a Google Maps interface, which was familiar to many participants, aided in collecting accurate and precise location information. The geospatial questions increased the length of the survey and warranted the inclusion of features to simplify it and speed it up. Using nicknames to refer to previously entered geographic locations limited the number of geospatial questions that appeared in the survey and reduced the time taken to complete it. The long-standing relationship between participants and the research team improved comfort to disclose sensitive geographic information related to drug use and sex. Participants in the cognitive interviews highlighted how trust and inclusive and validating language in the survey alleviated concerns related to privacy and representation. Conclusions: This study provides promising results regarding the feasibility of using a web-based mapping survey to collect sensitive location information relevant to ending the HIV epidemic. Data collection at several geographic levels will allow for insights into spatial recall of behaviors as well as future sensitivity analysis of the spatial scale of hotspots and network characteristics. This design also promotes the privacy and comfort of participants who provide location information for sensitive topics. Key considerations for implementing this type of survey include trust from participants, community partners, or research teams to overcome concerns related to privacy and comfort. The implementation of similar surveys should consider local characteristics and knowledge when crafting the geospatial components. UR - https://formative.jmir.org/2023/1/e45188 UR - http://dx.doi.org/10.2196/45188 UR - http://www.ncbi.nlm.nih.gov/pubmed/37347520 ID - info:doi/10.2196/45188 ER - TY - JOUR AU - Gendrin, Aline AU - Souliotis, Leonidas AU - Loudon-Griffiths, James AU - Aggarwal, Ravisha AU - Amoako, Daniel AU - Desouza, Gregory AU - Dimitrievska, Sashka AU - Metcalfe, Paul AU - Louvet, Emilie AU - Sahni, Harpreet PY - 2023/6/22 TI - Identifying Patient Populations in Texts Describing Drug Approvals Through Deep Learning?Based Information Extraction: Development of a Natural Language Processing Algorithm JO - JMIR Form Res SP - e44876 VL - 7 KW - algorithm KW - artificial intelligence KW - BERT KW - cancer KW - classification KW - data extraction KW - data mining KW - deep-learning KW - development KW - drug approval KW - free text KW - information retrieval KW - line of therapy KW - machine learning KW - natural language processing KW - NLP KW - oncology KW - pharmaceutic KW - pharmacology KW - pharmacy KW - stage of cancer KW - text extraction KW - text mining KW - unstructured data N2 - Background: New drug treatments are regularly approved, and it is challenging to remain up-to-date in this rapidly changing environment. Fast and accurate visualization is important to allow a global understanding of the drug market. Automation of this information extraction provides a helpful starting point for the subject matter expert, helps to mitigate human errors, and saves time. Objective: We aimed to semiautomate disease population extraction from the free text of oncology drug approval descriptions from the BioMedTracker database for 6 selected drug targets. More specifically, we intended to extract (1) line of therapy, (2) stage of cancer of the patient population described in the approval, and (3) the clinical trials that provide evidence for the approval. We aimed to use these results in downstream applications, aiding the searchability of relevant content against related drug project sources. Methods: We fine-tuned a state-of-the-art deep learning model, Bidirectional Encoder Representations from Transformers, for each of the 3 desired outputs. We independently applied rule-based text mining approaches. We compared the performances of deep learning and rule-based approaches and selected the best method, which was then applied to new entries. The results were manually curated by a subject matter expert and then used to train new models. Results: The training data set is currently small (433 entries) and will enlarge over time when new approval descriptions become available or if a choice is made to take another drug target into account. The deep learning models achieved 61% and 56% 5-fold cross-validated accuracies for line of therapy and stage of cancer, respectively, which were treated as classification tasks. Trial identification is treated as a named entity recognition task, and the 5-fold cross-validated F1-score is currently 87%. Although the scores of the classification tasks could seem low, the models comprise 5 classes each, and such scores are a marked improvement when compared to random classification. Moreover, we expect improved performance as the input data set grows, since deep learning models need to be trained on a large enough amount of data to be able to learn the task they are taught. The rule-based approach achieved 60% and 74% 5-fold cross-validated accuracies for line of therapy and stage of cancer, respectively. No attempt was made to define a rule-based approach for trial identification. Conclusions: We developed a natural language processing algorithm that is currently assisting subject matter experts in disease population extraction, which supports health authority approvals. This algorithm achieves semiautomation, enabling subject matter experts to leverage the results for deeper analysis and to accelerate information retrieval in a crowded clinical environment such as oncology. UR - https://formative.jmir.org/2023/1/e44876 UR - http://dx.doi.org/10.2196/44876 UR - http://www.ncbi.nlm.nih.gov/pubmed/37347514 ID - info:doi/10.2196/44876 ER - TY - JOUR AU - Bachelot, Guillaume AU - Dhombres, Ferdinand AU - Sermondade, Nathalie AU - Haj Hamid, Rahaf AU - Berthaut, Isabelle AU - Frydman, Valentine AU - Prades, Marie AU - Kolanska, Kamila AU - Selleret, Lise AU - Mathieu-D?Argent, Emmanuelle AU - Rivet-Danon, Diane AU - Levy, Rachel AU - Lamazière, Antonin AU - Dupont, Charlotte PY - 2023/6/21 TI - A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study JO - J Med Internet Res SP - e44047 VL - 25 KW - machine learning KW - azoospermia KW - prediction model KW - biomedical informatics KW - model KW - predict KW - sperm KW - men's health KW - infertility KW - infertile N2 - Background: Testicular sperm extraction (TESE) is an essential therapeutic tool for the management of male infertility. However, it is an invasive procedure with a success rate up to 50%. To date, no model based on clinical and laboratory parameters is sufficiently powerful to accurately predict the success of sperm retrieval in TESE. Objective: The aim of this study is to compare a wide range of predictive models under similar conditions for TESE outcomes in patients with nonobstructive azoospermia (NOA) to identify the correct mathematical approach to apply, most appropriate study size, and relevance of the input biomarkers. Methods: We analyzed 201 patients who underwent TESE at Tenon Hospital (Assistance Publique-Hôpitaux de Paris, Sorbonne University, Paris), distributed in a retrospective training cohort of 175 patients (January 2012 to April 2021) and a prospective testing cohort (May 2021 to December 2021) of 26 patients. Preoperative data (according to the French standard exploration of male infertility, 16 variables) including urogenital history, hormonal data, genetic data, and TESE outcomes (representing the target variable) were collected. A TESE was considered positive if we obtained sufficient spermatozoa for intracytoplasmic sperm injection. After preprocessing the raw data, 8 machine learning (ML) models were trained and optimized on the retrospective training cohort data set: The hyperparameter tuning was performed by random search. Finally, the prospective testing cohort data set was used for the model evaluation. The metrics used to evaluate and compare the models were the following: sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and accuracy. The importance of each variable in the model was assessed using the permutation feature importance technique, and the optimal number of patients to include in the study was assessed using the learning curve. Results: The ensemble models, based on decision trees, showed the best performance, especially the random forest model, which yielded the following results: AUC=0.90, sensitivity=100%, and specificity=69.2%. Furthermore, a study size of 120 patients seemed sufficient to properly exploit the preoperative data in the modeling process, since increasing the number of patients beyond 120 during model training did not bring any performance improvement. Furthermore, inhibin B and a history of varicoceles exhibited the highest predictive capacity. Conclusions: An ML algorithm based on an appropriate approach can predict successful sperm retrieval in men with NOA undergoing TESE, with promising performance. However, although this study is consistent with the first step of this process, a subsequent formal prospective multicentric validation study should be undertaken before any clinical applications. As future work, we consider the use of recent and clinically relevant data sets (including seminal plasma biomarkers, especially noncoding RNAs, as markers of residual spermatogenesis in NOA patients) to improve our results even more. UR - https://www.jmir.org/2023/1/e44047 UR - http://dx.doi.org/10.2196/44047 UR - http://www.ncbi.nlm.nih.gov/pubmed/37342078 ID - info:doi/10.2196/44047 ER - TY - JOUR AU - Diniz, Miguel José AU - Vasconcelos, Henrique AU - Souza, Júlio AU - Rb-Silva, Rita AU - Ameijeiras-Rodriguez, Carolina AU - Freitas, Alberto PY - 2023/6/19 TI - Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review JO - JMIR Res Protoc SP - e45823 VL - 12 KW - decentralized learning KW - distributed learning KW - federated learning KW - centralized learning KW - privacy KW - health KW - health data KW - secondary data use KW - health data model KW - blockchain KW - health care KW - data science N2 - Background: Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications. Objective: The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures. Methods: We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)?based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported. Results: The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings. Conclusions: We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients? privacy and future research. Trial Registration: PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126 International Registered Report Identifier (IRRID): PRR1-10.2196/45823 UR - https://www.researchprotocols.org/2023/1/e45823 UR - http://dx.doi.org/10.2196/45823 UR - http://www.ncbi.nlm.nih.gov/pubmed/37335606 ID - info:doi/10.2196/45823 ER - TY - JOUR AU - Wolters, Fabian AU - van Middendorp, Henriët AU - Van den Bergh, Omer AU - Biermasz, R. Nienke AU - Meijer, C. Onno AU - Evers, WM Andrea PY - 2023/6/19 TI - Conditioning of the Cortisol Awakening Response in Healthy Men: Study Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e38087 VL - 12 KW - conditioning KW - cortisol KW - cortisol awakening response KW - sleep KW - olfactory learning N2 - Background: The hormone cortisol plays important roles in human circadian and stress physiology and is an interesting target for interventions. Cortisol varies not only in response to stress but also as part of a diurnal rhythm. It shows a particularly sharp increase immediately after awakening, the cortisol awakening response (CAR). Cortisol can be affected by medication, but it is less clear whether it can also be affected by learning. Animal studies have consistently shown that cortisol can be affected by pharmacological conditioning, but the results in humans have been mixed. Other studies have suggested that conditioning is also possible during sleep and that the diurnal rhythm can be conditioned, but these findings have not yet been applied to cortisol conditioning. Objective: The objective of our study was to introduce a novel avenue for conditioning cortisol: by using the CAR as an unconditioned response and using scent conditioning while the participant is asleep. This study investigates an innovative way to study the effects of conditioning on cortisol and the diurnal rhythm, using a variety of devices and measures to make measurement possible at a distance and at unusual moments. Methods: The study protocol takes 2 weeks and is performed from the participant?s home. Measures in week 1 are taken to reflect the CAR and waking under baseline conditions. For the first 3 nights of week 2, participants are exposed to a scent from 30 minutes before awakening until their normal time of awakening to allow the scent to become associated with the CAR. On the final night, participants are forced to wake 4 hours earlier, when cortisol levels are normally low, and either the same (conditioned group) or a different (control group) scent is presented half an hour before this new time. This allows us to test whether cortisol levels are higher after the same scent is presented. The primary outcome is the CAR, assessed by saliva cortisol levels, 0, 15, 30, and 45 minutes after awakening. The secondary outcomes are heart rate variability, actigraphy measures taken during sleep, and self-reported mood after awakening. To perform manipulations and measurements, this study uses wearable devices, 2 smartphone apps, web-based questionnaires, and a programmed scent device. Results: We completed data collection as of December 24, 2021. Conclusions: This study can provide new insights into learning effects on cortisol and the diurnal rhythm. If the procedure does affect the CAR and associated measures, it also has potential clinical implications in the treatment of sleep and stress disorders. Trial Registration: Netherlands Trial Register NL58792.058.16; https://trialsearch.who.int/Trial2.aspx?TrialID=NL7791 International Registered Report Identifier (IRRID): DERR1-10.2196/38087 UR - https://www.researchprotocols.org/2023/1/e38087 UR - http://dx.doi.org/10.2196/38087 UR - http://www.ncbi.nlm.nih.gov/pubmed/37335592 ID - info:doi/10.2196/38087 ER - TY - JOUR AU - Sigle, Manuel AU - Berliner, Leon AU - Richter, Erich AU - van Iersel, Mart AU - Gorgati, Eleonora AU - Hubloue, Ives AU - Bamberg, Maximilian AU - Grasshoff, Christian AU - Rosenberger, Peter AU - Wunderlich, Robert PY - 2023/6/15 TI - Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study JO - J Med Internet Res SP - e44042 VL - 25 KW - novel triage algorithm KW - patient with trauma KW - dynamic patient simulation KW - mathematic model KW - artificial patient database KW - semisupervised generation of patients with artificial trauma KW - high-dimensional analysis of patient database KW - Germany KW - algorithm KW - trauma KW - proof-of-concept KW - model KW - emergency KW - triage KW - simulation KW - urgency KW - urgent KW - severity KW - rank KW - vital sign N2 - Background: In cases of terrorism, disasters, or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient?s current health status rather than their prognosis, thus leaving a fatal gap of patients who are under- or overtriaged. Objective: The aim of this proof-of-concept study is to demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aim to improve the prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods, and the availability of rescue resources. Methods: We designed a mathematical model that allows dynamic simulation of the time course of a patient?s vital parameters, depending on individual baseline vital signs and injury severity. The 2 variables were integrated using the well-established Revised Trauma Score (RTS) and the New Injury Severity Score (NISS). An artificial patient database of unique patients with trauma (N=82,277) was then generated and used for analysis of the time course modeling and triage classification. Comparative performance analysis of different triage algorithms was performed. In addition, we applied a sophisticated, state-of-the-art clustering method using the Gower distance to visualize patient cohorts at risk for mistriage. Results: The proposed triage algorithm realistically modeled the time course of a patient?s life, depending on injury severity and current vital parameters. Different casualties were ranked by their anticipated time course, reflecting their priority for treatment. Regarding the identification of patients at risk for mistriage, the model outperformed the Simple Triage And Rapid Treatment?s triage algorithm but also exclusive stratification by the RTS or the NISS. Multidimensional analysis separated patients with similar patterns of injuries and vital parameters into clusters with different triage classifications. In this large-scale analysis, our algorithm confirmed the previously mentioned conclusions during simulation and descriptive analysis and underlined the significance of this novel approach to triage. Conclusions: The findings of this study suggest the feasibility and relevance of our model, which is unique in terms of its ranking system, prognosis outline, and time course anticipation. The proposed triage-ranking algorithm could offer an innovative triage method with a wide range of applications in prehospital, disaster, and emergency medicine, as well as simulation and research. UR - https://www.jmir.org/2023/1/e44042 UR - http://dx.doi.org/10.2196/44042 UR - http://www.ncbi.nlm.nih.gov/pubmed/37318826 ID - info:doi/10.2196/44042 ER - TY - JOUR AU - Gresenz, Roan Carole AU - Singh, Lisa AU - Wang, Yanchen AU - Haber, Jaren AU - Liu, Yaguang PY - 2023/6/13 TI - Development and Assessment of a Social Media?Based Construct of Firearm Ownership: Computational Derivation and Benchmark Comparison JO - J Med Internet Res SP - e45187 VL - 25 KW - criterion validity KW - firearms ownership KW - gun violence KW - machine learning KW - social media data N2 - Background: Gun violence research is characterized by a dearth of data available for measuring key constructs. Social media data may offer a potential opportunity to significantly reduce that gap, but developing methods for deriving firearms-related constructs from social media data and understanding the measurement properties of such constructs are critical precursors to their broader use. Objective: This study aimed to develop a machine learning model of individual-level firearm ownership from social media data and assess the criterion validity of a state-level construct of ownership. Methods: We used survey responses to questions on firearm ownership linked with Twitter data to construct different machine learning models of firearm ownership. We externally validated these models using a set of firearm-related tweets hand-curated from the Twitter Streaming application programming interface and created state-level ownership estimates using a sample of users collected from the Twitter Decahose application programming interface. We assessed the criterion validity of state-level estimates by comparing their geographic variance to benchmark measures from the RAND State-Level Firearm Ownership Database. Results: We found that the logistic regression classifier for gun ownership performs the best with an accuracy of 0.7 and an F1-score of 0.69. We also found a strong positive correlation between Twitter-based estimates of gun ownership and benchmark ownership estimates. For states meeting a threshold requirement of a minimum of 100 labeled Twitter users, the Pearson and Spearman correlation coefficients are 0.63 (P<.001) and 0.64 (P<.001), respectively. Conclusions: Our success in developing a machine learning model of firearm ownership at the individual level with limited training data as well as a state-level construct that achieves a high level of criterion validity underscores the potential of social media data for advancing gun violence research. The ownership construct is an important precursor for understanding the representativeness of and variability in outcomes that have been the focus of social media analyses in gun violence research to date, such as attitudes, opinions, policy stances, sentiments, and perspectives on gun violence and gun policy. The high criterion validity we achieved for state-level gun ownership suggests that social media data may be a useful complement to traditional sources of information on gun ownership such as survey and administrative data, especially for identifying early signals of changes in geographic patterns of gun ownership, given the immediacy of the availability of social media data, their continuous generation, and their responsiveness. These results also lend support to the possibility that other computationally derived, social media?based constructs may be derivable, which could lend additional insight into firearm behaviors that are currently not well understood. More work is needed to develop other firearms-related constructs and to assess their measurement properties. UR - https://www.jmir.org/2023/1/e45187 UR - http://dx.doi.org/10.2196/45187 UR - http://www.ncbi.nlm.nih.gov/pubmed/37310779 ID - info:doi/10.2196/45187 ER - TY - JOUR AU - Zhang, Wei AU - Zheng, Xiaoran AU - Tang, Zeshen AU - Wang, Haoran AU - Li, Renren AU - Xie, Zengmai AU - Yan, Jiaxin AU - Zhang, Xiaochen AU - Yu, Qing AU - Wang, Fei AU - Li, Yunxia PY - 2023/6/9 TI - Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study JO - J Med Internet Res SP - e42637 VL - 25 KW - cognition impairment KW - Trail Making Test KW - vector quantization KW - screening KW - mixed mode KW - paper and electronic devices N2 - Background: Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method. Objective: This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices. Methods: This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant?s hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment?screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22). Results: We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data. Conclusions: The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants? cognitive impairment compared to conventional paper-based feature assessment. UR - https://www.jmir.org/2023/1/e42637 UR - http://dx.doi.org/10.2196/42637 UR - http://www.ncbi.nlm.nih.gov/pubmed/37294606 ID - info:doi/10.2196/42637 ER - TY - JOUR AU - Leung, W. Yvonne AU - Ng, Steve AU - Duan, Lauren AU - Lam, Claire AU - Chan, Kenith AU - Gancarz, Mathew AU - Rennie, Heather AU - Trachtenberg, Lianne AU - Chan, P. Kai AU - Adikari, Achini AU - Fang, Lin AU - Gratzer, David AU - Hirst, Graeme AU - Wong, Jiahui AU - Esplen, Jane Mary PY - 2023/6/9 TI - Therapist Feedback and Implications on Adoption of an Artificial Intelligence?Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study JO - JMIR Cancer SP - e40113 VL - 9 KW - cancer KW - recommender system KW - natural language processing KW - LIWC KW - emotion analysis KW - therapist adoption KW - therapist attitudes KW - legal implications of AI KW - therapist liability N2 - Background: The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes. Objective: The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants? distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers. Methods: Our study reports on the mixed methods evaluation of AICF, including therapists? opinions as well as quantitative measures. AICF?s ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised. Results: Although quantitative results showed only some validity of AICF?s ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF?s distress detection function. Conclusions: Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. International Registered Report Identifier (IRRID): RR2-10.2196/21453 UR - https://cancer.jmir.org/2023/1/e40113 UR - http://dx.doi.org/10.2196/40113 UR - http://www.ncbi.nlm.nih.gov/pubmed/37294610 ID - info:doi/10.2196/40113 ER - TY - JOUR AU - Fleischer, Jennifer AU - Ayton, Jeff AU - Riley, Maree AU - Binsted, Kim AU - Cowan, R. Devin AU - Fellows, M. Abigail AU - Weiss, A. Jeff AU - Buckey, C. Jay PY - 2023/6/9 TI - Web-Based, Interactive, Interest-Based Negotiation Training for Managing Conflict in Isolated Environments: Opportunistic Study With an e-Survey JO - JMIR Form Res SP - e42214 VL - 7 KW - conflict management KW - bargaining KW - confined environments KW - COVID-19 KW - pandemic KW - development KW - environment KW - skill KW - training KW - users KW - essential KW - Australia KW - management N2 - Background: Effective negotiation in relationships is critical for successful long-duration space missions; inadequate conflict resolution has shown serious consequences. Less desirable forms of negotiation, including positional bargaining (eg, negotiating prices), can exacerbate conflicts. Traditional positional bargaining may work for simple, low-stakes transactions but does not prioritize ongoing relationships. High-stakes situations warrant interest-based negotiation, where parties with competing interests or goals collaborate in a mutually beneficial agreement. This is learnable but must be practiced. Refresher training during conflicts is important to prevent out-of-practice crew members from using less effective negotiation techniques. Training should be self-directed and not involve others because, on a space mission, the only other people available may be part of the conflict. Objective: We aimed to develop and test an interactive module teaching principles and skills of interest-based negotiation in a way that users find acceptable, valuable for learning, and enjoyable. Methods: Using a web-based, interactive-media approach, we scripted, filmed, and programmed an interest-based negotiation interactive training module. In the module, the program mentor introduces users to ?The Circle of Value? approach to negotiation and highlights its key concepts through interactive scenarios requiring users to make selections at specific decision points. Each selection prompts feedback designed to reinforce a teaching point or highlight a particular negotiation technique. To evaluate the module, we sought populations experiencing isolation and confinement (an opportunistic design). This included 9 participants in isolated, confined environments in the Australian Antarctic Program and the Hawai'i Space Exploration Analog and Simulation Mars simulation, as well as a subset of people who self-identified as being isolated and confined during the COVID-19 pandemic. Feedback was collected from participants (n=54) through free-response answers and questionnaires with numerical scaling (0=strongly disagree to 4=strongly agree) at the end of the module. Results: In total, 51 of 54 (94%) participants found the activity valuable for learning about conflict management (identified by those who selected either ?somewhat agree? or ?strongly agree?), including 100% of participants in the isolated and confined environment subset (mode=3). In total, 79% (128/162) of participant responses indicated that the module was realistic (mode=3), including 85% (23/27) of responses from participants in isolated and confined environments (mode=3). Most participants felt that this would be particularly valuable for new team members in an isolated, confined environment (46/54, 85% of all participants, mode 4; 7/9, 78% of the isolated and confined environment subset, mode 3) as well as veterans. Conclusions: This module offers a self-directed, consistent approach to interest-based negotiation training, which is well received by users. Although the data are limited due to the opportunistic study design, the module could be useful for individuals in isolated and confined environments and for anyone involved in high-stakes negotiations where sustaining relationships is essential. UR - https://formative.jmir.org/2023/1/e42214 UR - http://dx.doi.org/10.2196/42214 UR - http://www.ncbi.nlm.nih.gov/pubmed/37075233 ID - info:doi/10.2196/42214 ER - TY - JOUR AU - Konopik, Jens AU - Blunck, Dominik PY - 2023/6/8 TI - Development of an Evidence-Based Conceptual Model of the Health Care Sector Under Digital Transformation: Integrative Review JO - J Med Internet Res SP - e41512 VL - 25 KW - digital transformation KW - health care KW - Healthcare 4.0 KW - digital health KW - eHealth KW - conceptual model KW - literature review KW - grounded theory KW - integrative review N2 - Background: Digital transformation is currently one of the most influential developments. It is fundamentally changing consumers? expectations and behaviors, challenging traditional firms, and disrupting numerous markets. Recent discussions in the health care sector tend to assess the influence of technological implications but neglect other factors needed for a holistic view on the digital transformation. This calls for a reevaluation of the current state of digital transformation in health care. Consequently, there is a need for a holistic view on the complex interdependencies of digital transformation in the health care sector. Objective: This study aimed to examine the effects of digital transformation on the health care sector. This is accomplished by providing a conceptual model of the health care sector under digital transformation. Methods: First, the most essential stakeholders in the health care sector were identified by a scoping review and grounded theory approach. Second, the effects on these stakeholders were assessed. PubMed, Web of Science, and Dimensions were searched for relevant studies. On the basis of an integrative review and grounded theory methodology, the relevant academic literature was systematized and quantitatively and qualitatively analyzed to evaluate the impact on the value creation of, and the relationships among, the stakeholders. Third, the findings were synthesized into a conceptual model of the health care sector under digital transformation. Results: A total of 2505 records were identified from the database search; of these, 140 (5.59%) were included and analyzed. The results revealed that providers of medical treatments, patients, governing institutions, and payers are the most essential stakeholders in the health care sector. As for the individual stakeholders, patients are experiencing a technology-enabled growth of influence in the sector. Providers are becoming increasingly dependent on intermediaries for essential parts of the value creation and patient interaction. Payers are expected to try to increase their influence on intermediaries to exploit the enormous amounts of data while seeing their business models be challenged by emerging technologies. Governing institutions regulating the health care sector are increasingly facing challenges from new entrants in the sector. Intermediaries increasingly interconnect all these stakeholders, which in turn drives new ways of value creation. These collaborative efforts have led to the establishment of a virtually integrated health care ecosystem. Conclusions: The conceptual model provides a novel and evidence-based perspective on the interrelations among actors in the health care sector, indicating that individual stakeholders need to recognize their role in the system. The model can be the basis of further evaluations of strategic actions of actors and their effects on other actors or the health care ecosystem itself. UR - https://www.jmir.org/2023/1/e41512 UR - http://dx.doi.org/10.2196/41512 UR - http://www.ncbi.nlm.nih.gov/pubmed/37289482 ID - info:doi/10.2196/41512 ER - TY - JOUR AU - Guven, Emine PY - 2023/6/6 TI - Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection JO - JMIR Bioinform Biotech SP - e43665 VL - 4 KW - gene expression data KW - nonnegative matrix factorization KW - rank factorization KW - optimal rank KW - unit invariant knee method KW - elbow method KW - consensus matrix N2 - Background: There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. The recent utilization of nonnegative matrix factorization (NMF) in computational biology has demonstrated the capability to derive essential details from a high amount of data in particular gene expression microarrays. A common problem in NMF is finding the proper number rank (r) of factors of the degraded demonstration, but no agreement exists on which technique is most appropriate to utilize for this purpose. Thus, various techniques have been suggested to select the optimal value of rank factorization (r). Objective: In this work, a new metric for rank selection is proposed based on the elbow method, which was methodically compared against the cophenetic metric. Methods: To decide the optimum number rank (r), this study focused on the unit invariant knee (UIK) method of the NMF on gene expression data sets. Since the UIK method requires an extremum distance estimator that is eventually employed for inflection and identification of a knee point, the proposed method finds the first inflection point of the curvature of the residual sum of squares of the proposed algorithms using the UIK method on gene expression data sets as a target matrix. Results: Computation was conducted for the UIK task using gene expression data of acute lymphoblastic leukemia and acute myeloid leukemia samples. Consequently, the distinct results of NMF were subjected to comparison on different algorithms. The proposed UIK method is easy to perform, fast, free of a priori rank value input, and does not require initial parameters that significantly influence the model?s functionality. Conclusions: This study demonstrates that the elbow method provides a credible prediction for both gene expression data and for precisely estimating simulated mutational processes data with known dimensions. The proposed UIK method is faster than conventional methods, including metrics utilizing the consensus matrix as a criterion for rank selection, while achieving significantly better computational efficiency without visual inspection on the curvatives. Finally, the suggested rank tuning method based on the elbow method for gene expression data is arguably theoretically superior to the cophenetic measure. UR - https://bioinform.jmir.org/2023/1/e43665 UR - http://dx.doi.org/10.2196/43665 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/43665 ER - TY - JOUR AU - Perez, Analay AU - Fetters, D. Michael AU - Creswell, W. John AU - Scerbo, Mark AU - Kron, W. Frederick AU - Gonzalez, Richard AU - An, Lawrence AU - Jimbo, Masahito AU - Klasnja, Predrag AU - Guetterman, C. Timothy PY - 2023/6/6 TI - Enhancing Nonverbal Communication Through Virtual Human Technology: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e46601 VL - 12 KW - human technology KW - MPathic-VR KW - nonverbal communication behavior KW - patient-provider communication KW - virtual human N2 - Background: Communication is a critical component of the patient-provider relationship; however, limited research exists on the role of nonverbal communication. Virtual human training is an informatics-based educational strategy that offers various benefits in communication skill training directed at providers. Recent informatics-based interventions aimed at improving communication have mainly focused on verbal communication, yet research is needed to better understand how virtual humans can improve verbal and nonverbal communication and further elucidate the patient-provider dyad. Objective: The purpose of this study is to enhance a conceptual model that incorporates technology to examine verbal and nonverbal components of communication and develop a nonverbal assessment that will be included in the virtual simulation for further testing. Methods: This study will consist of a multistage mixed methods design, including convergent and exploratory sequential components. A convergent mixed methods study will be conducted to examine the mediating effects of nonverbal communication. Quantitative (eg, MPathic game scores, Kinect nonverbal data, objective structured clinical examination communication score, and Roter Interaction Analysis System and Facial Action Coding System coding of video) and qualitative data (eg, video recordings of MPathic?virtual reality [VR] interventions and student reflections) will be collected simultaneously. Data will be merged to determine the most crucial components of nonverbal behavior in human-computer interaction. An exploratory sequential design will proceed, consisting of a grounded theory qualitative phase. Using theoretical, purposeful sampling, interviews will be conducted with oncology providers probing intentional nonverbal behaviors. The qualitative findings will aid the development of a nonverbal communication model that will be included in a virtual human. The subsequent quantitative strand will incorporate and validate a new automated nonverbal communication behavior assessment into the virtual human simulation, MPathic-VR, by assessing interrater reliability, code interactions, and dyadic data analysis by comparing Kinect responses (system recorded) to manually scored records for specific nonverbal behaviors. Data will be integrated using building integration to develop the automated nonverbal communication behavior assessment and conduct a quality check of these nonverbal features. Results: Secondary data from the MPathic-VR randomized controlled trial data set (210 medical students and 840 video recordings of interactions) were analyzed in the first part of this study. Results showed differential experiences by performance in the intervention group. Following the analysis of the convergent design, participants consisting of medical providers (n=30) will be recruited for the qualitative phase of the subsequent exploratory sequential design. We plan to complete data collection by July 2023 to analyze and integrate these findings. Conclusions: The results from this study contribute to the improvement of patient-provider communication, both verbal and nonverbal, including the dissemination of health information and health outcomes for patients. Further, this research aims to transfer to various topical areas, including medication safety, informed consent processes, patient instructions, and treatment adherence between patients and providers. International Registered Report Identifier (IRRID): DERR1-10.2196/46601 UR - https://www.researchprotocols.org/2023/1/e46601 UR - http://dx.doi.org/10.2196/46601 UR - http://www.ncbi.nlm.nih.gov/pubmed/37279041 ID - info:doi/10.2196/46601 ER - TY - JOUR AU - Lu, Chenmiao AU - Al-Juaid, Rawan AU - Al-Amri, Mohammad PY - 2023/6/5 TI - Gait Stability Characteristics in Able-Bodied Individuals During Self-paced Inclined Treadmill Walking: Within-Subject Repeated-Measures Study JO - JMIR Form Res SP - e42769 VL - 7 KW - healthy individuals KW - muscle activation KW - self-paced walking KW - slope walking KW - stability KW - treadmill-based gait analysis KW - virtual reality N2 - Background: Inclined walking is a challenging task that requires active neuromuscular control to maintain stability. However, the adaptive strategies that preserve stability during inclined walking are not well understood. Investigating the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation patterns of key lower limb muscles can provide insights into these strategies. Objective: The aim of this study was to investigate the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation of key lower limb muscles. Methods: Twenty-eight able-bodied individuals (mean age 25.02, SD 2.06 years) walked on an augmented instrumented treadmill for 3 minutes at 3 inclination angles (?8°, 0°, and 8°) at their preferred walking speed. Changes in gait characteristics (ie, stability, walking speed, spatial-temporal, kinematic, and muscle forces) across inclination angles were assessed using a repeated measures ANOVA and the Friedman test. Results: The study revealed that inclined treadmill walking has a significant impact on gait characteristics (P<.001). Changes were observed in spatial-temporal parameters, joint angles, and muscle activations depending on the treadmill inclination. Specifically, stability and walking speed decreased significantly during uphill walking, indicating that it was the most challenging walking condition. Uphill walking also led to a decrease in spatial parameters by at least 13.53% and a 5.26% to 10.96% increase in temporal parameters. Furthermore, joint kinematics and peak activation of several muscles, including the hamstrings (biceps femoris, long head=109.5%, biceps femoris, short head=53.3%, semimembranosus=98.9%, semitendinosus=90.9%), gastrocnemius (medial gastrocnemius=40.6%, lateral gastrocnemius=35.3%), and vastii muscles (vastus intermedius=12.8%, vastus lateralis=16.7%) increased significantly during uphill walking. In contrast, downhill walking resulted in bilateral reductions in spatial-temporal gait parameters, with knee flexion increasing and hip flexion and ankle dorsiflexion decreasing. The peak activation of antagonist muscles, such as the quadriceps, tibialis anterior, and tibialis posterior, significantly increased during downhill walking (rectus femoris=97.7%, vastus lateralis =70.6%, vastus intermedius=68.7%, tibialis anterior=72%, tibialis posterior=107.1%). Conclusions: Our findings demonstrate that able-bodied individuals adopt specific walking patterns during inclined treadmill walking to maintain a comfortable and safe walking performance. The results suggest that inclined treadmill walking has the potential to serve as a functional assessment and rehabilitation tool for gait stability by targeting muscle training. Future research should investigate the effects of inclined treadmill walking on individuals with gait impairments and the potential benefits of targeted muscle training. A better understanding of the adaptive strategies used during inclined walking may lead to the development of more effective rehabilitation interventions for individuals with lower limb injuries. UR - https://formative.jmir.org/2023/1/e42769 UR - http://dx.doi.org/10.2196/42769 UR - http://www.ncbi.nlm.nih.gov/pubmed/37276010 ID - info:doi/10.2196/42769 ER - TY - JOUR AU - Tran, Hong Hai AU - Hong, Kyung Jung AU - Jang, Hyeryung AU - Jung, Jinhwan AU - Kim, Jongmok AU - Hong, Joonki AU - Lee, Minji AU - Kim, Jeong-Whun AU - Kushida, A. Clete AU - Lee, Dongheon AU - Kim, Daewoo AU - Yoon, In-Young PY - 2023/6/1 TI - Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation JO - J Med Internet Res SP - e46216 VL - 25 KW - respiratory sounds KW - sleep stages KW - deep learning KW - smartphone KW - home environment N2 - Background: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. Objective: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. Methods: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. Results: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). Conclusions: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only. UR - https://www.jmir.org/2023/1/e46216 UR - http://dx.doi.org/10.2196/46216 UR - http://www.ncbi.nlm.nih.gov/pubmed/37261889 ID - info:doi/10.2196/46216 ER - TY - JOUR AU - Hussain, Yaqza AU - Bannaga, Ayman AU - Fisher, Neil AU - Krishnamoorthy, Ashwin AU - Kimani, Peter AU - Malik, Ahmad AU - Truslove, Maria AU - Joshi, Shivam AU - Hitchins, Megan AU - Abbasi, Abdullah AU - Corbett, Christopher AU - Brookes, Matthew AU - Randeva, Harpal AU - Than, Ni Nwe AU - Arasaradnam, P. Ramesh PY - 2023/5/31 TI - The Fatty Liver, Cirrhosis, and Liver Cancer Study (TENDENCY): Protocol for a Multicenter Case-Control Study JO - JMIR Res Protoc SP - e44264 VL - 12 KW - hepatocellular cancer KW - cirrhosis KW - methylated septin 9 KW - urinary volatile organic compounds KW - urinary peptides KW - fatty liver KW - fatty liver disease KW - hepatitis KW - liver cancer N2 - Background: Hepatocellular cancer (HCC) is associated with high mortality, and early diagnosis leads to better survival. Patients with cirrhosis, especially due to nonalcoholic fatty liver disease and viral hepatitis, are at higher risk of developing HCC and form the main screening group. The current screening methods for HCC (6-monthly screening with serum alpha fetoprotein and ultrasound liver) have low sensitivity; hence, there is a need for better screening markers for HCC. Objective: Our study, TENDENCY, aims to validate the novel screening markers (methylated septin 9, urinary volatile organic compounds, and urinary peptides) for HCC diagnosis and study these noninvasive biomarkers in liver disease. Methods: This is a multicenter, nested case-control study, which involves comparing the plasma levels of methylated septin 9 between confirmed HCC cases and patients with cirrhosis (control group). It also includes the comparison of urine samples for the detection of HCC-specific volatile organic compounds and peptides. Based on the findings of a pilot study carried out at University Hospital Coventry & Warwickshire, we estimated our sample size to be 308 (n=88, 29% patients with HCC; n=220, 71% patients with cirrhosis). Urine and plasma samples will be collected from all participants and will be frozen at ?80 °C until the end of recruitment. Gas chromatography?mass spectrometry will be used for urinary volatile organic compounds detection, and capillary electrophoresis?mass spectrometry will be used for urinary peptide identification. Real-time polymerase chain reaction will be used for the qualitative detection of plasma methylated septin 9. The study will be monitored by the Research and Development department at University Hospital Coventry & Warwickshire. Results: The recruitment stage was completed in March 2023. The TENDENCY study is currently in the analysis stage, which is expected to finish by November 2023. Conclusions: There is lack of effective screening tests for hepatocellular cancer despite higher mortality rates. The application of more sensitive plasma and urinary biomarkers for hepatocellular cancer screening in clinical practice will allow us to detect the disease at earlier stages and hence, overall, improve HCC outcomes. International Registered Report Identifier (IRRID): DERR1-10.2196/44264 UR - https://www.researchprotocols.org/2023/1/e44264 UR - http://dx.doi.org/10.2196/44264 UR - http://www.ncbi.nlm.nih.gov/pubmed/37256650 ID - info:doi/10.2196/44264 ER - TY - JOUR AU - Hernandez, Raymond AU - Hoogendoorn, Claire AU - Gonzalez, S. Jeffrey AU - Jin, Haomiao AU - Pyatak, A. Elizabeth AU - Spruijt-Metz, Donna AU - Junghaenel, U. Doerte AU - Lee, Pey-Jiuan AU - Schneider, Stefan PY - 2023/5/30 TI - Reliability and Validity of Noncognitive Ecological Momentary Assessment Survey Response Times as an Indicator of Cognitive Processing Speed in People?s Natural Environment: Intensive Longitudinal Study JO - JMIR Mhealth Uhealth SP - e45203 VL - 11 KW - cognitive performance KW - processing speed KW - ecological momentary assessment KW - ambulatory assessment KW - type 1 diabetes KW - survey response times KW - paradata KW - chronic illness KW - smartphone KW - mobile health KW - mHealth KW - mobile phone N2 - Background: Various populations with chronic conditions are at risk for decreased cognitive performance, making assessment of their cognition important. Formal mobile cognitive assessments measure cognitive performance with greater ecological validity than traditional laboratory-based testing but add to participant task demands. Given that responding to a survey is considered a cognitively demanding task itself, information that is passively collected as a by-product of ecological momentary assessment (EMA) may be a means through which people?s cognitive performance in their natural environment can be estimated when formal ambulatory cognitive assessment is not feasible. We specifically examined whether the item response times (RTs) to EMA questions (eg, mood) can serve as approximations of cognitive processing speed. Objective: This study aims to investigate whether the RTs from noncognitive EMA surveys can serve as approximate indicators of between-person (BP) differences and momentary within-person (WP) variability in cognitive processing speed. Methods: Data from a 2-week EMA study investigating the relationships among glucose, emotion, and functioning in adults with type 1 diabetes were analyzed. Validated mobile cognitive tests assessing processing speed (Symbol Search task) and sustained attention (Go-No Go task) were administered together with noncognitive EMA surveys 5 to 6 times per day via smartphones. Multilevel modeling was used to examine the reliability of EMA RTs, their convergent validity with the Symbol Search task, and their divergent validity with the Go-No Go task. Other tests of the validity of EMA RTs included the examination of their associations with age, depression, fatigue, and the time of day. Results: Overall, in BP analyses, evidence was found supporting the reliability and convergent validity of EMA question RTs from even a single repeatedly administered EMA item as a measure of average processing speed. BP correlations between the Symbol Search task and EMA RTs ranged from 0.43 to 0.58 (P<.001). EMA RTs had significant BP associations with age (P<.001), as expected, but not with depression (P=.20) or average fatigue (P=.18). In WP analyses, the RTs to 16 slider items and all 22 EMA items (including the 16 slider items) had acceptable (>0.70) WP reliability. After correcting for unreliability in multilevel models, EMA RTs from most combinations of items showed moderate WP correlations with the Symbol Search task (ranged from 0.29 to 0.58; P<.001) and demonstrated theoretically expected relationships with momentary fatigue and the time of day. The associations between EMA RTs and the Symbol Search task were greater than those between EMA RTs and the Go-No Go task at both the BP and WP levels, providing evidence of divergent validity. Conclusions: Assessing the RTs to EMA items (eg, mood) may be a method of approximating people?s average levels of and momentary fluctuations in processing speed without adding tasks beyond the survey questions. UR - https://mhealth.jmir.org/2023/1/e45203 UR - http://dx.doi.org/10.2196/45203 UR - http://www.ncbi.nlm.nih.gov/pubmed/37252787 ID - info:doi/10.2196/45203 ER - TY - JOUR AU - Jiwa, Moyez AU - Nyanhanda, Tafadzwa AU - Dodson, Michael PY - 2023/5/29 TI - Triggering Weight Management Using Digital Avatars: Prospective Cohort Study JO - Interact J Med Res SP - e42001 VL - 12 KW - weight management KW - digital avatar, behavior change, calorie awareness, obesity, health promotion, motivation, processes of change KW - stages of change KW - BMI KW - weight KW - body dysmorphia KW - diet KW - exercise KW - calorie KW - tool KW - digital N2 - Background: There is evidence that showing motivated people with a less-than-ideal BMI (>25 kg/m2) digital and personalized images of their future selves with reduced body weight will likely trigger them to achieve that new body weight. Objective: The purpose of this study is to assess whether digital avatars can trigger weight management action and identify some of the measurable factors that distinguish those who may be triggered. Methods: A prospective cohort study followed participants for 12 weeks through 5 recorded interviews. Participants were screened for suitability for the study using the Cosmetic Procedure Screening Questionnaire as a measure of body dysmorphia. At interview 1, participants were shown 10 images from a ?Food-pics? database and invited to estimate their calorie value. The intervention, the FutureMe app, delivered at interview 2, provided each participant an opportunity to see and take away a soft copy of an avatar of themselves as they might appear in the future depending on their calorie consumption and exercise regimen. Participants completed the readiness for change (S-Weight) survey based on Prochaska Stages of Change Model and the processes of change (P-Weight) survey. Any changes in diet, exercise, or weight were self-reported. Results: A total of 87 participants were recruited, and 42 participants completed the study (48% of recruited participants). Body dysmorphia was a rare but possible risk to participation. The majority (88.5%) of the participants were female and older than 40 years. The average BMI was 34.1 (SD 4.8). Most people wanted to reduce to a BMI of 30 kg/m2 or lose on average 10.5 kg within 13 weeks (?0.8 kg per week). Most participants stated that they would achieve these results by limiting their calorie intake to 1500 calories per day and taking the equivalent of 1 hour of bicycling per day. At interview 1, more participants were in the preparation stage of behavior change than in subsequent interviews. By interview 5, most of the participants were at the maintenance stage. Participants who overestimated the recommended number of calories were more likely to be in the contemplation stage (P=.03). Conclusions: Volunteers who participated in the study were mainly women older than 40 years and beyond the contemplation stage of change for weight management, and those who took weight management action were demonstrated to have a more accurate idea of the calorie content of different foods. Most participants set ambitious targets for weight loss, but few, if any, achieve these goals. However, most people who completed this study were actively taking action to manage their weight. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619001481167; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378055&isReview=true UR - https://www.i-jmr.org/2023/1/e42001 UR - http://dx.doi.org/10.2196/42001 UR - http://www.ncbi.nlm.nih.gov/pubmed/37247208 ID - info:doi/10.2196/42001 ER - TY - JOUR AU - Selder, L. Jasper AU - Te Kolste, Jan Henryk AU - Twisk, Jos AU - Schijven, Marlies AU - Gielen, Willem AU - Allaart, P. Cornelis PY - 2023/5/26 TI - Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study JO - J Med Internet Res SP - e44642 VL - 25 KW - smartwatch KW - atrial fibrillation KW - algorithm KW - fibrillation detection KW - wristband KW - diagnose KW - heart rhythm KW - cardioversion KW - environment KW - software algorithm KW - artificial intelligence KW - AI KW - electrocardiography KW - ECG KW - EKG N2 - Background: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)?driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. Objective: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). Methods: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. Results: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. Conclusions: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment. UR - https://www.jmir.org/2023/1/e44642 UR - http://dx.doi.org/10.2196/44642 UR - http://www.ncbi.nlm.nih.gov/pubmed/37234033 ID - info:doi/10.2196/44642 ER - TY - JOUR AU - Peretz, Gal AU - Taylor, Barr C. AU - Ruzek, I. Josef AU - Jefroykin, Samuel AU - Sadeh-Sharvit, Shiri PY - 2023/5/15 TI - Machine Learning Model to Predict Assignment of Therapy Homework in Behavioral Treatments: Algorithm Development and Validation JO - JMIR Form Res SP - e45156 VL - 7 KW - deep learning KW - empirically-based practice KW - natural language processing KW - behavioral treatment KW - machine learning KW - homework KW - treatment fidelity KW - artificial intelligence KW - intervention KW - therapy KW - mental health KW - mHealth N2 - Background: Therapeutic homework is a core element of cognitive and behavioral interventions, and greater homework compliance predicts improved treatment outcomes. To date, research in this area has relied mostly on therapists? and clients? self-reports or studies carried out in academic settings, and there is little knowledge on how homework is used as a treatment intervention in routine clinical care. Objective: This study tested whether a machine learning (ML) model using natural language processing could identify homework assignments in behavioral health sessions. By leveraging this technology, we sought to develop a more objective and accurate method for detecting the presence of homework in therapy sessions. Methods: We analyzed 34,497 audio-recorded treatment sessions provided in 8 behavioral health care programs via an artificial intelligence (AI) platform designed for therapy provided by Eleos Health. Therapist and client utterances were captured and analyzed via the AI platform. Experts reviewed the homework assigned in 100 sessions to create classifications. Next, we sampled 4000 sessions and labeled therapist-client microdialogues that suggested homework to train an unsupervised sentence embedding model. This model was trained on 2.83 million therapist-client microdialogues. Results: An analysis of 100 random sessions found that homework was assigned in 61% (n=61) of sessions, and in 34% (n=21) of these cases, more than one homework assignment was provided. Homework addressed practicing skills (n=34, 37%), taking action (n=26, 28.5%), journaling (n=17, 19%), and learning new skills (n=14, 15%). Our classifier reached a 72% F1-score, outperforming state-of-the-art ML models. The therapists reviewing the microdialogues agreed in 90% (n=90) of cases on whether or not homework was assigned. Conclusions: The findings of this study demonstrate the potential of ML and natural language processing to improve the detection of therapeutic homework assignments in behavioral health sessions. Our findings highlight the importance of accurately capturing homework in real-world settings and the potential for AI to support therapists in providing evidence-based care and increasing fidelity with science-backed interventions. By identifying areas where AI can facilitate homework assignments and tracking, such as reminding therapists to prescribe homework and reducing the charting associated with homework, we can ultimately improve the overall quality of behavioral health care. Additionally, our approach can be extended to investigate the impact of homework assignments on therapeutic outcomes, providing insights into the effectiveness of specific types of homework. UR - https://formative.jmir.org/2023/1/e45156 UR - http://dx.doi.org/10.2196/45156 UR - http://www.ncbi.nlm.nih.gov/pubmed/37184927 ID - info:doi/10.2196/45156 ER - TY - JOUR AU - Su, Ting AU - Calvo, A. Rafael AU - Jouaiti, Melanie AU - Daniels, Sarah AU - Kirby, Pippa AU - Dijk, Derk-Jan AU - della Monica, Ciro AU - Vaidyanathan, Ravi PY - 2023/5/11 TI - Assessing a Sleep Interviewing Chatbot to Improve Subjective and Objective Sleep: Protocol for an Observational Feasibility Study JO - JMIR Res Protoc SP - e45752 VL - 12 KW - automated chatbot KW - behavior analysis KW - conversational agents KW - older adults KW - sleep disorders KW - sleep interview N2 - Background: Sleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases. Objective: This work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants? self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep. Methods: We will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrupted sleep. We will analyze questionnaire responses before and after the study to assess their perception of our proposed chatbot; questionnaire responses before, during, and after the study to assess their subjective sleep quality changes; and sleep parameters recorded by the sleep analyzer throughout the study to assess their objective sleep quality changes. Results: Recruitment will begin in May 2023 through UK Dementia Research Institute Care Research and Technology Centre organized community outreach. Data collection will run from May 2023 until December 2023. We hypothesize that participants will perceive our proposed chatbot as intelligent and trustworthy; we also hypothesize that our proposed chatbot can help improve participants? subjective and objective sleep assessment throughout the study. Conclusions: The MotivSleep automated chatbot has the potential to provide additional care to older adults who wish to improve their sleep in more accessible and less costly ways than conventional face-to-face therapy. International Registered Report Identifier (IRRID): PRR1-10.2196/45752 UR - https://www.researchprotocols.org/2023/1/e45752 UR - http://dx.doi.org/10.2196/45752 UR - http://www.ncbi.nlm.nih.gov/pubmed/37166964 ID - info:doi/10.2196/45752 ER - TY - JOUR AU - Perrin Franck, Caroline AU - Babington-Ashaye, Awa AU - Dietrich, Damien AU - Bediang, Georges AU - Veltsos, Philippe AU - Gupta, Prasad Pramendra AU - Juech, Claudia AU - Kadam, Rigveda AU - Collin, Maxime AU - Setian, Lucy AU - Serrano Pons, Jordi AU - Kwankam, Yunkap S. AU - Garrette, Béatrice AU - Barbe, Solenne AU - Bagayoko, Oumar Cheick AU - Mehl, Garrett AU - Lovis, Christian AU - Geissbuhler, Antoine PY - 2023/5/10 TI - iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations JO - J Med Internet Res SP - e46694 VL - 25 KW - implementation science KW - knowledge management KW - reporting standards KW - publishing standards KW - guideline KW - Digital Health Hub KW - reporting guideline KW - digital health implementation KW - health outcome N2 - Background: Implementation of digital health technologies has grown rapidly, but many remain limited to pilot studies due to challenges, such as a lack of evidence or barriers to implementation. Overcoming these challenges requires learning from previous implementations and systematically documenting implementation processes to better understand the real-world impact of a technology and identify effective strategies for future implementation. Objective: A group of global experts, facilitated by the Geneva Digital Health Hub, developed the Guidelines and Checklist for the Reporting on Digital Health Implementations (iCHECK-DH, pronounced ?I checked?) to improve the completeness of reporting on digital health implementations. Methods: A guideline development group was convened to define key considerations and criteria for reporting on digital health implementations. To ensure the practicality and effectiveness of the checklist, it was pilot-tested by applying it to several real-world digital health implementations, and adjustments were made based on the feedback received. The guiding principle for the development of iCHECK-DH was to identify the minimum set of information needed to comprehensively define a digital health implementation, to support the identification of key factors for success and failure, and to enable others to replicate it in different settings. Results: The result was a 20-item checklist with detailed explanations and examples in this paper. The authors anticipate that widespread adoption will standardize the quality of reporting and, indirectly, improve implementation standards and best practices. Conclusions: Guidelines for reporting on digital health implementations are important to ensure the accuracy, completeness, and consistency of reported information. This allows for meaningful comparison and evaluation of results, transparency, and accountability and informs stakeholder decision-making. i-CHECK-DH facilitates standardization of the way information is collected and reported, improving systematic documentation and knowledge transfer that can lead to the development of more effective digital health interventions and better health outcomes. UR - https://www.jmir.org/2023/1/e46694 UR - http://dx.doi.org/10.2196/46694 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163336 ID - info:doi/10.2196/46694 ER - TY - JOUR AU - Sczuka, Sarah Kim AU - Schneider, Marc AU - Schellenbach, Michael AU - Kerse, Ngaire AU - Becker, Clemens AU - Klenk, Jochen PY - 2023/5/10 TI - Evaluating the Effect of Activity and Environment on Fall Risk in a Paradigm-Depending Laboratory Setting: Protocol for an Experimental Pilot Study JO - JMIR Res Protoc SP - e46930 VL - 12 KW - fall risk KW - fall risk factor KW - fall-related activity KW - laboratory setting KW - study protocol KW - fall KW - fall risk model KW - older people KW - elderly KW - analysis of fall N2 - Background: Knowledge about the causal factors leading to falls is still limited, and fall prevention interventions urgently need to be more effective to limit the otherwise increasing burden caused by falls in older people. To identify individual fall risk, it is important to understand the complex interplay of fall-related factors. Although fall events are common, they are seldom observed, and fall reports are often biased. Due to the rapid development of wearable inertial sensors, an objective approach to capture fall events and the corresponding circumstances is provided. Objective: The aim of this work is to operationalize a prototypical dynamic fall risk model regarding 4 ecologically valid real-world scenarios (opening a door, slipping, tripping, and usage of public transportation). We hypothesize that individual fall risk is associated with an interplay of intrinsic risk factors, activity, and environmental factors that can be estimated by using data measured within a laboratory simulation setting. Methods: We will recruit 30 community-dwelling people aged 60 years or older. To identify several fall-related intrinsic fall risk factors, appropriate clinical assessments will be selected. The experimental setup is adaptable so that the level of fall risk for each activity and each environmental factor is adjustable. By different levels of difficulty, the effect on the risk of falling will be investigated. An 8-camera motion tracking system will be used to record absolute body motions and limits of stability. All laboratory experiments will also be recorded by inertial sensors (L5, dominant leg) and video camera. Logistic regression analyses will be used to model the association between risk factors and falls. Continuous fall risk will be modeled by generalized linear regression models using margin of stability as outcome parameter. Results: The results of this project will prove the concept and establish methods to further use the dynamic fall risk model. Recruitment and measurement initially began in October 2020 but were halted because of the COVID-19 pandemic. Recruitment and measurements recommenced in October 2022, and by February 2023, a total of 25 of the planned 30 subjects have been measured. Conclusions: In the field of fall prevention, a more precise fall risk model will have a significant impact on research leading to more effective prevention approaches. Given the described burden related to falls and the high prevalence, considerable improvements in fall prevention will have a significant impact on individual quality of life and also on society in general by reducing institutionalization and health care costs. The setup will enable the analysis of fall events and their circumstances ecologically valid in a laboratory setting and thereby will provide important information to estimate the individual instantaneous fall risk. International Registered Report Identifier (IRRID): DERR1-10.2196/46930 UR - https://www.researchprotocols.org/2023/1/e46930 UR - http://dx.doi.org/10.2196/46930 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163327 ID - info:doi/10.2196/46930 ER - TY - JOUR AU - Richard-Lalonde, Melissa AU - Feeley, Nancy AU - Cossette, Sylvie AU - Chlan, L. Linda AU - Gélinas, Céline PY - 2023/5/10 TI - Acceptability and Feasibility of a Patient-Oriented Music Intervention to Reduce Pain in the Intensive Care Unit: Protocol for a Crossover Pilot Randomized Controlled Trial JO - JMIR Res Protoc SP - e40760 VL - 12 KW - music KW - pain KW - intensive care unit KW - pilot KW - feasibility KW - acceptability N2 - Background: Many patients experience pain in the intensive care unit (ICU) despite receiving pain medication. Research has shown that music can help reduce pain. Music interventions studied so far have not used music streaming to generate playlists based on patient preferences while incorporating recommended tempo and duration. Previous research has focused on postoperative ICU patients able to self-report, which is underrepresentative of the ICU population that might benefit from a music intervention for pain management. We developed a new patient-oriented music intervention (POMI) that incorporates features based on theoretical, empirical, and experiential data intended to be used in the ICU. Such a music intervention should consider the expertise of ICU patients, family members, and nursing staff, as well as the practicality of the intervention when used in practice. Objective: The primary objectives of this study are to (1) evaluate the acceptability and feasibility of the POMI to reduce pain in ICU patients and (2) evaluate the feasibility of conducting a crossover pilot randomized controlled trial (RCT) for intervention testing in the ICU. A secondary objective is to examine the preliminary efficacy of the POMI to reduce pain in ICU patients. Methods: A single-blind 2×2 crossover pilot RCT will be conducted. Patients will undergo 1 sequence of 2 interventions: the POMI which delivers music based on patients? preferences via headphones or music pillow for 20-30 minutes and the control intervention (headphones or pillow without music). The sequence of the interventions will be inverted with a 4-hour washout period. Timing of the interventions will be before a planned bed turning procedure. Each patient will undergo 1 session of music. Twenty-four patients will be recruited. Patients able to self-report (n=12), family members of patients unable to self-report (n=12), and nursing staff (n=12) involved in the bed turning procedure will be invited to complete a short questionnaire on the POMI acceptability. Data will be collected on the feasibility of the intervention delivery (ie, time spent creating a playlist, any issue related to headphones/pillow or music delivery, environmental noises, and intervention interruptions) and research methods (ie, number of patients screened, recruited, randomized, and included in the analysis). Pain scores will be obtained before and after intervention delivery. Results: Recruitment and data collection began in March 2022. As of July 5, 2022, in total, 22 patients, 12 family members, and 11 nurses were recruited. Conclusions: Methodological limitations and strengths are discussed. Study limitations include the lack of blinding for patients able to self-report. Strengths include collecting data from various sources, getting a comprehensive evaluation of the intervention, and using a crossover pilot RCT design, where participants act as their own control, thus reducing confounding factors. Trial Registration: ClinicalTrials.gov NCT05320224; https://clinicaltrials.gov/ct2/show/NCT05320224 International Registered Report Identifier (IRRID): DERR1-10.2196/40760 UR - https://www.researchprotocols.org/2023/1/e40760 UR - http://dx.doi.org/10.2196/40760 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163350 ID - info:doi/10.2196/40760 ER - TY - JOUR AU - Han, Jing AU - Montagna, Marco AU - Grammenos, Andreas AU - Xia, Tong AU - Bondareva, Erika AU - Siegele-Brown, Chloë AU - Chauhan, Jagmohan AU - Dang, Ting AU - Spathis, Dimitris AU - Floto, Andres R. AU - Cicuta, Pietro AU - Mascolo, Cecilia PY - 2023/5/9 TI - Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study JO - J Med Internet Res SP - e44804 VL - 25 KW - audio analysis KW - COVID-19 detection KW - deep learning KW - respiratory disease diagnosis KW - mobile health KW - detection KW - clinicians KW - machine learning KW - respiratory diagnosis KW - clinical decisions KW - respiratory N2 - Background: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. Objective: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Methods: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. Results: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians? and the model?s predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. Conclusions: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis. UR - https://www.jmir.org/2023/1/e44804 UR - http://dx.doi.org/10.2196/44804 UR - http://www.ncbi.nlm.nih.gov/pubmed/37126593 ID - info:doi/10.2196/44804 ER - TY - JOUR AU - Leenen, L. Jobbe P. AU - Rasing, M. Henriëtte J. AU - Kalkman, J. Cor AU - Schoonhoven, Lisette AU - Patijn, A. Gijsbert PY - 2023/5/4 TI - Process Evaluation of a Wireless Wearable Continuous Vital Signs Monitoring Intervention in 2 General Hospital Wards: Mixed Methods Study JO - JMIR Nursing SP - e44061 VL - 6 KW - physiological monitoring KW - implementation science KW - clinical deterioration KW - continuous vital sign monitoring KW - wearable wireless devices KW - wearables KW - process evaluation KW - mixed methods KW - intervention fidelity N2 - Background: Continuous monitoring of vital signs (CMVS) using wearable wireless sensors is increasingly available to patients in general wards and can improve outcomes and reduce nurse workload. To assess the potential impact of such systems, successful implementation is important. We developed a CMVS intervention and implementation strategy and evaluated its success in 2 general wards. Objective: We aimed to assess and compare intervention fidelity in 2 wards (internal medicine and general surgery) of a large teaching hospital. Methods: A mixed methods sequential explanatory design was used. After thorough training and preparation, CMVS was implemented?in parallel with the standard intermittent manual measurements?and executed for 6 months in each ward. Heart rate and respiratory rate were measured using a chest-worn wearable sensor, and vital sign trends were visualized on a digital platform. Trends were routinely assessed and reported each nursing shift without automated alarms. The primary outcome was intervention fidelity, defined as the proportion of written reports and related nurse activities in case of deviating trends comparing early (months 1-2), mid- (months 3-4), and late (months 5-6) implementation periods. Explanatory interviews with nurses were conducted. Results: The implementation strategy was executed as planned. A total of 358 patients were included, resulting in 45,113 monitored hours during 6142 nurse shifts. In total, 10.3% (37/358) of the sensors were replaced prematurely because of technical failure. Mean intervention fidelity was 70.7% (SD 20.4%) and higher in the surgical ward (73.6%, SD 18.1% vs 64.1%, SD 23.7%; P<.001). Fidelity decreased over the implementation period in the internal medicine ward (76%, 57%, and 48% at early, mid-, and late implementation, respectively; P<.001) but not significantly in the surgical ward (76% at early implementation vs 74% at midimplementation [P=.56] vs 70.7% at late implementation [P=.07]). No nursing activities were needed based on vital sign trends for 68.7% (246/358) of the patients. In 174 reports of 31.3% (112/358) of the patients, observed deviating trends led to 101 additional bedside assessments of patients and 73 consultations by physicians. The main themes that emerged during interviews (n=21) included the relative priority of CMVS in nurse work, the importance of nursing assessment, the relatively limited perceived benefits for patient care, and experienced mediocre usability of the technology. Conclusions: We successfully implemented a system for CMVS at scale in 2 hospital wards, but our results show that intervention fidelity decreased over time, more in the internal medicine ward than in the surgical ward. This decrease appeared to depend on multiple ward-specific factors. Nurses? perceptions regarding the value and benefits of the intervention varied. Implications for optimal implementation of CMVS include engaging nurses early, seamless integration into electronic health records, and sophisticated decision support tools for vital sign trend interpretation. UR - https://nursing.jmir.org/2023/1/e44061 UR - http://dx.doi.org/10.2196/44061 UR - http://www.ncbi.nlm.nih.gov/pubmed/37140977 ID - info:doi/10.2196/44061 ER - TY - JOUR AU - Mujirishvili, Tamara AU - Maidhof, Caterina AU - Florez-Revuelta, Francisco AU - Ziefle, Martina AU - Richart-Martinez, Miguel AU - Cabrero-García, Julio PY - 2023/5/1 TI - Acceptance and Privacy Perceptions Toward Video-based Active and Assisted Living Technologies: Scoping Review JO - J Med Internet Res SP - e45297 VL - 25 KW - video-based active assisted living technologies KW - video monitoring KW - life logging KW - user acceptance KW - privacy KW - older adults KW - disability KW - eHealth KW - virtual assistance KW - technology KW - assistive technology KW - virtual assistant KW - virtual reality N2 - Background: The aging society posits new socioeconomic challenges to which a potential solution is active and assisted living (AAL) technologies. Visual-based sensing systems are technologically among the most advantageous forms of AAL technologies in providing health and social care; however, they come at the risk of violating rights to privacy. With the immersion of video-based technologies, privacy-preserving smart solutions are being developed; however, the user acceptance research about these developments is not yet being systematized. Objective: With this scoping review, we aimed to gain an overview of existing studies examining the viewpoints of older adults and/or their caregivers on technology acceptance and privacy perceptions, specifically toward video-based AAL technology. Methods: A total of 22 studies were identified with a primary focus on user acceptance and privacy attitudes during a literature search of major databases. Methodological quality assessment and thematic analysis of the selected studies were executed and principal findings are summarized. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed at every step of this scoping review. Results: Acceptance attitudes toward video-based AAL technologies are rather conditional, and are summarized into five main themes seen from the two end-user perspectives: caregiver and care receiver. With privacy being a major barrier to video-based AAL technologies, security and medical safety were identified as the major benefits across the studies. Conclusions: This review reveals a very low methodological quality of the empirical studies assessing user acceptance of video-based AAL technologies. We propose that more specific and more end user? and real life?targeting research is needed to assess the acceptance of proposed solutions. UR - https://www.jmir.org/2023/1/e45297 UR - http://dx.doi.org/10.2196/45297 UR - http://www.ncbi.nlm.nih.gov/pubmed/37126390 ID - info:doi/10.2196/45297 ER - TY - JOUR AU - Sikstrom, Sverker AU - Dahl, Mats AU - Claesdotter-Knutsson, Emma PY - 2023/4/28 TI - Removing Biases in Communication of Severity Assessments of Intimate Partner Violence: Model Development and Evaluation JO - J Med Internet Res SP - e43499 VL - 25 KW - debiasing KW - violence KW - natural language processing KW - machine learning KW - psychological KW - physical N2 - Background: To support a victim of violence and establish the correct penalty for the perpetrator, it is crucial to correctly evaluate and communicate the severity of the violence. Recent data have shown these communications to be biased. However, computational language models provide opportunities for automated evaluation of the severity to mitigate the biases. Objective: We investigated whether these biases can be removed with computational algorithms trained to measure the severity of violence described. Methods: In phase 1 (P1), participants (N=71) were instructed to write some text and type 5 keywords describing an event where they experienced physical violence and 1 keyword describing an event where they experienced psychological violence in an intimate partner relationship. They were also asked to rate the severity. In phase 2 (P2), another set of participants (N=40) read the texts and rated them for severity of violence on the same scale as in P1. We also quantified the text data to word embeddings. Machine learning was used to train a model to predict the severity ratings. Results: For physical violence, there was a greater accuracy bias for humans (r2=0.22) compared to the computational model (r2=0.31; t38=?2.37, P=.023). For psychological violence, the accuracy bias was greater for humans (r2=0.058) than for the computational model (r2=0.35; t38=?14.58, P<.001). Participants in P1 experienced psychological violence as more severe (mean 6.46, SD 1.69) than participants rating the same events in P2 (mean 5.84, SD 2.80; t86=?2.22, P=.029<.05), whereas no calibration bias was found for the computational model (t134=1.30, P=.195). However, no calibration bias was found for physical violence for humans between P1 (mean 6.59, SD 1.81) and P2 (mean 7.54, SD 2.62; t86=1.32, P=.19) or for the computational model (t134=0.62, P=.534). There was no difference in the severity ratings between psychological and physical violence in P1. However, the bias (ie, the ratings in P2 minus the ratings in P1) was highly negatively correlated with the severity ratings in P1 (r2=0.29) and in P2 (r2=0.37), whereas the ratings in P1 and P2 were somewhat less correlated (r2=0.11) using the psychological and physical data combined. Conclusions: The results show that the computational model mitigates accuracy bias and removes calibration biases. These results suggest that computational models can be used for debiasing the severity evaluations of violence. These findings may have application in a legal context, prioritizing resources in society and how violent events are presented in the media. UR - https://www.jmir.org/2023/1/e43499 UR - http://dx.doi.org/10.2196/43499 UR - http://www.ncbi.nlm.nih.gov/pubmed/37115589 ID - info:doi/10.2196/43499 ER - TY - JOUR AU - Schucht, Philippe AU - Mathis, Maria Andrea AU - Murek, Michael AU - Zubak, Irena AU - Goldberg, Johannes AU - Falk, Stephanie AU - Raabe, Andreas PY - 2023/4/28 TI - Exploring Novel Innovation Strategies to Close a Technology Gap in Neurosurgery: HORAO Crowdsourcing Campaign JO - J Med Internet Res SP - e42723 VL - 25 KW - collective intelligence KW - crowdsourcing KW - fiber tracts KW - ideation KW - Mueller polarimetry KW - neuroscience KW - neurosurgery KW - open innovation KW - polarization N2 - Background: Scientific research is typically performed by expert individuals or groups who investigate potential solutions in a sequential manner. Given the current worldwide exponential increase in technical innovations, potential solutions for any new problem might already exist, even though they were developed to solve a different problem. Therefore, in crowdsourcing ideation, a research question is explained to a much larger group of individuals beyond the specialist community to obtain a multitude of diverse, outside-the-box solutions. These are then assessed in parallel by a group of experts for their capacity to solve the new problem.The 2 key problems in brain tumor surgery are the difficulty of discerning the exact border between a tumor and the surrounding brain, and the difficulty of identifying the function of a specific area of the brain. Both problems could be solved by a method that visualizes the highly organized fiber tracts within the brain; the absence of fibers would reveal the tumor, whereas the spatial orientation of the tracts would reveal the area?s function. To raise awareness about our challenge of developing a means of intraoperative, real-time, noninvasive identification of fiber tracts and tumor borders to improve neurosurgical oncology, we turned to the crowd with a crowdsourcing ideation challenge. Objective: Our objective was to evaluate the feasibility of a crowdsourcing ideation campaign for finding novel solutions to challenges in neuroscience. The purpose of this paper is to introduce our chosen crowdsourcing method and discuss it in the context of the current literature. Methods: We ran a prize-based crowdsourcing ideation competition called HORAO on the commercial platform HeroX. Prize money previously collected through a crowdfunding campaign was offered as an incentive. Using a multistage approach, an expert jury first selected promising technical solutions based on broad, predefined criteria, coached the respective solvers in the second stage, and finally selected the winners in a conference setting. We performed a postchallenge web-based survey among the solvers crowd to find out about their backgrounds and demographics. Results: Our web-based campaign reached more than 20,000 people (views). We received 45 proposals from 32 individuals and 7 teams, working in 26 countries on 4 continents. The postchallenge survey revealed that most of the submissions came from single solvers or teams working in engineering or the natural sciences, with additional submissions from other nonmedical fields. We engaged in further exchanges with 3 out of the 5 finalists and finally initiated a successful scientific collaboration with the winner of the challenge. Conclusions: This open innovation competition is the first of its kind in medical technology research. A prize-based crowdsourcing ideation campaign is a promising strategy for raising awareness about a specific problem, finding innovative solutions, and establishing new scientific collaborations beyond strictly disciplinary domains. UR - https://www.jmir.org/2023/1/e42723 UR - http://dx.doi.org/10.2196/42723 UR - http://www.ncbi.nlm.nih.gov/pubmed/37115612 ID - info:doi/10.2196/42723 ER - TY - JOUR AU - Zech, M. James AU - Johnson, Morgan AU - Pullmann, D. Michael AU - Hull, D. Thomas AU - Althoff, Tim AU - Munson, A. Sean AU - Fridling, Nicole AU - Litvin, Boris AU - Wu, Jerilyn AU - Areán, A. Patricia PY - 2023/4/26 TI - An Integrative Engagement Model of Digital Psychotherapy: Exploratory Focus Group Findings JO - JMIR Form Res SP - e41428 VL - 7 KW - Health Action Process Approach KW - lived informatics KW - digital engagement KW - messaging therapy N2 - Background: Digital mental health interventions, such as 2-way and asynchronous messaging therapy, are a growing part of the mental health care treatment ecosystem, yet little is known about how users engage with these interventions over the course of their treatment journeys. User engagement, or client behaviors and therapeutic relationships that facilitate positive treatment outcomes, is a necessary condition for the effectiveness of any digital treatment. Developing a better understanding of the factors that impact user engagement can impact the overall effectiveness of digital psychotherapy. Mapping the user experience in digital therapy may be facilitated by integrating theories from several fields. Specifically, health science?s Health Action Process Approach and human-computer interaction?s Lived Informatics Model may be usefully synthesized with relational constructs from psychotherapy process?outcome research to identify the determinants of engagement in digital messaging therapy. Objective: This study aims to capture insights into digital therapy users? engagement patterns through a qualitative analysis of focus group sessions. We aimed to synthesize emergent intrapersonal and relational determinants of engagement into an integrative framework of engagement in digital therapy. Methods: A total of 24 focus group participants were recruited to participate in 1 of 5 synchronous focus group sessions held between October and November 2021. Participant responses were coded by 2 researchers using thematic analysis. Results: Coders identified 10 relevant constructs and 24 subconstructs that can collectively account for users? engagement and experience trajectories in the context of digital therapy. Although users? engagement trajectories in digital therapy varied widely, they were principally informed by intrapsychic factors (eg, self-efficacy and outcome expectancy), interpersonal factors (eg, the therapeutic alliance and its rupture), and external factors (eg, treatment costs and social support). These constructs were organized into a proposed Integrative Engagement Model of Digital Psychotherapy. Notably, every participant in the focus groups indicated that their ability to connect with their therapist was among the most important factors that were considered in continuing or terminating treatment. Conclusions: Engagement in messaging therapy may be usefully approached through an interdisciplinary lens, linking constructs from health science, human-computer interaction studies, and clinical science in an integrative engagement framework. Taken together, our results suggest that users may not view the digital psychotherapy platform itself as a treatment so much as a means of gaining access to a helping provider, that is, users did not see themselves as engaging with a platform but instead viewed their experience as a healing relationship. The findings of this study suggest that a better understanding of user engagement is crucial for enhancing the effectiveness of digital mental health interventions, and future research should continue to explore the underlying factors that contribute to engagement in digital mental health interventions. Trial Registration: ClinicalTrials.gov NCT04507360; https://clinicaltrials.gov/ct2/show/NCT04507360 UR - https://formative.jmir.org/2023/1/e41428 UR - http://dx.doi.org/10.2196/41428 UR - http://www.ncbi.nlm.nih.gov/pubmed/37099363 ID - info:doi/10.2196/41428 ER - TY - JOUR AU - Shen, Alexander AU - Francisco, Luke AU - Sen, Srijan AU - Tewari, Ambuj PY - 2023/4/20 TI - Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks JO - J Med Internet Res SP - e43664 VL - 25 KW - privacy KW - data protection KW - machine learning KW - federated learning KW - neural networks KW - mobile health KW - mHealth KW - wearable electronic devices KW - differential privacy KW - learning KW - evidence KW - feasibility KW - applications KW - training KW - technology KW - mobile phone N2 - Background: Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions. Objective: Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model?s accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system?s performance associated with the chosen levels of privacy protection. Methods: A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target. Results: We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker?s AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker?s AUC fell to approximately 0.59 with only a 10% point decrease in the target?s R2 and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies. Conclusions: Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications. UR - https://www.jmir.org/2023/1/e43664 UR - http://dx.doi.org/10.2196/43664 UR - http://www.ncbi.nlm.nih.gov/pubmed/37079370 ID - info:doi/10.2196/43664 ER - TY - JOUR AU - Pariyo, George AU - Meghani, Ankita AU - Gibson, Dustin AU - Ali, Joseph AU - Labrique, Alain AU - Khan, Ansary Iqbal AU - Kibria, Al Gulam Muhammed AU - Masanja, Honorati AU - Hyder, Ali Adnan AU - Ahmed, Saifuddin PY - 2023/4/20 TI - Effect of the Data Collection Method on Mobile Phone Survey Participation in Bangladesh and Tanzania: Secondary Analyses of a Randomized Crossover Trial JO - JMIR Form Res SP - e38774 VL - 7 KW - mobile phone survey KW - interactive voice response survey KW - non-communicable disease surveillance KW - response rate KW - cooperation rate KW - phone KW - risk KW - survey KW - public health KW - interview KW - voice KW - response KW - cooperation KW - female KW - women KW - rural KW - school KW - countries KW - non-communicable disease KW - surveillance KW - interactive survey N2 - Background: Mobile phone surveys provide a novel opportunity to collect population-based estimates of public health risk factors; however, nonresponse and low participation challenge the goal of collecting unbiased survey estimates. Objective: This study compares the performance of computer-assisted telephone interview (CATI) and interactive voice response (IVR) survey modalities for noncommunicable disease risk factors in Bangladesh and Tanzania. Methods: This study used secondary data from a randomized crossover trial. Between June 2017 and August 2017, study participants were identified using the random digit dialing method. Mobile phone numbers were randomly allocated to either a CATI or IVR survey. The analysis examined survey completion, contact, response, refusal, and cooperation rates of those who received the CATI and IVR surveys. Differences in survey outcomes between modes were assessed using multilevel, multivariable logistic regression models to adjust for confounding covariates. These analyses were adjusted for clustering effects by mobile network providers. Results: For the CATI surveys, 7044 and 4399 phone numbers were contacted in Bangladesh and Tanzania, respectively, and 60,863 and 51,685 phone numbers, respectively, were contacted for the IVR survey. The total numbers of completed interviews in Bangladesh were 949 for CATI and 1026 for IVR and in Tanzania were 447 for CATI and 801 for IVR. Response rates for CATI were 5.4% (377/7044) in Bangladesh and 8.6% (376/4391) in Tanzania; response rates for IVR were 0.8% (498/60,377) in Bangladesh and 1.1% (586/51,483) in Tanzania. The distribution of the survey population was significantly different from the census distribution. In both countries, IVR respondents were younger, were predominantly male, and had higher education levels than CATI respondents. IVR respondents had a lower response rate than CATI respondents in Bangladesh (adjusted odds ratio [AOR]=0.73, 95% CI 0.54-0.99) and Tanzania (AOR=0.32, 95% CI 0.16-0.60). The cooperation rate was also lower with IVR than with CATI in Bangladesh (AOR=0.12, 95% CI 0.07-0.20) and Tanzania (AOR=0.28, 95% CI 0.14-0.56). Both in Bangladesh (AOR=0.33, 95% CI 0.25-0.43) and Tanzania (AOR=0.09, 95% CI 0.06-0.14), there were fewer completed interviews with IVR than with CATI; however, there were more partial interviews with IVR than with CATI in both countries. Conclusions: There were lower completion, response, and cooperation rates with IVR than with CATI in both countries. This finding suggests that, to increase representativeness in certain settings, a selective approach may be needed to design and deploy mobile phone surveys to increase population representativeness. Overall, CATI surveys may offer a promising approach for surveying potentially under-represented groups like women, rural residents, and participants with lower levels of education in some countries. UR - https://formative.jmir.org/2023/1/e38774 UR - http://dx.doi.org/10.2196/38774 UR - http://www.ncbi.nlm.nih.gov/pubmed/37079373 ID - info:doi/10.2196/38774 ER - TY - JOUR AU - Hopcroft, EM Lisa AU - Massey, Jon AU - Curtis, J. Helen AU - Mackenna, Brian AU - Croker, Richard AU - Brown, D. Andrew AU - O'Dwyer, Thomas AU - Macdonald, Orla AU - Evans, David AU - Inglesby, Peter AU - Bacon, CJ Sebastian AU - Goldacre, Ben AU - Walker, J. Alex PY - 2023/4/19 TI - Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England JO - JMIR Med Inform SP - e44237 VL - 11 KW - dashboard KW - data science KW - EHR KW - electronic health records KW - general practice KW - outliers KW - prescribing KW - primary care N2 - Background: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. Objective: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. Methods: Here we report a new data-driven approach to quantify how ?unusual? the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. Results: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. Conclusions: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance. UR - https://medinform.jmir.org/2023/1/e44237 UR - http://dx.doi.org/10.2196/44237 UR - http://www.ncbi.nlm.nih.gov/pubmed/37074763 ID - info:doi/10.2196/44237 ER - TY - JOUR AU - Garbey, Marc AU - Joerger, Guillaume AU - Lesport, Quentin AU - Girma, Helen AU - McNett, Sienna AU - Abu-Rub, Mohammad AU - Kaminski, Henry PY - 2023/4/19 TI - A Digital Telehealth System to Compute Myasthenia Gravis Core Examination Metrics: Exploratory Cohort Study JO - JMIR Neurotech SP - e43387 VL - 2 KW - telehealth KW - telemedicine KW - myasthenia gravis KW - ptosis KW - diplopia KW - deep learning KW - computer vision KW - eyes tracking KW - neurological disease N2 - Background: Telemedicine practice for neurological diseases has grown significantly during the COVID-19 pandemic. Telemedicine offers an opportunity to assess digitalization of examinations and enhances access to modern computer vision and artificial intelligence processing to annotate and quantify examinations in a consistent and reproducible manner. The Myasthenia Gravis Core Examination (MG-CE) has been recommended for the telemedicine evaluation of patients with myasthenia gravis. Objective: We aimed to assess the ability to take accurate and robust measurements during the examination, which would allow improvement in workflow efficiency by making the data acquisition and analytics fully automatic and thereby limit the potential for observation bias. Methods: We used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. The core examination tests required 2 broad categories of processing. First, computer vision algorithms were used to analyze videos with a focus on eye or body motions. Second, for the assessment of examinations involving vocalization, a different category of signal processing methods was required. In this way, we provide an algorithm toolbox to assist clinicians with the MG-CE. We used a data set of 6 patients recorded during 2 sessions. Results: Digitalization and control of quality of the core examination are advantageous and let the medical examiner concentrate on the patient instead of managing the logistics of the test. This approach showed the possibility of standardized data acquisition during telehealth sessions and provided real-time feedback on the quality of the metrics the medical doctor is assessing. Overall, our new telehealth platform showed submillimeter accuracy for ptosis and eye motion. In addition, the method showed good results in monitoring muscle weakness, demonstrating that continuous analysis is likely superior to pre-exercise and postexercise subjective assessment. Conclusions: We demonstrated the ability to objectively quantitate the MG-CE. Our results indicate that the MG-CE should be revisited to consider some of the new metrics that our algorithm identified. We provide a proof of concept involving the MG-CE, but the method and tools developed can be applied to many neurological disorders and have great potential to improve clinical care. UR - https://neuro.jmir.org/2023/1/e43387 UR - http://dx.doi.org/10.2196/43387 UR - http://www.ncbi.nlm.nih.gov/pubmed/37435094 ID - info:doi/10.2196/43387 ER - TY - JOUR AU - Angelucci, Alessandra AU - Greco, Massimiliano AU - Canali, Stefano AU - Marelli, Giovanni AU - Avidano, Gaia AU - Goretti, Giulia AU - Cecconi, Maurizio AU - Aliverti, Andrea PY - 2023/4/13 TI - Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study JO - J Med Internet Res SP - e42815 VL - 25 KW - wearable devices KW - smartwatch data KW - preoperative risk assessment KW - ethics in wearables KW - mobile phone N2 - Background: Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy. Objective: The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance. Methods: During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of ?2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT. Results: In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=?0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of ?0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001). Conclusions: Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies. UR - https://www.jmir.org/2023/1/e42815 UR - http://dx.doi.org/10.2196/42815 UR - http://www.ncbi.nlm.nih.gov/pubmed/37052980 ID - info:doi/10.2196/42815 ER - TY - JOUR AU - Brady, J. Christopher AU - Cockrell, Chase R. AU - Aldrich, R. Lindsay AU - Wolle, A. Meraf AU - West, K. Sheila PY - 2023/4/6 TI - A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study JO - J Med Internet Res SP - e41233 VL - 25 KW - trachoma KW - crowdsourcing KW - telemedicine KW - ophthalmic photography KW - Amazon Mechanical Turk KW - image analysis KW - diagnosis KW - detection KW - cloud-based KW - image interpretation KW - disease identification KW - diagnostics KW - image grading KW - disease grading KW - trachomatous inflammation?follicular KW - ophthalmology N2 - Background: As trachoma is eliminated, skilled field graders become less adept at correctly identifying active disease (trachomatous inflammation?follicular [TF]). Deciding if trachoma has been eliminated from a district or if treatment strategies need to be continued or reinstated is of critical public health importance. Telemedicine solutions require both connectivity, which can be poor in the resource-limited regions of the world in which trachoma occurs, and accurate grading of the images. Objective: Our purpose was to develop and validate a cloud-based ?virtual reading center? (VRC) model using crowdsourcing for image interpretation. Methods: The Amazon Mechanical Turk (AMT) platform was used to recruit lay graders to interpret 2299 gradable images from a prior field trial of a smartphone-based camera system. Each image received 7 grades for US $0.05 per grade in this VRC. The resultant data set was divided into training and test sets to internally validate the VRC. In the training set, crowdsourcing scores were summed, and the optimal raw score cutoff was chosen to optimize kappa agreement and the resulting prevalence of TF. The best method was then applied to the test set, and the sensitivity, specificity, kappa, and TF prevalence were calculated. Results: In this trial, over 16,000 grades were rendered in just over 60 minutes for US $1098 including AMT fees. After choosing an AMT raw score cut point to optimize kappa near the World Health Organization (WHO)?endorsed level of 0.7 (with a simulated 40% prevalence TF), crowdsourcing was 95% sensitive and 87% specific for TF in the training set with a kappa of 0.797. All 196 crowdsourced-positive images received a skilled overread to mimic a tiered reading center and specificity improved to 99%, while sensitivity remained above 78%. Kappa for the entire sample improved from 0.162 to 0.685 with overreads, and the skilled grader burden was reduced by over 80%. This tiered VRC model was then applied to the test set and produced a sensitivity of 99% and a specificity of 76% with a kappa of 0.775 in the entire set. The prevalence estimated by the VRC was 2.70% (95% CI 1.84%-3.80%) compared to the ground truth prevalence of 2.87% (95% CI 1.98%-4.01%). Conclusions: A VRC model using crowdsourcing as a first pass with skilled grading of positive images was able to identify TF rapidly and accurately in a low prevalence setting. The findings from this study support further validation of a VRC and crowdsourcing for image grading and estimation of trachoma prevalence from field-acquired images, although further prospective field testing is required to determine if diagnostic characteristics are acceptable in real-world surveys with a low prevalence of the disease. UR - https://www.jmir.org/2023/1/e41233 UR - http://dx.doi.org/10.2196/41233 UR - http://www.ncbi.nlm.nih.gov/pubmed/37023420 ID - info:doi/10.2196/41233 ER - TY - JOUR AU - Velummailum, Ruthiran Russanthy AU - McKibbon, Chelsea AU - Brenner, R. Darren AU - Stringer, Ann Elizabeth AU - Ekstrom, Leeland AU - Dron, Louis PY - 2023/4/5 TI - Data Challenges for Externally Controlled Trials: Viewpoint JO - J Med Internet Res SP - e43484 VL - 25 KW - external control arm KW - synthetic control arm KW - single-arm trial KW - real-world evidence KW - regulatory approval KW - data KW - clinical KW - decision-making KW - efficacy KW - rare conditions KW - trial UR - https://www.jmir.org/2023/1/e43484 UR - http://dx.doi.org/10.2196/43484 UR - http://www.ncbi.nlm.nih.gov/pubmed/37018021 ID - info:doi/10.2196/43484 ER - TY - JOUR AU - Sippel, Jeffrey AU - Podhajsky, Tim AU - Lin, Chen-Tan PY - 2023/4/5 TI - Patient Satisfaction With Speech Recognition in the Exam Room: Exploratory Survey JO - JMIR Hum Factors SP - e42739 VL - 10 KW - speech recognition KW - exam room KW - primary care KW - general practitioner KW - satisfaction KW - survey KW - perception KW - opinion KW - speech KW - voice KW - eHealth KW - digital health KW - health technology KW - communication technology N2 - Background: Medical speech recognition technology uses a microphone and computer software to transcribe the spoken word into text and is not typically used in outpatient clinical exam rooms. Patient perceptions regarding speech recognition in the exam room (SRIER) are therefore unknown. Objective: This study aims to characterize patient perceptions of SRIER by administering a survey to consecutive patients scheduled for acute, chronic, and wellness care in three outpatient clinic sites. Methods: We used a microphone and medical speech recognition software to complete the ?assessment and plan? portion of the after-visit summary in the patient?s presence, immediately printed the after-visit summary, and then administered a 4-question exploratory survey to 65 consecutive patients in internal medicine and pulmonary medicine clinics at an academic medical center and a community family practice clinic in 2021 to characterize patient perceptions of SRIER. All questions were completed by all participants. Results: When compared to patients? recollection of usual care (visits with no microphone and an after-visit summary without an ?assessment and plan?), 86% (n=56) of respondents agreed or strongly agreed that their provider addressed their concerns better, and 73% (n=48) agreed or strongly agreed that they understood their provider?s advice better. A total of 99% (n=64) of respondents agreed or strongly agreed that a printed after-visit summary including the ?assessment and plan? was helpful. By comparing the ?agree? and ?strongly agree? responses to the neutral responses, we found that patients felt that clinicians using SRIER addressed their concerns better (P<.001), they understood their clinician?s advice better (P<.001), and receiving a paper summary was helpful (P<.001). Patients were likely to recommend a provider using a microphone based on the Net Promoter Score of 58. Conclusions: This survey suggests patients have a very positive perception of speech recognition use in the exam room. UR - https://humanfactors.jmir.org/2023/1/e42739 UR - http://dx.doi.org/10.2196/42739 UR - http://www.ncbi.nlm.nih.gov/pubmed/37018039 ID - info:doi/10.2196/42739 ER - TY - JOUR AU - Toh, S. Melissa P. AU - Yang, Yuen Chui AU - Lim, Cze Phei AU - Loh, J. Hui Li AU - Bergonzelli, Gabriela AU - Lavalle, Luca AU - Mardhy, Elias AU - Samuel, Mary Tinu AU - Suniega-Tolentino, Elvira AU - Silva Zolezzi, Irma AU - Fries, R. Lisa AU - Chan, Yng Shiao PY - 2023/4/5 TI - A Probiotic Intervention With Bifidobacterium longum NCC3001 on Perinatal Mood Outcomes (PROMOTE Study): Protocol for a Decentralized Randomized Controlled Trial JO - JMIR Res Protoc SP - e41751 VL - 12 KW - perinatal mood disturbances KW - pregnancy KW - randomized clinical trial KW - low mood KW - stress KW - anxiety KW - depression KW - probiotics KW - mobile phone N2 - Background: Perinatal mood disorders such as depression and anxiety are common, with subclinical symptomology manifesting as perinatal mood disturbances being even more prevalent. These could potentially affect breastfeeding practices and infant development. Pregnant and lactating women usually limit their exposure to medications, including those for psychological symptoms. Interestingly, the naturally occurring probiotic Bifidobacterium longum (BL) NCC3001 has been shown to reduce anxious behavior in preclinical models and feelings of low mood in nonpregnant human adults. During the COVID-19 pandemic, mental health issues increased, and conventionally conducted clinical trials were restricted by social distancing regulations. Objective: This study, Probiotics on Mothers? Mood and Stress (PROMOTE), aimed to use a decentralized clinical trial design to test whether BL NCC3001 can reduce symptoms of depression, anxiety, and stress over the perinatal period. Methods: This double-blind, placebo-controlled, randomized, and 3-parallel-arm study aimed to recruit 180 women to evaluate the efficacy of the probiotic taken either during pregnancy and post partum (from 28-32 weeks? gestation until 12 weeks after delivery; n=60, 33.3%) or post partum only (from birth until 12 weeks after delivery; n=60, 33.3%) in comparison with a placebo control group (n=60, 33.3%). Participants consumed the probiotic or matched placebo in a drink once daily. Mood outcomes were measured using the State-Trait Anxiety Inventory and Edinburgh Postnatal Depression Scale questionnaires, captured electronically at baseline (28-32 weeks? gestation) and during e-study sessions over 5 further time points (36 weeks? gestation; 9 days post partum; and 4, 8, and 12 weeks post partum). Saliva and stool samples were collected longitudinally at home to provide mechanistic insights. Results: In total, 520 women registered their interest on our website, of whom 184 (35.4%) were eligible and randomized. Of these 184 participants, 5 (2.7%) withdrew after randomization, leaving 179 (97.3%) who completed the study. Recruitment occurred between November 7, 2020, and August 20, 2021. Advertising on social media brought in 46.9% (244/520) of the prospective participants, followed by parenting-specific websites (116/520, 22.3%). Nationwide recruitment was achieved. Data processing is ongoing, and there are no outcomes to report yet. Conclusions: Multiple converging factors contributed to speedy recruitment and retention of participants despite COVID-19?related restrictions. This decentralized trial design sets a precedent for similar studies, in addition to potentially providing novel evidence on the impact of BL NCC3001 on symptoms of perinatal mood disturbances. This study was ideal for remote conduct: because of the high digital literacy and public trust in digital security in Singapore, the intervention could be self-administered without regular clinical monitoring, and the eligibility criteria and outcomes were measured using electronic questionnaires and self-collected biological samples. This design was particularly suited for a group considered vulnerable?pregnant women?during the challenging times of COVID-19?related social restrictions. Trial Registration: ClinicalTrials.gov NCT04685252; https://clinicaltrials.gov/ct2/show/NCT04685252 International Registered Report Identifier (IRRID): DERR1-10.2196/41751 UR - https://www.researchprotocols.org/2023/1/e41751 UR - http://dx.doi.org/10.2196/41751 UR - http://www.ncbi.nlm.nih.gov/pubmed/37018024 ID - info:doi/10.2196/41751 ER - TY - JOUR AU - Bianchini, Edoardo AU - Warmerdam, Elke AU - Romijnders, Robbin AU - Stürner, Hanja Klarissa AU - Baron, Ralf AU - Heinzel, Sebastian AU - Pontieri, Ernesto Francesco AU - Hansen, Clint AU - Maetzler, Walter PY - 2023/3/30 TI - Turning When Using Smartphone in Persons With and Those Without Neurologic Conditions: Observational Study JO - J Med Internet Res SP - e41082 VL - 25 KW - turning KW - turning coordination KW - smartphone KW - dual task KW - dual task cost KW - Parkinson disease KW - Parkinson KW - stroke KW - multiple sclerosis KW - low back pain KW - neurology KW - neurological KW - movement KW - biomechanics KW - gait KW - balance KW - walk KW - kinesiology KW - fall N2 - Background: Turning during walking is a relevant and common everyday movement and it depends on a correct top-down intersegmental coordination. This could be reduced in several conditions (en bloc turning), and an altered turning kinematics has been linked to increased risk of falls. Smartphone use has been associated with poorer balance and gait; however, its effect on turning-while-walking has not been investigated yet. This study explores turning intersegmental coordination during smartphone use in different age groups and neurologic conditions. Objective: This study aims to evaluate the effect of smartphone use on turning behavior in healthy individuals of different ages and those with various neurological diseases. Methods: Younger (aged 18-60 years) and older (aged >60 years) healthy individuals and those with Parkinson disease, multiple sclerosis, subacute stroke (<4 weeks), or lower-back pain performed turning-while-walking alone (single task [ST]) and while performing 2 different cognitive tasks of increasing complexity (dual task [DT]). The mobility task consisted of walking up and down a 5-m walkway at self-selected speed, thus including 180° turns. Cognitive tasks consisted of a simple reaction time test (simple DT [SDT]) and a numerical Stroop test (complex DT [CDT]). General (turn duration and the number of steps while turning), segmental (peak angular velocity), and intersegmental turning parameters (intersegmental turning onset latency and maximum intersegmental angle) were extracted for head, sternum, and pelvis using a motion capture system and a turning detection algorithm. Results: In total, 121 participants were enrolled. All participants, irrespective of age and neurologic disease, showed a reduced intersegmental turning onset latency and a reduced maximum intersegmental angle of both pelvis and sternum relative to head, thus indicating an en bloc turning behavior when using a smartphone. With regard to change from the ST to turning when using a smartphone, participants with Parkinson disease reduced their peak angular velocity the most, which was significantly different from lower-back pain relative to the head (P<.01). Participants with stroke showed en bloc turning already without smartphone use. Conclusions: Smartphone use during turning-while-walking may lead to en bloc turning and thus increase fall risk across age and neurologic disease groups. This behavior is probably particularly dangerous for those groups with the most pronounced changes in turning parameters during smartphone use and the highest fall risk, such as individuals with Parkinson disease. Moreover, the experimental paradigm presented here might be useful in differentiating individuals with lower-back pain without and those with early or prodromal Parkinson disease. In individuals with subacute stroke, en bloc turning could represent a compensative strategy to overcome the newly occurring mobility deficit. Considering the ubiquitous smartphone use in daily life, this study should stimulate future studies in the area of fall risk and neurological and orthopedic diseases. Trial Registration: German Clinical Trials Register DRKS00022998; https://drks.de/search/en/trial/DRKS00022998 UR - https://www.jmir.org/2023/1/e41082 UR - http://dx.doi.org/10.2196/41082 UR - http://www.ncbi.nlm.nih.gov/pubmed/36995756 ID - info:doi/10.2196/41082 ER - TY - JOUR AU - Teferra, Gashaw Bazen AU - Rose, Jonathan PY - 2023/3/28 TI - Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study JO - JMIR Ment Health SP - e44325 VL - 10 KW - mental health KW - generalized anxiety disorder KW - impromptu speech KW - linguistic features KW - anxiety prediction KW - neural networks KW - natural language processing KW - transformer models KW - mobile phone N2 - Background: The ability to automatically detect anxiety disorders from speech could be useful as a screening tool for an anxiety disorder. Prior studies have shown that individual words in textual transcripts of speech have an association with anxiety severity. Transformer-based neural networks are models that have been recently shown to have powerful predictive capabilities based on the context of more than one input word. Transformers detect linguistic patterns and can be separately trained to make specific predictions based on these patterns. Objective: This study aimed to determine whether a transformer-based language model can be used to screen for generalized anxiety disorder from impromptu speech transcripts. Methods: A total of 2000 participants provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test (TSST). They also completed the Generalized Anxiety Disorder 7-item (GAD-7) scale. A transformer-based neural network model (pretrained on large textual corpora) was fine-tuned on the speech transcripts and the GAD-7 to predict whether a participant was above or below a screening threshold of the GAD-7. We reported the area under the receiver operating characteristic curve (AUROC) on the test data and compared the results with a baseline logistic regression model using the Linguistic Inquiry and Word Count (LIWC) features as input. Using the integrated gradient method to determine specific words that strongly affect the predictions, we inferred specific linguistic patterns that influence the predictions. Results: The baseline LIWC-based logistic regression model had an AUROC value of 0.58. The fine-tuned transformer model achieved an AUROC value of 0.64. Specific words that were often implicated in the predictions were also dependent on the context. For example, the first-person singular pronoun ?I? influenced toward an anxious prediction 88% of the time and a nonanxious prediction 12% of the time, depending on the context. Silent pauses in speech, also often implicated in predictions, influenced toward an anxious prediction 20% of the time and a nonanxious prediction 80% of the time. Conclusions: There is evidence that a transformer-based neural network model has increased predictive power compared with the single word?based LIWC model. We also showed that the use of specific words in a specific context?a linguistic pattern?is part of the reason for the better prediction. This suggests that such transformer-based models could play a useful role in anxiety screening systems. UR - https://mental.jmir.org/2023/1/e44325 UR - http://dx.doi.org/10.2196/44325 UR - http://www.ncbi.nlm.nih.gov/pubmed/36976636 ID - info:doi/10.2196/44325 ER - TY - JOUR AU - Min, Sooyeon AU - Shin, Daun AU - Rhee, Jin Sang AU - Park, Keun C. Hyung AU - Yang, Hun Jeong AU - Song, Yoojin AU - Kim, Ji Min AU - Kim, Kyungdo AU - Cho, Ik Won AU - Kwon, Chul Oh AU - Ahn, Min Yong AU - Lee, Hyunju PY - 2023/3/23 TI - Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality JO - J Med Internet Res SP - e45456 VL - 25 KW - suicide KW - voice analysis KW - mood disorder KW - artificial intelligence KW - screening KW - prediction KW - digital health tool N2 - Background: Assessing a patient?s suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. Objective: This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. Methods: We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. Results: A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. Conclusions: Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine. UR - https://www.jmir.org/2023/1/e45456 UR - http://dx.doi.org/10.2196/45456 UR - http://www.ncbi.nlm.nih.gov/pubmed/36951913 ID - info:doi/10.2196/45456 ER - TY - JOUR AU - Hunter, Victoria AU - Shapiro, Allison AU - Chawla, Devika AU - Drawnel, Faye AU - Ramirez, Ernesto AU - Phillips, Elizabeth AU - Tadesse-Bell, Sara AU - Foschini, Luca AU - Ukachukwu, Vincent PY - 2023/3/23 TI - Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study JO - J Med Internet Res SP - e41050 VL - 25 KW - influenza KW - influenza-like illness KW - wearable sensor KW - person-generated health care data N2 - Background: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. Objective: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non?care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. Methods: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care?seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. Results: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. Conclusions: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non?care-seeking populations, which may have future application in health care resource planning. Trial Registration: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800 UR - https://www.jmir.org/2023/1/e41050 UR - http://dx.doi.org/10.2196/41050 UR - http://www.ncbi.nlm.nih.gov/pubmed/36951890 ID - info:doi/10.2196/41050 ER - TY - JOUR AU - Banerjee, Agnik AU - Mutlu, Cezmi Onur AU - Kline, Aaron AU - Surabhi, Saimourya AU - Washington, Peter AU - Wall, Paul Dennis PY - 2023/3/21 TI - Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study JO - JMIR Form Res SP - e39917 VL - 7 KW - edge computing KW - affective computing KW - autism spectrum disorder KW - autism KW - ASD KW - classifier KW - classification KW - model KW - algorithm KW - mobile health KW - computer vision KW - deep learning KW - machine learning for health KW - pediatrics KW - emotion recognition KW - mHealth KW - diagnostic tool KW - digital therapy KW - child KW - developmental disorder KW - smartphone KW - image analysis KW - machine learning KW - Image classification KW - neural network N2 - Background: Implementing automated facial expression recognition on mobile devices could provide an accessible diagnostic and therapeutic tool for those who struggle to recognize facial expressions, including children with developmental behavioral conditions such as autism. Despite recent advances in facial expression classifiers for children, existing models are too computationally expensive for smartphone use. Objective: We explored several state-of-the-art facial expression classifiers designed for mobile devices, used posttraining optimization techniques for both classification performance and efficiency on a Motorola Moto G6 phone, evaluated the importance of training our classifiers on children versus adults, and evaluated the models? performance against different ethnic groups. Methods: We collected images from 12 public data sets and used video frames crowdsourced from the GuessWhat app to train our classifiers. All images were annotated for 7 expressions: neutral, fear, happiness, sadness, surprise, anger, and disgust. We tested 3 copies for each of 5 different convolutional neural network architectures: MobileNetV3-Small 1.0x, MobileNetV2 1.0x, EfficientNetB0, MobileNetV3-Large 1.0x, and NASNetMobile. We trained the first copy on images of children, second copy on images of adults, and third copy on all data sets. We evaluated each model against the entire Child Affective Facial Expression (CAFE) set and by ethnicity. We performed weight pruning, weight clustering, and quantize-aware training when possible and profiled each model?s performance on the Moto G6. Results: Our best model, a MobileNetV3-Large network pretrained on ImageNet, achieved 65.78% accuracy and 65.31% F1-score on the CAFE and a 90-millisecond inference latency on a Moto G6 phone when trained on all data. This accuracy is only 1.12% lower than the current state of the art for CAFE, a model with 13.91x more parameters that was unable to run on the Moto G6 due to its size, even when fully optimized. When trained solely on children, this model achieved 60.57% accuracy and 60.29% F1-score. When trained only on adults, the model received 53.36% accuracy and 53.10% F1-score. Although the MobileNetV3-Large trained on all data sets achieved nearly a 60% F1-score across all ethnicities, the data sets for South Asian and African American children achieved lower accuracy (as much as 11.56%) and F1-score (as much as 11.25%) than other groups. Conclusions: With specialized design and optimization techniques, facial expression classifiers can become lightweight enough to run on mobile devices and achieve state-of-the-art performance. There is potentially a ?data shift? phenomenon between facial expressions of children compared with adults; our classifiers performed much better when trained on children. Certain underrepresented ethnic groups (e.g., South Asian and African American) also perform significantly worse than groups such as European Caucasian despite similar data quality. Our models can be integrated into mobile health therapies to help diagnose autism spectrum disorder and provide targeted therapeutic treatment to children. UR - https://formative.jmir.org/2023/1/e39917 UR - http://dx.doi.org/10.2196/39917 UR - http://www.ncbi.nlm.nih.gov/pubmed/35962462 ID - info:doi/10.2196/39917 ER - TY - JOUR AU - Langener, M. Anna AU - Stulp, Gert AU - Kas, J. Martien AU - Bringmann, F. Laura PY - 2023/3/17 TI - Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review JO - JMIR Ment Health SP - e42646 VL - 10 KW - social context KW - experience sampling method KW - egocentric network KW - digital phenotyping KW - passive measures KW - ambulatory assessment KW - mobile phone N2 - Background: Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people?s social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people?s social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. Objective: Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. Methods: We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. Conclusions: We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants? burden and form a holistic perspective on the social environment. UR - https://mental.jmir.org/2023/1/e42646 UR - http://dx.doi.org/10.2196/42646 UR - http://www.ncbi.nlm.nih.gov/pubmed/36930210 ID - info:doi/10.2196/42646 ER - TY - JOUR AU - Zhuparris, Ahnjili AU - Maleki, Ghobad AU - Koopmans, Ingrid AU - Doll, J. Robert AU - Voet, Nicoline AU - Kraaij, Wessel AU - Cohen, Adam AU - van Brummelen, Emilie AU - De Maeyer, H. Joris AU - Groeneveld, Jan Geert PY - 2023/3/15 TI - Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study JO - JMIR Form Res SP - e41178 VL - 7 KW - facioscapulohumeral muscular dystrophy KW - FSHD KW - smartphone KW - wearables KW - machine learning KW - Time Up and Go KW - regression KW - mobile phone KW - neuromuscular disease KW - mHealth KW - mobile health N2 - Background: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions. Objective: We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic. Methods: In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data. Results: The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. Conclusions: We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. Trial Registration: ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735 UR - https://formative.jmir.org/2023/1/e41178 UR - http://dx.doi.org/10.2196/41178 UR - http://www.ncbi.nlm.nih.gov/pubmed/36920465 ID - info:doi/10.2196/41178 ER - TY - JOUR AU - ?uster, Simon AU - Baldwin, Timothy AU - Lau, Han Jey AU - Jimeno Yepes, Antonio AU - Martinez Iraola, David AU - Otmakhova, Yulia AU - Verspoor, Karin PY - 2023/3/13 TI - Automating Quality Assessment of Medical Evidence in Systematic Reviews: Model Development and Validation Study JO - J Med Internet Res SP - e35568 VL - 25 KW - critical appraisal KW - evidence synthesis KW - systematic reviews KW - bias detection KW - automated quality assessment N2 - Background: Assessment of the quality of medical evidence available on the web is a critical step in the preparation of systematic reviews. Existing tools that automate parts of this task validate the quality of individual studies but not of entire bodies of evidence and focus on a restricted set of quality criteria. Objective: We proposed a quality assessment task that provides an overall quality rating for each body of evidence (BoE), as well as finer-grained justification for different quality criteria according to the Grading of Recommendation, Assessment, Development, and Evaluation formalization framework. For this purpose, we constructed a new data set and developed a machine learning baseline system (EvidenceGRADEr). Methods: We algorithmically extracted quality-related data from all summaries of findings found in the Cochrane Database of Systematic Reviews. Each BoE was defined by a set of population, intervention, comparison, and outcome criteria and assigned a quality grade (high, moderate, low, or very low) together with quality criteria (justification) that influenced that decision. Different statistical data, metadata about the review, and parts of the review text were extracted as support for grading each BoE. After pruning the resulting data set with various quality checks, we used it to train several neural-model variants. The predictions were compared against the labels originally assigned by the authors of the systematic reviews. Results: Our quality assessment data set, Cochrane Database of Systematic Reviews Quality of Evidence, contains 13,440 instances, or BoEs labeled for quality, originating from 2252 systematic reviews published on the internet from 2002 to 2020. On the basis of a 10-fold cross-validation, the best neural binary classifiers for quality criteria detected risk of bias at 0.78 F1 (P=.68; R=0.92) and imprecision at 0.75 F1 (P=.66; R=0.86), while the performance on inconsistency, indirectness, and publication bias criteria was lower (F1 in the range of 0.3-0.4). The prediction of the overall quality grade into 1 of the 4 levels resulted in 0.5 F1. When casting the task as a binary problem by merging the Grading of Recommendation, Assessment, Development, and Evaluation classes (high+moderate vs low+very low-quality evidence), we attained 0.74 F1. We also found that the results varied depending on the supporting information that is provided as an input to the models. Conclusions: Different factors affect the quality of evidence in the context of systematic reviews of medical evidence. Some of these (risk of bias and imprecision) can be automated with reasonable accuracy. Other quality dimensions such as indirectness, inconsistency, and publication bias prove more challenging for machine learning, largely because they are much rarer. This technology could substantially reduce reviewer workload in the future and expedite quality assessment as part of evidence synthesis. UR - https://www.jmir.org/2023/1/e35568 UR - http://dx.doi.org/10.2196/35568 UR - http://www.ncbi.nlm.nih.gov/pubmed/36722350 ID - info:doi/10.2196/35568 ER - TY - JOUR AU - Harding, E. Eleanor AU - van der Wal-Huisman, Hanneke AU - van Leeuwen, L. Barbara PY - 2023/3/10 TI - Live and Recorded Music Interventions to Reduce Postoperative Pain: Protocol for a Nonrandomized Controlled Trial JO - JMIR Res Protoc SP - e40034 VL - 12 KW - live music intervention KW - recorded music intervention KW - pain VAS KW - postsurgical pain KW - neuroinflammation KW - pain management KW - intervention KW - music KW - postoperative KW - pain KW - perception KW - pain relief KW - live music KW - recorded music KW - music intervention KW - pain perception N2 - Background: Postoperative patients who were previously engaged in the live musical intervention Meaningful Music in Healthcare reported significantly reduced perception of pain than patients without the intervention. This encouraging finding indicates a potential for postsurgical musical interventions to have a place in standard care as therapeutic pain relief. However, live music is logistically complex in hospital settings, and previous studies have reported the more cost-effective recorded music to serve as a similar pain-reducing function in postsurgical patients. Moreover, little is known about the potential underlying physiological mechanisms that may be responsible for the reduced pain perceived by patients after the live music intervention. Objective: The primary objective is to see whether a live music intervention can significantly lower perceived postoperative pain compared to a recorded music intervention and do-nothing control. The secondary objective is to explore the neuroinflammatory underpinnings of postoperative pain and the potential role of a music intervention in mitigating neuroinflammation. Methods: This intervention study will compare subjective postsurgical pain ratings among 3 groups: live music intervention, recorded music intervention, and standard care control. The design will take the form of an on-off nonrandomized controlled trial. Adult patients undergoing elective surgery will be invited to participate. The intervention is a daily music session of up to 30 minutes for a maximum of 5 days. The live music intervention group is visited by professional musicians once a day for 15 minutes and will be asked to interact. The recorded music active control intervention group receives 15 minutes of preselected music over headphones. The do-nothing group receives typical postsurgical care that does not include music. Results: At study completion, we will have an empirical indication of whether live music or recorded music has a significant impact on postoperative perceived pain. We hypothesize that the live music intervention will have more impact than recorded music but that both will reduce the perceived pain more than care-as-usual. We will moreover have the preliminary evidence of the physiological underpinnings responsible for reducing the perceived pain during a music intervention, from which hypotheses for future research may be derived. Conclusions: Live music can provide relief from pain experienced by patients recovering from surgery; however, it is not known to what degree live music improves the patients? pain experience than the logistically simpler alternative of recorded music. Upon completion, this study will be able to statistically compare live versus recorded music. This study will moreover be able to provide insight into the neurophysiological mechanisms involved in reduced pain perception as a result of postoperative music listening. Trial Registration: The Netherlands Central Commission on Human Research NL76900.042.21; https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=F2CA4A88E6040A45C1258791001AEA44 International Registered Report Identifier (IRRID): PRR1-10.2196/40034 UR - https://www.researchprotocols.org/2023/1/e40034 UR - http://dx.doi.org/10.2196/40034 UR - http://www.ncbi.nlm.nih.gov/pubmed/36897643 ID - info:doi/10.2196/40034 ER - TY - JOUR AU - Martín-Carbonell, Marta AU - Espejo, Begoña AU - Castro-Melo, Patricia Greys AU - Sequeira-Daza, Doris AU - Checa, Irene PY - 2023/3/9 TI - Psychometric Properties of and Measurement Invariance in the Questionnaire of Stereotypes Toward Older Adulthood in Health Care College Students and Health Professionals of Colombia: Psychometric Study JO - J Med Internet Res SP - e42340 VL - 25 KW - psychometric properties KW - structural equation modeling KW - older adulthood KW - geriatric KW - gerontology KW - health care college students KW - health care professionals KW - questionnaire KW - stereotype KW - agism N2 - Background: In health professionals, negative stereotypes toward older adulthood have been associated with the difficulty in recognizing pathological processes and the refusal to care for older patients because of assuming that communication with them will be uncomfortable and frustrating. For these reasons, research on stereotypes in these groups has acquired growing importance. The usual strategy to identify and evaluate agist stereotypes is to use scales and questionnaires. Although multiple scales are currently used, in Latin America, the Questionnaire for the Evaluation of Negative Stereotypes Toward Older Adulthood (Cuestionario de Estereotipos Negativos sobre la Vejez [CENVE]), developed in Spain, is widely used but without evidence of construct validity in our context. In addition, although in the original version, a factorial structure of 3 factors was found, in later studies, a unifactorial structure was obtained. Objective: The objective is to study the construct validity of the CENVE in a sample of Colombian health personnel to clarify its factorial structure and concurrent validity. Likewise, the measurement invariance according to gender and age was studied. Methods: A nonprobabilistic sample of 877 Colombian health professionals and intern health students was obtained. The data were collected online using the LimeSurvey tool. To study the factor structure of the CENVE, 2 confirmatory factor analysis (CFA) models were carried out, one to test a single factor and the other to test the 3-related-factor structure. The factor measurement reliability was evaluated with the composite reliability index (CRI) and the average variance extracted (AVE). The measurement invariance was studied according to gender (men and women) and age (emerging adults, 18-29 years old, and adults, 30 years old or older). Using a structural equation model, the relationship between age and the latent CENVE total score was studied to obtain evidence of concurrent validity, since studies indicate that the younger the age, the greater the number of stereotypes. Results: The 1-factor structure was confirmed. The reliability results indicated that both indices show adequate values. Likewise, the existence of a strong invariance in measurement by gender and age group was verified. After contrasting the means of the groups, the results showed that men show more negative stereotypes toward old age than women. Likewise, emerging adults also showed more stereotypes than adults. We also verified that age is inversely related to the latent score of the questionnaire, such that the younger the age, the greater the stereotype. These results are in agreement with those obtained by other authors. Conclusions: The CENVE shows good construct and concurrent validity, as well as good reliability, and it can be used to assess stereotypes toward older adulthood in Colombian health professionals and health sciences college students. This will allow us to better understand the effect of stereotypes on agism. UR - https://www.jmir.org/2023/1/e42340 UR - http://dx.doi.org/10.2196/42340 UR - http://www.ncbi.nlm.nih.gov/pubmed/36892936 ID - info:doi/10.2196/42340 ER - TY - JOUR AU - Barton, J. Hanna AU - Salwei, E. Megan AU - Rutkowski, A. Rachel AU - Wust, Kathryn AU - Krause, Sheryl AU - Hoonakker, LT Peter AU - Dail, vW Paula AU - Buckley, M. Denise AU - Eastman, Alexis AU - Ehlenfeldt, Brad AU - Patterson, W. Brian AU - Shah, N. Manish AU - King, J. Barbara AU - Werner, E. Nicole AU - Carayon, Pascale PY - 2023/3/9 TI - Evaluating the Usability of an Emergency Department After Visit Summary: Staged Heuristic Evaluation JO - JMIR Hum Factors SP - e43729 VL - 10 KW - patient safety KW - heuristic evaluation KW - usability KW - emergency medicine KW - safety KW - emergency KW - human factors engineering KW - discharge summary KW - documentation KW - heuristic N2 - Background: Heuristic evaluations, while commonly used, may inadequately capture the severity of identified usability issues. In the domain of health care, usability issues can pose different levels of risk to patients. Incorporating diverse expertise (eg, clinical and patient) in the heuristic evaluation process can help assess and address potential negative impacts on patient safety that may otherwise go unnoticed. One document that should be highly usable for patients?with the potential to prevent adverse outcomes?is the after visit summary (AVS). The AVS is the document given to a patient upon discharge from the emergency department (ED), which contains instructions on how to manage symptoms, medications, and follow-up care. Objective: This study aims to assess a multistage method for integrating diverse expertise (ie, clinical, an older adult care partner, and health IT) with human factors engineering (HFE) expertise in the usability evaluation of the patient-facing ED AVS. Methods: We conducted a three-staged heuristic evaluation of an ED AVS using heuristics developed for use in evaluating patient-facing documentation. In stage 1, HFE experts reviewed the AVS to identify usability issues. In stage 2, 6 experts of varying expertise (ie, emergency medicine physicians, ED nurses, geriatricians, transitional care nurses, and an older adult care partner) rated each previously identified usability issue on its potential impact on patient comprehension and patient safety. Finally, in stage 3, an IT expert reviewed each usability issue to identify the likelihood of successfully addressing the issue. Results: In stage 1, we identified 60 usability issues that violated a total of 108 heuristics. In stage 2, 18 additional usability issues that violated 27 heuristics were identified by the study experts. Impact ratings ranged from all experts rating the issue as ?no impact? to 5 out of 6 experts rating the issue as having a ?large negative impact.? On average, the older adult care partner representative rated usability issues as being more significant more of the time. In stage 3, 31 usability issues were rated by an IT professional as ?impossible to address,? 21 as ?maybe,? and 24 as ?can be addressed.? Conclusions: Integrating diverse expertise when evaluating usability is important when patient safety is at stake. The non-HFE experts, included in stage 2 of our evaluation, identified 23% (18/78) of all the usability issues and, depending on their expertise, rated those issues as having differing impacts on patient comprehension and safety. Our findings suggest that, to conduct a comprehensive heuristic evaluation, expertise from all the contexts in which the AVS is used must be considered. Combining those findings with ratings from an IT expert, usability issues can be strategically addressed through redesign. Thus, a 3-staged heuristic evaluation method offers a framework for integrating context-specific expertise efficiently, while providing practical insights to guide human-centered design. UR - https://humanfactors.jmir.org/2023/1/e43729 UR - http://dx.doi.org/10.2196/43729 UR - http://www.ncbi.nlm.nih.gov/pubmed/36892941 ID - info:doi/10.2196/43729 ER - TY - JOUR AU - Wittenborn, John AU - Lee, Aaron AU - Lundeen, A. Elizabeth AU - Lamuda, Phoebe AU - Saaddine, Jinan AU - Su, L. Grace AU - Lu, Randy AU - Damani, Aashka AU - Zawadzki, S. Jonathan AU - Froines, P. Colin AU - Shen, Z. Jolie AU - Kung, H. Timothy-Paul AU - Yanagihara, T. Ryan AU - Maring, Morgan AU - Takahashi, M. Melissa AU - Blazes, Marian AU - Rein, B. David PY - 2023/3/7 TI - Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study JO - JMIR Public Health Surveill SP - e44552 VL - 9 KW - vision KW - blindness KW - surveillance KW - survey KW - acuity KW - validation KW - visual health KW - optometry clinic KW - eye disease KW - vision loss N2 - Background: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. Objective: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. Methods: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. Results: The survey question, ?Are you blind or do you have serious difficulty seeing, even when wearing glasses?? had the highest accuracy for identifying patients with blindness (BCVA ?20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of ?fair,? ?poor,? or ?very poor? to the question, ?At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor? (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. Conclusions: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA. UR - https://publichealth.jmir.org/2023/1/e44552 UR - http://dx.doi.org/10.2196/44552 UR - http://www.ncbi.nlm.nih.gov/pubmed/36881468 ID - info:doi/10.2196/44552 ER - TY - JOUR AU - Zhang, Xupin AU - Tao, Xinqi AU - Ji, Bingxiang AU - Wang, Renwu AU - Sörensen, Silvia PY - 2023/3/3 TI - The Success of Cancer Crowdfunding Campaigns: Project and Text Analysis JO - J Med Internet Res SP - e44197 VL - 25 KW - cancer KW - GoFundme KW - fundraising KW - emotional content KW - sentiment analysis KW - campaign features KW - crowdfunding KW - gender N2 - Background: Recent studies have analyzed the factors that contribute to variations in the success of crowdfunding campaigns for a specific cancer type; however, little is known about the influential factors among crowdfunding campaigns for multiple cancers. Objective: The purpose of this study was to examine the relationship between project features and the success of cancer crowdfunding campaigns and to determine whether text features affect campaign success for various cancers. Methods: Using cancer-related crowdfunding projects on the GoFundMe website, we transformed textual descriptions from the campaigns into structured data using natural language processing techniques. Next, we used penalized logistic regression and correlation analyses to examine the influence of project and text features on fundraising project outcomes. Finally, we examined the influence of campaign description sentiment on crowdfunding success using Linguistic Inquiry and Word Count software. Results: Campaigns were significantly more likely to be successful if they featured a lower target amount (Goal amount, ?=?1.949, z score=?82.767, P<.001) for fundraising, a higher number of previous donations, agency (vs individual) organizers, project pages containing updates, and project pages containing comments from readers. The results revealed an inverted U-shaped relationship between the length of the text and the amount of funds raised. In addition, more spelling mistakes negatively affected the funds raised (Number of spelling errors, ?=?1.068, z score=?38.79, P<.001). Conclusions: Difficult-to-treat cancers and high-mortality cancers tend to trigger empathy from potential donors, which increases the funds raised. Gender differences were observed in the effects of emotional words in the text on the amount of funds raised. For cancers that typically occur in women, links between emotional words used and the amount of funds raised were weaker than for cancers typically occurring among men. UR - https://www.jmir.org/2023/1/e44197 UR - http://dx.doi.org/10.2196/44197 UR - http://www.ncbi.nlm.nih.gov/pubmed/36692283 ID - info:doi/10.2196/44197 ER - TY - JOUR AU - Barata, Filipe AU - Cleres, David AU - Tinschert, Peter AU - Iris Shih, Chen-Hsuan AU - Rassouli, Frank AU - Boesch, Maximilian AU - Brutsche, Martin AU - Fleisch, Elgar PY - 2023/2/20 TI - Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study JO - JMIR Form Res SP - e38439 VL - 7 KW - cough monitoring KW - ward monitoring KW - mobile sensing KW - machine learning KW - convolutional neural network KW - COVID-19 KW - mobile phone N2 - Background: Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. Objective: This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. Methods: Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. Results: In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were ?1.0 (95% CI ?12.3 to 10.2) and ?0.9 (95% CI ?6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. Conclusions: The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward. UR - https://formative.jmir.org/2023/1/e38439 UR - http://dx.doi.org/10.2196/38439 UR - http://www.ncbi.nlm.nih.gov/pubmed/36655551 ID - info:doi/10.2196/38439 ER - TY - JOUR AU - Single, Michael AU - Bruhin, C. Lena AU - Schütz, Narayan AU - Naef, C. Aileen AU - Hegi, Heinz AU - Reuse, Pascal AU - Schindler, A. Kaspar AU - Krack, Paul AU - Wiest, Roland AU - Chan, Andrew AU - Nef, Tobias AU - Gerber, M. Stephan PY - 2023/2/17 TI - Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report JO - JMIR Form Res SP - e43092 VL - 7 KW - sensor recording software KW - on-demand deployment KW - digital measures KW - sensor platform KW - biomedical research N2 - Background: Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals? everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). Objective: The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. Methods: The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). Results: Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). Conclusions: Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies. UR - https://formative.jmir.org/2023/1/e43092 UR - http://dx.doi.org/10.2196/43092 UR - http://www.ncbi.nlm.nih.gov/pubmed/36800219 ID - info:doi/10.2196/43092 ER - TY - JOUR AU - Vandekerckhove, Pieter AU - Timmermans, Job AU - de Bont, Antoinette AU - de Mul, Marleen PY - 2023/2/14 TI - Diversity in Stakeholder Groups in Generative Co-design for Digital Health: Assembly Procedure and Preliminary Assessment JO - JMIR Hum Factors SP - e38350 VL - 10 KW - collaborative design KW - design methodology KW - stakeholder involvement KW - participatory design KW - digital health N2 - Background: Diverse knowledge and ways of thinking are claimed to be important when involving stakeholders such as patients, care professionals, and care managers in a generative co-design (GCD) process. However, this claim is rather general and has not been operationalized; therefore, the influence of various stakeholders on the GCD process has not been empirically tested. Objective: In this study, we aimed to take the first step in assessing stakeholder diversity by formulating a procedure to assemble a group of diverse stakeholders and test its influence in a GCD process. Methods: To test the procedure and assess its influence on the GCD process, a case was selected involving a foundation that planned to develop a serious game to help people with cancer return to work. The procedure for assembling a stakeholder group involves snowball sampling and individual interviews, leading to the formation of 2 groups of stakeholders. Thirteen people were identified through snowball sampling, and they were briefly interviewed to assess their knowledge, inference experience, and communication skills. Two diverse stakeholder groups were formed, with one more potent than the other. The influence of both stakeholder groups on the GCD process was qualitatively assessed by comparing the knowledge output and related knowledge processing in 2 identical GCD workshops. Results: Our hypothesis on diverse stakeholders was confirmed, although it also appeared that merely assessing the professional background of stakeholders was not sufficient to reach the full potential of the GCD process. The more potently diverse group had a stronger influence on knowledge output and knowledge processing, resulting in a more comprehensive problem definition and more precisely described solutions. In the less potently diverse group, none of the stakeholders had experience with abduction-2 inferencing, and this did not emerge in the GCD process, suggesting that at least one stakeholder should have previous abduction-2 experience. Conclusions: A procedure to assemble a stakeholder group with specific criteria to assess the diversity of knowledge, ways of thinking, and communication can improve the potential of the GCD process and the resulting digital health. UR - https://humanfactors.jmir.org/2023/1/e38350 UR - http://dx.doi.org/10.2196/38350 UR - http://www.ncbi.nlm.nih.gov/pubmed/36787170 ID - info:doi/10.2196/38350 ER - TY - JOUR AU - Haghayegh, Shahab AU - Hu, Kun AU - Stone, Katie AU - Redline, Susan AU - Schernhammer, Eva PY - 2023/2/10 TI - Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study JO - J Med Internet Res SP - e40211 VL - 25 KW - SleepInceptionNet KW - polysomnography KW - PSG KW - electroencephalogram KW - EEG KW - spectrogram KW - scalogram KW - short-time Fourier transform KW - continuous wavelet transform KW - Welch power spectral density KW - LeNet KW - ResNet KW - Alex Net KW - inception KW - convolutional neural network N2 - Background: Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. Objective: We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). Methods: SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). Results: The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen ? agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. Conclusions: SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages. UR - https://www.jmir.org/2023/1/e40211 UR - http://dx.doi.org/10.2196/40211 UR - http://www.ncbi.nlm.nih.gov/pubmed/36763454 ID - info:doi/10.2196/40211 ER - TY - JOUR AU - Inamoto, Yoko AU - Mukaino, Masahiko AU - Imaeda, Sayuri AU - Sawada, Manami AU - Satoji, Kumi AU - Nagai, Ayako AU - Hirano, Satoshi AU - Okazaki, Hideto AU - Saitoh, Eiichi AU - Sonoda, Shigeru AU - Otaka, Yohei PY - 2023/2/8 TI - A Tablet-Based Aphasia Assessment System ?STELA?: Feasibility and Validation Study JO - JMIR Form Res SP - e42219 VL - 7 KW - aphasia KW - tablet KW - assessment KW - validity KW - internal consistency KW - psychometric KW - psychological health KW - stress KW - digital mental health intervention KW - digital health intervention KW - heuristic evaluation KW - system usability KW - auditory comprehension KW - reading comprehension KW - naming and sentence formation KW - repetition KW - reading aloud KW - Cronbach ? KW - speech N2 - Background: There is an extensive library of language tests, each with excellent psychometric properties; however, many of the tests available take considerable administration time, possibly bearing psychological strain on patients. The Short and Tailored Evaluation of Language Ability (STELA) is a simplified, tablet-based language ability assessment system developed to address this issue, with a reduced number of items and automated testing process. Objective: The aim of this paper is to assess the administration time, internal consistency, and validity of the STELA. Methods: The STELA consists of a tablet app, a microphone, and an input keypad for clinician?s use. The system is designed to assess language ability with 52 questions grouped into 2 comprehension modalities (auditory comprehension and reading comprehension) and 3 expression modalities (naming and sentence formation, repetition, and reading aloud). Performance in each modality was scored as the correct answer rate (0-100), and overall performance expressed as the sum of modality scores (out of 500 points). Results: The time taken to complete the STELA was significantly less than the time for the WAB (mean 16.2, SD 9.4 vs mean 149.3, SD 64.1 minutes; P<.001). The STELA?s total score was strongly correlated with the WAB Aphasia Quotient (r=0.93, P<.001), supporting the former?s concurrent validity concerning the WAB, which is a gold-standard aphasia assessment. Strong correlations were also observed at the subscale level; STELA auditory comprehension versus WAB auditory comprehension (r=0.75, P<.001), STELA repetition versus WAB repetition (r=0.96, P<.001), STELA naming and sentence formation versus WAB naming and word finding (r=0.81, P<.001), and the sum of STELA reading comprehension or reading aloud versus WAB reading (r=0.82, P<.001). Cronbach ? obtained for each modality was .862 for auditory comprehension, .872 for reading comprehension, .902 for naming and sentence formation, .787 for repetition, and .892 for reading aloud. Global Cronbach ? was .961. The average of the values of item-total correlation to each subscale was 0.61 (SD 0.17). Conclusions: Our study confirmed significant time reduction in the assessment of language ability and provided evidence for good internal consistency and validity of the STELA tablet-based aphasia assessment system. UR - https://formative.jmir.org/2023/1/e42219 UR - http://dx.doi.org/10.2196/42219 UR - http://www.ncbi.nlm.nih.gov/pubmed/36753308 ID - info:doi/10.2196/42219 ER - TY - JOUR AU - Yu, Yanqiu AU - Fong, I. Vivian W. AU - Ng, Hoi-Yuk Joyce AU - Wang, Zixin AU - Tian, Xiaobing AU - Lau, F. Joseph T. PY - 2023/1/17 TI - The Associations Between Loneliness, Hopelessness, and Self-control and Internet Gaming Disorder Among University Students Who Were Men Who Have Sex With Men: Cross-sectional Mediation Study JO - J Med Internet Res SP - e43532 VL - 25 KW - men who have sex with men KW - internet gaming disorder KW - self-control KW - loneliness KW - hopelessness KW - structural equation modeling N2 - Background: The minority stress model postulates that men who have sex with men (MSM) often encounter multiple stressors because of their sexual minority status, which may lead to psychological problems and maladaptive coping such as addictive behaviors (eg, internet gaming disorder [IGD]). It was hypothesized that hopelessness and loneliness would be associated with IGD via self-control among MSM. Objective: This study investigated the prevalence of IGD and its associations with variables related to minority stress (loneliness and hopelessness) among MSM who were university students. Mediation involving such associations via self-control was also explored. Methods: With informed consent, 305 MSM attending universities in Sichuan, China participated in the study. The validated Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) checklist was used to assess IGD. Multivariable logistic regression adjusted for background factors and structural equation modeling were conducted. Results: The prevalence of IGD was 12.8% (n=39). Logistic regression found that IGD was positively associated with hopelessness and loneliness, and negatively associated with self-control. The structural equation modeling identified three significant paths between hopelessness/loneliness and IGD: (1) hopelessness ? lower self-control ? higher IGD (full mediation), (2) loneliness ? lower self-control ? higher IGD (partial mediation: effect size of 28%), and (3) a direct effect from loneliness to IGD. Conclusions: IGD was prevalent among young MSM and warrants interventions that may try to reduce the level of psychosocial problems such as loneliness and hopelessness and improve self-control. According to the socioecological model, the promotion of social acceptance and reduction in stigma toward MSM are important in reducing loneliness and hopefulness among MSM. Self-control links up the relationships between psychosocial problems and IGD and should be given special attention. Longitudinal studies are warranted to confirm the findings and test new mediations between loneliness/hopelessness and MSM with IGD. UR - https://www.jmir.org/2023/1/e43532 UR - http://dx.doi.org/10.2196/43532 UR - http://www.ncbi.nlm.nih.gov/pubmed/36649059 ID - info:doi/10.2196/43532 ER - TY - JOUR AU - Morse, Brad AU - Soares, Andrey AU - Ytell, Kate AU - DeSanto, Kristen AU - Allen, Marvyn AU - Holliman, Dorsey Brooke AU - Lee, S. Rita AU - Kwan, M. Bethany AU - Schilling, M. Lisa PY - 2023/1/10 TI - Co-design of the Transgender Health Information Resource: Web-Based Participatory Design JO - J Particip Med SP - e38078 VL - 15 KW - transgender KW - gender diverse KW - participatory design KW - web-based design KW - co-design KW - health information resource KW - smartphone KW - app KW - mobile phone N2 - Background: There is an urgent and unmet need for accessible and credible health information within the transgender and gender-diverse (TGD) community. Currently, TGD individuals often seek and must find relevant resources by vetting social media posts. A resource that provides accessible and credible health-related resources and content via a mobile phone app may have a positive impact on and support the TGD population. Objective: COVID-19 stay-at-home orders forced a shift in the methods used in participatory design. In this paper, we aimed to describe the web-based participatory methods used to develop the Transgender Health Information Resource. We also described and characterized the web-based engagement that occurred during a single session of the overall design process. Methods: We planned and conducted web-based design sessions to replace the proposed in-person sessions. We used web-based collaborative tools, including Zoom (Zoom Video Communications), Mural (Mural), REDCap (Research Electronic Data Capture; Vanderbilt University), and Justinmind (Justinmind), to engage the participants in the design process. Zoom was used as an integrated platform for design activities. Mural was used to perform exercises, such as free listing, brainstorming, and grouping. REDCap allowed us to collect survey responses. Justinmind was used to create prototypes that were shared and discussed via Zoom. Recruitment was led by one of our community partners, One Colorado, who used private Facebook groups in which web-based flyers were dispersed. The design process took place in several workshops over a period of 10 months. We described and characterized engagement during a single design session by tracking the number of influential interactions among participants. We defined an influential interaction as communication, either verbal or web-based content manipulation, that advanced the design process. Results: We presented data from a single design session that lasted 1 hour and 48 minutes and included 4 participants. During the session, there were 301 influential interactions, consisting of 79 verbal comments and 222 web-based content manipulations. Conclusions: Web-based participatory design can elicit input and decisions from participants to develop a health information resource, such as a mobile app user interface. Overall, participants were highly engaged. This approach maintained the benefits and fidelity of traditional in-person design sessions, mitigated deficits, and exploited the previously unconsidered benefits of web-based methods, such as enhancing the ability to participate for those who live far from academic institutions. The web-based approach to participatory design was an efficient and feasible methodological design approach. UR - https://jopm.jmir.org/2023/1/e38078 UR - http://dx.doi.org/10.2196/38078 UR - http://www.ncbi.nlm.nih.gov/pubmed/36626222 ID - info:doi/10.2196/38078 ER - TY - JOUR AU - Mai, Hang-Nga AU - Dam, Viet Van AU - Lee, Du-Hyeong PY - 2023/1/4 TI - Accuracy of Augmented Reality?Assisted Navigation in Dental Implant Surgery: Systematic Review and Meta-analysis JO - J Med Internet Res SP - e42040 VL - 25 KW - augmented reality KW - accuracy KW - computer-guided surgery KW - dental implants KW - systematic review KW - meta-analysis N2 - Background: The novel concept of immersive 3D augmented reality (AR) surgical navigation has recently been introduced in the medical field. This method allows surgeons to directly focus on the surgical objective without having to look at a separate monitor. In the dental field, the recently developed AR-assisted dental implant navigation system (AR navigation), which uses innovative image technology to directly visualize and track a presurgical plan over an actual surgical site, has attracted great interest. Objective: This study is the first systematic review and meta-analysis study that aimed to assess the accuracy of dental implants placed by AR navigation and compare it with that of the widely used implant placement methods, including the freehand method (FH), template-based static guidance (TG), and conventional navigation (CN). Methods: Individual search strategies were used in PubMed (MEDLINE), Scopus, ScienceDirect, Cochrane Library, and Google Scholar to search for articles published until March 21, 2022. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) database. Peer-reviewed journal articles evaluating the positional deviations of dental implants placed using AR-assisted implant navigation systems were included. Cohen d statistical power analysis was used to investigate the effect size estimate and CIs of standardized mean differences (SMDs) between data sets. Results: Among the 425 articles retrieved, 15 articles were considered eligible for narrative review, 8 articles were considered for single-arm meta-analysis, and 4 were included in a 2-arm meta-analysis. The mean lateral, global, depth, and angular deviations of the dental implant placed using AR navigation were 0.90 (95% CI 0.78-1.02) mm, 1.18 (95% CI 0.95-1.41) mm, 0.78 (95% CI 0.48-1.08) mm, and 3.96° (95% CI 3.45°-4.48°), respectively. The accuracy of AR navigation was significantly higher than that of the FH method (SMD=?1.01; 95% CI ?1.47 to ?0.55; P<.001) and CN method (SMD=?0.46; 95% CI ?0.64 to ?0.29; P<.001). However, the accuracies of the AR navigation and TG methods were similar (SMD=0.06; 95% CI ?0.62 to 0.74; P=.73). Conclusions: The positional deviations of AR-navigated implant placements were within the safety zone, suggesting clinically acceptable accuracy of the AR navigation method. Moreover, the accuracy of AR implant navigation was comparable with that of the highly recommended dental implant?guided surgery method, TG, and superior to that of the conventional FH and CN methods. This review highlights the possibility of using AR navigation as an effective and accurate immersive surgical guide for dental implant placement. UR - https://www.jmir.org/2023/1/e42040 UR - http://dx.doi.org/10.2196/42040 UR - http://www.ncbi.nlm.nih.gov/pubmed/36598798 ID - info:doi/10.2196/42040 ER - TY - JOUR AU - Maghsoudi, Arash AU - Nowakowski, Sara AU - Agrawal, Ritwick AU - Sharafkhaneh, Amir AU - Kunik, E. Mark AU - Naik, D. Aanand AU - Xu, Hua AU - Razjouyan, Javad PY - 2022/12/27 TI - Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre? and Peri?COVID-19 Pandemic Retrospective Study JO - J Med Internet Res SP - e41517 VL - 24 IS - 12 KW - COVID-19 KW - coronavirus KW - sleep KW - Twitter KW - natural language processing KW - sentiment analysis KW - transformers KW - Dempster-Shafer theory KW - sleeping KW - social media KW - pandemic KW - effect KW - viral infection N2 - Background: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. Objective: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. Methods: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users? insomnia experiences, using logistic regression. Results: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. Conclusions: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep. UR - https://www.jmir.org/2022/12/e41517 UR - http://dx.doi.org/10.2196/41517 UR - http://www.ncbi.nlm.nih.gov/pubmed/36417585 ID - info:doi/10.2196/41517 ER - TY - JOUR AU - Wang, Siyang AU - ?uster, Simon AU - Baldwin, Timothy AU - Verspoor, Karin PY - 2022/12/23 TI - Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study JO - J Med Internet Res SP - e38859 VL - 24 IS - 12 KW - clinical trials KW - study characteristics KW - machine learning KW - natural language processing KW - pretrained language models KW - publication success N2 - Background: Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation. It could also enhance our understanding of bias in the publication of trials and publication trends based on the research direction or strength of the findings. Although the publication of clinical trials has been addressed in several descriptive studies at an aggregate level, there is a lack of research on the predictive analysis of a trial?s publishability given an individual (planned) clinical trial description. Objective: We aimed to conduct a study that combined structured and unstructured features relevant to publication status in a single predictive approach. Established natural language processing techniques as well as recent pretrained language models enabled us to incorporate information from the textual descriptions of clinical trials into a machine learning approach. We were particularly interested in whether and which textual features could improve the classification accuracy for publication outcomes. Methods: In this study, we used metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a database of academic journal articles) to build a data set of clinical trials (N=76,950) that contained the description of a registered trial and its publication outcome (27,702/76,950, 36% published and 49,248/76,950, 64% unpublished). This is the largest data set of its kind, which we released as part of this work. The publication outcome in the data set was identified from MEDLINE based on clinical trial identifiers. We carried out a descriptive analysis and predicted the publication outcome using 2 approaches: a neural network with a large domain-specific language model and a random forest classifier using a weighted bag-of-words representation of text. Results: First, our analysis of the newly created data set corroborates several findings from the existing literature regarding attributes associated with a higher publication rate. Second, a crucial observation from our predictive modeling was that the addition of textual features (eg, eligibility criteria) offers consistent improvements over using only structured data (F1-score=0.62-0.64 vs F1-score=0.61 without textual features). Both pretrained language models and more basic word-based representations provide high-utility text representations, with no significant empirical difference between the two. Conclusions: Different factors affect the publication of a registered clinical trial. Our approach to predictive modeling combines heterogeneous features, both structured and unstructured. We show that methods from natural language processing can provide effective textual features to enable more accurate prediction of publication success, which has not been explored for this task previously. UR - https://www.jmir.org/2022/12/e38859 UR - http://dx.doi.org/10.2196/38859 UR - http://www.ncbi.nlm.nih.gov/pubmed/36563029 ID - info:doi/10.2196/38859 ER - TY - JOUR AU - Ding, Huitong AU - Mandapati, Amiya AU - Karjadi, Cody AU - Ang, Alvin Ting Fang AU - Lu, Sophia AU - Miao, Xiao AU - Glass, James AU - Au, Rhoda AU - Lin, Honghuang PY - 2022/12/22 TI - Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study JO - J Med Internet Res SP - e42886 VL - 24 IS - 12 KW - mild cognitive impairment KW - digital voice KW - neuropsychological test KW - association KW - prediction N2 - Background: Human voice has increasingly been recognized as an effective indicator for the detection of cognitive disorders. However, the association of acoustic features with specific cognitive functions and mild cognitive impairment (MCI) has yet to be evaluated in a large community-based population. Objective: This study aimed to investigate the association between acoustic features and neuropsychological (NP) tests across multiple cognitive domains and evaluate the added predictive power of acoustic composite scores for the classification of MCI. Methods: This study included participants without dementia from the Framingham Heart Study, a large community-based cohort with longitudinal surveillance for incident dementia. For each participant, 65 low-level acoustic descriptors were derived from voice recordings of NP test administration. The associations between individual acoustic descriptors and 18 NP tests were assessed with linear mixed-effect models adjusted for age, sex, and education. Acoustic composite scores were then built by combining acoustic features significantly associated with NP tests. The added prediction power of acoustic composite scores for prevalent and incident MCI was also evaluated. Results: The study included 7874 voice recordings from 4950 participants (age: mean 62, SD 14 years; 4336/7874, 55.07% women), of whom 453 were diagnosed with MCI. In all, 8 NP tests were associated with more than 15 acoustic features after adjusting for multiple testing. Additionally, 4 of the acoustic composite scores were significantly associated with prevalent MCI and 7 were associated with incident MCI. The acoustic composite scores can increase the area under the curve of the baseline model for MCI prediction from 0.712 to 0.755. Conclusions: Multiple acoustic features are significantly associated with NP test performance and MCI, which can potentially be used as digital biomarkers for early cognitive impairment monitoring. UR - https://www.jmir.org/2022/12/e42886 UR - http://dx.doi.org/10.2196/42886 UR - http://www.ncbi.nlm.nih.gov/pubmed/36548029 ID - info:doi/10.2196/42886 ER - TY - JOUR AU - Rey Velasco, Elena AU - Pedersen, Sæderup Hanne AU - Skinner, Timothy AU - PY - 2022/12/20 TI - Analysis of Patient Cues in Asynchronous Health Interactions: Pilot Study Combining Empathy Appraisal and Systemic Functional Linguistics JO - JMIR Form Res SP - e40058 VL - 6 IS - 12 KW - telehealth KW - telecoaching KW - asynchronous communication KW - empathy KW - systemic functional linguistics KW - communication KW - health promotion KW - coding KW - linguistic analysis KW - user experience KW - coach-user interaction KW - tool development KW - lifestyle-related disease N2 - Background: Lifestyle-related diseases are among the leading causes of death and disability. Their rapid increase worldwide has called for low-cost, scalable solutions to promote health behavior changes. Digital health coaching has proved to be effective in delivering affordable, scalable programs to support lifestyle change. This approach increasingly relies on asynchronous text-based interventions to motivate and support behavior change. Although we know that empathy is a core element for a successful coach-user relationship and positive patient outcomes, we lack research on how this is realized in text-based interactions. Systemic functional linguistics (SFL) is a linguistic theory that may support the identification of empathy opportunities (EOs) in text-based interactions, as well as the reasoning behind patients' linguistic choices in their formulation. Objective: This study aims to determine whether empathy and SFL approaches correspond and complement each other satisfactorily to study text-based communication in a health coaching context. We sought to explore whether combining empathic assessment with SFL categories can provide a means to understand client-coach interactions in asynchronous text-based coaching interactions. Methods: We retrieved 148 text messages sent by 29 women who participated in a randomized trial of telecoaching for the prevention of gestational diabetes mellitus (GDM) and postnatal weight loss. We conducted a pilot study to identify users' explicit and implicit EOs and further investigated these statements using the SFL approach, focusing on the analysis of transitivity and thematic analysis. Results: We identified 164 EOs present in 42.37% (3478/8209) of the word count in the corpus. These were mainly negative (n=90, 54.88%) and implicit (n=55, 60.00%). We distinguished opening, content and closing messages structures. Most of the wording was found in the content (n=7077, 86.21%) with a declarative structure (n=7084, 86.30%). Processes represented 22.4% (n=1839) of the corpus, with half being material (n=876, 10.67%) and mostly related to food and diet (n=196, 54.92%), physical activity (n=96, 26.89%), and lifestyle goals (n=40, 11.20%). Conclusions: Our findings show that empathy and SFL approaches are compatible. The results from our transitivity analysis reveal novel insights into the meanings of the users? EOs, such as their seek for help or praise, often missed by health care professionals (HCPs), and on the coach-user relationship. The absence of explicit EOs and direct questions could be attributed to low trust on or information about the coach?s abilities. In the future, we will conduct further research to explore additional linguistic features and code coach messages. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620001240932; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380020 UR - https://formative.jmir.org/2022/12/e40058 UR - http://dx.doi.org/10.2196/40058 UR - http://www.ncbi.nlm.nih.gov/pubmed/36538352 ID - info:doi/10.2196/40058 ER - TY - JOUR AU - Iyer, Ravi AU - Nedeljkovic, Maja AU - Meyer, Denny PY - 2022/12/19 TI - Using Vocal Characteristics To Classify Psychological Distress in Adult Helpline Callers: Retrospective Observational Study JO - JMIR Form Res SP - e42249 VL - 6 IS - 12 KW - machine learning KW - distress KW - voice KW - mental distress KW - psychological stress KW - artificial intelligence KW - emotional distress KW - voice biomarker KW - biomarker KW - digital health intervention KW - mental health KW - mental health intervention KW - psychological well being KW - speech analysis N2 - Background: Elevated psychological distress has demonstrated impacts on individuals? health. Reliable and efficient ways to detect distress are key to early intervention. Artificial intelligence has the potential to detect states of emotional distress in an accurate, efficient, and timely manner. Objective: The aim of this study was to automatically classify short segments of speech obtained from callers to national suicide prevention helpline services according to high versus low psychological distress and using a range of vocal characteristics in combination with machine learning approaches. Methods: A total of 120 telephone call recordings were initially converted to 16-bit pulse code modulation format. Short variable-length segments of each call were rated on psychological distress using the distress thermometer by the responding counselor and a second team of psychologists (n=6) blinded to the initial ratings. Following this, 24 vocal characteristics were initially extracted from 40-ms speech frames nested within segments within calls. After highly correlated variables were eliminated, 19 remained. Of 19 vocal characteristics, 7 were identified and validated as predictors of psychological distress using a penalized generalized additive mixed effects regression model, accounting for nonlinearity, autocorrelation, and moderation by sex. Speech frames were then grouped using k-means clustering based on the selected vocal characteristics. Finally, component-wise gradient boosting incorporating these clusters was used to classify each speech frame according to high versus low psychological distress. Classification accuracy was confirmed via leave-one-caller-out cross-validation, ensuring that speech segments from individual callers were not used in both the training and test data. Results: The sample comprised 87 female and 33 male callers. From an initial pool of 19 characteristics, 7 vocal characteristics were identified. After grouping speech frames into 2 separate clusters (correlation with sex of caller, Cramer?s V =0.02), the component-wise gradient boosting algorithm successfully classified psychological distress to a high level of accuracy, with an area under the receiver operating characteristic curve of 97.39% (95% CI 96.20-98.45) and an area under the precision-recall curve of 97.52 (95% CI 95.71-99.12). Thus, 39,282 of 41,883 (93.39%) speech frames nested within 728 of 754 segments (96.6%) were classified as exhibiting low psychological distress, and 71455 of 75503 (94.64%) speech frames nested within 382 of 423 (90.3%) segments were classified as exhibiting high psychological distress. As the probability of high psychological distress increases, male callers spoke louder, with greater vowel articulation but with greater roughness (subharmonic depth). In contrast, female callers exhibited decreased vocal clarity (entropy), greater proportion of signal noise, higher frequencies, increased breathiness (spectral slope), and increased roughness of speech with increasing psychological distress. Individual caller random effects contributed 68% to risk reduction in the classification algorithm, followed by cluster configuration (23.4%), spectral slope (4.4%), and the 50th percentile frequency (4.2%). Conclusions: The high level of accuracy achieved suggests possibilities for real-time detection of psychological distress in helpline settings and has potential uses in pre-emptive triage and evaluations of counseling outcomes. Trial Registration: ANZCTR ACTRN12622000486729; https://www.anzctr.org.au/ACTRN12622000486729.aspx UR - https://formative.jmir.org/2022/12/e42249 UR - http://dx.doi.org/10.2196/42249 UR - http://www.ncbi.nlm.nih.gov/pubmed/36534456 ID - info:doi/10.2196/42249 ER - TY - JOUR AU - Villius Zetterholm, My AU - Nilsson, Lina AU - Jokela, Päivi PY - 2022/12/12 TI - Using a Proximity-Detection Technology to Nudge for Physical Distancing in a Swedish Workplace During the COVID-19 Pandemic: Retrospective Case Study JO - JMIR Form Res SP - e39570 VL - 6 IS - 12 KW - case study KW - COVID-19 KW - feasibility KW - mixed methods KW - nudging KW - physical distance KW - preventive behavior KW - preventive technologies KW - proximity detecting technology KW - wearables N2 - Background: The recent COVID-19 pandemic has contributed to the emergence of several technologies for infectious disease management. Although much focus has been placed on contact-tracing apps, another promising new tactic is proximity tracing, which focuses on health-related behavior and can be used for primary prevention. Underpinned by theories on behavioral design, a proximity-detection system can be devised that provides a user with immediate nudges to maintain physical distance from others. However, the practical feasibility of proximity detection during an infectious disease outbreak has not been sufficiently investigated. Objective: We aimed to evaluate the feasibility of using a wearable device to nudge for distance and to gather important insights about how functionality and interaction are experienced by users. The results of this study can guide future research and design efforts in this emerging technology. Methods: In this retrospective case study, a wearable proximity-detection technology was used in a workplace for 6 weeks during the production of a music competition. The purpose of the technology was to nudge users to maintain their physical distance using auditory feedback. We used a mixed methods sequential approach, including interviews (n=8) and a survey (n=30), to compile the experiences of using wearable technology in a real-life setting. Results: We generated themes from qualitative analysis based on data from interviews and open-text survey responses. The quantitative data were subsequently integrated into these themes: feasibility (implementation and acceptance?establishing a shared problem; distance tags in context?strategy, environment, and activities; understanding and learning; and accomplishing the purpose) and design aspects (a purposefully annoying device; timing, tone, and proximity; and additional functions). Conclusions: This empirical study reports on the feasibility of using wearable technology based on proximity detection to nudge individuals to maintain physical distance in the workplace. The technology supports attention to distance, but the usability of this approach is dependent on the context and situation. In certain situations, the audio signal is frustrating, but most users agree that it needs to be annoying to ensure sufficient behavioral adaption. We proposed a dual nudge that involves vibration followed by sound. There are indications that the technology also facilitates learning how to maintain a greater distance from others, and that this behavior can persist beyond the context of technology use. This study demonstrates that the key value of this technology is that it places the user in control and enables immediate action when the distance to others is not maintained. This study provides insights into the emerging field of personal and wearable technologies used for primary prevention during infectious disease outbreaks. Future research is needed to evaluate the preventive effect on transmission and investigate behavioral changes in detail and in relation to different forms of feedback. UR - https://formative.jmir.org/2022/12/e39570 UR - http://dx.doi.org/10.2196/39570 UR - http://www.ncbi.nlm.nih.gov/pubmed/36343202 ID - info:doi/10.2196/39570 ER - TY - JOUR AU - Mughal, Fiza AU - Raffe, William AU - Stubbs, Peter AU - Kneebone, Ian AU - Garcia, Jaime PY - 2022/11/29 TI - Fitbits for Monitoring Depressive Symptoms in Older Aged Persons: Qualitative Feasibility Study JO - JMIR Form Res SP - e33952 VL - 6 IS - 11 KW - digital mental health KW - Fitbit KW - smartwatch KW - smart wearable KW - geriatric KW - aging KW - health informatics KW - feasibility KW - usability KW - older aged N2 - Background: In 2022, an estimated 1.105 billion people used smart wearables and 31 million used Fitbit devices worldwide. Although there is growing evidence for the use of smart wearables to benefit physical health, more research is required on the feasibility of using these devices for mental health and well-being. In studies focusing on emotion recognition, emotions are often inferred and dependent on external cues, which may not be representative of true emotional states. Objective: The aim of this study was to evaluate the feasibility and acceptability of using consumer-grade activity trackers for apps in the remote mental health monitoring of older aged people. Methods: Older adults were recruited using criterion sampling. Participants were provided an activity tracker (Fitbit Alta HR) and completed weekly online questionnaires, including the Geriatric Depression Scale, for 4 weeks. Before and after the study period, semistructured qualitative interviews were conducted to provide insight into the acceptance and feasibility of performing the protocol over a 4-week period. Interview transcripts were analyzed using a hybrid inductive-deductive thematic analysis. Results: In total, 12 participants enrolled in the study, and 9 returned for interviews after the study period. Participants had positive attitudes toward being remotely monitored, with 78% (7/9) of participants experiencing no inconvenience throughout the study period. Moreover, 67% (6/9) were interested in trialing our prototype when it is implemented. Participants stated they would feel more comfortable if mental well-being was being monitored by carers remotely. Conclusions: Fitbit-like devices were an unobtrusive and convenient tool to collect physiological user data. Future research should integrate physiological user inputs to differentiate and predict depressive tendencies in users. UR - https://formative.jmir.org/2022/11/e33952 UR - http://dx.doi.org/10.2196/33952 UR - http://www.ncbi.nlm.nih.gov/pubmed/36268552 ID - info:doi/10.2196/33952 ER - TY - JOUR AU - Nunes, Monara AU - Teles, Soares Ariel AU - Farias, Daniel AU - Diniz, Claudia AU - Bastos, Hugo Victor AU - Teixeira, Silmar PY - 2022/11/24 TI - A Telemedicine Platform for Aphasia: Protocol for a Development and Usability Study JO - JMIR Res Protoc SP - e40603 VL - 11 IS - 11 KW - aphasia KW - serious games KW - deep learning KW - telemedicine KW - diagnosis KW - treatment KW - language KW - machine learning KW - rehabilitation KW - smart platform N2 - Background: Aphasia is a central disorder of comprehension and expression of language that cannot be attributed to a peripheral sensory deficit or a peripheral motor disorder. The diagnosis and treatment of aphasia are complex. Interventions that facilitate this process can lead to an increase in the number of assisted patients and greater precision in the therapeutic choice by the health professional. Objective: This paper describes a protocol for a study that aims to implement a computer-based solution (ie, a telemedicine platform) that uses deep learning to classify vocal data from participants with aphasia and to develop serious games to treat aphasia. Additionally, this study aims to evaluate the usability and user experience of the proposed solution. Methods: Our interactive and smart platform will be developed to provide an alternative option for professionals and their patients with aphasia. We will design 2 serious games for aphasia rehabilitation and a deep learning?driven computational solution to aid diagnosis. A pilot evaluation of usability and user experience will reveal user satisfaction with platform features. Results: Data collection began in June 2022 and is currently ongoing. Results of system development as well as usability should be published by mid-2023. Conclusions: This research will contribute to the treatment and diagnosis of aphasia by developing a telemedicine platform based on a co-design process. Therefore, this research will provide an alternative method for health care to patients with aphasia. Additionally, it will guide further studies with the same purpose. International Registered Report Identifier (IRRID): PRR1-10.2196/40603 UR - https://www.researchprotocols.org/2022/11/e40603 UR - http://dx.doi.org/10.2196/40603 UR - http://www.ncbi.nlm.nih.gov/pubmed/36422881 ID - info:doi/10.2196/40603 ER - TY - JOUR AU - van Goor, R. Harriet M. AU - Vernooij, M. Lisette AU - Breteler, M. Martine J. AU - Kalkman, J. Cor AU - Kaasjager, H. Karin A. AU - van Loon, Kim PY - 2022/11/23 TI - Association of Continuously Measured Vital Signs With Respiratory Insufficiency in Hospitalized COVID-19 Patients: Retrospective Cohort Study JO - Interact J Med Res SP - e40289 VL - 11 IS - 2 KW - continuous monitoring KW - vital sign monitoring KW - COVID-19 KW - general ward KW - vital sign KW - monitoring KW - respiration KW - data KW - respiratory insufficiency KW - cohort study KW - respiratory rate KW - heart rate KW - oxygen KW - clinical N2 - Background: Continuous monitoring of vital signs has the potential to assist in the recognition of deterioration of patients admitted to the general ward. However, methods to efficiently process and use continuously measured vital sign data remain unclear. Objective: The aim of this study was to explore methods to summarize continuously measured vital sign data and evaluate their association with respiratory insufficiency in COVID-19 patients at the general ward. Methods: In this retrospective cohort study, we included patients admitted to a designated COVID-19 cohort ward equipped with continuous vital sign monitoring. We collected continuously measured data of respiratory rate, heart rate, and oxygen saturation. For each patient, 7 metrics to summarize vital sign data were calculated: mean, slope, variance, occurrence of a threshold breach, number of episodes, total duration, and area above/under a threshold. These summary measures were calculated over timeframes of either 4 or 8 hours, with a pause between the last data point and the endpoint (the ?lead?) of 4, 2, 1, or 0 hours, and with 3 predefined thresholds per vital sign. The association between each of the summary measures and the occurrence of respiratory insufficiency was calculated using logistic regression analysis. Results: Of the 429 patients that were monitored, 334 were included for analysis. Of these, 66 (19.8%) patients developed respiratory insufficiency. Summarized continuously measured vital sign data in timeframes close to the endpoint showed stronger associations than data measured further in the past (ie, lead 0 vs 1, 2, or 4 hours), and summarized estimates over 4 hours of data had stronger associations than estimates taken over 8 hours of data. The mean was consistently strongly associated with respiratory insufficiency for the three vital signs: in a 4-hour timeframe without a lead, the standardized odds ratio for heart rate, respiratory rate, and oxygen saturation was 2.59 (99% CI 1.74-4.04), 5.05 (99% CI 2.87-10.03), and 3.16 (99% CI 1.78-6.26), respectively. The strength of associations of summary measures varied per vital sign, timeframe, and lead. Conclusions: The mean of a vital sign showed a relatively strong association with respiratory insufficiency for the majority of vital signs and timeframes. The type of vital sign, length of the timeframe, and length of the lead influenced the strength of associations. Highly associated summary measures and their combinations could be used in a clinical prediction score or algorithm for an automatic alarm system. UR - https://www.i-jmr.org/2022/2/e40289 UR - http://dx.doi.org/10.2196/40289 UR - http://www.ncbi.nlm.nih.gov/pubmed/36256803 ID - info:doi/10.2196/40289 ER - TY - JOUR AU - Acien, Alejandro AU - Morales, Aythami AU - Vera-Rodriguez, Ruben AU - Fierrez, Julian AU - Mondesire-Crump, Ijah AU - Arroyo-Gallego, Teresa PY - 2022/11/21 TI - Detection of Mental Fatigue in the General Population: Feasibility Study of Keystroke Dynamics as a Real-world Biomarker JO - JMIR Biomed Eng SP - e41003 VL - 7 IS - 2 KW - fatigue KW - keystroke KW - biometrics KW - digital biomarker KW - TypeNet KW - domain adaptation KW - fatigue detection KW - typing patterns KW - circadian cycles KW - mental fatigue KW - psychomotor patterns KW - monitoring KW - mental health KW - keystroke dynamics N2 - Background: Mental fatigue is a common and potentially debilitating state that can affect individuals? health and quality of life. In some cases, its manifestation can precede or mask early signs of other serious mental or physiological conditions. Detecting and assessing mental fatigue can be challenging nowadays as it relies on self-evaluation and rating questionnaires, which are highly influenced by subjective bias. Introducing more objective, quantitative, and sensitive methods to characterize mental fatigue could be critical to improve its management and the understanding of its connection to other clinical conditions. Objective: This paper aimed to study the feasibility of using keystroke biometrics for mental fatigue detection during natural typing. As typing involves multiple motor and cognitive processes that are affected by mental fatigue, our hypothesis was that the information captured in keystroke dynamics can offer an interesting mean to characterize users? mental fatigue in a real-world setting. Methods: We apply domain transformation techniques to adapt and transform TypeNet, a state-of-the-art deep neural network, originally intended for user authentication, to generate a network optimized for the fatigue detection task. All experiments were conducted using 3 keystroke databases that comprise different contexts and data collection protocols. Results: Our preliminary results showed area under the curve performances ranging between 72.2% and 80% for fatigue versus rested sample classification, which is aligned with previously published models on daily alertness and circadian cycles. This demonstrates the potential of our proposed system to characterize mental fatigue fluctuations via natural typing patterns. Finally, we studied the performance of an active detection approach that leverages the continuous nature of keystroke biometric patterns for the assessment of users? fatigue in real time. Conclusions: Our results suggest that the psychomotor patterns that characterize mental fatigue manifest during natural typing, which can be quantified via automated analysis of users? daily interaction with their device. These findings represent a step towards the development of a more objective, accessible, and transparent solution to monitor mental fatigue in a real-world environment. UR - https://biomedeng.jmir.org/2022/2/e41003 UR - http://dx.doi.org/10.2196/41003 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875698 ID - info:doi/10.2196/41003 ER - TY - JOUR AU - Berdahl, T. Carl AU - Henreid, J. Andrew AU - Pevnick, M. Joshua AU - Zheng, Kai AU - Nuckols, K. Teryl PY - 2022/11/17 TI - Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review JO - J Med Internet Res SP - e36074 VL - 24 IS - 11 KW - anamnesis KW - informatics KW - emergency medicine KW - human-computer interaction KW - medical history taking KW - mobile phone N2 - Background: Many medical conditions, perhaps 80% of them, can be diagnosed by taking a thorough history of present illness (HPI). However, in the clinical setting, situational factors such as interruptions and time pressure may cause interactions with patients to be brief and fragmented. One solution for improving clinicians? ability to collect a thorough HPI and maximize efficiency and quality of care could be to use a digital tool to obtain the HPI before face-to-face evaluation by a clinician. Objective: Our objective was to identify and characterize digital tools that have been designed to obtain the HPI directly from patients or caregivers and present this information to clinicians before a face-to-face encounter. We also sought to describe outcomes reported in testing of these tools, especially those related to usability, efficiency, and quality of care. Methods: We conducted a scoping review using predefined search terms in the following databases: MEDLINE, CINAHL, PsycINFO, Web of Science, Embase, IEEE Xplore Digital Library, ACM Digital Library, and ProQuest Dissertations & Theses Global. Two reviewers screened titles and abstracts for relevance, performed full-text reviews of articles meeting the inclusion criteria, and used a pile-sorting procedure to identify distinguishing characteristics of the tools. Information describing the tools was primarily obtained from identified peer-reviewed sources; in addition, supplementary information was obtained from tool websites and through direct communications with tool creators. Results: We identified 18 tools meeting the inclusion criteria. Of these 18 tools, 14 (78%) used primarily closed-ended and multiple-choice questions, 1 (6%) used free-text input, and 3 (17%) used conversational (chatbot) style. More than half (10/18, 56%) of the tools were tailored to specific patient subpopulations; the remaining (8/18, 44%) tools did not specify a target subpopulation. Of the 18 tools, 7 (39%) included multilingual support, and 12 (67%) had the capability to transfer data directly into the electronic health record. Studies of the tools reported on various outcome measures related to usability, efficiency, and quality of care. Conclusions: The HPI tools we identified (N=18) varied greatly in their purpose and functionality. There was no consensus on how patient-generated information should be collected or presented to clinicians. Existing tools have undergone inconsistent levels of testing, with a wide variety of different outcome measures used in evaluation, including some related to usability, efficiency, and quality of care. There is substantial interest in using digital tools to obtain the HPI from patients, but the outcomes measured have been inconsistent. Future research should focus on whether using HPI tools can lead to improved patient experience and health outcomes, although surrogate end points could instead be used so long as patient safety is monitored. UR - https://www.jmir.org/2022/11/e36074 UR - http://dx.doi.org/10.2196/36074 UR - http://www.ncbi.nlm.nih.gov/pubmed/36394945 ID - info:doi/10.2196/36074 ER - TY - JOUR AU - Li, Xueying Sophia AU - Halabi, Ramzi AU - Selvarajan, Rahavi AU - Woerner, Molly AU - Fillipo, Griffith Isabell AU - Banerjee, Sreya AU - Mosser, Brittany AU - Jain, Felipe AU - Areán, Patricia AU - Pratap, Abhishek PY - 2022/11/14 TI - Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study JO - JMIR Form Res SP - e40765 VL - 6 IS - 11 KW - participant recruitment KW - participant retention KW - decentralized studies KW - active and passive data collection KW - retention KW - adherence KW - compliance KW - engagement KW - smartphone KW - mobile health KW - mHealth KW - sensor data KW - clinical research KW - data sharing KW - recruitment KW - mobile phone N2 - Background: Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner. Objective: We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated. Methods: We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression. Results: We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants? sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence. Conclusions: Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner. UR - https://formative.jmir.org/2022/11/e40765 UR - http://dx.doi.org/10.2196/40765 UR - http://www.ncbi.nlm.nih.gov/pubmed/36374539 ID - info:doi/10.2196/40765 ER - TY - JOUR AU - Kim, H. Lawrence AU - Saha, Gourab AU - Leon, Amelia Annel AU - King, C. Abby AU - Mauriello, Louis Matthew AU - Paredes, E. Pablo PY - 2022/11/9 TI - Shared Autonomy to Reduce Sedentary Behavior Among Sit-Stand Desk Users in the United States and India: Web-Based Study JO - JMIR Form Res SP - e35447 VL - 6 IS - 11 KW - shared autonomy KW - automation KW - sedentary behavior KW - sit-stand desk KW - nonvolitional behavior change KW - culture N2 - Background: Fitness technologies such as wearables and sit-stand desks are increasingly being used to fight sedentary lifestyles by encouraging physical activity. However, adherence to such technologies decreases over time because of apathy and increased dismissal of behavioral nudges. Objective: To address this problem, we introduced shared autonomy in the context of sit-stand desks, where user input is integrated with robot autonomy to control the desk and reduce sedentary behavior and investigated user reactions and preferences for levels of automation with a sit-stand desk. As demographics affect user acceptance of robotic technology, we also studied how perceptions of nonvolitional behavior change differ across cultures (United States and India), sex, familiarity, dispositional factors, and health priming messages. Methods: We conducted a web-based vignette study in the United States and India where a total of 279 participants watched video vignettes of a person interacting with sit-stand desks of various levels of automation and answered questions about their perceptions of the desks such as ranking of the different levels of automation. Results: Participants generally preferred either manual or semiautonomous desks over the fully autonomous option (P<.001). However, participants in India were generally more amenable to the idea of nonvolitional interventions from the desk than participants in the United States (P<.001). Male participants had a stronger desire for having control over the desk than female participants (P=.01). Participants who were more familiar with sit-stand desks were more likely to adopt autonomous sit-stand desks (P=.001). No effects of health priming messages were observed. We estimated the projected health outcome by combining ranking data and hazard ratios from previous work and found that the semiautonomous desk led to the highest projected health outcome. Conclusions: These results suggest that the shared autonomy desk is the optimal level of automation in terms of both user preferences and estimated projected health outcomes. Demographics such as culture and sex had significant effects on how receptive users were to autonomous intervention. As familiarity improves the likelihood of adoption, we propose a gradual behavior change intervention to increase acceptance and adherence, especially for populations with a high desire for control. UR - https://formative.jmir.org/2022/11/e35447 UR - http://dx.doi.org/10.2196/35447 UR - http://www.ncbi.nlm.nih.gov/pubmed/36350687 ID - info:doi/10.2196/35447 ER - TY - JOUR AU - Hagg, J. Lauryn AU - Merkouris, S. Stephanie AU - O?Dea, A. Gypsy AU - Francis, M. Lauren AU - Greenwood, J. Christopher AU - Fuller-Tyszkiewicz, Matthew AU - Westrupp, M. Elizabeth AU - Macdonald, A. Jacqui AU - Youssef, J. George PY - 2022/11/8 TI - Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review JO - J Med Internet Res SP - e33166 VL - 24 IS - 11 KW - latent Dirichlet allocation KW - LDA KW - review KW - analysis KW - methodology N2 - Background: Topic modeling approaches allow researchers to analyze and represent written texts. One of the commonly used approaches in psychology is latent Dirichlet allocation (LDA), which is used for rapidly synthesizing patterns of text within ?big data,? but outputs can be sensitive to decisions made during the analytic pipeline and may not be suitable for certain scenarios such as short texts, and we highlight resources for alternative approaches. This review focuses on the complex analytical practices specific to LDA, which existing practical guides for training LDA models have not addressed. Objective: This scoping review used key analytical steps (data selection, data preprocessing, and data analysis) as a framework to understand the methodological approaches being used in psychology research using LDA. Methods: A total of 4 psychology and health databases were searched. Studies were included if they used LDA to analyze written words and focused on a psychological construct or issue. The data charting processes were constructed and employed based on common data selection, preprocessing, and data analysis steps. Results: A total of 68 studies were included. These studies explored a range of research areas and mostly sourced their data from social media platforms. Although some studies reported on preprocessing and data analysis steps taken, most studies did not provide sufficient detail for reproducibility. Furthermore, the debate surrounding the necessity of certain preprocessing and data analysis steps is revealed. Conclusions: Our findings highlight the growing use of LDA in psychological science. However, there is a need to improve analytical reporting standards and identify comprehensive and evidence-based best practice recommendations. To work toward this, we developed an LDA Preferred Reporting Checklist that will allow for consistent documentation of LDA analytic decisions and reproducible research outcomes. UR - https://www.jmir.org/2022/11/e33166 UR - http://dx.doi.org/10.2196/33166 UR - http://www.ncbi.nlm.nih.gov/pubmed/36346659 ID - info:doi/10.2196/33166 ER - TY - JOUR AU - Lam, Ka-Hoo AU - Twose, James AU - Lissenberg-Witte, Birgit AU - Licitra, Giovanni AU - Meijer, Kim AU - Uitdehaag, Bernard AU - De Groot, Vincent AU - Killestein, Joep PY - 2022/11/7 TI - The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis JO - J Med Internet Res SP - e37614 VL - 24 IS - 11 KW - multiple sclerosis KW - smartphone KW - mobile app KW - digital technology KW - keystroke dynamics KW - typing KW - upper extremity KW - cognition KW - outpatient monitoring N2 - Background: Typing on smartphones, which has become a near daily activity, requires both upper limb and cognitive function. Analysis of keyboard interactions during regular typing, that is, keystroke dynamics, could therefore potentially be utilized for passive and continuous monitoring of function in patients with multiple sclerosis. Objective: To determine whether passively acquired smartphone keystroke dynamics correspond to multiple sclerosis outcomes, we investigated the association between keystroke dynamics and clinical outcomes (upper limb and cognitive function). This association was investigated longitudinally in order to study within-patient changes independently of between-patient differences. Methods: During a 1-year follow-up, arm function and information processing speed were assessed every 3 months in 102 patients with multiple sclerosis with the Nine-Hole Peg Test and Symbol Digit Modalities Test, respectively. Keystroke-dynamics data were continuously obtained from regular typing on the participants? own smartphones. Press-and-release latency of the alphanumeric keys constituted the fine motor score cluster, while latency of the punctuation and backspace keys constituted the cognition score cluster. The association over time between keystroke clusters and the corresponding clinical outcomes was assessed with linear mixed models with subjects as random intercepts. By centering around the mean and calculating deviation scores within subjects, between-subject and within-subject effects were distinguished. Results: Mean (SD) scores for the fine motor score cluster and cognition score cluster were 0.43 (0.16) and 0.94 (0.41) seconds, respectively. The fine motor score cluster was significantly associated with the Nine-Hole Peg Test: between-subject ? was 15.9 (95% CI 12.2-19.6) and within-subject ? was 6.9 (95% CI 2.0-11.9). The cognition score cluster was significantly associated with the Symbol Digit Modalities Test between subjects (between-subject ? ?11.2, 95% CI ?17.3 to ?5.2) but not within subjects (within-subject ? ?0.4, 95% CI ?5.6 to 4.9). Conclusions: Smartphone keystroke dynamics were longitudinally associated with multiple sclerosis outcomes. Worse arm function corresponded with longer latency in typing both across and within patients. Worse processing speed corresponded with higher latency in using punctuation and backspace keys across subjects. Hence, keystroke dynamics are a potential digital biomarker for remote monitoring and predicting clinical outcomes in patients with multiple sclerosis. Trial Registration: Netherlands Trial Register NTR7268; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7268 UR - https://www.jmir.org/2022/11/e37614 UR - http://dx.doi.org/10.2196/37614 UR - http://www.ncbi.nlm.nih.gov/pubmed/36342763 ID - info:doi/10.2196/37614 ER - TY - JOUR AU - Tsai, Ming-Chin AU - Lu, Horng-Shing Henry AU - Chang, Yueh-Chuan AU - Huang, Yung-Chieh AU - Fu, Lin-Shien PY - 2022/11/2 TI - Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach JO - JMIR Med Inform SP - e40878 VL - 10 IS - 11 KW - transfer learning KW - convolutional neural networks KW - pediatric renal ultrasound image KW - screening KW - pediatric KW - medical image KW - clinical informatics KW - deep learning KW - ultrasound image KW - artificial intelligence KW - diagnostic system N2 - Background: In recent years, the progress and generalization surrounding portable ultrasonic probes has made ultrasound (US) a useful tool for physicians when making a diagnosis. With the advent of machine learning and deep learning, the development of a computer-aided diagnostic system for screening renal US abnormalities can assist general practitioners in the early detection of pediatric kidney diseases. Objective: In this paper, we sought to evaluate the diagnostic performance of deep learning techniques to classify kidney images as normal and abnormal. Methods: We chose 330 normal and 1269 abnormal pediatric renal US images for establishing a model for artificial intelligence. The abnormal images involved stones, cysts, hyperechogenicity, space-occupying lesions, and hydronephrosis. We performed preprocessing of the original images for subsequent deep learning. We redefined the final connecting layers for classification of the extracted features as abnormal or normal from the ResNet-50 pretrained model. The performances of the model were tested by a validation data set using area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. Results: The deep learning model, 94 MB parameters in size, based on ResNet-50, was built for classifying normal and abnormal images. The accuracy, (%)/area under curve, of the validated images of stone, cyst, hyperechogenicity, space-occupying lesions, and hydronephrosis were 93.2/0.973, 91.6/0.940, 89.9/0.940, 91.3/0.934, and 94.1/0.996, respectively. The accuracy of normal image classification in the validation data set was 90.1%. Overall accuracy of (%)/area under curve was 92.9/0.959.. Conclusions: We established a useful, computer-aided model for automatic classification of pediatric renal US images in terms of normal and abnormal categories. UR - https://medinform.jmir.org/2022/11/e40878 UR - http://dx.doi.org/10.2196/40878 UR - http://www.ncbi.nlm.nih.gov/pubmed/36322109 ID - info:doi/10.2196/40878 ER - TY - JOUR AU - Teferra, Gashaw Bazen AU - Borwein, Sophie AU - DeSouza, D. Danielle AU - Rose, Jonathan PY - 2022/10/28 TI - Screening for Generalized Anxiety Disorder From Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study JO - JMIR Form Res SP - e39998 VL - 6 IS - 10 KW - mental health KW - generalized anxiety disorder KW - impromptu speech KW - acoustic features KW - linguistic features KW - anxiety prediction KW - mobile phone N2 - Background: Frequent interaction with mental health professionals is required to screen, diagnose, and track mental health disorders. However, high costs and insufficient access can make frequent interactions difficult. The ability to assess a mental health disorder passively and at frequent intervals could be a useful complement to the conventional treatment. It may be possible to passively assess clinical symptoms with high frequency by characterizing speech alterations collected using personal smartphones or other wearable devices. The association between speech features and mental health disorders can be leveraged as an objective screening tool. Objective: This study aimed to evaluate the performance of a model that predicts the presence of generalized anxiety disorder (GAD) from acoustic and linguistic features of impromptu speech on a larger and more generalizable scale than prior studies did. Methods: A total of 2000 participants were recruited, and they participated in a single web-based session. They completed the Generalized Anxiety Disorder-7 item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. We used the linguistic and acoustic features that were found to be associated with anxiety disorders in previous studies along with demographic information to predict whether participants fell above or below the screening threshold for GAD based on the Generalized Anxiety Disorder-7 item scale threshold of 10. Separate models for each sex were also evaluated. We reported the mean area under the receiver operating characteristic (AUROC) from a repeated 5-fold cross-validation to evaluate the performance of the models. Results: A logistic regression model using only acoustic and linguistic speech features achieved a significantly greater prediction accuracy than a random model did (mean AUROC 0.57, SD 0.03; P<.001). When separately assessing samples from female participants, we observed a mean AUROC of 0.55 (SD 0.05; P=.01). The model constructed from the samples from male participants achieved a mean AUROC of 0.57 (SD 0.07; P=.002). The mean AUROC increased to 0.62 (SD 0.03; P<.001) on the all-sample data set when demographic information (age, sex, and income) was included, indicating the importance of demographics when screening for anxiety disorders. The performance also increased for the female sample to a mean of 0.62 (SD 0.04; P<.001) when using demographic information (age and income). An increase in performance was not observed when demographic information was added to the model constructed from the male samples. Conclusions: A logistic regression model using acoustic and linguistic speech features, which have been suggested to be associated with anxiety disorders in prior studies, can achieve above-random accuracy for predicting GAD. Importantly, the addition of basic demographic variables further improves model performance, suggesting a role for speech and demographic information to be used as automated, objective screeners of GAD. UR - https://formative.jmir.org/2022/10/e39998 UR - http://dx.doi.org/10.2196/39998 UR - http://www.ncbi.nlm.nih.gov/pubmed/36306165 ID - info:doi/10.2196/39998 ER - TY - JOUR AU - Scheer, Jan AU - Volkert, Alisa AU - Brich, Nicolas AU - Weinert, Lina AU - Santhanam, Nandhini AU - Krone, Michael AU - Ganslandt, Thomas AU - Boeker, Martin AU - Nagel, Till PY - 2022/10/24 TI - Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review JO - J Med Internet Res SP - e38041 VL - 24 IS - 10 KW - patient data KW - comparison KW - visualization systems KW - visual analytics KW - information visualization KW - cohorts KW - multiple patients KW - single patients KW - time-oriented data N2 - Background: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. Objective: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. Methods: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. Results: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. Conclusions: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space. UR - https://www.jmir.org/2022/10/e38041 UR - http://dx.doi.org/10.2196/38041 UR - http://www.ncbi.nlm.nih.gov/pubmed/36279164 ID - info:doi/10.2196/38041 ER - TY - JOUR AU - Kim, Hyung Min AU - Ryu, Hyoung Un AU - Heo, Seok-Jae AU - Kim, Chan Yong AU - Park, Soo Yoon PY - 2022/10/18 TI - The Potential Role of an Adjunctive Real-Time Locating System in Preventing Secondary Transmission of SARS-CoV-2 in a Hospital Environment: Retrospective Case-Control Study JO - J Med Internet Res SP - e41395 VL - 24 IS - 10 KW - real-time locating system KW - COVID-19 KW - contact tracing KW - secondary transmission KW - SARS-CoV-2 N2 - Background: There has been an increasing demand for new technologies regarding infection control in hospital settings to reduce the burden of contact tracing. Objective: This study aimed to compare the validity of a real-time locating system (RTLS) with that of the conventional contact tracing method for identifying high-risk contact cases associated with the secondary transmission of SARS-CoV-2. Methods: A retrospective case-control study involving in-hospital contact cases of confirmed COVID-19 patients, who were diagnosed from January 23 to March 25, 2022, was conducted at a university hospital in South Korea. Contact cases were identified using either the conventional method or the RTLS. The primary endpoint of this study was secondary transmission of SARS-CoV-2 among contact cases. Univariate and multivariable logistic regression analysis comparing test positive and versus negative contact cases were performed. Results: Overall, 509 and 653 cases were confirmed by the conventional method and the RTLS, respectively. Only 74 contact cases were identified by both methods, which could be attributed to the limitations of each method. Sensitivity was higher for the RTLS tracing method (653/1088, 60.0%) than the conventional tracing method (509/1088, 46.8%) considering all contact cases identified by both methods. The secondary transmission rate in the RTLS model was 8.1%, while that in the conventional model was 5.3%. The multivariable logistic regression model revealed that the RTLS was more capable of detecting secondary transmission than the conventional method (adjusted odds ratio 6.15, 95% CI 1.92-28.69; P=.007). Conclusions: This study showed that the RTLS is beneficial when used as an adjunctive approach to the conventional method for contact tracing associated with secondary transmission. However, the RTLS cannot completely replace traditional contact tracing. UR - https://www.jmir.org/2022/10/e41395 UR - http://dx.doi.org/10.2196/41395 UR - http://www.ncbi.nlm.nih.gov/pubmed/36197844 ID - info:doi/10.2196/41395 ER - TY - JOUR AU - Langford, Tom AU - Fleming, Victoria AU - Upton, Emily AU - Doogan, Catherine AU - Leff, Alexander AU - Romano, M. Daniela PY - 2022/10/18 TI - Design Innovation for Engaging and Accessible Digital Aphasia Therapies: Framework Analysis of the iReadMore App Co-Design Process JO - JMIR Neurotech SP - e39855 VL - 1 IS - 1 KW - aphasia KW - reading impairment KW - co-design KW - framework analysis KW - speech and language therapy KW - digital health KW - accessibility N2 - Background: iReadMore is a digital therapy for people with acquired reading impairments (known as alexia) caused by brain injury or neurodegeneration. A phase II clinical trial demonstrated the efficacy of the digital therapy research prototype for improving reading speed and accuracy in people with poststroke aphasia (acquired language impairment) and alexia. However, it also highlighted the complexities and barriers to delivering self-managed therapies at home. Therefore, in order to translate the positive study results into real-world benefits, iReadMore required subsequent design innovation. Here, we present qualitative findings from the co-design process as well as the methodology. Objective: We aimed to present a methodology for inclusive co-design in the redesign of a digital therapy prototype, focusing on elements of accessibility and user engagement. We used framework analysis to explore the themes of the communications and interactions from the co-design process. Methods: This study included 2 stages. In the first stage, 5 in-person co-design sessions were held with participants living with poststroke aphasia (n=22) and their carers (n=3), and in the second stage, remote one-to-one beta-testing sessions were held with participants with aphasia (n=20) and their carers (n=5) to test and refine the final design. Data collection included video recordings of the co-design sessions in addition to participants? written notes and drawings. Framework analysis was used to identify themes within the data relevant to the design of digital aphasia therapies in general. Results: From a qualitative framework analysis of the data generated in the co-design process, 7 key areas of consideration for digital aphasia therapies have been proposed and discussed in context. The themes generated were agency, intuitive design, motivation, personal trajectory, recognizable and relatable content, social and sharing, and widening participation. This study enabled the deployment of the iReadMore app in an accessible and engaging format. Conclusions: Co-design is a valuable strategy for innovating beyond traditional therapy designs to utilize what is achievable with technology-based therapies in user-centered design. The co-designed iReadMore app has been publicly released for use in the rehabilitation of acquired reading impairments. This paper details the co-design process for the iReadMore therapy app and provides a methodology for how inclusive co-design can be conducted with people with aphasia. The findings of the framework analysis offer insights into design considerations for digital therapies that are important to people living with aphasia. UR - https://neuro.jmir.org/2022/1/e39855 UR - http://dx.doi.org/10.2196/39855 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/39855 ER - TY - JOUR AU - Babrak, Marie Lmar AU - Smakaj, Erand AU - Agac, Teyfik AU - Asprion, Maria Petra AU - Grimberg, Frank AU - der Werf, Van Daan AU - van Ginkel, Willem Erwin AU - Tosoni, David Deniz AU - Clay, Ieuan AU - Degen, Markus AU - Brodbeck, Dominique AU - Natali, Noel Eriberto AU - Schkommodau, Erik AU - Miho, Enkelejda PY - 2022/10/18 TI - RWD-Cockpit: Application for Quality Assessment of Real-world Data JO - JMIR Form Res SP - e29920 VL - 6 IS - 10 KW - real-world data KW - real-world evidence KW - quality assessment KW - application KW - mobile phone N2 - Background: Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. Objective: To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. Methods: The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. Results: To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets?molecular, phenotypical, and social?and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies?de novo?generated sleep data and publicly available data sets?the RWD-Cockpit could identify and provide researchers with variables that might increase quality. Conclusions: The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores?quality identifiers?provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings. UR - https://formative.jmir.org/2022/10/e29920 UR - http://dx.doi.org/10.2196/29920 UR - http://www.ncbi.nlm.nih.gov/pubmed/35266872 ID - info:doi/10.2196/29920 ER - TY - JOUR AU - Kim, Sang Yoon AU - Won, JuHye AU - Jang, Seong-Wook AU - Ko, Junho PY - 2022/10/17 TI - Effects of Cybersickness Caused by Head-Mounted Display?Based Virtual Reality on Physiological Responses: Cross-sectional Study JO - JMIR Serious Games SP - e37938 VL - 10 IS - 4 KW - cybersickness KW - physiological responses KW - virtual reality KW - VR KW - head-mounted displays KW - heart rate KW - cortisol N2 - Background: Although more people are experiencing cybersickness due to the popularization of virtual reality (VR), no official standard for the cause and reduction of cybersickness exists to date. One of the main reasons is that an objective method to assess cybersickness has not been established. To resolve this, research on evaluating cybersickness with physiological responses that can be measured in real time is required. Since research on deriving physiological responses that can assess cybersickness is at an early stage, further studies examining various physiological responses are needed. Objective: This study analyzed the effects of cybersickness caused by head-mounted display?based VR on physiological responses. Methods: We developed content that provided users with a first-person view of an aircraft that moved (with translation and combined rotation) over a city via a predetermined trajectory. In the experiment, cybersickness and the physiological responses of participants were measured. Cybersickness was assessed by the Simulator Sickness Questionnaire (SSQ). The measured physiological responses were heart rate, blood pressure, body temperature, and cortisol level. Results: Our measurement confirmed that all SSQ scores increased significantly (all Ps<.05) when participants experienced cybersickness. Heart rate and cortisol level increased significantly (P=.01 and P=.001, respectively). Body temperature also increased, but there was no statistically significant difference (P=.02). Systolic blood pressure and diastolic blood pressure decreased significantly (P=.001). Conclusions: Based on the results of our analysis, the following conclusions were drawn: (1) cybersickness causes significant disorientation, and research on this topic should focus on factors that affect disorientation; and (2) the physiological responses that are suitable for measuring cybersickness are heart rate and cortisol level. UR - https://games.jmir.org/2022/4/e37938 UR - http://dx.doi.org/10.2196/37938 UR - http://www.ncbi.nlm.nih.gov/pubmed/36251360 ID - info:doi/10.2196/37938 ER - TY - JOUR AU - Lamer, Antoine AU - Fruchart, Mathilde AU - Paris, Nicolas AU - Popoff, Benjamin AU - Payen, Anaïs AU - Balcaen, Thibaut AU - Gacquer, William AU - Bouzillé, Guillaume AU - Cuggia, Marc AU - Doutreligne, Matthieu AU - Chazard, Emmanuel PY - 2022/10/17 TI - Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study JO - JMIR Med Inform SP - e38936 VL - 10 IS - 10 KW - feature extraction KW - data reuse KW - data warehouse KW - database KW - algorithm KW - Observation Medical Outcomes Partnership N2 - Background: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. Objective: The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. Methods: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). Results: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. ?Track? is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). ?Feature? is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables ?TRACK? and ?FEATURE? to store variables obtained in feature extraction and extend the OMOP CDM. Conclusions: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies. UR - https://medinform.jmir.org/2022/10/e38936 UR - http://dx.doi.org/10.2196/38936 UR - http://www.ncbi.nlm.nih.gov/pubmed/36251369 ID - info:doi/10.2196/38936 ER - TY - JOUR AU - Fossouo Tagne, Joel AU - Yakob, Amin Reginald AU - Mcdonald, Rachael AU - Wickramasinghe, Nilmini PY - 2022/10/11 TI - Barriers and Facilitators Influencing Real-time and Digital-Based Reporting of Adverse Drug Reactions by Community Pharmacists: Qualitative Study Using the Task-Technology Fit Framework JO - Interact J Med Res SP - e40597 VL - 11 IS - 2 KW - pharmacovigilance KW - adverse drug reaction KW - pharmacist KW - Task-Technology Fit KW - digital health N2 - Background: Medication use can result in adverse drug reactions (ADRs) that cause increased morbidity and health care consumption for patients and could potentially be fatal. Timely reporting of ADRs to regulators may contribute to patient safety by facilitating information gathering on drug safety data. Currently, little is known about how community pharmacists (CPs) monitor, handle, and report ADRs in Australia. Objective: This study aimed to identify perceived barriers to and facilitators of ADR reporting by CPs in Australia and suggest digital interventions. Methods: A qualitative study with individual interviews was conducted with CPs working across Victoria, Australia, between April 2022 and May 2022. A semistructured interview guide was used to identify perceived barriers to and facilitators of ADR reporting among CPs. The data were analyzed using thematic analysis. We constructed themes from the CP-reported barriers and facilitators. The themes were subsequently aligned with the Task-Technology Fit framework. Results: A total of 12 CPs were interviewed. Identified barriers were lack of knowledge of both the ADR reporting process and ADR reporting systems, time constraints, lack of financial incentives, lack of organizational support for ADR reporting, inadequate IT systems, and preference to refer consumers to physicians. The proposed facilitators of ADR reporting included enhancing CPs knowledge and awareness of ADRs, financial incentives for ADR reporting, workflow-integrated ADR reporting technology systems, feedback provision to CPs on the reported ADRs, and promoting consumer ADR reporting. Conclusions: Barriers to and facilitators of ADR reporting spanned both the task and technology aspects of the Task-Technology Fit model. Addressing the identified barriers to ADR reporting and providing workplace technologies that support ADR reporting may improve ADR reporting by CPs. Further investigations to observe ADR handling and reporting within community pharmacies can enhance patient safety by increasing ADR reporting by CPs. UR - https://www.i-jmr.org/2022/2/e40597 UR - http://dx.doi.org/10.2196/40597 UR - http://www.ncbi.nlm.nih.gov/pubmed/36222800 ID - info:doi/10.2196/40597 ER - TY - JOUR AU - Bardia, Amit AU - Deshpande, Ranjit AU - Michel, George AU - Yanez, David AU - Dai, Feng AU - Pace, L. Nathan AU - Schuster, Kevin AU - Mathis, R. Michael AU - Kheterpal, Sachin AU - Schonberger, B. Robert PY - 2022/10/5 TI - Demonstration and Performance Evaluation of Two Novel Algorithms for Removing Artifacts From Automated Intraoperative Temperature Data Sets: Multicenter, Observational, Retrospective Study JO - JMIR Perioper Med SP - e37174 VL - 5 IS - 1 KW - temperature KW - intraoperative KW - artifacts KW - algorithms KW - perioperative KW - surgery KW - temperature probe KW - artifact reduction KW - data acquisition KW - accuracy N2 - Background: The automated acquisition of intraoperative patient temperature data via temperature probes leads to the possibility of producing a number of artifacts related to probe positioning that may impact these probes? utility for observational research. Objective: We sought to compare the performance of two de novo algorithms for filtering such artifacts. Methods: In this observational retrospective study, the intraoperative temperature data of adults who received general anesthesia for noncardiac surgery were extracted from the Multicenter Perioperative Outcomes Group registry. Two algorithms were developed and then compared to the reference standard?anesthesiologists? manual artifact detection process. Algorithm 1 (a slope-based algorithm) was based on the linear curve fit of 3 adjacent temperature data points. Algorithm 2 (an interval-based algorithm) assessed for time gaps between contiguous temperature recordings. Sensitivity and specificity values for artifact detection were calculated for each algorithm, as were mean temperatures and areas under the curve for hypothermia (temperatures below 36 °C) for each patient, after artifact removal via each methodology. Results: A total of 27,683 temperature readings from 200 anesthetic records were analyzed. The overall agreement among the anesthesiologists was 92.1%. Both algorithms had high specificity but moderate sensitivity (specificity: 99.02% for algorithm 1 vs 99.54% for algorithm 2; sensitivity: 49.13% for algorithm 1 vs 37.72% for algorithm 2; F-score: 0.65 for algorithm 1 vs 0.55 for algorithm 2). The areas under the curve for time × hypothermic temperature and the mean temperatures recorded for each case after artifact removal were similar between the algorithms and the anesthesiologists. Conclusions: The tested algorithms provide an automated way to filter intraoperative temperature artifacts that closely approximates manual sorting by anesthesiologists. Our study provides evidence demonstrating the efficacy of highly generalizable artifact reduction algorithms that can be readily used by observational studies that rely on automated intraoperative data acquisition. UR - https://periop.jmir.org/2022/1/e37174 UR - http://dx.doi.org/10.2196/37174 UR - http://www.ncbi.nlm.nih.gov/pubmed/36197702 ID - info:doi/10.2196/37174 ER - TY - JOUR AU - Koyama, Takafumi AU - Matsui, Ryota AU - Yamamoto, Akiko AU - Yamada, Eriku AU - Norose, Mio AU - Ibara, Takuya AU - Kaburagi, Hidetoshi AU - Nimura, Akimoto AU - Sugiura, Yuta AU - Saito, Hideo AU - Okawa, Atsushi AU - Fujita, Koji PY - 2022/10/3 TI - High-Dimensional Analysis of Finger Motion and Screening of Cervical Myelopathy With a Noncontact Sensor: Diagnostic Case-Control Study JO - JMIR Biomed Eng SP - e41327 VL - 7 IS - 2 KW - cervical myelopathy KW - myelopathy KW - spinal cord disease KW - spinal cord disorder KW - nervous system disorder KW - nervous system disease KW - clumsiness KW - screening KW - 10-second hand grip and release test KW - machine learning KW - carpal tunnel syndrome KW - Leap Motion KW - clinical informatics KW - system validation KW - screening system KW - sensor KW - model KW - diagnosis KW - diagnostic KW - high-dimensional analysis KW - motion sensor KW - motion detection KW - high-dimensional data analysis KW - high-dimensional statistics N2 - Background: Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders. Objective: This study aims to develop a system that can diagnose CM with higher sensitivity and specificity, and distinguish CM from carpal tunnel syndrome (CTS), a common hand disorder. We then validated the system with a modified Leap Motion that can record the joints of each finger. Methods: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model). Results: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74. Conclusions: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients. UR - https://biomedeng.jmir.org/2022/2/e41327 UR - http://dx.doi.org/10.2196/41327 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875599 ID - info:doi/10.2196/41327 ER - TY - JOUR AU - Daniore, Paola AU - Nittas, Vasileios AU - von Wyl, Viktor PY - 2022/9/9 TI - Enrollment and Retention of Participants in Remote Digital Health Studies: Scoping Review and Framework Proposal JO - J Med Internet Res SP - e39910 VL - 24 IS - 9 KW - remote digital health studies KW - remote clinical trials KW - remote cohorts KW - digital epidemiology KW - digital health KW - health outcome KW - conceptual framework KW - user-centered design KW - population-based digital health KW - participant recruitment KW - interventional study N2 - Background: Digital technologies are increasingly used in health research to collect real-world data from wider populations. A new wave of digital health studies relies primarily on digital technologies to conduct research entirely remotely. Remote digital health studies hold promise to significant cost and time advantages over traditional, in-person studies. However, such studies have been reported to typically suffer from participant attrition, the sources for which are still largely understudied. Objective: To contribute to future remote digital health study planning, we present a conceptual framework and hypotheses for study enrollment and completion. The framework introduces 3 participation criteria that impact remote digital health study outcomes: (1) participant motivation profile and incentives or nudges, (2) participant task complexity, and (3) scientific requirements. The goal of this study is to inform the planning and implementation of remote digital health studies from a person-centered perspective. Methods: We conducted a scoping review to collect information on participation in remote digital health studies, focusing on methodological aspects that impact participant enrollment and retention. Comprehensive searches were conducted on the PubMed, CINAHL, and Web of Science databases, and additional sources were included in our study from citation searching. We included digital health studies that were fully conducted remotely, included information on at least one of the framework criteria during recruitment, onboarding or retention phases of the studies, and included study enrollment or completion outcomes. Qualitative analyses were performed to synthesize the findings from the included studies. Results: We report qualitative findings from 37 included studies that reveal high values of achieved median participant enrollment based on target sample size calculations, 128% (IQR 100%-234%), and median study completion, 48% (IQR 35%-76%). Increased median study completion is observed for studies that provided incentives or nudges to extrinsically motivated participants (62%, IQR 43%-78%). Reducing task complexity for participants in the absence of incentives or nudges did not improve median study enrollment (103%, IQR 102%-370%) or completion (43%, IQR 22%-60%) in observational studies, in comparison to interventional studies that provided more incentives or nudges (median study completion rate of 55%, IQR 38%-79%). Furthermore, there were inconsistencies in measures of completion across the assessed remote digital health studies, where only around half of the studies with completion measures (14/27, 52%) were based on participant retention throughout the study period. Conclusions: Few studies reported on participatory factors and study outcomes in a consistent manner, which may have limited the evidence base for our study. Our assessment may also have suffered from publication bias or unrepresentative study samples due to an observed preference for participants with digital literacy skills in digital health studies. Nevertheless, we find that future remote digital health study planning can benefit from targeting specific participant profiles, providing incentives and nudges, and reducing study complexity to improve study outcomes. UR - https://www.jmir.org/2022/9/e39910 UR - http://dx.doi.org/10.2196/39910 UR - http://www.ncbi.nlm.nih.gov/pubmed/36083626 ID - info:doi/10.2196/39910 ER - TY - JOUR AU - Lowe, Cabella AU - Browne, Mitchell AU - Marsh, William AU - Morrissey, Dylan PY - 2022/8/30 TI - Usability Testing of a Digital Assessment Routing Tool for Musculoskeletal Disorders: Iterative, Convergent Mixed Methods Study JO - J Med Internet Res SP - e38352 VL - 24 IS - 8 KW - mobile health KW - mHealth KW - eHealth KW - digital health KW - digital technology KW - musculoskeletal KW - triage KW - physiotherapy triage KW - usability KW - acceptability KW - mobile phone N2 - Background: Musculoskeletal disorders negatively affect millions of patients worldwide, placing significant demand on health care systems. Digital technologies that improve clinical outcomes and efficiency across the care pathway are development priorities. We developed the musculoskeletal Digital Assessment Routing Tool (DART) to enable self-assessment and immediate direction to the right care. Objective: We aimed to assess and resolve all serious DART usability issues to create a positive user experience and enhance system adoption before conducting randomized controlled trials for the integration of DART into musculoskeletal management pathways. Methods: An iterative, convergent mixed methods design was used, with 22 adult participants assessing 50 different clinical presentations over 5 testing rounds across 4 DART iterations. Participants were recruited using purposive sampling, with quotas for age, habitual internet use, and English-language ability. Quantitative data collection was defined by the constructs within the International Organization for Standardization 9241-210-2019 standard, with user satisfaction measured by the System Usability Scale. Study end points were resolution of all grade 1 and 2 usability problems and a mean System Usability Scale score of ?80 across a minimum of 3 user group sessions. Results: All participants (mean age 48.6, SD 15.2; range 20-77 years) completed the study. Every assessment resulted in a recommendation with no DART system errors and a mean completion time of 5.2 (SD 4.44, range 1-18) minutes. Usability problems were reduced from 12 to 0, with trust and intention to act improving during the study. The relationship between eHealth literacy and age, as explored with a scatter plot and calculation of the Pearson correlation coefficient, was performed for all participants (r=?0.2; 20/22, 91%) and repeated with a potential outlier removed (r=?0.23), with no meaningful relationships observed or found for either. The mean satisfaction for daily internet users was highest (19/22, 86%; mean 86.5, SD 4.48; 90% confidence level [CL] 1.78 or ?1.78), with nonnative English speakers (6/22, 27%; mean 78.1, SD 4.60; 90% CL 3.79 or ?3.79) and infrequent internet users scoring the lowest (3/22, 14%; mean 70.8, SD 5.44; 90% CL 9.17 or ?9.17), although the CIs overlap. The mean score across all groups was 84.3 (SD 4.67), corresponding to an excellent system, with qualitative data from all participants confirming that DART was simple to use. Conclusions: All serious DART usability issues were resolved, and a good level of satisfaction, trust, and willingness to act on the DART recommendation was demonstrated, thus allowing progression to randomized controlled trials that assess safety and effectiveness against usual care comparators. The iterative, convergent mixed methods design proved highly effective in fully evaluating DART from a user perspective and could provide a blueprint for other researchers of mobile health systems. International Registered Report Identifier (IRRID): RR2-10.2196/27205 UR - https://www.jmir.org/2022/8/e38352 UR - http://dx.doi.org/10.2196/38352 UR - http://www.ncbi.nlm.nih.gov/pubmed/36040787 ID - info:doi/10.2196/38352 ER - TY - JOUR AU - Ayad, Ahmad AU - Hallawa, Ahmed AU - Peine, Arne AU - Martin, Lukas AU - Fazlic, Begic Lejla AU - Dartmann, Guido AU - Marx, Gernot AU - Schmeink, Anke PY - 2022/8/24 TI - Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study JO - JMIR Med Inform SP - e37658 VL - 10 IS - 8 KW - anomaly detection KW - DNN KW - time series classification KW - lab values KW - ICU KW - CNN KW - medical Informatics KW - EHR KW - machine learning KW - lightGBM N2 - Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient?s case. Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values. Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting?based algorithm and compared their performance on both data sets. Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%. Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients? diagnosis and treatment. UR - https://medinform.jmir.org/2022/8/e37658 UR - http://dx.doi.org/10.2196/37658 UR - http://www.ncbi.nlm.nih.gov/pubmed/36001363 ID - info:doi/10.2196/37658 ER - TY - JOUR AU - Bricker, B. Jonathan AU - Mull, E. Kristin AU - Santiago-Torres, Margarita AU - Miao, Zhen AU - Perski, Olga AU - Di, Chongzhi PY - 2022/8/18 TI - Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial JO - J Med Internet Res SP - e39208 VL - 24 IS - 8 KW - acceptance and commitment therapy KW - ACT KW - digital interventions KW - eHealth KW - engagement KW - iCanQuit KW - QuitGuide KW - mobile health KW - mHealth KW - smartphone apps KW - trajectories KW - tobacco KW - smoking KW - mobile phone N2 - Background: Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. Objective: In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. Methods: Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. Results: For iCanQuit, participants were clustered into 3 groups: ?1-week users? (610/1069, 57.06%), ?4-week users? (303/1069, 28.34%), and ?26-week users? (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: ?1-week users? (695/1064, 65.32%) and ?3-week users? (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. Conclusions: Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful. UR - https://www.jmir.org/2022/8/e39208 UR - http://dx.doi.org/10.2196/39208 UR - http://www.ncbi.nlm.nih.gov/pubmed/35831180 ID - info:doi/10.2196/39208 ER - TY - JOUR AU - Wang, Xin AU - Wang, Jian AU - Xu, Bo AU - Lin, Hongfei AU - Zhang, Bo AU - Yang, Zhihao PY - 2022/8/15 TI - Exploiting Intersentence Information for Better Question-Driven Abstractive Summarization: Algorithm Development and Validation JO - JMIR Med Inform SP - e38052 VL - 10 IS - 8 KW - question-driven abstractive summarization KW - transformer KW - multi-head attention KW - pointer network KW - question answering KW - factual consistency KW - algorithm KW - validation KW - natural language processing N2 - Background: Question-driven summarization has become a practical and accurate approach to summarizing the source document. The generated summary should be concise and consistent with the concerned question, and thus, it could be regarded as the answer to the nonfactoid question. Existing methods do not fully exploit question information over documents and dependencies across sentences. Besides, most existing summarization evaluation tools like recall-oriented understudy for gisting evaluation (ROUGE) calculate N-gram overlaps between the generated summary and the reference summary while neglecting the factual consistency problem. Objective: This paper proposes a novel question-driven abstractive summarization model based on transformer, including a two-step attention mechanism and an overall integration mechanism, which can generate concise and consistent summaries for nonfactoid question answering. Methods: Specifically, the two-step attention mechanism is proposed to exploit the mutual information both of question to context and sentence over other sentences. We further introduced an overall integration mechanism and a novel pointer network for information integration. We conducted a question-answering task to evaluate the factual consistency between the generated summary and the reference summary. Results: The experimental results of question-driven summarization on the PubMedQA data set showed that our model achieved ROUGE-1, ROUGE-2, and ROUGE-L measures of 36.01, 15.59, and 30.22, respectively, which is superior to the state-of-the-art methods with a gain of 0.79 (absolute) in the ROUGE-2 score. The question-answering task demonstrates that the generated summaries of our model have better factual constancy. Our method achieved 94.2% accuracy and a 77.57% F1 score. Conclusions: Our proposed question-driven summarization model effectively exploits the mutual information among the question, document, and summary to generate concise and consistent summaries. UR - https://medinform.jmir.org/2022/8/e38052 UR - http://dx.doi.org/10.2196/38052 UR - http://www.ncbi.nlm.nih.gov/pubmed/35969463 ID - info:doi/10.2196/38052 ER - TY - JOUR AU - Kannampallil, Thomas AU - Ronneberg, R. Corina AU - Wittels, E. Nancy AU - Kumar, Vikas AU - Lv, Nan AU - Smyth, M. Joshua AU - Gerber, S. Ben AU - Kringle, A. Emily AU - Johnson, A. Jillian AU - Yu, Philip AU - Steinman, E. Lesley AU - Ajilore, A. Olu AU - Ma, Jun PY - 2022/8/12 TI - Design and Formative Evaluation of a Virtual Voice-Based Coach for Problem-solving Treatment: Observational Study JO - JMIR Form Res SP - e38092 VL - 6 IS - 8 KW - voice assistants KW - behavioral therapy KW - problem-solving therapy KW - mental health KW - artificial intelligence KW - user evaluation N2 - Background: Artificial intelligence has provided new opportunities for human interactions with technology for the practice of medicine. Among the recent artificial intelligence innovations, personal voice assistants have been broadly adopted. This highlights their potential for health care?related applications such as behavioral counseling to promote healthy lifestyle habits and emotional well-being. However, the use of voice-based applications for behavioral therapy has not been previously evaluated. Objective: This study aimed to conduct a formative user evaluation of Lumen, a virtual voice-based coach developed as an Alexa skill that delivers evidence-based, problem-solving treatment for patients with mild to moderate depression and/or anxiety. Methods: A total of 26 participants completed 2 therapy sessions?an introductory (session 1) and a problem-solving (session 2)?with Lumen. Following each session with Lumen, participants completed user experience, task-related workload, and work alliance surveys. They also participated in semistructured interviews addressing the benefits, challenges and barriers to Lumen use, and design recommendations. We evaluated the differences in user experience, task load, and work alliance between sessions using 2-tailed paired t tests. Interview transcripts were coded using an inductive thematic analysis to characterize the participants? perspectives regarding Lumen use. Results: Participants found Lumen to provide high pragmatic usability and favorable user experience, with marginal task load during interactions for both Lumen sessions. However, participants experienced a higher temporal workload during the problem-solving session, suggesting a feeling of being rushed during their communicative interactions. On the basis of the qualitative analysis, the following themes were identified: Lumen?s on-demand accessibility and the delivery of a complex problem-solving treatment task with a simplistic structure for achieving therapy goals; themes related to Lumen improvements included streamlining and improved personalization of conversations, slower pacing of conversations, and providing additional context during therapy sessions. Conclusions: On the basis of an in-depth formative evaluation, we found that Lumen supported the ability to conduct cognitively plausible interactions for the delivery of behavioral therapy. Several design suggestions identified from the study including reducing temporal and cognitive load during conversational interactions, developing more natural conversations, and expanding privacy and security features were incorporated in the revised version of Lumen. Although further research is needed, the promising findings from this study highlight the potential for using Lumen to deliver personalized and accessible mental health care, filling a gap in traditional mental health services. UR - https://formative.jmir.org/2022/8/e38092 UR - http://dx.doi.org/10.2196/38092 UR - http://www.ncbi.nlm.nih.gov/pubmed/35969431 ID - info:doi/10.2196/38092 ER - TY - JOUR AU - Tremblay, Melanie AU - Hamel, Christine AU - Viau-Guay, Anabelle AU - Giroux, Dominique PY - 2022/8/9 TI - User Experience of the Co-design Research Approach in eHealth: Activity Analysis With the Course-of-Action Framework JO - JMIR Hum Factors SP - e35577 VL - 9 IS - 3 KW - co-design KW - caregivers KW - activity analysis KW - course-of-action framework KW - participant experience KW - intrinsic description KW - guidelines KW - affordances N2 - Background: The cocreation of eHealth solutions with potential users, or co-design, can help make the solution more acceptable. However, the co-design research approach requires substantial investment, and projects are not always fruitful. Researchers have provided guidelines for the co-design approach, but these are either applicable only in specific situations or not supported by empirical data. Ways to optimize the experience of the co-design process from the point of view of the participants are also missing. Scientific literature in the co-design field generally provides an extrinsic description of the experience of participants in co-design projects. Objective: We addressed this issue by describing a co-design project and focusing on the participants? experiences looking at what was significant from their point of view. Methods: We used a qualitative situated cognitive anthropology approach for this study. Data were collected on a co-design research project that aimed to support the help-seeking process of caregivers of functionally dependent older adults. The methodology was based on the perspective of experience by Dewey and used the course-of-action theoretical and methodological framework. Data collection was conducted in 2 phases: observation of participants and recording of sessions and participant self-confrontation interviews using the session recordings. We interviewed 27% (20/74) of the participants. We analyzed the data through nonexclusive emerging categorization of themes using the constant comparative method. Results: In total, 5 emerging themes were identified. The perception of extrinsic constraints and the effects of the situation was central and the most important theme, affecting other themes (frustrating interactions with others, learning together, destabilization, and getting personal benefits). Co-occurrences between codes allowed for a visual and narrative understanding of what was significant for the participants during this project. The results highlighted the importance of the role of the research team in preparing and moderating the sessions. They also provided a detailed description of the interactions between participants during the sessions, which is a core aspect of the co-design approach. There were positive and negative aspects of the participants? experiences during this co-design project. Reflecting on our results, we provided potential affordances to shape the experience of participants in co-design. Conclusions: Potential users are an essential component of the co-design research approach. Researchers and designers should seek to offer these users a positive and contributory experience to encourage participation in further co-design initiatives. Future research should explore how the proposed affordances influence the success of the intervention. UR - https://humanfactors.jmir.org/2022/3/e35577 UR - http://dx.doi.org/10.2196/35577 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943783 ID - info:doi/10.2196/35577 ER - TY - JOUR AU - Kaur, Manpreet AU - Costello, Jeremy AU - Willis, Elyse AU - Kelm, Karen AU - Reformat, Z. Marek AU - Bolduc, V. Francois PY - 2022/8/5 TI - Deciphering the Diversity of Mental Models in Neurodevelopmental Disorders: Knowledge Graph Representation of Public Data Using Natural Language Processing JO - J Med Internet Res SP - e39888 VL - 24 IS - 8 KW - concept map KW - neurodevelopmental disorder KW - knowledge graph KW - text analysis KW - semantic relatedness KW - PubMed KW - forums KW - mental model N2 - Background: Understanding how individuals think about a topic, known as the mental model, can significantly improve communication, especially in the medical domain where emotions and implications are high. Neurodevelopmental disorders (NDDs) represent a group of diagnoses, affecting up to 18% of the global population, involving differences in the development of cognitive or social functions. In this study, we focus on 2 NDDs, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), which involve multiple symptoms and interventions requiring interactions between 2 important stakeholders: parents and health professionals. There is a gap in our understanding of differences between mental models for each stakeholder, making communication between stakeholders more difficult than it could be. Objective: We aim to build knowledge graphs (KGs) from web-based information relevant to each stakeholder as proxies of mental models. These KGs will accelerate the identification of shared and divergent concerns between stakeholders. The developed KGs can help improve knowledge mobilization, communication, and care for individuals with ADHD and ASD. Methods: We created 2 data sets by collecting the posts from web-based forums and PubMed abstracts related to ADHD and ASD. We utilized the Unified Medical Language System (UMLS) to detect biomedical concepts and applied Positive Pointwise Mutual Information followed by truncated Singular Value Decomposition to obtain corpus-based concept embeddings for each data set. Each data set is represented as a KG using a property graph model. Semantic relatedness between concepts is calculated to rank the relation strength of concepts and stored in the KG as relation weights. UMLS disorder-relevant semantic types are used to provide additional categorical information about each concept?s domain. Results: The developed KGs contain concepts from both data sets, with node sizes representing the co-occurrence frequency of concepts and edge sizes representing relevance between concepts. ADHD- and ASD-related concepts from different semantic types shows diverse areas of concerns and complex needs of the conditions. KG identifies converging and diverging concepts between health professionals literature (PubMed) and parental concerns (web-based forums), which may correspond to the differences between mental models for each stakeholder. Conclusions: We show for the first time that generating KGs from web-based data can capture the complex needs of families dealing with ADHD or ASD. Moreover, we showed points of convergence between families and health professionals? KGs. Natural language processing?based KG provides access to a large sample size, which is often a limiting factor for traditional in-person mental model mapping. Our work offers a high throughput access to mental model maps, which could be used for further in-person validation, knowledge mobilization projects, and basis for communication about potential blind spots from stakeholders in interactions about NDDs. Future research will be needed to identify how concepts could interact together differently for each stakeholder. UR - https://www.jmir.org/2022/8/e39888 UR - http://dx.doi.org/10.2196/39888 UR - http://www.ncbi.nlm.nih.gov/pubmed/35930346 ID - info:doi/10.2196/39888 ER - TY - JOUR AU - Reyes, McNaughton H. Luz AU - Langoni, Armora Eliana Gabriela AU - Sharpless, Laurel AU - Blackburn, Natalie AU - McCort, Agnieszka AU - Macy, J. Rebecca AU - Moracco, E. Kathryn AU - Foshee, A. Vangie PY - 2022/8/5 TI - Web-Based Delivery of a Family-Based Dating Violence Prevention Program for Youth Who Have Been Exposed to Intimate Partner Violence: Protocol for an Acceptability and Feasibility Study JO - JMIR Res Protoc SP - e35487 VL - 11 IS - 8 KW - dating violence KW - adolescents KW - family-based prevention KW - web-based delivery KW - feasibility and acceptability KW - mobile phone N2 - Background: Children exposed to intimate partner violence (IPV) between caregivers are at an increased risk of becoming involved in dating violence during adolescence. However, to date, few adolescent dating violence (ADV) prevention programs have been developed for and evaluated with youth exposed to IPV. An exception is Moms and Teens for Safe Dates (MTSD), an evidence-based ADV prevention program for mothers or maternal caregivers (mothers) exposed to IPV and their teenagers. The MTSD program comprises a series of booklets that families complete together in a home that includes activities to promote positive family communication and healthy teenager relationships. We developed a web-adapted version of the MTSD program?entitled eMoms and Teens for Safe Dates (eMTSD)?to provide a delivery format that may increase program appeal for digitally oriented teenagers, lower dissemination costs, lower reading burden for low-literacy participants, and incorporate built-in cues and reminders to boost program adherence. Objective: This protocol is for a research study that has the following three main objectives: to assess the acceptability of eMTSD; to identify the feasibility of the research process, including program adherence and participant recruitment and assessment; and to explore the acceptability, feasibility, and preliminary efficacy of 2 features?text reminders and the creation of an action plan for engaging with the program?that may increase program uptake and completion. Methods: Approximately 100 mothers and their teenagers will be invited to complete eMTSD, which includes six 30-minute web-based modules over a 6-week period. Mothers will be recruited through community organizations and social media advertising and will be eligible to participate if they have at least 1 teenager aged 12 to 16 years living with them, have experienced IPV after the teenager was born, are not currently living with an abusive partner, and have access to an internet-enabled device. Using a factorial design, enrolled dyads will be randomized to the following four adherence support groups (n=25 dyads per group): text reminders and action planning, text reminders only, action planning only, and no adherence supports. All participants will complete brief web-based assessments at enrollment after each module is completed, after the full program is completed, and 90 days after enrollment. Program adherence will be tracked using website use metrics. Results: The data collected will be synthesized to assess the acceptability of the program and the feasibility of the study procedures. An exploratory analysis will examine the impact of adherence support on program completion levels. In November 2021, ethical approval was received and recruitment was initiated. Data collection is expected to continue until December 2022. Conclusions: The web-based delivery of a family-based healthy relationship program for teenagers exposed to IPV may offer a convenient, low-cost, and engaging approach to preventing ADV. The findings from this study are expected to guide future research. International Registered Report Identifier (IRRID): DERR1-10.2196/35487 UR - https://www.researchprotocols.org/2022/8/e35487 UR - http://dx.doi.org/10.2196/35487 UR - http://www.ncbi.nlm.nih.gov/pubmed/35930332 ID - info:doi/10.2196/35487 ER - TY - JOUR AU - Helgerud, Jan AU - Haglo, Håvard AU - Hoff, Jan PY - 2022/8/4 TI - Prediction of VO2max From Submaximal Exercise Using the Smartphone Application Myworkout GO: Validation Study of a Digital Health Method JO - JMIR Cardio SP - e38570 VL - 6 IS - 2 KW - high-intensity interval training KW - cardiovascular health KW - physical inactivity KW - endurance training KW - measurement accuracy N2 - Background: Physical inactivity remains the largest risk factor for the development of cardiovascular disease worldwide. Wearable devices have become a popular method of measuring activity-based outcomes and facilitating behavior change to increase cardiorespiratory fitness (CRF) or maximal oxygen consumption (VO2max) and reduce weight. However, it is critical to determine their accuracy in measuring these variables. Objective: This study aimed to determine the accuracy of using a smartphone and the application Myworkout GO for submaximal prediction of VO2max. Methods: Participants included 162 healthy volunteers: 58 women and 104 men (17-73 years old). The study consisted of 3 experimental tests randomized to 3 separate days. One-day VO2max was assessed with Metamax II, with the participant walking or running on the treadmill. On the 2 other days, the application Myworkout GO used standardized high aerobic intensity interval training (HIIT) on the treadmill to predict VO2max. Results: There were no significant differences between directly measured VO2max (mean 49, SD 14 mL/kg/min) compared with the VO2max predicted by Myworkout GO (mean 50, SD 14 mL/kg/min). The direct and predicted VO2max values were highly correlated, with an R2 of 0.97 (P<.001) and standard error of the estimate (SEE) of 2.2 mL/kg/min, with no sex differences. Conclusions: Myworkout GO accurately calculated VO2max, with an SEE of 4.5% in the total group. The submaximal HIIT session (4 x 4 minutes) incorporated in the application was tolerated well by the participants. We present health care providers and their patients with a more accurate and practical version of health risk estimation. This might increase physical activity and improve exercise habits in the general population. UR - https://cardio.jmir.org/2022/2/e38570 UR - http://dx.doi.org/10.2196/38570 UR - http://www.ncbi.nlm.nih.gov/pubmed/35925653 ID - info:doi/10.2196/38570 ER - TY - JOUR AU - Kurz, Alexander AU - Hauser, Katja AU - Mehrtens, Alexander Hendrik AU - Krieghoff-Henning, Eva AU - Hekler, Achim AU - Kather, Nikolas Jakob AU - Fröhling, Stefan AU - von Kalle, Christof AU - Brinker, Josef Titus PY - 2022/8/2 TI - Uncertainty Estimation in Medical Image Classification: Systematic Review JO - JMIR Med Inform SP - e36427 VL - 10 IS - 8 KW - uncertainty estimation KW - network calibration KW - out-of-distribution detection KW - medical image classification KW - deep learning KW - medical imaging N2 - Background: Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network?s uncertainty together with its prediction. Objective: In this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation Methods: Google Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published between 2016 and 2021, that deal with uncertainty estimation in medical image classification. The search terms ?uncertainty,? ?uncertainty estimation,? ?network calibration,? and ?out-of-distribution detection? were used in combination with the terms ?medical images,? ?medical image analysis,? and ?medical image classification.? Results: A total of 22 papers were chosen for detailed analysis through the systematic review process. This paper provides a table for a systematic comparison of the included works with respect to the applied method for estimating the uncertainty. Conclusions: The applied methods for estimating uncertainties are diverse, but the sampling-based methods Monte-Carlo Dropout and Deep Ensembles are used most frequently. We concluded that future works can investigate the benefits of uncertainty estimation in collaborative settings of artificial intelligence systems and human experts. International Registered Report Identifier (IRRID): RR2-10.2196/11936 UR - https://medinform.jmir.org/2022/8/e36427 UR - http://dx.doi.org/10.2196/36427 UR - http://www.ncbi.nlm.nih.gov/pubmed/35916701 ID - info:doi/10.2196/36427 ER - TY - JOUR AU - Zahradka, Nicole AU - Pugmire, Juliana AU - Lever Taylor, Jessie AU - Wolfberg, Adam AU - Wilkes, Matt PY - 2022/7/29 TI - Deployment of an End-to-End Remote, Digitalized Clinical Study Protocol in COVID-19: Process Evaluation JO - JMIR Form Res SP - e37832 VL - 6 IS - 7 KW - evaluation study KW - telemedicine KW - remote consultation KW - digital divide KW - research design KW - virtual clinical trial KW - decentralized KW - COVID-19 KW - primary recruitment KW - social media KW - virtual care KW - heart rate KW - wearable KW - health care cost KW - health technology N2 - Background: The SARS-CoV-2 (COVID-19) pandemic may accelerate the adoption of digital, decentralized clinical trials. Conceptual recommendations for digitalized and remote clinical studies and technology are available to enable digitalization. Fully remote studies may break down some of the participation barriers in traditional trials. However, they add logistical complexity and offer fewer opportunities to intervene following a technical failure or adverse event. Objective: Our group designed an end-to-end digitalized clinical study protocol, using the Food and Drug Administration (FDA)?cleared Current Health (CH) remote monitoring platform to collect symptoms and continuous physiological data of individuals recently infected with COVID-19 in the community. The purpose of this work is to provide a detailed example of an end-to-end digitalized protocol implementation based on conceptual recommendations by describing the study setup in detail, evaluating its performance, and identifying points of success and failure. Methods: Primary recruitment was via social media and word of mouth. Informed consent was obtained during a virtual appointment, and the CH-monitoring kit was shipped directly to the participants. The wearable continuously recorded pulse rate (PR), respiratory rate (RR), oxygen saturation (SpO2), skin temperature, and step count, while a tablet administered symptom surveys. Data were transmitted in real time to the CH cloud-based platform and displayed in the web-based dashboard, with alerts to the study team if the wearable was not charged or worn. The study duration was up to 30 days. The time to recruit, screen, consent, set up equipment, and collect data was quantified, and advertising engagement was tracked with a web analytics service. Results: Of 13 different study advertisements, 5 (38.5%) were live on social media at any one time. In total, 38 eligibility forms were completed, and 19 (50%) respondents met the eligibility criteria. Of these, 9 (47.4%) were contactable and 8 (88.9%) provided informed consent. Deployment times ranged from 22 to 110 hours, and participants set up the equipment and started transmitting vital signs within 7.6 (IQR 6.3-10) hours of delivery. The mean wearable adherence was 70% (SD 19%), and the mean daily survey adherence was 88% (SD 21%) for the 8 participants. Vital signs were in normal ranges during study participation, and symptoms decreased over time. Conclusions: Evaluation of clinical study implementation is important to capture what works and what might need to be modified. A well-calibrated approach to online advertising and enrollment can remove barriers to recruitment and lower costs but remains the most challenging part of research. Equipment was effectively and promptly shipped to participants and removed the risk of illness transmission associated with in-person encounters during a pandemic. Wearable technology incorporating continuous, clinical-grade monitoring offered an unprecedented level of detail and ecological validity. However, study planning, relationship building, and troubleshooting are more complex in the remote setting. The relevance of a study to potential participants remains key to its success. UR - https://formative.jmir.org/2022/7/e37832 UR - http://dx.doi.org/10.2196/37832 UR - http://www.ncbi.nlm.nih.gov/pubmed/35852933 ID - info:doi/10.2196/37832 ER - TY - JOUR AU - Funk, Luke AU - Liu, Natalie PY - 2022/6/29 TI - Authors? Reply to: To Screen or Not to Screen? At Which BMI Cut Point? Comment on ?Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study? JO - J Med Internet Res SP - e39717 VL - 24 IS - 6 KW - obesity KW - body mass index KW - BMI KW - risk factors KW - screening KW - health services KW - chronic disease KW - heart disease KW - myocardial perfusion imaging KW - anxiety KW - depression UR - https://www.jmir.org/2022/6/e39717 UR - http://dx.doi.org/10.2196/39717 UR - http://www.ncbi.nlm.nih.gov/pubmed/35767330 ID - info:doi/10.2196/39717 ER - TY - JOUR AU - Sioka, Chrissa PY - 2022/6/29 TI - To Screen or Not to Screen? At Which BMI Cut Point? Comment on ?Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study? JO - J Med Internet Res SP - e37267 VL - 24 IS - 6 KW - obesity KW - body mass index KW - BMI KW - risk factors KW - screening KW - health services KW - chronic disease KW - heart disease KW - myocardial perfusion imaging KW - anxiety KW - depression UR - https://www.jmir.org/2022/6/e37267 UR - http://dx.doi.org/10.2196/37267 UR - http://www.ncbi.nlm.nih.gov/pubmed/35767333 ID - info:doi/10.2196/37267 ER - TY - JOUR AU - Lipsmeier, Florian AU - Simillion, Cedric AU - Bamdadian, Atieh AU - Tortelli, Rosanna AU - Byrne, M. Lauren AU - Zhang, Yan-Ping AU - Wolf, Detlef AU - Smith, V. Anne AU - Czech, Christian AU - Gossens, Christian AU - Weydt, Patrick AU - Schobel, A. Scott AU - Rodrigues, B. Filipe AU - Wild, J. Edward AU - Lindemann, Michael PY - 2022/6/28 TI - A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study JO - J Med Internet Res SP - e32997 VL - 24 IS - 6 KW - Huntington disease KW - digital monitoring KW - digital biomarkers KW - remote monitoring KW - smartphone KW - smartwatch KW - cognition KW - motor KW - clinical trials KW - mobile phone N2 - Background: Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring. Objective: The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD. Methods: This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts?the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic. Results: Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90). Conclusions: These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD. UR - https://www.jmir.org/2022/6/e32997 UR - http://dx.doi.org/10.2196/32997 UR - http://www.ncbi.nlm.nih.gov/pubmed/35763342 ID - info:doi/10.2196/32997 ER - TY - JOUR AU - Zhuang, Yan AU - Zhang, Luxia AU - Gao, Xiyuan AU - Shae, Zon-Yin AU - Tsai, P. Jeffrey J. AU - Li, Pengfei AU - Shyu, Chi-Ren PY - 2022/6/27 TI - Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies JO - J Med Internet Res SP - e36774 VL - 24 IS - 6 KW - blockchain KW - clinical trials KW - clinical trial management system KW - electronic data capture KW - smart contract N2 - Background: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. Objective: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. Methods: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. Results: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. Conclusions: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks. UR - https://www.jmir.org/2022/6/e36774 UR - http://dx.doi.org/10.2196/36774 UR - http://www.ncbi.nlm.nih.gov/pubmed/35759315 ID - info:doi/10.2196/36774 ER - TY - JOUR AU - Lim Jit Fan, Christina AU - Boon Kwang, Goh AU - Chee Wing Ling, Vivian AU - Woh Peng, Tang AU - Goh Qiuling, Bandy PY - 2022/6/27 TI - Remodeling the Medication Collection Process With Prescription in Locker Box (PILBOX): Prospective Cross-sectional Study JO - J Med Internet Res SP - e23266 VL - 24 IS - 6 KW - PILBOX KW - medication collection process KW - dispensing KW - remodeling KW - locker box KW - medication KW - pharmaceuticals KW - prescription N2 - Background: Traditionally, patients wishing to obtain their prescription medications have had to physically go to pharmacy counters and collect their medications via face-to-face interactions with pharmacy staff. Prescription in Locker Box (PILBOX) is a new innovation allowing patients and their caregivers to collect medication asynchronously, 24/7 at their convenience, from medication lockers instead of from pharmacy staff. Objective: This study aimed to determine the willingness of patients and caregivers to use this new innovation and factors that affect their willingness. Methods: This prospective cross-sectional study was conducted over 2 months at 2 public primary health care centers in Singapore. Patients or caregivers aged 21 years and older who came to pharmacies to collect medications were administered a 3-part questionnaire face-to-face by trained study team members after they gave their consent to participate in the study. Results: A total of 222 participants completed the study. About 40% (89/222, 40.1%) of participants were willing to use PILBOX to collect their medications. Among participants who were keen to use the PILBOX service, slightly more than half (47/89, 53%) were willing to pay for the PILBOX service. Participants felt that ease of use (3.5 [SD 1.2]) of PILBOX was the most important factor affecting their willingness to use the medication pickup service. This was followed by waiting time (3.4 [SD 1.3]), cost of using the medication pickup service (3.0 [SD 1.4]), and 24/7 accessibility (2.6 [SD 1.4]). This study also found that age (P=.01), language literacy (P<.001), education level (P<.001), working status (P=.01), and personal monthly income (P=.01) were factors affecting the willingness of patients or caregivers to use PILBOX. Conclusions: Patients and caregivers are keen to use PILBOX to collect their medications for its convenience and the opportunity to save time if it is easy to use and not costly. UR - https://www.jmir.org/2022/6/e23266 UR - http://dx.doi.org/10.2196/23266 UR - http://www.ncbi.nlm.nih.gov/pubmed/35759321 ID - info:doi/10.2196/23266 ER - TY - JOUR AU - Dixit, Abhishek AU - Lee, Michael PY - 2022/6/24 TI - Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps JO - JMIR Form Res SP - e36687 VL - 6 IS - 6 KW - Scalable Vector Graphics KW - SVG KW - pain drawing KW - pain location KW - Body Pain Map KW - overlap computation KW - heat map KW - pain frequency map KW - algorithm N2 - Background: Pain is an unpleasant sensation that signals potential or actual bodily injury. The locations of bodily pain can be communicated and recorded by freehand drawing on 2D or 3D (manikin) surface maps. Freehand pain drawings are often part of validated pain questionnaires (eg, the Brief Pain Inventory) and use 2D templates with undemarcated body outlines. The simultaneous analysis of drawings allows the generation of pain frequency maps that are clinically useful for identifying areas of common pain in a disease. The grid-based approach (dividing a template into cells) allows easy generation of pain frequency maps, but the grid?s granularity influences data capture accuracy and end-user usability. The grid-free templates circumvent the problem related to grid creation and selection and provide an unbiased basis for drawings that most resemble paper drawings. However, the precise capture of drawn areas poses considerable challenges in producing pain frequency maps. While web-based applications and mobile-based apps for freehand digital drawings are widely available, tools for generating pain frequency maps from grid-free drawings are lacking. Objective: We sought to provide an algorithm that can process any number of freehand drawings on any grid-free 2D body template to generate a pain frequency map. We envisage the use of the algorithm in clinical or research settings to facilitate fine-grain comparisons of human pain anatomy between disease diagnosis or disorders or as an outcome metric to guide monitoring or discovery of treatments. Methods: We designed a web-based tool to capture freehand pain drawings using a grid-free 2D body template. Each drawing consisted of overlapping rectangles (Scalable Vector Graphics elements) created by scribbling in the same area of the body template. An algorithm was developed and implemented in Python to compute the overlap of rectangles and generate a pain frequency map. The utility of the algorithm was demonstrated on drawings obtained from 2 clinical data sets, one of which was a clinical drug trial (ISRCTN68734605). We also used simulated data sets of overlapping rectangles to evaluate the performance of the algorithm. Results: The algorithm produced nonoverlapping rectangles representing unique locations on the body template. Each rectangle carries an overlap frequency that denotes the number of participants with pain at that location. When transformed into an HTML file, the output is feasibly rendered as a pain frequency map on web browsers. The layout (vertical-horizontal) of the output rectangles can be specified based on the dimensions of the body regions. The output can also be exported to a CSV file for further analysis. Conclusions: Although further validation in much larger clinical data sets is required, the algorithm in its current form allows for the generation of pain frequency maps from any number of freehand drawings on any 2D body template. UR - https://formative.jmir.org/2022/6/e36687 UR - http://dx.doi.org/10.2196/36687 UR - http://www.ncbi.nlm.nih.gov/pubmed/35749160 ID - info:doi/10.2196/36687 ER - TY - JOUR AU - Gauld, Christophe AU - Maquet, Julien AU - Micoulaud-Franchi, Jean-Arthur AU - Dumas, Guillaume PY - 2022/6/15 TI - Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice JO - J Med Internet Res SP - e32912 VL - 24 IS - 6 KW - autism spectrum disorder KW - Twitter KW - natural language processing KW - network analysis KW - popular understanding of illness KW - knowledge translation KW - autism KW - tweets KW - psychiatry KW - text mining N2 - Background: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities. Objective: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. Methods: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term ?autism? in PubMed without time restriction. Lastly, we compared the results of the 2 databases. Results: We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. Conclusions: This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries. UR - https://www.jmir.org/2022/6/e32912 UR - http://dx.doi.org/10.2196/32912 UR - http://www.ncbi.nlm.nih.gov/pubmed/35704359 ID - info:doi/10.2196/32912 ER - TY - JOUR AU - Elnakib, Shatha AU - Vecino-Ortiz, I. Andres AU - Gibson, G. Dustin AU - Agarwal, Smisha AU - Trujillo, J. Antonio AU - Zhu, Yifan AU - Labrique, B. Alain PY - 2022/6/14 TI - A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set JO - J Med Internet Res SP - e36787 VL - 24 IS - 6 KW - C-Score KW - validation KW - mortality KW - predictive models KW - mobile phone N2 - Background: The C-Score, which is an individual health score, is based on a predictive model validated in the UK and US populations. It was designed to serve as an individualized point-in-time health assessment tool that could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications that reduce the risk of premature death. Objective: Our study aimed to conduct an external validation of the C-Score in the US population and expand the original score to improve its predictive capabilities in the US population. The C-Score is intended for mobile health apps on wearable devices. Methods: We conducted a literature review to identify relevant variables that were missing in the original C-Score. Subsequently, we used data from the 2005 to 2014 US National Health and Nutrition Examination Survey (NHANES; N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III data from 1988 to 1994 (N=1440) to conduct an external validation of the test. Only participants with complete data were included in this study. Discrimination and calibration tests were conducted to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test. Results: Higher C-Scores were associated with reduced odds of all-cause mortality (odds ratio 0.96, P<.001). We found a good fit of the C-Score for all-cause mortality with an area under the curve (AUC) of 0.72. Among participants aged between 40 and 69 years, C-Score models had a good fit for all-cause mortality and an AUC >0.72. A sensitivity analysis using NHANES III data (1988-1994) was performed, yielding similar results. The inclusion of sociodemographic and clinical variables in the basic C-Score increased the AUCs from 0.72 (95% CI 0.71-0.73) to 0.87 (95% CI 0.85-0.88). Conclusions: Our study shows that this digital biomarker, the C-Score, has good capabilities to predict all-cause mortality in the general US population. An expanded health score can predict 87% of the mortality in the US population. This model can be used as an instrument to assess individual mortality risk and as a counseling tool to motivate behavior changes and lifestyle modifications. UR - https://www.jmir.org/2022/6/e36787 UR - http://dx.doi.org/10.2196/36787 UR - http://www.ncbi.nlm.nih.gov/pubmed/35483022 ID - info:doi/10.2196/36787 ER - TY - JOUR AU - Itelman, Edward AU - Shlomai, Gadi AU - Leibowitz, Avshalom AU - Weinstein, Shiri AU - Yakir, Maya AU - Tamir, Idan AU - Sagiv, Michal AU - Muhsen, Aia AU - Perelman, Maxim AU - Kant, Daniella AU - Zilber, Eyal AU - Segal, Gad PY - 2022/6/9 TI - Assessing the Usability of a Novel Wearable Remote Patient Monitoring Device for the Early Detection of In-Hospital Patient Deterioration: Observational Study JO - JMIR Form Res SP - e36066 VL - 6 IS - 6 KW - remote patient monitoring KW - noninvasive monitoring KW - general ward KW - early warning score system KW - patient deterioration KW - clinical prediction KW - wearable devices KW - uHealth N2 - Background: Patients admitted to general wards are inherently at risk of deterioration. Thus, tools that can provide early detection of deterioration may be lifesaving. Frequent remote patient monitoring (RPM) has the potential to allow such early detection, leading to a timely intervention by health care providers. Objective: This study aimed to assess the potential of a novel wearable RPM device to provide timely alerts in patients at high risk for deterioration. Methods: This prospective observational study was conducted in two general wards of a large tertiary medical center. Patients determined to be at high risk to deteriorate upon admission and assigned to a telemetry bed were included. On top of the standard monitoring equipment, a wearable monitor was attached to each patient, and monitoring was conducted in parallel. The data gathered by the wearable monitors were analyzed retrospectively, with the medical staff being blinded to them in real time. Several early warning scores of the risk for deterioration were used, all calculated from frequent data collected by the wearable RPM device: these included (1) the National Early Warning Score (NEWS), (2) Airway, Breathing, Circulation, Neurology, and Other (ABCNO) score, and (3) deterioration criteria defined by the clinical team as a ?wish list? score. In all three systems, the risk scores were calculated every 5 minutes using the data frequently collected by the wearable RPM device. Data generated by the early warning scores were compared with those obtained from the clinical records of actual deterioration among these patients. Results: In total, 410 patients were recruited and 217 were included in the final analysis. The median age was 71 (IQR 62-78) years and 130 (59.9%) of them were male. Actual clinical deterioration occurred in 24 patients. The NEWS indicated high alert in 16 of these 24 (67%) patients, preceding actual clinical deterioration by 29 hours on average. The ABCNO score indicated high alert in 18 (75%) of these patients, preceding actual clinical deterioration by 38 hours on average. Early warning based on wish list scoring criteria was observed for all 24 patients 40 hours on average before clinical deterioration was detected by the medical staff. Importantly, early warning based on the wish list scoring criteria was also observed among all other patients who did not deteriorate. Conclusions: Frequent remote patient monitoring has the potential for early detection of a high risk to deteriorate among hospitalized patients, using both grouped signal-based scores and algorithm-based prediction. In this study, we show the ability to formulate scores for early warning by using RPM. Nevertheless, early warning scores compiled on the basis of these data failed to deliver reasonable specificity. Further efforts should be directed at improving the specificity and sensitivity of such tools. Trial Registration: ClinicalTrials.gov NCT04220359; https://clinicaltrials.gov/ct2/show/NCT04220359 UR - https://formative.jmir.org/2022/6/e36066 UR - http://dx.doi.org/10.2196/36066 UR - http://www.ncbi.nlm.nih.gov/pubmed/35679119 ID - info:doi/10.2196/36066 ER - TY - JOUR AU - Lee, JooHyun AU - Lee, Seon Tae AU - Lee, SeungWoo AU - Jang, JiHye AU - Yoo, SuYoung AU - Choi, YeJin AU - Park, Rang Yu PY - 2022/6/8 TI - Development and Application of a Metaverse-Based Social Skills Training Program for Children With Autism Spectrum Disorder to Improve Social Interaction: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e35960 VL - 11 IS - 6 KW - metaverse KW - social skills KW - Autism KW - ASD KW - digital therapy KW - Roblox KW - RCT KW - social skill KW - social interaction KW - human interaction KW - child KW - youth KW - development KW - wearable KW - biometric KW - communication KW - digital technology KW - eHealth KW - mhealth KW - stress KW - emotional change KW - online platform N2 - Background: Autism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive behavioral patterns. Children with ASD who lack social communication skills will eventually not interact with others and will lack peer relationships when compared to ordinary people. Thus, it is necessary to develop a program to improve social communication abilities using digital technology in people with ASD. Objective: We intend to develop and apply a metaverse-based child social skills training program aimed at improving the social interaction abilities of children with ASD aged 7-12 years. We plan to compare and analyze the biometric information collected through wearable devices when applying the metaverse-based social skills training program to evaluate emotional changes in children with ASD in stressful situations. Methods: This parallel randomized controlled study will be conducted on children aged 7-12 years diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administered to children who voluntarily wish to participate in the research with consent from their legal guardians. The treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4 weeks. The control group will not intervene during the experiment. The treatment group will use wearable devices during the experiment to collect real-time biometric information. Results: The study is expected to recruit and enroll participants in March 2022. After registering the participants, the study will be conducted from March 2022 to May 2022. This research will be jointly conducted by Yonsei University and Dobrain Co Ltd. Children participating in the program will use the internet-based platform. Conclusions: The metaverse-based Program for the Education and Enrichment of Relational Skills (PEERS) will be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and is inexpensive because it can be administered at home; thus, it is expected to be effective in many children with ASD. If a method can be applied to detect children's emotional changes early using biometric information collected through wearable devices, then emotional changes such as anxiety and anger can be alleviated in advance, thus reducing issues in children with ASD. Trial Registration: Clinical Research Information Service KCT0006859; https://tinyurl.com/4r3k7cmj International Registered Report Identifier (IRRID): PRR1-10.2196/35960 UR - https://www.researchprotocols.org/2022/6/e35960 UR - http://dx.doi.org/10.2196/35960 UR - http://www.ncbi.nlm.nih.gov/pubmed/35675112 ID - info:doi/10.2196/35960 ER - TY - JOUR AU - Gambril, John AU - Boyd, Carter AU - Egbaria, Jamal PY - 2022/5/30 TI - Application of Nonfungible Tokens to Health Care. Comment on ?Blockchain Technology Projects to Provide Telemedical Services: Systematic Review? JO - J Med Internet Res SP - e34276 VL - 24 IS - 5 KW - telemedicine KW - distributed ledger KW - health information exchange KW - blockchain KW - cryptocurrency KW - nonfungible token KW - non-fungible token KW - medical education KW - internet KW - finance UR - https://www.jmir.org/2022/5/e34276 UR - http://dx.doi.org/10.2196/34276 UR - http://www.ncbi.nlm.nih.gov/pubmed/35635749 ID - info:doi/10.2196/34276 ER - TY - JOUR AU - Gamhewage, Gaya AU - Mahmoud, Essam Mohamed AU - Tokar, Anna AU - Attias, Melissa AU - Mylonas, Christos AU - Canna, Sara AU - Utunen, Heini PY - 2022/5/26 TI - Digital Transformation of Face-To-Face Focus Group Methodology: Engaging a Globally Dispersed Audience to Manage Institutional Change at the World Health Organization JO - J Med Internet Res SP - e28911 VL - 24 IS - 5 KW - qualitative research KW - digitalization KW - WHO KW - World Health Organization KW - FGDs KW - focus group discussions UR - https://www.jmir.org/2022/5/e28911 UR - http://dx.doi.org/10.2196/28911 UR - http://www.ncbi.nlm.nih.gov/pubmed/35617007 ID - info:doi/10.2196/28911 ER - TY - JOUR AU - Huguet, Marius AU - Sarazin, Marianne AU - Perrier, Lionel AU - Augusto, Vincent PY - 2022/5/20 TI - How We Can Reap the Full Benefit of Teleconsultations: Economic Evaluation Combined With a Performance Evaluation Through a Discrete-Event Simulation JO - J Med Internet Res SP - e32002 VL - 24 IS - 5 KW - telemedicine KW - telehealth KW - teleconsultation KW - discrete-event simulation KW - economic evaluation KW - propensity score matching N2 - Background: In recent years, the rapid development of information and communications technology enabled by innovations in videoconferencing solutions and the emergence of connected medical devices has contributed to expanding the scope of application and expediting the development of telemedicine. Objective: This study evaluates the use of teleconsultations (TCs) for specialist consultations at hospitals in terms of costs, resource consumption, and patient travel time. The key feature of our evaluation framework is the combination of an economic evaluation through a cost analysis and a performance evaluation through a discrete-event simulation (DES) approach. Methods: Three data sets were used to obtain detailed information on the characteristics of patients, characteristics of patients? residential locations, and usage of telehealth stations. A total of 532 patients who received at least one TC and 18,559 patients who received solely physical consultations (CSs) were included in the initial sample. The TC patients were recruited during a 7-month period (ie, 2020 data) versus 19 months for the CS patients (ie, 2019 and 2020 data). A propensity score matching procedure was applied in the economic evaluation. To identify the best scenarios for reaping the full benefits of TCs, various scenarios depicting different population types and deployment strategies were explored in the DES model. Associated break-even levels were calculated. Results: The results of the cost evaluation reveal a higher cost for the TC group, mainly induced by higher volumes of (tele)consultations per patient and the substantial initial investment required for TC equipment. On average, the total cost per patient over 298 days of follow-up was ?356.37 (US $392) per TC patient and ?305.18 (US $336) per CS patient. However, the incremental cost of TCs was not statistically significant: ?356.37 ? ?305.18 = ?51.19 or US $392 ? US $336 = US $56 (95% CI ?35.99 to 114.25; P=.18). Sensitivity analysis suggested heterogeneous economic profitability levels within subpopulations and based on the intensity of use of TC solutions. In fact, the DES model results show that TCs could be a cost-saving strategy in some cases, depending on population characteristics, the amortization speed of telehealth equipment, and the locations of telehealth stations. Conclusions: The use of TCs has the potential to lead to a major organizational change in the health care system in the near future. Nevertheless, TC performance is strongly related to the context and deployment strategy involved. UR - https://www.jmir.org/2022/5/e32002 UR - http://dx.doi.org/10.2196/32002 UR - http://www.ncbi.nlm.nih.gov/pubmed/35594065 ID - info:doi/10.2196/32002 ER - TY - JOUR AU - Read, A. Emily AU - Gagnon, A. Danie AU - Donelle, Lorie AU - Ledoux, Kathleen AU - Warner, Grace AU - Hiebert, Brad AU - Sharma, Ridhi PY - 2022/5/11 TI - Stakeholder Perspectives on In-home Passive Remote Monitoring to Support Aging in Place in the Province of New Brunswick, Canada: Rapid Qualitative Investigation JO - JMIR Aging SP - e31486 VL - 5 IS - 2 KW - aging in place KW - home care KW - older adults KW - passive remote monitoring N2 - Background: The province of New Brunswick (NB) has one of the oldest populations in Canada, providing an opportunity to develop and test innovative strategies to address the unique health challenges faced by older adults. Passive remote monitoring technology has the potential to support independent living among older adults. Limited research has examined the benefits of and barriers to the adoption of this technology among community-dwelling older adults. Objective: This study aimed to explore perceptions of in-home passive remote monitoring technology designed to support aging in place from the perspective of older adults, their family or friend caregivers, social workers, and government decision-makers in the province of NB, Canada. Methods: Between October 2018 and March 2020, a rapid qualitative investigation of 28 one-on-one interviews was conducted in person or via telephone. Participants included 2 home support services clients and 11 family or friend caregivers who had used passive remote monitoring technology in their homes; 8 social workers who had worked as case managers for home support services clients; and 7 individuals who were key government decision-makers in the adoption, policy development, and use of the technology in the province of NB. The interviews focused on the following topics: decision to adopt the passive remote monitoring system, barriers to adopting the passive remote monitoring system, benefits of the passive remote monitoring system, impact on client health outcomes, and privacy concerns. The interviews were audio recorded, transcribed, and analyzed by a team of 6 researchers. Data analysis was conducted using a rapid assessment process approach that included matrix analysis. Results: Participants reported that the use of the remote monitoring system allowed older adults to live at home longer and provided caregiver relief. Stakeholders were invested in meeting the home support (home care) needs of older adults. However, when it came to the use of remote monitoring, there was a lack of consensus about which clients it was well-suited for and the role that social workers should play in informing clients and caregivers about the service (role ambiguity, gatekeeping, and perceived conflicts of interest). Conclusions: Our findings highlight many benefits and challenges of the adoption of passive remote monitoring for clients, their family or friend caregivers, and public provincial health and social services systems. Passive remote monitoring is a valuable tool that can provide support to older adults and their family or friend caregivers when it is a good fit with client needs. Further work is needed in NB to increase public and social workers? awareness of the service and its benefits. UR - https://aging.jmir.org/2022/2/e31486 UR - http://dx.doi.org/10.2196/31486 UR - http://www.ncbi.nlm.nih.gov/pubmed/35544304 ID - info:doi/10.2196/31486 ER - TY - JOUR AU - Scherer, A. Emily AU - Kim, Jung Sunny AU - Metcalf, A. Stephen AU - Sweeney, Ann Mary AU - Wu, Jialing AU - Xie, Haiyi AU - Mazza, L. Gina AU - Valente, J. Matthew AU - MacKinnon, P. David AU - Marsch, A. Lisa PY - 2022/5/10 TI - Momentary Self-regulation: Scale Development and Preliminary Validation JO - JMIR Ment Health SP - e35273 VL - 9 IS - 5 KW - self-regulation KW - momentary self-regulation KW - ecological momentary assessment KW - psychometric KW - health behavior change KW - health risk behaviors KW - mobile phone N2 - Background: Self-regulation refers to a person?s ability to manage their cognitive, emotional, and behavioral processes to achieve long-term goals. Most prior research has examined self-regulation at the individual level; however, individual-level assessments do not allow the examination of dynamic patterns of intraindividual variability in self-regulation and thus cannot aid in understanding potential malleable processes of self-regulation that may occur in response to the daily environment. Objective: This study aims to develop a brief, psychometrically sound momentary self-regulation scale that can be practically administered through participants? mobile devices at a momentary level. Methods: This study was conducted in 2 phases. In the first phase, in a sample of 522 adults collected as part of a larger self-regulation project, we examined 23 previously validated assessments of self-regulation containing 594 items in total to evaluate the underlying structure of self-regulation via exploratory and confirmatory factor analyses. We then selected 20 trait-level items to be carried forward to the second phase. In the second phase, we converted each item into a momentary question and piloted the momentary items in a sample of 53 adults over 14 days. Using the results from the momentary pilot study, we explored the psychometric properties of the items and assessed their underlying structure. We then proposed a set of subscale and total score calculations. Results: In the first phase, the selected individual-level items appeared to measure 4 factors of self-regulation. The factors identified were perseverance, sensation seeking, emotion regulation, and mindfulness. In the second phase of the ecological momentary assessment pilot, the selected items demonstrated strong construct validity as well as predictive validity for health risk behaviors. Conclusions: Our findings provide preliminary evidence for a 12-item momentary self-regulation scale comprising 4 subscales designed to capture self-regulatory dynamics at the momentary level. UR - https://mental.jmir.org/2022/5/e35273 UR - http://dx.doi.org/10.2196/35273 UR - http://www.ncbi.nlm.nih.gov/pubmed/35536605 ID - info:doi/10.2196/35273 ER - TY - JOUR AU - Kaushal, Aradhna AU - Bravo, Caroline AU - Duffy, Stephen AU - Lewins, Douglas AU - Möhler, Ralph AU - Raine, Rosalind AU - Vlaev, Ivo AU - Waller, Jo AU - von Wagner, Christian PY - 2022/5/9 TI - Developing Reporting Guidelines for Social Media Research (RESOME) by Using a Modified Delphi Method: Protocol for Guideline Development JO - JMIR Res Protoc SP - e31739 VL - 11 IS - 5 KW - social media KW - research design KW - web-based social networking KW - health behavior KW - health promotion KW - public health N2 - Background: Social media platforms, such as Facebook, Twitter, and Instagram, are being increasingly used to deliver public health interventions. Despite the high level of research interest, there is no consensus or guidance on how to report on social media interventions. Reporting guidelines that incorporate elements from behavior change theories and social media engagement frameworks could foster more robust evaluations that capture outcomes that have an impact on behavior change and engagement. Objective: The aim of this project is to develop, publish, and promote a list of items for our Reporting Guidelines for Social Media Research (RESOME) checklist. Methods: RESOME will be developed by using a modified Delphi approach wherein 2 rounds of questionnaires will be sent to experts and stakeholders. The questionnaires will ask them to rate their agreement with a series of statements until a level of consensus is reached. This will be followed by a web-based consensus meeting to finalize the reporting guidelines. After the consensus meeting, the reporting guidelines will be published in the form of a paper outlining the need for the new guidelines and how the guidelines were developed, along with the finalized checklist for reporting. Prior to publication, the guidelines will be piloted to check for understanding and simplify the language used, if necessary. Results: The first draft of RESOME has been developed. Round 1 of the Delphi survey took place between July and December 2021. Round 2 is due to take place in February 2022, and the web-based consensus meeting will be scheduled for the spring of 2022. Conclusions: Developing RESOME has the potential to contribute to improved reporting, and such guidelines will make it easier to assess the effectiveness of social media interventions. Future work will be needed to evaluate our guidelines? usefulness and practicality. International Registered Report Identifier (IRRID): PRR1-10.2196/31739 UR - https://www.researchprotocols.org/2022/5/e31739 UR - http://dx.doi.org/10.2196/31739 UR - http://www.ncbi.nlm.nih.gov/pubmed/35532999 ID - info:doi/10.2196/31739 ER - TY - JOUR AU - Rodriguez-Ferrer, M. Jose AU - Manzano-León, Ana AU - Cangas, J. Adolfo AU - Aguilar-Parra, M. Jose PY - 2022/5/5 TI - A Web-Based Escape Room to Raise Awareness About Severe Mental Illness Among University Students: Randomized Controlled Trial JO - JMIR Serious Games SP - e34222 VL - 10 IS - 2 KW - escape room KW - severe mental disorder KW - higher education KW - nursing education KW - mental health KW - mental disorder KW - serious games N2 - Background: People with severe mental illness (SMI) face discriminatory situations because of prejudice toward them, even among health care personnel. Escape rooms can be a novel educational strategy for learning about and empathizing with SMI, thus reducing stigma among health care students. Objective: This study aimed to examine the effect of the Without Memories escape room on nursing students? stigma against SMI. Methods: A pre- and postintervention study was conducted with a control group and an experimental group. A total of 306 students from 2 Andalusian universities participated in the study. Data were collected through a pre-post study questionnaire, consisting of an adapted version of the Attributional Style Questionnaire and a questionnaire on motivation for cooperative playful learning strategies. The control group carried out an escape room scenario without sensitizing content, whereas the experimental group carried out an escape room scenario on SMI, with both escape rooms being carried out in a 1-hour session of subjects related to mental health. To answer the research questions, a 2-way analysis of variance with repeated measures, a linear regression, and a 2-way analysis of variance were performed. Results: After the intervention, a significant reduction (P<.001) was observed in the experimental group in stigmatizing attitudes compared with the control group, in which no statistically significant changes (P>.05) were observed. In contrast, the linear regression (t195=?22.15; P<.001) showed that there was an inverse relationship between flow and the level of reduced stigma. When controlling for having or not having a close relative, the intervention was also shown to be effective (P<.001) in reducing the stigma displayed, both for people with affected and unaffected relatives. Conclusions: Our findings suggest that the Without Memories escape room can be used as an effective tool to educate and raise awareness about stigmatizing attitudes toward SMI in university students studying health care. Future testing of the effectiveness of educational escape rooms should be designed with new programs through playful strategies of longer duration to evaluate whether they can achieve a greater impact on motivation, acquisition of knowledge, and awareness. In addition, the feasibility of implementing the Without Memories escape room in other careers related to health and community should be investigated. UR - https://games.jmir.org/2022/2/e34222 UR - http://dx.doi.org/10.2196/34222 UR - http://www.ncbi.nlm.nih.gov/pubmed/35511232 ID - info:doi/10.2196/34222 ER - TY - JOUR AU - Schneider, Manuel PY - 2022/5/5 TI - A Platform to Develop and Apply Digital Methods for Empirical Bioethics Research: Mixed Methods Design and Development Study JO - JMIR Form Res SP - e28558 VL - 6 IS - 5 KW - digital bioethics KW - digital humanities KW - digital methods KW - computational methods KW - empirical bioethics KW - research platform KW - digital health KW - bioethics KW - digital platform N2 - Background: The rise of digital methods and computational tools has opened up the possibility of collecting and analyzing data from novel sources, such as discussions on social media. At the same time, these methods and tools introduce a dependence on technology, often resulting in a need for technical skills and expertise. Researchers from various disciplines engage in empirical bioethics research, and software development and similar skills are not usually part of their background. Therefore, researchers often depend on technical experts to develop and apply digital methods, which can create a bottleneck and hinder the broad use of digital methods in empirical bioethics research. Objective: This study aimed to develop a research platform that would offer researchers the means to better leverage implemented digital methods, and that would simplify the process of developing new methods. Methods: This study used a mixed methods approach to design and develop a research platform prototype. I combined established methods from user-centered design, rapid prototyping, and agile software development to iteratively develop the platform prototype. In collaboration with two other researchers, I tested and extended the platform prototype in situ by carrying out a study using the prototype. Results: The resulting research platform prototype provides three digital methods, which are composed of functional components. This modular concept allows researchers to use existing methods for their own experiments and combine implemented components into new methods. Conclusions: The platform prototype illustrates the potential of the modular concept and empowers researchers without advanced technical skills to carry out experiments using digital methods and develop new methods. However, more work is needed to bring the prototype to a production-ready state. UR - https://formative.jmir.org/2022/5/e28558 UR - http://dx.doi.org/10.2196/28558 UR - http://www.ncbi.nlm.nih.gov/pubmed/35511234 ID - info:doi/10.2196/28558 ER - TY - JOUR AU - Moreno, A. Megan AU - Binger, Kole AU - Zhao, Qianqian AU - Eickhoff, Jens AU - Minich, Matt AU - Uhls, Tehranian Yalda PY - 2022/5/4 TI - Digital Technology and Media Use by Adolescents: Latent Class Analysis JO - JMIR Pediatr Parent SP - e35540 VL - 5 IS - 2 KW - digital technology KW - adolescents KW - latent class analysis KW - social media KW - mobile phone N2 - Background: Digital technology and media use is integral to adolescents? lives and has been associated with both positive and negative health consequences. Previous studies have largely focused on understanding technology behaviors and outcomes within adolescent populations, which can promote assumptions about adolescent technology use as homogeneous. Furthermore, many studies on adolescent technology use have focused on risks and negative outcomes. To better understand adolescent digital technology use, we need new approaches that can assess distinct profiles within study populations and take a balanced approach to understanding the risks and benefits of digital technology use. Objective: The purpose of this study was to identify profiles of adolescent technology use within a large study population focusing on four evidence-based constructs: technology ownership and use, parental involvement, health outcomes, and well-being indicators. Methods: Adolescent-parent dyads were recruited for a cross-sectional web-based survey using the Qualtrics (Qualtrics International, Inc) platform and panels. Technology use measures included ownership of devices, social media use frequency, and the Adolescents? Digital Technology Interactions and Importance scale. Parent involvement measures included household media rules, technology-related parenting practices, parent social media use frequency, and the parent-child relationship. Health outcome measures included physical activity, sleep, problematic internet use, and mental health assessments. Well-being indicators included mental wellness, communication, and empathy. We used latent class analysis (LCA) to identify distinct profile groups across the aforementioned 4 critical constructs. Results: Among the 3981 adolescent-parent dyads recruited, adolescent participants had a mean age of 15.0 (SD 1.43) years; a total of 46.3% (1842/3981) were female, 67.8% (2701/3981) were White, and 75% (2986/3981) lived in a household with an income above the poverty line. The LCA identified 2 discrete classes. Class 1 was made up of 62.8% (2501/3981) of the participants. Class 1 participants were more likely than Class 2 participants to report family-owned devices, have lower technology importance scores, have household technology rules often centered on content, have positive parent relationships and lower parent social media use, and report better health outcomes and well-being indicators. Conclusions: Findings from this national cross-sectional survey using LCA led to 2 distinct profile groups of adolescent media use and their association with technology use and parent involvement as well as health and well-being outcomes. The two classes included a larger Class 1 (Family-Engaged Adolescents) and a smaller Class 2 (At-Risk Adolescents). The findings of this study can inform interventions to reinforce positive technology use and family support. UR - https://pediatrics.jmir.org/2022/2/e35540 UR - http://dx.doi.org/10.2196/35540 UR - http://www.ncbi.nlm.nih.gov/pubmed/35507401 ID - info:doi/10.2196/35540 ER - TY - JOUR AU - Golder, Su AU - Stevens, Robin AU - O'Connor, Karen AU - James, Richard AU - Gonzalez-Hernandez, Graciela PY - 2022/4/29 TI - Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review JO - J Med Internet Res SP - e35788 VL - 24 IS - 4 KW - twitter KW - social media KW - race KW - ethnicity N2 - Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective: This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods: We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results: Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions: There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice. UR - https://www.jmir.org/2022/4/e35788 UR - http://dx.doi.org/10.2196/35788 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486433 ID - info:doi/10.2196/35788 ER - TY - JOUR AU - Polhemus, Ashley AU - Novak, Jan AU - Majid, Shazmin AU - Simblett, Sara AU - Morris, Daniel AU - Bruce, Stuart AU - Burke, Patrick AU - Dockendorf, F. Marissa AU - Temesi, Gergely AU - Wykes, Til PY - 2022/4/28 TI - Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives JO - JMIR Ment Health SP - e25249 VL - 9 IS - 4 KW - digital health KW - remote measurement technology KW - neurology KW - mental health KW - data visualization KW - user-centered design N2 - Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. Results: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users? experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not ?one-size-fits-all,? and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health. UR - https://mental.jmir.org/2022/4/e25249 UR - http://dx.doi.org/10.2196/25249 UR - http://www.ncbi.nlm.nih.gov/pubmed/35482368 ID - info:doi/10.2196/25249 ER - TY - JOUR AU - Redlinger, Eric AU - Glas, Bernhard AU - Rong, Yang PY - 2022/4/28 TI - Impact of Visual Game-Like Features on Cognitive Performance in a Virtual Reality Working Memory Task: Within-Subjects Experiment JO - JMIR Serious Games SP - e35295 VL - 10 IS - 2 KW - HMD KW - working memory KW - gamification KW - cognitive training KW - serious game KW - game KW - cognitive activity KW - user performance KW - visual memory KW - cognitive KW - mobile phone N2 - Background: Although the pursuit of improved cognitive function through working memory training has been the subject of decades of research, the recent growth in commercial adaptations of classic working memory tasks in the form of gamified apps warrants additional scrutiny. In particular, the emergence of virtual reality as a platform for cognitive training presents opportunities for the use of novel visual features. Objective: This study aimed to add to the body of knowledge regarding the use of game-like visual design elements by specifically examining the application of two particular visual features common to virtual reality environments: immersive, colorful backgrounds and the use of 3D depth. In addition, electroencephalography (EEG) data were collected to identify potential neural correlates of any observed changes in performance. Methods: A simple visual working memory task was presented to participants in several game-like adaptations, including the use of colorful, immersive backgrounds and 3D depth. The impact of each adaptation was separately assessed using both EEG and performance assessment outcomes and compared with an unmodified version of the task. Results: Results suggest that although accuracy and reaction time may be slightly affected by the introduction of such game elements, the effects were small and not statistically significant. Changes in EEG power, particularly in the beta and theta rhythms, were significant but failed to correlate with any corresponding changes in performance. Therefore, they may only reflect cognitive changes at the perceptual level. Conclusions: Overall, the data suggest that the addition of these specific visual features to simple cognitive tasks does not appear to significantly affect performance or task-dependent cognitive load. UR - https://games.jmir.org/2022/2/e35295 UR - http://dx.doi.org/10.2196/35295 UR - http://www.ncbi.nlm.nih.gov/pubmed/35482373 ID - info:doi/10.2196/35295 ER - TY - JOUR AU - Nißen, Marcia AU - Rüegger, Dominik AU - Stieger, Mirjam AU - Flückiger, Christoph AU - Allemand, Mathias AU - v Wangenheim, Florian AU - Kowatsch, Tobias PY - 2022/4/27 TI - The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study JO - J Med Internet Res SP - e32630 VL - 24 IS - 4 KW - chatbot KW - conversational agent KW - social roles KW - interpersonal closeness KW - social role theory KW - working alliance KW - design KW - persona KW - digital health intervention KW - web-based experiment N2 - Background: The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective: This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients? experiences and the development of an affective bond with the chatbot, depending on clients? characteristics (ie, age and gender) and whether they can freely choose a chatbot?s social role. Methods: Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings?institution, expert, peer, and dialogical self?and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results: While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants? demographic profiles: main effects for gender (P=.04, ?p2=0.115) and age (P<.001, ?p2=0.192) and a significant interaction effect of persona and age (P=.01, ?p2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ?p2=0.117). Conclusions: Manipulating a chatbot?s social role is a possible avenue for health care chatbot designers to tailor clients? chatbot experiences using user-specific demographic factors and to improve clients? perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots. UR - https://www.jmir.org/2022/4/e32630 UR - http://dx.doi.org/10.2196/32630 UR - http://www.ncbi.nlm.nih.gov/pubmed/35475761 ID - info:doi/10.2196/32630 ER - TY - JOUR AU - Jacobson, Natasha AU - Lithgow, Brian AU - Jafari Jozani, Mohammad AU - Moussavi, Zahra PY - 2022/4/27 TI - The Effect of Transcranial Alternating Current Stimulation With Cognitive Training on Executive Brain Function in Individuals With Dementia: Protocol for a Crossover Randomized Controlled Trial JO - JMIR Res Protoc SP - e37282 VL - 11 IS - 4 KW - transcranial alternating current stimulation KW - Alzheimer disease KW - cognitive impairment KW - double blind KW - treatment KW - placebo-controlled KW - randomized KW - crossover KW - dementia KW - cognitive N2 - Background: Although memory and cognitive declines are associated with normal brain aging, they may also be precursors to dementia. Objective: We aim to offer a novel approach to prevent or slow the progress of neurodegenerative dementia, or plausibly, improve the cognitive functions of individuals with dementia. Methods: We will recruit and enroll 75 participants (older than 50 years old with either mild cognitive impairment or probable early or moderate dementia) for this double-blind randomized controlled study to estimate the efficacy of active transcranial alternating current stimulation with cognitive treatment (in comparison with sham transcranial alternating current stimulation). This will be a crossover study; a cycle consists of sham or active treatment for a period of 4 weeks (5 days per week, in two 30-minute sessions with a half-hour break in between), and participants are randomized into 2 groups, with stratification by age, sex, and cognitive level (measured with the Montreal Cognitive Assessment). Outcomes will be assessed before and after each treatment cycle. The primary outcomes are changes in Wechsler Memory Scale Older Adult Battery and Alzheimer Disease Assessment Scale scores. Secondary outcomes are changes in performance on tests of frontal lobe functioning (verbal fluency), neuropsychiatric symptoms (Neuropsychiatric Inventory Questionnaire), mood changes (Montgomery-Åsberg Depression Rating Scale), and short-term recall (visual 1-back task). Exploratory outcome measures will also be assessed: static and dynamic vestibular response using electrovestibulography, neuronal changes using functional near-infrared spectroscopy, and change in spatial orientation using virtual reality navigation. Results: As of February 10, 2022, the study is ongoing: 7 patients have been screened, and all were deemed eligible for and enrolled in the study; 4 participants have completed baseline assessments. Conclusions: We anticipate that transcranial alternating current stimulation will be a well-tolerated treatment, with no serious side effects and with considerable short- and long-term cognitive improvements. Trial Registration: Clinicaltrials.gov NCT05203523; https://clinicaltrials.gov/show/NCT05203523 International Registered Report Identifier (IRRID): DERR1-10.2196/37282 UR - https://www.researchprotocols.org/2022/4/e37282 UR - http://dx.doi.org/10.2196/37282 UR - http://www.ncbi.nlm.nih.gov/pubmed/35475789 ID - info:doi/10.2196/37282 ER - TY - JOUR AU - Fuchs-Neuhold, Bianca AU - Staubmann, Wolfgang AU - Peterseil, Marie AU - Rath, Anna AU - Schweighofer, Natascha AU - Kronberger, Anika AU - Riederer, Monika AU - van der Kleyn, Moenie AU - Martin, Jochen AU - Hörmann-Wallner, Marlies AU - Waldner, Irmgard AU - Konrad, Manuela AU - Aufschnaiter, Lena Anna AU - Siegmund, Barbara AU - Berghold, Andrea AU - Holasek, Sandra AU - Pail, Elisabeth PY - 2022/4/27 TI - Investigating New Sensory Methods Related to Taste Sensitivity, Preferences, and Diet of Mother-Infant Pairs and Their Relationship With Body Composition and Biomarkers: Protocol for an Explorative Study JO - JMIR Res Protoc SP - e37279 VL - 11 IS - 4 KW - taste KW - preferences KW - nutrition KW - biomarkers KW - body composition KW - air displacement plethysmography KW - Baby Facial Actions Coding System KW - mother KW - infant KW - parenting KW - pediatrics KW - prenatal KW - postnatal N2 - Background: Early experiences with different flavors play an important role in infant development, including food and taste acceptance. Flavors are already perceived in utero with the development of the taste and olfactory system and are passed on to the child through breast and bottle feeding. Therefore, the first 1000 days of life are considered a critical window for infant developmental programming. Objective: The objective of our study is to investigate, both in the prenatal and postnatal period, taste sensitivity, preferences, and dietary diversity of mother-infant pairs. The explorative study design will also report on the impact of these variables on body composition (BC) and biomarkers. In contrast to conventional methods, this study involves long-term follow-up data collection from mother-infant pairs; moreover, the integration of audiovisual tools for recording infants' expressions pertaining to taste stimuli is a novelty of this study. Considering these new methodological approaches, the study aims to assess taste-related data in conjunction with BC parameters like fat-free mass or fat mass, biomarkers, and nutritional intake in infants and children. Methods: Healthy pregnant women aged between 18 and 50 years (BMI?18.5 kg/m2 to ?30 kg/m2; <28 weeks of gestation) were recruited from January 2014 to October 2014. The explorative design implies 2 center visits during pregnancy (24-28 weeks of gestation and 32-34 weeks of gestation) and 2 center visits after delivery (6-8 weeks postpartum and 14-16 weeks postpartum) as well as follow-up visits at 1, 3-3.5, and 6 years after delivery. Data collection encompasses anthropometric and biochemical measurements as well as BC analyses with air displacement plethysmography, taste perception assessments, and multicomponent questionnaires on demographics, feeding practices, and nutritional and lifestyle behaviors. Audiovisual data from infants? reactions to sensory stimuli are collected and coded by trained staff using Baby Facial Action Coding and the Body Action Posture System. Birth outcomes and weight development are obtained from medical records, and additional qualitative data are gathered from 24 semistructured interviews. Results: Our cohort represents a homogenous group of healthy women with stringent exclusion criteria. A total of 54 women met the eligibility criteria, whereas 47 mother-child pairs completed data collection at 4 center visits during and after pregnancy. Follow-up phases, data analyses, and dissemination of the findings are scheduled for the end of 2023. The study was approved by the ethics committee of the Medical University of Graz (EC No 26?066 ex 13/14), and all participants provided informed consent. Conclusions: The results of this study could be useful for elucidating the connections between maternal and infant statuses regarding diet, taste, biomarkers, and prenatal and postnatal weight development. This study may also be relevant to the establishment of further diagnostic and interventional strategies targeting childhood obesity and early body fat development. International Registered Report Identifier (IRRID): DERR1-10.2196/37279 UR - https://www.researchprotocols.org/2022/4/e37279 UR - http://dx.doi.org/10.2196/37279 UR - http://www.ncbi.nlm.nih.gov/pubmed/35475790 ID - info:doi/10.2196/37279 ER - TY - JOUR AU - Newson, Jane Jennifer AU - Pastukh, Vladyslav AU - Thiagarajan, C. Tara PY - 2022/4/20 TI - Assessment of Population Well-being With the Mental Health Quotient: Validation Study JO - JMIR Ment Health SP - e34105 VL - 9 IS - 4 KW - psychiatry KW - public health KW - methods KW - mental health KW - population health KW - social determinants of health KW - global health KW - behavioral symptoms KW - diagnosis KW - symptom assessment KW - psychopathology KW - mental disorders KW - mHealth KW - depression KW - anxiety KW - attention deficit disorder with hyperactivity KW - autistic disorder KW - internet N2 - Background: The Mental Health Quotient (MHQ) is an anonymous web-based assessment of mental health and well-being that comprehensively covers symptoms across 10 major psychiatric disorders, as well as positive elements of mental function. It uses a novel life impact scale and provides a score to the individual that places them on a spectrum from Distressed to Thriving along with a personal report that offers self-care recommendations. Since April 2020, the MHQ has been freely deployed as part of the Mental Health Million Project. Objective: This paper demonstrates the reliability and validity of the MHQ, including the construct validity of the life impact scale, sample and test-retest reliability of the assessment, and criterion validation of the MHQ with respect to clinical burden and productivity loss. Methods: Data were taken from the Mental Health Million open-access database (N=179,238) and included responses from English-speaking adults (aged?18 years) from the United States, Canada, the United Kingdom, Ireland, Australia, New Zealand, South Africa, Singapore, India, and Nigeria collected during 2021. To assess sample reliability, random demographically matched samples (each 11,033/179,238, 6.16%) were compared within the same 6-month period. Test-retest reliability was determined using the subset of individuals who had taken the assessment twice ?3 days apart (1907/179,238, 1.06%). To assess the construct validity of the life impact scale, additional questions were asked about the frequency and severity of an example symptom (feelings of sadness, distress, or hopelessness; 4247/179,238, 2.37%). To assess criterion validity, elements rated as having a highly negative life impact by a respondent (equivalent to experiencing the symptom ?5 days a week) were mapped to clinical diagnostic criteria to calculate the clinical burden (174,618/179,238, 97.42%). In addition, MHQ scores were compared with the number of workdays missed or with reduced productivity in the past month (7625/179,238, 4.25%). Results: Distinct samples collected during the same period had indistinguishable MHQ distributions and MHQ scores were correlated with r=0.84 between retakes within an 8- to 120-day period. Life impact ratings were correlated with frequency and severity of symptoms, with a clear linear relationship (R2>0.99). Furthermore, the aggregate MHQ scores were systematically related to both clinical burden and productivity. At one end of the scale, 89.08% (8986/10,087) of those in the Distressed category mapped to one or more disorders and had an average productivity loss of 15.2 (SD 11.2; SEM [standard error of measurement] 0.5) days per month. In contrast, at the other end of the scale, 0% (1/24,365) of those in the Thriving category mapped to any of the 10 disorders and had an average productivity loss of 1.3 (SD 3.6; SEM 0.1) days per month. Conclusions: The MHQ is a valid and reliable assessment of mental health and well-being when delivered anonymously on the web. UR - https://mental.jmir.org/2022/4/e34105 UR - http://dx.doi.org/10.2196/34105 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442210 ID - info:doi/10.2196/34105 ER - TY - JOUR AU - Sharifi-Heris, Zahra AU - Laitala, Juho AU - Airola, Antti AU - Rahmani, M. Amir AU - Bender, Miriam PY - 2022/4/20 TI - Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review JO - JMIR Med Inform SP - e33875 VL - 10 IS - 4 KW - preterm birth KW - prediction model KW - machine learning approach KW - artificial intelligence N2 - Background: Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs). Objective: This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach. Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported. Results: A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models? characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Conclusions: Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set. UR - https://medinform.jmir.org/2022/4/e33875 UR - http://dx.doi.org/10.2196/33875 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442214 ID - info:doi/10.2196/33875 ER - TY - JOUR AU - Nguyen, N. Tam PY - 2022/4/20 TI - Toward Human Digital Twins for Cybersecurity Simulations on the Metaverse: Ontological and Network Science Approach JO - JMIRx Med SP - e33502 VL - 3 IS - 2 KW - human behavior modeling KW - cognitive twins KW - human digital twins KW - cybersecurity KW - cognitive systems KW - digital twins KW - Metaverse KW - artificial intelligence N2 - Background: Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response, Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple domains on the Metaverse. Simulated results may predict adversaries' behaviors and tactics, leading to more proactive cyber defense strategies. However, current HDTs? cognitive architectures are underdeveloped for such use. Objective: This paper aims to make a case for extending the current digital cognitive architectures as the first step toward more robust HDTs that are suitable for realistic Metaverse cybersecurity simulations. Methods: This study formally documented 108 psychology constructs and thousands of related paths based on 20 time-tested psychology theories, all of which were packaged as Cybonto?a novel ontology. Then, this study applied 20 network science centrality algorithms in ranking the Cybonto psychology constructs by their influences. Results: Out of 108 psychology constructs, the top 10 are Behavior, Arousal, Goals, Perception, Self-efficacy, Circumstances, Evaluating, Behavior-Controllability, Knowledge, and Intentional Modality. In this list, only Behaviors, Goals, Perception, Evaluating, and Knowledge are parts of existing digital cognitive architectures. Notably, some of the constructs are not explicitly implemented. Early usability tests demonstrate that Cybonto can also be useful for immediate uses such as manual analysis of hackers? behaviors and automatic analysis of behavioral cybersecurity knowledge texts. Conclusions: The results call for specific extensions of current digital cognitive architectures such as explicitly implementing more refined structures of Long-term Memory and Perception, placing a stronger focus on noncognitive yet influential constructs such as Arousal, and creating new capabilities for simulating, reasoning about, and selecting circumstances. UR - https://med.jmirx.org/2022/2/e33502 UR - http://dx.doi.org/10.2196/33502 UR - http://www.ncbi.nlm.nih.gov/pubmed/27666280 ID - info:doi/10.2196/33502 ER - TY - JOUR AU - Yuan, Jing AU - Au, Rhoda AU - Karjadi, Cody AU - Ang, Fang Ting AU - Devine, Sherral AU - Auerbach, Sanford AU - DeCarli, Charles AU - Libon, J. David AU - Mez, Jesse AU - Lin, Honghuang PY - 2022/4/15 TI - Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study) JO - J Med Internet Res SP - e34513 VL - 24 IS - 4 KW - Clock Drawing Test KW - digital KW - neuropsychological test KW - cognitive KW - technology KW - Boston Process Approach KW - neurology KW - Framingham Heart Study KW - dementia KW - Alzheimer N2 - Background: The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. Objective: We aimed to investigate the association between dCDT features and brain volume. Methods: This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. Results: A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. Conclusions: dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI. UR - https://www.jmir.org/2022/4/e34513 UR - http://dx.doi.org/10.2196/34513 UR - http://www.ncbi.nlm.nih.gov/pubmed/35436225 ID - info:doi/10.2196/34513 ER - TY - JOUR AU - Wiegersma, Sytske AU - Hidajat, Maurice AU - Schrieken, Bart AU - Veldkamp, Bernard AU - Olff, Miranda PY - 2022/4/11 TI - Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation JO - JMIR Ment Health SP - e21111 VL - 9 IS - 4 KW - supervised text classification KW - multi-class classification KW - screening KW - mental health disorders KW - computerized CBT KW - automated intake and referral N2 - Background: Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. Objective: The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. Methods: On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. Results: The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. Conclusions: A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process. UR - https://mental.jmir.org/2022/4/e21111 UR - http://dx.doi.org/10.2196/21111 UR - http://www.ncbi.nlm.nih.gov/pubmed/35404261 ID - info:doi/10.2196/21111 ER - TY - JOUR AU - Zhang, Lili AU - Vashisht, Himanshu AU - Nethra, Alekhya AU - Slattery, Brian AU - Ward, Tomas PY - 2022/4/6 TI - Differences in Learning and Persistency Characterizing Behavior in Chronic Pain for the Iowa Gambling Task: Web-Based Laboratory-in-the-Field Study JO - J Med Internet Res SP - e26307 VL - 24 IS - 4 KW - chronic pain KW - decision-making KW - computational modeling KW - Iowa Gambling Task KW - lab-in-the-field experiment N2 - Background: Chronic pain is a significant worldwide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional laboratory experiments to date. In such experiments, researchers have extensive control of conditions and can more precisely eliminate potential confounds. In contrast, there is much less known regarding how chronic pain affects decision-making captured via laboratory-in-the-field experiments. Although such settings can introduce more experimental uncertainty, collecting data in more ecologically valid contexts can better characterize the real-world impact of chronic pain. Objective: We aim to quantify decision-making differences between individuals with chronic pain and healthy controls in a laboratory-in-the-field environment by taking advantage of internet technologies and social media. Methods: A cross-sectional design with independent groups was used. A convenience sample of 45 participants was recruited through social media: 20 (44%) participants who self-reported living with chronic pain, and 25 (56%) people with no pain or who were living with pain for <6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making (ie, the Iowa Gambling Task) in their web browser at a time and location of their choice without supervision. Results: Standard behavioral analysis revealed no differences in learning strategies between the 2 groups, although qualitative differences could be observed in the learning curves. However, computational modeling revealed that individuals with chronic pain were quicker to update their behavior than healthy controls, which reflected their increased learning rate (95% highest?posterior-density interval [HDI] 0.66-0.99) when fitted to the Values-Plus-Perseverance model. This result was further validated and extended on the Outcome-Representation Learning model as higher differences (95% HDI 0.16-0.47) between the reward and punishment learning rates were observed when fitted to this model, indicating that individuals with chronic pain were more sensitive to rewards. It was also found that they were less persistent in their choices during the Iowa Gambling Task compared with controls, a fact reflected by their decreased outcome perseverance (95% HDI ?4.38 to ?0.21) when fitted using the Outcome-Representation Learning model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments. Conclusions: We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our laboratory-in-the-field experiment. In this case study, it was demonstrated that, compared with standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand, and explain the differences in decision-making behavior in the context of chronic pain outside the laboratory. UR - https://www.jmir.org/2022/4/e26307 UR - http://dx.doi.org/10.2196/26307 UR - http://www.ncbi.nlm.nih.gov/pubmed/35384855 ID - info:doi/10.2196/26307 ER - TY - JOUR AU - Röhling, Marie Hanna AU - Althoff, Patrik AU - Arsenova, Radina AU - Drebinger, Daniel AU - Gigengack, Norman AU - Chorschew, Anna AU - Kroneberg, Daniel AU - Rönnefarth, Maria AU - Ellermeyer, Tobias AU - Rosenkranz, Cathérine Sina AU - Heesen, Christoph AU - Behnia, Behnoush AU - Hirano, Shigeki AU - Kuwabara, Satoshi AU - Paul, Friedemann AU - Brandt, Ulrich Alexander AU - Schmitz-Hübsch, Tanja PY - 2022/4/1 TI - Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study JO - JMIR Hum Factors SP - e26825 VL - 9 IS - 2 KW - instrumented motion analysis KW - markerless motion capture KW - visual perceptive computing KW - quality control KW - quality reporting KW - gait analysis N2 - Background: Instrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns. Objective: The main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings. Methods: We developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result. Results: The approach developed here has proven user-friendly and applicable on a large scale. Raters? decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6%-85.1% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0%-26.3%, both raters agreed to discard the recording. Conclusions: We present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets. UR - https://humanfactors.jmir.org/2022/2/e26825 UR - http://dx.doi.org/10.2196/26825 UR - http://www.ncbi.nlm.nih.gov/pubmed/35363150 ID - info:doi/10.2196/26825 ER - TY - JOUR AU - Uehara, Fumiko AU - Hori, Kazuhiro AU - Hasegawa, Yoko AU - Yoshimura, Shogo AU - Hori, Shoko AU - Kitamura, Mari AU - Akazawa, Kohei AU - Ono, Takahiro PY - 2022/3/24 TI - Impact of Masticatory Behaviors Measured With Wearable Device on Metabolic Syndrome: Cross-sectional Study JO - JMIR Mhealth Uhealth SP - e30789 VL - 10 IS - 3 KW - metabolic syndrome KW - mastication behaviors KW - wearable device KW - daily meal KW - energy intake KW - chew KW - internet of things N2 - Background: It has been widely recognized that mastication behaviors are related to the health of the whole body and to lifestyle-related diseases. However, many studies were based on subjective questionnaires or were limited to small-scale research in the laboratory due to the lack of a device for measuring mastication behaviors during the daily meal objectively. Recently, a small wearable masticatory counter device, called bitescan (Sharp Co), for measuring masticatory behavior was developed. This wearable device is designed to assess objective masticatory behavior by being worn on the ear in daily life. Objective: This study aimed to investigate the relation between mastication behaviors in the laboratory and in daily meals and to clarify the difference in mastication behaviors between those with metabolic syndrome (MetS) and those without (non-MetS) measured using a wearable device. Methods: A total of 99 healthy volunteers (50 men and 49 women, mean age 36.4 [SD 11.7] years) participated in this study. The mastication behaviors (ie, number of chews and bites, number of chews per bite, and chewing rate) were measured using a wearable ear-hung device. Mastication behaviors while eating a rice ball (100 g) in the laboratory and during usual meals for an entire day were monitored, and the daily energy intake was calculated. Participants? abdominal circumference, fasting glucose concentration, blood pressure, and serum lipids were also measured. Mastication behaviors in the laboratory and during meals for 1 entire day were compared. The participants were divided into 2 groups using the Japanese criteria for MetS (positive/negative for MetS or each MetS component), and mastication behaviors were compared. Results: Mastication behaviors in the laboratory and during daily meals were significantly correlated (number of chews r=0.36; P<.001; number of bites r=0.49; P<.001; number of chews per bite r=0.33; P=.001; and chewing rate r=0.51; P<.001). Although a positive correlation was observed between the number of chews during the 1-day meals and energy intake (r=0.26, P=.009), the number of chews per calorie ingested was negatively correlated with energy intake (r=?0.32, P=.002). Of the 99 participants, 8 fit the criteria for MetS and 14 for pre-MetS. The number of chews and bites for a rice ball in the pre-MetS(+) group was significantly lower than the pre-MetS(?) group (P=.02 and P=.04, respectively). Additionally, scores for the positive abdominal circumference and hypertension subgroups were also less than the counterpart groups (P=.004 and P=.01 for chews, P=.006 and P=.02 for bites, respectively). The number of chews and bites for an entire day in the hypertension subgroup were significantly lower than in the other groups (P=.02 and P=.006). Furthermore, the positive abdominal circumference and hypertension subgroups showed lower numbers of chews per calorie ingested for 1-day meals (P=.03 and P=.02, respectively). Conclusions: These results suggest a relationship between masticatory behaviors in the laboratory and those during daily meals and that masticatory behaviors are associated with MetS and MetS components. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry R000034453; https://tinyurl.com/mwzrhrua UR - https://mhealth.jmir.org/2022/3/e30789 UR - http://dx.doi.org/10.2196/30789 UR - http://www.ncbi.nlm.nih.gov/pubmed/35184033 ID - info:doi/10.2196/30789 ER - TY - JOUR AU - Vadathya, Kumar Anil AU - Musaad, Salma AU - Beltran, Alicia AU - Perez, Oriana AU - Meister, Leo AU - Baranowski, Tom AU - Hughes, O. Sheryl AU - Mendoza, A. Jason AU - Sabharwal, Ashutosh AU - Veeraraghavan, Ashok AU - O'Connor, Teresia PY - 2022/3/24 TI - An Objective System for Quantitative Assessment of Television Viewing Among Children (Family Level Assessment of Screen Use in the Home-Television): System Development Study JO - JMIR Pediatr Parent SP - e33569 VL - 5 IS - 1 KW - television KW - screen media KW - digital media KW - measurement KW - child KW - gaze KW - machine learning KW - mobile phone N2 - Background: Television viewing among children is associated with developmental and health outcomes, yet measurement techniques for television viewing are prone to errors, biases, or both. Objective: This study aims to develop a system to objectively and passively measure children?s television viewing time. Methods: The Family Level Assessment of Screen Use in the Home-Television (FLASH-TV) system includes three sequential algorithms applied to video data collected in front of a television screen: face detection, face verification, and gaze estimation. A total of 21 families of diverse race and ethnicity were enrolled in 1 of 4 design studies to train the algorithms and provide proof of concept testing for the integrated FLASH-TV system. Video data were collected from each family in a laboratory mimicking a living room or in the child?s home. Staff coded the video data for the target child as the gold standard. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each algorithm, as compared with the gold standard. Prevalence and biased adjusted ? scores and an intraclass correlation using a generalized linear mixed model compared FLASH-TV?s estimation of television viewing duration to the gold standard. Results: FLASH-TV demonstrated high sensitivity for detecting faces (95.5%-97.9%) and performed well on face verification when the child?s gaze was on the television. Each of the metrics for estimating the child?s gaze on the screen was moderate to good (range: 55.1% negative predictive value to 91.2% specificity). When combining the 3 sequential steps, FLASH-TV estimation of the child?s screen viewing was overall good, with an intraclass correlation for an overall time watching television of 0.725 across conditions. Conclusions: FLASH-TV offers a critical step forward in improving the assessment of children?s television viewing. UR - https://pediatrics.jmir.org/2022/1/e33569 UR - http://dx.doi.org/10.2196/33569 UR - http://www.ncbi.nlm.nih.gov/pubmed/35323113 ID - info:doi/10.2196/33569 ER - TY - JOUR AU - Ljubenovic, Arsène AU - Said, Sadiq AU - Braun, Julia AU - Grande, Bastian AU - Kolbe, Michaela AU - Spahn, R. Donat AU - Nöthiger, B. Christoph AU - Tscholl, W. David AU - Roche, R. Tadzio PY - 2022/3/22 TI - Visual Attention of Anesthesia Providers in Simulated Anesthesia Emergencies Using Conventional Number-Based and Avatar-Based Patient Monitoring: Prospective Eye-Tracking Study JO - JMIR Serious Games SP - e35642 VL - 10 IS - 1 KW - Anesthesia KW - eye-tracking technology KW - patient monitoring KW - patient simulation KW - situation awareness KW - task performance KW - visual attention KW - avatar based model KW - simulated anesthesia KW - perioperative N2 - Background: Inadequate situational awareness accounts for two-thirds of preventable complications in anesthesia. An essential tool for situational awareness in the perioperative setting is the patient monitor. However, the conventional monitor has several weaknesses. Avatar-based patient monitoring may address these shortcomings and promote situation awareness, a prerequisite for good decision making. Objective: The spatial distribution of visual attention is a fundamental process for achieving adequate situation awareness and thus a potential quantifiable surrogate for situation awareness. Moreover, measuring visual attention with a head-mounted eye-tracker may provide insights into usage and acceptance of the new avatar-based patient monitoring modality. Methods: This prospective eye-tracking study compared anesthesia providers' visual attention on conventional and avatar-based patient monitors during simulated critical anesthesia events. We defined visual attention, measured as fixation count and dwell time, as our primary outcome. We correlated visual attention with the potential confounders: performance in managing simulated critical anesthesia events (task performance), work experience, and profession. We used mixed linear models to analyze the results. Results: Fifty-two teams performed 156 simulations. After a manual quality check of the eye-tracking footage, we excluded 57 simulations due to technical problems and quality issues. Participants had a median of 198 (IQR 92.5-317.5) fixations on the patient monitor with a median dwell time of 30.2 (IQR 14.9-51.3) seconds. We found no significant difference in participants' visual attention when using avatar-based patient monitoring or conventional patient monitoring. However, we found that with each percentage point of better task performance, the number of fixations decreased by about 1.39 (coefficient ?1.39; 95% CI ?2.44 to ?0.34; P=.02), and the dwell time diminished by 0.23 seconds (coefficient ?0.23; 95% CI: ?0.4 to ?0.06; P=.01). Conclusions: Using eye tracking, we found no significant difference in visual attention when anesthesia providers used avatar-based monitoring or conventional patient monitoring in simulated critical anesthesia events. However, we identified visual attention in conjunction with task performance as a surrogate for situational awareness. UR - https://games.jmir.org/2022/1/e35642 UR - http://dx.doi.org/10.2196/35642 UR - http://www.ncbi.nlm.nih.gov/pubmed/35172958 ID - info:doi/10.2196/35642 ER - TY - JOUR AU - Niemeijer, Koen AU - Mestdagh, Merijn AU - Kuppens, Peter PY - 2022/3/18 TI - Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study JO - J Med Internet Res SP - e25643 VL - 24 IS - 3 KW - mobile sensing KW - sleep KW - subjective sleep quality KW - negative affect KW - depression KW - multiverse KW - multilevel modeling KW - machine learning KW - mood KW - mood disorder KW - mobile sensors KW - sleep quality KW - clinical applications N2 - Background: Sleep influences moods and mood disorders. Existing methods for tracking the quality of people?s sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications. Objective: Our goal was to evaluate the potential of mobile sensing data to obtain information about a person?s sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model. Methods: We used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting. Results: Most models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2?0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set. Conclusions: The approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic. UR - https://www.jmir.org/2022/3/e25643 UR - http://dx.doi.org/10.2196/25643 UR - http://www.ncbi.nlm.nih.gov/pubmed/35302502 ID - info:doi/10.2196/25643 ER - TY - JOUR AU - Akbas, Samira AU - Said, Sadiq AU - Roche, Raoul Tadzio AU - Nöthiger, B. Christoph AU - Spahn, R. Donat AU - Tscholl, W. David AU - Bergauer, Lisa PY - 2022/3/18 TI - User Perceptions of Different Vital Signs Monitor Modalities During High-Fidelity Simulation: Semiquantitative Analysis JO - JMIR Hum Factors SP - e34677 VL - 9 IS - 1 KW - avatar KW - patient monitoring KW - semiquantitative research KW - simulation study KW - situation awareness KW - user-centered design KW - visual-patient-avatar N2 - Background: Patient safety during anesthesia is crucially dependent on the monitoring of vital signs. However, the values obtained must also be perceived and correctly classified by the attending care providers. To facilitate these processes, we developed Visual-Patient-avatar, an animated virtual model of the monitored patient, which innovatively presents numerical and waveform data following user-centered design principles. After a high-fidelity simulation study, we analyzed the participants? perceptions of 3 different monitor modalities, including this newly introduced technique. Objective: The aim of this study was to collect and evaluate participants? opinions and experiences regarding 3 different monitor modalities, which are Visual-Patient-avatar, Split Screen (avatar and Conventional monitor alongside each other), and Conventional monitor after using them during simulated critical anesthetic events. Methods: This study was a researcher-initiated, single-center, semiquantitative study. We asked 92 care providers right after finishing 3 simulated emergency scenarios about their positive and negative opinions concerning the different monitor modalities. We processed the field notes obtained and derived the main categories and corresponding subthemes following qualitative research methods. Results: We gained a total of 307 statements. Through a context-based analysis, we identified the 3 main categories of ?Visual-Patient-avatar,? ?Split Screen,? and ?Conventional monitor? and divided them into 11 positive and negative subthemes. We achieved substantial interrater reliability in assigning the statements to 1 of the topics. Most of the statements concerned the design and usability features of the avatar or the Split Screen mode. Conclusions: This study semiquantitatively reviewed the clinical applicability of the Visual-Patient-avatar technique in a high-fidelity simulation study and revealed the strengths and limitations of the avatar only and Split Screen modality. In addition to valuable suggestions for improving the design, the requirement for training prior to clinical implementation was emphasized. The responses to the Split Screen suggest that this symbiotic modality generates better situation awareness in combination with numerical data and accurate curves. As a subsequent development step, a real-life introduction study is planned, where we will test the avatar in Split Screen mode under actual clinical conditions. UR - https://humanfactors.jmir.org/2022/1/e34677 UR - http://dx.doi.org/10.2196/34677 UR - http://www.ncbi.nlm.nih.gov/pubmed/35119375 ID - info:doi/10.2196/34677 ER - TY - JOUR AU - Staffini, Alessio AU - Fujita, Kento AU - Svensson, Kishi Akiko AU - Chung, Ung-Il AU - Svensson, Thomas PY - 2022/3/18 TI - Statistical Methods for Item Reduction in a Representative Lifestyle Questionnaire: Pilot Questionnaire Study JO - Interact J Med Res SP - e28692 VL - 11 IS - 1 KW - item reduction KW - surveys and lifestyle questionnaires KW - feedback measures KW - questionnaire design KW - variance inflation factor KW - factor analysis KW - mobile phone N2 - Background: Reducing the number of items in a questionnaire while maintaining relevant information is important as it is associated with advantages such as higher respondent engagement and reduced response error. However, in health care, after the original design, an a posteriori check of the included items in a questionnaire is often overlooked or considered to be of minor importance. When conducted, this is often based on a single selected method. We argue that before finalizing any lifestyle questionnaire, a posteriori validation should always be conducted using multiple approaches to ensure the robustness of the results. Objective: The objectives of this study are to compare the results of two statistical methods for item reduction (variance inflation factor [VIF] and factor analysis [FA]) in a lifestyle questionnaire constructed by combining items from different sources and analyze the different results obtained from the 2 methods and the conclusions that can be made about the original items. Methods: Data were collected from 79 participants (heterogeneous in age and sex) with a high risk of metabolic syndrome working in a financial company based in Tokyo. The lifestyle questionnaire was constructed by combining items (asked with daily, weekly, and monthly frequency) from multiple validated questionnaires and other selected questions. Item reduction was conducted using VIF and exploratory FA. Adequacy tests were used to check the data distribution and sampling adequacy. Results: Among the daily and weekly questions, both VIF and FA identified redundancies in sleep-related items. Among the monthly questions, both approaches identified redundancies in stress-related items. However, the number of items suggested for reduction often differed: VIF suggested larger reductions than FA for daily questions but fewer reductions for weekly questions. Adequacy tests always confirmed that the structural detection was adequate for the considered items. Conclusions: As expected, our analyses showed that VIF and FA produced both similar and different findings, suggesting that questionnaire designers should consider using multiple methods for item reduction. Our findings using both methods indicate that many questions, especially those related to sleep, are redundant, indicating that the considered lifestyle questionnaire can be shortened. UR - https://www.i-jmr.org/2022/1/e28692 UR - http://dx.doi.org/10.2196/28692 UR - http://www.ncbi.nlm.nih.gov/pubmed/35302507 ID - info:doi/10.2196/28692 ER - TY - JOUR AU - Bakre, Shivani AU - Shea, Benjamin AU - Langheier, Jason AU - Hu, A. Emily PY - 2022/3/17 TI - Blood Pressure Control in Individuals With Hypertension Who Used a Digital, Personalized Nutrition Platform: Longitudinal Study JO - JMIR Form Res SP - e35503 VL - 6 IS - 3 KW - blood pressure KW - hypertension KW - systolic KW - diastolic KW - digital KW - nutrition KW - meal planning KW - food environment KW - food ordering KW - food purchasing KW - cardiology KW - digital health KW - digital platform KW - health technology KW - platform usability N2 - Background: While there is a strong association between adhering to a healthy dietary pattern and reductions in blood pressure, adherence remains low. New technologies aimed to help facilitate behavior change may have an effect on reducing blood pressure among individuals with hypertension. Objective: This study aims to evaluate characteristics of participants with stage 2 hypertension who used Foodsmart and to assess changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP). Methods: We analyzed demographic, dietary, and clinical characteristics collected from 11,934 adults with at least two blood pressure readings who used the Foodsmart platform. Stage 2 hypertension was defined as SBP ?140 mmHg or DBP ?90 mmHg. We calculated mean changes in blood pressure among participants with stage 2 hypertension and stratified by length of follow-up and the covariates associated with achieving blood pressure levels below stage 2 hypertension. We compared changes in diet quality and weight between participants with stage 2 hypertension at baseline who achieved stage 1 hypertension or below and those who did not. Results: We found that 10.63% (1269/11,934) of participants had stage 2 hypertension at baseline. Among Foodsmart participants with stage 2 hypertension at baseline, SBP and DBP decreased, on average, by 5.7 and 4.0 mmHg, respectively; 33.02% (419/1269) of participants with stage 2 hypertension at baseline achieved blood pressure levels below stage 2 hypertension (SBP <140 mmHg and DBP <90 mmHg). Using a multivariable ordinal logistic regression model, changes in Nutriscore (P=.001) and weight (P=.04) were statistically significantly associated with changes in blood pressure categories for users with stage 2 hypertension at baseline. Using a multivariable logistic regression model, we found that baseline Nutriscore, change in Nutriscore, and change in weight were associated with greater likelihood of users with stage 2 hypertension at baseline achieving a lower blood pressure category. Conclusions: This study evaluated changes in SBP and DBP among users (with hypertension) of the Foodsmart platform and found that those with stage 2 hypertension, on average, improved their blood pressure levels over time. UR - https://formative.jmir.org/2022/3/e35503 UR - http://dx.doi.org/10.2196/35503 UR - http://www.ncbi.nlm.nih.gov/pubmed/35297775 ID - info:doi/10.2196/35503 ER - TY - JOUR AU - ten Klooster, Iris AU - Wentzel, Jobke AU - Sieverink, Floor AU - Linssen, Gerard AU - Wesselink, Robin AU - van Gemert-Pijnen, Lisette PY - 2022/3/15 TI - Personas for Better Targeted eHealth Technologies: User-Centered Design Approach JO - JMIR Hum Factors SP - e24172 VL - 9 IS - 1 KW - personas KW - clustering KW - heart failure KW - eHealth KW - user-centered design N2 - Background: The full potential of eHealth technologies to support self-management and disease management for patients with chronic diseases is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes, and context of the prospective end users. To overcome such issues, the user-centered design practice of creating personas is widely accepted to ensure the fit between a technology and the target group or end users throughout all phases of development. Objective: In this study, we integrate several approaches to persona development into the Persona Approach Twente to attain a more holistic and structured approach that aligns with the iterative process of eHealth development. Methods: In 3 steps, a secondary analysis was carried out on different parts of the data set using the Partitioning Around Medoids clustering method. First, we used health-related electronic patient record data only. Second, we added person-related data that were gathered through interviews and questionnaires. Third, we added log data. Results: In the first step, 2 clusters were found, with average silhouette widths of 0.12 and 0.27. In the second step, again 2 clusters were found, with average silhouette widths of 0.08 and 0.12. In the third step, 3 clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04. Conclusions: The Persona Approach Twente is applicable for mixed types of data and allows alignment of this user-centered design method to the iterative approach of eHealth development. A variety of characteristics can be used that stretches beyond (standardized) medical and demographic measurements. Challenges lie in data quality and fitness for (quantitative) clustering. UR - https://humanfactors.jmir.org/2022/1/e24172 UR - http://dx.doi.org/10.2196/24172 UR - http://www.ncbi.nlm.nih.gov/pubmed/35289759 ID - info:doi/10.2196/24172 ER - TY - JOUR AU - Yang, Hsuan-Chia AU - Rahmanti, Ristya Annisa AU - Huang, Chih-Wei AU - Li, Jack Yu-Chuan PY - 2022/3/4 TI - How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? JO - J Med Internet Res SP - e29506 VL - 24 IS - 3 KW - artificial empathy KW - deepfakes KW - doctor-patient relationship KW - face emotion recognition KW - artificial intelligence KW - facial recognition KW - facial emotion recognition KW - medical images KW - patient KW - physician KW - therapy UR - https://www.jmir.org/2022/3/e29506 UR - http://dx.doi.org/10.2196/29506 UR - http://www.ncbi.nlm.nih.gov/pubmed/35254278 ID - info:doi/10.2196/29506 ER - TY - JOUR AU - Hiragi, Shusuke AU - Hatanaka, Jun AU - Sugiyama, Osamu AU - Saito, Kenichi AU - Nambu, Masayuki AU - Kuroda, Tomohiro PY - 2022/3/4 TI - Token Economy?Based Hospital Bed Allocation to Mitigate Information Asymmetry: Proof-of-Concept Study Through Simulation Implementation JO - JMIR Form Res SP - e28877 VL - 6 IS - 3 KW - hospital administration KW - resource allocation KW - token economy KW - bed occupancy KW - hospital management KW - simulation KW - decision-making KW - organization N2 - Background: Hospital bed management is an important resource allocation task in hospital management, but currently, it is a challenging task. However, acquiring an optimal solution is also difficult because intraorganizational information asymmetry exists. Signaling, as defined in the fields of economics, can be used to mitigate this problem. Objective: We aimed to develop an assignment process that is based on a token economy as signaling intermediary. Methods: We implemented a game-like simulation, representing token economy?based bed assignments, in which 3 players act as ward managers of 3 inpatient wards (1 each). As a preliminary evaluation, we recruited 9 nurse managers to play and then participate in a survey about qualitative perceptions for current and proposed methods (7-point Likert scale). We also asked them about preferred rewards for collected tokens. In addition, we quantitatively recorded participant pricing behavior. Results: Participants scored the token economy?method positively in staff satisfaction (3.89 points vs 2.67 points) and patient safety (4.38 points vs 3.50 points) compared to the current method, but they scored the proposed method negatively for managerial rivalry, staff employee development, and benefit for patients. The majority of participants (7 out of 9) listed human resources as the preferred reward for tokens. There were slight associations between workload information and pricing. Conclusions: Survey results indicate that the proposed method can improve staff satisfaction and patient safety by increasing the decision-making autonomy of staff but may also increase managerial rivalry, as expected from existing criticism for decentralized decision-making. Participant behavior indicated that token-based pricing can act as a signaling intermediary. Given responses related to rewards, a token system that is designed to incorporate human resource allocation is a promising method. Based on aforementioned discussion, we concluded that a token economy?based bed allocation system has the potential to be an optimal method by mitigating information asymmetry. UR - https://formative.jmir.org/2022/3/e28877 UR - http://dx.doi.org/10.2196/28877 UR - http://www.ncbi.nlm.nih.gov/pubmed/35254264 ID - info:doi/10.2196/28877 ER - TY - JOUR AU - Zhang, Bo AU - Deng, Kaiwen AU - Shen, Jie AU - Cai, Lingrui AU - Ratitch, Bohdana AU - Fu, Haoda AU - Guan, Yuanfang PY - 2022/3/1 TI - Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study JO - J Med Internet Res SP - e27934 VL - 24 IS - 3 KW - deep learning KW - eating KW - digital watch N2 - Background: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set. Objective: This study aims to fill in this gap by an integrative engineering and machine learning effort and conducting a large-scale study in terms of monitoring hours on wearable-based eating detection. Methods: This prospective, longitudinal, passively collected study, covering 3828 hours of records, was made possible by programming a digital system that streams diary, accelerometer, and gyroscope data from Apple Watches to iPhones and then transfers the data to the cloud. Results: On the basis of this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an area under the curve (AUC) of 0.825 within 5 minutes in the general population. In addition, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal-level aggregation). Conclusions: The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care. UR - https://www.jmir.org/2022/3/e27934 UR - http://dx.doi.org/10.2196/27934 UR - http://www.ncbi.nlm.nih.gov/pubmed/35230244 ID - info:doi/10.2196/27934 ER - TY - JOUR AU - Marcolin, Barb AU - Saunders, Chad AU - Aubert, Benoit PY - 2022/2/23 TI - Persuasive Technologies and Social Interactions in Professional Environments: Embedded Qualitative Case Study JO - JMIR Form Res SP - e32613 VL - 6 IS - 2 KW - persuasive technology KW - patient experience platforms KW - group effects KW - professional work management KW - services co-design KW - self-management KW - health and wellness outcomes KW - social environments KW - work influence N2 - Background: Although previous studies have highlighted the impact of interactions on the web in the context of patient?health care professional (HCP) dyads, this paper extends that context to a triad that includes the role of employers and associated settings with social groups. Objective: This study aims to evaluate how the interactions between individuals and the social use of the platform affect individuals? use of persuasive technology and, in turn, their work environment actions and responses, by implementing a persuasive technology health and wellness platform in a work environment. Methods: For 8 months, we deployed a persuasive technology platform with different combinations of health-related features and content in 1 embedded case design with 8 fire stations for a small Canadian city (total number of participant firefighters, n=141) assigned to 1 of 2 treatments?interactive or static webpages. We used text-based content analysis techniques for outcome measures, drawn from a total of 29 participant exit interviews. In addition, medical assessments were conducted at baseline, midpoint, and end point by 7 HCPs and 1 researcher (BM), who also served as the data steward and managed the study. Results: Our results reveal that group, social, and work influences introduce new elements to the use of persuasive technology, which interact to foster higher levels of individual success. The platform in our study served as part of a larger social system, providing information that facilitated new behaviors at work and home. The 8-month group programs centered on exercise, nutrition, and smoking cessation. Groups of participants coached by certified professionals showed significant increases in sodium awareness, levels of actual exercise, and consistency of activities. As a result of the study, of 141 people, 15 (10.6%) were notified of serious medical health issues and 29 (20.6%) underwent blood work assessments and a privacy shield (protected by federal law) was enacted to protect employees from losing their employment based on any health concerns disclosed. Conclusions: The persuasive technology platform, in combination with self-management and professional management and social interactions, significantly altered work management behaviors. Interactions among individual outcomes, group influences, and social situations strongly influenced individuals? behaviors in their work and home environments. Three things further improved the positive results that we observed: privacy shields (which allowed employees to reveal health concerns without fear of professional consequences), individual private activities aligned with group activities, and integration between HCP work with localized, organizational work roles. UR - https://formative.jmir.org/2022/2/e32613 UR - http://dx.doi.org/10.2196/32613 UR - http://www.ncbi.nlm.nih.gov/pubmed/35195527 ID - info:doi/10.2196/32613 ER - TY - JOUR AU - Cha, Jinhee AU - West, W. Ian AU - Brockman, A. Tabetha AU - Soto, Valdez Miguel AU - Balls-Berry, E. Joyce AU - Eder, Milton AU - Patten, A. Christi AU - Cohen, L. Elisia PY - 2022/2/18 TI - Use of Live Community Events on Facebook to Share Health and Clinical Research Information With a Minnesota Statewide Community: Exploratory Study JO - JMIR Form Res SP - e30973 VL - 6 IS - 2 KW - social media KW - virtual KW - digital KW - community engagement KW - engagement KW - retention KW - Facebook KW - health information KW - information sharing KW - communication KW - participation KW - eHealth N2 - Background: Community engagement can make a substantial difference in health outcomes and strengthen the capacity to deal with disruptive public health events such as the COVID-19 pandemic. Social media platforms such as Facebook are a promising avenue to reach the broader public and enhance access to clinical and translational science, and require further evaluation from the scientific community. Objective: This study aims to describe the use of live community events to enhance communication about clinical and health research through a Facebook platform case study (Minnesota [MN] Research Link) with a Minnesota statewide community. We examined variables associated with video engagement including video length and type of posting. Methods: From June 2019 to February 2021, MN Research Link streamed 38 live community events on its public Facebook page, MN Research Link. Live community events highlighted different investigators? clinical and health research in the areas of mental health, health and wellness, chronic diseases, and immunology/infectious diseases. Facebook analytics were used to determine the number of views, total minutes viewed, engagement metrics, and audience retention. An engagement rate was calculated by the total number of interactions (likes, shares, and comments) divided by the total length of the live event by the type of live community event. Results: The 38 live community events averaged 23 minutes and 1 second in duration. The total time viewed for all 38 videos was 10 hours, 44 minutes, and 40 seconds. Viewers? watch time averaged 23 seconds of content per video. After adjusting for video length, promotional videos and research presentations had the highest engagement and retention rates. Events that included audience participation did not have higher retention rates compared to events without audience participation. Conclusions: The use of live community events showed adequate levels of engagement from participants. A view time of 23 seconds on average per video suggests that short informational videos engage viewers of clinical and translational science content. Live community events on Facebook can be an effective method of advancing health promotion and clinical and translational science content; however, certain types of events have more impact on engagement than others. UR - https://formative.jmir.org/2022/2/e30973 UR - http://dx.doi.org/10.2196/30973 UR - http://www.ncbi.nlm.nih.gov/pubmed/35179514 ID - info:doi/10.2196/30973 ER - TY - JOUR AU - Hawthorne, Grace AU - Greening, Neil AU - Esliger, Dale AU - Briggs-Price, Samuel AU - Richardson, Matthew AU - Chaplin, Emma AU - Clinch, Lisa AU - Steiner, C. Michael AU - Singh, J. Sally AU - Orme, W. Mark PY - 2022/2/16 TI - Usability of Wearable Multiparameter Technology to Continuously Monitor Free-Living Vital Signs in People Living With Chronic Obstructive Pulmonary Disease: Prospective Observational Study JO - JMIR Hum Factors SP - e30091 VL - 9 IS - 1 KW - chronic obstructive pulmonary disease KW - digital health KW - physical activity KW - respiratory rate KW - wearable technology KW - wearable device KW - vital signs monitor N2 - Background: Vital signs monitoring (VSM) is routine for inpatients, but monitoring during free-living conditions is largely untested in chronic obstructive pulmonary disease (COPD). Objective: This study investigated the usability and acceptability of continuous VSM for people with COPD using wearable multiparameter technology. Methods: In total, 50 people following hospitalization for an acute exacerbation of COPD (AECOPD) and 50 people with stable COPD symptoms were asked to wear an Equivital LifeMonitor during waking hours for 6 weeks (42 days). The device recorded heart rate (HR), respiratory rate (RR), skin temperature, and physical activity. Adherence was defined by the number of days the vest was worn and daily wear time. Signal quality was examined, with thresholds of ?85% for HR and ?80% for RR, based on the device?s proprietary confidence algorithm. Data quality was calculated as the percentage of wear time with acceptable signal quality. Participant feedback was assessed during follow-up phone calls. Results: In total, 84% of participants provided data, with average daily wear time of 11.8 (SD 2.2) hours for 32 (SD 11) days (average of study duration 76%, SD 26%). There was greater adherence in the stable group than in the post-AECOPD group (?5 weeks wear: 71.4% vs 45.7%; P=.02). For all 84 participants, the median HR signal quality was 90% (IQR 80%-94%) and the median RR signal quality was 93% (IQR 92%-95%). The median HR data quality was 81% (IQR 58%-91%), and the median RR data quality was 85% (IQR 77%-91%). Stable group BMI was associated with HR signal quality (rs=0.45, P=.008) and HR data quality (rs=0.44, P=.008). For the AECOPD group, RR data quality was associated with waist circumference and BMI (rs=?0.49, P=.009; rs=?0.44, P=.02). In total, 36 (74%) participants in the Stable group and 21 (60%) participants in the AECOPD group accepted the technology, but 10 participants (12%) expressed concerns with wearing a device around their chest. Conclusions: This wearable multiparametric technology showed good user acceptance and was able to measure vital signs in a COPD population. Data quality was generally high but was influenced by body composition. Overall, it was feasible to continuously measure vital signs during free-living conditions in people with COPD symptoms but with additional challenges in the post-AECOPD context. UR - https://humanfactors.jmir.org/2022/1/e30091 UR - http://dx.doi.org/10.2196/30091 UR - http://www.ncbi.nlm.nih.gov/pubmed/35171101 ID - info:doi/10.2196/30091 ER - TY - JOUR AU - Varma, Maya AU - Washington, Peter AU - Chrisman, Brianna AU - Kline, Aaron AU - Leblanc, Emilie AU - Paskov, Kelley AU - Stockham, Nate AU - Jung, Jae-Yoon AU - Sun, Woo Min AU - Wall, P. Dennis PY - 2022/2/15 TI - Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods JO - J Med Internet Res SP - e31830 VL - 24 IS - 2 KW - mobile health KW - autism spectrum disorder KW - social phenotyping KW - computer vision KW - gaze KW - mobile diagnostics KW - pattern recognition KW - autism KW - diagnostic KW - pattern KW - engagement KW - gaming KW - app KW - insight KW - vision KW - video N2 - Background: Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos. Such techniques can improve objectivity and contribute quantitatively to the diagnostic process. Objective: In this work, we evaluate whether home videos collected from a game-based mobile app can be used to provide diagnostic insights into ASD. To the best of our knowledge, this is the first study attempting to identify potential social indicators of ASD from mobile phone videos without the use of eye-tracking hardware, manual annotations, and structured scenarios or clinical environments. Methods: Here, we used a mobile health app to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We used automated data set annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns, which represent regions of an individual?s visual focus and (2) visual scanning methods, which refer to the ways in which individuals scan their surrounding environment. We compared the gaze fixation and visual scanning methods used by children during a 90-second gameplay video to identify statistically significant differences between the 2 cohorts; we then trained a long short-term memory (LSTM) neural network to determine if gaze indicators could be predictive of ASD. Results: Our results show that gaze fixation patterns differ between the 2 cohorts; specifically, we could identify 1 statistically significant region of fixation (P<.001). In addition, we also demonstrate that there are unique visual scanning patterns that exist for individuals with ASD when compared to NT children (P<.001). A deep learning model trained on coarse gaze fixation annotations demonstrates mild predictive power in identifying ASD. Conclusions: Ultimately, our study demonstrates that heterogeneous video data sets collected from mobile devices hold potential for quantifying visual patterns and providing insights into ASD. We show the importance of automated labeling techniques in generating large-scale data sets while simultaneously preserving the privacy of participants, and we demonstrate that specific social engagement indicators associated with ASD can be identified and characterized using such data. UR - https://www.jmir.org/2022/2/e31830 UR - http://dx.doi.org/10.2196/31830 UR - http://www.ncbi.nlm.nih.gov/pubmed/35166683 ID - info:doi/10.2196/31830 ER - TY - JOUR AU - Santos, Mauro AU - Vollam, Sarah AU - Pimentel, AF Marco AU - Areia, Carlos AU - Young, Louise AU - Roman, Cristian AU - Ede, Jody AU - Piper, Philippa AU - King, Elizabeth AU - Harford, Mirae AU - Shah, Akshay AU - Gustafson, Owen AU - Tarassenko, Lionel AU - Watkinson, Peter PY - 2022/2/15 TI - The Use of Wearable Pulse Oximeters in the Prompt Detection of Hypoxemia and During Movement: Diagnostic Accuracy Study JO - J Med Internet Res SP - e28890 VL - 24 IS - 2 KW - diagnostic accuracy KW - hypoxia KW - hypoxemia KW - wearable pulse oximeter KW - continuous monitoring KW - mHealth KW - wearable technology KW - patient monitoring KW - deterioration KW - blood oxygen KW - hospital N2 - Background: Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms; however, their reliability in usual hospital settings is not known. Objective: We report the performance of wearable pulse oximeters in a simulated clinical setting when challenged by motion and low levels of arterial blood oxygen saturation (SaO2). Methods: The performance of 1 wrist-worn (Wavelet) and 3 finger-worn (CheckMe O2+, AP-20, and WristOx2 3150) wearable, wireless transmission?mode pulse oximeters was evaluated. For this, 7 motion tasks were performed: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia exposure followed, in which inspired gases were adjusted to achieve decreasing SaO2 levels at 100%, 95%, 90%, 87%, 85%, 83%, and 80%. Peripheral oxygen saturation (SpO2) estimates were compared with simultaneous SaO2 samples to calculate the root-mean-square error (RMSE). The area under the receiver operating characteristic curve was used to analyze the detection of hypoxemia (ie, SaO2<90%). Results: SpO2 estimates matching 215 SaO2 samples in both study phases, from 33 participants, were analyzed. Tapping, rubbing, turning pages, and using a tablet degraded SpO2 estimation (RMSE>4% for at least 1 device). All finger-worn pulse oximeters detected hypoxemia, with an overall sensitivity of ?0.87 and specificity of ?0.80, comparable to that of the Philips MX450 pulse oximeter. Conclusions: The SpO2 accuracy of wearable finger-worn pulse oximeters was within that required by the International Organization for Standardization guidelines. Performance was degraded by motion, but all pulse oximeters could detect hypoxemia. Our findings support the use of wearable, wireless transmission?mode pulse oximeters to detect the onset of clinical deterioration in hospital settings. Trial Registration: ISRCTN Registry 61535692; http://www.isrctn.com/ISRCTN61535692 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-034404 UR - https://www.jmir.org/2022/2/e28890 UR - http://dx.doi.org/10.2196/28890 UR - http://www.ncbi.nlm.nih.gov/pubmed/35166690 ID - info:doi/10.2196/28890 ER - TY - JOUR AU - Tahri Sqalli, Mohammed AU - Al-Thani, Dena AU - Elshazly, B. Mohamed AU - Al-Hijji, Mohammed AU - Alahmadi, Alaa AU - Sqalli Houssaini, Yahya PY - 2022/2/9 TI - Understanding Cardiology Practitioners? Interpretations of Electrocardiograms: An Eye-Tracking Study JO - JMIR Hum Factors SP - e34058 VL - 9 IS - 1 KW - eye tracking KW - electrocardiogram KW - ECG interpretation KW - cardiology practitioners KW - human-computer interaction KW - cardiology KW - ECG N2 - Background: Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners? visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately. Objective: This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs. Methods: A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated. Results: Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation. Conclusions: The results show that visual expertise for ECG interpretation is linked to the practitioner?s role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels. UR - https://humanfactors.jmir.org/2022/1/e34058 UR - http://dx.doi.org/10.2196/34058 UR - http://www.ncbi.nlm.nih.gov/pubmed/35138258 ID - info:doi/10.2196/34058 ER - TY - JOUR AU - Hadian, Kimia AU - Fernie, Geoff AU - Roshan Fekr, Atena PY - 2022/2/2 TI - A New Performance Metric to Estimate the Risk of Exposure to Infection in a Health Care Setting: Descriptive Study JO - JMIR Form Res SP - e32384 VL - 6 IS - 2 KW - hand hygiene KW - health care-acquired KW - infection control KW - compliance KW - electronic monitoring KW - exposure KW - risk KW - hygiene KW - monitoring KW - surveillance KW - performance KW - metric KW - method KW - estimate KW - predict KW - development N2 - Background: Despite several measures to monitor and improve hand hygiene (HH) in health care settings, health care-acquired infections (HAIs) remain prevalent. The measures used to calculate HH performance are not able to fully benefit from the high-resolution data collected using electronic monitoring systems. Objective: This study proposes a novel parameter for quantifying the HAI exposure risk of individual patients by considering temporal and spatial features of health care workers? HH adherence. Methods: Patient exposure risk is calculated as a function of the number of consecutive missed HH opportunities, the number of unique rooms visited by the health care professional, and the time duration that the health care professional spends inside and outside the patient?s room without performing HH. The patient exposure risk is compared to the entrance compliance rate (ECR) defined as the ratio of the number of HH actions performed at a room entrance to the total number of entrances into the room. The compliance rate is conventionally used to measure HH performance. The ECR and the patient exposure risk are analyzed using the data collected from an inpatient nursing unit for 12 weeks. Results: The analysis of data collected from 59 nurses and more than 25,600 records at a musculoskeletal rehabilitation unit at the Toronto Rehabilitation Institute, KITE, showed that there is no strong linear relation between the ECR and patient exposure risk (r=0.7, P<.001). Since the ECR is calculated based on the number of missed HH actions upon room entrance, this parameter is already included in the patient exposure risk. Therefore, there might be scenarios that these 2 parameters are correlated; however, in several cases, the ECR contrasted with the reported patient exposure risk. Generally, the patients in rooms with a significantly high ECR can be potentially exposed to a considerable risk of infection. By contrast, small ECRs do not necessarily result in a high patient exposure risk. The results clearly explained the important role of the factors incorporated in patient exposure risk for quantifying the risk of infection for the patients. Conclusions: Patient exposure risk might provide a more reliable estimation of the risk of developing HAIs compared to ECR by considering both the temporal and spatial aspects of HH records. UR - https://formative.jmir.org/2022/2/e32384 UR - http://dx.doi.org/10.2196/32384 UR - http://www.ncbi.nlm.nih.gov/pubmed/35107424 ID - info:doi/10.2196/32384 ER - TY - JOUR AU - Oberschmidt, Kira AU - Grünloh, Christiane AU - Nijboer, Femke AU - van Velsen, Lex PY - 2022/1/28 TI - Best Practices and Lessons Learned for Action Research in eHealth Design and Implementation: Literature Review JO - J Med Internet Res SP - e31795 VL - 24 IS - 1 KW - action research KW - eHealth KW - best practices KW - lessons learned N2 - Background: Action research (AR) is an established research framework to introduce change in a community following a cyclical approach and involving stakeholders as coresearchers in the process. In recent years, it has also been used for eHealth development. However, little is known about the best practices and lessons learned from using AR for eHealth development. Objective: This literature review aims to provide more knowledge on the best practices and lessons learned from eHealth AR studies. Additionally, an overview of the context in which AR eHealth studies take place is given. Methods: A semisystematic review of 44 papers reporting on 40 different AR projects was conducted to identify the best practices and lessons learned in the research studies while accounting for the particular contextual setting and used AR approach. Results: Recommendations include paying attention to the training of stakeholders? academic skills, as well as the various roles and tasks of action researchers. The studies also highlight the need for constant reflection and accessible dissemination suiting the target group. Conclusions: This literature review identified room for improvements regarding communicating and specifying the particular AR definition and applied approach. UR - https://www.jmir.org/2022/1/e31795 UR - http://dx.doi.org/10.2196/31795 UR - http://www.ncbi.nlm.nih.gov/pubmed/35089158 ID - info:doi/10.2196/31795 ER - TY - JOUR AU - Renner, Simon AU - Marty, Tom AU - Khadhar, Mickaïl AU - Foulquié, Pierre AU - Voillot, Paméla AU - Mebarki, Adel AU - Montagni, Ilaria AU - Texier, Nathalie AU - Schück, Stéphane PY - 2022/1/28 TI - A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation JO - J Med Internet Res SP - e31528 VL - 24 IS - 1 KW - health-related quality of life KW - social media use KW - measures KW - real world KW - natural language processing KW - social media KW - NLP KW - infoveillance KW - quality of life KW - digital health KW - social listening N2 - Background: Monitoring social media has been shown to be a useful means to capture patients? opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life (HRQoL) is a useful indicator of overall patients? health, which can be captured online. Objective: This study aimed to describe a social media listening algorithm able to detect the impact of diseases or treatments on specific dimensions of HRQoL based on posts written by patients in social media and forums. Methods: Using a web crawler, 19 forums in France were harvested, and messages related to patients? experience with disease or treatment were specifically collected. The SF-36 (Short Form Health Survey) and EQ-5D (Euro Quality of Life 5 Dimensions) HRQoL surveys were mixed and adapted for a tailored social media listening system. This was carried out to better capture the variety of expression on social media, resulting in 5 dimensions of the HRQoL, which are physical, psychological, activity-based, social, and financial. Models were trained using cross-validation and hyperparameter optimization. Oversampling was used to increase the infrequent dimension: after annotation, SMOTE (synthetic minority oversampling technique) was used to balance the proportions of the dimensions among messages. Results: The training set was composed of 1399 messages, randomly taken from a batch of 20,000 health-related messages coming from forums. The algorithm was able to detect a general impact on HRQoL (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70), and a financial impact (0.79 and 0.74). Conclusions: The development of an innovative method to extract health data from social media as real time assessment of patients? HRQoL is useful to a patient-centered medical care. As a source of real-world data, social media provide a complementary point of view to understand patients? concerns and unmet needs, as well as shedding light on how diseases and treatments can be a burden in their daily lives. UR - https://www.jmir.org/2022/1/e31528 UR - http://dx.doi.org/10.2196/31528 UR - http://www.ncbi.nlm.nih.gov/pubmed/35089152 ID - info:doi/10.2196/31528 ER - TY - JOUR AU - Trang, Kathy AU - Le, X. Lam AU - Brown, A. Carolyn AU - To, Q. Margaret AU - Sullivan, S. Patrick AU - Jovanovic, Tanja AU - Worthman, M. Carol AU - Giang, Minh Le PY - 2022/1/27 TI - Feasibility, Acceptability, and Design of a Mobile Ecological Momentary Assessment for High-Risk Men Who Have Sex With Men in Hanoi, Vietnam: Qualitative Study JO - JMIR Form Res SP - e30360 VL - 6 IS - 1 KW - men who have sex with men KW - HIV KW - mental disorder KW - ecological momentary assessment KW - mobile phone KW - mHealth KW - sexual minorities KW - pilot projects N2 - Background: Men who have sex with men (MSM) are at a disproportionate risk for HIV infection and common mental disorders worldwide. In the context of HIV, common mental disorders are important and are frequent drivers of suboptimal prevention and treatment outcomes. Mobile ecological momentary assessments (EMAs), or the repeated sampling of people?s behaviors and psychological states in their daily lives using mobile phones, can clarify the triggers and HIV-related sequelae of depressive-anxious symptoms and contribute toward the design of ecological momentary interventions (EMIs) that cater to the contextually varying needs of individuals to optimize prevention and treatment outcomes. Objective: This study aims to characterize the feasibility and acceptability of mobile EMA among high-risk MSM in Hanoi, Vietnam. It aims to evaluate the perceived relevance, usability, and concerns of this group with regard to the content and delivery of mobile EMA and the potential of leveraging such platforms in the future to deliver EMIs. Methods: Between January and April 2018, a total of 46 participants were recruited. The participants completed 6 to 8 mobile EMA surveys daily for 7 days. Surveys occurred once upon waking, 4 to 6 times throughout the day, and once before sleeping. All surveys queried participants? perceived safety, social interactions, psychological state, and mental health symptoms. The morning survey further queried on sleep and medication use within the past 24 hours, whereas the night survey queried on sexual activity and substance use and allowed participants to share an audio recording of a stressful experience they had that day. At the end of the week, participants were interviewed about their experiences with using the app. Results: Participants completed an average of 21.7 (SD 12.7) prompts over the 7-day period. Excluding nonresponders, the average compliance rate was 61.8% (SD 26.6%). A thematic analysis of qualitative interviews suggested an overall positive reception of the app and 5 recurring themes, which were centered on the relevance of psychological and behavioral items to daily experiences (eg, mental health symptoms and audio recording), benefits of using the app (eg, increased self-understanding), worries and concerns (eg, privacy), usability (eg, confusion about the interface), and recommendations for future design (eg, integrating more open-ended questions). Conclusions: Mobile EMA is feasible and acceptable among young MSM in Vietnam; however, more research is needed to adapt EMA protocols to this context and enhance compliance. Most participants eagerly provided information about their mental health status and daily activities. As several participants looked toward the app for further mental health and psychosocial support, EMIs have the potential to reduce HIV and mental health comorbidity among MSM. UR - https://formative.jmir.org/2022/1/e30360 UR - http://dx.doi.org/10.2196/30360 UR - http://www.ncbi.nlm.nih.gov/pubmed/35084340 ID - info:doi/10.2196/30360 ER - TY - JOUR AU - Yu, Jia-Ruei AU - Chen, Chun-Hsien AU - Huang, Tsung-Wei AU - Lu, Jang-Jih AU - Chung, Chia-Ru AU - Lin, Ting-Wei AU - Wu, Min-Hsien AU - Tseng, Yi-Ju AU - Wang, Hsin-Yao PY - 2022/1/25 TI - Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study JO - J Med Internet Res SP - e28036 VL - 24 IS - 1 KW - medical informatics KW - machine learning KW - algorithms KW - energy consumption KW - artificial intelligence KW - energy efficient KW - medical domain KW - medical data sets KW - informatics N2 - Background: The use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied. Objective: The aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms?logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms?when applied to clinical laboratory datasets. Methods: We applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5. Results: The experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency. Conclusions: The XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications. UR - https://www.jmir.org/2022/1/e28036 UR - http://dx.doi.org/10.2196/28036 UR - http://www.ncbi.nlm.nih.gov/pubmed/35076405 ID - info:doi/10.2196/28036 ER - TY - JOUR AU - Birnbaum, L. Michael AU - Abrami, Avner AU - Heisig, Stephen AU - Ali, Asra AU - Arenare, Elizabeth AU - Agurto, Carla AU - Lu, Nathaniel AU - Kane, M. John AU - Cecchi, Guillermo PY - 2022/1/24 TI - Acoustic and Facial Features From Clinical Interviews for Machine Learning?Based Psychiatric Diagnosis: Algorithm Development JO - JMIR Ment Health SP - e24699 VL - 9 IS - 1 KW - audiovisual patterns KW - speech analysis KW - facial analysis KW - psychiatry KW - schizophrenia spectrum disorders KW - bipolar disorder KW - symptom prediction KW - diagnostic prediction KW - machine learning KW - audiovisual KW - speech KW - schizophrenia KW - spectrum disorders N2 - Background: In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions. Objective: We aimed to investigate whether reliable inferences?psychiatric signs, symptoms, and diagnoses?can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder. Methods: We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation. Results: The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner?pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61). Conclusions: This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis. UR - https://mental.jmir.org/2022/1/e24699 UR - http://dx.doi.org/10.2196/24699 UR - http://www.ncbi.nlm.nih.gov/pubmed/35072648 ID - info:doi/10.2196/24699 ER - TY - JOUR AU - Parra, Federico AU - Benezeth, Yannick AU - Yang, Fan PY - 2022/1/24 TI - Automatic Assessment of Emotion Dysregulation in American, French, and Tunisian Adults and New Developments in Deep Multimodal Fusion: Cross-sectional Study JO - JMIR Ment Health SP - e34333 VL - 9 IS - 1 KW - emotion dysregulation KW - deep multimodal fusion KW - small data KW - psychometrics N2 - Background: Emotion dysregulation is a key dimension of adult psychological functioning. There is an interest in developing a computer-based, multimodal, and automatic measure. Objective: We wanted to train a deep multimodal fusion model to estimate emotion dysregulation in adults based on their responses to the Multimodal Developmental Profile, a computer-based psychometric test, using only a small training sample and without transfer learning. Methods: Two hundred and forty-eight participants from 3 different countries took the Multimodal Developmental Profile test, which exposed them to 14 picture and music stimuli and asked them to express their feelings about them, while the software extracted the following features from the video and audio signals: facial expressions, linguistic and paralinguistic characteristics of speech, head movements, gaze direction, and heart rate variability derivatives. Participants also responded to the brief version of the Difficulties in Emotional Regulation Scale. We separated and averaged the feature signals that corresponded to the responses to each stimulus, building a structured data set. We transformed each person?s per-stimulus structured data into a multimodal codex, a grayscale image created by projecting each feature?s normalized intensity value onto a cartesian space, deriving each pixel?s position by applying the Uniform Manifold Approximation and Projection method. The codex sequence was then fed to 2 network types. First, 13 convolutional neural networks dealt with the spatial aspect of the problem, estimating emotion dysregulation by analyzing each of the codified responses. These convolutional estimations were then fed to a transformer network that decoded the temporal aspect of the problem, estimating emotional dysregulation based on the succession of responses. We introduce a Feature Map Average Pooling layer, which computes the mean of the convolved feature maps produced by our convolution layers, dramatically reducing the number of learnable weights and increasing regularization through an ensembling effect. We implemented 8-fold cross-validation to provide a good enough estimation of the generalization ability to unseen samples. Most of the experiments mentioned in this paper are easily replicable using the associated Google Colab system. Results: We found an average Pearson correlation (r) of 0.55 (with an average P value of <.001) between ground truth emotion dysregulation and our system?s estimation of emotion dysregulation. An average mean absolute error of 0.16 and a mean concordance correlation coefficient of 0.54 were also found. Conclusions: In psychometry, our results represent excellent evidence of convergence validity, suggesting that the Multimodal Developmental Profile could be used in conjunction with this methodology to provide a valid measure of emotion dysregulation in adults. Future studies should replicate our findings using a hold-out test sample. Our methodology could be implemented more generally to train deep neural networks where only small training samples are available. UR - https://mental.jmir.org/2022/1/e34333 UR - http://dx.doi.org/10.2196/34333 UR - http://www.ncbi.nlm.nih.gov/pubmed/35072643 ID - info:doi/10.2196/34333 ER - TY - JOUR AU - Abbas, Anzar AU - Hansen, J. Bryan AU - Koesmahargyo, Vidya AU - Yadav, Vijay AU - Rosenfield, J. Paul AU - Patil, Omkar AU - Dockendorf, F. Marissa AU - Moyer, Matthew AU - Shipley, A. Lisa AU - Perez-Rodriguez, Mercedez M. AU - Galatzer-Levy, R. Isaac PY - 2022/1/21 TI - Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study JO - JMIR Form Res SP - e26276 VL - 6 IS - 1 KW - digital biomarkers KW - phenotyping KW - computer vision KW - facial expressivity KW - negative symptoms KW - vocal acoustics N2 - Background: Machine learning?based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage. Objective: This study aimed to determine the accuracy of machine learning?based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones. Methods: Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale. Results: Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity. Conclusions: Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed. UR - https://formative.jmir.org/2022/1/e26276 UR - http://dx.doi.org/10.2196/26276 UR - http://www.ncbi.nlm.nih.gov/pubmed/35060906 ID - info:doi/10.2196/26276 ER - TY - JOUR AU - Shakeri Hossein Abad, Zahra AU - Butler, P. Gregory AU - Thompson, Wendy AU - Lee, Joon PY - 2022/1/18 TI - Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk JO - J Med Internet Res SP - e28749 VL - 24 IS - 1 KW - crowdsourcing KW - machine learning KW - digital public health surveillance KW - public health database KW - social media analysis N2 - Background: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. Objective: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Methods: We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning?based classification model, and a hybrid convolutional neural network model. Results: Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. Conclusions: Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models. UR - https://www.jmir.org/2022/1/e28749 UR - http://dx.doi.org/10.2196/28749 UR - http://www.ncbi.nlm.nih.gov/pubmed/35040794 ID - info:doi/10.2196/28749 ER - TY - JOUR AU - Schmälzle, Ralf AU - Wilcox, Shelby PY - 2022/1/18 TI - Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine JO - J Med Internet Res SP - e28858 VL - 24 IS - 1 KW - human-centered AI KW - campaigns KW - health communication KW - NLP KW - health promotion N2 - Background: Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. Objective: In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness. Methods: We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages. Results: We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness. Conclusions: The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage. UR - https://www.jmir.org/2022/1/e28858 UR - http://dx.doi.org/10.2196/28858 UR - http://www.ncbi.nlm.nih.gov/pubmed/35040800 ID - info:doi/10.2196/28858 ER - TY - JOUR AU - Cao, Rui AU - Azimi, Iman AU - Sarhaddi, Fatemeh AU - Niela-Vilen, Hannakaisa AU - Axelin, Anna AU - Liljeberg, Pasi AU - Rahmani, M. Amir PY - 2022/1/18 TI - Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis JO - J Med Internet Res SP - e27487 VL - 24 IS - 1 KW - electrocardiography KW - ECG KW - wearable device KW - heart rate variability KW - Oura smart ring N2 - Background: Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. Objective: This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. Methods: We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland?Altman plot were used to compare the measurements of the 2 devices. Results: Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. Conclusions: The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests. UR - https://www.jmir.org/2022/1/e27487 UR - http://dx.doi.org/10.2196/27487 UR - http://www.ncbi.nlm.nih.gov/pubmed/35040799 ID - info:doi/10.2196/27487 ER - TY - JOUR AU - Leong, Victoria AU - Raheel, Kausar AU - Sim, Yi Jia AU - Kacker, Kriti AU - Karlaftis, M. Vasilis AU - Vassiliu, Chrysoula AU - Kalaivanan, Kastoori AU - Chen, Annabel S. H. AU - Robbins, W. Trevor AU - Sahakian, J. Barbara AU - Kourtzi, Zoe PY - 2022/1/6 TI - A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High-Quality Data: Development and Usability Study JO - J Med Internet Res SP - e28368 VL - 24 IS - 1 KW - web-based testing KW - neurocognitive assessment KW - COVID-19 KW - executive functions KW - learning N2 - Background: The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the forefront as a promising solution for the rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. This study examines the opportunities and challenges afforded by the societal shift toward web-based testing and highlights an urgent need to establish a standard data quality assurance framework for online studies. Objective: This study aims to develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods: A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory, and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (n=41) or online using remote guided testing (n=44) and delivered using identical web-based platforms (Cambridge Neuropsychological Test Automated Battery, Inquisit, and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, and response times) and overall task performance measures. Results: The results indicated that, across all data quality and performance measures, RGT data was statistically-equivalent to in-person data collected in the lab (P>.40 for all comparisons). Moreover, RGT participants out-performed the lab group on measured verbal intelligence (P<.001), which could reflect test environment differences, including possible effects of mask-wearing on communication. Conclusions: These data suggest that the RGT methodology could help ameliorate concerns regarding online data quality?particularly for studies involving high-risk or rare cohorts?and offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance. UR - https://www.jmir.org/2022/1/e28368 UR - http://dx.doi.org/10.2196/28368 UR - http://www.ncbi.nlm.nih.gov/pubmed/34989691 ID - info:doi/10.2196/28368 ER - TY - JOUR AU - Ko, Hoon AU - Huh, Jimi AU - Kim, Won Kyung AU - Chung, Heewon AU - Ko, Yousun AU - Kim, Keun Jai AU - Lee, Hee Jei AU - Lee, Jinseok PY - 2022/1/3 TI - A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation JO - J Med Internet Res SP - e34415 VL - 24 IS - 1 KW - ascites KW - computed tomography KW - deep residual U-Net KW - artificial intelligence N2 - Background: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial. Objective: We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). Methods: We developed 2D DLMs based on deep residual U-Net, U-Net, bidirectional U-Net, and recurrent residual U-Net (R2U-Net) algorithms to segment areas of ascites on abdominopelvic CT images. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and nonascites images. The AI algorithms were trained using 6337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of the AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. Results: The segmentation accuracy was the highest for the deep residual U-Net model with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bidirectional U-Net, and R2U-Net models (mIoU values of 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest for the deep residual U-Net model (0.96), followed by U-Net, bidirectional U-Net, and R2U-Net models (0.90, 0.88, and 0.82, respectively). The deep residual U-Net model also achieved high sensitivity (0.96) and high specificity (0.96). Conclusions: We propose a deep residual U-Net?based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance. UR - https://www.jmir.org/2022/1/e34415 UR - http://dx.doi.org/10.2196/34415 UR - http://www.ncbi.nlm.nih.gov/pubmed/34982041 ID - info:doi/10.2196/34415 ER - TY - JOUR AU - Mariakakis, Alex AU - Karkar, Ravi AU - Patel, N. Shwetak AU - Kientz, A. Julie AU - Fogarty, James AU - Munson, A. Sean PY - 2022/1/3 TI - Using Health Concept Surveying to Elicit Usable Evidence: Case Studies of a Novel Evaluation Methodology JO - JMIR Hum Factors SP - e30474 VL - 9 IS - 1 KW - mobile health KW - survey instrument KW - health screening KW - health belief model KW - path analysis KW - user design KW - health technology KW - health intervention technology KW - digital health KW - mobile phone N2 - Background: Developers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs. Objective: In this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community. Methods: We proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables. Results: We demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people?s decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition. Conclusions: Beyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations. UR - https://humanfactors.jmir.org/2022/1/e30474 UR - http://dx.doi.org/10.2196/30474 UR - http://www.ncbi.nlm.nih.gov/pubmed/34982038 ID - info:doi/10.2196/30474 ER - TY - JOUR AU - Yang, Lin AU - Chan, Long Ka AU - Yuen, M. John W. AU - Wong, Y. Frances K. AU - Han, Lefei AU - Ho, Chak Hung AU - Chang, P. Katherine K. AU - Ho, Shan Yuen AU - Siu, Yuen-Man Judy AU - Tian, Linwei AU - Wong, Sing Man PY - 2021/12/30 TI - Effects of Urban Green Space on Cardiovascular and Respiratory Biomarkers in Chinese Adults: Panel Study Using Digital Tracking Devices JO - JMIR Cardio SP - e31316 VL - 5 IS - 2 KW - green space KW - biomarker KW - cardiovascular disease KW - respiratory disease N2 - Background: The health benefits of urban green space have been widely reported in the literature; however, the biological mechanisms remain unexplored, and a causal relationship cannot be established between green space exposure and cardiorespiratory health. Objective: Our aim was to conduct a panel study using personal tracking devices to continuously collect individual exposure data from healthy Chinese adults aged 50 to 64 years living in Hong Kong. Methods: A panel of cardiorespiratory biomarkers was tested each week for a period of 5 consecutive weeks. Data on weekly exposure to green space, air pollution, and the physical activities of individual participants were collected by personal tracking devices. The effects of green space exposure measured by the normalized difference vegetation index (NDVI) at buffer zones of 100, 250, and 500 meters on a panel of cardiorespiratory biomarkers were estimated by a generalized linear mixed-effects model, with adjustment for confounding variables of sociodemographic characteristics, exposure to air pollutants and noise, exercise, and nutrient intake. Results: A total of 39 participants (mean age 56.4 years, range 50-63 years) were recruited and followed up for 5 consecutive weeks. After adjustment for sex, income, occupation, physical activities, dietary intake, noise, and air pollution, significant negative associations with the NDVI for the 250-meter buffer zone were found in total cholesterol (?21.6% per IQR increase in NDVI, 95% CI ?32.7% to ?10.6%), low-density lipoprotein (?14.9%, 95% CI ?23.4% to ?6.4%), glucose (?11.2%, 95% CI ?21.9% to ?0.5%), and high-sensitivity C-reactive protein (?41.3%, 95% CI ?81.7% to ?0.9%). Similar effect estimates were found for the 100-meter and 250-meter buffer zones. After adjustment for multiple testing, the effect estimates of glucose and high-sensitivity C-reactive protein were no longer significant. Conclusions: The health benefits of green space can be found in some metabolic and inflammatory biomarkers. Further studies are warranted to establish the causal relationship between green space and cardiorespiratory health. UR - https://cardio.jmir.org/2021/2/e31316 UR - http://dx.doi.org/10.2196/31316 UR - http://www.ncbi.nlm.nih.gov/pubmed/34967754 ID - info:doi/10.2196/31316 ER - TY - JOUR AU - Aguayo, A. Gloria AU - Goetzinger, Catherine AU - Scibilia, Renza AU - Fischer, Aurélie AU - Seuring, Till AU - Tran, Viet-Thi AU - Ravaud, Philippe AU - Bereczky, Tamás AU - Huiart, Laetitia AU - Fagherazzi, Guy PY - 2021/12/23 TI - Methods to Generate Innovative Research Ideas and Improve Patient and Public Involvement in Modern Epidemiological Research: Review, Patient Viewpoint, and Guidelines for Implementation of a Digital Cohort Study JO - J Med Internet Res SP - e25743 VL - 23 IS - 12 KW - patient and public involvement KW - workshops KW - surveys KW - focus groups KW - co-design KW - digital cohort study KW - digital epidemiology KW - social media KW - mobile phone N2 - Background: Patient and public involvement (PPI) in research aims to increase the quality and relevance of research by incorporating the perspective of those ultimately affected by the research. Despite these potential benefits, PPI is rarely included in epidemiology protocols. Objective: The aim of this study is to provide an overview of methods used for PPI and offer practical recommendations for its efficient implementation in epidemiological research. Methods: We conducted a review on PPI methods. We mirrored it with a patient advocate?s viewpoint about PPI. We then identified key steps to optimize PPI in epidemiological research based on our review and the viewpoint of the patient advocate, taking into account the identification of barriers to, and facilitators of, PPI. From these, we provided practical recommendations to launch a patient-centered cohort study. We used the implementation of a new digital cohort study as an exemplary use case. Results: We analyzed data from 97 studies, of which 58 (60%) were performed in the United Kingdom. The most common methods were workshops (47/97, 48%); surveys (33/97, 34%); meetings, events, or conferences (28/97, 29%); focus groups (25/97, 26%); interviews (23/97, 24%); consensus techniques (8/97, 8%); James Lind Alliance consensus technique (7/97, 7%); social media analysis (6/97, 6%); and experience-based co-design (3/97, 3%). The viewpoint of a patient advocate showed a strong interest in participating in research. The most usual PPI modalities were research ideas (60/97, 62%), co-design (42/97, 43%), defining priorities (31/97, 32%), and participation in data analysis (25/97, 26%). We identified 9 general recommendations and 32 key PPI-related steps that can serve as guidelines to increase the relevance of epidemiological studies. Conclusions: PPI is a project within a project that contributes to improving knowledge and increasing the relevance of research. PPI methods are mainly used for idea generation. On the basis of our review and case study, we recommend that PPI be included at an early stage and throughout the research cycle and that methods be combined for generation of new ideas. For e-cohorts, the use of digital tools is essential to scale up PPI. We encourage investigators to rely on our practical recommendations to extend PPI in future epidemiological studies. UR - https://www.jmir.org/2021/12/e25743 UR - http://dx.doi.org/10.2196/25743 UR - http://www.ncbi.nlm.nih.gov/pubmed/34941554 ID - info:doi/10.2196/25743 ER - TY - JOUR AU - Gundersen, Alexander Daniel AU - Wivagg, Jonathan AU - Young, J. William AU - Yan, Ting AU - Delnevo, D. Cristine PY - 2021/12/20 TI - The Use of Multimode Data Collection in Random Digit Dialing Cell Phone Surveys for Young Adults: Feasibility Study JO - J Med Internet Res SP - e31545 VL - 23 IS - 12 KW - web mode KW - web survey KW - random digit dialing KW - mixed mode surveys KW - survey methodology KW - data capture KW - research methods KW - recruitment KW - survey KW - feasibility KW - smoking N2 - Background: Young adults? early adoption of new cell phone technologies have created challenges to survey recruitment but offer opportunities to combine random digit dialing (RDD) sampling with web mode data collection. The National Young Adult Health Survey was designed to test the feasibility of this methodology. Objective: In this study, we compared response rates across the telephone mode and web mode, assessed sample representativeness, examined design effects (DEFFs), and compared cigarette smoking prevalence to a gold standard national survey. Methods: We conducted a survey experiment where the sampling frame was randomized to single-mode telephone interviews, telephone-to-web sequential mixed mode, and single-mode web survey. A total of 831 respondents aged 18 to 34 years were recruited via RDD at baseline. A soft launch was conducted prior to main launch. We compared the web mode to the telephone modes (ie, single-mode and mixed mode) at wave 1 based on the American Association for Public Opinion Research response rate 3 for screening and extended surveys. Base-weighted demographic distributions were compared to the American Community Survey. The sample was calibrated to the US Census Bureau's American Community Survey to calculate DEFFs and to compare cigarette smoking prevalence to the National Health Interview Survey. Prevalence estimates are estimated with sampling weights and are presented with unweighted sample sizes. Consistency of estimates was judged by 95% CI. Results: The American Association for Public Opinion Research response rate 3 was higher in the telephone mode than in the web mode (24% and 30% vs 6.1% and 12.5%, for soft launch and main launch, respectively), which was reflected in response rate 3 for screening and extended surveys. During the soft launch, the extended survey and eligibility rate were low for respondents pushed to the web mode. To boost productivity and survey completes for the web condition, the main launch used cell phone numbers from the sampling frame where the sample vendor matched the number to auxiliary data, which suggested that the number likely belonged to an adult in the target age range. This increased the eligibility rate, but the screener response rate was lower. Compared to population distribution from the US Census Bureau, the telephone mode overrepresented men (57.1% [unweighted n=412] vs 50.9%) and those enrolled in college (40.3% [unweighted n=269] vs 23.8%); it also underrepresented those with a Bachelor of Arts or Science (34.4% [unweighted n=239] vs 55%). The web mode overrepresented White, non-Latinos (70.7% [unweighted n=90] vs 54.4%) and those with some college education (30.4% [unweighted n=40] vs 7.6%); it also underrepresented Latinos (13.6% [unweighted n=20] vs 20.7%) and those with a high school or General Education Development diploma (15.3% [unweighted n=20] vs 29.3%). The DEFF measure was 1.28 (subpopulation range 0.96-1.93). The National Young Adult Health Survey cigarette smoking prevalence was consistent with the National Health Interview Survey overall (15%, CI 12.4%-18% [unweighted 149/831] vs 13.5%, CI 12.3%-14.7% [unweighted 823/5552]), with notable deviation among 18- to 24-year-olds (15.6%, CI 11.3%-22.2% [unweighted 51/337] vs 8.7%, CI 7.1%-10.6% [unweighted 167/1647]), and those with education levels lower than Bachelor of Arts or Science (24%, CI 19.3%-29.4% [unweighted 123/524] vs 17.1%, CI 15.6%-18.7% [unweighted 690/3493]). Conclusions: RDD sampling for a web survey is not feasible for young adults due to its low response rate. However, combining this methodology with RDD telephone surveys may have a great potential for including media and collecting autophotographic data in population surveys. UR - https://www.jmir.org/2021/12/e31545 UR - http://dx.doi.org/10.2196/31545 UR - http://www.ncbi.nlm.nih.gov/pubmed/34932017 ID - info:doi/10.2196/31545 ER - TY - JOUR AU - Divi, Nomita AU - Smolinski, Mark PY - 2021/12/15 TI - EpiHacks, a Process for Technologists and Health Experts to Cocreate Optimal Solutions for Disease Prevention and Control: User-Centered Design Approach JO - J Med Internet Res SP - e34286 VL - 23 IS - 12 KW - epidemiology KW - public health KW - diagnostic KW - tool KW - disease surveillance KW - technology solution KW - innovative approaches to disease surveillance KW - One Health KW - surveillance KW - hack KW - innovation KW - expert KW - solution KW - prevention KW - control N2 - Background: Technology-based innovations that are created collaboratively by local technology specialists and health experts can optimize the addressing of priority needs for disease prevention and control. An EpiHack is a distinct, collaborative approach to developing solutions that combines the science of epidemiology with the format of a hackathon. Since 2013, a total of 12 EpiHacks have collectively brought together over 500 technology and health professionals from 29 countries. Objective: We aimed to define the EpiHack process and summarize the impacts of the technology-based innovations that have been created through this approach. Methods: The key components and timeline of an EpiHack were described in detail. The focus areas, outputs, and impacts of the twelve EpiHacks that were conducted between 2013 and 2021 were summarized. Results: EpiHack solutions have served to improve surveillance for influenza, dengue, and mass gatherings, as well as laboratory sample tracking and One Health surveillance, in rural and urban communities. Several EpiHack tools were scaled during the COVID-19 pandemic to support local governments in conducting active surveillance. All tools were designed to be open source to allow for easy replication and adaptation by other governments or parties. Conclusions: EpiHacks provide an efficient, flexible, and replicable new approach to generating relevant and timely innovations that are locally developed and owned, are scalable, and are sustainable. UR - https://www.jmir.org/2021/12/e34286 UR - http://dx.doi.org/10.2196/34286 UR - http://www.ncbi.nlm.nih.gov/pubmed/34807832 ID - info:doi/10.2196/34286 ER - TY - JOUR AU - Park, Dohyun AU - Cho, Jin Soo AU - Kim, Kyunga AU - Woo, Hyunki AU - Kim, Eun Jee AU - Lee, Jin-Young AU - Koh, Janghyun AU - Lee, JeanHyoung AU - Choi, Soo Jong AU - Chang, Kyung Dong AU - Choi, Yoon-Ho AU - Chung, In Ji AU - Cha, Chul Won AU - Jeong, Soon Ok AU - Jekal, Yong Se AU - Kang, Mira PY - 2021/12/8 TI - Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation JO - JMIR Med Inform SP - e29212 VL - 9 IS - 12 KW - blood pressure KW - pulse transit time KW - pulse wave velocity KW - prediction model KW - algorithms KW - medical informatics KW - wearable devices N2 - Background: Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP. Objective: This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models. Methods: This study was conducted on 1362 healthy men older than 18 years who visited the Samsung Medical Center. The systolic blood pressure and diastolic blood pressure were estimated using the multiple linear regression method. Models were divided into two groups based on age: younger than 60 years and 60 years or older; 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error were used as performance metrics. Results: The model divided into two age groups (younger than 60 years and 60 years and older) performed better than the model without division. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, and age met the criteria presented by the American Association for the Advancement of Medical Instrumentation. The prediction errors were within the range of about 9 to 12 mmHg that can occur with a gold standard mercury sphygmomanometer. Conclusions: Dividing age based on the age of 60 years showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with the minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP. UR - https://medinform.jmir.org/2021/12/e29212 UR - http://dx.doi.org/10.2196/29212 UR - http://www.ncbi.nlm.nih.gov/pubmed/34889753 ID - info:doi/10.2196/29212 ER - TY - JOUR AU - Mehta, Paul AU - Raymond, Jaime AU - Han, Kwon Moon AU - Larson, Theodore AU - Berry, D. James AU - Paganoni, Sabrina AU - Mitsumoto, Hiroshi AU - Bedlack, Stanley Richard AU - Horton, Kevin D. PY - 2021/12/7 TI - Recruitment of Patients With Amyotrophic Lateral Sclerosis for Clinical Trials and Epidemiological Studies: Descriptive Study of the National ALS Registry?s Research Notification Mechanism JO - J Med Internet Res SP - e28021 VL - 23 IS - 12 KW - amyotrophic lateral sclerosis KW - Lou Gehrig disease KW - motor neuron disease KW - clinical trials KW - patient recruitment KW - National ALS Registry KW - research notification mechanism N2 - Background: Researchers face challenges in patient recruitment, especially for rare, fatal diseases such as amyotrophic lateral sclerosis (ALS). These challenges include obtaining sufficient statistical power as well as meeting eligibility requirements such as age, sex, and study proximity. Similarly, persons with ALS (PALS) face difficulty finding and enrolling in research studies for which they are eligible. Objective: The aim of this study was to describe how the federal Agency for Toxic Substances and Disease Registry?s (ATSDR) National ALS Registry is linking PALS to scientists who are conducting research, clinical trials, and epidemiological studies. Methods: Through the Registry?s online research notification mechanism (RNM), PALS can elect to be notified about new research opportunities. This mechanism allows researchers to upload a standardized application outlining their study design and objectives, and proof of Institutional Review Board approval. If the application is approved, ATSDR queries the Registry for PALS meeting the study?s specific eligibility criteria, and then distributes the researcher?s study material and contact information to PALS via email. PALS then need to contact the researcher directly to take part in any research. Such an approach allows ATSDR to protect the confidentiality of Registry enrollees. Results: From 2013 to 2019, a total of 46 institutions around the United States and abroad have leveraged this tool and over 600,000 emails have been sent, resulting in over 2000 patients conservatively recruited for clinical trials and epidemiological studies. Patients between the ages of 60 and 69 had the highest level of participation, whereas those between the ages of 18 and 39 and aged over 80 had the lowest. More males participated (4170/7030, 59.32%) than females (2860/7030, 40.68%). Conclusions: The National ALS Registry?s RNM benefits PALS by connecting them to appropriate ALS research. Simultaneously, the system benefits researchers by expediting recruitment, increasing sample size, and efficiently identifying PALS meeting specific eligibility requirements. As more researchers learn about and use this mechanism, both PALS and researchers can hasten research and expand trial options for PALS. UR - https://www.jmir.org/2021/12/e28021 UR - http://dx.doi.org/10.2196/28021 UR - http://www.ncbi.nlm.nih.gov/pubmed/34878988 ID - info:doi/10.2196/28021 ER - TY - JOUR AU - Taylor, Salima AU - Korpusik, Mandy AU - Das, Sai AU - Gilhooly, Cheryl AU - Simpson, Ryan AU - Glass, James AU - Roberts, Susan PY - 2021/12/6 TI - Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study JO - J Med Internet Res SP - e26988 VL - 23 IS - 12 KW - energy intake KW - macronutrient intakes KW - 24-hour recall KW - machine learning KW - convolutional neural networks KW - nutrition KW - diet KW - app KW - natural language processing N2 - Background: Self-monitoring food intake is a cornerstone of national recommendations for health, but existing apps for this purpose are burdensome for users and researchers, which limits use. Objective: We developed and pilot tested a new app (COCO Nutritionist) that combines speech understanding technology with technologies for mapping foods to appropriate food composition codes in national databases, for lower-burden and automated nutritional analysis of self-reported dietary intake. Methods: COCO was compared with the multiple-pass, interviewer-administered 24-hour recall method for assessment of energy intake. COCO was used for 5 consecutive days, and 24-hour dietary recalls were obtained for two of the days. Participants were 35 women and men with a mean age of 28 (range 20-58) years and mean BMI of 24 (range 17-48) kg/m2. Results: There was no significant difference in energy intake between values obtained by COCO and 24-hour recall for days when both methods were used (mean 2092, SD 1044 kcal versus mean 2030, SD 687 kcal, P=.70). There were also no significant differences between the methods for percent of energy from protein, carbohydrate, and fat (P=.27-.89), and no trend in energy intake obtained with COCO over the entire 5-day study period (P=.19). Conclusions: This first demonstration of a dietary assessment method using natural spoken language to map reported foods to food composition codes demonstrates a promising new approach to automate assessments of dietary intake. UR - https://www.jmir.org/2021/12/e26988 UR - http://dx.doi.org/10.2196/26988 UR - http://www.ncbi.nlm.nih.gov/pubmed/34874885 ID - info:doi/10.2196/26988 ER - TY - JOUR AU - Müller, Lars AU - Srinivasan, Aditya AU - Abeles, R. Shira AU - Rajagopal, Amutha AU - Torriani, J. Francesca AU - Aronoff-Spencer, Eliah PY - 2021/12/3 TI - A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis JO - J Med Internet Res SP - e23571 VL - 23 IS - 12 KW - antimicrobial resistance KW - clinical decision support KW - antibiotic stewardship KW - data visualization N2 - Background: There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. Objective: The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. Methods: We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. Results: We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians? reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. Conclusions: The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship. UR - https://www.jmir.org/2021/12/e23571 UR - http://dx.doi.org/10.2196/23571 UR - http://www.ncbi.nlm.nih.gov/pubmed/34870601 ID - info:doi/10.2196/23571 ER - TY - JOUR AU - Wu, Hong AU - Ji, Jiatong AU - Tian, Haimei AU - Chen, Yao AU - Ge, Weihong AU - Zhang, Haixia AU - Yu, Feng AU - Zou, Jianjun AU - Nakamura, Mitsuhiro AU - Liao, Jun PY - 2021/12/1 TI - Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding?Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model JO - JMIR Med Inform SP - e26407 VL - 9 IS - 12 KW - deep learning KW - BERT KW - adverse drug reaction KW - named entity recognition KW - electronic medical records N2 - Background: With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective: This study describes how to identify ADR-related information from Chinese ADE reports. Methods: Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results: The NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity. Conclusions: The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation. UR - https://medinform.jmir.org/2021/12/e26407 UR - http://dx.doi.org/10.2196/26407 UR - http://www.ncbi.nlm.nih.gov/pubmed/34855616 ID - info:doi/10.2196/26407 ER - TY - JOUR AU - Donnat, Claire AU - Bunbury, Freddy AU - Kreindler, Jack AU - Liu, David AU - Filippidis, T. Filippos AU - Esko, Tonu AU - El-Osta, Austen AU - Harris, Matthew PY - 2021/12/1 TI - Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach JO - JMIR Public Health Surveill SP - e30648 VL - 7 IS - 12 KW - COVID-19 KW - transmission dynamics KW - live event management KW - Monte Carlo simulation N2 - Background: Modelling COVID-19 transmission at live events and public gatherings is essential to controlling the probability of subsequent outbreaks and communicating to participants their personalized risk. Yet, despite the fast-growing body of literature on COVID-19 transmission dynamics, current risk models either neglect contextual information including vaccination rates or disease prevalence or do not attempt to quantitatively model transmission. Objective: This paper attempted to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty. Methods: Building upon existing models, our approach ties together 3 main components: (1) reliable modelling of the number of infectious cases at the time of the event, (2) evaluation of the efficiency of pre-event screening, and (3) modelling of the event?s transmission dynamics and their uncertainty using Monte Carlo simulations. Results: We illustrated the application of our pipeline for a concert at the Royal Albert Hall and highlighted the risk?s dependency on factors such as prevalence, mask wearing, and event duration. We demonstrate how this event held on 3 different dates (August 20, 2020; January 20, 2021; and March 20, 2021) would likely lead to transmission events that are similar to community transmission rates (0.06 vs 0.07, 2.38 vs 2.39, and 0.67 vs 0.60, respectively). However, differences between event and background transmissions substantially widened in the upper tails of the distribution of the number of infections (as denoted by their respective 99th quantiles: 1 vs 1, 19 vs 8, and 6 vs 3, respectively, for our 3 dates), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event. Conclusions: Despite the unknowns surrounding COVID-19 transmission, our estimation pipeline opens the discussion on contextualized risk assessment by combining the best tools at hand to assess the order of magnitude of the risk. Our model can be applied to any future event and is presented in a user-friendly RShiny interface. Finally, we discussed our model?s limitations as well as avenues for model evaluation and improvement. UR - https://publichealth.jmir.org/2021/12/e30648 UR - http://dx.doi.org/10.2196/30648 UR - http://www.ncbi.nlm.nih.gov/pubmed/34583317 ID - info:doi/10.2196/30648 ER - TY - JOUR AU - Schulz, Johannes Peter AU - Andersson, M. Elin AU - Bizzotto, Nicole AU - Norberg, Margareta PY - 2021/11/29 TI - Using Ecological Momentary Assessment to Study the Development of COVID-19 Worries in Sweden: Longitudinal Study JO - J Med Internet Res SP - e26743 VL - 23 IS - 11 KW - COVID-19 KW - coronavirus KW - longitudinal studies KW - EMA KW - worry KW - fear KW - pandemics N2 - Background: The foray of COVID-19 around the globe has certainly instigated worries in many people, and lockdown measures may well have triggered more specific worries. Sweden, more than other countries, relied on voluntary measures to fight the pandemic. This provides a particularly interesting context to assess people?s reactions to the threat of the pandemic. Objective: The general aim of this study was to better understand the worried reactions to the virus and the associated lockdown measures. As there have been very few longitudinal studies in this area published to date, development of feelings of worry over time was analyzed over a longer range than in previous research. Affective variables, worry in particular, were included because most of the research in this field has focused on cognitive variables. To employ new methodology, ecological momentary assessment was used for data collection and a multilevel modeling approach was adopted for data analysis. Methods: Results were based on an unbalanced panel sample of 260 Swedish participants filling in 3226 interview questionnaires by smartphone over a 7-week period in 2020 during the rapid rise of cases in the early phase of the pandemic. Causal factors considered in this study included the perceived severity of an infection, susceptibility of a person to the threat posed by the virus, perceived efficacy of safeguarding measures, and assessment of government action against the spread of COVID-19. The effect of these factors on worries was traced in two analytical steps: the effects at the beginning of the study and the effect on the trend during the study. Results: The level of general worry related to COVID-19 was modest (mean 6.67, SD 2.54 on an 11-point Likert scale); the increase during the study period was small, but the interindividual variation of both the worry level and its increase over time was large. Findings confirmed that the hypothesized causal factors (severity of infection, susceptibility to the threat of the virus, efficacy of safeguarding, and assessment of government preventive action) did indeed affect the level of worry. Conclusions: The results confirmed earlier research in a very special case and demonstrated the usefulness of a different study design, which takes a longitudinal perspective, and a new type of data analysis borrowed from multilevel study design. UR - https://www.jmir.org/2021/11/e26743 UR - http://dx.doi.org/10.2196/26743 UR - http://www.ncbi.nlm.nih.gov/pubmed/34847065 ID - info:doi/10.2196/26743 ER - TY - JOUR AU - Kennedy, Mari-Rose AU - Huxtable, Richard AU - Birchley, Giles AU - Ives, Jonathan AU - Craddock, Ian PY - 2021/11/26 TI - ?A Question of Trust? and ?a Leap of Faith??Study Participants? Perspectives on Consent, Privacy, and Trust in Smart Home Research: Qualitative Study JO - JMIR Mhealth Uhealth SP - e25227 VL - 9 IS - 11 KW - smart homes KW - assistive technology KW - research ethics KW - informed consent KW - privacy KW - anonymization KW - trust N2 - Background: Ubiquitous, smart technology has the potential to assist humans in numerous ways, including with health and social care. COVID-19 has notably hastened the move to remotely delivering many health services. A variety of stakeholders are involved in the process of developing technology. Where stakeholders are research participants, this poses practical and ethical challenges, particularly if the research is conducted in people?s homes. Researchers must observe prima facie ethical obligations linked to participants? interests in having their autonomy and privacy respected. Objective: This study aims to explore the ethical considerations around consent, privacy, anonymization, and data sharing with participants involved in SPHERE (Sensor Platform for Healthcare in a Residential Environment), a project for developing smart technology for monitoring health behaviors at home. Participants? unique insights from being part of this unusual experiment offer valuable perspectives on how to properly approach informed consent for similar smart home research in the future. Methods: Semistructured qualitative interviews were conducted with 7 households (16 individual participants) recruited from SPHERE. Purposive sampling was used to invite participants from a range of household types and ages. Interviews were conducted in participants? homes or on-site at the University of Bristol. Interviews were digitally recorded, transcribed verbatim, and analyzed using an inductive thematic approach. Results: Four themes were identified?motivation for participating; transparency, understanding, and consent; privacy, anonymity, and data use; and trust in research. Motivations to participate in SPHERE stemmed from an altruistic desire to support research directed toward the public good. Participants were satisfied with the consent process despite reporting some difficulties?recalling and understanding the information received, the timing and amount of information provision, and sometimes finding the information to be abstract. Participants were satisfied that privacy was assured and judged that the goals of the research compensated for threats to privacy. Participants trusted SPHERE. The factors that were relevant to developing and maintaining this trust were the trustworthiness of the research team, the provision of necessary information, participants? control over their participation, and positive prior experiences of research involvement. Conclusions: This study offers valuable insights into the perspectives of participants in smart home research on important ethical considerations around consent and privacy. The findings may have practical implications for future research regarding the types of information researchers should convey, the extent to which anonymity can be assured, and the long-term duty of care owed to the participants who place trust in researchers not only on the basis of this information but also because of their institutional affiliation. This study highlights important ethical implications. Although autonomy matters, trust appears to matter the most. Therefore, researchers should be alert to the need to foster and maintain trust, particularly as failing to do so might have deleterious effects on future research. UR - https://mhealth.jmir.org/2021/11/e25227 UR - http://dx.doi.org/10.2196/25227 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842551 ID - info:doi/10.2196/25227 ER - TY - JOUR AU - Medina, Rafael AU - Bouhaben, Jaime AU - de Ramón, Ignacio AU - Cuesta, Pablo AU - Antón-Toro, Luis AU - Pacios, Javier AU - Quintero, Javier AU - Ramos-Quiroga, Antoni Josep AU - Maestú, Fernando PY - 2021/11/26 TI - Electrophysiological Brain Changes Associated With Cognitive Improvement in a Pediatric Attention Deficit Hyperactivity Disorder Digital Artificial Intelligence-Driven Intervention: Randomized Controlled Trial JO - J Med Internet Res SP - e25466 VL - 23 IS - 11 KW - ADHD KW - cognitive stimulation KW - magnetoencephalography KW - artificial intelligence KW - Conners continuous performance test KW - KAD_SCL_01 KW - AI KW - cognitive impairment KW - attention deficit hyperactivity disorder KW - pediatrics KW - children KW - rehabilitation N2 - Background: Cognitive stimulation therapy appears to show promising results in the rehabilitation of impaired cognitive processes in attention deficit hyperactivity disorder. Objective: Encouraged by this evidence and the ever-increasing use of technology and artificial intelligence for therapeutic purposes, we examined whether cognitive stimulation therapy implemented on a mobile device and controlled by an artificial intelligence engine can be effective in the neurocognitive rehabilitation of these patients. Methods: In this randomized study, 29 child participants (25 males) underwent training with a smart, digital, cognitive stimulation program (KAD_SCL_01) or with 3 commercial video games for 12 weeks, 3 days a week, 15 minutes a day. Participants completed a neuropsychological assessment and a preintervention and postintervention magnetoencephalography study in a resting state with their eyes closed. In addition, information on clinical symptoms was collected from the child´s legal guardians. Results: In line with our main hypothesis, we found evidence that smart, digital, cognitive treatment results in improvements in inhibitory control performance. Improvements were also found in visuospatial working memory performance and in the cognitive flexibility, working memory, and behavior and general executive functioning behavioral clinical indexes in this group of participants. Finally, the improvements found in inhibitory control were related to increases in alpha-band power in all participants in the posterior regions, including 2 default mode network regions of the interest: the bilateral precuneus and the bilateral posterior cingulate cortex. However, only the participants who underwent cognitive stimulation intervention (KAD_SCL_01) showed a significant increase in this relationship. Conclusions: The results seem to indicate that smart, digital treatment can be effective in the inhibitory control and visuospatial working memory rehabilitation in patients with attention deficit hyperactivity disorder. Furthermore, the relation of the inhibitory control with alpha-band power changes could mean that these changes are a product of plasticity mechanisms or changes in the neuromodulatory dynamics. Trial Registration: ISRCTN Registry ISRCTN71041318; https://www.isrctn.com/ISRCTN71041318 UR - https://www.jmir.org/2021/11/e25466 UR - http://dx.doi.org/10.2196/25466 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842533 ID - info:doi/10.2196/25466 ER - TY - JOUR AU - Ramachandran, Raghav AU - McShea, J. Michael AU - Howson, N. Stephanie AU - Burkom, S. Howard AU - Chang, Hsien-Yen AU - Weiner, P. Jonathan AU - Kharrazi, Hadi PY - 2021/11/25 TI - Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data JO - JMIR Med Inform SP - e31442 VL - 9 IS - 11 KW - persistent high users KW - persistent high utilizers KW - latent class analysis KW - comorbidity patterns KW - utilization prediction KW - unsupervised clustering KW - population health analytics KW - health care KW - prediction models KW - health care services KW - health care costs N2 - Background: A high proportion of health care services are persistently utilized by a small subpopulation of patients. To improve clinical outcomes while reducing costs and utilization, population health management programs often provide targeted interventions to patients who may become persistent high users/utilizers (PHUs). Enhanced prediction and management of PHUs can improve health care system efficiencies and improve the overall quality of patient care. Objective: The aim of this study was to detect key classes of diseases and medications among the study population and to assess the predictive value of these classes in identifying PHUs. Methods: This study was a retrospective analysis of insurance claims data of patients from the Johns Hopkins Health Care system. We defined a PHU as a patient incurring health care costs in the top 20% of all patients? costs for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in 2014-2015. We applied latent class analysis (LCA), an unsupervised clustering approach, to identify patient subgroups with similar diagnostic and medication patterns to differentiate variations in health care utilization across PHUs. Logistic regression models were then built to predict PHUs in the full population and in select subpopulations. Predictors included LCA membership probabilities, demographic covariates, and health utilization covariates. Predictive powers of the regression models were assessed and compared using standard metrics. Results: We identified 164,221 patients with continuous enrollment between 2013 and 2015. The mean study population age was 19.7 years, 55.9% were women, 3.3% had ?1 hospitalization, and 19.1% had 10+ outpatient visits in 2013. A total of 8359 (5.09%) patients were identified as PHUs in both 2014 and 2015. The LCA performed optimally when assigning patients to four probability disease/medication classes. Given the feedback provided by clinical experts, we further divided the population into four diagnostic groups for sensitivity analysis: acute upper respiratory infection (URI) (n=53,232; 4.6% PHUs), mental health (n=34,456; 12.8% PHUs), otitis media (n=24,992; 4.5% PHUs), and musculoskeletal (n=24,799; 15.5% PHUs). For the regression models predicting PHUs in the full population, the F1-score classification metric was lower using a parsimonious model that included LCA categories (F1=38.62%) compared to that of a complex risk stratification model with a full set of predictors (F1=48.20%). However, the LCA-enabled simple models were comparable to the complex model when predicting PHUs in the mental health and musculoskeletal subpopulations (F1-scores of 48.69% and 48.15%, respectively). F1-scores were lower than that of the complex model when the LCA-enabled models were limited to the otitis media and acute URI subpopulations (45.77% and 43.05%, respectively). Conclusions: Our study illustrates the value of LCA in identifying subgroups of patients with similar patterns of diagnoses and medications. Our results show that LCA-derived classes can simplify predictive models of PHUs without compromising predictive accuracy. Future studies should investigate the value of LCA-derived classes for predicting PHUs in other health care settings. UR - https://medinform.jmir.org/2021/11/e31442 UR - http://dx.doi.org/10.2196/31442 UR - http://www.ncbi.nlm.nih.gov/pubmed/34592712 ID - info:doi/10.2196/31442 ER - TY - JOUR AU - Li, Chong AU - Song, Xinyu AU - Chen, Shugeng AU - Wang, Chuankai AU - He, Jieying AU - Zhang, Yongli AU - Xu, Shuo AU - Yan, Zhijie AU - Jia, Jie AU - Shull, Peter PY - 2021/11/23 TI - Long-term Effectiveness and Adoption of a Cellphone Augmented Reality System on Patients with Stroke: Randomized Controlled Trial JO - JMIR Serious Games SP - e30184 VL - 9 IS - 4 KW - stroke KW - augmented reality KW - serious game KW - upper limb motor function KW - cognitive function KW - home-based rehabilitation N2 - Background: A serious game?based cellphone augmented reality system (CARS) was developed for rehabilitation of stroke survivors, which is portable, convenient, and suitable for self-training. Objective: This study aims to examine the effectiveness of CARS in improving upper limb motor function and cognitive function of stroke survivors via conducting a long-term randomized controlled trial and analyze the patient?s acceptance of the proposed system. Methods: A double-blind randomized controlled trial was performed with 30 poststroke, subacute phase patients. All patients in both the experimental group (n=15) and the control group (n=15) performed a 1-hour session of therapy each day, 5 days per week for 2 weeks. Patients in the experimental group received 30 minutes of rehabilitation training with CARS and 30 minutes of conventional occupational therapy (OT) each session, while patients in the control group received conventional OT for the full 1 hour each session. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) subscale, Action Research Arm Test (ARAT), manual muscle test and Brunnstrom stage were used to assess motor function; the Mini-Mental State Examination, Add VS Sub, and Stroop Game were used to assess cognitive function; and the Barthel index was used to assess activities of daily living before and after the 2-week treatment period. In addition, the User Satisfaction Evaluation Questionnaire was used to reflect the patients? adoption of the system in the experimental group after the final intervention. Results: All the assessment scores of the experimental group and control group were significantly improved after intervention. After the intervention. The experimental group?s FMA-UE and ARAT scores increased by 11.47 and 5.86, respectively, and were both significantly higher than the increase of the control group. Similarly, the score of the Add VS Sub and Stroop Game in the experimental group increased by 7.53 and 6.83, respectively, after the intervention, which also represented a higher increase than that in the control group. The evaluation of the adoption of this system had 3 sub-dimensions. In terms of accessibility, the patients reported a mean score of 4.27 (SD 0.704) for the enjoyment of their experience with the system, a mean 4.33 (SD 0.816) for success in using the system, and a mean 4.67 (SD 0.617) for the ability to control the system. In terms of comfort, the patients reported a mean 4.40 (SD 0.737) for the clarity of information provided by the system and a mean 4.40 (SD 0.632) for comfort. In terms of acceptability, the patients reported a mean 4.27 (SD 0.884) for usefulness in their rehabilitation and a mean 4.67 (0.617) in agreeing that CARS is a suitable tool for home-based rehabilitation. Conclusions: The rehabilitation based on combined CARS and conventional OT was more effective in improving both upper limb motor function and cognitive function than was conventional OT. Due to the low cost and ease of use, CARS is also potentially suitable for home-based rehabilitation. Trial Registration: Chinese Clinical Trial Registry ChiCTR1800017568; https://tinyurl.com/xbkkyfyz UR - https://games.jmir.org/2021/4/e30184 UR - http://dx.doi.org/10.2196/30184 UR - http://www.ncbi.nlm.nih.gov/pubmed/34817390 ID - info:doi/10.2196/30184 ER - TY - JOUR AU - Chao, Yi-Ping AU - Chuang, Hai-Hua AU - Hsin, Li-Jen AU - Kang, Chung-Jan AU - Fang, Tuan-Jen AU - Li, Hsueh-Yu AU - Huang, Chung-Guei AU - Kuo, J. Terry B. AU - Yang, H. Cheryl C. AU - Shyu, Hsin-Yih AU - Wang, Shu-Ling AU - Shyu, Liang-Yu AU - Lee, Li-Ang PY - 2021/11/22 TI - Using a 360° Virtual Reality or 2D Video to Learn History Taking and Physical Examination Skills for Undergraduate Medical Students: Pilot Randomized Controlled Trial JO - JMIR Serious Games SP - e13124 VL - 9 IS - 4 KW - cognitive load KW - heart rate variability KW - video learning KW - learning outcome KW - secondary-task reaction time KW - virtual reality N2 - Background: Learning through a 360° virtual reality (VR) or 2D video represents an alternative way to learn a complex medical education task. However, there is currently no consensus on how best to assess the effects of different learning materials on cognitive load estimates, heart rate variability (HRV), outcomes, and experience in learning history taking and physical examination (H&P) skills. Objective: The aim of this study was to investigate how learning materials (ie, VR or 2D video) impact learning outcomes and experience through changes in cognitive load estimates and HRV for learning H&P skills. Methods: This pilot system?design study included 32 undergraduate medical students at an academic teaching hospital. The students were randomly assigned, with a 1:1 allocation, to a 360° VR video group or a 2D video group, matched by age, sex, and cognitive style. The contents of both videos were different with regard to visual angle and self-determination. Learning outcomes were evaluated using the Milestone reporting form. Subjective and objective cognitive loads were estimated using the Paas Cognitive Load Scale, the National Aeronautics and Space Administration Task Load Index, and secondary-task reaction time. Cardiac autonomic function was assessed using HRV measurements. Learning experience was assessed using the AttrakDiff2 questionnaire and qualitative feedback. Statistical significance was accepted at a two-sided P value of <.01. Results: All 32 participants received the intended intervention. The sample consisted of 20 (63%) males and 12 (38%) females, with a median age of 24 (IQR 23-25) years. The 360° VR video group seemed to have a higher Milestone level than the 2D video group (P=.04). The reaction time at the 10th minute in the 360° VR video group was significantly higher than that in the 2D video group (P<.001). Multiple logistic regression models of the overall cohort showed that the 360° VR video module was independently and positively associated with a reaction time at the 10th minute of ?3.6 seconds (exp B=18.8, 95% CI 3.2-110.8; P=.001) and a Milestone level of ?3 (exp B=15.0, 95% CI 2.3-99.6; P=.005). However, a reaction time at the 10th minute of ?3.6 seconds was not related to a Milestone level of ?3. A low-frequency to high-frequency ratio between the 5th and 10th minute of ?1.43 seemed to be inversely associated with a hedonic stimulation score of ?2.0 (exp B=0.14, 95% CI 0.03-0.68; P=.015) after adjusting for video module. The main qualitative feedback indicated that the 360° VR video module was fun but caused mild dizziness, whereas the 2D video module was easy to follow but tedious. Conclusions: Our preliminary results showed that 360° VR video learning may be associated with a better Milestone level than 2D video learning, and that this did not seem to be related to cognitive load estimates or HRV indexes in the novice learners. Of note, an increase in sympathovagal balance may have been associated with a lower hedonic stimulation score, which may have met the learners? needs and prompted learning through the different video modules. Trial Registration: ClinicalTrials.gov NCT03501641; https://clinicaltrials.gov/ct2/show/NCT03501641 UR - https://games.jmir.org/2021/4/e13124 UR - http://dx.doi.org/10.2196/13124 UR - http://www.ncbi.nlm.nih.gov/pubmed/34813485 ID - info:doi/10.2196/13124 ER - TY - JOUR AU - Hou, Xinyao AU - Zhang, Yu AU - Wang, Yanping AU - Wang, Xinyi AU - Zhao, Jiahao AU - Zhu, Xiaobo AU - Su, Jianbo PY - 2021/11/19 TI - A Markerless 2D Video, Facial Feature Recognition?Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study JO - J Med Internet Res SP - e29554 VL - 23 IS - 11 KW - Parkinson disease KW - facial features KW - artificial intelligence KW - diagnosis N2 - Background: Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible. Objective: The study aimed to develop a markerless 2D video, facial feature recognition?based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis. Methods: We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists. Results: The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD. Conclusions: PD patients commonly exhibit masked facial features. Videos of a facial feature recognition?based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient?s condition, especially during the COVID-19 pandemic. UR - https://www.jmir.org/2021/11/e29554 UR - http://dx.doi.org/10.2196/29554 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806994 ID - info:doi/10.2196/29554 ER - TY - JOUR AU - Paetzold, Isabell AU - Hermans, M. Karlijn S. F. AU - Schick, Anita AU - Nelson, Barnaby AU - Velthorst, Eva AU - Schirmbeck, Frederike AU - AU - van Os, Jim AU - Morgan, Craig AU - van der Gaag, Mark AU - de Haan, Lieuwe AU - Valmaggia, Lucia AU - McGuire, Philip AU - Kempton, Matthew AU - Myin-Germeys, Inez AU - Reininghaus, Ulrich PY - 2021/11/19 TI - Momentary Manifestations of Negative Symptoms as Predictors of Clinical Outcomes in People at High Risk for Psychosis: Experience Sampling Study JO - JMIR Ment Health SP - e30309 VL - 8 IS - 11 KW - ecological momentary assessment KW - psychotic disorder KW - psychopathology N2 - Background: Negative symptoms occur in individuals at ultrahigh risk (UHR) for psychosis. Although there is evidence that observer ratings of negative symptoms are associated with level of functioning, the predictive value of subjective experience in daily life for individuals at UHR has not been studied yet. Objective: This study therefore aims to investigate the predictive value of momentary manifestations of negative symptoms for clinical outcomes in individuals at UHR. Methods: Experience sampling methodology was used to measure momentary manifestations of negative symptoms (blunted affective experience, lack of social drive, anhedonia, and social anhedonia) in the daily lives of 79 individuals at UHR. Clinical outcomes (level of functioning, illness severity, UHR status, and transition status) were assessed at baseline and at 1- and 2-year follow-ups. Results: Lack of social drive, operationalized as greater experienced pleasantness of being alone, was associated with poorer functioning at the 2-year follow-up (b=?4.62, P=.01). Higher levels of anhedonia were associated with poorer functioning at the 1-year follow-up (b=5.61, P=.02). Higher levels of social anhedonia were associated with poorer functioning (eg, disability subscale: b=6.36, P=.006) and greater illness severity (b=?0.38, P=.045) at the 1-year follow-up. In exploratory analyses, there was evidence that individuals with greater variability of positive affect (used as a measure of blunted affective experience) experienced a shorter time to remission from UHR status at follow-up (hazard ratio=4.93, P=.005). Conclusions: Targeting negative symptoms in individuals at UHR may help to predict clinical outcomes and may be a promising target for interventions in the early stages of psychosis. UR - https://mental.jmir.org/2021/11/e30309 UR - http://dx.doi.org/10.2196/30309 UR - http://www.ncbi.nlm.nih.gov/pubmed/34807831 ID - info:doi/10.2196/30309 ER - TY - JOUR AU - Wang, Huan AU - Wu, Wei AU - Han, Chunxia AU - Zheng, Jiaqi AU - Cai, Xinyu AU - Chang, Shimin AU - Shi, Junlong AU - Xu, Nan AU - Ai, Zisheng PY - 2021/11/19 TI - Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning?Based Development and Validation Study JO - JMIR Med Inform SP - e30079 VL - 9 IS - 11 KW - femoral neck fracture KW - osteonecrosis of the femoral head KW - machine learning KW - interpretability N2 - Background: The absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis. Objective: The aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation. Methods: We retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model. Results: A total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual?s probability of ONFH. Conclusions: Machine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF. UR - https://medinform.jmir.org/2021/11/e30079 UR - http://dx.doi.org/10.2196/30079 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806984 ID - info:doi/10.2196/30079 ER - TY - JOUR AU - Kiekens, Glenn AU - Robinson, Kealagh AU - Tatnell, Ruth AU - Kirtley, J. Olivia PY - 2021/11/19 TI - Opening the Black Box of Daily Life in Nonsuicidal Self-injury Research: With Great Opportunity Comes Great Responsibility JO - JMIR Ment Health SP - e30915 VL - 8 IS - 11 KW - real-time monitoring KW - nonsuicidal self-injury KW - NSSI KW - experience sampling KW - ecological momentary assessment KW - digital psychiatry UR - https://mental.jmir.org/2021/11/e30915 UR - http://dx.doi.org/10.2196/30915 UR - http://www.ncbi.nlm.nih.gov/pubmed/34807835 ID - info:doi/10.2196/30915 ER - TY - JOUR AU - Böttcher, Sebastian AU - Bruno, Elisa AU - Manyakov, V. Nikolay AU - Epitashvili, Nino AU - Claes, Kasper AU - Glasstetter, Martin AU - Thorpe, Sarah AU - Lees, Simon AU - Dümpelmann, Matthias AU - Van Laerhoven, Kristof AU - Richardson, P. Mark AU - Schulze-Bonhage, Andreas AU - PY - 2021/11/19 TI - Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation JO - JMIR Mhealth Uhealth SP - e27674 VL - 9 IS - 11 KW - wearables KW - epilepsy KW - seizure detection KW - multimodal data KW - mHealth KW - mobile health KW - digital health KW - eHealth N2 - Background: Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. Objective: Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. Methods: An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. Results: In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. Conclusions: We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection. UR - https://mhealth.jmir.org/2021/11/e27674 UR - http://dx.doi.org/10.2196/27674 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806993 ID - info:doi/10.2196/27674 ER - TY - JOUR AU - Woelfle, Tim AU - Pless, Silvan AU - Wiencierz, Andrea AU - Kappos, Ludwig AU - Naegelin, Yvonne AU - Lorscheider, Johannes PY - 2021/11/18 TI - Practice Effects of Mobile Tests of Cognition, Dexterity, and Mobility on Patients With Multiple Sclerosis: Data Analysis of a Smartphone-Based Observational Study JO - J Med Internet Res SP - e30394 VL - 23 IS - 11 KW - multiple sclerosis KW - digital biomarkers KW - practice effects KW - learning effects KW - learning curves KW - nonlinear mixed models KW - quantile regression KW - information processing speed KW - symbol digit modalities test KW - smartphones KW - wearable electronic devices KW - mobile phones N2 - Background: Smartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration. Objective: The aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing. Methods: We analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility. Results: Strong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (?=1.50-2.00). Only small differences were observed for Finger Pinching (?=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed. Conclusions: Smartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered. UR - https://www.jmir.org/2021/11/e30394 UR - http://dx.doi.org/10.2196/30394 UR - http://www.ncbi.nlm.nih.gov/pubmed/34792480 ID - info:doi/10.2196/30394 ER - TY - JOUR AU - Zhang, Qi AU - Fu, Yu AU - Lu, Yanhui AU - Zhang, Yating AU - Huang, Qifang AU - Yang, Yajie AU - Zhang, Ke AU - Li, Mingzi PY - 2021/11/17 TI - Impact of Virtual Reality-Based Therapies on Cognition and Mental Health of Stroke Patients: Systematic Review and Meta-analysis JO - J Med Internet Res SP - e31007 VL - 23 IS - 11 KW - virtual reality KW - stroke KW - cognition KW - depression KW - mental health N2 - Background: Stroke remains one of the major chronic illnesses worldwide that health care organizations will need to address for the next several decades. Individuals poststroke are subject to levels of cognitive impairment and mental health problems. Virtual reality (VR)-based therapies are new technologies used for cognitive rehabilitation and the management of psychological outcomes. Objective: This study performed a meta-analysis to evaluate the effects of VR-based therapies on cognitive function and mental health in patients with stroke. Methods: A comprehensive database search was performed using PubMed, MEDLINE (Ovid), Embase, Cochrane Library, and APA PsycINFO databases for randomized controlled trials (RCTs) that studied the effects of VR on patients with stroke. We included trials published up to April 15, 2021, that fulfilled our inclusion and exclusion criteria. The literature was screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.3 software. Results: A total of 894 patients from 23 RCTs were included in our meta-analysis. Compared to traditional rehabilitation therapies, the executive function (standard mean difference [SMD]=0.88, 95% confidence interval [CI]=0.06-1.70, P=.03), memory (SMD=1.44, 95% CI=0.21-2.68, P=.02), and visuospatial function (SMD=0.78, 95% CI=0.23-1.33, P=.006) significantly improved among patients after VR intervention. However, there were no significant differences observed in global cognitive function, attention, verbal fluency, depression, and the quality of life (QoL). Conclusions: The findings of our meta-analysis showed that VR-based therapies are efficacious in improving executive function, memory, and visuospatial function in patients with stroke. For global cognitive function, attention, verbal fluency, depression, and the QoL, further research is required. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021252788; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252788 UR - https://www.jmir.org/2021/11/e31007 UR - http://dx.doi.org/10.2196/31007 UR - http://www.ncbi.nlm.nih.gov/pubmed/34787571 ID - info:doi/10.2196/31007 ER - TY - JOUR AU - Ahn, Imjin AU - Gwon, Hansle AU - Kang, Heejun AU - Kim, Yunha AU - Seo, Hyeram AU - Choi, Heejung AU - Cho, Na Ha AU - Kim, Minkyoung AU - Jun, Joon Tae AU - Kim, Young-Hak PY - 2021/11/17 TI - Machine Learning?Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study JO - JMIR Med Inform SP - e32662 VL - 9 IS - 11 KW - electronic health records KW - cardiovascular diseases KW - discharge prediction KW - bed management KW - explainable artificial intelligence N2 - Background: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient?s hospitalization period may support the making of judicious decisions regarding bed management. Objective: First, this study aims to develop a machine learning (ML)?based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. Methods: We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. Results: We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. Conclusions: In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources. UR - https://medinform.jmir.org/2021/11/e32662 UR - http://dx.doi.org/10.2196/32662 UR - http://www.ncbi.nlm.nih.gov/pubmed/34787584 ID - info:doi/10.2196/32662 ER - TY - JOUR AU - Beukenhorst, L. Anna AU - Sergeant, C. Jamie AU - Schultz, M. David AU - McBeth, John AU - Yimer, B. Belay AU - Dixon, G. Will PY - 2021/11/16 TI - Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study JO - JMIR Mhealth Uhealth SP - e28857 VL - 9 IS - 11 KW - geolocation KW - global positioning system KW - smartphones KW - mobile phone KW - mobile health KW - environmental exposures KW - data analysis KW - digital epidemiology KW - missing data KW - location data KW - mobile application N2 - Background: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants? time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants. UR - https://mhealth.jmir.org/2021/11/e28857 UR - http://dx.doi.org/10.2196/28857 UR - http://www.ncbi.nlm.nih.gov/pubmed/34783661 ID - info:doi/10.2196/28857 ER - TY - JOUR AU - Yoo, Whi Dong AU - Ernala, Kiranmai Sindhu AU - Saket, Bahador AU - Weir, Domino AU - Arenare, Elizabeth AU - Ali, F. Asra AU - Van Meter, R. Anna AU - Birnbaum, L. Michael AU - Abowd, D. Gregory AU - De Choudhury, Munmun PY - 2021/11/16 TI - Clinician Perspectives on Using Computational Mental Health Insights From Patients? Social Media Activities: Design and Qualitative Evaluation of a Prototype JO - JMIR Ment Health SP - e25455 VL - 8 IS - 11 KW - mental health KW - social media KW - information technology N2 - Background: Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored. Objective: The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients? social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations. Methods: We developed a prototype that can analyze consented patients? Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient?s social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis. Results: Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients? verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients? social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings. Conclusions: Our findings support the touted potential of computational mental health insights from patients? social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology. UR - https://mental.jmir.org/2021/11/e25455 UR - http://dx.doi.org/10.2196/25455 UR - http://www.ncbi.nlm.nih.gov/pubmed/34783667 ID - info:doi/10.2196/25455 ER - TY - JOUR AU - Sylcott, Brian AU - Lin, Chia-Cheng AU - Williams, Keith AU - Hinderaker, Mark PY - 2021/11/15 TI - Investigating the Use of Virtual Reality Headsets for Postural Control Assessment: Instrument Validation Study JO - JMIR Rehabil Assist Technol SP - e24950 VL - 8 IS - 4 KW - postural sway KW - virtual reality KW - force plate KW - center of pressure N2 - Background: Accurately measuring postural sway is an important part of balance assessment and rehabilitation. Although force plates give accurate measurements, their costs and space requirements make their use impractical in many situations. Objective: The work presented in this paper aimed to address this issue by validating a virtual reality (VR) headset as a relatively low-cost alternative to force plates for postural sway measurement. The HTC Vive (HTC Corporation) VR headset has built-in sensors that allow for position and orientation tracking, making it a potentially e?ective tool for balance assessments. Methods: Participants in this study were asked to stand upright on a force plate (NeuroCom; Natus Medical Incorporated) while wearing the HTC Vive. Position data were collected from the headset and force plate simultaneously as participants experienced a custom-built VR environment that covered their entire field of view. The intraclass correlation coefficient (ICC) was used to examine the test-retest reliability of the postural control variables, which included the normalized path length, root mean square (RMS), and peak-to-peak (P2P) value. These were computed from the VR position output data and the center of pressure (COP) data from the force plate. Linear regression was used to investigate the correlations between the VR and force plate measurements. Results: Our results showed that the test-retest reliability of the RMS and P2P value of VR headset outputs (ICC: range 0.285-0.636) was similar to that of the RMS and P2P value of COP outputs (ICC: range 0.228-0.759). The linear regression between VR and COP measures showed significant correlations in RMSs and P2P values. Conclusions: Based on our results, the VR headset has the potential to be used for postural control measurements. However, the further development of software and testing protocols for balance assessments is needed. UR - https://rehab.jmir.org/2021/4/e24950 UR - http://dx.doi.org/10.2196/24950 UR - http://www.ncbi.nlm.nih.gov/pubmed/34779789 ID - info:doi/10.2196/24950 ER - TY - JOUR AU - Wasfi, Rania AU - Poirier Stephens, Zoe AU - Sones, Meridith AU - Laberee, Karen AU - Pugh, Caitlin AU - Fuller, Daniel AU - Winters, Meghan AU - Kestens, Yan PY - 2021/11/12 TI - Recruiting Participants for Population Health Intervention Research: Effectiveness and Costs of Recruitment Methods for a Cohort Study JO - J Med Internet Res SP - e21142 VL - 23 IS - 11 KW - recruitment methods KW - Facebook recruitment KW - cost-effectiveness KW - built environment KW - intervention research KW - natural experiment KW - mobile phone N2 - Background: Public health research studies often rely on population-based participation and draw on various recruitment methods to establish samples. Increasingly, researchers are turning to web-based recruitment tools. However, few studies detail traditional and web-based recruitment efforts in terms of costs and potential biases. Objective: This study aims to report on and evaluate the cost-effectiveness, time effectiveness, and sociodemographic representation of diverse recruitment methods used to enroll participants in 3 cities of the Interventions, Research, and Action in Cities Team (INTERACT) study, a cohort study conducted in Canadian cities. Methods: Over 2017 and 2018 in Vancouver, Saskatoon, and Montreal, the INTERACT study used the following recruitment methods: mailed letters, social media (including sponsored Facebook advertisements), news media, partner communications, snowball recruitment, in-person recruitment, and posters. Participation in the study involved answering web-based questionnaires (at minimum), activating a smartphone app to share sensor data, and wearing a device for mobility and physical activity monitoring. We describe sociodemographic characteristics by the recruitment method and analyze performance indicators, including cost, completion rate, and time effectiveness. Effectiveness included calculating cost per completer (ie, a participant who completed at least one questionnaire), the completion rate of a health questionnaire, and the delay between completion of eligibility and health questionnaires. Cost included producing materials (ie, printing costs), transmitting recruitment messages (ie, mailing list rental, postage, and sponsored Facebook posts charges), and staff time. In Montreal, the largest INTERACT sample, we modeled the number of daily recruits through generalized linear models accounting for the distributed lagged effects of recruitment campaigns. Results: Overall, 1791 participants were recruited from 3 cities and completed at least one questionnaire: 318 in Vancouver, 315 in Saskatoon, and 1158 in Montreal. In all cities, most participants chose to participate fully (questionnaires, apps, and devices). The costs associated with a completed participant varied across recruitment methods and by city. Facebook advertisements generated the most recruits (n=687), at a cost of CAD $15.04 (US $11.57; including staff time) per completer. Mailed letters were the costliest, at CAD $108.30 (US $83.3) per completer but served to reach older participants. All methods resulted in a gender imbalance, with women participating more, specifically with social media. Partner newsletters resulted in the participation of younger adults and were cost-efficient (CAD $5.16 [US $3.97] per completer). A generalized linear model for daily Montreal recruitment identified 2-day lag effects on most recruitment methods, except for the snowball campaign (4 days), letters (15 days), and reminder cards (5 days). Conclusions: This study presents comprehensive data on the costs, effectiveness, and bias of population recruitment in a cohort study in 3 Canadian cities. More comprehensive documentation and reporting of recruitment efforts across studies are needed to improve our capacity to conduct inclusive intervention research. UR - https://www.jmir.org/2021/11/e21142 UR - http://dx.doi.org/10.2196/21142 UR - http://www.ncbi.nlm.nih.gov/pubmed/34587586 ID - info:doi/10.2196/21142 ER - TY - JOUR AU - Martinez, J. Gonzalo AU - Mattingly, M. Stephen AU - Robles-Granda, Pablo AU - Saha, Koustuv AU - Sirigiri, Anusha AU - Young, Jessica AU - Chawla, Nitesh AU - De Choudhury, Munmun AU - D'Mello, Sidney AU - Mark, Gloria AU - Striegel, Aaron PY - 2021/11/12 TI - Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study JO - JMIR Mhealth Uhealth SP - e22218 VL - 9 IS - 11 KW - adherence KW - compliance KW - wearables KW - smartphones KW - research design KW - ecological momentary assessment KW - mobile sensing KW - mobile phone N2 - Background: Studies that use ecological momentary assessments (EMAs) or wearable sensors to track numerous attributes, such as physical activity, sleep, and heart rate, can benefit from reductions in missing data. Maximizing compliance is one method of reducing missing data to increase the return on the heavy investment of time and money into large-scale studies. Objective: This paper aims to identify the extent to which compliance can be prospectively predicted from individual attributes and initial compliance. Methods: We instrumented 757 information workers with fitness trackers for 1 year and conducted EMAs in the first 56 days of study participation as part of an observational study. Their compliance with the EMA and fitness tracker wearing protocols was analyzed. Overall, 31 individual characteristics (eg, demographics and personalities) and behavioral variables (eg, early compliance and study portal use) were considered, and 14 variables were selected to create beta regression models for predicting compliance with EMAs 56 days out and wearable compliance 1 year out. We surveyed study participation and correlated the results with compliance. Results: Our modeling indicates that 16% and 25% of the variance in EMA compliance and wearable compliance, respectively, could be explained through a survey of demographics and personality in a held-out sample. The likelihood of higher EMA and wearable compliance was associated with being older (EMA: odds ratio [OR] 1.02, 95% CI 1.00-1.03; wearable: OR 1.02, 95% CI 1.01-1.04), speaking English as a first language (EMA: OR 1.38, 95% CI 1.05-1.80; wearable: OR 1.39, 95% CI 1.05-1.85), having had a wearable before joining the study (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.50, 95% CI 1.23-1.83), and exhibiting conscientiousness (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.34, 95% CI 1.14-1.58). Compliance was negatively associated with exhibiting extraversion (EMA: OR 0.74, 95% CI 0.64-0.85; wearable: OR 0.67, 95% CI 0.57-0.78) and having a supervisory role (EMA: OR 0.65, 95% CI 0.54-0.79; wearable: OR 0.66, 95% CI 0.54-0.81). Furthermore, higher wearable compliance was negatively associated with agreeableness (OR 0.68, 95% CI 0.56-0.83) and neuroticism (OR 0.85, 95% CI 0.73-0.98). Compliance in the second week of the study could help explain more variance; 62% and 66% of the variance in EMA compliance and wearable compliance, respectively, was explained. Finally, compliance correlated with participants? self-reflection on the ease of participation, usefulness of our compliance portal, timely resolution of issues, and compensation adequacy, suggesting that these are avenues for improving compliance. Conclusions: We recommend conducting an initial 2-week pilot to measure trait-like compliance and identify participants at risk of long-term noncompliance, performing oversampling based on participants? individual characteristics to avoid introducing bias in the sample when excluding data based on noncompliance, using an issue tracking portal, and providing special care in troubleshooting to help participants maintain compliance. UR - https://mhealth.jmir.org/2021/11/e22218 UR - http://dx.doi.org/10.2196/22218 UR - http://www.ncbi.nlm.nih.gov/pubmed/34766911 ID - info:doi/10.2196/22218 ER - TY - JOUR AU - Lee, Kyu Jeong AU - Lin, Lavinia AU - Kang, Hyunjin PY - 2021/11/12 TI - The Influence of Normative Perceptions on the Uptake of the COVID-19 TraceTogether Digital Contact Tracing System: Cross-sectional Study JO - JMIR Public Health Surveill SP - e30462 VL - 7 IS - 11 KW - COVID-19 KW - social norms KW - TraceTogether KW - Singapore KW - contact tracing KW - mobile app KW - token N2 - Background: In 2020, the Singapore government rolled out the TraceTogether program, a digital system to facilitate contact tracing efforts in response to the COVID-19 pandemic. This system is available as a smartphone app and Bluetooth-enabled token to help identify close contacts. As of February 1, 2021, more than 80% of the population has either downloaded the mobile app or received the token in Singapore. Despite the high adoption rate of the TraceTogether mobile app and token (ie, device), it is crucial to understand the role of social and normative perceptions in uptake and usage by the public, given the collective efforts for contact tracing. Objective: This study aimed to examine normative influences (descriptive and injunctive norms) on TraceTogether device use for contact tracing purposes, informed by the theory of normative social behavior, a theoretical framework to explain how perceived social norms are related to behaviors. Methods: From January to February 2021, cross-sectional data were collected by a local research company through emailing their panel members who were (1) Singapore citizens or permanent residents aged 21 years or above; (2) able to read English; and (3) internet users with access to a personal email account. The study sample (n=1137) was restricted to those who had either downloaded the TraceTogether mobile app or received the token. Results: Multivariate (linear and ordinal logistic) regression analyses were carried out to assess the relationships of the behavioral outcome variables (TraceTogether device usage and intention of TraceTogether device usage) with potential correlates, including perceived social norms, perceived community, and interpersonal communication. Multivariate regression analyses indicated that descriptive norms (unstandardized regression coefficient ?=0.31, SE=0.05; P<.001) and injunctive norms (unstandardized regression coefficient ?=0.16, SE=0.04; P<.001) were significantly positively associated with the intention to use the TraceTogether device. It was also found that descriptive norms were a significant correlate of TraceTogether device use frequency (adjusted odds ratio [aOR] 2.08, 95% CI 1.66-2.61; P<.001). Though not significantly related to TraceTogether device use frequency, injunctive norms moderated the relationship between descriptive norms and the outcome variable (aOR 1.12, 95% CI 1.03-1.21; P=.005). Conclusions: This study provides useful implications for the design of effective intervention strategies to promote the uptake and usage of digital methods for contact tracing in a multiethnic Asian population. Our findings highlight that influence from social networks plays an important role in developing normative perceptions in relation to TraceTogether device use for contact tracing. To promote the uptake of the TraceTogether device and other preventive behaviors for COVID-19, it would be useful to devise norm-based interventions that address these normative perceptions by presenting high prevalence and approval of important social referents, such as family and close friends. UR - https://publichealth.jmir.org/2021/11/e30462 UR - http://dx.doi.org/10.2196/30462 UR - http://www.ncbi.nlm.nih.gov/pubmed/34623956 ID - info:doi/10.2196/30462 ER - TY - JOUR AU - Klein, Arno AU - Clucas, Jon AU - Krishnakumar, Anirudh AU - Ghosh, S. Satrajit AU - Van Auken, Wilhelm AU - Thonet, Benjamin AU - Sabram, Ihor AU - Acuna, Nino AU - Keshavan, Anisha AU - Rossiter, Henry AU - Xiao, Yao AU - Semenuta, Sergey AU - Badioli, Alessandra AU - Konishcheva, Kseniia AU - Abraham, Ann Sanu AU - Alexander, M. Lindsay AU - Merikangas, R. Kathleen AU - Swendsen, Joel AU - Lindner, B. Ariel AU - Milham, P. Michael PY - 2021/11/11 TI - Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study JO - J Med Internet Res SP - e22369 VL - 23 IS - 11 KW - mental health KW - mHealth KW - mobile health KW - digital health KW - eHealth KW - digital psychiatry KW - digital phenotyping KW - teletherapy KW - mobile device KW - mobile phone KW - smartphone KW - ecological momentary assessment KW - ecological momentary intervention KW - EMA KW - EMI KW - ESM KW - experience sampling KW - experience sampling methods N2 - Background: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. Objective: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. Methods: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform?s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. Results: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users? data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser?based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. Conclusions: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health?related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population. UR - https://www.jmir.org/2021/11/e22369 UR - http://dx.doi.org/10.2196/22369 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762054 ID - info:doi/10.2196/22369 ER - TY - JOUR AU - Kundu, Anasua AU - Chaiton, Michael AU - Billington, Rebecca AU - Grace, Daniel AU - Fu, Rui AU - Logie, Carmen AU - Baskerville, Bruce AU - Yager, Christina AU - Mitsakakis, Nicholas AU - Schwartz, Robert PY - 2021/11/11 TI - Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review JO - JMIR Med Inform SP - e28962 VL - 9 IS - 11 KW - sexual and gender minorities KW - mental health KW - mental disorders KW - substance-related disorders KW - machine learning N2 - Background: A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. Objective: This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. Methods: The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. Results: We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%). Conclusions: ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health. UR - https://medinform.jmir.org/2021/11/e28962 UR - http://dx.doi.org/10.2196/28962 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762059 ID - info:doi/10.2196/28962 ER - TY - JOUR AU - Soulard, Julie AU - Vaillant, Jacques AU - Baillet, Athan AU - Gaudin, Philippe AU - Vuillerme, Nicolas PY - 2021/11/9 TI - Gait and Axial Spondyloarthritis: Comparative Gait Analysis Study Using Foot-Worn Inertial Sensors JO - JMIR Mhealth Uhealth SP - e27087 VL - 9 IS - 11 KW - ankylosing spondylitis KW - spondyloarthritis KW - gait KW - locomotion KW - pain KW - mobility KW - spatiotemporal KW - digital health KW - sensors N2 - Background: Axial spondyloarthritis (axSpA) can lead to spinal mobility restrictions associated with restricted lower limb ranges of motion, thoracic kyphosis, spinopelvic ankylosis, or decrease in muscle strength. It is well known that these factors can have consequences on spatiotemporal gait parameters during walking. However, no study has assessed spatiotemporal gait parameters in patients with axSpA. Divergent results have been obtained in the studies assessing spatiotemporal gait parameters in ankylosing spondylitis, a subgroup of axSpA, which could be partly explained by self-reported pain intensity scores at time of assessment. Inertial measurement units (IMUs) are increasingly popular and may facilitate gait assessment in clinical practice. Objective: This study compared spatiotemporal gait parameters assessed with foot-worn IMUs in patients with axSpA and matched healthy individuals without and with pain intensity score as a covariate. Methods: A total of 30 patients with axSpA and 30 age- and sex-matched healthy controls performed a 10-m walk test at comfortable speed. Various spatiotemporal gait parameters were computed from foot-worn inertial sensors including gait speed in ms?1 (mean walking velocity), cadence in steps/minute (number of steps in a minute), stride length in m (distance between 2 consecutive footprints of the same foot on the ground), swing time in percentage (portion of the cycle during which the foot is in the air), stance time in percentage (portion of the cycle during which part of the foot touches the ground), and double support time in percentage (portion of the cycle where both feet touch the ground). Results: Age, height, and weight were not significantly different between groups. Self-reported pain intensity was significantly higher in patients with axSpA than healthy controls (P<.001). Independent sample t tests indicated that patients with axSpA presented lower gait speed (P<.001) and cadence (P=.004), shorter stride length (P<.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than healthy controls. When using pain intensity as a covariate, spatiotemporal gait parameters were still significant with patients with axSpA exhibiting lower gait speed (P<.001), shorter stride length (P=.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than matched healthy controls. Interestingly, there were no longer statistically significant between-group differences observed for the cadence (P=.17). Conclusions: Gait was significantly altered in patients with axSpA with reduced speed, cadence, stride length, and swing time and increased double support and stance time. Taken together, these changes in spatiotemporal gait parameters could be interpreted as the adoption of a so-called cautious gait pattern in patients with axSpA. Among factors that may influence gait in patients with axSpA, patient self-reported pain intensity could play a role. Finally, IMUs allowed computation of spatiotemporal gait parameters and are usable to assess gait in patients with axSpA in clinical routine. Trial Registration: ClinicalTrials.gov NCT03761212; https://clinicaltrials.gov/ct2/show/NCT03761212 International Registered Report Identifier (IRRID): RR2-10.1007/s00296-019-04396-4 UR - https://mhealth.jmir.org/2021/11/e27087 UR - http://dx.doi.org/10.2196/27087 UR - http://www.ncbi.nlm.nih.gov/pubmed/34751663 ID - info:doi/10.2196/27087 ER - TY - JOUR AU - Baumgartner, L. Susan AU - Buffkin Jr, Eric D. AU - Rukavina, Elise AU - Jones, Jason AU - Weiler, Elizabeth AU - Carnes, C. Tony PY - 2021/11/8 TI - A Novel Digital Pill System for Medication Adherence Measurement and Reporting: Usability Validation Study JO - JMIR Hum Factors SP - e30786 VL - 8 IS - 4 KW - digital pills KW - digital medication KW - ingestible event marker KW - ingestible sensor KW - human factors KW - usability KW - validation study KW - medication adherence KW - medication nonadherence KW - remote patient monitoring KW - mobile phone N2 - Background: Medication nonadherence is a costly problem that is common in clinical use and clinical trials alike, with significant adverse consequences. Digital pill systems have proved to be effective and safe solutions to the challenges of nonadherence, with documented success in improving adherence and health outcomes. Objective: The aim of this human factors validation study is to evaluate a novel digital pill system, the ID-Cap System from etectRx, for usability among patient users in a simulated real-world use environment. Methods: A total of 17 patients with diverse backgrounds who regularly take oral prescription medications were recruited. After training and a period of training decay, the participants were asked to complete 12 patient-use scenarios during which errors or difficulties were logged. The participants were also interviewed about their experiences with the ID-Cap System. Results: The participants ranged in age from 27 to 74 years (mean 51 years, SD 13.8 years), and they were heterogeneous in other demographic factors as well, such as education level, handedness, and sex. In this human factors validation study, the patient users completed 97.5% (196/201) of the total use scenarios successfully; 75.1% (151/201) were completed without any failures or errors. The participants found the ID-Cap System easy to use, and they were able to accurately and proficiently record ingestion events using the device. Conclusions: The participants demonstrated the ability to safely and effectively use the ID-Cap System for its intended use. The ID-Cap System has great potential as a useful tool for encouraging medication adherence and can be easily implemented by patient users. UR - https://humanfactors.jmir.org/2021/4/e30786 UR - http://dx.doi.org/10.2196/30786 UR - http://www.ncbi.nlm.nih.gov/pubmed/34747709 ID - info:doi/10.2196/30786 ER - TY - JOUR AU - Stecher, Chad AU - Berardi, Vincent AU - Fowers, Rylan AU - Christ, Jaclyn AU - Chung, Yunro AU - Huberty, Jennifer PY - 2021/11/4 TI - Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study JO - J Med Internet Res SP - e27282 VL - 23 IS - 11 KW - behavioral habits KW - habit formation KW - mindfulness meditation KW - mental health KW - mHealth KW - mobile health KW - dynamic time warping KW - mobile phone N2 - Background: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits. Objective: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits based on temporal similarity and estimate the association between temporal similarity and meditation app users? perceived health benefits. Methods: App-use data from June 2019 to June 2020 were analyzed for 2771 paying subscribers of a meditation mobile phone app, of whom 86.06% (2359/2771) were female, 72.61% (2012/2771) were college educated, 86.29% (2391/2771) were White, and 60.71% (1664/2771) were employed full-time. Participants volunteered to complete a survey assessing their perceived changes in physical and mental health from using the app. Receiver operating characteristic curve analysis was used to evaluate the ability of the temporal similarity measure to predict future behavior, and variable importance statistics from random forest models were used to corroborate these findings. Logistic regression was used to estimate the association between temporal similarity and self-reported physical and mental health benefits. Results: The temporal similarity of users? daily app use before completing the survey, as measured by the dynamic time warping (DTW) distance between app use on consecutive days, significantly predicted app use at 28 days and at 6 months after the survey, even after controlling for users? demographic and socioeconomic characteristics, total app sessions, duration of app use, and number of days with any app use. In addition, the temporal similarity measure significantly increased in the area under the receiver operating characteristic curve (AUC) for models predicting any future app use in 28 days (AUC=0.868 with DTW and 0.850 without DTW; P<.001) and for models predicting any app use in 6 months (AUC=0.821 with DTW and 0.802 without DTW; P<.001). Finally, a 1% increase in the temporal similarity of users? daily meditation practice with the app over 6 weeks before the survey was associated with increased odds of reporting mental health improvements, with an odds ratio of 2.94 (95% CI 1.832-6.369). Conclusions: The temporal similarity of the meditation app use was a significant predictor of future behavior, which suggests that this measure can identify reflexive meditation habits. In addition, temporal similarity was associated with greater perceived mental health benefits, which demonstrates that additional mental health benefits may be derived from forming reflexive meditation habits. UR - https://www.jmir.org/2021/11/e27282 UR - http://dx.doi.org/10.2196/27282 UR - http://www.ncbi.nlm.nih.gov/pubmed/34734826 ID - info:doi/10.2196/27282 ER - TY - JOUR AU - Mestrom, Eveline AU - Deneer, Ruben AU - Bonomi, G. Alberto AU - Margarito, Jenny AU - Gelissen, Jos AU - Haakma, Reinder AU - Korsten, M. Hendrikus H. AU - Scharnhorst, Volkher AU - Bouwman, Arthur R. PY - 2021/11/4 TI - Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study JO - JMIR Cardio SP - e27765 VL - 5 IS - 2 KW - validation KW - heart rate KW - photoplethysmography KW - perioperative patients KW - unobtrusive sensing N2 - Background: Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. Objective: The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. Methods: In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor?s ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient?s wrist. The agreement between the HR reported by the patient?s monitor and the HR extracted from the OHRM?s PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. Results: A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was ?0.14 bpm (LoA between ?3.08 bpm and 2.79 bpm) and ?0.19% (LoA between ?5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was ?0.11 bpm (LoA between ?2.79 bpm and 2.59 bpm) and ?0.15% (LoA between ?3.92% and 3.64%). Conclusions: This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of ?5 bpm to 5 bpm or ?10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients. UR - https://cardio.jmir.org/2021/2/e27765 UR - http://dx.doi.org/10.2196/27765 UR - http://www.ncbi.nlm.nih.gov/pubmed/34734834 ID - info:doi/10.2196/27765 ER - TY - JOUR AU - Gielis, Karsten AU - Vanden Abeele, Marie-Elena AU - De Croon, Robin AU - Dierick, Paul AU - Ferreira-Brito, Filipa AU - Van Assche, Lies AU - Verbert, Katrien AU - Tournoy, Jos AU - Vanden Abeele, Vero PY - 2021/11/4 TI - Dissecting Digital Card Games to Yield Digital Biomarkers for the Assessment of Mild Cognitive Impairment: Methodological Approach and Exploratory Study JO - JMIR Serious Games SP - e18359 VL - 9 IS - 4 KW - mild cognitive impairment KW - Klondike Solitaire KW - card games KW - generalized linear mixed effects analysis KW - expert study KW - monitoring KW - screening KW - cognition KW - dementia KW - older adults KW - mobile phone N2 - Background: Mild cognitive impairment (MCI), the intermediate cognitive status between normal cognitive decline and pathological decline, is an important clinical construct for signaling possible prodromes of dementia. However, this condition is underdiagnosed. To assist monitoring and screening, digital biomarkers derived from commercial off-the-shelf video games may be of interest. These games maintain player engagement over a longer period of time and support longitudinal measurements of cognitive performance. Objective: This paper aims to explore how the player actions of Klondike Solitaire relate to cognitive functions and to what extent the digital biomarkers derived from these player actions are indicative of MCI. Methods: First, 11 experts in the domain of cognitive impairments were asked to correlate 21 player actions to 11 cognitive functions. Expert agreement was verified through intraclass correlation, based on a 2-way, fully crossed design with type consistency. On the basis of these player actions, 23 potential digital biomarkers of performance for Klondike Solitaire were defined. Next, 23 healthy participants and 23 participants living with MCI were asked to play 3 rounds of Klondike Solitaire, which took 17 minutes on average to complete. A generalized linear mixed model analysis was conducted to explore the differences in digital biomarkers between the healthy participants and those living with MCI, while controlling for age, tablet experience, and Klondike Solitaire experience. Results: All intraclass correlations for player actions and cognitive functions scored higher than 0.75, indicating good to excellent reliability. Furthermore, all player actions had, according to the experts, at least one cognitive function that was on average moderately to strongly correlated to a cognitive function. Of the 23 potential digital biomarkers, 12 (52%) were revealed by the generalized linear mixed model analysis to have sizeable effects and significance levels. The analysis indicates sensitivity of the derived digital biomarkers to MCI. Conclusions: Commercial off-the-shelf games such as digital card games show potential as a complementary tool for screening and monitoring cognition. Trial Registration: ClinicalTrials.gov NCT02971124; https://clinicaltrials.gov/ct2/show/NCT02971124 UR - https://games.jmir.org/2021/4/e18359 UR - http://dx.doi.org/10.2196/18359 UR - http://www.ncbi.nlm.nih.gov/pubmed/34734825 ID - info:doi/10.2196/18359 ER - TY - JOUR AU - Engelhard, M. Matthew AU - D'Arcy, Joshua AU - Oliver, A. Jason AU - Kozink, Rachel AU - McClernon, Joseph F. PY - 2021/11/1 TI - Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation JO - J Med Internet Res SP - e27875 VL - 23 IS - 11 KW - smoking KW - smoking cessation KW - machine learning KW - computer vision KW - digital health KW - eHealth KW - behavior KW - CNN KW - neural network KW - artificial intelligence KW - AI KW - images KW - environment KW - ecological momentary assessment KW - mobile health KW - mHealth KW - mobile phone N2 - Background: Viewing their habitual smoking environments increases smokers? craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers? daily environments. Objective: In this study, we aim to predict environment-associated risk from continuously acquired images of smokers? daily environments. We also aim to understand how model performance varies by location type, as reported by participants. Methods: Smokers from Durham, North Carolina and surrounding areas completed ecological momentary assessments both immediately after smoking and at randomly selected times throughout the day for 2 weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network?based model was trained to predict smoking, craving, whether smoking was permitted in the current environment and whether the participant was outside based on images of participants? daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for out-of-sample prediction as well as personalized models trained on images from days 1 to 10. The models were optimized for mobile devices and implemented as a smartphone app. Results: A total of 48 participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was permitted in the current environment (AUC=0.932; AP=0.981), and whether the participant was outside (AUC=0.977; AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723; AP=0.785), whether smoking was permitted in the current environment (AUC=0.815; AP=0.937), and whether the participant was outside (AUC=0.949; AP=0.922); however, they were not effective in predicting craving (AUC=0.522; AP=0.427). Omitting image features reduced AUC by over 0.1 when predicting all outcomes except craving. Prediction of smoking was more effective for participants whose self-reported location type was more variable (Spearman ?=0.48; P=.001). Conclusions: Images of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device and can be incorporated into device-based smoking cessation interventions. UR - https://www.jmir.org/2021/11/e27875 UR - http://dx.doi.org/10.2196/27875 UR - http://www.ncbi.nlm.nih.gov/pubmed/34723819 ID - info:doi/10.2196/27875 ER - TY - JOUR AU - Akbarian, Sina AU - Ghahjaverestan, Montazeri Nasim AU - Yadollahi, Azadeh AU - Taati, Babak PY - 2021/11/1 TI - Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation JO - J Med Internet Res SP - e26524 VL - 23 IS - 11 KW - sleep apnea KW - deep learning KW - noncontact monitoring KW - computer vision KW - positional sleep apnea KW - 3D convolutional neural network KW - 3D-CNN N2 - Background: Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective: The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods: A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results: The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions: This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app. UR - https://www.jmir.org/2021/11/e26524 UR - http://dx.doi.org/10.2196/26524 UR - http://www.ncbi.nlm.nih.gov/pubmed/34723817 ID - info:doi/10.2196/26524 ER - TY - JOUR AU - Worth, Chris AU - Harper, Simon AU - Salomon-Estebanez, Maria AU - O'Shea, Elaine AU - Nutter, W. Paul AU - Dunne, J. Mark AU - Banerjee, Indraneel PY - 2021/10/29 TI - Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis JO - J Med Internet Res SP - e26957 VL - 23 IS - 10 KW - hyperinsulinism KW - continuous glucose monitoring KW - digital phenotype KW - hypoglycemia KW - nocturnal hypoglycemia N2 - Background: Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycemia is the cornerstone of the management of HI, but the risk of hypoglycemia at night or the timing of hypoglycemia in children with HI has not been studied; thus, the digital phenotype remains incomplete and management suboptimal. Objective: This study aims to quantify the timing of hypoglycemia in patients with HI to describe glycemic variability and to extend the digital phenotype. This will facilitate future work using computational modeling to enable behavior change and reduce exposure of patients with HI to injurious hypoglycemic events. Methods: Patients underwent continuous glucose monitoring (CGM) with a Dexcom G4 or G6 CGM device as part of their clinical assessment for either HI (N=23) or idiopathic ketotic hypoglycemia (IKH; N=24). The CGM data were analyzed for temporal trends. Hypoglycemia was defined as glucose levels <3.5 mmol/L. Results: A total of 449 hypoglycemic events totaling 15,610 minutes were captured over 237 days from 47 patients (29 males; mean age 70 months, SD 53). The mean length of hypoglycemic events was 35 minutes. There was a clear tendency for hypoglycemia in the early hours (3-7 AM), particularly for patients with HI older than 10 months who experienced hypoglycemia 7.6% (1480/19,370 minutes) of time in this period compared with 2.6% (2405/92,840 minutes) of time outside this period (P<.001). This tendency was less pronounced in patients with HI who were younger than 10 months, patients with a negative genetic test result, and patients with IKH. Despite real-time CGM, there were 42 hypoglycemic events from 13 separate patients with HI lasting >30 minutes. Conclusions: This is the first study to have taken the first step in extending the digital phenotype of HI by describing the glycemic trends and identifying the timing of hypoglycemia measured by CGM. We have identified the early hours as a time of high hypoglycemia risk for patients with HI and demonstrated that simple provision of CGM data to patients is not sufficient to eliminate hypoglycemia. Future work in HI should concentrate on the early hours as a period of high risk for hypoglycemia and must target personalized hypoglycemia predictions. Focus must move to the human-computer interaction as an aspect of the digital phenotype that is susceptible to change rather than simple mathematical modeling to produce small improvements in hypoglycemia prediction accuracy. UR - https://www.jmir.org/2021/10/e26957 UR - http://dx.doi.org/10.2196/26957 UR - http://www.ncbi.nlm.nih.gov/pubmed/34435596 ID - info:doi/10.2196/26957 ER - TY - JOUR AU - Li, Po-Hung Lieber AU - Han, Ji-Yan AU - Zheng, Wei-Zhong AU - Huang, Ren-Jie AU - Lai, Ying-Hui PY - 2021/10/28 TI - Improved Environment-Aware?Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study JO - J Med Internet Res SP - e25460 VL - 23 IS - 10 KW - cochlear implants KW - noise reduction KW - deep learning KW - noise classification KW - hearing KW - deaf KW - sound KW - audio KW - cochlear N2 - Background: Cochlear implant technology is a well-known approach to help deaf individuals hear speech again and can improve speech intelligibility in quiet conditions; however, it still has room for improvement in noisy conditions. More recently, it has been proven that deep learning?based noise reduction, such as noise classification and deep denoising autoencoder (NC+DDAE), can benefit the intelligibility performance of patients with cochlear implants compared to classical noise reduction algorithms. Objective: Following the successful implementation of the NC+DDAE model in our previous study, this study aimed to propose an advanced noise reduction system using knowledge transfer technology, called NC+DDAE_T; examine the proposed NC+DDAE_T noise reduction system using objective evaluations and subjective listening tests; and investigate which layer substitution of the knowledge transfer technology in the NC+DDAE_T noise reduction system provides the best outcome. Methods: The knowledge transfer technology was adopted to reduce the number of parameters of the NC+DDAE_T compared with the NC+DDAE. We investigated which layer should be substituted using short-time objective intelligibility and perceptual evaluation of speech quality scores as well as t-distributed stochastic neighbor embedding to visualize the features in each model layer. Moreover, we enrolled 10 cochlear implant users for listening tests to evaluate the benefits of the newly developed NC+DDAE_T. Results: The experimental results showed that substituting the middle layer (ie, the second layer in this study) of the noise-independent DDAE (NI-DDAE) model achieved the best performance gain regarding short-time objective intelligibility and perceptual evaluation of speech quality scores. Therefore, the parameters of layer 3 in the NI-DDAE were chosen to be replaced, thereby establishing the NC+DDAE_T. Both objective and listening test results showed that the proposed NC+DDAE_T noise reduction system achieved similar performances compared with the previous NC+DDAE in several noisy test conditions. However, the proposed NC+DDAE_T only required a quarter of the number of parameters compared to the NC+DDAE. Conclusions: This study demonstrated that knowledge transfer technology can help reduce the number of parameters in an NC+DDAE while keeping similar performance rates. This suggests that the proposed NC+DDAE_T model may reduce the implementation costs of this noise reduction system and provide more benefits for cochlear implant users. UR - https://www.jmir.org/2021/10/e25460 UR - http://dx.doi.org/10.2196/25460 UR - http://www.ncbi.nlm.nih.gov/pubmed/34709193 ID - info:doi/10.2196/25460 ER - TY - JOUR AU - Wang, Jie-Teng AU - Lin, Wen-Yang PY - 2021/10/28 TI - Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation JO - JMIR Med Inform SP - e28752 VL - 9 IS - 10 KW - adverse drug reaction KW - data anonymization KW - incremental data publishing KW - privacy preserving data publishing KW - spontaneous reporting system KW - drug KW - data set KW - anonymous KW - privacy KW - security KW - algorithm KW - development KW - validation KW - data N2 - Background: Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals? privacy. We have previously proposed a privacy model called MS(k, ?*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, ?*)-bounding. Objective: We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data. Methods: We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, ?*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio). Results: The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal. Conclusions: The proposed PPMS(k, ?*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection. UR - https://medinform.jmir.org/2021/10/e28752 UR - http://dx.doi.org/10.2196/28752 UR - http://www.ncbi.nlm.nih.gov/pubmed/34709197 ID - info:doi/10.2196/28752 ER - TY - JOUR AU - Sekandi, Nabbuye Juliet AU - Kasiita, Vicent AU - Onuoha, Amara Nicole AU - Zalwango, Sarah AU - Nakkonde, Damalie AU - Kaawa-Mafigiri, David AU - Turinawe, Julius AU - Kakaire, Robert AU - Davis-Olwell, Paula AU - Atuyambe, Lynn AU - Buregyeya, Esther PY - 2021/10/27 TI - Stakeholders? Perceptions of Benefits of and Barriers to Using Video-Observed Treatment for Monitoring Patients With Tuberculosis in Uganda: Exploratory Qualitative Study JO - JMIR Mhealth Uhealth SP - e27131 VL - 9 IS - 10 KW - tuberculosis KW - adherence KW - mHealth KW - video directly observed therapy KW - Uganda KW - mobile phone N2 - Background: Nonadherence to treatment remains a barrier to tuberculosis (TB) control. Directly observed therapy (DOT) is the standard for monitoring adherence to TB treatment worldwide, but its implementation is challenging, especially in resource-limited settings. DOT is labor-intensive and inconvenient to both patients and health care workers. Video DOT (VDOT) is a novel patient-centered alternative that uses mobile technology to observe patients taking medication remotely. However, the perceptions and acceptability of potential end users have not been evaluated in Africa. Objective: This study explores stakeholders? acceptability of, as well as perceptions of potential benefits of and barriers to, using VDOT to inform a pilot study for monitoring patients with TB in urban Uganda. Methods: An exploratory, qualitative, cross-sectional study with an exit survey was conducted in Kampala, Uganda, from April to May 2018. We conducted 5 focus group discussions, each comprising 6 participants. Groups included patients with TB (n=2 groups; male and female), health care providers (n=1), caregivers (n=1), and community DOT volunteer workers (n=1). The questions that captured perceived benefits and barriers were guided by domains adopted from the Technology Acceptance Model. These included perceived usefulness, ease of use, and intent to use technology. Eligible participants were aged ?18 years and provided written informed consent. For patients with TB, we included only those who had completed at least 2 months of treatment to minimize the likelihood of infection. A purposive sample of patients, caregivers, health care providers, and community DOT workers was recruited at 4 TB clinics in Kampala. Trained interviewers conducted unstructured interviews that were audio-recorded, transcribed, and analyzed using inductive content analysis to generate emerging themes. Results: The average age of participants was 34.5 (SD 10.7) years. VDOT was acceptable to most participants on a scale of 1 to 10. Of the participants, 70% (21/30) perceived it as highly acceptable, with scores ?8, whereas 30% (9/30) scored between 5 and 7. Emergent themes on perceived benefits of VDOT were facilitation of easy adherence monitoring, timely follow-up on missed doses, patient-provider communication, and saving time and money because of minimal travel to meet in person. Perceived barriers included limited technology usability skills, inadequate cellular connectivity, internet access, availability of electricity, cost of the smartphone, and use of the internet. Some female patients raised concerns about the disruption of their domestic work routines to record videos. The impact of VDOT on privacy and confidentiality emerged as both a perceived benefit and barrier. Conclusions: VDOT was acceptable and perceived as beneficial by most study participants, despite potential technical and cost barriers. Mixed perceptions emerged about the impact of VDOT on privacy and confidentiality. Future efforts should focus on training users, ensuring adequate technical infrastructure, assuring privacy, and performing comparative cost analyses in the local context. UR - https://mhealth.jmir.org/2021/10/e27131 UR - http://dx.doi.org/10.2196/27131 UR - http://www.ncbi.nlm.nih.gov/pubmed/34704961 ID - info:doi/10.2196/27131 ER - TY - JOUR AU - Dasgupta, Soham AU - Jayagopal, Aishwarya AU - Jun Hong, Lim Abel AU - Mariappan, Ragunathan AU - Rajan, Vaibhav PY - 2021/10/25 TI - Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation JO - JMIR Med Inform SP - e32730 VL - 9 IS - 10 KW - adverse drug event KW - knowledge graph KW - Embedding of Semantic Predications KW - biomedical literature N2 - Background: Adverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilance, is routinely conducted to find unknown ADEs. A wealth of information, which facilitates ADE discovery, lies in the growing body of biomedical literature. Knowledge graphs (KGs) encode information from the literature, where the vertices and the edges represent clinical concepts and their relations, respectively. The scale and unstructured form of the literature necessitates the use of natural language processing (NLP) to automatically create such KGs. Previous studies have demonstrated the utility of such literature-derived KGs in ADE prediction. Through unsupervised learning of the representations (features) of clinical concepts from the KG, which are used in machine learning models, state-of-the-art results for ADE prediction were obtained on benchmark data sets. Objective: Due to the use of NLP to infer literature-derived KGs, there is noise in the form of false positive (erroneous) and false negative (absent) nodes and edges. Previous representation learning methods do not account for such inaccuracies in the graph. NLP algorithms can quantify the confidence in their inference of extracted concepts and relations from the literature. Our hypothesis, which motivates this work, is that by using such confidence scores during representation learning, the learned embeddings would yield better features for ADE prediction models. Methods: We developed methods to use these confidence scores on two well-known representation learning methods?DeepWalk and Translating Embeddings for Modeling Multi-relational Data (TransE)?to develop their weighted versions: Weighted DeepWalk and Weighted TransE. These methods were used to learn representations from a large literature-derived KG, the Semantic MEDLINE Database, which contains more than 93 million clinical relations. They were compared with Embedding of Semantic Predications, which, to our knowledge, is the best reported representation learning method using the Semantic MEDLINE Database with state-of-the-art results for ADE prediction. Representations learned from different methods were used (separately) as features of drugs and diseases to build classification models for ADE prediction using benchmark data sets. The methods were compared rigorously over multiple cross-validation settings. Results: The weighted versions we designed were able to learn representations that yielded more accurate predictive models than the corresponding unweighted versions of both DeepWalk and TransE, as well as Embedding of Semantic Predications, in our experiments. There were performance improvements of up to 5.75% in the F1-score and 8.4% in the area under the receiver operating characteristic curve value, thus advancing the state of the art in ADE prediction from literature-derived KGs. Conclusions: Our classification models can be used to aid pharmacovigilance teams in detecting potentially new ADEs. Our experiments demonstrate the importance of modeling inaccuracies in the inferred KGs for representation learning. UR - https://medinform.jmir.org/2021/10/e32730 UR - http://dx.doi.org/10.2196/32730 UR - http://www.ncbi.nlm.nih.gov/pubmed/34694230 ID - info:doi/10.2196/32730 ER - TY - JOUR AU - Cilia, Federica AU - Carette, Romuald AU - Elbattah, Mahmoud AU - Dequen, Gilles AU - Guérin, Jean-Luc AU - Bosche, Jérôme AU - Vandromme, Luc AU - Le Driant, Barbara PY - 2021/10/25 TI - Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning JO - JMIR Hum Factors SP - e27706 VL - 8 IS - 4 KW - autism spectrum disorder KW - screening KW - eye tracking KW - data visualization KW - machine learning KW - deep learning KW - AI KW - ASS KW - artificial intelligence KW - ML KW - adolescent KW - diagnosis N2 - Background: The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective: This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods: The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results: The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions: Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders. UR - https://humanfactors.jmir.org/2021/4/e27706 UR - http://dx.doi.org/10.2196/27706 UR - http://www.ncbi.nlm.nih.gov/pubmed/34694238 ID - info:doi/10.2196/27706 ER - TY - JOUR AU - Lavertu, Adam AU - Hamamsy, Tymor AU - Altman, B. Russ PY - 2021/10/21 TI - Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis JO - J Med Internet Res SP - e27714 VL - 23 IS - 10 KW - social media for health KW - pharmacovigilance KW - adverse drug reactions KW - machine learning KW - network analysis KW - word embeddings KW - drug safety KW - social media N2 - Background: Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. Objective: The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. Methods: We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Results: Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and ?0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Conclusions: Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data. UR - https://www.jmir.org/2021/10/e27714 UR - http://dx.doi.org/10.2196/27714 UR - http://www.ncbi.nlm.nih.gov/pubmed/34673524 ID - info:doi/10.2196/27714 ER - TY - JOUR AU - Hao, Tianyong AU - Huang, Zhengxing AU - Liang, Likeng AU - Weng, Heng AU - Tang, Buzhou PY - 2021/10/21 TI - Health Natural Language Processing: Methodology Development and Applications JO - JMIR Med Inform SP - e23898 VL - 9 IS - 10 KW - health care KW - unstructured text KW - natural language processing KW - methodology KW - application UR - https://medinform.jmir.org/2021/10/e23898 UR - http://dx.doi.org/10.2196/23898 UR - http://www.ncbi.nlm.nih.gov/pubmed/34673533 ID - info:doi/10.2196/23898 ER - TY - JOUR AU - Khaksar, Siavash AU - Pan, Huizhu AU - Borazjani, Bita AU - Murray, Iain AU - Agrawal, Himanshu AU - Liu, Wanquan AU - Elliott, Catherine AU - Imms, Christine AU - Campbell, Amity AU - Walmsley, Corrin PY - 2021/10/20 TI - Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial JO - JMIR Rehabil Assist Technol SP - e29769 VL - 8 IS - 4 KW - inertial measurement unit KW - wearable sensors KW - biomedical sensors KW - machine learning KW - human joint measurement KW - occupational therapy KW - range of motion KW - wearable KW - sensor KW - children KW - cerebral palsy KW - therapy KW - disability KW - N2 - Background: Cerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP. Objective: This paper presents collaborative research between the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University and a team of clinicians in a multicenter randomized controlled trial involving children with CP. This study aims to develop a digital solution for mass data collection using inertial measurement units (IMUs) and the application of machine learning (ML) to classify the movement features associated with CP to determine the effectiveness of therapy. The results were calculated without the need to measure Euler, quaternion, and joint measurement calculation, reducing the time required to classify the data. Methods: Custom IMUs were developed to record the usual wrist movements of participants in 2 age groups. The first age group consisted of participants approaching 3 years of age, and the second age group consisted of participants approaching 15 years of age. Both groups consisted of participants with and without CP. The IMU data were used to calculate the joint angle of the wrist movement and determine the range of motion. A total of 9 different ML algorithms were used to classify the movement features associated with CP. This classification can also confirm if the current treatment (in this case, the use of wrist extension) is effective. Results: Upon completion of the project, the wrist joint angle was successfully calculated and validated against Vicon motion capture. In addition, the CP movement was classified as a feature using ML on raw IMU data. The Random Forrest algorithm achieved the highest accuracy of 87.75% for the age range approaching 15 years, and C4.5 decision tree achieved the highest accuracy of 89.39% for the age range approaching 3 years. Conclusions: Anecdotal feedback from Minimising Impairment Trial researchers was positive about the potential for IMUs to contribute accurate data about active range of motion, especially in children, for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movements throughout the day. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001276640, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367398; ANZCTR ACTRN12614001275651, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367422 UR - https://rehab.jmir.org/2021/4/e29769 UR - http://dx.doi.org/10.2196/29769 UR - http://www.ncbi.nlm.nih.gov/pubmed/34668870 ID - info:doi/10.2196/29769 ER - TY - JOUR AU - Rahman, Wasifur AU - Lee, Sangwu AU - Islam, Saiful Md AU - Antony, Nikhil Victor AU - Ratnu, Harshil AU - Ali, Rafayet Mohammad AU - Mamun, Al Abdullah AU - Wagner, Ellen AU - Jensen-Roberts, Stella AU - Waddell, Emma AU - Myers, Taylor AU - Pawlik, Meghan AU - Soto, Julia AU - Coffey, Madeleine AU - Sarkar, Aayush AU - Schneider, Ruth AU - Tarolli, Christopher AU - Lizarraga, Karlo AU - Adams, Jamie AU - Little, A. Max AU - Dorsey, Ray E. AU - Hoque, Ehsan PY - 2021/10/19 TI - Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study JO - J Med Internet Res SP - e26305 VL - 23 IS - 10 KW - Parkinson?s disease KW - speech analysis KW - improving access and equity in health care KW - mobile phone N2 - Background: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases?fueled mostly by environmental pollution and an aging population?can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. Objective: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. Methods: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, ?the quick brown fox jumps over the lazy dog.? We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning?based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model?s output. Results: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost?a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing ?ahh?) influence the model?s decision the most. Conclusions: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care. UR - https://www.jmir.org/2021/10/e26305 UR - http://dx.doi.org/10.2196/26305 UR - http://www.ncbi.nlm.nih.gov/pubmed/34665148 ID - info:doi/10.2196/26305 ER - TY - JOUR AU - Abujarad, Fuad AU - Peduzzi, Peter AU - Mun, Sophia AU - Carlson, Kristina AU - Edwards, Chelsea AU - Dziura, James AU - Brandt, Cynthia AU - Alfano, Sandra AU - Chupp, Geoffrey PY - 2021/10/19 TI - Comparing a Multimedia Digital Informed Consent Tool With Traditional Paper-Based Methods: Randomized Controlled Trial JO - JMIR Form Res SP - e20458 VL - 5 IS - 10 KW - digital consent KW - digital health KW - e-consent KW - informed consent KW - mobile phone N2 - Background: The traditional informed consent (IC) process rarely emphasizes research participants? comprehension of medical information, leaving them vulnerable to unknown risks and consequences associated with procedures or studies. Objective: This paper explores how we evaluated the feasibility of a digital health tool called Virtual Multimedia Interactive Informed Consent (VIC) for advancing the IC process and compared the results with traditional paper-based methods of IC. Methods: Using digital health and web-based coaching, we developed the VIC tool that uses multimedia and other digital features to improve the current IC process. The tool was developed on the basis of the user-centered design process and Mayer?s cognitive theory of multimedia learning. This study is a randomized controlled trial that compares the feasibility of VIC with standard paper consent to understand the impact of interactive digital consent. Participants were recruited from the Winchester Chest Clinic at Yale New Haven Hospital in New Haven, Connecticut, and healthy individuals were recruited from the community using fliers. In this coordinator-assisted trial, participants were randomized to complete the IC process using VIC on the iPad or with traditional paper consent. The study was conducted at the Winchester Chest Clinic, and the outcomes were self-assessed through coordinator-administered questionnaires. Results: A total of 50 participants were recruited in the study (VIC, n=25; paper, n=25). The participants in both groups had high comprehension. VIC participants reported higher satisfaction, higher perceived ease of use, higher ability to complete the consent independently, and shorter perceived time to complete the consent process. Conclusions: The use of dynamic, interactive audiovisual elements in VIC may improve participants? satisfaction and facilitate the IC process. We believe that using VIC in an ongoing, real-world study rather than a hypothetical study improved the reliability of our findings, which demonstrates VIC?s potential to improve research participants? comprehension and the overall process of IC. Trial Registration: ClinicalTrials.gov NCT02537886; https://clinicaltrials.gov/ct2/show/NCT02537886 UR - https://formative.jmir.org/2021/10/e20458 UR - http://dx.doi.org/10.2196/20458 UR - http://www.ncbi.nlm.nih.gov/pubmed/34665142 ID - info:doi/10.2196/20458 ER - TY - JOUR AU - Follmann, Andreas AU - Ruhl, Alexander AU - Gösch, Michael AU - Felzen, Marc AU - Rossaint, Rolf AU - Czaplik, Michael PY - 2021/10/18 TI - Augmented Reality for Guideline Presentation in Medicine: Randomized Crossover Simulation Trial for Technically Assisted Decision-making JO - JMIR Mhealth Uhealth SP - e17472 VL - 9 IS - 10 KW - augmented reality KW - smart glasses KW - wearables KW - guideline presentation KW - decision support KW - triage N2 - Background: Guidelines provide instructions for diagnostics and therapy in modern medicine. Various mobile devices are used to represent the potential complex decision trees. An example of time-critical decisions is triage in case of a mass casualty incident. Objective: In this randomized controlled crossover study, the potential of augmented reality for guideline presentation was evaluated and compared with the guideline presentation provided in a tablet PC as a conventional device. Methods: A specific Android app was designed for use with smart glasses and a tablet PC for the presentation of a triage algorithm as an example for a complex guideline. Forty volunteers simulated a triage based on 30 fictional patient descriptions, each with technical support from smart glasses and a tablet PC in a crossover trial design. The time to come to a decision and the accuracy were recorded and compared between both devices. Results: A total of 2400 assessments were performed by the 40 volunteers. A significantly faster time to triage was achieved in total with the tablet PC (median 12.8 seconds, IQR 9.4-17.7; 95% CI 14.1-14.9) compared to that to triage with smart glasses (median 17.5 seconds, IQR 13.2-22.8, 95% CI 18.4-19.2; P=.001). Considering the difference in the triage time between both devices, the additional time needed with the smart glasses could be reduced significantly in the course of assessments (21.5 seconds, IQR 16.5-27.3, 95% CI 21.6-23.2) in the first run, 17.4 seconds (IQR 13-22.4, 95% CI 17.6-18.9) in the second run, and 14.9 seconds (IQR 11.7-18.6, 95% CI 15.2-16.3) in the third run (P=.001). With regard to the accuracy of the guideline decisions, there was no significant difference between both the devices. Conclusions: The presentation of a guideline on a tablet PC as well as through augmented reality achieved good results. The implementation with smart glasses took more time owing to their more complex operating concept but could be accelerated in the course of the study after adaptation. Especially in a non?time-critical working area where hands-free interfaces are useful, a guideline presentation with augmented reality can be of great use during clinical management. UR - https://mhealth.jmir.org/2021/10/e17472 UR - http://dx.doi.org/10.2196/17472 UR - http://www.ncbi.nlm.nih.gov/pubmed/34661548 ID - info:doi/10.2196/17472 ER - TY - JOUR AU - Cheng, Christina AU - Elsworth, Gerald AU - Osborne, H. Richard PY - 2021/10/14 TI - Validity Evidence Based on Relations to Other Variables of the eHealth Literacy Questionnaire (eHLQ): Bayesian Approach to Test for Known-Groups Validity JO - J Med Internet Res SP - e30243 VL - 23 IS - 10 KW - eHealth KW - digital health KW - health literacy KW - health equity KW - questionnaire design KW - health literacy questionnaire KW - validity evidence KW - mediation effect KW - mobile phone N2 - Background: As health resources and services are increasingly delivered through digital platforms, eHealth literacy is becoming a set of essential capabilities to improve consumer health in the digital era. To understand eHealth literacy needs, a meaningful measure is required. Strong initial evidence for the reliability and construct validity of inferences drawn from the eHealth Literacy Questionnaire (eHLQ) was obtained during its development in Denmark, but validity testing for varying purposes is an ongoing and cumulative process. Objective: This study aims to examine validity evidence based on relations to other variables?using data collected with the known-groups approach?to further explore if the eHLQ is a robust tool to understand eHealth literacy needs in different contexts. A priori hypotheses are set for the expected score differences among age, sex, education, and information and communication technology (ICT) use for each of the 7 eHealth literacy constructs represented by the 7 eHLQ scales. Methods: A Bayesian mediated multiple indicators multiple causes model approach was used to simultaneously identify group differences and test measurement invariance through differential item functioning across the groups, with ICT use as a mediator. A sample size of 500 participants was estimated. Data were collected at 3 diverse health sites in Australia. Results: Responses from 525 participants were included for analysis. Being older was significantly related to lower scores in 4 eHLQ scales, with 3. Ability to actively engage with digital services having the strongest effect (total effect ?0.37; P<.001), followed by 1. Using technology to process health information (total effect ?0.32; P<.001), 5. Motivated to engage with digital services (total effect ?0.21; P=.01), and 7. Digital services that suit individual needs (total effect ?0.21; P=.02). However, the effects were only partially mediated by ICT use. Higher education was associated with higher scores in 1. Using technology to process health information (total effect 0.22; P=.01) and 3. Ability to actively engage with digital services (total effect 0.25; P<.001), with the effects mostly mediated by ICT use. Higher ICT use was related to higher scores in all scales except 2. Understanding health concepts and language and 4. Feel safe and in control. Either no or ignorable cases of differential item functioning were found across the 4 groups. Conclusions: By using a Bayesian mediated multiple indicators multiple causes model, this study provides supportive validity evidence for the eHLQ based on relations to other variables as well as established evidence regarding internal structure related to measurement invariance across the groups for the 7 scales in the Australian community health context. This study also demonstrates that the eHLQ can be used to gain valuable insights into people?s eHealth literacy needs to help optimize access and use of digital health and promote health equity. UR - https://www.jmir.org/2021/10/e30243 UR - http://dx.doi.org/10.2196/30243 UR - http://www.ncbi.nlm.nih.gov/pubmed/34647897 ID - info:doi/10.2196/30243 ER - TY - JOUR AU - Li, Xinyue AU - Kane, Michael AU - Zhang, Yunting AU - Sun, Wanqi AU - Song, Yuanjin AU - Dong, Shumei AU - Lin, Qingmin AU - Zhu, Qi AU - Jiang, Fan AU - Zhao, Hongyu PY - 2021/10/14 TI - Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach JO - J Med Internet Res SP - e18403 VL - 23 IS - 10 KW - wearable device KW - actigraphy KW - circadian rhythm KW - physical activity KW - early childhood development N2 - Background: Wearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers. Objective: This study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development. Methods: We developed a machine learning?based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis. Results: The PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third?day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth?day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third?day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third?day periodicity (P<.001). Conclusions: The proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood. UR - https://www.jmir.org/2021/10/e18403 UR - http://dx.doi.org/10.2196/18403 UR - http://www.ncbi.nlm.nih.gov/pubmed/34647895 ID - info:doi/10.2196/18403 ER - TY - JOUR AU - Hu, Xiao-Su AU - Beard, Katherine AU - Sherbel, Catherine Mary AU - Nascimento, D. Thiago AU - Petty, Sean AU - Pantzlaff, Eddie AU - Schwitzer, David AU - Kaciroti, Niko AU - Maslowski, Eric AU - Ashman, M. Lawrence AU - Feinberg, E. Stephen AU - DaSilva, F. Alexandre PY - 2021/10/12 TI - Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near-infrared Spectroscopy Study JO - J Med Internet Res SP - e27298 VL - 23 IS - 10 KW - virtual reality breathing KW - traditional mindful breathing KW - pain KW - functional near-infrared spectroscopy KW - mobile phone N2 - Background: Pain is a complex experience that involves sensory-discriminative and cognitive-emotional neuronal processes. It has long been known across cultures that pain can be relieved by mindful breathing (MB). There is a common assumption that MB exerts its analgesic effect through interoception. Interoception refers to consciously refocusing the mind?s attention to the physical sensation of internal organ function. Objective: In this study, we dissect the cortical analgesic processes by imaging the brains of healthy subjects exposed to traditional MB (TMB) and compare them with another group for which we augmented MB to an outside sensory experience via virtual reality breathing (VRB). Methods: The VRB protocol involved in-house?developed virtual reality 3D lungs that synchronized with the participants? breathing cycles in real time, providing them with an immersive visual-auditory exteroception of their breathing. Results: We found that both breathing interventions led to a significant increase in pain thresholds after week-long practices, as measured by a thermal quantitative sensory test. However, the underlying analgesic brain mechanisms were opposite, as revealed by functional near-infrared spectroscopy data. In the TMB practice, the anterior prefrontal cortex uniquely modulated the premotor cortex. This increased its functional connection with the primary somatosensory cortex (S1), thereby facilitating the S1-based sensory-interoceptive processing of breathing but inhibiting its other role in sensory-discriminative pain processing. In contrast, virtual reality induced an immersive 3D exteroception with augmented visual-auditory cortical activations, which diminished the functional connection with the S1 and consequently weakened the pain processing function of the S1. Conclusions: In summary, our study suggested two analgesic neuromechanisms of VRB and TMB practices?exteroception and interoception?that distinctively modulated the S1 processing of the ascending noxious inputs. This is in line with the concept of dualism (Yin and Yang). UR - https://www.jmir.org/2021/10/e27298 UR - http://dx.doi.org/10.2196/27298 UR - http://www.ncbi.nlm.nih.gov/pubmed/34636731 ID - info:doi/10.2196/27298 ER - TY - JOUR AU - Woo, MinJae AU - Mishra, Prabodh AU - Lin, Ju AU - Kar, Snigdhaswin AU - Deas, Nicholas AU - Linduff, Caleb AU - Niu, Sufeng AU - Yang, Yuzhe AU - McClendon, Jerome AU - Smith, Hudson D. AU - Shelton, L. Stephen AU - Gainey, E. Christopher AU - Gerard, C. William AU - Smith, C. Melissa AU - Griffin, F. Sarah AU - Gimbel, W. Ronald AU - Wang, Kuang-Ching PY - 2021/10/12 TI - Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study JO - JMIR Mhealth Uhealth SP - e32301 VL - 9 IS - 10 KW - emergency medical services KW - prehospital documentation KW - speech recognition software KW - natural language processing KW - military medicine KW - documentation KW - development KW - challenge KW - paramedic KW - disruption KW - attention KW - medical information KW - audio KW - speech recognition KW - qualitative KW - simulation N2 - Background: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. Objective: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. Methods: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. Results: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P<.001). Conclusions: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation. UR - https://mhealth.jmir.org/2021/10/e32301 UR - http://dx.doi.org/10.2196/32301 UR - http://www.ncbi.nlm.nih.gov/pubmed/34636729 ID - info:doi/10.2196/32301 ER - TY - JOUR AU - Chen, Hung-Chang AU - Tzeng, Shin-Shi AU - Hsiao, Yen-Chang AU - Chen, Ruei-Feng AU - Hung, Erh-Chien AU - Lee, K. Oscar PY - 2021/10/8 TI - Smartphone-Based Artificial Intelligence?Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study JO - JMIR Mhealth Uhealth SP - e32444 VL - 9 IS - 10 KW - artificial intelligence KW - AI KW - deep learning KW - margin reflex distance 1 KW - margin reflex distance 2 KW - levator muscle function KW - smartphone KW - measurement KW - eye KW - prediction KW - processing KW - limit KW - image KW - algorithm KW - observational N2 - Background: Margin reflex distance 1 (MRD1), margin reflex distance 2 (MRD2), and levator muscle function (LF) are crucial metrics for ptosis evaluation and management. However, manual measurements of MRD1, MRD2, and LF are time-consuming, subjective, and prone to human error. Smartphone-based artificial intelligence (AI) image processing is a potential solution to overcome these limitations. Objective: We propose the first smartphone-based AI-assisted image processing algorithm for MRD1, MRD2, and LF measurements. Methods: This observational study included 822 eyes of 411 volunteers aged over 18 years from August 1, 2020, to April 30, 2021. Six orbital photographs (bilateral primary gaze, up-gaze, and down-gaze) were taken using a smartphone (iPhone 11 Pro Max). The gold-standard measurements and normalized eye photographs were obtained from these orbital photographs and compiled using AI-assisted software to create MRD1, MRD2, and LF models. Results: The Pearson correlation coefficients between the gold-standard measurements and the predicted values obtained with the MRD1 and MRD2 models were excellent (r=0.91 and 0.88, respectively) and that obtained with the LF model was good (r=0.73). The intraclass correlation coefficient demonstrated excellent agreement between the gold-standard measurements and the values predicted by the MRD1 and MRD2 models (0.90 and 0.84, respectively), and substantial agreement with the LF model (0.69). The mean absolute errors were 0.35 mm, 0.37 mm, and 1.06 mm for the MRD1, MRD2, and LF models, respectively. The 95% limits of agreement were ?0.94 to 0.94 mm for the MRD1 model, ?0.92 to 1.03 mm for the MRD2 model, and ?0.63 to 2.53 mm for the LF model. Conclusions: We developed the first smartphone-based AI-assisted image processing algorithm for eyelid measurements. MRD1, MRD2, and LF measures can be taken in a quick, objective, and convenient manner. Furthermore, by using a smartphone, the examiner can check these measurements anywhere and at any time, which facilitates data collection. UR - https://mhealth.jmir.org/2021/10/e32444 UR - http://dx.doi.org/10.2196/32444 UR - http://www.ncbi.nlm.nih.gov/pubmed/34538776 ID - info:doi/10.2196/32444 ER - TY - JOUR AU - Wichmann, Johannes AU - Leyer, Michael PY - 2021/10/5 TI - Factors Influencing the Intention of Actors in Hospitals to Use Indoor Positioning Systems: Reasoned Action Approach JO - J Med Internet Res SP - e28193 VL - 23 IS - 10 KW - indoor positioning systems KW - indoor navigation KW - indoor localization KW - hospital KW - clinic KW - reasoned action approach KW - survey KW - hospital visitors KW - hospital employees N2 - Background: Indoor positioning systems (IPS) have become increasingly important for several branches of the economy (eg, in shopping malls) but are relatively new to hospitals and underinvestigated in that context. This research analyzes the intention of actors within a hospital to use an IPS to address this gap. Objective: To investigate the intentions of hospital visitors and employees (as the main actors in a hospital) to use an IPS in a hospital. Methods: The reasoned action approach was used, according to which the behavior of an individual is caused by behavioral intentions that are affected by (1) a persuasion that represents the individual?s attitude toward the behavior, (2) perceived norms that describe the influence of other individuals, and (3) perceived norms that reflect the possibility of the individual influencing the behavior. Results: The survey responses of 323 hospital visitors and 304 hospital employees were examined separately using SmartPLS 3.3.3. Bootstrapping procedures with 5000 subsamples were used to test the models (one-tailed test with a significance level of .05). The results show that attitude (?=.536; P<.001; f²=.381) and perceived norms (?=.236; P<.001; f²=.087) are predictors of hospital visitors? intention to use an IPS. In addition, attitude (?=.283; P<.001; f²=.114), perceived norms (?=.301; P<.001; f²=.126), and perceived behavioral control (?=.178; P=.005; f²=.062) are predictors of hospital employees? intention to use an IPS. Conclusions: This study has two major implications: (1) our extended reasoned action approach model, which takes into account spatial abilities and personal innovativeness, is appropriate for determining hospital visitors? and employees? intention to use an IPS; and (2) hospitals should invest in implementing IPS with a focus on (a) navigational services for hospital visitors and (b) asset tracking for hospital employees. UR - https://www.jmir.org/2021/10/e28193 UR - http://dx.doi.org/10.2196/28193 UR - http://www.ncbi.nlm.nih.gov/pubmed/34609318 ID - info:doi/10.2196/28193 ER - TY - JOUR AU - Stucky, Benjamin AU - Clark, Ian AU - Azza, Yasmine AU - Karlen, Walter AU - Achermann, Peter AU - Kleim, Birgit AU - Landolt, Hans-Peter PY - 2021/10/5 TI - Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study JO - J Med Internet Res SP - e26476 VL - 23 IS - 10 KW - wearables KW - actigraphy KW - polysomnography KW - validation KW - multisensory KW - mobile phone N2 - Background: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. Objective: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. Methods: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. Results: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non?rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. Conclusions: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data. UR - https://www.jmir.org/2021/10/e26476 UR - http://dx.doi.org/10.2196/26476 UR - http://www.ncbi.nlm.nih.gov/pubmed/34609317 ID - info:doi/10.2196/26476 ER - TY - JOUR AU - Mouchabac, Stephane AU - Leray, Philippe AU - Adrien, Vladimir AU - Gollier-Briant, Fanny AU - Bonnot, Olivier PY - 2021/9/30 TI - Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations JO - J Med Internet Res SP - e24560 VL - 23 IS - 9 KW - suicide KW - bayesian network KW - smartphone application KW - digital psychiatry KW - artificial intelligence N2 - Background: Recently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months. Objective: Our paper aims to present our development of a BN algorithm as a medical device in accordance with the American Psychiatric Association digital healthcare guidelines and to provide results from a preclinical phase. Methods: The experts are psychiatrists working in university hospitals who are experienced and trained in managing suicidal crises. As recommended when building a BN, we divided the process into 2 tasks. Task 1 is structure determination, representing the qualitative part of the BN. The factors were chosen for their known and demonstrated link with suicidal risk in the literature (clinical, behavioral, and psychometrics) and therapeutic accuracy (advice). Task 2 is parameter elicitation, with the conditional probabilities corresponding to the quantitative part. The 4-step simulation (use case) process allowed us to ensure that the advice was adapted to the clinical states of patients and the context. Results: For task 1, in this formative part, we defined clinical questions related to the mental state of the patients, and we proposed specific factors related to the questions. Subsequently, we suggested specific advice related to the patient?s state. We obtained a structure for the BN with a graphical representation of causal relations between variables. For task 2, several runs of simulations confirmed the a priori model of experts regarding mental state, refining the precision of our model. Moreover, we noticed that the advice had the same distribution as the previous state and was clinically relevant. After 2 rounds of simulation, the experts found the exact match. Conclusions: BN is an efficient methodology to build an algorithm for a digital assistant dedicated to suicidal crisis management. Digital psychiatry is an emerging field, but it needs validation and testing before being used with patients. Similar to psychotropics, any medical device requires a phase II (preclinical) trial. With this method, we propose another step to respond to the American Psychiatric Association guidelines. Trial Registration: ClinicalTrials.gov NCT03975881; https://clinicaltrials.gov/ct2/show/NCT03975881 UR - https://www.jmir.org/2021/9/e24560 UR - http://dx.doi.org/10.2196/24560 UR - http://www.ncbi.nlm.nih.gov/pubmed/34591030 ID - info:doi/10.2196/24560 ER - TY - JOUR AU - van Eijk, A. Ruben P. AU - Beelen, Anita AU - Kruitwagen, T. Esther AU - Murray, Deirdre AU - Radakovic, Ratko AU - Hobson, Esther AU - Knox, Liam AU - Helleman, Jochem AU - Burke, Tom AU - Rubio Pérez, Ángel Miguel AU - Reviers, Evy AU - Genge, Angela AU - Steyn, J. Frederik AU - Ngo, Shyuan AU - Eaglesham, John AU - Roes, B. Kit C. AU - van den Berg, H. Leonard AU - Hardiman, Orla AU - McDermott, J. Christopher PY - 2021/9/22 TI - A Road Map for Remote Digital Health Technology for Motor Neuron Disease JO - J Med Internet Res SP - e28766 VL - 23 IS - 9 KW - amyotrophic lateral sclerosis KW - digital health care technology KW - e-health UR - https://www.jmir.org/2021/9/e28766 UR - http://dx.doi.org/10.2196/28766 UR - http://www.ncbi.nlm.nih.gov/pubmed/34550089 ID - info:doi/10.2196/28766 ER - TY - JOUR AU - Park, Jung Chae AU - Cho, Sang Young AU - Chung, Jin Myung AU - Kim, Yi-Kyung AU - Kim, Hyung-Jin AU - Kim, Kyunga AU - Ko, Jae-Wook AU - Chung, Won-Ho AU - Cho, Hwan Baek PY - 2021/9/21 TI - A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study JO - J Med Internet Res SP - e29678 VL - 23 IS - 9 KW - deep learning KW - magnetic resonance imaging KW - medical image segmentation KW - Ménière disease KW - inner ear KW - endolymphatic hydrops KW - artificial intelligence KW - machine learning KW - multi-class segmentation KW - convolutional neural network KW - end-to-end system KW - clinician support KW - clinical decision support system KW - image selection KW - clinical usability KW - automation N2 - Background: Recently, the analysis of endolymphatic hydropses (EHs) via inner ear magnetic resonance imaging (MRI) for patients with Ménière disease has been attempted in various studies. In addition, artificial intelligence has rapidly been incorporated into the medical field. In our previous studies, an automated algorithm for EH analysis was developed by using a convolutional neural network. However, several limitations existed, and further studies were conducted to compensate for these limitations. Objective: The aim of this study is to develop a fully automated analytic system for measuring EH ratios that enhances EH analysis accuracy and clinical usability when studying Ménière disease via MRI. Methods: We proposed the 3into3Inception and 3intoUNet networks. Their network architectures were based on those of the Inception-v3 and U-Net networks, respectively. The developed networks were trained for inner ear segmentation by using the magnetic resonance images of 124 people and were embedded in a new, automated EH analysis system?inner-ear hydrops estimation via artificial intelligence (INHEARIT)-version 2 (INHEARIT-v2). After fivefold cross-validation, an additional test was performed by using 60 new, unseen magnetic resonance images to evaluate the performance of our system. The INHEARIT-v2 system has a new function that automatically selects representative images from a full MRI stack. Results: The average segmentation performance of the fivefold cross-validation was measured via the intersection of union method, resulting in performance values of 0.743 (SD 0.030) for the 3into3Inception network and 0.811 (SD 0.032) for the 3intoUNet network. The representative magnetic resonance slices (ie, from a data set of unseen magnetic resonance images) that were automatically selected by the INHEARIT-v2 system only differed from a maximum of 2 expert-selected slices. After comparing the ratios calculated by experienced physicians and those calculated by the INHEARIT-v2 system, we found that the average intraclass correlation coefficient for all cases was 0.941; the average intraclass correlation coefficient of the vestibules was 0.968, and that of the cochleae was 0.914. The time required for the fully automated system to accurately analyze EH ratios based on a patient's MRI stack was approximately 3.5 seconds. Conclusions: In this study, a fully automated full-stack magnetic resonance analysis system for measuring EH ratios was developed (named INHEARIT-v2), and the results showed that there was a high correlation between the expert-calculated EH ratio values and those calculated by the INHEARIT-v2 system. The system is an upgraded version of the INHEARIT system; it has higher segmentation performance and automatically selects representative images from an MRI stack. The new model can help clinicians by providing objective analysis results and reducing the workload for interpreting magnetic resonance images. UR - https://www.jmir.org/2021/9/e29678 UR - http://dx.doi.org/10.2196/29678 UR - http://www.ncbi.nlm.nih.gov/pubmed/34546181 ID - info:doi/10.2196/29678 ER - TY - JOUR AU - Tobias, Guy AU - Spanier, B. Assaf PY - 2021/9/16 TI - Using an mHealth App (iGAM) to Reduce Gingivitis Remotely (Part 2): Prospective Observational Study JO - JMIR Mhealth Uhealth SP - e24955 VL - 9 IS - 9 KW - mHealth KW - public health KW - oral health promotion KW - gum health KW - COVID-19 N2 - Background: Gingivitis is a nonpainful, inflammatory condition that can be managed at home. Left untreated, gingivitis can lead to tooth loss. Periodic dental examinations are important for early diagnosis and treatment of gum diseases. To contain the spread of the coronavirus, governments, including in Israel, have restricted movements of their citizens which might have caused routine dental checkups to be postponed. Objective: This study aimed to examine the ability of a mobile health app, iGAM, to reduce gingivitis, and to determine the most effective interval between photograph submissions. Methods: A prospective observational cohort study with 160 unpaid participants divided into 2 equal groups using the iGAM app was performed. The intervention group photographed their gums weekly for 8 weeks. The wait-list control group photographed their gums at the time of recruitment and 8 weeks later. After photo submission, the participants received the same message ?we recommended that you read the information in the app regarding oral hygiene habits.? A single-blinded researcher examined all the images and scored them according to the Modified Gingival Index (MGI). Results: The average age of the intervention group was 26.77 (SD 7.43) and 28.53 (SD 10.44) for the wait-list control group. Most participants were male (intervention group: 56/75,74.7%; wait-list control group: 34/51, 66.7%) and described themselves as ?secular?; most were ?single? non-smokers (intervention group: 56/75, 74.7%; wait-list control group: 40/51, 78.4%), and did not take medications (intervention group: 64/75, 85.3%; wait-list control group: 40/51, 78.4%). A total of 126 subjects completed the study. A statistically significant difference (P<.001) was found in the dependent variable (MGI). Improvements in gingival health were noted over time, and the average gingivitis scores were significantly lower in the intervention group (mean 1.16, SD 1.18) than in the wait-list control group (mean 2.16, SD 1.49) after 8 weeks. Those with more recent dental visits had a lower MGI (P=.04). No association was found between knowledge and behavior. Most participants were familiar with the recommendations for maintaining oral health, yet they only performed some of them. Conclusions: A dental selfie taken once a week using an mobile health app (iGAM) reduced the signs of gingivitis and promoted oral health. Selfies taken less frequently yielded poorer results. During the current pandemic, where social distancing recommendations may be causing people to avoid dental clinics, this app can remotely promote gum health. UR - https://mhealth.jmir.org/2021/9/e24955 UR - http://dx.doi.org/10.2196/24955 UR - http://www.ncbi.nlm.nih.gov/pubmed/34528897 ID - info:doi/10.2196/24955 ER - TY - JOUR AU - Morgado Areia, Carlos AU - Santos, Mauro AU - Vollam, Sarah AU - Pimentel, Marco AU - Young, Louise AU - Roman, Cristian AU - Ede, Jody AU - Piper, Philippa AU - King, Elizabeth AU - Gustafson, Owen AU - Harford, Mirae AU - Shah, Akshay AU - Tarassenko, Lionel AU - Watkinson, Peter PY - 2021/9/15 TI - A Chest Patch for Continuous Vital Sign Monitoring: Clinical Validation Study During Movement and Controlled Hypoxia JO - J Med Internet Res SP - e27547 VL - 23 IS - 9 KW - clinical validation KW - chest patch KW - vital signs KW - remote monitoring KW - wearable KW - heart rate KW - respiratory rate N2 - Background: The standard of care in general wards includes periodic manual measurements, with the data entered into track-and-trigger charts, either on paper or electronically. Wearable devices may support health care staff, improve patient safety, and promote early deterioration detection in the interval between periodic measurements. However, regulatory standards for ambulatory cardiac monitors estimating heart rate (HR) and respiratory rate (RR) do not specify performance criteria during patient movement or clinical conditions in which the patient?s oxygen saturation varies. Therefore, further validation is required before clinical implementation and deployment of any wearable system that provides continuous vital sign measurements. Objective: The objective of this study is to determine the agreement between a chest-worn patch (VitalPatch) and a gold standard reference device for HR and RR measurements during movement and gradual desaturation (modeling a hypoxic episode) in a controlled environment. Methods: After the VitalPatch and gold standard devices (Philips MX450) were applied, participants performed different movements in seven consecutive stages: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia was then induced, and the participants? oxygen saturation gradually reduced to 80% in a controlled environment. The primary outcome measure was accuracy, defined as the mean absolute error (MAE) of the VitalPatch estimates when compared with HR and RR gold standards (3-lead electrocardiography and capnography, respectively). We defined these as clinically acceptable if the rates were within 5 beats per minute for HR and 3 respirations per minute (rpm) for RR. Results: Complete data sets were acquired for 29 participants. In the movement phase, the HR estimates were within prespecified limits for all movements. For RR, estimates were also within the acceptable range, with the exception of the sit-to-stand and turning page movements, showing an MAE of 3.05 (95% CI 2.48-3.58) rpm and 3.45 (95% CI 2.71-4.11) rpm, respectively. For the hypoxia phase, both HR and RR estimates were within limits, with an overall MAE of 0.72 (95% CI 0.66-0.78) beats per minute and 1.89 (95% CI 1.75-2.03) rpm, respectively. There were no significant differences in the accuracy of HR and RR estimations between normoxia (?90%), mild (89.9%-85%), and severe hypoxia (<85%). Conclusions: The VitalPatch was highly accurate throughout both the movement and hypoxia phases of the study, except for RR estimation during the two types of movements. This study demonstrated that VitalPatch can be safely tested in clinical environments to support earlier detection of cardiorespiratory deterioration. Trial Registration: ISRCTN Registry ISRCTN61535692; https://www.isrctn.com/ISRCTN61535692 UR - https://www.jmir.org/2021/9/e27547 UR - http://dx.doi.org/10.2196/27547 UR - http://www.ncbi.nlm.nih.gov/pubmed/34524087 ID - info:doi/10.2196/27547 ER - TY - JOUR AU - Chi, Chien-Yu AU - Ao, Shuang AU - Winkler, Adrian AU - Fu, Kuan-Chun AU - Xu, Jie AU - Ho, Yi-Lwun AU - Huang, Chien-Hua AU - Soltani, Rohollah PY - 2021/9/13 TI - Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach JO - J Med Internet Res SP - e27798 VL - 23 IS - 9 KW - in-hospital cardiac arrest KW - 30-day mortality KW - 30-day readmission KW - machine learning KW - imbalanced dataset N2 - Background: In-hospital cardiac arrest (IHCA) is associated with high mortality and health care costs in the recovery phase. Predicting adverse outcome events, including readmission, improves the chance for appropriate interventions and reduces health care costs. However, studies related to the early prediction of adverse events of IHCA survivors are rare. Therefore, we used a deep learning model for prediction in this study. Objective: This study aimed to demonstrate that with the proper data set and learning strategies, we can predict the 30-day mortality and readmission of IHCA survivors based on their historical claims. Methods: National Health Insurance Research Database claims data, including 168,693 patients who had experienced IHCA at least once and 1,569,478 clinical records, were obtained to generate a data set for outcome prediction. We predicted the 30-day mortality/readmission after each current record (ALL-mortality/ALL-readmission) and 30-day mortality/readmission after IHCA (cardiac arrest [CA]-mortality/CA-readmission). We developed a hierarchical vectorizer (HVec) deep learning model to extract patients? information and predict mortality and readmission. To embed the textual medical concepts of the clinical records into our deep learning model, we used Text2Node to compute the distributed representations of all medical concept codes as a 128-dimensional vector. Along with the patient?s demographic information, our novel HVec model generated embedding vectors to hierarchically describe the health status at the record-level and patient-level. Multitask learning involving two main tasks and auxiliary tasks was proposed. As CA-mortality and CA-readmission were rare, person upsampling of patients with CA and weighting of CA records were used to improve prediction performance. Results: With the multitask learning setting in the model learning process, we achieved an area under the receiver operating characteristic of 0.752 for CA-mortality, 0.711 for ALL-mortality, 0.852 for CA-readmission, and 0.889 for ALL-readmission. The area under the receiver operating characteristic was improved to 0.808 for CA-mortality and 0.862 for CA-readmission after solving the extremely imbalanced issue for CA-mortality/CA-readmission by upsampling and weighting. Conclusions: This study demonstrated the potential of predicting future outcomes for IHCA survivors by machine learning. The results showed that our proposed approach could effectively alleviate data imbalance problems and train a better model for outcome prediction. UR - https://www.jmir.org/2021/9/e27798 UR - http://dx.doi.org/10.2196/27798 UR - http://www.ncbi.nlm.nih.gov/pubmed/34515639 ID - info:doi/10.2196/27798 ER - TY - JOUR AU - Jung, Young Se AU - Kim, Taehyun AU - Hwang, Ju Hyung AU - Hong, Kyungpyo PY - 2021/9/13 TI - Mechanism Design of Health Care Blockchain System Token Economy: Development Study Based on Simulated Real-World Scenarios JO - J Med Internet Res SP - e26802 VL - 23 IS - 9 KW - mechanism design KW - optimization KW - blockchain KW - token economy KW - eHealth KW - electronic health records KW - healthcare KW - economy KW - health records N2 - Background: Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. Objective: The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. Methods: First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company?s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. Results: To minimize the company?s costs, reward?time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum?an optimal point?for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. Conclusions: In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform. UR - https://www.jmir.org/2021/9/e26802 UR - http://dx.doi.org/10.2196/26802 UR - http://www.ncbi.nlm.nih.gov/pubmed/34515640 ID - info:doi/10.2196/26802 ER - TY - JOUR AU - Sahandi Far, Mehran AU - Eickhoff, B. Simon AU - Goni, Maria AU - Dukart, Juergen PY - 2021/9/13 TI - Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study JO - J Med Internet Res SP - e26608 VL - 23 IS - 9 KW - health sciences KW - medical research KW - biomarkers KW - diagnostic markers KW - neurological disorders KW - Parkinson disease KW - mobile phone N2 - Background: Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD). Objective: Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC). Methods: Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects. Results: Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task. Conclusions: These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications. UR - https://www.jmir.org/2021/9/e26608 UR - http://dx.doi.org/10.2196/26608 UR - http://www.ncbi.nlm.nih.gov/pubmed/34515645 ID - info:doi/10.2196/26608 ER - TY - JOUR AU - Han, Changho AU - Song, Youngjae AU - Lim, Hong-Seok AU - Tae, Yunwon AU - Jang, Jong-Hwan AU - Lee, Tak Byeong AU - Lee, Yeha AU - Bae, Woong AU - Yoon, Dukyong PY - 2021/9/10 TI - Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals?Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study JO - J Med Internet Res SP - e31129 VL - 23 IS - 9 KW - wearables KW - smartwatches KW - asynchronous electrocardiogram KW - artificial intelligence KW - deep learning KW - automatic diagnosis KW - myocardial infarction KW - timely diagnosis KW - machine learning KW - digital health KW - cardiac health KW - cardiology N2 - Background: When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored. Objective: We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead?based AI-enabled ECG algorithms on smartwatches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power. Methods: We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked. Results: The performance of lead sets with 3 or more leads compared favorably with that of the automatic ECG interpretations provided by a commercial ECG analysis software, with 8.1%-13.9% gain in sensitivity when the specificity was matched. Our results indicate that multiple lead-based AI-enabled ECG algorithms can be implemented on smartwatches. Model performance generally increased as the number of leads increased (12-lead sets: area under the receiver operating characteristic curve [AUROC] 0.880; 4-lead sets: AUROC 0.858, SD 0.008; 3-lead sets: AUROC 0.845, SD 0.011; 2-lead sets: AUROC 0.813, SD 0.018; single-lead sets: AUROC 0.768, SD 0.001). Considering the short amount of time needed to measure additional leads, measuring at least 3 leads?ideally more than 4 leads?is necessary for minimizing the risk of failing to detect acute myocardial infarction occurring in a certain spatial location or direction. Conclusions: By developing an AI model for detecting acute myocardial infarction with asynchronous ECG lead sets, we demonstrated the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for automated diagnosis of cardiac disorders. We also demonstrated the necessity of measuring at least 3 leads for accurate detection. Our results can be used as reference for the development of other AI models using sequentially measured asynchronous ECG leads via smartwatches for detecting various cardiac disorders. UR - https://www.jmir.org/2021/9/e31129 UR - http://dx.doi.org/10.2196/31129 UR - http://www.ncbi.nlm.nih.gov/pubmed/34505839 ID - info:doi/10.2196/31129 ER - TY - JOUR AU - Liu, Lei AU - Ni, Yizhao AU - Beck, F. Andrew AU - Brokamp, Cole AU - Ramphul, C. Ryan AU - Highfield, D. Linda AU - Kanjia, Karkera Megha AU - Pratap, ?Nick? J. PY - 2021/9/10 TI - Understanding Pediatric Surgery Cancellation: Geospatial Analysis JO - J Med Internet Res SP - e26231 VL - 23 IS - 9 KW - surgery cancellation KW - socioeconomic factors KW - spatial regression models KW - machine learning N2 - Background: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients? and families? behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood. Objective: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children?s Hospital Medical Center (CCHMC) and of Texas Children?s Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods. Methods: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients? health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients? socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set. Results: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate. Conclusions: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children?s surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account. UR - https://www.jmir.org/2021/9/e26231 UR - http://dx.doi.org/10.2196/26231 UR - http://www.ncbi.nlm.nih.gov/pubmed/34505837 ID - info:doi/10.2196/26231 ER - TY - JOUR AU - Li, Patrick AU - Chen, Bob AU - Rhodes, Evan AU - Slagle, Jason AU - Alrifai, Wael Mhd AU - France, Daniel AU - Chen, You PY - 2021/9/3 TI - Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study JO - JMIR Med Inform SP - e28998 VL - 9 IS - 9 KW - collaboration KW - electronic health records KW - audit logs KW - health care workers KW - neonatal intensive care unit KW - network analysis KW - clustering KW - visualization KW - concurrent interaction KW - human-computer interaction KW - survey instrument KW - informatics framework KW - secondary data analysis N2 - Background: Collaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods. Objective: We aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage. Methods: By extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center?s neonatal intensive care unit (NICU) to validate the effectiveness of our framework. Results: There was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters. Conclusions: Leveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout. UR - https://medinform.jmir.org/2021/9/e28998 UR - http://dx.doi.org/10.2196/28998 UR - http://www.ncbi.nlm.nih.gov/pubmed/34477566 ID - info:doi/10.2196/28998 ER - TY - JOUR AU - Shin, In-Soo AU - Rim, Hong Chai PY - 2021/9/2 TI - Stepwise-Hierarchical Pooled Analysis for Synergistic Interpretation of Meta-analyses Involving Randomized and Observational Studies: Methodology Development JO - J Med Internet Res SP - e29642 VL - 23 IS - 9 KW - meta-analysis KW - observational study KW - randomized study KW - interpretation KW - combination KW - statistics KW - synergy KW - methodology KW - hypothesis KW - validity N2 - Background: The necessity of including observational studies in meta-analyses has been discussed in the literature, but a synergistic analysis method for combining randomized and observational studies has not been reported. Observational studies differ in validity depending on the degree of the confounders? influence. Combining interpretations may be challenging, especially if the statistical directions are similar but the magnitude of the pooled results are different between randomized and observational studies (the ?gray zone?). Objective: To overcome these hindrances, in this study, we aim to introduce a logical method for clinical interpretation of randomized and observational studies. Methods: We designed a stepwise-hierarchical pooled analysis method to analyze both distribution trends and individual pooled results by dividing the included studies into at least three stages (eg, all studies, balanced studies, and randomized studies). Results: According to the model, the validity of a hypothesis is mostly based on the pooled results of randomized studies (the highest stage). Ascending patterns in which effect size and statistical significance increase gradually with stage strengthen the validity of the hypothesis; in this case, the effect size of the observational studies is lower than that of the true effect (eg, because of the uncontrolled effect of negative confounders). Descending patterns in which decreasing effect size and statistical significance gradually weaken the validity of the hypothesis suggest that the effect size and statistical significance of the observational studies is larger than the true effect (eg, because of researchers? bias). Conclusions: We recommend using the stepwise-hierarchical pooled analysis approach for meta-analyses involving randomized and observational studies. UR - https://www.jmir.org/2021/9/e29642 UR - http://dx.doi.org/10.2196/29642 UR - http://www.ncbi.nlm.nih.gov/pubmed/34315697 ID - info:doi/10.2196/29642 ER - TY - JOUR AU - de Buisonjé, David AU - Van der Geer, Jessica AU - Keesman, Mike AU - Van der Vaart, Roos AU - Reijnders, Thomas AU - Wentzel, Jobke AU - Kemps, Hareld AU - Kraaijenhagen, Roderik AU - Janssen, Veronica AU - Evers, Andrea PY - 2021/8/30 TI - Financial Incentives for Healthy Living for Patients With Cardiac Disease From the Perspective of Health Care Professionals: Interview Study JO - JMIR Cardio SP - e27867 VL - 5 IS - 2 KW - financial incentives KW - material rewards KW - healthy lifestyle KW - cardiovascular disease KW - cardiac rehabilitation KW - CVD N2 - Background: A promising new approach to support lifestyle changes in patients with cardiovascular disease (CVD) is the use of financial incentives. Although financial incentives have proven to be effective, their implementation remains controversial, and ethical objections have been raised. It is unknown whether health care professionals (HCPs) involved in CVD care find it acceptable to provide financial incentives to patients with CVD as support for lifestyle change. Objective: This study aims to investigate HCPs? perspectives on using financial incentives to support healthy living for patients with CVD. More specifically, we aim to provide insight into attitudes toward using financial incentives as well as obstacles and facilitators of implementing financial incentives in current CVD care. Methods: A total of 16 semistructured, in-depth, face-to-face interviews were conducted with Dutch HCPs involved in supporting patients with CVD with lifestyle changes. The topics discussed were attitudes toward an incentive system, obstacles to using an incentive system, and possible solutions to facilitate the use of an incentive system. Results: HCPs perceived an incentive system for healthy living for patients with CVD as possibly effective and showed generally high acceptance. However, there were concerns related to focusing too much on the extrinsic aspects of lifestyle change, disengagement when rewards are insignificant, paternalization and threatening autonomy, and low digital literacy in the target group. According to HCPs, solutions to mitigate these concerns included emphasizing intrinsic aspects of healthy living while giving extrinsic rewards, integrating social aspects to increase engagement, supporting autonomy by allowing freedom of choice in rewards, and aiming for a target group that can work with the necessary technology. Conclusions: This study mapped perspectives of Dutch HCPs and showed that attitudes are predominantly positive, provided that contextual factors, design, and target groups are accurately considered. Concerns about digital literacy in the target group are novel findings that warrant further investigation. Follow-up research is needed to validate these insights among patients with CVD. UR - https://cardio.jmir.org/2021/2/e27867 UR - http://dx.doi.org/10.2196/27867 UR - http://www.ncbi.nlm.nih.gov/pubmed/34459748 ID - info:doi/10.2196/27867 ER - TY - JOUR AU - Zhao, Zhong AU - Tang, Haiming AU - Zhang, Xiaobin AU - Qu, Xingda AU - Hu, Xinyao AU - Lu, Jianping PY - 2021/8/26 TI - Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation JO - J Med Internet Res SP - e29328 VL - 23 IS - 8 KW - autism spectrum disorder KW - eye tracking KW - face-to-face interaction KW - machine learning KW - visual fixation N2 - Background: Previous studies have shown promising results in identifying individuals with autism spectrum disorder (ASD) by applying machine learning (ML) to eye-tracking data collected while participants viewed varying images (ie, pictures, videos, and web pages). Although gaze behavior is known to differ between face-to-face interaction and image-viewing tasks, no study has investigated whether eye-tracking data from face-to-face conversations can also accurately identify individuals with ASD. Objective: The objective of this study was to examine whether eye-tracking data from face-to-face conversations could classify children with ASD and typical development (TD). We further investigated whether combining features on visual fixation and length of conversation would achieve better classification performance. Methods: Eye tracking was performed on children with ASD and TD while they were engaged in face-to-face conversations (including 4 conversational sessions) with an interviewer. By implementing forward feature selection, four ML classifiers were used to determine the maximum classification accuracy and the corresponding features: support vector machine (SVM), linear discriminant analysis, decision tree, and random forest. Results: A maximum classification accuracy of 92.31% was achieved with the SVM classifier by combining features on both visual fixation and session length. The classification accuracy of combined features was higher than that obtained using visual fixation features (maximum classification accuracy 84.62%) or session length (maximum classification accuracy 84.62%) alone. Conclusions: Eye-tracking data from face-to-face conversations could accurately classify children with ASD and TD, suggesting that ASD might be objectively screened in everyday social interactions. However, these results will need to be validated with a larger sample of individuals with ASD (varying in severity and balanced sex ratio) using data collected from different modalities (eg, eye tracking, kinematic, electroencephalogram, and neuroimaging). In addition, individuals with other clinical conditions (eg, developmental delay and attention deficit hyperactivity disorder) should be included in similar ML studies for detecting ASD. UR - https://www.jmir.org/2021/8/e29328 UR - http://dx.doi.org/10.2196/29328 UR - http://www.ncbi.nlm.nih.gov/pubmed/34435957 ID - info:doi/10.2196/29328 ER - TY - JOUR AU - Brasier, Noe AU - Osthoff, Michael AU - De Ieso, Fiorangelo AU - Eckstein, Jens PY - 2021/8/19 TI - Next-Generation Digital Biomarkers for Tuberculosis and Antibiotic Stewardship: Perspective on Novel Molecular Digital Biomarkers in Sweat, Saliva, and Exhaled Breath JO - J Med Internet Res SP - e25907 VL - 23 IS - 8 KW - digital biomarkers KW - active tuberculosis KW - drug resistance KW - wearable KW - smart biosensors KW - iSudorology KW - infectious diseases UR - https://www.jmir.org/2021/8/e25907 UR - http://dx.doi.org/10.2196/25907 UR - http://www.ncbi.nlm.nih.gov/pubmed/34420925 ID - info:doi/10.2196/25907 ER - TY - JOUR AU - Koshechkin, Konstantin AU - Lebedev, Georgy AU - Radzievsky, George AU - Seepold, Ralf AU - Martinez, Madrid Natividad PY - 2021/8/18 TI - Blockchain Technology Projects to Provide Telemedical Services: Systematic Review JO - J Med Internet Res SP - e17475 VL - 23 IS - 8 KW - telemedicine KW - distributed ledger KW - health information exchange KW - blockchain N2 - Background: One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment. Objective: This paper aims to review the feasibility of blockchain technology for telemedicine. Methods: The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex). Results: Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%). Conclusions: These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains. UR - https://www.jmir.org/2021/8/e17475 UR - http://dx.doi.org/10.2196/17475 UR - http://www.ncbi.nlm.nih.gov/pubmed/34407924 ID - info:doi/10.2196/17475 ER - TY - JOUR AU - Di Matteo, Daniel AU - Fotinos, Kathryn AU - Lokuge, Sachinthya AU - Mason, Geneva AU - Sternat, Tia AU - Katzman, A. Martin AU - Rose, Jonathan PY - 2021/8/13 TI - Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study JO - J Med Internet Res SP - e28918 VL - 23 IS - 8 KW - mobile sensing KW - passive EMA KW - passive sensing KW - psychiatric assessment KW - mood and anxiety disorders KW - mobile apps KW - mhealth KW - mobile phone KW - digital health KW - digital phenotyping N2 - Background: The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals? behaviors to infer their mental states and therefore screen for anxiety disorders and depression. Objective: The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. Methods: An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. Results: Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. Conclusions: We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries. UR - https://www.jmir.org/2021/8/e28918 UR - http://dx.doi.org/10.2196/28918 UR - http://www.ncbi.nlm.nih.gov/pubmed/34397386 ID - info:doi/10.2196/28918 ER - TY - JOUR AU - Zhang, Xiaoyi AU - Luo, Gang PY - 2021/8/11 TI - Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study JO - JMIR Med Inform SP - e28287 VL - 9 IS - 8 KW - asthma KW - clinical decision support KW - machine learning KW - patient care management KW - forecasting N2 - Background: Asthma hospital encounters impose a heavy burden on the health care system. To improve preventive care and outcomes for patients with asthma, we recently developed a black-box machine learning model to predict whether a patient with asthma will have one or more asthma hospital encounters in the succeeding 12 months. Our model is more accurate than previous models. However, black-box machine learning models do not explain their predictions, which forms a barrier to widespread clinical adoption. To solve this issue, we previously developed a method to automatically provide rule-based explanations for the model?s predictions and to suggest tailored interventions without sacrificing model performance. For an average patient correctly predicted by our model to have future asthma hospital encounters, our explanation method generated over 5000 rule-based explanations, if any. However, the user of the automated explanation function, often a busy clinician, will want to quickly obtain the most useful information for a patient by viewing only the top few explanations. Therefore, a methodology is required to appropriately rank the explanations generated for a patient. However, this is currently an open problem. Objective: The aim of this study is to develop a method to appropriately rank the rule-based explanations that our automated explanation method generates for a patient. Methods: We developed a ranking method that struck a balance among multiple factors. Through a secondary analysis of 82,888 data instances of adults with asthma from the University of Washington Medicine between 2011 and 2018, we demonstrated our ranking method on the test case of predicting asthma hospital encounters in patients with asthma. Results: For each patient predicted to have asthma hospital encounters in the succeeding 12 months, the top few explanations returned by our ranking method typically have high quality and low redundancy. Many top-ranked explanations provide useful insights on the various aspects of the patient?s situation, which cannot be easily obtained by viewing the patient?s data in the current electronic health record system. Conclusions: The explanation ranking module is an essential component of the automated explanation function, and it addresses the interpretability issue that deters the widespread adoption of machine learning predictive models in clinical practice. In the next few years, we plan to test our explanation ranking method on predictive modeling problems addressing other diseases as well as on data from other health care systems. International Registered Report Identifier (IRRID): RR2-10.2196/5039 UR - https://medinform.jmir.org/2021/8/e28287 UR - http://dx.doi.org/10.2196/28287 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383673 ID - info:doi/10.2196/28287 ER - TY - JOUR AU - Nickels, Stefanie AU - Edwards, D. Matthew AU - Poole, F. Sarah AU - Winter, Dale AU - Gronsbell, Jessica AU - Rozenkrants, Bella AU - Miller, P. David AU - Fleck, Mathias AU - McLean, Alan AU - Peterson, Bret AU - Chen, Yuanwei AU - Hwang, Alan AU - Rust-Smith, David AU - Brant, Arthur AU - Campbell, Andrew AU - Chen, Chen AU - Walter, Collin AU - Arean, A. Patricia AU - Hsin, Honor AU - Myers, J. Lance AU - Marks Jr, J. William AU - Mega, L. Jessica AU - Schlosser, A. Danielle AU - Conrad, J. Andrew AU - Califf, M. Robert AU - Fromer, Menachem PY - 2021/8/10 TI - Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling JO - JMIR Ment Health SP - e27589 VL - 8 IS - 8 KW - mental health KW - mobile sensing KW - mobile phone KW - mHealth KW - depression KW - location KW - GPS KW - app usage KW - voice diaries KW - adherence KW - engagement KW - mobility KW - sleep KW - physical activity KW - digital phenotyping KW - user-centered design N2 - Background: Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. Objective: This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. Methods: A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). Results: A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary?derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). Conclusions: This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness. UR - https://mental.jmir.org/2021/8/e27589 UR - http://dx.doi.org/10.2196/27589 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383685 ID - info:doi/10.2196/27589 ER - TY - JOUR AU - Stojanov, Riste AU - Popovski, Gorjan AU - Cenikj, Gjorgjina AU - Korou?i? Seljak, Barbara AU - Eftimov, Tome PY - 2021/8/9 TI - A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation JO - J Med Internet Res SP - e28229 VL - 23 IS - 8 KW - food information extraction KW - named-entity recognition KW - fine-tuning BERT KW - semantic annotation KW - information extraction KW - BERT KW - bidirectional encoder representations from transformers KW - natural language processing KW - machine learning N2 - Background: Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only few food semantic resources and few rule-based methods for food information extraction, which often depend on some external resources. However, an annotated corpus with food entities along with their normalization was published in 2019 by using several food semantic resources. Objective: In this study, we investigated how the recently published bidirectional encoder representations from transformers (BERT) model, which provides state-of-the-art results in information extraction, can be fine-tuned for food information extraction. Methods: We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags. Results: All BERT models provided very promising results with 93.30% to 94.31% macro F1 scores in the task of distinguishing food versus nonfood entity, which represents the new state-of-the-art technology in food information extraction. Considering the tasks where semantic tags are predicted, all BERT models obtained very promising results once again, with their macro F1 scores ranging from 73.39% to 78.96%. Conclusions: FoodNER can be used to extract and annotate food entities in 5 different tasks: food versus nonfood entities and distinguishing food entities on the level of food groups by using the closest Hansard semantic tags, the parent Hansard semantic tags, the FoodOn semantic tags, or the Systematized Nomenclature of Medicine Clinical Terms semantic tags. UR - https://www.jmir.org/2021/8/e28229 UR - http://dx.doi.org/10.2196/28229 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383671 ID - info:doi/10.2196/28229 ER - TY - JOUR AU - Liu, Natalie AU - Birstler, Jen AU - Venkatesh, Manasa AU - Hanrahan, Lawrence AU - Chen, Guanhua AU - Funk, Luke PY - 2021/8/9 TI - Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study JO - J Med Internet Res SP - e24017 VL - 23 IS - 8 KW - obesity KW - body mass index (BMI) KW - risk factors KW - screening KW - health services KW - chronic disease N2 - Background: Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown. Objective: The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities. Methods: US adults aged 18-75 years who had ?3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index. Results: We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m2, respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m2), coronary artery disease (27.7 kg/m2), hypertension (28.4 kg/m2), osteoarthritis (28.7 kg/m2), obstructive sleep apnea (30.1 kg/m2), and type 2 diabetes (30.9 kg/m2). Conclusions: The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised. UR - https://www.jmir.org/2021/8/e24017 UR - http://dx.doi.org/10.2196/24017 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383661 ID - info:doi/10.2196/24017 ER - TY - JOUR AU - Wongkoblap, Akkapon AU - Vadillo, A. Miguel AU - Curcin, Vasa PY - 2021/8/6 TI - Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study JO - JMIR Ment Health SP - e19824 VL - 8 IS - 8 KW - depression KW - mental health KW - Twitter KW - social media KW - deep learning KW - anaphora resolution KW - multiple-instance learning KW - depression markers N2 - Background: Mental health problems are widely recognized as a major public health challenge worldwide. This concern highlights the need to develop effective tools for detecting mental health disorders in the population. Social networks are a promising source of data wherein patients publish rich personal information that can be mined to extract valuable psychological cues; however, these data come with their own set of challenges, such as the need to disambiguate between statements about oneself and third parties. Traditionally, natural language processing techniques for social media have looked at text classifiers and user classification models separately, hence presenting a challenge for researchers who want to combine text sentiment and user sentiment analysis. Objective: The objective of this study is to develop a predictive model that can detect users with depression from Twitter posts and instantly identify textual content associated with mental health topics. The model can also address the problem of anaphoric resolution and highlight anaphoric interpretations. Methods: We retrieved the data set from Twitter by using a regular expression or stream of real-time tweets comprising 3682 users, of which 1983 self-declared their depression and 1699 declared no depression. Two multiple instance learning models were developed?one with and one without an anaphoric resolution encoder?to identify users with depression and highlight posts related to the mental health of the author. Several previously published models were applied to our data set, and their performance was compared with that of our models. Results: The maximum accuracy, F1 score, and area under the curve of our anaphoric resolution model were 92%, 92%, and 90%, respectively. The model outperformed alternative predictive models, which ranged from classical machine learning models to deep learning models. Conclusions: Our model with anaphoric resolution shows promising results when compared with other predictive models and provides valuable insights into textual content that is relevant to the mental health of the tweeter. UR - https://mental.jmir.org/2021/8/e19824 UR - http://dx.doi.org/10.2196/19824 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383688 ID - info:doi/10.2196/19824 ER - TY - JOUR AU - Holdnack, A. James AU - Brennan, Flatley Patricia PY - 2021/8/4 TI - Usability and Effectiveness of Immersive Virtual Grocery Shopping for Assessing Cognitive Fatigue in Healthy Controls: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e28073 VL - 10 IS - 8 KW - cognitive fatigue KW - immersive VR KW - user experience KW - virtual grocery shopping KW - instrumental activity of daily living N2 - Background: Cognitive fatigue (CF) is a human response to stimulation and stress and is a common comorbidity in many medical conditions that can result in serious consequences; however, studying CF under controlled conditions is difficult. Immersive virtual reality provides an experimental environment that enables the precise measurement of the response of an individual to complex stimuli in a controlled environment. Objective: We aim to examine the development of an immersive virtual shopping experience to measure subjective and objective indicators of CF induced by instrumental activities of daily living. Methods: We will recruit 84 healthy participants (aged 18-75 years) for a 2-phase study. Phase 1 is a user experience study for testing the software functionality, user interface, and realism of the virtual shopping environment. Phase 2 uses a 3-arm randomized controlled trial to determine the effect that the immersive environment has on fatigue. Participants will be randomized into 1 of 3 conditions exploring fatigue response during a typical human activity (grocery shopping). The level of cognitive and emotional challenges will change during each activity. The primary outcome of phase 1 is the experience of user interface difficulties. The primary outcome of phase 2 is self-reported CF. The core secondary phase 2 outcomes include subjective cognitive load, change in task performance behavior, and eye tracking. Phase 2 uses within-subject repeated measures analysis of variance to compare pre- and postfatigue measures under 3 conditions (control, cognitive challenge, and emotional challenge). Results: This study was approved by the scientific review committee of the National Institute of Nursing Research and was identified as an exempt study by the institutional review board of the National Institutes of Health. Data collection will begin in spring 2021. Conclusions: Immersive virtual reality may be a useful research platform for simulating the induction of CF associated with the cognitive and emotional challenges of instrumental activities of daily living. Trial Registration: ClinicalTrials.gov NCT04883359; http://clinicaltrials.gov/ct2/show/NCT04883359 International Registered Report Identifier (IRRID): PRR1-10.2196/28073 UR - https://www.researchprotocols.org/2021/8/e28073 UR - http://dx.doi.org/10.2196/28073 UR - http://www.ncbi.nlm.nih.gov/pubmed/34346898 ID - info:doi/10.2196/28073 ER - TY - JOUR AU - Ganti, Venu AU - Carek, M. Andrew AU - Jung, Hewon AU - Srivatsa, V. Adith AU - Cherry, Deborah AU - Johnson, Neicey Levather AU - Inan, T. Omer PY - 2021/8/2 TI - Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study JO - JMIR Mhealth Uhealth SP - e27466 VL - 9 IS - 8 KW - wearable sensing KW - pulse transit time KW - cuffless blood pressure KW - noninvasive blood pressure estimation KW - health equity KW - mobile phone N2 - Background: Noninvasive and cuffless approaches to monitor blood pressure (BP), in light of their convenience and accuracy, have paved the way toward remote screening and management of hypertension. However, existing noninvasive methodologies, which operate on mechanical, electrical, and optical sensing modalities, have not been thoroughly evaluated in demographically and racially diverse populations. Thus, the potential accuracy of these technologies in populations where they could have the greatest impact has not been sufficiently addressed. This presents challenges in clinical translation due to concerns about perpetuating existing health disparities. Objective: In this paper, we aim to present findings on the feasibility of a cuffless, wrist-worn, pulse transit time (PTT)?based device for monitoring BP in a diverse population. Methods: We recruited a diverse population through a collaborative effort with a nonprofit organization working with medically underserved areas in Georgia. We used our custom, multimodal, wrist-worn device to measure the PTT through seismocardiography, as the proximal timing reference, and photoplethysmography, as the distal timing reference. In addition, we created a novel data-driven beat-selection algorithm to reduce noise and improve the robustness of the method. We compared the wearable PTT measurements with those from a finger-cuff continuous BP device over the course of several perturbations used to modulate BP. Results: Our PTT-based wrist-worn device accurately monitored diastolic blood pressure (DBP) and mean arterial pressure (MAP) in a diverse population (N=44 participants) with a mean absolute difference of 2.90 mm Hg and 3.39 mm Hg for DBP and MAP, respectively, after calibration. Meanwhile, the mean absolute difference of our systolic BP estimation was 5.36 mm Hg, a grade B classification based on the Institute for Electronics and Electrical Engineers standard. We have further demonstrated the ability of our device to capture the commonly observed demographic differences in underlying arterial stiffness. Conclusions: Accurate DBP and MAP estimation, along with grade B systolic BP estimation, using a convenient wearable device can empower users and facilitate remote BP monitoring in medically underserved areas, thus providing widespread hypertension screening and management for health equity. UR - https://mhealth.jmir.org/2021/8/e27466 UR - http://dx.doi.org/10.2196/27466 UR - http://www.ncbi.nlm.nih.gov/pubmed/34338646 ID - info:doi/10.2196/27466 ER - TY - JOUR AU - Cho, Ho Jae AU - Choi, Jung-Yeon AU - Kim, Nak-Hyun AU - Lim, Yejee AU - Ohn, Hun Jung AU - Kim, Sun Eun AU - Ryu, Jiwon AU - Kim, Jangsun AU - Kim, Yiseob AU - Kim, Sun-wook AU - Kim, Kwang-Il PY - 2021/7/30 TI - A Smart Diaper System Using Bluetooth and Smartphones to Automatically Detect Urination and Volume of Voiding: Prospective Observational Pilot Study in an Acute Care Hospital JO - J Med Internet Res SP - e29979 VL - 23 IS - 7 KW - smart diaper KW - urinary incontinence KW - enuresis KW - voided volume KW - diaper rash KW - smartphone KW - mobile phone KW - app KW - eHealth KW - mHealth KW - urine KW - medical device KW - sensor KW - prospective KW - pilot study KW - observational N2 - Background: Caregivers of patients who wear conventional diapers are required to check for voiding every hour because prolonged wearing of wet diapers causes health problems including diaper dermatitis and urinary tract infections. However, frequent checking is labor intensive and disturbs patients? and caregivers? sleep. Furthermore, assessing patients? urine output with diapers in an acute care setting is difficult. Recently, a smart diaper system with wetness detection technology was developed to solve these issues. Objective: We aimed to evaluate the applicability of the smart diaper system for urinary detection, its accuracy in measuring voiding volume, and its effect on incontinence-associated dermatitis (IAD) occurrence in an acute care hospital. Methods: This prospective, observational, single-arm pilot study was conducted at a single tertiary hospital. We recruited 35 participants aged ?50 years who were wearing diapers due to incontinence between August and November 2020. When the smart diaper becomes wet, the smart diaper system notifies the caregiver to change the diaper and measures voiding volume automatically. Caregivers were instructed to record the weight of wet diapers on frequency volume charts (FVCs). We determined the voiding detection rate of the smart diaper system and compared the urine volume as automatically calculated by the smart diaper system with the volume recorded on FVCs. Agreement between the two measurements was estimated using a Bland-Altman plot. We also checked for the occurrence or aggravation of IAD and bed sores. Results: A total of 30 participants completed the protocol and 390 episodes of urination were recorded. There were 108 records (27.7%) on both the FVCs and the smart diaper system, 258 (66.2%) on the FVCs alone, 18 (4.6%) on the smart diaper system alone, and 6 (1.5%) on the FVCs with sensing device lost. The detection rate of the smart diaper system was 32.8% (126/384). When analyzing records concurrently listed in both the FVCs and the smart diaper system, linear regression showed a strong correlation between the two measurements (R2=0.88, P<.001). The Bland-Altman assessment showed good agreement between the two measurements, with a mean difference of ?4.2 mL and 95% limits of agreement of ?96.7 mL and 88.3 mL. New occurrence and aggravation of IAD and bed sores were not observed. Bed sores improved in one participant. Conclusions: The smart diaper system showed acceptable accuracy for measuring urine volume and it could replace conventional FVCs in acute setting hospitals. Furthermore, the smart diaper system has the potential advantage of preventing IAD development and bed sore worsening. However, the detection rate of the smart diaper system was lower than expected. Detection rate polarization among participants was observed, and improvements in the user interface and convenience are needed for older individuals who are unfamiliar with the smart diaper system. UR - https://www.jmir.org/2021/7/e29979 UR - http://dx.doi.org/10.2196/29979 UR - http://www.ncbi.nlm.nih.gov/pubmed/34328427 ID - info:doi/10.2196/29979 ER - TY - JOUR AU - Zhang, Yuezhou AU - Folarin, A. Amos AU - Sun, Shaoxiong AU - Cummins, Nicholas AU - Ranjan, Yatharth AU - Rashid, Zulqarnain AU - Conde, Pauline AU - Stewart, Callum AU - Laiou, Petroula AU - Matcham, Faith AU - Oetzmann, Carolin AU - Lamers, Femke AU - Siddi, Sara AU - Simblett, Sara AU - Rintala, Aki AU - Mohr, C. David AU - Myin-Germeys, Inez AU - Wykes, Til AU - Haro, Maria Josep AU - Penninx, H. Brenda W. J. AU - Narayan, A. Vaibhav AU - Annas, Peter AU - Hotopf, Matthew AU - Dobson, B. Richard J. AU - PY - 2021/7/30 TI - Predicting Depressive Symptom Severity Through Individuals? Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study JO - JMIR Mhealth Uhealth SP - e29840 VL - 9 IS - 7 KW - mental health KW - depression KW - digital biomarkers KW - digital phenotyping KW - digital health KW - Bluetooth KW - hierarchical Bayesian model KW - mobile health KW - mHealth KW - monitoring N2 - Background: Research in mental health has found associations between depression and individuals? behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective: This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods: The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals? life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results: A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions: Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals? behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings. UR - https://mhealth.jmir.org/2021/7/e29840 UR - http://dx.doi.org/10.2196/29840 UR - http://www.ncbi.nlm.nih.gov/pubmed/34328441 ID - info:doi/10.2196/29840 ER - TY - JOUR AU - Harrison, J. Conrad AU - Sidey-Gibbons, J. Chris AU - Klassen, F. Anne AU - Wong Riff, Y. Karen W. AU - Furniss, Dominic AU - Swan, C. Marc AU - Rodrigues, N. Jeremy PY - 2021/7/30 TI - Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study JO - J Med Internet Res SP - e26412 VL - 23 IS - 7 KW - cleft Lip KW - cleft palate KW - patient-reported outcome measures KW - outcome assessment KW - CLEFT-Q KW - computerized adaptive test KW - machine learning KW - decision tree KW - regression tree N2 - Background: Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate. Objective: We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models. Methods: We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson?s correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error. Results: Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data. Conclusions: When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study. UR - https://www.jmir.org/2021/7/e26412 UR - http://dx.doi.org/10.2196/26412 UR - http://www.ncbi.nlm.nih.gov/pubmed/34328443 ID - info:doi/10.2196/26412 ER - TY - JOUR AU - Brassel, Sophie AU - Power, Emma AU - Campbell, Andrew AU - Brunner, Melissa AU - Togher, Leanne PY - 2021/7/30 TI - Recommendations for the Design and Implementation of Virtual Reality for Acquired Brain Injury Rehabilitation: Systematic Review JO - J Med Internet Res SP - e26344 VL - 23 IS - 7 KW - virtual reality KW - acquired brain injury KW - traumatic brain injury KW - rehabilitation KW - systematic review KW - recommendations KW - cognitive communication KW - mobile phone N2 - Background: Virtual reality (VR) is increasingly being used for the assessment and treatment of impairments arising from acquired brain injuries (ABIs) due to perceived benefits over traditional methods. However, no tailored options exist for the design and implementation of VR for ABI rehabilitation and, more specifically, traumatic brain injury (TBI) rehabilitation. In addition, the evidence base lacks systematic reviews of immersive VR use for TBI rehabilitation. Recommendations for this population are important because of the many complex and diverse impairments that individuals can experience. Objective: This study aims to conduct a two-part systematic review to identify and synthesize existing recommendations for designing and implementing therapeutic VR for ABI rehabilitation, including TBI, and to identify current evidence for using immersive VR for TBI assessment and treatment and to map the degree to which this literature includes recommendations for VR design and implementation. Methods: This review was guided by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). A comprehensive search of 11 databases and gray literature was conducted in August 2019 and repeated in June 2020. Studies were included if they met relevant search terms, were peer-reviewed, were written in English, and were published between 2009 and 2020. Studies were reviewed to determine the level of evidence and methodological quality. For the first part, qualitative data were synthesized and categorized via meta-synthesis. For the second part, findings were analyzed and synthesized descriptively owing to the heterogeneity of data extracted from the included studies. Results: In the first part, a total of 14 papers met the inclusion criteria. Recommendations for VR design and implementation were not specific to TBI but rather to stroke or ABI rehabilitation more broadly. The synthesis and analysis of data resulted in three key phases and nine categories of recommendations for designing and implementing VR for ABI rehabilitation. In the second part, 5 studies met the inclusion criteria. A total of 2 studies reported on VR for assessment and three for treatment. Studies were varied in terms of therapeutic targets, VR tasks, and outcome measures. VR was used to assess or treat impairments in cognition, balance, and anxiety, with positive outcomes. However, the levels of evidence, methodological quality, and inclusion of recommendations for VR design and implementation were poor. Conclusions: There is limited research on the use of immersive VR for TBI rehabilitation. Few studies have been conducted, and there is limited inclusion of recommendations for therapeutic VR design and implementation. Future research in ABI rehabilitation should consider a stepwise approach to VR development, from early co-design studies with end users to larger controlled trials. A list of recommendations is offered to provide guidance and a more consistent model to advance clinical research in this area. UR - https://www.jmir.org/2021/7/e26344 UR - http://dx.doi.org/10.2196/26344 UR - http://www.ncbi.nlm.nih.gov/pubmed/34328434 ID - info:doi/10.2196/26344 ER - TY - JOUR AU - Zhang, Zhongxing AU - Qi, Ming AU - Hügli, Gordana AU - Khatami, Ramin PY - 2021/7/29 TI - The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study JO - J Med Internet Res SP - e24171 VL - 23 IS - 7 KW - obstructive sleep apnea KW - wearable devices KW - smartwatch KW - oxygen saturation KW - near-infrared spectroscopy KW - continuous positive airway pressure therapy KW - photoplethysmography N2 - Background: Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ?30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. Objective: We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy. Methods: Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. Results: In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t667=28.28; P<.001; 95% limits of agreement [LoA] ?2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t1516=31.99; P<.001; 95% LoA ?2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively. Conclusions: arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ?3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness. UR - https://www.jmir.org/2021/7/e24171 UR - http://dx.doi.org/10.2196/24171 UR - http://www.ncbi.nlm.nih.gov/pubmed/34326039 ID - info:doi/10.2196/24171 ER - TY - JOUR AU - Cha, KyeongMin AU - Woo, Hyun-Ki AU - Park, Dohyun AU - Chang, Kyung Dong AU - Kang, Mira PY - 2021/7/28 TI - Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach JO - JMIR Med Inform SP - e26000 VL - 9 IS - 7 KW - pill recognition KW - deep neural network KW - image processing KW - color space KW - color difference KW - pharmaceutical KW - imaging KW - photography KW - neural network KW - mobile phone N2 - Background: Pill image recognition systems are difficult to develop due to differences in pill color, which are influenced by external factors such as the illumination from and the presence of a flash. Objective: In this study, the differences in color between reference images and real-world images were measured to determine the accuracy of a pill recognition system under 12 real-world conditions (ie, different background colors, the presence and absence of a flash, and different exposure values [EVs]). Methods: We analyzed 19 medications with different features (ie, different colors, shapes, and dosages). The average color difference was calculated based on the color distance between a reference image and a real-world image. Results: For images with black backgrounds, as the EV decreased, the top-1 and top-5 accuracies increased independently of the presence of a flash. The top-5 accuracy for images with black backgrounds increased from 26.8% to 72.6% when the flash was on and increased from 29.5% to 76.8% when the flash was off as the EV decreased. However, the top-5 accuracy increased from 62.1% to 78.4% for images with white backgrounds when the flash was on. The best top-1 accuracy was 51.1% (white background; flash on; EV of +2.0). The best top-5 accuracy was 78.4% (white background; flash on; EV of 0). Conclusions: The accuracy generally increased as the color difference decreased, except for images with black backgrounds and an EV of ?2.0. This study revealed that background colors, the presence of a flash, and EVs in real-world conditions are important factors that affect the performance of a pill recognition model. UR - https://medinform.jmir.org/2021/7/e26000 UR - http://dx.doi.org/10.2196/26000 UR - http://www.ncbi.nlm.nih.gov/pubmed/34319239 ID - info:doi/10.2196/26000 ER - TY - JOUR AU - Hendrickx, Iris AU - Voets, Tim AU - van Dyk, Pieter AU - Kool, B. Rudolf PY - 2021/7/27 TI - Using Text Mining Techniques to Identify Health Care Providers With Patient Safety Problems: Exploratory Study JO - J Med Internet Res SP - e19064 VL - 23 IS - 7 KW - text mining KW - risk management KW - health care quality improvement N2 - Background: Regulatory bodies such as health care inspectorates can identify potential patient safety problems in health care providers by analyzing patient complaints. However, it is challenging to analyze the large number of complaints. Text mining techniques may help identify signals of problems with patient safety at health care providers. Objective: The aim of this study was to explore whether employing text mining techniques on patient complaint databases can help identify potential problems with patient safety at health care providers and automatically predict the severity of patient complaints. Methods: We performed an exploratory study on the complaints database of the Dutch Health and Youth Care Inspectorate with more than 22,000 written complaints. Severe complaints are defined as those cases where the inspectorate contact point experts deemed it worthy of a triage by the inspectorate, or complaints that led to direct action by the inspectorate. We investigated a range of supervised machine learning techniques to assign a severity label to complaints that can be used to prioritize which incoming complaints need the most attention. We studied several features based on the complaints? written content, including sentiment analysis, to decide which were helpful for severity prediction. Finally, we showcased how we could combine these severity predictions and automatic keyword analysis on the complaints database and listed health care providers and their organization-specific complaints to determine the average severity of complaints per organization. Results: A straightforward text classification approach using a bag-of-words feature representation worked best for the severity prediction of complaints. We obtained an accuracy of 87%-93% (2658-2990 of 3319 complaints) on the held-out test set and an F1 score of 45%-51% on the severe complaints. The skewed class distribution led to only reasonable recall (47%-54%) and precision (44%-49%) scores. The use of sentiment analysis for severity prediction was not helpful. By combining the predicted severity outcomes with an automatic keyword analysis, we identified several health care providers that could have patient safety problems. Conclusions: Text mining techniques for analyzing complaints by civilians can support inspectorates. They can automatically predict the severity of the complaints, or they can be used for keyword analysis. This can help the inspectorate detect potential patient safety problems, or support prioritizing follow-up supervision activities by sorting complaints based on the severity per organization or per sector. UR - https://www.jmir.org/2021/7/e19064 UR - http://dx.doi.org/10.2196/19064 UR - http://www.ncbi.nlm.nih.gov/pubmed/34313604 ID - info:doi/10.2196/19064 ER - TY - JOUR AU - Cunha, Rodrigues Bruna Carolina AU - Rodrigues, Hora Kamila Rios Da AU - Zaine, Isabela AU - da Silva, Nogueira Elias Adriano AU - Viel, César Caio AU - Pimentel, Campos Maria Da Graça PY - 2021/7/12 TI - Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports JO - J Med Internet Res SP - e24278 VL - 23 IS - 7 KW - mobile apps KW - mHealth KW - intervention KW - experience sampling KW - method KW - monitoring KW - Experience Sampling and Programmed Intervention Method KW - experience sampling method KW - ecological momentary assessment KW - just-in-time adaptive intervention N2 - Background: Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones. Objective: The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system). Methods: We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method?s conceptual model, support to 8 real case studies, and postdesign interviews. Results: The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists? target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited. Conclusions: The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology?based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems. UR - https://www.jmir.org/2021/7/e24278 UR - http://dx.doi.org/10.2196/24278 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255652 ID - info:doi/10.2196/24278 ER - TY - JOUR AU - Shih, Chi-Huang AU - Chou, Pai-Chien AU - Chou, Ting-Ling AU - Huang, Tsai-Wei PY - 2021/7/5 TI - Measurement of Cancer-Related Fatigue Based on Heart Rate Variability: Observational Study JO - J Med Internet Res SP - e25791 VL - 23 IS - 7 KW - cancer-related fatigue KW - heart rate variability KW - LF-HF ratio KW - photoplethysmography KW - wearables KW - chemotherapy N2 - Background: Cancer-related fatigue is a serious side effect of cancer, and its treatment can disrupt the quality of life of patients. Clinically, the standard method for assessing cancer-related fatigue relies on subjective experience retrieved from patient self-reports, such as the Brief Fatigue Inventory (BFI). However, most patients do not self-report their fatigue levels. Objective: In this study, we aim to develop an objective cancer-related fatigue assessment method to track and monitor fatigue in patients with cancer. Methods: In total, 12 patients with lung cancer who were undergoing chemotherapy or targeted therapy were enrolled. We developed frequency-domain parameters of heart rate variability (HRV) and BFI based on a wearable-based HRV measurement system. All patients completed the BFI-Taiwan version questionnaire and wore the device for 7 consecutive days to record HRV parameters such as low frequency (LF), high frequency (HF), and LF-HF ratio (LF-HF). Statistical analysis was used to map the correlation between subjective fatigue and objective data. Results: A moderate positive correlation was observed between the average LF-HF ratio and BFI in the sleep phase (?=0.86). The mapped BFI score derived by the BFI mapping method could approximate the BFI from the patient self-report. The mean absolute error rate between the subjective and objective BFI scores was 3%. Conclusions: LF-HF is highly correlated with the cancer-related fatigue experienced by patients with lung cancer undergoing chemotherapy or targeted therapy. Beyond revealing fatigue levels objectively, continuous HRV recordings through the photoplethysmography watch device and the defined parameters (LF-HF) can define the active phase and sleep phase in patients with lung cancer who undergo chemotherapy or targeted chemotherapy, allowing a deduction of their sleep patterns. UR - https://www.jmir.org/2021/7/e25791 UR - http://dx.doi.org/10.2196/25791 UR - http://www.ncbi.nlm.nih.gov/pubmed/36260384 ID - info:doi/10.2196/25791 ER - TY - JOUR AU - Caine, A. Joshua AU - Klein, Britt AU - Edwards, L. Stephen PY - 2021/6/17 TI - The Impact of a Novel Mimicry Task for Increasing Emotion Recognition in Adults with Autism Spectrum Disorder and Alexithymia: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e24543 VL - 10 IS - 6 KW - alexithymia hypothesis KW - training facial expression emotion recognition KW - mimicry task KW - autism spectrum disorder KW - interoception KW - facial expression KW - emotion KW - emotion recognition KW - autism KW - spectrum disorder KW - mimicry KW - therapy KW - protocol KW - expression KW - disability N2 - Background: Impaired facial emotion expression recognition (FEER) has typically been considered a correlate of autism spectrum disorder (ASD). Now, the alexithymia hypothesis is suggesting that this emotion processing problem is instead related to alexithymia, which frequently co-occurs with ASD. By combining predictive coding theories of ASD and simulation theories of emotion recognition, it is suggested that facial mimicry may improve the training of FEER in ASD and alexithymia. Objective: This study aims to evaluate a novel mimicry task to improve FEER in adults with and without ASD and alexithymia. Additionally, this study will aim to determine the contributions of alexithymia and ASD to FEER ability and assess which of these 2 populations benefit from this training task. Methods: Recruitment will primarily take place through an ASD community group with emphasis put on snowball recruiting. Included will be 64 consenting adults equally divided between participants without an ASD and participants with an ASD. Participants will be screened online using the Kessler Psychological Distress Scale (K-10; cut-off score of 22), Autism Spectrum Quotient (AQ-10), and Toronto Alexithymia Scale (TAS-20) followed by a clinical interview with a provisional psychologist at the Federation University psychology clinic. The clinical interview will include assessment of ability, anxiety, and depression as well as discussion of past ASD diagnosis and confirmatory administration of the Autism Mental Status Exam (AMSE). Following the clinical interview, the participant will complete the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and then undertake a baseline assessment of FEER. Consenting participants will then be assigned using a permuted blocked randomization method into either the control task condition or the mimicry task condition. A brief measure of satisfaction of the task and a debriefing session will conclude the study. Results: The study has Federation University Human Research Ethics Committee approval and is registered with the Australian New Zealand Clinical Trials. Participant recruitment is predicted to begin in the third quarter of 2021. Conclusions: This study will be the first to evaluate the use of a novel facial mimicry task condition to increase FEER in adults with ASD and alexithymia. If efficacious, this task could prove useful as a cost-effective adjunct intervention that could be used at home and thus remove barriers to entry. This study will also explore the unique effectiveness of this task in people without an ASD, with an ASD, and with alexithymia. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12619000705189p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377455 International Registered Report Identifier (IRRID): PRR1-10.2196/24543 UR - https://www.researchprotocols.org/2021/6/e24543/ UR - http://dx.doi.org/10.2196/24543 UR - http://www.ncbi.nlm.nih.gov/pubmed/34170257 ID - info:doi/10.2196/24543 ER - TY - JOUR AU - Feusner, D. Jamie AU - Mohideen, Reza AU - Smith, Stephen AU - Patanam, Ilyas AU - Vaitla, Anil AU - Lam, Christopher AU - Massi, Michelle AU - Leow, Alex PY - 2021/6/21 TI - Semantic Linkages of Obsessions From an International Obsessive-Compulsive Disorder Mobile App Data Set: Big Data Analytics Study JO - J Med Internet Res SP - e25482 VL - 23 IS - 6 KW - OCD KW - natural language processing KW - clinical subtypes KW - semantic KW - word embedding KW - clustering N2 - Background: Obsessive-compulsive disorder (OCD) is characterized by recurrent intrusive thoughts, urges, or images (obsessions) and repetitive physical or mental behaviors (compulsions). Previous factor analytic and clustering studies suggest the presence of three or four subtypes of OCD symptoms. However, these studies have relied on predefined symptom checklists, which are limited in breadth and may be biased toward researchers? previous conceptualizations of OCD. Objective: In this study, we examine a large data set of freely reported obsession symptoms obtained from an OCD mobile app as an alternative to uncovering potential OCD subtypes. From this, we examine data-driven clusters of obsessions based on their latent semantic relationships in the English language using word embeddings. Methods: We extracted free-text entry words describing obsessions in a large sample of users of a mobile app, NOCD. Semantic vector space modeling was applied using the Global Vectors for Word Representation algorithm. A domain-specific extension, Mittens, was also applied to enhance the corpus with OCD-specific words. The resulting representations provided linear substructures of the word vector in a 100-dimensional space. We applied principal component analysis to the 100-dimensional vector representation of the most frequent words, followed by k-means clustering to obtain clusters of related words. Results: We obtained 7001 unique words representing obsessions from 25,369 individuals. Heuristics for determining the optimal number of clusters pointed to a three-cluster solution for grouping subtypes of OCD. The first had themes relating to relationship and just-right; the second had themes relating to doubt and checking; and the third had themes relating to contamination, somatic, physical harm, and sexual harm. All three clusters showed close semantic relationships with each other in the central area of convergence, with themes relating to harm. An equal-sized split-sample analysis across individuals and a split-sample analysis over time both showed overall stable cluster solutions. Words in the third cluster were the most frequently occurring words, followed by words in the first cluster. Conclusions: The clustering of naturally acquired obsessional words resulted in three major groupings of semantic themes, which partially overlapped with predefined checklists from previous studies. Furthermore, the closeness of the overall embedded relationships across clusters and their central convergence on harm suggests that, at least at the level of self-reported obsessional thoughts, most obsessions have close semantic relationships. Harm to self or others may be an underlying organizing theme across many obsessions. Notably, relationship-themed words, not previously included in factor-analytic studies, clustered with just-right words. These novel insights have potential implications for understanding how an apparent multitude of obsessional symptoms are connected by underlying themes. This observation could aid exposure-based treatment approaches and could be used as a conceptual framework for future research. UR - https://www.jmir.org/2021/6/e25482 UR - http://dx.doi.org/10.2196/25482 UR - http://www.ncbi.nlm.nih.gov/pubmed/33892466 ID - info:doi/10.2196/25482 ER - TY - JOUR AU - Verdonck, Michaël AU - Carvalho, Hugo AU - Berghmans, Johan AU - Forget, Patrice AU - Poelaert, Jan PY - 2021/6/21 TI - Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach JO - J Med Internet Res SP - e25913 VL - 23 IS - 6 KW - neuromuscular monitoring KW - outlier analysis KW - acceleromyography KW - postoperative residual curarization KW - train-of-four KW - monitoring devices KW - neuromuscular KW - machine learning KW - monitors KW - anesthesiology N2 - Background: Perioperative quantitative monitoring of neuromuscular function in patients receiving neuromuscular blockers hasbecome internationally recognized as an absolute and core necessity in modern anesthesia care. Because of their kinetic nature, artifactual recordings of acceleromyography-based neuromuscular monitoring devices are not unusual. These generate a great deal of cynicism among anesthesiologists, constituting an obstacle toward their widespread adoption. Through outlier analysis techniques, monitoring devices can learn to detect and flag signal abnormalities. Outlier analysis (or anomaly detection) refers to the problem of finding patterns in data that do not conform to expected behavior. Objective: This study was motivated by the development of a smartphone app intended for neuromuscular monitoring based on combined accelerometric and angular hand movement data. During the paired comparison stage of this app against existing acceleromyography monitoring devices, it was noted that the results from both devices did not always concur. This study aims to engineer a set of features that enable the detection of outliers in the form of erroneous train-of-four (TOF) measurements from an acceleromyographic-based device. These features are tested for their potential in the detection of erroneous TOF measurements by developing an outlier detection algorithm. Methods: A data set encompassing 533 high-sensitivity TOF measurements from 35 patients was created based on a multicentric open label trial of a purpose-built accelero- and gyroscopic-based neuromuscular monitoring app. A basic set of features was extracted based on raw data while a second set of features was purpose engineered based on TOF pattern characteristics. Two cost-sensitive logistic regression (CSLR) models were deployed to evaluate the performance of these features. The final output of the developed models was a binary classification, indicating if a TOF measurement was an outlier or not. Results: A total of 7 basic features were extracted based on raw data, while another 8 features were engineered based on TOF pattern characteristics. The model training and testing were based on separate data sets: one with 319 measurements (18 outliers) and a second with 214 measurements (12 outliers). The F1 score (95% CI) was 0.86 (0.48-0.97) for the CSLR model with engineered features, significantly larger than the CSLR model with the basic features (0.29 [0.17-0.53]; P<.001). Conclusions: The set of engineered features and their corresponding incorporation in an outlier detection algorithm have the potential to increase overall neuromuscular monitoring data consistency. Integrating outlier flagging algorithms within neuromuscular monitors could potentially reduce overall acceleromyography-based reliability issues. Trial Registration: ClinicalTrials.gov NCT03605225; https://clinicaltrials.gov/ct2/show/NCT03605225 UR - https://www.jmir.org/2021/6/e25913/ UR - http://dx.doi.org/10.2196/25913 UR - http://www.ncbi.nlm.nih.gov/pubmed/34152273 ID - info:doi/10.2196/25913 ER - TY - JOUR AU - Yokotani, Kenji PY - 2021/6/21 TI - A Change Talk Model for Abstinence Based on Web-Based Anonymous Gambler Chat Meeting Data by Using an Automatic Change Talk Classifier: Development Study JO - J Med Internet Res SP - e24088 VL - 23 IS - 6 KW - problem gambling KW - web-based anonymous gambler chat meetings KW - self-help group KW - change talk classifier KW - computerized text analysis KW - long-term data with dropout gamblers KW - recovery gradient KW - gradient descent method KW - gambling KW - addiction KW - abstinence N2 - Background: Change and sustain talks (negative and positive comments) on gambling have been relevant for determining gamblers? outcomes but they have not been used to clarify the abstinence process in anonymous gambler meetings. Objective: The aim of this study was to develop a change talk model for abstinence based on data extracted from web-based anonymous gambler chat meetings by using an automatic change talk classifier. Methods: This study used registry data from the internet. The author accessed web-based anonymous gambler chat meetings in Japan and sampled 1.63 million utterances (two-sentence texts) from 267 abstinent gamblers who have remained abstinent for at least three years and 1625 nonabstinent gamblers. The change talk classifier in this study automatically classified gamblers? utterances into change and sustain talks. Results: Abstinent gamblers showed higher proportions of change talks and lower probability of sustain talks compared with nonabstinent gamblers. The change talk model for abstinence, involving change and sustain talks, classified abstinent and nonabstinent gamblers through the use of a support vector machine with a radial basis kernel function. The model also indicated individual evaluation scores for abstinence and the ideal proportion of change talks for all participants according to their previous utterances. Conclusions: Abstinence likelihood among gamblers can be increased by providing personalized evaluation values and indicating the optimal proportion of change talks. Moreover, this may help to prevent severe mental, social, and financial problems caused by the gambling disorder. UR - https://www.jmir.org/2021/6/e24088 UR - http://dx.doi.org/10.2196/24088 UR - http://www.ncbi.nlm.nih.gov/pubmed/34152282 ID - info:doi/10.2196/24088 ER - TY - JOUR AU - Habukawa, Chizu AU - Ohgami, Naoto AU - Arai, Takahiko AU - Makata, Haruyuki AU - Tomikawa, Morimitsu AU - Fujino, Tokihiko AU - Manabe, Tetsuharu AU - Ogihara, Yoshihito AU - Ohtani, Kiyotaka AU - Shirao, Kenichiro AU - Sugai, Kazuko AU - Asai, Kei AU - Sato, Tetsuya AU - Murakami, Katsumi PY - 2021/6/17 TI - Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study JO - JMIR Pediatr Parent SP - e28865 VL - 4 IS - 2 KW - asthma KW - children KW - infant KW - wheezing KW - wheeze recognition algorithm KW - pediatrics KW - remote KW - medical devices KW - validation KW - home management KW - algorithm KW - detection KW - chronic illness N2 - Background: Since 2020, peoples? lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign. Objective: We developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions. Methods: A total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ?6 years were diagnosed with bronchial asthma and patients ?5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as ?wheeze? files and 197 as ?no-wheeze? files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity. Results: Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm. Conclusions: The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care. UR - https://pediatrics.jmir.org/2021/2/e28865 UR - http://dx.doi.org/10.2196/28865 UR - http://www.ncbi.nlm.nih.gov/pubmed/33875413 ID - info:doi/10.2196/28865 ER - TY - JOUR AU - Polsky, Michael AU - Moraveji, Neema PY - 2021/6/16 TI - Early Identification of COVID-19 Infection Using Remote Cardiorespiratory Monitoring: Three Case Reports JO - Interact J Med Res SP - e27823 VL - 10 IS - 2 KW - COVID-19 KW - remote patient monitoring KW - wearable sensors KW - monitoring KW - case study KW - preidentification KW - lung KW - data collection KW - respiration KW - prediction N2 - Background: The adoption of remote patient monitoring (RPM) in routine medical care requires increased understanding of the physiologic changes accompanying disease development and the proactive interventions that will improve outcomes. Objective: The aim of this study is to present three case reports that highlight the capability of RPM to enable early identification of viral infection with COVID-19 in patients with chronic respiratory disease. Methods: Patients at a large pulmonary practice who were enrolled in a respiratory RPM program and who had contracted COVID-19 were identified. The RPM system (Spire Health) contains three components: (1) Health Tags (Spire Health), undergarment waistband-adhered physiologic monitors that include a respiratory rate sensor; (2) an app on a smartphone; and (3) a web dashboard for use by respiratory therapists. The physiologic data of 9 patients with COVID out of 1000 patients who were enrolled for monitoring were retrospectively reviewed, and 3 instances were identified where the RPM system had notified clinicians of physiologic deviation due to the viral infection. Results: Physiologic deviations from respective patient baselines occurred during infection onset and, although the infection manifested differently in each case, were identified by the RPM system. In the first case, the patient was symptomatic; in the second case, the patient was presymptomatic; and in the third case, the patient varied from asymptomatic to mildly symptomatic. Conclusions: RPM systems intended for long-term use and that use patient-specific baselines can highlight physiologic changes early in the course of acute disease, such as COVID-19 infection. These cases demonstrate opportunities for earlier diagnosis, treatment, and isolation. This study supports the need for further research into how RPM can be effectively integrated into clinical practice. UR - https://www.i-jmr.org/2021/2/e27823 UR - http://dx.doi.org/10.2196/27823 UR - http://www.ncbi.nlm.nih.gov/pubmed/34086588 ID - info:doi/10.2196/27823 ER - TY - JOUR AU - Goldberg, B. Simon AU - Bolt, M. Daniel AU - Davidson, J. Richard PY - 2021/6/15 TI - Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses JO - J Med Internet Res SP - e26749 VL - 23 IS - 6 KW - missing data KW - randomized controlled trial KW - differential attrition KW - sensitivity analysis KW - statistical methodology KW - mobile phone N2 - Background: Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out. Objective: This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR. Methods: We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach). Results: Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects. Conclusions: MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results. UR - https://www.jmir.org/2021/6/e26749 UR - http://dx.doi.org/10.2196/26749 UR - http://www.ncbi.nlm.nih.gov/pubmed/34128810 ID - info:doi/10.2196/26749 ER - TY - JOUR AU - Lee, Ji-Hyun AU - Park, Hyeoun-Ae AU - Song, Tae-Min PY - 2021/6/14 TI - A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology JO - J Med Internet Res SP - e25028 VL - 23 IS - 6 KW - ontology KW - fertility KW - public policy KW - South Korea KW - social media KW - future KW - infodemiology KW - infoveillance N2 - Background: South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. Objective: The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. Methods: An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea?s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. Results: A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. Conclusions: The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility. UR - https://www.jmir.org/2021/6/e25028 UR - http://dx.doi.org/10.2196/25028 UR - http://www.ncbi.nlm.nih.gov/pubmed/34125068 ID - info:doi/10.2196/25028 ER - TY - JOUR AU - Zhang, Fuguo AU - Xue, Bingyu AU - Li, Yiran AU - Li, Hui AU - Liu, Qihua PY - 2021/6/11 TI - Effect of Textual Features on the Success of Medical Crowdfunding: Model Development and Econometric Analysis from the Tencent Charity Platform JO - J Med Internet Res SP - e22395 VL - 23 IS - 6 KW - medical crowdfunding KW - textual features KW - project title KW - project details KW - fundraising success KW - theory of persuasion N2 - Background: Medical crowdfunding utilizes the internet to raise medical funds. Medical crowdfunding has developed rapidly worldwide; however, most medical crowdfunding projects fail to raise the targeted funds. Therefore, a very important research problem that has not received sufficient attention from the existing literature is identifying which factors affect the success of medical crowdfunding projects. Objective: The aim of this study was to examine the effect of textual features of medical crowdfunding projects on their success rate using 4903 real projects from the Tencent Charity platform, a well-known medical crowdfunding platform in China. In particular, according to Aristotle?s theory of persuasion, we divided the project text of medical crowdfunding into the project title and project details, which were analyzed from two perspectives (existence and extent) to explore their respective impacts. Methods: We established a research framework to meet our research goals. The process was divided into five main parts. We first collected data from Tencent Charity using Python programs and cleaned the datasets. Second, we selected variables and built the research model based on previous studies and the theory of persuasion. Next, the selected variables were extracted from the project text. We then performed econometric analysis using multiple regression analysis. Finally, we evaluated the results of econometric analysis to extract knowledge. Results: In the project title, the presence of the patient?s disease (P=.04) and occupation (P=.01) had a positive impact on the success rate of fundraising, whereas the presence of age (P<.001), gender (P=.001), and negative emotions (P=.04) had a negative impact. In the project details, the presence of the patient?s occupation (P=.01), monetary evidence (P=.02), and negative emotions (P=.04) played a positive role in the fundraising success rate, whereas the presence of age (P<.001) and positive emotions (P<.001) played a negative role. Moreover, in the project details, high-frequency monetary evidence (P=.02) and negative words (P=.02), as well as a short narrative length (P=.01) were conducive to succeeding in medical crowdfunding. Younger patients were more likely to obtain a higher success rate in medical crowdfunding. For patients whose occupations were national civil servant, professional skill worker, clerk, business and service worker, solider, child, student, and public-spirited person, the success rate of fundraising decreased sequentially. Conclusions: This study collected 4903 valid data from Tencent Charity, and identified which factors in the project text play an important role in the success rate of medical crowdfunding from the perspective of existence and extent. We found that in addition to the project details, the features of the project title also have an important impact on the success rate of fundraising. These findings provide important theoretical and managerial implications for medical crowdfunding. UR - https://www.jmir.org/2021/6/e22395 UR - http://dx.doi.org/10.2196/22395 UR - http://www.ncbi.nlm.nih.gov/pubmed/34114959 ID - info:doi/10.2196/22395 ER - TY - JOUR AU - Thomas, E. Beena AU - Kumar, Vignesh J. AU - Periyasamy, Murugesan AU - Khandewale, Subhash Amit AU - Hephzibah Mercy, J. AU - Raj, Michael E. AU - Kokila, S. AU - Walgude, Shashikant Apurva AU - Gaurkhede, Rahul Gunjan AU - Kumbhar, Dattatraya Jagannath AU - Ovung, Senthanro AU - Paul, Mariyamma AU - Rajkumar, Sathyan B. AU - Subbaraman, Ramnath PY - 2021/6/10 TI - Acceptability of the Medication Event Reminder Monitor for Promoting Adherence to Multidrug-Resistant Tuberculosis Therapy in Two Indian Cities: Qualitative Study of Patients and Health Care Providers JO - J Med Internet Res SP - e23294 VL - 23 IS - 6 KW - tuberculosis KW - drug-resistant KW - medication adherence KW - mHealth KW - digital adherence technologies KW - India N2 - Background: Patients with multidrug-resistant tuberculosis (MDR-TB) face challenges adhering to medications, given that treatment is prolonged and has a high rate of adverse effects. The Medication Event Reminder Monitor (MERM) is a digital pillbox that provides pill-taking reminders and facilitates the remote monitoring of medication adherence. Objective: This study aims to assess the MERM?s acceptability to patients and health care providers (HCPs) during pilot implementation in India?s public sector MDR-TB program. Methods: From October 2017 to September 2018, we conducted qualitative interviews with patients who were undergoing MDR-TB therapy and were being monitored with the MERM and HCPs in the government program in Chennai and Mumbai. Interview transcripts were independently coded by 2 researchers and analyzed to identify the emergent themes. We organized findings by using the Unified Theory of Acceptance and Use of Technology (UTAUT), which outlines 4 constructs that predict technology acceptance?performance expectancy, effort expectancy, social influence, and facilitating conditions. Results: We interviewed 65 patients with MDR-TB and 10 HCPs. In patient interviews, greater acceptance of the MERM was related to perceptions that the audible and visual reminders improved medication adherence and that remote monitoring reduced the frequency of clinic visits (performance expectancy), that the device?s organization and labeling of medications made it easier to take them correctly (effort expectancy), that the device facilitated positive family involvement in the patient?s care (social influences), and that remote monitoring made patients feel more cared for by the health system (facilitating conditions). Lower patient acceptance was related to problems with the durability of the MERM?s cardboard construction and difficulties with portability and storage because of its large size (effort expectancy), concerns regarding stigma and the disclosure of patients? MDR-TB diagnoses (social influences), and the incorrect understanding of the MERM because of suboptimal counseling (facilitating conditions). In their interviews, HCPs reported that MERM implementation resulted in fewer in-person interactions with patients and thus allowed HCPs to dedicate more time to other tasks, which improved job satisfaction. Conclusions: Several features of the MERM support its acceptability among patients with MDR-TB and HCPs, and some barriers to patient use could be addressed by improving the design of the device. However, some barriers, such as disease-related stigma, are more difficult to modify and may limit use of the MERM among some patients with MDR-TB. Further research is needed to assess the accuracy of MERM for measuring adherence, its effectiveness for improving treatment outcomes, and patients? sustained use of the device in larger scale implementation. UR - https://www.jmir.org/2021/6/e23294 UR - http://dx.doi.org/10.2196/23294 UR - http://www.ncbi.nlm.nih.gov/pubmed/34110300 ID - info:doi/10.2196/23294 ER - TY - JOUR AU - Yuan, Jing AU - Libon, J. David AU - Karjadi, Cody AU - Ang, A. Alvin F. AU - Devine, Sherral AU - Auerbach, H. Sanford AU - Au, Rhoda AU - Lin, Honghuang PY - 2021/6/8 TI - Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study) JO - J Med Internet Res SP - e27407 VL - 23 IS - 6 KW - clock drawing test KW - neuropsychological test KW - cognition KW - technology KW - digital assessment KW - mild cognitive impairment KW - association KW - neurology KW - Framingham Heart Study N2 - Background: The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function. Objective: We aimed to investigate the association between dCDT features and cognitive performance across multiple domains. Methods: Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition). Results: The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment. Conclusions: The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations. UR - https://www.jmir.org/2021/6/e27407 UR - http://dx.doi.org/10.2196/27407 UR - http://www.ncbi.nlm.nih.gov/pubmed/34100766 ID - info:doi/10.2196/27407 ER - TY - JOUR AU - Zhu, Y. Tracy AU - Rothenbühler, Martina AU - Hamvas, Györgyi AU - Hofmann, Anja AU - Welter, JoEllen AU - Kahr, Maike AU - Kimmich, Nina AU - Shilaih, Mohaned AU - Leeners, Brigitte PY - 2021/6/8 TI - The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study JO - J Med Internet Res SP - e20710 VL - 23 IS - 6 KW - ovulation KW - basal body temperature KW - BBT KW - oral temperature KW - wrist skin temperature KW - diagnostic accuracy KW - thermometer KW - fertility KW - menstruation KW - wearable KW - sensor KW - mobile phone N2 - Background: As a daily point measurement, basal body temperature (BBT) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation. Objective: This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles. Methods: This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily BBT was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases. Results: Wrist skin temperature was more sensitive than BBT (sensitivity 0.62 vs 0.23; P<.001) and had a higher true-positive rate (54.9% vs 20.2%) for detecting ovulation; however, it also had a higher false-positive rate (8.8% vs 3.6%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2% for wrist skin temperature and 84.8% for BBT. Both temperatures had low negative predictive values (8.8% for wrist skin temperature and 10.9% for BBT). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than BBT (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves. Conclusions: For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than BBT for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures. UR - https://www.jmir.org/2021/6/e20710 UR - http://dx.doi.org/10.2196/20710 UR - http://www.ncbi.nlm.nih.gov/pubmed/34100763 ID - info:doi/10.2196/20710 ER - TY - JOUR AU - Hu, Hao-Chun AU - Chang, Shyue-Yih AU - Wang, Chuen-Heng AU - Li, Kai-Jun AU - Cho, Hsiao-Yun AU - Chen, Yi-Ting AU - Lu, Chang-Jung AU - Tsai, Tzu-Pei AU - Lee, Kuang-Sheng Oscar PY - 2021/6/8 TI - Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study JO - J Med Internet Res SP - e25247 VL - 23 IS - 6 KW - artificial intelligence KW - convolutional neural network KW - dysphonia KW - pathological voice KW - vocal fold disease KW - voice pathology identification N2 - Background: Dysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis. Objective: This study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence. Methods: We collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets: 593 samples as the training set and 148 samples as the testing set. A convolutional neural network approach was applied to train the model, and findings were compared with those of human specialists. Results: The convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. Compared with the accuracy of human specialists, the overall accuracy rates were 60.1% and 56.1% for the 2 laryngologists and 51.4% and 43.2% for the 2 general ear, nose, and throat doctors. Conclusions: Voice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies. UR - https://www.jmir.org/2021/6/e25247 UR - http://dx.doi.org/10.2196/25247 UR - http://www.ncbi.nlm.nih.gov/pubmed/34100770 ID - info:doi/10.2196/25247 ER - TY - JOUR AU - Salvi, Dario AU - Poffley, Emma AU - Tarassenko, Lionel AU - Orchard, Elizabeth PY - 2021/6/7 TI - App-Based Versus Standard Six-Minute Walk Test in Pulmonary Hypertension: Mixed Methods Study JO - JMIR Mhealth Uhealth SP - e22748 VL - 9 IS - 6 KW - cardiology KW - exercise test KW - pulmonary hypertension KW - mobile apps KW - GPS N2 - Background: Pulmonary arterial hypertension (PAH) is a chronic disease of the pulmonary vasculature that can lead to heart failure and premature death. Assessment of patients with PAH includes performing a 6-minute walk test (6MWT) in clinics. We developed a smartphone app to compute the walked distance (6MWD) indoors, by counting U-turns, and outdoors, by using satellite positioning. Objective: The goal of the research was to assess (1) accuracy of the indoor 6MWTs in clinical settings, (2) validity and test-retest reliability of outdoor 6MWTs in the community, (3) compliance, usability, and acceptance of the app, and (4) feasibility of pulse oximetry during 6MWTs. Methods: We tested the app on 30 PAH patients over 6 months. Patients were asked to perform 3 conventional 6MWTs in clinic while using the app in the indoor mode and one or more app-based 6MWTs in outdoor mode in the community per month. Results: Bland-Altman analysis of 70 pairs of conventional versus app-based indoor 6MWDs suggests that the app is sometimes inaccurate (14.6 m mean difference, lower and upper limit of agreement: ?133.35 m to 162.55 m). The comparison of 69 pairs of conventional 6MWDs and community-based outdoor 6MWDs within 7 days shows that community tests are strongly related to those performed in clinic (correlation 0.89), but the interpretation of the distance should consider that differences above the clinically significant threshold are not uncommon. Analysis of 89 pairs of outdoor tests performed by the same patient within 7 days shows that community-based tests are repeatable (intraclass correlation 0.91, standard error of measurement 36.97 m, mean coefficient of variation 12.45%). Questionnaires and semistructured interviews indicate that the app is usable and well accepted, but motivation to use it could be affected if the data are not used for clinical decision, which may explain low compliance in 52% of our cohort. Analysis of pulse oximetry data indicates that conventional pulse oximeters are unreliable if used during a walk. Conclusions: App-based outdoor 6MWTs in community settings are valid, repeatable, and well accepted by patients. More studies would be needed to assess the benefits of using the app in clinical practice. Trial Registration: ClinicalTrials.gov NCT04633538; https://clinicaltrials.gov/ct2/show/NCT04633538 UR - https://mhealth.jmir.org/2021/6/e22748 UR - http://dx.doi.org/10.2196/22748 UR - http://www.ncbi.nlm.nih.gov/pubmed/34096876 ID - info:doi/10.2196/22748 ER - TY - JOUR AU - Galatzer-Levy, Isaac AU - Abbas, Anzar AU - Ries, Anja AU - Homan, Stephanie AU - Sels, Laura AU - Koesmahargyo, Vidya AU - Yadav, Vijay AU - Colla, Michael AU - Scheerer, Hanne AU - Vetter, Stefan AU - Seifritz, Erich AU - Scholz, Urte AU - Kleim, Birgit PY - 2021/6/3 TI - Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study JO - J Med Internet Res SP - e25199 VL - 23 IS - 6 KW - digital phenotyping KW - digital biomarkers KW - digital health KW - depression KW - suicidal ideation KW - digital markers KW - digital KW - facial KW - suicide KW - suicide risk KW - visual KW - auditory N2 - Background: Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. Objective: We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. Methods: We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. Results: Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (?=?0.68, P=.02, r2=0.40), overall expressivity (?=?0.46, P=.10, r2=0.27), and head movement measured as head pitch variability (?=?1.24, P=.006, r2=0.48) and head yaw variability (?=?0.54, P=.06, r2=0.32). Conclusions: Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation. UR - https://www.jmir.org/2021/6/e25199 UR - http://dx.doi.org/10.2196/25199 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081022 ID - info:doi/10.2196/25199 ER - TY - JOUR AU - Oliveira J e Silva, Lucas AU - Maldonado, Graciela AU - Brigham, Tara AU - Mullan, F. Aidan AU - Utengen, Audun AU - Cabrera, Daniel PY - 2021/5/31 TI - Evaluating Scholars? Impact and Influence: Cross-sectional Study of the Correlation Between a Novel Social Media?Based Score and an Author-Level Citation Metric JO - J Med Internet Res SP - e28859 VL - 23 IS - 5 KW - social media KW - Twitter KW - journal impact factor KW - h-index KW - digital scholarship KW - digital platform KW - Scopus KW - metrics KW - scientometrics KW - altmetrics KW - stakeholders KW - health care KW - digital health care N2 - Background: The development of an author-level complementary metric could play a role in the process of academic promotion through objective evaluation of scholars? influence and impact. Objective: The objective of this study was to evaluate the correlation between the Healthcare Social Graph (HSG) score, a novel social media influence and impact metric, and the h-index, a traditional author-level metric. Methods: This was a cross-sectional study of health care stakeholders with a social media presence randomly sampled from the Symplur database in May 2020. We performed stratified random sampling to obtain a representative sample with all strata of HSG scores. We manually queried the h-index in two reference-based databases (Scopus and Google Scholar). Continuous features (HSG score and h-index) from the included profiles were summarized as the median and IQR. We calculated the Spearman correlation coefficients (?) to evaluate the correlation between the HSG scores and h-indexes obtained from Google Scholar and Scopus. Results: A total of 286 (31.2%) of the 917 stakeholders had a Google Scholar h-index available. The median HSG score for these profiles was 61.1 (IQR 48.2), and the median h-index was 14.5 (IQR 26.0). For the 286 subjects with the HSG score and Google Scholar h-index available, the Spearman correlation coefficient ? was 0.1979 (P<.001), indicating a weak positive correlation between these two metrics. A total of 715 (78%) of 917 stakeholders had a Scopus h-index available. The median HSG score for these profiles was 57.6 (IQR 46.4), and the median h-index was 7 (IQR 16). For the 715 subjects with the HSG score and Scopus h-index available, ? was 0.2173 (P<.001), also indicating a weak positive correlation. Conclusions: We found a weak positive correlation between a novel author-level complementary metric and the h-index. More than a chiasm between traditional citation metrics and novel social media?based metrics, our findings point toward a bridge between the two domains. UR - https://www.jmir.org/2021/5/e28859 UR - http://dx.doi.org/10.2196/28859 UR - http://www.ncbi.nlm.nih.gov/pubmed/34057413 ID - info:doi/10.2196/28859 ER - TY - JOUR AU - Malik, Raza Ahmed AU - Boger, Jennifer PY - 2021/5/31 TI - Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study JO - JMIR Rehabil Assist Technol SP - e25996 VL - 8 IS - 2 KW - ballistocardiography KW - heart rate KW - ambient health monitoring KW - zero-effort technology KW - continuous wavelet transform N2 - Background: Cardiovascular diseases are a leading cause of death worldwide and result in significant economic costs to health care systems. The prevalence of cardiovascular conditions that require monitoring is expected to increase as the average age of the global population continues to rise. Although an accurate cardiac assessment can be performed at medical centers, frequent visits for assessment are not feasible for most people, especially those with limited mobility. Monitoring of vital signs at home is becoming an increasingly desirable, accessible, and practical alternative. As wearable devices are not the ideal solution for everyone, it is necessary to develop parallel and complementary approaches. Objective: This research aims to develop a zero-effort, unobtrusive, cost-effective, and portable option for home-based ambient heart rate monitoring. Methods: The prototype seat cushion uses load cells to acquire a user?s ballistocardiogram (BCG). The analog signal from the load cells is amplified and filtered by a signal-conditioning circuit before being digitally recorded. A pilot study with 20 participants was conducted to analyze the prototype?s ability to capture the BCG during five real-world tasks: sitting still, watching a video on a computer screen, reading, using a computer, and having a conversation. A novel algorithm based on the continuous wavelet transform was developed to extract the heart rate by detecting the largest amplitude values (J-peaks) in the BCG signal. Results: The pilot study data showed that the BCG signals from all five tasks had sufficiently large portions to extract heart rate. The continuous wavelet transform?based algorithm for J-peak detection demonstrated an overall accuracy of 91.4% compared with electrocardiography. Excluding three outliers that had significantly noisy BCG data, the algorithm achieved 94.6% accuracy, which was aligned with that of wearable devices. Conclusions: This study suggests that BCG acquired through a seat cushion is a viable alternative to wearable technologies. The prototype seat cushion presented in this study is an example of a relatively accessible, affordable, portable, and unobtrusive zero-effort approach to achieve frequent home-based ambient heart rate monitoring. UR - https://rehab.jmir.org/2021/2/e25996 UR - http://dx.doi.org/10.2196/25996 UR - http://www.ncbi.nlm.nih.gov/pubmed/34057420 ID - info:doi/10.2196/25996 ER - TY - JOUR AU - Shaklai, Sigal AU - Gilad-Bachrach, Ran AU - Yom-Tov, Elad AU - Stern, Naftali PY - 2021/5/28 TI - Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data JO - J Med Internet Res SP - e27084 VL - 23 IS - 5 KW - search engines KW - diagnosis KW - screening KW - stroke KW - risk KW - internet KW - trend KW - infodemiology KW - archive KW - prospective KW - algorithm N2 - Background: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension). Objective: Here, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event. Methods: We analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions. Results: The model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1% false-positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session. Conclusions: The proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events. UR - https://www.jmir.org/2021/5/e27084 UR - http://dx.doi.org/10.2196/27084 UR - http://www.ncbi.nlm.nih.gov/pubmed/34047699 ID - info:doi/10.2196/27084 ER - TY - JOUR AU - Poncette, Akira-Sebastian AU - Wunderlich, Markus Maximilian AU - Spies, Claudia AU - Heeren, Patrick AU - Vorderwülbecke, Gerald AU - Salgado, Eduardo AU - Kastrup, Marc AU - Feufel, A. Markus AU - Balzer, Felix PY - 2021/5/28 TI - Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions JO - J Med Internet Res SP - e26494 VL - 23 IS - 5 KW - digital health KW - patient monitoring KW - intensive care unit KW - technological innovation KW - data science KW - alarm fatigue KW - alarm management KW - patient safety KW - ICU KW - alarm system KW - alarm system quality KW - medical devices KW - clinical alarms N2 - Background: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients? vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients. Objective: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU?s patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies. Methods: This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU?s alarm situation. Results: We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). Conclusions: Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff?s work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data. UR - https://www.jmir.org/2021/5/e26494 UR - http://dx.doi.org/10.2196/26494 UR - http://www.ncbi.nlm.nih.gov/pubmed/34047701 ID - info:doi/10.2196/26494 ER - TY - JOUR AU - Rager, L. Theresa AU - Koepfli, Cristian AU - Khan, A. Wasif AU - Ahmed, Sabeena AU - Mahmud, Hayat Zahid AU - Clayton, N. Katherine PY - 2021/5/12 TI - Usability of Rapid Cholera Detection Device (OmniVis) for Water Quality Workers in Bangladesh: Iterative Convergent Mixed Methods Study JO - J Med Internet Res SP - e22973 VL - 23 IS - 5 KW - cholera KW - environmental surveillance KW - mHealth KW - usability N2 - Background: Cholera poses a significant global health burden. In Bangladesh, cholera is endemic and causes more than 100,000 cases each year. Established environmental reservoirs leave millions at risk of infection through the consumption of contaminated water. The Global Task Force for Cholera Control has called for increased environmental surveillance to detect contaminated water sources prior to human infection in an effort to reduce cases and deaths. The OmniVis rapid cholera detection device uses loop-mediated isothermal amplification and particle diffusometry detection methods integrated into a handheld hardware device that attaches to an iPhone 6 to identify and map contaminated water sources. Objective: The aim of this study was to evaluate the usability of the OmniVis device with targeted end users to advance the iterative prototyping process and ultimately design a device that easily integrates into users? workflow. Methods: Water quality workers were trained to use the device and subsequently completed an independent device trial and usability questionnaire. Pretraining and posttraining knowledge assessments were administered to ensure training quality did not confound trial and questionnaire Results: Device trials identified common user errors and device malfunctions including incorrect test kit insertion and device powering issues. We did not observe meaningful differences in user errors or device malfunctions accumulated per participant across demographic groups. Over 25 trials, the mean time to complete a test was 47 minutes, a significant reduction compared with laboratory protocols, which take approximately 3 days. Overall, participants found the device easy to use and expressed confidence and comfort in using the device independently. Conclusions: These results are used to advance the iterative prototyping process of the OmniVis rapid cholera detection device so it can achieve user uptake, workflow integration, and scale to ultimately impact cholera control and elimination strategies. We hope this methodology will promote robust usability evaluations of rapid pathogen detection technologies in device development. UR - https://www.jmir.org/2021/5/e22973 UR - http://dx.doi.org/10.2196/22973 UR - http://www.ncbi.nlm.nih.gov/pubmed/33978590 ID - info:doi/10.2196/22973 ER - TY - JOUR AU - Gwon, Danbi AU - Cho, Hakyung AU - Shin, Hangsik PY - 2021/5/11 TI - Feasibility of a Waistband-Type Wireless Wearable Electrocardiogram Monitoring System Based on a Textile Electrode: Development and Usability Study JO - JMIR Mhealth Uhealth SP - e26469 VL - 9 IS - 5 KW - electrocardiogram KW - telehealth KW - telemetry KW - telemonitoring KW - textile electrode KW - wearable system KW - smartphone KW - mobile phone N2 - Background: Electrocardiogram (ECG) monitoring in daily life is essential for effective management of cardiovascular disease, a leading cause of death. Wearable ECG measurement systems in the form of clothing have been proposed to replace Holter monitors used for clinical ECG monitoring; however, they have limitations in daily use because they compress the upper body and, in doing so, cause discomfort during wear. Objective: The purpose of this study was to develop a wireless wearable ECG monitoring system that includes a textile ECG electrode that can be applied to the lining of pants and can be used in the same way that existing lower clothing is worn, without compression to the upper body. Methods: A textile electrode with stretchable characteristics was fabricated by knitting a conductive yarn together with polyester-polyurethane fiber, which was then coated with silver compound; an ECG electrode was developed by placing it on an elastic band in a modified limb lead configuration. In addition, a system with analog-to-digital conversion, wireless communication, and a smartphone app was developed, allowing users to be able to check and store their own ECGs in real time. A signal processing algorithm was also developed to remove noise from the obtained signal and to calculate the heart rate. To evaluate the ECG and heart rate measurement performance of the developed module, a comparative evaluation with a commercial device was performed. ECGs were measured for 5 minutes each in standing, sitting, and lying positions; the mean absolute percentage errors of heart rates measured with both systems were then compared. Results: The system was developed in the form of a belt buckle with a size of 53 × 45 × 12 mm (width × height × depth) and a weight of 23 g. In a qualitative evaluation, it was confirmed that the P-QRS-T waveform was clearly observed in ECGs obtained with the wearable system. From the results of the heart rate estimation, the developed system could track changes in heart rate as calculated by a commercial ECG measuring device; in addition, the mean absolute percentage errors of heart rates were 1.80%, 2.84%, and 2.48% in the standing, sitting, and lying positions, respectively. Conclusions: The developed system was able to effectively measure ECG and calculate heart rate simply through being worn as existing clothing without upper body pressure. It is anticipated that general usability can be secured through further evaluation under more diverse conditions. UR - https://mhealth.jmir.org/2021/5/e26469 UR - http://dx.doi.org/10.2196/26469 UR - http://www.ncbi.nlm.nih.gov/pubmed/33973860 ID - info:doi/10.2196/26469 ER - TY - JOUR AU - Guay, Manon AU - Labbé, Mathieu AU - Séguin-Tremblay, Noémie AU - Auger, Claudine AU - Goyer, Geneviève AU - Veloza, Emily AU - Chevalier, Natalie AU - Polgar, Jan AU - Michaud, François PY - 2021/5/11 TI - Adapting a Person?s Home in 3D Using a Mobile App (MapIt): Participatory Design Framework Investigating the App?s Acceptability JO - JMIR Rehabil Assist Technol SP - e24669 VL - 8 IS - 2 KW - occupational therapy KW - mobile phone KW - aging KW - disability KW - telehealth KW - 3D visualization KW - universal design KW - built environment KW - camera KW - remote assessment KW - assistive technology N2 - Background: Home adaptation processes enhancing occupational engagement rely on identifying environmental barriers, generally during time-consuming home visits performed by occupational therapists (OTs). Relevance of a 3D model to the OT?s work has been attested, but a convenient and consumer-available technology to map the home environment in 3D is currently lacking. For instance, such a technology would support the exploration of home adaptations for a person with disability, with or without an OT visit. Objective: The aim of this study was to document the development and acceptability of a 3D mapping eHealth technology, optimizing its contribution to the OT?s work when conducting assessments in which home representations are essential to fit a person?s needs. Methods: A user-centered perspective, embedded in a participatory design framework where users are considered as research partners (not as just study participants), is reported. OTs, engineers, clinicians, researchers, and students, as well as the relatives of older adults contributed by providing ongoing feedback (eg, demonstrations, brainstorming, usability testing, questionnaires, prototyping). System acceptability, as per the Nielsen model, is documented by deductively integrating the data. Results: A total of 24 stakeholders contributed significantly to MapIt technology?s co-design over a span of 4 years. Fueled by the objective to enhance MapIt?s acceptability, 11 iterations lead to a mobile app to scan a room and produce its 3D model in less than 5 minutes. The app is available for smartphones and paired with computer software. Scanning, visualization, and automatic measurements are done on a smartphone equipped with a motion sensor and a camera with depth perception, and the computer software facilitates visualization, while allowing custom measurement of architectural elements directly on the 3D model. Stakeholders? perception was favorable regarding MapIt?s acceptability, testifying to its usefulness (ie, usability and utility). Residual usability issues as well as concerns about accessibility and scan rendering still need to be addressed to foster its integration to a clinical context. Conclusions: MapIt allows to scan a room quickly and simply, providing a 3D model from images taken in real-world settings and to remotely but jointly explore home adaptations to enhance a person?s occupational engagement. UR - https://rehab.jmir.org/2021/2/e24669 UR - http://dx.doi.org/10.2196/24669 UR - http://www.ncbi.nlm.nih.gov/pubmed/33973867 ID - info:doi/10.2196/24669 ER - TY - JOUR AU - Helou, Samar AU - Abou-Khalil, Victoria AU - Iacobucci, Riccardo AU - El Helou, Elie AU - Kiyono, Ken PY - 2021/5/10 TI - Automatic Classification of Screen Gaze and Dialogue in Doctor-Patient-Computer Interactions: Computational Ethnography Algorithm Development and Validation JO - J Med Internet Res SP - e25218 VL - 23 IS - 5 KW - computational ethnography KW - patient-physician communication KW - doctor-patient-computer interaction KW - electronic medical records KW - pose estimation KW - gaze KW - voice activity KW - dialogue KW - clinic layout N2 - Background: The study of doctor-patient-computer interactions is a key research area for examining doctor-patient relationships; however, studying these interactions is costly and obtrusive as researchers usually set up complex mechanisms or intrude on consultations to collect, then manually analyze the data. Objective: We aimed to facilitate human-computer and human-human interaction research in clinics by providing a computational ethnography tool: an unobtrusive automatic classifier of screen gaze and dialogue combinations in doctor-patient-computer interactions. Methods: The classifier?s input is video taken by doctors using their computers' internal camera and microphone. By estimating the key points of the doctor's face and the presence of voice activity, we estimate the type of interaction that is taking place. The classification output of each video segment is 1 of 4 interaction classes: (1) screen gaze and dialogue, wherein the doctor is gazing at the computer screen while conversing with the patient; (2) dialogue, wherein the doctor is gazing away from the computer screen while conversing with the patient; (3) screen gaze, wherein the doctor is gazing at the computer screen without conversing with the patient; and (4) other, wherein no screen gaze or dialogue are detected. We evaluated the classifier using 30 minutes of video provided by 5 doctors simulating consultations in their clinics both in semi- and fully inclusive layouts. Results: The classifier achieved an overall accuracy of 0.83, a performance similar to that of a human coder. Similar to the human coder, the classifier was more accurate in fully inclusive layouts than in semi-inclusive layouts. Conclusions: The proposed classifier can be used by researchers, care providers, designers, medical educators, and others who are interested in exploring and answering questions related to screen gaze and dialogue in doctor-patient-computer interactions. UR - https://www.jmir.org/2021/5/e25218 UR - http://dx.doi.org/10.2196/25218 UR - http://www.ncbi.nlm.nih.gov/pubmed/33970117 ID - info:doi/10.2196/25218 ER - TY - JOUR AU - Lin, Shu-Han Donna AU - Lee, Jen-Kuang PY - 2021/5/7 TI - Mobile Health?Based Thermometer for Monitoring Wound Healing After Endovascular Therapy in Patients With Chronic Foot Ulcer: Prospective Cohort Study JO - JMIR Mhealth Uhealth SP - e26468 VL - 9 IS - 5 KW - temperature KW - peripheral artery disease KW - endovascular therapy KW - mHealth KW - app KW - foot KW - therapy KW - wound KW - thermometer KW - monitoring KW - ulcer KW - artery KW - prospective KW - cohort KW - healing N2 - Background: Foot temperature may increase after endovascular therapy, but the relationship between foot temperature and wound healing is unclear. Objective: This study was performed to evaluate the feasibility of a mobile health (mHealth)?based thermometer for foot temperature monitoring in patients with chronic foot ulcer before and after endovascular therapy and to determine the association between temperature change and wound healing time. Methods: This was a prospective cohort study. Patients who had a chronic foot ulcer (>3 months) and underwent endovascular therapy between July 2019 and December 2019 were included. The participants received standard medical care and endovascular therapy for revascularization. The mHealth-based thermometer, composed of 4 temperature-sensing chips, was put on the foot before and after endovascular therapy. Data from the chips were transferred to an associated mobile phone app via Bluetooth. Wound healing time was estimated using the Kaplan-Meier method, and the associations between baseline characteristics and clinical outcomes were evaluated using a Cox proportional hazard model. Results: A total of 163 patients with chronic foot ulcer who underwent endovascular therapy were enrolled and followed up until wound healing was complete or for 180 days. The mean foot temperature before endovascular therapy was 30.6 (SD 2.8 °C). Foot temperature increased significantly (mean 32.1 °C, SD 2.8 °C; P=.01) after the procedure. Wound healing time was significantly different in the Kaplan-Meier curves of the patient group with temperature changes ?2 °C and the group with temperature changes ?2 °C (log-rank P<.001). A foot temperature increase ?2 °C after endovascular therapy was associated with increased wound healing in univariate analysis (hazard ratio [HR] 1.78, 95% CI 1.24-2.76, P=.02), and the association remained significant in multivariate analysis (HR 1.69, 95% CI 1.21-2.67, P=.03). Conclusions: The mHealth-based thermometer was feasible and useful for foot temperature monitoring, which may provide health care professionals with a new endpoint for endovascular therapy. Foot temperature increases ?2 °C after endovascular therapy were associated with faster wound healing in patients with chronic foot ulcer. Further studies are needed, however, to confirm these findings. UR - https://mhealth.jmir.org/2021/5/e26468 UR - http://dx.doi.org/10.2196/26468 UR - http://www.ncbi.nlm.nih.gov/pubmed/33960955 ID - info:doi/10.2196/26468 ER - TY - JOUR AU - Onie, Sandersan AU - Li, Xun AU - Liang, Morgan AU - Sowmya, Arcot AU - Larsen, Erik Mark PY - 2021/5/7 TI - The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions JO - JMIR Ment Health SP - e27663 VL - 8 IS - 5 KW - suicide KW - suicide prevention KW - CCTV KW - video KW - computer vision KW - machine learning N2 - Background: Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video. Objective: The objective of this study was to review the literature to understand how CCTV and video data have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision?based system to identify behaviors prior to a suicide attempt. Methods: We conducted a search using the keywords ?suicide,? ?cctv,? and ?video? on PubMed, Inspec, and Web of Science. We included any studies which used CCTV or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how CCTV and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies. Results: The review found that CCTV and video have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors prior to an attempt at a hotspot, an important gap identified by papers in the literature. Conclusions: Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention. UR - https://mental.jmir.org/2021/5/e27663 UR - http://dx.doi.org/10.2196/27663 UR - http://www.ncbi.nlm.nih.gov/pubmed/33960952 ID - info:doi/10.2196/27663 ER - TY - JOUR AU - Lee, Hyeonhoon AU - Kang, Jaehyun AU - Yeo, Jonghyeon PY - 2021/5/6 TI - Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment JO - J Med Internet Res SP - e27460 VL - 23 IS - 5 KW - artificial intelligence KW - chatbot KW - COVID-19 KW - deep learning KW - deployment KW - development KW - machine learning KW - medical specialty KW - natural language processing KW - recommendation KW - smartphone N2 - Background: The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients? symptoms and recommends the appropriate medical specialty could provide a valuable solution. Objective: In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning?based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. Methods: We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called ?Alpha? to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. Results: The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F1-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F1-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. Conclusions: With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning?based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers. UR - https://www.jmir.org/2021/5/e27460 UR - http://dx.doi.org/10.2196/27460 UR - http://www.ncbi.nlm.nih.gov/pubmed/33882012 ID - info:doi/10.2196/27460 ER - TY - JOUR AU - Areán, A. Patricia AU - Pratap, Abhishek AU - Hsin, Honor AU - Huppert, K. Tierney AU - Hendricks, E. Karin AU - Heagerty, J. Patrick AU - Cohen, Trevor AU - Bagge, Courtney AU - Comtois, Anne Katherine PY - 2021/5/6 TI - Perceived Utility and Characterization of Personal Google Search Histories to Detect Data Patterns Proximal to a Suicide Attempt in Individuals Who Previously Attempted Suicide: Pilot Cohort Study JO - J Med Internet Res SP - e27918 VL - 23 IS - 5 KW - real-world data KW - web searches KW - suicide risk factors KW - suicide detection KW - suicide KW - eHealth KW - internet KW - website KW - search history KW - risk KW - EHR KW - social media KW - behavior KW - mental health KW - personalized KW - online seeking behavior N2 - Background: Despite decades of research to better understand suicide risk and to develop detection and prevention methods, suicide is still one of the leading causes of death globally. While large-scale studies using real-world evidence from electronic health records can identify who is at risk, they have not been successful at pinpointing when someone is at risk. Personalized social media and online search history data, by contrast, could provide an ongoing real-world datastream revealing internal thoughts and personal states of mind. Objective: We conducted this study to determine the feasibility and acceptability of using personalized online information-seeking behavior in the identification of risk for suicide attempts. Methods: This was a cohort survey study to assess attitudes of participants with a prior suicide attempt about using web search data for suicide prevention purposes, dates of lifetime suicide attempts, and an optional one-time download of their past web searches on Google. The study was conducted at the University of Washington School of Medicine Psychiatry Research Offices. The main outcomes were participants? opinions on internet search data for suicide prediction and intervention and any potential change in online information-seeking behavior proximal to a suicide attempt. Individualized nonparametric association analysis was used to assess the magnitude of difference in web search data features derived from time periods proximal (7, 15, 30, and 60 days) to the suicide attempts versus the typical (baseline) search behavior of participants. Results: A total of 62 participants who had attempted suicide in the past agreed to participate in the study. Internet search activity varied from person to person (median 2-24 searches per day). Changes in online search behavior proximal to suicide attempts were evident up to 60 days before attempt. For a subset of attempts (7/30, 23%) search features showed associations from 2 months to a week before the attempt. The top 3 search constructs associated with attempts were online searching patterns (9/30 attempts, 30%), semantic relatedness of search queries to suicide methods (7/30 attempts, 23%), and anger (7/30 attempts, 23%). Participants (40/59, 68%) indicated that use of this personalized web search data for prevention purposes was acceptable with noninvasive potential interventions such as connection to a real person (eg, friend, family member, or counselor); however, concerns were raised about detection accuracy, privacy, and the potential for overly invasive intervention. Conclusions: Changes in online search behavior may be a useful and acceptable means of detecting suicide risk. Personalized analysis of online information-seeking behavior showed notable changes in search behavior and search terms that are tied to early warning signs of suicide and are evident 2 months to 7 days before a suicide attempt. UR - https://www.jmir.org/2021/5/e27918 UR - http://dx.doi.org/10.2196/27918 UR - http://www.ncbi.nlm.nih.gov/pubmed/33955838 ID - info:doi/10.2196/27918 ER - TY - JOUR AU - McGee, Beth AU - Leonte, Marie AU - Wildenhaus, Kevin AU - Wilcox, Marsha AU - Reps, Jenna AU - LaCross, Lauren PY - 2021/4/27 TI - Leveraging Digital Technology in Conducting Longitudinal Research on Mental Health in Pregnancy: Longitudinal Panel Survey Study JO - JMIR Pediatr Parent SP - e16280 VL - 4 IS - 2 KW - digital KW - longitudinal KW - pregnancy KW - postpartum KW - perinatal KW - panel KW - study design KW - mental health N2 - Background: Collecting longitudinal data during and shortly after pregnancy is difficult, as pregnant women often avoid studies with repeated surveys. In contrast, pregnant women interact with certain websites at multiple stages throughout pregnancy and the postpartum period. This digital connection presents the opportunity to use a website as a way to recruit and enroll pregnant women into a panel study and collect valuable longitudinal data for research. These data can then be used to learn new scientific insights and improve health care. Objective: The objective of this paper is to describe the approaches applied and lessons learned from designing and conducting an online panel for health care research, specifically perinatal mood disorders. Our panel design and approach aimed to recruit a large sample (N=1200) of pregnant women representative of the US population and to minimize attrition over time. Methods: We designed an online panel to enroll participants from the pregnancy and parenting website BabyCenter. We enrolled women into the panel from weeks 4 to 10 of pregnancy (Panel 1) or from weeks 28 to 33 of pregnancy (Panel 2) and administered repeated psychometric assessments from enrollment through 3 months postpartum. We employed a combination of adaptive digital strategies to recruit, communicate with, and build trust with participants to minimize attrition over time. We were transparent at baseline about expectations, used monetary and information-based incentives, and sent personalized reminders to reduce attrition. The approach was participant-centric and leveraged many aspects of flexibility that digital methods afford. Results: We recruited 1179 pregnant women?our target was 1200?during a 26-day period between August 25 and September 19, 2016. Our strategy to recruit participants using adaptive sampling tactics resulted in a large panel that was similar to the US population of pregnant women. Attrition was on par with existing longitudinal observational studies in pregnant populations, and 79.2% (934/1179) of our panel completed another survey after enrollment. There were 736 out of 1179 (62.4%) women who completed at least one assessment in both the prenatal and postnatal periods, and 709 out of 1179 (60.1%) women who completed the final assessment. To validate the data, we compared participation rates and factors of perinatal mood disorders ascertained from this study with prior research, suggesting reliability of our approach. Conclusions: A suitably designed online panel created in partnership with a digital media source that reaches the target audience is a means to leverage a conveniently sized and viable sample for scientific research. Our key lessons learned are as follows: sampling tactics may need to be adjusted to enroll a representative sample, attrition can be reduced by adapting to participants? needs, and study engagement can be boosted by personalizing interactions with the flexibility afforded by digital technologies. UR - https://pediatrics.jmir.org/2021/2/e16280 UR - http://dx.doi.org/10.2196/16280 UR - http://www.ncbi.nlm.nih.gov/pubmed/33904826 ID - info:doi/10.2196/16280 ER - TY - JOUR AU - Holyoke, Paul AU - Yogaratnam, Karthika AU - Kalles, Elizabeth PY - 2021/4/23 TI - Web-Based Smartphone Algorithm for Calculating Blood Pressure From Photoplethysmography Remotely in a General Adult Population: Validation Study JO - J Med Internet Res SP - e19187 VL - 23 IS - 4 KW - blood pressure measurement KW - remote monitoring KW - hypertension N2 - Background: Outside of a clinical setting, oscillometric devices make remote monitoring of blood pressure and virtual care more convenient and feasible. HeartBeat Technologies Ltd developed a novel approach to measuring blood pressure remotely after an initial blood pressure reading by a nurse using the conventional measurement method. Using a finger pulse oximeter, a photoplethysmogram wave is transmitted by Bluetooth to a smartphone or tablet. A smartphone app (MediBeat) transmits the photoplethysmogram to a server for analysis by a proprietary algorithm?the person?s current blood pressure is sent back to the smartphone and to the individual?s health care provider. Objective: This study sought to determine whether the HeartBeat algorithm calculates blood pressure as accurately as required by the European Society of Hypertension International Protocol revision 2010 (ESH-IP2) for validation of blood pressure measuring devices. Methods: ESH-IP2 requirements, modified to conform to a more recent international consensus statement, were followed. The ESH-IP2 establishes strict guidelines for the conduct and reporting of any validation of any device to measure blood pressure, including using the standard manual blood pressure instrument as a comparator and specific required accuracy levels for low, medium, and high ranges of blood pressure readings. The consensus statement requires a greater number of study participants for each of the blood pressure ranges. The validation of the accuracy of the algorithm was conducted with a Contec CMS50EW pulse oximeter and a Samsung Galaxy XCover 4 smartphone. Results: The differences between the HeartBeat-calculated and the manually measured blood pressures of 62 study participants did not meet the ESH-IP2 standards for accuracy for either systolic or diastolic blood pressure measurements. There was no discernible pattern in the inaccuracies of the HeartBeat-calculated measurements. Conclusions: The October 4, 2019 version of the HeartBeat algorithm, implemented in combination with the MediBeat app, a pulse oximeter, and an Android smartphone, was not sufficiently accurate for use in a general adult population. Trial Registration: ClinicalTrials.gov NCT04082819; http://clinicaltrials.gov/ct2/show/NCT04082819 UR - https://www.jmir.org/2021/4/e19187 UR - http://dx.doi.org/10.2196/19187 UR - http://www.ncbi.nlm.nih.gov/pubmed/33890856 ID - info:doi/10.2196/19187 ER - TY - JOUR AU - Brons, Annette AU - de Schipper, Antoine AU - Mironcika, Svetlana AU - Toussaint, Huub AU - Schouten, Ben AU - Bakkes, Sander AU - Kröse, Ben PY - 2021/4/22 TI - Assessing Children?s Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach JO - J Med Internet Res SP - e24237 VL - 23 IS - 4 KW - motor development KW - fine motor function KW - gamification KW - playful KW - motor skill assessment KW - Movement ABC (MABC) KW - machine learning KW - motor function KW - motor skills KW - toys KW - children KW - game KW - movement assessment N2 - Background: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. Objective: This study examines whether sensor-augmented toys can be used to assess children?s fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. Methods: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called ?Futuro Cube.? The game ?roadrunner? focused on speed while the game ?maze? focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used ?=.05. Results: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046). Conclusions: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty. UR - https://www.jmir.org/2021/4/e24237 UR - http://dx.doi.org/10.2196/24237 UR - http://www.ncbi.nlm.nih.gov/pubmed/33885371 ID - info:doi/10.2196/24237 ER - TY - JOUR AU - D´Ancona, Giuseppe AU - Murero, Monica AU - Feickert, Sebastian AU - Kaplan, Hilmi AU - Öner, Alper AU - Ortak, Jasmin AU - Ince, Hueseyin PY - 2021/4/21 TI - Implantation of an Innovative Intracardiac Microcomputer System for Web-Based Real-Time Monitoring of Heart Failure: Usability and Patients? Attitudes JO - JMIR Cardio SP - e21055 VL - 5 IS - 1 KW - heart KW - failure KW - left atrial KW - pressure KW - intracardiac KW - device KW - monitoring KW - implantable KW - wireless KW - transmission KW - web-based N2 - Background: Heart failure (HF) management guided by the measurement of intracardiac and pulmonary pressure values obtained through innovative permanent intracardiac microsensors has been recently proposed as a valid strategy to individualize treatment and anticipate hemodynamic destabilization. These sensors have potential to reduce patient hospitalization rates and optimize quality of life. Objective: The aim of this study was to evaluate the usability and patients? attitudes toward a new permanent intracardiac device implanted to remotely monitor left intra-atrial pressures (V-LAP, Vectorious Medical Technologies, Tel Aviv, Israel) in patients with chronic HF. Methods: The V-LAP system is a miniaturized sensor implanted percutaneously across the interatrial septum. The system communicates wirelessly with a ?companion device? (a wearable belt) that is placed on the patient?s chest at the time of acquisition/transmission of left heart pressure measurements. At first follow-up after implantation, the patients and health care providers were asked to fill out a questionnaire on the usability of the system, ease in performing the various required tasks (data acquisition and transmission), and overall satisfaction. Replies to the questions were mainly given using a 5-point Likert scale (1: very poor, 2: poor, 3: average, 4: good, 5: excellent). Further patient follow-ups were performed at 3, 6, and 12 months. Results: Use and acceptance of the first 14 patients receiving the V-LAP technology worldwide and related health care providers have been analyzed to date. No periprocedural morbidity/mortality was observed. Before discharge, a tailored educational session was performed after device implantation with the patients and their health care providers. At the first follow-up, the mean score for overall comfort in technology use was 3.7 (SD 1.2) with 93% (13/14) of patients succeeding in applying and operating the system independently. For health care providers, the mean score for overall ease and comfort in use of the technology was 4.2 (SD 0.8). No significant differences were found between the patients? and health care providers? replies to the questionnaires. There was a general trend for higher scores in patients? usability reports at later follow-ups, in which the score related to overall comfort with using the technology increased from 3.0 (SD 1.4) to 4.0 (SD 0.7) (P=.40) and comfort with wearing and adjusting the measuring thoracic belt increased from 2.8 (SD 1.0) to 4.2 (SD 0.4) (P=.02). Conclusions: Despite the gravity of their HF pathology and the complexity of their comorbid profile, patients are comfortable in using the V-LAP technology and, in the majority of cases, they can correctly and consistently acquire and transmit hemodynamic data. Although the overall patient/care provider satisfaction with the V-LAP system seems to be acceptable, improvements can be achieved after ameliorating the design of the measuring tools. Trial Registration: ClincalTrials.gov NCT03775161; https://clinicaltrials.gov/ct2/show/NCT03775161 UR - https://cardio.jmir.org/2021/1/e21055 UR - http://dx.doi.org/10.2196/21055 UR - http://www.ncbi.nlm.nih.gov/pubmed/33881400 ID - info:doi/10.2196/21055 ER - TY - JOUR AU - König, Alexandra AU - Riviere, Kevin AU - Linz, Nicklas AU - Lindsay, Hali AU - Elbaum, Julia AU - Fabre, Roxane AU - Derreumaux, Alexandre AU - Robert, Philippe PY - 2021/4/19 TI - Measuring Stress in Health Professionals Over the Phone Using Automatic Speech Analysis During the COVID-19 Pandemic: Observational Pilot Study JO - J Med Internet Res SP - e24191 VL - 23 IS - 4 KW - stress detection KW - speech KW - voice analysis KW - COVID-19 KW - phone monitoring KW - computer linguistics N2 - Background: During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior. Objective: This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants? speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks. Methods: Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed. Results: Certain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31). Conclusions: Automatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety. UR - https://www.jmir.org/2021/4/e24191 UR - http://dx.doi.org/10.2196/24191 UR - http://www.ncbi.nlm.nih.gov/pubmed/33739930 ID - info:doi/10.2196/24191 ER - TY - JOUR AU - Liu, Sam AU - Perdew, Megan AU - Lithopoulos, Alexander AU - Rhodes, E. Ryan PY - 2021/4/19 TI - The Feasibility of Using Instagram Data to Predict Exercise Identity and Physical Activity Levels: Cross-sectional Observational Study JO - J Med Internet Res SP - e20954 VL - 23 IS - 4 KW - social media KW - exercise identity KW - physical activity KW - physical fitness N2 - Background: Exercise identity is an important predictor for regular physical activity (PA). There is a lack of research on the potential mechanisms or antecedents of identity development. Theories of exercise identity have proposed that investment, commitment and self-referential (eg, I am an exerciser) statements, and social activation (comparison, support) may be crucial to identity development. Social media may be a potential mechanism to shape identity. Objective: The objectives of this study were to (1) explore whether participants were willing to share their Instagram data with researchers to predict their lifestyle behaviors; (2) examine whether PA-related Instagram uses (ie, the percentage of PA-related Instagram posts, fitness-related followings, and the number of likes received on PA-related posts) were positively associated with exercise identity; and (3) evaluate whether exercise identity mediates the relationship between PA-related Instagram use and weekly PA minutes. Methods: Participants (18-30 years old) were asked to complete a questionnaire to evaluate their current levels of exercise identity and PA. Participants? Instagram data for the past 12 months before the completion of the questionnaire were extracted and analyzed with their permission. Instagram posts related to PA in the 12 months before their assessment, the number of likes received for each PA-related post, and verified fitness- or PA-related followings by the participants were extracted and analyzed. Pearson correlation analyses were used to evaluate the relationship among exercise identity, PA, and Instagram uses. We conducted mediation analyses using the PROCESS macro modeling tool to examine whether exercise identity mediated the relationship between Instagram use variables and PA. Descriptive statistical analyses were used to compare the number of willing participants versus those who were not willing to share their Instagram data. Results: Of the 76 participants recruited to participate, 54% (n=41) shared their Instagram data. The percentage of PA-related Instagram posts (r=0.38; P=.01) and fitness-related Instagram followings (r=0.39; P=.01) were significantly associated with exercise identity. The average number of ?likes? received (r=0.05, P=.75) was not significantly associated with exercise identity. Exercise identity significantly influenced the relationship between Instagram usage metrics (ie, the percentage of PA-related Instagram posts [P=.01] and verified fitness-related Instagram accounts [P=.01]) and PA level. Exercise identity did not significantly influence the relationship between the average number of ?likes? received for the PA-related Instagram posts and PA level. Conclusions: Our results suggest that an increase in PA-related Instagram posts and fitness-related followings were associated with a greater sense of exercise identity. Higher exercise identity led to higher PA levels. Exercise identity significantly influenced the relationship between PA-related Instagram posts (P=.01) and fitness-related followings on PA levels (P=.01). These results suggest that Instagram may influence a person?s exercise identity and PA levels. Future intervention studies are warranted. UR - https://www.jmir.org/2021/4/e20954 UR - http://dx.doi.org/10.2196/20954 UR - http://www.ncbi.nlm.nih.gov/pubmed/33871380 ID - info:doi/10.2196/20954 ER - TY - JOUR AU - He, Qian AU - Du, Fei AU - Simonse, L. Lianne W. PY - 2021/4/12 TI - A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth JO - JMIR Med Inform SP - e23238 VL - 9 IS - 4 KW - COVID-19 KW - design KW - eHealth KW - artificial intelligence KW - service design KW - patient journey map KW - user-centered design KW - digital service solutions in health KW - home isolation KW - AI KW - touchpoint N2 - Background: In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking. Objective: The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services. Methods: A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map. Results: The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support. Conclusions: The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support. UR - https://medinform.jmir.org/2021/4/e23238 UR - http://dx.doi.org/10.2196/23238 UR - http://www.ncbi.nlm.nih.gov/pubmed/33444156 ID - info:doi/10.2196/23238 ER - TY - JOUR AU - Vanegas, Erik AU - Salazar, Yolocuauhtli AU - Igual, Raúl AU - Plaza, Inmaculada PY - 2021/4/9 TI - Force-Sensitive Mat for Vertical Jump Measurement to Assess Lower Limb Strength: Validity and Reliability Study JO - JMIR Mhealth Uhealth SP - e27336 VL - 9 IS - 4 KW - vertical jump KW - mHealth KW - mobile health KW - force-sensitive resistor KW - lower limb strength KW - leg strength N2 - Background: Vertical jump height is widely used in health care and sports fields to assess muscle strength and power from lower limb muscle groups. Different approaches have been proposed for vertical jump height measurement. Some commonly used approaches need no sensor at all; however, these methods tend to overestimate the height reached by the subjects. There are also novel systems using different kind of sensors like force-sensitive resistors, capacitive sensors, and inertial measurement units, among others, to achieve more accurate measurements. Objective: The objective of this study is twofold. The first objective is to validate the functioning of a developed low-cost system able to measure vertical jump height. The second objective is to assess the effects on obtained measurements when the sampling frequency of the system is modified. Methods: The system developed in this study consists of a matrix of force-sensitive resistor sensors embedded in a mat with electronics that allow a full scan of the mat. This mat detects pressure exerted on it. The system calculates the jump height by using the flight-time formula, and the result is sent through Bluetooth to any mobile device or PC. Two different experiments were performed. In the first experiment, a total of 38 volunteers participated with the objective of validating the performance of the system against a high-speed camera used as reference (120 fps). In the second experiment, a total of 15 volunteers participated. Raw data were obtained in order to assess the effects of different sampling frequencies on the performance of the system with the same reference device. Different sampling frequencies were obtained by performing offline downsampling of the raw data. In both experiments, countermovement jump and countermovement jump with arm swing techniques were performed. Results: In the first experiment an overall mean relative error (MRE) of 1.98% and a mean absolute error of 0.38 cm were obtained. Bland-Altman and correlation analyses were performed, obtaining a coefficient of determination equal to R2=.996. In the second experiment, sampling frequencies of 200 Hz, 100 Hz, and 66.6 Hz show similar performance with MRE below 3%. Slower sampling frequencies show an exponential increase in MRE. On both experiments, when dividing jump trials in different heights reached, a decrease in MRE with higher height trials suggests that the precision of the proposed system increases as height reached increases. Conclusions: In the first experiment, we concluded that results between the proposed system and the reference are systematically the same. In the second experiment, the relevance of a sufficiently high sampling frequency is emphasized, especially for jump trials whose height is below 10 cm. For trials with heights above 30 cm, MRE decreases in general for all sampling frequencies, suggesting that at higher heights reached, the impact of high sampling frequencies is lesser. UR - https://mhealth.jmir.org/2021/4/e27336 UR - http://dx.doi.org/10.2196/27336 UR - http://www.ncbi.nlm.nih.gov/pubmed/33835040 ID - info:doi/10.2196/27336 ER - TY - JOUR AU - Yamada, Yasunori AU - Shinkawa, Kaoru AU - Kobayashi, Masatomo AU - Takagi, Hironobu AU - Nemoto, Miyuki AU - Nemoto, Kiyotaka AU - Arai, Tetsuaki PY - 2021/4/8 TI - Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study JO - J Med Internet Res SP - e27667 VL - 23 IS - 4 KW - cognitive impairment KW - smart speaker KW - speech analysis KW - accident KW - prevention KW - older adults KW - prediction KW - risk KW - assistant N2 - Background: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. Objective: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. Methods: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data?neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)?from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. Results: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. Conclusions: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function. UR - https://www.jmir.org/2021/4/e27667 UR - http://dx.doi.org/10.2196/27667 UR - http://www.ncbi.nlm.nih.gov/pubmed/33830066 ID - info:doi/10.2196/27667 ER - TY - JOUR AU - Jagesar, R. Raj AU - Vorstman, A. Jacob AU - Kas, J. Martien PY - 2021/4/7 TI - Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study JO - J Med Internet Res SP - e20996 VL - 23 IS - 4 KW - digital phenotyping KW - mobile behavioral monitoring KW - passive behavioral monitoring KW - smartphone-based behavioral monitoring KW - research data management KW - psychoinformatics KW - mobile phone N2 - Background: Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way. Objective: This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform. Methods: We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform. Results: We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design. Conclusions: Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping?driven research. UR - https://www.jmir.org/2021/4/e20996 UR - http://dx.doi.org/10.2196/20996 UR - http://www.ncbi.nlm.nih.gov/pubmed/33825695 ID - info:doi/10.2196/20996 ER - TY - JOUR AU - Kazevman, Gill AU - Mercado, Marck AU - Hulme, Jennifer AU - Somers, Andrea PY - 2021/4/6 TI - Prescribing Phones to Address Health Equity Needs in the COVID-19 Era: The PHONE-CONNECT Program JO - J Med Internet Res SP - e23914 VL - 23 IS - 4 KW - digital health equity KW - health inequity KW - digital determinants of health KW - emergency medicine KW - COVID-19 KW - public health KW - health policy KW - primary care KW - cell phone UR - https://www.jmir.org/2021/4/e23914 UR - http://dx.doi.org/10.2196/23914 UR - http://www.ncbi.nlm.nih.gov/pubmed/33760753 ID - info:doi/10.2196/23914 ER - TY - JOUR AU - Banholzer, Nicolas AU - Feuerriegel, Stefan AU - Fleisch, Elgar AU - Bauer, Friedrich Georg AU - Kowatsch, Tobias PY - 2021/4/2 TI - Computer Mouse Movements as an Indicator of Work Stress: Longitudinal Observational Field Study JO - J Med Internet Res SP - e27121 VL - 23 IS - 4 KW - work stress KW - psychological stress KW - stress indicator KW - computer mouse movements KW - human-computer interactions N2 - Background: Work stress affects individual health and well-being. These negative effects could be mitigated through regular monitoring of employees? stress. Such monitoring becomes even more important as the digital transformation of the economy implies profound changes in working conditions. Objective: The goal of this study was to investigate the association between computer mouse movements and work stress in the field. Methods: We hypothesized that stress is associated with a speed-accuracy trade-off in computer mouse movements. To test this hypothesis, we conducted a longitudinal field study at a large business organization, where computer mouse movements from regular work activities were monitored over 7 weeks; the study included 70 subjects and 1829 observations. A Bayesian regression model was used to estimate whether self-reported acute work stress was associated with a speed-accuracy trade-off in computer mouse movements. Results: There was a negative association between stress and the two-way interaction term of mouse speed and accuracy (mean ?0.32, 95% highest posterior density interval ?0.58 to ?0.08), which means that stress was associated with a speed-accuracy trade-off. The estimated association was not sensitive to different processing of the data and remained negative after controlling for the demographics, health, and personality traits of subjects. Conclusions: Self-reported acute stress is associated with computer mouse movements, specifically in the form of a speed-accuracy trade-off. This finding suggests that the regular analysis of computer mouse movements could indicate work stress. UR - https://www.jmir.org/2021/4/e27121 UR - http://dx.doi.org/10.2196/27121 UR - http://www.ncbi.nlm.nih.gov/pubmed/33632675 ID - info:doi/10.2196/27121 ER - TY - JOUR AU - Hwang, Youjin AU - Shin, Donghoon AU - Eun, Jinsu AU - Suh, Bongwon AU - Lee, Joonhwan PY - 2021/3/29 TI - Design Guidelines of a Computer-Based Intervention for Computer Vision Syndrome: Focus Group Study and Real-World Deployment JO - J Med Internet Res SP - e22099 VL - 23 IS - 3 KW - computer-based intervention KW - computer vision syndrome KW - system interface KW - deployment study N2 - Background: Prolonged time of computer use increases the prevalence of ocular problems, including eye strain, tired eyes, irritation, redness, blurred vision, and double vision, which are collectively referred to as computer vision syndrome (CVS). Approximately 70% of computer users have vision-related problems. For these reasons, properly designed interventions for users with CVS are required. To design an effective screen intervention for preventing or improving CVS, we must understand the effective interfaces of computer-based interventions. Objective: In this study, we aimed to explore the interface elements of computer-based interventions for CVS to set design guidelines based on the pros and cons of each interface element. Methods: We conducted an iterative user study to achieve our research objective. First, we conducted a workshop to evaluate the overall interface elements that were included in previous systems for CVS (n=7). Through the workshop, participants evaluated existing interface elements. Based on the evaluation results, we eliminated the elements that negatively affect intervention outcomes. Second, we designed our prototype system LiquidEye that includes multiple interface options (n=11). Interface options included interface elements that were positively evaluated in the workshop study. Lastly, we deployed LiquidEye in the real world to see how the included elements affected the intervention outcomes. Participants used LiquidEye for 14 days, and during this period, we collected participants? daily logs (n=680). Additionally, we conducted prestudy and poststudy surveys, and poststudy interviews to explore how each interface element affects participation in the system. Results: User data logs collected from the 14 days of deployment were analyzed with multiple regression analysis to explore the interface elements affecting user participation in the intervention (LiquidEye). Statistically significant elements were the instruction page of the eye resting strategy (P=.01), goal setting of the resting period (P=.009), compliment feedback after completing resting (P<.001), a mid-size popup window (P=.02), and CVS symptom-like effects (P=.004). Conclusions: Based on the study results, we suggested design implications to consider when designing computer-based interventions for CVS. The sophisticated design of the customization interface can make it possible for users to use the system more interactively, which can result in higher engagement in managing eye conditions. There are important technical challenges that still need to be addressed, but given the fact that this study was able to clarify the various factors related to computer-based interventions, the findings are expected to contribute greatly to the research of various computer-based intervention designs in the future. UR - https://www.jmir.org/2021/3/e22099 UR - http://dx.doi.org/10.2196/22099 UR - http://www.ncbi.nlm.nih.gov/pubmed/33779568 ID - info:doi/10.2196/22099 ER - TY - JOUR AU - Suppan, Mélanie AU - Abbas, Mohamed AU - Catho, Gaud AU - Stuby, Loric AU - Regard, Simon AU - Achab, Sophia AU - Harbarth, Stephan AU - Suppan, Laurent PY - 2021/3/25 TI - Impact of a Serious Game (Escape COVID-19) on the Intention to Change COVID-19 Control Practices Among Employees of Long-term Care Facilities: Web-Based Randomized Controlled Trial JO - J Med Internet Res SP - e27443 VL - 23 IS - 3 KW - COVID-19 KW - transmission KW - serious game KW - infection prevention KW - health care worker KW - SARS-CoV-2 KW - nursing homes KW - randomized controlled trial KW - long-term care facilities KW - impact KW - game KW - intention KW - control KW - elderly N2 - Background: Most residents of long-term care facilities (LTCFs) are at high risk of complications and death following SARS-CoV-2 infection. In these facilities, viral transmission can be facilitated by shortages of human and material resources, which can lead to suboptimal application of infection prevention and control (IPC) procedures. To improve the dissemination of COVID-19 IPC guidelines, we developed a serious game called ?Escape COVID-19? using Nicholson?s RECIPE for meaningful gamification, as engaging serious games have the potential to induce behavioral change. Objective: As the probability of executing an action is strongly linked to the intention of performing it, the objective of this study was to determine whether LTCF employees were willing to change their IPC practices after playing ?Escape COVID-19.? Methods: This was a web-based, triple-blind, randomized controlled trial, which took place between November 5 and December 4, 2020. The health authorities of Geneva, Switzerland, asked the managers of all LTCFs under their jurisdiction to forward information regarding the study to all their employees, regardless of professional status. Participants were unaware that they would be randomly allocated to one of two different study paths upon registration. In the control group, participants filled in a first questionnaire designed to gather demographic data and assess baseline knowledge before accessing regular online IPC guidelines. They then answered a second questionnaire, which assessed their willingness to change their IPC practices and identified the reasons underlying their decision. They were then granted access to the serious game. Conversely, the serious game group played ?Escape COVID-19? after answering the first questionnaire but before answering the second one. This group accessed the control material after answering the second set of questions. There was no time limit. The primary outcome was the proportion of LTCF employees willing to change their IPC practices. Secondary outcomes included the factors underlying participants? decisions, the domains these changes would affect, changes in the use of protective equipment items, and attrition at each stage of the study. Results: A total of 295 answer sets were analyzed. Willingness to change behavior was higher in the serious game group (82% [119/145] versus 56% [84/150]; P<.001), with an odds ratio of 3.86 (95% CI 2.18-6.81; P<.001) after adjusting for professional category and baseline knowledge, using a mixed effects logistic regression model with LTCF as a random effect. For more than two-thirds (142/203) of the participants, the feeling of playing an important role against the epidemic was the most important factor explaining their willingness to change behavior. Most of the participants unwilling to change their behavior answered that they were already applying all the guidelines. Conclusions: The serious game ?Escape COVID-19? was more successful than standard IPC material in convincing LTCF employees to adopt COVID-19?safe IPC behavior. International Registered Report Identifier (IRRID): RR2-10.2196/25595 UR - https://www.jmir.org/2021/3/e27443 UR - http://dx.doi.org/10.2196/27443 UR - http://www.ncbi.nlm.nih.gov/pubmed/33685854 ID - info:doi/10.2196/27443 ER - TY - JOUR AU - Casanova, Morgane AU - Clavreul, Anne AU - Soulard, Gwénaëlle AU - Delion, Matthieu AU - Aubin, Ghislaine AU - Ter Minassian, Aram AU - Seguier, Renaud AU - Menei, Philippe PY - 2021/3/24 TI - Immersive Virtual Reality and Ocular Tracking for Brain Mapping During Awake Surgery: Prospective Evaluation Study JO - J Med Internet Res SP - e24373 VL - 23 IS - 3 KW - virtual reality KW - eye tracking KW - brain mapping KW - awake surgery KW - visuospatial cognition KW - nonverbal language KW - mobile phone N2 - Background: Language mapping during awake brain surgery is currently a standard procedure. However, mapping is rarely performed for other cognitive functions that are important for social interaction, such as visuospatial cognition and nonverbal language, including facial expressions and eye gaze. The main reason for this omission is the lack of tasks that are fully compatible with the restrictive environment of an operating room and awake brain surgery procedures. Objective: This study aims to evaluate the feasibility and safety of a virtual reality headset equipped with an eye-tracking device that is able to promote an immersive visuospatial and social virtual reality (VR) experience for patients undergoing awake craniotomy. Methods: We recruited 15 patients with brain tumors near language and/or motor areas. Language mapping was performed with a naming task, DO 80, presented on a computer tablet and then in 2D and 3D via the VRH. Patients were also immersed in a visuospatial and social VR experience. Results: None of the patients experienced VR sickness, whereas 2 patients had an intraoperative focal seizure without consequence; there was no reason to attribute these seizures to virtual reality headset use. The patients were able to perform the VR tasks. Eye tracking was functional, enabling the medical team to analyze the patients? attention and exploration of the visual field of the virtual reality headset directly. Conclusions: We found that it is possible and safe to immerse the patient in an interactive virtual environment during awake brain surgery, paving the way for new VR-based brain mapping procedures. Trial Registration: ClinicalTrials.gov NCT03010943; https://clinicaltrials.gov/ct2/show/NCT03010943. UR - https://www.jmir.org/2021/3/e24373 UR - http://dx.doi.org/10.2196/24373 UR - http://www.ncbi.nlm.nih.gov/pubmed/33759794 ID - info:doi/10.2196/24373 ER - TY - JOUR AU - Eberle, Claudia AU - Stichling, Stefanie AU - Löhnert, Maxine PY - 2021/3/24 TI - Diabetology 4.0: Scoping Review of Novel Insights and Possibilities Offered by Digitalization JO - J Med Internet Res SP - e23475 VL - 23 IS - 3 KW - diabetes mellitus KW - telemedicine KW - mobile apps KW - electronic health records KW - digital technology KW - eHealth KW - mobile phone N2 - Background: The increasing prevalence of diabetes mellitus and associated morbidity worldwide justifies the need to create new approaches and strategies for diabetes therapy. Therefore, the ongoing digitalization offers novel opportunities in this field. Objective: The aim of this study is to provide an updated overview of available technologies, possibilities, and novel insights into diabetes therapy 4.0. Methods: A scoping review was carried out, and a literature search was performed using electronic databases (MEDLINE [PubMed], Cochrane Library, Embase, CINAHL, and Web of Science). The results were categorized according to the type of technology presented. Results: Different types of technology (eg, glucose monitoring systems, insulin pens, insulin pumps, closed-loop systems, mobile health apps, telemedicine, and electronic medical records) may help to improve diabetes treatment. These improvements primarily affect glycemic control. However, they may also help in increasing the autonomy and quality of life of people who are diagnosed with diabetes mellitus. Conclusions: Diabetes technologies have developed rapidly over the last few years and offer novel insights into diabetes therapy and a chance to improve and individualize diabetes treatment. Challenges that need to be addressed in the following years relate to data security, interoperability, and the development of standards. UR - https://www.jmir.org/2021/3/e23475 UR - http://dx.doi.org/10.2196/23475 UR - http://www.ncbi.nlm.nih.gov/pubmed/33759789 ID - info:doi/10.2196/23475 ER - TY - JOUR AU - Wang, Xuancong AU - Vouk, Nikola AU - Heaukulani, Creighton AU - Buddhika, Thisum AU - Martanto, Wijaya AU - Lee, Jimmy AU - Morris, JT Robert PY - 2021/3/15 TI - HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning JO - J Med Internet Res SP - e23984 VL - 23 IS - 3 KW - digital phenotyping KW - eHealth KW - mHealth KW - mobile phone KW - phenotype KW - data collection KW - outpatient monitoring KW - machine learning UR - https://www.jmir.org/2021/3/e23984 UR - http://dx.doi.org/10.2196/23984 UR - http://www.ncbi.nlm.nih.gov/pubmed/33720028 ID - info:doi/10.2196/23984 ER - TY - JOUR AU - Zhao, Zhixiang AU - Wu, Che-Ming AU - Zhang, Shuping AU - He, Fanping AU - Liu, Fangfen AU - Wang, Ben AU - Huang, Yingxue AU - Shi, Wei AU - Jian, Dan AU - Xie, Hongfu AU - Yeh, Chao-Yuan AU - Li, Ji PY - 2021/3/15 TI - A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study JO - JMIR Med Inform SP - e23415 VL - 9 IS - 3 KW - rosacea KW - artificial intelligence KW - convolutional neural networks N2 - Background: Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. Objective: The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. Methods: In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. Results: The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. Conclusions: The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist. UR - https://medinform.jmir.org/2021/3/e23415 UR - http://dx.doi.org/10.2196/23415 UR - http://www.ncbi.nlm.nih.gov/pubmed/33720027 ID - info:doi/10.2196/23415 ER - TY - JOUR AU - Romero-Lopez-Alberca, Cristina AU - Alonso-Trujillo, Federico AU - Almenara-Abellan, Luis Jose AU - Salinas-Perez, A. Jose AU - Gutierrez-Colosia, R. Mencia AU - Gonzalez-Caballero, Juan-Luis AU - Pinzon Pulido, Sandra AU - Salvador-Carulla, Luis PY - 2021/3/15 TI - A Semiautomated Classification System for Producing Service Directories in Social and Health Care (DESDE-AND): Maturity Assessment Study JO - J Med Internet Res SP - e24930 VL - 23 IS - 3 KW - DESDE-LTC KW - DESDE-AND KW - services coding KW - service directories KW - decision support system KW - impact analysis KW - maturity N2 - Background: DESDE-LTC (Description and Evaluation of Services and DirectoriEs for Long-Term Care) is an international classification system that allows standardized coding and comparisons between different territories and care sectors, such as health and social care, in defined geographic areas. We adapted DESDE-LTC into a computer tool (DESDE-AND) for compiling a directory of care services in Andalucia, Spain. Objective: The aim of this study was to evaluate the maturity of DESDE-AND. A secondary objective of this study is to show the practicality of a new combined set of standard evaluation tools for measuring the maturity of health technology products. Methods: A system for semiautomated coding of service provision has been co-designed. A panel of 23 domain experts and a group of 68 end users participated in its maturity assessment that included its technology readiness level (TRL), usability, validity, adoption (Adoption Impact Ladder [AIL]), and overall degree of maturity [implementation maturity model [IMM]). We piloted the prototype in an urban environment (Seville, Spain). Results: The prototype was demonstrated in an operational environment (TRL 7). Sixty-eight different care services were coded, generating fact sheets for each service and its geolocation map. The observed agreement was 90%, with moderate reliability. The tool was partially adopted by the regional government of Andalucia (Spain), reaching a level 5 in adoption (AIL) and a level 4 in maturity (IMM) and is ready for full implementation. Conclusions: DESDE-AND is a usable and manageable system for coding and compiling service directories and it can be used as a core module of decision support systems to guide planning in complex cross-sectoral areas such as combined social and health care. UR - https://www.jmir.org/2021/3/e24930 UR - http://dx.doi.org/10.2196/24930 UR - http://www.ncbi.nlm.nih.gov/pubmed/33720035 ID - info:doi/10.2196/24930 ER - TY - JOUR AU - Zanetti, Michele AU - Clavenna, Antonio AU - Pandolfini, Chiara AU - Pansieri, Claudia AU - Calati, Grazia Maria AU - Cartabia, Massimo AU - Miglio, Daniela AU - Bonati, Maurizio PY - 2021/3/12 TI - Informatics Methodology Used in the Web-Based Portal of the NASCITA Cohort Study: Development and Implementation Study JO - J Med Internet Res SP - e23087 VL - 23 IS - 3 KW - internet KW - computer systems KW - cohort studies KW - pediatricians KW - infant KW - newborn N2 - Background: Many diseases occurring in adults can be pinned down to early childhood and birth cohorts are the optimal means to study this connection. Birth cohorts have contributed to the understanding of many diseases and their risk factors. Objective: To improve the knowledge of the health status of Italian children early on and how it is affected by social and health determinants, we set up a longitudinal, prospective, national-level, population-based birth cohort, the NASCITA study (NAscere e creSCere in ITAlia). The main aim of this cohort is to evaluate physical, cognitive, and psychological development; health status; and health resource use in the first 6 years of life in newborns, and potential associated factors. A web-based system was set up with the aim to host the cohort; provide ongoing information to pediatricians and to families; and facilitate accurate data input, monitoring, and analysis. This article describes the informatics methodology used to set up and maintain the NASCITA cohort with its web-based platform, and provides a general description of the data on children aged over 7 months. Methods: Family pediatricians were contacted for participation in the cohort and enrolled newborns from April 2019 to July 2020 at their first well-child visit. Information collected included basic data that are part of those routinely collected by the family pediatricians, but also parental data, such as medical history, characteristics and lifestyle, and indoor and outdoor environment. A specific web portal for the NASCITA cohort study was developed and an electronic case report form for data input was created and tested. Interactive data charts, including growth curves, are being made available to pediatricians with their patients? data. Newsletters covering the current biomedical literature on child cohorts are periodically being put up for pediatricians, and, for parents, evidence-based information on common illnesses and problems in children. Results: The entire cohort population consists of 5166 children, with 139 participating pediatricians, distributed throughout Italy. The number of children enrolled per pediatrician ranged from 1 to 100. The 5166 enrolled children represent 66.55% (5166/7763) of the children born in all of 2018 covered by the same pediatricians participating in the cohort. The number of children aged over 7 months at the time of these analyses, and for whom the most complete data were available upon initial analyses, was 4386 (2226/4381 males [50.81%] and 142/4370 twins [3.25%]). The age of the mothers at birth of the 4386 children ranged from 16 to 54 years. Most newborns? mothers (3758/4367, 86.05%) were born in Italy, followed by mothers born in Romania (101/4367, 2.31%), Albania (75/4367, 1.72%), and Morocco (60/4367, 1.37%). Concerning the newborns, 138/4386 (3.15%) were born with malformations and 352/4386 (8.03%) had a disease, most commonly neonatal respiratory distress syndrome (n=52), neonatal jaundice (n=46), and neonatal hypoglycemia (n=45). Conclusions: The NASCITA cohort is well underway and the population size will permit significant conclusions to be drawn. The key role of pediatricians in obtaining clinical data directly, along with the national-level representativity, will make the findings even more solid. In addition to promoting accurate data input, the multiple functions of the web portal, with its interactive platform, help maintain a solid relationship with the pediatricians and keep parents informed and interested in participating. Trial Registration: ClinicalTrials.gov NCT03894566; https://clinicaltrials.gov/ct2/show/NCT03894566 UR - https://www.jmir.org/2021/3/e23087 UR - http://dx.doi.org/10.2196/23087 UR - http://www.ncbi.nlm.nih.gov/pubmed/33709930 ID - info:doi/10.2196/23087 ER - TY - JOUR AU - Shepperd, A. James AU - Pogge, Gabrielle AU - Hunleth, M. Jean AU - Ruiz, Sienna AU - Waters, A. Erika PY - 2021/3/11 TI - Guidelines for Conducting Virtual Cognitive Interviews During a Pandemic JO - J Med Internet Res SP - e25173 VL - 23 IS - 3 KW - cognitive interview KW - COVID-19 KW - guidelines KW - teleresearch KW - pandemic KW - tablet computer KW - telehealth KW - training UR - https://www.jmir.org/2021/3/e25173 UR - http://dx.doi.org/10.2196/25173 UR - http://www.ncbi.nlm.nih.gov/pubmed/33577464 ID - info:doi/10.2196/25173 ER - TY - JOUR AU - Walsh, Caoimhe AU - Zargaran, David AU - Patel, Nikhil AU - White, Amelia AU - Koumpa, Stefania Foteini AU - Tanna, Ravina AU - Ashraf, Arsalan Muhammad PY - 2021/3/11 TI - Practical Considerations and Successful Implementation of Vital Signs Monitoring. Comment on ?Continuous Versus Intermittent Vital Signs Monitoring Using a Wearable, Wireless Patch in Patients Admitted to Surgical Wards: Pilot Cluster Randomized Controlled Trial? JO - J Med Internet Res SP - e14042 VL - 23 IS - 3 KW - general surgery KW - monitoring KW - observations KW - vital signs UR - https://www.jmir.org/2021/3/e14042 UR - http://dx.doi.org/10.2196/14042 UR - http://www.ncbi.nlm.nih.gov/pubmed/33704079 ID - info:doi/10.2196/14042 ER - TY - JOUR AU - Tena, Alberto AU - Claria, Francec AU - Solsona, Francesc AU - Meister, Einar AU - Povedano, Monica PY - 2021/3/10 TI - Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study JO - JMIR Med Inform SP - e21331 VL - 9 IS - 3 KW - amyotrophic lateral sclerosis KW - bulbar involvement KW - voice KW - diagnosis KW - machine learning N2 - Background: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. Objective: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. Methods: The study focused on the extraction of features from the phonatory subsystem?jitter, shimmer, harmonics-to-noise ratio, and pitch?from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. Results: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement. Conclusions: The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement. UR - https://medinform.jmir.org/2021/3/e21331 UR - http://dx.doi.org/10.2196/21331 UR - http://www.ncbi.nlm.nih.gov/pubmed/33688838 ID - info:doi/10.2196/21331 ER - TY - JOUR AU - Patel, Shivani AU - Craigen, Gerry AU - Pinto da Costa, Mariana AU - Inkster, Becky PY - 2021/3/9 TI - Opportunities and Challenges for Digital Social Prescribing in Mental Health: Questionnaire Study JO - J Med Internet Res SP - e17438 VL - 23 IS - 3 KW - mental health KW - technology KW - psychiatry KW - mobile phone N2 - Background: The concept of digital social prescription usually refers to social prescriptions that are facilitated by using technology. Tools that enable such digital social prescriptions may be beneficial in recommending nonmedical activities to people with mental illness. As these tools are still somewhat novel and emerging, little is known about their potential advantages and disadvantages. Objective: The objective of this study is to identify the potential opportunities and challenges that may arise from digital social prescriptions. Methods: We developed a qualitative questionnaire that was disseminated through social media (Facebook and Twitter). A purposive sample targeting digital mental health experts and nonexperts was approached. The questionnaire asked participants? views about digital social prescription; the core elements linked with a definition of digital social prescription; and the strengths, weaknesses, opportunities, and threats associated with digital social prescription. Results: Four core elements were recommended to define the concept of digital social prescription: digital, facilitate, user, and social. The main strength identified was the possibility to rapidly start using digital social prescription tools, which were perceived as cost-effective. The main weaknesses were their poor adherence and difficulties with using such tools. The main opportunities were an increased access to social prescription services and the prevention of serious mental illness. The main threats were certain groups being disadvantaged, patients being subject to unintended negative consequences, and issues relating to confidentiality and data protection. Conclusions: Although digital social prescriptions may be able to effectively augment the social prescriptions, a careful consideration of practical challenges and data ethics is imperative in the design and implementation of such technologies. UR - https://www.jmir.org/2021/3/e17438 UR - http://dx.doi.org/10.2196/17438 UR - http://www.ncbi.nlm.nih.gov/pubmed/33687338 ID - info:doi/10.2196/17438 ER - TY - JOUR AU - Yan, Chao AU - Zhang, Xinmeng AU - Gao, Cheng AU - Wilfong, Erin AU - Casey, Jonathan AU - France, Daniel AU - Gong, Yang AU - Patel, Mayur AU - Malin, Bradley AU - Chen, You PY - 2021/3/8 TI - Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study JO - JMIR Hum Factors SP - e25724 VL - 8 IS - 1 KW - COVID-19 KW - intensive care unit KW - collaboration structure KW - critically ill patient KW - health care worker KW - network analysis KW - electronic health record KW - collaboration KW - critical care KW - relationship KW - safety KW - teamwork N2 - Background: Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. Objective: We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. Methods: In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics?eigencentrality and betweenness?to quantify HCWs? statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs? broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients? EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non?COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs. Results: HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non?COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non?COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non?COVID-19 care (P<.001). Compared to HCWs in non?COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). Conclusions: Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non?COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care. UR - https://humanfactors.jmir.org/2021/1/e25724 UR - http://dx.doi.org/10.2196/25724 UR - http://www.ncbi.nlm.nih.gov/pubmed/33621187 ID - info:doi/10.2196/25724 ER - TY - JOUR AU - Bai, Ran AU - Xiao, Le AU - Guo, Yu AU - Zhu, Xuequan AU - Li, Nanxi AU - Wang, Yashen AU - Chen, Qinqin AU - Feng, Lei AU - Wang, Yinghua AU - Yu, Xiangyi AU - Wang, Chunxue AU - Hu, Yongdong AU - Liu, Zhandong AU - Xie, Haiyong AU - Wang, Gang PY - 2021/3/8 TI - Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study JO - JMIR Mhealth Uhealth SP - e24365 VL - 9 IS - 3 KW - digital phenotype KW - major depressive disorder KW - machine learning KW - mobile phone N2 - Background: Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time. Objective: The aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data. Methods: We constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients? Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. Results: A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). Conclusions: Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173 UR - https://mhealth.jmir.org/2021/3/e24365 UR - http://dx.doi.org/10.2196/24365 UR - http://www.ncbi.nlm.nih.gov/pubmed/33683207 ID - info:doi/10.2196/24365 ER - TY - JOUR AU - Hill, R. Jordan AU - Harrington, B. Addison AU - Adeoye, Philip AU - Campbell, L. Noll AU - Holden, J. Richard PY - 2021/3/4 TI - Going Remote?Demonstration and Evaluation of Remote Technology Delivery and Usability Assessment With Older Adults: Survey Study JO - JMIR Mhealth Uhealth SP - e26702 VL - 9 IS - 3 KW - COVID-19 KW - mobile usability testing KW - usability inspection KW - methods KW - aging KW - agile KW - mobile phone N2 - Background: The COVID-19 pandemic necessitated ?going remote? with the delivery, support, and assessment of a study intervention targeting older adults enrolled in a clinical trial. While remotely delivering and assessing technology is not new, there are few methods available in the literature that are proven to be effective with diverse populations, and none for older adults specifically. Older adults comprise a diverse population, including in terms of their experience with and access to technology, making this a challenging endeavor. Objective: Our objective was to remotely deliver and conduct usability testing for a mobile health (mHealth) technology intervention for older adult participants enrolled in a clinical trial of the technology. This paper describes the methodology used, its successes, and its limitations. Methods: We developed a conceptual model for remote operations, called the Framework for Agile and Remote Operations (FAR Ops), that combined the general requirements for spaceflight operations with Agile project management processes to quickly respond to this challenge. Using this framework, we iteratively created care packages that differed in their contents based on participant needs and were sent to study participants to deliver the study intervention?a medication management app?and assess its usability. Usability data were collected using the System Usability Scale (SUS) and a novel usability questionnaire developed to collect more in-depth data. Results: In the first 6 months of the project, we successfully delivered 21 care packages. We successfully designed and deployed a minimum viable product in less than 6 weeks, generally maintained a 2-week sprint cycle, and achieved a 40% to 50% return rate for both usability assessment instruments. We hypothesize that lack of engagement due to the pandemic and our use of asynchronous communication channels contributed to the return rate of usability assessments being lower than desired. We also provide general recommendations for performing remote usability testing with diverse populations based on the results of our work, including implementing screen sharing capabilities when possible, and determining participant preference for phone or email communications. Conclusions: The FAR Ops model allowed our team to adopt remote operations for our mHealth trial in response to interruptions from the COVID-19 pandemic. This approach can be useful for other research or practice-based projects under similar circumstances or to improve efficiency, cost, effectiveness, and participant diversity in general. In addition to offering a replicable approach, this paper tells the often-untold story of practical challenges faced by mHealth projects and practical strategies used to address them. Trial Registration: ClinicalTrials.gov NCT04121858; https://clinicaltrials.gov/ct2/show/NCT04121858 UR - https://mhealth.jmir.org/2021/3/e26702 UR - http://dx.doi.org/10.2196/26702 UR - http://www.ncbi.nlm.nih.gov/pubmed/33606655 ID - info:doi/10.2196/26702 ER - TY - JOUR AU - Kabir, Ashad Muhammad AU - Rahman, Sowmen Sheikh AU - Islam, Mainul Mohammad AU - Ahmed, Sayed AU - Laird, Craig PY - 2021/3/4 TI - Mobile Apps for Foot Measurement in Pedorthic Practice: Scoping Review JO - JMIR Mhealth Uhealth SP - e24202 VL - 9 IS - 3 KW - foot measurement KW - foot scanning KW - mobile app KW - custom shoes making KW - apps review KW - diabetic foot KW - pedorthics KW - footcare N2 - Background: As the use of smartphones increases globally across various fields of research and technology, significant contributions to the sectors related to health, specifically foot health, can be observed. Numerous smartphone apps are now being used for providing accurate information about various foot-related properties. Corresponding to this abundance of foot scanning and measuring apps available in app stores, there is a need for evaluating these apps, as limited information regarding their evidence-based quality is available. Objective: The aim of this review was to assess the measurement techniques and essential software quality characteristics of mobile foot measurement apps, and to determine their potential as commercial tools used by foot care health professionals, to assist in measuring feet for custom shoes, and for individuals to enhance their awareness of foot health and hygiene to ultimately prevent foot-related problems. Methods: An electronic search across Android and iOS app stores was performed between July and August 2020 to identify apps related to foot measurement and general foot health. The selected apps were rated by three independent raters, and all discrepancies were resolved by discussion among raters and other investigators. Based on previous work on app rating tools, a modified rating scale tool was devised to rate the selected apps. The internal consistency of the rating tool was tested with a group of three people who rated the selected apps over 2-3 weeks. This scale was then used to produce evaluation scores for the selected foot measurement apps and to assess the interrater reliability. Results: Evaluation inferences showed that all apps failed to meet even half of the measurement-specific criteria required for the proper manufacturing of custom-made footwear. Only 23% (6/26) of the apps reportedly used external scanners or advanced algorithms to reconstruct 3D models of a user?s foot that could possibly be used for ordering custom-made footwear (shoes, insoles/orthoses), and medical casts to fit irregular foot sizes and shapes. The apps had varying levels of performance and usability, although the overall measurement functionality was subpar with a mean of 1.93 out of 5. Apps linked to online shops and stores (shoe recommendation) were assessed to be more usable than other apps but lacked some features (eg, custom shoe sizes and shapes). Overall, the current apps available for foot measurement do not follow any specific guidelines for measurement purposes. Conclusions: Most commercial apps currently available in app stores are not viable for use as tools in assisting foot care health professionals or individuals to measure their feet for custom-made footwear. Current apps lack software quality characteristics and need significant improvements to facilitate proper measurement, enhance awareness of foot health, and induce motivation to prevent and cure foot-related problems. Guidelines similar to the essential criteria items introduced in this study need to be developed for future apps aimed at foot measurement for custom-made or individually fitted footwear and to create awareness of foot health. UR - https://mhealth.jmir.org/2021/3/e24202 UR - http://dx.doi.org/10.2196/24202 UR - http://www.ncbi.nlm.nih.gov/pubmed/33661124 ID - info:doi/10.2196/24202 ER - TY - JOUR AU - Srinivasan, Balaji AU - Finkelstein, L. Julia AU - Erickson, David AU - Mehta, Saurabh PY - 2021/3/3 TI - Point-of-Care Quantification of Serum Alpha-Fetoprotein for Screening Birth Defects in Resource-Limited Settings: Proof-of-Concept Study JO - JMIR Biomed Eng SP - e23527 VL - 6 IS - 1 KW - alpha-fetoprotein KW - point-of-care testing KW - screening KW - neural tube defects KW - mobile phone N2 - Background: Maternal serum alpha-fetoprotein (MSAFP) concentration typically increases during pregnancy and is routinely measured during the second trimester as a part of screening for fetal neural tube defects and Down syndrome. However, most pregnancy screening tests are not available in the settings they are needed the most. A mobile device?enabled technology based on MSAFP for screening birth defects could enable the rapid screening and triage of high-risk pregnancies, especially where maternal serum screening and fetal ultrasound scan facilities are not easily accessible. Shifting the approach from clinic- and laboratory-dependent care to a mobile platform based on our point-of-care approach will enable translation to resource-limited settings and the global health care market. Objective: The objective of this study is to develop and perform proof-of-concept testing of a lateral flow immunoassay on a mobile platform for rapid, point-of-care quantification of serum alpha-fetoprotein (AFP) levels, from a drop of human serum, within a few minutes. Methods: The development of the immunoassay involved the selection of commercially available antibodies and optimization of their concentrations by an iterative method to achieve the required detection limits. We compared the performance of our method with that of commercially obtained human serum samples, with known AFP concentrations quantified by the Abbott ARCHITECT chemiluminescent magnetic microparticle immunoassay (CMIA). Results: We tested commercially obtained serum samples (N=20) with concentrations ranging from 2.2 to 446 ng/mL to compare the results of our point-of-care assay with results from the Abbott ARCHITECT CMIA. A correlation of 0.98 (P<.001) was observed on preliminary testing and comparison with the CMIA. The detection range of our point-of-care assay covers the range of maternal serum AFP levels observed during pregnancy. Conclusions: The preliminary test results from the AFP test on the mobile platform performed in this study represent a proof of concept that will pave the way for our future work focused on developing a mobile device?enabled quad-screen point-of-care testing with the potential to enable the screening of high-risk pregnancies in various settings. The AFP test on the mobile platform can be applied to enable screening for high-risk pregnancies, within a few minutes, at the point of care even in remote areas where maternal serum tests and fetal ultrasound scans are not easily accessible; assessment of whether clinical follow-up and diagnostic testing may be needed after a positive initial screening evaluation; and development of surveillance tools for birth defects. UR - https://biomedeng.jmir.org/2021/1/e23527 UR - http://dx.doi.org/10.2196/23527 UR - http://www.ncbi.nlm.nih.gov/pubmed/34746648 ID - info:doi/10.2196/23527 ER - TY - JOUR AU - Li, Guanjian AU - Li, Weiran AU - Song, Bing AU - Wang, Chao AU - Shen, Qunshan AU - Li, Bo AU - Tang, Dongdong AU - Xu, Chuan AU - Geng, Hao AU - Gao, Yang AU - Wang, Guanxiong AU - Wu, Huan AU - Zhang, Zhiguo AU - Xu, Xiaofeng AU - Zhou, Ping AU - Wei, Zhaolian AU - He, Xiaojin AU - Cao, Yunxia PY - 2021/2/25 TI - Differences in the Gut Microbiome of Women With and Without Hypoactive Sexual Desire Disorder: Case Control Study JO - J Med Internet Res SP - e25342 VL - 23 IS - 2 KW - gut microbiome KW - metabolome KW - sexual desire KW - online recruitment KW - biomarkers N2 - Background: The gut microbiome is receiving considerable attention as a potentially modifiable risk factor and therapeutic target for numerous mental and neurological diseases. Objective: This study aimed to explore and assess the difference in the composition of gut microbes and fecal metabolites between women with hypoactive sexual desire disorder (HSDD) and healthy controls. Methods: We employed an online recruitment method to enroll ?hard-to-reach? HSDD populations. After a stringent diagnostic and exclusion process based on DSM-IV criteria, fecal samples collected from 24 women with HSDD and 22 age-matched, healthy controls underwent microbiome analysis using 16S ribosomal RNA gene sequencing and metabolome analysis using untargeted liquid chromatography?mass spectrometry. Results: We found a decreased abundance of Ruminococcaceae and increased abundance of Bifidobacterium and Lactobacillus among women with HSDD. Fecal samples from women with HSDD showed significantly altered metabolic signatures compared with healthy controls. The abundance of Bifidobacterium, Lactobacillus, and several fecal metabolites correlated negatively with the sexual desire score, while the number of Ruminococcaceae correlated positively with the sexual desire score in all subjects. Conclusions: Our analysis of fecal samples from women with HSDD and healthy controls identified significantly different gut microbes and metabolic signatures. These preliminary findings could be useful for developing strategies to adjust the level of human sexual desire by modifying gut microbiota. Trial Registration: Chinese Clinical Trial Registry ChiCTR1800020321; http://www.chictr.org.cn/showproj.aspx?proj=34267 UR - https://www.jmir.org/2021/2/e25342 UR - http://dx.doi.org/10.2196/25342 UR - http://www.ncbi.nlm.nih.gov/pubmed/33629964 ID - info:doi/10.2196/25342 ER - TY - JOUR AU - Ozkaynak, Mustafa AU - Valdez, Rupa AU - Hannah, Katia AU - Woodhouse, Gina AU - Klem, Patrick PY - 2021/2/25 TI - Understanding Gaps Between Daily Living and Clinical Settings in Chronic Disease Management: Qualitative Study JO - J Med Internet Res SP - e17590 VL - 23 IS - 2 KW - health information systems KW - workflow KW - self-management KW - activities of daily living KW - mobile phone N2 - Background: Management of chronic conditions entails numerous activities in both clinical and daily living settings. Activities across these settings interact, creating a high potential for a gap to occur if there is an inconsistency or disconnect between controlled clinical settings and complex daily living environments. Objective: The aim of this study is to characterize gaps (from the patient?s perspective) between health-related activities across home-based and clinical settings using anticoagulation treatment as an example. The causes, consequences, and mitigation strategies (reported by patients) were identified to understand these gaps. We conceptualized gaps as latent phenomena (ie, a break in continuity). Methods: Patients (n=39) and providers (n=4) from the anticoagulation clinic of an urban, western mountain health care system were recruited. Data were collected through primary interviews with patients, patient journaling with tablet computers, exit interviews with patients, and provider interviews. Data were analyzed qualitatively using a theory-driven approach and framework method of analysis. Results: The causes of gaps included clinician recommendations not fitting into patients? daily routines, recommendations not fitting into patients? living contexts, and information not transferred across settings. The consequences of these gaps included increased cognitive and physical workload on the patient, poor patient satisfaction, and compromised adherence to the therapy plan. We identified resources and strategies used to overcome these consequences as patient-generated strategies, routines, collaborative management, social environment, and tools and technologies. Conclusions: Understanding gaps, their consequences, and mitigating strategies can lead to the development of interventions that help narrow these gaps. Such interventions could take the form of collaborative health information technologies, novel patient and clinician education initiatives, and programs that strongly integrate health systems and community resources. Current technologies are insufficient to narrow the gaps between clinical and daily living settings due to the limited number and types of routines that are tracked. UR - https://www.jmir.org/2021/2/e17590 UR - http://dx.doi.org/10.2196/17590 UR - http://www.ncbi.nlm.nih.gov/pubmed/33629657 ID - info:doi/10.2196/17590 ER - TY - JOUR AU - Lee, Min-Kyung AU - Lee, Young Da AU - Ahn, Hong-Yup AU - Park, Cheol-Young PY - 2021/2/24 TI - A Novel User Utility Score for Diabetes Management Using Tailored Mobile Coaching: Secondary Analysis of a Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e17573 VL - 9 IS - 2 KW - type 2 diabetes KW - mobile applications KW - diabetes management KW - patient engagement N2 - Background: Mobile health applications have been developed to support diabetes self-management, but their effectiveness could depend on patient engagement. Therefore, patient engagement must be examined through multifactorial tailored behavioral interventions from an individual perspective. Objective: This study aims to evaluate the usefulness of a novel user utility score (UUS) as a tool to measure patient engagement by using a mobile health application for diabetes management. Methods: We conducted a subanalysis of results from a 12-month randomized controlled trial of a tailored mobile coaching (TMC) system among insurance policyholders with type 2 diabetes. UUS was calculated as the sum of the scores for 4 major core components (range 0-8): frequency of self-monitoring blood glucose testing, dietary and exercise records, and message reading rate. We explored the association between UUS for the first 3 months and glycemic control over 12 months. In addition, we investigated the relationship of UUS with blood pressure, lipid profile, and self-report scales assessing diabetes self-management. Results: We divided 72 participants into 2 groups based on UUS for the first 3 months: UUS:0-4 (n=38) and UUS:5-8 (n=34). There was a significant between-group difference in glycated hemoglobin test (HbA1c) levels for the 12-months study period (P=.011). The HbA1c decrement at 12 months in the UUS:5-8 group was greater than that of the UUS:0-4 group [?0.92 (SD 1.24%) vs ?0.33 (SD 0.80%); P=.049]. After adjusting for confounding factors, UUS was significantly associated with changes in HbA1c at 3, 6, and 12 months; the regression coefficients were ?0.113 (SD 0.040; P=.006), ?0.143 (SD 0.045; P=.002), and ?0.136 (SD 0.052; P=.011), respectively. Change differences in other health outcomes between the 2 groups were not observed throughout a 12-month follow-up. Conclusions: UUS as a measure of patient engagement was associated with changes in HbA1c over the study period of the TMC system and could be used to predict improved glycemic control in diabetes self-management through mobile health interventions. Trial Registration: ClinicalTrial.gov NCT03033407; https://clinicaltrials.gov/ct2/show/NCT03033407 UR - https://mhealth.jmir.org/2021/2/e17573 UR - http://dx.doi.org/10.2196/17573 UR - http://www.ncbi.nlm.nih.gov/pubmed/33625363 ID - info:doi/10.2196/17573 ER - TY - JOUR AU - Spinazze, Pier AU - Aardoom, Jiska AU - Chavannes, Niels AU - Kasteleyn, Marise PY - 2021/2/24 TI - The Computer Will See You Now: Overcoming Barriers to Adoption of Computer-Assisted History Taking (CAHT) in Primary Care JO - J Med Internet Res SP - e19306 VL - 23 IS - 2 KW - computer-assisted history taking KW - history taking KW - clinical consultation KW - digital health KW - electronic health record KW - patient-provided health information UR - https://www.jmir.org/2021/2/e19306 UR - http://dx.doi.org/10.2196/19306 UR - http://www.ncbi.nlm.nih.gov/pubmed/33625360 ID - info:doi/10.2196/19306 ER - TY - JOUR AU - Sang, Shengtian AU - Sun, Ran AU - Coquet, Jean AU - Carmichael, Harris AU - Seto, Tina AU - Hernandez-Boussard, Tina PY - 2021/2/22 TI - Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study JO - J Med Internet Res SP - e23026 VL - 23 IS - 2 KW - COVID-19 KW - invasive mechanical ventilation KW - all-cause mortality KW - machine learning KW - artificial intelligence KW - respiratory KW - infection KW - outcome KW - data KW - feasibility KW - framework N2 - Background: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective: This study aimed to develop and test the feasibility of a ?patients-like-me? framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods: Our framework used COVID-19?like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19?like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19?like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results: Compared to the COVID-19?like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19?like patients. In the COVID-19?like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19?like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic. UR - https://www.jmir.org/2021/2/e23026 UR - http://dx.doi.org/10.2196/23026 UR - http://www.ncbi.nlm.nih.gov/pubmed/33534724 ID - info:doi/10.2196/23026 ER - TY - JOUR AU - Giaretto, Simone AU - Renne, Lorenzo Salvatore AU - Rahal, Daoud AU - Bossi, Paola AU - Colombo, Piergiuseppe AU - Spaggiari, Paola AU - Manara, Sofia AU - Sollai, Mauro AU - Fiamengo, Barbara AU - Brambilla, Tatiana AU - Fernandes, Bethania AU - Rao, Stefania AU - Elamin, Abubaker AU - Valeri, Marina AU - De Carlo, Camilla AU - Belsito, Vincenzo AU - Lancellotti, Cesare AU - Cieri, Miriam AU - Cagini, Angelo AU - Terracciano, Luigi AU - Roncalli, Massimo AU - Di Tommaso, Luca PY - 2021/2/22 TI - Digital Pathology During the COVID-19 Outbreak in Italy: Survey Study JO - J Med Internet Res SP - e24266 VL - 23 IS - 2 KW - COVID19 KW - digital pathology KW - Bayesian data analysis KW - probabilistic modeling N2 - Background: Transition to digital pathology usually takes months or years to be completed. We were familiarizing ourselves with digital pathology solutions at the time when the COVID-19 outbreak forced us to embark on an abrupt transition to digital pathology. Objective: The aim of this study was to quantitatively describe how the abrupt transition to digital pathology might affect the quality of diagnoses, model possible causes by probabilistic modeling, and qualitatively gauge the perception of this abrupt transition. Methods: A total of 17 pathologists and residents participated in this study; these participants reviewed 25 additional test cases from the archives and completed a final psychologic survey. For each case, participants performed several different diagnostic tasks, and their results were recorded and compared with the original diagnoses performed using the gold standard method (ie, conventional microscopy). We performed Bayesian data analysis with probabilistic modeling. Results: The overall analysis, comprising 1345 different items, resulted in a 9% (117/1345) error rate in using digital slides. The task of differentiating a neoplastic process from a nonneoplastic one accounted for an error rate of 10.7% (42/392), whereas the distinction of a malignant process from a benign one accounted for an error rate of 4.2% (11/258). Apart from residents, senior pathologists generated most discrepancies (7.9%, 13/164). Our model showed that these differences among career levels persisted even after adjusting for other factors. Conclusions: Our findings are in line with previous findings, emphasizing that the duration of transition (ie, lengthy or abrupt) might not influence the diagnostic performance. Moreover, our findings highlight that senior pathologists may be limited by a digital gap, which may negatively affect their performance with digital pathology. These results can guide the process of digital transition in the field of pathology. UR - https://www.jmir.org/2021/2/e24266 UR - http://dx.doi.org/10.2196/24266 UR - http://www.ncbi.nlm.nih.gov/pubmed/33503002 ID - info:doi/10.2196/24266 ER - TY - JOUR AU - Abrami, Avner AU - Gunzler, Steven AU - Kilbane, Camilla AU - Ostrand, Rachel AU - Ho, Bryan AU - Cecchi, Guillermo PY - 2021/2/22 TI - Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study JO - J Med Internet Res SP - e21037 VL - 23 IS - 2 KW - Parkinson disease KW - hypomimia KW - computer vision KW - telemedicine N2 - Background: Facial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to ?hypomimia? or ?masked facies.? Objective: We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD. Methods: We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD. Results: The algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda?s seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7). Conclusions: This proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient?s motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine. UR - https://www.jmir.org/2021/2/e21037 UR - http://dx.doi.org/10.2196/21037 UR - http://www.ncbi.nlm.nih.gov/pubmed/33616535 ID - info:doi/10.2196/21037 ER - TY - JOUR AU - Hirten, P. Robert AU - Danieletto, Matteo AU - Tomalin, Lewis AU - Choi, Hyewon Katie AU - Zweig, Micol AU - Golden, Eddye AU - Kaur, Sparshdeep AU - Helmus, Drew AU - Biello, Anthony AU - Pyzik, Renata AU - Charney, Alexander AU - Miotto, Riccardo AU - Glicksberg, S. Benjamin AU - Levin, Matthew AU - Nabeel, Ismail AU - Aberg, Judith AU - Reich, David AU - Charney, Dennis AU - Bottinger, P. Erwin AU - Keefer, Laurie AU - Suarez-Farinas, Mayte AU - Nadkarni, N. Girish AU - Fayad, A. Zahi PY - 2021/2/22 TI - Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study JO - J Med Internet Res SP - e26107 VL - 23 IS - 2 KW - wearable device KW - COVID-19 KW - identification KW - prediction KW - heart rate variability KW - physiological KW - wearable KW - app KW - data KW - infectious disease KW - symptom KW - diagnosis KW - observational N2 - Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19?related symptom compared to all other symptom-free days (P=.01). Conclusions: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19?related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection. UR - https://www.jmir.org/2021/2/e26107 UR - http://dx.doi.org/10.2196/26107 UR - http://www.ncbi.nlm.nih.gov/pubmed/33529156 ID - info:doi/10.2196/26107 ER - TY - JOUR AU - Barr, J. Paul AU - Ryan, James AU - Jacobson, C. Nicholas PY - 2021/2/19 TI - Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice JO - J Med Internet Res SP - e20545 VL - 23 IS - 2 KW - communication KW - coronavirus KW - COVID-19 KW - Machine Learning KW - natural language processing KW - patient-physician communication KW - patient records KW - recording UR - http://www.jmir.org/2021/2/e20545/ UR - http://dx.doi.org/10.2196/20545 UR - http://www.ncbi.nlm.nih.gov/pubmed/33556031 ID - info:doi/10.2196/20545 ER - TY - JOUR AU - Andy, U. Anietie AU - Guntuku, C. Sharath AU - Adusumalli, Srinath AU - Asch, A. David AU - Groeneveld, W. Peter AU - Ungar, H. Lyle AU - Merchant, M. Raina PY - 2021/2/19 TI - Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models JO - JMIR Cardio SP - e24473 VL - 5 IS - 1 KW - ASCVD KW - machine learning KW - natural language processing KW - atherosclerotic KW - cardiovascular disease KW - social media language KW - social media N2 - Background: Current atherosclerotic cardiovascular disease (ASCVD) predictive models have limitations; thus, efforts are underway to improve the discriminatory power of ASCVD models. Objective: We sought to evaluate the discriminatory power of social media posts to predict the 10-year risk for ASCVD as compared to that of pooled cohort risk equations (PCEs). Methods: We consented patients receiving care in an urban academic emergency department to share access to their Facebook posts and electronic medical records (EMRs). We retrieved Facebook status updates up to 5 years prior to study enrollment for all consenting patients. We identified patients (N=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine-learning model to predict 10-year ASCVD risk scores. Using a machine-learning model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count, we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively. Results: The machine-learning model predicted the 10-year ASCVD risk scores for the categories <5%, 5%-7.4%, 7.5%-9.9%, and ?10% with area under the curve (AUC) values of 0.78, 0.57, 0.72, and 0.61, respectively. The machine-learning model distinguished between low risk (<10%) and high risk (>10%) with an AUC of 0.69. Additionally, the machine-learning model predicted the ASCVD risk score with Pearson r=0.26. Using Linguistic Inquiry and Word Count, patients with higher ASCVD scores were more likely to use words associated with sadness (r=0.32). Conclusions: Language used on social media can provide insights about an individual?s ASCVD risk and inform approaches to risk modification. UR - http://cardio.jmir.org/2021/1/e24473/ UR - http://dx.doi.org/10.2196/24473 UR - http://www.ncbi.nlm.nih.gov/pubmed/33605888 ID - info:doi/10.2196/24473 ER - TY - JOUR AU - Desai, Varma Anjali AU - Michael, L. Chelsea AU - Kuperman, J. Gilad AU - Jordan, Gregory AU - Mittelstaedt, Haley AU - Epstein, S. Andrew AU - Connor, MaryAnn AU - B Villar, Paula Rika AU - Bernal, Camila AU - Kramer, Dana AU - Davis, Elizabeth Mary AU - Chen, Yuxiao AU - Malisse, Catherine AU - Markose, Gigi AU - Nelson, E. Judith PY - 2021/2/17 TI - A Novel Patient Values Tab for the Electronic Health Record: A User-Centered Design Approach JO - J Med Internet Res SP - e21615 VL - 23 IS - 2 KW - electronic health record KW - health informatics KW - supportive care KW - palliative care KW - oncology N2 - Background: The COVID-19 pandemic has shined a harsh light on a critical deficiency in our health care system: our inability to access important information about patients? values, goals, and preferences in the electronic health record (EHR). At Memorial Sloan Kettering Cancer Center (MSK), we have integrated and systematized health-related values discussions led by oncology nurses for newly diagnosed cancer patients as part of routine comprehensive cancer care. Such conversations include not only the patient?s wishes for care at the end of life but also more holistic personal values, including sources of strength, concerns, hopes, and their definition of an acceptable quality of life. In addition, health care providers use a structured template to document their discussions of patient goals of care. Objective: To provide ready access to key information about the patient as a person with individual values, goals, and preferences, we undertook the creation of the Patient Values Tab in our center?s EHR to display this information in a single, central location. Here, we describe the interprofessional, interdisciplinary, iterative process and user-centered design methodology that we applied to build this novel functionality as well as our initial implementation experience and plans for evaluation. Methods: We first convened a working group of experts from multiple departments, including medical oncology, health informatics, information systems, nursing informatics, nursing education, and supportive care, and a user experience designer. We conducted in-depth, semistructured, audiorecorded interviews of over 100 key stakeholders. The working group sought consensus on the tab?s main content, homing in on high-priority areas identified by the stakeholders. The core content was mapped to various EHR data sources. We established a set of high-level design principles to guide our process. Our user experience designer then created wireframes of the tab design. The designer conducted usability testing with physicians, nurses, and other health professionals. Data validation testing was conducted. Results: We have already deployed the Patient Values Tab to a pilot sample of users in the MSK Gastrointestinal Medical Oncology Service, including physicians, advanced practice providers, nurses, and administrative staff. We have early evidence of the positive impact of this EHR innovation. Audit logs show increasing use. Many of the initial user comments have been enthusiastically positive, while others have provided constructive suggestions for additional tab refinements with respect to format and content. Conclusions: It is our challenge and obligation to enrich the EHR with information about the patient as a person. Realization of this capability is a pressing public health need requiring the collaboration of technological experts with a broad range of clinical leaders, users, patients, and families to achieve solutions that are both principled and practical. Our new Patient Values Tab represents a step forward in this important direction. UR - http://www.jmir.org/2021/2/e21615/ UR - http://dx.doi.org/10.2196/21615 UR - http://www.ncbi.nlm.nih.gov/pubmed/33595448 ID - info:doi/10.2196/21615 ER - TY - JOUR AU - Kim, Jeongsim AU - Shin, EunJi AU - Han, KyungHwa AU - Park, Soowon AU - Youn, Hae Jung AU - Jin, Guixiang AU - Lee, Jun-Young PY - 2021/2/16 TI - Efficacy of Smart Speaker?Based Metamemory Training in Older Adults: Case-Control Cohort Study JO - J Med Internet Res SP - e20177 VL - 23 IS - 2 KW - smart speaker KW - cognitive training KW - cognitive decline N2 - Background: Metamemory training (MMT) is a useful training strategy for improving cognitive functioning in the older adult population. Despite the advantages, there are limitations imposed by location and time constraints. Objective: This study aimed to develop a smart speaker?based MMT program and evaluate the efficacy of the program in older adults without cognitive impairment. Methods: This study used a case-control cohort design. The smart speaker?based MMT program comprised 3 training sessions per day, 5 days a week, for 8 weeks. Each training session took approximately 15 minutes. This program was implemented using smart speakers, not human trainers. All participants completed the Mini-Mental State Examination, Subjective Memory Complaints Questionnaire, Verbal Learning Test, Digit Span Test, fluency tests, and a short-form version of the Geriatric Depression Scale before and after training. Results: A total of 60 subjects (29 in the MMT group and 31 in the control group) participated in the study. The training group showed significant increases in the delayed free recall, digit span forward, digit span backward, and fluency test scores compared with the control group. Conclusions: This study confirmed the efficacy of smart speaker?based MMT in older adults. Home-based smart speaker?based MMT is not limited with respect to location or constrained by space and may help older adults with subjective cognitive decline without requiring intervention by human professionals. UR - http://www.jmir.org/2021/2/e20177/ UR - http://dx.doi.org/10.2196/20177 UR - http://www.ncbi.nlm.nih.gov/pubmed/33591276 ID - info:doi/10.2196/20177 ER - TY - JOUR AU - Kurita, Junko AU - Sugishita, Yoshiyuki AU - Sugawara, Tamie AU - Ohkusa, Yasushi PY - 2021/2/15 TI - Evaluating Apple Inc Mobility Trend Data Related to the COVID-19 Outbreak in Japan: Statistical Analysis JO - JMIR Public Health Surveill SP - e20335 VL - 7 IS - 2 KW - peak KW - COVID-19 KW - effective reproduction number KW - mobility trend data KW - Apple KW - countermeasure N2 - Background: In Japan, as a countermeasure against the COVID-19 outbreak, both the national and local governments issued voluntary restrictions against going out from residences at the end of March 2020 in preference to the lockdowns instituted in European and North American countries. The effect of such measures can be studied with mobility data, such as data which is generated by counting the number of requests made to Apple Maps for directions in select countries/regions, sub-regions, and cities. Objective: We investigate the associations of mobility data provided by Apple Inc and an estimate an an effective reproduction number R(t). Methods: We regressed R(t) on a polynomial function of daily Apple data, estimated using the whole period, and analyzed subperiods delimited by March 10, 2020. Results: In the estimation results, R(t) was 1.72 when voluntary restrictions against going out ceased and mobility reverted to a normal level. However, the critical level of reducing R(t) to <1 was obtained at 89.3% of normal mobility. Conclusions: We demonstrated that Apple mobility data are useful for short-term prediction of R(t). The results indicate that the number of trips should decrease by 10% until herd immunity is achieved and that higher voluntary restrictions against going out might not be necessary for avoiding a re-emergence of the outbreak. UR - http://publichealth.jmir.org/2021/2/e20335/ UR - http://dx.doi.org/10.2196/20335 UR - http://www.ncbi.nlm.nih.gov/pubmed/33481755 ID - info:doi/10.2196/20335 ER - TY - JOUR AU - Yeh, Chun-Yin AU - Chung, Yi-Ting AU - Chuang, Kun-Ta AU - Shu, Yu-Chen AU - Kao, Hung-Yu AU - Chen, Po-Lin AU - Ko, Wen-Chien AU - Ko, Nai-Ying PY - 2021/2/10 TI - An Innovative Wearable Device For Monitoring Continuous Body Surface Temperature (HEARThermo): Instrument Validation Study JO - JMIR Mhealth Uhealth SP - e19210 VL - 9 IS - 2 KW - body surface temperature KW - wearable device KW - validation KW - continuous monitoring N2 - Background: Variations in body temperature are highly informative during an illness. To date, there are not many adequate studies that have investigated the feasibility of a wearable wrist device for the continuous monitoring of body surface temperatures in humans. Objective: The objective of this study was to validate the performance of HEARThermo, an innovative wearable device, which was developed to continuously monitor the body surface temperature in humans. Methods: We implemented a multi-method research design in this study, which included 2 validation studies?one in the laboratory and one with human subjects. In validation study I, we evaluated the test-retest reliability of HEARThermo in the laboratory to measure the temperature and to correct the values recorded by each HEARThermo by using linear regression models. We conducted validation study II on human subjects who wore HEARThermo for the measurement of their body surface temperatures. Additionally, we compared the HEARThermo temperature recordings with those recorded by the infrared skin thermometer simultaneously. We used intraclass correlation coefficients (ICCs) and Bland-Altman plots to analyze the criterion validity and agreement between the 2 measurement tools. Results: A total of 66 participants (age range, 10-77 years) were recruited, and 152,881 completed data were analyzed in this study. The 2 validation studies in the laboratory and on human skin indicated that HEARThermo showed a good test-retest reliability (ICC 0.96-0.98) and adequate criterion validity with the infrared skin thermometer at room temperatures of 20°C-27.9°C (ICC 0.72, P<.001). The corrected measurement bias averaged ?0.02°C, which was calibrated using a water bath ranging in temperature from 16°C to 40°C. The values of each HEARThermo improved by the regression models were not significantly different from the temperature of the water bath (P=.19). Bland-Altman plots showed no visualized systematic bias. HEARThermo had a bias of 1.51°C with a 95% limit of agreement between ?1.34°C and 4.35°C. Conclusions: The findings of our study show the validation of HEARThermo for the continuous monitoring of body surface temperatures in humans. UR - http://mhealth.jmir.org/2021/2/e19210/ UR - http://dx.doi.org/10.2196/19210 UR - http://www.ncbi.nlm.nih.gov/pubmed/33565990 ID - info:doi/10.2196/19210 ER - TY - JOUR AU - Coughlan, Helen AU - Quin, David AU - O'Brien, Kevin AU - Healy, Colm AU - Deacon, Jack AU - Kavanagh, Naoise AU - Humphries, Niamh AU - Clarke, C. Mary AU - Cannon, Mary PY - 2021/2/9 TI - Online Mental Health Animations for Young People: Qualitative Empirical Thematic Analysis and Knowledge Transfer JO - J Med Internet Res SP - e21338 VL - 23 IS - 2 KW - mental health KW - public health KW - mental health literacy KW - social media KW - youth KW - qualitative KW - knowledge translation KW - anxiety KW - bullying KW - depression KW - loneliness KW - internet N2 - Background: Mental ill-health is one of the most significant health and social issues affecting young people globally. To address the mental health crisis, a number of cross-sectoral research and action priorities have been identified. These include improving mental health literacy, translating research findings into accessible public health outputs, and the use of digital technologies. There are, however, few examples of public health?oriented knowledge transfer activities involving collaborations between researchers, the Arts, and online platforms in the field of youth mental health. Objective: The primary aim of this project was to translate qualitative research findings into a series of online public mental health animations targeting young people between the ages of 16 and 25 years. A further aim was to track online social media engagement and viewing data for the animations for a period of 12 months. Methods: Qualitative data were collected from a sample of 17 youth in Ireland, aged 18-21 years, as part of the longitudinal population-based Adolescent Brain Development study. Interviews explored the life histories and the emotional and mental health of participants. The narrative analysis revealed 5 thematic findings relating to young people?s emotional and mental health. Through a collaboration between research, the Arts, and the online sector, the empirical thematic findings were translated into 5 public health animations. The animations were hosted and promoted on 3 social media platforms of the Irish youth health website called SpunOut. Viewing data, collected over a 12-month period, were analyzed to determine the reach of the animations. Results: Narrative thematic analysis identified anxiety, depression, feeling different, loneliness, and being bullied as common experiences for young people. These thematic findings formed the basis of the animations. During the 12 months following the launch of the animations, they were viewed 15,848 times. A majority of views occurred during the period of the social media ad campaign at a cost of ?0.035 (approximately US $0.042) per view. Animations on feeling different and being bullied accounted for the majority of views. Conclusions: This project demonstrates that online animations provide an accessible means of translating empirical research findings into meaningful public health outputs. They offer a cost-effective way to provide targeted online information about mental health, coping, and help-seeking to young people. Cross-sectoral collaboration is required to leverage the knowledge and expertise required to maximize the quality and potential reach of any knowledge transfer activities. A high level of engagement is possible by targeting non?help-seeking young people on their native social media platforms. Paid promotion is, therefore, an important consideration when budgeting for online knowledge translation and dissemination activities in health research. UR - http://www.jmir.org/2021/2/e21338/ UR - http://dx.doi.org/10.2196/21338 UR - http://www.ncbi.nlm.nih.gov/pubmed/33560231 ID - info:doi/10.2196/21338 ER - TY - JOUR AU - Braune, Katarina AU - Rojas, Pablo-David AU - Hofferbert, Joscha AU - Valera Sosa, Alvaro AU - Lebedev, Anastasiya AU - Balzer, Felix AU - Thun, Sylvia AU - Lieber, Sascha AU - Kirchberger, Valerie AU - Poncette, Akira-Sebastian PY - 2021/2/8 TI - Interdisciplinary Online Hackathons as an Approach to Combat the COVID-19 Pandemic: Case Study JO - J Med Internet Res SP - e25283 VL - 23 IS - 2 KW - hackathon KW - COVID-19 KW - digital health KW - mentoring KW - interdisciplinarity KW - interoperability KW - SARS-CoV-2 KW - public health KW - innovation KW - collaboration KW - hack KW - mentor KW - case study KW - online health care KW - challenge KW - implementation KW - plan N2 - Background: The COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on many health care systems and the global economy. This devastating pandemic has brought together communities across the globe to work on this issue in an unprecedented manner. Objective: This case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges posed by the COVID-19 pandemic. It aims to deliver a clear implementation road map for other organizations to follow. Methods: This 4-day hackathon was conducted in April 2020, based on six COVID-19?related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (1) individual experience satisfaction, (2) level of interprofessional skills exchange, (3) maturity of the projects realized, and (4) overall quality of the event. At the end of the event, participants were invited to take part in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions that assessed their experience regarding the remote nature of the event and their individual project, interprofessional skills exchange, and their confidence in working on a digital health project before and after the hackathon. Mentors, who guided the participants through the event, also provided feedback to the organizers through an online survey. Results: A total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and stated that they were likely to continue working on their projects. Of the participants who provided feedback, 60% (n=18) would not have started their project without this particular hackathon and indicated that the hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype. Conclusions: This study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful for other institutions and initiatives that are willing to introduce similar event formats in the fight against COVID-19. UR - http://www.jmir.org/2021/2/e25283/ UR - http://dx.doi.org/10.2196/25283 UR - http://www.ncbi.nlm.nih.gov/pubmed/33497350 ID - info:doi/10.2196/25283 ER - TY - JOUR AU - Hendriks, S. Maartje M. AU - van Lotringen, H. Jaap AU - Vos-van der Hulst, Marije AU - Keijsers, W. Noël L. PY - 2021/2/8 TI - Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study JO - JMIR Mhealth Uhealth SP - e24339 VL - 9 IS - 2 KW - continuous sleep monitoring device KW - bed sensor technology KW - mHealth KW - nocturnal heart rate KW - nocturnal respiratory rate KW - nocturnal movement activity KW - neurological disorders KW - incomplete spinal cord injury KW - stroke KW - inpatient rehabilitation KW - clinical application N2 - Background: Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective: The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods: Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results: In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ?120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=?2.1, P=.04) and movement activity (t18=?1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. Conclusions: It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants. UR - http://mhealth.jmir.org/2021/2/e24339/ UR - http://dx.doi.org/10.2196/24339 UR - http://www.ncbi.nlm.nih.gov/pubmed/33555268 ID - info:doi/10.2196/24339 ER - TY - JOUR AU - Dangerfield II, T. Derek AU - Wylie, Charleen AU - Anderson, N. Janeane PY - 2021/2/8 TI - Conducting Virtual, Synchronous Focus Groups Among Black Sexual Minority Men: Qualitative Study JO - JMIR Public Health Surveill SP - e22980 VL - 7 IS - 2 KW - engagement KW - recruitment KW - sexual health KW - telehealth N2 - Background: Focus groups are useful to support HIV prevention research among US subpopulations, such as Black gay, Black bisexual, and other Black sexual minority men (BSMM). Virtual synchronous focus groups provide an electronic means to obtain qualitative data and are convenient to implement; however, the protocols and acceptability for conducting virtual synchronous focus groups in HIV prevention research among BSMM are lacking. Objective: This paper describes the protocols and acceptability of conducting virtual synchronous focus groups in HIV prevention research among BSMM Methods: Data for this study came from 8 virtual synchronous focus groups examined in 2 studies of HIV-negative BSMM in US cities, stratified by age (N=39): 2 groups of BSMM ages 18-24 years, 5 groups of BSMM ages 25-34 years, and 1 group of BSMM 35 years and older. Virtual synchronous focus groups were conducted via Zoom, and participants were asked to complete an electronic satisfaction survey distributed to their email via Qualtrics. Results: The age of participants ranged from 18 to 44 years (mean 28.3, SD 6.0). All participants ?strongly agreed? or ?agreed? that they were satisfied participating in an online focus group. Only 17% (5/30) preferred providing written informed consent versus oral consent. Regarding privacy, most (30/30,100%) reported ?strongly agree? or ?agree? that their information was safe to share with other participants in the group. Additionally, 97% (29/30) reported being satisfied with the incentive. Conclusions: Conducting virtual synchronous focus groups in HIV prevention research among BSMM is feasible. However, thorough oral informed consent with multiple opportunities for questions, culturally relevant facilitation procedures, and appropriate incentives are needed for optimal focus group participation. UR - http://publichealth.jmir.org/2021/2/e22980/ UR - http://dx.doi.org/10.2196/22980 UR - http://www.ncbi.nlm.nih.gov/pubmed/33427671 ID - info:doi/10.2196/22980 ER - TY - JOUR AU - Elghafari, Anas AU - Finkelstein, Joseph PY - 2021/2/8 TI - Automated Identification of Common Disease-Specific Outcomes for Comparative Effectiveness Research Using ClinicalTrials.gov: Algorithm Development and Validation Study JO - JMIR Med Inform SP - e18298 VL - 9 IS - 2 KW - clinical trials KW - clinical outcomes KW - common data elements KW - data processing KW - ClinicalTrials.gov N2 - Background: Common disease-specific outcomes are vital for ensuring comparability of clinical trial data and enabling meta analyses and interstudy comparisons. Traditionally, the process of deciding which outcomes should be recommended as common for a particular disease relied on assembling and surveying panels of subject-matter experts. This is usually a time-consuming and laborious process. Objective: The objectives of this work were to develop and evaluate a generalized pipeline that can automatically identify common outcomes specific to any given disease by finding, downloading, and analyzing data of previous clinical trials relevant to that disease. Methods: An automated pipeline to interface with ClinicalTrials.gov?s application programming interface and download the relevant trials for the input condition was designed. The primary and secondary outcomes of those trials were parsed and grouped based on text similarity and ranked based on frequency. The quality and usefulness of the pipeline?s output were assessed by comparing the top outcomes identified by it for chronic obstructive pulmonary disease (COPD) to a list of 80 outcomes manually abstracted from the most frequently cited and comprehensive reviews delineating clinical outcomes for COPD. Results: The common disease-specific outcome pipeline successfully downloaded and processed 3876 studies related to COPD. Manual verification indicated that the pipeline was downloading and processing the same number of trials as were obtained from the self-service ClinicalTrials.gov portal. Evaluating the automatically identified outcomes against the manually abstracted ones showed that the pipeline achieved a recall of 92% and precision of 79%. The precision number indicated that the pipeline was identifying many outcomes that were not covered in the literature reviews. Assessment of those outcomes indicated that they are relevant to COPD and could be considered in future research. Conclusions: An automated evidence-based pipeline can identify common clinical trial outcomes of comparable breadth and quality as the outcomes identified in comprehensive literature reviews. Moreover, such an approach can highlight relevant outcomes for further consideration. UR - http://medinform.jmir.org/2021/2/e18298/ UR - http://dx.doi.org/10.2196/18298 UR - http://www.ncbi.nlm.nih.gov/pubmed/33460388 ID - info:doi/10.2196/18298 ER - TY - JOUR AU - Chelabi, Khadidja AU - Balli, Fabio AU - Bransi, Myriam AU - Gervais, Yannick AU - Marthe, Clement AU - Tse, Man Sze PY - 2021/1/29 TI - Validation of a Portable Game Controller to Assess Peak Expiratory Flow Against Conventional Spirometry in Children: Cross-sectional Study JO - JMIR Serious Games SP - e25052 VL - 9 IS - 1 KW - asthma KW - pediatrics KW - serious game KW - peak expiratory flow KW - pulmonary function test, adherence, self-management N2 - Background: International asthma guidelines recommend the monitoring of peak expiratory flow (PEF) as part of asthma self-management in children and adolescents who poorly perceive airflow obstruction, those with a history of severe exacerbations, or those who have difficulty controlling asthma. Measured with a peak flow meter, PEF represents a person?s maximum speed of expiration and helps individuals to follow their disease evolution and, ultimately, to prevent asthma exacerbations. However, patient adherence to regular peak flow meter use is poor, particularly in pediatric populations. To address this, we developed an interactive tablet-based game with a portable game controller that can transduce a signal from the user?s breath to generate a PEF value. Objective: The purpose of this study was to evaluate the concordance between PEF values obtained with the game controller and various measures derived from conventional pulmonary function tests (ie, spirometry) and to synthesize the participants? feedback. Methods: In this cross-sectional multicenter study, 158 children (aged 8-15 years old) with a diagnosis or suspicion of asthma performed spirometry and played the game in one of two hospital university centers. We evaluated the correlation between PEF measured by both the game controller and spirometry, forced expiratory volume at 1 second (FEV1), and forced expiratory flow at 25%-75% of pulmonary volume (FEF25-75), using Spearman correlation. A Bland-Altman plot was generated for comparison of PEF measured by the game controller against PEF measured by spirometry. A post-game user feedback questionnaire was administered and analyzed. Results: The participants had a mean age of 10.9 (SD 2.5) years, 44% (71/158) were female, and 88% (139/158) were White. On average, the pulmonary function of the participants was normal, including FEV1, PEF, and FEV1/forced vital capacity (FVC). The PEF measured by the game controller was reproducible in 96.2% (152/158) of participants according to standardized criteria. The PEF measured by the game controller presented a good correlation with PEF measured by spirometry (r=0.83, P<.001), with FEV1 (r=0.74, P<.001), and with FEF25-75 (r=0.65, P<.001). The PEF measured by the game controller presented an expected mean bias of ?36.4 L/min as compared to PEF measured by spirometry. The participants? feedback was strongly positive, with 78.3% (123/157) reporting they would use the game if they had it at home. Conclusions: The game controller we developed is an interactive tool appreciated by children with asthma, and the PEF values measured by the game controller are reproducible, with a good correlation to values measured by conventional spirometry. Future studies are necessary to evaluate the clinical impact this novel tool might have on asthma management and its potential use in an out-of-hospital setting. UR - http://games.jmir.org/2021/1/e25052/ UR - http://dx.doi.org/10.2196/25052 UR - http://www.ncbi.nlm.nih.gov/pubmed/33512326 ID - info:doi/10.2196/25052 ER - TY - JOUR AU - Bahador, Nooshin AU - Ferreira, Denzil AU - Tamminen, Satu AU - Kortelainen, Jukka PY - 2021/1/28 TI - Deep Learning?Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors JO - JMIR Mhealth Uhealth SP - e21926 VL - 9 IS - 1 KW - deep learning KW - image processing KW - data fusion KW - covariance distribution KW - food intake episode KW - wearable sensors N2 - Background: Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative information from high-dimensional data collected from multiple sources. The available fusion algorithms with their complex structure are poorly adopted to the computationally constrained environment which requires integrating information directly at the source. As a result, more simple low-level fusion methods are needed. Objective: In the absence of a data combining process, the cost of directly applying high-dimensional raw data to a deep classifier would be computationally expensive with regard to the response time, energy consumption, and memory requirement. Taking this into account, we aimed to develop a data fusion technique in a computationally efficient way to achieve a more comprehensive insight of human activity dynamics in a lower dimension. The major objective was considering statistical dependency of multisensory data and exploring intermodality correlation patterns for different activities. Methods: In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and the covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep model to learn specific patterns associated with specific activity. Results: In order to show the generalizability of the proposed fusion algorithm, 2 different scenarios were taken into account. These scenarios were different in terms of temporal segment size, type of activity, wearable device, subjects, and deep learning architecture. The first scenario used a data set in which a single participant performed a limited number of activities while wearing the Empatica E4 wristband. In the second scenario, a data set related to the activities of daily living was used where 10 different participants wore inertial measurement units while performing a more complex set of activities. The precision metric obtained from leave-one-subject-out cross-validation for the second scenario reached 0.803. The impact of missing data on performance degradation was also evaluated. Conclusions: To conclude, the proposed fusion technique provides the possibility of embedding joint variability information over different modalities in just a single 2D representation which results in obtaining a more global view of different aspects of daily human activities at hand, and yet preserving the desired performance level in activity recognition. UR - http://mhealth.jmir.org/2021/1/e21926/ UR - http://dx.doi.org/10.2196/21926 UR - http://www.ncbi.nlm.nih.gov/pubmed/33507156 ID - info:doi/10.2196/21926 ER - TY - JOUR AU - Burtch, Gordon AU - Greenwood, N. Brad AU - McCullough, S. Jeffrey PY - 2021/1/27 TI - Ride-Hailing Services and Alcohol Consumption: Longitudinal Analysis JO - J Med Internet Res SP - e15402 VL - 23 IS - 1 KW - binge drinking KW - drunk driving KW - road traffic safety KW - ride-hailing KW - alcohol consumption KW - difference in differences KW - Uber N2 - Background: Alcohol consumption is associated with a wide range of adverse health consequences and a leading cause of preventable deaths. Ride-hailing services such as Uber have been found to prevent alcohol-related motor vehicle fatalities. These services may, however, facilitate alcohol consumption generally and binge drinking in particular. Objective: The goal of the research is to measure the impact of ride-hailing services on the extent and intensity of alcohol consumption. We allow these associations to depend on population density as the use of ride-hailing services varies across markets. Methods: We exploit the phased rollout of the ride-hailing platform Uber using a difference-in-differences approach. We use this variation to measure changes in alcohol consumption among a local population following Uber?s entry. Data are drawn from Uber press releases to capture platform entry and the Behavioral Risk Factor Surveillance Systems (BRFSS) Annual Survey to measure alcohol consumption in 113 metropolitan areas. Models are estimated using fixed-effects Poisson regression. Pre- and postentry trends are used to validate this approach. Results: Ride-hailing has no association with the extent of alcohol consumption in high (0.61 [95% CI ?0.05% to 1.28%]) or low (0.61 [95% CI ?0.05% to 1.28%]) density markets, but is associated with increases in the binge drinking rate in high-density markets (0.71 [95% CI 0.13% to 1.29%]). This corresponds to a 4% increase in binge drinking within a Metropolitan Statistical Area. Conclusions: Ride-hailing services are associated with an increase in binge drinking, which has been associated with a wide array of adverse health outcomes. Drunk driving rates have fallen for more than a decade, while binge drinking continues to climb. Both trends may be accelerated by ride-hailing services. This suggests that health information messaging should increase emphasis on the direct dangers of alcohol consumption and binge drinking. UR - http://www.jmir.org/2021/1/e15402/ UR - http://dx.doi.org/10.2196/15402 UR - http://www.ncbi.nlm.nih.gov/pubmed/33502328 ID - info:doi/10.2196/15402 ER - TY - JOUR AU - Taeger, Johannes AU - Bischoff, Stefanie AU - Hagen, Rudolf AU - Rak, Kristen PY - 2021/1/26 TI - Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study JO - JMIR Mhealth Uhealth SP - e19346 VL - 9 IS - 1 KW - facial nerve KW - facial palsy KW - app development KW - medical informatics KW - eHealth KW - mHealth KW - Stennert?s index KW - depth mapping camera KW - smartphone sensors N2 - Background: For the classification of facial paresis, various systems of description and evaluation in the form of clinician-graded or software-based scoring systems are available. They serve the purpose of scientific and clinical assessment of the spontaneous course of the disease or monitoring therapeutic interventions. Nevertheless, none have been able to achieve universal acceptance in everyday clinical practice. Hence, a quick and precise tool for assessing the functional status of the facial nerve would be desirable. In this context, the possibilities that the TrueDepth camera of recent iPhone models offer have sparked our interest. Objective: This paper describes the utilization of the iPhone?s TrueDepth camera via a specially developed app prototype for quick, objective, and reproducible quantification of facial asymmetries. Methods: After conceptual and user interface design, a native app prototype for iOS was programmed that accesses and processes the data of the TrueDepth camera. Using a special algorithm, a new index for the grading of unilateral facial paresis ranging from 0% to 100% was developed. The algorithm was adapted to the well-established Stennert index by weighting the individual facial regions based on functional and cosmetic aspects. Test measurements with healthy subjects using the app were performed in order to prove the reliability of the system. Results: After the development process, the app prototype had no runtime or buildtime errors and also worked under suboptimal conditions such as different measurement angles, so it met our criteria for a safe and reliable app. The newly defined index expresses the result of the measurements as a generally understandable percentage value for each half of the face. The measurements that correctly rated the facial expressions of healthy individuals as symmetrical in all cases were reproducible and showed no statistically significant intertest variability. Conclusions: Based on the experience with the app prototype assessing healthy subjects, the use of the TrueDepth camera should have considerable potential for app-based grading of facial movement disorders. The app and its algorithm, which is based on theoretical considerations, should be evaluated in a prospective clinical study and correlated with common facial scores. UR - http://mhealth.jmir.org/2021/1/e19346/ UR - http://dx.doi.org/10.2196/19346 UR - http://www.ncbi.nlm.nih.gov/pubmed/33496670 ID - info:doi/10.2196/19346 ER - TY - JOUR AU - Garnier, Romain AU - Benetka, R. Jan AU - Kraemer, John AU - Bansal, Shweta PY - 2021/1/22 TI - Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study JO - J Med Internet Res SP - e24591 VL - 23 IS - 1 KW - COVID-19 KW - SARS-CoV-2 KW - disease ecology KW - nonpharmaceutical interventions KW - mobility data KW - economic KW - disparity KW - social distancing KW - equity KW - access KW - socioeconomic KW - infectious disease KW - mobility N2 - Background: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. Objective: We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. Methods: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. Results: We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. Conclusions: Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts. UR - http://www.jmir.org/2021/1/e24591/ UR - http://dx.doi.org/10.2196/24591 UR - http://www.ncbi.nlm.nih.gov/pubmed/33351774 ID - info:doi/10.2196/24591 ER - TY - JOUR AU - Nittas, Vasileios AU - Puhan, Alan Milo AU - von Wyl, Viktor PY - 2021/1/21 TI - Toward a Working Definition of eCohort Studies in Health Research: Narrative Literature Review JO - JMIR Public Health Surveill SP - e24588 VL - 7 IS - 1 KW - cohorts KW - digital epidemiology KW - eCohorts KW - eHealth N2 - Background: The wide availability of internet-connected devices and new sensor technologies increasingly infuse longitudinal observational study designs and cohort studies. Simultaneously, the costly and time-consuming nature of traditional cohorts has given rise to alternative, technology-driven designs such as eCohorts, which remain inadequately described in the scientific literature. Objective: The aim of this study was to outline and discuss what may constitute an eCohort, as well as to formulate a first working definition for health researchers based on a review of the relevant literature. Methods: A two-staged review and synthesis process was performed comparing 10 traditional cohorts and 10 eCohorts across the six core steps in the life cycle of cohort designs. Results: eCohorts are a novel type of technology-driven cohort study that are not physically linked to a clinical setting, follow more relaxed and not necessarily random sampling procedures, are primarily based on self-reported and digitally collected data, and systematically aim to leverage the internet and digitalization to achieve flexibility, interactivity, patient-centeredness, and scalability. This approach comes with some hurdles such as data quality, generalizability, and privacy concerns. Conclusions: eCohorts have similarities to their traditional counterparts; however, they are sufficiently distinct to be treated as a separate type of cohort design. The novelty of eCohorts is associated with a range of strengths and weaknesses that require further exploration. UR - http://publichealth.jmir.org/2021/1/e24588/ UR - http://dx.doi.org/10.2196/24588 UR - http://www.ncbi.nlm.nih.gov/pubmed/33475521 ID - info:doi/10.2196/24588 ER - TY - JOUR AU - Elbers, Stefan AU - van Gessel, Christa AU - Renes, Jan Reint AU - van der Lugt, Remko AU - Wittink, Harriët AU - Hermsen, Sander PY - 2021/1/20 TI - Innovation in Pain Rehabilitation Using Co-Design Methods During the Development of a Relapse Prevention Intervention: Case Study JO - J Med Internet Res SP - e18462 VL - 23 IS - 1 KW - co-design KW - participatory design KW - chronic pain KW - intervention development KW - rehabilitation KW - behavior change KW - relapse KW - prevention N2 - Background: Many intervention development projects fail to bridge the gap from basic research to clinical practice. Instead of theory-based approaches to intervention development, co-design prioritizes the end users? perspective as well as continuous collaboration between stakeholders, designers, and researchers throughout the project. This alternative approach to the development of interventions is expected to promote the adaptation to existing treatment activities and to be responsive to the requirements of end users. Objective: The first objective was to provide an overview of all activities that were employed during the course of a research project to develop a relapse prevention intervention for interdisciplinary pain treatment programs. The second objective was to examine how co-design may contribute to stakeholder involvement, generation of relevant insights and ideas, and incorporation of stakeholder input into the intervention design. Methods: We performed an embedded single case study and used the double diamond model to describe the process of intervention development. Using all available data sources, we also performed deductive content analysis to reflect on this process. Results: By critically reviewing the value and function of a co-design project with respect to idea generation, stakeholder involvement, and incorporation of stakeholder input into the intervention design, we demonstrated how co-design shaped the transition from ideas, via concepts, to a prototype for a relapse prevention intervention. Conclusions: Structural use of co-design throughout the project resulted in many different participating stakeholders and stimulating design activities. As a consequence, the majority of the components of the final prototype can be traced back to the information that stakeholders provided during the project. Although this illustrates how co-design facilitates the integration of contextual information into the intervention design, further experimental testing is required to evaluate to what extent this approach ultimately leads to improved usability as well as patient outcomes in the context of clinical practice. UR - http://www.jmir.org/2021/1/e18462/ UR - http://dx.doi.org/10.2196/18462 UR - http://www.ncbi.nlm.nih.gov/pubmed/33470937 ID - info:doi/10.2196/18462 ER - TY - JOUR AU - Yang, Chuan AU - Zhang, Wei AU - Pang, Zhixuan AU - Zhang, Jing AU - Zou, Deling AU - Zhang, Xinzhong AU - Guo, Sicong AU - Wan, Jiye AU - Wang, Ke AU - Pang, Wenyue PY - 2021/1/19 TI - A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study JO - JMIR Med Inform SP - e22753 VL - 9 IS - 1 KW - stethoscope KW - auscultation KW - COVID-19 KW - Raspberry Pi KW - Python KW - ear-contactless KW - low-cost KW - phonocardiogram KW - digital health N2 - Background: Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients. Objective: The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope. Methods: We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope. Results: We were able to conveniently operate Auscul Pi and precisely record the patients? auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001). Conclusions: Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19. Trial Registration: ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971. UR - https://medinform.jmir.org/2021/1/e22753 UR - http://dx.doi.org/10.2196/22753 UR - http://www.ncbi.nlm.nih.gov/pubmed/33436354 ID - info:doi/10.2196/22753 ER - TY - JOUR AU - Hager, Andreas AU - Lindblad, Staffan AU - Brommels, Mats AU - Salomonsson, Stina AU - Wannheden, Carolina PY - 2021/1/19 TI - Sharing Patient-Controlled Real-World Data Through the Application of the Theory of Commons: Action Research Case Study JO - J Med Internet Res SP - e16842 VL - 23 IS - 1 KW - knowledge commons KW - learning networks KW - patient and family centered care KW - eHealth N2 - Background: Technological advances have radically changed the opportunities for individuals with chronic conditions to practice self-care and to coproduce health care and research. Digital technologies enable patients to perform tasks traditionally carried out by health care professionals in a more convenient way, at lower costs, and without compromising quality. Patients may also share real-world data with other stakeholders to promote individual and population health. However, there is a need for legal frameworks that enable patient privacy and control in such sharing of real-world data. We believe that this need could be met by the conceptualization of patient-controlled real-world data as knowledge commons, which is a resource shared by a group of people. Objective: This study aimed to propose a conceptual model that describes how patient-controlled real-world data can be shared effectively in chronic care management, in a way that supports individual and population health, while respecting personal data privacy and control. Methods: An action research approach was used to develop a solution to enable patients, in a self-determined way, to share patient-controlled data to other settings. We chose the context of cystic fibrosis (CF) care in Sweden, where coproduction between patients, their families, and health care professionals is critical in the introduction of new drugs. The first author, who is a lawyer and parent of children with CF, was a driver in the change process. All coauthors collaborated in the analysis. We collected primary and secondary data reflecting changes during the time period from 2012 to 2020, and performed a qualitative content analysis guided by the knowledge commons framework. Results: Through a series of changes, a national system for enabling patients to share patient-controlled real-world data to different stakeholders in CF care was implemented. The case analysis resulted in a conceptual model consisting of the following three knowledge commons arenas that contributed to patient-controlled real-world data collection, use, and sharing: (1) patient world arena involving the private sphere of patients and families; (2) clinical microsystem arena involving the professional sphere at frontline health care clinics; and (3) round table arena involving multiple stakeholders from different settings. Based on the specification of property rights, as presented in our model, the patient can keep control over personal health information and may grant use rights to other stakeholders. Conclusions: Health information exchanges for sharing patient-controlled real-world data are pivotal to enable patients, health care professionals, health care funders, researchers, authorities, and the industry to coproduce high-quality care and to introduce and follow-up novel health technologies. Our model proposes how technical and legal structures that protect the integrity and self-determination of patients can be implemented, which may be applicable in other chronic care settings as well. UR - http://www.jmir.org/2021/1/e16842/ UR - http://dx.doi.org/10.2196/16842 UR - http://www.ncbi.nlm.nih.gov/pubmed/33464212 ID - info:doi/10.2196/16842 ER - TY - JOUR AU - Suzuki, Ryusuke AU - Suzuki, Teppei AU - Tsuji, Shintaro AU - Fujiwara, Kensuke AU - Yamashina, Hiroko AU - Endoh, Akira AU - Ogasawara, Katsuhiko PY - 2021/1/19 TI - A Bayesian Network?Based Browsing Model for Patients Seeking Radiology-Related Information on Hospital Websites: Development and Usability Study JO - J Med Internet Res SP - e14794 VL - 23 IS - 1 KW - web marketing KW - internet KW - hospitals KW - radiology KW - information-seeking behavior N2 - Background: An increasing number of people are visiting hospital websites to seek better services and treatments compared to the past. It is therefore important for hospitals to develop websites to meet the needs of their patients. However, few studies have investigated whether and how the current hospital websites meet the patient?s needs. Above all, in radiation departments, it may be difficult for patients to obtain the desired information regarding modality and diagnosis because such information is subdivided when described on a website. Objective: The purpose of this study is to suggest a hospital website search behavior model by analyzing the browsing behavior model using a Bayesian network from the perspective of one-to-one marketing. Methods: First, we followed the website access log of Hokkaido University Hospital, which was collected from September 1, 2016, to August 31, 2017, and analyzed the access log using Google Analytics. Second, we specified the access records related to radiology from visitor browsing pages and keywords. Third, using these resources, we structured 3 Bayesian network models based on specific patient needs: radiotherapy, nuclear medicine examination, and radiological diagnosis. Analyzing each model, this study considered why some visitors could not reach their desired page and improvements to meet the needs of visitors seeking radiology-related information. Results: The radiotherapy model showed that 74% (67/90) of the target visitors could reach their requested page, but only 2% (2/90) could reach the Center page where inspection information, one of their requested pages, is posted. By analyzing the behavior of the visitors, we clarified that connecting with the radiotherapy and radiological diagnosis pages is useful for increasing the proportion of patients reaching their requested page. Conclusions: We proposed solutions for patient web-browsing accessibility based on a Bayesian network. Further analysis is necessary to verify the accuracy of the proposed model in comparison to other models. It is expected that information provided on hospital websites will be improved using this method. UR - https://www.jmir.org/2021/1/e14794 UR - http://dx.doi.org/10.2196/14794 UR - http://www.ncbi.nlm.nih.gov/pubmed/33464211 ID - info:doi/10.2196/14794 ER - TY - JOUR AU - Abd-Alrazaq, A. Alaa AU - Alajlani, Mohannad AU - Ali, Nashva AU - Denecke, Kerstin AU - Bewick, M. Bridgette AU - Househ, Mowafa PY - 2021/1/13 TI - Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review JO - J Med Internet Res SP - e17828 VL - 23 IS - 1 KW - chatbots KW - conversational agents KW - mental health KW - mental disorders KW - perceptions KW - opinions KW - mobile phone N2 - Background: Chatbots have been used in the last decade to improve access to mental health care services. Perceptions and opinions of patients influence the adoption of chatbots for health care. Many studies have been conducted to assess the perceptions and opinions of patients about mental health chatbots. To the best of our knowledge, there has been no review of the evidence surrounding perceptions and opinions of patients about mental health chatbots. Objective: This study aims to conduct a scoping review of the perceptions and opinions of patients about chatbots for mental health. Methods: The scoping review was carried out in line with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews guidelines. Studies were identified by searching 8 electronic databases (eg, MEDLINE and Embase) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. In total, 2 reviewers independently selected studies and extracted data from the included studies. Data were synthesized using thematic analysis. Results: Of 1072 citations retrieved, 37 unique studies were included in the review. The thematic analysis generated 10 themes from the findings of the studies: usefulness, ease of use, responsiveness, understandability, acceptability, attractiveness, trustworthiness, enjoyability, content, and comparisons. Conclusions: The results demonstrated overall positive perceptions and opinions of patients about chatbots for mental health. Important issues to be addressed in the future are the linguistic capabilities of the chatbots: they have to be able to deal adequately with unexpected user input, provide high-quality responses, and have to show high variability in responses. To be useful for clinical practice, we have to find ways to harmonize chatbot content with individual treatment recommendations, that is, a personalization of chatbot conversations is required. UR - http://www.jmir.org/2021/1/e17828/ UR - http://dx.doi.org/10.2196/17828 UR - http://www.ncbi.nlm.nih.gov/pubmed/33439133 ID - info:doi/10.2196/17828 ER - TY - JOUR AU - Mitre-Hernandez, Hugo AU - Covarrubias Carrillo, Roberto AU - Lara-Alvarez, Carlos PY - 2021/1/11 TI - Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study JO - JMIR Serious Games SP - e21620 VL - 9 IS - 1 KW - video games KW - pupil KW - metacognitive monitoring KW - educational technology KW - machine learning N2 - Background: A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty. Objective: This study aims to assess the use of task-evoked pupillary responses to measure the cognitive load measure for describing the difficulty levels in a video game. In addition, it proposes an image filter to better estimate baseline pupil size and to reduce the screen luminescence effect. Methods: We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Then, a classifier with different pupil features was used to classify the difficulty of a data set containing information from students playing a video game for practicing math fractions. Results: We observed that the proposed filter better estimates a baseline. Mauchly?s test of sphericity indicated that the assumption of sphericity had been violated (?214=0.05; P=.001); therefore, a Greenhouse-Geisser correction was used (?=0.47). There was a significant difference in mean pupil diameter change (MPDC) estimated from different baseline images with the scramble filter (F5,78=30.965; P<.001). Moreover, according to the Wilcoxon signed rank test, pupillary response features that better describe the difficulty level were MPDC (z=?2.15; P=.03) and peak dilation (z=?3.58; P<.001). A random forest classifier for easy and hard levels of difficulty showed an accuracy of 75% when the gamer data were used, but the accuracy increased to 87.5% when pupillary measurements were included. Conclusions: The screen luminescence effect on pupil size is reduced with a scrambled filter on the background video game image. Finally, pupillary response data can improve classifier accuracy for the perceived difficulty of levels in educational video games. UR - http://games.jmir.org/2021/1/e21620/ UR - http://dx.doi.org/10.2196/21620 UR - http://www.ncbi.nlm.nih.gov/pubmed/33427677 ID - info:doi/10.2196/21620 ER - TY - JOUR AU - Aubourg, Timothée AU - Demongeot, Jacques AU - Vuillerme, Nicolas PY - 2021/1/8 TI - Gaining Insights Into the Estimation of the Circadian Rhythms of Social Activity in Older Adults From Their Telephone Call Activity With Statistical Learning: Observational Study JO - J Med Internet Res SP - e22339 VL - 23 IS - 1 KW - circadian rhythms KW - phone call detail records KW - older population KW - statistics KW - machine learning N2 - Background: Understanding the social mechanisms of the circadian rhythms of activity represents a major issue in better managing the mechanisms of age-related diseases occurring over time in the elderly population. The automated analysis of call detail records (CDRs) provided by modern phone technologies can help meet such an objective. At this stage, however, whether and how the circadian rhythms of telephone call activity can be automatically and properly modeled in the elderly population remains to be established. Objective: Our goal for this study is to address whether and how the circadian rhythms of social activity observed through telephone calls could be automatically modeled in older adults. Methods: We analyzed a 12-month data set of outgoing telephone CDRs of 26 adults older than 65 years of age. We designed a statistical learning modeling approach adapted for exploratory analysis. First, Gaussian mixture models (GMMs) were calculated to automatically model each participant?s circadian rhythm of telephone call activity. Second, k-means clustering was used for grouping participants into distinct groups depending on the characteristics of their personal GMMs. Results: The results showed the existence of specific structures of telephone call activity in the daily social activity of older adults. At the individual level, GMMs allowed the identification of personal habits, such as morningness-eveningness for making calls. At the population level, k-means clustering allowed the structuring of these individual habits into specific morningness or eveningness clusters. Conclusions: These findings support the potential of phone technologies and statistical learning approaches to automatically provide personalized and precise information on the social rhythms of telephone call activity of older individuals. Futures studies could integrate such digital insights with other sources of data to complete assessments of the circadian rhythms of activity in elderly populations. UR - http://www.jmir.org/2021/1/e22339/ UR - http://dx.doi.org/10.2196/22339 UR - http://www.ncbi.nlm.nih.gov/pubmed/33416502 ID - info:doi/10.2196/22339 ER - TY - JOUR AU - Muntaner-Mas, Adria AU - Martinez-Nicolas, Antonio AU - Quesada, Alberto AU - Cadenas-Sanchez, Cristina AU - Ortega, B. Francisco PY - 2021/1/8 TI - Smartphone App (2kmFIT-App) for Measuring Cardiorespiratory Fitness: Validity and Reliability Study JO - JMIR Mhealth Uhealth SP - e14864 VL - 9 IS - 1 KW - exercise test KW - mobile apps KW - reproducibility of results KW - physical fitness KW - telemedicine KW - cardiorespiratory fitness N2 - Background: There is strong evidence suggesting that higher levels of cardiorespiratory fitness (CRF) are associated with a healthier metabolic profile, and that CRF can serve as a powerful predictor of morbidity and mortality. In this context, a smartphone app based on the 2-km walk test (UKK test) would provide the possibility to assess CRF remotely in individuals geographically distributed around a country or continent, and even between continents, with minimal equipment and low costs. Objective: The overall aim of this study was to evaluate the validity and reliability of 2kmFIT-App developed for Android and iOS mobile operating systems to estimate maximum oxygen consumption (VO2max) as an indicator of CRF. The specific aims of the study were to determine the validity of 2kmFIT-App to track distance and calculate heart rate (HR). Methods: Twenty participants were included for field-testing validation and reliability analysis. The participants completed the UKK test twice using 2kmFIT-App. Distance and HR were measured with the app as well as with accurate methods, and VO2max was estimated using the UKK test equation. Results: The validity results showed the following mean differences (app minus criterion): distance (?70.40, SD 51.47 meters), time (?0.59, SD 0.45 minutes), HR (?16.75, SD 9.96 beats/minute), and VO2max (3.59, SD 2.01 ml/kg/min). There was moderate validity found for HR (intraclass correlation coefficient [ICC] 0.731, 95% CI ?0.211 to 0.942) and good validity found for VO2max (ICC 0.878, 95% CI ?0.125 to 0.972). The reliability results showed the following mean differences (retest minus test): app distance (25.99, SD 43.21 meters), app time (?0.15, SD 0.94 seconds), pace (?0.18, SD 0.33 min/km), app HR (?4.5, 13.44 beats/minute), and app VO2max (0.92, SD 3.04 ml/kg/min). There was good reliability for app HR (ICC 0.897, 95% CI 0.742-0.959) and excellent validity for app VO2max (ICC 0.932, 95% CI 0.830-0.973). All of these findings were observed when using the app with an Android operating system, whereas validity was poor when the app was used with iOS. Conclusions: This study shows that 2kmFIT-App is a new, scientifically valid and reliable tool able to objectively and remotely estimate CRF, HR, and distance with an Android but not iOS mobile operating system. However, certain limitations such as the time required by 2kmFIT-App to calculate HR or the temperature environment should be considered when using the app. UR - http://mhealth.jmir.org/2021/1/e14864/ UR - http://dx.doi.org/10.2196/14864 UR - http://www.ncbi.nlm.nih.gov/pubmed/33416503 ID - info:doi/10.2196/14864 ER - TY - JOUR AU - Salmi, Salim AU - Mérelle, Saskia AU - Gilissen, Renske AU - Brinkman, Willem-Paul PY - 2021/1/7 TI - Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study JO - J Med Internet Res SP - e21690 VL - 23 IS - 1 KW - suicide prevention KW - content based recommender system KW - chat corpus KW - crisis line KW - sentence embedding KW - suicide KW - mental health N2 - Background: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer?s block. Objective: This study evaluates and then designs supportive technology to determine if a support system that provides inspiration can help counselors resolve writer?s block when they encounter difficult situations in chats with help-seekers. Methods: A content-based recommender system with sentence embedding was used to search a chat corpus for similar chat situations. The system showed a counselor the most similar parts of former chat conversations so that the counselor would be able to use approaches previously taken by their colleagues as inspiration. In a within-subject experiment, counselors? chat replies when confronted with a difficult situation were analyzed to determine if experts could see a noticeable difference in chat replies that were obtained in 3 conditions: (1) with the help of the support system, (2) with written advice from a senior counselor, or (3) when receiving no help. In addition, the system?s utility and usability were measured, and the validity of the algorithm was examined. Results: A total of 24 counselors used a prototype of the support system; the results showed that, by reading chat replies, experts were able to significantly predict if counselors had received help from the support system or from a senior counselor (P=.004). Counselors scored the information they received from a senior counselor (M=1.46, SD 1.91) as significantly more helpful than the information received from the support system or when no help was given at all (M=?0.21, SD 2.26). Finally, compared with randomly selected former chat conversations, counselors rated the ones identified by the content-based recommendation system as significantly more similar to their current chats (?=.30, P<.001). Conclusions: Support given to counselors influenced how they responded in difficult conversations. However, the higher utility scores given for the advice from senior counselors seem to indicate that specific actionable instructions are preferred. We expect that these findings will be beneficial for developing a system that can use similar chat situations to generate advice in a descriptive style, hence helping counselors through writer?s block. UR - https://www.jmir.org/2021/1/e21690 UR - http://dx.doi.org/10.2196/21690 UR - http://www.ncbi.nlm.nih.gov/pubmed/33410755 ID - info:doi/10.2196/21690 ER - TY - JOUR AU - Agley, Jon AU - Jun, Mikyoung AU - Eldridge, Lori AU - Agley, L. Daniel AU - Xiao, Yunyu AU - Sussman, Steve AU - Golzarri-Arroyo, Lilian AU - Dickinson, L. Stephanie AU - Jayawardene, Wasantha AU - Gassman, Ruth PY - 2021/1/6 TI - Effects of ACT Out! Social Issue Theater on Social-Emotional Competence and Bullying in Youth and Adolescents: Cluster Randomized Controlled Trial JO - JMIR Ment Health SP - e25860 VL - 8 IS - 1 KW - cyberbullying KW - bullying KW - social-emotional learning KW - SEL KW - social-emotional competence KW - RCT KW - randomized controlled trial KW - outcome KW - emotion KW - bully KW - prevention KW - school KW - intervention KW - assessment KW - effectiveness KW - implementation KW - fidelity KW - reception KW - children KW - young adults KW - adolescents N2 - Background: Schools increasingly prioritize social-emotional competence and bullying and cyberbullying prevention, so the development of novel, low-cost, and high-yield programs addressing these topics is important. Further, rigorous assessment of interventions prior to widespread dissemination is crucial. Objective: This study assesses the effectiveness and implementation fidelity of the ACT Out! Social Issue Theater program, a 1-hour psychodramatic intervention by professional actors; it also measures students? receptiveness to the intervention. Methods: This study is a 2-arm cluster randomized control trial with 1:1 allocation that randomized either to the ACT Out! intervention or control (treatment as usual) at the classroom level (n=76 classrooms in 12 schools across 5 counties in Indiana, comprised of 1571 students at pretest in fourth, seventh, and tenth grades). The primary outcomes were self-reported social-emotional competence, bullying perpetration, and bullying victimization; the secondary outcomes were receptiveness to the intervention, implementation fidelity (independent observer observation), and prespecified subanalyses of social-emotional competence for seventh- and tenth-grade students. All outcomes were collected at baseline and 2-week posttest, with planned 3-months posttest data collection prevented due to the COVID-19 pandemic. Results: Intervention fidelity was uniformly excellent (>96% adherence), and students were highly receptive to the program. However, trial results did not support the hypothesis that the intervention would increase participants? social-emotional competence. The intervention?s impact on bullying was complicated to interpret and included some evidence of small interaction effects (reduced cyberbullying victimization and increased physical bullying perpetration). Additionally, pooled within-group reductions were also observed and discussed but were not appropriate for causal attribution. Conclusions: This study found no superiority for a 1-hour ACT Out! intervention compared to treatment as usual for social-emotional competence or offline bullying, but some evidence of a small effect for cyberbullying. On the basis of these results and the within-group effects, as a next step, we encourage research into whether the ACT Out! intervention may engender a bystander effect not amenable to randomization by classroom. Therefore, we recommend a larger trial of the ACT Out! intervention that focuses specifically on cyberbullying, measures bystander behavior, is randomized by school, and is controlled for extant bullying prevention efforts at each school. Trial Registration: Clinicaltrials.gov NCT04097496; https://clinicaltrials.gov/ct2/show/NCT04097496 International Registered Report Identifier (IRRID): RR2-10.2196/17900 UR - http://mental.jmir.org/2021/1/e25860/ UR - http://dx.doi.org/10.2196/25860 UR - http://www.ncbi.nlm.nih.gov/pubmed/33338986 ID - info:doi/10.2196/25860 ER - TY - JOUR AU - Wang, Yameng AU - Ren, Xiaotong AU - Liu, Xiaoqian AU - Zhu, Tingshao PY - 2021/1/6 TI - Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study JO - JMIR Mhealth Uhealth SP - e19046 VL - 9 IS - 1 KW - depression KW - digital phenotyping KW - social behavior KW - smartphone usage KW - mobile sensing N2 - Background: As smartphone has been widely used, understanding how depression correlates with social behavior on smartphones can be beneficial for early diagnosis of depression. An enormous amount of research relied on self-report questionnaires, which is not objective. Only recently the increased availability of rich data about human behavior in digital space has provided new perspectives for the investigation of individual differences. Objective: The objective of this study was to explore depressed Chinese individuals? social behavior in digital space through metadata collected via smartphones. Methods: A total of 120 participants were recruited to carry a smartphone with a metadata collection app (MobileSens). At the end of metadata collection, they were instructed to complete the Center for Epidemiological Studies-Depression Scale (CES-D). We then separated participants into nondepressed and depressed groups based on their scores on CES-D. From the metadata of smartphone usage, we extracted 44 features, including traditional social behaviors such as making calls and sending SMS text messages, and the usage of social apps (eg, WeChat and Sina Weibo, 2 popular social apps in China). The 2-way ANOVA (nondepressed vs depressed × male vs female) and multiple logistic regression analysis were conducted to investigate differences in social behaviors on smartphones among users. Results: The results found depressed users received less calls from contacts (all day: F1,116=3.995, P=.048, ?2=0.033; afternoon: F1,116=5.278, P=.02, ?2=0.044), and used social apps more frequently (all day: F1,116=6.801, P=.01, ?2=0.055; evening: F1,116=6.902, P=.01, ?2=0.056) than nondepressed ones. In the depressed group, females used Weibo more frequently than males (all day: F1,116=11.744, P=.001, ?2=0.092; morning: F1,116=9.105, P=.003, ?2=0.073; afternoon: F1,116=14.224, P<.001, ?2=0.109; evening: F1,116=9.052, P=.003, ?2=0.072). Moreover, usage of social apps in the evening emerged as a predictor of depressive symptoms for all participants (odds ratio [OR] 1.007, 95% CI 1.001-1.013; P=.02) and male (OR 1.013, 95% CI 1.003-1.022; P=.01), and usage of Weibo in the morning emerged as a predictor for female (OR 1.183, 95% CI 1.015-1.378; P=.03). Conclusions: This paper finds that there exists a certain correlation between depression and social behavior on smartphones. The result may be useful to improve social interaction for depressed individuals in the daily lives and may be insightful for early diagnosis of depression. UR - https://mhealth.jmir.org/2021/1/e19046 UR - http://dx.doi.org/10.2196/19046 UR - http://www.ncbi.nlm.nih.gov/pubmed/33404512 ID - info:doi/10.2196/19046 ER - TY - JOUR AU - Vinci, Christine AU - Brandon, O. Karen AU - Kleinjan, Marloes AU - Hernandez, M. Laura AU - Sawyer, E. Leslie AU - Haneke, Jody AU - Sutton, K. Steven AU - Brandon, H. Thomas PY - 2020/12/31 TI - Augmented Reality for Smoking Cessation: Development and Usability Study JO - JMIR Mhealth Uhealth SP - e21643 VL - 8 IS - 12 KW - augmented reality KW - smoking cessation KW - cue exposure therapy KW - cue reactivity KW - behavior change KW - smoking KW - smartphone app KW - mobile phone N2 - Background: The recent widespread availability of augmented reality via smartphone offers an opportunity to translate cue exposure therapy for smoking cessation from the laboratory to the real world. Despite significant reductions in the smoking rates in the last decade, approximately 13.7% of the adults in the United States continue to smoke. Smoking-related cue exposure has demonstrated promise as an adjuvant therapy in the laboratory, but practical limitations have prevented its success in the real world. Augmented reality technology presents an innovative approach to overcome these limitations. Objective: The aim of this study was to develop a smartphone app that presents smoking-related augmented reality images for cue exposure. Smokers provided feedback on the images and reported on the perceived urge to smoke, qualities of reality/coexistence, and general feedback about quality and functioning. The feedback was used to refine the augmented reality images within the app. Methods: In collaboration with an augmented reality design company, we developed 6 smoking-related images (cigarette, lighter, ashtray, lit cigarette in ashtray, etc) and 6 neutral images similar in size or complexity for comparison (pen, eraser, notebook, soda bottle with droplets, etc). Ten smokers completed a survey of demographic characteristics, smoking history and behavior, dependence on nicotine, motivation to quit smoking, and familiarity with augmented reality technology. Then, participants viewed each augmented reality image and provided ratings on 10-point Likert scales for urge to smoke and reality/coexistence of the image into the scene. Participants were also queried with open-ended questions regarding the features of the images. Results: Of the 10 participants, 5 (50%) had experienced augmented reality prior to the laboratory visit, but only 4 of those 5 participants used augmented reality at least weekly. Although the sample was small (N=10), smokers reported significantly higher urge to smoke after viewing the smoking-related augmented reality images (median 4.58, SD 3.49) versus the neutral images (median 1.42, SD 3.01) (Z=?2.14, P=.03; d=0.70). The average reality and coexistence ratings of the images did not differ between smoking-related and neutral images (all P>.29). Augmented reality images were found on average to be realistic (mean [SD] score 6.49 [3.11]) and have good environmental coexistence (mean [SD] score 6.93 [3.04]) and user coexistence (mean [SD] score 6.38 [3.27]) on the 10-point scale. Participant interviews revealed some areas of excellence (eg, details of the lit cigarette) and areas for improvement (eg, stability of images, lighting). Conclusions: All images were generally perceived as being realistic and well-integrated into the environment. However, the smoking augmented reality images produced higher urge to smoke than the neutral augmented reality images. In total, our findings support the potential utility of augmented reality for cue exposure therapy. Future directions and next steps are discussed. UR - http://mhealth.jmir.org/2020/12/e21643/ UR - http://dx.doi.org/10.2196/21643 UR - http://www.ncbi.nlm.nih.gov/pubmed/33382377 ID - info:doi/10.2196/21643 ER - TY - JOUR AU - Kawamoto, Eiji AU - Ito-Masui, Asami AU - Esumi, Ryo AU - Ito, Mami AU - Mizutani, Noriko AU - Hayashi, Tomoyo AU - Imai, Hiroshi AU - Shimaoka, Motomu PY - 2020/12/31 TI - Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study JO - J Med Internet Res SP - e23184 VL - 22 IS - 12 KW - wearable KW - interprofessional communication KW - clinician interaction KW - social network analysis N2 - Background: Use of wearable sensor technology for studying human teamwork behavior is expected to generate a better understanding of the interprofessional interactions between health care professionals. Objective: We used wearable sociometric sensor badges to study how intensive care unit (ICU) health care professionals interact and are socially connected. Methods: We studied the face-to-face interaction data of 76 healthcare professionals in the ICU at Mie University Hospital collected over 4 weeks via wearable sensors. Results: We detail the spatiotemporal distributions of staff members? inter- and intraprofessional active face-to-face interactions, thereby generating a comprehensive visualization of who met whom, when, where, and for how long in the ICU. Social network analysis of these active interactions, concomitant with centrality measurements, revealed that nurses constitute the core members of the network, while doctors remain in the periphery. Conclusions: Our social network analysis using the comprehensive ICU interaction data obtained by wearable sensors has revealed the leading roles played by nurses within the professional communication network. UR - http://www.jmir.org/2020/12/e23184/ UR - http://dx.doi.org/10.2196/23184 UR - http://www.ncbi.nlm.nih.gov/pubmed/33258785 ID - info:doi/10.2196/23184 ER - TY - JOUR AU - Stamm, Oskar AU - Heimann-Steinert, Anika PY - 2020/12/21 TI - Accuracy of Monocular Two-Dimensional Pose Estimation Compared With a Reference Standard for Kinematic Multiview Analysis: Validation Study JO - JMIR Mhealth Uhealth SP - e19608 VL - 8 IS - 12 KW - 2D human pose estimation KW - motion capturing KW - kinematics KW - clinical practice KW - mobility KW - smartphone app KW - analysis N2 - Background: Expensive optoelectronic systems, considered the gold standard, require a laboratory environment and the attachment of markers, and they are therefore rarely used in everyday clinical practice. Two-dimensional (2D) human pose estimations for clinical purposes allow kinematic analyses to be carried out via a camera-based smartphone app. Since clinical specialists highly depend on the validity of information, there is a need to evaluate the accuracy of 2D pose estimation apps. Objective: The aim of the study was to investigate the accuracy of the 2D pose estimation of a mobility analysis app (Lindera-v2), using the PanopticStudio Toolbox data set as a reference standard. The study aimed to assess the differences in joint angles obtained by 2D video information generated with the Lindera-v2 algorithm and the reference standard. The results can provide an important assessment of the adequacy of the app for clinical use. Methods: To evaluate the accuracy of the Lindera-v2 algorithm, 10 video sequences were analyzed. Accuracy was evaluated by assessing a total of 30,000 data pairs for each joint (10 joints in total), comparing the angle data obtained from the Lindera-v2 algorithm with those of the reference standard. The mean differences of the angles were calculated for each joint, and a comparison was made between the estimated values and the reference standard values. Furthermore, the mean absolute error (MAE), root mean square error, and symmetric mean absolute percentage error of the 2D angles were calculated. Agreement between the 2 measurement methods was calculated using the intraclass correlation coefficient (ICC[A,2]). A cross-correlation was calculated for the time series to verify whether there was a temporal shift in the data. Results: The mean difference of the Lindera-v2 data in the right hip was the closest to the reference standard, with a mean value difference of ?0.05° (SD 6.06°). The greatest difference in comparison with the baseline was found in the neck, with a measurement of ?3.07° (SD 6.43°). The MAE of the angle measurement closest to the baseline was observed in the pelvis (1.40°, SD 1.48°). In contrast, the largest MAE was observed in the right shoulder (6.48°, SD 8.43°). The medians of all acquired joints ranged in difference from 0.19° to 3.17° compared with the reference standard. The ICC values ranged from 0.951 (95% CI 0.914-0.969) in the neck to 0.997 (95% CI 0.997-0.997) in the left elbow joint. The cross-correlation showed that the Lindera-v2 algorithm had no temporal lag. Conclusions: The results of the study indicate that a 2D pose estimation by means of a smartphone app can have excellent agreement compared with a validated reference standard. An assessment of kinematic variables can be performed with the analyzed algorithm, showing only minimal deviations compared with data from a massive multiview system. UR - http://mhealth.jmir.org/2020/12/e19608/ UR - http://dx.doi.org/10.2196/19608 UR - http://www.ncbi.nlm.nih.gov/pubmed/33346739 ID - info:doi/10.2196/19608 ER - TY - JOUR AU - Kim, Ki-Hun AU - Kim, Kwang-Jae PY - 2020/12/17 TI - Missing-Data Handling Methods for Lifelogs-Based Wellness Index Estimation: Comparative Analysis With Panel Data JO - JMIR Med Inform SP - e20597 VL - 8 IS - 12 KW - lifelogs-based wellness index KW - missing-data handling KW - health behavior lifelogs KW - panel data KW - smart wellness service N2 - Background: A lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch). A wellness score intuitively shows the users of smart wellness services the overall condition of their health behaviors. LWI development includes estimation (ie, estimating coefficients in LWI with data). A panel data set comprising health behavior lifelogs allows LWI estimation to control for unobserved variables, thereby resulting in less bias. However, these data sets typically have missing data due to events that occur in daily life (eg, smart devices stop collecting data when batteries are depleted), which can introduce biases into LWI coefficients. Thus, the appropriate choice of method to handle missing data is important for reducing biases in LWI estimations with panel data. However, there is a lack of research in this area. Objective: This study aims to identify a suitable missing-data handling method for LWI estimation with panel data. Methods: Listwise deletion, mean imputation, expectation maximization?based multiple imputation, predictive-mean matching?based multiple imputation, k-nearest neighbors?based imputation, and low-rank approximation?based imputation were comparatively evaluated by simulating an existing case of LWI development. A panel data set comprising health behavior lifelogs of 41 college students over 4 weeks was transformed into a reference data set without any missing data. Then, 200 simulated data sets were generated by randomly introducing missing data at proportions from 1% to 80%. The missing-data handling methods were each applied to transform the simulated data sets into complete data sets, and coefficients in a linear LWI were estimated for each complete data set. For each proportion for each method, a bias measure was calculated by comparing the estimated coefficient values with values estimated from the reference data set. Results: Methods performed differently depending on the proportion of missing data. For 1% to 30% proportions, low-rank approximation?based imputation, predictive-mean matching?based multiple imputation, and expectation maximization?based multiple imputation were superior. For 31% to 60% proportions, low-rank approximation?based imputation and predictive-mean matching?based multiple imputation performed best. For over 60% proportions, only low-rank approximation?based imputation performed acceptably. Conclusions: Low-rank approximation?based imputation was the best of the 6 data-handling methods regardless of the proportion of missing data. This superiority is generalizable to other panel data sets comprising health behavior lifelogs given their verified low-rank nature, for which low-rank approximation?based imputation is known to perform effectively. This result will guide missing-data handling in reducing coefficient biases in new development cases of linear LWIs with panel data. UR - http://medinform.jmir.org/2020/12/e20597/ UR - http://dx.doi.org/10.2196/20597 UR - http://www.ncbi.nlm.nih.gov/pubmed/33331831 ID - info:doi/10.2196/20597 ER - TY - JOUR AU - Kondylakis, Haridimos AU - Axenie, Cristian AU - (Kiran) Bastola, Dhundy AU - Katehakis, G. Dimitrios AU - Kouroubali, Angelina AU - Kurz, Daria AU - Larburu, Nekane AU - Macía, Iván AU - Maguire, Roma AU - Maramis, Christos AU - Marias, Kostas AU - Morrow, Philip AU - Muro, Naiara AU - Núñez-Benjumea, José Francisco AU - Rampun, Andrik AU - Rivera-Romero, Octavio AU - Scotney, Bryan AU - Signorelli, Gabriel AU - Wang, Hui AU - Tsiknakis, Manolis AU - Zwiggelaar, Reyer PY - 2020/12/15 TI - Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study JO - J Med Internet Res SP - e22034 VL - 22 IS - 12 KW - neoplasms KW - inventions KW - data-driven science N2 - Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)?funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020?funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations. UR - https://www.jmir.org/2020/12/e22034 UR - http://dx.doi.org/10.2196/22034 UR - http://www.ncbi.nlm.nih.gov/pubmed/33320099 ID - info:doi/10.2196/22034 ER - TY - JOUR AU - Jani, Anant AU - Liyanage, Harshana AU - Okusi, Cecilia AU - Sherlock, Julian AU - Hoang, Uy AU - Ferreira, Filipa AU - Yonova, Ivelina AU - de Lusignan, Simon PY - 2020/12/11 TI - Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study JO - J Med Internet Res SP - e23721 VL - 22 IS - 12 KW - social prescribing KW - clinical informatics KW - ontology KW - social determinants of health N2 - Background: National Health Service (NHS) England supports social prescribing in order to address social determinants of health, which account for approximately 80% of all health outcomes. Nevertheless, data on ongoing social prescribing activities are lacking. Although NHS England has attempted to overcome this problem by recommending 3 standardized primary care codes, these codes do not capture the social prescribing activity to a level of granularity that would allow for fair attribution of outcomes to social prescribing. Objective: In this study, we explored whether an alternative approach to coding social prescribing activity, specifically through a social prescribing ontology, can be used to capture the social prescriptions used in primary care in greater detail. Methods: The social prescribing ontology, implemented according to the Web Ontology Language, was designed to cover several key concepts encompassing social determinants of health. Readv2 and Clinical Terms Version 3 codes were identified using the NHS Terms Browser. The Royal College of General Practitioners Research Surveillance Centre, a sentinel network of over 1000 primary care practices across England covering a population of more than 4,000,000 registered patients, was used for data analyses for a defined period (ie, January 2011 to December 2019). Results: In all, 668 codes capturing social prescriptions addressing different social determinants of health were identified for the social prescribing ontology. For the study period, social prescribing ontology codes were used 5,504,037 times by primary care practices of the Royal College of General Practitioners Research Surveillance Centre as compared to 29,606 instances of use of social prescribing codes, including NHS England?s recommended codes. Conclusions: A social prescribing ontology provides a powerful alternative to the codes currently recommended by NHS England to capture detailed social prescribing activity in England. The more detailed information thus obtained will allow for explorations about whether outputs or outcomes of care delivery can be attributed to social prescriptions, which is essential for demonstrating the overall value that social prescribing can deliver to the NHS and health care systems. UR - http://www.jmir.org/2020/12/e23721/ UR - http://dx.doi.org/10.2196/23721 UR - http://www.ncbi.nlm.nih.gov/pubmed/33306032 ID - info:doi/10.2196/23721 ER - TY - JOUR AU - Van Asbroeck, Stephanie AU - Matthys, Christophe PY - 2020/12/7 TI - Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study JO - JMIR Form Res SP - e15602 VL - 4 IS - 12 KW - image recognition KW - dietary assessment KW - automated food recognition KW - accuracy N2 - Background: In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. Objective: This is a comparative performance study of commercial image recognition platforms. Methods: A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. Results: Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. Conclusions: Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods. UR - https://formative.jmir.org/2020/12/e15602 UR - http://dx.doi.org/10.2196/15602 UR - http://www.ncbi.nlm.nih.gov/pubmed/33284118 ID - info:doi/10.2196/15602 ER - TY - JOUR AU - Braune, Katarina AU - Wäldchen, Mandy AU - Raile, Klemens AU - Hahn, Sigrid AU - Ubben, Tebbe AU - Römer, Susanne AU - Hoeber, Daniela AU - Reibel, Johanna Nora AU - Launspach, Michael AU - Blankenstein, Oliver AU - Bührer, Christoph PY - 2020/12/4 TI - Open-Source Technology for Real-Time Continuous Glucose Monitoring in the Neonatal Intensive Care Unit: Case Study in a Neonate With Transient Congenital Hyperinsulinism JO - J Med Internet Res SP - e21770 VL - 22 IS - 12 KW - open-source KW - mobile health KW - continuous glucose monitoring KW - off-label use KW - neonatal hypoglycemia KW - congenital hyperinsulinism KW - transient hyperinsulinism N2 - Background: Use of real-time continuous glucose monitoring (rtCGM) systems has been shown to be a low-pain, safe, and effective method of preventing hypoglycemia and hyperglycemia in people with diabetes of various age groups. Evidence on rtCGM use in infants and in patients with conditions other than diabetes remains limited. Objective: This case study describes the off-label use of rtCGM and the use of an open-source app for glucose monitoring in a newborn with prolonged hypoglycemia secondary to transient congenital hyperinsulinism during the perinatal period. Methods: The Dexcom G6 rtCGM system (Dexcom, Inc) was introduced at 39 hours of age. Capillary blood glucose checks were performed regularly. In order to benefit from customizable alert settings and detect hypoglycemic episodes, the open-source rtCGM app xDrip+ was introduced at 9 days of age. Results: Time in range (45-180 mg/dL) for interstitial glucose remained consistently above 90%, whereas time in hypoglycemia (<45 mg/dL) decreased. Mean glucose was maintained above 70 mg/dL at 72 hours of life and thereafter. Daily sensor glucose profiles showed cyclic fluctuations that were less pronounced over time. Conclusions: While off-label use of medication is both common practice and a necessity in newborn infants, there are few examples of off-label uses of medical devices, rtCGM being a notable exception. Real-time information allowed us to better understand glycemic patterns and to improve the quality of glycemic control accordingly. Severe hypoglycemia was prevented, and measurement of serum levels of insulin and further lab diagnostics were performed much faster, while the patient?s individual burden caused by invasive procedures was reduced. Greater customizability of threshold and alert settings would be beneficial for user groups with glycemic instability other than people with diabetes, and for hospitalized newborn infants in particular. Further research in the field of personal and off-label rtCGM use, efficacy studies evaluating the accuracy of low glucose readings, and studies on the differences between algorithms in translating raw sensor data, as well as customization of commercially available rtCGM systems, is needed. UR - http://www.jmir.org/2020/12/e21770/ UR - http://dx.doi.org/10.2196/21770 UR - http://www.ncbi.nlm.nih.gov/pubmed/33275114 ID - info:doi/10.2196/21770 ER - TY - JOUR AU - Xu, Paiheng AU - Dredze, Mark AU - Broniatowski, A. David PY - 2020/12/3 TI - The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets JO - J Med Internet Res SP - e21499 VL - 22 IS - 12 KW - COVID-19 KW - social distancing KW - mobility KW - Twitter KW - social media KW - surveillance KW - tracking KW - travel KW - index N2 - Background: Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and ?flattens the curve? so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. Objective: The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. Methods: We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. Results: We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. Conclusions: We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning. UR - https://www.jmir.org/2020/12/e21499 UR - http://dx.doi.org/10.2196/21499 UR - http://www.ncbi.nlm.nih.gov/pubmed/33048823 ID - info:doi/10.2196/21499 ER - TY - JOUR AU - Chishtie, Ahmed Jawad AU - Marchand, Jean-Sebastien AU - Turcotte, A. Luke AU - Bielska, Anna Iwona AU - Babineau, Jessica AU - Cepoiu-Martin, Monica AU - Irvine, Michael AU - Munce, Sarah AU - Abudiab, Sally AU - Bjelica, Marko AU - Hossain, Saima AU - Imran, Muhammad AU - Jeji, Tara AU - Jaglal, Susan PY - 2020/12/3 TI - Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review JO - J Med Internet Res SP - e17892 VL - 22 IS - 12 KW - visual analytics KW - machine learning KW - data visualization KW - data mining KW - population health KW - health services research KW - mobile phone N2 - Background: Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. Objective: This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). Methods: Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. Results: After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. Conclusions: With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. International Registered Report Identifier (IRRID): RR2-10.2196/14019 UR - https://www.jmir.org/2020/12/e17892 UR - http://dx.doi.org/10.2196/17892 UR - http://www.ncbi.nlm.nih.gov/pubmed/33270029 ID - info:doi/10.2196/17892 ER - TY - JOUR AU - Wang, Chi-Te AU - Han, Ji-Yan AU - Fang, Shih-Hau AU - Lai, Ying-Hui PY - 2020/12/3 TI - Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation JO - JMIR Mhealth Uhealth SP - e16746 VL - 8 IS - 12 KW - voice disorder KW - speech envelope KW - phonation habits KW - background noise KW - noise reduction KW - adaptive threshold KW - dosimetry KW - phonotrauma N2 - Background: Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. Objective: This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. Methods: We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. Results: The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. Conclusions: This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users. UR - https://mhealth.jmir.org/2020/12/e16746 UR - http://dx.doi.org/10.2196/16746 UR - http://www.ncbi.nlm.nih.gov/pubmed/33270033 ID - info:doi/10.2196/16746 ER - TY - JOUR AU - Davoudi, Anahita AU - Lee, S. Natalie AU - Chivers, Corey AU - Delaney, Timothy AU - Asch, L. Elizabeth AU - Reitz, Catherine AU - Mehta, J. Shivan AU - Chaiyachati, H. Krisda AU - Mowery, L. Danielle PY - 2020/12/3 TI - Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study JO - J Med Internet Res SP - e22493 VL - 22 IS - 12 KW - text messaging KW - hypertension KW - telemedicine KW - cluster analysis N2 - Background: Automated texting platforms have emerged as a tool to facilitate communication between patients and health care providers with variable effects on achieving target blood pressure (BP). Understanding differences in the way patients interact with these communication platforms can inform their use and design for hypertension management. Objective: Our primary aim was to explore the unique phenotypes of patient interactions with an automated text messaging platform for BP monitoring. Our secondary aim was to estimate associations between interaction phenotypes and BP control. Methods: This study was a secondary analysis of data from a randomized controlled trial for adults with poorly controlled hypertension. A total of 201 patients with established primary care were assigned to the automated texting platform; messages exchanged throughout the 4-month program were analyzed. We used the k-means clustering algorithm to characterize two different interaction phenotypes: program conformity and engagement style. First, we identified unique clusters signifying differences in program conformity based on the frequency over time of error alerts, which were generated to patients when they deviated from the requested text message format (eg, ###/## for BP). Second, we explored overall engagement styles, defined by error alerts and responsiveness to text prompts, unprompted messages, and word count averages. Finally, we applied the chi-square test to identify associations between each interaction phenotype and achieving the target BP. Results: We observed 3 categories of program conformity based on their frequency of error alerts: those who immediately and consistently submitted texts without system errors (perfect users, 51/201), those who did so after an initial learning period (adaptive users, 66/201), and those who consistently submitted messages generating errors to the platform (nonadaptive users, 38/201). Next, we observed 3 categories of engagement style: the enthusiast, who tended to submit unprompted messages with high word counts (17/155); the student, who inconsistently engaged (35/155); and the minimalist, who engaged only when prompted (103/155). Of all 6 phenotypes, we observed a statistically significant association between patients demonstrating the minimalist communication style (high adherence, few unprompted messages, limited information sharing) and achieving target BP (P<.001). Conclusions: We identified unique interaction phenotypes among patients engaging with an automated text message platform for remote BP monitoring. Only the minimalist communication style was associated with achieving target BP. Identifying and understanding interaction phenotypes may be useful for tailoring future automated texting interactions and designing future interventions to achieve better BP control. UR - https://www.jmir.org/2020/12/e22493 UR - http://dx.doi.org/10.2196/22493 UR - http://www.ncbi.nlm.nih.gov/pubmed/33270032 ID - info:doi/10.2196/22493 ER - TY - JOUR AU - Weiger, Caitlin AU - Smith, C. Katherine AU - Cohen, E. Joanna AU - Dredze, Mark AU - Moran, Bridgid Meghan PY - 2020/12/2 TI - How Internet Contracts Impact Research: Content Analysis of Terms of Service on Consumer Product Websites JO - JMIR Public Health Surveill SP - e23579 VL - 6 IS - 4 KW - marketing KW - contracts KW - internet KW - jurisprudence KW - ethics N2 - Background: Companies use brand websites as a promotional tool to engage consumers on the web, which can increase product use. Given that some products are harmful to the health of consumers, it is important for marketing associated with these products to be subject to public health surveillance. However, terms of service (TOS) governing the use of brand website content may impede such important research. Objective: The aim of this study is to explore the TOS for brand websites with public health significance to assess possible legal and ethical challenges for conducting research on consumer product websites. Methods: Using Statista, we purposefully constructed a sample of 15 leading American tobacco, alcohol, psychiatric pharmaceutical, fast-food, and gun brands that have associated websites. We developed and implemented a structured coding system for the TOS on these websites and coded for the presence versus absence of different types of restriction that might impact the ability to conduct research. Results: All TOS stated that by accessing the website, users agreed to abide by the TOS (15/15, 100%). A total of 11 out of 15 (73%) websites had age restrictions in their TOS. All alcohol brand websites (5/15, 33%) required users to enter their age or date of birth before viewing website content. Both websites for tobacco brands (2/15, 13%) further required that users register and verify their age and identity to access any website content and agree that they use tobacco products. Only one website (1/15, 7%) allowed users to display, download, copy, distribute, and translate the website content as long as it was for personal and not commercial use. A total of 33% (5/15) of TOS unconditionally prohibited or put substantial restrictions on all of these activities and/or failed to specify if they were allowed or prohibited. Moreover, 87% (13/15) of TOS indicated that website access could be restricted at any time. A total of 73% (11/15) of websites specified that violating TOS could result in deleting user content from the website, revoking access by having the user?s Internet Protocol address blocked, terminating log-in credentials, or enforcing legal action resulting in civil or criminal penalties. Conclusions: TOS create complications for public health surveillance related to e-marketing on brand websites. Recent court opinions have reduced the risk of federal criminal charges for violating TOS on public websites, but this risk remains unclear for private websites. The public health community needs to establish standards to guide and protect researchers from the possibility of legal repercussions related to such efforts. UR - http://publichealth.jmir.org/2020/4/e23579/ UR - http://dx.doi.org/10.2196/23579 UR - http://www.ncbi.nlm.nih.gov/pubmed/33263555 ID - info:doi/10.2196/23579 ER - TY - JOUR AU - Maté, Tomás AU - Hoyos, Juan AU - Guerras, Miguel Juan AU - Agustí, Cristina AU - Chanos, Sophocles AU - Kuske, Matthias AU - Fuertes, Ricardo AU - Stefanescu, Roxana AU - Pulido, Jose AU - Sordo, Luis AU - de la Fuente, Luis AU - Belza, José María AU - PY - 2020/11/30 TI - Potential of HIV Self-Sampling to Increase Testing Frequency Among Gay, Bisexual, and Other Men Who Have Sex With Men, and the Role of Online Result Communication: Online Cross-Sectional Study JO - J Med Internet Res SP - e21268 VL - 22 IS - 11 KW - early diagnosis KW - HIV KW - testing KW - men who have sex with men KW - online testing KW - MSM KW - diagnosis KW - self-sampling KW - frequency KW - cross-sectional KW - communication N2 - Background: Late HIV diagnosis remains frequent among the gay, bisexual, and other men who have sex with men (GBMSM) population across Europe. HIV self-sampling could help remove barriers and facilitate access to testing for this high-risk population. Objective: We assessed the capacity of HIV self-sampling to increase the testing frequency among GBMSM living in Denmark, Germany, Greece, Portugal, Romania, and Spain, and evaluated the role of new technologies in the result communication phase. Methods: We analyzed a convenience sample of 5019 GBMSM with prior HIV testing experience who were recruited during 2016 through gay dating websites. We estimated the proportion of GBMSM who reported that the availability of self-sampling would result in an increase of their current testing frequency. We constructed a Poisson regression model for each country to calculate prevalence ratios and 95% CIs of factors associated with an increase of testing frequency as a result of self-sampling availability. Results: Overall, 59% (between country range 54.2%-77.2%) of the participants considered that they would test more frequently for HIV if self-sampling was available in their country. In the multivariate analysis, the increase of testing frequency as a result of self-sampling availability was independently associated with reporting a higher number of unprotected anal intercourse events in all countries except for Greece. Independent associations were also observed among GBMSM who were not open about their sex life in Germany, Greece, Portugal, and Spain; those with a lower number of previous HIV tests in Denmark, Greece, Portugal, and Spain; and for those that took their last test more than 3 months previously in Germany, Portugal, Romania, and Spain. In addition, 58.4% (range 40.5%-73.6%) of the participants indicated a preference for learning their result through one-way interaction methods, mainly via email (25.6%, range 16.8%-35.2%) and through a secure website (20.3%, range 7.3%-23.7%). Almost two thirds (65%) of GBMSM indicated preferring one of these methods even if the result was reactive. Conclusions: Availability of HIV self-sampling kits as an additional testing methodology would lead to a much-needed increase of testing frequency, especially for the hidden, high-risk, and undertested GBMSM population. Online-based technologies without any personal interaction were preferred for the communication of the results, even for reactive results. UR - http://www.jmir.org/2020/11/e21268/ UR - http://dx.doi.org/10.2196/21268 UR - http://www.ncbi.nlm.nih.gov/pubmed/33252346 ID - info:doi/10.2196/21268 ER - TY - JOUR AU - Sahu, Sundar Kirti AU - Oetomo, Arlene AU - Morita, Pelegrini Plinio PY - 2020/11/20 TI - Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study JO - JMIR Mhealth Uhealth SP - e21016 VL - 8 IS - 11 KW - disruptive technology KW - health information systems KW - public health surveillance KW - health behavior KW - Internet of Things KW - cell phone KW - mobile phone N2 - Background: Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. Objective: The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an individual?s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. Methods: We conducted a pilot study with a sample size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators; sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. Results: The results showed a significant Spearman rank correlation coefficient of 0.8 (P<.001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. Conclusions: The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home. UR - https://mhealth.jmir.org/2020/11/e21016 UR - http://dx.doi.org/10.2196/21016 UR - http://www.ncbi.nlm.nih.gov/pubmed/33216001 ID - info:doi/10.2196/21016 ER - TY - JOUR AU - Tsai, FS Vincent AU - Zhuang, Bin AU - Pong, Yuan-Hung AU - Hsieh, Ju-Ton AU - Chang, Hong-Chiang PY - 2020/11/19 TI - Web- and Artificial Intelligence?Based Image Recognition For Sperm Motility Analysis: Verification Study JO - JMIR Med Inform SP - e20031 VL - 8 IS - 11 KW - Male infertility KW - semen analysis KW - home sperm test KW - smartphone KW - artificial intelligence KW - cloud computing KW - telemedicine N2 - Background: Human sperm quality fluctuates over time. Therefore, it is crucial for couples preparing for natural pregnancy to monitor sperm motility. Objective: This study verified the performance of an artificial intelligence?based image recognition and cloud computing sperm motility testing system (Bemaner, Createcare) composed of microscope and microfluidic modules and designed to adapt to different types of smartphones. Methods: Sperm videos were captured and uploaded to the cloud with an app. Analysis of sperm motility was performed by an artificial intelligence?based image recognition algorithm then results were displayed. According to the number of motile sperm in the vision field, 47 (deidentified) videos of sperm were scored using 6 grades (0-5) by a male-fertility expert with 10 years of experience. Pearson product-moment correlation was calculated between the grades and the results (concentration of total sperm, concentration of motile sperm, and motility percentage) computed by the system. Results: Good correlation was demonstrated between the grades and results computed by the system for concentration of total sperm (r=0.65, P<.001), concentration of motile sperm (r=0.84, P<.001), and motility percentage (r=0.90, P<.001). Conclusions: This smartphone-based sperm motility test (Bemaner) accurately measures motility-related parameters and could potentially be applied toward the following fields: male infertility detection, sperm quality test during preparation for pregnancy, and infertility treatment monitoring. With frequent at-home testing, more data can be collected to help make clinical decisions and to conduct epidemiological research. UR - http://medinform.jmir.org/2020/11/e20031/ UR - http://dx.doi.org/10.2196/20031 UR - http://www.ncbi.nlm.nih.gov/pubmed/33211025 ID - info:doi/10.2196/20031 ER - TY - JOUR AU - El Emam, Khaled AU - Mosquera, Lucy AU - Bass, Jason PY - 2020/11/16 TI - Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation JO - J Med Internet Res SP - e23139 VL - 22 IS - 11 KW - synthetic data KW - privacy KW - data sharing KW - data access KW - de-identification KW - open data N2 - Background: There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them. Objective: The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data. Methods: A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this ?meaningful identity disclosure risk.? The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data. Results: The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively. Conclusions: We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data. UR - http://www.jmir.org/2020/11/e23139/ UR - http://dx.doi.org/10.2196/23139 UR - http://www.ncbi.nlm.nih.gov/pubmed/33196453 ID - info:doi/10.2196/23139 ER - TY - JOUR AU - Schucht, Philippe AU - Roccaro-Waldmeyer, M. Diana AU - Murek, Michael AU - Zubak, Irena AU - Goldberg, Johannes AU - Falk, Stephanie AU - Dahlweid, Fried-Michael AU - Raabe, Andreas PY - 2020/11/11 TI - Exploring Novel Funding Strategies for Innovative Medical Research: The HORAO Crowdfunding Campaign JO - J Med Internet Res SP - e19715 VL - 22 IS - 11 KW - science funding KW - crowdfunding KW - neurosurgery KW - neurosciences KW - brain tumor N2 - Background: The rise of the internet and social media has boosted online crowdfunding as a novel strategy to raise funds for kick-starting projects, but it is rarely used in science. Objective: We report on an online crowdfunding campaign launched in the context of the neuroscience project HORAO. The aim of HORAO was to develop a noninvasive real-time method to visualize neuronal fiber tracts during brain surgery in order to better delineate tumors and to identify crucial cerebral landmarks. The revenue from the crowdfunding campaign was to be used to sponsor a crowdsourcing campaign for the HORAO project. Methods: We ran a 7-week reward-based crowdfunding campaign on a national crowdfunding platform, offering optional material and experiential rewards in return for a contribution toward raising our target of Swiss francs (CHF) 50,000 in financial support (roughly equivalent to US $50,000 at the time of the campaign). We used various owned media (websites and social media), as well as earned media (press releases and news articles) to raise awareness about our project. Results: The production of an explanatory video took 60 hours, and 31 posts were published on social media (Facebook, Instagram, and Twitter). The campaign raised a total of CHF 69,109. Approximately half of all donations came from donors who forwent a reward (CHF 28,786, 48.74%); the other half came from donors who chose experiential and material rewards in similar proportions (CHF 14,958, 25.33% and CHF 15,315.69, 25.93%, respectively). Of those with an identifiable relationship to the crowdfunding team, patients and their relatives contributed the largest sum (CHF 17,820, 30.17%), followed by friends and family (CHF 9288, 15.73%) and work colleagues (CHF 6028, 10.21%), while 43.89% of funds came from donors who were either anonymous or had an unknown relationship to the crowdfunding team. Patients and their relatives made the largest donations, with a median value of CHF 200 (IQR 90). Conclusions: Crowdfunding proved to be a successful strategy to fund a neuroscience project and to raise awareness of a specific clinical problem. Focusing on potential donors with a personal interest in the issue, such as patients and their relatives in our project, is likely to increase funding success. Compared with traditional grant applications, new skills are needed to explain medical challenges to the crowd through video messages and social media. UR - https://www.jmir.org/2020/11/e19715 UR - http://dx.doi.org/10.2196/19715 UR - http://www.ncbi.nlm.nih.gov/pubmed/33174857 ID - info:doi/10.2196/19715 ER - TY - JOUR AU - Choo, Hyunwoo AU - Kim, Myeongchan AU - Choi, Jiyun AU - Shin, Jaewon AU - Shin, Soo-Yong PY - 2020/10/29 TI - Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study JO - J Med Internet Res SP - e21369 VL - 22 IS - 10 KW - influenza KW - screening tool KW - patient-generated health data KW - mobile health KW - mHealth KW - deep learning N2 - Background: Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. Objective: The aim of this study was to develop a machine learning?based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. Methods: We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient?s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user?s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. Results: We achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). Conclusions: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions. UR - http://www.jmir.org/2020/10/e21369/ UR - http://dx.doi.org/10.2196/21369 UR - http://www.ncbi.nlm.nih.gov/pubmed/33118941 ID - info:doi/10.2196/21369 ER - TY - JOUR AU - Lee, Hyeong Geun AU - Shin, Soo-Yong PY - 2020/10/26 TI - Federated Learning on Clinical Benchmark Data: Performance Assessment JO - J Med Internet Res SP - e20891 VL - 22 IS - 10 KW - federated learning KW - medical data KW - privacy protection KW - machine learning KW - deep learning N2 - Background: Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas. Objective: The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset. Methods: To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data. Results: FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807. Conclusions: FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data. UR - http://www.jmir.org/2020/10/e20891/ UR - http://dx.doi.org/10.2196/20891 UR - http://www.ncbi.nlm.nih.gov/pubmed/33104011 ID - info:doi/10.2196/20891 ER - TY - JOUR AU - Goel, Rahul AU - An, Michael AU - Alayrangues, Hugo AU - Koneshloo, Amirhossein AU - Lincoln, Thierry Emmanuel AU - Paredes, Enrique Pablo PY - 2020/10/23 TI - Stress Tracker?Detecting Acute Stress From a Trackpad: Controlled Study JO - J Med Internet Res SP - e22743 VL - 22 IS - 10 KW - precision health KW - well-being KW - trackpad KW - computer input device KW - computer interaction KW - stress sensing KW - affective interfaces KW - mental health N2 - Background: Stress is a risk factor associated with physiological and mental health problems. Unobtrusive, continuous stress sensing would enable precision health monitoring and proactive interventions, but current sensing methods are often inconvenient, expensive, or suffer from limited adherence. Prior work has shown the possibility to detect acute stress using biomechanical models derived from passive logging of computer input devices. Objective: Our objective is to detect acute stress from passive movement measurements of everyday interactions on a laptop trackpad: (1) click, (2) steer, and (3) drag and drop. Methods: We built upon previous work, detecting acute stress through the biomechanical analyses of canonical computer mouse interactions and extended it to study similar interactions with the trackpad. A total of 18 participants carried out 40 trials each of three different types of movement?(1) click, (2) steer, and (3) drag and drop?under both relaxed and stressed conditions. Results: The mean and SD of the contact area under the finger were higher when clicking trials were performed under stressed versus relaxed conditions (mean area: P=.009, effect size=0.76; SD area: P=.01, effect size=0.69). Further, our results show that as little as 4 clicks on a trackpad can be used to detect binary levels of acute stress (ie, whether it is present or not). Conclusions: We present evidence that scalable, inexpensive, and unobtrusive stress sensing can be done via repurposing passive monitoring of computer trackpad usage. UR - http://www.jmir.org/2020/10/e22743/ UR - http://dx.doi.org/10.2196/22743 UR - http://www.ncbi.nlm.nih.gov/pubmed/33095176 ID - info:doi/10.2196/22743 ER - TY - JOUR AU - Mai, Hang-Nga AU - Lee, Du-Hyeong PY - 2020/10/23 TI - Accuracy of Mobile Device?Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e22228 VL - 22 IS - 10 KW - accuracy KW - facial digitization KW - facial scanners KW - systematic review KW - meta-analysis N2 - Background: The accurate assessment and acquisition of facial anatomical information significantly contributes to enhancing the reliability of treatments in dental and medical fields, and has applications in fields such as craniomaxillofacial surgery, orthodontics, prosthodontics, orthopedics, and forensic medicine. Mobile device?compatible 3D facial scanners have been reported to be an effective tool for clinical use, but the accuracy of digital facial impressions obtained with the scanners has not been explored. Objective: We aimed to review comparisons of the accuracy of mobile device?compatible face scanners for facial digitization with that of systems for professional 3D facial scanning. Methods: Individual search strategies were employed in PubMed (MEDLINE), Scopus, Science Direct, and Cochrane Library databases to search for articles published up to May 27, 2020. Peer-reviewed journal articles evaluating the accuracy of 3D facial models generated by mobile device?compatible face scanners were included. Cohen d effect size estimates and confidence intervals of standardized mean difference (SMD) data sets were used for meta-analysis. Results: By automatic database searching, 3942 articles were identified, of which 11 articles were considered eligible for narrative review, with 6 studies included in the meta-analysis. Overall, the accuracy of face models obtained using mobile device?compatible face scanners was significantly lower than that of face models obtained using professional 3D facial scanners (SMD 3.96 mm, 95% CI 2.81-5.10 mm; z=6.78; P<.001). The difference between face scanning when performed on inanimate facial models was significantly higher (SMD 10.53 mm, 95% CI 6.29-14.77 mm) than that when performed on living participants (SMD 2.58 mm, 95% CI 1.70-3.47 mm, P<.001, df=12.94). Conclusions: Overall, mobile device?compatible face scanners did not perform as well as professional scanning systems in 3D facial acquisition, but the deviations were within the clinically acceptable range of <1.5 mm. Significant differences between results when 3D facial scans were performed on inanimate facial objects and when performed on the faces of living participants were found; thus, caution should be exercised when interpreting results from studies conducted on inanimate objects. UR - http://www.jmir.org/2020/10/e22228/ UR - http://dx.doi.org/10.2196/22228 UR - http://www.ncbi.nlm.nih.gov/pubmed/33095178 ID - info:doi/10.2196/22228 ER - TY - JOUR AU - Pantel, Tori Jean AU - Hajjir, Nurulhuda AU - Danyel, Magdalena AU - Elsner, Jonas AU - Abad-Perez, Teresa Angela AU - Hansen, Peter AU - Mundlos, Stefan AU - Spielmann, Malte AU - Horn, Denise AU - Ott, Claus-Eric AU - Mensah, Atta Martin PY - 2020/10/22 TI - Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study JO - J Med Internet Res SP - e19263 VL - 22 IS - 10 KW - facial phenotyping KW - DeepGestalt KW - facial recognition KW - Face2Gene KW - medical genetics KW - diagnostic accuracy KW - genetic syndrome KW - machine learning N2 - Background: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt?s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning?based framework for the automated differentiation of DeepGestalt?s output on such images. Methods: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt?s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt?s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt?s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt?s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt?s results and may help enhance it and similar computer-aided facial phenotyping tools. UR - http://www.jmir.org/2020/10/e19263/ UR - http://dx.doi.org/10.2196/19263 UR - http://www.ncbi.nlm.nih.gov/pubmed/33090109 ID - info:doi/10.2196/19263 ER - TY - JOUR AU - Lim, Cherry AU - Miliya, Thyl AU - Chansamouth, Vilada AU - Aung, Thazin Myint AU - Karkey, Abhilasha AU - Teparrukkul, Prapit AU - Rahul, Batra AU - Lan, Huong Nguyen Phu AU - Stelling, John AU - Turner, Paul AU - Ashley, Elizabeth AU - van Doorn, Rogier H. AU - Lin, Naing Htet AU - Ling, Clare AU - Hinjoy, Soawapak AU - Iamsirithaworn, Sopon AU - Dunachie, Susanna AU - Wangrangsimakul, Tri AU - Hantrakun, Viriya AU - Schilling, William AU - Yen, Minh Lam AU - Tan, Van Le AU - Hlaing, Htay Htay AU - Mayxay, Mayfong AU - Vongsouvath, Manivanh AU - Basnyat, Buddha AU - Edgeworth, Jonathan AU - Peacock, J. Sharon AU - Thwaites, Guy AU - Day, PJ Nicholas AU - Cooper, S. Ben AU - Limmathurotsakul, Direk PY - 2020/10/2 TI - Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries JO - J Med Internet Res SP - e19762 VL - 22 IS - 10 KW - antimicrobial resistance KW - surveillance KW - report KW - data analysis KW - application N2 - Background: Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. Objective: This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. Methods: An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People?s Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. Results: We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. Conclusions: The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports. UR - https://www.jmir.org/2020/10/e19762 UR - http://dx.doi.org/10.2196/19762 UR - http://www.ncbi.nlm.nih.gov/pubmed/33006570 ID - info:doi/10.2196/19762 ER - TY - JOUR AU - Marler, D. Jennifer AU - Fujii, A. Craig AU - Wong, S. Kristine AU - Galanko, A. Joseph AU - Balbierz, J. Daniel AU - Utley, S. David PY - 2020/10/2 TI - Assessment of a Personal Interactive Carbon Monoxide Breath Sensor in People Who Smoke Cigarettes: Single-Arm Cohort Study JO - J Med Internet Res SP - e22811 VL - 22 IS - 10 KW - smoking cessation KW - digital health KW - smartphone KW - digital sensor KW - carbon monoxide KW - breath sensor KW - biofeedback N2 - Background: Tobacco use is the leading cause of preventable morbidity and mortality. Existing evidence-based treatments are underutilized and have seen little recent innovation. The success of personal biofeedback interventions in other disease states portends a similar opportunity in smoking cessation. The Pivot Breath Sensor is a personal interactive FDA-cleared (over-the-counter) device that measures carbon monoxide (CO) in exhaled breath, enabling users to link their smoking behavior and CO values, and track their progress in reducing or quitting smoking. Objective: The objective of this study is to assess the Pivot Breath Sensor in people who smoke cigarettes, evaluating changes in attitudes toward quitting smoking, changes in smoking behavior, and use experience. Methods: US adults (18-80 years of age, ?10 cigarettes per day [CPD]) were recruited online for this remote 12-week study. Participants completed a screening call, informed consent, and baseline questionnaire, and then were mailed their sensor. Participants were asked to submit 4 or more breath samples per day and complete questionnaires at 1-4, 8, and 12 weeks. Outcomes included attitudes toward quitting smoking (Stage of Change, success to quit, and perceived difficulty of quitting), smoking behavior (quit attempts, CPD reduction, and 7-, 30-day point prevalence abstinence [PPA]), and use experience (impact and learning). Results: Participants comprised 234 smokers, mean age 39.9 (SD 11.3) years, 52.6% (123/234) female, mean CPD 20.3 (SD 8.0). The 4- and 12-week questionnaires were completed by 92.3% (216/234) and 91.9% (215/234) of participants, respectively. Concerning attitude outcomes, at baseline, 15.4% (36/234) were seriously thinking of quitting in the next 30 days, increasing to 38.9% (84/216) at 4 weeks and 47.9% (103/215) at 12 weeks (both P<.001). At 12 weeks, motivation to quit was increased in 39.1% (84/215), unchanged in 54.9% (118/215), and decreased in 6.0% (13/215; P<.001). Additional attitudes toward quitting improved from baseline to 12 weeks: success to quit 3.3 versus 5.0 (P<.001) and difficulty of quitting 2.8 versus 4.3 (P<.001). Regarding smoking behavior, at 4 weeks, 28.2% (66/234) had made 1 or more quit attempts (?1 day of abstinence), increasing to 48.3% (113/234) at 12 weeks. At 4 weeks, 23.1% (54/234) had reduced CPD by 50% or more, increasing to 38.5% (90/234) at 12 weeks. At 12 weeks, CPD decreased by 41.1% from baseline (P<.001), and 7- and 30-day PPA were 12.0% (28/234) and 6.0% (14/234), respectively. Concerning use experience, 75.3% (171/227) reported the sensor increased their motivation to quit. More than 90% (>196/214) indicated the sensor taught them about their CO levels and smoking behavior, and 73.1% (166/227) reported that seeing their CO values made them want to quit smoking. Conclusions: Use of the Pivot Breath Sensor resulted in a significant increase in motivation to quit, a reduction in CPD, and favorable quit attempt rates. These outcomes confer increased likelihood of quitting smoking. Accordingly, the results support a role for biofeedback via personal CO breath sampling in smoking cessation. Trial Registration: ClinicalTrials.gov NCT04133064; https://clinicaltrials.gov/ct2/show/NCT04133064 UR - https://www.jmir.org/2020/10/e22811 UR - http://dx.doi.org/10.2196/22811 UR - http://www.ncbi.nlm.nih.gov/pubmed/32894829 ID - info:doi/10.2196/22811 ER - TY - JOUR AU - Sultana, Madeena AU - Al-Jefri, Majed AU - Lee, Joon PY - 2020/9/29 TI - Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study JO - JMIR Mhealth Uhealth SP - e17818 VL - 8 IS - 9 KW - mHealth KW - mental health KW - emotion detection KW - emotional transition detection KW - spatiotemporal context KW - supervised machine learning KW - artificial intelligence KW - mobile phone KW - digital biomarkers KW - digital phenotyping N2 - Background: Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. Automated detection of emotional states and transitions on a daily basis could be an effective solution to this problem. However, the relationship between emotional transitions and everyday context remains to be unexplored. Objective: This study aims to explore the relationship between contextual information and emotional transitions and states to evaluate the feasibility of detecting emotional transitions and states from daily contextual information using machine learning (ML) techniques. Methods: This study was conducted on the data of 18 individuals from a publicly available data set called ExtraSensory. Contextual and sensor data were collected using smartphone and smartwatch sensors in a free-living condition, where the number of days for each person varied from 3 to 9. Sensors included an accelerometer, a gyroscope, a compass, location services, a microphone, a phone state indicator, light, temperature, and a barometer. The users self-reported approximately 49 discrete emotions at different intervals via a smartphone app throughout the data collection period. We mapped the 49 reported discrete emotions to the 3 dimensions of the pleasure, arousal, and dominance model and considered 6 emotional states: discordant, pleased, dissuaded, aroused, submissive, and dominant. We built general and personalized models for detecting emotional transitions and states every 5 min. The transition detection problem is a binary classification problem that detects whether a person?s emotional state has changed over time, whereas state detection is a multiclass classification problem. In both cases, a wide range of supervised ML algorithms were leveraged, in addition to data preprocessing, feature selection, and data imbalance handling techniques. Finally, an assessment was conducted to shed light on the association between everyday context and emotional states. Results: This study obtained promising results for emotional state and transition detection. The best area under the receiver operating characteristic (AUROC) curve for emotional state detection reached 60.55% in the general models and an average of 96.33% across personalized models. Despite the highly imbalanced data, the best AUROC curve for emotional transition detection reached 90.5% in the general models and an average of 88.73% across personalized models. In general, feature analyses show that spatiotemporal context, phone state, and motion-related information are the most informative factors for emotional state and transition detection. Our assessment showed that lifestyle has an impact on the predictability of emotion. Conclusions: Our results demonstrate a strong association of daily context with emotional states and transitions as well as the feasibility of detecting emotional states and transitions using data from smartphone and smartwatch sensors. UR - http://mhealth.jmir.org/2020/9/e17818/ UR - http://dx.doi.org/10.2196/17818 UR - http://www.ncbi.nlm.nih.gov/pubmed/32990638 ID - info:doi/10.2196/17818 ER - TY - JOUR AU - Jones, V. Helen AU - Smith, Harry AU - Cooksley, Tim AU - Jones, Philippa AU - Woolley, Toby AU - Gwyn Murdoch, Derick AU - Thomas, Dafydd AU - Foster, Betty AU - Wakefield, Valerie AU - Innominato, Pasquale AU - Mullard, Anna AU - Ghosal, Niladri AU - Subbe, Christian PY - 2020/9/25 TI - Checklists for Complications During Systemic Cancer Treatment Shared by Patients, Friends, and Health Care Professionals: Prospective Interventional Cohort Study JO - JMIR Mhealth Uhealth SP - e19225 VL - 8 IS - 9 KW - cancer KW - patient safety KW - checklist KW - quality of life KW - anxiety KW - depression KW - health economics KW - mHealth KW - smartphone KW - redundancy N2 - Background: Advances in cancer management have been associated with an increased incidence of emergency presentations with disease- or treatment-related complications. Objective: This study aimed to measure the ability of patients and members of their social network to complete checklists for complications of systemic treatment for cancer and examine the impact on patient-centered and health-economic outcomes. Methods: A prospective interventional cohort study was performed to assess the impact of a smartphone app used by patients undergoing systemic cancer therapy and members of their network to monitor for common complications. The app was used by patients, a nominated ?safety buddy,? and acute oncology services. The control group was made up of patients from the same institution. Measures were based on process (completion of checklists over 60 days), patient experience outcomes (Hospital Anxiety and Depression Scale and the General version of the Functional Assessment of Cancer Therapy at baseline, 1 month, and 2 months) and health-economic outcomes (usage of appointments in primary care and elective and unscheduled hospital admissions). Results: At the conclusion of the study, 50 patients had completed 2882 checklists, and their 50 ?safety buddies? had completed 318 checklists. Near daily usage was maintained over the 60-day study period. When compared to a cohort of 50 patients with matching disease profiles from the same institution, patients in the intervention group had comparable changes in Hospital Anxiety and Depression Scale and General version of the Functional Assessment of Cancer Therapy. Patients in the Intervention Group required a third (32 vs 97 nights) of the hospital days with overnight stay compared to patients in the Control Group, though the difference was not significant. The question, ?I feel safer with the checklist,? received a mean score of 4.27 (SD 0.87) on a Likert scale (1-5) for patients and 4.55 (SD 0.65) for family and friends. Conclusions: Patients undergoing treatment for cancer and their close contacts can complete checklists for common complications of systemic treatments and take an active role in systems supporting their own safety. A larger sample size will be needed to assess the impact on clinical outcomes and health economics. UR - http://mhealth.jmir.org/2020/9/e19225/ UR - http://dx.doi.org/10.2196/19225 UR - http://www.ncbi.nlm.nih.gov/pubmed/32975526 ID - info:doi/10.2196/19225 ER - TY - JOUR AU - Schneider, Stefan AU - Junghaenel, U. Doerte AU - Gutsche, Tania AU - Mak, Wa Hio AU - Stone, A. Arthur PY - 2020/9/24 TI - Comparability of Emotion Dynamics Derived From Ecological Momentary Assessments, Daily Diaries, and the Day Reconstruction Method: Observational Study JO - J Med Internet Res SP - e19201 VL - 22 IS - 9 KW - ecological momentary assessment KW - daily diaries KW - day reconstruction method KW - emotion dynamics KW - emotion variability KW - mobile phone N2 - Background: Interest in the measurement of the temporal dynamics of people?s emotional lives has risen substantially in psychological and medical research. Emotions fluctuate and change over time, and measuring the ebb and flow of people?s affective experiences promises enhanced insights into people?s health and functioning. Researchers have used a variety of intensive longitudinal assessment (ILA) methods to create measures of emotion dynamics, including ecological momentary assessments (EMAs), end-of-day (EOD) diaries, and the day reconstruction method (DRM). To date, it is unclear whether they can be used interchangeably or whether ostensibly similar emotion dynamics captured by the methods differ in meaningful ways. Objective: This study aims to examine the extent to which different ILA methods yield comparable measures of intraindividual emotion dynamics. Methods: Data from 90 participants aged 50 years or older were collected in a probability-based internet panel, the Understanding America Study, and analyzed. Participants provided positive and negative affect ratings using 3 ILA methods: (1) smartphone-based EMA, administered 6 times per day over 1 week, (2) web-based EOD diaries, administered daily over the same week, and (3) web-based DRM, administered once during that week. We calculated 11 measures of emotion dynamics (addressing mean levels, variability, instability, and inertia separately for positive and negative affect, as well as emotion network density, mixed emotions, and emotional dialecticism) from each ILA method. The analyses examined mean differences and correlations of scores addressing the same emotion dynamic across the ILA methods. We also compared the patterns of intercorrelations among the emotion dynamics and their relationships with health outcomes (general health, pain, and fatigue) across ILA methods. Results: Emotion dynamics derived from EMAs and EOD diaries demonstrated moderate-to-high correspondence for measures of mean emotion levels (??0.95), variability (??0.68), instability (??0.51), mixed emotions (?=0.92), and emotional dialecticism (?=0.57), and low correspondence for measures of inertia (??0.17) and emotion network density (?=0.36). DRM-derived measures showed correlations with EMAs and EOD diaries that were high for mean emotion levels and mixed emotions (??0.74), moderate for variability (?=0.38-.054), and low to moderate for other measures (?=0.03-0.41). Intercorrelations among the emotion dynamics showed high convergence across EMAs and EOD diaries, and moderate convergence between the DRM and EMAs as well as EOD diaries. Emotion dynamics from all 3 ILA methods produced very similar patterns of relationships with health outcomes. Conclusions: EMAs and EOD diaries provide corresponding information about individual differences in various emotion dynamics, whereas the DRM provides corresponding information about emotion levels and (to a lesser extent) variability, but not about more complex emotion dynamics. Our results caution researchers against viewing these ILA methods as universally interchangeable. UR - http://www.jmir.org/2020/9/e19201/ UR - http://dx.doi.org/10.2196/19201 UR - http://www.ncbi.nlm.nih.gov/pubmed/32969835 ID - info:doi/10.2196/19201 ER - TY - JOUR AU - Zehetmair, Catharina AU - Nagy, Ede AU - Leetz, Carla AU - Cranz, Anna AU - Kindermann, David AU - Reddemann, Luise AU - Nikendei, Christoph PY - 2020/9/23 TI - Self-Practice of Stabilizing and Guided Imagery Techniques for Traumatized Refugees via Digital Audio Files: Qualitative Study JO - J Med Internet Res SP - e17906 VL - 22 IS - 9 KW - stabilizing techniques KW - guided imagery KW - refugees KW - qualitative analyses KW - posttraumatic stress disorder KW - mental health KW - PTSD KW - audio KW - therapy N2 - Background: Refugees have an increased risk of developing mental health problems. There are insufficient psychosocial care structures to meet the resulting need for support. Stabilizing and guided imagery techniques have shown promising results in increasing traumatized refugees? emotional stabilization. If delivered via audio files, the techniques can be practiced autonomously and independent of time, space, and human resources or stable treatment settings. Objective: This study aimed to evaluate the self-practice of stabilizing and guided imagery techniques via digital audio files for traumatized refugees living in a reception and registration center in Germany. Methods: From May 2018 to February 2019, 42 traumatized refugees participated in our study. At T1, patients received digital audio files in English, French, Arabic, Farsi, Turkish, or Serbian for self-practice. Nine days later, at T2, a face-to-face interview was conducted. Two months after T2, a follow-up interview took place via telephone. Results: At T2, about half of the patients reported the daily practice of stabilizing and guided imagery techniques. At follow-up, the average frequency of practice was once weekly or more for those experiencing worse symptoms. No technical difficulties were reported. According to T2 and follow-up statements, the techniques helped the patients dealing with arousal, concentration, sleep, mood, thoughts, empowerment, and tension. The guided imagery technique ?The Inner Safe Place? was the most popular. Self-practice was impeded by postmigratory distress factors, like overcrowded accommodations. Conclusions: The results show that self-practice of stabilizing and guided imagery techniques via digital audio files was helpful to and well accepted by the assessed refugees. Even though postmigratory distress factors hampered self-practice, ?The Inner Safe Place? technique was particularly well received. Overall, the self-practiced audio-based stabilizing and guided imagery techniques showed promising results among the highly vulnerable group of newly arrived traumatized refugees. UR - http://www.jmir.org/2020/9/e17906/ UR - http://dx.doi.org/10.2196/17906 UR - http://www.ncbi.nlm.nih.gov/pubmed/32965229 ID - info:doi/10.2196/17906 ER - TY - JOUR AU - Khurshid, Anjum PY - 2020/9/22 TI - Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic JO - JMIR Med Inform SP - e20477 VL - 8 IS - 9 KW - blockchain KW - privacy KW - trust KW - contact tracing KW - COVID-19 KW - coronavirus N2 - Background: The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. Objective: This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. Methods: Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. Results: This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create ?trustless? systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. Conclusions: Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care. UR - http://medinform.jmir.org/2020/9/e20477/ UR - http://dx.doi.org/10.2196/20477 UR - http://www.ncbi.nlm.nih.gov/pubmed/32903197 ID - info:doi/10.2196/20477 ER - TY - JOUR AU - Gazi, H. Asim AU - Gurel, Z. Nil AU - Richardson, S. Kristine L. AU - Wittbrodt, T. Matthew AU - Shah, J. Amit AU - Vaccarino, Viola AU - Bremner, Douglas J. AU - Inan, T. Omer PY - 2020/9/22 TI - Digital Cardiovascular Biomarker Responses to Transcutaneous Cervical Vagus Nerve Stimulation: State-Space Modeling, Prediction, and Simulation JO - JMIR Mhealth Uhealth SP - e20488 VL - 8 IS - 9 KW - vagus nerve stimulation KW - noninvasive KW - wearable sensing KW - digital biomarkers KW - dynamic models KW - state space KW - biomarker KW - cardiovascular KW - neuromodulation KW - bioelectronic medicine N2 - Background: Transcutaneous cervical vagus nerve stimulation (tcVNS) is a promising alternative to implantable stimulation of the vagus nerve. With demonstrated potential in myriad applications, ranging from systemic inflammation reduction to traumatic stress attenuation, closed-loop tcVNS during periods of risk could improve treatment efficacy and reduce ineffective delivery. However, achieving this requires a deeper understanding of biomarker changes over time. Objective: The aim of the present study was to reveal the dynamics of relevant cardiovascular biomarkers, extracted from wearable sensing modalities, in response to tcVNS. Methods: Twenty-four human subjects were recruited for a randomized double-blind clinical trial, for whom electrocardiography and photoplethysmography were used to measure heart rate and photoplethysmogram amplitude responses to tcVNS, respectively. Modeling these responses in state-space, we (1) compared the biomarkers in terms of their predictability and active vs sham differentiation, (2) studied the latency between stimulation onset and measurable effects, and (3) visualized the true and model-simulated biomarker responses to tcVNS. Results: The models accurately predicted future heart rate and photoplethysmogram amplitude values with root mean square errors of approximately one-fifth the standard deviations of the data. Moreover, (1) the photoplethysmogram amplitude showed superior predictability (P=.03) and active vs sham separation compared to heart rate; (2) a consistent delay of greater than 5 seconds was found between tcVNS onset and cardiovascular effects; and (3) dynamic characteristics differentiated responses to tcVNS from the sham stimulation. Conclusions: This work furthers the state of the art by modeling pertinent biomarker responses to tcVNS. Through subsequent analysis, we discovered three key findings with implications related to (1) wearable sensing devices for bioelectronic medicine, (2) the dominant mechanism of action for tcVNS-induced effects on cardiovascular physiology, and (3) the existence of dynamic biomarker signatures that can be leveraged when titrating therapy in closed loop. Trial Registration: ClinicalTrials.gov NCT02992899; https://clinicaltrials.gov/ct2/show/NCT02992899 International Registered Report Identifier (IRRID): RR2-10.1016/j.brs.2019.08.002 UR - http://mhealth.jmir.org/2020/9/e20488/ UR - http://dx.doi.org/10.2196/20488 UR - http://www.ncbi.nlm.nih.gov/pubmed/32960179 ID - info:doi/10.2196/20488 ER - TY - JOUR AU - Li, Juan AU - Maharjan, Bikesh AU - Xie, Bo AU - Tao, Cui PY - 2020/9/21 TI - A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation JO - J Med Internet Res SP - e19897 VL - 22 IS - 9 KW - Alzheimer disease KW - dementia KW - diet KW - knowledge KW - ontology KW - voice assistant N2 - Background: The world?s aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD?s health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD?s diet is important. Objective: This study aims to design, develop, and test an artificial intelligence?powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge. Methods: The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations. Results: A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness. Conclusions: The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers? use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD?s diet. UR - http://www.jmir.org/2020/9/e19897/ UR - http://dx.doi.org/10.2196/19897 UR - http://www.ncbi.nlm.nih.gov/pubmed/32955452 ID - info:doi/10.2196/19897 ER - TY - JOUR AU - Dolci, Elisa AU - Schärer, Barbara AU - Grossmann, Nicole AU - Musy, Naima Sarah AU - Zúñiga, Franziska AU - Bachnick, Stefanie AU - Simon, Michael PY - 2020/9/21 TI - Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study JO - J Med Internet Res SP - e19516 VL - 22 IS - 9 KW - falls KW - adverse event KW - harm KW - algorithm KW - natural language processing N2 - Background: Falls are common adverse events in hospitals, frequently leading to additional health costs due to prolonged stays and extra care. Therefore, reliable fall detection is vital to develop and test fall prevention strategies. However, conventional methods?voluntary incident reports and manual chart reviews?are error-prone and time consuming, respectively. Using a search algorithm to examine patients? electronic health record data and flag fall indicators offers an inexpensive, sensitive, cost-effective alternative. Objective: This study?s purpose was to develop a fall detection algorithm for use with electronic health record data, then to evaluate it alongside the Global Trigger Tool, incident reports, a manual chart review, and patient-reported falls. Methods: Conducted on 2 campuses of a large hospital system in Switzerland, this retrospective diagnostic accuracy study consisted of 2 substudies: the first, targeting 240 patients, for algorithm development and the second, targeting 298 patients, for validation. In the development study, we compared the new algorithm?s in-hospital fall rates with those indicated by the Global Trigger Tool and incident reports; in the validation study, we compared the algorithm?s in-hospital fall rates with those from patient-reported falls and manual chart review. We compared the various methods by calculating sensitivity, specificity, and predictive values. Results: Twenty in-hospital falls were discovered in the development study sample. Of these, the algorithm detected 19 (sensitivity 95%), the Global Trigger Tool detected 18 (90%), and incident reports detected 14 (67%). Of the 15 falls found in the validation sample, the algorithm identified all 15 (100%), the manual chart review identified 14 (93%), and the patient-reported fall measure identified 5 (33%). Owing to relatively high numbers of false positives based on falls present on admission, the algorithm?s positive predictive values were 50% (development sample) and 47% (validation sample). Instead of requiring 10 minutes per case for a full manual review or 20 minutes to apply the Global Trigger Tool, the algorithm requires only a few seconds, after which only the positive results (roughly 11% of the full case number) require review. Conclusions: The newly developed electronic health record algorithm demonstrated very high sensitivity for fall detection. Applied in near real time, the algorithm can record in-hospital falls events effectively and help to develop and test fall prevention measures. UR - http://www.jmir.org/2020/9/e19516/ UR - http://dx.doi.org/10.2196/19516 UR - http://www.ncbi.nlm.nih.gov/pubmed/32955445 ID - info:doi/10.2196/19516 ER - TY - JOUR AU - Wolffsohn, S. James AU - Leteneux-Pantais, Claudia AU - Chiva-Razavi, Sima AU - Bentley, Sarah AU - Johnson, Chloe AU - Findley, Amy AU - Tolley, Chloe AU - Arbuckle, Rob AU - Kommineni, Jyothi AU - Tyagi, Nishith PY - 2020/9/21 TI - Social Media Listening to Understand the Lived Experience of Presbyopia: Systematic Search and Content Analysis Study JO - J Med Internet Res SP - e18306 VL - 22 IS - 9 KW - presbyopia KW - near vision KW - social media KW - social media listening KW - infodemiology N2 - Background: Presbyopia is defined as the age-related deterioration of near vision over time which is experienced in over 80% of people aged 40 years or older. Individuals with presbyopia have difficulty with tasks that rely on near vision. It is not currently possible to stop or reverse the aging process that causes presbyopia; generally, it is corrected with glasses, contact lenses, surgery, or the use of a magnifying glass. Objective: This study aimed to explore how individuals used social media to describe their experience of presbyopia with regard to the symptoms experienced and the impacts of presbyopia on their quality of life. Methods: Social media sources including Twitter, forums, blogs, and news outlets were searched using a predefined search string relating to symptoms and impacts of presbyopia. The data that were downloaded, based on the keywords, underwent manual review to identify relevant data points. Relevant posts were further manually analyzed through a process of data tagging, categorization, and clustering. Key themes relating to symptoms, impacts, treatment, and lived experiences were identified. Results: A total of 4456 social media posts related to presbyopia were identified between May 2017 and August 2017. Using a random sampling methodology, we selected 2229 (50.0%) posts for manual review, with 1470 (65.9%) of these 2229 posts identified as relevant to the study objectives. Twitter was the most commonly used channel for discussions on presbyopia compared to forums and blogs. The majority of relevant posts originated in Spain (559/1470, 38.0%) and the United States (426/1470, 29.0%). Of the relevant posts, 270/1470 (18.4%) were categorized as posts written by individuals who have presbyopia, of which 37 of the 270 posts (13.7%) discussed symptoms. On social media, individuals with presbyopia most frequently reported experiencing difficulty reading small print (24/37, 64.9%), difficulty focusing on near objects (15/37, 40.5%), eye strain (12/37, 32.4%), headaches (9/37, 24.3%), and blurred vision (8/37, 21.6%). 81 of the 270 posts (30.0%) discussed impacts of presbyopia?emotional burden (57/81, 70.4%), functional or daily living impacts (46/81, 56.8%), such as difficulty reading (46/81, 56.8%) and using electronic devices (21/81, 25.9%), and impacts on work (3/81, 3.7%). Conclusions: Findings from this social media listening study provided insight into how people with presbyopia discuss their condition online and highlight the impact of presbyopia on individuals? quality of life. The social media listening methodology can be used to generate insights into the lived experience of a condition, but it is recommended that this research be combined with prospective qualitative research for added rigor and for confirmation of the relevance of the findings. UR - http://www.jmir.org/2020/9/e18306/ UR - http://dx.doi.org/10.2196/18306 UR - http://www.ncbi.nlm.nih.gov/pubmed/32955443 ID - info:doi/10.2196/18306 ER - TY - JOUR AU - Kouri, Andrew AU - Yamada, Janet AU - Sale, M. Joanna E. AU - Straus, E. Sharon AU - Gupta, Samir PY - 2020/9/18 TI - Primary Care Pre-Visit Electronic Patient Questionnaire for Asthma: Uptake Analysis and Predictor Modeling JO - J Med Internet Res SP - e19358 VL - 22 IS - 9 KW - electronic questionnaire KW - tablet KW - mHealth uptake KW - asthma KW - modeling N2 - Background: mHealth tablet-based interventions are increasingly being studied and deployed in various health care settings, yet little knowledge exists regarding patient uptake and acceptance or how patient demographics influence these important implementation metrics. Objective: To determine which factors influence the uptake and successful completion of an mHealth tablet questionnaire by analyzing its implementation in a primary care setting. Methods: We prospectively studied a patient-facing electronic touch-tablet asthma questionnaire deployed as part of the Electronic Asthma Management System. We describe tablet uptake and completion rates and corresponding predictor models for these behaviors. Results: The tablet was offered to and accepted by patients in 891/1715 (52.0%) visits. Patients refused the tablet in 33.0% (439/1330) visits in which it was successfully offered. Patients aged older than 65 years of age (odds ratio [OR] 2.30, 95% CI 1.33-3.95) and with concurrent chronic obstructive pulmonary disease (OR 2.22, 95% CI 1.05-4.67) were more likely to refuse the tablet, and those on an asthma medication (OR 0.55, 95% CI 0.30-0.99) were less likely to refuse it. Once accepted, the questionnaire was completed in 784/891 (88.0%) instances, with those on an asthma medication (OR 0.53, 95% CI 0.32-0.88) being less likely to leave it incomplete. Conclusions: Older age predicted initial tablet refusal but not tablet questionnaire completion, suggesting that perceptions of mHealth among older adults may negatively impact uptake, independent of usability. The influence of being on an asthma medication suggests that disease severity may also mediate mHealth acceptance. Although use of mHealth questionnaires is growing rapidly across health care settings and diseases, few studies describe their real-world acceptance and its predictors. Our results should be complemented by qualitative methods to identify barriers and enablers to uptake and may inform technological and implementation strategies to drive successful usage. UR - http://www.jmir.org/2020/9/e19358/ UR - http://dx.doi.org/10.2196/19358 UR - http://www.ncbi.nlm.nih.gov/pubmed/32945779 ID - info:doi/10.2196/19358 ER - TY - JOUR AU - Sadek, Ibrahim AU - Heng, Soon Terry Tan AU - Seet, Edwin AU - Abdulrazak, Bessam PY - 2020/9/18 TI - A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study JO - J Med Internet Res SP - e18297 VL - 22 IS - 9 KW - ballistocardiography KW - sleep apnea KW - vital signs KW - eHealth KW - mobile health KW - home care N2 - Background: At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient?s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient?s everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective: This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods: In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient?s mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results: For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. Conclusions: Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection. UR - http://www.jmir.org/2020/9/e18297/ UR - http://dx.doi.org/10.2196/18297 UR - http://www.ncbi.nlm.nih.gov/pubmed/32945773 ID - info:doi/10.2196/18297 ER - TY - JOUR AU - Adnan, Mehnaz AU - Gao, Xiaoying AU - Bai, Xiaohan AU - Newbern, Elizabeth AU - Sherwood, Jill AU - Jones, Nicholas AU - Baker, Michael AU - Wood, Tim AU - Gao, Wei PY - 2020/9/17 TI - Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering JO - JMIR Public Health Surveill SP - e18281 VL - 6 IS - 3 KW - Campylobacter KW - disease outbreaks KW - forecasting KW - spatio-temporal analysis N2 - Background: Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective: The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods: We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran?s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results: Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions: Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice. UR - http://publichealth.jmir.org/2020/3/e18281/ UR - http://dx.doi.org/10.2196/18281 UR - http://www.ncbi.nlm.nih.gov/pubmed/32940617 ID - info:doi/10.2196/18281 ER - TY - JOUR AU - Gaynor, Mark AU - Tuttle-Newhall, Janet AU - Parker, Jessica AU - Patel, Arti AU - Tang, Clare PY - 2020/9/17 TI - Adoption of Blockchain in Health Care JO - J Med Internet Res SP - e17423 VL - 22 IS - 9 KW - blockchain adoption KW - blockchain technology in health care KW - supply chain KW - data management UR - https://www.jmir.org/2020/9/e17423 UR - http://dx.doi.org/10.2196/17423 UR - http://www.ncbi.nlm.nih.gov/pubmed/32940618 ID - info:doi/10.2196/17423 ER - TY - JOUR AU - Yom-Tov, Elad AU - Cherlow, Yuval PY - 2020/9/16 TI - Ethical Challenges and Opportunities Associated With the Ability to Perform Medical Screening From Interactions With Search Engines: Viewpoint JO - J Med Internet Res SP - e21922 VL - 22 IS - 9 KW - search engines KW - diagnosis KW - screening UR - http://www.jmir.org/2020/9/e21922/ UR - http://dx.doi.org/10.2196/21922 UR - http://www.ncbi.nlm.nih.gov/pubmed/32936082 ID - info:doi/10.2196/21922 ER - TY - JOUR AU - Bergman, G. Brandon AU - Wu, Weiyi AU - Marsch, A. Lisa AU - Crosier, S. Benjamin AU - DeLise, C. Timothy AU - Hassanpour, Saeed PY - 2020/9/16 TI - Associations Between Substance Use and Instagram Participation to Inform Social Network?Based Screening Models: Multimodal Cross-Sectional Study JO - J Med Internet Res SP - e21916 VL - 22 IS - 9 KW - substance use KW - social network sites KW - health risk KW - screening KW - machine learning KW - social media KW - Instagram KW - alcohol KW - drug N2 - Background: Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools. Objective: This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity. Methods: Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram?s approval, participants? Instagram photo posts were downloaded with an application program interface. Participants? past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having ?had more than a few alcoholic drinks a day,? drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level. Results: Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts. Conclusions: Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches. UR - http://www.jmir.org/2020/9/e21916/ UR - http://dx.doi.org/10.2196/21916 UR - http://www.ncbi.nlm.nih.gov/pubmed/32936081 ID - info:doi/10.2196/21916 ER - TY - JOUR AU - Zhang, Liang AU - Qu, Yue AU - Jin, Bo AU - Jing, Lu AU - Gao, Zhan AU - Liang, Zhanhua PY - 2020/9/16 TI - An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System JO - JMIR Med Inform SP - e18689 VL - 8 IS - 9 KW - Parkinson disease KW - speech disorder KW - remote diagnosis KW - artificial intelligence KW - mobile phone app KW - mobile health N2 - Background: Parkinson disease (PD) is one of the most common neurological diseases. At present, because the exact cause is still unclear, accurate diagnosis and progression monitoring remain challenging. In recent years, exploring the relationship between PD and speech impairment has attracted widespread attention in the academic world. Most of the studies successfully validated the effectiveness of some vocal features. Moreover, the noninvasive nature of speech signal?based testing has pioneered a new way for telediagnosis and telemonitoring. In particular, there is an increasing demand for artificial intelligence?powered tools in the digital health era. Objective: This study aimed to build a real-time speech signal analysis tool for PD diagnosis and severity assessment. Further, the underlying system should be flexible enough to integrate any machine learning or deep learning algorithm. Methods: At its core, the system we built consists of two parts: (1) speech signal processing: both traditional and novel speech signal processing technologies have been employed for feature engineering, which can automatically extract a few linear and nonlinear dysphonia features, and (2) application of machine learning algorithms: some classical regression and classification algorithms from the machine learning field have been tested; we then chose the most efficient algorithms and relevant features. Results: Experimental results showed that our system had an outstanding ability to both diagnose and assess severity of PD. By using both linear and nonlinear dysphonia features, the accuracy reached 88.74% and recall reached 97.03% in the diagnosis task. Meanwhile, mean absolute error was 3.7699 in the assessment task. The system has already been deployed within a mobile app called No Pa. Conclusions: This study performed diagnosis and severity assessment of PD from the perspective of speech order detection. The efficiency and effectiveness of the algorithms indirectly validated the practicality of the system. In particular, the system reflects the necessity of a publicly accessible PD diagnosis and assessment system that can perform telediagnosis and telemonitoring of PD. This system can also optimize doctors? decision-making processes regarding treatments. UR - http://medinform.jmir.org/2020/9/e18689/ UR - http://dx.doi.org/10.2196/18689 UR - http://www.ncbi.nlm.nih.gov/pubmed/32936086 ID - info:doi/10.2196/18689 ER - TY - JOUR AU - Ferrario, Andrea AU - Demiray, Burcu AU - Yordanova, Kristina AU - Luo, Minxia AU - Martin, Mike PY - 2020/9/15 TI - Social Reminiscence in Older Adults? Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning JO - J Med Internet Res SP - e19133 VL - 22 IS - 9 KW - aging KW - dementia KW - reminiscence KW - real-life conversations KW - electronically activated recorder (EAR) KW - natural language processing KW - machine learning KW - imbalanced learning N2 - Background: Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations. Objective: The aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts. Methods: The methods in this study comprise (1) collecting and coding of transcripts of older adults? conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. Results: Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs. Conclusions: This study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults? everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults? well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health. UR - http://www.jmir.org/2020/9/e19133/ UR - http://dx.doi.org/10.2196/19133 UR - http://www.ncbi.nlm.nih.gov/pubmed/32866108 ID - info:doi/10.2196/19133 ER - TY - JOUR AU - Thorsen, Kær Ida AU - Rossen, Sine AU - Glümer, Charlotte AU - Midtgaard, Julie AU - Ried-Larsen, Mathias AU - Kayser, Lars PY - 2020/9/15 TI - Health Technology Readiness Profiles Among Danish Individuals With Type 2 Diabetes: Cross-Sectional Study JO - J Med Internet Res SP - e21195 VL - 22 IS - 9 KW - readiness for health technology KW - telemedicine KW - diabetes mellitus, type 2 KW - socioeconomic factors KW - mental health KW - psychological distress KW - healthcare disparities KW - delivery of healthcare KW - exercise N2 - Background: Information technologies (IT) are increasingly implemented in type 2 diabetes (T2D) treatment as a resource for remotely supported health care. However, possible pitfalls of introducing IT in health care are generally overlooked. Specifically, the effectiveness of IT to improve health care may depend on the user?s readiness for health technology. Objective: We aim to investigate readiness for health technology in relation to mental well-being, sociodemographic, and disease-related characteristics among individuals with T2D. Methods: Individuals with T2D (aged ?18 years) who had been referred to self-management education, exercise, diet counseling, smoking cessation, or alcohol counseling completed a questionnaire survey covering (1) background information, (2) the 5-item World Health Organization Well-Being Index (WHO-5), (3) receptiveness to IT use in physical activity, and (4) the Readiness and Enablement Index for Health Technology (READHY), constituted by dimensions related to self-management, social support, and eHealth literacy. Individuals were divided into profiles using cluster analysis based on their READHY scores. Outcomes included differences across profiles in mental well-being, sociodemographic, and disease-related characteristics. Results: Participants in the study were 155 individuals with T2D with a mean age of 60.2 (SD 10.7) years, 55.5% (86/155) of which were men and 44.5% (69/155) of which were women. Participants were stratified into 5 health technology readiness profiles based on the cluster analysis: Profile 1, high health technology readiness; Profile 2, medium health technology readiness; Profile 3, medium health technology readiness and high level of emotional distress; Profile 4, medium health technology readiness and low-to-medium eHealth literacy; Profile 5, low health technology readiness. No differences in sociodemographic and disease-related characteristics were observed across profiles; however, we identified 3 vulnerable subgroups of individuals: Profile 3 (21/155, 13.5%), younger individuals (mean age of 53.4 years, SD 8.9 years) with low mental well-being (mean 42.7, SD 14.7) and emotional distress (mean 1.69, SD 0.38); Profile 4 (20/155, 12.9%), older individuals (mean age 66.3 years, SD 9.0 years) with less IT use (50.0% used IT for communication) and low-to-medium eHealth literacy; and Profile 5 (36/155, 23.2%) with low mental well-being (mean 43.4, SD 20.1) and low readiness for health technology. Conclusions: Implementation of IT in health care of individuals with T2D should be based on comprehensive consideration of mental well-being, emotional distress, and readiness for health technology rather than sociodemographic and disease-related characteristics to identify the individuals in need of social support, self-management education, and extensive IT support. A one-size-fits-all approach to IT implementation in health care will potentially increase the risk of treatment failure among the most vulnerable individuals. UR - http://www.jmir.org/2020/9/e21195/ UR - http://dx.doi.org/10.2196/21195 UR - http://www.ncbi.nlm.nih.gov/pubmed/32930669 ID - info:doi/10.2196/21195 ER - TY - JOUR AU - Cotté, François-Emery AU - Voillot, Paméla AU - Bennett, Bryan AU - Falissard, Bruno AU - Tzourio, Christophe AU - Foulquié, Pierre AU - Gaudin, Anne-Françoise AU - Lemasson, Hervé AU - Grumberg, Valentine AU - McDonald, Laura AU - Faviez, Carole AU - Schück, Stéphane PY - 2020/9/11 TI - Exploring the Health-Related Quality of Life of Patients Treated With Immune Checkpoint Inhibitors: Social Media Study JO - J Med Internet Res SP - e19694 VL - 22 IS - 9 KW - health-related quality of life KW - immunotherapy KW - patients with cancer KW - social media use KW - measures KW - real world N2 - Background: Immune checkpoint inhibitors (ICIs) are increasingly used to treat several types of tumors. Impact of this emerging therapy on patients? health-related quality of life (HRQoL) is usually collected in clinical trials through standard questionnaires. However, this might not fully reflect HRQoL of patients under real-world conditions. In parallel, users? narratives from social media represent a potential new source of research concerning HRQoL. Objective: The aim of this study is to assess and compare coverage of ICI-treated patients? HRQoL domains and subdomains in standard questionnaires from clinical trials and in real-world setting from social media posts. Methods: A retrospective study was carried out by collecting social media posts in French language written by internet users mentioning their experiences with ICIs between January 2011 and August 2018. Automatic and manual extractions were implemented to create a corpus where domains and subdomains of HRQoL were classified. These annotations were compared with domains covered by 2 standard HRQoL questionnaires, the EORTC QLQ-C30 and the FACT-G. Results: We identified 150 users who described their own experience with ICI (89/150, 59.3%) or that of their relative (61/150, 40.7%), with 137 users (91.3%) reporting at least one HRQoL domain in their social media posts. A total of 8 domains and 42 subdomains of HRQoL were identified: Global health (1 subdomain; 115 patients), Symptoms (13; 76), Emotional state (10; 49), Role (7; 22), Physical activity (4; 13), Professional situation (3; 9), Cognitive state (2; 2), and Social state (2; 2). The QLQ-C30 showed a wider global coverage of social media HRQoL subdomains than the FACT-G, 45% (19/42) and 29% (12/42), respectively. For both QLQ-C30 and FACT-G questionnaires, coverage rates were particularly suboptimal for Symptoms (68/123, 55.3% and 72/123, 58.5%, respectively), Emotional state (7/49, 14% and 24/49, 49%, respectively), and Role (17/22, 77% and 15/22, 68%, respectively). Conclusions: Many patients with cancer are using social media to share their experiences with immunotherapy. Collecting and analyzing their spontaneous narratives are helpful to capture and understand their HRQoL in real-world setting. New measures of HRQoL are needed to provide more in-depth evaluation of Symptoms, Emotional state, and Role among patients with cancer treated with immunotherapy. UR - http://www.jmir.org/2020/9/e19694/ UR - http://dx.doi.org/10.2196/19694 UR - http://www.ncbi.nlm.nih.gov/pubmed/32915159 ID - info:doi/10.2196/19694 ER - TY - JOUR AU - Turicchi, Jake AU - O'Driscoll, Ruairi AU - Finlayson, Graham AU - Duarte, Cristiana AU - Palmeira, L. A. AU - Larsen, C. Sofus AU - Heitmann, L. Berit AU - Stubbs, James R. PY - 2020/9/11 TI - Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study JO - JMIR Mhealth Uhealth SP - e17977 VL - 8 IS - 9 KW - weight variability KW - weight fluctuation KW - weight cycling KW - weight instability KW - imputation KW - validation KW - digital tracking KW - smart scales KW - body weight KW - energy balance N2 - Background: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. Objective: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches Methods: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. Results: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. Conclusions: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data. UR - http://mhealth.jmir.org/2020/9/e17977/ UR - http://dx.doi.org/10.2196/17977 UR - http://www.ncbi.nlm.nih.gov/pubmed/32915155 ID - info:doi/10.2196/17977 ER - TY - JOUR AU - Knights, Jonathan AU - Heidary, Zahra AU - Cochran, M. Jeffrey PY - 2020/9/10 TI - Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System JO - JMIR Ment Health SP - e21378 VL - 7 IS - 9 KW - digital medicine KW - mobile phone KW - entropy rate KW - Markov chains KW - medication adherence KW - contextual anomaly KW - psychiatric disorders N2 - Background: Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. Objective: This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. Methods: We defined the term adherence volatility as ?the degree to which medication ingestion behavior fits expected behavior based on historically observed data? and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient?s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. Results: Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period?this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. Conclusions: Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area. UR - https://mental.jmir.org/2020/9/e21378 UR - http://dx.doi.org/10.2196/21378 UR - http://www.ncbi.nlm.nih.gov/pubmed/32909950 ID - info:doi/10.2196/21378 ER - TY - JOUR AU - van Kraaij, Jacobus Alex Wilhelmus AU - Schiavone, Giuseppina AU - Lutin, Erika AU - Claes, Stephan AU - Van Hoof, Chris PY - 2020/9/9 TI - Relationship Between Chronic Stress and Heart Rate Over Time Modulated by Gender in a Cohort of Office Workers: Cross-Sectional Study Using Wearable Technologies JO - J Med Internet Res SP - e18253 VL - 22 IS - 9 KW - chronic stress KW - heart rate KW - circadian rhythm KW - gender KW - age KW - wearable device N2 - Background: Chronic stress is increasing in prevalence and is associated with several physical and mental disorders. Although it is proven that acute stress changes physiology, much less is known about the relationship between physiology and long-term stress. Continuous measurement of vital signs in daily life and chronic stress detection algorithms could serve this purpose. For this, it is paramount to model the effects of chronic stress on human physiology and include other cofounders, such as demographics, enabling the enrichment of a population-wide approach with individual variations. Objective: The main objectives of this study were to investigate the effect of chronic stress on heart rate (HR) over time while correcting for weekdays versus weekends and to test a possible modulation effect by gender and age in a healthy cohort. Methods: Throughout 2016 and 2017, healthy employees of technology companies were asked to participate in a 5-day observation stress study. They were required to wear two wearables, of which one included an electrocardiogram sensor. The derived HR was averaged per hour and served as an output for a mixed design model including a trigonometric fit over time with four harmonics (periods of 24, 12, 8, and 6 hours), gender, age, whether it was a workday or weekend day, and a chronic stress score derived from the Perceived Stress Scale (PSS) as predictors. Results: The study included 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (?21=24.02, P<.001); the hour of the day (?21=73.22, P<.001); the circadian harmonic (?22=284.4, P<.001); and the harmonic over 12 hours (?22=242.1, P<.001), over 8 hours (?22=23.78, P<.001), and over 6 hours (?22=82.96, P<.001) had a significant effect on HR. Two three-way interaction effects were found. The interaction of age, whether it was a workday or weekend day, and the circadian harmonic over time were significantly correlated with HR (?22=7.13, P=.03), as well as the interaction of gender, PSS score, and the circadian harmonic over time (?22=7.59, P=.02). Conclusions: The results show a relationship between HR and the three-way interaction of chronic stress, gender, and the circadian harmonic. The modulation by gender might be related to evolution-based energy utilization strategies, as suggested in related literature studies. More research, including daily cortisol assessment, longer recordings, and a wider population, should be performed to confirm this interpretation. This would enable the development of more complete and personalized models of chronic stress. UR - http://www.jmir.org/2020/9/e18253/ UR - http://dx.doi.org/10.2196/18253 UR - http://www.ncbi.nlm.nih.gov/pubmed/32902392 ID - info:doi/10.2196/18253 ER - TY - JOUR AU - ter Stal, Silke AU - Broekhuis, Marijke AU - van Velsen, Lex AU - Hermens, Hermie AU - Tabak, Monique PY - 2020/9/4 TI - Embodied Conversational Agent Appearance for Health Assessment of Older Adults: Explorative Study JO - JMIR Hum Factors SP - e19987 VL - 7 IS - 3 KW - embodied conversational agent KW - appearance design KW - health status assessment KW - older adults KW - eHealth N2 - Background: Embodied conversational agents (ECAs) have great potential for health apps but are rarely investigated as part of such apps. To promote the uptake of health apps, we need to understand how the design of ECAs can influence the preferences, motivation, and behavior of users. Objective: This is one of the first studies that investigates how the appearance of an ECA implemented within a health app affects users? likeliness of following agent advice, their perception of agent characteristics, and their feeling of rapport. In addition, we assessed usability and intention to use. Methods: The ECA was implemented within a frailty assessment app in which three health questionnaires were translated into agent dialogues. In a within-subject experiment, questionnaire dialogues were randomly offered by a young female agent or an older male agent. Participants were asked to think aloud during interaction. Afterward, they rated the likeliness of following the agent?s advice, agent characteristics, rapport, usability, and intention to use and participated in a semistructured interview. Results: A total of 20 older adults (72.2 [SD 3.5] years) participated. The older male agent was perceived as more authoritative than the young female agent (P=.03), but no other differences were found. The app scored high on usability (median 6.1) and intention to use (median 6.0). Participants indicated they did not see an added value of the agent to the health app. Conclusions: Agent age and gender little influence users? impressions after short interaction but remain important at first glance to lower the threshold to interact with the agent. Thus, it is important to take the design of ECAs into account when implementing them into health apps. UR - https://humanfactors.jmir.org/2020/3/e19987 UR - http://dx.doi.org/10.2196/19987 UR - http://www.ncbi.nlm.nih.gov/pubmed/32886068 ID - info:doi/10.2196/19987 ER - TY - JOUR AU - Hall, William Eric AU - Luisi, Nicole AU - Zlotorzynska, Maria AU - Wilde, Gretchen AU - Sullivan, Patrick AU - Sanchez, Travis AU - Bradley, Heather AU - Siegler, J. Aaron PY - 2020/9/3 TI - Willingness to Use Home Collection Methods to Provide Specimens for SARS-CoV-2/COVID-19 Research: Survey Study JO - J Med Internet Res SP - e19471 VL - 22 IS - 9 KW - COVID-19 KW - SARS-CoV-2 KW - specimen collection KW - survey KW - research KW - public health KW - infectious disease KW - virus KW - test N2 - Background: Innovative laboratory testing approaches for SARS-CoV-2 infection and immune response are needed to conduct research to establish estimates of prevalence and incidence. Self-specimen collection methods have been successfully used in HIV and sexually transmitted infection research and can provide a feasible opportunity to scale up SARS-CoV-2 testing for research purposes. Objective: The aim of this study was to assess the willingness of adults to use different specimen collection modalities for themselves and children as part of a COVID-19 research study. Methods: Between March 27 and April 1, 2020, we recruited 1435 adults aged 18 years or older though social media advertisements. Participants completed a survey that included 5-point Likert scale items stating how willing they were to use the following specimen collection testing modalities as part of a research study: home collection of a saliva sample, home collection of a throat swab, home finger-prick blood collection, drive-through site throat swab, clinic throat swab, and clinic blood collection. Additionally, participants indicated how the availability of home-based collection methods would impact their willingness to participate compared to drive-through and clinic-based specimen collection. We used Kruskal-Wallis tests and Spearman rank correlations to assess if willingness to use each testing modality differed by demographic variables and characteristics of interest. We compared the overall willingness to use each testing modality and estimated effect sizes with Cohen d. Results: We analyzed responses from 1435 participants with a median age of 40.0 (SD=18.2) years and over half of which were female (761/1435, 53.0%). Most participants agreed or strongly agreed that they would be willing to use specimens self-collected at home to participate in research, including willingness to collect a saliva sample (1259/1435, 87.7%) or a throat swab (1191/1435, 83.1%). Willingness to collect a throat swab sample was lower in both a drive-through setting (64%) and clinic setting (53%). Overall, 69.0% (990/1435) of participants said they would be more likely to participate in a research study if they could provide a saliva sample or throat swab at home compared to going to a drive-through site; only 4.4% (63/1435) of participants said they would be less likely to participate using self-collected samples. For each specimen collection modality, willingness to collect specimens from children for research was lower than willingness to use on oneself, but the ranked order of modalities was similar. Conclusions: Most participants were willing to participate in a COVID-19 research study that involves laboratory testing; however, there was a strong preference for home specimen collection procedures over drive-through or clinic-based testing. To increase participation and minimize bias, epidemiologic research studies of SARS-CoV-2 infection and immune response should consider home specimen collection methods. UR - https://www.jmir.org/2020/9/e19471 UR - http://dx.doi.org/10.2196/19471 UR - http://www.ncbi.nlm.nih.gov/pubmed/32790639 ID - info:doi/10.2196/19471 ER - TY - JOUR AU - Kim, Ben AU - McKay, M. Sandra AU - Lee, Joon PY - 2020/9/3 TI - Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study JO - J Med Internet Res SP - e19732 VL - 22 IS - 9 KW - frailty KW - mobile health KW - wearables KW - physical activity KW - home care KW - prediction KW - predictive modeling, older adults KW - activities of daily living, sleep N2 - Background: Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population. Objective: The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods: From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty. Results: A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions: We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services. UR - https://www.jmir.org/2020/9/e19732 UR - http://dx.doi.org/10.2196/19732 UR - http://www.ncbi.nlm.nih.gov/pubmed/32880582 ID - info:doi/10.2196/19732 ER - TY - JOUR AU - Birnbaum, Leo Michael AU - Kulkarni, "Param" Prathamesh AU - Van Meter, Anna AU - Chen, Victor AU - Rizvi, F. Asra AU - Arenare, Elizabeth AU - De Choudhury, Munmun AU - Kane, M. John PY - 2020/9/1 TI - Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study JO - JMIR Ment Health SP - e19348 VL - 7 IS - 9 KW - schizophrenia spectrum disorders KW - internet search activity KW - Google KW - diagnostic prediction KW - relapse prediction KW - machine learning KW - digital data KW - digital phenotyping KW - digital biomarkers N2 - Background: Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. Objective: We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. Methods: We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. Results: Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. Conclusions: Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring. UR - https://mental.jmir.org/2020/9/e19348 UR - http://dx.doi.org/10.2196/19348 UR - http://www.ncbi.nlm.nih.gov/pubmed/32870161 ID - info:doi/10.2196/19348 ER - TY - JOUR AU - Blair, K. Cindy AU - Harding, Elizabeth AU - Herman, Carla AU - Boyce, Tawny AU - Demark-Wahnefried, Wendy AU - Davis, Sally AU - Kinney, Y. Anita AU - Pankratz, S. Vernon PY - 2020/9/1 TI - Remote Assessment of Functional Mobility and Strength in Older Cancer Survivors: Protocol for a Validity and Reliability Study JO - JMIR Res Protoc SP - e20834 VL - 9 IS - 9 KW - physical function KW - physical performance KW - older adults KW - remote assessment KW - videoconferencing KW - cancer survivors KW - cancer KW - elderly KW - physical activity KW - telehealth N2 - Background: Older cancer survivors, faced with both age- and treatment-related morbidity, are at increased and premature risk for physical function limitations. Physical performance is an important predictor of disability, quality of life, and premature mortality, and thus is considered an important target of interventions designed to prevent, delay, or attenuate the physical functional decline. Currently, low-cost, valid, and reliable methods to remotely assess physical performance tests that are self-administered by older adults in the home-setting do not exist, thus limiting the reach, scalability, and dissemination of interventions. Objective: This paper will describe the rationale and design for a study to evaluate the accuracy, reliability, safety, and acceptability of videoconferencing and self-administered tests of functional mobility and strength by older cancer survivors in their own homes. Methods: To enable remote assessment, participants receive a toolkit and instructions for setting up their test course and communicating with the investigator. Two standard gerontologic performance tests are being evaluated: the Timed Up and Go test and the 30-second chair stand test. Phase 1 of the study evaluates proof-of-concept that older cancer survivors (age ?60 years) can follow the testing protocol and use a tablet PC to communicate with the study investigator. Phase 2 evaluates the criterion validity of videoconference compared to direct observation of the two physical performance tests. Phase 3 evaluates reliability by enrolling 5-10 participants who agree to repeat the remote assessment (without direct observation). Phase 4 enrolls 5-10 new study participants to complete the remote assessment test protocol. Feedback from participants in each phase is used to refine the test protocol and instructions. Results: Enrollment began in December 2019. Ten participants completed the Phase 1 proof-of-concept. The study was paused in mid-March 2020 due to the COVID-19 pandemic. The study is expected to be completed by the end of 2020. Conclusions: This validity and reliability study will provide important information on the acceptability and safety of using videoconferencing to remotely assess two tests of functional mobility and strength, self-administered by older adults in their homes. Videoconferencing has the potential to expand the reach, scalability, and dissemination of interventions to older cancer survivors, and potentially other older adults, especially in rural areas. Trial Registration: ClinicalTrials.gov NCT04339959; https://clinicaltrials.gov/ct2/show/NCT04339959 International Registered Report Identifier (IRRID): DERR1-10.2196/20834 UR - https://www.researchprotocols.org/2020/9/e20834 UR - http://dx.doi.org/10.2196/20834 UR - http://www.ncbi.nlm.nih.gov/pubmed/32769075 ID - info:doi/10.2196/20834 ER - TY - JOUR AU - Adler, A. Daniel AU - Ben-Zeev, Dror AU - Tseng, W-S Vincent AU - Kane, M. John AU - Brian, Rachel AU - Campbell, T. Andrew AU - Hauser, Marta AU - Scherer, A. Emily AU - Choudhury, Tanzeem PY - 2020/8/31 TI - Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks JO - JMIR Mhealth Uhealth SP - e19962 VL - 8 IS - 8 KW - psychotic disorders KW - schizophrenia KW - mHealth KW - mental health KW - mobile health KW - smartphone applications KW - machine learning KW - passive sensing KW - digital biomarkers KW - digital phenotyping KW - artificial intelligence KW - deep learning KW - mobile phone N2 - Background: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient?s condition worsens. Objective: In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse. Methods: Data used to train and test the models were collected during the CrossCheck study. Hourly features derived from smartphone passive sensing data were extracted from 60 patients with SSDs (42 nonrelapse and 18 relapse >1 time throughout the study) and used to train models and test performance. We trained 2 types of encoder-decoder neural network models and a clustering-based local outlier factor model to predict behavioral anomalies that occurred within the 30-day period before a participant's date of relapse (the near relapse period). Models were trained to recreate participant behavior on days of relative health (DRH, outside of the near relapse period), following which a threshold to the recreation error was applied to predict anomalies. The neural network model architecture and the percentage of relapse participant data used to train all models were varied. Results: A total of 20,137 days of collected data were analyzed, with 726 days of data (0.037%) within any 30-day near relapse period. The best performing model used a fully connected neural network autoencoder architecture and achieved a median sensitivity of 0.25 (IQR 0.15-1.00) and specificity of 0.88 (IQR 0.14-0.96; a median 108% increase in behavioral anomalies near relapse). We conducted a post hoc analysis using the best performing model to identify behavioral features that had a medium-to-large effect (Cohen d>0.5) in distinguishing anomalies near relapse from DRH among 4 participants who relapsed multiple times throughout the study. Qualitative validation using clinical notes collected during the original CrossCheck study showed that the identified features from our analysis were presented to clinicians during relapse events. Conclusions: Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs. UR - https://mhealth.jmir.org/2020/8/e19962 UR - http://dx.doi.org/10.2196/19962 UR - http://www.ncbi.nlm.nih.gov/pubmed/32865506 ID - info:doi/10.2196/19962 ER - TY - JOUR AU - Chang, Ernest Shuchih AU - Chen, YiChian PY - 2020/8/31 TI - Blockchain in Health Care Innovation: Literature Review and Case Study From a Business Ecosystem Perspective JO - J Med Internet Res SP - e19480 VL - 22 IS - 8 KW - blockchain KW - health care industry KW - business ecosystem KW - smart contract KW - paradigm shift N2 - Background: Blockchain technology is leveraging its innovative potential in various sectors and its transformation of business-related processes has drawn much attention. Topics of research interest have focused on medical and health care applications, while research implications have generally concluded in system design, literature reviews, and case studies. However, a general overview and knowledge about the impact on the health care ecosystem is limited. Objective: This paper explores a potential paradigm shift and ecosystem evolution in health care utilizing blockchain technology. Methods: A literature review with a case study on a pioneering initiative was conducted. With a systematic life cycle analysis, this study sheds light on the evolutionary development of blockchain in health care scenarios and its interactive relationship among stakeholders. Results: Four stages?birth, expansion, leadership, and self-renewal or death?in the life cycle of the business ecosystem were explored to elucidate the evolving trajectories of blockchain-based health care implementation. Focused impacts on the traditional health care industry are highlighted within each stage to further support the potential health care paradigm shift in the future. Conclusions: This paper enriches the existing body of literature in this field by illustrating the potential of blockchain in fulfilling stakeholders? needs and elucidating the phenomenon of coevolution within the health care ecosystem. Blockchain not only catalyzes the interactions among players but also facilitates the formation of the ecosystem life cycle. The collaborative network linked by blockchain may play a critical role on value creation, transfer, and sharing among the health care community. Future efforts may focus on empirical or case studies to validate the proposed evolution of the health care ecosystem. UR - http://www.jmir.org/2020/8/e19480/ UR - http://dx.doi.org/10.2196/19480 UR - http://www.ncbi.nlm.nih.gov/pubmed/32865501 ID - info:doi/10.2196/19480 ER - TY - JOUR AU - Jurkeviciute, Monika AU - Eriksson, Henrik PY - 2020/8/28 TI - Exploring the Use of Evidence From the Development and Evaluation of an Electronic Health (eHealth) Trial: Case Study JO - J Med Internet Res SP - e17718 VL - 22 IS - 8 KW - evidence-based practice KW - evidence use KW - eHealth KW - evaluation KW - evaluation use N2 - Background: Evidence-based practice refers to building clinical decisions on credible research evidence, professional experience, and patient preferences. However, there is a growing concern that evidence in the context of electronic health (eHealth) is not sufficiently used when forming policies and practice of health care. In this context, using evaluation and research evidence in clinical or policy decisions dominates the discourse. However, the use of additional types of evidence, such as professional experience, is underexplored. Moreover, there might be other ways of using evidence than in clinical or policy decisions. Objective: This study aimed to analyze how different types of evidence (such as evaluation outcomes [including patient preferences], professional experiences, and existing scientific evidence from other research) obtained within the development and evaluation of an eHealth trial are used by diverse stakeholders. An additional aim was to identify barriers to the use of evidence and ways to support its use. Methods: This study was built on a case of an eHealth trial funded by the European Union. The project included 4 care centers, 2 research and development companies that provided the web-based physical exercise program and an activity monitoring device, and 2 science institutions. The qualitative data collection included 9 semistructured interviews conducted 8 months after the evaluation was concluded. The data analysis concerned (1) activities and decisions that were made based on evidence after the project ended, (2) evidence used for those activities and decisions, (3) in what way the evidence was used, and (4) barriers to the use of evidence. Results: Evidence generated from eHealth trials can be used by various stakeholders for decisions regarding clinical integration of eHealth solutions, policy making, scientific publishing, research funding applications, eHealth technology, and teaching. Evaluation evidence has less value than professional experiences to local decision making regarding eHealth integration into clinical practice. Professional experiences constitute the evidence that is valuable to the highest variety of activities and decisions in relation to eHealth trials. When using existing scientific evidence related to eHealth trials, it is important to consider contextual relevance, such as location or disease. To support the use of evidence, it is suggested to create possibilities for health care professionals to gain experience, assess a few rather than a large number of variables, and design for shorter iterative cycles of evaluation. Conclusions: Initiatives to support and standardize evidence-based practice in the context of eHealth should consider the complexities in how the evidence is used in order to achieve better uptake of evidence in practice. However, one should be aware that the assumption of fact-based decision making in organizations is misleading. In order to create better chances that the evidence produced would be used, this should be addressed through the design of eHealth trials. UR - http://www.jmir.org/2020/8/e17718/ UR - http://dx.doi.org/10.2196/17718 UR - http://www.ncbi.nlm.nih.gov/pubmed/32857057 ID - info:doi/10.2196/17718 ER - TY - JOUR AU - Eke, Ransome AU - Li, Tong AU - Bond, Kiersten AU - Ho, Arlene AU - Graves, Lisa PY - 2020/8/24 TI - Viewing Trends and Users? Perceptions of the Effect of Sleep-Aiding Music on YouTube: Quantification and Thematic Content Analysis JO - J Med Internet Res SP - e15697 VL - 22 IS - 8 KW - insomnia KW - sleep deprivation KW - YouTube KW - utilization KW - pattern KW - perception KW - content analysis N2 - Background: Sleep plays an essential role in the psychological and physiological functioning of humans. A report from the Centers for Disease Control and Prevention (CDC) found that sleep duration was significantly reduced among US adults in 2012 compared to 1985. Studies have described a significant association between listening to soothing music and an improvement in sleep quality and sleep duration. YouTube is a platform where users can access sleep-aiding music videos. No literature exists pertaining to the use of sleep-aiding music on YouTube. Objective: This study aimed to examine the patterns of viewing sleep-aiding music videos on YouTube. We also performed a content analysis of the comments left on sleep-aiding music video posts, to describe the perception of users regarding the effects of these music videos on their sleep quality. Methods: We searched for sleep-aiding music videos published on YouTube between January 1, 2012, and December 31, 2017. We sorted videos by view number (highest to lowest) and used a targeted sampling approach to select eligible videos for qualitative content analysis. To perform the content analysis, we imported comments into a mixed-method analytical software. We summarized variables including total views, likes, dislikes, play duration, and age of published music videos. All descriptive statistics were completed with SAS statistical software. Results: We found a total of 238 sleep-aiding music videos on YouTube that met the inclusion criteria. The total view count was 1,467,747,018 and the total playtime was 84,252 minutes. The median play length was 186 minutes (IQR 122 to 480 minutes) and the like to dislike ratio was approximately 9 to 1. In total, 135 (56.7%) videos had over 1 million views, and 124 (52.1%) of the published sleep-aiding music videos had stayed active for 1 to 2 years. Overall, 4023 comments were extracted from 20 selected sleep-aiding music videos. Five overarching themes emerged in the reviewed comments, including viewers experiencing a sleep problem, perspective on the positive impact of the sleep-aiding music videos, no effect of the sleep-aiding music videos, time to initiation of sleep or sleep duration, and location of viewers. The overall ? statistic for the codes was 0.87 (range 0.85-0.96). Conclusions: This is the first study to examine the patterns of viewing sleep-aiding music videos on YouTube. We observed a substantial increase in the number of people using sleep-aiding music videos, with a wide variation in viewer location. This study supports the hypothesis that listening to soothing music has a positive impact on sleep habits. UR - http://www.jmir.org/2020/8/e15697/ UR - http://dx.doi.org/10.2196/15697 UR - http://www.ncbi.nlm.nih.gov/pubmed/32831182 ID - info:doi/10.2196/15697 ER - TY - JOUR AU - Grima-Murcia, D. M. AU - Sanchez-Ferrer, Francisco AU - Ramos-Rincón, Manuel Jose AU - Fernández, Eduardo PY - 2020/8/21 TI - Use of Eye-Tracking Technology by Medical Students Taking the Objective Structured Clinical Examination: Descriptive Study JO - J Med Internet Res SP - e17719 VL - 22 IS - 8 KW - visual perception KW - medical education KW - eye tracking KW - objective structured clinical examination KW - medical evaluation N2 - Background: The objective structured clinical examination (OSCE) is a test used throughout Spain to evaluate the clinical competencies, decision making, problem solving, and other skills of sixth-year medical students. Objective: The main goal of this study is to explore the possible applications and utility of portable eye-tracking systems in the setting of the OSCE, particularly questions associated with attention and engagement. Methods: We used a portable Tobii Glasses 2 eye tracker, which allows real-time monitoring of where the students were looking and records the voice and ambient sounds. We then performed a qualitative and a quantitative analysis of the fields of vision and gaze points attracting attention as well as the visual itinerary. Results: Eye-tracking technology was used in the OSCE with no major issues. This portable system was of the greatest value in the patient simulators and mannequin stations, where interaction with the simulated patient or areas of interest in the mannequin can be quantified. This technology proved useful to better identify the areas of interest in the medical images provided. Conclusions: Portable eye trackers offer the opportunity to improve the objective evaluation of candidates and the self-evaluation of the stations used as well as medical simulations by examiners. We suggest that this technology has enough resolution to identify where a student is looking at and could be useful for developing new approaches for evaluating specific aspects of clinical competencies. UR - http://www.jmir.org/2020/8/e17719/ UR - http://dx.doi.org/10.2196/17719 UR - http://www.ncbi.nlm.nih.gov/pubmed/32821060 ID - info:doi/10.2196/17719 ER - TY - JOUR AU - Liu, Dianbo AU - Clemente, Leonardo AU - Poirier, Canelle AU - Ding, Xiyu AU - Chinazzi, Matteo AU - Davis, Jessica AU - Vespignani, Alessandro AU - Santillana, Mauricio PY - 2020/8/17 TI - Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models JO - J Med Internet Res SP - e20285 VL - 22 IS - 8 KW - COVID-19 KW - coronavirus KW - digital epidemiology KW - modeling KW - modeling disease outbreaks KW - emerging outbreak KW - machine learning KW - precision public health KW - machine learning in public health KW - forecasting KW - digital data KW - mechanistic model KW - hybrid simulation KW - hybrid model KW - simulation N2 - Background: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. Objective: We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Methods: Our method uses the following as inputs: (a) official health reports, (b) COVID-19?related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Results: Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Conclusions: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention. UR - http://www.jmir.org/2020/8/e20285/ UR - http://dx.doi.org/10.2196/20285 UR - http://www.ncbi.nlm.nih.gov/pubmed/32730217 ID - info:doi/10.2196/20285 ER - TY - JOUR AU - Martin, Guy AU - Koizia, Louis AU - Kooner, Angad AU - Cafferkey, John AU - Ross, Clare AU - Purkayastha, Sanjay AU - Sivananthan, Arun AU - Tanna, Anisha AU - Pratt, Philip AU - Kinross, James AU - PY - 2020/8/14 TI - Use of the HoloLens2 Mixed Reality Headset for Protecting Health Care Workers During the COVID-19 Pandemic: Prospective, Observational Evaluation JO - J Med Internet Res SP - e21486 VL - 22 IS - 8 KW - COVID-19 KW - mixed reality KW - telemedicine KW - protection KW - acceptability KW - feasibility KW - impact KW - headset KW - virtual reality KW - augmented reality KW - pilot N2 - Background: The coronavirus disease (COVID-19) pandemic has led to rapid acceleration in the deployment of new digital technologies to improve both accessibility to and quality of care, and to protect staff. Mixed-reality (MR) technology is the latest iteration of telemedicine innovation; it is a logical next step in the move toward the provision of digitally supported clinical care and medical education. This technology has the potential to revolutionize care both during and after the COVID-19 pandemic. Objective: This pilot project sought to deploy the HoloLens2 MR device to support the delivery of remote care in COVID-19 hospital environments. Methods: A prospective, observational, nested cohort evaluation of the HoloLens2 was undertaken across three distinct clinical clusters in a teaching hospital in the United Kingdom. Data pertaining to staff exposure to high-risk COVID-19 environments and personal protective equipment (PPE) use by clinical staff (N=28) were collected, and assessments of acceptability and feasibility were conducted. Results: The deployment of the HoloLens2 led to a 51.5% reduction in time exposed to harm for staff looking after COVID-19 patients (3.32 vs 1.63 hours/day/staff member; P=.002), and an 83.1% reduction in the amount of PPE used (178 vs 30 items/round/day; P=.02). This represents 222.98 hours of reduced staff exposure to COVID-19, and 3100 fewer PPE items used each week across the three clusters evaluated. The majority of staff using the device agreed it was easy to set up and comfortable to wear, improved the quality of care and decision making, and led to better teamwork and communication. In total, 89.3% (25/28) of users felt that their clinical team was safer when using the HoloLens2. Conclusions: New technologies have a role in minimizing exposure to nosocomial infection, optimizing the use of PPE, and enhancing aspects of care. Deploying such technologies at pace requires context-specific information security, infection control, user experience, and workflow integration to be addressed at the outset and led by clinical end-users. The deployment of new telemedicine technology must be supported with objective evidence for its safety and effectiveness to ensure maximum impact. UR - http://www.jmir.org/2020/8/e21486/ UR - http://dx.doi.org/10.2196/21486 UR - http://www.ncbi.nlm.nih.gov/pubmed/32730222 ID - info:doi/10.2196/21486 ER - TY - JOUR AU - Thiabaud, Amaury AU - Triulzi, Isotta AU - Orel, Erol AU - Tal, Kali AU - Keiser, Olivia PY - 2020/8/14 TI - Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review JO - J Med Internet Res SP - e18747 VL - 22 IS - 8 KW - HIV/AIDS KW - topic modelling KW - text mining KW - Malawi KW - risk factors KW - machine learning N2 - Background: Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention. Objective: To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process. Methods: Due to the choice of broad search terms (?HIV AND Malawi?), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest. Results: Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes. Conclusions: Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a ?seed dataset? of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries. UR - http://www.jmir.org/2020/8/e18747/ UR - http://dx.doi.org/10.2196/18747 UR - http://www.ncbi.nlm.nih.gov/pubmed/32795992 ID - info:doi/10.2196/18747 ER - TY - JOUR AU - Sung, MinDong AU - Park, SungJun AU - Jung, Sungjae AU - Lee, Eunsol AU - Lee, Jaehoon AU - Park, Rang Yu PY - 2020/8/14 TI - Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study JO - J Med Internet Res SP - e19657 VL - 22 IS - 8 KW - blockchain KW - monitoring app KW - clinical documents N2 - Background: Although we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent. Objective: Using blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents. We then examined whether patients can effectively verify the monitoring of their primary care clinical medical records in a system based on blockchain technology. Methods: We enrolled participants who visited two primary care clinics in Korea. Three substudies were performed: (1) a survey of the recognition of blockchain medical records changes and the digital literacy of participants; (2) an observational study on participants using the blockchain-based mobile alert app; and (3) a usability survey study. The participants? medical documents were profiled with HL7 Fast Healthcare Interoperability Resources, hashed, and transacted to the blockchain. The app checked the changes in the documents by querying the blockchain. Results: A total of 70 participants were enrolled in this study. Considering their recognition of changes to their medical records, participants tended to not allow these changes. Participants also generally expressed a desire for a medical record monitoring system. Concerning digital literacy, most questions were answered with ?good,? indicating fair digital literacy. In the second survey, only 44 participants?those who logged into the app more than once and used the app for more than 28 days?were included in the analysis to determine whether they exhibited usage patterns. The app was accessed a mean of 5.1 (SD 2.6) times for 33.6 (SD 10.0) days. The mean System Usability Scale score was 63.21 (SD 25.06), which indicated satisfactory usability. Conclusions: Patients showed great interest in a blockchain-based system to monitor changes in their medical records. The blockchain system is useful for informing patients of changes in their records via the app without uploading the medical record itself to the network. This ensures the transparency of medical records as well as patient empowerment. UR - http://www.jmir.org/2020/8/e19657/ UR - http://dx.doi.org/10.2196/19657 UR - http://www.ncbi.nlm.nih.gov/pubmed/32795988 ID - info:doi/10.2196/19657 ER - TY - JOUR AU - Min, Kyoung-Bok AU - Song, Sung-Hee AU - Min, Jin-Young PY - 2020/8/13 TI - Topic Modeling of Social Networking Service Data on Occupational Accidents in Korea: Latent Dirichlet Allocation Analysis JO - J Med Internet Res SP - e19222 VL - 22 IS - 8 KW - topic modeling KW - occupational accident KW - social media KW - knowledge KW - workplace KW - accident KW - model KW - analysis KW - safety N2 - Background: In most industrialized societies, regulations, inspections, insurance, and legal options are established to support workers who suffer injury, disease, or death in relation to their work; in practice, these resources are imperfect or even unavailable due to workplace or employer obstruction. Thus, limitations exist to identify unmet needs in occupational safety and health information. Objective: The aim of this study was to explore hidden issues related to occupational accidents by examining social network services (SNS) data using topic modeling. Methods: Based on the results of a Google search for the phrases occupational accident, industrial accident and occupational diseases, a total of 145 websites were selected. From among these websites, we collected 15,244 documents on queries related to occupational accidents between 2002 and 2018. To transform unstructured text into structure data, natural language processing of the Korean language was conducted. We performed the latent Dirichlet allocation (LDA) as a topic model using a Python library. A time-series linear regression analysis was also conducted to identify yearly trends for the given documents. Results: The results of the LDA model showed 14 topics with 3 themes: workers? compensation benefits (Theme 1), illicit agreements with the employer (Theme 2), and fatal and non-fatal injuries and vulnerable workers (Theme 3). Theme 1 represented the largest cluster (52.2%) of the collected documents and included keywords related to workers? compensation (ie, company, occupational injury, insurance, accident, approval, and compensation) and keywords describing specific compensation benefits such as medical expense benefits, temporary incapacity benefits, and disability benefits. In the yearly trend, Theme 1 gradually decreased; however, other themes showed an overall increasing pattern. Certain queries (ie, musculoskeletal system, critical care, and foreign workers) showed no significant variation in the number of queries. Conclusions: We conducted LDA analysis of SNS data of occupational accident?related queries and discovered that the primary concerns of workers posting about occupational injuries and diseases were workers? compensation benefits, fatal and non-fatal injuries, vulnerable workers, and illicit agreements with employers. While traditional systems focus mainly on quantitative monitoring of occupational accidents, qualitative aspects formulated by topic modeling from unstructured SNS queries may be valuable to address inequalities and improve occupational health and safety. UR - http://www.jmir.org/2020/8/e19222/ UR - http://dx.doi.org/10.2196/19222 UR - http://www.ncbi.nlm.nih.gov/pubmed/32663156 ID - info:doi/10.2196/19222 ER - TY - JOUR AU - Kim, Woon Ko AU - Lee, Yun Sung AU - Choi, Jongdoo AU - Chin, Juhee AU - Lee, Hwa Byung AU - Na, L. Duk AU - Choi, Hyun Jee PY - 2020/8/12 TI - A Comprehensive Evaluation of the Process of Copying a Complex Figure in Early- and Late-Onset Alzheimer Disease: A Quantitative Analysis of Digital Pen Data JO - J Med Internet Res SP - e18136 VL - 22 IS - 8 KW - alzheimer disease KW - Rey-Osterrieth Complex Figure KW - digital biomarkers KW - copying process N2 - Background: The Rey-Osterrieth Complex Figure Test (RCFT) is a neuropsychological test that is widely used to assess visual memory and visuoconstructional deficits in patients with cognitive impairment, including Alzheimer disease (AD). Patients with AD have an increased tendency for exhibiting extraordinary behaviors in the RCFT for selecting the drawing area, organizing the figure, and deciding the order of images, among other activities. However, the conventional scoring system based on pen and paper has a limited ability to reflect these detailed behaviors. Objective: This study aims to establish a scoring system that addresses not only the spatial arrangement of the finished drawing but also the drawing process of patients with AD by using digital pen data. Methods: A digital pen and tablet were used to copy complex figures. The stroke patterns and kinetics of normal controls (NCs) and patients with early-onset AD (EOAD) and late-onset AD (LOAD) were analyzed by comparing the pen tip trajectory, spatial arrangement, and similarity of the finished drawings. Results: Patients with AD copied the figure in a more fragmented way with a longer pause than NCs (EOAD: P=.045; LOAD: P=.01). Patients with AD showed an increased tendency to draw the figures closer toward the target image in comparison with the NCs (EOAD: P=.005; LOAD: P=.01) Patients with AD showed the lower accuracy than NCs (EOAD: P=.004; LOAD: P=.002). Patients with EOAD and LOAD showed similar but slightly different drawing behaviors, especially in space use and in the initial stage of drawing. Conclusions: The digitalized complex figure test evaluated copying performance quantitatively and further elucidated the patients? ongoing process during copying. We believe that this novel approach can be used as a digital biomarker of AD. In addition, the repeatability of the test will delineate the process of executive functions and constructional organization abilities with disease progression. UR - https://www.jmir.org/2020/8/e18136 UR - http://dx.doi.org/10.2196/18136 UR - http://www.ncbi.nlm.nih.gov/pubmed/32491988 ID - info:doi/10.2196/18136 ER - TY - JOUR AU - Bonten, N. Tobias AU - Rauwerdink, Anneloek AU - Wyatt, C. Jeremy AU - Kasteleyn, J. Marise AU - Witkamp, Leonard AU - Riper, Heleen AU - van Gemert-Pijnen, JEWC Lisette AU - Cresswell, Kathrin AU - Sheikh, Aziz AU - Schijven, P. Marlies AU - Chavannes, H. Niels AU - PY - 2020/8/12 TI - Online Guide for Electronic Health Evaluation Approaches: Systematic Scoping Review and Concept Mapping Study JO - J Med Internet Res SP - e17774 VL - 22 IS - 8 KW - eHealth KW - mHealth KW - digital health KW - methodology KW - study design KW - health technology assessment KW - evaluation KW - scoping review KW - concept mapping N2 - Background: Despite the increase in use and high expectations of digital health solutions, scientific evidence about the effectiveness of electronic health (eHealth) and other aspects such as usability and accuracy is lagging behind. eHealth solutions are complex interventions, which require a wide array of evaluation approaches that are capable of answering the many different questions that arise during the consecutive study phases of eHealth development and implementation. However, evaluators seem to struggle in choosing suitable evaluation approaches in relation to a specific study phase. Objective: The objective of this project was to provide a structured overview of the existing eHealth evaluation approaches, with the aim of assisting eHealth evaluators in selecting a suitable approach for evaluating their eHealth solution at a specific evaluation study phase. Methods: Three consecutive steps were followed. Step 1 was a systematic scoping review, summarizing existing eHealth evaluation approaches. Step 2 was a concept mapping study asking eHealth researchers about approaches for evaluating eHealth. In step 3, the results of step 1 and 2 were used to develop an ?eHealth evaluation cycle? and subsequently compose the online ?eHealth methodology guide.? Results: The scoping review yielded 57 articles describing 50 unique evaluation approaches. The concept mapping study questioned 43 eHealth researchers, resulting in 48 unique approaches. After removing duplicates, 75 unique evaluation approaches remained. Thereafter, an ?eHealth evaluation cycle? was developed, consisting of six evaluation study phases: conceptual and planning, design, development and usability, pilot (feasibility), effectiveness (impact), uptake (implementation), and all phases. Finally, the ?eHealth methodology guide? was composed by assigning the 75 evaluation approaches to the specific study phases of the ?eHealth evaluation cycle.? Conclusions: Seventy-five unique evaluation approaches were found in the literature and suggested by eHealth researchers, which served as content for the online ?eHealth methodology guide.? By assisting evaluators in selecting a suitable evaluation approach in relation to a specific study phase of the ?eHealth evaluation cycle,? the guide aims to enhance the quality, safety, and successful long-term implementation of novel eHealth solutions. UR - https://www.jmir.org/2020/8/e17774 UR - http://dx.doi.org/10.2196/17774 UR - http://www.ncbi.nlm.nih.gov/pubmed/32784173 ID - info:doi/10.2196/17774 ER - TY - JOUR AU - Challet-Bouju, Gaëlle AU - Hardouin, Jean-Benoit AU - Thiabaud, Elsa AU - Saillard, Anaïs AU - Donnio, Yann AU - Grall-Bronnec, Marie AU - Perrot, Bastien PY - 2020/8/12 TI - Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis JO - J Med Internet Res SP - e17675 VL - 22 IS - 8 KW - gambling KW - internet KW - trajectory KW - latent class analysis KW - growth mixture modeling KW - gambling tracking data KW - early detection N2 - Background: Individuals who gamble online may be at risk of gambling excessively, but internet gambling also provides a unique opportunity to monitor gambling behavior in real environments which may allow intervention for those who encounter difficulties. Objective: The objective of this study was to model the early gambling trajectories of individuals who play online lottery. Methods: Anonymized gambling?related records of the initial 6 months of 1152 clients of the French national lottery who created their internet gambling accounts between September 2015 and February 2016 were analyzed using a two-step approach that combined growth mixture modeling and latent class analysis. The analysis was based upon behavior indicators of gambling activity (money wagered and number of gambling days) and indicators of gambling problems (breadth of involvement and chasing). Profiles were described based upon the probabilities of following the trajectories that were identified for the four indicators, and upon several covariates (age, gender, deposits, type of play, net losses, voluntary self-exclusion, and Playscan classification?a responsible gambling tool that provides each player with a risk assessment: green for low risk, orange for medium risk and red for high risk). Net losses, voluntary self-exclusion, and Playscan classification were used as external verification of problem gambling. Results: We identified 5 distinct profiles of online lottery gambling. Classes 1 (56.8%), 2 (14.8%) and 3 (13.9%) were characterized by low to medium gambling activity and low values for markers of problem gambling. They displayed low net losses, did not use the voluntary self-exclusion measure, and were classified predominantly with green Playscan tags (range 90%-98%). Class 4 (9.7%) was characterized by medium to high gambling activity, played a higher breadth of game types (range 1-6), and had zero to few chasing episodes. They had high net losses but were classified with green (66%) or orange (25%) Playscan tags and did not use the voluntary self-exclusion measure. Class 5 (4.8%) was characterized by medium to very high gambling activity, played a higher breadth of game types (range 1-17), and had a high number of chasing episodes (range 0-5). They experienced the highest net losses, the highest proportion of orange (32%) and red (39%) tags within the Playscan classification system and represented the only class in which voluntary self-exclusion was present. Conclusions: Classes 1, 2, 3 may be considered to represent recreational gambling. Class 4 had higher gambling activity and higher breadth of involvement and may be representative of players at risk for future gambling problems. Class 5 stood out in terms of much higher gambling activity and breadth of involvement, and the presence of chasing behavior. Individuals in classes 4 and 5 may benefit from early preventive measures. UR - http://www.jmir.org/2020/8/e17675/ UR - http://dx.doi.org/10.2196/17675 UR - http://www.ncbi.nlm.nih.gov/pubmed/32254041 ID - info:doi/10.2196/17675 ER - TY - JOUR AU - Yoo, Whi Dong AU - Birnbaum, L. Michael AU - Van Meter, R. Anna AU - Ali, F. Asra AU - Arenare, Elizabeth AU - Abowd, D. Gregory AU - De Choudhury, Munmun PY - 2020/8/12 TI - Designing a Clinician-Facing Tool for Using Insights From Patients? Social Media Activity: Iterative Co-Design Approach JO - JMIR Ment Health SP - e16969 VL - 7 IS - 8 KW - social media KW - psychotic disorders KW - information technology N2 - Background: Recent research has emphasized the need for accessing information about patients to augment mental health patients? verbal reports in clinical settings. Although it has not been introduced in clinical settings, computational linguistic analysis on social media has proved it can infer mental health attributes, implying a potential use as collateral information at the point of care. To realize this potential and make social media insights actionable to clinical decision making, the gaps between computational linguistic analysis on social media and the current work practices of mental health clinicians must be bridged. Objective: This study aimed to identify information derived from patients? social media data that can benefit clinicians and to develop a set of design implications, via a series of low-fidelity (lo-fi) prototypes, on how to deliver the information at the point of care. Methods: A team of clinical researchers and human-computer interaction (HCI) researchers conducted a long-term co-design activity for over 6 months. The needs-affordances analysis framework was used to refine the clinicians? potential needs, which can be supported by patients? social media data. On the basis of those identified needs, the HCI researchers iteratively created 3 different lo-fi prototypes. The prototypes were shared with both groups of researchers via a videoconferencing software for discussion and feedback. During the remote meetings, potential clinical utility, potential use of the different prototypes in a treatment setting, and areas of improvement were discussed. Results: Our first prototype was a card-type interface that supported treatment goal tracking. Each card included attribute levels: depression, anxiety, social activities, alcohol, and drug use. This version confirmed what types of information are helpful but revealed the need for a glanceable dashboard that highlights the trends of these information. As a result, we then developed the second prototype, an interface that shows the clinical state and trend. We found that focusing more on the changes since the last visit without visual representation can be more compatible with clinicians? work practices. In addition, the second phase of needs-affordances analysis identified 3 categories of information relevant to patients with schizophrenia: symptoms related to psychosis, symptoms related to mood and anxiety, and social functioning. Finally, we developed the third prototype, a clinical summary dashboard that showed changes from the last visit in plain texts and contrasting colors. Conclusions: This exploratory co-design research confirmed that mental health attributes inferred from patients? social media data can be useful for clinicians, although it also revealed a gap between computational social media analyses and clinicians? expectations and conceptualizations of patients? mental health states. In summary, the iterative co-design process crystallized design directions for the future interface, including how we can organize and provide symptom-related information in a way that minimizes the clinicians? workloads. UR - http://mental.jmir.org/2020/8/e16969/ UR - http://dx.doi.org/10.2196/16969 UR - http://www.ncbi.nlm.nih.gov/pubmed/32784180 ID - info:doi/10.2196/16969 ER - TY - JOUR AU - Woldaregay, Zebene Ashenafi AU - Launonen, Kalervo Ilkka AU - Albers, David AU - Igual, Jorge AU - Årsand, Eirik AU - Hartvigsen, Gunnar PY - 2020/8/12 TI - A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism JO - J Med Internet Res SP - e18912 VL - 22 IS - 8 KW - type 1 diabetes KW - self-recorded health data KW - infection detection KW - decision support techniques KW - outbreak detection system KW - syndromic surveillance N2 - Background: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model. Objective: This study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio. Methods: Three groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required. Results: The one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time. Conclusions: We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale. UR - https://www.jmir.org/2020/8/e18912 UR - http://dx.doi.org/10.2196/18912 UR - http://www.ncbi.nlm.nih.gov/pubmed/32784179 ID - info:doi/10.2196/18912 ER - TY - JOUR AU - Woldaregay, Zebene Ashenafi AU - Launonen, Kalervo Ilkka AU - Årsand, Eirik AU - Albers, David AU - Holubová, Anna AU - Hartvigsen, Gunnar PY - 2020/8/12 TI - Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System JO - J Med Internet Res SP - e18911 VL - 22 IS - 8 KW - type 1 diabetes KW - self-recorded health data KW - infection incidence KW - decision making KW - infectious disease outbreaks KW - public health surveillance N2 - Background: Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. Objective: The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. Methods: We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). Results: Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions: We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field. UR - https://www.jmir.org/2020/8/e18911 UR - http://dx.doi.org/10.2196/18911 UR - http://www.ncbi.nlm.nih.gov/pubmed/32784178 ID - info:doi/10.2196/18911 ER - TY - JOUR AU - Lee, JeeEun AU - Yoo, K. Sun PY - 2020/8/10 TI - Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study JO - JMIR Mhealth Uhealth SP - e17803 VL - 8 IS - 8 KW - respiration rate KW - accelerometer KW - smartphone KW - independent component analysis KW - quefrency KW - mobile phone N2 - Background: As the mobile environment has developed recently, there have been studies on continuous respiration monitoring. However, it is not easy for general users to access the sensors typically used to measure respiration. There is also random noise caused by various environmental variables when respiration is measured using noncontact methods in a mobile environment. Objective: In this study, we aimed to estimate the respiration rate using an accelerometer sensor in a smartphone. Methods: First, data were acquired from an accelerometer sensor by a smartphone, which can easily be accessed by the general public. Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns. Results: From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95% limits of agreement. Conclusions: There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment. UR - https://mhealth.jmir.org/2020/8/e17803 UR - http://dx.doi.org/10.2196/17803 UR - http://www.ncbi.nlm.nih.gov/pubmed/32773384 ID - info:doi/10.2196/17803 ER - TY - JOUR AU - Berrocal, Allan AU - Concepcion, Waldo AU - De Dominicis, Stefano AU - Wac, Katarzyna PY - 2020/8/7 TI - Complementing Human Behavior Assessment by Leveraging Personal Ubiquitous Devices and Social Links: An Evaluation of the Peer-Ceived Momentary Assessment Method JO - JMIR Mhealth Uhealth SP - e15947 VL - 8 IS - 8 KW - peer-ceived momentary assessment KW - PeerMA KW - ecological momentary assessment KW - EMA KW - human state assessment KW - behavior modeling KW - human-smartphone interaction KW - digital health KW - well-being KW - mobile phone N2 - Background: Ecological momentary assessment (EMA) enables individuals to self-report their subjective momentary physical and emotional states. However, certain conditions, including routine observable behaviors (eg, moods, medication adherence) as well as behaviors that may suggest declines in physical or mental health (eg, memory losses, compulsive disorders) cannot be easily and reliably measured via self-reports. Objective: This study aims to examine a method complementary to EMA, denoted as peer-ceived momentary assessment (PeerMA), which enables the involvement of peers (eg, family members, friends) to report their perception of the individual?s subjective physical and emotional states. In this paper, we aim to report the feasibility results and identified human factors influencing the acceptance and reliability of the PeerMA Methods: We conducted two studies of 4 weeks each, collecting self-reports from 20 participants about their stress, fatigue, anxiety, and well-being, in addition to collecting peer-reported perceptions from 27 of their peers. Results: Preliminary results showed that some of the peers reported daily assessments for stress, fatigue, anxiety, and well-being statistically equal to those reported by the participant. We also showed how pairing assessments of participants and peers in time enables a qualitative and quantitative exploration of unique research questions not possible with EMA-only based assessments. We reported on the usability and implementation aspects based on the participants? experience to guide the use of the PeerMA to complement the information obtained via self-reports for observable behaviors and physical and emotional states among healthy individuals. Conclusions: It is possible to leverage the PeerMA method as a complement to EMA to assess constructs that fall in the realm of observable behaviors and states in healthy individuals. UR - https://mhealth.jmir.org/2020/8/e15947 UR - http://dx.doi.org/10.2196/15947 UR - http://www.ncbi.nlm.nih.gov/pubmed/32763876 ID - info:doi/10.2196/15947 ER - TY - JOUR AU - Wang, Hsin-Yao AU - Lin, Ting-Wei AU - Chiu, Yueh-Hsia Sherry AU - Lin, Wan-Ying AU - Huang, Song-Bin AU - Hsieh, Chia-Hsun Jason AU - Chen, Cheng Hsieh AU - Lu, Jang-Jih AU - Wu, Min-Hsien PY - 2020/8/7 TI - Novel Toilet Paper?Based Point-Of-Care Test for the Rapid Detection of Fecal Occult Blood: Instrument Validation Study JO - J Med Internet Res SP - e20261 VL - 22 IS - 8 KW - fecal occult blood test KW - point-of-care diagnostics KW - paper-based analytical devices KW - diagnostic KW - testing KW - detection KW - validation KW - cancer KW - public health N2 - Background: Colorectal cancer screening by fecal occult blood testing has been an important public health test and shown to reduce colorectal cancer?related mortality. However, the low participation rate in colorectal cancer screening by the general public remains a problematic public health issue. This fact could be attributed to the complex and unpleasant operation of the screening tool. Objective: This study aimed to validate a novel toilet paper?based point-of-care test (ie, JustWipe) as a public health instrument to detect fecal occult blood and provide detailed results from the evaluation of the analytic characteristics in the clinical validation. Methods: The mechanism of fecal specimen collection by the toilet-paper device was verified with repeatability and reproducibility tests. We also evaluated the analytical characteristics of the test reagents. For clinical validation, we conducted comparisons between JustWipe and other fecal occult blood tests. The first comparison was between JustWipe and typical fecal occult blood testing in a central laboratory setting with 70 fecal specimens from the hospital. For the second comparison, a total of 58 volunteers were recruited, and JustWipe was compared with the commercially available Hemoccult SENSA in a point-of-care setting. Results: Adequate amounts of fecal specimens were collected using the toilet-paper device with small day-to-day and person-to-person variations. The limit of detection of the test reagent was evaluated to be 3.75 µg of hemoglobin per milliliter of reagent. Moreover, the test reagent also showed high repeatability (100%) on different days and high reproducibility (>96%) among different users. The overall agreement between JustWipe and a typical fecal occult blood test in a central laboratory setting was 82.9%. In the setting of point-of-care tests, the overall agreement between JustWipe and Hemoccult SENSA was 89.7%. Moreover, the usability questionnaire showed that the novel test tool had high scores in operation friendliness (87.3/100), ease of reading results (97.4/100), and information usefulness (96.1/100). Conclusions: We developed and validated a toilet paper?based fecal occult blood test for use as a point-of-care test for the rapid (in 60 seconds) and easy testing of fecal occult blood. These favorable characteristics render it a promising tool for colorectal cancer screening as a public health instrument. UR - https://www.jmir.org/2020/8/e20261 UR - http://dx.doi.org/10.2196/20261 UR - http://www.ncbi.nlm.nih.gov/pubmed/32763879 ID - info:doi/10.2196/20261 ER - TY - JOUR AU - Guo, Xujun AU - Yang, Yarui AU - Takiff, E. Howard AU - Zhu, Minmin AU - Ma, Jianping AU - Zhong, Tao AU - Fan, Yuzheng AU - Wang, Jian AU - Liu, Shengyuan PY - 2020/7/31 TI - A Comprehensive App That Improves Tuberculosis Treatment Management Through Video-Observed Therapy: Usability Study JO - JMIR Mhealth Uhealth SP - e17658 VL - 8 IS - 7 KW - tuberculosis KW - management KW - video-observed therapy KW - directly observed therapy KW - mobile phone N2 - Background: Treatment of pulmonary tuberculosis (TB) requires at least six months and is compromised by poor adherence. In the directly observed therapy (DOT) scheme recommended by the World Health Organization, the patient is directly observed taking their medications at a health post. An alternative to DOT is video-observed therapy (VOT), in which the patients take videos of themselves taking the medication and the video is uploaded into the app and reviewed by a health care worker. We developed a comprehensive TB management system by using VOT that is installed as an app on the smartphones of both patients and health care workers. It was implemented into the routine TB control program of the Nanshan District of Shenzhen, China. Objective: The aim of this study was to compare the effectiveness of VOT with that of DOT in managing the treatment of patients with pulmonary TB and to evaluate the acceptance of VOT for TB management by patients and health care workers. Methods: Patients beginning treatment between September 2017 and August 2018 were enrolled into the VOT group and their data were compared with the retrospective data of patients who began TB treatment and were managed with routine DOT between January 2016 and August 2017. Sociodemographic characteristics, clinical features, treatment adherence, positive findings of sputum smears, reporting of side effects, time and costs of transportation, and satisfaction were compared between the 2 treatment groups. The attitudes of the health care workers toward the VOT-based system were also analyzed. Results: This study included 158 patients in the retrospective DOT group and 235 patients in the VOT group. The VOT group showed a significantly higher fraction of doses observed (P<.001), less missed observed doses (P<.001), and fewer treatment discontinuations (P<.05) than the DOT group. Over 79.1% (186/235) of the VOT patients had >85% of their doses observed, while only 16.4% (26/158) of the DOT patients had >85% of their doses observed. All patients were cured without recurrences. The VOT management required significantly (P<.001) less median patient time (300 minutes vs 1240 minutes, respectively) and transportation costs (¥53 [US $7.57] vs ¥276 [US $39.43], respectively; P<.001) than DOT. Significantly more patients (191/235, 81.3%) in the VOT group preferred their treatment method compared to those on DOT (37/131, 28.2%) (P<.001), and 92% (61/66) of the health care workers thought that the VOT method was more convenient than DOT for managing patients with TB. Conclusions: Implementation of the VOT-based system into the routine program of TB management was simple and it significantly increased patient adherence to their drug regimens. Our study shows that a comprehensive VOT-based TB management represents a viable and improved evolution of DOT. UR - https://mhealth.jmir.org/2020/7/e17658 UR - http://dx.doi.org/10.2196/17658 UR - http://www.ncbi.nlm.nih.gov/pubmed/32735222 ID - info:doi/10.2196/17658 ER - TY - JOUR AU - Han, Yangyang AU - Lie, K. Reidar AU - Guo, Rui PY - 2020/7/29 TI - The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis JO - J Med Internet Res SP - e17995 VL - 22 IS - 7 KW - Internet hospital KW - telehealth KW - telemedicine KW - ehealth KW - digital health KW - digital medicine KW - health services research KW - China N2 - Background: The internet hospital is an innovative organizational form and service mode under the tide of internet plus in the Chinese medical industry. It is the product of the interaction between consumer health needs and supply-side reform. However, there has still been no systematic summary of its establishment and definition, nor has there been an analysis of its service content. Objective: The primary purpose of this study was to understand the definition, establishment, and development status of internet hospitals. Methods: Data on internet hospitals were obtained via the Baidu search engine for results up until January 1, 2019. Based on the results of the search, we obtained more detailed information from the official websites and apps of 130 online hospitals and formed a database for descriptive analysis. Results: By January 2019, the number of registered internet hospitals had expanded to approximately 130 in 25 provinces, accounting for 73.5% of all provinces or province-level municipalities in China. Internet hospitals, as a new telehealth model, are distinct but overlap with online health, telemedicine, and mobile medical. They offer four kinds of services?convenience services, online medical services, telemedicine, and related industries. In general, there is an underlying common treatment flowchart of care in ordinary and internet hospitals. There are three different sponsors?government-led integration, hospital-led, and enterprise-led internet hospitals?for which stakeholders have different supporting content and responsibilities. Conclusions: Internet hospitals are booming in China, and it is the joint effort of the government and the market to alleviate the coexistence of shortages of medical resources and wasted medical supplies. The origin of internet hospitals in the eastern and western regions, the purpose of the establishment initiator, and the content of online and offline services are different. Only further standardized management and reasonable industry freedom can realize the original intention of the internet hospital of meeting various health needs. UR - http://www.jmir.org/2020/7/e17995/ UR - http://dx.doi.org/10.2196/17995 UR - http://www.ncbi.nlm.nih.gov/pubmed/32723721 ID - info:doi/10.2196/17995 ER - TY - JOUR AU - Wintergerst, M. Maximilian W. AU - Jansen, G. Linus AU - Holz, G. Frank AU - Finger, P. Robert PY - 2020/7/29 TI - A Novel Device for Smartphone-Based Fundus Imaging and Documentation in Clinical Practice: Comparative Image Analysis Study JO - JMIR Mhealth Uhealth SP - e17480 VL - 8 IS - 7 KW - smartphone-based fundus imaging KW - smartphone-based funduscopy KW - smartphone KW - retinal imaging KW - mHealth KW - mobile phone KW - smartphone imaging KW - smartphone funduscopy KW - smartphone ophthalmoscope N2 - Background: Smartphone-based fundus imaging allows for mobile and inexpensive fundus examination with the potential to revolutionize eye care, particularly in lower-resource settings. However, most smartphone-based fundus imaging adapters convey image quality not comparable to conventional fundus imaging. Objective: The purpose of this study was to evaluate a novel smartphone-based fundus imaging device for documentation of a variety of retinal/vitreous pathologies in a patient sample with wide refraction and age ranges. Methods: Participants? eyes were dilated and imaged with the iC2 funduscope (HEINE Optotechnik) using an Apple iPhone 6 in single-image acquisition (image resolution of 2448 × 3264 pixels) or video mode (1248 × 1664 pixels) and a subgroup of participants was also examined by conventional fundus imaging (Zeiss VISUCAM 500). Smartphone-based image quality was compared to conventional fundus imaging in terms of sharpness (focus), reflex artifacts, contrast, and illumination on semiquantitative scales. Results: A total of 47 eyes from 32 participants (age: mean 62.3, SD 19.8 years; range 7-93; spherical equivalent: mean ?0.78, SD 3.21 D; range: ?7.88 to +7.0 D) were included in the study. Mean (SD) visual acuity (logMAR) was 0.48 (0.66; range 0-2.3); 30% (14/47) of the eyes were pseudophakic. Image quality was sufficient in all eyes irrespective of refraction. Images acquired with conventional fundus imaging were sharper and had less reflex artifacts, and there was no significant difference in contrast and illumination (P<.001, P=.03, and P=.10, respectively). When comparing image quality at the posterior pole, the mid periphery, and the far periphery, glare increased as images were acquired from a more peripheral part of the retina. Reflex artifacts were more frequent in pseudophakic eyes. Image acquisition was also possible in children. Documentation of deep optic nerve cups in video mode conveyed a mock 3D impression. Conclusions: Image quality of conventional fundus imaging was superior to that of smartphone-based fundus imaging, although this novel smartphone-based fundus imaging device achieved image quality high enough to document various fundus pathologies including only subtle findings. High-quality smartphone-based fundus imaging might represent a mobile alternative for fundus documentation in clinical practice. UR - https://mhealth.jmir.org/2020/7/e17480 UR - http://dx.doi.org/10.2196/17480 UR - http://www.ncbi.nlm.nih.gov/pubmed/32723717 ID - info:doi/10.2196/17480 ER - TY - JOUR AU - Signal, June Nada Elizabeth AU - McLaren, Ruth AU - Rashid, Usman AU - Vandal, Alain AU - King, Marcus AU - Almesfer, Faisal AU - Henderson, Jeanette AU - Taylor, Denise PY - 2020/7/29 TI - Haptic Nudges Increase Affected Upper Limb Movement During Inpatient Stroke Rehabilitation: Multiple-Period Randomized Crossover Study JO - JMIR Mhealth Uhealth SP - e17036 VL - 8 IS - 7 KW - stroke KW - rehabilitation KW - physical activity KW - movement KW - disability KW - technology KW - upper limb KW - wearable KW - haptic KW - nudge N2 - Background: As many as 80% of stroke survivors experience upper limb (UL) disability. The strong relationships between disability, lost productivity, and ongoing health care costs mean reducing disability after stroke is critical at both individual and society levels. Unfortunately, the amount of UL-focused rehabilitation received by people with stroke is extremely low. Activity monitoring and promotion using wearable devices offer a potential technology-based solution to address this gap. Commonly, wearable devices are used to deliver a haptic nudge to the wearer with the aim of promoting a particular behavior. However, little is known about the effectiveness of haptic nudging in promoting behaviors in patient populations. Objective: This study aimed to estimate the effect of haptic nudging delivered via a wrist-worn wearable device on UL movement in people with UL disability following stroke undertaking inpatient rehabilitation. Methods: A multiple-period randomized crossover design was used to measure the association of UL movement with the occurrence of haptic nudge reminders to move the affected UL in 20 people with stroke undertaking inpatient rehabilitation. UL movement was observed and classified using movement taxonomy across 72 one-minute observation periods from 7:00 AM to 7:00 PM on a single weekday. On 36 occasions, a haptic nudge to move the affected UL was provided just before the observation period. On the other 36 occasions, no haptic nudge was given. The timing of the haptic nudge was randomized across the observation period for each participant. Statistical analysis was performed using mixed logistic regression. The effect of a haptic nudge was evaluated from the intention-to-treat dataset as the ratio of the odds of affected UL movement during the observation period following a ?Planned Nudge? to the odds of affected limb movement during the observation period following ?No Nudge.? Results: The primary intention-to-treat analysis showed the odds ratio (OR) of affected UL movement following a haptic nudge was 1.44 (95% CI 1.28-1.63, P<.001). The secondary analysis revealed an increased odds of affected UL movement following a Planned Nudge was predominantly due to increased odds of spontaneous affected UL movement (OR 2.03, 95% CI 1.65-2.51, P<.001) rather than affected UL movement in conjunction with unaffected UL movement (OR 1.13, 95% CI 0.99-1.29, P=.07). Conclusions: Haptic nudging delivered via a wrist-worn wearable device increases affected UL movement in people with UL disability following stroke undertaking inpatient rehabilitation. The promoted movement appears to be specific to the instructions given. Trial Registration: Australia New Zealand Clinical Trials Registry 12616000654459; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370687&isReview=true UR - https://mhealth.jmir.org/2020/7/e17036 UR - http://dx.doi.org/10.2196/17036 UR - http://www.ncbi.nlm.nih.gov/pubmed/32723718 ID - info:doi/10.2196/17036 ER - TY - JOUR AU - Zhang, Lei AU - Shang, Xianwen AU - Sreedharan, Subhashaan AU - Yan, Xixi AU - Liu, Jianbin AU - Keel, Stuart AU - Wu, Jinrong AU - Peng, Wei AU - He, Mingguang PY - 2020/7/28 TI - Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study JO - JMIR Med Inform SP - e16850 VL - 8 IS - 7 KW - diabetes KW - machine learning KW - risk prediction KW - cohort study N2 - Background: Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. Objective: We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. Methods: We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. Results: Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001). Conclusions: A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM. UR - https://medinform.jmir.org/2020/7/e16850 UR - http://dx.doi.org/10.2196/16850 UR - http://www.ncbi.nlm.nih.gov/pubmed/32720912 ID - info:doi/10.2196/16850 ER - TY - JOUR AU - Chang, Ernest Shuchih AU - Chen, YiChian AU - Lu, MingFang AU - Luo, Louis Hueimin PY - 2020/7/28 TI - Development and Evaluation of a Smart Contract?Enabled Blockchain System for Home Care Service Innovation: Mixed Methods Study JO - JMIR Med Inform SP - e15472 VL - 8 IS - 7 KW - home care service KW - trust KW - innovation KW - blockchain KW - smart contract KW - automation N2 - Background: In the home care industry, the assignment and tracking of care services are controlled by care centers that are centralized in nature and prone to inefficient information transmission. A lack of trust among the involved parties, information opaqueness, and large manual manipulation result in lower process efficiency. Objective: This study aimed to explore and demonstrate the application of blockchain and smart contract technologies to innovate/renovate home care services for harvesting the desired blockchain benefits of process transparency, traceability, and interoperability. Methods: An object-oriented analysis/design combined with a unified modeling language tool was used to construct the architecture of the proposed home care service system. System feasibility was evaluated via an implementation test, and a questionnaire survey was performed to collect opinions from home care service respondents knowledgeable about blockchain and smart contracts. Results: According to the comparative analysis results, the proposed design outperformed the existing system in terms of traceability, system efficiency, and process automation. Moreover, for the questionnaire survey, the quantitative analysis results showed that the proposed blockchain-based system had significantly (P<.001) higher mean scores (when compared with the existing system) in terms of important factors, including timeliness, workflow efficiency, automatic notifications, insurance functionality, and auditable traceability. In summary, blockchain-based home care service participants will be able to enjoy improved efficiency, better transparency, and higher levels of process automation. Conclusions: Blockchain and smart contracts can provide valuable benefits to the home care service industry via distributed data management and process automation. The proposed system enhances user experiences by mitigating human intervention and improving service interoperability, transparency/traceability, and real-time response to home care service events. Efforts in exploring and integrating blockchain-based home care services with emerging technologies, such as the internet of things and artificial intelligence, are expected to provide further benefits and therefore are subject to future research. UR - https://medinform.jmir.org/2020/7/e15472 UR - http://dx.doi.org/10.2196/15472 UR - http://www.ncbi.nlm.nih.gov/pubmed/32720903 ID - info:doi/10.2196/15472 ER - TY - JOUR AU - Rael, Tagliaferri Christine AU - Lentz, Cody AU - Carballo-Diéguez, Alex AU - Giguere, Rebecca AU - Dolezal, Curtis AU - Feller, Daniel AU - D'Aquila, T. Richard AU - Hope, J. Thomas PY - 2020/7/27 TI - Understanding the Acceptability of Subdermal Implants as a Possible New HIV Prevention Method: Multi-Stage Mixed Methods Study JO - J Med Internet Res SP - e16904 VL - 22 IS - 7 KW - PrEP implant KW - YouTube KW - acceptability KW - long-acting PrEP KW - systemic PrEP KW - human-centered design KW - HIV prevention KW - removable implant KW - long-acting HIV prevention KW - gay and bisexual men N2 - Background: A long-acting implant for HIV pre-exposure prophylaxis (PrEP) is in development in the Sustained Long-Action Prevention Against HIV (SLAP-HIV) trial. This could provide an alternative to oral PrEP. Objective: Our mixed methods study aimed to understand (1) users? experiences with a similar subdermal implant for contraception and (2) factors influencing the likelihood that gay and bisexual men (GBM) would use a proposed PrEP implant. Methods: Work was completed in 4 stages. In stage 1, we conducted a scientific literature review on existing subdermal implants, focusing on users? experiences with implant devices. In stage 2, we reviewed videos on YouTube, focusing on the experiences of current or former contraceptive implant users (as these implants are similar to those in development in SLAP-HIV). In stage 3, individuals who indicated use of a subdermal implant for contraception in the last 5 years were recruited via a web-based questionnaire. Eligible participants (n=12 individuals who liked implants a lot and n=12 individuals who disliked implants a lot) completed in-depth phone interviews (IDIs) about their experiences. In stage 4, results from IDIs were used to develop a web-based survey for HIV-negative GBM to rate their likelihood of using a PrEP implant on a scale (1=very unlikely and 5=very likely) based on likely device characteristics and implant concerns identified in the IDIs. Results: In the scientific literature review (stage 1), concerns about contraceptive implants that could apply to the PrEP implants in development included potential side effects (eg, headache), anticipated high cost of the device, misconceptions about PrEP implants (eg, specific contraindications), and difficulty accessing PrEP implants. In the stage 2 YouTube review, individuals who had used contraceptive implants reported mild side effects related to their device. In stage 3, implant users reported that devices were comfortable, unintrusive, and presented only minor discomfort (eg, bruising) before or after insertion and removal. They mainly reported removing or disliking the device due to contraceptive-related side effects (eg, prolonged menstruation). Participants in the stage 4 quantitative survey (N=304) were mainly gay (204/238, 85.7%), white (125/238, 52.5%), cisgender men (231/238, 97.1%), and 42.0% (73/174) of them were on oral PrEP. Not having to take a daily pill increased the likelihood of using PrEP implants (mean 4.13). Requiring >1 device to achieve 1 year of protection (mean range 1.79-2.94) mildly discouraged PrEP implant use. Participants did not mind moderate bruising, a small scar, tenderness, or bleeding after insertion or removal, and an implant with a size slightly larger than a matchstick (mean ratings 3.18-3.69). Conclusions: PrEP implants are promising among GBM. Implant features and insertion or removal-related concerns do not seem to discourage potential users. To ensure acceptability, PrEP implants should require the fewest possible implants for the greatest protection duration. UR - https://www.jmir.org/2020/7/e16904 UR - http://dx.doi.org/10.2196/16904 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348277 ID - info:doi/10.2196/16904 ER - TY - JOUR AU - Mazoteras-Pardo, Victoria AU - Becerro-De-Bengoa-Vallejo, Ricardo AU - Losa-Iglesias, Elena Marta AU - Martínez-Jiménez, María Eva AU - Calvo-Lobo, César AU - Romero-Morales, Carlos AU - López-López, Daniel AU - Palomo-López, Patricia PY - 2020/7/24 TI - QardioArm Blood Pressure Monitoring in a Population With Type 2 Diabetes: Validation Study JO - J Med Internet Res SP - e19781 VL - 22 IS - 7 KW - blood pressure KW - hypertension KW - type 2 diabetes KW - mobile applications KW - software validation N2 - Background: Home blood pressure monitoring has many benefits, even more so, in populations prone to high blood pressure, such as persons with diabetes. Objective: The purpose of this research was to validate the QardioArm mobile device in a sample of individuals with noninsulin-dependent type 2 diabetes in accordance with the guidelines of the second International Protocol of the European Society of Hypertension. Methods: The sample consisted of 33 patients with type 2 diabetes. To evaluate the validity of QardioArm by comparing its data with that obtained with a digital sphygmomanometer (Omron M3 Intellisense), two nurses collected diastolic blood pressure, systolic blood pressure, and heart rate with both devices. Results: The analysis indicated that the test device QardioArm met all the validation requirements using a sample population with type 2 diabetes. Conclusions: This paper reports the first validation of QardioArm in a population of individuals with noninsulin-dependent type 2 diabetes. QardioArm for home monitoring of blood pressure and heart rate met the requirements of the second International Protocol of the European Society of Hypertension. UR - http://www.jmir.org/2020/7/e19781/ UR - http://dx.doi.org/10.2196/19781 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706672 ID - info:doi/10.2196/19781 ER - TY - JOUR AU - Weerdmeester, Joanneke AU - van Rooij, MJW Marieke AU - Engels, CME Rutger AU - Granic, Isabela PY - 2020/7/23 TI - An Integrative Model for the Effectiveness of Biofeedback Interventions for Anxiety Regulation: Viewpoint JO - J Med Internet Res SP - e14958 VL - 22 IS - 7 KW - biofeedback KW - neurofeedback KW - anxiety KW - appraisal KW - mechanisms KW - mental health KW - eHealth KW - video games KW - wearable technology KW - review KW - mobile phone UR - https://www.jmir.org/2020/7/e14958 UR - http://dx.doi.org/10.2196/14958 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706654 ID - info:doi/10.2196/14958 ER - TY - JOUR AU - Jang, Jong-Hwan AU - Choi, Junggu AU - Roh, Woong Hyun AU - Son, Joon Sang AU - Hong, Hyung Chang AU - Kim, Young Eun AU - Kim, Young Tae AU - Yoon, Dukyong PY - 2020/7/23 TI - Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study JO - JMIR Mhealth Uhealth SP - e16113 VL - 8 IS - 7 KW - accelerometer KW - actigraphy KW - imputation KW - autoencoder KW - deep learning N2 - Background: Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks. Objective: The aim of this study was to impute missing values in data using a deep learning approach. Methods: To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning?based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning?based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression). Results: The zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts. Conclusions: Our deep learning?based imputation model performed better than the other methods when imputing missing values in actigraphy data. UR - http://mhealth.jmir.org/2020/7/e16113/ UR - http://dx.doi.org/10.2196/16113 UR - http://www.ncbi.nlm.nih.gov/pubmed/32445459 ID - info:doi/10.2196/16113 ER - TY - JOUR AU - Huang, Rendong AU - Xu, Mei AU - Li, Xiuting AU - Wang, Yinping AU - Wang, Bin AU - Cui, Naixue PY - 2020/7/22 TI - Internet-Based Sharing Nurse Program and Nurses? Perceptions in China: Cross-Sectional Survey JO - J Med Internet Res SP - e16644 VL - 22 IS - 7 KW - sharing nurse KW - home visiting KW - internet plus nursing program KW - perception KW - China N2 - Background: China is currently piloting a ?Sharing Nurse? program that aims to increase the accessibility of nursing services to at-home patients by enabling patients to order nursing services using mobile apps or online platforms. Objective: This study aims to assess nurses? perceptions of the Sharing Nurse program, including their acceptance, concerns, needs, and willingness to take part in the program. Methods: A total of 694 nurses participated in the questionnaire survey. The survey collected their sociodemographic and work-related information and their perceptions of the Sharing Nurse program using a self-developed questionnaire. Results: The 694 respondents agreed that the Sharing Nurse program could provide patients with better access to nursing care (n=483, 69.6%). Their main concerns about the program were unclear liability division when medical disputes occur (n=637, 90.3%) and potential personal safety issues (n=604, 87%). They reported that insurance (n=611, 88%), permits from their affiliated hospital (n=562, 81.0%), clear instructions concerning rights and duties (n=580, 83.6%), real time positioning while delivering the service (n=567, 81.7%), and one-key alarm equipment (n=590, 85.0%) were necessary for better implementation of the program. More than half of the respondents (n=416, 60%) had an optimistic attitude toward the development of the Sharing Nurse program in China. However, only 19.4% (n=135) of the respondents expressed their willingness to be a ?shared nurse.? Further analyses found that nurses with a master?s degree or above (?23=28.835, P<.001) or from tertiary hospitals (?23=18.669, P<.001) were more likely to be aware of the Sharing Nurse program and that male nurses were more willing to be shared nurses (Z=?2.275, P=.02). Conclusions: The Chinese Sharing Nurse program is still in its infancy and many refinements are needed before it can be implemented nationwide. Generally, Chinese nurses are positive about the Sharing Nurse program and are willing to participate if the program is thoroughly regulated and supervised. UR - http://www.jmir.org/2020/7/e16644/ UR - http://dx.doi.org/10.2196/16644 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706711 ID - info:doi/10.2196/16644 ER - TY - JOUR AU - Waterman, D. Amy AU - Wood, H. Emily AU - Ranasinghe, N. Omesh AU - Faye Lipsey, Amanda AU - Anderson, Crystal AU - Balliet, Wendy AU - Holland-Carter, Lauren AU - Maurer, Stacey AU - Aurora Posadas Salas, Maria PY - 2020/7/21 TI - A Digital Library for Increasing Awareness About Living Donor Kidney Transplants: Formative Study JO - JMIR Form Res SP - e17441 VL - 4 IS - 7 KW - living donor kidney transplant KW - living donation KW - health education KW - informed decision-making KW - awareness KW - health literacy KW - video library KW - health technology KW - kidney diseases KW - diffusion of innovation KW - digital library KW - mobile phone N2 - Background: It is not common for people to come across a living kidney donor, let alone consider whether they would ever donate a kidney themselves while they are alive. Narrative storytelling, the sharing of first-person narratives based on lived experience, may be an important way to improve education about living donor kidney transplants (LDKTs). Developing ways to easily standardize and disseminate diverse living donor stories using digital technology could inspire more people to consider becoming living donors and reduce the kidney shortage nationally. Objective: This paper aimed to describe the development of the Living Donation Storytelling Project, a web-based digital library of living donation narratives from multiple audiences using video capture technology. Specifically, we aimed to describe the theoretical foundation and development of the library, a protocol to capture diverse storytellers, the characteristics and experiences of participating storytellers, and the frequency with which any ethical concerns about the content being shared emerged. Methods: This study invited kidney transplant recipients who had received LDKTs, living donors, family members, and patients seeking LDKTs to record personal stories using video capture technology by answering a series of guided prompts on their computer or smartphone and answering questions about their filming experience. The digital software automatically spliced responses to open-ended prompts, creating a seamless story available for uploading to a web-based library and posting to social media. Each story was reviewed by a transplant professional for the disclosure of protected health information (PHI), pressuring others to donate, and medical inaccuracies. Disclosures were edited. Results: This study recruited diverse storytellers through social media, support groups, churches, and transplant programs. Of the 137 storytellers who completed the postsurvey, 105/137 (76.6%) were white and 99/137 (72.2%) were female. They spent 62.5 min, on average, recording their story, with a final median story length of 10 min (00:46 seconds to 32:16 min). A total of 94.8% (130/137) of storytellers were motivated by a desire to educate the public; 78.1% (107/137) were motivated to help more people become living donors; and 75.9% (104/137) were motivated to dispel myths. The ease of using the technology and telling their story varied, with the fear of being on film, emotional difficulty talking about their experiences, and some technological barriers being reported. PHI, most commonly surnames and transplant center names, was present in 62.9% (85/135) of stories and was edited out. Conclusions: With appropriate sensitivity to ensure diverse recruitment, ethical review of content, and support for storytellers, web-based storytelling platforms may be a cost-effective and convenient way to further engage patients and increase the curiosity of the public in learning more about the possibility of becoming living donors. UR - https://formative.jmir.org/2020/7/e17441 UR - http://dx.doi.org/10.2196/17441 UR - http://www.ncbi.nlm.nih.gov/pubmed/32480362 ID - info:doi/10.2196/17441 ER - TY - JOUR AU - Li, Xiaoying AU - Lin, Xin AU - Ren, Huiling AU - Guo, Jinjing PY - 2020/7/20 TI - Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study JO - J Med Internet Res SP - e20443 VL - 22 IS - 7 KW - ontology KW - adverse drug reactions KW - package inserts KW - information retrieval KW - natural language processing KW - bioinformatics KW - drug KW - adverse events KW - machine-understandable knowledge KW - clinical applications N2 - Background: Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. Objective: This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. Methods: Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. Results: We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. Conclusions: Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications. UR - https://www.jmir.org/2020/7/e20443 UR - http://dx.doi.org/10.2196/20443 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706718 ID - info:doi/10.2196/20443 ER - TY - JOUR AU - Dai, Hengfen AU - Zheng, Caiyun AU - Lin, Chun AU - Zhang, Yan AU - Zhang, Hong AU - Chen, Fan AU - Liu, Yunchun AU - Xiao, Jingwen AU - Chen, Chaoxin PY - 2020/7/15 TI - Technology-Based Interventions in Oral Anticoagulation Management: Meta-Analysis of Randomized Controlled Trials JO - J Med Internet Res SP - e18386 VL - 22 IS - 7 KW - technology-based KW - oral anticoagulation management KW - meta-analysis KW - randomized controlled trials KW - telehealth KW - warfarin N2 - Background: An increasing number of patients have received prophylactic or therapeutic oral anticoagulants (OACs) for thromboembolic complications of diseases. The use of OACs is associated with both clinical benefits and risks. Considering the challenges imposed by this class of drugs, as well as the enormous progress made in portable device technology, it is possible that technology-based interventions may improve clinical benefits for patients and optimize anticoagulation management. Objective: This study was designed to comprehensively evaluate the role of technology-based interventions in the management of OACs. Methods: We searched 6 databases?PubMed, EMBASE, Cochrane, Cumulative Index to Nursing and Allied Health Literature, Scopus, and PsycINFO?to retrieve relevant studies published as of November 1, 2019, to evaluate the effect of technology-based interventions on oral anticoagulation management. RevMan (version 5.3; Cochrane) software was used to evaluate and analyze clinical outcomes. The methodological quality of studies was assessed by the Cochrane risk of bias tool. Results: A total of 15 randomized controlled trials (RCTs) were selected for analysis. They reported data for 2218 patients (1110 patients in the intervention groups and 1108 patients in the control groups). A meta-analysis was performed on the effectiveness and safety data reported in the RCTs. Technology-based interventions significantly improved the effectiveness of oral anticoagulation management (mean difference [MD]=6.07; 95% CI 0.84-11.30; I2=72%; P=.02). The safety of oral anticoagulation management was also improved, but the results were not statistically significant. Bleeding events were reduced (major bleeding events MD=1.02; 95% CI 0.78-1.32; I2=0%; P=.90; minor bleeding events MD=1.06, 95% CI 0.77-1.44; I2=41%; P=.73) and thromboembolism events were reduced (MD=0.71; 95% CI 0.49-1.01; I2=0%; P=.06). In general, patients were more satisfied with technology-based interventions, which could also improve their knowledge of anticoagulation management, improve their quality of life, and reduce mortality and hospitalization events. Conclusions: Using technology to manage OACs can improve the effectiveness and safety of oral anticoagulation management, result in higher patient satisfaction, and allow greater understanding of anticoagulation. UR - https://www.jmir.org/2020/7/e18386 UR - http://dx.doi.org/10.2196/18386 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673227 ID - info:doi/10.2196/18386 ER - TY - JOUR AU - Vuorinen, Anna-Leena AU - Erkkola, Maijaliisa AU - Fogelholm, Mikael AU - Kinnunen, Satu AU - Saarijärvi, Hannu AU - Uusitalo, Liisa AU - Näppilä, Turkka AU - Nevalainen, Jaakko PY - 2020/7/15 TI - Characterization and Correction of Bias Due to Nonparticipation and the Degree of Loyalty in Large-Scale Finnish Loyalty Card Data on Grocery Purchases: Cohort Study JO - J Med Internet Res SP - e18059 VL - 22 IS - 7 KW - loyalty card data KW - diet KW - selection bias KW - weighting KW - raking KW - food N2 - Background: To date, the evaluation of diet has mostly been based on questionnaires and diaries that have their limitations in terms of being time and resource intensive, and a tendency toward social desirability. Loyalty card data obtained in retailing provides timely and objective information on diet-related behaviors. In Finland, the market is highly concentrated, which provides a unique opportunity to investigate diet through grocery purchases. Objective: The aims of this study were as follows: (1) to investigate and quantify the selection bias in large-scale (n=47,066) loyalty card (LoCard) data and correct the bias by developing weighting schemes and (2) to investigate how the degree of loyalty relates to food purchases. Methods: Members of a loyalty card program from a large retailer in Finland were contacted via email and invited to take part in the study, which involved consenting to the release of their grocery purchase data for research purposes. Participants? sociodemographic background was obtained through a web-based questionnaire and was compared to that of the general Finnish adult population obtained via Statistics Finland. To match the distributions of sociodemographic variables, poststratification weights were constructed by using the raking method. The degree of loyalty was self-estimated on a 5-point rating scale. Results: On comparing our study sample with the general Finnish adult population, in our sample, there were more women (65.25%, 30,696/47,045 vs 51.12%, 2,273,139/4,446,869), individuals with higher education (56.91%, 20,684/36,348 vs 32.21%, 1,432,276/4,446,869), and employed individuals (60.53%, 22,086/36,487 vs 52.35%, 2,327,730/4,446,869). Additionally, in our sample, there was underrepresentation of individuals aged under 30 years (14.44%, 6,791/47,045 vs 18.04%, 802,295/4,446,869) and over 70 years (7.94%, 3,735/47,045 vs 18.20%, 809,317/4,446,869), as well as retired individuals (23.51%, 8,578/36,487 vs 31.82%, 1,414,785/4,446,869). Food purchases differed by the degree of loyalty, with higher shares of vegetable, red meat & processed meat, and fat spread purchases in the higher loyalty groups. Conclusions: Individuals who consented to the use of their loyalty card data for research purposes tended to diverge from the general Finnish adult population. However, the high volume of data enabled the inclusion of sociodemographically diverse subgroups and successful correction of the differences found in the distributions of sociodemographic variables. In addition, it seems that food purchases differ according to the degree of loyalty, which should be taken into account when researching loyalty card data. Despite the limitations, loyalty card data provide a cost-effective approach to reach large groups of people, including hard-to-reach population subgroups. UR - http://www.jmir.org/2020/7/e18059/ UR - http://dx.doi.org/10.2196/18059 UR - http://www.ncbi.nlm.nih.gov/pubmed/32459633 ID - info:doi/10.2196/18059 ER - TY - JOUR AU - Twomey, B. Michael AU - Sammon, David AU - Nagle, Tadhg PY - 2020/7/14 TI - The Tango of Problem Formulation: A Patient?s/Researcher?s Reflection on an Action Design Research Journey JO - J Med Internet Res SP - e16916 VL - 22 IS - 7 KW - action design research KW - patient KW - reflection KW - problem formulation KW - checklist KW - cystic fibrosis UR - https://www.jmir.org/2020/7/e16916 UR - http://dx.doi.org/10.2196/16916 UR - http://www.ncbi.nlm.nih.gov/pubmed/32285802 ID - info:doi/10.2196/16916 ER - TY - JOUR AU - Barata, Filipe AU - Tinschert, Peter AU - Rassouli, Frank AU - Steurer-Stey, Claudia AU - Fleisch, Elgar AU - Puhan, Alan Milo AU - Brutsche, Martin AU - Kotz, David AU - Kowatsch, Tobias PY - 2020/7/14 TI - Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study JO - J Med Internet Res SP - e18082 VL - 22 IS - 7 KW - asthma KW - cough recognition KW - cough segmentation KW - sex assignment KW - deep learning KW - smartphone KW - mobile phone N2 - Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean ?0.1 (95% CI ?12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI ?3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch?based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma. UR - https://www.jmir.org/2020/7/e18082 UR - http://dx.doi.org/10.2196/18082 UR - http://www.ncbi.nlm.nih.gov/pubmed/32459641 ID - info:doi/10.2196/18082 ER - TY - JOUR AU - Jin, Bo AU - Qu, Yue AU - Zhang, Liang AU - Gao, Zhan PY - 2020/7/10 TI - Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis JO - J Med Internet Res SP - e18697 VL - 22 IS - 7 KW - Parkinson disease KW - face landmarks KW - machine learning KW - artificial intelligence N2 - Background: The number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). Objective: This study proposes methods to diagnose PD through facial expression recognition. Methods: We collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. Results: The experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. Conclusions: This study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis. UR - https://www.jmir.org/2020/7/e18697 UR - http://dx.doi.org/10.2196/18697 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673247 ID - info:doi/10.2196/18697 ER - TY - JOUR AU - Lee, WJ Edmund AU - Bekalu, Awoke Mesfin AU - McCloud, Rachel AU - Vallone, Donna AU - Arya, Monisha AU - Osgood, Nathaniel AU - Li, Xiaoyan AU - Minsky, Sara AU - Viswanath, Kasisomayajula PY - 2020/7/7 TI - The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study JO - J Med Internet Res SP - e17451 VL - 22 IS - 7 KW - mobile health KW - mobile phone KW - tobacco use KW - big data KW - spatial analysis KW - data science N2 - Background: People from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States?when used alongside data sources on TRO locations?apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the locations of TROs as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health promotion organizations to better reach underserved communities in their tobacco control efforts. Objective: This study aimed to demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts. Methods: Data for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data. Results: Participants reported encountering protobacco messages mostly from store signs and displays and antitobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown. Conclusions: Smartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment. UR - https://www.jmir.org/2020/7/e17451 UR - http://dx.doi.org/10.2196/17451 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673252 ID - info:doi/10.2196/17451 ER - TY - JOUR AU - Ren, Jie AU - Raghupathi, Viju AU - Raghupathi, Wullianallur PY - 2020/7/3 TI - Understanding the Dimensions of Medical Crowdfunding: A Visual Analytics Approach JO - J Med Internet Res SP - e18813 VL - 22 IS - 7 KW - crowdfunding KW - medical crowdfunding KW - GoFundMe KW - fundraising KW - health care KW - health care affordability KW - patient KW - Facebook KW - fundraiser N2 - Background: Medical crowdfunding has emerged as a growing field for fundraising opportunities. Some environmental trends have driven the emergence of campaigns to raise funds for medical care. These trends include lack of medical insurance, economic backlash following the 2008 financial collapse, and shortcomings of health care regulations. Objective: Research regarding crowdfunding campaign use, reasons, and effects on the provision of medical care and individual relationships in health systems is limited. This study aimed to explore the nature and dimensions of the phenomenon of medical crowdfunding using a visual analytics approach and data crawled from the GoFundMe crowdfunding platform in 2019. We aimed to explore and identify the factors that contribute to a successful campaign. Methods: This data-driven study used a visual analytics approach. It focused on descriptive analytics to obtain a panoramic insight into medical projects funded through the GoFundMe crowdfunding platform. Results: This study highlighted the relevance of positioning the campaign for fundraising. In terms of motivating donors, it appears that people are typically more generous in contributing to campaigns for children rather than those for adults. The results emphasized the differing dynamics that a picture posted in the campaign brings to the potential for medical crowdfunding. In terms of donor?s motivation, the results show that a picture depicting the pediatric patient by himself or herself is the most effective. In addition, a picture depicting the current medical condition of the patient as severe is more effective than one depicting relative normalcy in the condition. This study also drew attention to the optimum length of the title. Finally, an interesting trend in the trajectory of donations is that the average amount of a donation decreases with an increase in the number of donors. This indicates that the first donors tend to be the most generous. Conclusions: This study examines the relationship between social media, the characteristics of a campaign, and the potential for fundraising. Its analysis of medical crowdfunding campaigns across the states offers a window into the status of the country?s health care affordability. This study shows the nurturing role that social media can play in the domain of medical crowdfunding. In addition, it discusses the drivers of a successful fundraising campaign with respect to the GoFundMe platform. UR - https://www.jmir.org/2020/7/e18813 UR - http://dx.doi.org/10.2196/18813 UR - http://www.ncbi.nlm.nih.gov/pubmed/32618573 ID - info:doi/10.2196/18813 ER - TY - JOUR AU - Sapci, Hasan A. AU - Sapci, Aylin H. PY - 2020/6/30 TI - Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review JO - JMIR Med Educ SP - e19285 VL - 6 IS - 1 KW - artificial intelligence KW - education KW - machine learning KW - deep learning KW - medical education KW - health informatics KW - systematic review N2 - Background: The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. Objective: The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. Methods: A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. Results: This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. Conclusions: To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed. UR - http://mededu.jmir.org/2020/1/e19285/ UR - http://dx.doi.org/10.2196/19285 UR - http://www.ncbi.nlm.nih.gov/pubmed/32602844 ID - info:doi/10.2196/19285 ER - TY - JOUR AU - Pryss, Rüdiger AU - Schlee, Winfried AU - Hoppenstedt, Burkhard AU - Reichert, Manfred AU - Spiliopoulou, Myra AU - Langguth, Berthold AU - Breitmayer, Marius AU - Probst, Thomas PY - 2020/6/30 TI - Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study JO - J Med Internet Res SP - e15547 VL - 22 IS - 6 KW - mHealth KW - crowdsensing KW - tinnitus KW - machine learning KW - mobile operating system differences KW - ecological momentary assessment KW - mobile phone N2 - Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient?s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)?Android and iOS?to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective: In this study, we explored whether the mobile OS?Android and iOS?used during user assessments can be predicted by the dynamic daily-life TYT data. Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods?a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine?were applied to address the research question. Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder. UR - http://www.jmir.org/2020/6/e15547/ UR - http://dx.doi.org/10.2196/15547 UR - http://www.ncbi.nlm.nih.gov/pubmed/32602842 ID - info:doi/10.2196/15547 ER - TY - JOUR AU - Si, Yan AU - Wu, Hong AU - Liu, Qing PY - 2020/6/29 TI - Factors Influencing Doctors? Participation in the Provision of Medical Services Through Crowdsourced Health Care Information Websites: Elaboration-Likelihood Perspective Study JO - JMIR Med Inform SP - e16704 VL - 8 IS - 6 KW - crowdsourcing KW - crowdsourced medical services KW - online health communities KW - doctors? participation KW - elaboration-likelihood model N2 - Background: Web-based crowdsourcing promotes the goals achieved effectively by gaining solutions from public groups via the internet, and it has gained extensive attention in both business and academia. As a new mode of sourcing, crowdsourcing has been proven to improve efficiency, quality, and diversity of tasks. However, little attention has been given to crowdsourcing in the health sector. Objective: Crowdsourced health care information websites enable patients to post their questions in the question pool, which is accessible to all doctors, and the patients wait for doctors to respond to their questions. Since the sustainable development of crowdsourced health care information websites depends on the participation of the doctors, we aimed to investigate the factors influencing doctors? participation in providing health care information in these websites from the perspective of the elaboration-likelihood model. Methods: We collected 1524 questions with complete patient-doctor interaction processes from an online health community in China to test all the hypotheses. We divided the doctors into 2 groups based on the sequence of the answers: (1) doctor who answered the patient?s question first and (2) the doctors who answered that question after the doctor who answered first. All analyses were conducted using the ordinary least squares method. Results: First, the ability of the doctor who first answered the health-related question was found to positively influence the participation of the following doctors who answered after the first doctor responded to the question (?offline1=.177, P<.001; ?offline2=.063, P=.048; ?online=.418, P<.001). Second, the reward that the patient offered for the best answer showed a positive effect on doctors? participation (?=.019, P<.001). Third, the question?s complexity was found to positively moderate the relationships between the ability of the first doctor who answered and the participation of the following doctors (?=.186, P=.05) and to mitigate the effect between the reward and the participation of the following doctors (?=?.003, P=.10). Conclusions: This study has both theoretical and practical contributions. Online health community managers can build effective incentive mechanisms to encourage highly competent doctors to participate in the provision of medical services in crowdsourced health care information websites and they can increase the reward incentives for each question to increase the participation of the doctors. UR - http://medinform.jmir.org/2020/6/e16704/ UR - http://dx.doi.org/10.2196/16704 UR - http://www.ncbi.nlm.nih.gov/pubmed/32597787 ID - info:doi/10.2196/16704 ER - TY - JOUR AU - Flitcroft, Leah AU - Chen, Sun Won AU - Meyer, Denny PY - 2020/6/26 TI - The Demographic Representativeness and Health Outcomes of Digital Health Station Users: Longitudinal Study JO - J Med Internet Res SP - e14977 VL - 22 IS - 6 KW - population health KW - health behavior KW - health technology KW - eHealth KW - health status N2 - Background: Digital health stations offer an affordable and accessible platform for people to monitor their health; however, there is limited information regarding the demographic profile of users and the health benefits of this technology. Objective: This study aimed to assess the demographic representativeness of health station users, identify the factors associated with repeat utilization of stations, and determine if the health status of repeat users changed between baseline and final health check. Methods: Data from 180,442 health station users in Australia, including 8441 repeat users, were compared with 2014-2015 Australian National Health Survey (NHS) participants on key demographic and health characteristics. Binary logistic regression analyses were used to compare demographic and health characteristics of repeat and one-time users. Baseline and final health checks of repeat users were compared using McNemar tests and Wilcoxon signed rank tests. The relationship between the number of checks and final health scores was investigated using generalized linear models. Results: The demographic profile of SiSU health station users differs from that of the general population. A larger proportion of SiSU users were female (100,814/180,442, 55.87% vs 7807/15,393, 50.72%), younger (86,387/180,442, 47.88% vs 5309/15,393, 34.49% aged less than 35 years), and socioeconomically advantaged (64,388/180,442, 35.68% vs 3117/15,393, 20.25%). Compared with NHS participants, a smaller proportion of SiSU health station users were overweight or obese, were smokers, had high blood pressure (BP), or had diabetes. When data were weighted for demographic differences, only rates of high BP were found to be lower for SiSU users compared with the NHS participants (odds ratio [OR] 1.26; P<.001). Repeat users were more likely to be female (OR 1.37; P<.001), younger (OR 0.99; P<.001), and from high socioeconomic status areas?those residing in socioeconomic index for areas quintiles 4 and 5 were significantly more likely to be repeat users compared with those residing in quintile 1 (OR 1.243; P<.001 and OR 1.151; P<.001, respectively). Repeat users were more likely to have a higher BMI (OR 1.02; P<.001), high BP (OR 1.15; P<.001), and less likely to be smokers (OR 0.77; P<.001). Significant improvements in health status were observed for repeat users. Mean BMI decreased by 0.97 kg/m2 from baseline to final check (z=?14.24; P<.001), whereas the proportion of people with high BP decreased from 15.77% (1080/6848) to 12.90% (885/6860; ?21=38.2; P<.001). The proportion of smokers decreased from 11.91% (1005/8438) to 10.13% (853/8421; ?21=48.4; P<.001). Number of repeat health checks was significantly associated with smoking status (OR 0.96; P<.048) but not with higher BP (P=.14) or BMI (P=.23). Conclusions: These findings provide valuable insight into the benefits of health stations for self-monitoring and partially support previous research regarding the effect of demographics and health status on self-management of health. UR - http://www.jmir.org/2020/6/e14977/ UR - http://dx.doi.org/10.2196/14977 UR - http://www.ncbi.nlm.nih.gov/pubmed/32589150 ID - info:doi/10.2196/14977 ER - TY - JOUR AU - Ding, Xiaodong AU - Cheng, Feng AU - Morris, Robert AU - Chen, Cong AU - Wang, Yiqin PY - 2020/6/22 TI - Machine Learning?Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation JO - JMIR Med Inform SP - e18134 VL - 8 IS - 6 KW - pulse wave KW - quality evaluation KW - single period KW - segmentation KW - machine learning N2 - Background: The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. Objective: The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. Methods: Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. Results: It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. Conclusions: We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status. UR - http://medinform.jmir.org/2020/6/e18134/ UR - http://dx.doi.org/10.2196/18134 UR - http://www.ncbi.nlm.nih.gov/pubmed/32568091 ID - info:doi/10.2196/18134 ER - TY - JOUR AU - Chu, Kuo-Chung AU - Lu, Hsin-Ke AU - Huang, Ming-Chun AU - Lin, Shr-Jie AU - Liu, Wen-I AU - Huang, Yu-Shu AU - Hsu, Jen-Fu AU - Wang, Chih-Huan PY - 2020/6/19 TI - Using Mobile Electroencephalography and Actigraphy to Diagnose Attention-Deficit/Hyperactivity Disorder: Case-Control Comparison Study JO - JMIR Ment Health SP - e12158 VL - 7 IS - 6 KW - actigraphy KW - ADHD KW - attention deficit disorder with hyperactivity KW - clinical decision-making KW - electroencephalography KW - neuropsychological tests N2 - Background: Children with attention-deficit/hyperactivity disorder (ADHD), a neurobehavioral disorder, display behaviors of inattention, hyperactivity, or impulsivity, which can affect their ability to learn and establish proper family and social relationships. Various tools are currently used by child and adolescent psychiatric clinics to diagnose, evaluate, and collect information and data. The tools allow professional physicians to assess if patients need further treatment, following a thorough and careful clinical diagnosis process. Objective: We aim to determine potential indicators extracted from a mobile electroencephalography (EEG) device (Mindset; NeuroSky) and an actigraph (MotionWatch 8; CamNtech) and to validate them for diagnosis of ADHD. The 3 indicators are (1) attention, measured by the EEG; (2) meditation, measured by the EEG; and (3) activity, measured by the actigraph. Methods: A total of 63 participants were recruited. The case group comprised 40 boys and 9 girls, while the control group comprised 5 boys and 9 girls. The groups were age matched. The test was divided into 3 stages?pretest, in-test, and posttest?with a testing duration of 20 minutes each. We used correlation analysis, repeated measures analysis of variance, and regression analysis to investigate which indicators can be used for ADHD diagnosis. Results: With the EEG indicators, the analysis results show a significant correlation of attention with both hit reaction time (RT) interstimulus interval (ISI) change (r=?0.368; P=.003) and hit standard error (SE) ISI change (r=?0.336; P=.007). This indicates that the higher the attention of the participants, the smaller both the hit RT change and the hit SE ISI change. With the actigraph indicator, confidence index (r=0.352; P=.005), omissions (r=0.322; P=.01), hit RT SE (r=0.393; P=.001), and variability (r=0.351; P=.005) were significant. This indicates that the higher the activity amounts, the higher the impulsive behavior of the participants and the more target omissions in the continuous performance test (CPT). The results show that the participants with ADHD present a significant difference in activity amounts (P<0.001). The actigraph outperforms the EEG in screening ADHD. Conclusions: When the participants with ADHD are stimulated under restricted conditions, they will present different amounts of activity than in unrestricted conditions due to participants? inability to exercise control over their concentration. This finding could be a new electronic physiological biomarker of ADHD. An actigraph can be used to detect the amount of activity exhibited and to help physicians diagnose the disorder in order to develop more objective, rapid auxiliary diagnostic tools. UR - http://mental.jmir.org/2020/6/e12158/ UR - http://dx.doi.org/10.2196/12158 UR - http://www.ncbi.nlm.nih.gov/pubmed/32558658 ID - info:doi/10.2196/12158 ER - TY - JOUR AU - Grethlein, David AU - Winston, Koplin Flaura AU - Walshe, Elizabeth AU - Tanner, Sean AU - Kandadai, Venk AU - Ontañón, Santiago PY - 2020/6/18 TI - Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data JO - J Med Internet Res SP - e13995 VL - 22 IS - 6 KW - simulated driving assessment KW - on-road exam KW - machine learning KW - adolescent KW - child KW - support vector machines KW - humans KW - accidents, traffic KW - cause of death KW - licensure KW - automobile driving KW - motor vehicle KW - motor vehicles N2 - Background: A large Midwestern state commissioned a virtual driving test (VDT) to assess driving skills preparedness before the on-road examination (ORE). Since July 2017, a pilot deployment of the VDT in state licensing centers (VDT pilot) has collected both VDT and ORE data from new license applicants with the aim of creating a scoring algorithm that could predict those who were underprepared. Objective: Leveraging data collected from the VDT pilot, this study aimed to develop and conduct an initial evaluation of a novel machine learning (ML)?based classifier using limited domain knowledge and minimal feature engineering to reliably predict applicant pass/fail on the ORE. Such methods, if proven useful, could be applicable to the classification of other time series data collected within medical and other settings. Methods: We analyzed an initial dataset that comprised 4308 drivers who completed both the VDT and the ORE, in which 1096 (25.4%) drivers went on to fail the ORE. We studied 2 different approaches to constructing feature sets to use as input to ML algorithms: the standard method of reducing the time series data to a set of manually defined variables that summarize driving behavior and a novel approach using time series clustering. We then fed these representations into different ML algorithms to compare their ability to predict a driver?s ORE outcome (pass/fail). Results: The new method using time series clustering performed similarly compared with the standard method in terms of overall accuracy for predicting pass or fail outcome (76.1% vs 76.2%) and area under the curve (0.656 vs 0.682). However, the time series clustering slightly outperformed the standard method in differentially predicting failure on the ORE. The novel clustering method yielded a risk ratio for failure of 3.07 (95% CI 2.75-3.43), whereas the standard variables method yielded a risk ratio for failure of 2.68 (95% CI 2.41-2.99). In addition, the time series clustering method with logistic regression produced the lowest ratio of false alarms (those who were predicted to fail but went on to pass the ORE; 27.2%). Conclusions: Our results provide initial evidence that the clustering method is useful for feature construction in classification tasks involving time series data when resources are limited to create multiple, domain-relevant variables. UR - https://www.jmir.org/2020/6/e13995 UR - http://dx.doi.org/10.2196/13995 UR - http://www.ncbi.nlm.nih.gov/pubmed/32554384 ID - info:doi/10.2196/13995 ER - TY - JOUR AU - Ferguson, Caleb AU - Inglis, C. Sally AU - Breen, P. Paul AU - Gargiulo, D. Gaetano AU - Byiers, Victoria AU - Macdonald, S. Peter AU - Hickman, D. Louise PY - 2020/6/18 TI - Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study JO - JMIR Aging SP - e17299 VL - 3 IS - 1 KW - technology KW - arrhythmia KW - monitoring KW - older people KW - cardiology KW - qualitative KW - wearable N2 - Background: New wearable devices (for example, AliveCor or Zio patch) offer promise in detecting arrhythmia and monitoring cardiac health status, among other clinically useful parameters in older adults. However, the clinical utility and usability from the perspectives of clinicians is largely unexplored. Objective: This study aimed to explore clinician perspectives on the use of wearable cardiac monitoring technology for older adults. Methods: A descriptive qualitative study was conducted using semistructured focus group interviews. Clinicians were recruited through purposive sampling of physicians, nurses, and allied health staff working in 3 tertiary-level hospitals. Verbatim transcripts were analyzed using thematic content analysis to identify themes. Results: Clinicians representing physicians, nurses, and allied health staff working in 3 tertiary-level hospitals completed 4 focus group interviews between May 2019 and July 2019. There were 50 participants (28 men and 22 women), including cardiologists, geriatricians, nurses, and allied health staff. The focus groups generated the following 3 overarching, interrelated themes: (1) the current state of play, understanding the perceived challenges of patient cardiac monitoring in hospitals, (2) priorities in cardiac monitoring, what parameters new technologies should measure, and (3) cardiac monitoring of the future, ?the ideal device.? Conclusions: There remain pitfalls related to the design of wearable cardiac technology for older adults that present clinical challenges. These pitfalls and challenges likely negatively impact the uptake of wearable cardiac monitoring in routine clinical care. Partnering with clinicians and patients in the co-design of new wearable cardiac monitoring technologies is critical to optimize the use of these devices and their uptake in clinical care. UR - http://aging.jmir.org/2020/1/e17299/ UR - http://dx.doi.org/10.2196/17299 UR - http://www.ncbi.nlm.nih.gov/pubmed/32554377 ID - info:doi/10.2196/17299 ER - TY - JOUR AU - Faruqui, Akhter Syed Hasib AU - Alaeddini, Adel AU - Chang, C. Mike AU - Shirinkam, Sara AU - Jaramillo, Carlos AU - NajafiRad, Peyman AU - Wang, Jing AU - Pugh, Jo Mary PY - 2020/6/17 TI - Summarizing Complex Graphical Models of Multiple Chronic Conditions Using the Second Eigenvalue of Graph Laplacian: Algorithm Development and Validation JO - JMIR Med Inform SP - e16372 VL - 8 IS - 6 KW - graphical models KW - graph summarization KW - graph Laplacian KW - disease network KW - multiple chronic conditions N2 - Background: It is important but challenging to understand the interactions of multiple chronic conditions (MCC) and how they develop over time in patients and populations. Clinical data on MCC can now be represented using graphical models to study their interaction and identify the path toward the development of MCC. However, the current graphical models representing MCC are often complex and difficult to analyze. Therefore, it is necessary to develop improved methods for generating these models. Objective: This study aimed to summarize the complex graphical models of MCC interactions to improve comprehension and aid analysis. Methods: We examined the emergence of 5 chronic medical conditions (ie, traumatic brain injury [TBI], posttraumatic stress disorder [PTSD], depression [Depr], substance abuse [SuAb], and back pain [BaPa]) over 5 years among 257,633 veteran patients. We developed 3 algorithms that utilize the second eigenvalue of the graph Laplacian to summarize the complex graphical models of MCC by removing less significant edges. The first algorithm learns a sparse probabilistic graphical model of MCC interactions directly from the data. The second algorithm summarizes an existing probabilistic graphical model of MCC interactions when a supporting data set is available. The third algorithm, which is a variation of the second algorithm, summarizes the existing graphical model of MCC interactions with no supporting data. Finally, we examined the coappearance of the 100 most common terms in the literature of MCC to validate the performance of the proposed model. Results: The proposed summarization algorithms demonstrate considerable performance in extracting major connections among MCC without reducing the predictive accuracy of the resulting graphical models. For the model learned directly from the data, the area under the curve (AUC) performance for predicting TBI, PTSD, BaPa, SuAb, and Depr, respectively, during the next 4 years is as follows?year 2: 79.91%, 84.04%, 78.83%, 82.50%, and 81.47%; year 3: 76.23%, 80.61%, 73.51%, 79.84%, and 77.13%; year 4: 72.38%, 78.22%, 72.96%, 77.92%, and 72.65%; and year 5: 69.51%, 76.15%, 73.04%, 76.72%, and 69.99%, respectively. This demonstrates an overall 12.07% increase in the cumulative sum of AUC in comparison with the classic multilevel temporal Bayesian network. Conclusions: Using graph summarization can improve the interpretability and the predictive power of the complex graphical models of MCC. UR - http://medinform.jmir.org/2020/6/e16372/ UR - http://dx.doi.org/10.2196/16372 UR - http://www.ncbi.nlm.nih.gov/pubmed/32554376 ID - info:doi/10.2196/16372 ER - TY - JOUR AU - Salisbury, Chris AU - Murphy, Mairead AU - Duncan, Polly PY - 2020/6/16 TI - The Impact of Digital-First Consultations on Workload in General Practice: Modeling Study JO - J Med Internet Res SP - e18203 VL - 22 IS - 6 KW - general practice KW - family practice KW - electronic consultations KW - remote consultation KW - telemedicine KW - telephone consultation KW - video KW - access to health care KW - health care quality, access, and evaluation N2 - Background: Health services in many countries are promoting digital-first models of access to general practice based on offering online, video, or telephone consultations before a face-to-face consultation. It is claimed that this will improve access for patients and moderate the workload of doctors. However, improved access could also potentially increase doctors? workload. Objective: The aim of this study was to explore whether and under what circumstances digital-first access to general practice is likely to decrease or increase general practice workload. Methods: A process map to delineate primary care access pathways was developed and a model to estimate general practice workload constructed in Microsoft Excel (Microsoft Corp). The model was populated using estimates of key variables obtained from a systematic review of published studies. A MEDLINE search was conducted for studies published in English between January 1, 2000, and September 30, 2019. Included papers provided quantitative data about online, telephone, or video consultations for unselected patients requesting a general practice in-hours consultation for any problem. We excluded studies of general practitioners consulting specialists, consultations not conducted by doctors, and consultations conducted after hours, in secondary care, in specialist services, or for a specific health care problem. Data about the following variables were extracted from the included papers to form the model inputs: the proportion of consultations managed digitally, the proportion of digital consultations completed without a subsequent consultation, the proportion of subsequent consultations conducted by telephone rather than face-to-face, consultation duration, and the proportion of digital consultations that represent new demand. The outcome was general practice workload. The model was used to test the likely impact of different digital-first scenarios, based on the best available evidence and the plausible range of estimates from the published studies. The model allows others to test the impact on workload of varying assumptions about model inputs. Results: Digital-first approaches are likely to increase general practice workload unless they are shorter, and a higher proportion of patients are managed without a subsequent consultation than observed in most published studies. In our base-case scenarios (based on the best available evidence), digital-first access models using online, telephone, or video consultations are likely to increase general practitioner workload by 25%, 3%, and 31%, respectively. An important determinant of workload is whether the availability of digital-first approaches changes the demand for general practice consultations, but there is little robust evidence to answer this question. Conclusions: Digital-first approaches to primary care could increase general practice workload unless stringent conditions are met. Justification for these approaches should be based on evidence about the benefits in relation to the costs, rather than assumptions about reductions in workload. Given the potential increase in workload, which in due course could worsen problems of access, these initiatives should be implemented in a staged way alongside careful evaluation. UR - http://www.jmir.org/2020/6/e18203/ UR - http://dx.doi.org/10.2196/18203 UR - http://www.ncbi.nlm.nih.gov/pubmed/32543441 ID - info:doi/10.2196/18203 ER - TY - JOUR AU - Lowres, Nicole AU - Duckworth, Andrew AU - Redfern, Julie AU - Thiagalingam, Aravinda AU - Chow, K. Clara PY - 2020/6/16 TI - Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text?Based Secondary Prevention Program: Feasibility Study JO - JMIR Mhealth Uhealth SP - e19200 VL - 8 IS - 6 KW - eHealth KW - machine learning, secondary prevention, SMS text messaging, cardiovascular, mHealth, digital health, mobile phone N2 - Background: SMS text messaging programs are increasingly being used for secondary prevention, and have been shown to be effective in a number of health conditions including cardiovascular disease. SMS text messaging programs have the potential to increase the reach of an intervention, at a reduced cost, to larger numbers of people who may not access traditional programs. However, patients regularly reply to the SMS text messages, leading to additional staffing requirements to monitor and moderate the patients? SMS text messaging replies. This additional staff requirement directly impacts the cost-effectiveness and scalability of SMS text messaging interventions. Objective: This study aimed to test the feasibility and accuracy of developing a machine learning (ML) program to triage SMS text messaging replies (ie, identify which SMS text messaging replies require a health professional review). Methods: SMS text messaging replies received from 2 clinical trials were manually coded (1) into ?Is staff review required?? (binary response of yes/no); and then (2) into 12 general categories. Five ML models (Naïve Bayes, OneVsRest, Random Forest Decision Trees, Gradient Boosted Trees, and Multilayer Perceptron) and an ensemble model were tested. For each model run, data were randomly allocated into training set (2183/3118, 70.01%) and test set (935/3118, 29.98%). Accuracy for the yes/no classification was calculated using area under the receiver operating characteristics curve (AUC), false positives, and false negatives. Accuracy for classification into 12 categories was compared using multiclass classification evaluators. Results: A manual review of 3118 SMS text messaging replies showed that 22.00% (686/3118) required staff review. For determining need for staff review, the Multilayer Perceptron model had highest accuracy (AUC 0.86; 4.85% false negatives; and 4.63% false positives); with addition of heuristics (specified keywords) fewer false negatives were identified (3.19%), with small increase in false positives (7.66%) and AUC 0.79. Application of this model would result in 26.7% of SMS text messaging replies requiring review (true + false positives). The ensemble model produced the lowest false negatives (1.43%) at the expense of higher false positives (16.19%). OneVsRest was the most accurate (72.3%) for the 12-category classification. Conclusions: The ML program has high sensitivity for identifying the SMS text messaging replies requiring staff input; however, future research is required to validate the models against larger data sets. Incorporation of an ML program to review SMS text messaging replies could significantly reduce staff workload, as staff would not have to review all incoming SMS text messages. This could lead to substantial improvements in cost-effectiveness, scalability, and capacity of SMS text messaging?based interventions. UR - http://mhealth.jmir.org/2020/6/e19200/ UR - http://dx.doi.org/10.2196/19200 UR - http://www.ncbi.nlm.nih.gov/pubmed/32543439 ID - info:doi/10.2196/19200 ER - TY - JOUR AU - Di Tosto, Gennaro AU - McAlearney, Scheck Ann AU - Fareed, Naleef AU - Huerta, R. Timothy PY - 2020/6/12 TI - Metrics for Outpatient Portal Use Based on Log File Analysis: Algorithm Development JO - J Med Internet Res SP - e16849 VL - 22 IS - 6 KW - patient portals KW - health records, personal KW - health information technology KW - electronic health record N2 - Background: Web-based outpatient portals help patients engage in the management of their health by allowing them to access their medical information, schedule appointments, track their medications, and communicate with their physicians and care team members. Initial studies have shown that portal adoption positively affects health outcomes; however, early studies typically relied on survey data. Using data from health portal applications, we conducted systematic assessments of patients? use of an outpatient portal to examine how patients engage with the tool. Objective: This study aimed to document the functionality of an outpatient portal in the context of outpatient care by mining portal usage data and to provide insights into how patients use this tool. Methods: Using audit log files from the outpatient portal associated with the electronic health record system implemented at a large multihospital academic medical center, we investigated the behavioral traces of a study population of 2607 patients who used the portal between July 2015 and February 2019. Patient portal use was defined as having an active account and having accessed any portal function more than once during the study time frame. Results: Through our analysis of audit log file data of the number and type of user interactions, we developed a taxonomy of functions and actions and computed analytic metrics, including frequency and comprehensiveness of use. We additionally documented the computational steps required to diagnose artifactual data and arrive at valid usage metrics. Of the 2607 patients in our sample, 2511 were active users of the patients portal where the median number of sessions was 94 (IQR 207). Function use was comprehensive at the patient level, while each session was instead limited to the use of one specific function. Only 17.45% (78,787/451,762) of the sessions were linked to activities involving more than one portal function. Conclusions: In discussing the full methodological choices made in our analysis, we hope to promote the replicability of our study at other institutions and contribute to the establishment of best practices that can facilitate the adoption of behavioral metrics that enable the measurement of patient engagement based on the outpatient portal use. UR - https://www.jmir.org/2020/6/e16849 UR - http://dx.doi.org/10.2196/16849 UR - http://www.ncbi.nlm.nih.gov/pubmed/32530435 ID - info:doi/10.2196/16849 ER - TY - JOUR AU - Knopp, U. Melanie AU - Binzel, Katherine AU - Wright, L. Chadwick AU - Zhang, Jun AU - Knopp, V. Michael PY - 2020/6/12 TI - Enhancing Patient Experience With Internet Protocol Addressable Digital Light-Emitting Diode Lighting in Imaging Environments: A Phase I Study JO - J Med Internet Res SP - e11839 VL - 22 IS - 6 KW - ambient lighting KW - patient comfort KW - medical imaging KW - color perception KW - health care environment KW - internet protocol?based light-emitting diode lighting N2 - Background: Conventional approaches to improve the quality of clinical patient imaging studies focus predominantly on updating or replacing imaging equipment; however, it is often not considered that patients can also highly influence the diagnostic quality of clinical imaging studies. Patient-specific artifacts can limit the diagnostic image quality, especially when patients are uncomfortable, anxious, or agitated. Imaging facility or environmental conditions can also influence the patient?s comfort and willingness to participate in diagnostic imaging studies, especially when performed in visually unesthetic, anxiety-inducing, and technology-intensive imaging centers. When given the opportunity to change a single aspect of the environmental or imaging facility experience, patients feel much more in control of the otherwise unfamiliar and uncomfortable setting. Incorporating commercial, easily adaptable, ambient lighting products within clinical imaging environments allows patients to individually customize their environment for a more personalized and comfortable experience. Objective: The aim of this pilot study was to use a customizable colored light-emitting diode (LED) lighting system within a clinical imaging environment and demonstrate the feasibility and initial findings of enabling healthy subjects to customize the ambient lighting and color. Improving the patient experience within clinical imaging environments with patient-preferred ambient lighting and color may improve overall patient comfort, compliance, and participation in the imaging study and indirectly contribute to improving diagnostic image quality. Methods: We installed consumer-based internet protocol addressable LED lights using the ZigBee standard in different imaging rooms within a clinical imaging environment. We recruited healthy volunteers (n=35) to generate pilot data in order to develop a subsequent clinical trial. The visual perception assessment procedure utilized questionnaires with preprogrammed light/color settings and further assessed how subjects preferred ambient light and color within a clinical imaging setting. Results: Technical implementation using programmable LED lights was performed without any hardware or electrical modifications to the existing clinical imaging environment. Subject testing revealed substantial variabilities in color perception; however, clear trends in subject color preference were noted. In terms of the color hue of the imaging environment, 43% (15/35) found blue and 31% (11/35) found yellow to be the most relaxing. Conversely, 69% (24/35) found red, 17% (6/35) found yellow, and 11% (4/35) found green to be the least relaxing. Conclusions: With the majority of subjects indicating that colored lighting within a clinical imaging environment would contribute to an improved patient experience, we predict that enabling patients to customize environmental factors like lighting and color to individual preferences will improve patient comfort and patient satisfaction. Improved patient comfort in clinical imaging environments may also help to minimize patient-specific imaging artifacts that can otherwise limit diagnostic image quality. Trial Registration: ClinicalTrials.gov NCT03456895; https://clinicaltrials.gov/ct2/show/NCT03456895 UR - http://www.jmir.org/2020/6/e11839/ UR - http://dx.doi.org/10.2196/11839 UR - http://www.ncbi.nlm.nih.gov/pubmed/32530434 ID - info:doi/10.2196/11839 ER - TY - JOUR AU - Peng, Li-Ning AU - Hsiao, Fei-Yuan AU - Lee, Wei-Ju AU - Huang, Shih-Tsung AU - Chen, Liang-Kung PY - 2020/6/11 TI - Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach JO - J Med Internet Res SP - e16213 VL - 22 IS - 6 KW - multimorbidity frailty index KW - machine learning KW - random forest KW - unplanned hospitalizations KW - intensive care unit admissions KW - mortality N2 - Background: Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. Objective: This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. Methods: In this study, we used Taiwan?s National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. Results: The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. Conclusions: The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society. UR - http://www.jmir.org/2020/6/e16213/ UR - http://dx.doi.org/10.2196/16213 UR - http://www.ncbi.nlm.nih.gov/pubmed/32525481 ID - info:doi/10.2196/16213 ER - TY - JOUR AU - Shao, Fang AU - He, Zhiqiang AU - Zhu, Zheng AU - Wang, Xiang AU - Zhang, Jianping AU - Shan, Jinhua AU - Pan, Jiajia AU - Wang, Hui PY - 2020/6/10 TI - Internet Influence of Assisted Reproduction Technology Centers in China: Qualitative Study Based on WeChat Official Accounts JO - J Med Internet Res SP - e17997 VL - 22 IS - 6 KW - ART center KW - WeChat official account KW - Internet influence N2 - Background: The prevalence of infertility in China is high, but the advent of assisted reproduction technology (ART) has greatly eased this situation. Social media, such as WeChat official accounts, have become the preferred tool for ART centers to communicate with patients, but their attention and operational status differ, and the Internet influence of WeChat official accounts is insufficient. In addition, questions about whether Internet influence is consistent with academic influence and whether the Internet can influence patients? choice of medical treatment to a certain extent have not been explored. Objective: This study aimed to examine the operational status and Internet influence of WeChat official accounts for ART centers and to explore the degree of Internet influence on patients? choices of medical treatment. Methods: We collected information from the WeChat official accounts for ART centers approved by the National Health Commission of the People?s Republic of China and used the technique for order of preference by similarity to ideal solution to build an Internet influence model of the ART centers and obtained a Ranking of Internet Influence on Reproductive Centers (RIIRC) for each center. Results: We found there were 451 ART centers throughout the country by the end of 2016 and 498 by the end of 2018. The number of medical institutions is quite large, but their distribution is uneven, and their level of medical technical ability is very different. Analysis of the text data of posts of WeChat official accounts showed the ART centers have insufficient awareness of network exposure and publicity, and the RIIRC of some medical institutions was inconsistent with their medical level and academic status. Conclusions: ART institutions have varying degrees of emphasis and use of WeChat official accounts in China. They fail to realize that the Internet influence of WeChat may bring them potential patient resources and that Internet influence may affect the future market structure of ART and may also potentially affect academic rankings. UR - https://www.jmir.org/2020/6/e17997 UR - http://dx.doi.org/10.2196/17997 UR - http://www.ncbi.nlm.nih.gov/pubmed/32357124 ID - info:doi/10.2196/17997 ER - TY - JOUR AU - Weenk, Mariska AU - Bredie, J. Sebastian AU - Koeneman, Mats AU - Hesselink, Gijs AU - van Goor, Harry AU - van de Belt, H. Tom PY - 2020/6/10 TI - Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial JO - J Med Internet Res SP - e15471 VL - 22 IS - 6 KW - remote sensing technology KW - wireless technology KW - continuous monitoring KW - vital signs KW - wearable electronic devices KW - remote monitoring KW - digital health N2 - Background: Wearable devices can be used for continuous patient monitoring in the general ward, increasing patient safety. Little is known about the experiences and expectations of patients and health care professionals regarding continuous monitoring with these devices. Objective: This study aimed to identify positive and negative effects as well as barriers and facilitators for the use of two wearable devices: ViSi Mobile (VM) and HealthPatch (HP). Methods: In this randomized controlled trial, 90 patients admitted to the internal medicine and surgical wards of a university hospital in the Netherlands were randomly assigned to continuous vital sign monitoring using VM or HP and a control group. Users? experiences and expectations were addressed using semistructured interviews. Nurses, physician assistants, and medical doctors were also interviewed. Interviews were analyzed using thematic content analysis. Psychological distress was assessed using the State Trait Anxiety Inventory and the Pain Catastrophizing Scale. The System Usability Scale was used to assess the usability of both devices. Results: A total of 60 patients, 20 nurses, 3 physician assistants, and 6 medical doctors were interviewed. We identified 47 positive and 30 negative effects and 19 facilitators and 36 barriers for the use of VM and HP. Frequently mentioned topics included earlier identification of clinical deterioration, increased feelings of safety, and VM lines and electrodes. No differences related to psychological distress and usability were found between randomization groups or devices. Conclusions: Both devices were well received by most patients and health care professionals, and the majority of them encouraged the idea of monitoring vital signs continuously in the general ward. This comprehensive overview of barriers and facilitators of using wireless devices may serve as a guide for future researchers, developers, and health care institutions that consider implementing continuous monitoring in the ward. Trial Registration: Clinicaltrials.gov NCT02933307; http://clinicaltrials.gov/ct2/show/NCT02933307. UR - https://www.jmir.org/2020/6/e15471 UR - http://dx.doi.org/10.2196/15471 UR - http://www.ncbi.nlm.nih.gov/pubmed/32519972 ID - info:doi/10.2196/15471 ER - TY - JOUR AU - Miyashita, Hirotaka AU - Nakamura, Mitsuteru AU - Svensson, Kishi Akiko AU - Nakamura, Masahiro AU - Tokuno, Shinichi AU - Chung, Ung-Il AU - Svensson, Thomas PY - 2020/6/9 TI - Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study JO - JMIR Form Res SP - e16880 VL - 4 IS - 6 KW - voice analysis KW - emotional status KW - vitality KW - sleep KW - mobile phone N2 - Background: Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective: The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods: A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (?vitality) and specific sleep measures. Results: A significant association was identified between total sleep time and ?vitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and ?vitality (regression coefficient: ?0.026, P=.001). There was no significant association between ?vitality and sleep efficiency or number of awakenings. Conclusions: Total sleep time and sleep onset latency are significantly associated with ?vitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures. UR - https://formative.jmir.org/2020/6/e16880 UR - http://dx.doi.org/10.2196/16880 UR - http://www.ncbi.nlm.nih.gov/pubmed/32515745 ID - info:doi/10.2196/16880 ER - TY - JOUR AU - Kahler, W. Christopher AU - Cohn, M. Amy AU - Costantino, Catherine AU - Toll, A. Benjamin AU - Spillane, S. Nichea AU - Graham, L. Amanda PY - 2020/6/8 TI - A Digital Smoking Cessation Program for Heavy Drinkers: Pilot Randomized Controlled Trial JO - JMIR Form Res SP - e7570 VL - 4 IS - 6 KW - smoking cessation KW - alcohol drinking KW - internet KW - text messaging KW - therapy N2 - Background: Heavy drinking (HD) is far more common among smokers compared with nonsmokers and interferes with successful smoking cessation. Alcohol-focused smoking cessation interventions delivered by counselors have shown promise, but digital versions of these interventions?which could have far greater population reach?have not yet been tested. Objective: This pilot randomized controlled trial aimed to examine the feasibility, acceptability, and effect sizes of an automated digital smoking cessation program that specifically addresses HD using an interactive web-based intervention with an optional text messaging component. Methods: Participants (83/119, 69.7% female; 98/119, 82.4% white; mean age 38.0 years) were daily smokers recruited on the web from a free automated digital smoking cessation program (BecomeAnEX.org, EX) who met the criteria for HD: women drinking 8+ drinks/week or 4+ drinks on any day and men drinking 15+ drinks/week or 5+ drinks on any day. Participants were randomized to receive EX with standard content (EX-S) or an EX with additional content specific to HD (EX-HD). Outcomes were assessed by web-based surveys at 1 and 6 months. Results: Participants reported high satisfaction with the website and the optional text messaging component. Total engagement with both EX-S and EX-HD was modest, with participants visiting the website a median of 2 times, and 52.9% of the participants enrolled to receive text messages. Participants in both the conditions showed substantial, significant reductions in drinking across 6 months of follow-up, with no condition effects observed. Although smoking outcomes tended to favor EX-HD, the condition effects were small and nonsignificant. A significantly smaller proportion of participants in EX-HD reported having a lapse back to smoking when drinking alcohol (7/58, 16%) compared with those in EX-S (18/61, 41%; ?21=6.2; P=.01). Conclusions: This is the first trial to examine a digital smoking cessation program tailored to HD smokers. The results provide some initial evidence that delivering such a program is feasible and may reduce the risk of alcohol-involved smoking lapses. However, increasing engagement in this and other web-based interventions is a crucial challenge to address in future work. Trial Registration: ClinicalTrials.gov NCT03068611; https://clinicaltrials.gov/ct2/show/NCT03068611 UR - https://formative.jmir.org/2020/6/e7570 UR - http://dx.doi.org/10.2196/formative.7570 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348286 ID - info:doi/10.2196/formative.7570 ER - TY - JOUR AU - Fu, Weifeng PY - 2020/6/3 TI - Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study JO - JMIR Med Inform SP - e18677 VL - 8 IS - 6 KW - speech recognition KW - isolated words KW - mental health KW - small vocabulary KW - HMM KW - hidden Markov model KW - programming N2 - Background: Speech recognition is a technology that enables machines to understand human language. Objective: In this study, speech recognition of isolated words from a small vocabulary was applied to the field of mental health counseling. Methods: A software platform was used to establish a human-machine chat for psychological counselling. The software uses voice recognition technology to decode the user's voice information. The software system analyzes and processes the user's voice information according to many internal related databases, and then gives the user accurate feedback. For users who need psychological treatment, the system provides them with psychological education. Results: The speech recognition system included features such as speech extraction, endpoint detection, feature value extraction, training data, and speech recognition. Conclusions: The Hidden Markov Model was adopted, based on multithread programming under a VC2005 compilation environment, to realize the parallel operation of the algorithm and improve the efficiency of speech recognition. After the design was completed, simulation debugging was performed in the laboratory. The experimental results showed that the designed program met the basic requirements of a speech recognition system. UR - https://medinform.jmir.org/2020/6/e18677 UR - http://dx.doi.org/10.2196/18677 UR - http://www.ncbi.nlm.nih.gov/pubmed/32384054 ID - info:doi/10.2196/18677 ER - TY - JOUR AU - Jacobson, C. Nicholas AU - Summers, Berta AU - Wilhelm, Sabine PY - 2020/5/29 TI - Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors JO - J Med Internet Res SP - e16875 VL - 22 IS - 5 KW - biomarkers KW - machine learning KW - technology assessment, biomedical KW - social anxiety KW - social anxiety disorder KW - mobile phone N2 - Background: Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. Objective: This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods: In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants? social anxiety symptom severity. Results: The results suggested that these passive sensor data could be utilized to accurately predict participants? social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions: These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect. UR - http://www.jmir.org/2020/5/e16875/ UR - http://dx.doi.org/10.2196/16875 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348284 ID - info:doi/10.2196/16875 ER - TY - JOUR AU - Ford, Helen AU - Herbert, Jeremy AU - Horsham, Caitlin AU - Wall, Alexander AU - Hacker, Elke PY - 2020/5/28 TI - Internet of Things Smart Sunscreen Station: Descriptive Proof-of-Concept Study JO - J Med Internet Res SP - e17079 VL - 22 IS - 5 KW - skin neoplasms KW - melanoma KW - health promotion KW - public health KW - preventive medicine KW - web applications N2 - Background: Skin cancer is the most prevalent but also most preventable cancer in Australia. Outdoor workers are at increased risk of developing skin cancer, and improvements in sun protection are needed. Sunscreen, when applied at the recommended concentration (2 mg/cm2), has been shown to block the harmful molecular effects of ultraviolet radiation in vivo. However, sunscreen is often not applied, reapplied sufficiently, or stored adequately to yield protection and reduce sunburns. Objective: The primary aim of this study was to test an Internet of Things approach by deploying a smart sunscreen station to an outdoor regional mining site. Methods: We deployed a smart sunscreen station and examined the key technological considerations including connectivity, security, and data management systems. Results: The smart sunscreen station was deployed for 12 days at a mining workplace (Dalby, Australia). The smart sunscreen station?s electrical components remained operational during field testing, and data were received by the message queuing telemetry transport server automatically at the end of each day of field testing (12/12 days, 100% connectivity). Conclusions: This study highlights that an Internet of Things technology approach can successfully measure sunscreen usage and temperature storage conditions. UR - http://www.jmir.org/2020/5/e17079/ UR - http://dx.doi.org/10.2196/17079 UR - http://www.ncbi.nlm.nih.gov/pubmed/32463378 ID - info:doi/10.2196/17079 ER - TY - JOUR AU - Khundaqji, Hamzeh AU - Hing, Wayne AU - Furness, James AU - Climstein, Mike PY - 2020/5/27 TI - Smart Shirts for Monitoring Physiological Parameters: Scoping Review JO - JMIR Mhealth Uhealth SP - e18092 VL - 8 IS - 5 KW - wearable electronic devices KW - biomedical technology KW - telemedicine KW - fitness trackers KW - sports KW - exercise KW - physiology KW - clinical decision making KW - vital signs N2 - Background: The recent trends of technological innovation and widescale digitization as potential solutions to challenges in health care, sports, and emergency service operations have led to the conception of smart textile technology. In health care, these smart textile systems present the potential to aid preventative medicine and early diagnosis through continuous, noninvasive tracking of physical and mental health while promoting proactive involvement of patients in their medical management. In areas such as sports and emergency response, the potential to provide comprehensive and simultaneous physiological insights across multiple body systems is promising. However, it is currently unclear what type of evidence exists surrounding the use of smart textiles for the monitoring of physiological outcome measures across different settings. Objective: This scoping review aimed to systematically survey the existing body of scientific literature surrounding smart textiles in their most prevalent form, the smart shirt, for monitoring physiological outcome measures. Methods: A total of 5 electronic bibliographic databases were systematically searched (Ovid Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Scopus, Cumulative Index to Nursing and Allied Health Literature, and SPORTDiscus). Publications from the inception of the database to June 24, 2019 were reviewed. Nonindexed literature relevant to this review was also systematically searched. The results were then collated, summarized, and reported. Results: Following the removal of duplicates, 7871 citations were identified. On the basis of title and abstract screening, 7632 citations were excluded, whereas 239 were retrieved and assessed for eligibility. Of these, 101 citations were included in the final analysis. Included studies were categorized into four themes: (1) prototype design, (2) validation, (3) observational, and (4) reviews. Among the 101 analyzed studies, prototype design was the most prevalent theme (50/101, 49.5%), followed by validation (29/101, 28.7%), observational studies (21/101, 20.8%), and reviews (1/101, 0.1%). Presented prototype designs ranged from those capable of monitoring one physiological metric to those capable of monitoring several simultaneously. In 29 validation studies, 16 distinct smart shirts were validated against reference technology under various conditions and work rates, including rest, submaximal exercise, and maximal exercise. The identified observational studies used smart shirts in clinical, healthy, and occupational populations for aims such as early diagnosis and stress detection. One scoping review was identified, investigating the use of smart shirts for electrocardiograph signal monitoring in cardiac patients. Conclusions: Although smart shirts have been found to be valid and reliable in the monitoring of specific physiological metrics, results were variable for others, demonstrating the need for further systematic validation. Analysis of the results has also demonstrated gaps in knowledge, such as a considerable lag of validation and observational studies in comparison with prototype design and limited investigation using smart shirts in pediatric, elite sports, and emergency service populations. UR - http://mhealth.jmir.org/2020/5/e18092/ UR - http://dx.doi.org/10.2196/18092 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348279 ID - info:doi/10.2196/18092 ER - TY - JOUR AU - Bian, Yanyan AU - Xiang, Yongbo AU - Tong, Bingdu AU - Feng, Bin AU - Weng, Xisheng PY - 2020/5/26 TI - Artificial Intelligence?Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study JO - J Med Internet Res SP - e16896 VL - 22 IS - 5 KW - artificial intelligence KW - conversational agent KW - follow-up KW - cost-effectiveness N2 - Background: Patient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically. Objective: The primary objective of this study was to compare the cost-effectiveness of AI-assisted follow-up to manual follow-up of patients after surgery. The secondary objective was to compare the feedback from AI-assisted follow-up to feedback from manual follow-up. Methods: The AI-assisted follow-up system was adopted in the Orthopedic Department of Peking Union Medical College Hospital in April 2019. A total of 270 patients were followed up through this system. Prior to that, 2656 patients were followed up by phone calls manually. Patient characteristics, telephone connection rate, follow-up rate, feedback collection rate, time spent, and feedback composition were compared between the two groups of patients. Results: There was no statistically significant difference in age, gender, or disease between the two groups. There was no significant difference in telephone connection rate (manual: 2478/2656, 93.3%; AI-assisted: 249/270, 92.2%; P=.50) or successful follow-up rate (manual: 2301/2478, 92.9%; AI-assisted: 231/249, 92.8%; P=.96) between the two groups. The time spent on 100 patients in the manual follow-up group was about 9.3 hours. In contrast, the time spent on the AI-assisted follow-up was close to 0 hours. The feedback rate in the AI-assisted follow-up group was higher than that in the manual follow-up group (manual: 68/2656, 2.5%; AI-assisted: 28/270, 10.3%; P<.001). The composition of feedback was different in the two groups. Feedback from the AI-assisted follow-up group mainly included nursing, health education, and hospital environment content, while feedback from the manual follow-up group mostly included medical consultation content. Conclusions: The effectiveness of AI-assisted follow-up was not inferior to that of manual follow-up. Human resource costs are saved by AI. AI can help obtain comprehensive feedback from patients, although its depth and pertinence of communication need to be improved. UR - http://www.jmir.org/2020/5/e16896/ UR - http://dx.doi.org/10.2196/16896 UR - http://www.ncbi.nlm.nih.gov/pubmed/32452807 ID - info:doi/10.2196/16896 ER - TY - JOUR AU - Akbarian, Sina AU - Montazeri Ghahjaverestan, Nasim AU - Yadollahi, Azadeh AU - Taati, Babak PY - 2020/5/22 TI - Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study JO - J Med Internet Res SP - e17252 VL - 22 IS - 5 KW - noncontact monitoring KW - sleep apnea KW - motion analysis KW - computer vision KW - obstructive apnea KW - central apnea KW - machine learning KW - deep learning N2 - Background: Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). Objective: The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. Methods: Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. Results: Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an F1 score of 89% in differentiating OA vs CA. Conclusions: In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition. UR - http://www.jmir.org/2020/5/e17252/ UR - http://dx.doi.org/10.2196/17252 UR - http://www.ncbi.nlm.nih.gov/pubmed/32441656 ID - info:doi/10.2196/17252 ER - TY - JOUR AU - Snyder, Jeremy AU - Zenone, Marco AU - Crooks, Valorie AU - Schuurman, Nadine PY - 2020/5/22 TI - What Medical Crowdfunding Campaigns Can Tell Us About Local Health System Gaps and Deficiencies: Exploratory Analysis of British Columbia, Canada JO - J Med Internet Res SP - e16982 VL - 22 IS - 5 KW - crowdfunding KW - exploratory analysis KW - Canada KW - health system N2 - Background: There are a range of perceived gaps and shortcomings in the publicly funded Canadian health system. These include wait times for care, lack of public insurance coverage for dental care and pharmaceuticals, and difficulties accessing specialist care. Medical crowdfunding is a response to these gaps where individuals raise funds from their social networks to address health-related needs. Objective: This study aimed to investigate the potential of crowdfunding data to better understand what health-related needs individuals are using crowdfunding for, how these needs compare with the existing commentary on health system deficiencies, and the advantages and limitations of using crowdfunding campaigns to enhance or augment our understanding of perceived health system deficiencies. Methods: Crowdfunding campaigns were scraped from the GoFundMe website. These campaigns were then limited to those originating in the metropolitan Vancouver region of two health authorities during 2018. These campaigns were then further limited to those raising funds to allow the treatment of a medical problem or related to needs arising from ill health. These campaigns were then reviewed to identify the underlying health issue and motivation for pursuing crowdfunding. Results: We identified 423 campaigns for health-related needs. These campaigns requested CAD $8,715,806 (US $6,088,078) in funding and were pledged CAD $3,477,384 (US $2,428,987) from 27,773 donors. The most common underlying medical condition for campaign recipients was cancer, followed by traumatic injuries from collisions and brain injury and stroke. By far, the most common factor of motivation for crowdfunding was seeking financial support for wages lost because of illness (232/684, 33.9%). Some campaigns (65/684, 9.5%) sought help with purchasing medical equipment and supplies; 8.2% (56/684) sought to fund complementary, alternative, or unproven treatments including experimental interventions; 7.2% (49/684) sought financial support to cover travel-related costs, including in-province and out-of-province (49/684, 7.2%) travel; and 6.3% (43/684) campaigns sought help to pay for medication. Conclusions: This analysis demonstrates the potential of crowdfunding data to present timely and context-specific user-created insights into the perceived health-related financial needs of some Canadians. Although the literature on perceived limitations of the Canadian health system focuses on wait times for care and limited access to specialist services, among other issues, these campaigners were much more motivated by gaps in the wider social system such as costs related to unpaid time off work and travel to access care. Our findings demonstrate spatial differences in the underlying medical problems, motivations for crowdfunding, and success using crowdfunding that warrants additional attention. These differences may support established concerns that medical crowdfunding is most commonly used by individuals from relatively privileged socioeconomic backgrounds. We encourage the development of new resources to harness the power of crowdfunding data as a supplementary source of information for Canadian health system stakeholders. UR - http://www.jmir.org/2020/5/e16982/ UR - http://dx.doi.org/10.2196/16982 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348269 ID - info:doi/10.2196/16982 ER - TY - JOUR AU - Kwon, Soonil AU - Hong, Joonki AU - Choi, Eue-Keun AU - Lee, Byunghwan AU - Baik, Changhyun AU - Lee, Euijae AU - Jeong, Eui-Rim AU - Koo, Bon-Kwon AU - Oh, Seil AU - Yi, Yung PY - 2020/5/21 TI - Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study JO - J Med Internet Res SP - e16443 VL - 22 IS - 5 KW - atrial fibrillation KW - deep learning KW - diagnosis KW - photoplethysmography KW - wearable electronic devices N2 - Background: Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective: We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods: Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results: In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions: A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration: ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188 UR - http://www.jmir.org/2020/5/e16443/ UR - http://dx.doi.org/10.2196/16443 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348254 ID - info:doi/10.2196/16443 ER - TY - JOUR AU - Chai, R. Peter AU - Schwartz, Emily AU - Hasdianda, Adrian Mohammad AU - Azizoddin, R. Desiree AU - Kikut, Anna AU - Jambaulikar, D. Guruprasad AU - Edwards, R. Robert AU - Boyer, W. Edward AU - Schreiber, L. Kristin PY - 2020/5/20 TI - A Brief Music App to Address Pain in the Emergency Department: Prospective Study JO - J Med Internet Res SP - e18537 VL - 22 IS - 5 KW - music therapy KW - pain KW - smartphone KW - technology KW - telemedicine KW - emergency service, hospital N2 - Background: Emergency physicians face the challenge of relieving acute pain daily. While opioids are a potent treatment for pain, the opioid epidemic has ignited a search for nonopioid analgesic alternatives that may decrease the dose or duration of opioid exposure. While behavioral therapies and complementary medicine are effective, they are difficult to deploy in the emergency department. Music is a potential adjunctive therapy that has demonstrated effectiveness in managing pain. Objective: Our objective was to understand the feasibility and potential for an effect of a novel music app to address acute pain and anxiety in patients admitted to an emergency department observation unit. Methods: This prospective cohort study enrolled patients admitted to an emergency department observation unit with pain who had received orders for opioids. We gathered baseline pain and psychosocial characteristics including anxiety, sleep disturbance, and pain catastrophizing using validated questionnaires. Participants received a smartphone-based music intervention and listened to the music in either a supervised (research assistant?delivered music session 3 times during their stay) or unsupervised manner (music used ad lib by participant). The app collected premusic and postmusic pain and anxiety scores, and participants provided qualitative feedback regarding acceptability of operating the music intervention. Results: We enrolled 81 participants and randomly assigned 38 to an unsupervised and 43 to a supervised group. Mean pain in both groups was 6.1 (1.8) out of a possible score of 10. A total of 43 (53%) reported previous use of music apps at home. We observed an overall modest but significant decrease in pain (mean difference ?0.81, 95% CI ?0.45 to ?1.16) and anxiety (mean difference ?0.72, 95% CI ?0.33 to ?1.12) after music sessions. Reduction of pain and anxiety varied substantially among participants. Individuals with higher baseline pain, catastrophizing (about pain), or anxiety reported greater relief. Changes in pain were correlated to changes in anxiety (Pearson ?=0.3, P=.02) but did not vary between supervised and unsupervised groups. Upon conclusion of the study, 46/62 (74%) reported they liked the music intervention, 57/62 (92%) reported the app was easy to use, and 49/62 (79%) reported they would be willing to use the music intervention at home. Conclusions: A smartphone-based music intervention decreased pain and anxiety among patients in an emergency department observation unit, with no difference between supervised and unsupervised use. Individuals reporting the greatest reduction in pain after music sessions included those scoring highest on baseline assessment of catastrophic thinking, suggesting there may be specific patient populations that may benefit more from using music as an analgesic adjunct in the emergency department. Qualitative feedback suggested that this intervention was feasible and acceptable by emergency department patients. UR - http://www.jmir.org/2020/5/e18537/ UR - http://dx.doi.org/10.2196/18537 UR - http://www.ncbi.nlm.nih.gov/pubmed/32432550 ID - info:doi/10.2196/18537 ER - TY - JOUR AU - De Cannière, Hélène AU - Smeets, P. Christophe J. AU - Schoutteten, Melanie AU - Varon, Carolina AU - Van Hoof, Chris AU - Van Huffel, Sabine AU - Groenendaal, Willemijn AU - Vandervoort, Pieter PY - 2020/5/20 TI - Using Biosensors and Digital Biomarkers to Assess Response to Cardiac Rehabilitation: Observational Study JO - J Med Internet Res SP - e17326 VL - 22 IS - 5 KW - wearables KW - sensor KW - 6MWT KW - rehabilitation KW - cardiovascular N2 - Background: Cardiac rehabilitation (CR) is known for its beneficial effects on functional capacity and is a key component within current cardiovascular disease management strategies. In addition, a larger increase in functional capacity is accompanied by better clinical outcomes. However, not all patients respond in a similar way to CR. Therefore, a patient-tailored approach to CR could open up the possibility to achieve an optimal increase in functional capacity in every patient. Before treatment can be optimized, the differences in response of patients in terms of cardiac adaptation to exercise should first be understood. In addition, digital biomarkers to steer CR need to be identified. Objective: The aim of the study was to investigate the difference in cardiac response between patients characterized by a clear improvement in functional capacity and patients showing only a minor improvement following CR therapy. Methods: A total of 129 patients in CR performed a 6-minute walking test (6MWT) at baseline and during four consecutive short-term follow-up tests while being equipped with a wearable electrocardiogram (ECG) device. The 6MWTs were used to evaluate functional capacity. Patients were divided into high- and low-response groups, based on the improvement in functional capacity during the CR program. Commonly used heart rate parameters and cardiac digital biomarkers representative of the heart rate behavior during the 6MWT and their evolution over time were investigated. Results: All participating patients improved in functional capacity throughout the CR program (P<.001). The heart rate parameters, which are commonly used in practice, evolved differently for both groups throughout CR. The peak heart rate (HRpeak) from patients in the high-response group increased significantly throughout CR, while no change was observed in the low-response group (F4,92=8.321, P<.001). Similar results were obtained for the recovery heart rate (HRrec) values, which increased significantly over time during every minute of recuperation, for the high-response group (HRrec1: P<.001, HRrec2: P<.001, HRrec3: P<.001, HRrec4: P<.001, and HRrec5: P=.02). The other digital biomarkers showed that the evolution of heart rate behavior during a standardized activity test differed throughout CR between both groups. These digital biomarkers, derived from the continuous measurements, contribute to more in-depth insight into the progression of patients? cardiac responses. Conclusions: This study showed that when using wearable sensor technology, the differences in response of patients to CR can be characterized by means of commonly used heart rate parameters and digital biomarkers that are representative of cardiac response to exercise. These digital biomarkers, derived by innovative analysis techniques, allow for more in-depth insights into the cardiac response of cardiac patients during standardized activity. These results open up the possibility to optimized and more patient-tailored treatment strategies and to potentially improve CR outcome. UR - http://www.jmir.org/2020/5/e17326/ UR - http://dx.doi.org/10.2196/17326 UR - http://www.ncbi.nlm.nih.gov/pubmed/32432552 ID - info:doi/10.2196/17326 ER - TY - JOUR AU - Park, Yong-Seok AU - Kim, Sung-Hoon AU - Lee, Se Yoon AU - Choi, Seung-Ho AU - Ku, Seung-Woo AU - Hwang, Gyu-Sam PY - 2020/5/15 TI - Real-Time Monitoring of Blood Pressure Using Digitalized Pulse Arrival Time Calculation Technology for Prompt Detection of Sudden Hypertensive Episodes During Laryngeal Microsurgery: Retrospective Observational Study JO - J Med Internet Res SP - e13156 VL - 22 IS - 5 KW - larynx KW - blood pressure KW - photoplethysmography KW - pulse N2 - Background: Laryngeal microsurgery (LMS) is often accompanied by a sudden increase in blood pressure (BP) during surgery because of stimulation around the larynx. This sudden change in the hemodynamic status is not immediately reflected in a casual cuff-type measurement that takes intermittent readings every 3 to 5 min. Objective: This study aimed to investigate the potential of pulse arrival time (PAT) as a marker for a BP surge, which usually occurs in patients undergoing LMS. Methods: Intermittent measurements of BP and electrocardiogram (ECG) and photoplethysmogram (PPG) signals were recorded during LMS. PAT was defined as the interval between the R-peak on the ECG and the maximum slope on the PPG. Mean PAT values before and after BP increase were compared. PPG-related parameters and the correlations between changes in these variables were calculated. Results: BP surged because of laryngoscopic manipulation (mean systolic BP [SBP] from 115.3, SD 21.4 mmHg, to 159.9, SD 25.2 mmHg; P<.001), whereas PAT decreased significantly (from mean 460.6, SD 51.9 ms, to 405.8, SD 50.1 ms; P<.001) in most of the cases. The change in SBP showed a significant correlation with the inverse of the PAT (r=0.582; P<.001). Receiver-operating characteristic curve analysis indicated that an increase of 11.5% in the inverse of the PAT could detect a 40% increase in SBP, and the area under the curve was 0.814. Conclusions: During LMS, where invasive arterial catheterization is not always possible, PAT shows good correlation with SBP and may, therefore, have the potential to identify abrupt BP surges during laryngoscopic manipulations in a noninvasive manner. UR - https://www.jmir.org/2020/5/e13156 UR - http://dx.doi.org/10.2196/13156 UR - http://www.ncbi.nlm.nih.gov/pubmed/32412413 ID - info:doi/10.2196/13156 ER - TY - JOUR AU - Howard, Derek AU - Maslej, M. Marta AU - Lee, Justin AU - Ritchie, Jacob AU - Woollard, Geoffrey AU - French, Leon PY - 2020/5/13 TI - Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study JO - J Med Internet Res SP - e15371 VL - 22 IS - 5 KW - triage KW - classification KW - natural language processing KW - transfer learning KW - machine learning KW - data interpretation, statistical KW - mental health KW - social support N2 - Background: Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. Objective: This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. Methods: We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. Results: The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. Conclusions: In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available. UR - https://www.jmir.org/2020/5/e15371 UR - http://dx.doi.org/10.2196/15371 UR - http://www.ncbi.nlm.nih.gov/pubmed/32401222 ID - info:doi/10.2196/15371 ER - TY - JOUR AU - Wyatt, D. Kirk AU - Finley, Anissa AU - Uribe, Richard AU - Pallagi, Peter AU - Willaert, Brian AU - Ommen, Steve AU - Yiannias, James AU - Hellmich, Thomas PY - 2020/5/12 TI - Patients' Experiences and Attitudes of Using a Secure Mobile Phone App for Medical Photography: Qualitative Survey Study JO - J Med Internet Res SP - e14412 VL - 22 IS - 5 KW - photography KW - mobile apps KW - telemedicine KW - electronic health records KW - mobile phone KW - digital imaging KW - dermatology KW - vascular medicine KW - family medicine N2 - Background: Point-of-care clinical photography using mobile devices is coming of age as a new standard of care for clinical documentation. High-quality cameras in modern smartphones facilitate faithful reproduction of clinical findings in photographs; however, clinical photographs captured on mobile devices are often taken using the native camera app on the device and transmitted using relatively insecure methods (eg, SMS text message and email) that do not preserve images as part of the electronic medical records. Native camera apps lack robust security features and direct integration with electronic health records (EHRs), which may limit patient acceptability and usefulness to clinicians. In March 2015, Mayo Clinic overcame these barriers by launching an internally developed mobile app that allows health care providers to securely capture clinical photographs and upload them to the EHR in a manner that is compliant with patient privacy and confidentiality regulations. Objective: The study aimed to understand the perceptions, attitudes, and experiences of patients who were photographed using a mobile point-of-care clinical image capture app. Methods: The study included a mail-out survey sent to 292 patients in Rochester, Minnesota, who were photographed using a mobile point-of-care clinical image capture app within a preceding 2-week period. Results: The surveys were completed by 71 patients who recalled being photographed. Patients were seen in 18 different departments, with the most common departments being dermatology (19/71, 27%), vascular medicine (17/71, 24%), and family medicine (10/71, 14%). Most patients (49/62, 79%) reported that photographs were taken to simply document the appearance of a clinical finding for future reference. Only 16% (10/62) of patients said the photographs were used to obtain advice from a specialist. Furthermore, 74% (51/69) of the patients said they would recommend medical photography to others and 67% (46/69) of them thought the photos favorably affected their care. Patients were largely indifferent about the device used for photography (mobile device vs professional camera; 40/69, 58%) or the identity of the photographer (provider vs professional photographer; 52/69, 75%). In addition, 90% (64/71) of patients found reuse of photographs for one-on-one learner education to be acceptable. Acceptability for other uses declined as the size of the audience increased, with only 42% (30/71) of patients deeming reuse on social media for medical education as appropriate. Only 3% (2/71) of patients expressed privacy or confidentiality concerns. Furthermore, 52% (33/63) of patients preferred to provide consent verbally, and 21% (13/63) of them did not think a specific consent process was necessary. Conclusions: Patient attitudes regarding medical photography using a secure EHR-integrated app were favorable. Patients perceived that photography improved their care despite the most common reason for photography being to simply document the appearance of a clinical finding for future reference. Whenever possible, health care providers should utilize secure EHR-integrated apps for point-of-care medical photography using mobile devices. UR - http://www.jmir.org/2020/5/e14412/ UR - http://dx.doi.org/10.2196/14412 UR - http://www.ncbi.nlm.nih.gov/pubmed/32396127 ID - info:doi/10.2196/14412 ER - TY - JOUR AU - Shaw, E. Sara AU - Seuren, Martinus Lucas AU - Wherton, Joseph AU - Cameron, Deborah AU - A'Court, Christine AU - Vijayaraghavan, Shanti AU - Morris, Joanne AU - Bhattacharya, Satyajit AU - Greenhalgh, Trisha PY - 2020/5/11 TI - Video Consultations Between Patients and Clinicians in Diabetes, Cancer, and Heart Failure Services: Linguistic Ethnographic Study of Video-Mediated Interaction JO - J Med Internet Res SP - e18378 VL - 22 IS - 5 KW - delivery of health care KW - physical examination KW - remote consultation KW - telemedicine KW - health communication KW - language KW - nonverbal communication KW - mobile phone N2 - Background: Video-mediated clinical consultations offer potential benefits over conventional face-to-face in terms of access, convenience, and sometimes cost. The improved technical quality and dependability of video-mediated consultations has opened up the possibility for more widespread use. However, questions remain regarding clinical quality and safety. Video-mediated consultations are sometimes criticized for being not as good as face-to-face, but there has been little previous in-depth research on their interactional dynamics, and no agreement on what a good video consultation looks like. Objective: Using conversation analysis, this study aimed to identify and analyze the communication strategies through which video-mediated consultations are accomplished and to produce recommendations for patients and clinicians to improve the communicative quality of such consultations. Methods: We conducted an in-depth analysis of the clinician-patient interaction in a sample of video-mediated consultations and a comparison sample of face-to-face consultations drawn from 4 clinical settings across 2 trusts (1 community and 1 acute care) in the UK National Health Service. The video dataset consisted of 37 recordings of video-mediated consultations (with diabetes, antenatal diabetes, cancer, and heart failure patients), 28 matched audio recordings of face-to-face consultations, and fieldnotes from before and after each consultation. We also conducted 37 interviews with staff and 26 interviews with patients. Using linguistic ethnography (combining analysis of communication with an appreciation of the context in which it takes place), we examined in detail how video interaction was mediated by 2 software platforms (Skype and FaceTime). Results: Patients had been selected by their clinician as appropriate for video-mediated consultation. Most consultations in our sample were technically and clinically unproblematic. However, we identified 3 interactional challenges: (1) opening the video consultation, (2) dealing with disruption to conversational flow (eg, technical issues with audio and/or video), and (3) conducting an examination. Operational and technological issues were the exception rather than the norm. In all but 1 case, both clinicians and patients (deliberately or intuitively) used established communication strategies to successfully negotiate these challenges. Remote physical examinations required the patient (and, in some cases, a relative) to simultaneously follow instructions and manipulate technology (eg, camera) to make it possible for the clinician to see and hear adequately. Conclusions: A remote video link alters how patients and clinicians interact and may adversely affect the flow of conversation. However, our data suggest that when such problems occur, clinicians and patients can work collaboratively to find ways to overcome them. There is potential for a limited physical examination to be undertaken remotely with some patients and in some conditions, but this appears to need complex interactional work by the patient and/or their relatives. We offer preliminary guidance for patients and clinicians on what is and is not feasible when consulting via a video link. International Registered Report Identifier (IRRID): RR2-10.2196/10913 UR - http://www.jmir.org/2020/5/e18378/ UR - http://dx.doi.org/10.2196/18378 UR - http://www.ncbi.nlm.nih.gov/pubmed/32391799 ID - info:doi/10.2196/18378 ER - TY - JOUR AU - D'Souza, Marcus AU - Van Munster, P. Caspar E. AU - Dorn, F. Jonas AU - Dorier, Alexis AU - Kamm, P. Christian AU - Steinheimer, Saskia AU - Dahlke, Frank AU - Uitdehaag, J. Bernard M. AU - Kappos, Ludwig AU - Johnson, Matthew PY - 2020/5/8 TI - Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study JO - J Med Internet Res SP - e16669 VL - 22 IS - 5 KW - autoencoder KW - video-rating KW - machine learning algorithms KW - deep neuronal network KW - Neurostatus-EDSS N2 - Background: In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non?health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. Objective: The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. Methods: In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. Results: In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. Conclusions: The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non?health-care professionals are involved. UR - https://www.jmir.org/2020/5/e16669 UR - http://dx.doi.org/10.2196/16669 UR - http://www.ncbi.nlm.nih.gov/pubmed/32191621 ID - info:doi/10.2196/16669 ER - TY - JOUR AU - van Dooren, M. Marierose M. AU - Visch, Valentijn AU - Spijkerman, Renske AU - Goossens, M. Richard H. AU - Hendriks, M. Vincent PY - 2020/5/6 TI - Mental Health Therapy Protocols and eHealth Design: Focus Group Study JO - JMIR Form Res SP - e15568 VL - 4 IS - 5 KW - eHealth design KW - mental health care KW - personalization KW - protocol KW - youth addiction care N2 - Background: Electronic health (eHealth) programs are often based on protocols developed for the original face-to-face therapies. However, in practice, therapists and patients may not always follow the original therapy protocols. This form of personalization may also interfere with the intended implementation and effects of eHealth interventions if designers do not take these practices into account. Objective: The aim of this explorative study was to gain insights into the personalization practices of therapists and patients using cognitive behavioral therapy, one of the most commonly applied types of psychotherapy, in a youth addiction care center as a case context. Methods: Focus group discussions were conducted asking therapists and patients to estimate the extent to which a therapy protocol was followed and about the type and reasons for personalization of a given therapy protocol. A total of 7 focus group sessions were organized involving therapists and patients. We used a commonly applied protocol for cognitive behavioral therapy as a therapy protocol example in youth mental health care. The first focus group discussions aimed at assessing the extent to which patients (N=5) or therapists (N=6) adapted the protocol. The second focus group discussions aimed at estimating the extent to which the therapy protocol is applied and personalized based on findings from the first focus groups to gain further qualitative insight into the reasons for personalization with groups of therapists and patients together (N=7). Qualitative data were analyzed using thematic analysis. Results: Therapists used the protocol as a ?toolbox? comprising different therapy tools, and personalized the protocol to enhance the therapeutic alliance and based on their therapy-provision experiences. Therapists estimated that they strictly follow 48% of the protocol, adapt 30%, and replace 22% by other nonprotocol therapeutic components. Patients personalized their own therapy to conform the assignments to their daily lives and routines, and to reduce their levels of stress and worry. Patients estimated that 29% of the provided therapy had been strictly followed by the therapist, 48% had been adjusted, and 23% had been replaced by other nonprotocol therapeutic components. Conclusions: A standard cognitive behavioral therapy protocol is not strictly and fully applied but is mainly personalized. Based on these results, the following recommendations for eHealth designers are proposed to enhance alignment of eHealth to therapeutic practice and implementation: (1) study and copy at least the applied parts of a protocol, (2) co-design eHealth with therapists and patients so they can allocate the components that should be open for user customization, and (3) investigate if components of the therapy protocol that are not applied should remain part of the eHealth applied. To best generate this information, we suggest that eHealth designers should collaborate with therapists, patients, protocol developers, and mental health care managers during the development process. UR - https://formative.jmir.org/2020/5/e15568 UR - http://dx.doi.org/10.2196/15568 UR - http://www.ncbi.nlm.nih.gov/pubmed/32374271 ID - info:doi/10.2196/15568 ER - TY - JOUR AU - Rawtaer, Iris AU - Mahendran, Rathi AU - Kua, Heok Ee AU - Tan, Pink Hwee AU - Tan, Xian Hwee AU - Lee, Tih-Shih AU - Ng, Pin Tze PY - 2020/5/5 TI - Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study JO - J Med Internet Res SP - e16854 VL - 22 IS - 5 KW - dementia KW - neurocognitive disorder KW - pattern recognition, automated/methods KW - internet of things KW - early diagnosis N2 - Background: Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection. Objective: The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively. Methods: We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered. Results: Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system. Conclusions: We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information. UR - https://www.jmir.org/2020/5/e16854 UR - http://dx.doi.org/10.2196/16854 UR - http://www.ncbi.nlm.nih.gov/pubmed/32369031 ID - info:doi/10.2196/16854 ER - TY - JOUR AU - Pak, Kyoungjune AU - Oh, Sae-Ock AU - Goh, Sik Tae AU - Heo, Jin Hye AU - Han, Myoung-Eun AU - Jeong, Cheon Dae AU - Lee, Chi-Seung AU - Sun, Hokeun AU - Kang, Junho AU - Choi, Suji AU - Lee, Soohwan AU - Kwon, Jung Eun AU - Kang, Wan Ji AU - Kim, Hak Yun PY - 2020/5/5 TI - A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study JO - J Med Internet Res SP - e16084 VL - 22 IS - 5 KW - survival analysis KW - grouped variable selection KW - The Cancer Genome Atlas KW - web-based tool KW - user service N2 - Background: Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective: Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods: We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results: Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions: Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses. UR - https://www.jmir.org/2020/5/e16084 UR - http://dx.doi.org/10.2196/16084 UR - http://www.ncbi.nlm.nih.gov/pubmed/32369034 ID - info:doi/10.2196/16084 ER - TY - JOUR AU - Nag, Anish AU - Haber, Nick AU - Voss, Catalin AU - Tamura, Serena AU - Daniels, Jena AU - Ma, Jeffrey AU - Chiang, Bryan AU - Ramachandran, Shasta AU - Schwartz, Jessey AU - Winograd, Terry AU - Feinstein, Carl AU - Wall, P. Dennis PY - 2020/4/22 TI - Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses JO - J Med Internet Res SP - e13810 VL - 22 IS - 4 KW - autism spectrum disorder KW - translational medicine KW - eye tracking KW - wearable technologies KW - artificial intelligence KW - machine learning KW - precision health KW - digital therapy N2 - Background: Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings. Objective: This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs). Methods: We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision?enabled methods for pupil tracking and world?gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants. Results: Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects. Conclusions: Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype. UR - http://www.jmir.org/2020/4/e13810/ UR - http://dx.doi.org/10.2196/13810 UR - http://www.ncbi.nlm.nih.gov/pubmed/32319961 ID - info:doi/10.2196/13810 ER - TY - JOUR AU - Sczuka, Sarah Kim AU - Schwickert, Lars AU - Becker, Clemens AU - Klenk, Jochen PY - 2020/4/3 TI - Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study JO - J Med Internet Res SP - e13961 VL - 22 IS - 4 KW - falls KW - simulation KW - inertial sensor KW - method N2 - Background: Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective: The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods: Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results: A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person?s center of mass during fall events based on the available sensor information. Conclusions: Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available. UR - https://www.jmir.org/2020/4/e13961 UR - http://dx.doi.org/10.2196/13961 UR - http://www.ncbi.nlm.nih.gov/pubmed/32242825 ID - info:doi/10.2196/13961 ER - TY - JOUR AU - Poncette, Akira-Sebastian AU - Rojas, Pablo-David AU - Hofferbert, Joscha AU - Valera Sosa, Alvaro AU - Balzer, Felix AU - Braune, Katarina PY - 2020/3/24 TI - Hackathons as Stepping Stones in Health Care Innovation: Case Study With Systematic Recommendations JO - J Med Internet Res SP - e17004 VL - 22 IS - 3 KW - digital health KW - transdisciplinary research KW - hackathon KW - technological innovation KW - patient-centered care KW - social media N2 - Background: Until recently, developing health technologies was time-consuming and expensive, and often involved patients, doctors, and other health care professionals only as passive recipients of the end product. So far, users have been minimally involved in the ideation and creation stages of digital health technologies. In order to best address users? unmet needs, a transdisciplinary and user-led approach, involving cocreation and direct user feedback, is required. In this context, hackathon events have become increasingly popular in generating enthusiasm for user-centered innovation. Objective: This case study describes preparatory steps and the performance of a health hackathon directly involving patients and health care professionals at all stages. Feasibility and outcomes were assessed, leading to the development of systematic recommendations for future hackathons as a vehicle for bottom-up innovation in health care. Methods: A 2-day hackathon was conducted in February 2017 in Berlin, Germany. Data were collected through a field study. Collected field notes were subsequently discussed in 15 informal meetings among the research team. Experiences of conducting two further hackathons in December 2017 and November 2018 were included. Results: In total, 30 participants took part, with 63% (19/30) of participants between 25 and 34 years of age, 30% (9/30) between 35 and 44 years of age, and 7% (2/30) younger than 25 years of age. A total of 43% (13/30) of the participants were female. The participation rate of medical experts, including patients and health care professionals, was 30% (9/30). Five multidisciplinary teams were formed and each tackled a specific health care problem. All presented projects were apps: a chatbot for skin cancer recognition, an augmented reality exposure-based therapy (eg, for arachnophobia), an app for medical neighborhood connectivity, a doctor appointment platform, and a self-care app for people suffering from depression. Patients and health care professionals initiated all of the projects. Conducting the hackathon resulted in significant growth of the digital health community of Berlin and was followed up by larger hackathons. Systematic recommendations for conducting cost-efficient hackathons (n?30) were developed, including aspects of community building, stakeholder engagement, mentoring, themes, announcements, follow-up, and timing for each step. Conclusions: This study shows that hackathons are effective in bringing innovation to health care and are more cost- and time-efficient and potentially more sustainable than traditional medical device and digital product development. Our systematic recommendations can be useful to other individuals and organizations that want to establish user-led innovation in academic hospitals by conducting transdisciplinary hackathons. UR - http://www.jmir.org/2020/3/e17004/ UR - http://dx.doi.org/10.2196/17004 UR - http://www.ncbi.nlm.nih.gov/pubmed/32207691 ID - info:doi/10.2196/17004 ER - TY - JOUR AU - Seuren, Martinus Lucas AU - Wherton, Joseph AU - Greenhalgh, Trisha AU - Cameron, Deborah AU - A'Court, Christine AU - Shaw, E. Sara PY - 2020/2/20 TI - Physical Examinations via Video for Patients With Heart Failure: Qualitative Study Using Conversation Analysis JO - J Med Internet Res SP - e16694 VL - 22 IS - 2 KW - remote consultation KW - telemedicine KW - videoconferencing KW - communication KW - language KW - linguistics KW - gestures KW - physical examination N2 - Background: Video consultations are increasingly seen as a possible replacement for face-to-face consultations. Direct physical examination of the patient is impossible; however, a limited examination may be undertaken via video (eg, using visual signals or asking a patient to press their lower legs and assess fluid retention). Little is currently known about what such video examinations involve. Objective: This study aimed to explore the opportunities and challenges of remote physical examination of patients with heart failure using video-mediated communication technology. Methods: We conducted a microanalysis of video examinations using conversation analysis (CA), an established approach for studying the details of communication and interaction. In all, seven video consultations (using FaceTime) between patients with heart failure and their community-based specialist nurses were video recorded with consent. We used CA to identify the challenges of remote physical examination over video and the verbal and nonverbal communication strategies used to address them. Results: Apart from a general visual overview, remote physical examination in patients with heart failure was restricted to assessing fluid retention (by the patient or relative feeling for leg edema), blood pressure with pulse rate and rhythm (using a self-inflating blood pressure monitor incorporating an irregular heartbeat indicator and put on by the patient or relative), and oxygen saturation (using a finger clip device). In all seven cases, one or more of these examinations were accomplished via video, generating accurate biometric data for assessment by the clinician. However, video examinations proved challenging for all involved. Participants (patients, clinicians, and, sometimes, relatives) needed to collaboratively negotiate three recurrent challenges: (1) adequate design of instructions to guide video examinations (with nurses required to explain tasks using lay language and to check instructions were followed), (2) accommodation of the patient?s desire for autonomy (on the part of nurses and relatives) in light of opportunities for involvement in their own physical assessment, and (3) doing the physical examination while simultaneously making it visible to the nurse (with patients and relatives needing adequate technological knowledge to operate a device and make the examination visible to the nurse as well as basic biomedical knowledge to follow nurses? instructions). Nurses remained responsible for making a clinical judgment of the adequacy of the examination and the trustworthiness of the data. In sum, despite significant challenges, selected participants in heart failure consultations managed to successfully complete video examinations. Conclusions: Video examinations are possible in the context of heart failure services. However, they are limited, time consuming, and challenging for all involved. Guidance and training are needed to support rollout of this new service model, along with research to understand if the challenges identified are relevant to different patients and conditions and how they can be successfully negotiated. UR - https://www.jmir.org/2020/2/e16694 UR - http://dx.doi.org/10.2196/16694 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130133 ID - info:doi/10.2196/16694 ER - TY - JOUR AU - Funk, Burkhardt AU - Sadeh-Sharvit, Shiri AU - Fitzsimmons-Craft, E. Ellen AU - Trockel, Todd Mickey AU - Monterubio, E. Grace AU - Goel, J. Neha AU - Balantekin, N. Katherine AU - Eichen, M. Dawn AU - Flatt, E. Rachael AU - Firebaugh, Marie-Laure AU - Jacobi, Corinna AU - Graham, K. Andrea AU - Hoogendoorn, Mark AU - Wilfley, E. Denise AU - Taylor, Barr C. PY - 2020/2/19 TI - A Framework for Applying Natural Language Processing in Digital Health Interventions JO - J Med Internet Res SP - e13855 VL - 22 IS - 2 KW - Digital Health Interventions Text Analytics (DHITA) KW - digital health interventions KW - eating disorders KW - guided self-help KW - natural language processing KW - text mining N2 - Background: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. Objective: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. Methods: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. Results: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. Conclusions: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts. UR - https://www.jmir.org/2020/2/e13855 UR - http://dx.doi.org/10.2196/13855 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130118 ID - info:doi/10.2196/13855 ER - TY - JOUR AU - Sezgin, Emre AU - Noritz, Garey AU - Elek, Alexander AU - Conkol, Kimberly AU - Rust, Steve AU - Bailey, Matthew AU - Strouse, Robert AU - Chandawarkar, Aarti AU - von Sadovszky, Victoria AU - Lin, Simon AU - Huang, Yungui PY - 2020/2/13 TI - Capturing At-Home Health and Care Information for Children With Medical Complexity Using Voice Interactive Technologies: Multi-Stakeholder Viewpoint JO - J Med Internet Res SP - e14202 VL - 22 IS - 2 KW - care coordination KW - self-management KW - children with medical complexity KW - voice technology KW - voice assistant KW - digital health KW - conversational agents UR - https://www.jmir.org/2020/2/e14202 UR - http://dx.doi.org/10.2196/14202 UR - http://www.ncbi.nlm.nih.gov/pubmed/32053114 ID - info:doi/10.2196/14202 ER - TY - JOUR AU - Escalona-Marfil, Carles AU - Coda, Andrea AU - Ruiz-Moreno, Jorge AU - Riu-Gispert, Miquel Lluís AU - Gironès, Xavier PY - 2020/2/12 TI - Validation of an Electronic Visual Analog Scale mHealth Tool for Acute Pain Assessment: Prospective Cross-Sectional Study JO - J Med Internet Res SP - e13468 VL - 22 IS - 2 KW - pain KW - visual analog pain scale KW - pain measurement KW - mobile phone KW - mHealth KW - validation KW - tablet N2 - Background: Accurate measurement of pain is required to improve its management and in research. The visual analog scale (VAS) on paper format has been shown to be an accurate, valid, reliable, and reproducible way to measure pain intensity. However, some limitations should be considered, some of which can be implemented with the introduction of an electronic VAS version, suitable to be used both in a tablet and a smartphone. Objective: This study aimed to validate a new method of recording pain level by comparing the traditional paper VAS with the pain level module on the newly designed Interactive Clinics app. Methods: A prospective observational cross-sectional study was designed. The sample consisted of 102 participants aged 18 to 65 years. A Force Dial FDK 20 algometer (Wagner Instruments) was employed to induce mild pressure symptoms on the participants? thumbs. Pain was measured using a paper VAS (10 cm line) and the app. Results: Intermethod reliability estimated by ICC(3,1) was 0.86 with a 95% confidence interval of 0.81 to 0.90, indicating good reliability. Intramethod reliability estimated by ICCa(3,1) was 0.86 with a 95% confidence interval of 0.81 to 0.90, also indicating good reliability. Bland-Altman analysis showed a difference of 0.175 (0.49), and limits of agreement ranged from ?0.79 to 1.14. Conclusions: The pain level module on the app is highly reliable and interchangeable with the paper VAS version. This tool could potentially help clinicians and researchers precisely assess pain in a simple, economic way with the use of a ubiquitous technology. UR - http://www.jmir.org/2020/2/e13468/ UR - http://dx.doi.org/10.2196/13468 UR - http://www.ncbi.nlm.nih.gov/pubmed/32049063 ID - info:doi/10.2196/13468 ER - TY - JOUR AU - Prior, Sarah AU - Miller, Andrea AU - Campbell, Steven AU - Linegar, Karen AU - Peterson, Gregory PY - 2020/2/7 TI - The Challenges of Including Patients With Aphasia in Qualitative Research for Health Service Redesign: Qualitative Interview Study JO - J Participat Med SP - e12336 VL - 12 IS - 1 KW - stroke KW - communication KW - research KW - qualitative KW - aphasia KW - participatory research N2 - Background: Aphasia is an impairment of language, affecting the production or comprehension of speech and the ability to read or write. Aphasia is a frequent complication of stroke and is a major disability for patients and their families. The provision of services for stroke patients differs across health care providers and regions, and strategies directed at improving these services have benefited from the involvement of patients. However, patients with aphasia are often excluded from these co-design activities due to a diminished capacity to communicate verbally and a lack of health researcher experience in working with patients with aphasia. Objective: The primary aim of this paper is to identify approaches appropriate for working with patients with aphasia in an interview situation and, more generally, determine the importance of including people with aphasia in health service improvement research. The secondary aim is to describe the experiences of researchers involved in interviewing patients with aphasia. Methods: A total of 5 poststroke patients with aphasia participated in face-to-face interviews in their homes to gain insight into their in-hospital experience following their stroke. Interviews were audio-recorded, and thematic analysis was performed. The experiences of the researchers interviewing these patients were informally recorded postinterview, and themes were derived from these reflections. Results: The interview technique utilized in this study was unsuitable to gain rich, qualitative data from patients with aphasia. The experience of researchers performing these interviews suggests that preparation, emotion, and understanding were three of the main factors influencing their ability to gather useful experiential information from patients with aphasia. Patients with aphasia are valuable contributors to qualitative health services research, and researchers need to be flexible and adaptable in their methods of engagement. Conclusions: Including patients with aphasia in health service redesign research requires the use of nontraditional interview techniques. Researchers intending to engage patients with aphasia must devise appropriate strategies and methods to maximize the contributions and valuable communications of these participants. UR - https://jopm.jmir.org/2020/1/e12336 UR - http://dx.doi.org/10.2196/12336 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/12336 ER - TY - JOUR AU - Chou, H. Joseph AU - Roumiantsev, Sergei AU - Singh, Rachana PY - 2020/1/30 TI - PediTools Electronic Growth Chart Calculators: Applications in Clinical Care, Research, and Quality Improvement JO - J Med Internet Res SP - e16204 VL - 22 IS - 1 KW - growth charts KW - pediatrics KW - infant, newborn KW - infant, premature KW - failure to thrive KW - internet KW - software N2 - Background: Parameterization of pediatric growth charts allows precise quantitation of growth metrics that would be difficult or impossible with traditional paper charts. However, limited availability of growth chart calculators for use by clinicians and clinical researchers currently restricts broader application. Objective: The aim of this study was to assess the deployment of electronic calculators for growth charts using the lambda-mu-sigma (LMS) parameterization method, with examples of their utilization for patient care delivery, clinical research, and quality improvement projects. Methods: The publicly accessible PediTools website of clinical calculators was developed to allow LMS-based calculations on anthropometric measurements of individual patients. Similar calculations were applied in a retrospective study of a population of patients from 7 Massachusetts neonatal intensive care units (NICUs) to compare interhospital growth outcomes (change in weight Z-score from birth to discharge [?Z weight]) and their association with gestational age at birth. At 1 hospital, a bundle of quality improvement interventions targeting improved growth was implemented, and the outcomes were assessed prospectively via monitoring of ?Z weight pre- and postintervention. Results: The PediTools website was launched in January 2012, and as of June 2019, it received over 500,000 page views per month, with users from over 21 countries. A retrospective analysis of 7975 patients at 7 Massachusetts NICUs, born between 2006 and 2011, at 23 to 34 completed weeks gestation identified an overall ?Z weight from birth to discharge of ?0.81 (P<.001). However, the degree of ?Z weight differed significantly by hospital, ranging from ?0.56 to ?1.05 (P<.001). Also identified was the association between inferior growth outcomes and lower gestational age at birth, as well as that the degree of association between ?Z weight and gestation at birth also differed by hospital. At 1 hospital, implementing a bundle of interventions targeting growth resulted in a significant and sustained reduction in loss of weight Z-score from birth to discharge. Conclusions: LMS-based anthropometric measurement calculation tools on a public website have been widely utilized. Application in a retrospective clinical study on a large dataset demonstrated inferior growth at lower gestational age and interhospital variation in growth outcomes. Change in weight Z-score has potential utility as an outcome measure for monitoring clinical quality improvement. We also announce the release of open-source computer code written in R to allow other clinicians and clinical researchers to easily perform similar analyses. UR - https://www.jmir.org/2020/1/e16204 UR - http://dx.doi.org/10.2196/16204 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012066 ID - info:doi/10.2196/16204 ER - TY - JOUR AU - Meyer, D. Ashley N. AU - Giardina, D. Traber AU - Spitzmueller, Christiane AU - Shahid, Umber AU - Scott, T. Taylor M. AU - Singh, Hardeep PY - 2020/1/30 TI - Patient Perspectives on the Usefulness of an Artificial Intelligence?Assisted Symptom Checker: Cross-Sectional Survey Study JO - J Med Internet Res SP - e14679 VL - 22 IS - 1 KW - clinical decision support systems KW - technology KW - diagnosis KW - patient safety KW - symptom checker KW - computer-assisted diagnosis N2 - Background: Patients are increasingly seeking Web-based symptom checkers to obtain diagnoses. However, little is known about the characteristics of the patients who use these resources, their rationale for use, and whether they find them accurate and useful. Objective: The study aimed to examine patients? experiences using an artificial intelligence (AI)?assisted online symptom checker. Methods: An online survey was administered between March 2, 2018, through March 15, 2018, to US users of the Isabel Symptom Checker within 6 months of their use. User characteristics, experiences of symptom checker use, experiences discussing results with physicians, and prior personal history of experiencing a diagnostic error were collected. Results: A total of 329 usable responses was obtained. The mean respondent age was 48.0 (SD 16.7) years; most were women (230/304, 75.7%) and white (271/304, 89.1%). Patients most commonly used the symptom checker to better understand the causes of their symptoms (232/304, 76.3%), followed by for deciding whether to seek care (101/304, 33.2%) or where (eg, primary or urgent care: 63/304, 20.7%), obtaining medical advice without going to a doctor (48/304, 15.8%), and understanding their diagnoses better (39/304, 12.8%). Most patients reported receiving useful information for their health problems (274/304, 90.1%), with half reporting positive health effects (154/302, 51.0%). Most patients perceived it to be useful as a diagnostic tool (253/301, 84.1%), as a tool providing insights leading them closer to correct diagnoses (231/303, 76.2%), and reported they would use it again (278/304, 91.4%). Patients who discussed findings with their physicians (103/213, 48.4%) more often felt physicians were interested (42/103, 40.8%) than not interested in learning about the tool?s results (24/103, 23.3%) and more often felt physicians were open (62/103, 60.2%) than not open (21/103, 20.4%) to discussing the results. Compared with patients who had not previously experienced diagnostic errors (missed or delayed diagnoses: 123/304, 40.5%), patients who had previously experienced diagnostic errors (181/304, 59.5%) were more likely to use the symptom checker to determine where they should seek care (15/123, 12.2% vs 48/181, 26.5%; P=.002), but they less often felt that physicians were interested in discussing the tool?s results (20/34, 59% vs 22/69, 32%; P=.04). Conclusions: Despite ongoing concerns about symptom checker accuracy, a large patient-user group perceived an AI-assisted symptom checker as useful for diagnosis. Formal validation studies evaluating symptom checker accuracy and effectiveness in real-world practice could provide additional useful information about their benefit. UR - http://www.jmir.org/2020/1/e14679/ UR - http://dx.doi.org/10.2196/14679 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012052 ID - info:doi/10.2196/14679 ER - TY - JOUR AU - Salvi, Dario AU - Poffley, Emma AU - Orchard, Elizabeth AU - Tarassenko, Lionel PY - 2020/1/3 TI - The Mobile-Based 6-Minute Walk Test: Usability Study and Algorithm Development and Validation JO - JMIR Mhealth Uhealth SP - e13756 VL - 8 IS - 1 KW - cardiology KW - exercise test KW - pulmonary hypertension KW - mobile apps KW - digital signal processing KW - global positioning system N2 - Background: The 6-min walk test (6MWT) is a convenient method for assessing functional capacity in patients with cardiopulmonary conditions. It is usually performed in the context of a hospital clinic and thus requires the involvement of hospital staff and facilities, with their associated costs. Objective: This study aimed to develop a mobile phone?based system that allows patients to perform the 6MWT in the community. Methods: We developed 2 algorithms to compute the distance walked during a 6MWT using sensors embedded in a mobile phone. One algorithm makes use of the global positioning system to track the location of the phone when outdoors and hence computes the distance travelled. The other algorithm is meant to be used indoors and exploits the inertial sensors built into the phone to detect U-turns when patients walk back and forth along a corridor of fixed length. We included these algorithms in a mobile phone app, integrated with wireless pulse oximeters and a back-end server. We performed Bland-Altman analysis of the difference between the distances estimated by the phone and by a reference trundle wheel on 49 indoor tests and 30 outdoor tests, with 11 different mobile phones (both Apple iOS and Google Android operating systems). We also assessed usability aspects related to the app in a discussion group with patients and clinicians using a technology acceptance model to guide discussion. Results: The mean difference between the mobile phone-estimated distances and the reference values was ?2.013 m (SD 7.84 m) for the indoor algorithm and ?0.80 m (SD 18.56 m) for the outdoor algorithm. The absolute maximum difference was, in both cases, below the clinically significant threshold. A total of 2 pulmonary hypertension patients, 1 cardiologist, 2 physiologists, and 1 nurse took part in the discussion group, where issues arising from the use of the 6MWT in hospital were identified. The app was demonstrated to be usable, and the 2 patients were keen to use it in the long term. Conclusions: The system described in this paper allows patients to perform the 6MWT at a place of their convenience. In addition, the use of pulse oximetry allows more information to be generated about the patient?s health status and, possibly, be more relevant to the real-life impact of their condition. Preliminary assessment has shown that the developed 6MWT app is highly accurate and well accepted by its users. Further tests are needed to assess its clinical value. UR - https://mhealth.jmir.org/2020/1/e13756 UR - http://dx.doi.org/10.2196/13756 UR - http://www.ncbi.nlm.nih.gov/pubmed/31899457 ID - info:doi/10.2196/13756 ER - TY - JOUR AU - Moon, Jae Sun AU - Hwang, Jinseub AU - Kana, Rajesh AU - Torous, John AU - Kim, Won Jung PY - 2019/12/20 TI - Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies JO - JMIR Ment Health SP - e14108 VL - 6 IS - 12 KW - autism spectrum disorder KW - machine learning KW - sensitivity and specificity KW - systematic review KW - meta-analysis N2 - Background: In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. Objective: This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. Methods: The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). Results: A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. Conclusions: The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. Trial Registration: PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779 UR - https://mental.jmir.org/2019/12/e14108 UR - http://dx.doi.org/10.2196/14108 UR - http://www.ncbi.nlm.nih.gov/pubmed/31562756 ID - info:doi/10.2196/14108 ER - TY - JOUR AU - Bendtsen, Marcus PY - 2019/12/17 TI - An Electronic Screening and Brief Intervention for Hazardous and Harmful Drinking Among Swedish University Students: Reanalysis of Findings From a Randomized Controlled Trial Using a Bayesian Framework JO - J Med Internet Res SP - e14420 VL - 21 IS - 12 KW - Bayesian analysis KW - telemedicine KW - digital health KW - internet interventions KW - alcohol KW - randomized controlled trial N2 - Background: Due to a resurgent debate on the misuse of P values, the Journal of Medical Internet Research is hosting a standing theme issue inviting the reanalysis of (primarily digital health) trial data using a Bayesian framework. This first paper in this series focuses on an electronic screening and brief intervention (eSBI), targeting harmful and hazardous alcohol consumption, which student health care centers across Sweden have routinely administerd to all students during the past decade. The second Alcohol Email Assessment and Feedback Study Dismantling Effectiveness for University Students (AMADEUS-2) trial aimed to assess the effect of the eSBI on alcohol consumption among students who were harmful and hazardous drinkers. A two-arm randomized controlled trial design was employed, randomizing eligible participants to either a waiting list or direct access to an eSBI. Follow-up assessments were conducted 2 months after randomization. Subsequent analysis of the trial followed the conventional null hypothesis approach, and no statistical significance was found between groups at follow-up with respect to the number of standard drinks consumed weekly. However, in an unspecified sensitivity analysis, it was discovered that removing three potential outliers made the difference between the groups significant. Objective: The objective of this study is to reperform the primary and sensitivity analysis of the AMADEUS-2 trial using a Bayesian framework and to compare the results with those of the original analysis. Methods: The same regression models used in the original analysis were employed in this reanalysis (negative binomial regression). Model parameters were given uniform priors. Markov chain Monte Carlo was used for Bayesian inference, and posterior probabilities were calculated for prespecified thresholds of interest. Results: Null hypothesis tests did not identify a statistically significant difference between the intervention and control groups, potentially due to a few extreme data points. The Bayesian analysis indicated a 93.6% probability that there was a difference in grams of alcohol consumed at follow-up between the intervention and control groups and a 71.5% probability that the incidence rate ratio was <0.96. Posterior probabilities increased when excluding three potential outliers, yet such post hoc analyses were not necessary to show the preference toward offering an eSBI to harmful and hazardous drinkers among university students. Conclusions: The null hypothesis framework relies on point estimates of parameters. P values can therefore swing heavily, depending on a single or few data points alone, casting doubt on the value of the analysis. Bayesian analysis results in a distribution over parameter values and is therefore less sensitive to outliers and extreme values. Results from analyses of trials of interventions where small-to-modest effect sizes are expected can be more robust in a Bayesian framework, making this a potentially better approach for analyzing digital health research data. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 02335307; http://www.isrctn.com/ISRCTN02335307 UR - http://www.jmir.org/2019/12/e14420/ UR - http://dx.doi.org/10.2196/14420 UR - http://www.ncbi.nlm.nih.gov/pubmed/31845903 ID - info:doi/10.2196/14420 ER - TY - JOUR AU - Peng, Suyuan AU - Shen, Feichen AU - Wen, Andrew AU - Wang, Liwei AU - Fan, Yadan AU - Liu, Xusheng AU - Liu, Hongfang PY - 2019/12/10 TI - Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data JO - J Med Internet Res SP - e14204 VL - 21 IS - 12 KW - chronic kidney disease KW - association rule mining KW - Behavioral Risk Factor Surveillance System KW - noncommunicable diseases N2 - Background: The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. Objective: The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). Methods: We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. Results: The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly. Conclusions: We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs. UR - https://www.jmir.org/2019/12/e14204 UR - http://dx.doi.org/10.2196/14204 UR - http://www.ncbi.nlm.nih.gov/pubmed/31821152 ID - info:doi/10.2196/14204 ER - TY - JOUR AU - Cambron, C. Julia AU - Wyatt, D. Kirk AU - Lohse, M. Christine AU - Underwood, Y. Page AU - Hellmich, R. Thomas PY - 2019/12/3 TI - Medical Videography Using a Mobile App: Retrospective Analysis JO - JMIR Mhealth Uhealth SP - e14919 VL - 7 IS - 12 KW - photography KW - video recording KW - telemedicine KW - medical informatics applications N2 - Background: As mobile devices and apps grow in popularity, they are increasingly being used by health care providers to aid clinical care. At our institution, we developed and implemented a point-of-care clinical photography app that also permitted the capture of video recordings; however, the clinical findings it was used to capture and the outcomes that resulted following video recording were unclear. Objective: The study aimed to assess the use of a mobile clinical video recording app at our institution and its impact on clinical care. Methods: A single reviewer retrospectively reviewed video recordings captured between April 2016 and July 2017, associated metadata, and patient records. Results: We identified 362 video recordings that were eligible for inclusion. Most video recordings (54.1%; 190/351) were captured by attending physicians. Specialties recording a high number of video recordings included orthopedic surgery (33.7%; 122/362), neurology (21.3%; 77/362), and ophthalmology (15.2%; 55/362). Consent was clearly documented in the medical record in less than one-third (31.8%; 115/362) of the records. People other than the patient were incidentally captured in 29.6% (107/362) of video recordings. Although video recordings were infrequently referenced in notes corresponding to the clinical encounter (12.2%; 44/362), 7.7% (22/286) of patients were video recorded in subsequent clinical encounters, with 82% (18/22) of these corresponding to the same finding seen in the index video. Store-and-forward telemedicine was documented in clinical notes in only 2 cases (0.5%; 2/362). Videos appeared to be of acceptable quality for clinical purposes. Conclusions: Video recordings were captured in a variety of clinical settings. Documentation of consent was inconsistent, and other individuals were incidentally included in videos. Although clinical impact was not always clearly evident through retrospective review because of limited documentation, potential uses include documentation for future reference and store-and-forward telemedicine. Repeat video recordings of the same finding provide evidence of use to track the findings over time. Clinical video recordings have the potential to support clinical care; however, documentation of consent requires standardization. UR - https://mhealth.jmir.org/2019/12/e14919 UR - http://dx.doi.org/10.2196/14919 UR - http://www.ncbi.nlm.nih.gov/pubmed/31793894 ID - info:doi/10.2196/14919 ER - TY - JOUR AU - Larrivée, Samuel AU - Balg, Frédéric AU - Léonard, Guillaume AU - Bédard, Sonia AU - Tousignant, Michel AU - Boissy, Patrick PY - 2019/12/3 TI - Wrist-Based Accelerometers and Visual Analog Scales as Outcome Measures for Shoulder Activity During Daily Living in Patients With Rotator Cuff Tendinopathy: Instrument Validation Study JO - JMIR Rehabil Assist Technol SP - e14468 VL - 6 IS - 2 KW - shoulder KW - wearable sensors KW - activity count KW - validation KW - test-retest KW - sensitivity to change N2 - Background: Shoulder pain secondary to rotator cuff tendinopathy affects a large proportion of patients in orthopedic surgery practices. Corticosteroid injections are a common intervention proposed for these patients. The clinical evaluation of a response to corticosteroid injections is usually based only on the patient?s self-evaluation of his function, activity, and pain by multiple questionnaires with varying metrological qualities. Objective measures of upper extremity functions are lacking, but wearable sensors are emerging as potential tools to assess upper extremity function and activity. Objective: This study aimed (1) to evaluate and compare test-retest reliability and sensitivity to change of known clinical assessments of shoulder function to wrist-based accelerometer measures and visual analog scales (VAS) of shoulder activity during daily living in patients with rotator cuff tendinopathy convergent validity and (2) to determine the acceptability and compliance of using wrist-based wearable sensors. Methods: A total of 38 patients affected by rotator cuff tendinopathy wore wrist accelerometers on the affected side for a total of 5 weeks. Western Ontario Rotator Cuff (WORC) index; Short version of the Disability of the Arm, Shoulder, and Hand questionnaire (QuickDASH); and clinical examination (range of motion and strength) were performed the week before the corticosteroid injections, the day of the corticosteroid injections, and 2 and 4 weeks after the corticosteroid injections. Daily Single Assessment Numeric Evaluation (SANE) and VAS were filled by participants to record shoulder pain and activity. Accelerometer data were processed to extract daily upper extremity activity in the form of active time; activity counts; and ratio of low-intensity activities, medium-intensity activities, and high-intensity activities. Results: Daily pain measured using VAS and SANE correlated well with the WORC and QuickDASH questionnaires (r=0.564-0.815) but not with accelerometry measures, amplitude, and strength. Daily activity measured with VAS had good correlation with active time (r=0.484, P=.02). All questionnaires had excellent test-retest reliability at 1 week before corticosteroid injections (intraclass correlation coefficient [ICC]=0.883-0.950). Acceptable reliability was observed with accelerometry (ICC=0.621-0.724), apart from low-intensity activities (ICC=0.104). Sensitivity to change was excellent at 2 and 4 weeks for all questionnaires (standardized response mean=1.039-2.094) except for activity VAS (standardized response mean=0.50). Accelerometry measures had low sensitivity to change at 2 weeks, but excellent sensitivity at 4 weeks (standardized response mean=0.803-1.032). Conclusions: Daily pain VAS and SANE had good correlation with the validated questionnaires, excellent reliability at 1 week, and excellent sensitivity to change at 2 and 4 weeks. Daily activity VAS and accelerometry-derived active time correlated well together. Activity VAS had excellent reliability, but moderate sensitivity to change. Accelerometry measures had moderate reliability and acceptable sensitivity to change at 4 weeks. UR - http://rehab.jmir.org/2019/2/e14468/ UR - http://dx.doi.org/10.2196/14468 UR - http://www.ncbi.nlm.nih.gov/pubmed/31793896 ID - info:doi/10.2196/14468 ER - TY - JOUR AU - Mazoteras-Pardo, Victoria AU - Becerro-De-Bengoa-Vallejo, Ricardo AU - Losa-Iglesias, Elena Marta AU - López-López, Daniel AU - Rodríguez-Sanz, David AU - Casado-Hernández, Israel AU - Calvo-Lobo, Cesar AU - Palomo-López, Patricia PY - 2019/12/2 TI - QardioArm Upper Arm Blood Pressure Monitor Against Omron M3 Upper Arm Blood Pressure Monitor in Patients With Chronic Kidney Disease: A Validation Study According to the European Society of Hypertension International Protocol Revision 2010 JO - J Med Internet Res SP - e14686 VL - 21 IS - 12 KW - blood pressure KW - hypertension KW - kidney disease KW - mobile apps KW - software validation N2 - Background: Hypertension is considered as a main risk factor for chronic kidney disease development and progression. Thus, the control and evaluation of this disease with new software and devices are especially important in patients who suffer from chronic kidney disease. Objective: This study aimed to validate the QardioArm mobile device, which is used for blood pressure (BP) self-measurement in patients who suffer from chronic kidney disease, by following the European Society of Hypertension International Protocol 2 (ESH-IP2) guidelines. Methods: A validation study was carried out by following the ESH-IP2 guidelines. A sample of 33 patients with chronic kidney disease self-measured their BP by using the QardioArm and Omron M3 Intellisense devices. Heart rate (HR), diastolic BP, and systolic BP were measured. Results: The QardioArm fulfilled the ESH-IP2 validation criteria in patients who suffered from chronic kidney disease. Conclusions: Thus, this study is considered as the first validation using a wireless upper arm oscillometric device connected to an app to measure BP and HR meeting the ESH-IP2 requirements in patients who suffer from chronic kidney disease. New validation studies following the ESH-IP2 guidelines should be carried out using different BP devices in patients with specific diseases. UR - https://www.jmir.org/2019/12/e14686 UR - http://dx.doi.org/10.2196/14686 UR - http://www.ncbi.nlm.nih.gov/pubmed/31789600 ID - info:doi/10.2196/14686 ER - TY - JOUR AU - Johnson, Amanda AU - Yang, Fan AU - Gollarahalli, Siddharth AU - Banerjee, Tanvi AU - Abrams, Daniel AU - Jonassaint, Jude AU - Jonassaint, Charles AU - Shah, Nirmish PY - 2019/12/2 TI - Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study JO - JMIR Mhealth Uhealth SP - e13671 VL - 7 IS - 12 KW - pain KW - sickle cell disease KW - SCD KW - machine learning N2 - Background: Sickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain. Objective: The objective of this study was to show the feasibility of using objective, physiologic measurements obtained from a wearable device during an acute pain crisis to predict patient-reported pain scores (in an app and to nursing staff) using machine learning techniques. Methods: For this feasibility study, we enrolled 27 adult patients presenting to the day hospital with acute pain. At the beginning of pain treatment, each participant was given a wearable device (Microsoft Band 2) that collected physiologic measurements. Pain scores from our mobile app, Technology Resources to Understand Pain Assessment in Patients with Pain, and those obtained by nursing staff were both used with wearable signals to complete time stamp matching and feature extraction and selection. Following this, we constructed regression and classification machine learning algorithms to build between-subject pain prediction models. Results: Patients were monitored for an average of 3.79 (SD 2.23) hours, with an average of 5826 (SD 2667) objective data values per patient. As expected, we found that pain scores and heart rate decreased for most patients during the course of their stay. Using the wearable sensor data and pain scores, we were able to create a regression model to predict subjective pain scores with a root mean square error of 1.430 and correlation between observations and predictions of 0.706. Furthermore, we verified the hypothesis that the regression model outperformed the classification model by comparing the performances of the support vector machines (SVM) and the SVM for regression. Conclusions: The Microsoft Band 2 allowed easy collection of objective, physiologic markers during an acute pain crisis in adults with SCD. Features can be extracted from these data signals and matched with pain scores. Machine learning models can then use these features to feasibly predict patient pain scores. UR - https://mhealth.jmir.org/2019/12/e13671 UR - http://dx.doi.org/10.2196/13671 UR - http://www.ncbi.nlm.nih.gov/pubmed/31789599 ID - info:doi/10.2196/13671 ER - TY - JOUR AU - Sapci, Hasan A. AU - Sapci, Aylin H. PY - 2019/11/29 TI - Innovative Assisted Living Tools, Remote Monitoring Technologies, Artificial Intelligence-Driven Solutions, and Robotic Systems for Aging Societies: Systematic Review JO - JMIR Aging SP - e15429 VL - 2 IS - 2 KW - innovative assisted living tools for aging society KW - artificially intelligent home monitoring KW - older adults KW - robotic technologies KW - smart home N2 - Background: The increase in life expectancy and recent advancements in technology and medical science have changed the way we deliver health services to the aging societies. Evidence suggests that home telemonitoring can significantly decrease the number of readmissions, and continuous monitoring of older adults? daily activities and health-related issues might prevent medical emergencies. Objective: The primary objective of this review was to identify advances in assistive technology devices for seniors and aging-in-place technology and to determine the level of evidence for research on remote patient monitoring, smart homes, telecare, and artificially intelligent monitoring systems. Methods: A literature review was conducted using Cumulative Index to Nursing and Allied Health Literature Plus, MEDLINE, EMBASE, Institute of Electrical and Electronics Engineers Xplore, ProQuest Central, Scopus, and Science Direct. Publications related to older people?s care, independent living, and novel assistive technologies were included in the study. Results: A total of 91 publications met the inclusion criteria. In total, four themes emerged from the data: technology acceptance and readiness, novel patient monitoring and smart home technologies, intelligent algorithm and software engineering, and robotics technologies. The results revealed that most studies had poor reference standards without an explicit critical appraisal. Conclusions: The use of ubiquitous in-home monitoring and smart technologies for aged people?s care will increase their independence and the health care services available to them as well as improve frail elderly people?s health care outcomes. This review identified four different themes that require different conceptual approaches to solution development. Although the engineering teams were focused on prototype and algorithm development, the medical science teams were concentrated on outcome research. We also identified the need to develop custom technology solutions for different aging societies. The convergence of medicine and informatics could lead to the development of new interdisciplinary research models and new assistive products for the care of older adults. UR - http://aging.jmir.org/2019/2/e15429/ UR - http://dx.doi.org/10.2196/15429 UR - http://www.ncbi.nlm.nih.gov/pubmed/31782740 ID - info:doi/10.2196/15429 ER - TY - JOUR AU - Pham, Quynh AU - Shaw, James AU - Morita, P. Plinio AU - Seto, Emily AU - Stinson, N. Jennifer AU - Cafazzo, A. Joseph PY - 2019/11/11 TI - The Service of Research Analytics to Optimize Digital Health Evidence Generation: Multilevel Case Study JO - J Med Internet Res SP - e14849 VL - 21 IS - 11 KW - research analytics KW - effective engagement KW - digital health KW - mobile health KW - implementation KW - log data KW - service design KW - chronic disease N2 - Background: The widespread adoption of digital health interventions for chronic disease self-management has catalyzed a paradigm shift in the selection of methodologies used to evidence them. Recently, the application of digital health research analytics has emerged as an efficient approach to evaluate these data-rich interventions. However, there is a growing mismatch between the promising evidence base emerging from analytics mediated trials and the complexity of introducing these novel research methods into evaluative practice. Objective: This study aimed to generate transferable insights into the process of implementing research analytics to evaluate digital health interventions. We sought to answer the following two research questions: (1) how should the service of research analytics be designed to optimize digital health evidence generation? and (2) what are the challenges and opportunities to scale, spread, and sustain this service in evaluative practice? Methods: We conducted a qualitative multilevel embedded single case study of implementing research analytics in evaluative practice that comprised a review of the policy and regulatory climate in Ontario (macro level), a field study of introducing a digital health analytics platform into evaluative practice (meso level), and interviews with digital health innovators on their perceptions of analytics and evaluation (microlevel). Results: The practice of research analytics is an efficient and effective means of supporting digital health evidence generation. The introduction of a research analytics platform to evaluate effective engagement with digital health interventions into a busy research lab was ultimately accepted by research staff, became routinized in their evaluative practice, and optimized their existing mechanisms of log data analysis and interpretation. The capacity for research analytics to optimize digital health evaluations is highest when there is (1) a collaborative working relationship between research client and analytics service provider, (2) a data-driven research agenda, (3) a robust data infrastructure with clear documentation of analytic tags, (4) in-house software development expertise, and (5) a collective tolerance for methodological change. Conclusions: Scientific methods and practices that can facilitate the agile trials needed to iterate and improve digital health interventions warrant continued implementation. The service of research analytics may help to accelerate the pace of digital health evidence generation and build a data-rich research infrastructure that enables continuous learning and evaluation. UR - http://www.jmir.org/2019/11/e14849/ UR - http://dx.doi.org/10.2196/14849 UR - http://www.ncbi.nlm.nih.gov/pubmed/31710296 ID - info:doi/10.2196/14849 ER - TY - JOUR AU - Wisse, L. Robert P. AU - Muijzer, B. Marc AU - Cassano, Francesco AU - Godefrooij, A. Daniel AU - Prevoo, M. Yves F. D. AU - Soeters, Nienke PY - 2019/11/8 TI - Validation of an Independent Web-Based Tool for Measuring Visual Acuity and Refractive Error (the Manifest versus Online Refractive Evaluation Trial): Prospective Open-Label Noninferiority Clinical Trial. JO - J Med Internet Res SP - e14808 VL - 21 IS - 11 KW - digital refraction KW - easee KW - telemedicine KW - medical informatics KW - refractive error N2 - Background: Digital tools provide a unique opportunity to increase access to eye care. We developed a Web-based test that measures visual acuity and both spherical and cylindrical refractive errors. This test is Conformité Européenne marked and available on the Easee website. The purpose of this study was to compare the efficacy of this Web-based tool with traditional subjective manifest refraction in a prospective open-label noninferiority clinical trial. Objective: The aim of this study was to evaluate the outcome of a Web-based refraction compared with a manifest refraction (golden standard). Methods: Healthy volunteers from 18 to 40 years of age, with a refraction error between ?6 and +4 diopter (D), were eligible. Each participant performed the Web-based test, and the reference test was performed by an optometrist. An absolute difference in refractive error of <0.5 D was considered noninferior. Reliability was assessed by using an intraclass correlation coefficient (ICC). Both uncorrected and corrected visual acuity were measured. Results: A total of 200 eyes in 100 healthy volunteers were examined. The Web-based assessment of refractive error had excellent correlation with the reference test (ICC=0.92) and was considered noninferior to the reference test. Uncorrected visual acuity was similar with the Web-based test and the reference test (P=.21). Visual acuity was significantly improved using the prescription obtained by using the Web-based tool (P<.01). The Web-based test provided the best results in participants with mild myopia (ie, <3 D), with a mean difference of 0.02 (SD 0.49) D (P=.48) and yielding a corrected visual acuity of >1.0 in 90% (n=77) of participants. Conclusions: Our results indicate that Web-based eye testing is a valid and safe method for measuring visual acuity and refractive error in healthy eyes, particularly for mild myopia. This tool can be used for screening purposes, and it is an easily accessible alternative to the subjective manifest refraction test. Trial Registration: Clinicaltrials.gov NCT03313921; https://clinicaltrials.gov/ct2/show/NCT03313921. UR - https://www.jmir.org/2019/11/e14808 UR - http://dx.doi.org/10.2196/14808 UR - http://www.ncbi.nlm.nih.gov/pubmed/31702560 ID - info:doi/10.2196/14808 ER - TY - JOUR AU - Kocaballi, Baki Ahmet AU - Berkovsky, Shlomo AU - Quiroz, C. Juan AU - Laranjo, Liliana AU - Tong, Ly Huong AU - Rezazadegan, Dana AU - Briatore, Agustina AU - Coiera, Enrico PY - 2019/11/7 TI - The Personalization of Conversational Agents in Health Care: Systematic Review JO - J Med Internet Res SP - e15360 VL - 21 IS - 11 KW - conversational interfaces KW - conversational agents KW - dialogue systems KW - personalization KW - customization KW - adaptive systems KW - health care N2 - Background: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective: The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. Methods: We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. Results: The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Conclusions: Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making. UR - https://www.jmir.org/2019/11/e15360 UR - http://dx.doi.org/10.2196/15360 UR - http://www.ncbi.nlm.nih.gov/pubmed/31697237 ID - info:doi/10.2196/15360 ER - TY - JOUR AU - Bendtsen, Marcus PY - 2019/11/7 TI - Electronic Screening for Alcohol Use and Brief Intervention by Email for University Students: Reanalysis of Findings From a Randomized Controlled Trial Using a Bayesian Framework JO - J Med Internet Res SP - e14419 VL - 21 IS - 11 KW - Bayesian analysis KW - telemedicine KW - alcohol KW - randomized controlled trial N2 - Background: Almost a decade ago, Sweden became the first country to implement a national system enabling student health care centers across all universities to routinely administer (via email) an electronic alcohol screening and brief intervention to their students. The Alcohol email assessment and feedback study dismantling effectiveness for university students (AMADEUS-1) trial aimed to assess the effect of the student health care centers? routine practices by exploiting the lack of any standard timing for the email invitation and by masking trial participation from students. The original analyses adopted the conventional null hypothesis framework, and the results were consistently in the expected direction. However, since for some tests the P values did not pass the conventional .05 threshold, some of the analyses were necessarily inconclusive. Objective: The outcomes of the AMADEUS-1 trial were derived from the first 3 items of the Alcohol Use Disorders Identification Test (AUDIT-C). The aim of this paper was to reanalyze the two primary outcomes of the AMADEUS-1 trial (AUDIT-C scores and prevalence of risky drinking), using the same models used in the original publication but applying a Bayesian inference framework and interpretation. Methods: The same regression models used in the original analysis were employed in this reanalysis (linear and logistic regression). Model parameters were given uniform priors. Markov chain Monte Carlo was used for Bayesian inference, and posterior probabilities were calculated for prespecified thresholds of interest. Results: Where the null hypothesis tests showed inconclusive results, the Bayesian analysis showed that offering an intervention at baseline was preferable compared to offering nothing. At follow-up, the probability of a lower AUDIT-C score among those who had been offered an intervention at baseline was greater than 95%, as was the case when comparing the prevalence of risky drinking. Conclusions: The Bayesian analysis allows for a more consistent perspective of the data collected in the trial, since dichotomization of evidence is not looked for at some arbitrary threshold. Results are presented that represent the data collected in the trial rather than trying to make conclusions about the existence of a population effect. Thus, policy makers can think about the value of keeping the national system without having to navigate the treacherous landscape of statistical significance. Trial Registration: ISRCTN Registry ISRCTN28328154; http://www.isrctn.com/ISRCTN28328154 UR - https://www.jmir.org/2019/11/e14419 UR - http://dx.doi.org/10.2196/14419 UR - http://www.ncbi.nlm.nih.gov/pubmed/31697242 ID - info:doi/10.2196/14419 ER - TY - JOUR AU - Shah, Zubair AU - Surian, Didi AU - Dyda, Amalie AU - Coiera, Enrico AU - Mandl, D. Kenneth AU - Dunn, G. Adam PY - 2019/11/4 TI - Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study JO - J Med Internet Res SP - e14007 VL - 21 IS - 11 KW - health misinformation KW - credibility appraisal KW - machine learning KW - social media N2 - Background: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Objective: The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts. Methods: Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist. Results: The best-performing classifiers were able to distinguish between low, medium, and high credibility with an accuracy of 78% and labeled low-credibility Web pages with a precision of over 96%. Across the set of unique Web pages, 11.86% (16,961 of 143,003) were estimated as low credibility and they generated 9.34% (1.64 billion of 17.6 billion) of potential exposures. The 100 most popular links to low credibility Web pages were each potentially seen by an estimated 2 million to 80 million Twitter users globally. Conclusions: The results indicate that although a small minority of low-credibility Web pages reach a large audience, low-credibility Web pages tend to reach fewer users than other Web pages overall and are more commonly shared within certain subpopulations. An automatic credibility appraisal tool may be useful for finding communities of users at higher risk of exposure to low-credibility vaccine communications. UR - https://www.jmir.org/2019/11/e14007 UR - http://dx.doi.org/10.2196/14007 UR - http://www.ncbi.nlm.nih.gov/pubmed/31682571 ID - info:doi/10.2196/14007 ER - TY - JOUR AU - Maar, A. Marion AU - Beaudin, Valerie AU - Yeates, Karen AU - Boesch, Lisa AU - Liu, Peter AU - Madjedi, Kian AU - Perkins, Nancy AU - Hua-Stewart, Diane AU - Beaudin, Faith AU - Wabano, Jo Mary AU - Tobe, W. Sheldon PY - 2019/11/4 TI - Wise Practices for Cultural Safety in Electronic Health Research and Clinical Trials With Indigenous People: Secondary Analysis of a Randomized Clinical Trial JO - J Med Internet Res SP - e14203 VL - 21 IS - 11 KW - mobile health KW - process evaluation KW - implementation science KW - Indigenous peoples KW - health care texting KW - SMS KW - hypertension KW - task shifting KW - community-based participatory research KW - DREAM-GLOBAL N2 - Background: There is a paucity of controlled clinical trial data based on research with Indigenous peoples. A lack of data specific to Indigenous peoples means that new therapeutic methods, such as those involving electronic health (eHealth), will be extrapolated to these groups based on research with other populations. Rigorous, ethical research can be undertaken in collaboration with Indigenous communities but requires careful attention to culturally safe research practices. Literature on how to involve Indigenous peoples in the development and evaluation of eHealth or mobile health apps that responds to the needs of Indigenous patients, providers, and communities is still scarce; however, the need for community-based participatory research to develop culturally safe technologies is emerging as an essential focus in Indigenous eHealth research. To be effective, researchers must first gain an in-depth understanding of Indigenous determinants of health, including the harmful consequences of colonialism. Second, researchers need to learn how colonialism affects the research process. The challenge then for eHealth researchers is to braid Indigenous ethical values with the requirements of good research methodologies into a culturally safe research protocol. Objective: A recent systematic review showed that Indigenous peoples are underrepresented in randomized controlled trials (RCTs), primarily due to a lack of attention to providing space for Indigenous perspectives within the study frameworks of RCTs. Given the lack of guidelines for conducting RCTs with Indigenous communities, we conducted an analysis of our large evaluation data set collected in the Diagnosing Hypertension-Engaging Action and Management in Getting Lower Blood Pressure in Indigenous Peoples and Low- and Middle- Income Countries (DREAM-GLOBAL) trial over a period of five years. Our goal is to identify wise practices for culturally safe, collaborative eHealth and RCT research with Indigenous communities. Methods: We thematically analyzed survey responses and qualitative interview/focus group data that we collected over five years in six culturally diverse Indigenous communities in Canada during the evaluation of the clinical trial DREAM-GLOBAL. We established themes that reflect culturally safe approaches to research and then developed wise practices for culturally safe research in pragmatic eHealth research. Results: Based on our analysis, successful eHealth research in collaboration with Indigenous communities requires a focus on cultural safety that includes: (1) building a respectful relationship; (2) maintaining a respectful relationship; (3) good communication and support for the local team during the RCT; (4) commitment to co-designing the innovation; (5) supporting task shifting with the local team; and (6) reflecting on our mistakes and lessons learned or areas for improvement that support learning and cultural safety. Conclusions: Based on evaluation data collected in the DREAM-GLOBAL RCT, we found that there are important cultural safety considerations in Indigenous eHealth research. Building on the perspectives of Indigenous staff and patients, we gleaned wise practices for RCTs in Indigenous communities. Trial Registration: ClinicalTrials.gov NCT02111226; https://clinicaltrials.gov/ct2/show/NCT02111226 UR - https://www.jmir.org/2019/11/e14203 UR - http://dx.doi.org/10.2196/14203 UR - http://www.ncbi.nlm.nih.gov/pubmed/31682574 ID - info:doi/10.2196/14203 ER - TY - JOUR AU - Ralph-Nearman, Christina AU - Arevian, C. Armen AU - Puhl, Maria AU - Kumar, Rajay AU - Villaroman, Diane AU - Suthana, Nanthia AU - Feusner, D. Jamie AU - Khalsa, S. Sahib PY - 2019/10/29 TI - A Novel Mobile Tool (Somatomap) to Assess Body Image Perception Pilot Tested With Fashion Models and Nonmodels: Cross-Sectional Study JO - JMIR Ment Health SP - e14115 VL - 6 IS - 10 KW - body image KW - body perception KW - body representation KW - body image disorder KW - eating disorder KW - mobile health KW - mental health KW - mobile app KW - digital health N2 - Background: Distorted perception of one?s body and appearance, in general, is a core feature of several psychiatric disorders including anorexia nervosa and body dysmorphic disorder and is operative to varying degrees in nonclinical populations. Yet, body image perception is challenging to assess, given its subjective nature and variety of manifestations. The currently available methods have several limitations including restricted ability to assess perceptions of specific body areas. To address these limitations, we created Somatomap, a mobile tool that enables individuals to visually represent their perception of body-part sizes and shapes as well as areas of body concerns and record the emotional valence of concerns. Objective: This study aimed to develop and pilot test the feasibility of a novel mobile tool for assessing 2D and 3D body image perception. Methods: We developed a mobile 2D tool consisting of a manikin figure on which participants outline areas of body concern and indicate the nature, intensity, and emotional valence of the concern. We also developed a mobile 3D tool consisting of an avatar on which participants select individual body parts and use sliders to manipulate their size and shape. The tool was pilot tested on 103 women: 65 professional fashion models, a group disproportionately exposed to their own visual appearance, and 38 nonmodels from the general population. Acceptability was assessed via a usability rating scale. To identify areas of body concern in 2D, topographical body maps were created by combining assessments across individuals. Statistical body maps of group differences in body concern were subsequently calculated using the formula for proportional z-score. To identify areas of body concern in 3D, participants? subjective estimates from the 3D avatar were compared to corresponding measurements of their actual body parts. Discrepancy scores were calculated based on the difference between the perceived and actual body parts and evaluated using multivariate analysis of covariance. Results: Statistical body maps revealed different areas of body concern between models (more frequently about thighs and buttocks) and nonmodels (more frequently about abdomen/waist). Models were more accurate at estimating their overall body size, whereas nonmodels tended to underestimate the size of individual body parts, showing greater discrepancy scores for bust, biceps, waist, hips, and calves but not shoulders and thighs. Models and nonmodels reported high ease-of-use scores (8.4/10 and 8.5/10, respectively), and the resulting 3D avatar closely resembled their actual body (72.7% and 75.2%, respectively). Conclusions: These pilot results suggest that Somatomap is feasible to use and offers new opportunities for assessment of body image perception in mobile settings. Although further testing is needed to determine the applicability of this approach to other populations, Somatomap provides unique insight into how humans perceive and represent the visual characteristics of their body. UR - http://mental.jmir.org/2019/10/e14115/ UR - http://dx.doi.org/10.2196/14115 UR - http://www.ncbi.nlm.nih.gov/pubmed/31469647 ID - info:doi/10.2196/14115 ER - TY - JOUR AU - Crawford, Danielle Natalie AU - Haardöerfer, Regine AU - Cooper, Hannah AU - McKinnon, Izraelle AU - Jones-Harrell, Carla AU - Ballard, April AU - von Hellens, Shantel Sierra AU - Young, April PY - 2019/10/4 TI - Characterizing the Rural Opioid Use Environment in Kentucky Using Google Earth: Virtual Audit JO - J Med Internet Res SP - e14923 VL - 21 IS - 10 KW - opioid-related disorders KW - rural health KW - built environment N2 - Background: The opioid epidemic has ravaged rural communities in the United States. Despite extensive literature relating the physical environment to substance use in urban areas, little is known about the role of physical environment on the opioid epidemic in rural areas. Objective: This study aimed to examine the reliability of Google Earth to collect data on the physical environment related to substance use in rural areas. Methods: Systematic virtual audits were performed in 5 rural Kentucky counties using Google Earth between 2017 and 2018 to capture land use, health care facilities, entertainment venues, and businesses. In-person audits were performed for a subset of the census blocks. Results: We captured 533 features, most of which were images taken before 2015 (71.8%, 383/533). Reliability between the virtual audits and the gold standard was high for health care facilities (>83%), entertainment venues (>95%), and businesses (>61%) but was poor for land use features (>18%). Reliability between the virtual audit and in-person audit was high for health care facilities (83%) and entertainment venues (62%) but was poor for land use (0%) and businesses (12.5%). Conclusions: Poor reliability for land use features may reflect difficulty characterizing features that require judgment or natural changes in the environment that are not reflective of the Google Earth imagery because it was captured several years before the audit was performed. Virtual Google Earth audits were an efficient way to collect rich neighborhood data that are generally not available from other sources. However, these audits should use caution when the images in the observation area are dated. UR - https://www.jmir.org/2019/10/e14923 UR - http://dx.doi.org/10.2196/14923 UR - http://www.ncbi.nlm.nih.gov/pubmed/31588903 ID - info:doi/10.2196/14923 ER - TY - JOUR AU - Abuelezam, N. Nadia AU - Reshef, A. Yakir AU - Novak, David AU - Grad, Hagai Yonatan AU - Seage III, R. George AU - Mayer, Kenneth AU - Lipsitch, Marc PY - 2019/09/12 TI - Interaction Patterns of Men Who Have Sex With Men on a Geosocial Networking Mobile App in Seven United States Metropolitan Areas: Observational Study JO - J Med Internet Res SP - e13766 VL - 21 IS - 9 KW - men who have sex with men KW - sexual behavior KW - race factors KW - population dynamics N2 - Background: The structure of the sexual networks and partnership characteristics of young black men who have sex with men (MSM) may be contributing to their high risk of contracting HIV in the United States. Assortative mixing, which refers to the tendency of individuals to have partners from one?s own group, has been proposed as a potential explanation for disparities. Objective: The objective of this study was to identify the age- and race-related search patterns of users of a diverse geosocial networking mobile app in seven metropolitan areas in the United States to understand the disparities in sexually transmitted infection and HIV risk in MSM communities. Methods: Data were collected on user behavior between November 2015 and May 2016. Data pertaining to behavior on the app were collected for men who had searched for partners with at least one search parameter narrowed from defaults or used the app to send at least one private chat message and used the app at least once during the study period. Newman assortativity coefficient (R) was calculated from the study data to understand assortativity patterns of men by race. Pearson correlation coefficient was used to assess assortativity patterns by age. Heat maps were used to visualize the relationship between searcher?s and candidate?s characteristics by age band, race, or age band and race. Results: From November 2015 through May 2016, there were 2,989,737 searches in all seven metropolitan areas among 122,417 searchers. Assortativity by age was important for looking at the profiles of candidates with correlation coefficients ranging from 0.284 (Birmingham) to 0.523 (San Francisco). Men tended to look at the profiles of candidates that matched their race in a highly assortative manner with R ranging from 0.310 (Birmingham) to 0.566 (Los Angeles). For the initiation of chats, race appeared to be slightly assortative for some groups with R ranging from 0.023 (Birmingham) to 0.305 (Los Angeles). Asian searchers were most assortative in initiating chats with Asian candidates in Boston, Los Angeles, New York, and San Francisco. In Birmingham and Tampa, searchers from all races tended to initiate chats with black candidates. Conclusions: Our results indicate that the age preferences of MSM are relatively consistent across cities, that is, younger MSM are more likely to be chatted with and have their profiles viewed compared with older MSM, but the patterns of racial mixing are more variable. Although some generalizations can be made regarding Web-based behaviors across all cities, city-specific usage patterns and trends should be analyzed to create targeted and localized interventions that may make the most difference in the lives of MSM in these areas. UR - https://www.jmir.org/2019/9/e13766/ UR - http://dx.doi.org/10.2196/13766 UR - http://www.ncbi.nlm.nih.gov/pubmed/31516124 ID - info:doi/10.2196/13766 ER - TY - JOUR AU - Huang, Lu-Lu AU - Wang, Yang-Yang AU - Liu, Li-Ying AU - Tang, Hong-Ping AU - Zhang, Meng-Na AU - Ma, Shu-Fang AU - Zou, Li-Ping PY - 2019/09/12 TI - Home Videos as a Cost-Effective Tool for the Diagnosis of Paroxysmal Events in Infants: Prospective Study JO - JMIR Mhealth Uhealth SP - e11229 VL - 7 IS - 9 KW - paroxysmal events KW - infant KW - home videos KW - online consultation N2 - Background: The diagnosis of paroxysmal events in infants is often challenging. Reasons include the child?s inability to express discomfort and the inability to record video electroencephalography at home. The prevalence of mobile phones, which can record videos, may be beneficial to these patients. In China, this advantage may be even more significant given the vast population and the uneven distribution of medical resources. Objective: The aim of this study is to investigate the value of mobile phone videos in increasing the diagnostic accuracy and cost savings of paroxysmal events in infants. Methods: Clinical data, including descriptions and home videos of episodes, from 12 patients with paroxysmal events were collected. The investigation was conducted in six centers during pediatric academic conferences. All 452 practitioners present were asked to make their diagnoses by just the descriptions of the events, and then remake their diagnoses after watching the corresponding home videos of the episodes. The doctor?s information, including educational background, profession, working years, and working hospital level, was also recorded. The cost savings from accurate diagnoses were measured on the basis of using online consultation, which can also be done easily by mobile phone. All data were recorded in the form of questionnaires designed for this study. Results: We collected 452 questionnaires, 301 of which met the criteria (66.6%) and were analyzed. The mean correct diagnoses with and without videos was 8.4 (SD 1.7) of 12 and 7.5 (SD 1.7) of 12, respectively. For epileptic seizures, mobile phone videos increased the mean accurate diagnoses by 3.9%; for nonepileptic events, it was 11.5% and both were statistically different (P=.006 for epileptic events; P<.001 for nonepileptic events). Pediatric neurologists with longer working years had higher diagnostic accuracy; whereas, their working hospital level and educational background made no difference. For patients with paroxysmal events, at least US $673.90 per capita and US $128 million nationwide could be saved annually, which is 12.02% of the total cost for correct diagnosis. Conclusions: Home videos made on mobile phones are a cost-effective tool for the diagnosis of paroxysmal events in infants. They can facilitate the diagnosis of paroxysmal events in infants and thereby save costs. The best choice for infants with paroxysmal events on their initial visit is to record their events first and then show the video to a neurologist with longer working years through online consultation. UR - https://mhealth.jmir.org/2019/9/e11229/ UR - http://dx.doi.org/10.2196/11229 UR - http://www.ncbi.nlm.nih.gov/pubmed/31516128 ID - info:doi/10.2196/11229 ER - TY - JOUR AU - Ijaz, Kiran AU - Ahmadpour, Naseem AU - Naismith, L. Sharon AU - Calvo, A. Rafael PY - 2019/09/03 TI - An Immersive Virtual Reality Platform for Assessing Spatial Navigation Memory in Predementia Screening: Feasibility and Usability Study JO - JMIR Ment Health SP - e13887 VL - 6 IS - 9 KW - virtual reality KW - healthy aging KW - memory KW - cognition KW - dementia N2 - Background: Traditional methods for assessing memory are expensive and have high administrative costs. Memory assessment is important for establishing cognitive impairment in cases such as detecting dementia in older adults. Virtual reality (VR) technology can assist in establishing better quality outcome in such crucial screening by supporting the well-being of individuals and offering them an engaging, cognitively challenging task that is not stressful. However, unmet user needs can compromise the validity of the outcome. Therefore, screening technology for older adults must address their specific design and usability requirements. Objective: This study aimed to design and evaluate the feasibility of an immersive VR platform to assess spatial navigation memory in older adults and establish its compatibility by comparing the outcome to a standard screening platform on a personal computer (PC). Methods: VR-CogAssess is a platform integrating an Oculus Rift head-mounted display and immersive photorealistic imagery. In a pilot study with healthy older adults (N=42; mean age 73.22 years, SD 9.26), a landmark recall test was conducted, and assessment on the VR-CogAssess was compared against a standard PC (SPC) setup. Results: Results showed that participants in VR were significantly more engaged (P=.003), achieved higher landmark recall scores (P=.004), made less navigational mistakes (P=.04), and reported a higher level of presence (P=.002) than those in SPC setup. In addition, participants in VR indicated no significantly higher stress than SPC setup (P=.87). Conclusions: The study findings suggest immersive VR is feasible and compatible with SPC counterpart for spatial navigation memory assessment. The study provides a set of design guidelines for creating similar platforms in the future. UR - https://mental.jmir.org/2019/9/e13887/ UR - http://dx.doi.org/10.2196/13887 UR - http://www.ncbi.nlm.nih.gov/pubmed/31482851 ID - info:doi/10.2196/13887 ER - TY - JOUR AU - Katapally, Reddy Tarun PY - 2019/08/30 TI - The SMART Framework: Integration of Citizen Science, Community-Based Participatory Research, and Systems Science for Population Health Science in the Digital Age JO - JMIR Mhealth Uhealth SP - e14056 VL - 7 IS - 8 KW - community-based participatory research KW - smartphones KW - mobile phones KW - population health KW - mHealth KW - eHealth KW - digital health KW - big data KW - evidence-based framework KW - citizen science KW - participatory research KW - participatory surveillance KW - systems science KW - ubiquitous tools UR - http://mhealth.jmir.org/2019/8/e14056/ UR - http://dx.doi.org/10.2196/14056 UR - http://www.ncbi.nlm.nih.gov/pubmed/31471963 ID - info:doi/10.2196/14056 ER - TY - JOUR AU - Theofanopoulou, Nikki AU - Isbister, Katherine AU - Edbrooke-Childs, Julian AU - Slovák, Petr PY - 2019/08/05 TI - A Smart Toy Intervention to Promote Emotion Regulation in Middle Childhood: Feasibility Study JO - JMIR Ment Health SP - e14029 VL - 6 IS - 8 KW - mental health KW - children KW - families KW - stress, psychological KW - emotional adjustment N2 - Background: A common challenge with existing psycho-social prevention interventions for children is the lack of effective, engaging, and scalable delivery mechanisms, especially beyond in-person therapeutic or school-based contexts. Although digital technology has the potential to address these issues, existing research on technology-enabled interventions for families remains limited. This paper focuses on emotion regulation (ER) as an example of a core protective factor that is commonly targeted by prevention interventions. Objective: The aim of this pilot study was to provide an initial validation of the logic model and feasibility of in situ deployment for a new technology-enabled intervention, designed to support children?s in-the-moment ER efforts. The novelty of the proposed approach relies on delivering the intervention through an interactive object (a smart toy) sent home with the child, without any prior training necessary for either the child or their carer. This study examined (1) engagement and acceptability of the toy in the homes during 1-week deployments, and (2) qualitative indicators of ER effects, as reported by parents and children. In total, 10 families (altogether 11 children aged 6-10 years) were recruited from 3 predominantly underprivileged communities in the United Kingdom, as low SES populations have been shown to be particularly at risk for less developed ER competencies. Children were given the prototype, a discovery book, and a simple digital camera to keep at home for 7 to 8 days. Data were gathered through a number of channels: (1) semistructured interviews with parents and children prior to and right after the deployment, (2) photos children took during the deployment, and (3) touch interactions automatically logged by the prototype throughout the deployment. Results: Across all families, parents and children reported that the smart toy was incorporated into the children?s ER practices and engaged with naturally in moments the children wanted to relax or calm down. Data suggested that the children interacted with the toy throughout the deployment, found the experience enjoyable, and all requested to keep the toy longer. Children?s emotional connection to the toy appears to have driven this strong engagement. Parents reported satisfaction with and acceptability of the toy. Conclusions: This is the first known study on the use of technology-enabled intervention delivery to support ER in situ. The strong engagement, incorporation into children?s ER practices, and qualitative indications of effects are promising. Further efficacy research is needed to extend these indicative data by examining the psychological efficacy of the proposed intervention. More broadly, our findings argue for the potential of a technology-enabled shift in how future prevention interventions are designed and delivered: empowering children and parents through child-led, situated interventions, where participants learn through actionable support directly within family life, as opposed to didactic in-person workshops and a subsequent skills application. UR - https://mental.jmir.org/2019/8/e14029/ UR - http://dx.doi.org/10.2196/14029 UR - http://www.ncbi.nlm.nih.gov/pubmed/31381502 ID - info:doi/10.2196/14029 ER - TY - JOUR AU - Dusseljee-Peute, W. Linda AU - Van der Togt, Remko AU - Jansen, Bas AU - Jaspers, W. Monique PY - 2019/08/05 TI - The Value of Radio Frequency Identification in Quality Management of the Blood Transfusion Chain in an Academic Hospital Setting JO - JMIR Med Inform SP - e9510 VL - 7 IS - 3 KW - radio waves KW - automatic data processing KW - blood transfusion KW - geographic information systems KW - temperature KW - technology KW - guideline adherence N2 - Background: A complex process like the blood transfusion chain could benefit from modern technologies such as radio frequency identification (RFID). RFID could, for example, play an important role in generating logistic and temperature data of blood products, which are important in assessing the quality of the logistic process of blood transfusions and the product itself. Objective: This study aimed to evaluate whether location, time stamp, and temperature data generated in real time by an active RFID system containing temperature sensors attached to red blood cell (RBC) products can be used to assess the compliance of the management of RBCs to 4 intrahospital European and Dutch guidelines prescribing logistic and temperature constraints in an academic hospital setting. Methods: An RFID infrastructure supported the tracking and tracing of 243 tagged RBCs in a clinical setting inside the hospital at the blood transfusion laboratory, the operating room complex, and the intensive care unit within the Academic Medical Center, a large academic hospital in Amsterdam, the Netherlands. The compliance of the management of 182 out of the 243 tagged RBCs could be assessed on their adherence to the following guidelines on intrahospital storage, transport, and distribution: (1) RBCs must be preserved within an environment with a temperature between 2°C and 6°C; (2) RBCs have to be transfused within 1 hour after they have left a validated cooling system; (3) RBCs that have reached a temperature above 10°C must not be restored or must be transfused within 24 hours or else be destroyed; (4) unused RBCs are to be returned to the BTL within 24 hours after they left the transfusion laboratory. Results: In total, 4 blood products (4/182 compliant; 2.2%) complied to all applicable guidelines. Moreover, 15 blood products (15/182 not compliant to 1 out of several guidelines; 8.2%) were not compliant to one of the guidelines of either 2 or 3 relevant guidelines. Finally, 148 blood products (148/182 not compliant to 2 guidelines; 81.3%) were not compliant to 2 out of the 3 relevant guidelines. Conclusions: The results point out the possibilities of using RFID technology to assess the quality of the blood transfusion chain itself inside a hospital setting in reference to intrahospital guidelines concerning the storage, transport, and distribution conditions of RBCs. This study shows the potentials of RFID in identifying potential bottlenecks in hospital organizations? processes by use of objective data, which are to be tackled in process redesign efforts. The effect of these efforts can subsequently be evaluated by the use of RFID again. As such, RFID can play a significant role in optimization of the quality of the blood transfusion chain. UR - https://medinform.jmir.org/2019/3/e9510/ UR - http://dx.doi.org/10.2196/medinform.9510 UR - http://www.ncbi.nlm.nih.gov/pubmed/31381503 ID - info:doi/10.2196/medinform.9510 ER - TY - JOUR AU - Gillum, Shalu AU - Williams, Natasha AU - Brink, Brittany AU - Ross, Edward PY - 2019/07/05 TI - Clinician Job Searches in the Internet Era: Internet-Based Study JO - J Med Internet Res SP - e12638 VL - 21 IS - 7 KW - personnel selection KW - internet KW - academic medical centers N2 - Background: Traditional methods using print media and commercial firms for clinician recruiting are often limited by cost, slow pace, and suboptimal results. An efficient and fiscally sound approach is needed for searching online to recruit clinicians. Objective: The aim of the study was to assess the Web-based methods by which clinicians might be searching for jobs in a broad range of specialties and how academic medical centers can advertise clinical job openings to prominently appear on internet searches that would yield the greatest return on investment. Methods: We used a search engine (Google) to identify 8 query terms for each of the specialties and specialists (eg, dermatology and dermatologist) to determine internet job search methodologies for 12 clinical disciplines. Searches were conducted, and the data used for analysis were the first 20 results. Results: In total, 176 searches were conducted at varying times over the course of several months, and 3520 results were recorded. The following 4 types of websites appeared in the top 10 search results across all specialties searched, accounting for 52.27% (920/1760) of the results: (1) a single no-cost job aggregator (229/1760, 13.01%); (2) 2 prominent journal-based paid digital job listing services (157/1760, 8.92% and 91/1760, 5.17%, respectively); (3) a fee-based Web-based agency (137/1760, 7.78%) offering candidate profiles; and (4) society-based paid advertisements (totaling 306/1760, 17.38%). These sites accounted for 75.45% (664/880) of results limited to the top 5 results. Repetitive short-term testing yielded similar results with minor changes in the rank order. Conclusions: On the basis of our findings, we offer a specific financially prudent internet strategy for both clinicians searching the internet for employment and employers hiring clinicians in academic medical centers. UR - https://www.jmir.org/2019/7/e12638/ UR - http://dx.doi.org/10.2196/12638 UR - http://www.ncbi.nlm.nih.gov/pubmed/31278735 ID - info:doi/10.2196/12638 ER - TY - JOUR AU - Araujo Almeida, Vanessa AU - Littlejohn, Paula AU - Cop, Irene AU - Brown, Erin AU - Afroze, Rimi AU - Davison, M. Karen PY - 2019/06/28 TI - Comparison of Nutrigenomics Technology Interface Tools for Consumers and Health Professionals: A Sequential Explanatory Mixed Methods Investigation JO - J Med Internet Res SP - e12580 VL - 21 IS - 6 KW - nutrigenomics KW - nutrigenetics KW - genomics KW - epigenomics KW - interface, user-computer N2 - Background: Nutrigenomics forms the basisof personalized nutrition by customizing an individual?s dietaryplan based on the integration of life stage, current health status,and genome information. Some common genes that are includedin nutrition-based multigene test panels include CYP1A2 (rateof caffeine break down), MTHFR (folate usage),NOS3 (risk of elevated triglyceride levels related to omega-3fat intake), and ACE (blood pressure response in related tosodium intake). The complexity of gene test?based personalized nutrition presents barriers to its implementation. Objective: This study aimed to compare a self-driven approach to gene test?based nutrition education versus an integrated practitioner-facilitated method to help develop improved interface tools for personalized nutrition practice. Methods: A sequential, explanatory mixed methods investigation of 55 healthy adults (35 to 55 years) was conducted that included (1) a 9-week randomized controlled trial where participants were randomized to receive a standard nutrition-based gene test report (control; n=19) or a practitioner-facilitated personalized nutrition intervention (intervention; n=36) and (2) an interpretative thematic analysis of focus group interview data. Outcome measures included differences in the diet quality score (Healthy Eating Index?Canadian [HEI-C]; proportion [%] of calories from total fat, saturated fat, and sugar; omega 3 fatty acid intake [grams]; sodium intake [milligrams]); as well as health-related quality of life (HRQoL) scale score. Results: Of the 55 (55/58 enrolled, 95%) participants who completed the study, most were aged between 40 and 51 years (n=37, 67%), were female (n=41, 75%), and earned a high household income (n=32, 58%). Compared with baseline measures, group differences were found for the percentage of calories from total fat (mean difference [MD]=?5.1%; Wilks lambda (?)=0.817, F1,53=11.68; P=.001; eta-squared [?²]=0.183) and saturated fat (MD=?1.7%; ?=0.816; F1,53=11.71; P=.001; ?²=0.18) as well as HRQoL scores (MD=8.1 points; ?=0.914; F1,53=4.92; P=.03; ?²=0.086) compared with week 9 postintervention measures. Interactions of time-by-group assignment were found for sodium intakes (?=0.846; F1,53=9.47; P=.003; ?²=0.15) and HEI-C scores (?=0.660; F1,53=27.43; P<.001; ?²=0.35). An analysis of phenotypic and genotypic information by group assignment found improved total fat (MD=?5%; ?=0.815; F1,51=11.36; P=.001; ?²=0.19) and saturated fat (MD=?1.3%; ?=0.822; F1,51=10.86; P=.002; ?²=0.18) intakes. Time-by-group interactions were found for sodium (?=0.844; F3,51=3.09; P=.04; ?²=0.16); a post hoc analysis showed pre/post differences for those in the intervention group that did (preintervention mean 3611 mg, 95% CI 3039-4182; postintervention mean 2135 mg, 95% CI 1564-2705) and did not have the gene risk variant (preintervention mean 3722 mg, 95% CI 2949-4496; postintervention mean 2071 mg, 95% CI 1299-2843). Pre- and postdifferences related to the Dietary Reference Intakes showed increases in the proportion of intervention participants within the acceptable macronutrient distribution ranges for fat (pre/post mean difference=41.2%; P=.02). Analysis of textual data revealed 3 categories of feedback: (1) translation of nutrition-related gene test information to action; (2) facilitation of eating behavior change, particularly for the macronutrients and sodium; and (3) directives for future personalized nutrition practice. Conclusions: Although improvements were observed in both groups, healthy adults appear to derive more health benefits from practitioner-led personalized nutrition interventions. Further work is needed to better facilitate positive changes in micronutrient intakes. Trial Registration: ClinicalTrials.gov NCT03310814; http://clinicaltrials.gov/ct2/show/NCT03310814 International Registered Report Identifier (IRRID): RR2-10.2196/resprot.9846 UR - http://www.jmir.org/2019/6/e12580/ UR - http://dx.doi.org/10.2196/12580 UR - http://www.ncbi.nlm.nih.gov/pubmed/31254340 ID - info:doi/10.2196/12580 ER - TY - JOUR AU - Hu, Xiao-Su AU - Nascimento, D. Thiago AU - Bender, C. Mary AU - Hall, Theodore AU - Petty, Sean AU - O?Malley, Stephanie AU - Ellwood, P. Roger AU - Kaciroti, Niko AU - Maslowski, Eric AU - DaSilva, F. Alexandre PY - 2019/06/28 TI - Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain JO - J Med Internet Res SP - e13594 VL - 21 IS - 6 KW - pain KW - spectroscopy, near-infrared KW - virtual reality KW - artificial intelligence N2 - Background: For many years, clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy. Objective: This study aimed to test the feasibility of a mobile neuroimaging-based clinical augmented reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient?s brain in real time. Methods: Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32°C-0°C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy, to gauge their cortical activity during evoked acute clinical pain. The data were decoded using a neural network (NN)?based AI algorithm to classify hemodynamic response data into pain and no-pain brain states in real time. We tested the performance of several networks (NN with 7 layers, 6 layers, 5 layers, 3 layers, recurrent NN, and long short-term memory network) upon reorganized data features on pain diction and localization in a simulated real-time environment. In addition, we also tested the feasibility of transmitting the neuroimaging data to an AR device, HoloLens, in the same simulated environment, allowing visualization of the ongoing cortical activity on a 3-dimensional brain template virtually plotted on the patients? head during clinical consult. Results: The artificial neutral network (3-layer NN) achieved an optimal classification accuracy at 80.37% (126,000/156,680) for pain and no pain discrimination, with positive likelihood ratio (PLR) at 2.35. We further explored a 3-class localization task of left/right side pain and no-pain states, and convolutional NN-6 (6-layer NN) achieved highest classification accuracy at 74.23% (1040/1401) with PLR at 2.02. Conclusions: Additional studies are needed to optimize and validate our prototype CLARAi framework for other pains and neurologic disorders. However, we presented an innovative and feasible neuroimaging-based AR/AI concept that can potentially transform the human brain into an objective target to visualize and precisely measure and localize pain in real time where it is most needed: in the doctor?s office. International Registered Report Identifier (IRRID): RR1-10.2196/13594 UR - https://www.jmir.org/2019/6/e13594/ UR - http://dx.doi.org/10.2196/13594 UR - http://www.ncbi.nlm.nih.gov/pubmed/31254336 ID - info:doi/10.2196/13594 ER - TY - JOUR AU - Crawford, Joanna AU - Wilhelm, Kay AU - Proudfoot, Judy PY - 2019/06/27 TI - Web-Based Benefit-Finding Writing for Adults with Type 1 or Type 2 Diabetes: Preliminary Randomized Controlled Trial JO - JMIR Diabetes SP - e13857 VL - 4 IS - 2 KW - diabetes KW - adult KW - distress KW - benefit-finding KW - depression KW - anxiety KW - emotions KW - internet KW - writing KW - surveys and questionnaires KW - treatment outcome N2 - Background: The high prevalence of diabetes distress and subclinical depression in adults with type 1 and type 2 diabetes mellitus (T1DM and T2DM, respectively) indicates the need for low-intensity self-help interventions that can be used in a stepped care approach to address some of their psychological needs. However, people with diabetes can be reluctant to engage in mental health care. Benefit-finding writing (BFW) is a brief intervention that involves writing about any positive thoughts and feelings concerning a stressful experience such as an illness, avoiding potential mental health stigma. It has been associated with increases in positive affect and positive growth and has demonstrated promising results in trials in other clinical populations. However, BFW has not been examined in people with diabetes. Objective: This study aimed to evaluate the efficacy of a Web-based BFW intervention for reducing diabetes distress and increasing benefit finding in diabetic adults with T1DM or T2DM compared to a control writing condition. Methods: Adults with T1DM or T2DM and diabetes distress were recruited online through the open access Writing for Health program. After completing baseline questionnaires, they were randomly allocated to receive online BFW or an active control condition of online writing about the use of time (CW). Both groups completed 15-minute online writing sessions, once per day, for 3 consecutive days. Online measures were administered at baseline, 1 month, and 3 months postintervention. Participants were also asked to rate their current mood immediately prior to and following each writing session. Results: Seventy-two adults with T1DM or T2DM were recruited and randomly allocated to receive BFW (n=24) or CW (n=48). Participants adhered to the BFW regimen. Greater increases in positive affect immediately postwriting were found in the BFW group than in the CW group. However, there were no significant group-by-time interactions (indicating intervention effects) for benefit finding or diabetes distress at either the 1-month or 3-month follow-up. Both the BFW and CW groups demonstrated small, significant decreases in diabetes distress over time. Conclusions: BFW was well tolerated by adults with diabetes in this study but did not demonstrate efficacy in improving diabetes distress or benefit finding compared to an active control writing condition. However, due to recruitment difficulties, the study was underpowered and the sample was skewed to individuals with minimal diabetes distress and none to minimal depression and anxiety at baseline. Future research should continue to investigate the efficacy of variants of therapeutic writing for adults with T1DM or T2DM, using larger samples of participants with elevated diabetes distress. Trial Registration: Australiand New Zealand Clinical Trials Registry ACTRN12615000241538; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368146 UR - http://diabetes.jmir.org/2019/2/e13857/ UR - http://dx.doi.org/10.2196/13857 UR - http://www.ncbi.nlm.nih.gov/pubmed/31250827 ID - info:doi/10.2196/13857 ER - TY - JOUR AU - Ogink, AM Paula AU - de Jong, M. Jelske AU - Koeneman, Mats AU - Weenk, Mariska AU - Engelen, JLPG Lucien AU - van Goor, Harry AU - van de Belt, H. Tom AU - Bredie, JH Sebastian PY - 2019/06/19 TI - Feasibility of a New Cuffless Device for Ambulatory Blood Pressure Measurement in Patients With Hypertension: Mixed Methods Study JO - J Med Internet Res SP - e11164 VL - 21 IS - 6 KW - ambulatory blood pressure monitoring KW - home blood pressure monitoring KW - cuffless blood pressure device KW - hypertension N2 - Background: Frequent home blood pressure (BP) measurements result in a better estimation of the true BP. However, traditional cuff-based BP measurements are troublesome for patients. Objective: This study aimed to evaluate the feasibility of a cuffless device for ambulatory systolic blood pressure (SBP) measurement. Methods: This was a mixed method feasibility study in patients with hypertension. Performance of ambulatory SBPs with the device was analyzed quantitatively by intrauser reproducibility and comparability to a classic home BP monitor. Correct use by the patients was checked with video, and user-friendliness was assessed using a validated questionnaire, the System Usability Scale (SUS). Patient experiences were assessed using qualitative interviews. Results: A total of 1020 SBP measurements were performed using the Checkme monitor in 11 patients with hypertension. Duplicate SBPs showed a high intrauser correlation (R=0.86, P<.001). SBPs measured by the Checkme monitor did not correlate well with those of the different home monitors (R=0.47, P=.007). However, the mean SBPs measured by the Checkme and home monitors over the 3-week follow-up were strongly correlated (R=0.75, P=.008). In addition, 36.4% (n=4) of the participants performed the Checkme measurements without any mistakes. The mean SUS score was 86.4 (SD 8.3). The most important facilitator was the ease of using the Checkme monitor. Most important barriers included the absence of diastolic BP and the incidental difficulties in obtaining an SBP result. Conclusions: Given the good intrauser reproducibility, user-friendliness, and patient experience, all of which facilitate patients to perform frequent measurements, cuffless BP monitoring may change the way patients measure their BP at home in the context of ambulant hypertension management. UR - http://www.jmir.org/2019/6/e11164/ UR - http://dx.doi.org/10.2196/11164 UR - http://www.ncbi.nlm.nih.gov/pubmed/31219050 ID - info:doi/10.2196/11164 ER - TY - JOUR AU - Lalaye, Didier AU - de Bruijn, E. Mirjam AU - de Jong, PVM Tom PY - 2019/06/18 TI - Prevalence of Schistosoma Haematobium Measured by a Mobile Health System in an Unexplored Endemic Region in the Subprefecture of Torrock, Chad JO - JMIR Public Health Surveill SP - e13359 VL - 5 IS - 2 KW - Schistosoma haematobium KW - prevalence KW - Chad KW - neglected tropical diseases KW - mobile health N2 - Background: Schistosoma haematobium is a parasitic digenetic trematode responsible for schistosomiasis (also known as bilharzia). The disease is caused by penetration of the skin by the parasite, spread by intermediate host molluscs in stagnant waters, and can be treated by administration of praziquantel. Schistosomiasis is considered to be an important but neglected tropical disease. Objective: The aim of this pilot study was to investigate the prevalence of schistosomiasis in the subprefecture of Torrock, an endemic area in Chad where no earlier investigation had been conducted and no distribution system for pharmacotherapy has ever existed. Methods: This study examined 1875 children aged 1 to 14 years over a period of 1 year. After centrifugation, urine examination was performed by a direct microscopic investigation for eggs. The investigation was conducted with a mobile health (mHealth) approach, using short message service (SMS) for communication among parents, local health workers, a pharmacist, and a medical doctor. An initial awareness campaign requested parents to have their children examined for schistosomiasis. Urine was then collected at home by the parents following the SMS request. Urine results that proved positive were sent to a medical doctor by SMS, who in turn ordered a pharmacist by SMS to distribute praziquantel to the infected children. Results: Direct microscopic examination of urine found 467 positive cases (24.9% of the total sample). Of all male and female samples, 341 (34%) and 127 (14.4%) samples were positive, respectively. The infection rate was equally distributed over age groups. The newly developed mHealth system had a limited level of participation (8%) from an estimated total of 25,000 children in the target group. Conclusions: The prevalence of schistosomiasis in children in the subprefecture of Torrock is moderately high. Efforts will be required to enhance the awareness of parents and to reach a larger percentage of the population. Systematic governmental measures should be put in place as soon as possible to increase awareness in the area and to diagnose and treat cases of schistosomiasis. UR - http://publichealth.jmir.org/2019/2/e13359/ UR - http://dx.doi.org/10.2196/13359 UR - http://www.ncbi.nlm.nih.gov/pubmed/31215519 ID - info:doi/10.2196/13359 ER - TY - JOUR AU - Cacheda, Fidel AU - Fernandez, Diego AU - Novoa, J. Francisco AU - Carneiro, Victor PY - 2019/6/10 TI - Early Detection of Depression: Social Network Analysis and Random Forest Techniques JO - J Med Internet Res SP - e12554 VL - 21 IS - 6 KW - depression KW - major depressive disorder KW - social media KW - artificial intelligence KW - machine learning N2 - Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects? behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks. UR - http://www.jmir.org/2019/6/e12554/ UR - http://dx.doi.org/10.2196/12554 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199323 ID - info:doi/10.2196/12554 ER - TY - JOUR AU - Li, Jiawei AU - Xu, Qing AU - Shah, Neal AU - Mackey, K. Tim PY - 2019/6/15 TI - A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study JO - J Med Internet Res SP - e13803 VL - 21 IS - 6 KW - opioids KW - social media KW - narcotics KW - substance abuse KW - machine learning KW - internet KW - prescription drug abuse KW - artificial intelligence N2 - Background: Social media use is now ubiquitous, but the growth in social media communications has also made it a convenient digital platform for drug dealers selling controlled substances, opioids, and other illicit drugs. Previous studies and news investigations have reported the use of popular social media platforms as conduits for opioid sales. This study uses deep learning to detect illicit drug dealing on the image and video sharing platform Instagram. Objective: The aim of this study was to develop and evaluate a machine learning approach to detect Instagram posts related to illegal internet drug dealing. Methods: In this paper, we describe an approach to detect drug dealers by using a deep learning model on Instagram. We collected Instagram posts using a Web scraper between July 2018 and October 2018 and then compared our deep learning model against 3 different machine learning models (eg, random forest, decision tree, and support vector machine) to assess the performance and accuracy of the model. For our deep learning model, we used the long short-term memory unit in the recurrent neural network to learn the pattern of the text of drug dealing posts. We also manually annotated all posts collected to evaluate our model performance and to characterize drug selling conversations. Results: From the 12,857 posts we collected, we detected 1228 drug dealer posts comprising 267 unique users. We used cross-validation to evaluate the 4 models, with our deep learning model reaching 95% on F1 score and performing better than the other 3 models. We also found that by removing the hashtags in the text, the model had better performance. Detected posts contained hashtags related to several drugs, including the controlled substance Xanax (1078/1228, 87.78%), oxycodone/OxyContin (321/1228, 26.14%), and illicit drugs lysergic acid diethylamide (213/1228, 17.34%) and 3,4-methylenedioxy-methamphetamine (94/1228, 7.65%). We also observed the use of communication applications for suspected drug trading through user comments. Conclusions: Our approach using a combination of Web scraping and deep learning was able to detect illegal online drug sellers on Instagram, with high accuracy. Despite increased scrutiny by regulators and policymakers, the Instagram platform continues to host posts from drug dealers, in violation of federal law. Further action needs to be taken to ensure the safety of social media communities and help put an end to this illicit digital channel of sourcing. UR - http://www.jmir.org/2019/6/e13803/ UR - http://dx.doi.org/10.2196/13803 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199298 ID - info:doi/10.2196/13803 ER - TY - JOUR AU - Hawig, David AU - Zhou, Chao AU - Fuhrhop, Sebastian AU - Fialho, S. Andre AU - Ramachandran, Navin PY - 2019/6/14 TI - Designing a Distributed Ledger Technology System for Interoperable and General Data Protection Regulation?Compliant Health Data Exchange: A Use Case in Blood Glucose Data JO - J Med Internet Res SP - e13665 VL - 21 IS - 6 KW - distributed ledger technology KW - directed acyclic graph KW - IOTA KW - IPFS KW - blockchain KW - Masked Authenticated Messaging, MAM KW - mobile health KW - blood glucose KW - diabetes KW - FHIR N2 - Background: Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices. Objective: The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)?compliant exchange of blood glucose data. Methods: Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity. Results: The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity. Conclusions: For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system. UR - http://www.jmir.org/2019/6/e13665/ UR - http://dx.doi.org/10.2196/13665 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199293 ID - info:doi/10.2196/13665 ER - TY - JOUR AU - Zhang, Zhongxing AU - Cajochen, Christian AU - Khatami, Ramin PY - 2019/5/11 TI - Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices JO - J Med Internet Res SP - e13482 VL - 21 IS - 6 KW - chronotypes KW - social jetlag KW - wearable devices KW - nap KW - cardiopulmonary coupling KW - sleep KW - big data N2 - Background: Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China. Objective: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. Methods: We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc. Results: The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively). Conclusions: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL. UR - https://www.jmir.org/2019/6/e13482/ UR - http://dx.doi.org/10.2196/13482 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199292 ID - info:doi/10.2196/13482 ER - TY - JOUR AU - Kwon, Soonil AU - Hong, Joonki AU - Choi, Eue-Keun AU - Lee, Euijae AU - Hostallero, Earl David AU - Kang, Ju Wan AU - Lee, Byunghwan AU - Jeong, Eui-Rim AU - Koo, Bon-Kwon AU - Oh, Seil AU - Yi, Yung PY - 2019/6/6 TI - Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study JO - JMIR Mhealth Uhealth SP - e12770 VL - 7 IS - 6 KW - atrial fibrillation KW - deep learning KW - photoplethysmography KW - pulse oximetry KW - diagnosis N2 - Background: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or handcrafted machine learning techniques that are limited in terms of diagnostic accuracy and reliability. Objective: This study aimed to develop deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion. Methods: We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). Electrocardiogram and pulse oximetry data over a 15-min period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the 2 DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (support vector machine with root-mean-square of successive difference of RR intervals and Shannon entropy, autocorrelation, and ensemble by combining 2 previous methods) using 10 5-fold cross-validation processes. Results: Among the 14,298 training samples containing PPG data, 7157 samples were obtained during the post-DCC period. The PAC indicator estimated 29.79% (2132/7157) of post-DCC samples had PACs. The diagnostic accuracy of AF versus SR was 99.32% (70,925/71,410) versus 95.85% (68,602/71,570) in 1D-CNN and 98.27% (70,176/71,410) versus 96.04% (68,736/71,570) in RNN methods. The area under receiver operating characteristic curves of the 2 DL classifiers was 0.998 (95% CI 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors (P<.001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers improved their diagnostic performances even further especially for the samples with a high burden of PACs. The average CLs for true versus false classification were 98.56% versus 78.75% for 1D-CNN and 98.37% versus 82.57% for RNN (P<.001 for all cases). Conclusions: New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL. Wearable devices sensing PPG signals with DL classifiers should be validated as tools to screen for AF. UR - http://mhealth.jmir.org/2019/6/e12770/ UR - http://dx.doi.org/10.2196/12770 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199302 ID - info:doi/10.2196/12770 ER - TY - JOUR AU - Luz, Friedemann Christian AU - Berends, S. Matthijs AU - Dik, H. Jan-Willem AU - Lokate, Mariëtte AU - Pulcini, Céline AU - Glasner, Corinna AU - Sinha, Bhanu PY - 2019/5/24 TI - Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship JO - J Med Internet Res SP - e12843 VL - 21 IS - 6 KW - antimicrobial stewardship KW - software KW - hospital records KW - data visualization KW - infection, medical informatics applications N2 - Background: Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams. Objective: The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge. Methods: RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions. Results: RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features. Conclusions: AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings. UR - https://www.jmir.org/2019/6/e12843/ UR - http://dx.doi.org/10.2196/12843 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199325 ID - info:doi/10.2196/12843 ER - TY - JOUR AU - Safarishahrbijari, Anahita AU - Osgood, D. Nathaniel PY - 2019/5/26 TI - Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study JO - JMIR Public Health Surveill SP - e11615 VL - 5 IS - 2 KW - machine learning KW - infectious disease transmission KW - disease models KW - system dynamics analysis KW - social media KW - outbreaks KW - infodemiology KW - infoveillance N2 - Background: Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources. Objective: This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts. Methods: We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation. Results: Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods. Conclusions: The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets. UR - http://publichealth.jmir.org/2019/2/e11615/ UR - http://dx.doi.org/10.2196/11615 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199339 ID - info:doi/10.2196/11615 ER - TY - JOUR AU - Kuipers, Esther AU - Poot, C. Charlotte AU - Wensing, Michel AU - Chavannes, H. Niels AU - de Smet, AGM Peter AU - Teichert, Martina PY - 2019/05/30 TI - Self-Management Maintenance Inhalation Therapy With eHealth (SELFIE): Observational Study on the Use of an Electronic Monitoring Device in Respiratory Patient Care and Research JO - J Med Internet Res SP - e13551 VL - 21 IS - 5 KW - eHealth KW - pharmacy KW - inhalation therapy KW - asthma KW - COPD KW - pharmacy practice research N2 - Background: Electronic inhalation monitoring devices (EIMDs) are available to remind patients with respiratory diseases to take their medication and register inhalations for feedback to patients and health care providers as well as for data collection in research settings. Objective: This study aimed to assess the validity as well as the patient-reported usability and acceptability of an EIMD. Methods: This observational study planned to include 21 community pharmacies in the Netherlands. Patient-reported inhalations were collected and compared to EIMD registrations to evaluate the positive predictive value of these registrations as actual patient inhalations. Patients received questionnaires on their experiences and acceptance. Results: A convenience sample of 32 patients was included from across 18 pharmacies, and 932 medication doses were validated. Of these, 796 registrations matched with patient-reported use (true-positive, 85.4%), and 33 inhalation registrations did not match with patient-reported use (false-positive, 3.5%). The positive predictive value was 96.0%, and 103 patient-reported inhalations were not recorded in the database (false-negative, 11.1%). Overall, patients considered the EIMD to be acceptable and easy to use, but many hesitated to continue its use. Reminders and motivational messages were not appreciated by all users, and more user-tailored features in the app were desired. Conclusions: Patients? interaction with the device in real-world settings is critical for objective measurement of medication adherence. The positive predictive value of this EIMD was found to be acceptable. However, patients reported false-negative registrations and a desire to include more user-tailored features to increase the usability and acceptability of the EIMD. UR - http://www.jmir.org/2019/5/e13551/ UR - http://dx.doi.org/10.2196/13551 UR - http://www.ncbi.nlm.nih.gov/pubmed/31148542 ID - info:doi/10.2196/13551 ER - TY - JOUR AU - Alvarez-Mon, Angel Miguel AU - Llavero-Valero, María AU - Sánchez-Bayona, Rodrigo AU - Pereira-Sanchez, Victor AU - Vallejo-Valdivielso, Maria AU - Monserrat, Jorge AU - Lahera, Guillermo AU - Asunsolo del Barco, Angel AU - Alvarez-Mon, Melchor PY - 2019/05/28 TI - Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter JO - J Med Internet Res SP - e14110 VL - 21 IS - 5 KW - social stigma KW - social media KW - psychosis KW - breast cancer KW - HIV KW - dementia KW - public opinion KW - diabetes N2 - Background: Twitter is an indicator of real-world performance, thus, is an appropriate arena to assess the social consideration and attitudes toward psychosis. Objective: The aim of this study was to perform a mixed-methods study of the content and key metrics of tweets referring to psychosis in comparison with tweets referring to control diseases (breast cancer, diabetes, Alzheimer, and human immunodeficiency virus). Methods: Each tweet?s content was rated as nonmedical (NM: testimonies, health care products, solidarity or awareness and misuse) or medical (M: included a reference to the illness?s diagnosis, treatment, prognosis, or prevention). NM tweets were classified as positive or pejorative. We assessed the appropriateness of the medical content. The number of retweets generated and the potential reach and impact of the hashtags analyzed was also investigated. Results: We analyzed a total of 15,443 tweets: 8055 classified as NM and 7287 as M. Psychosis-related tweets (PRT) had a significantly higher frequency of misuse 33.3% (212/636) vs 1.15% (853/7419; P<.001) and pejorative content 36.2% (231/636) vs 11.33% (840/7419; P<.001). The medical content of the PRT showed the highest scientific appropriateness 100% (391/391) vs 93.66% (6030/6439; P<.001) and had a higher frequency of content about disease prevention. The potential reach and impact of the tweets related to psychosis were low, but they had a high retweet-to-tweet ratio. Conclusions: We show a reduced number and a different pattern of contents in tweets about psychosis compared with control diseases. PRT showed a predominance of nonmedical content with increased frequencies of misuse and pejorative tone. However, the medical content of PRT showed high scientific appropriateness aimed toward prevention. UR - http://www.jmir.org/2019/5/e14110/ UR - http://dx.doi.org/10.2196/14110 UR - http://www.ncbi.nlm.nih.gov/pubmed/31140438 ID - info:doi/10.2196/14110 ER - TY - JOUR AU - Vidal-Alaball, Josep AU - Fernandez-Luque, Luis AU - Marin-Gomez, X. Francesc AU - Ahmed, Wasim PY - 2019/05/28 TI - A New Tool for Public Health Opinion to Give Insight Into Telemedicine: Twitter Poll Analysis JO - JMIR Form Res SP - e13870 VL - 3 IS - 2 KW - telemedicine KW - Twitter messaging KW - health care surveys N2 - Background: Telemedicine draws on information technologies in order to enable the delivery of clinical health care from a distance. Twitter is a social networking platform that has 316 million monthly active users with 500 million tweets per day; its potential for real-time monitoring of public health has been well documented. There is a lack of empirical research that has critically examined the potential of Twitter polls for providing insight into public health. One of the benefits of utilizing Twitter polls is that it is possible to gain access to a large audience that can provide instant and real-time feedback. Moreover, Twitter polls are completely anonymized. Objective: The overall aim of this study was to develop and disseminate Twitter polls based on existing surveys to gain real-time feedback on public views and opinions toward telemedicine. Methods: Two Twitter polls were developed utilizing questions from previously used questionnaires to explore acceptance of telemedicine among Twitter users. The polls were placed on the Twitter timeline of one of the authors, which had more than 9300 followers, and the account followers were asked to answer the poll and retweet it to reach a larger audience. Results: In a population where telemedicine was expected to enjoy big support, a significant number of Twitter users responding to the poll felt that telemedicine was not as good as traditional care. Conclusions: Our results show the potential of Twitter polls for gaining insight into public health topics on a range of health issues not just limited to telemedicine. Our study also sheds light on how Twitter polls can be used to validate and test survey questions. UR - http://formative.jmir.org/2019/2/e13870/ UR - http://dx.doi.org/10.2196/13870 UR - http://www.ncbi.nlm.nih.gov/pubmed/31140442 ID - info:doi/10.2196/13870 ER - TY - JOUR AU - Washington, Peter AU - Kalantarian, Haik AU - Tariq, Qandeel AU - Schwartz, Jessey AU - Dunlap, Kaitlyn AU - Chrisman, Brianna AU - Varma, Maya AU - Ning, Michael AU - Kline, Aaron AU - Stockham, Nathaniel AU - Paskov, Kelley AU - Voss, Catalin AU - Haber, Nick AU - Wall, Paul Dennis PY - 2019/05/23 TI - Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks JO - J Med Internet Res SP - e13668 VL - 21 IS - 5 KW - crowdsourcing KW - autism KW - mechanical turk KW - pediatrics KW - diagnostics KW - diagnosis KW - neuropsychiatric conditions KW - human-computer interaction KW - citizen healthcare KW - biomedical data science KW - mobile health KW - digital health N2 - Background: Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis. Objective: We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips. Methods: Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as ?Autism? or ?Not autism,? (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ?8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study. Results: For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ?8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity (?=?0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=?0.19, P=.04), number of family members with autism and sensitivity (r=?0.195, P=.04), and number of family members with autism and precision (r=?0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001). Conclusions: Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations. UR - http://www.jmir.org/2019/5/e13668/ UR - http://dx.doi.org/10.2196/13668 UR - http://www.ncbi.nlm.nih.gov/pubmed/31124463 ID - info:doi/10.2196/13668 ER - TY - JOUR AU - Shah, Zubair AU - Martin, Paige AU - Coiera, Enrico AU - Mandl, D. Kenneth AU - Dunn, G. Adam PY - 2019/05/08 TI - Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations JO - J Med Internet Res SP - e12881 VL - 21 IS - 5 KW - text mining KW - social media KW - public health N2 - Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective: The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods: Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results: In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions: In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes. UR - https://www.jmir.org/2019/5/e12881/ UR - http://dx.doi.org/10.2196/12881 UR - http://www.ncbi.nlm.nih.gov/pubmed/31344669 ID - info:doi/10.2196/12881 ER - TY - JOUR AU - Liu, Xingyun AU - Liu, Xiaoqian AU - Sun, Jiumo AU - Yu, Xiaonan Nancy AU - Sun, Bingli AU - Li, Qing AU - Zhu, Tingshao PY - 2019/05/08 TI - Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors JO - J Med Internet Res SP - e11705 VL - 21 IS - 5 KW - suicide identification KW - crisis management KW - machine learning KW - microblog direct message KW - social network KW - Chinese young people N2 - Background: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and behaviors to seek help. Objective: The goal of the research was to test the feasibility and acceptability of Proactive Suicide Prevention Online (PSPO), a new approach based on social media that combines proactive identification of suicide-prone individuals with specialized crisis management. Methods: We first located a microblog group online. Their comments on a suicide note were analyzed by experts to provide a training set for the machine learning models for suicide identification. The best-performing model was used to automatically identify posts that suggested suicidal thoughts and behaviors. Next, a microblog direct message containing crisis management information, including measures that covered suicide-related issues, depression, help-seeking behavior and an acceptability test, was sent to users who had been identified by the model to be at risk of suicide. For those who replied to the message, trained counselors provided tailored crisis management. The Simplified Chinese Linguistic Inquiry and Word Count was also used to analyze the users? psycholinguistic texts in 1-month time slots prior to and postconsultation. Results: A total of 27,007 comments made in April 2017 were analyzed. Among these, 2786 (10.32%) were classified as indicative of suicidal thoughts and behaviors. The performance of the detection model was good, with high precision (.86), recall (.78), F-measure (.86), and accuracy (.88). Between July 3, 2017, and July 3, 2018, we sent out a total of 24,727 direct messages to 12,486 social media users, and 5542 (44.39%) responded. Over one-third of the users who were contacted completed the questionnaires included in the direct message. Of the valid responses, 89.73% (1259/1403) reported suicidal ideation, but more than half (725/1403, 51.67%) reported that they had not sought help. The 9-Item Patient Health Questionnaire (PHQ-9) mean score was 17.40 (SD 5.98). More than two-thirds of the participants (968/1403, 69.00%) thought the PSPO approach was acceptable. Moreover, 2321 users replied to the direct message. In a comparison of the frequency of word usage in their microblog posts 1-month before and after the consultation, we found that the frequency of death-oriented words significantly declined while the frequency of future-oriented words significantly increased. Conclusions: The PSPO model is suitable for identifying populations that are at risk of suicide. When followed up with proactive crisis management, it may be a useful supplement to existing prevention programs because it has the potential to increase the accessibility of antisuicide information to people with suicidal thoughts and behaviors but a low motivation to seek help. UR - https://www.jmir.org/2019/5/e11705/ UR - http://dx.doi.org/10.2196/11705 UR - http://www.ncbi.nlm.nih.gov/pubmed/31344675 ID - info:doi/10.2196/11705 ER - TY - JOUR AU - Holter, TS Marianne AU - Johansen, B. Ayna AU - Ness, Ottar AU - Brinkmann, Svend AU - Høybye, T. Mette AU - Brendryen, Håvar PY - 2019/05/06 TI - Qualitative Interview Studies of Working Mechanisms in Electronic Health: Tools to Enhance Study Quality JO - J Med Internet Res SP - e10354 VL - 21 IS - 5 KW - telemedicine KW - eHealth KW - mobile health KW - telehealth KW - mHealth KW - interviews as topic KW - health care evaluation mechanisms KW - data collection UR - https://www.jmir.org/2019/5/e10354/ UR - http://dx.doi.org/10.2196/10354 UR - http://www.ncbi.nlm.nih.gov/pubmed/31066683 ID - info:doi/10.2196/10354 ER - TY - JOUR AU - Alvarez-Lopez, Fernando AU - Maina, Fabián Marcelo AU - Saigí-Rubió, Francesc PY - 2019/05/03 TI - Use of Commercial Off-The-Shelf Devices for the Detection of Manual Gestures in Surgery: Systematic Literature Review JO - J Med Internet Res SP - e11925 VL - 21 IS - 5 KW - minimally invasive surgery KW - user-computer interface KW - operating room KW - education, medical KW - computer-assisted surgery N2 - Background: The increasingly pervasive presence of technology in the operating room raises the need to study the interaction between the surgeon and computer system. A new generation of tools known as commercial off-the-shelf (COTS) devices enabling touchless gesture?based human-computer interaction is currently being explored as a solution in surgical environments. Objective: The aim of this systematic literature review was to provide an account of the state of the art of COTS devices in the detection of manual gestures in surgery and to identify their use as a simulation tool for motor skills teaching in minimally invasive surgery (MIS). Methods: For this systematic literature review, a search was conducted in PubMed, Excerpta Medica dataBASE, ScienceDirect, Espacenet, OpenGrey, and the Institute of Electrical and Electronics Engineers databases. Articles published between January 2000 and December 2017 on the use of COTS devices for gesture detection in surgical environments and in simulation for surgical skills learning in MIS were evaluated and selected. Results: A total of 3180 studies were identified, 86 of which met the search selection criteria. Microsoft Kinect (Microsoft Corp) and the Leap Motion Controller (Leap Motion Inc) were the most widely used COTS devices. The most common intervention was image manipulation in surgical and interventional radiology environments, followed by interaction with virtual reality environments for educational or interventional purposes. The possibility of using this technology to develop portable low-cost simulators for skills learning in MIS was also examined. As most of the articles identified in this systematic review were proof-of-concept or prototype user testing and feasibility testing studies, we concluded that the field was still in the exploratory phase in areas requiring touchless manipulation within environments and settings that must adhere to asepsis and antisepsis protocols, such as angiography suites and operating rooms. Conclusions: COTS devices applied to hand and instrument gesture?based interfaces in the field of simulation for skills learning and training in MIS could open up a promising field to achieve ubiquitous training and presurgical warm up. UR - https://www.jmir.org/2019/5/e11925/ UR - http://dx.doi.org/10.2196/11925 UR - http://www.ncbi.nlm.nih.gov/pubmed/31066679 ID - info:doi/10.2196/11925 ER - TY - JOUR AU - McKay, Rana AU - Mills, Hannah AU - Werner, Lillian AU - Choudhury, Atish AU - Choueiri, Toni AU - Jacobus, Susanna AU - Pace, Amanda AU - Polacek, Laura AU - Pomerantz, Mark AU - Prisby, Judith AU - Sweeney, Christopher AU - Walsh, Meghara AU - Taplin, Mary-Ellen PY - 2019/05/02 TI - Evaluating a Video-Based, Personalized Webpage in Genitourinary Oncology Clinical Trials: A Phase 2 Randomized Trial JO - J Med Internet Res SP - e12044 VL - 21 IS - 5 KW - cancer KW - prostatic neoplasms KW - kidney neoplasms KW - clinical trial KW - instructional films and videos KW - education N2 - Background: The pace of drug discovery and approvals has led to expanding treatments for cancer patients. Although extensive research exists regarding barriers to enrollment in oncology clinical trials, there are limited studies evaluating processes to optimize patient education, oral anticancer therapy administration, and adherence for patients enrolled in clinical trials. In this study, we assess the feasibility of a video-based, personalized webpage for patients enrolled in genitourinary oncology clinical trials involving 1 or more oral anticancer therapy. Objective: The primary objective of this trial was to assess the differences in the number of patient-initiated violations in the intervention arm compared with a control arm over 4 treatment cycles. Secondary objectives included patient satisfaction, frequently asked questions by patients on the intervention arm, patient-initiated calls to study team members, and patient-reported stress levels. Methods: Eligible patients enrolling on a therapeutic clinical trial for a genitourinary malignancy were randomized 2:1 to the intervention arm or control arm. Patients randomized to the intervention arm received access to a video-based, personalized webpage, which included videos of patients? own clinic encounters with their providers, instructional videos on medication administration and side effects, and electronic versions of educational documents. Results: A total of 99 patients were enrolled (89 were evaluable; 66 completed 4 cycles). In total, 71% (40/56) of patients in the intervention arm had 1 or more patient-initiated violation compared with 70% (23/33) in the control arm. There was no difference in the total number of violations across 4 cycles between the 2 arms (estimate=?0.0939, 95% CI?0.6295 to 0.4418, P value=.73). Median baseline satisfaction scores for the intervention and control arms were 72 and 73, respectively, indicating high levels of patient satisfaction in both arms. Median baseline patient-reported stress levels were 10 and 13 for the intervention and control arms, respectively, indicating low stress levels in both arms at baseline. Conclusions: This study is among the first to evaluate a video-based, personalized webpage that provides patients with educational videos and video recordings of clinical trial appointments. Despite not meeting the primary endpoint of reduced patient-initiated violations, this study demonstrates the feasibility of a video-based, personalized webpage in clinical trials. Future research assessing this tool might be better suited for realms outside of clinical trials and might consider the use of an endpoint that assesses patient-reported outcomes directly. A major limitation of this study was the lack of prior data for estimating the null hypothesis in this population. UR - https://www.jmir.org/2019/5/e12044/ UR - http://dx.doi.org/10.2196/12044 UR - http://www.ncbi.nlm.nih.gov/pubmed/31045501 ID - info:doi/10.2196/12044 ER - TY - JOUR AU - Huang, Ming AU - Zolnoori, Maryam AU - Balls-Berry, E. Joyce AU - Brockman, A. Tabetha AU - Patten, A. Christi AU - Yao, Lixia PY - 2019/04/30 TI - Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017 JO - J Med Internet Res SP - e13316 VL - 21 IS - 4 KW - patent KW - technological innovation KW - disease KW - research opportunity index KW - public health index KW - text mining KW - topic modeling KW - dynamic topic model KW - resource allocation KW - research priority N2 - Background: Patents are important intellectual property protecting technological innovations that inspire efficient research and development in biomedicine. The number of awarded patents serves as an important indicator of economic growth and technological innovation. Researchers have mined patents to characterize the focuses and trends of technological innovations in many fields. Objective: To expand patent mining to biomedicine and facilitate future resource allocation in biomedical research for the United States, we analyzed US patent documents to determine the focuses and trends of protected technological innovations across the entire disease landscape. Methods: We analyzed more than 5 million US patent documents between 1995 and 2017, using summary statistics and dynamic topic modeling. More specifically, we investigated the disease coverage and latent topics in patent documents over time. We also incorporated the patent data into the calculation of our recently developed Research Opportunity Index (ROI) and Public Health Index (PHI), to recalibrate the resource allocation in biomedical research. Results: Our analysis showed that protected technological innovations have been primarily focused on socioeconomically critical diseases such as ?other cancers? (malignant neoplasm of head, face, neck, abdomen, pelvis, or limb; disseminated malignant neoplasm; Merkel cell carcinoma; and malignant neoplasm, malignant carcinoid tumors, neuroendocrine tumor, and carcinoma in situ of an unspecified site), diabetes mellitus, and obesity. The United States has significantly improved resource allocation to biomedical research and development over the past 17 years, as illustrated by the decreasing PHI. Diseases with positive ROI, such as ankle and foot fracture, indicate potential research opportunities for the future. Development of novel chemical or biological drugs and electrical devices for diagnosis and disease management is the dominating topic in patented inventions. Conclusions: This multifaceted analysis of patent documents provides a deep understanding of the focuses and trends of technological innovations in disease management in patents. Our findings offer insights into future research and innovation opportunities and provide actionable information to facilitate policy makers, payers, and investors to make better evidence-based decisions regarding resource allocation in biomedicine. UR - http://www.jmir.org/2019/4/e13316/ UR - http://dx.doi.org/10.2196/13316 UR - http://www.ncbi.nlm.nih.gov/pubmed/31038462 ID - info:doi/10.2196/13316 ER - TY - JOUR AU - Nama, Nassr AU - Sampson, Margaret AU - Barrowman, Nicholas AU - Sandarage, Ryan AU - Menon, Kusum AU - Macartney, Gail AU - Murto, Kimmo AU - Vaccani, Jean-Philippe AU - Katz, Sherri AU - Zemek, Roger AU - Nasr, Ahmed AU - McNally, Dayre James PY - 2019/04/29 TI - Crowdsourcing the Citation Screening Process for Systematic Reviews: Validation Study JO - J Med Internet Res SP - e12953 VL - 21 IS - 4 KW - crowdsourcing KW - systematic reviews as topic KW - meta-analysis as topic KW - research design N2 - Background: Systematic reviews (SRs) are often cited as the highest level of evidence available as they involve the identification and synthesis of published studies on a topic. Unfortunately, it is increasingly challenging for small teams to complete SR procedures in a reasonable time period, given the exponential rise in the volume of primary literature. Crowdsourcing has been postulated as a potential solution. Objective: The feasibility objective of this study was to determine whether a crowd would be willing to perform and complete abstract and full text screening. The validation objective was to assess the quality of the crowd?s work, including retention of eligible citations (sensitivity) and work performed for the investigative team, defined as the percentage of citations excluded by the crowd. Methods: We performed a prospective study evaluating crowdsourcing essential components of an SR, including abstract screening, document retrieval, and full text assessment. Using CrowdScreenSR citation screening software, 2323 articles from 6 SRs were available to an online crowd. Citations excluded by less than or equal to 75% of the crowd were moved forward for full text assessment. For the validation component, performance of the crowd was compared with citation review through the accepted, gold standard, trained expert approach. Results: Of 312 potential crowd members, 117 (37.5%) commenced abstract screening and 71 (22.8%) completed the minimum requirement of 50 citation assessments. The majority of participants were undergraduate or medical students (192/312, 61.5%). The crowd screened 16,988 abstracts (median: 8 per citation; interquartile range [IQR] 7-8), and all citations achieved the minimum of 4 assessments after a median of 42 days (IQR 26-67). Crowd members retrieved 83.5% (774/927) of the articles that progressed to the full text phase. A total of 7604 full text assessments were completed (median: 7 per citation; IQR 3-11). Citations from all but 1 review achieved the minimum of 4 assessments after a median of 36 days (IQR 24-70), with 1 review remaining incomplete after 3 months. When complete crowd member agreement at both levels was required for exclusion, sensitivity was 100% (95% CI 97.9-100) and work performed was calculated at 68.3% (95% CI 66.4-70.1). Using the predefined alternative 75% exclusion threshold, sensitivity remained 100% and work performed increased to 72.9% (95% CI 71.0-74.6; P<.001). Finally, when a simple majority threshold was considered, sensitivity decreased marginally to 98.9% (95% CI 96.0-99.7; P=.25) and work performed increased substantially to 80.4% (95% CI 78.7-82.0; P<.001). Conclusions: Crowdsourcing of citation screening for SRs is feasible and has reasonable sensitivity and specificity. By expediting the screening process, crowdsourcing could permit the investigative team to focus on more complex SR tasks. Future directions should focus on developing a user-friendly online platform that allows research teams to crowdsource their reviews. UR - http://www.jmir.org/2019/4/e12953/ UR - http://dx.doi.org/10.2196/12953 UR - http://www.ncbi.nlm.nih.gov/pubmed/31033444 ID - info:doi/10.2196/12953 ER - TY - JOUR AU - Ozella, Laura AU - Gauvin, Laetitia AU - Carenzo, Luca AU - Quaggiotto, Marco AU - Ingrassia, Luigi Pier AU - Tizzoni, Michele AU - Panisson, André AU - Colombo, Davide AU - Sapienza, Anna AU - Kalimeri, Kyriaki AU - Della Corte, Francesco AU - Cattuto, Ciro PY - 2019/04/26 TI - Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study JO - J Med Internet Res SP - e12251 VL - 21 IS - 4 KW - contact patterns KW - contact networks KW - wearable proximity sensors KW - mass casualty incident KW - simulation KW - medical staff ? patient interaction KW - patients? flow N2 - Background: Over the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved. Objective: In this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital. Methods: Data were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital. Results: The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care teams with the victims is related to the severity of the patient?s injury. The analysis of patients? flow showed that the presence of patients in the rooms of the hospital is consistent with the triage code and diagnosis, and no obvious bottlenecks were found. Conclusions: Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection?ie, without the need for the involvement of observers, which could compromise the realism of the exercise?of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management. UR - http://www.jmir.org/2019/4/e12251/ UR - http://dx.doi.org/10.2196/12251 UR - http://www.ncbi.nlm.nih.gov/pubmed/31025944 ID - info:doi/10.2196/12251 ER - TY - JOUR AU - Alwashmi, F. Meshari AU - Hawboldt, John AU - Davis, Erin AU - Fetters, D. Michael PY - 2019/04/26 TI - The Iterative Convergent Design for Mobile Health Usability Testing: Mixed Methods Approach JO - JMIR Mhealth Uhealth SP - e11656 VL - 7 IS - 4 KW - mHealth KW - mixed methods KW - usability KW - eHealth KW - methods UR - http://mhealth.jmir.org/2019/4/e11656/ UR - http://dx.doi.org/10.2196/11656 UR - http://www.ncbi.nlm.nih.gov/pubmed/31025951 ID - info:doi/10.2196/11656 ER - TY - JOUR AU - Tariq, Qandeel AU - Fleming, Lanyon Scott AU - Schwartz, Nicole Jessey AU - Dunlap, Kaitlyn AU - Corbin, Conor AU - Washington, Peter AU - Kalantarian, Haik AU - Khan, Z. Naila AU - Darmstadt, L. Gary AU - Wall, Paul Dennis PY - 2019/04/24 TI - Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study JO - J Med Internet Res SP - e13822 VL - 21 IS - 4 KW - autism KW - autism spectrum disorder KW - machine learning KW - developmental delays KW - clinical resources KW - Bangladesh KW - Biomedical Data Science N2 - Background: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children?s ?risk scores? for autism. We achieved an accuracy of 92% (95% CI 88%-97%) on US videos using a classifier built on five features. Objective: Using videos of Bangladeshi children collected from Dhaka Shishu Children?s Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions. Methods: Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75% accuracy, 95% CI 71%-78%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers? process aligns with clinical intuition. Results: Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76% (SD 3%) and sensitivity of 76% (SD 4%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85% (SD 5%) and sensitivity of 76% (SD 6%) for identifying children with ASD from those predicted to have other developmental delays. Conclusions: These results show promise for using a mobile video-based and machine learning?directed approach for early and remote detection of autism in Bangladeshi children. This strategy could provide important resources for developmental health in developing countries with few clinical resources for diagnosis, helping children get access to care at an early age. Future research aimed at extending the application of this approach to identify a range of other conditions and determine the population-level burden of developmental disabilities and impairments will be of high value. UR - http://www.jmir.org/2019/4/e13822/ UR - http://dx.doi.org/10.2196/13822 UR - http://www.ncbi.nlm.nih.gov/pubmed/31017583 ID - info:doi/10.2196/13822 ER - TY - JOUR AU - Hao, Yiming AU - Cheng, Feng AU - Pham, Minh AU - Rein, Hayley AU - Patel, Devashru AU - Fang, Yuchen AU - Feng, Yiyi AU - Yan, Jin AU - Song, Xueyang AU - Yan, Haixia AU - Wang, Yiqin PY - 2019/04/23 TI - A Noninvasive, Economical, and Instant-Result Method to Diagnose and Monitor Type 2 Diabetes Using Pulse Wave: Case-Control Study JO - JMIR Mhealth Uhealth SP - e11959 VL - 7 IS - 4 KW - type 2 diabetes KW - hypertension KW - hyperlipidemia KW - pulse wave analysis KW - diagnosis N2 - Background: We should pay more attention to the long-term monitoring and early warning of type 2 diabetes and its complications. The traditional blood glucose tests are traumatic and cannot effectively monitor the development of diabetic complications. The development of mobile health is changing rapidly. Therefore, we are interested in developing a new noninvasive, economical, and instant-result method to accurately diagnose and monitor type 2 diabetes and its complications. Objective: We aimed to determine whether type 2 diabetes and its complications, including hypertension and hyperlipidemia, could be diagnosed and monitored by using pulse wave. Methods: We collected the pulse wave parameters from 50 healthy people, 139 diabetic patients without hypertension and hyperlipidemia, 133 diabetic patients with hypertension, 70 diabetic patients with hyperlipidemia, and 75 diabetic patients with hypertension and hyperlipidemia. The pulse wave parameters showing significant differences among these groups were identified. Various machine learning models such as linear discriminant analysis, support vector machines (SVMs), and random forests were applied to classify the control group, diabetic patients, and diabetic patients with complications. Results: There were significant differences in several pulse wave parameters among the 5 groups. The parameters height of tidal wave (h3), time distance between the start point of pulse wave and dominant wave (t1), and width of percussion wave in its one-third height position (W) increase and the height of dicrotic wave (h5) decreases when people develop diabetes. The parameters height of dominant wave (h1), h3, and height of dicrotic notch (h4) are found to be higher in diabetic patients with hypertension, whereas h5 is lower in diabetic patients with hyperlipidemia. For detecting diabetes, the method with the highest out-of-sample prediction accuracy is SVM with polynomial kernel. The algorithm can detect diabetes with 96.35% accuracy. However, all the algorithms have a low accuracy when predicting diabetic patients with hypertension and hyperlipidemia (below 70%). Conclusions: The results demonstrated that the noninvasive and convenient pulse-taking diagnosis described in this paper has the potential to become a low-cost and accurate method to monitor the development of diabetes. We are collecting more data to improve the accuracy for detecting hypertension and hyperlipidemia among diabetic patients. Mobile devices such as sport bands, smart watches, and other diagnostic tools are being developed based on the pulse wave method to improve the diagnosis and monitoring of diabetes, hypertension, and hyperlipidemia. UR - http://mhealth.jmir.org/2019/4/e11959/ UR - http://dx.doi.org/10.2196/11959 UR - http://www.ncbi.nlm.nih.gov/pubmed/31012863 ID - info:doi/10.2196/11959 ER - TY - JOUR AU - Mandryk, Lee Regan AU - Birk, Valentin Max PY - 2019/04/23 TI - The Potential of Game-Based Digital Biomarkers for Modeling Mental Health JO - JMIR Ment Health SP - e13485 VL - 6 IS - 4 KW - digital games KW - digital phenotyping KW - mental health KW - computational modeling KW - big data KW - video games KW - biomarkers N2 - Background: Assessment for mental health is performed by experts using interview techniques, questionnaires, and test batteries and following standardized manuals; however, there would be myriad benefits if behavioral correlates could predict mental health and be used for population screening or prevalence estimations. A variety of digital sources of data (eg, online search data and social media posts) have been previously proposed as candidates for digital biomarkers in the context of mental health. Playing games on computers, gaming consoles, or mobile devices (ie, digital gaming) has become a leading leisure activity of choice and yields rich data from a variety of sources. Objective: In this paper, we argue that game-based data from commercial off-the-shelf games have the potential to be used as a digital biomarker to assess and model mental health and health decline. Although there is great potential in games developed specifically for mental health assessment (eg, Sea Hero Quest), we focus on data gathered ?in-the-wild? from playing commercial off-the-shelf games designed primarily for entertainment. Methods: We argue that the activity traces left behind by natural interactions with digital games can be modeled using computational approaches for big data. To support our argument, we present an investigation of existing data sources, a categorization of observable traits from game data, and examples of potentially useful game-based digital biomarkers derived from activity traces. Results: Our investigation reveals different types of data that are generated from play and the sources from which these data can be accessed. Based on these insights, we describe five categories of digital biomarkers that can be derived from game-based data, including behavior, cognitive performance, motor performance, social behavior, and affect. For each type of biomarker, we describe the data type, the game-based sources from which it can be derived, its importance for mental health modeling, and any existing statistical associations with mental health that have been demonstrated in prior work. We end with a discussion on the limitations and potential of data from commercial off-the-shelf games for use as a digital biomarker of mental health. Conclusions: When people play commercial digital games, they produce significant volumes of high-resolution data that are not only related to play frequency, but also include performance data reflecting low-level cognitive and motor processing; text-based data that are indicative of the affective state; social data that reveal networks of relationships; content choice data that imply preferred genres; and contextual data that divulge where, when, and with whom the players are playing. These data provide a source for digital biomarkers that may indicate mental health. Produced by engaged human behavior, game data have the potential to be leveraged for population screening or prevalence estimations, leading to at-scale, nonintrusive assessment of mental health. UR - http://mental.jmir.org/2019/4/e13485/ UR - http://dx.doi.org/10.2196/13485 UR - http://www.ncbi.nlm.nih.gov/pubmed/31012857 ID - info:doi/10.2196/13485 ER - TY - JOUR AU - McCaig, Duncan AU - Elliott, T. Mark AU - Siew, SQ Cynthia AU - Walasek, Lukasz AU - Meyer, Caroline PY - 2019/04/22 TI - Profiling Commenters on Mental Health?Related Online Forums: A Methodological Example Focusing on Eating Disorder?Related Commenters JO - JMIR Ment Health SP - e12555 VL - 6 IS - 4 KW - mental health KW - eating disorders KW - social media KW - social networks N2 - Background: Understanding the characteristics of commenters on mental health?related online forums is vital for the development of effective psychological interventions in these communities. The way in which commenters interact can enhance our understanding of their characteristics. Objective: Using eating disorder?related (EDR) forums as an example, this study detailed a methodology that aimed to determine subtypes of mental health?related forums and profile their commenters based on the other forums to which they contributed. Methods: The researchers identified all public EDR forums (with ?500 contributing commenters between March 2017 and February 2018) on a large Web-based discussion platform (Reddit). A mixed-methods approach comprising network analysis with community detection, text mining, and manual review identified subtypes of EDR forums. For each subtype, another network analysis with community detection was conducted using the EDR forum commenter overlap between 50 forums on which the commenters also commented. The topics of forums in each detected community were then manually reviewed to identify the shared interests of each subtype of EDR forum commenters. Results: Six subtypes of EDR forums were identified, to which 14,024 commenters had contributed. The results focus on 2 subtypes?proeating disorder and thinspiration?and communities of commenters within both subtypes. Within the proeating disorder subtype, 3 communities of commenters were detected that related to the body and eating, mental health, and women, appearance, and mixed topics. With regard to the thinspiration group, 78.17% (849/1086) of commenters had also commented on pornographic forums and 16.66% (181/1086) had contributed to proeating disorder forums. Conclusions: The article exemplifies a methodology that provides insight into subtypes of mental health?related forums and the characteristics of their commenters. The findings have implications for future research and Web-based psychological interventions. With the publicly available data and code provided, researchers can easily reproduce the analyses or utilize the methodology to investigate other mental health?related forums. UR - http://mental.jmir.org/2019/4/e12555/ UR - http://dx.doi.org/10.2196/12555 UR - http://www.ncbi.nlm.nih.gov/pubmed/31008715 ID - info:doi/10.2196/12555 ER - TY - JOUR AU - Goodale, Mae Brianna AU - Shilaih, Mohaned AU - Falco, Lisa AU - Dammeier, Franziska AU - Hamvas, Györgyi AU - Leeners, Brigitte PY - 2019/04/18 TI - Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study JO - J Med Internet Res SP - e13404 VL - 21 IS - 4 KW - algorithms KW - fertility/physiology KW - heart rate KW - machine learning KW - menstrual cycle KW - ovulation detection/methods KW - respiratory rate KW - perfusion KW - skin temperature KW - wearable electronic devices N2 - Background: Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women?s nightly basal body temperature increases from 0.28 to 0.56 ?C following postovulation progesterone production. Women?s resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window?s closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. Objective: In this study, we probed what phase-based differences a wearable bracelet could detect in users? WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. Methods: We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. Results: We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Conclusions: Our contributions highlight the impact of artificial intelligence and machine learning?s integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation. UR - http://www.jmir.org/2019/4/e13404/ UR - http://dx.doi.org/10.2196/13404 UR - http://www.ncbi.nlm.nih.gov/pubmed/30998226 ID - info:doi/10.2196/13404 ER - TY - JOUR AU - Chen, T. Annie AU - Swaminathan, Aarti AU - Kearns, R. William AU - Alberts, M. Nicole AU - Law, F. Emily AU - Palermo, M. Tonya PY - 2019/04/15 TI - Understanding User Experience: Exploring Participants? Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain JO - J Med Internet Res SP - e11756 VL - 21 IS - 4 KW - data visualization KW - natural language processing KW - chronic pain KW - cluster analysis KW - technology N2 - Background: Delivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs. Objective: In this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents. Methods: We explored the main themes in coaches? and participants? messages using an automated textual analysis method, topic modeling. We then clustered participants? messages to identify subgroups of participants with similar engagement patterns. Results: First, we performed topic modeling on coaches? messages. The themes in coaches? messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants? message histories. Similar to the coaches? topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster. Conclusions: In this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content. UR - http://www.jmir.org/2019/4/e11756/ UR - http://dx.doi.org/10.2196/11756 UR - http://www.ncbi.nlm.nih.gov/pubmed/30985288 ID - info:doi/10.2196/11756 ER - TY - JOUR AU - Leavens, Schneider Eleanor Ladd AU - Stevens, Marie Elise AU - Brett, Irene Emma AU - Molina, Neil AU - Leffingwell, Ryan Thad AU - Wagener, Lee Theodore PY - 2019/04/08 TI - Use of Rideshare Services to Increase Participant Recruitment and Retention in Research: Participant Perspectives JO - J Med Internet Res SP - e11166 VL - 21 IS - 4 KW - rideshare service KW - recruitment KW - retention KW - attrition KW - transportation N2 - Background: Recruitment and retention of participants are important factors in empirical studies. Methods that increase recruitment and retention can reduce costs and burden on researchers related to the need for over-recruitment because of attrition. Rideshare services such as Uber and Lyft are a potential means for decreasing this burden. Objective: This study aimed to understand the role rideshare utilization plays in participant recruitment and retention in research trials. Methods: Data are presented for a study (N=42) in which rideshare services were utilized for participant transportation to and from study visits during a 2-session, in-laboratory research study. Results: Retention at visit 2 was greater than 95% (42/44) in the initial study. In a follow-up survey of the participants from the original trial, participants (N=32) reported that the rideshare service was an important reason they returned for all study visits. Participants reported whether they would prefer differing levels of additional monetary compensation or a ride from a rideshare service. When the additional compensation was less than US $15, participants reported a preference for the rideshare service. Conclusions: Rideshare services may represent a relatively low cost means for increasing study retention. Specifically, findings indicate that rideshare services may not be crucial for initial participant recruitment but for their retention in multi-visit studies. UR - https://www.jmir.org/2019/4/e11166/ UR - http://dx.doi.org/10.2196/11166 UR - http://www.ncbi.nlm.nih.gov/pubmed/30958268 ID - info:doi/10.2196/11166 ER - TY - JOUR AU - Percha, Bethany AU - Baskerville, B. Edward AU - Johnson, Matthew AU - Dudley, T. Joel AU - Zimmerman, Noah PY - 2019/04/01 TI - Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations JO - J Med Internet Res SP - e12641 VL - 21 IS - 4 KW - n-of-1 studies KW - computer simulation KW - patient-specific modeling KW - precision medicine KW - cross-over studies KW - inter-individual biological variation KW - individual differences N2 - Background: Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual. Objective: N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance. Methods: Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics. Results: Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community. Conclusions: Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden. UR - https://www.jmir.org/2019/4/e12641/ UR - http://dx.doi.org/10.2196/12641 UR - http://www.ncbi.nlm.nih.gov/pubmed/30932871 ID - info:doi/10.2196/12641 ER - TY - JOUR AU - Wirtz, L. Andrea AU - Cooney, E. Erin AU - Chaudhry, Aeysha AU - Reisner, L. Sari AU - PY - 2019/03/29 TI - Computer-Mediated Communication to Facilitate Synchronous Online Focus Group Discussions: Feasibility Study for Qualitative HIV Research Among Transgender Women Across the United States JO - J Med Internet Res SP - e12569 VL - 21 IS - 3 KW - transgender KW - qualitative research KW - formative research KW - technology N2 - Background: Novel, technology-based methods are rapidly increasing in popularity across multiple facets of quantitative research. Qualitative research, however, has been slower to integrate technology into research methodology. One method, computer-mediated communication (CMC), has been utilized to a limited extent for focus group discussions. Objective: This study aimed to assess feasibility of an online video conferencing system to further adapt CMC to facilitate synchronous focus group discussions among transgender women living in six cities in eastern and southern United States. Methods: Between August 2017 and January 2018, focus group discussions with adult transgender women were conducted in English and Spanish by research teams based in Boston, MA, and Baltimore, MD. Participants were sampled from six cities: Baltimore, MD; Boston, MA; New York, NY; Washington, DC; Atlanta, GA; and Miami, FL. This was formative research to inform a technology-enhanced cohort study to assess HIV acquisition among transgender women. This analysis focused on the methodologic use of CMC focus groups conducted synchronously using online software that enabled video or phone discussion. Findings were based on qualitative observations of attendance and study team debriefing on topics of individual, social, technical, and logistical challenges encountered. Results: A total of 41 transgender women from all six cities participated in seven online focus group discussions?five English and two Spanish. There was equal racial distribution of black/African American (14/41, 34%) and white (14/41, 34%) attendees, with 29% (12/41) identifying as Hispanic/Latina ethnicity. Overall, 29 of 70 (41%) eligible and scheduled transgender women failed to attend the focus group discussions. The most common reason for nonattendance was forgetting or having a scheduling conflict (16/29, 55%). A total of 14% (4/29) reported technical challenges associated with accessing the CMC focus group discussion. CMC focus group discussions were found to facilitate geographic diversity; allow participants to control anonymity and privacy (eg, use of pseudonyms and option to use video); ease scheduling by eliminating challenges related to travel to a data collection site; and offer flexibility to join via a variety of devices. Challenges encountered were related to overlapping conversations; variable audio quality in cases where Internet or cellular connection was poor; and distribution of incentives (eg, cash versus gift cards). As with all focus group discussions, establishment of ground rules and employing both a skilled facilitator and a notetaker who could troubleshoot technology issues were critical to the success of CMC focus group discussions. Conclusions: Synchronous CMC focus group discussions provide a secure opportunity to convene participants across geographic space with minimal time burden and without losing the standardized approach that is expected of focus group discussions. This method may provide an optimal alternative to engaging hard-to-reach participants in focus group discussions. Participants with limited technological literacy or inconsistent access to a phone and/or cellular data or service, as well as circumstances necessitating immediate cash incentives may, however, require additional support and accommodation when participating in CMC focus group discussions. UR - http://www.jmir.org/2019/3/e12569/ UR - http://dx.doi.org/10.2196/12569 UR - http://www.ncbi.nlm.nih.gov/pubmed/30924782 ID - info:doi/10.2196/12569 ER - TY - JOUR AU - Pan, Yuan-Chien AU - Lin, Hsiao-Han AU - Chiu, Yu-Chuan AU - Lin, Sheng-Hsuan AU - Lin, Yu-Hsuan PY - 2019/03/26 TI - Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle JO - JMIR Mhealth Uhealth SP - e12171 VL - 7 IS - 3 KW - temporal stability KW - smartphone use KW - smartphone addiction KW - smartphone KW - mobile phone N2 - Background: Assessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research. Objective: The study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters. Methods: Using the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use. Results: Our findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles. Conclusions: This study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods. UR - http://mhealth.jmir.org/2019/3/e12171/ UR - http://dx.doi.org/10.2196/12171 UR - http://www.ncbi.nlm.nih.gov/pubmed/30912751 ID - info:doi/10.2196/12171 ER - TY - JOUR AU - Østervang, Christina AU - Vestergaard, Vedel Lene AU - Dieperink, Brochstedt Karin AU - Danbjørg, Boe Dorthe PY - 2019/03/25 TI - Patient Rounds With Video-Consulted Relatives: Qualitative Study on Possibilities and Barriers From the Perspective of Healthcare Providers JO - J Med Internet Res SP - e12584 VL - 21 IS - 3 KW - telehealth KW - family KW - relatives KW - cancer KW - technology KW - qualitative research N2 - Background: In cancer settings, relatives are often seen as a resource as they are able to support the patient and remember information during hospitalization. However, geographic distance to hospitals, work, and family obligations are reasons that may cause difficulties for relatives? physical participation during hospitalization. This provided inspiration to uncover the possibility of telehealth care in connection with enabling participation by relatives during patient rounds. Telehealth is used advantageously in health care systems but is also at risk of failing during the implementation process because of, for instance, health care professionals? resistance to change. Research on the implications for health care professionals in involving relatives? participation through virtual presence during patient rounds is limited. Objective: This study aimed to investigate health care professionals? experiences in using and implementing technology to involve relatives during video-consulted patient rounds. Methods: The design was a qualitative approach. Methods used were focus group interviews, short open interviews, and field observations of health care professionals working at a cancer department. The text material was analyzed using interpretative phenomenological analysis. Results: Field observational studies were conducted for 15 days, yielding 75 hours of observation. A total of 14 sessions of video-consulted patient rounds were observed and 15 pages of field notes written, along with 8 short open interviews with physicians, nurses, and staff from management. Moreover, 2 focus group interviews with 9 health care professionals were conducted. Health care professionals experienced the use of technology as a way to facilitate involvement of the patient?s relatives, without them being physically present. Moreover, it raised questions about whether this way of conducting patient rounds could address the needs of both the patients and the relatives. Time, culture, and change of work routines were found to be the major barriers when implementing new technology involving relatives. Conclusions: This study identified a double change by introducing both new technology and virtual participation by relatives at the same time. The change had consequences on health care professionals? work routines with regard to work load, culture, and organization because of the complexity in health care systems. UR - http://www.jmir.org/2019/3/e12584/ UR - http://dx.doi.org/10.2196/12584 UR - http://www.ncbi.nlm.nih.gov/pubmed/30907746 ID - info:doi/10.2196/12584 ER - TY - JOUR AU - Huerta, Timothy AU - Fareed, Naleef AU - Hefner, L. Jennifer AU - Sieck, J. Cynthia AU - Swoboda, Christine AU - Taylor, Robert AU - McAlearney, Scheck Ann PY - 2019/03/25 TI - Patient Engagement as Measured by Inpatient Portal Use: Methodology for Log File Analysis JO - J Med Internet Res SP - e10957 VL - 21 IS - 3 KW - patient portals KW - health records, personal KW - health information technology KW - inpatient portals N2 - Background: Inpatient portals (IPPs) have the potential to increase patient engagement and satisfaction with their health care. An IPP provides a hospitalized patient with similar functions to those found in outpatient portals, including the ability to view vital signs, laboratory results, and medication information; schedule appointments; and communicate with their providers. However, IPPs may offer additional functions such as meal planning, real-time messaging with the inpatient care team, daily schedules, and access to educational materials relevant to their specific condition. In practice, IPPs have been developed as websites and tablet apps, with hospitals providing the required technology as a component of care during the patient?s stay. Objective: This study aimed to describe how inpatients are using IPPs at the first academic medical center to implement a system-wide IPP and document the challenges and choices associated with this analytic process. Methods: We analyzed the audit log files of IPP users hospitalized between January 2014 and January 2016. Data regarding the date/time and duration of interactions with each of the MyChart Bedside modules (eg, view lab results or medications and patient schedule) and activities (eg, messaging the provider and viewing educational videos) were captured as part of the system audit logs. The development of a construct to describe the length of time associated with a single coherent use of the tool?which we call a session?provides a foundational unit of analysis. We defined frequency as the number of sessions a patient has during a given provision day. We defined comprehensiveness in terms of the percentage of functions that an individual uses during a given provision day. Results: The analytic process presented data challenges such as length of stay and tablet-provisioning factors. This study presents data visualizations to illustrate a series of data-cleaning issues. In the presence of these robust approaches to data cleaning, we present the baseline usage patterns associated with our patient panel. In addition to frequency and comprehensiveness, we present considerations of median data to mitigate the effect of outliers. Conclusions: Although other studies have published usage data associated with IPPs, most have not explicated the challenges and choices associated with the analytic approach deployed within each study. Our intent in this study was to be somewhat exhaustive in this area, in part, because replicability requires common metrics. Our hope is that future researchers in this area will avail themselves of these perspectives to engage in critical assessment moving forward. UR - http://www.jmir.org/2019/3/e10957/ UR - http://dx.doi.org/10.2196/10957 UR - http://www.ncbi.nlm.nih.gov/pubmed/30907733 ID - info:doi/10.2196/10957 ER - TY - JOUR AU - Faust, Louis AU - Wang, Cheng AU - Hachen, David AU - Lizardo, Omar AU - Chawla, V. Nitesh PY - 2019/03/12 TI - Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data JO - JMIR Mhealth Uhealth SP - e11075 VL - 7 IS - 3 KW - mHealth KW - fitness trackers KW - activity trackers KW - exercise KW - health behavior KW - physical activity KW - health KW - mental health KW - perception KW - social network N2 - Background: Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. Objective: The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. Methods: Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks? output in supporting MVPA behavior change studies, we applied it to 2 case studies. Results: Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. Conclusions: By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA. UR - http://mhealth.jmir.org/2019/3/e11075/ UR - http://dx.doi.org/10.2196/11075 UR - http://www.ncbi.nlm.nih.gov/pubmed/30860488 ID - info:doi/10.2196/11075 ER - TY - JOUR AU - Karystianis, George AU - Adily, Armita AU - Schofield, W. Peter AU - Greenberg, David AU - Jorm, Louisa AU - Nenadic, Goran AU - Butler, Tony PY - 2019/03/12 TI - Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study JO - J Med Internet Res SP - e13067 VL - 21 IS - 3 KW - domestic violence KW - injuries KW - abuse types KW - text mining KW - rule-based approach KW - police narratives N2 - Background: The police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives. Objective: We aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force. Methods: We used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports. Results: Testing our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events; 35.97%) contained victim injuries. ?Emotional/verbal abuse? (33.46%; 117,488) was the most common abuse type, followed by ?punching? (86,322 events; 24.58%) and ?property damage? (22.27%; 78,203 events). ?Bruising? was the most common form of injury sustained (51,455 events; 29.03%), with ?cut/abrasion? (28.93%; 51,284 events) and ?red marks/signs? (23.71%; 42,038 events) ranking second and third, respectively. Conclusions: The results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status. UR - http://www.jmir.org/2019/3/e13067/ UR - http://dx.doi.org/10.2196/13067 UR - http://www.ncbi.nlm.nih.gov/pubmed/30860490 ID - info:doi/10.2196/13067 ER - TY - JOUR AU - McManus, D. David AU - Trinquart, Ludovic AU - Benjamin, J. Emelia AU - Manders, S. Emily AU - Fusco, Kelsey AU - Jung, S. Lindsey AU - Spartano, L. Nicole AU - Kheterpal, Vik AU - Nowak, Christopher AU - Sardana, Mayank AU - Murabito, M. Joanne PY - 2019/03/01 TI - Design and Preliminary Findings From a New Electronic Cohort Embedded in the Framingham Heart Study JO - J Med Internet Res SP - e12143 VL - 21 IS - 3 KW - smartphone KW - tele-medicine KW - blood pressure monitoring KW - ambulatory KW - cohort studies N2 - Background: New models of scalable population-based data collection that integrate digital and mobile health (mHealth) data are necessary. Objective: The aim of this study was to describe a cardiovascular digital and mHealth electronic cohort (e-cohort) embedded in a traditional longitudinal cohort study, the Framingham Heart Study (FHS). Methods: We invited eligible and consenting FHS Generation 3 and Omni participants to download the electronic Framingham Heart Study (eFHS) app onto their mobile phones and co-deployed a digital blood pressure (BP) cuff. Thereafter, participants were also offered a smartwatch (Apple Watch). Participants are invited to complete surveys through the eFHS app, to perform weekly BP measurements, and to wear the smartwatch daily. Results: Up to July 2017, we enrolled 790 eFHS participants, representing 76% (790/1044) of potentially eligible FHS participants. eFHS participants were, on average, 53±8 years of age and 57% were women. A total of 85% (675/790) of eFHS participants completed all of the baseline survey and 59% (470/790) completed the 3-month survey. A total of 42% (241/573) and 76% (306/405) of eFHS participants adhered to weekly digital BP and heart rate (HR) uploads, respectively, over 12 weeks. Conclusions: We have designed an e-cohort focused on identifying novel cardiovascular disease risk factors using a new smartphone app, a digital BP cuff, and a smartwatch. Despite minimal training and support, preliminary findings over a 3-month follow-up period show that uptake is high and adherence to periodic app-based surveys, weekly digital BP assessments, and smartwatch HR measures is acceptable. UR - http://www.jmir.org/2019/3/e12143/ UR - http://dx.doi.org/10.2196/12143 UR - http://www.ncbi.nlm.nih.gov/pubmed/30821691 ID - info:doi/10.2196/12143 ER - TY - JOUR AU - Wakamiya, Shoko AU - Morita, Mizuki AU - Kano, Yoshinobu AU - Ohkuma, Tomoko AU - Aramaki, Eiji PY - 2019/02/20 TI - Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations JO - J Med Internet Res SP - e12783 VL - 21 IS - 2 KW - text mining KW - social media KW - machine learning KW - natural language processing KW - artificial intelligence KW - surveillance KW - infodemiology KW - infoveillance N2 - Background: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media?based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient?s symptom and those that do not. Objective: This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP. Methods: In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. Results: The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively. Conclusions: This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications. UR - http://www.jmir.org/2019/2/e12783/ UR - http://dx.doi.org/10.2196/12783 UR - http://www.ncbi.nlm.nih.gov/pubmed/30785407 ID - info:doi/10.2196/12783 ER - TY - JOUR AU - Zeleke, Alamirrew Atinkut AU - Worku, Gebeyehu Abebaw AU - Demissie, Adina AU - Otto-Sobotka, Fabian AU - Wilken, Marc AU - Lipprandt, Myriam AU - Tilahun, Binyam AU - Röhrig, Rainer PY - 2019/02/11 TI - Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation JO - JMIR Mhealth Uhealth SP - e10995 VL - 7 IS - 2 KW - public health KW - maternal health KW - surveillance KW - survey KW - data collection KW - data quality KW - tablet computer KW - mHealth KW - Ethiopia N2 - Background: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. Objective: This study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. Methods: A randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit?based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. Results: From the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. Conclusions: EDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site. UR - http://mhealth.jmir.org/2019/2/e10995/ UR - http://dx.doi.org/10.2196/10995 UR - http://www.ncbi.nlm.nih.gov/pubmed/30741642 ID - info:doi/10.2196/10995 ER - TY - JOUR AU - Marcu, Afrodita AU - Muller, Cecile AU - Ream, Emma AU - Whitaker, L. Katriina PY - 2019/02/06 TI - Online Information-Seeking About Potential Breast Cancer Symptoms: Capturing Online Behavior With an Internet Browsing Tracking Tool JO - J Med Internet Res SP - e12400 VL - 21 IS - 2 KW - breast cancer KW - health information KW - internet search KW - online information seeking N2 - Background: People engage in health information-seeking online when experiencing unusual or unfamiliar bodily changes. It is not well understood how people consult the internet for health information after the onset of unfamiliar symptoms and before receiving a potential diagnosis and how online information-seeking can help people appraise their symptoms. This lack of evidence may be partly due to methodological limitations in capturing in real time the online information-seeking process. Objective: We explored women?s symptom attribution and online health information-seeking in response to a hypothetical and unfamiliar breast change suggestive of cancer (nipple rash). We also aimed to establish the feasibility of capturing in real time the online information-seeking process with a tool designed to track participant online searches and visited websites, the Vizzata browser tracker. Methods: An online survey was completed by 56 cancer-free women (mean age 60.34 [SD 7.73] years) responding to a scenario asking them to imagine noticing a red scaly rash on the nipple. Participants were asked to make symptom attributions when presented with the scenario (T1) and again after seeking information online (T2). The online tracking tool, embedded in the survey, was used to capture in real time participant search terms and accessed websites. Results: The tracking tool captured the search terms and accessed websites of most of the participants (46/56, 82%). For the rest (10/56, 18%), there was evidence of engagement in online information-seeking (eg, medical terminology and cancer attribution at T2) despite their searching activity not being recorded. A total of 25 participants considered cancer as a potential cause for the nipple rash at T1, yet only one of these used cancer as a search term. Most participants (40/46, 87%) used rash-related search terms, particularly nipple rash and rash on nipple. The majority (41/46, 89%) accessed websites containing breast cancer information, with the National Health Service webpage ?Paget disease of the nipple? being the most visited one. At T2, after engaging in the internet search task, more participants attributed the nipple rash to breast cancer than at T1 (37/46, 66% vs 25/46, 45%), although a small number of participants (6/46) changed from making a cancer attribution at T1 to a noncancer one at T2. Conclusions: Making a cancer attribution for an unfamiliar breast change did not necessarily translate into cancer-termed searches. Equally, not all internet searches led to a cancer attribution. The findings suggest that online information-seeking may not necessarily help women who experience unfamiliar breast cancer symptoms understand their condition. Despite some technical issues, this study showed that it is feasible to use an online browser tracking tool to capture in real time information-seeking about unfamiliar symptoms. UR - http://www.jmir.org/2019/2/e12400/ UR - http://dx.doi.org/10.2196/12400 UR - http://www.ncbi.nlm.nih.gov/pubmed/30724741 ID - info:doi/10.2196/12400 ER - TY - JOUR AU - Mandl, D. Kenneth AU - Gottlieb, Daniel AU - Ellis, Alyssa PY - 2019/02/01 TI - Beyond One-Off Integrations: A Commercial, Substitutable, Reusable, Standards-Based, Electronic Health Record?Connected App JO - J Med Internet Res SP - e12902 VL - 21 IS - 2 KW - electronic medical records KW - application programming interfaces UR - http://www.jmir.org/2019/2/e12902/ UR - http://dx.doi.org/10.2196/12902 UR - http://www.ncbi.nlm.nih.gov/pubmed/30707097 ID - info:doi/10.2196/12902 ER - TY - JOUR AU - Birckhead, Brandon AU - Khalil, Carine AU - Liu, Xiaoyu AU - Conovitz, Samuel AU - Rizzo, Albert AU - Danovitch, Itai AU - Bullock, Kim AU - Spiegel, Brennan PY - 2019/01/31 TI - Recommendations for Methodology of Virtual Reality Clinical Trials in Health Care by an International Working Group: Iterative Study JO - JMIR Ment Health SP - e11973 VL - 6 IS - 1 KW - clinical trials KW - consensus KW - virtual reality N2 - Background: Therapeutic virtual reality (VR) has emerged as an efficacious treatment modality for a wide range of health conditions. However, despite encouraging outcomes from early stage research, a consensus for the best way to develop and evaluate VR treatments within a scientific framework is needed. Objective: We aimed to develop a methodological framework with input from an international working group in order to guide the design, implementation, analysis, interpretation, and communication of trials that develop and test VR treatments. Methods: A group of 21 international experts was recruited based on their contributions to the VR literature. The resulting Virtual Reality Clinical Outcomes Research Experts held iterative meetings to seek consensus on best practices for the development and testing of VR treatments. Results: The interactions were transcribed, and key themes were identified to develop a scientific framework in order to support best practices in methodology of clinical VR trials. Using the Food and Drug Administration Phase I-III pharmacotherapy model as guidance, a framework emerged to support three phases of VR clinical study designs?VR1, VR2, and VR3. VR1 studies focus on content development by working with patients and providers through the principles of human-centered design. VR2 trials conduct early testing with a focus on feasibility, acceptability, tolerability, and initial clinical efficacy. VR3 trials are randomized, controlled studies that evaluate efficacy against a control condition. Best practice recommendations for each trial were provided. Conclusions: Patients, providers, payers, and regulators should consider this best practice framework when assessing the validity of VR treatments. UR - https://mental.jmir.org/2019/1/e11973/ UR - http://dx.doi.org/10.2196/11973 UR - http://www.ncbi.nlm.nih.gov/pubmed/30702436 ID - info:doi/10.2196/11973 ER - TY - JOUR AU - Crossley, Morgan Sam Graeme AU - McNarry, Anne Melitta AU - Hudson, Joanne AU - Eslambolchilar, Parisa AU - Knowles, Zoe AU - Mackintosh, Alexandra Kelly PY - 2019/01/30 TI - Perceptions of Visualizing Physical Activity as a 3D-Printed Object: Formative Study JO - J Med Internet Res SP - e12064 VL - 21 IS - 1 KW - 3D printing KW - feedback KW - youth KW - education KW - school N2 - Background: The UK government recommends that children engage in moderate-to-vigorous physical activity for at least 60 min every day. Despite associated physiological and psychosocial benefits of physical activity, many youth fail to meet these guidelines partly due to sedentary screen-based pursuits displacing active behaviors. However, technological advances such as 3D printing have enabled innovative methods of visualizing and conceptualizing physical activity as a tangible output. Objective: The aim of this study was to elicit children?s, adolescents?, parents?, and teachers? perceptions and understanding of 3D physical activity objects to inform the design of future 3D models of physical activity. Methods: A total of 28 primary school children (aged 8.4 [SD 0.3] years; 15 boys) and 42 secondary school adolescents (aged 14.4 [SD 0.3] years; 22 boys) participated in semistructured focus groups, with individual interviews conducted with 8 teachers (2 male) and 7 parents (2 male). Questions addressed understanding of the physical activity guidelines, 3D model design, and both motivation for and potential engagement with a 3D physical activity model intervention. Pupils were asked to use Play-Doh to create and describe a model that could represent their physical activity levels (PAL). Data were transcribed verbatim and thematically analyzed, and key emergent themes were represented using pen profiles. Results: Pupils understood the concept of visualizing physical activity as a 3D object, although adolescents were able to better analyze and critique differences between low and high PAL. Both youths and adults preferred a 3D model representing a week of physical activity data when compared with other temporal representations. Furthermore, all participants highlighted that 3D models could act as a motivational tool to enhance youths? physical activity. From the Play-Doh designs, 2 key themes were identified by pupils, with preferences indicated for models of abstract representations of physical activity or bar charts depicting physical activity, respectively. Conclusions: These novel findings highlight the potential utility of 3D objects of physical activity as a mechanism to enhance children?s and adolescents? understanding of, and motivation to increase, their PAL. This study suggests that 3D printing may offer a unique strategy for promoting physical activity in these groups. UR - http://www.jmir.org/2019/1/e12064/ UR - http://dx.doi.org/10.2196/12064 UR - http://www.ncbi.nlm.nih.gov/pubmed/30698532 ID - info:doi/10.2196/12064 ER - TY - JOUR AU - Lang, Michael PY - 2019/01/30 TI - Automatic Near Real-Time Outlier Detection and Correction in Cardiac Interbeat Interval Series for Heart Rate Variability Analysis: Singular Spectrum Analysis-Based Approach JO - JMIR Biomed Eng SP - e10740 VL - 4 IS - 1 KW - change-point detection KW - cumulative sum KW - forecasting KW - heart rate variability KW - R-R series KW - singular spectrum analysis KW - ventricular premature complexes N2 - Background: Heart rate variability (HRV) is derived from the series of R-R intervals extracted from an electrocardiographic (ECG) measurement. Ideally all components of the R-R series are the result of sinoatrial node depolarization. However, the actual R-R series are contaminated by outliers due to heart rhythm disturbances such as ectopic beats, which ought to be detected and corrected appropriately before HRV analysis. Objective: We have introduced a novel, lightweight, and near real-time method to detect and correct anomalies in the R-R series based on the singular spectrum analysis (SSA). This study aimed to assess the performance of the proposed method in terms of (1) detection performance (sensitivity, specificity, and accuracy); (2) root mean square error (RMSE) between the actual N-N series and the approximated outlier-cleaned R-R series; and (3) how it benchmarks against a competitor in terms of the relative RMSE. Methods: A lightweight SSA-based change-point detection procedure, improved through the use of a cumulative sum control chart with adaptive thresholds to reduce detection delays, monitored the series of R-R intervals in real time. Upon detection of an anomaly, the corrupted segment was substituted with the respective outlier-cleaned approximation obtained using recurrent SSA forecasting. Next, N-N intervals from a 5-minute ECG segment were extracted from each of the 18 records in the MIT-BIH Normal Sinus Rhythm Database. Then, for each such series, a number (randomly drawn integer between 1 and 6) of simulated ectopic beats were inserted at random positions within the series and results were averaged over 1000 Monte Carlo runs. Accordingly, 18,000 R-R records corresponding to 5-minute ECG segments were used to assess the detection performance whereas another 180,000 (10,000 for each record) were used to assess the error introduced in the correction step. Overall 198,000 R-R series were used in this study. Results: The proposed SSA-based algorithm reliably detected outliers in the R-R series and achieved an overall sensitivity of 96.6%, specificity of 98.4% and accuracy of 98.4%. Furthermore, it compared favorably in terms of discrepancies of the cleaned R-R series compared with the actual N-N series, outperforming an established correction method on average by almost 30%. Conclusions: The proposed algorithm, which leverages the power and versatility of the SSA to both automatically detect and correct artifacts in the R-R series, provides an effective and efficient complementary method and a potential alternative to the current manual-editing gold standard. Other important characteristics of the proposed method include the ability to operate in near real-time, the almost entirely model-free nature of the framework which does not require historical training data, and its overall low computational complexity. UR - https://biomedeng.jmir.org/2019/1/e10740/ UR - http://dx.doi.org/10.2196/10740 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/10740 ER - TY - JOUR AU - Turner, M. Anne AU - Choi, K. Yong AU - Dew, Kristin AU - Tsai, Ming-Tse AU - Bosold, L. Alyssa AU - Wu, Shuyang AU - Smith, Donahue AU - Meischke, Hendrika PY - 2019/01/28 TI - Evaluating the Usefulness of Translation Technologies for Emergency Response Communication: A Scenario-Based Study JO - JMIR Public Health Surveill SP - e11171 VL - 5 IS - 1 KW - Chinese KW - Emergency Medical Services KW - emergency response KW - language barriers KW - language translation KW - public health informatics KW - Spanish KW - limited English proficient KW - translation technologies N2 - Background: In the United States, language barriers pose challenges to communication in emergency response and impact emergency care delivery and quality for individuals who are limited English proficient (LEP). There is a growing interest among Emergency Medical Services (EMS) personnel in using automated translation tools to improve communications with LEP individuals in the field. However, little is known about whether automated translation software can be used successfully in EMS settings to improve communication with LEP individuals. Objective: The objective of this work is to use scenario-based methods with EMS providers and nonnative English-speaking users who identified themselves as LEP (henceforth referred to as LEP participants) to evaluate the potential of two automated translation technologies in improving emergency communication. Methods: We developed mock emergency scenarios and enacted them in simulation sessions with EMS personnel and Spanish-speaking and Chinese-speaking (Mandarin) LEP participants using two automated language translation tools: an EMS domain-specific fixed-sentence translation tool (QuickSpeak) and a statistical machine translation tool (Google Translate). At the end of the sessions, we gathered feedback from both groups through a postsession questionnaire. EMS participants also completed the System Usability Scale (SUS). Results: We conducted a total of 5 group sessions (3 Chinese and 2 Spanish) with 12 Chinese-speaking LEP participants, 14 Spanish-speaking LEP participants, and 17 EMS personnel. Overall, communications between EMS and LEP participants remained limited, even with the use of the two translation tools. QuickSpeak had higher mean SUS scores than Google Translate (65.3 vs 48.4; P=.04). Although both tools were deemed less than satisfactory, LEP participants showed preference toward the domain-specific system with fixed questions (QuickSpeak) over the free-text translation tool (Google Translate) in terms of understanding the EMS personnel?s questions (Chinese 11/12, 92% vs 3/12, 25%; Spanish 12/14, 86% vs 4/14, 29%). While both EMS and LEP participants appreciated the flexibility of the free-text tool, multiple translation errors and difficulty responding to questions limited its usefulness. Conclusions: Technologies are emerging that have the potential to assist with language translation in emergency response; however, improvements in accuracy and usability are needed before these technologies can be used safely in the field. UR - http://publichealth.jmir.org/2019/1/e11171/ UR - http://dx.doi.org/10.2196/11171 UR - http://www.ncbi.nlm.nih.gov/pubmed/30688652 ID - info:doi/10.2196/11171 ER - TY - JOUR AU - Follmann, Andreas AU - Ohligs, Marian AU - Hochhausen, Nadine AU - Beckers, K. Stefan AU - Rossaint, Rolf AU - Czaplik, Michael PY - 2019/01/03 TI - Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine JO - J Med Internet Res SP - e11939 VL - 21 IS - 1 KW - augmented reality KW - disaster medicine KW - emergency medical service physician KW - mass casualty incident KW - Smart Glasses KW - telemedicine KW - triage N2 - Background: To treat many patients despite lacking personnel resources, triage is important in disaster medicine. Various triage algorithms help but often are used incorrectly or not at all. One potential problem-solving approach is to support triage with Smart Glasses. Objective: In this study, augmented reality was used to display a triage algorithm and telemedicine assistance was enabled to compare the duration and quality of triage with a conventional one. Methods: A specific Android app was designed for use with Smart Glasses, which added information in terms of augmented reality with two different methods?through the display of a triage algorithm in data glasses and a telemedical connection to a senior emergency physician realized by the integrated camera. A scenario was created (ie, randomized simulation study) in which 31 paramedics carried out a triage of 12 patients in 3 groups as follows: without technical support (control group), with a triage algorithm display, and with telemedical contact. Results: A total of 362 assessments were performed. The accuracy in the control group was only 58%, but the assessments were quicker (on average 16.6 seconds). In contrast, an accuracy of 92% (P=.04) was achieved when using technical support by displaying the triage algorithm. This triaging took an average of 37.0 seconds. The triage group wearing data glasses and being telemedically connected achieved 90% accuracy (P=.01) in 35.0 seconds. Conclusions: Triage with data glasses required markedly more time. While only a tally was recorded in the control group, Smart Glasses led to digital capture of the triage results, which have many tactical advantages. We expect a high potential in the application of Smart Glasses in disaster scenarios when using telemedicine and augmented reality features to improve the quality of triage. UR - https://www.jmir.org/2019/1/e11939/ UR - http://dx.doi.org/10.2196/11939 UR - http://www.ncbi.nlm.nih.gov/pubmed/30609988 ID - info:doi/10.2196/11939 ER - TY - JOUR AU - Reyzelman, M. Alexander AU - Koelewyn, Kristopher AU - Murphy, Maryam AU - Shen, Xuening AU - Yu, E. AU - Pillai, Raji AU - Fu, Jie AU - Scholten, Jan Henk AU - Ma, Ran PY - 2018/12/17 TI - Continuous Temperature-Monitoring Socks for Home Use in Patients With Diabetes: Observational Study JO - J Med Internet Res SP - e12460 VL - 20 IS - 12 KW - diabetes KW - diabetic foot ulcer KW - continuous temperature monitoring KW - Charcot arthropathy KW - digital health KW - wearable KW - neurofabric KW - mobile phone KW - wireless KW - Bluetooth KW - neuropathy KW - home use N2 - Background: Over 30 million people in the United States (over 9%) have been diagnosed with diabetes. About 25% of people with diabetes will experience a diabetic foot ulcer (DFU) in their lifetime. Unresolved DFUs may lead to sepsis and are the leading cause of lower-limb amputations. DFU rates can be reduced by screening patients with diabetes to enable risk-based interventions. Skin temperature assessment has been shown to reduce the risk of foot ulceration. While several tools have been developed to measure plantar temperatures, they only measure temperature once a day or are designed for clinic use only. In this report, wireless sensor-embedded socks designed for daily wear are introduced, which perform continuous temperature monitoring of the feet of persons with diabetes in the home environment. Combined with a mobile app, this wearable device informs the wearer about temperature increases in one foot relative to the other, to facilitate early detection of ulcers and timely intervention. Objective: A pilot study was conducted to assess the accuracy of sensors used in daily wear socks, obtain user feedback on how comfortable sensor-embedded socks were for home use, and examine whether observed temperatures correlated with clinical observations. Methods: Temperature accuracy of sensors was assessed both prior to incorporation in the socks, as well as in the completed design. The measured temperatures were compared to the reference standard, a high-precision thermostatic water bath in the range 20°C-40°C. A total of 35 patients, 18 years of age and older, with diabetic peripheral neuropathy were enrolled in a single-site study conducted under an Institutional Review Board?approved protocol. This study evaluated the usability of the sensor-embedded socks and correlated the observed temperatures with clinical findings. Results: The temperatures measured by the stand-alone sensors were within 0.2°C of the reference standard. In the sensor-embedded socks, across multiple measurements for each of the six sensors, a high agreement (R2=1) between temperatures measured and the reference standard was observed. Patients reported that the socks were easy to use and comfortable, ranking them at a median score of 9 or 10 for comfort and ease of use on a 10-point scale. Case studies are presented showing that the temperature differences observed between the feet were consistent with clinical observations. Conclusions: We report the first use of wireless continuous temperature monitoring for daily wear and home use in patients with diabetes and neuropathy. The wearers found the socks to be no different from standard socks. The temperature studies conducted show that the sensors used in the socks are reliable and accurate at detecting temperature and the findings matched clinical observations. Continuous temperature monitoring is a promising approach as an early warning system for foot ulcers, Charcot foot, and reulceration. UR - http://www.jmir.org/2018/12/e12460/ UR - http://dx.doi.org/10.2196/12460 UR - http://www.ncbi.nlm.nih.gov/pubmed/30559091 ID - info:doi/10.2196/12460 ER - TY - JOUR AU - Downey, Candice AU - Randell, Rebecca AU - Brown, Julia AU - Jayne, G. David PY - 2018/12/11 TI - Continuous Versus Intermittent Vital Signs Monitoring Using a Wearable, Wireless Patch in Patients Admitted to Surgical Wards: Pilot Cluster Randomized Controlled Trial JO - J Med Internet Res SP - e10802 VL - 20 IS - 12 KW - general surgery KW - monitoring KW - physiological KW - randomized controlled trial KW - vital signs N2 - Background: Vital signs monitoring is a universal tool for the detection of postoperative complications; however, unwell patients can be missed between traditional observation rounds. New remote monitoring technologies promise to convey the benefits of continuous monitoring to patients in general wards. Objective: The aim of this pilot study was to evaluate whether continuous remote vital signs monitoring is a practical and acceptable way of monitoring surgical patients and to optimize the delivery of a definitive trial. Methods: We performed a prospective, cluster-randomized, parallel-group, unblinded, controlled pilot study. Patients admitted to 2 surgical wards at a large tertiary hospital received either continuous and intermittent vital signs monitoring or intermittent monitoring alone using an early warning score system. Continuous monitoring was provided by a wireless patch, worn on the patient?s chest, with data transmitted wirelessly every 2 minutes to a central monitoring station or a mobile device carried by the patient?s nurse. The primary outcome measure was time to administration of antibiotics in sepsis. The secondary outcome measures included the length of hospital stay, 30-day readmission rate, mortality, and patient acceptability. Results: Overall, 226 patients were randomized between January and June 2017. Of 226 patients, 140 were randomized to continuous remote monitoring and 86 to intermittent monitoring alone. On average, patients receiving continuous monitoring were administered antibiotics faster after evidence of sepsis (626 minutes, n=22, 95% CI 431.7-820.3 minutes vs 1012.8 minutes, n=12, 95% CI 425.0-1600.6 minutes), had a shorter average length of hospital stay (13.3 days, 95% CI 11.3-15.3 days vs 14.6 days, 95% CI 11.5-17.7 days), and were less likely to require readmission within 30 days of discharge (11.4%, 95% CI 6.16-16.7 vs 20.9%, 95% CI 12.3-29.5). Wide CIs suggest these differences are not statistically significant. Patients found the monitoring device to be acceptable in terms of comfort and perceived an enhanced sense of safety, despite 24% discontinuing the intervention early. Conclusions: Remote continuous vital signs monitoring on surgical wards is practical and acceptable to patients. Large, well-controlled studies in high-risk populations are required to determine whether the observed trends translate into a significant benefit for continuous over intermittent monitoring. Trial Registration: International Standard Randomised Controlled Trial Number ISRCTN60999823; http://www.isrctn.com /ISRCTN60999823 (Archived by WebCite at http://www.webcitation.org/73ikP6OQz) UR - https://www.jmir.org/2018/12/e10802/ UR - http://dx.doi.org/10.2196/10802 UR - http://www.ncbi.nlm.nih.gov/pubmed/30538086 ID - info:doi/10.2196/10802 ER - TY - JOUR AU - Tan, Ling Shu AU - Whittal, Amanda AU - Lippke, Sonia PY - 2018/12/06 TI - Testing a Photo Story Intervention in Paper Versus Electronic Tablet Format Compared to a Traditional Brochure Among Older Adults in Germany: Randomized Controlled Trial JO - JMIR Aging SP - e12145 VL - 1 IS - 2 KW - photo story KW - traditional brochure KW - health literacy KW - communication KW - older adults KW - tablet intervention KW - electronic/information technology KW - primary care consultation N2 - Background: To increase effective communication in primary care consultations among older adults in Germany, the photo story is considered to be a useful tool based on Bandura?s social cognitive theory. With information technology helping to increase effective communication, the use of tablets is gaining attention in health care settings, especially with older adults. However, the effectiveness of tablet technology and photo stories has rarely been tested. Objective: The aim is to compare the effectiveness of a photo story intervention to a traditional brochure. Both were delivered either in paper or tablet format. Methods: A trial was conducted with 126 older adults, aged 50 years and older, who were approached and recruited by researchers and administrative staff from senior day care, doctors in rehabilitation centers, and trainers in sports clubs in Germany. Open and face-to-face assessment methodologies were used. Participants were randomly assigned to one of four intervention conditions: traditional brochure in paper format (condition 1) and tablet format (condition 2), and photo story in paper format (condition 3) and tablet format (condition 4). Each participant received a questionnaire and either the traditional brochure or photo story in a paper or tablet version. To evaluate the effectiveness of each intervention, participants completed evaluation questionnaires before and after each intervention. The second part of the questionnaire measured different indicators of health literacy, communication skills, health measurements, and possible underlying mechanisms. Results: Compared to the traditional brochure, participants considered the photo story easier to understand (t124=2.62, P=.01) and more informative (t124=?2.17, P=.03). Participants preferred the paper format because they found it less monotonous (t124=?3.05, P=.003), less boring (t124=?2.65, P=.009), and not too long (t124=?2.26, P=.03) compared to the tablet format. Among all conditions, the traditional brochure with a tablet (condition 2) was also perceived as more monotonous (mean 3.07, SD 1.08), boring (mean 2.77, SD 1.19), and too long to read (mean 2.50, SD 1.33) in comparison to the traditional brochure in paper format (condition 1). Moreover, the participants scored significantly higher on self-referencing on the traditional brochure in paper format (condition 1) than tablet format for both types of the brochure (conditions 2 and 4). Conclusions: Traditional brochures on a tablet seem to be the least effective communication option in primary care consultations among all conditions for older adults. The findings might be specific for the current generation of older adults in Germany and need to be replicated in other countries with larger sample sizes. Although information technology brings advantages, such as effective interventions in different fields and settings, it may also come with several disadvantages, such as technical requirements of the users and devices. These should be considered when integrating information technology into wider situations and populations. Trial Registration: ClinicalTrials.gov NCT02502292; https://clinicaltrials.gov/ct2/show/NCT02502292 (Archived by Webcite at http://www.webcitation.org/747jdJ8pU) UR - http://aging.jmir.org/2018/2/e12145/ UR - http://dx.doi.org/10.2196/12145 UR - http://www.ncbi.nlm.nih.gov/pubmed/31518254 ID - info:doi/10.2196/12145 ER - TY - JOUR AU - Lattie, G. Emily AU - Kaiser, M. Susan AU - Alam, Nameyeh AU - Tomasino, N. Kathryn AU - Sargent, Elizabeth AU - Rubanovich, Kseniya Caryn AU - Palac, L. Hannah AU - Mohr, C. David PY - 2018/11/29 TI - A Practical Do-It-Yourself Recruitment Framework for Concurrent eHealth Clinical Trials: Identification of Efficient and Cost-Effective Methods for Decision Making (Part 2) JO - J Med Internet Res SP - e11050 VL - 20 IS - 11 KW - eHealth KW - mHealth KW - mental health KW - recruitment N2 - Background: The ability to successfully recruit participants for electronic health (eHealth) clinical trials is largely dependent on the use of efficient and effective recruitment strategies. Determining which types of recruitment strategies to use presents a challenge for many researchers. Objective: The aim of this study was to present an analysis of the time-efficiency and cost-effectiveness of recruitment strategies for eHealth clinical trials, and it describes a framework for cost-effective trial recruitment. Methods: Participants were recruited for one of 5 eHealth trials of interventions for common mental health conditions. A multipronged recruitment approach was used, including digital (eg, social media and Craigslist), research registry-based, print (eg, flyers and posters on public transportation), clinic-based (eg, a general internal medicine clinic within an academic medical center and a large nonprofit health care organization), a market research recruitment firm, and traditional media strategies (eg, newspaper and television coverage in response to press releases). The time costs and fees for each recruitment method were calculated, and the participant yield on recruitment costs was calculated by dividing the number of enrolled participants by the total cost for each method. Results: A total of 777 participants were enrolled across all trials. Digital recruitment strategies yielded the largest number of participants across the 5 clinical trials and represented 34.0% (264/777) of the total enrolled participants. Registry-based recruitment strategies were in second place by enrolling 28.0% (217/777) of the total enrolled participants across trials. Research registry-based recruitment had a relatively high conversion rate from potential participants who contacted our center for being screened to be enrolled, and it was also the most cost-effective for enrolling participants in this set of clinical trials with a total cost per person enrolled at US $8.99. Conclusions: On the basis of these results, a framework is proposed for participant recruitment. To make decisions on initiating and maintaining different types of recruitment strategies, the resources available and requirements of the research study (or studies) need to be carefully examined. UR - https://www.jmir.org/2018/11/e11050/ UR - http://dx.doi.org/10.2196/11050 UR - http://www.ncbi.nlm.nih.gov/pubmed/30497997 ID - info:doi/10.2196/11050 ER - TY - JOUR AU - Zhang, Youshan AU - Allem, Jon-Patrick AU - Unger, Beth Jennifer AU - Boley Cruz, Tess PY - 2018/11/21 TI - Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification JO - J Med Internet Res SP - e10513 VL - 20 IS - 11 KW - convolutional neural network KW - feature extraction KW - image classification KW - Instagram KW - social media KW - support vector machine N2 - Background: Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between objects within images. Objective: This study aimed to demonstrate how a convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image. Methods: Images of waterpipes or hookah (an emerging tobacco product possessing similar harms to that of cigarettes) were collected from Instagram and used in the analyses (N=840). A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy. Results: As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5% (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone. Conclusions: A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies. UR - http://www.jmir.org/2018/11/e10513/ UR - http://dx.doi.org/10.2196/10513 UR - http://www.ncbi.nlm.nih.gov/pubmed/30452385 ID - info:doi/10.2196/10513 ER - TY - JOUR AU - Plante, B. Timothy AU - O'Kelly, C. Anna AU - Urrea, Bruno AU - Macfarlane, T. Zane AU - Appel, J. Lawrence AU - Miller III, R. Edgar AU - Blumenthal, S. Roger AU - Martin, S. Seth PY - 2018/11/21 TI - Auralife Instant Blood Pressure App in Measuring Resting Heart Rate: Validation Study JO - JMIR Biomed Eng SP - e11057 VL - 3 IS - 1 KW - mHealth KW - digital health KW - heart rate KW - validation study KW - photoplethysmography KW - medical informatics KW - mobile phones N2 - Background: mHealth apps that measure heart rate using pulse photoplethysmography (PPG) are classified as class II (moderate-risk) Food and Drug Administration devices; therefore, these devices need clinical validation prior to public release. The Auralife Instant Blood Pressure app (AuraLife IBP app) is an mHealth app that measures blood pressure inaccurately based on a previous validation study. Its ability to measure heart rate has not been previously reported. Objective: The objective of our study was to assess the accuracy and precision of the AuraLife IBP app in measuring heart rate. Methods: We enrolled 85 adults from ambulatory clinics. Two measurements were obtained using the AuraLife IBP app, and 2 other measurements were achieved with a oscillometric device. The order of devices was randomized. Accuracy was assessed by calculating the relative and absolute mean differences between heart rate measurements obtained using each AuraLife IBP app and an average of both standard heart rate measurements. Precision was assessed by calculating the relative and absolute mean differences between individual measurements in the pair for each device. Results: The relative and absolute mean (SD) differences between the devices were 1.1 (3.5) and 2.8 (2.4) beats per minute (BPM), respectively. Meanwhile, the within-device relative and absolute mean differences, respectively, were <0.1 (2.2) and 1.7 (1.4) BPM for the standard device and ?0.1 (3.2) and 2.2 (2.3) BPM for the AuraLife IBP app. Conclusions: The AuraLife IBP app had a high degree of accuracy and precision in the measurement of heart rate. This supports the use of PPG technology in smartphones for monitoring resting heart rate. UR - http://biomedeng.jmir.org/2018/1/e11057/ UR - http://dx.doi.org/10.2196/11057 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/11057 ER - TY - JOUR AU - Gower, D. Aubrey AU - Moreno, A. Megan PY - 2018/11/19 TI - A Novel Approach to Evaluating Mobile Smartphone Screen Time for iPhones: Feasibility and Preliminary Findings JO - JMIR Mhealth Uhealth SP - e11012 VL - 6 IS - 11 KW - smartphone KW - youth KW - mobile apps KW - mobile phone KW - screenshot N2 - Background: Increasingly high levels of smartphone ownership and use pose the potential risk for addictive behaviors and negative health outcomes, particularly among younger populations. Previous methodologies to understand mobile screen time have relied on self-report surveys or ecological momentary assessments (EMAs). Self-report is subject to bias and unreliability, while EMA can be burdensome to participants. Thus, a new methodology is needed to advance the understanding of mobile screen time. Objective: The objective of this study was to test the feasibility of a novel methodology to record and evaluate mobile smartphone screen time and use: battery use screenshot (BUS). Methods: The BUS approach, defined for this study as uploading a mobile phone screenshot of a specific page within a smartphone, was utilized within a Web-based cross-sectional survey of adolescents aged 12-15 years through the survey platform Qualtrics. Participants were asked to provide a screenshot of their battery use page, a feature within an iPhone, to upload within the Web-based survey. Feasibility was assessed by smartphone ownership and response rate to the BUS upload request. Data availability was evaluated as apps per BUS, completeness of data within the screenshot, and five most used apps based on battery use percentage. Results: Among those surveyed, 26.73% (309/1156) indicated ownership of a smartphone. A total of 105 screenshots were evaluated. For data availability, screenshots contained an average of 10.2 (SD 2.0) apps per screenshot and over half (58/105, 55.2%) had complete data available. The most common apps or functions included Safari and Home and Lock Screen. Conclusions: Study findings describe the BUS as a novel approach for real-time data collection focused on iPhone screen time and use among young adolescents. Although feasibility showed some challenges in the upload capacity of young teens, data availability was generally strong across this large dataset. These data from screenshots have the potential to provide key insights into precise mobile smartphone screen use and time spent per mobile app. Future studies could explore the use of the BUS methodology on other mobile smartphones such as Android phones to correlate mobile smartphone screen time with health outcomes. UR - http://mhealth.jmir.org/2018/11/e11012/ UR - http://dx.doi.org/10.2196/11012 UR - http://www.ncbi.nlm.nih.gov/pubmed/30455163 ID - info:doi/10.2196/11012 ER - TY - JOUR AU - White, B. Elizabeth AU - Meyer, J. Amanda AU - Ggita, M. Joseph AU - Babirye, Diana AU - Mark, David AU - Ayakaka, Irene AU - Haberer, E. Jessica AU - Katamba, Achilles AU - Armstrong-Hough, Mari AU - Davis, Lucian John PY - 2018/11/15 TI - Feasibility, Acceptability, and Adoption of Digital Fingerprinting During Contact Investigation for Tuberculosis in Kampala, Uganda: A Parallel-Convergent Mixed-Methods Analysis JO - J Med Internet Res SP - e11541 VL - 20 IS - 11 KW - biometrics KW - mHealth KW - mobile phone KW - tuberculosis N2 - Background: In resource-constrained settings, challenges with unique patient identification may limit continuity of care, monitoring and evaluation, and data integrity. Biometrics offers an appealing but understudied potential solution. Objective: The objective of this mixed-methods study was to understand the feasibility, acceptability, and adoption of digital fingerprinting for patient identification in a study of household tuberculosis contact investigation in Kampala, Uganda. Methods: Digital fingerprinting was performed using multispectral fingerprint scanners. We tested associations between demographic, clinical, and temporal characteristics and failure to capture a digital fingerprint. We used generalized estimating equations and a robust covariance estimator to account for clustering. In addition, we evaluated the clustering of outcomes by household and community health workers (CHWs) by calculating intraclass correlation coefficients (ICCs). To understand the determinants of intended and actual use of fingerprinting technology, we conducted 15 in-depth interviews with CHWs and applied a widely used conceptual framework, the Technology Acceptance Model 2 (TAM2). Results: Digital fingerprints were captured for 75.5% (694/919) of participants, with extensive clustering by household (ICC=.99) arising from software (108/179, 60.3%) and hardware (65/179, 36.3%) failures. Clinical and demographic characteristics were not markedly associated with fingerprint capture. CHWs successfully fingerprinted all contacts in 70.1% (213/304) of households, with modest clustering of outcomes by CHWs (ICC=.18). The proportion of households in which all members were successfully fingerprinted declined over time (?=.30, P<.001). In interviews, CHWs reported that fingerprinting failures lowered their perceptions of the quality of the technology, threatened their social image as competent health workers, and made the technology more difficult to use. Conclusions: We found that digital fingerprinting was feasible and acceptable for individual identification, but problems implementing the hardware and software lead to a high failure rate. Although CHWs found fingerprinting to be acceptable in principle, their intention to use the technology was tempered by perceptions that it was inconsistent and of questionable value. TAM2 provided a valuable framework for understanding the motivations behind CHWs? intentions to use the technology. We emphasize the need for routine process evaluation of biometrics and other digital technologies in resource-constrained settings to assess implementation effectiveness and guide improvement of delivery. UR - http://www.jmir.org/2018/11/e11541/ UR - http://dx.doi.org/10.2196/11541 UR - http://www.ncbi.nlm.nih.gov/pubmed/30442637 ID - info:doi/10.2196/11541 ER - TY - JOUR AU - Sturesson, Linda AU - Groth, Kristina PY - 2018/11/05 TI - Clinicians? Selection Criteria for Video Visits in Outpatient Care: Qualitative Study JO - J Med Internet Res SP - e288 VL - 20 IS - 11 KW - outpatient care KW - selection criteria KW - telemedicine KW - telehealth KW - ethnography N2 - Background: Video visits with patients were introduced into outpatient care at a hospital in Sweden. New behaviors and tasks emerged due to changes in roles, work processes, and responsibilities. This study investigates the effects of the digital transformation?in this case, how video visits in outpatient care change work processes and introduce new tasks?to further improve the concept of video visits. The overarching goal was to increase the value of these visits, with a focus on the value of conducting the treatment for the patient. Objective: Through the real-time, social interactional features of preparing for and conducting video visits with patients with obesity, this study examines which patients the clinicians considered suitable for video visits and why. The aim was to identify the criteria used by clinicians when selecting patients for video visits to understand what criteria the clinicians used as the grounds for their selection. Methods: Qualitative methods were used, including 13 observations of video visits at 2 different clinics and 14 follow-up interviews with clinicians. Transcripts of interviews and field notes were thematically analyzed, discussed, and synthesized into themes. Results: From the interviews, 20 different arguments for selecting a specific patient for video visits were identified. Analyzing interviews and field notes also revealed unexpressed arguments that played a part in the selection process. The unexpressed arguments, as well as the implicit reasons, for why a patient was given the option of video visits can be understood as the selection criteria for helping clinicians in their decision about whether to offer video visits or not. The criteria identified in the collected data were divided into 3 themes: practicalities, patient ability, and meeting content. Conclusions: Not all patients with obesity undergoing treatment programs should be offered video visits. Patients? new responsibilities could influence the content of the meeting and the progress of the treatment program. The selection criteria developed and used by the clinicians could be a tool for finding a balance between what the patient wants and what the clinician thinks the patient can manage and achieving good results in the treatment program. The criteria could also reduce the number and severity of disturbances and limitations during the meeting and could be used to communicate the requirements they represent to the patient. Some of the criteria are based on facts, whereas others are subjective. A method for how and when to involve the patient in the selection process is recommended as it may strengthen the patient?s sense of responsibility and the relationship with the clinician. UR - https://www.jmir.org/2018/11/e288/ UR - http://dx.doi.org/10.2196/jmir.9851 UR - http://www.ncbi.nlm.nih.gov/pubmed/30401661 ID - info:doi/10.2196/jmir.9851 ER - TY - JOUR AU - Bendtsen, Marcus PY - 2018/10/24 TI - A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials JO - J Med Internet Res SP - e10873 VL - 20 IS - 10 KW - null hypothesis testing KW - Bayesian analysis KW - randomized controlled trials KW - Bayes theorem KW - randomized controlled trials as topic UR - http://www.jmir.org/2018/10/e10873/ UR - http://dx.doi.org/10.2196/10873 UR - http://www.ncbi.nlm.nih.gov/pubmed/30148453 ID - info:doi/10.2196/10873 ER - TY - JOUR AU - Brinker, Josef Titus AU - Hekler, Achim AU - Utikal, Sven Jochen AU - Grabe, Niels AU - Schadendorf, Dirk AU - Klode, Joachim AU - Berking, Carola AU - Steeb, Theresa AU - Enk, H. Alexander AU - von Kalle, Christof PY - 2018/10/17 TI - Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review JO - J Med Internet Res SP - e11936 VL - 20 IS - 10 KW - skin cancer KW - convolutional neural networks KW - lesion classification KW - deep learning KW - melanoma classification KW - carcinoma classification N2 - Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability. UR - http://www.jmir.org/2018/10/e11936/ UR - http://dx.doi.org/10.2196/11936 UR - http://www.ncbi.nlm.nih.gov/pubmed/30333097 ID - info:doi/10.2196/11936 ER - TY - JOUR AU - Zolnoori, Maryam AU - Fung, Wah Kin AU - Fontelo, Paul AU - Kharrazi, Hadi AU - Faiola, Anthony AU - Wu, Shirley Yi Shuan AU - Stoffel, Virginia AU - Patrick, Timothy PY - 2018/9/30 TI - Identifying the Underlying Factors Associated With Patients? Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews JO - JMIR Ment Health SP - e10726 VL - 5 IS - 4 KW - medication adherence KW - attitude KW - perception KW - antidepressive agents KW - patient-centered care KW - chronic disease KW - depression KW - community networks KW - internet KW - social media KW - data mining KW - framework method N2 - Background: Nonadherence to antidepressants is a major obstacle to deriving antidepressants? therapeutic benefits, resulting in significant burdens on the individuals and the health care system. Several studies have shown that nonadherence is weakly associated with personal and clinical variables but strongly associated with patients? beliefs and attitudes toward medications. Patients? drug review posts in online health care communities might provide a significant insight into patients? attitude toward antidepressants and could be used to address the challenges of self-report methods such as patients? recruitment. Objective: The aim of this study was to use patient-generated data to identify factors affecting the patient?s attitude toward 4 antidepressants drugs (sertraline [Zoloft], escitalopram [Lexapro], duloxetine [Cymbalta], and venlafaxine [Effexor XR]), which in turn, is a strong determinant of treatment nonadherence. We hypothesized that clinical variables (drug effectiveness; adverse drug reactions, ADRs; perceived distress from ADRs, ADR-PD; and duration of treatment) and personal variables (age, gender, and patients? knowledge about medications) are associated with patients? attitude toward antidepressants, and experience of ADRs and drug ineffectiveness are strongly associated with negative attitude. Methods: We used both qualitative and quantitative methods to analyze the dataset. Patients? drug reviews were randomly selected from a health care forum called askapatient. The Framework method was used to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. Then, 4 annotators coded the drug reviews at the sentence level using the analytical framework. After managing missing values, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude. Results: A total of 892 reviews posted between February 2001 and September 2016 were analyzed. Most of the patients were females (680/892, 76.2%) and aged less than 40 years (540/892, 60.5%). Patient attitude was significantly (P<.001) associated with experience of ADRs, ADR-PD, drug effectiveness, perceived lack of knowledge, experience of withdrawal, and duration of usage, whereas oth age (F4,874=0.72, P=.58) and gender (?24=2.7, P=.21) were not found to be associated with patient attitudes. Moreover, modeling the relationship between variables and attitudes showed that drug effectiveness and perceived distress from adverse drug reactions were the 2 most significant factors affecting patients? attitude toward antidepressants. Conclusions: Patients? self-report experiences of medications in online health care communities can provide a direct insight into the underlying factors associated with patients? perceptions and attitudes toward antidepressants. However, it cannot be used as a replacement for self-report methods because of the lack of information for some of the variables, colloquial language, and the unstructured format of the reports. UR - http://mental.jmir.org/2018/4/e10726/ UR - http://dx.doi.org/10.2196/10726 UR - http://www.ncbi.nlm.nih.gov/pubmed/30287417 ID - info:doi/10.2196/10726 ER - TY - JOUR AU - Karystianis, George AU - Adily, Armita AU - Schofield, Peter AU - Knight, Lee AU - Galdon, Clara AU - Greenberg, David AU - Jorm, Louisa AU - Nenadic, Goran AU - Butler, Tony PY - 2018/09/13 TI - Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study JO - J Med Internet Res SP - e11548 VL - 20 IS - 9 KW - text mining KW - rule-based approach KW - police narratives KW - mental health disorders KW - domestic violence N2 - Background: Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. Objective: In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. Methods: We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. Results: The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.25%, 3269) and POIs (18.70%, 8944), followed by alcohol abuse for POIs (12.19%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.66%, 1714). Conclusions: The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV. UR - http://www.jmir.org/2018/9/e11548/ UR - http://dx.doi.org/10.2196/11548 UR - http://www.ncbi.nlm.nih.gov/pubmed/30213778 ID - info:doi/10.2196/11548 ER - TY - JOUR AU - Barr, J. Paul AU - Bonasia, Kyra AU - Verma, Kanak AU - Dannenberg, D. Michelle AU - Yi, Cameron AU - Andrews, Ethan AU - Palm, Marisha AU - Cavanaugh, L. Kerri AU - Masel, Meredith AU - Durand, Marie-Anne PY - 2018/09/12 TI - Audio-/Videorecording Clinic Visits for Patient?s Personal Use in the United States: Cross-Sectional Survey JO - J Med Internet Res SP - e11308 VL - 20 IS - 9 KW - audiorecording KW - health care KW - health system KW - policy KW - United States KW - videorecording N2 - Background: Few clinics in the United States routinely offer patients audio or video recordings of their clinic visits. While interest in this practice has increased, to date, there are no data on the prevalence of recording clinic visits in the United States. Objective: Our objectives were to (1) determine the prevalence of audiorecording clinic visits for patients? personal use in the United States, (2) assess the attitudes of clinicians and public toward recording, and (3) identify whether policies exist to guide recording practices in 49 of the largest health systems in the United States. Methods: We administered 2 parallel cross-sectional surveys in July 2017 to the internet panels of US-based clinicians (SERMO Panel) and the US public (Qualtrics Panel). To ensure a diverse range of perspectives, we set quotas to capture clinicians from 8 specialties. Quotas were also applied to the public survey based on US census data (gender, race, ethnicity, and language other than English spoken at home) to approximate the US adult population. We contacted 49 of the largest health systems (by clinician number) in the United States by email and telephone to determine the existence, or absence, of policies to guide audiorecordings of clinic visits for patients? personal use. Multiple logistic regression models were used to determine factors associated with recording. Results: In total, 456 clinicians and 524 public respondents completed the surveys. More than one-quarter of clinicians (129/456, 28.3%) reported that they had recorded a clinic visit for patients? personal use, while 18.7% (98/524) of the public reported doing so, including 2.7% (14/524) who recorded visits without the clinician?s permission. Amongst clinicians who had not recorded a clinic visit, 49.5% (162/327) would be willing to do so in the future, while 66.0% (346/524) of the public would be willing to record in the future. Clinician specialty was associated with prior recording: specifically oncology (odds ratio [OR] 5.1, 95% CI 1.9-14.9; P=.002) and physical rehabilitation (OR 3.9, 95% CI 1.4-11.6; P=.01). Public respondents who were male (OR 2.11, 95% CI 1.26-3.61; P=.005), younger (OR 0.73 for a 10-year increase in age, 95% CI 0.60-0.89; P=.002), or spoke a language other than English at home (OR 1.99; 95% CI 1.09-3.59; P=.02) were more likely to have recorded a clinic visit. None of the large health systems we contacted reported a dedicated policy; however, 2 of the 49 health systems did report an existing policy that would cover the recording of clinic visits for patient use. The perceived benefits of recording included improved patient understanding and recall. Privacy and medicolegal concerns were raised. Conclusions: Policy guidance from health systems and further examination of the impact of recordings?positive or negative?on care delivery, clinician-related outcomes, and patients? behavioral and health-related outcomes is urgently required. UR - http://www.jmir.org/2018/9/e11308/ UR - http://dx.doi.org/10.2196/11308 UR - http://www.ncbi.nlm.nih.gov/pubmed/30209029 ID - info:doi/10.2196/11308 ER - TY - JOUR AU - Heraz, Alicia AU - Clynes, Manfred PY - 2018/08/30 TI - Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques JO - JMIR Ment Health SP - e10104 VL - 5 IS - 3 KW - emotional artificial intelligence KW - human-computer interaction KW - smartphone KW - force-sensitive screens KW - mental health KW - positive computing KW - artificial intelligence KW - emotions KW - emotional intelligence N2 - Background: Emotions affect our mental health: they influence our perception, alter our physical strength, and interfere with our reason. Emotions modulate our face, voice, and movements. When emotions are expressed through the voice or face, they are difficult to measure because cameras and microphones are not often used in real life in the same laboratory conditions where emotion detection algorithms perform well. With the increasing use of smartphones, the fact that we touch our phones, on average, thousands of times a day, and that emotions modulate our movements, we have an opportunity to explore emotional patterns in passive expressive touches and detect emotions, enabling us to empower smartphone apps with emotional intelligence. Objective: In this study, we asked 2 questions. (1) As emotions modulate our finger movements, will humans be able to recognize emotions by only looking at passive expressive touches? (2) Can we teach machines how to accurately recognize emotions from passive expressive touches? Methods: We were interested in 8 emotions: anger, awe, desire, fear, hate, grief, laughter, love (and no emotion). We conducted 2 experiments with 2 groups of participants: good imagers and emotionally aware participants formed group A, with the remainder forming group B. In the first experiment, we video recorded, for a few seconds, the expressive touches of group A, and we asked group B to guess the emotion of every expressive touch. In the second experiment, we trained group A to express every emotion on a force-sensitive smartphone. We then collected hundreds of thousands of their touches, and applied feature selection and machine learning techniques to detect emotions from the coordinates of participant? finger touches, amount of force, and skin area, all as functions of time. Results: We recruited 117 volunteers: 15 were good imagers and emotionally aware (group A); the other 102 participants formed group B. In the first experiment, group B was able to successfully recognize all emotions (and no emotion) with a high 83.8% (769/918) accuracy: 49.0% (50/102) of them were 100% (450/450) correct and 25.5% (26/102) were 77.8% (182/234) correct. In the second experiment, we achieved a high 91.11% (2110/2316) classification accuracy in detecting all emotions (and no emotion) from 9 spatiotemporal features of group A touches. Conclusions: Emotions modulate our touches on force-sensitive screens, and humans have a natural ability to recognize other people?s emotions by watching prerecorded videos of their expressive touches. Machines can learn the same emotion recognition ability and do better than humans if they are allowed to continue learning on new data. It is possible to enable force-sensitive screens to recognize users? emotions and share this emotional insight with users, increasing users? emotional awareness and allowing researchers to design better technologies for well-being. UR - http://mental.jmir.org/2018/3/e10104/ UR - http://dx.doi.org/10.2196/10104 UR - http://www.ncbi.nlm.nih.gov/pubmed/30166276 ID - info:doi/10.2196/10104 ER - TY - JOUR AU - Bremer, Vincent AU - Becker, Dennis AU - Kolovos, Spyros AU - Funk, Burkhardt AU - van Breda, Ward AU - Hoogendoorn, Mark AU - Riper, Heleen PY - 2018/08/21 TI - Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis JO - J Med Internet Res SP - e10275 VL - 20 IS - 8 KW - treatment recommendation KW - cost effectiveness KW - mental health KW - machine learning N2 - Background: Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level. Objective: This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation. Methods: Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment. Results: Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 ?/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%). Conclusions: Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs. UR - http://www.jmir.org/2018/8/e10275/ UR - http://dx.doi.org/10.2196/10275 UR - http://www.ncbi.nlm.nih.gov/pubmed/30131318 ID - info:doi/10.2196/10275 ER - TY - JOUR AU - Brinker, Josef Titus AU - Brieske, Martin Christian AU - Esser, Stefan AU - Klode, Joachim AU - Mons, Ute AU - Batra, Anil AU - Rüther, Tobias AU - Seeger, Werner AU - Enk, H. Alexander AU - von Kalle, Christof AU - Berking, Carola AU - Heppt, V. Markus AU - Gatzka, V. Martina AU - Bernardes-Souza, Breno AU - Schlenk, F. Richard AU - Schadendorf, Dirk PY - 2018/08/15 TI - A Face-Aging App for Smoking Cessation in a Waiting Room Setting: Pilot Study in an HIV Outpatient Clinic JO - J Med Internet Res SP - e10976 VL - 20 IS - 8 KW - face aging KW - smoking cessation KW - HIV KW - mobile apps KW - HIV patients KW - HIV seropositivity KW - smoking KW - cessation KW - tobacco smoking KW - morphing N2 - Background: There is strong evidence for the effectiveness of addressing tobacco use in health care settings. However, few smokers receive cessation advice when visiting a hospital. Implementing smoking cessation technology in outpatient waiting rooms could be an effective strategy for change, with the potential to expose almost all patients visiting a health care provider without preluding physician action needed. Objective: The objective of this study was to develop an intervention for smoking cessation that would make use of the time patients spend in a waiting room by passively exposing them to a face-aging, public morphing, tablet-based app, to pilot the intervention in a waiting room of an HIV outpatient clinic, and to measure the perceptions of this intervention among smoking and nonsmoking HIV patients. Methods: We developed a kiosk version of our 3-dimensional face-aging app Smokerface, which shows the user how their face would look with or without cigarette smoking 1 to 15 years in the future. We placed a tablet with the app running on a table in the middle of the waiting room of our HIV outpatient clinic, connected to a large monitor attached to the opposite wall. A researcher noted all the patients who were using the waiting room. If a patient did not initiate app use within 30 seconds of waiting time, the researcher encouraged him or her to do so. Those using the app were asked to complete a questionnaire. Results: During a 19-day period, 464 patients visited the waiting room, of whom 187 (40.3%) tried the app and 179 (38.6%) completed the questionnaire. Of those who completed the questionnaire, 139 of 176 (79.0%) were men and 84 of 179 (46.9%) were smokers. Of the smokers, 55 of 81 (68%) said the intervention motivated them to quit (men: 45, 68%; women: 10, 67%); 41 (51%) said that it motivated them to discuss quitting with their doctor (men: 32, 49%; women: 9, 60%); and 72 (91%) perceived the intervention as fun (men: 57, 90%; women: 15, 94%). Of the nonsmokers, 92 (98%) said that it motivated them never to take up smoking (men: 72, 99%; women: 20, 95%). Among all patients, 102 (22.0%) watched another patient try the app without trying it themselves; thus, a total of 289 (62.3%) of the 464 patients were exposed to the intervention (average waiting time 21 minutes). Conclusions: A face-aging app implemented in a waiting room provides a novel opportunity to motivate patients visiting a health care provider to quit smoking, to address quitting at their subsequent appointment and thereby encourage physician-delivered smoking cessation, or not to take up smoking. UR - http://www.jmir.org/2018/8/e10976/ UR - http://dx.doi.org/10.2196/10976 UR - http://www.ncbi.nlm.nih.gov/pubmed/30111525 ID - info:doi/10.2196/10976 ER - TY - JOUR AU - Bott, Nicholas AU - Madero, N. Erica AU - Glenn, Jordan AU - Lange, Alexander AU - Anderson, John AU - Newton, Doug AU - Brennan, Adam AU - Buffalo, A. Elizabeth AU - Rentz, Dorene AU - Zola, Stuart PY - 2018/07/24 TI - Device-Embedded Cameras for Eye Tracking?Based Cognitive Assessment: Validation With Paper-Pencil and Computerized Cognitive Composites JO - J Med Internet Res SP - e11143 VL - 20 IS - 7 KW - eye tracking KW - visual paired comparison KW - preclinical Alzheimer?s disease KW - neuropsychological testing N2 - Background: As eye tracking-based assessment of cognition becomes more widely used in older adults, particularly those at risk for dementia, reliable and scalable methods to collect high-quality data are required. Eye tracking-based cognitive tests that utilize device-embedded cameras have the potential to reach large numbers of people as a screening tool for preclinical cognitive decline. However, to fully validate this approach, more empirical evidence about the comparability of eyetracking-based paradigms to existing cognitive batteries is needed. Objective: Using a population of clinically normal older adults, we examined the relationship between a 30-minute Visual Paired Comparison (VPC) recognition memory task and cognitive composite indices sensitive to a subtle decline in domains associated with Alzheimer disease. Additionally, the scoring accuracy between software used with a commercial grade eye tracking camera at 60 frames per second (FPS) and a manually scored procedure used with a laptop-embedded web camera (3 FPS) on the VPC task was compared, as well as the relationship between VPC task performance and domain-specific cognitive function. Methods: A group of 49 clinically normal older adults completed a 30-min VPC recognition memory task with simultaneous recording of eye movements by a commercial-grade eye-tracking camera and a laptop-embedded camera. Relationships between webcam VPC performance and the Preclinical Alzheimer Cognitive Composite (PACC) and National Institutes of Health Toolbox Cognitive Battery (NIHTB-CB) were examined. Inter-rater reliability for manually scored tests was analyzed using Krippendorff?s kappa formula, and we used Spearman?s Rho correlations to investigate the relationship between VPC performance scores with both cameras. We also examined the relationship between VPC performance with the device-embedded camera and domain-specific cognitive performance. Results: Modest relationships were seen between mean VPC novelty preference and the PACC (r=.39, P=.007) and NIHTB-CB (r=.35, P=.03) composite scores, and additional individual neurocognitive task scores including letter fluency (r=.33, P=.02), category fluency (r=.36, P=.01), and Trail Making Test A (?.40, P=.006). Robust relationships were observed between the 60 FPS eye tracker and 3 FPS webcam on both trial-level VPC novelty preference (r=.82, P<.001) and overall mean VPC novelty preference (r=.92 P<.001). Inter-rater agreement of manually scored web camera data was high (kappa=.84). Conclusions: In a sample of clinically normal older adults, performance on a 30-minute VPC task correlated modestly with computerized and paper-pencil based cognitive composites that serve as preclinical Alzheimer disease cognitive indices. The strength of these relationships did not differ between camera devices. We suggest that using a device-embedded camera is a reliable and valid way to assess performance on VPC tasks accurately and that these tasks correlate with existing cognitive composites. UR - http://www.jmir.org/2018/7/e11143/ UR - http://dx.doi.org/10.2196/11143 UR - http://www.ncbi.nlm.nih.gov/pubmed/30042093 ID - info:doi/10.2196/11143 ER - TY - JOUR AU - Gao, Fangjian AU - Thiebes, Scott AU - Sunyaev, Ali PY - 2018/07/11 TI - Rethinking the Meaning of Cloud Computing for Health Care: A Taxonomic Perspective and Future Research Directions JO - J Med Internet Res SP - e10041 VL - 20 IS - 7 KW - cloud computing KW - taxonomy KW - health IT innovation N2 - Background: Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care. Objective: We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care. Methods: We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations. Results: Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing. Conclusions: By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care. UR - http://www.jmir.org/2018/7/e10041/ UR - http://dx.doi.org/10.2196/10041 UR - http://www.ncbi.nlm.nih.gov/pubmed/29997108 ID - info:doi/10.2196/10041 ER - TY - JOUR AU - Guetterman, C. Timothy AU - Chang, Tammy AU - DeJonckheere, Melissa AU - Basu, Tanmay AU - Scruggs, Elizabeth AU - Vydiswaran, Vinod V. G. PY - 2018/06/29 TI - Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study JO - J Med Internet Res SP - e231 VL - 20 IS - 6 KW - qualitative research KW - natural language processing KW - text data KW - methodology KW - coding N2 - Background: Qualitative research methods are increasingly being used across disciplines because of their ability to help investigators understand the perspectives of participants in their own words. However, qualitative analysis is a laborious and resource-intensive process. To achieve depth, researchers are limited to smaller sample sizes when analyzing text data. One potential method to address this concern is natural language processing (NLP). Qualitative text analysis involves researchers reading data, assigning code labels, and iteratively developing findings; NLP has the potential to automate part of this process. Unfortunately, little methodological research has been done to compare automatic coding using NLP techniques and qualitative coding, which is critical to establish the viability of NLP as a useful, rigorous analysis procedure. Objective: The purpose of this study was to compare the utility of a traditional qualitative text analysis, an NLP analysis, and an augmented approach that combines qualitative and NLP methods. Methods: We conducted a 2-arm cross-over experiment to compare qualitative and NLP approaches to analyze data generated through 2 text (short message service) message survey questions, one about prescription drugs and the other about police interactions, sent to youth aged 14-24 years. We randomly assigned a question to each of the 2 experienced qualitative analysis teams for independent coding and analysis before receiving NLP results. A third team separately conducted NLP analysis of the same 2 questions. We examined the results of our analyses to compare (1) the similarity of findings derived, (2) the quality of inferences generated, and (3) the time spent in analysis. Results: The qualitative-only analysis for the drug question (n=58) yielded 4 major findings, whereas the NLP analysis yielded 3 findings that missed contextual elements. The qualitative and NLP-augmented analysis was the most comprehensive. For the police question (n=68), the qualitative-only analysis yielded 4 primary findings and the NLP-only analysis yielded 4 slightly different findings. Again, the augmented qualitative and NLP analysis was the most comprehensive and produced the highest quality inferences, increasing our depth of understanding (ie, details and frequencies). In terms of time, the NLP-only approach was quicker than the qualitative-only approach for the drug (120 vs 270 minutes) and police (40 vs 270 minutes) questions. An approach beginning with qualitative analysis followed by qualitative- or NLP-augmented analysis took longer time than that beginning with NLP for both drug (450 vs 240 minutes) and police (390 vs 220 minutes) questions. Conclusions: NLP provides both a foundation to code qualitatively more quickly and a method to validate qualitative findings. NLP methods were able to identify major themes found with traditional qualitative analysis but were not useful in identifying nuances. Traditional qualitative text analysis added important details and context. UR - http://www.jmir.org/2018/6/e231/ UR - http://dx.doi.org/10.2196/jmir.9702 UR - http://www.ncbi.nlm.nih.gov/pubmed/29959110 ID - info:doi/10.2196/jmir.9702 ER - TY - JOUR AU - Dietrich, Damien AU - Dekova, Ralitza AU - Davy, Stephan AU - Fahrni, Guillaume AU - Geissbühler, Antoine PY - 2018/06/27 TI - Applications of Space Technologies to Global Health: Scoping Review JO - J Med Internet Res SP - e230 VL - 20 IS - 6 KW - satellite imagery KW - satellite communications KW - public health KW - remote sensing technology KW - global positioning system KW - geographic information systems KW - telemedicine KW - spaceflight KW - space medicine KW - global health N2 - Background: Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations? Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health. Objective: The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges. Methods: We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs? Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified. Results: A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use. Conclusions: Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed. UR - http://www.jmir.org/2018/6/e230/ UR - http://dx.doi.org/10.2196/jmir.9458 UR - http://www.ncbi.nlm.nih.gov/pubmed/29950289 ID - info:doi/10.2196/jmir.9458 ER - TY - JOUR AU - DelPozo-Banos, Marcos AU - John, Ann AU - Petkov, Nicolai AU - Berridge, Mark Damon AU - Southern, Kate AU - LLoyd, Keith AU - Jones, Caroline AU - Spencer, Sarah AU - Travieso, Manuel Carlos PY - 2018/06/22 TI - Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study JO - JMIR Ment Health SP - e10144 VL - 5 IS - 2 KW - suicide prevention KW - risk assessment KW - electronic health records KW - routine data KW - machine learning KW - artificial neural networks N2 - Background: Each year, approximately 800,000 people die by suicide worldwide, accounting for 1?2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person?s motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge. Objective: To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services. Methods: Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks. Results: We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks < 0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by ~0.4. At least 95% of those presenting these factors were identified as suicide cases. Conclusions: Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects. UR - http://mental.jmir.org/2018/2/e10144/ UR - http://dx.doi.org/10.2196/10144 UR - http://www.ncbi.nlm.nih.gov/pubmed/29934287 ID - info:doi/10.2196/10144 ER - TY - JOUR AU - Odenheimer, Sandra AU - Goyal, Deepika AU - Jones, Goel Veena AU - Rosenblum, Ruth AU - Ho, Lam AU - Chan, S. Albert PY - 2018/06/21 TI - Patient Acceptance of Remote Scribing Powered by Google Glass in Outpatient Dermatology: Cross-Sectional Study JO - J Med Internet Res SP - e10762 VL - 20 IS - 6 KW - acceptance, clinician burnout, communication, Google Glass, health care provider, patient, remote scribing, trust N2 - Background: The ubiquitous use of electronic health records (EHRs) during medical office visits using a computer monitor and keyboard can be distracting and can disrupt patient-health care provider (HCP) nonverbal eye contact cues, which are integral to effective communication. Provider use of a remote medical scribe with face-mounted technology (FMT), such as Google Glass, may preserve patient-HCP communication dynamics in health care settings by allowing providers to maintain direct eye contact with their patients while still having access to the patient?s relevant EHR information. The medical scribe is able to chart patient encounters in real-time working in an offsite location, document the visit directly into EHR, and free HCP to focus only on the patient. Objective: The purpose of this study was to examine patient perceptions of their interactions with an HCP who used FMT with a remote medical scribe during office visits. This includes an examination of any association between patient privacy and trust in their HCP when FMT is used in the medical office setting. Methods: For this descriptive, cross-sectional study, a convenience sample of patients was recruited from an outpatient dermatology clinic in Northern California. Participants provided demographic data and completed a 12-item questionnaire to assess their familiarity, comfort, privacy, and perceptions following routine office visits with an HCP where FMT was used to document the clinical encounter. Data were analyzed using appropriate descriptive and inferential statistics. Results: Over half of the 170 study participants were female (102/170, 59.4%), 60.0% were Caucasian (102/170), 24.1% were Asian (41/170), and 88.8% were college-educated (151/170). Age ranged between 18 and 90 years (mean 50.5, SD 17.4). The majority of participants (118/170, 69.4%) were familiar with FMT, not concerned with privacy issues (132/170, 77.6%), and stated that the use of FMT did not affect their trust in their HCP (139/170, 81.8%). Moreover, participants comfortable with the use of FMT were less likely to be concerned about privacy (P<.001) and participants who trusted their HCP were less likely to be concerned about their HCP using Google Glass (P<.009). Almost one-third of them self-identified as early technology adopters (49/170, 28.8%) and 87% (148/170) preferred their HCP using FMT if it delivered better care. Conclusions: Our study findings support the patient acceptance of Google Glass use for outpatient dermatology visits. Future research should explore the use of FMT in other areas of health care and strive to include a socioeconomically diverse patient population in study samples. UR - http://www.jmir.org/2018/6/e10762/ UR - http://dx.doi.org/10.2196/10762 UR - http://www.ncbi.nlm.nih.gov/pubmed/29929947 ID - info:doi/10.2196/10762 ER - TY - JOUR AU - Komarzynski, Sandra AU - Huang, Qi AU - Innominato, F. Pasquale AU - Maurice, Monique AU - Arbaud, Alexandre AU - Beau, Jacques AU - Bouchahda, Mohamed AU - Ulusakarya, Ayhan AU - Beaumatin, Nicolas AU - Breda, Gabrièle AU - Finkenstädt, Bärbel AU - Lévi, Francis PY - 2018/06/11 TI - Relevance of a Mobile Internet Platform for Capturing Inter- and Intrasubject Variabilities in Circadian Coordination During Daily Routine: Pilot Study JO - J Med Internet Res SP - e204 VL - 20 IS - 6 KW - circadian clock KW - eHealth KW - temperature rhythm KW - rest-activity rhythm KW - time series analyses KW - domomedicine KW - biomarkers N2 - Background: Experimental and epidemiologic studies have shown that circadian clocks? disruption can play an important role in the development of cancer and metabolic diseases. The cellular clocks outside the brain are effectively coordinated by the body temperature rhythm. We hypothesized that concurrent measurements of body temperature and rest-activity rhythms would assess circadian clocks coordination in individual patients, thus enabling the integration of biological rhythms into precision medicine. Objective: The objective was to evaluate the circadian clocks? coordination in healthy subjects and patients through simultaneous measurements of rest-activity and body temperature rhythms. Methods: Noninvasive real-time measurements of rest-activity and chest temperature rhythms were recorded during the subject?s daily life, using a dedicated new mobile electronic health platform (PiCADo). It involved a chest sensor that jointly measured accelerations, 3D orientation, and skin surface temperature every 1-5 min and relayed them out to a mobile gateway via Bluetooth Low Energy. The gateway tele-transmitted all stored data to a server via General Packet Radio Service every 24 hours. The technical capabilities of PiCADo were validated in 55 healthy subjects and 12 cancer patients, whose rhythms were e-monitored during their daily routine for 3-30 days. Spectral analyses enabled to compute rhythm parameters values, with their 90% confidence limits, and their dynamics in each subject. Results: All the individuals displayed a dominant circadian rhythm in activity with maxima occurring from 12:09 to 20:25. This was not the case for the dominant temperature period, which clustered around 24 hours for 51 out of 67 subjects (76%), and around 12 hours for 13 others (19%). Statistically significant sex- and age-related differences in circadian coordination were identified in the noncancerous subjects, based upon the range of variations in temperature rhythm amplitudes, maxima (acrophases), and phase relations with rest-activity. The circadian acrophase of chest temperature was located at night for the majority of people, but it occurred at daytime for 26% (14/55) of the noncancerous people and 33% (4/12) of the cancer patients, thus supporting important intersubject differences in circadian coordination. Sex, age, and cancer significantly impacted the circadian coordination of both rhythms, based on their phase relationships. Conclusions: Complementing rest-activity with chest temperature circadian e-monitoring revealed striking intersubject differences regarding human circadian clocks? coordination and timing during daily routine. To further delineate the clinical importance of such finding, the PiCADo platform is currently applied for both the assessment of health effects resulting from atypical work schedules and the identification of the key determinants of circadian disruption in cancer patients. UR - http://www.jmir.org/2018/6/e204/ UR - http://dx.doi.org/10.2196/jmir.9779 UR - http://www.ncbi.nlm.nih.gov/pubmed/29704408 ID - info:doi/10.2196/jmir.9779 ER - TY - JOUR AU - Zanaboni, Paolo AU - Ngangue, Patrice AU - Mbemba, Claudine Gisele Irène AU - Schopf, Roger Thomas AU - Bergmo, Strand Trine AU - Gagnon, Marie-Pierre PY - 2018/06/07 TI - Methods to Evaluate the Effects of Internet-Based Digital Health Interventions for Citizens: Systematic Review of Reviews JO - J Med Internet Res SP - e10202 VL - 20 IS - 6 KW - review KW - electronic health records KW - patient access to records KW - patient portals KW - epidemiological methods KW - evaluation studies as topic N2 - Background: Digital health can empower citizens to manage their health and address health care system problems including poor access, uncoordinated care and increasing costs. Digital health interventions are typically complex interventions. Therefore, evaluations present methodological challenges. Objective: The objective of this study was to provide a systematic overview of the methods used to evaluate the effects of internet-based digital health interventions for citizens. Three research questions were addressed to explore methods regarding approaches (study design), effects and indicators. Methods: We conducted a systematic review of reviews of the methods used to measure the effects of internet-based digital health interventions for citizens. The protocol was developed a priori according to Preferred Reporting Items for Systematic review and Meta-Analysis Protocols and the Cochrane Collaboration methodology for overviews of reviews. Qualitative, mixed-method, and quantitative reviews published in English or French from January 2010 to October 2016 were included. We searched for published reviews in PubMed, EMBASE, The Cochrane Database of Systematic Reviews, CINHAL and Epistemonikos. We categorized the findings based on a thematic analysis of the reviews structured around study designs, indicators, types of interventions, effects and perspectives. Results: A total of 20 unique reviews were included. The most common digital health interventions for citizens were patient portals and patients' access to electronic health records, covered by 10/20 (50%) and 6/20 (30%) reviews, respectively. Quantitative approaches to study design included observational study (15/20 reviews, 75%), randomized controlled trial (13/20 reviews, 65%), quasi-experimental design (9/20 reviews, 45%), and pre-post studies (6/20 reviews, 30%). Qualitative studies or mixed methods were reported in 13/20 (65%) reviews. Five main categories of effects were identified: (1) health and clinical outcomes, (2) psychological and behavioral outcomes, (3) health care utilization, (4) system adoption and use, and (5) system attributes. Health and clinical outcomes were measured with both general indicators and disease-specific indicators and reported in 11/20 (55%) reviews. Patient-provider communication and patient satisfaction were the most investigated psychological and behavioral outcomes, reported in 13/20 (65%) and 12/20 (60%) reviews, respectively. Evaluation of health care utilization was included in 8/20 (40%) reviews, most of which focused on the economic effects on the health care system. Conclusions: Although observational studies and surveys have provided evidence of benefits and satisfaction for patients, there is still little reliable evidence from randomized controlled trials of improved health outcomes. Future evaluations of digital health interventions for citizens should focus on specific populations or chronic conditions which are more likely to achieve clinically meaningful benefits and use high-quality approaches such as randomized controlled trials. Implementation research methods should also be considered. We identified a wide range of effects and indicators, most of which focused on patients as main end users. Implications for providers and the health system should also be included in evaluations or monitoring of digital health interventions. UR - http://www.jmir.org/2018/6/e10202/ UR - http://dx.doi.org/10.2196/10202 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/10202 ER - TY - JOUR AU - Appiah, Bernard AU - Burdine, N. James AU - Aftab, Ammar AU - Asamoah-Akuoko, Lucy AU - Anum, A. David AU - Kretchy, A. Irene AU - Samman, W. Elfreda AU - Appiah, B. Patience AU - Bates, Imelda PY - 2018/05/04 TI - Determinants of Intention to Use Mobile Phone Caller Tunes to Promote Voluntary Blood Donation: Cross-Sectional Study JO - JMIR Mhealth Uhealth SP - e117 VL - 6 IS - 5 KW - caller tunes KW - blood donation KW - sub-Saharan Africa KW - technology acceptance model KW - mobile health N2 - Background: Voluntary blood donation rates are low in sub-Saharan Africa. Sociobehavioral factors such as a belief that donated blood would be used for performing rituals deter people from donating blood. There is a need for culturally appropriate communication interventions to encourage individuals to donate blood. Health care interventions that use mobile phones have increased in developing countries, although many of them focus on SMS text messaging (short message service, SMS). A unique feature of mobile phones that has so far not been used for aiding blood donation is caller tunes. Caller tunes replace the ringing sound heard by a caller to a mobile phone before the called party answers the call. In African countries such as Ghana, instead of the typical ringing sound, a caller may hear a message or song. Despite the popularity of such caller tunes, there is a lack of empirical studies on their potential use for promoting blood donation. Objective: The aim of this study was to use the technology acceptance model to explore the influence of the factors?perceived ease of use, perceived usefulness, attitude, and free of cost?on intentions of blood or nonblood donors to download blood donation-themed caller tunes to promote blood donation, if available. Methods: A total of 478 blood donors and 477 nonblood donors were purposively sampled for an interviewer-administered questionnaire survey at blood donation sites in Accra, Ghana. Data were analyzed using descriptive statistics, exploratory factor analysis, and confirmatory factory analysis or structural equation modeling, leading to hypothesis testing to examine factors that determine intention to use caller tunes for blood donation among blood or nonblood donors who use or do not use mobile phone caller tunes. Results: Perceived usefulness had a significant effect on intention to use caller tunes among blood donors with caller tunes (beta=.293, P<.001), blood donors without caller tunes (beta=.165, P=.02, nonblood donors with caller tunes (beta=.278, P<.001), and nonblood donors without caller tunes (beta=.164, P=.01). Attitudes had significant effect on intention to use caller tunes among blood donors without caller tunes (beta=.351, P<.001), nonblood donors with caller tunes (beta=.384, P<.001), nonblood donors without caller tunes (beta=.539, P<.001) but not among blood donors with caller tunes (beta=.056, P=.44). The effect of free-of-cost caller tunes on the intention to use for blood donation was statistically significant (beta=.169, P<.001) only in the case of nonblood donors without caller tunes, whereas this path was statistically not significant in other models. Conclusions: Our results provide empirical evidence for designing caller tunes to promote blood donation in Ghana. The study found that making caller tunes free is particularly relevant for nonblood donors with no caller tunes. UR - http://mhealth.jmir.org/2018/5/e117/ UR - http://dx.doi.org/10.2196/mhealth.9752 UR - http://www.ncbi.nlm.nih.gov/pubmed/29728343 ID - info:doi/10.2196/mhealth.9752 ER - TY - JOUR AU - Galvão Gomes da Silva, Joana AU - Kavanagh, J. David AU - Belpaeme, Tony AU - Taylor, Lloyd AU - Beeson, Konna AU - Andrade, Jackie PY - 2018/05/03 TI - Experiences of a Motivational Interview Delivered by a Robot: Qualitative Study JO - J Med Internet Res SP - e116 VL - 20 IS - 5 KW - robotics KW - counseling KW - motivational interviewing KW - motivation KW - exercise KW - qualitative research KW - computer-assisted therapy KW - person-centered therapy N2 - Background: Motivational interviewing is an effective intervention for supporting behavior change but traditionally depends on face-to-face dialogue with a human counselor. This study addressed a key challenge for the goal of developing social robotic motivational interviewers: creating an interview protocol, within the constraints of current artificial intelligence, which participants will find engaging and helpful. Objective: The aim of this study was to explore participants? qualitative experiences of a motivational interview delivered by a social robot, including their evaluation of usability of the robot during the interaction and its impact on their motivation. Methods: NAO robots are humanoid, child-sized social robots. We programmed a NAO robot with Choregraphe software to deliver a scripted motivational interview focused on increasing physical activity. The interview was designed to be comprehensible even without an empathetic response from the robot. Robot breathing and face-tracking functions were used to give an impression of attentiveness. A total of 20 participants took part in the robot-delivered motivational interview and evaluated it after 1 week by responding to a series of written open-ended questions. Each participant was left alone to speak aloud with the robot, advancing through a series of questions by tapping the robot?s head sensor. Evaluations were content-analyzed utilizing Boyatzis? steps: (1) sampling and design, (2) developing themes and codes, and (3) validating and applying the codes. Results: Themes focused on interaction with the robot, motivation, change in physical activity, and overall evaluation of the intervention. Participants found the instructions clear and the navigation easy to use. Most enjoyed the interaction but also found it was restricted by the lack of individualized response from the robot. Many positively appraised the nonjudgmental aspect of the interview and how it gave space to articulate their motivation for change. Some participants felt that the intervention increased their physical activity levels. Conclusions: Social robots can achieve a fundamental objective of motivational interviewing, encouraging participants to articulate their goals and dilemmas aloud. Because they are perceived as nonjudgmental, robots may have advantages over more humanoid avatars for delivering virtual support for behavioral change. UR - http://www.jmir.org/2018/5/e116/ UR - http://dx.doi.org/10.2196/jmir.7737 UR - http://www.ncbi.nlm.nih.gov/pubmed/29724701 ID - info:doi/10.2196/jmir.7737 ER - TY - JOUR AU - Burleson, Winslow AU - Lozano, Cecil AU - Ravishankar, Vijay AU - Lee, Jisoo AU - Mahoney, Diane PY - 2018/05/01 TI - An Assistive Technology System that Provides Personalized Dressing Support for People Living with Dementia: Capability Study JO - JMIR Med Inform SP - e21 VL - 6 IS - 2 KW - Alzheimer disease KW - disorders, neurocognitive KW - image processing, computer-assisted N2 - Background: Individuals living with advancing stages of dementia (persons with dementia, PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. Objective: In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals). Methods: This laboratory study evaluated the DRESS prototype?s capacity to detect dressing events. These events were engaged in by 11 healthy participants simulating common correct and incorrect dressing scenarios. The events ranged from donning a shirt and pants inside out or backwards to partial dressing?typical issues that challenge a PWD and their caregivers. Results: A set of expected detections for correct dressing was prepared via video analysis of all participants? dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only 4 out of 388 expected detections. The prototype?s ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only 5 of 22 cases. Further analyses identified opportunities to improve the DRESS prototype?s reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype. Conclusions: This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite. UR - http://medinform.jmir.org/2018/2/e21/ UR - http://dx.doi.org/10.2196/medinform.5587 UR - http://www.ncbi.nlm.nih.gov/pubmed/29716885 ID - info:doi/10.2196/medinform.5587 ER - TY - JOUR AU - Melville, Sarah AU - Teskey, Robert AU - Philip, Shona AU - Simpson, A. Jeremy AU - Lutchmedial, Sohrab AU - Brunt, R. Keith PY - 2018/04/25 TI - A Comparison and Calibration of a Wrist-Worn Blood Pressure Monitor for Patient Management: Assessing the Reliability of Innovative Blood Pressure Devices JO - J Med Internet Res SP - e111 VL - 20 IS - 4 KW - patient self-management KW - diastolic hypertension KW - telemonitoring KW - vital signs KW - smartphone applications N2 - Background: Clinical guidelines recommend monitoring of blood pressure at home using an automatic blood pressure device for the management of hypertension. Devices are not often calibrated against direct blood pressure measures, leaving health care providers and patients with less reliable information than is possible with current technology. Rigorous assessments of medical devices are necessary for establishing clinical utility. Objective: The purpose of our study was 2-fold: (1) to assess the validity and perform iterative calibration of indirect blood pressure measurements by a noninvasive wrist cuff blood pressure device in direct comparison with simultaneously recorded peripheral and central intra-arterial blood pressure measurements and (2) to assess the validity of the measurements thereafter of the noninvasive wrist cuff blood pressure device in comparison with measurements by a noninvasive upper arm blood pressure device to the Canadian hypertension guidelines. Methods: The cloud-based blood pressure algorithms for an oscillometric wrist cuff device were iteratively calibrated to direct pressure measures in 20 consented patient participants. We then assessed measurement validity of the device, using Bland-Altman analysis during routine cardiovascular catheterization. Results: The precalibrated absolute mean difference between direct intra-arterial to wrist cuff pressure measurements were 10.8 (SD 9.7) for systolic and 16.1 (SD 6.3) for diastolic. The postcalibrated absolute mean difference was 7.2 (SD 5.1) for systolic and 4.3 (SD 3.3) for diastolic pressures. This is an improvement in accuracy of 33% systolic and 73% diastolic with a 48% reduction in the variability for both measures. Furthermore, the wrist cuff device demonstrated similar sensitivity in measuring high blood pressure compared with the direct intra-arterial method. The device, when calibrated to direct aortic pressures, demonstrated the potential to reduce a treatment gap in high blood pressure measurements. Conclusions: The systolic pressure measurements of the wrist cuff have been iteratively calibrated using gold standard central (ascending aortic) pressure. This improves the accuracy of the indirect measures and potentially reduces the treatment gap. Devices that undergo auscultatory (indirect) calibration for licensing can be greatly improved by additional iterative calibration via intra-arterial (direct) measures of blood pressure. Further clinical trials with repeated use of the device over time are needed to assess the reliability of the device in accordance with current and evolving guidelines for informed decision making in the management of hypertension. Trial Registration: ClinicalTrials.gov NCT03015363; https://clinicaltrials.gov/ct2/show/NCT03015363 (Archived by WebCite at http://www.webcitation.org/6xPZgseYS) UR - https://www.jmir.org/2018/4/e111/ UR - http://dx.doi.org/10.2196/jmir.8009 UR - http://www.ncbi.nlm.nih.gov/pubmed/29695375 ID - info:doi/10.2196/jmir.8009 ER - TY - JOUR AU - Ben-Sasson, Ayelet AU - Robins, L. Diana AU - Yom-Tov, Elad PY - 2018/04/24 TI - Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach JO - J Med Internet Res SP - e134 VL - 20 IS - 4 KW - autistic disorder KW - early diagnosis KW - screening KW - parents KW - child KW - expression of concern KW - technology KW - machine learning N2 - Background: Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned parents. Objective: The objective of this study was to test the feasibility of assessing autism spectrum disorder risk in parental concerns from Web-based sources, using automated text analysis tools and minimal standard questioning. Methods: Participants were 115 parents with concerns regarding their child?s social-communication development. Children were 16- to 30-months old, and 57.4% (66/115) had a family history of autism spectrum disorder. Parents reported their concerns online, and completed an autism spectrum disorder-specific screener, the Modified Checklist for Autism in Toddlers-Revised, with Follow-up (M-CHAT-R/F), and a broad developmental screener, the Ages and Stages Questionnaire (ASQ). An algorithm predicted autism spectrum disorder risk using a combination of the parent's text and a single screening question, selected by the algorithm to enhance prediction accuracy. Results: Screening measures identified 58% (67/115) to 88% (101/115) of children at risk for autism spectrum disorder. Children with a family history of autism spectrum disorder were 3 times more likely to show autism spectrum disorder risk on screening measures. The prediction of a child?s risk on the ASQ or M-CHAT-R was significantly more accurate when predicted from text combined with an M-CHAT-R question selected (automatically) than from the text alone. The frequently automatically selected M-CHAT-R questions that predicted risk were: following a point, make-believe play, and concern about deafness. Conclusions: The internet can be harnessed to prescreen for autism spectrum disorder using parental concerns by administering a few standardized screening questions to augment this process. UR - http://www.jmir.org/2018/4/e134/ UR - http://dx.doi.org/10.2196/jmir.9496 UR - http://www.ncbi.nlm.nih.gov/pubmed/29691210 ID - info:doi/10.2196/jmir.9496 ER - TY - JOUR AU - Magistro, Daniele AU - Sessa, Salvatore AU - Kingsnorth, P. Andrew AU - Loveday, Adam AU - Simeone, Alessandro AU - Zecca, Massimiliano AU - Esliger, W. Dale PY - 2018/04/20 TI - A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation JO - JMIR Mhealth Uhealth SP - e100 VL - 6 IS - 4 KW - context KW - indoor location KW - activity monitor KW - behavior KW - wearable sensor KW - beacons/proximity KW - algorithm KW - physical activity KW - sedentary behavior N2 - Background: Unfortunately, global efforts to promote ?how much? physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets. Objective: The aim of this study was to develop and implement an innovative algorithm to determine ?where? physical activity occurs using proximity sensors coupled with a widely used physical activity monitor. Methods: A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters. Results: Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer. Conclusions: This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of ?context sensing? will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction). UR - http://mhealth.jmir.org/2018/4/e100/ UR - http://dx.doi.org/10.2196/mhealth.8516 UR - http://www.ncbi.nlm.nih.gov/pubmed/29678806 ID - info:doi/10.2196/mhealth.8516 ER - TY - JOUR AU - Greenhalgh, Trisha AU - Shaw, Sara AU - Wherton, Joseph AU - Vijayaraghavan, Shanti AU - Morris, Joanne AU - Bhattacharya, Satya AU - Hanson, Philippa AU - Campbell-Richards, Desirée AU - Ramoutar, Seendy AU - Collard, Anna AU - Hodkinson, Isabel PY - 2018/04/17 TI - Real-World Implementation of Video Outpatient Consultations at Macro, Meso, and Micro Levels: Mixed-Method Study JO - J Med Internet Res SP - e150 VL - 20 IS - 4 KW - remote consultations KW - diabetes mellitus KW - ethnography KW - interviews KW - organizational case studies KW - health systems N2 - Background: There is much interest in virtual consultations using video technology. Randomized controlled trials have shown video consultations to be acceptable, safe, and effective in selected conditions and circumstances. However, this model has rarely been mainstreamed and sustained in real-world settings. Objective: The study sought to (1) define good practice and inform implementation of video outpatient consultations and (2) generate transferable knowledge about challenges to scaling up and routinizing this service model. Methods: A multilevel, mixed-method study of Skype video consultations (micro level) was embedded in an organizational case study (meso level), taking account of national context and wider influences (macro level). The study followed the introduction of video outpatient consultations in three clinical services (diabetes, diabetes antenatal, and cancer surgery) in a National Health Service trust (covering three hospitals) in London, United Kingdom. Data sources included 36 national-level stakeholders (exploratory and semistructured interviews), longitudinal organizational ethnography (300 hours of observations; 24 staff interviews), 30 videotaped remote consultations, 17 audiotaped face-to-face consultations, and national and local documents. Qualitative data, analyzed using sociotechnical change theories, addressed staff and patient experience and organizational and system drivers. Quantitative data, analyzed via descriptive statistics, included uptake of video consultations by staff and patients and microcategorization of different kinds of talk (using the Roter interaction analysis system). Results: When clinical, technical, and practical preconditions were met, video consultations appeared safe and were popular with some patients and staff. Compared with face-to-face consultations for similar conditions, video consultations were very slightly shorter, patients did slightly more talking, and both parties sometimes needed to make explicit things that typically remained implicit in a traditional encounter. Video consultations appeared to work better when the clinician and patient already knew and trusted each other. Some clinicians used Skype adaptively to respond to patient requests for ad hoc encounters in a way that appeared to strengthen supported self-management. The reality of establishing video outpatient services in a busy and financially stretched acute hospital setting proved more complex and time-consuming than originally anticipated. By the end of this study, between 2% and 22% of consultations were being undertaken remotely by participating clinicians. In the remainder, clinicians chose not to participate, or video consultations were considered impractical, technically unachievable, or clinically inadvisable. Technical challenges were typically minor but potentially prohibitive. Conclusions: Video outpatient consultations appear safe, effective, and convenient for patients in situations where participating clinicians judge them clinically appropriate, but such situations are a fraction of the overall clinic workload. As with other technological innovations, some clinicians will adopt readily, whereas others will need incentives and support. There are complex challenges to embedding video consultation services within routine practice in organizations that are hesitant to change, especially in times of austerity. UR - http://www.jmir.org/2018/4/e150/ UR - http://dx.doi.org/10.2196/jmir.9897 UR - http://www.ncbi.nlm.nih.gov/pubmed/29625956 ID - info:doi/10.2196/jmir.9897 ER - TY - JOUR AU - Arroyo-Gallego, Teresa AU - Ledesma-Carbayo, J. María AU - Butterworth, Ian AU - Matarazzo, Michele AU - Montero-Escribano, Paloma AU - Puertas-Martín, Verónica AU - Gray, L. Martha AU - Giancardo, Luca AU - Sánchez-Ferro, Álvaro PY - 2018/03/26 TI - Detecting Motor Impairment in Early Parkinson?s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting JO - J Med Internet Res SP - e89 VL - 20 IS - 3 KW - eHealth KW - machine learning KW - telemedicine N2 - Background: Parkinson?s disease (PD) is the second most prevalent neurodegenerative disease and one of the most common forms of movement disorder. Although there is no known cure for PD, existing therapies can provide effective symptomatic relief. However, optimal titration is crucial to avoid adverse effects. Today, decision making for PD management is challenging because it relies on subjective clinical evaluations that require a visit to the clinic. This challenge has motivated recent research initiatives to develop tools that can be used by nonspecialists to assess psychomotor impairment. Among these emerging solutions, we recently reported the neuroQWERTY index, a new digital marker able to detect motor impairment in an early PD cohort through the analysis of the key press and release timing data collected during a controlled in-clinic typing task. Objective: The aim of this study was to extend the in-clinic implementation to an at-home implementation by validating the applicability of the neuroQWERTY approach in an uncontrolled at-home setting, using the typing data from subjects? natural interaction with their laptop to enable remote and unobtrusive assessment of PD signs. Methods: We implemented the data-collection platform and software to enable access and storage of the typing data generated by users while using their computer at home. We recruited a total of 60 participants; of these participants 52 (25 people with Parkinson?s and 27 healthy controls) provided enough data to complete the analysis. Finally, to evaluate whether our in-clinic-built algorithm could be used in an uncontrolled at-home setting, we compared its performance on the data collected during the controlled typing task in the clinic and the results of our method using the data passively collected at home. Results: Despite the randomness and sparsity introduced by the uncontrolled setting, our algorithm performed nearly as well in the at-home data (area under the receiver operating characteristic curve [AUC] of 0.76 and sensitivity/specificity of 0.73/0.69) as it did when used to evaluate the in-clinic data (AUC 0.83 and sensitivity/specificity of 0.77/0.72). Moreover, the keystroke metrics presented a strong correlation between the 2 typing settings, which suggests a minimal influence of the in-clinic typing task in users? normal typing. Conclusions: The finding that an algorithm trained on data from an in-clinic setting has comparable performance with that tested on data collected through naturalistic at-home computer use reinforces the hypothesis that subtle differences in motor function can be detected from typing behavior. This work represents another step toward an objective, user-convenient, and quasi-continuous monitoring tool for PD. UR - http://www.jmir.org/2018/3/e89/ UR - http://dx.doi.org/10.2196/jmir.9462 UR - http://www.ncbi.nlm.nih.gov/pubmed/29581092 ID - info:doi/10.2196/jmir.9462 ER - TY - JOUR AU - Wenham, Clare AU - Gray, R. Eleanor AU - Keane, E. Candice AU - Donati, Matthew AU - Paolotti, Daniela AU - Pebody, Richard AU - Fragaszy, Ellen AU - McKendry, A. Rachel AU - Edmunds, John W. PY - 2018/03/01 TI - Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study JO - J Med Internet Res SP - e71 VL - 20 IS - 3 KW - influenza KW - influenza-like illness KW - surveillance KW - online KW - cohort study KW - virological confirmation N2 - Background: Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK?s largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza. Objective: We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory. Methods: Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing. Results: A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35%). Conclusions: This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity. UR - http://www.jmir.org/2018/3/e71/ UR - http://dx.doi.org/10.2196/jmir.9084 UR - http://www.ncbi.nlm.nih.gov/pubmed/29496658 ID - info:doi/10.2196/jmir.9084 ER - TY - JOUR AU - Stubberud, Anker AU - Omland, Moe Petter AU - Tronvik, Erling AU - Olsen, Alexander AU - Sand, Trond AU - Linde, Mattias PY - 2018/02/23 TI - Wireless Surface Electromyography and Skin Temperature Sensors for Biofeedback Treatment of Headache: Validation Study with Stationary Control Equipment JO - JMIR Biomed Eng SP - e1 VL - 3 IS - 1 KW - biofeedback KW - mobile phone KW - app KW - migraine KW - pediatric N2 - Background: The use of wearables and mobile phone apps in medicine is gaining attention. Biofeedback has the potential to exploit the recent advances in mobile health (mHealth) for the treatment of headaches. Objectives: The aim of this study was to assess the validity of selected wireless wearable health monitoring sensors (WHMS) for measuring surface electromyography (SEMG) and peripheral skin temperature in combination with a mobile phone app. This proof of concept will form the basis for developing innovative mHealth delivery of biofeedback treatment among young persons with primary headache. Methods: Sensors fulfilling the following predefined criteria were identified: wireless, small size, low weight, low cost, and simple to use. These sensors were connected to an app and used by 20 healthy volunteers. Validity was assessed through the agreement with simultaneous control measurements made with stationary neurophysiological equipment. The main variables were (1) trapezius muscle tension during different degrees of voluntary contraction and (2) voluntary increase in finger temperature. Data were statistically analyzed using Bland-Altman plots, intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Results: The app was programmed to receive data from the wireless sensors, process them, and feed them back to the user through a simple interface. Excellent agreement was found for the temperature sensor regarding increase in temperature (CCC .90; 95% CI 0.83-0.97). Excellent to fair agreement was found for the SEMG sensor. The ICC for the average of 3 repetitions during 4 different target levels ranged from .58 to .81. The wireless sensor showed consistency in muscle tension change during moderate muscle activity. Electrocardiography artifacts were avoided through right-sided use of the SEMG sensors. Participants evaluated the setup as usable and tolerable. Conclusions: This study confirmed the validity of wireless WHMS connected to a mobile phone for monitoring neurophysiological parameters of relevance for biofeedback therapy. UR - http://biomedeng.jmir.org/2018/1/e1/ UR - http://dx.doi.org/10.2196/biomedeng.9062 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/biomedeng.9062 ER - TY - JOUR AU - Beaulieu-Jones, K. Brett AU - Lavage, R. Daniel AU - Snyder, W. John AU - Moore, H. Jason AU - Pendergrass, A. Sarah AU - Bauer, R. Christopher PY - 2018/02/23 TI - Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis JO - JMIR Med Inform SP - e11 VL - 6 IS - 1 KW - imputation KW - missing data KW - clinical laboratory test results KW - electronic health records N2 - Background: Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results. Objective: The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. Methods: We analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling). Results: Our results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation. Conclusions: The analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available. UR - http://medinform.jmir.org/2018/1/e11/ UR - http://dx.doi.org/10.2196/medinform.8960 UR - http://www.ncbi.nlm.nih.gov/pubmed/29475824 ID - info:doi/10.2196/medinform.8960 ER - TY - JOUR AU - Sillice, A. Marie AU - Morokoff, J. Patricia AU - Ferszt, Ginette AU - Bickmore, Timothy AU - Bock, C. Beth AU - Lantini, Ryan AU - Velicer, F. Wayne PY - 2018/02/07 TI - Using Relational Agents to Promote Exercise and Sun Protection: Assessment of Participants? Experiences With Two Interventions JO - J Med Internet Res SP - e48 VL - 20 IS - 2 KW - relational agents KW - eHealth KW - exercise KW - sun protection KW - qualitative methods N2 - Background: Relational agents (RAs) are electronic computational figures designed to engage participants in the change process. A recent study, Project RAISE, tested the effectiveness of RAs, combined with existing computer-based interventions to increase regular exercise and sun protection behaviors. Results showed these interventions can be effective but need further development. Objective: The purpose of this study was to examine participants? experiences using RAs to increase participant engagement and promote behavior change . Methods: A qualitative approach was primarily utilized. A 25-question interview guide assessed different components of participants? experiences with the intervention, including motivation, engagement, satisfaction or dissatisfaction, quality of their interaction with the RA, and behavior change. Quantitative assessment of satisfaction was based on a scale of 1 to 10, with 1 representing least satisfied and 10 representing most satisfied. A summative analytic approach was used to assess individuals? qualitative responses. A single analysis of variance (ANOVA) examined levels of satisfaction by gender. Results: Of the original 1354 participants enrolled in Project RAISE, 490 of 1354 (36%) were assigned to the RA group. A sample of 216 out of 490 (44%) participants assigned to the RA group completed the interventions, and follow-up assessments were contacted to participate in the semistructured interview. A total of 34 out of 216 (16%) completed the interview. Participants were motivated by, and satisfied with, the intervention. Participants viewed the RA as supportive, informative, caring, and reported positive behavior change in both exercise and sun protection. Some participants (15/34, 44%) noted the RA was less judgmental and less ?overbearing? compared with a human counselor; other participants (12/34, 35%) said that the interaction was sometimes repetitive or overly general. The majority of participants (22/34, 65%) viewed the RA as an important contributor to their behavior change for exercise, sun protection, or both. Levels of satisfaction ranged between 7 and 10. There were no gender differences noted in levels of satisfaction (P=.51). Conclusions: RAs provide an innovative and attractive platform to increase exercise and sun protection behaviors and potentially other health behaviors. UR - http://www.jmir.org/2018/2/e48/ UR - http://dx.doi.org/10.2196/jmir.7640 UR - http://www.ncbi.nlm.nih.gov/pubmed/29415873 ID - info:doi/10.2196/jmir.7640 ER - TY - JOUR AU - Salisbury, P. Joseph AU - Keshav, U. Neha AU - Sossong, D. Anthony AU - Sahin, T. Ned PY - 2018/01/23 TI - Concussion Assessment With Smartglasses: Validation Study of Balance Measurement Toward a Lightweight, Multimodal, Field-Ready Platform JO - JMIR Mhealth Uhealth SP - e15 VL - 6 IS - 1 KW - postural balance KW - wearable technology KW - accelerometry KW - mild traumatic brain injury N2 - Background: Lightweight and portable devices that objectively measure concussion-related impairments could improve injury detection and critical decision-making in contact sports and the military, where brain injuries commonly occur but remain underreported. Current standard assessments often rely heavily on subjective methods such as symptom self-reporting. Head-mounted wearables, such as smartglasses, provide an emerging platform for consideration that could deliver the range of assessments necessary to develop a rapid and objective screen for brain injury. Standing balance assessment, one parameter that may inform a concussion diagnosis, could theoretically be performed quantitatively using current off-the-shelf smartglasses with an internal accelerometer. However, the validity of balance measurement using smartglasses has not been investigated. Objective: This study aimed to perform preliminary validation of a smartglasses-based balance accelerometer measure (BAM) compared with the well-described and characterized waist-based BAM. Methods: Forty-two healthy individuals (26 male, 16 female; mean age 23.8 [SD 5.2] years) participated in the study. Following the BAM protocol, each subject performed 2 trials of 6 balance stances while accelerometer and gyroscope data were recorded from smartglasses (Glass Explorer Edition). Test-retest reliability and correlation were determined relative to waist-based BAM as used in the National Institutes of Health?s Standing Balance Toolbox. Results: Balance measurements obtained using a head-mounted wearable were highly correlated with those obtained through a waist-mounted accelerometer (Spearman rho, ?=.85). Test-retest reliability was high (intraclass correlation coefficient, ICC2,1=0.85, 95% CI 0.81-0.88) and in good agreement with waist balance measurements (ICC2,1=0.84, 95% CI 0.80-0.88). Considering the normalized path length magnitude across all 3 axes improved interdevice correlation (?=.90) while maintaining test-retest reliability (ICC2,1=0.87, 95% CI 0.83-0.90). All subjects successfully completed the study, demonstrating the feasibility of using a head-mounted wearable to assess balance in a healthy population. Conclusions: Balance measurements derived from the smartglasses-based accelerometer were consistent with those obtained using a waist-mounted accelerometer. Additional research is necessary to determine to what extent smartglasses-based accelerometry measures can detect balance dysfunction associated with concussion. However, given the potential for smartglasses to perform additional concussion-related assessments in an integrated, wearable platform, continued development and validation of a smartglasses-based balance assessment is warranted. This approach could lead to a wearable platform for real-time assessment of concussion-related impairments that could be further augmented with telemedicine capabilities to integrate professional clinical guidance. Smartglasses may be superior to fully immersive virtual reality headsets for this application, given their lighter weight and reduced likelihood of potential safety concerns. UR - http://mhealth.jmir.org/2018/1/e15/ UR - http://dx.doi.org/10.2196/mhealth.8478 UR - http://www.ncbi.nlm.nih.gov/pubmed/29362210 ID - info:doi/10.2196/mhealth.8478 ER - TY - JOUR AU - Comerford, Megan AU - Fogel, Rachel AU - Bailey, Robert James AU - Chilukuri, Prianka AU - Chalasani, Naga AU - Lammert, Steven Craig PY - 2018/01/18 TI - Leveraging Social Networking Sites for an Autoimmune Hepatitis Genetic Repository: Pilot Study to Evaluate Feasibility JO - J Med Internet Res SP - e14 VL - 20 IS - 1 KW - autoimmune hepatitis KW - social media KW - rare disease N2 - Background: Conventional approaches to participant recruitment are often inadequate in rare disease investigation. Social networking sites such as Facebook may provide a vehicle to circumvent common research limitations and pitfalls. We report our preliminary experience with Facebook-based methodology for participant recruitment and participation into an ongoing study of autoimmune hepatitis (AIH). Objective: The goal of our research was to conduct a pilot study to assess whether a Facebook-based methodology is capable of recruiting geographically widespread participants into AIH patient-oriented research and obtaining quality phenotypic data. Methods: We established a Facebook community, the Autoimmune Hepatitis Research Network (AHRN), in 2014 to provide a secure and reputable distillation of current literature and AIH research opportunities. Quarterly advertisements for our ongoing observational AIH study were posted on the AHRN over 2 years. Interested and self-reported AIH participants were subsequently enrolled after review of study materials and completion of an informed consent by our study coordinator. Participants returned completed study materials, including epidemiologic questionnaires and genetic material, to our facility via mail. Outside medical records were obtained and reviewed by a study physician. Results: We successfully obtained all study materials from 29 participants with self-reported AIH within 2 years from 20 different states. Liver biopsy results were available for 90% (26/29) of participants, of which 81% (21/29) had findings consistent with AIH, 15% (4/29) were suggestive of AIH with features of primary biliary cholangitis (PBC), and 4% (1/29) had PBC alone. A total of 83% (24/29) had at least 2 of 3 proposed criteria: positive autoimmune markers, consistent histologic findings of AIH on liver biopsy, and reported treatment with immunosuppressant medications. Self-reported and physician records were discrepant for immunosuppressant medications or for AIH/PBC diagnoses in 4 patients. Conclusions: Facebook can be an effective ancillary tool for facilitating patient-oriented research in rare diseases. A social media-based approach transcends established limitations in rare disease research and can further develop research communities. UR - http://www.jmir.org/2018/1/e14/ UR - http://dx.doi.org/10.2196/jmir.7683 UR - http://www.ncbi.nlm.nih.gov/pubmed/29348111 ID - info:doi/10.2196/jmir.7683 ER - TY - JOUR AU - Shi, Jingyuan AU - Salmon, T. Charles PY - 2018/01/09 TI - Identifying Opinion Leaders to Promote Organ Donation on Social Media: Network Study JO - J Med Internet Res SP - e7 VL - 20 IS - 1 KW - social media KW - health promotion KW - organ donation KW - opinion leaders KW - social network analysis N2 - Background: In the recent years, social networking sites (SNSs, also called social media) have been adopted in organ donation campaigns, and recruiting opinion leaders for such campaigns has been found effective in promoting behavioral changes. Objective: The aim of this paper was to focus on the dissemination of organ donation tweets on Weibo, the Chinese equivalent of Twitter, and to examine the opinion leadership in the retweet network of popular organ donation messages using social network analysis. It also aimed to investigate how personal and social attributes contribute to a user?s opinion leadership on the topic of organ donation. Methods: All messages about organ donation posted on Weibo from January 1, 2015 to December 31, 2015 were extracted using Python Web crawler. A retweet network with 505,047 nodes and 545,312 edges of the popular messages (n=206) was constructed and analyzed. The local and global opinion leaderships were measured using network metrics, and the roles of personal attributes, professional knowledge, and social positions in obtaining the opinion leadership were examined using general linear model. Results: The findings revealed that personal attributes, professional knowledge, and social positions predicted individual?s local opinion leadership in the retweet network of popular organ donation messages. Alternatively, personal attributes and social positions, but not professional knowledge, were significantly associated with global opinion leadership. Conclusions: The findings of this study indicate that health campaign designers may recruit peer leaders in SNS organ donation promotions to facilitate information sharing among the target audience. Users who are unverified, active, well connected, and experienced with information and communications technology (ICT) will accelerate the sharing of organ donation messages in the global environment. Medical professionals such as organ transplant surgeons who can wield a great amount of influence on their direct connections could also effectively participate in promoting organ donation on social media. UR - http://www.jmir.org/2018/1/e7/ UR - http://dx.doi.org/10.2196/jmir.7643 UR - http://www.ncbi.nlm.nih.gov/pubmed/29317384 ID - info:doi/10.2196/jmir.7643 ER - TY - JOUR AU - Jacquemin, Charlotte AU - Servy, Hervé AU - Molto, Anna AU - Sellam, Jérémie AU - Foltz, Violaine AU - Gandjbakhch, Frédérique AU - Hudry, Christophe AU - Mitrovic, Stéphane AU - Fautrel, Bruno AU - Gossec, Laure PY - 2018/01/02 TI - Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study JO - JMIR Mhealth Uhealth SP - e1 VL - 6 IS - 1 KW - fitness tracker KW - exercise KW - rheumatoid arthritis KW - axial spondylarthritis N2 - Background: Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA). Objective: The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population. Methods: This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire. Results: A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10. Conclusions: Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases. UR - http://mhealth.jmir.org/2018/1/e1/ UR - http://dx.doi.org/10.2196/mhealth.7948 UR - http://www.ncbi.nlm.nih.gov/pubmed/29295810 ID - info:doi/10.2196/mhealth.7948 ER - TY - JOUR AU - Frie, Kerstin AU - Hartmann-Boyce, Jamie AU - Jebb, Susan AU - Albury, Charlotte AU - Nourse, Rebecca AU - Aveyard, Paul PY - 2017/12/22 TI - Insights From Google Play Store User Reviews for the Development of Weight Loss Apps: Mixed-Method Analysis JO - JMIR Mhealth Uhealth SP - e203 VL - 5 IS - 12 KW - weight loss KW - mobile applications KW - telemedicine KW - consumer behavior N2 - Background: Significant weight loss takes several months to achieve, and behavioral support can enhance weight loss success. Weight loss apps could provide ongoing support and deliver innovative interventions, but to do so, developers must ensure user satisfaction. Objective: The aim of this study was to conduct a review of Google Play Store apps to explore what users like and dislike about weight loss and weight-tracking apps and to examine qualitative feedback through analysis of user reviews. Methods: The Google Play Store was searched and screened for weight loss apps using the search terms weight loss and weight track*, resulting in 179 mobile apps. A content analysis was conducted based on the Oxford Food and Activity Behaviors taxonomy. Correlational analyses were used to assess the association between complexity of mobile health (mHealth) apps and popularity indicators. The sample was then screened for popular apps that primarily focus on weight-tracking. For the resulting subset of 15 weight-tracking apps, 569 user reviews were sampled from the Google Play Store. Framework and thematic analysis of user reviews was conducted to assess which features users valued and how design influenced users? responses. Results: The complexity (number of components) of weight loss apps was significantly positively correlated with the rating (r=.25; P=.001), number of reviews (r=.28; P<.001), and number of downloads (r=.48; P<.001) of the app. In contrast, in the qualitative analysis of weight-tracking apps, users expressed preference for simplicity and ease of use. In addition, we found that positive reinforcement through detailed feedback fostered users? motivation for further weight loss. Smooth functioning and reliable data storage emerged as critical prerequisites for long-term app usage. Conclusions: Users of weight-tracking apps valued simplicity, whereas users of comprehensive weight loss apps appreciated availability of more features, indicating that complexity demands are specific to different target populations. The provision of feedback on progress can motivate users to continue their weight loss attempts. Users value seamless functioning and reliable data storage. UR - http://mhealth.jmir.org/2017/12/e203/ UR - http://dx.doi.org/10.2196/mhealth.8791 UR - http://www.ncbi.nlm.nih.gov/pubmed/29273575 ID - info:doi/10.2196/mhealth.8791 ER - TY - JOUR AU - Low, A. Carissa AU - Dey, K. Anind AU - Ferreira, Denzil AU - Kamarck, Thomas AU - Sun, Weijing AU - Bae, Sangwon AU - Doryab, Afsaneh PY - 2017/12/19 TI - Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study JO - J Med Internet Res SP - e420 VL - 19 IS - 12 KW - patient reported outcome measures KW - cancer KW - mobile health N2 - Background: Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. Objective: The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. Methods: A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. Results: Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. Conclusions: Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms. UR - http://www.jmir.org/2017/12/e420/ UR - http://dx.doi.org/10.2196/jmir.9046 UR - http://www.ncbi.nlm.nih.gov/pubmed/29258977 ID - info:doi/10.2196/jmir.9046 ER - TY - JOUR AU - Cole, A. Casey AU - Anshari, Dien AU - Lambert, Victoria AU - Thrasher, F. James AU - Valafar, Homayoun PY - 2017/12/13 TI - Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study JO - JMIR Mhealth Uhealth SP - e189 VL - 5 IS - 12 KW - machine learning KW - neural networks KW - automated pattern recognition KW - smoking cessation KW - ecological momentary assessment KW - digital signal processing KW - data mining N2 - Background: Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. Objective: This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. Methods: A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. Results: In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. Conclusions: Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events. UR - http://mhealth.jmir.org/2017/12/e189/ UR - http://dx.doi.org/10.2196/mhealth.9035 UR - http://www.ncbi.nlm.nih.gov/pubmed/29237580 ID - info:doi/10.2196/mhealth.9035 ER - TY - JOUR AU - Dendere, Ronald AU - Mutsvangwa, Tinashe AU - Goliath, Rene AU - Rangaka, X. Molebogeng AU - Abubakar, Ibrahim AU - Douglas, S. Tania PY - 2017/12/07 TI - Measurement of Skin Induration Size Using Smartphone Images and Photogrammetric Reconstruction: Pilot Study JO - JMIR Biomed Eng SP - e3 VL - 2 IS - 1 KW - tuberculosis KW - skin tests KW - telemedicine KW - computer assisted diagnosis N2 - Background: The tuberculin skin test (TST) is the most common method for detecting latent tuberculosis infection (LTBI). The test requires that a patient return to the health facility or be visited by a health care worker 48 to 72 hours after the intradermal placement of tuberculin so that the size of the resulting skin induration, if any, can be measured. Objective: This study aimed to propose and evaluate an image-based method for measuring induration size from images captured using a smartphone camera. Methods: We imaged simulated skin indurations, ranging from 4.0 to 19 mm, in 10 subjects using a handheld smartphone, and performed three-dimensional reconstruction of the induration sites using photogrammetry software. An experienced TST reader measured the size of each induration using the standard clinical method. The experienced reader and an inexperienced observer both measured the size of each induration using the software. The agreement between measurements generated by the standard clinical and image-based methods was assessed using the intraclass correlation coefficient (ICC). Inter- and intraobserver agreement for the image-based method was similarly evaluated. Results: Results showed excellent agreement between the standard and image-based measurements performed by the experienced reader with an ICC value of .965. Inter- and intraobserver agreements were also excellent, indicating that experience in reading TSTs is not required with our proposed method. Conclusions: We conclude that the proposed smartphone image-based method is a potential alternative to standard induration size measurement and would enable remote data collection for LTBI screening. UR - http://biomedeng.jmir.org/2017/1/e3/ UR - http://dx.doi.org/10.2196/biomedeng.8333 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/biomedeng.8333 ER - TY - JOUR AU - Sohda, Satoshi AU - Suzuki, Kenta AU - Igari, Ichiro PY - 2017/11/27 TI - Relationship Between the Menstrual Cycle and Timing of Ovulation Revealed by New Protocols: Analysis of Data from a Self-Tracking Health App JO - J Med Internet Res SP - e391 VL - 19 IS - 11 KW - self-tracking KW - person generated health data KW - calendar calculation KW - fertility KW - menstrual cycle N2 - Background: There are many mobile phone apps aimed at helping women map their ovulation and menstrual cycles and facilitating successful conception (or avoiding pregnancy). These apps usually ask users to input various biological features and have accumulated the menstrual cycle data of a vast number of women. Objective: The purpose of our study was to clarify how the data obtained from a self-tracking health app for female mobile phone users can be used to improve the accuracy of prediction of the date of next ovulation. Methods: Using the data of 7043 women who had reliable menstrual and ovulation records out of 8,000,000 users of a mobile phone app of a health care service, we analyzed the relationship between the menstrual cycle length, follicular phase length, and luteal phase length. Then we fitted a linear function to the relationship between the length of the menstrual cycle and timing of ovulation and compared it with the existing calendar-based methods. Results: The correlation between the length of the menstrual cycle and the length of the follicular phase was stronger than the correlation between the length of the menstrual cycle and the length of the luteal phase, and there was a positive correlation between the lengths of past and future menstrual cycles. A strong positive correlation was also found between the mean length of past cycles and the length of the follicular phase. The correlation between the mean cycle length and the luteal phase length was also statistically significant. In most of the subjects, our method (ie, the calendar-based method based on the optimized function) outperformed the Ogino method of predicting the next ovulation date. Our method also outperformed the ovulation date prediction method that assumes the middle day of a mean menstrual cycle as the date of the next ovulation. Conclusions: The large number of subjects allowed us to capture the relationships between the lengths of the menstrual cycle, follicular phase, and luteal phase in more detail than previous studies. We then demonstrated how the present calendar methods could be improved by the better grouping of women. This study suggested that even without integrating various biological metrics, the dataset collected by a self-tracking app can be used to develop formulas that predict the ovulation day when the data are aggregated. Because the method that we developed requires data only on the first day of menstruation, it would be the best option for couples during the early stages of their attempt to have a baby or for those who want to avoid the cost associated with other methods. Moreover, the result will be the baseline for more advanced methods that integrate other biological metrics. UR - http://www.jmir.org/2017/11/e391/ UR - http://dx.doi.org/10.2196/jmir.7468 UR - http://www.ncbi.nlm.nih.gov/pubmed/29180346 ID - info:doi/10.2196/jmir.7468 ER - TY - JOUR AU - Smeets, JP Christophe AU - Vranken, Julie AU - Van der Auwera, Jo AU - Verbrugge, H. Frederik AU - Mullens, Wilfried AU - Dupont, Matthias AU - Grieten, Lars AU - De Cannière, Hélène AU - Lanssens, Dorien AU - Vandenberk, Thijs AU - Storms, Valerie AU - Thijs, M. Inge AU - Vandervoort, M. Pieter PY - 2017/11/23 TI - Bioimpedance Alerts from Cardiovascular Implantable Electronic Devices: Observational Study of Diagnostic Relevance and Clinical Outcomes JO - J Med Internet Res SP - e393 VL - 19 IS - 11 KW - defibrillators, implantable KW - cardiac resynchronization therapy KW - telemedicine KW - electric impedance KW - algorithms KW - call centers N2 - Background: The use of implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy (CRT) devices is expanding in the treatment of heart failure. Most of the current devices are equipped with remote monitoring functions, including bioimpedance for fluid status monitoring. The question remains whether bioimpedance measurements positively impact clinical outcome. Objective: The aim of this study was to provide a comprehensive overview of the clinical interventions taken based on remote bioimpedance monitoring alerts and their impact on clinical outcome. Methods: This is a single-center observational study of consecutive ICD and CRT patients (n=282) participating in protocol-driven remote follow-up. Bioimpedance alerts were analyzed with subsequently triggered interventions. Results: A total of 55.0% (155/282) of patients had an ICD or CRT device equipped with a remote bioimpedance algorithm. During 34 (SD 12) months of follow-up, 1751 remote monitoring alarm notifications were received (2.2 per patient-year of follow-up), comprising 2096 unique alerts (2.6 per patient-year of follow-up). Since 591 (28.2%) of all incoming alerts were bioimpedance-related, patients with an ICD or CRT including a bioimpedance algorithm had significantly more alerts (3.4 versus 1.8 alerts per patient-year of follow-up, P<.001). Bioimpedance-only alerts resulted in a phone contact in 91.0% (498/547) of cases, which triggered an actual intervention in 15.9% (87/547) of cases, since in 75.1% (411/547) of cases reenforcing heart failure education sufficed. Overall survival was lower in patients with a cardiovascular implantable electronic device with a bioimpedance algorithm; however, this difference was driven by differences in baseline characteristics (adjusted hazard ratio of 2.118, 95% CI 0.845-5.791). No significant differences between both groups were observed in terms of the number of follow-up visits in the outpatient heart failure clinic, the number of hospital admissions with a primary diagnosis of heart failure, or mean length of hospital stay. Conclusions: Bioimpedance-only alerts constituted a substantial amount of incoming alerts when turned on during remote follow-up and triggered an additional intervention in only 16% of cases since in 75% of cases, providing general heart failure education sufficed. The high frequency of heart failure education that was provided could have contributed to fewer heart failure?related hospitalizations despite significant differences in baseline characteristics. UR - http://www.jmir.org/2017/11/e393/ UR - http://dx.doi.org/10.2196/jmir.8066 UR - http://www.ncbi.nlm.nih.gov/pubmed/29170147 ID - info:doi/10.2196/jmir.8066 ER - TY - JOUR AU - Almalki, Manal AU - Gray, Kathleen AU - Martin-Sanchez, Fernando PY - 2017/11/03 TI - Development and Validation of a Taxonomy for Characterizing Measurements in Health Self-Quantification JO - J Med Internet Res SP - e378 VL - 19 IS - 11 KW - health KW - self-management KW - self-experimentation KW - wearables KW - quantified self KW - taxonomy KW - classification N2 - Background: The use of wearable tools for health self-quantification (SQ) introduces new ways of thinking about one?s body and about how to achieve desired health outcomes. Measurements from individuals, such as heart rate, respiratory volume, skin temperature, sleep, mood, blood pressure, food consumed, and quality of surrounding air can be acquired, quantified, and aggregated in a holistic way that has never been possible before. However, health SQ still lacks a formal common language or taxonomy for describing these kinds of measurements. Establishing such taxonomy is important because it would enable systematic investigations that are needed to advance in the use of wearable tools in health self-care. For a start, a taxonomy would help to improve the accuracy of database searching when doing systematic reviews and meta-analyses in this field. Overall, more systematic research would contribute to build evidence of sufficient quality to determine whether and how health SQ is a worthwhile health care paradigm. Objective: The aim of this study was to investigate a sample of SQ tools and services to build and test a taxonomy of measurements in health SQ, titled: the classification of data and activity in self-quantification systems (CDA-SQS). Methods: Eight health SQ tools and services were selected to be examined: Zeo Sleep Manager, Fitbit Ultra, Fitlinxx Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, and uBiome. An open coding analytical approach was used to find all the themes related to the research aim. Results: This study distinguished three types of measurements in health SQ: body structures and functions, body actions and activities, and around the body. Conclusions: The CDA-SQS classification should be applicable to align health SQ measurement data from people with many different health objectives, health states, and health conditions. CDA-SQS is a critical contribution to a much more consistent way of studying health SQ. UR - http://www.jmir.org/2017/11/e378/ UR - http://dx.doi.org/10.2196/jmir.6903 UR - http://www.ncbi.nlm.nih.gov/pubmed/29101092 ID - info:doi/10.2196/jmir.6903 ER - TY - JOUR AU - Rodríguez, Iyubanit AU - Herskovic, Valeria AU - Gerea, Carmen AU - Fuentes, Carolina AU - Rossel, O. Pedro AU - Marques, Maíra AU - Campos, Mauricio PY - 2017/10/27 TI - Understanding Monitoring Technologies for Adults With Pain: Systematic Literature Review JO - J Med Internet Res SP - e364 VL - 19 IS - 10 KW - systematic review KW - pain KW - technology KW - patient monitoring KW - ubiquitous and mobile computing N2 - Background: Monitoring of patients may decrease treatment costs and improve quality of care. Pain is the most common health problem that people seek help for in hospitals. Therefore, monitoring patients with pain may have significant impact in improving treatment. Several studies have studied factors affecting pain; however, no previous study has reviewed the contextual information that a monitoring system may capture to characterize a patient?s situation. Objective: The objective of this study was to conduct a systematic review to (1) determine what types of technologies have been used to monitor adults with pain, and (2) construct a model of the context information that may be used to implement apps and devices aimed at monitoring adults with pain. Methods: A literature search (2005-2015) was conducted in electronic databases pertaining to medical and computer science literature (PubMed, Science Direct, ACM Digital Library, and IEEE Xplore) using a defined search string. Article selection was done through a process of removing duplicates, analyzing title and abstract, and then reviewing the full text of the article. Results: In the final analysis, 87 articles were included and 53 of them (61%) used technologies to collect contextual information. A total of 49 types of context information were found and a five-dimension (activity, identity, wellness, environment, physiological) model of context information to monitor adults with pain was proposed, expanding on a previous model. Most technological interfaces for pain monitoring were wearable, possibly because they can be used in more realistic contexts. Few studies focused on older adults, creating a relevant avenue of research on how to create devices for users that may have impaired cognitive skills or low digital literacy. Conclusions: The design of monitoring devices and interfaces for adults with pain must deal with the challenge of selecting relevant contextual information to understand the user?s situation, and not overburdening or inconveniencing users with information requests. A model of contextual information may be used by researchers to choose possible contextual information that may be monitored during studies on adults with pain. UR - http://www.jmir.org/2017/10/e364/ UR - http://dx.doi.org/10.2196/jmir.7279 UR - http://www.ncbi.nlm.nih.gov/pubmed/29079550 ID - info:doi/10.2196/jmir.7279 ER - TY - JOUR AU - Bayen, Eleonore AU - Jacquemot, Julien AU - Netscher, George AU - Agrawal, Pulkit AU - Tabb Noyce, Lynn AU - Bayen, Alexandre PY - 2017/10/17 TI - Reduction in Fall Rate in Dementia Managed Care Through Video Incident Review: Pilot Study JO - J Med Internet Res SP - e339 VL - 19 IS - 10 KW - video monitoring KW - video review KW - mobile app KW - deep learning KW - fall KW - Alzheimer disease KW - dementia N2 - Background: Falls of individuals with dementia are frequent, dangerous, and costly. Early detection and access to the history of a fall is crucial for efficient care and secondary prevention in cognitively impaired individuals. However, most falls remain unwitnessed events. Furthermore, understanding why and how a fall occurred is a challenge. Video capture and secure transmission of real-world falls thus stands as a promising assistive tool. Objective: The objective of this study was to analyze how continuous video monitoring and review of falls of individuals with dementia can support better quality of care. Methods: A pilot observational study (July-September 2016) was carried out in a Californian memory care facility. Falls were video-captured (24×7), thanks to 43 wall-mounted cameras (deployed in all common areas and in 10 out of 40 private bedrooms of consenting residents and families). Video review was provided to facility staff, thanks to a customized mobile device app. The outcome measures were the count of residents? falls happening in the video-covered areas, the acceptability of video recording, the analysis of video review, and video replay possibilities for care practice. Results: Over 3 months, 16 falls were video-captured. A drop in fall rate was observed in the last month of the study. Acceptability was good. Video review enabled screening for the severity of falls and fall-related injuries. Video replay enabled identifying cognitive-behavioral deficiencies and environmental circumstances contributing to the fall. This allowed for secondary prevention in high-risk multi-faller individuals and for updated facility care policies regarding a safer living environment for all residents. Conclusions: Video monitoring offers high potential to support conventional care in memory care facilities. UR - http://www.jmir.org/2017/10/e339/ UR - http://dx.doi.org/10.2196/jmir.8095 UR - http://www.ncbi.nlm.nih.gov/pubmed/29042342 ID - info:doi/10.2196/jmir.8095 ER - TY - JOUR AU - Schrempft, Stephanie AU - van Jaarsveld, HM Cornelia AU - Fisher, Abigail PY - 2017/10/12 TI - Exploring the Potential of a Wearable Camera to Examine the Early Obesogenic Home Environment: Comparison of SenseCam Images to the Home Environment Interview JO - J Med Internet Res SP - e332 VL - 19 IS - 10 KW - environment and public health KW - obesity KW - parents N2 - Background: The obesogenic home environment is usually examined via self-report, and objective measures are required. Objective: This study explored whether the wearable camera SenseCam can be used to examine the early obesogenic home environment and whether it is useful for validation of self-report measures. Methods: A total of 15 primary caregivers of young children (mean age of child 4 years) completed the Home Environment Interview (HEI). Around 12 days after the HEI, participants wore the SenseCam at home for 4 days. A semistructured interview assessed participants? experience of wearing the SenseCam. Intraclass correlation coefficients (ICCs), percent agreement, and kappa statistics were used as validity estimates for 54 home environment features. Results: Wearing the SenseCam was generally acceptable to those who participated. The SenseCam captured all 54 HEI features but with varying detail; 36 features (67%) had satisfactory validity (ICC or kappa ?0.40; percent agreement ?80 where kappa could not be calculated). Validity was good or excellent (ICC or kappa ?0.60) for fresh fruit and vegetable availability, fresh vegetable variety, display of food and drink (except sweet snacks), family meals, child eating lunch or dinner while watching TV, garden and play equipment, the number of TVs and DVD players, and media equipment in the child?s bedroom. Validity was poor (ICC or kappa <0.40) for tinned and frozen vegetable availability and variety, and sweet snack availability. Conclusions: The SenseCam has the potential to objectively examine and validate multiple aspects of the obesogenic home environment. Further research should aim to replicate the findings in a larger, representative sample. UR - http://www.jmir.org/2017/10/e332/ UR - http://dx.doi.org/10.2196/jmir.7748 UR - http://www.ncbi.nlm.nih.gov/pubmed/29025695 ID - info:doi/10.2196/jmir.7748 ER - TY - JOUR AU - Amann, Julia AU - Rubinelli, Sara PY - 2017/10/10 TI - Views of Community Managers on Knowledge Co-creation in Online Communities for People With Disabilities: Qualitative Study JO - J Med Internet Res SP - e320 VL - 19 IS - 10 KW - community networks KW - internet KW - patient-centered care KW - telemedicine KW - community participation KW - co-creation N2 - Background: The use of online communities to promote end user involvement and co-creation in the product and service innovation process is well documented in the marketing and management literature. Whereas online communities are widely used for health care service provision and peer-to-peer support, only little is known about how they could be integrated into the health care innovation process. Objective: The overall objective of this qualitative study was to explore community managers? views on and experiences with knowledge co-creation in online communities for people with disabilities. Methods: A descriptive qualitative research design was used. Data were collected through semi-structured interviews with nine community managers. To complement the interview data, additional information was retrieved from the communities in the form of structural information (number of registered users, number and names of topic areas covered by the forum) and administrative information (terms and conditions and privacy statements, forum rules). Data were analyzed using thematic analysis. Results: Our results highlight two main aspects: peer-to-peer knowledge co-creation and types of collaboration with external actors. Although community managers strongly encouraged peer-to-peer knowledge co-creation, our findings indicated that these activities were not common practice in the communities under investigation. In fact, much of what related to co-creation, prototyping, and product development was still perceived to be directed by professionals and experts. Community managers described the role of their respective communities as informing this process rather than a driving force. The role of community members as advisors to researchers, health care professionals, and businesses was discussed in the context of types of collaboration with external actors. According to the community managers, most of the external inquiries related to research projects of students or health care professionals in training, who often joined a community for the sole purpose of recruiting participants for their research. Despite this unilateral form of knowledge co-creation, community managers acknowledged the mere interest of these user groups as beneficial, as long as their interest was not purely financially motivated. Being able to contribute to advancing research, improving products, and informing the planning and design of health care services were described as some of the key motivations to engage with external stakeholders. Conclusions: This paper draws attention to the currently under-investigated role of online communities as platforms for collaboration and co-creation between patients, health care professionals, researchers, and businesses. It describes community managers? views on and experiences with knowledge co-creation and provides recommendations on how these activities can be leveraged to foster knowledge co-creation in health care. Engaging in knowledge co-creation with online health communities may ultimately help to inform the planning and design of products, services, and research activities that better meet the actual needs of those living with a disability. UR - https://www.jmir.org/2017/10/e320/ UR - http://dx.doi.org/10.2196/jmir.7406 UR - http://www.ncbi.nlm.nih.gov/pubmed/29017993 ID - info:doi/10.2196/jmir.7406 ER - TY - JOUR AU - Mizdrak, Anja AU - Waterlander, Elzeline Wilma AU - Rayner, Mike AU - Scarborough, Peter PY - 2017/10/09 TI - Using a UK Virtual Supermarket to Examine Purchasing Behavior Across Different Income Groups in the United Kingdom: Development and Feasibility Study JO - J Med Internet Res SP - e343 VL - 19 IS - 10 KW - food KW - diet KW - public health KW - United Kingdom KW - socioeconomic status N2 - Background: The majority of food in the United Kingdom is purchased in supermarkets, and therefore, supermarket interventions provide an opportunity to improve diets. Randomized controlled trials are costly, time-consuming, and difficult to conduct in real stores. Alternative approaches of assessing the impact of supermarket interventions on food purchases are needed, especially with respect to assessing differential impacts on population subgroups. Objective: The aim of this study was to assess the feasibility of using the United Kingdom Virtual Supermarket (UKVS), a three-dimensional (3D) computer simulation of a supermarket, to measure food purchasing behavior across income groups. Methods: Participants (primary household shoppers in the United Kingdom with computer access) were asked to conduct two shopping tasks using the UKVS and complete questionnaires on demographics, food purchasing habits, and feedback on the UKVS software. Data on recruitment method and rate, completion of study procedure, purchases, and feedback on usability were collected to inform future trial protocols. Results: A total of 98 participants were recruited, and 46 (47%) fully completed the study procedure. Low-income participants were less likely to complete the study (P=.02). Most participants found the UKVS easy to use (38/46, 83%) and reported that UKVS purchases resembled their usual purchases (41/46, 89%). Conclusions: The UKVS is likely to be a useful tool to examine the effects of nutrition interventions using randomized controlled designs. Feedback was positive from participants who completed the study and did not differ by income group. However, retention was low and needs to be addressed in future studies. This study provides purchasing data to establish sample size requirements for full trials using the UKVS. UR - http://www.jmir.org/2017/10/e343/ UR - http://dx.doi.org/10.2196/jmir.7982 UR - http://www.ncbi.nlm.nih.gov/pubmed/28993301 ID - info:doi/10.2196/jmir.7982 ER - TY - JOUR AU - van Lint, Céline AU - Wang, Wenxin AU - van Dijk, Sandra AU - Brinkman, Willem-Paul AU - Rövekamp, JM Ton AU - Neerincx, A. Mark AU - Rabelink, J. Ton AU - van der Boog, JM Paul PY - 2017/09/26 TI - Self-Monitoring Kidney Function Post Transplantation: Reliability of Patient-Reported Data JO - J Med Internet Res SP - e316 VL - 19 IS - 9 KW - self-care KW - kidney transplantation KW - creatinine KW - patient compliance KW - data accuracy KW - patient reported outcomes N2 - Background: The high frequency of outpatient visits after kidney transplantation is burdensome to both the recovering patient and health care capacity. Self-monitoring kidney function offers a promising strategy to reduce the number of these outpatient visits. Objective: The objective of this study was to investigate whether it is safe to rely on patients? self-measurements of creatinine and blood pressure, using data from a self-management randomized controlled trial. Methods: For self-monitoring creatinine, each participant received a StatSensor Xpress-i Creatinine Meter and related test material. For self-monitoring blood pressure, each participant received a Microlife WatchBP Home, an oscillometric device for blood pressure self-measurement on the upper arm. Both devices had a memory function and the option to download stored values to a computer. During the first year post transplantation, 54 patients registered their self-measured creatinine values in a Web-based Self-Management Support System (SMSS) which provided automatic feedback on the registered values (eg, seek contact with hospital). Values registered in the SMSS were compared with those logged automatically in the creatinine device to study reliability of registered data. Adherence to measurement frequency was determined by comparing the number of requested with the number of performed measurements. To study adherence to provided feedback, SMSS-logged feedback and information from the electronic hospital files were analyzed. Results: Level of adherence was highest during months 2-4 post transplantation with over 90% (42/47) of patients performing at least 75% of the requested measurements. Overall, 87.00% (3448/3963) of all registered creatinine values were entered correctly, although values were often registered several days later. If (the number of) measured and registered values deviated, the mean of registered creatinine values was significantly lower than what was measured, suggesting active selection of lower creatinine values. Adherence to SMSS feedback ranged from 53% (14/24) to 85% (33/39), depending on the specific feedback. Conclusions: Patients? tendency to postpone registration and to select lower creatinine values for registration and the suboptimal adherence to the feedback provided by the SMSS might challenge safety. This should be well considered when designing self-monitoring care systems, for example by ensuring that self-measured data are transferred automatically to an SMSS. UR - http://www.jmir.org/2017/9/e316/ UR - http://dx.doi.org/10.2196/jmir.7542 UR - http://www.ncbi.nlm.nih.gov/pubmed/28951385 ID - info:doi/10.2196/jmir.7542 ER - TY - JOUR AU - Pasman, J. Wilrike AU - Boessen, Ruud AU - Donner, Yoni AU - Clabbers, Nard AU - Boorsma, André PY - 2017/09/07 TI - Effect of Caffeine on Attention and Alertness Measured in a Home-Setting, Using Web-Based Cognition Tests JO - JMIR Res Protoc SP - e169 VL - 6 IS - 9 KW - caffeine KW - at-home testing KW - cognition KW - EFSA claim N2 - Background: There is an increasing interest among nutritional researchers to perform lifestyle and nutritional intervention studies in a home setting instead of testing subjects in a clinical unit. The term used in other disciplines is ?ecological validity? stressing a realistic situation. This becomes more and more feasible because devices and self-tests that enable such studies are more commonly available. Here, we present such a study in which we reproduced the effect of caffeine on attention and alertness in an at-home setting. Objective: The study was aimed to reproduce the effect of caffeine on attention and alertness using a Web-based study environment of subjects, at home, performing different Web-based cognition tests. Methods: The study was designed as a randomized, placebo-controlled, double-blind, crossover study. Subjects were provided with coffee sachets (2 with and 2 without caffeine). They were also provided with a written instruction of the test days. Healthy volunteers consumed a cup of coffee after an overnight fast. Each intervention was repeated once. Before and 1 hour after coffee consumption subjects performed Web-based cognitive performance tests at home, which measured alertness and attention, established by 3 computerized tests provided by QuantifiedMind. Each test was performed for 5 minutes. Results: Web-based recruitment was fast and efficient. Within 2 weeks, 102 subjects applied, of whom 70 were eligible. Of the 66 subjects who started the study, 53 completed all 4 test sessions (80%), indicating that they were able to perform the do it yourself tests, at home, correctly. The Go-No Go cognition test performed at home showed the same significant improvement in reaction time with caffeine as found in controlled studies in a metabolic ward (P=.02). For coding and N-back the second block was performed approximately 10% faster. No effect was seen on correctness. Conclusions: The study showed that the effects of caffeine consumption on a cognition test in an at-home setting revealed similar results as in a controlled setting. The Go-No Go test applied showed improved results after caffeine intake, similar as seen in clinical trials. This type of study is a fast, reliable, economical, and easy way to demonstrate effectiveness of a supplement and is rapidly becoming a viable alternative for the classical randomized control trial to evaluate life style and nutritional interventions. Trial Registration: Clinicaltrials.gov NCT02061982; https://clinicaltrials.gov/ct2/show/NCT02061982 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02061982) UR - http://www.researchprotocols.org/2017/9/e169/ UR - http://dx.doi.org/10.2196/resprot.6727 UR - http://www.ncbi.nlm.nih.gov/pubmed/28882811 ID - info:doi/10.2196/resprot.6727 ER - TY - JOUR AU - Austin, Johanna AU - Hollingshead, Kristy AU - Kaye, Jeffrey PY - 2017/09/06 TI - Internet Searches and Their Relationship to Cognitive Function in Older Adults: Cross-Sectional Analysis JO - J Med Internet Res SP - e307 VL - 19 IS - 9 KW - Internet KW - geriatrics KW - cognition KW - executive function N2 - Background: Alzheimer disease (AD) is a very challenging experience for all those affected. Unfortunately, detection of Alzheimer disease in its early stages when clinical treatments may be most effective is challenging, as the clinical evaluations are time-consuming and costly. Recent studies have demonstrated a close relationship between cognitive function and everyday behavior, an avenue of research that holds great promise for the early detection of cognitive decline. One area of behavior that changes with cognitive decline is language use. Multiple groups have demonstrated a close relationship between cognitive function and vocabulary size, verbal fluency, and semantic ability, using conventional in-person cognitive testing. An alternative to this approach which is inherently ecologically valid may be to take advantage of automated computer monitoring software to continually capture and analyze language use while on the computer. Objective: The aim of this study was to understand the relationship between Internet searches as a measure of language and cognitive function in older adults. We hypothesize that individuals with poorer cognitive function will search using fewer unique terms, employ shorter words, and use less obscure words in their searches. Methods: Computer monitoring software (WorkTime, Nestersoft Inc) was used to continuously track the terms people entered while conducting searches in Google, Yahoo, Bing, and Ask.com. For all searches, punctuation, accents, and non-ASCII characters were removed, and the resulting search terms were spell-checked before any analysis. Cognitive function was evaluated as a z-normalized summary score capturing five unique cognitive domains. Linear regression was used to determine the relationship between cognitive function and Internet searches by controlling for variables such as age, sex, and education. Results: Over a 6-month monitoring period, 42 participants (mean age 81 years [SD 10.5], 83% [35/42] female) conducted 2915 searches using these top search engines. Participants averaged 3.08 words per search (SD 1.6) and 5.77 letters per word (SD 2.2). Individuals with higher cognitive function used more unique terms per search (beta=.39, P=.002) and employed less common terms in their searches (beta=1.39, P=.02). Cognitive function was not significantly associated with the length of the words used in the searches. Conclusions: These results suggest that early decline in cognitive function may be detected from the terms people search for when they use the Internet. By continuously tracking basic aspects of Internet search terms, it may be possible to detect cognitive decline earlier than currently possible, thereby enabling proactive treatment and intervention. UR - http://www.jmir.org/2017/9/e307/ UR - http://dx.doi.org/10.2196/jmir.7671 UR - http://www.ncbi.nlm.nih.gov/pubmed/28877864 ID - info:doi/10.2196/jmir.7671 ER - TY - JOUR AU - Kenny, Avi AU - Gordon, Nicholas AU - Griffiths, Thomas AU - Kraemer, D. John AU - Siedner, J. Mark PY - 2017/08/18 TI - Validation Relaxation: A Quality Assurance Strategy for Electronic Data Collection JO - J Med Internet Res SP - e297 VL - 19 IS - 8 KW - data accuracy KW - data collection KW - surveys KW - survey methodology KW - research methodology KW - questionnaire design KW - mHealth KW - eHealth N2 - Background: The use of mobile devices for data collection in developing world settings is becoming increasingly common and may offer advantages in data collection quality and efficiency relative to paper-based methods. However, mobile data collection systems can hamper many standard quality assurance techniques due to the lack of a hardcopy backup of data. Consequently, mobile health data collection platforms have the potential to generate datasets that appear valid, but are susceptible to unidentified database design flaws, areas of miscomprehension by enumerators, and data recording errors. Objective: We describe the design and evaluation of a strategy for estimating data error rates and assessing enumerator performance during electronic data collection, which we term ?validation relaxation.? Validation relaxation involves the intentional omission of data validation features for select questions to allow for data recording errors to be committed, detected, and monitored. Methods: We analyzed data collected during a cluster sample population survey in rural Liberia using an electronic data collection system (Open Data Kit). We first developed a classification scheme for types of detectable errors and validation alterations required to detect them. We then implemented the following validation relaxation techniques to enable data error conduct and detection: intentional redundancy, removal of ?required? constraint, and illogical response combinations. This allowed for up to 11 identifiable errors to be made per survey. The error rate was defined as the total number of errors committed divided by the number of potential errors. We summarized crude error rates and estimated changes in error rates over time for both individuals and the entire program using logistic regression. Results: The aggregate error rate was 1.60% (125/7817). Error rates did not differ significantly between enumerators (P=.51), but decreased for the cohort with increasing days of application use, from 2.3% at survey start (95% CI 1.8%-2.8%) to 0.6% at day 45 (95% CI 0.3%-0.9%; OR=0.969; P<.001). The highest error rate (84/618, 13.6%) occurred for an intentional redundancy question for a birthdate field, which was repeated in separate sections of the survey. We found low error rates (0.0% to 3.1%) for all other possible errors. Conclusions: A strategy of removing validation rules on electronic data capture platforms can be used to create a set of detectable data errors, which can subsequently be used to assess group and individual enumerator error rates, their trends over time, and categories of data collection that require further training or additional quality control measures. This strategy may be particularly useful for identifying individual enumerators or systematic data errors that are responsive to enumerator training and is best applied to questions for which errors cannot be prevented through training or software design alone. Validation relaxation should be considered as a component of a holistic data quality assurance strategy. UR - http://www.jmir.org/2017/8/e297/ UR - http://dx.doi.org/10.2196/jmir.7813 UR - http://www.ncbi.nlm.nih.gov/pubmed/28821474 ID - info:doi/10.2196/jmir.7813 ER - TY - JOUR AU - Sieverink, Floor AU - Kelders, Saskia AU - Poel, Mannes AU - van Gemert-Pijnen, Lisette PY - 2017/08/07 TI - Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data JO - JMIR Res Protoc SP - e156 VL - 6 IS - 8 KW - eHealth KW - black box KW - evaluation KW - log data analysis UR - http://www.researchprotocols.org/2017/8/e156/ UR - http://dx.doi.org/10.2196/resprot.6452 UR - http://www.ncbi.nlm.nih.gov/pubmed/28784592 ID - info:doi/10.2196/resprot.6452 ER - TY - JOUR AU - Hartkopf, D. Andreas AU - Graf, Joachim AU - Simoes, Elisabeth AU - Keilmann, Lucia AU - Sickenberger, Nina AU - Gass, Paul AU - Wallwiener, Diethelm AU - Matthies, Lina AU - Taran, Florin-Andrei AU - Lux, P. Michael AU - Wallwiener, Stephanie AU - Belleville, Eric AU - Sohn, Christof AU - Fasching, A. Peter AU - Schneeweiss, Andreas AU - Brucker, Y. Sara AU - Wallwiener, Markus PY - 2017/08/07 TI - Electronic-Based Patient-Reported Outcomes: Willingness, Needs, and Barriers in Adjuvant and Metastatic Breast Cancer Patients JO - JMIR Cancer SP - e11 VL - 3 IS - 2 KW - breast cancer KW - patient-reported outcome measures KW - electronic patient- reported outcome KW - technical skills KW - willingness to use KW - needs and barriers N2 - Background: Patient-reported outcomes (PROs) play an increasingly important role as an adjunct to clinical outcome parameters in measuring health-related quality of life (HRQoL). In fact, PROs are already the accepted gold standard for collecting data about patients? subjective perception of their own state of health. Currently, paper-based surveys of PRO still predominate; however, knowledge regarding the feasibility of and barriers to electronic-based PRO (ePRO) acceptance remains limited. Objective: The objective of this trial was to analyze the willingness, specific needs, and barriers of adjuvant breast cancer (aBC) and metastatic breast cancer (mBC) patients in nonexposed (no exposure to electronic assessment) and exposed (after exposure to electronic assessment decision, whether a tablet-based questionnaire is favored) settings before implementing digital ePRO assessment in relation to health status. We also investigated whether providing support can increase the patients? willingness to participate in such programs. Methods: The nonexposed patients only answered a paper-based questionnaire, whereas the exposed patients filled out both paper- and tablet-based questionnaires. The assessment comprised socioeconomic variables, HRQoL, preexisting technical skills, general attitude toward electronic-based surveys, and potential barriers in relation to health status. Furthermore, nonexposed patients were asked about the existing need for technological support structures. In the course of data evaluation, we performed a frequency analysis as well as chi-square tests and Wilcoxon signed-rank tests. Subsequently, relative risks analysis, univariate categorical regression (CATREG), and mediation analyses (Hayes? bias-corrected bootstrap) were performed. Results: A total of 202 female breast cancer patients completed the PRO assessment (nonexposed group: n=96 patients; exposed group: n=106 patients). Self-reported technical skills were higher in exposed patients (2.79 vs 2.33, P ?.001). Significant differences were found in relation to willingness to use ePRO (92.3% in the exposed group vs 59% in the nonexposed group; P=.001). Multiple barriers were identified, and most of them showed statistically significant differences in favor of the exposed patients (ie, data security [13% in the exposed patients vs 30% in the nonexposed patients; P=.003] and no prior technology usage [5% in the exposed group vs 15% in the nonexposed group; P=.02]), whereas the differences in disease burden (somatic dimension: 4% in the exposed group vs 9% in the nonexposed group; P=.13) showed no significance. In nonexposed patients, requests for support services were identified, which could increase their ePRO willingness. Conclusions: Significant barriers in relation to HRQoL, cancer-related restrictions, and especially the setting of the survey were identified in this trial. Thus, it is necessary to address and eliminate these barriers to ensure data accuracy and reliability for future ePRO assessments. Exposure seems to be a potential option to increase willingness to use ePRO and to reduce barriers. UR - http://cancer.jmir.org/2017/2/e11/ UR - http://dx.doi.org/10.2196/cancer.6996 UR - http://www.ncbi.nlm.nih.gov/pubmed/28784595 ID - info:doi/10.2196/cancer.6996 ER - TY - JOUR AU - Andriesen, Jessica AU - Bull, Sheana AU - Dietrich, Janan AU - Haberer, E. Jessica AU - Van Der Pol, Barbara AU - Voronin, Yegor AU - Wall, M. Kristin AU - Whalen, Christopher AU - Priddy, Frances PY - 2017/07/31 TI - Using Digital Technologies in Clinical HIV Research: Real-World Applications and Considerations for Future Work JO - J Med Internet Res SP - e274 VL - 19 IS - 7 KW - clinical trial KW - HIV KW - mobile phone KW - text messaging KW - biometric identification KW - observational study privacy KW - data collection N2 - Background: Digital technologies, especially if used in novel ways, provide a number of potential advantages to clinical research in trials related to human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) and may greatly facilitate operations as well as data collection and analysis. These technologies may even allow answering questions that are not answerable with older technologies. However, they come with a variety of potential concerns for both the participants and the trial sponsors. The exact challenges and means for alleviation depend on the technology and on the population in which it is deployed, and the rapidly changing landscape of digital technologies presents a challenge for creating future-proof guidelines for technology application. Objective: The aim of this study was to identify and summarize some common themes that are frequently encountered by researchers in this context and highlight those that should be carefully considered before making a decision to include these technologies in their research. Methods: In April 2016, the Global HIV Vaccine Enterprise surveyed the field for research groups with recent experience in novel applications of digital technologies in HIV clinical research and convened these groups for a 1-day meeting. Real-world uses of various technologies were presented and discussed by 46 attendees, most of whom were researchers involved in the design and conduct of clinical trials of biomedical HIV prevention and treatment approaches. After the meeting, a small group of organizers reviewed the presentations and feedback obtained during the meeting and categorized various lessons-learned to identify common themes. A group of 9 experts developed a draft summary of the findings that was circulated via email to all 46 attendees for review. Taking into account the feedback received, the group finalized the considerations that are presented here. Results: Meeting presenters and attendees discussed the many successful applications of digital technologies to improve research outcomes, such as those for recruitment and enrollment, participant identification, informed consent, data collection, data quality, and protocol or treatment adherence. These discussions also revealed unintended consequence of technology usage, including risks to study participants and risks to study integrity. Conclusions: Key lessons learned from these discussions included the need to thoroughly evaluate systems to be used, the idea that early success may not be sustained throughout the study, that some failures will occur, and considerations for study-provided devices. Additionally, taking these key lessons into account, the group generated recommendations on how to move forward with the use of technology in HIV vaccine and biomedical prevention trials. UR - http://www.jmir.org/2017/7/e274/ UR - http://dx.doi.org/10.2196/jmir.7513 UR - http://www.ncbi.nlm.nih.gov/pubmed/28760729 ID - info:doi/10.2196/jmir.7513 ER - TY - JOUR AU - Tapi Nzali, Donald Mike AU - Bringay, Sandra AU - Lavergne, Christian AU - Mollevi, Caroline AU - Opitz, Thomas PY - 2017/07/31 TI - What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer JO - JMIR Med Inform SP - e23 VL - 5 IS - 3 KW - breast cancer KW - text mining KW - social media KW - unsupervised learning N2 - Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients? concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life. UR - http://medinform.jmir.org/2017/3/e23/ UR - http://dx.doi.org/10.2196/medinform.7779 UR - http://www.ncbi.nlm.nih.gov/pubmed/28760725 ID - info:doi/10.2196/medinform.7779 ER - TY - JOUR AU - Payne, Philip AU - Lele, Omkar AU - Johnson, Beth AU - Holve, Erin PY - 2017/07/31 TI - Enabling Open Science for Health Research: Collaborative Informatics Environment for Learning on Health Outcomes (CIELO) JO - J Med Internet Res SP - e276 VL - 19 IS - 7 KW - healthcare research KW - information dissemination KW - open access to information KW - social networking KW - reproducibility of results N2 - Background: There is an emergent and intensive dialogue in the United States with regard to the accessibility, reproducibility, and rigor of health research. This discussion is also closely aligned with the need to identify sustainable ways to expand the national research enterprise and to generate actionable results that can be applied to improve the nation?s health. The principles and practices of Open Science offer a promising path to address both goals by facilitating (1) increased transparency of data and methods, which promotes research reproducibility and rigor; and (2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge in proximal domains, thereby resulting in greater productivity and a reduction in redundant research investments. Objectives: AcademyHealth?s Electronic Data Methods (EDM) Forum implemented a proof-of-concept open science platform for health research called the Collaborative Informatics Environment for Learning on Health Outcomes (CIELO). Methods: The EDM Forum conducted a user-centered design process to elucidate important and high-level requirements for creating and sustaining an open science paradigm. Results: By implementing CIELO and engaging a variety of potential users in its public beta testing, the EDM Forum has been able to elucidate a broad range of stakeholder needs and requirements related to the use of an open science platform focused on health research in a variety of ?real world? settings. Conclusions: Our initial design and development experience over the course of the CIELO project has provided the basis for a vigorous dialogue between stakeholder community members regarding the capabilities that will add the greatest value to an open science platform for the health research community. A number of important questions around user incentives, sustainability, and scalability will require further community dialogue and agreement. UR - http://www.jmir.org/2017/7/e276/ UR - http://dx.doi.org/10.2196/jmir.6937 UR - http://www.ncbi.nlm.nih.gov/pubmed/28760728 ID - info:doi/10.2196/jmir.6937 ER - TY - JOUR AU - Brinker, Josef Titus AU - Schadendorf, Dirk AU - Klode, Joachim AU - Cosgarea, Ioana AU - Rösch, Alexander AU - Jansen, Philipp AU - Stoffels, Ingo AU - Izar, Benjamin PY - 2017/07/26 TI - Photoaging Mobile Apps as a Novel Opportunity for Melanoma Prevention: Pilot Study JO - JMIR Mhealth Uhealth SP - e101 VL - 5 IS - 7 KW - melanoma KW - skin cancer KW - prevention KW - mobile apps KW - smartphones KW - photoaging N2 - Background: Around 90% of melanomas are caused by ultraviolet (UV) exposure and are therefore eminently preventable. Unhealthy tanning behavior is mostly initiated in early adolescence, often with the belief that it increases attractiveness; the problems related to skin atrophy and malignant melanoma are too far in the future to fathom. Photoaging desktop programs, in which an image is altered to predict future appearance, have been successful in positively influencing behavior in adiposity or tobacco prevention settings. Objective: To develop and test a photoaging app designed for melanoma prevention. Methods: We harnessed the widespread availability of mobile phones and adolescents? interest in appearance to develop a free mobile app called Sunface. This app has the user take a self-portrait (ie, a selfie), and then photoages the image based on Fitzpatrick skin type and individual UV protection behavior. Afterward, the app explains the visual results and aims at increasing self-competence on skin cancer prevention by providing guideline recommendations on sun protection and the ABCDE rule for melanoma self-detection. The underlying aging algorithms are based on publications showing UV-induced skin damage by outdoor as well as indoor tanning. To get a first impression on how well the app would be received in a young target group, we included a total sample of 25 students in our cross-sectional pilot study with a median age of 22 (range 19-25) years of both sexes (11/25, 44% female; 14/25, 56% male) attending the University of Essen in Germany. Results: The majority of enrolled students stated that they would download the app (22/25, 88%), that the intervention had the potential to motivate them to use sun protection (23/25, 92%) and that they thought such an app could change their perceptions that tanning makes you attractive (19/25, 76%). Only a minority of students disagreed or fully disagreed that they would download such an app (2/25, 8%) or that such an app could change their perceptions on tanning and attractiveness (4/25, 16%). Conclusions: Based on previous studies and the initial study results presented here, it is reasonable to speculate that the app may induce behavioral change in the target population. Further work is required to implement and examine the effectiveness of app-based photoaging interventions within risk groups from various cultural backgrounds. UR - http://mhealth.jmir.org/2017/7/e101/ UR - http://dx.doi.org/10.2196/mhealth.8231 UR - http://www.ncbi.nlm.nih.gov/pubmed/28747297 ID - info:doi/10.2196/mhealth.8231 ER - TY - JOUR AU - Dogan, Ezgi AU - Sander, Christian AU - Wagner, Xenija AU - Hegerl, Ulrich AU - Kohls, Elisabeth PY - 2017/07/24 TI - Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review JO - J Med Internet Res SP - e262 VL - 19 IS - 7 KW - review KW - mood disorders KW - smartphone KW - ecological momentary assessment N2 - Background: Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones. Objective: We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes. Methods: We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. Results: A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage. Conclusions: The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field. UR - http://www.jmir.org/2017/7/e262/ UR - http://dx.doi.org/10.2196/jmir.7006 UR - http://www.ncbi.nlm.nih.gov/pubmed/28739561 ID - info:doi/10.2196/jmir.7006 ER - TY - JOUR AU - Kaiser, Tim AU - Laireiter, Rupert Anton PY - 2017/07/20 TI - DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy JO - JMIR Med Inform SP - e20 VL - 5 IS - 3 KW - health information management KW - mental health KW - mental disorders KW - psychotherapeutic processes KW - algorithms N2 - Background: In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners. Objective: Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes. Methods: In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible. Results: The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice. Conclusions: With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy. UR - http://medinform.jmir.org/2017/3/e20/ UR - http://dx.doi.org/10.2196/medinform.6808 UR - http://www.ncbi.nlm.nih.gov/pubmed/28729233 ID - info:doi/10.2196/medinform.6808 ER - TY - JOUR AU - Knell, Gregory AU - Gabriel, Pettee Kelley AU - Businelle, S. Michael AU - Shuval, Kerem AU - Wetter, W. David AU - Kendzor, E. Darla PY - 2017/07/18 TI - Ecological Momentary Assessment of Physical Activity: Validation Study JO - J Med Internet Res SP - e253 VL - 19 IS - 7 KW - accelerometry KW - behavioral risk factor surveillance system KW - ecological momentary assessment KW - self-report KW - data accuracy N2 - Background: Ecological momentary assessment (EMA) may elicit physical activity (PA) estimates that are less prone to bias than traditional self-report measures while providing context. Objectives: The objective of this study was to examine the convergent validity of EMA-assessed PA compared with accelerometry. Methods: The participants self-reported their PA using International Physical Activity Questionnaire (IPAQ) and Behavioral Risk Factor Surveillance System (BRFSS) and wore an accelerometer while completing daily EMAs (delivered through the mobile phone) for 7 days. Weekly summary estimates included sedentary time and moderate-, vigorous-, and moderate-to vigorous-intensity physical activity (MVPA). Spearman coefficients and Lin?s concordance correlation coefficients (LCC) examined the linear association and agreement for EMA and the questionnaires as compared with accelerometry. Results: Participants were aged 43.3 (SD 13.1) years, 51.7% (123/238) were African American, 74.8% (178/238) were overweight or obese, and 63.0% (150/238) were low income. The linear associations of EMA and traditional self-reports with accelerometer estimates were statistically significant (P<.05) for sedentary time (EMA: ?=.16), moderate-intensity PA (EMA: ?=.29; BRFSS: ?=.17; IPAQ: ?=.24), and MVPA (EMA: ?=.31; BRFSS: ?=.17; IPAQ: ?=.20). Only EMA estimates of PA were statistically significant compared with accelerometer for agreement. Conclusions: The mobile EMA showed better correlation and agreement to accelerometer estimates than traditional self-report methods. These findings suggest that mobile EMA may be a practical alternative to accelerometers to assess PA in free-living settings. UR - http://www.jmir.org/2017/7/e253/ UR - http://dx.doi.org/10.2196/jmir.7602 UR - http://www.ncbi.nlm.nih.gov/pubmed/28720556 ID - info:doi/10.2196/jmir.7602 ER - TY - JOUR AU - Wongkoblap, Akkapon AU - Vadillo, A. Miguel AU - Curcin, Vasa PY - 2017/06/29 TI - Researching Mental Health Disorders in the Era of Social Media: Systematic Review JO - J Med Internet Res SP - e228 VL - 19 IS - 6 KW - mental health KW - mental disorders KW - social networking KW - artificial intelligence KW - machine learning KW - public health informatics KW - depression KW - anxiety KW - infodemiology N2 - Background: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. UR - http://www.jmir.org/2017/6/e228/ UR - http://dx.doi.org/10.2196/jmir.7215 UR - http://www.ncbi.nlm.nih.gov/pubmed/28663166 ID - info:doi/10.2196/jmir.7215 ER - TY - JOUR AU - Kahler, W. Christopher AU - Lechner, J. William AU - MacGlashan, James AU - Wray, B. Tyler AU - Littman, L. Michael PY - 2017/06/28 TI - Initial Progress Toward Development of a Voice-Based Computer-Delivered Motivational Intervention for Heavy Drinking College Students: An Experimental Study JO - JMIR Ment Health SP - e25 VL - 4 IS - 2 KW - Computer-delivered intervention KW - voice-based systems KW - alcohol intervention KW - heavy drinking N2 - Background: Computer-delivered interventions have been shown to be effective in reducing alcohol consumption in heavy drinking college students. However, these computer-delivered interventions rely on mouse, keyboard, or touchscreen responses for interactions between the users and the computer-delivered intervention. The principles of motivational interviewing suggest that in-person interventions may be effective, in part, because they encourage individuals to think through and speak aloud their motivations for changing a health behavior, which current computer-delivered interventions do not allow. Objective: The objective of this study was to take the initial steps toward development of a voice-based computer-delivered intervention that can ask open-ended questions and respond appropriately to users? verbal responses, more closely mirroring a human-delivered motivational intervention. Methods: We developed (1) a voice-based computer-delivered intervention that was run by a human controller and that allowed participants to speak their responses to scripted prompts delivered by speech generation software and (2) a text-based computer-delivered intervention that relied on the mouse, keyboard, and computer screen for all interactions. We randomized 60 heavy drinking college students to interact with the voice-based computer-delivered intervention and 30 to interact with the text-based computer-delivered intervention and compared their ratings of the systems as well as their motivation to change drinking and their drinking behavior at 1-month follow-up. Results: Participants reported that the voice-based computer-delivered intervention engaged positively with them in the session and delivered content in a manner consistent with motivational interviewing principles. At 1-month follow-up, participants in the voice-based computer-delivered intervention condition reported significant decreases in quantity, frequency, and problems associated with drinking, and increased perceived importance of changing drinking behaviors. In comparison to the text-based computer-delivered intervention condition, those assigned to voice-based computer-delivered intervention reported significantly fewer alcohol-related problems at the 1-month follow-up (incident rate ratio 0.60, 95% CI 0.44-0.83, P=.002). The conditions did not differ significantly on perceived importance of changing drinking or on measures of drinking quantity and frequency of heavy drinking. Conclusions: Results indicate that it is feasible to construct a series of open-ended questions and a bank of responses and follow-up prompts that can be used in a future fully automated voice-based computer-delivered intervention that may mirror more closely human-delivered motivational interventions to reduce drinking. Such efforts will require using advanced speech recognition capabilities and machine-learning approaches to train a program to mirror the decisions made by human controllers in the voice-based computer-delivered intervention used in this study. In addition, future studies should examine enhancements that can increase the perceived warmth and empathy of voice-based computer-delivered intervention, possibly through greater personalization, improvements in the speech generation software, and embodying the computer-delivered intervention in a physical form. UR - http://mental.jmir.org/2017/2/e25/ UR - http://dx.doi.org/10.2196/mental.7571 UR - http://www.ncbi.nlm.nih.gov/pubmed/28659259 ID - info:doi/10.2196/mental.7571 ER - TY - JOUR AU - Brady, John Christopher AU - Mudie, Iluka Lucy AU - Wang, Xueyang AU - Guallar, Eliseo AU - Friedman, Steven David PY - 2017/06/20 TI - Improving Consensus Scoring of Crowdsourced Data Using the Rasch Model: Development and Refinement of a Diagnostic Instrument JO - J Med Internet Res SP - e222 VL - 19 IS - 6 KW - crowdsourcing KW - diabetic retinopathy KW - Rasch analysis KW - Amazon Mechanical Turk N2 - Background: Diabetic retinopathy (DR) is a leading cause of vision loss in working age individuals worldwide. While screening is effective and cost effective, it remains underutilized, and novel methods are needed to increase detection of DR. This clinical validation study compared diagnostic gradings of retinal fundus photographs provided by volunteers on the Amazon Mechanical Turk (AMT) crowdsourcing marketplace with expert-provided gold-standard grading and explored whether determination of the consensus of crowdsourced classifications could be improved beyond a simple majority vote (MV) using regression methods. Objective: The aim of our study was to determine whether regression methods could be used to improve the consensus grading of data collected by crowdsourcing. Methods: A total of 1200 retinal images of individuals with diabetes mellitus from the Messidor public dataset were posted to AMT. Eligible crowdsourcing workers had at least 500 previously approved tasks with an approval rating of 99% across their prior submitted work. A total of 10 workers were recruited to classify each image as normal or abnormal. If half or more workers judged the image to be abnormal, the MV consensus grade was recorded as abnormal. Rasch analysis was then used to calculate worker ability scores in a random 50% training set, which were then used as weights in a regression model in the remaining 50% test set to determine if a more accurate consensus could be devised. Outcomes of interest were the percent correctly classified images, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) for the consensus grade as compared with the expert grading provided with the dataset. Results: Using MV grading, the consensus was correct in 75.5% of images (906/1200), with 75.5% sensitivity, 75.5% specificity, and an AUROC of 0.75 (95% CI 0.73-0.78). A logistic regression model using Rasch-weighted individual scores generated an AUROC of 0.91 (95% CI 0.88-0.93) compared with 0.89 (95% CI 0.86-92) for a model using unweighted scores (chi-square P value<.001). Setting a diagnostic cut-point to optimize sensitivity at 90%, 77.5% (465/600) were graded correctly, with 90.3% sensitivity, 68.5% specificity, and an AUROC of 0.79 (95% CI 0.76-0.83). Conclusions: Crowdsourced interpretations of retinal images provide rapid and accurate results as compared with a gold-standard grading. Creating a logistic regression model using Rasch analysis to weight crowdsourced classifications by worker ability improves accuracy of aggregated grades as compared with simple majority vote. UR - http://www.jmir.org/2017/6/e222/ UR - http://dx.doi.org/10.2196/jmir.7984 UR - http://www.ncbi.nlm.nih.gov/pubmed/28634154 ID - info:doi/10.2196/jmir.7984 ER - TY - JOUR AU - Chen, Yuzen Robert AU - Feltes, Robert Jordan AU - Tzeng, Shun William AU - Lu, Yunzhu Zoe AU - Pan, Michael AU - Zhao, Nan AU - Talkin, Rebecca AU - Javaherian, Kavon AU - Glowinski, Anne AU - Ross, Will PY - 2017/06/16 TI - Phone-Based Interventions in Adolescent Psychiatry: A Perspective and Proof of Concept Pilot Study With a Focus on Depression and Autism JO - JMIR Res Protoc SP - e114 VL - 6 IS - 6 KW - telemedicine KW - depression KW - autistic disorder KW - mobile applications KW - text messaging KW - child KW - mental health N2 - Background: Telemedicine has emerged as an innovative platform to diagnose and treat psychiatric disorders in a cost-effective fashion. Previous studies have laid the functional framework for monitoring and treating child psychiatric disorders electronically using videoconferencing, mobile phones (smartphones), and Web-based apps. However, phone call and text message (short message service, SMS) interventions in adolescent psychiatry are less studied than other electronic platforms. Further investigations on the development of these interventions are needed. Objective: The aim of this paper was to explore the utility of text message interventions in adolescent psychiatry and describe a user feedback-driven iterative design process for text message systems. Methods: We developed automated text message interventions using a platform for both depression (EpxDepression) and autism spectrum disorder (ASD; EpxAutism) and conducted 2 pilot studies for each intervention (N=3 and N=6, respectively). The interventions were prescribed by and accessible to the patients? healthcare providers. EpxDepression and EpxAutism utilized an automated system to triage patients into 1 of 3 risk categories based on their text responses and alerted providers directly via phone and an online interface when patients met provider-specified risk criteria. Rapid text-based feedback from participants and interviews with providers allowed for quick iterative cycles to improve interventions. Results: Patients using EpxDepression had high weekly response rates (100% over 2 to 4 months), but exhibited message fatigue with daily prompts with mean (SD) overall response rates of 66.3% (21.6%) and 64.7% (8.2%) for mood and sleep questionnaires, respectively. In contrast, parents using EpxAutism displayed both high weekly and overall response rates (100% and 85%, respectively, over 1 to 4 months) that did not decay significantly with time. Monthly participant feedback surveys for EpxDepression (7 surveys) and EpxAutism (18 surveys) preliminarily indicated that for both interventions, daily messages constituted the ?perfect amount? of contact and that EpxAutism, but not EpxDepression, improved patient communication with providers. Notably, EpxDepression detected thoughts of self-harm in patients before their case managers or caregivers were aware of such ideation. Conclusions: Text-message interventions in adolescent psychiatry can provide a cost-effective and engaging method to track symptoms, behavior, and ideation over time. Following the collection of pilot data and feedback from providers and patients, larger studies are already underway to validate the clinical utility of EpxDepression and EpxAutism. Trial Registration: Clinicaltrials.gov NCT03002311; https://clinicaltrials.gov/ct2/show/NCT03002311 (Archived by WebCite at http://www.webcitation.org/6qQtlCIS0) UR - http://www.researchprotocols.org/2017/6/e114/ UR - http://dx.doi.org/10.2196/resprot.7245 UR - http://www.ncbi.nlm.nih.gov/pubmed/28623183 ID - info:doi/10.2196/resprot.7245 ER - TY - JOUR AU - Modi, A. Riddhi AU - Mugavero, J. Michael AU - Amico, K. Rivet AU - Keruly, Jeanne AU - Quinlivan, Byrd Evelyn AU - Crane, M. Heidi AU - Guzman, Alfredo AU - Zinski, Anne AU - Montue, Solange AU - Roytburd, Katya AU - Church, Anna AU - Willig, H. James PY - 2017/06/16 TI - A Web-Based Data Collection Platform for MultisiteRandomized Behavioral Intervention Trials: Development, Key Software Features, and Results of a User Survey JO - JMIR Res Protoc SP - e115 VL - 6 IS - 6 KW - iENGAGE KW - software design KW - behavioral research KW - survey KW - Web application KW - HIV KW - user perspective N2 - Background: Meticulous tracking of study data must begin early in the study recruitment phase and must account for regulatory compliance, minimize missing data, and provide high information integrity and/or reduction of errors. In behavioral intervention trials, participants typically complete several study procedures at different time points. Among HIV-infected patients, behavioral interventions can favorably affect health outcomes. In order to empower newly diagnosed HIV positive individuals to learn skills to enhance retention in HIV care, we developed the behavioral health intervention Integrating ENGagement and Adherence Goals upon Entry (iENGAGE) funded by the National Institute of Allergy and Infectious Diseases (NIAID), where we deployed an in-clinic behavioral health intervention in 4 urban HIV outpatient clinics in the United States. To scale our intervention strategy homogenously across sites, we developed software that would function as a behavioral sciences research platform. Objective: This manuscript aimed to: (1) describe the design and implementation of a Web-based software application to facilitate deployment of a multisite behavioral science intervention; and (2) report on results of a survey to capture end-user perspectives of the impact of this platform on the conduct of a behavioral intervention trial. Methods: In order to support the implementation of the NIAID-funded trial iENGAGE, we developed software to deploy a 4-site behavioral intervention for new clinic patients with HIV/AIDS. We integrated the study coordinator into the informatics team to participate in the software development process. Here, we report the key software features and the results of the 25-item survey to evaluate user perspectives on research and intervention activities specific to the iENGAGE trial (N=13). Results: The key features addressed are study enrollment, participant randomization, real-time data collection, facilitation of longitudinal workflow, reporting, and reusability. We found 100% user agreement (13/13) that participation in the database design and/or testing phase made it easier to understand user roles and responsibilities and recommended participation of research teams in developing databases for future studies. Users acknowledged ease of use, color flags, longitudinal work flow, and data storage in one location as the most useful features of the software platform and issues related to saving participant forms, security restrictions, and worklist layout as least useful features. Conclusions: The successful development of the iENGAGE behavioral science research platform validated an approach of early and continuous involvement of the study team in design development. In addition, we recommend post-hoc collection of data from users as this led to important insights on how to enhance future software and inform standard clinical practices. Trial Registration: Clinicaltrials.gov NCT01900236; (https://clinicaltrials.gov/ct2/show/NCT01900236 (Archived by WebCite at http://www.webcitation.org/6qAa8ld7v) UR - http://www.researchprotocols.org/2017/6/e115/ UR - http://dx.doi.org/10.2196/resprot.6768 UR - http://www.ncbi.nlm.nih.gov/pubmed/28623185 ID - info:doi/10.2196/resprot.6768 ER - TY - JOUR AU - Thompson, David AU - Mackay, Teresa AU - Matthews, Maria AU - Edwards, Judith AU - Peters, S. Nicholas AU - Connolly, B. Susan PY - 2017/06/12 TI - Direct Adherence Measurement Using an Ingestible Sensor Compared With Self-Reporting in High-Risk Cardiovascular Disease Patients Who Knew They Were Being Measured: A Prospective Intervention JO - JMIR Mhealth Uhealth SP - e76 VL - 5 IS - 6 KW - cardiac prevention and rehabilitation KW - adherence KW - mHealth KW - remote monitoring KW - cardiovascular diseases KW - primary prevention KW - medication adherence KW - telemedicine N2 - Background: Use of appropriate cardioprotective medication is a cornerstone of cardiovascular disease prevention, but less-than-optimal patient adherence is common. Thus, strategies for improving adherence are recommended to adopt a multifaceted approach. Objective: The objective of our study was to test a system comprising a biodegradable, ingestible sensor for direct measurement of medication ingestion in a group of patients at elevated cardiovascular risk attending a cardiac prevention and rehabilitation program. Methods: In this prospective intervention trial in a single group of 21 patients running from April 2014 to June 2015, we measured adherence by self-report and adherence determined objectively by the system. The sensor emits a signal when it encounters the acidic environment of the stomach, detectable by an externally worn patch and linked software app. Longitudinal adherence data in the form of daily progress charts for sensed dosing events as compared with scheduled dosing are visible to patients on their tablet computer?s medication dosing app, thus providing patients with continuous medication adherence feedback. We sought feedback on patient acceptability by questionnaire assessment. Participants used the system for the 12-week period of their cardiac prevention and rehabilitation program. Results: Only 1 patient at initial assessment and 1 patient at end-of-program assessment reported often missing medication. The remaining patients reported never missing medication or had missing data. Only 12 (57%) of patients overall achieved system-determined adherence of 80% or more, and 3 patients had scores below 40%. Participants reported high levels of acceptability. Conclusions: This integrated system was well tolerated in a group of 21 patients over an appreciable time frame. Its ability to measure adherence reveals the sizeable disconnect between patient self-reported adherence and actual medication taking and has promising potential for clinical use as a tool to encourage better medication-taking behavior due to its ability to provide continuous patient-level feedback. UR - http://mhealth.jmir.org/2017/6/e76/ UR - http://dx.doi.org/10.2196/mhealth.6998 UR - http://www.ncbi.nlm.nih.gov/pubmed/28606895 ID - info:doi/10.2196/mhealth.6998 ER - TY - JOUR AU - Bauermeister, Jose AU - Giguere, Rebecca AU - Leu, Cheng-Shiun AU - Febo, Irma AU - Cranston, Ross AU - Mayer, Kenneth AU - Carballo-Diéguez, Alex PY - 2017/06/09 TI - Interactive Voice Response System: Data Considerations and Lessons Learned During a Rectal Microbicide Placebo Adherence Trial for Young Men Who Have Sex With Men JO - J Med Internet Res SP - e207 VL - 19 IS - 6 KW - user-computer interface KW - speech recognition software KW - HIV KW - survey methodology N2 - Background: Rectal microbicides, if proven effective, may aid in reducing human immunodeficiency virus (HIV) incidence; however, demonstration of efficacy and effectiveness is contingent on accurate measurement of product adherence. Delays in self-report, in particular, may affect the accuracy of behavioral data. Objective: The aim of this study was to capitalize on mobile phone use by young men who have sex with men (YMSM), and examine the use of an interactive voice response system (IVRS) by YMSM aged 18-30 years enrolled in a multisite, 12-week microbicide safety and acceptability trial. Methods: YMSM (N=95) enrolled across 3 sites (Boston, Pittsburgh, and San Juan) were asked to report their use of an applicator applied placebo rectal gel product during receptive anal intercourse (RAI) using the IVRS. IVRS was available in Spanish and English. After the 12-week trial, we examined whether IVRS problems were associated with YMSM?s sociodemographic characteristics (eg, age, race and ethnicity, and education), sexual behavior, or recruitment site. We used a multinomial logistic regression to compare YMSM who experienced no IVRS problems (n=40) with those who reported one IVRS problem (n=25) or two or more IVRS problems (n=30). Results: We recorded 1494 IVRS calls over 12 weeks. Over half of the participants (55/95; 58%) experienced challenges using the IVRS during the 12-week trial. YMSM reporting greater RAI occasions during the trial were more likely to experience one (odds ratio [OR]=1.08, 95% CI (1.02-1.14); P ?.01) or more (OR=1.10, 95% CI (1.03-1.16); P ?.001) IVRS challenges. Greater educational attainment was associated with multiple IVRS challenges (OR=7.08, 95% CI (1.6-31.6); P ?.01). Participants in the Puerto Rico site were most likely to report multiple IVRS problems. Conclusions: Although IVRS was a useful data collection technology in our trial, several challenges experienced by English and Spanish speaking YMSM diminish its overall acceptability. We discuss strategies to optimize future development of IVRS data quality protocols based on lessons learned. UR - http://www.jmir.org/2017/6/e207/ UR - http://dx.doi.org/10.2196/jmir.7682 UR - http://www.ncbi.nlm.nih.gov/pubmed/28600275 ID - info:doi/10.2196/jmir.7682 ER - TY - JOUR AU - Aramaki, Eiji AU - Shikata, Shuko AU - Ayaya, Satsuki AU - Kumagaya, Shin-Ichiro PY - 2017/05/16 TI - Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services JO - JMIR Res Protoc SP - e83 VL - 6 IS - 5 KW - allergy KW - crowdsourcing KW - disease risk KW - automatic abduction KW - Tohjisha-Kenkyu KW - self-support study N2 - Background: Hypothesis generation is an essential task for clinical research, and it can require years of research experience to formulate a meaningful hypothesis. Recent studies have endeavored to apply crowdsourcing to generate novel hypotheses for research. In this study, we apply crowdsourcing to explore previously unknown allergy-associated factors. Objective: In this study, we aimed to collect and test hypotheses of unknown allergy-associated factors using a crowdsourcing service. Methods: Using a series of questionnaires, we asked crowdsourcing participants to provide hypotheses on associated factors for seven different allergies, and validated the candidate hypotheses with odds ratios calculated for each associated factor. We repeated this abductive validation process to identify a set of reliable hypotheses. Results: We obtained two primary findings: (1) crowdsourcing showed that 8 of the 13 known hypothesized allergy risks were statically significant; and (2) among the total of 157 hypotheses generated by the crowdsourcing service, 75 hypotheses were statistically significant allergy-associated factors, comprising the 8 known risks and 53 previously unknown allergy-associated factors. These findings suggest that there are still many topics to be examined in future allergy studies. Conclusions: Crowdsourcing generated new hypotheses on allergy-associated factors. In the near future, clinical trials should be conducted to validate the hypotheses generated in this study. UR - http://www.researchprotocols.org/2017/5/e83/ UR - http://dx.doi.org/10.2196/resprot.5851 UR - http://www.ncbi.nlm.nih.gov/pubmed/28512079 ID - info:doi/10.2196/resprot.5851 ER - TY - JOUR AU - Mantokoudis, Georgios AU - Koller, Roger AU - Guignard, Jérémie AU - Caversaccio, Marco AU - Kompis, Martin AU - Senn, Pascal PY - 2017/04/24 TI - Influence of Telecommunication Modality, Internet Transmission Quality, and Accessories on Speech Perception in Cochlear Implant Users JO - J Med Internet Res SP - e135 VL - 19 IS - 4 KW - communication aids for disabled KW - telecommunications devices for the deaf KW - cochlear implants KW - speech discrimination tests KW - hearing loss KW - telephone N2 - Background: Telecommunication is limited or even impossible for more than one-thirds of all cochlear implant (CI) users. Objective: We sought therefore to study the impact of voice quality on speech perception with voice over Internet protocol (VoIP) under real and adverse network conditions. Methods: Telephone speech perception was assessed in 19 CI users (15-69 years, average 42 years), using the German HSM (Hochmair-Schulz-Moser) sentence test comparing Skype and conventional telephone (public switched telephone networks, PSTN) transmission using a personal computer (PC) and a digital enhanced cordless telecommunications (DECT) telephone dual device. Five different Internet transmission quality modes and four accessories (PC speakers, headphones, 3.5 mm jack audio cable, and induction loop) were compared. As a secondary outcome, the subjective perceived voice quality was assessed using the mean opinion score (MOS). Results: Speech telephone perception was significantly better (median 91.6%, P<.001) with Skype compared with PSTN (median 42.5%) under optimal conditions. Skype calls under adverse network conditions (data packet loss > 15%) were not superior to conventional telephony. In addition, there were no significant differences between the tested accessories (P>.05) using a PC. Coupling a Skype DECT phone device with an audio cable to the CI, however, resulted in higher speech perception (median 65%) and subjective MOS scores (3.2) than using PSTN (median 7.5%, P<.001). Conclusions: Skype calls significantly improve speech perception for CI users compared with conventional telephony under real network conditions. Listening accessories do not further improve listening experience. Current Skype DECT telephone devices do not fully offer technical advantages in voice quality. UR - http://www.jmir.org/2017/4/e135/ UR - http://dx.doi.org/10.2196/jmir.6954 UR - http://www.ncbi.nlm.nih.gov/pubmed/28438727 ID - info:doi/10.2196/jmir.6954 ER - TY - JOUR AU - Berry, Natalie AU - Lobban, Fiona AU - Belousov, Maksim AU - Emsley, Richard AU - Nenadic, Goran AU - Bucci, Sandra PY - 2017/04/05 TI - #WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems JO - J Med Internet Res SP - e107 VL - 19 IS - 4 KW - mental health KW - Twitter KW - social media N2 - Background: Use of the social media website Twitter is highly prevalent and has led to a plethora of Web-based social and health-related data available for use by researchers. As such, researchers are increasingly using data from social media to retrieve and analyze mental health-related content. However, there is limited evidence regarding why people use this emerging platform to discuss mental health problems in the first place. Objectives: The aim of this study was to explore the reasons why individuals discuss mental health on the social media website Twitter. The study was the first of its kind to implement a study-specific hashtag for research; therefore, we also examined how feasible it was to circulate and analyze a study-specific hashtag for mental health research. Methods: Text mining methods using the Twitter Streaming Application Programming Interface (API) and Twitter Search API were used to collect and organize tweets from the hashtag #WhyWeTweetMH, circulated between September 2015 and November 2015. Tweets were analyzed thematically to understand the key reasons for discussing mental health using the Twitter platform. Results: Four overarching themes were derived from the 132 tweets collected: (1) sense of community; (2) raising awareness and combatting stigma; (3) safe space for expression; and (4) coping and empowerment. In addition, 11 associated subthemes were also identified. Conclusions: The themes derived from the content of the tweets highlight the perceived therapeutic benefits of Twitter through the provision of support and information and the potential for self-management strategies. The ability to use Twitter to combat stigma and raise awareness of mental health problems indicates the societal benefits that can be facilitated via the platform. The number of tweets and themes identified demonstrates the feasibility of implementing study-specific hashtags to explore research questions in the field of mental health and can be used as a basis for other health-related research. UR - http://www.jmir.org/2017/4/e107/ UR - http://dx.doi.org/10.2196/jmir.6173 UR - http://www.ncbi.nlm.nih.gov/pubmed/28381392 ID - info:doi/10.2196/jmir.6173 ER - TY - JOUR AU - Rupert, J. Douglas AU - Poehlman, A. Jon AU - Hayes, J. Jennifer AU - Ray, E. Sarah AU - Moultrie, R. Rebecca PY - 2017/03/22 TI - Virtual Versus In-Person Focus Groups: Comparison of Costs, Recruitment, and Participant Logistics JO - J Med Internet Res SP - e80 VL - 19 IS - 3 KW - focus groups KW - virtual systems KW - online systems KW - videoconferencing KW - qualitative research KW - communication KW - mobile apps KW - diabetes mellitus N2 - Background: Virtual focus groups?such as online chat and video groups?are increasingly promoted as qualitative research tools. Theoretically, virtual groups offer several advantages, including lower cost, faster recruitment, greater geographic diversity, enrollment of hard-to-reach populations, and reduced participant burden. However, no study has compared virtual and in-person focus groups on these metrics. Objective: To rigorously compare virtual and in-person focus groups on cost, recruitment, and participant logistics. We examined 3 focus group modes and instituted experimental controls to ensure a fair comparison. Methods: We conducted 6 1-hour focus groups in August 2014 using in-person (n=2), live chat (n=2), and video (n=2) modes with individuals who had type 2 diabetes (n=48 enrolled, n=39 completed). In planning groups, we solicited bids from 6 virtual platform vendors and 4 recruitment firms. We then selected 1 platform or facility per mode and a single recruitment firm across all modes. To minimize bias, the recruitment firm employed different recruiters by mode who were blinded to recruitment efforts for other modes. We tracked enrollment during a 2-week period. A single moderator conducted all groups using the same guide, which addressed the use of technology to communicate with health care providers. We conducted the groups at the same times of day on Monday to Wednesday during a single week. At the end of each group, participants completed a short survey. Results: Virtual focus groups offered minimal cost savings compared with in-person groups (US $2000 per chat group vs US $2576 per in-person group vs US $2,750 per video group). Although virtual groups did not incur travel costs, they often had higher management fees and miscellaneous expenses (eg, participant webcams). Recruitment timing did not differ by mode, but show rates were higher for in-person groups (94% [15/16] in-person vs 81% [13/16] video vs 69% [11/16] chat). Virtual group participants were more geographically diverse (but with significant clustering around major metropolitan areas) and more likely to be non-white, less educated, and less healthy. Internet usage was higher among virtual group participants, yet virtual groups still reached light Internet users. In terms of burden, chat groups were easiest to join and required the least preparation (chat = 13 minutes, video = 40 minutes, in-person = 78 minutes). Virtual group participants joined using laptop or desktop computers, and most virtual participants (82% [9/11] chat vs 62% [8/13] video) reported having no other people in their immediate vicinity. Conclusions: Virtual focus groups offer potential advantages for participant diversity and reaching less healthy populations. However, virtual groups do not appear to cost less or recruit participants faster than in-person groups. Further research on virtual group data quality and group dynamics is needed to fully understand their advantages and limitations. UR - http://www.jmir.org/2017/3/e80/ UR - http://dx.doi.org/10.2196/jmir.6980 UR - http://www.ncbi.nlm.nih.gov/pubmed/28330832 ID - info:doi/10.2196/jmir.6980 ER - TY - JOUR AU - Sebo, Paul AU - Maisonneuve, Hubert AU - Cerutti, Bernard AU - Fournier, Pascal Jean AU - Senn, Nicolas AU - Haller, M. Dagmar PY - 2017/03/22 TI - Rates, Delays, and Completeness of General Practitioners? Responses to a Postal Versus Web-Based Survey: A Randomized Trial JO - J Med Internet Res SP - e83 VL - 19 IS - 3 KW - participation rate KW - response time KW - completeness KW - survey methods KW - primary care N2 - Background: Web-based surveys have become a new and popular method for collecting data, but only a few studies have directly compared postal and Web-based surveys among physicians, and none to our knowledge among general practitioners (GPs). Objective: Our aim is to compare two modes of survey delivery (postal and Web-based) in terms of participation rates, response times, and completeness of questionnaires in a study assessing GPs? preventive practices. Methods: This randomized study was conducted in Western Switzerland (Geneva and Vaud) and in France (Alsace and Pays de la Loire) in 2015. A random selection of community-based GPs (1000 GPs in Switzerland and 2400 GPs in France) were randomly allocated to receive a questionnaire about preventive care activities either by post (n=700 in Switzerland, n=400 in France) or by email (n=300 in Switzerland, n=2000 in France). Reminder messages were sent once in the postal group and twice in the Web-based group. Any GPs practicing only complementary and alternative medicine were excluded from the study. Results: Among the 3400 contacted GPs, 764 (22.47%, 95% CI 21.07%-23.87%) returned the questionnaire. Compared to the postal group, the participation rate in the Web-based group was more than four times lower (246/2300, 10.70% vs 518/1100, 47.09%, P<.001), but median response time was much shorter (1 day vs 1-3 weeks, P<.001) and the number of GPs having fully completed the questionnaire was almost twice as high (157/246, 63.8% vs 179/518, 34.6%, P<.001). Conclusions: Web-based surveys offer many advantages such as reduced response time, higher completeness of data, and large cost savings, but our findings suggest that postal surveys can be still considered for GP research. The use of mixed-mode approaches is probably a good strategy to increase GPs? participation in surveys while reducing costs. UR - http://www.jmir.org/2017/3/e83/ UR - http://dx.doi.org/10.2196/jmir.6308 UR - http://www.ncbi.nlm.nih.gov/pubmed/28330830 ID - info:doi/10.2196/jmir.6308 ER - TY - JOUR AU - Delgado-Gomez, David AU - Peñuelas-Calvo, Inmaculada AU - Masó-Besga, Eduardo Antonio AU - Vallejo-Oñate, Silvia AU - Baltasar Tello, Itziar AU - Arrua Duarte, Elsa AU - Vera Varela, Constanza María AU - Carballo, Juan AU - Baca-García, Enrique PY - 2017/03/20 TI - Microsoft Kinect-based Continuous Performance Test: An Objective Attention Deficit Hyperactivity Disorder Assessment JO - J Med Internet Res SP - e79 VL - 19 IS - 3 KW - kinect KW - attention deficit hyperactivity disorder KW - continuous performance test KW - impulsivity KW - hyperactivity N2 - Background: One of the major challenges in mental medical care is finding out new instruments for an accurate and objective evaluation of the attention deficit hyperactivity disorder (ADHD). Early ADHD identification, severity assessment, and prompt treatment are essential to avoid the negative effects associated with this mental condition. Objective: The aim of our study was to develop a novel ADHD assessment instrument based on Microsoft Kinect, which identifies ADHD cardinal symptoms in order to provide a more accurate evaluation. Methods: A group of 30 children, aged 8-12 years (10.3 [SD 1.4]; male 70% [21/30]), who were referred to the Child and Adolescent Psychiatry Unit of the Department of Psychiatry at Fundación Jiménez Díaz Hospital (Madrid, Spain), were included in this study. Children were required to meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria of ADHD diagnosis. One of the parents or guardians of the children filled the Spanish version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN) rating scale used in clinical practice. Each child conducted a Kinect-based continuous performance test (CPT) in which the reaction time (RT), the commission errors, and the time required to complete the reaction (CT) were calculated. The correlations of the 3 predictors, obtained using Kinect methodology, with respect to the scores of the SWAN scale were calculated. Results: The RT achieved a correlation of -.11, -.29, and -.37 with respect to the inattention, hyperactivity, and impulsivity factors of the SWAN scale. The correlations of the commission error with respect to these 3 factors were -.03, .01, and .24, respectively. Conclusions: Our findings show a relation between the Microsoft Kinect-based version of the CPT and ADHD symptomatology assessed through parental report. Results point out the importance of future research on the development of objective measures for the diagnosis of ADHD among children and adolescents. UR - http://www.jmir.org/2017/3/e79/ UR - http://dx.doi.org/10.2196/jmir.6985 UR - http://www.ncbi.nlm.nih.gov/pubmed/28320691 ID - info:doi/10.2196/jmir.6985 ER - TY - JOUR AU - Place, Skyler AU - Blanch-Hartigan, Danielle AU - Rubin, Channah AU - Gorrostieta, Cristina AU - Mead, Caroline AU - Kane, John AU - Marx, P. Brian AU - Feast, Joshua AU - Deckersbach, Thilo AU - Pentland, ?Sandy? Alex AU - Nierenberg, Andrew AU - Azarbayejani, Ali PY - 2017/03/16 TI - Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders JO - J Med Internet Res SP - e75 VL - 19 IS - 3 KW - mHealth KW - post-traumatic stress disorders KW - depression KW - behavioral symptoms N2 - Background: There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. Objective: The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. Methods: A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants? mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. Results: Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). Conclusions: Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed. UR - http://www.jmir.org/2017/3/e75/ UR - http://dx.doi.org/10.2196/jmir.6678 UR - http://www.ncbi.nlm.nih.gov/pubmed/28302595 ID - info:doi/10.2196/jmir.6678 ER - TY - JOUR AU - Burke, E. Lora AU - Shiffman, Saul AU - Music, Edvin AU - Styn, A. Mindi AU - Kriska, Andrea AU - Smailagic, Asim AU - Siewiorek, Daniel AU - Ewing, J. Linda AU - Chasens, Eileen AU - French, Brian AU - Mancino, Juliet AU - Mendez, Dara AU - Strollo, Patrick AU - Rathbun, L. Stephen PY - 2017/03/15 TI - Ecological Momentary Assessment in Behavioral Research: Addressing Technological and Human Participant Challenges JO - J Med Internet Res SP - e77 VL - 19 IS - 3 KW - ecological momentary assessment KW - relapse KW - obesity KW - smartphone KW - eating behavior KW - adherence N2 - Background: Ecological momentary assessment (EMA) assesses individuals? current experiences, behaviors, and moods as they occur in real time and in their natural environment. EMA studies, particularly those of longer duration, are complex and require an infrastructure to support the data flow and monitoring of EMA completion. Objective: Our objective is to provide a practical guide to developing and implementing an EMA study, with a focus on the methods and logistics of conducting such a study. Methods: The EMPOWER study was a 12-month study that used EMA to examine the triggers of lapses and relapse following intentional weight loss. We report on several studies that informed the implementation of the EMPOWER study: (1) a series of pilot studies, (2) the EMPOWER study?s infrastructure, (3) training of study participants in use of smartphones and the EMA protocol and, (4) strategies used to enhance adherence to completing EMA surveys. Results: The study enrolled 151 adults and had 87.4% (132/151) retention rate at 12 months. Our learning experiences in the development of the infrastructure to support EMA assessments for the 12-month study spanned several topic areas. Included were the optimal frequency of EMA prompts to maximize data collection without overburdening participants; the timing and scheduling of EMA prompts; technological lessons to support a longitudinal study, such as proper communication between the Android smartphone, the Web server, and the database server; and use of a phone that provided access to the system?s functionality for EMA data collection to avoid loss of data and minimize the impact of loss of network connectivity. These were especially important in a 1-year study with participants who might travel. It also protected the data collection from any server-side failure. Regular monitoring of participants? response to EMA prompts was critical, so we built in incentives to enhance completion of EMA surveys. During the first 6 months of the 12-month study interval, adherence to completing EMA surveys was high, with 88.3% (66,978/75,888) completion of random assessments and around 90% (23,411/25,929 and 23,343/26,010) completion of time-contingent assessments, despite the duration of EMA data collection and challenges with implementation. Conclusions: This work informed us of the necessary preliminary steps to plan and prepare a longitudinal study using smartphone technology and the critical elements to ensure participant engagement in the potentially burdensome protocol, which spanned 12 months. While this was a technology-supported and -programmed study, it required close oversight to ensure all elements were functioning correctly, particularly once human participants became involved. UR - http://www.jmir.org/2017/3/e77/ UR - http://dx.doi.org/10.2196/jmir.7138 UR - http://www.ncbi.nlm.nih.gov/pubmed/28298264 ID - info:doi/10.2196/jmir.7138 ER - TY - JOUR AU - Zhang, Jing AU - Sun, Lei AU - Liu, Yu AU - Wang, Hongyi AU - Sun, Ningling AU - Zhang, Puhong PY - 2017/03/08 TI - Mobile Device?Based Electronic Data Capture System Used in a Clinical Randomized Controlled Trial: Advantages and Challenges JO - J Med Internet Res SP - e66 VL - 19 IS - 3 KW - mEDC KW - electronic data capture KW - mobile data capture KW - mhealth KW - randomized controlled trial KW - clinical research N2 - Background: Electronic data capture (EDC) systems have been widely used in clinical research, but mobile device?based electronic data capture (mEDC) system has not been well evaluated. Objective: The aim of our study was to evaluate the feasibility, advantages, and challenges of mEDC in data collection, project management, and telemonitoring in a randomized controlled trial (RCT). Methods: We developed an mEDC to support an RCT called ?Telmisartan and Hydrochlorothiazide Antihypertensive Treatment (THAT)? study, which was a multicenter, double-blinded, RCT, with the purpose of comparing the efficacy of telmisartan and hydrochlorothiazide (HCTZ) monotherapy in high-sodium-intake patients with mild to moderate hypertension during a 60 days follow-up. Semistructured interviews were conducted during and after the trial to evaluate the feasibility, advantage, and challenge of mEDC. Nvivo version 9.0 (QSR International) was used to analyze records of interviews, and a thematic framework method was used to obtain outcomes. Results: The mEDC was successfully used to support the data collection and project management in all the 14 study hospitals. A total of 1333 patients were recruited with support of mEDC, of whom 1037 successfully completed all 4 visits. Across all visits, the average time needed for 141 questions per patient was 53 min, which were acceptable to both doctors and patients. All the interviewees, including 24 doctors, 53 patients, 1 clinical research associate (CRA), 1 project manager (PM), and 1 data manager (DM), expressed their satisfaction to nearly all the functions of the innovative mEDC in randomization, data collection, project management, quality control, and remote monitoring in real time. The average satisfaction score was 9.2 (scale, 0-10). The biggest challenge came from the stability of the mobile or Wi-Fi signal although it was not a problem in THAT study. Conclusions: The innovative mEDC has many merits and is well acceptable in supporting data collection and project management in a timely manner in clinical trial. UR - http://www.jmir.org/2017/3/e66/ UR - http://dx.doi.org/10.2196/jmir.6978 UR - http://www.ncbi.nlm.nih.gov/pubmed/28274907 ID - info:doi/10.2196/jmir.6978 ER - TY - JOUR AU - Pilkington, Karen AU - Loef, Martin AU - Polley, Marie PY - 2017/02/02 TI - Searching for Real-World Effectiveness of Health Care Innovations: Scoping Study of Social Prescribing for Diabetes JO - J Med Internet Res SP - e20 VL - 19 IS - 2 KW - diabetes mellitus, type 2 KW - evaluation studies KW - primary health care KW - program evaluation N2 - Background: Social prescribing is a process whereby primary care patients are linked or referred to nonmedical sources of support in the community and voluntary sector. It is a concept that has arisen in practice and implemented widely in the United Kingdom and has been evaluated by various organizations. Objective: The aim of our study was to characterize, collate, and analyze the evidence from evaluation of social prescribing for type 2 diabetes in the United Kingdom and Ireland, comparing information available on publicly available websites with the published literature. Methods: We used a broad, pragmatic definition of social prescribing and conducted Web-based searches for websites of organizations providing potentially relevant services. We also explored linked information. In parallel, we searched Medline, PubMed, Cochrane Library, Google Scholar, and reference lists for relevant studies published in peer-reviewed journals. We extracted the data systematically on the characteristics, any reported evaluation, outcomes measured and results, and terminology used to describe each service. Results: We identified 40 UK- or Ireland-based projects that referred people with type 2 diabetes and prediabetes to nonmedical interventions or services provided in the community. We located evaluations of 24 projects; 11 as published papers, 12 as Web-based reports, and 1 as both a paper and a Web-based report. The interventions and services identified included structured group educational programs, exercise referral schemes, and individualized advice and support with signposting of health-related activities in the community. Although specific interventions such as community-based group educational programs and exercise referral have been evaluated in randomized controlled trials, evaluation of individualized social prescribing services involving people with type 2 diabetes has, in most cases, used pre-post and mixed methods approaches. These evaluations report generic improvement in a broad range of outcomes and provide an insight into the criteria for the success of social prescribing services. Conclusions: Our study revealed the varied models of social prescribing and nonmedical, community-based services available to people with type 2 diabetes and the extent of evaluation of these, which would not have been achieved by searching databases alone. The findings of this scoping study do not prove that social prescribing is an effective measure for people with type 2 diabetes in the United Kingdom, but can be used to inform future evaluation and contribute to the development of the evidence base for social prescribing. Accessing Web-based information provides a potential method for investigating how specific innovative health concepts, such as social prescribing, have been translated, implemented, and evaluated in practice. Several challenges were encountered including defining the concept, focusing on process plus intervention, and searching diverse, evolving Web-based sources. Further exploration of this approach will inform future research on the application of innovative health care concepts into practice. UR - http://www.jmir.org/2017/2/e20/ UR - http://dx.doi.org/10.2196/jmir.6431 UR - http://www.ncbi.nlm.nih.gov/pubmed/28153817 ID - info:doi/10.2196/jmir.6431 ER - TY - JOUR AU - Chai, R. Peter AU - Carreiro, Stephanie AU - Innes, J. Brendan AU - Rosen, K. Rochelle AU - O'Cleirigh, Conall AU - Mayer, H. Kenneth AU - Boyer, W. Edward PY - 2017/01/13 TI - Digital Pills to Measure Opioid Ingestion Patterns in Emergency Department Patients With Acute Fracture Pain: A Pilot Study JO - J Med Internet Res SP - e19 VL - 19 IS - 1 KW - medication adherence KW - opioid KW - digital pills KW - digital health KW - emergency medicine KW - pain management N2 - Background: Nonadherence to prescribed regimens for opioid analgesic agents contributes to increasing opioid abuse and overdose death. Opioids are frequently prescribed on an as-needed basis, placing the responsibility to determine opioid dose and frequency with the patient. There is wide variability in physician prescribing patterns because of the lack of data describing how patients actually use as-needed opioid analgesics. Digital pill systems have a radiofrequency emitter that directly measures medication ingestion events, and they provide an opportunity to discover the dose, timing, and duration of opioid therapy. Objective: The purpose of this study was to determine the feasibility of a novel digital pill system to measure as-needed opioid ingestion patterns in patients discharged from the emergency department (ED) after an acute bony fracture. Methods: We used a digital pill with individuals who presented to a teaching hospital ED with an acute extremity fracture. The digital pill consisted of a digital radiofrequency emitter within a standard gelatin capsule that encapsulated an oxycodone tablet. When ingested, the gastric chloride ion gradient activated the digital pill, transmitting a radiofrequency signal that was received by a hip-worn receiver, which then transmitted the ingestion data to a cloud-based server. After a brief, hands-on training session in the ED, study participants were discharged home and used the digital pill system to ingest oxycodone prescribed as needed for pain for one week. We conducted pill counts to verify digital pill data and open-ended interviews with participants at their follow-up appointment with orthopedics or at one week after enrollment in the study to determine the knowledge, attitudes, beliefs, and practices regarding digital pills. We analyzed open-ended interviews using applied thematic analysis. Results: We recruited 10 study participants and recorded 96 ingestion events (87.3%, 96/110 accuracy). Study participants reported being able to operate all aspects of the digital pill system after their training. Two participants stopped using the digital pill, reporting they were in too much pain to focus on the novel technology. The digital pill system detected multiple simultaneous ingestion events by the digital pill system. Participants ingested a mean 8 (SD 5) digital pills during the study period and four participants continued on opioids at the end of the study period. After interacting with the digital pill system in the real world, participants found the system highly acceptable (80%, 8/10) and reported a willingness to continue to use a digital pill to improve medication adherence monitoring (90%, 9/10). Conclusions: The digital pill is a feasible method to measure real-time opioid ingestion patterns in individuals with acute pain and to develop real-time interventions if opioid abuse is detected. Deploying digital pills is possible through the ED with a short instructional course. Patients who used the digital pill accepted the technology. UR - http://www.jmir.org/2017/1/e19/ UR - http://dx.doi.org/10.2196/jmir.7050 UR - http://www.ncbi.nlm.nih.gov/pubmed/28087496 ID - info:doi/10.2196/jmir.7050 ER - TY - JOUR AU - Skonnord, Trygve AU - Steen, Finn AU - Skjeie, Holgeir AU - Fetveit, Arne AU - Brekke, Mette AU - Klovning, Atle PY - 2016/11/22 TI - Survey Email Scheduling and Monitoring in eRCTs (SESAMe): A Digital Tool to Improve Data Collection in Randomized Controlled Clinical Trials JO - J Med Internet Res SP - e311 VL - 18 IS - 11 KW - randomized controlled trials KW - data collection KW - surveys and questionnaires KW - quality improvement KW - sample size KW - Internet KW - email KW - text messaging N2 - Background: Electronic questionnaires can ease data collection in randomized controlled trials (RCTs) in clinical practice. We found no existing software that could automate the sending of emails to participants enrolled into an RCT at different study participant inclusion time points. Objective: Our aim was to develop suitable software to facilitate data collection in an ongoing multicenter RCT of low back pain (the Acuback study). For the Acuback study, we determined that we would need to send a total of 5130 emails to 270 patients recruited at different centers and at 19 different time points. Methods: The first version of the software was tested in a pilot study in November 2013 but was unable to deliver multiuser or Web-based access. We resolved these shortcomings in the next version, which we tested on the Web in February 2014. Our new version was able to schedule and send the required emails in the full-scale Acuback trial that started in March 2014. The system architecture evolved through an iterative, inductive process between the project study leader and the software programmer. The program was tested and updated when errors occurred. To evaluate the development of the software, we used a logbook, a research assistant dialogue, and Acuback trial participant queries. Results: We have developed a Web-based app, Survey Email Scheduling and Monitoring in eRCTs (SESAMe), that monitors responses in electronic surveys and sends reminders by emails or text messages (short message service, SMS) to participants. The overall response rate for the 19 surveys in the Acuback study increased from 76.4% (655/857) before we introduced reminders to 93.11% (1149/1234) after the new function (P<.001). Further development will aim at securing encryption and data storage. Conclusions: The SESAMe software facilitates consecutive patient data collection in RCTs and can be used to increase response rates and quality of research, both in general practice and in other clinical trial settings. UR - http://www.jmir.org/2016/11/e311/ UR - http://dx.doi.org/10.2196/jmir.6560 UR - http://www.ncbi.nlm.nih.gov/pubmed/27876689 ID - info:doi/10.2196/jmir.6560 ER - TY - JOUR AU - Kesse-Guyot, Emmanuelle AU - Assmann, Karen AU - Andreeva, Valentina AU - Castetbon, Katia AU - Méjean, Caroline AU - Touvier, Mathilde AU - Salanave, Benoît AU - Deschamps, Valérie AU - Péneau, Sandrine AU - Fezeu, Léopold AU - Julia, Chantal AU - Allès, Benjamin AU - Galan, Pilar AU - Hercberg, Serge PY - 2016/10/18 TI - Lessons Learned From Methodological Validation Research in E-Epidemiology JO - JMIR Public Health Surveill SP - e160 VL - 2 IS - 2 KW - cohort studies KW - bias, epidemiology N2 - Background: Traditional epidemiological research methods exhibit limitations leading to high logistics, human, and financial burden. The continued development of innovative digital tools has the potential to overcome many of the existing methodological issues. Nonetheless, Web-based studies remain relatively uncommon, partly due to persistent concerns about validity and generalizability. Objective: The objective of this viewpoint is to summarize findings from methodological studies carried out in the NutriNet-Santé study, a French Web-based cohort study. Methods: On the basis of the previous findings from the NutriNet-Santé e-cohort (>150,000 participants are currently included), we synthesized e-epidemiological knowledge on sample representativeness, advantageous recruitment strategies, and data quality. Results: Overall, the reported findings support the usefulness of Web-based studies in overcoming common methodological deficiencies in epidemiological research, in particular with regard to data quality (eg, the concordance for body mass index [BMI] classification was 93%), reduced social desirability bias, and access to a wide range of participant profiles, including the hard-to-reach subgroups such as young (12.30% [15,118/122,912], <25 years) and old people (6.60% [8112/122,912], ?65 years), unemployed or homemaker (12.60% [15,487/122,912]), and low educated (38.50% [47,312/122,912]) people. However, some selection bias remained (78.00% (95,871/122,912) of the participants were women, and 61.50% (75,590/122,912) had postsecondary education), which is an inherent aspect of cohort study inclusion; other specific types of bias may also have occurred. Conclusions: Given the rapidly growing access to the Internet across social strata, the recruitment of participants with diverse socioeconomic profiles and health risk exposures was highly feasible. Continued efforts concerning the identification of specific biases in e-cohorts and the collection of comprehensive and valid data are still needed. This summary of methodological findings from the NutriNet-Santé cohort may help researchers in the development of the next generation of high-quality Web-based epidemiological studies. UR - http://publichealth.jmir.org/2016/2/e160/ UR - http://dx.doi.org/10.2196/publichealth.5880 UR - http://www.ncbi.nlm.nih.gov/pubmed/27756715 ID - info:doi/10.2196/publichealth.5880 ER - TY - JOUR AU - Elgendi, Mohamed AU - Howard, Newton AU - Lovell, Nigel AU - Cichocki, Andrzej AU - Brearley, Matt AU - Abbott, Derek AU - Adatia, Ian PY - 2016/10/17 TI - A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives JO - JMIR Biomed Eng SP - e1 VL - 1 IS - 1 KW - mobile health KW - smart healthcare KW - affordable diagnostics KW - wearable devices KW - global health KW - eHealth KW - mHealth KW - point-of-care devices UR - http://biomedeng.jmir.org/2016/1/e1/ UR - http://dx.doi.org/10.2196/biomedeng.6401 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/biomedeng.6401 ER - TY - JOUR AU - Powell, Lauren AU - Parker, Jack AU - Martyn St-James, Marrissa AU - Mawson, Susan PY - 2016/10/07 TI - The Effectiveness of Lower-Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: A Systematic Review JO - J Med Internet Res SP - e259 VL - 18 IS - 10 KW - wearable technology KW - stroke KW - gait KW - rehabilitation N2 - Background: With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Regaining ambulation is a top priority for an increasing number of stroke survivors. However, despite an increase in research exploring these devices for lower limb rehabilitation, little is known of the effectiveness. Objective: This review aims to assess the effectiveness of lower limb wearable technology for improving activity and participation in adult stroke survivors. Methods: Randomized controlled trials (RCTs) of lower limb wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs. Results: In the review, we included 11 RCTs with collectively 550 participants at baseline and 474 participants at final follow-up including control groups and participants post stroke. Participants' stroke type and severity varied. Only one study found significant between-group differences for systems functioning and activity. Across the included RCTs, the lowest number of participants was 12 and the highest was 151 with a mean of 49 participants. The lowest number of participants to drop out of an RCT was zero in two of the studies and 19 in one study. Significant between-group differences were found across three of the 11 included trials. Out of the activity and participation measures alone, P values ranged from P=.87 to P ?.001. Conclusions: This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes, appropriateness of the RCT methodology for complex interventions, a lack of appropriate analysis of outcome data, and participant stroke severity. UR - http://www.jmir.org/2016/10/e259/ UR - http://dx.doi.org/10.2196/jmir.5891 UR - http://www.ncbi.nlm.nih.gov/pubmed/27717920 ID - info:doi/10.2196/jmir.5891 ER - TY - JOUR AU - Carpenter, Jordan AU - Crutchley, Patrick AU - Zilca, D. Ran AU - Schwartz, Andrew H. AU - Smith, K. Laura AU - Cobb, M. Angela AU - Parks, C. Acacia PY - 2016/08/31 TI - Seeing the ?Big? Picture: Big Data Methods for Exploring Relationships Between Usage, Language, and Outcome in Internet Intervention Data JO - J Med Internet Res SP - e241 VL - 18 IS - 8 KW - well-being intervention KW - big data KW - qualitative analysis KW - linguistic analysis KW - word cloud KW - multilevel modeling N2 - Background: Assessing the efficacy of Internet interventions that are already in the market introduces both challenges and opportunities. While vast, often unprecedented amounts of data may be available (hundreds of thousands, and sometimes millions of participants with high dimensions of assessed variables), the data are observational in nature, are partly unstructured (eg, free text, images, sensor data), do not include a natural control group to be used for comparison, and typically exhibit high attrition rates. New approaches are therefore needed to use these existing data and derive new insights that can augment traditional smaller-group randomized controlled trials. Objective: Our objective was to demonstrate how emerging big data approaches can help explore questions about the effectiveness and process of an Internet well-being intervention. Methods: We drew data from the user base of a well-being website and app called Happify. To explore effectiveness, multilevel models focusing on within-person variation explored whether greater usage predicted higher well-being in a sample of 152,747 users. In addition, to explore the underlying processes that accompany improvement, we analyzed language for 10,818 users who had a sufficient volume of free-text response and timespan of platform usage. A topic model constructed from this free text provided language-based correlates of individual user improvement in outcome measures, providing insights into the beneficial underlying processes experienced by users. Results: On a measure of positive emotion, the average user improved 1.38 points per week (SE 0.01, t122,455=113.60, P<.001, 95% CI 1.36?1.41), about a 27% increase over 8 weeks. Within a given individual user, more usage predicted more positive emotion and less usage predicted less positive emotion (estimate 0.09, SE 0.01, t6047=9.15, P=.001, 95% CI .07?.12). This estimate predicted that a given user would report positive emotion 1.26 points higher after a 2-week period when they used Happify daily than during a week when they didn?t use it at all. Among highly engaged users, 200 automatically clustered topics showed a significant (corrected P<.001) effect on change in well-being over time, illustrating which topics may be more beneficial than others when engaging with the interventions. In particular, topics that are related to addressing negative thoughts and feelings were correlated with improvement over time. Conclusions: Using observational analyses on naturalistic big data, we can explore the relationship between usage and well-being among people using an Internet well-being intervention and provide new insights into the underlying mechanisms that accompany it. By leveraging big data to power these new types of analyses, we can explore the workings of an intervention from new angles, and harness the insights that surface to feed back into the intervention and improve it further in the future. UR - http://www.jmir.org/2016/8/e241/ UR - http://dx.doi.org/10.2196/jmir.5725 UR - http://www.ncbi.nlm.nih.gov/pubmed/27580524 ID - info:doi/10.2196/jmir.5725 ER - TY - JOUR AU - Probst, Yasmine AU - Morrison, Evan AU - Sullivan, Emma AU - Dam, Khanh Hoa PY - 2016/07/28 TI - First-Stage Development and Validation of a Web-Based Automated Dietary Modeling Tool: Using Constraint Optimization Techniques to Streamline Food Group and Macronutrient Focused Dietary Prescriptions for Clinical Trials JO - J Med Internet Res SP - e190 VL - 18 IS - 7 KW - decision modeling KW - linear models KW - dietary requirements KW - clinical trial KW - food KW - programming, linear N2 - Background: Standardizing the background diet of participants during a dietary randomized controlled trial is vital to trial outcomes. For this process, dietary modeling based on food groups and their target servings is employed via a dietary prescription before an intervention, often using a manual process. Partial automation has employed the use of linear programming. Validity of the modeling approach is critical to allow trial outcomes to be translated to practice. Objective: This paper describes the first-stage development of a tool to automatically perform dietary modeling using food group and macronutrient requirements as a test case. The Dietary Modeling Tool (DMT) was then compared with existing approaches to dietary modeling (manual and partially automated), which were previously available to dietitians working within a dietary intervention trial. Methods: Constraint optimization techniques were implemented to determine whether nonlinear constraints are best suited to the development of the automated dietary modeling tool using food composition and food consumption data. Dietary models were produced and compared with a manual Microsoft Excel calculator, a partially automated Excel Solver approach, and the automated DMT that was developed. Results: The web-based DMT was produced using nonlinear constraint optimization, incorporating estimated energy requirement calculations, nutrition guidance systems, and the flexibility to amend food group targets for individuals. Percentage differences between modeling tools revealed similar results for the macronutrients. Polyunsaturated fatty acids and monounsaturated fatty acids showed greater variation between tools (practically equating to a 2-teaspoon difference), although it was not considered clinically significant when the whole diet, as opposed to targeted nutrients or energy requirements, were being addressed. Conclusions: Automated modeling tools can streamline the modeling process for dietary intervention trials ensuring consistency of the background diets, although appropriate constraints must be used in their development to achieve desired results. The DMT was found to be a valid automated tool producing similar results to tools with less automation. The results of this study suggest interchangeability of the modeling approaches used, although implementation should reflect the requirements of the dietary intervention trial in which it is used. UR - http://www.jmir.org/2016/7/e190/ UR - http://dx.doi.org/10.2196/jmir.5459 UR - http://www.ncbi.nlm.nih.gov/pubmed/27471104 ID - info:doi/10.2196/jmir.5459 ER - TY - JOUR AU - Suzuki, Teppei AU - Tani, Yuji AU - Ogasawara, Katsuhiko PY - 2016/07/25 TI - Behavioral Analysis of Visitors to a Medical Institution?s Website Using Markov Chain Monte Carlo Methods JO - J Med Internet Res SP - e199 VL - 18 IS - 7 KW - information-seeking behavior KW - Internet KW - media, social KW - Bayesian analysis KW - Web marketing N2 - Background: Consistent with the ?attention, interest, desire, memory, action? (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. Objective: We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. Methods: We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. Results: In the case of the keyword ?clinic name,? the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the keyword ?clinic name and regional name,? the probability for a repeated visit to the website and the mammography screening page was negative. In the case of the keyword ?clinic name + medical examination,? the visit probability to the website was positive, and the visit probability to the information page was negative. When visitors referred to the keywords ?mammography screening,? the visit probability to the mammography screening page was positive (95% highest posterior density interval = 3.38-26.66). Conclusions: Further analysis for not only the clinic website but also various other medical institution websites is necessary to build a general inspection model for medical institution websites; we want to consider this in future research. Additionally, we hope to use the results obtained in this study as a prior distribution for future work to conduct higher-precision analysis. UR - http://www.jmir.org/2016/7/e199/ UR - http://dx.doi.org/10.2196/jmir.5139 UR - http://www.ncbi.nlm.nih.gov/pubmed/27457537 ID - info:doi/10.2196/jmir.5139 ER - TY - JOUR AU - Lumsden, Jim AU - Edwards, A. Elizabeth AU - Lawrence, S. Natalia AU - Coyle, David AU - Munafò, R. Marcus PY - 2016/07/15 TI - Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy JO - JMIR Serious Games SP - e11 VL - 4 IS - 2 KW - gamification KW - gamelike KW - cognition KW - computer games KW - review N2 - Background: Cognitive tasks are typically viewed as effortful, frustrating, and repetitive, which often leads to participant disengagement. This, in turn, may negatively impact data quality and/or reduce intervention effects. However, gamification may provide a possible solution. If game design features can be incorporated into cognitive tasks without undermining their scientific value, then data quality, intervention effects, and participant engagement may be improved. Objectives: This systematic review aims to explore and evaluate the ways in which gamification has already been used for cognitive training and assessment purposes. We hope to answer 3 questions: (1) Why have researchers opted to use gamification? (2) What domains has gamification been applied in? (3) How successful has gamification been in cognitive research thus far? Methods: We systematically searched several Web-based databases, searching the titles, abstracts, and keywords of database entries using the search strategy (gamif* OR game OR games) AND (cognit* OR engag* OR behavi* OR health* OR attention OR motiv*). Searches included papers published in English between January 2007 and October 2015. Results: Our review identified 33 relevant studies, covering 31 gamified cognitive tasks used across a range of disorders and cognitive domains. We identified 7 reasons for researchers opting to gamify their cognitive training and testing. We found that working memory and general executive functions were common targets for both gamified assessment and training. Gamified tests were typically validated successfully, although mixed-domain measurement was a problem. Gamified training appears to be highly engaging and does boost participant motivation, but mixed effects of gamification on task performance were reported. Conclusions: Heterogeneous study designs and typically small sample sizes highlight the need for further research in both gamified training and testing. Nevertheless, careful application of gamification can provide a way to develop engaging and yet scientifically valid cognitive assessments, and it is likely worthwhile to continue to develop gamified cognitive tasks in the future. UR - http://games.jmir.org/2016/2/e11/ UR - http://dx.doi.org/10.2196/games.5888 UR - http://www.ncbi.nlm.nih.gov/pubmed/27421244 ID - info:doi/10.2196/games.5888 ER - TY - JOUR AU - Stokke, Randi PY - 2016/07/14 TI - The Personal Emergency Response System as a Technology Innovation in Primary Health Care Services: An Integrative Review JO - J Med Internet Res SP - e187 VL - 18 IS - 7 KW - home care services KW - caring practice KW - personal emergency alarm system KW - PERS KW - safety alarm KW - social alarm KW - telecare KW - review N2 - Background: Most western countries are experiencing greater pressure on community care services due to increased life expectancy and changes in policy toward prioritizing independent living. This has led to a demand for change and innovation in caring practices with an expected increased use of technology. Despite numerous attempts, it has proven surprisingly difficult to implement and adopt technological innovations. The main established technological innovation in home care services for older people is the personal emergency response system (PERS), which is widely adopted and used throughout most western countries aiming to support ?aging safely in place.? Objective: This integrative review examines how research literature describes use of the PERS focusing on the users? perspective, thus exploring how different actors experience the technology in use and how it affects the complex interactions between multiple actors in caring practices. Methods: The review presents an overview of the body of research on this well-established telecare solution, indicating what is important for different actors in regard to accepting and using this technology in community care services. An integrative review, recognized by a systematic search in major databases followed by a review process, was conducted. Results: The search resulted in 33 included studies describing different actors? experiences with the PERS in use. The overall focus was on the end users? experiences and the consequences of having and using the alarm, and how the technology changes caring practices and interactions between the actors. Conclusions: The PERS contributes to safety and independent living for users of the alarm, but there are also unforeseen consequences and possible improvements in the device and the integrated service. This rather simple and well-established telecare technology in use interacts with the actors involved, creating changes in daily living and even affecting their identities. This review argues for an approach to telecare in which the complexity of practice is accounted for and shows how the plug-and-play expectations producers tend to generate is a simplification of the reality. This calls for a recognition that place and actors matter, as does a sensitivity to technology as an integrated part of complex caring practices. UR - http://www.jmir.org/2016/7/e187/ UR - http://dx.doi.org/10.2196/jmir.5727 UR - http://www.ncbi.nlm.nih.gov/pubmed/27417422 ID - info:doi/10.2196/jmir.5727 ER - TY - JOUR AU - Liao, Yue AU - Skelton, Kara AU - Dunton, Genevieve AU - Bruening, Meg PY - 2016/06/21 TI - A Systematic Review of Methods and Procedures Used in Ecological Momentary Assessments of Diet and Physical Activity Research in Youth: An Adapted STROBE Checklist for Reporting EMA Studies (CREMAS) JO - J Med Internet Res SP - e151 VL - 18 IS - 6 KW - ecological momentary assessment KW - nutrition KW - physical activity KW - youth KW - systematic review KW - reporting checklist N2 - Background: Ecological momentary assessment (EMA) is a method of collecting real-time data based on careful timing, repeated measures, and observations that take place in a participant?s typical environment. Due to methodological advantages and rapid advancement in mobile technologies in recent years, more studies have adopted EMA in addressing topics of nutrition and physical activity in youth. Objective: The aim of this systematic review is to describe EMA methodology that has been used in studies addressing nutrition and physical activity in youth and provide a comprehensive checklist for reporting EMA studies. Methods: Thirteen studies were reviewed and analyzed for the following 5 areas of EMA methodology: (1) sampling and measures, (2) schedule, (3) technology and administration, (4) prompting strategy, and (5) response and compliance. Results: Results of this review showed a wide variability in the design and reporting of EMA studies in nutrition and physical activity among youth. The majority of studies (69%) monitored their participants during one period of time, although the monitoring period ranged from 4 to 14 days, and EMA surveys ranged from 2 to 68 times per day. More than half (54%) of the studies employed some type of electronic technology. Most (85%) of the studies used interval-contingent prompting strategy. For studies that utilized electronic devices with interval-contingent prompting strategy, none reported the actual number of EMA prompts received by participants out of the intended number of prompts. About half (46%) of the studies failed to report information about EMA compliance rates. For those who reported, compliance rates ranged from 44-96%, with an average of 71%. Conclusions: Findings from this review suggest that in order to identify best practices for EMA methodology in nutrition and physical activity research among youth, more standardized EMA reporting is needed. Missing the key information about EMA design features and participant compliance might lead to misinterpretation of results. Future nutrition and physical activity EMA studies need to be more rigorous and thorough in descriptions of methodology and results. A reporting checklist was developed with the goal of enhancing reliability, efficacy, and overall interpretation of the findings for future studies that use EMAs. UR - http://www.jmir.org/2016/6/e151/ UR - http://dx.doi.org/10.2196/jmir.4954 UR - http://www.ncbi.nlm.nih.gov/pubmed/27328833 ID - info:doi/10.2196/jmir.4954 ER - TY - JOUR AU - Hamann, Christoph AU - Schultze-Lutter, Frauke AU - Tarokh, Leila PY - 2016/06/15 TI - Web-Based Assessment of Mental Well-Being in Early Adolescence: A Reliability Study JO - J Med Internet Res SP - e138 VL - 18 IS - 6 KW - early adolescence KW - online assessment KW - reliability N2 - Background: The ever-increasing use of the Internet among adolescents represents an emerging opportunity for researchers to gain access to larger samples, which can be queried over several years longitudinally. Among adolescents, young adolescents (ages 11 to 13 years) are of particular interest to clinicians as this is a transitional stage, during which depressive and anxiety symptoms often emerge. However, it remains unclear whether these youngest adolescents can accurately answer questions about their mental well-being using a Web-based platform. Objective: The aim of the study was to examine the accuracy of responses obtained from Web-based questionnaires by comparing Web-based with paper-and-pencil versions of depression and anxiety questionnaires. Methods: The primary outcome was the score on the depression and anxiety questionnaires under two conditions: (1) paper-and-pencil and (2) Web-based versions. Twenty-eight adolescents (aged 11-13 years, mean age 12.78 years and SD 0.78; 18 females, 64%) were randomly assigned to complete either the paper-and-pencil or the Web-based questionnaire first. Intraclass correlation coefficients (ICCs) were calculated to measure intrarater reliability. Intraclass correlation coefficients were calculated separately for depression (Children?s Depression Inventory, CDI) and anxiety (Spence Children?s Anxiety Scale, SCAS) questionnaires. Results: On average, it took participants 17 minutes (SD 6) to answer 116 questions online. Intraclass correlation coefficient analysis revealed high intrarater reliability when comparing Web-based with paper-and-pencil responses for both CDI (ICC=.88; P<.001) and the SCAS (ICC=.95; P<.001). According to published criteria, both of these values are in the ?almost perfect? category indicating the highest degree of reliability. Conclusions: The results of the study show an excellent reliability of Web-based assessment in 11- to 13-year-old children as compared with the standard paper-pencil assessment. Furthermore, we found that Web-based assessments with young adolescents are highly feasible, with all enrolled participants completing the Web-based form. As early adolescence is a time of remarkable social and behavioral changes, these findings open up new avenues for researchers from diverse fields who are interested in studying large samples of young adolescents over time. UR - http://www.jmir.org/2016/6/e138/ UR - http://dx.doi.org/10.2196/jmir.5482 UR - http://www.ncbi.nlm.nih.gov/pubmed/27306932 ID - info:doi/10.2196/jmir.5482 ER - TY - JOUR AU - Crosier, Sage Benjamin AU - Brian, Marie Rachel AU - Ben-Zeev, Dror PY - 2016/06/14 TI - Using Facebook to Reach People Who Experience Auditory Hallucinations JO - J Med Internet Res SP - e160 VL - 18 IS - 6 KW - hearing voices KW - auditory hallucinations KW - social media KW - Facebook KW - survey KW - advertisements N2 - Background: Auditory hallucinations (eg, hearing voices) are relatively common and underreported false sensory experiences that may produce distress and impairment. A large proportion of those who experience auditory hallucinations go unidentified and untreated. Traditional engagement methods oftentimes fall short in reaching the diverse population of people who experience auditory hallucinations. Objective: The objective of this proof-of-concept study was to examine the viability of leveraging Web-based social media as a method of engaging people who experience auditory hallucinations and to evaluate their attitudes toward using social media platforms as a resource for Web-based support and technology-based treatment. Methods: We used Facebook advertisements to recruit individuals who experience auditory hallucinations to complete an 18-item Web-based survey focused on issues related to auditory hallucinations and technology use in American adults. We systematically tested multiple elements of the advertisement and survey layout including image selection, survey pagination, question ordering, and advertising targeting strategy. Each element was evaluated sequentially and the most cost-effective strategy was implemented in the subsequent steps, eventually deriving an optimized approach. Three open-ended question responses were analyzed using conventional inductive content analysis. Coded responses were quantified into binary codes, and frequencies were then calculated. Results: Recruitment netted N=264 total sample over a 6-week period. Ninety-seven participants fully completed all measures at a total cost of $8.14 per participant across testing phases. Systematic adjustments to advertisement design, survey layout, and targeting strategies improved data quality and cost efficiency. People were willing to provide information on what triggered their auditory hallucinations along with strategies they use to cope, as well as provide suggestions to others who experience auditory hallucinations. Women, people who use mobile phones, and those experiencing more distress, were reportedly more open to using Facebook as a support and/or therapeutic tool in the future. Conclusions: Facebook advertisements can be used to recruit research participants who experience auditory hallucinations quickly and in a cost-effective manner. Most (58%) Web-based respondents are open to Facebook-based support and treatment and are willing to describe their subjective experiences with auditory hallucinations. UR - http://www.jmir.org/2016/6/e160/ UR - http://dx.doi.org/10.2196/jmir.5420 UR - http://www.ncbi.nlm.nih.gov/pubmed/27302017 ID - info:doi/10.2196/jmir.5420 ER - TY - JOUR AU - Vaughan, S. Adam AU - Kramer, R. Michael AU - Cooper, LF Hannah AU - Rosenberg, S. Eli AU - Sullivan, S. Patrick PY - 2016/06/09 TI - Completeness and Reliability of Location Data Collected on the Web: Assessing the Quality of Self-Reported Locations in an Internet Sample of Men Who Have Sex With Men JO - J Med Internet Res SP - e142 VL - 18 IS - 6 KW - HIV KW - digital mapping KW - geographic locations KW - survey KW - men who have sex with men N2 - Background: Place is critical to our understanding of human immunodeficiency virus (HIV) infections among men who have sex with men (MSM) in the United States. However, within the scientific literature, place is almost always represented by residential location, suggesting a fundamental assumption of equivalency between neighborhood of residence, place of risk, and place of prevention. However, the locations of behaviors among MSM show significant spatial variation, and theory has posited the importance of nonresidential contextual exposures. This focus on residential locations has been at least partially necessitated by the difficulties in collecting detailed geolocated data required to explore nonresidential locations. Objective: Using a Web-based map tool to collect locations, which may be relevant to the daily lives and health behaviors of MSM, this study examines the completeness and reliability of the collected data. Methods: MSM were recruited on the Web and completed a Web-based survey. Within this survey, men used a map tool embedded within a question to indicate their homes and multiple nonresidential locations, including those representing work, sex, socialization, physician, and others. We assessed data quality by examining data completeness and reliability. We used logistic regression to identify demographic, contextual, and location-specific predictors of answering all eligible map questions and answering specific map questions. We assessed data reliability by comparing selected locations with other participant-reported data. Results: Of 247 men completing the survey, 167 (67.6%) answered the entire set of eligible map questions. Most participants (>80%) answered specific map questions, with sex locations being the least reported (80.6%). Participants with no college education were less likely than those with a college education to answer all map questions (prevalence ratio, 0.4; 95% CI, 0.2-0.8). Participants who reported sex at their partner?s home were less likely to indicate the location of that sex (prevalence ratio, 0.8; 95% CI, 0.7-1.0). Overall, 83% of participants placed their home?s location within the boundaries of their reported residential ZIP code. Of locations having a specific text description, the median distance between the participant-selected location and the location determined using the specific text description was 0.29 miles (25th and 75th percentiles, 0.06-0.88). Conclusions: Using this Web-based map tool, this Web-based sample of MSM was generally willing and able to provide accurate data regarding both home and nonresidential locations. This tool provides a mechanism to collect data that can be used in more nuanced studies of place and sexual risk and preventive behaviors of MSM. UR - http://www.jmir.org/2016/6/e142/ UR - http://dx.doi.org/10.2196/jmir.5701 UR - http://www.ncbi.nlm.nih.gov/pubmed/27283957 ID - info:doi/10.2196/jmir.5701 ER - TY - JOUR AU - Romano, Francesca Maria AU - Sardella, Vittoria Maria AU - Alboni, Fabrizio PY - 2016/06/06 TI - Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems JO - JMIR Res Protoc SP - e101 VL - 5 IS - 2 KW - Web questionnaire KW - Web health monitoring survey KW - telemedicine KW - virtual checkup KW - survey quality KW - quality indicators KW - paradata N2 - Background: Aging of the European population and interest in a healthy population in western countries have contributed to an increase in the number of health surveys, where the role of survey design, data collection, and data analysis methodology is clear and recognized by the whole scientific community. Survey methodology has had to couple with the challenges deriving from data collection through information and communications technology (ICT). Telemedicine systems have not used patients as a source of information, often limiting them to collecting only biometric data. A more effective telemonitoring system would be able to collect objective and subjective data (biometric parameters and symptoms reported by the patients themselves), and to control the quality of subjective data collected: this goal be achieved only by using and merging competencies from both survey methodology and health research. Objective: The objective of our study was to propose new metrics to control the quality of data, along with the well-known indicators of survey methodology. Web questionnaires administered daily to a group of patients for an extended length of time are a Web health monitoring survey (WHMS) in a telemedicine system. Methods: We calculated indicators based on paradata collected during a WHMS study involving 12 patients, who signed in to the website daily for 2 months. Results: The patients? involvement was very high: the patients? response rate ranged between 1.00 and 0.82, with an outlier of 0.65. Item nonresponse rate was very low, ranging between 0.0% and 7.4%. We propose adherence to the chosen time to connect to the website as a measure of involvement and cooperation by the patients: the difference from the median time ranged between 11 and 24 minutes, demonstrating very good cooperation and involvement from all patients. To measure habituation to the questionnaire, we also compared nonresponse rates to the items between the first and the second month of the study, and found no significant difference. We computed the time to complete the questionnaire both as a measure of possible burden for patient, and to detect the risk of automatic responses. Neither of these hypothesis was confirmed, and differences in time to completion seemed to depend on health conditions. Focus groups with patients confirmed their appreciation for this ?new? active role in a telemonitoring system. Conclusions: The main and innovative aspect of our proposal is the use of a Web questionnaire to virtually recreate a checkup visit, integrating subjective (patient?s information) with objective data (biometric information). Our results, although preliminary and if need of further study, appear promising in proposing more effective telemedicine systems. Survey methodology could have an effective role in this growing field of research and applications. UR - http://www.researchprotocols.org/2016/2/e101/ UR - http://dx.doi.org/10.2196/resprot.5187 UR - http://www.ncbi.nlm.nih.gov/pubmed/27268949 ID - info:doi/10.2196/resprot.5187 ER - TY - JOUR AU - Dunton, Fridlund Genevieve AU - Dzubur, Eldin AU - Intille, Stephen PY - 2016/06/01 TI - Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity JO - J Med Internet Res SP - e106 VL - 18 IS - 6 KW - mobile phones KW - ecological momentary assessment KW - accelerometer KW - physical activity N2 - Background: Objective physical activity monitors (eg, accelerometers) have high rates of nonwear and do not provide contextual information about behavior. Objective: This study tested performance and value of a mobile phone app that combined objective and real-time self-report methods to measure physical activity using sensor-informed context-sensitive ecological momentary assessment (CS-EMA). Methods: The app was programmed to prompt CS-EMA surveys immediately after 3 types of events detected by the mobile phone?s built-in motion sensor: (1) Activity (ie, mobile phone movement), (2) No-Activity (ie, mobile phone nonmovement), and (3) No-Data (ie, mobile phone or app powered off). In addition, the app triggered random (ie, signal-contingent) ecological momentary assessment (R-EMA) prompts (up to 7 per day). A sample of 39 ethnically diverse high school students in the United States (aged 14-18, 54% female) tested the app over 14 continuous days during nonschool time. Both CS-EMA and R-EMA prompts assessed activity type (eg, reading or doing homework, eating or drinking, sports or exercising) and contextual characteristics of the activity (eg, location, social company, purpose). Activity was also measured with a waist-worn Actigraph accelerometer. Results: The average CS-EMA + R-EMA prompt compliance and survey completion rates were 80.5% and 98.5%, respectively. More moderate-to-vigorous intensity physical activity was recorded by the waist-worn accelerometer in the 30 minutes before CS-EMA activity prompts (M=5.84 minutes) than CS-EMA No-Activity (M=1.11 minutes) and CS-EMA No-Data (M=0.76 minute) prompts (P?s<.001). Participants were almost 5 times as likely to report going somewhere (ie, active or motorized transit) in the 30 minutes before CS-EMA Activity than R-EMA prompts (odds ratio=4.91, 95% confidence interval=2.16-11.12). Conclusions: Mobile phone apps using motion sensor?informed CS-EMA are acceptable among high school students and may be used to augment objective physical activity data collected from traditional waist-worn accelerometers. UR - http://www.jmir.org/2016/6/e106/ UR - http://dx.doi.org/10.2196/jmir.5398 UR - http://www.ncbi.nlm.nih.gov/pubmed/27251313 ID - info:doi/10.2196/jmir.5398 ER - TY - JOUR AU - Levac, Danielle AU - Nawrotek, Joanna AU - Deschenes, Emilie AU - Giguere, Tia AU - Serafin, Julie AU - Bilodeau, Martin AU - Sveistrup, Heidi PY - 2016/06/01 TI - Development and Reliability Evaluation of the Movement Rating Instrument for Virtual Reality Video Game Play JO - JMIR Serious Games SP - e9 VL - 4 IS - 1 KW - active video games, virtual reality, physical therapy, movement, reliability N2 - Background: Virtual reality active video games are increasingly popular physical therapy interventions for children with cerebral palsy. However, physical therapists require educational resources to support decision making about game selection to match individual patient goals. Quantifying the movements elicited during virtual reality active video game play can inform individualized game selection in pediatric rehabilitation. Objective: The objectives of this study were to develop and evaluate the feasibility and reliability of the Movement Rating Instrument for Virtual Reality Game Play (MRI-VRGP). Methods: Item generation occurred through an iterative process of literature review and sample videotape viewing. The MRI-VRGP includes 25 items quantifying upper extremity, lower extremity, and total body movements. A total of 176 videotaped 90-second game play sessions involving 7 typically developing children and 4 children with cerebral palsy were rated by 3 raters trained in MRI-VRGP use. Children played 8 games on 2 virtual reality and active video game systems. Intraclass correlation coefficients (ICCs) determined intra-rater and interrater reliability. Results: Excellent intrarater reliability was evidenced by ICCs of >0.75 for 17 of the 25 items across the 3 raters. Interrater reliability estimates were less precise. Excellent interrater reliability was achieved for far reach upper extremity movements (ICC=0.92 [for right and ICC=0.90 for left) and for squat (ICC=0.80) and jump items (ICC=0.99), with 9 items achieving ICCs of >0.70, 12 items achieving ICCs of between 0.40 and 0.70, and 4 items achieving poor reliability (close-reach upper extremity-ICC=0.14 for right and ICC=0.07 for left) and single-leg stance (ICC=0.55 for right and ICC=0.27 for left). Conclusions: Poor video quality, differing item interpretations between raters, and difficulty quantifying the high-speed movements involved in game play affected reliability. With item definition clarification and further psychometric property evaluation, the MRI-VRGP could inform the content of educational resources for therapists by ranking games according to frequency and type of elicited body movements. UR - http://games.jmir.org/2016/1/e9/ UR - http://dx.doi.org/10.2196/games.5528 UR - http://www.ncbi.nlm.nih.gov/pubmed/27251029 ID - info:doi/10.2196/games.5528 ER - TY - JOUR AU - Juusola, L. Jessie AU - Quisel, R. Thomas AU - Foschini, Luca AU - Ladapo, A. Joseph PY - 2016/06/01 TI - The Impact of an Online Crowdsourcing Diagnostic Tool on Health Care Utilization: A Case Study Using a Novel Approach to Retrospective Claims Analysis JO - J Med Internet Res SP - e127 VL - 18 IS - 6 KW - crowdsourcing KW - diagnosis KW - mHealth KW - online systems N2 - Background: Patients with difficult medical cases often remain undiagnosed despite visiting multiple physicians. A new online platform, CrowdMed, uses crowdsourcing to quickly and efficiently reach an accurate diagnosis for these patients. Objective: This study sought to evaluate whether CrowdMed decreased health care utilization for patients who have used the service. Methods: Novel, electronic methods of patient recruitment and data collection were utilized. Patients who completed cases on CrowdMed?s platform between July 2014 and April 2015 were recruited for the study via email and screened via an online survey. After providing eConsent, participants provided identifying information used to access their medical claims data, which was retrieved through a third-party web application program interface (API). Utilization metrics including frequency of provider visits and medical charges were compared pre- and post-case resolution to assess the impact of resolving a case on CrowdMed. Results: Of 45 CrowdMed users who completed the study survey, comprehensive claims data was available via API for 13 participants, who made up the final enrolled sample. There were a total of 221 health care provider visits collected for the study participants, with service dates ranging from September 2013 to July 2015. Frequency of provider visits was significantly lower after resolution of a case on CrowdMed (mean of 1.07 visits per month pre-resolution vs. 0.65 visits per month post-resolution, P=.01). Medical charges were also significantly lower after case resolution (mean of US $719.70 per month pre-resolution vs. US $516.79 per month post-resolution, P=.03). There was no significant relationship between study results and disease onset date, and there was no evidence of regression to the mean influencing results. Conclusions: This study employed technology-enabled methods to demonstrate that patients who used CrowdMed had lower health care utilization after case resolution. However, since the final sample size was limited, results should be interpreted as a case study. Despite this limitation, the statistically significant results suggest that online crowdsourcing shows promise as an efficient method of solving difficult medical cases. UR - http://www.jmir.org/2016/6/e127/ UR - http://dx.doi.org/10.2196/jmir.5644 UR - http://www.ncbi.nlm.nih.gov/pubmed/27251384 ID - info:doi/10.2196/jmir.5644 ER - TY - JOUR AU - Masalski, Marcin AU - Kipi?ski, Lech AU - Grysi?ski, Tomasz AU - Kr?cicki, Tomasz PY - 2016/05/30 TI - Hearing Tests on Mobile Devices: Evaluation of the Reference Sound Level by Means of Biological Calibration JO - J Med Internet Res SP - e130 VL - 18 IS - 5 KW - hearing test, mobile device, calibration N2 - Background: Hearing tests carried out in home setting by means of mobile devices require previous calibration of the reference sound level. Mobile devices with bundled headphones create a possibility of applying the predefined level for a particular model as an alternative to calibrating each device separately. Objective: The objective of this study was to determine the reference sound level for sets composed of a mobile device and bundled headphones. Methods: Reference sound levels for Android-based mobile devices were determined using an open access mobile phone app by means of biological calibration, that is, in relation to the normal-hearing threshold. The examinations were conducted in 2 groups: an uncontrolled and a controlled one. In the uncontrolled group, the fully automated self-measurements were carried out in home conditions by 18- to 35-year-old subjects, without prior hearing problems, recruited online. Calibration was conducted as a preliminary step in preparation for further examination. In the controlled group, audiologist-assisted examinations were performed in a sound booth, on normal-hearing subjects verified through pure-tone audiometry, recruited offline from among the workers and patients of the clinic. In both the groups, the reference sound levels were determined on a subject?s mobile device using the Bekesy audiometry. The reference sound levels were compared between the groups. Intramodel and intermodel analyses were carried out as well. Results: In the uncontrolled group, 8988 calibrations were conducted on 8620 different devices representing 2040 models. In the controlled group, 158 calibrations (test and retest) were conducted on 79 devices representing 50 models. Result analysis was performed for 10 most frequently used models in both the groups. The difference in reference sound levels between uncontrolled and controlled groups was 1.50 dB (SD 4.42). The mean SD of the reference sound level determined for devices within the same model was 4.03 dB (95% CI 3.93-4.11). Statistically significant differences were found across models. Conclusions: Reference sound levels determined in the uncontrolled group are comparable to the values obtained in the controlled group. This validates the use of biological calibration in the uncontrolled group for determining the predefined reference sound level for new devices. Moreover, due to a relatively small deviation of the reference sound level for devices of the same model, it is feasible to conduct hearing screening on devices calibrated with the predefined reference sound level. UR - http://www.jmir.org/2016/5/e130/ UR - http://dx.doi.org/10.2196/jmir.4987 UR - http://www.ncbi.nlm.nih.gov/pubmed/27241793 ID - info:doi/10.2196/jmir.4987 ER - TY - JOUR AU - Rowe, Christopher AU - Hern, Jaclyn AU - DeMartini, Anna AU - Jennings, Danielle AU - Sommers, Mathew AU - Walker, John AU - Santos, Glenn-Milo PY - 2016/05/26 TI - Concordance of Text Message Ecological Momentary Assessment and Retrospective Survey Data Among Substance-Using Men Who Have Sex With Men: A Secondary Analysis of a Randomized Controlled Trial JO - JMIR mHealth uHealth SP - e44 VL - 4 IS - 2 KW - data collection KW - cell phones KW - drug users KW - drinking behavior KW - homosexuality, male N2 - Background: Alcohol and illicit drug use is more prevalent among men who have sex with men (MSM) compared to the general population and has been linked to HIV transmission in this population. Research assessing individual patterns of substance use often utilizes questionnaires or interviews that rely on retrospective self-reported information, which can be subject to recall bias. Ecological momentary assessment (EMA) is a set of methods developed to mitigate recall bias by collecting data about subjects? mental states and behaviors on a near real-time basis. EMA remains underutilized in substance use and HIV research. Objective: To assess the concordance between daily reports of substance use collected by EMA text messages (short message service, SMS) and retrospective questionnaires and identify predictors of daily concordance in a sample of MSM. Methods: We conducted a secondary analysis of EMA text responses (regarding behavior on the previous day) and audio computer-assisted self-interview (ACASI) survey data (14-day recall) from June 2013 to September 2014 as part of a randomized controlled trial assessing a pharmacologic intervention to reduce methamphetamine and alcohol use among nondependent MSM in San Francisco, California. Reports of daily methamphetamine use, alcohol use, and binge alcohol use (5 or more drinks on one occasion) were collected via EMA and ACASI and compared using McNemar?s tests. Demographic and behavioral correlates of daily concordance between EMA and ACASI were assessed for each substance, using separate multivariable logistic regression models, fit with generalized estimating equations. Results: Among 30 MSM, a total of 994 days were included in the analysis for methamphetamine use, 987 for alcohol use, and 981 for binge alcohol use. Methamphetamine (EMA 20%, ACASI 11%, P<.001) and alcohol use (EMA 40%, ACASI 35%, P=.001) were reported significantly more frequently via EMA versus ACASI. In multivariable analysis, text reporting of methamphetamine (adjusted odds ratio 0.06, 95% CI 0.04-0.10), alcohol (0.48, 0.33-0.69), and binge alcohol use (0.27, 0.17-0.42) was negatively associated with daily concordance in the reporting of each respective substance. Compared to white participants, African American participants were less likely to have daily concordance in methamphetamine (0.15, 0.05-0.43) and alcohol (0.2, 0.05-0.54) reporting, and other participants of color (ie, Asian, Hispanic, multi-racial) were less likely to have daily concordance in methamphetamine reporting (0.34, 0.12-1.00). College graduates were more likely to have daily concordance in methamphetamine reporting (6.79, 1.84-25.04) compared to those with no college experience. Conclusions: We found that methamphetamine and alcohol use were reported more frequently with daily EMA texts compared to retrospective ACASI, concordance varied among different racial/ethnic subgroups and education levels, and reported substance use by EMA text was associated with lower daily concordance with retrospective ACASI. These findings suggest that EMA methods may provide more complete reporting of frequent, discrete behaviors such as substance use. UR - http://mhealth.jmir.org/2016/2/e44/ UR - http://dx.doi.org/10.2196/mhealth.5368 UR - http://www.ncbi.nlm.nih.gov/pubmed/27230545 ID - info:doi/10.2196/mhealth.5368 ER - TY - JOUR AU - Fink, C. Jeffrey AU - Doerfler, M. Rebecca AU - Yoffe, R. Marni AU - Diamantidis, J. Clarissa AU - Blumenthal, B. Jacob AU - Siddiqui, Tariq AU - Gardner, F. James AU - Snitker, Soren AU - Zhan, Min PY - 2016/05/26 TI - Patient-Reported Safety Events in Chronic Kidney Disease Recorded With an Interactive Voice-Inquiry Dial-Response System: Monthly Report Analysis JO - J Med Internet Res SP - e125 VL - 18 IS - 5 KW - patient-reported outcomes KW - CKD KW - interactive voice-response system KW - patient safety N2 - Background: Monitoring patient-reported outcomes (PROs) may improve safety of chronic kidney disease (CKD) patients. Objective: Evaluate the performance of an interactive voice-inquiry dial-response system (IVRDS) in detecting CKD-pertinent adverse safety events outside of the clinical environment and compare the incidence of events using the IVDRS to that detected by paper diary. Methods: This was a 6-month study of Stage III-V CKD patients in the Safe Kidney Care (SKC) study. Participants crossed over from a paper diary to the IVDRS for recording patient-reported safety events defined as symptoms or events attributable to medications or care. The IVDRS was adapted from the SKC paper diary to record event frequency and remediation. Participants were auto-called weekly and permitted to self-initiate calls. Monthly reports were reviewed by two physician adjudicators for their clinical significance. Results: 52 participants were followed over a total of 1384 weeks. 28 out of 52 participants (54%) reported events using the IVDRS versus 8 out of 52 (15%) with the paper diary; hypoglycemia was the most common event for both methods. All IVDRS menu options were selected at least once except for confusion and rash. Events were reported on 121 calls, with 8 calls reporting event remediation by ambulance or emergency room (ER) visit. The event rate with the IVDRS and paper diary, with and without hypoglycemia, was 26.7 versus 4.7 and 18.3 versus 0.8 per 100 person weeks, respectively (P=.002 and P<.001). The frequent users (ie, >10 events) largely differed by method, and event rates excluding the most frequent user of each were 16.9 versus 2.5 per 100 person weeks, respectively (P<.001). Adjudicators found approximately half the 80 reports clinically significant, with about a quarter judged as actionable. Hypoglycemia was often associated with additional reports of fatigue and falling. Participants expressed favorable satisfaction with the IVDRS. Conclusions: Use of the IVDRS among CKD patients reveals a high frequency of clinically significant safety events and has the potential to be used as an important supplement to clinical care for improving patient safety. UR - http://www.jmir.org/2016/5/e125/ UR - http://dx.doi.org/10.2196/jmir.5203 UR - http://www.ncbi.nlm.nih.gov/pubmed/27230267 ID - info:doi/10.2196/jmir.5203 ER - TY - JOUR AU - Rhyner, Daniel AU - Loher, Hannah AU - Dehais, Joachim AU - Anthimopoulos, Marios AU - Shevchik, Sergey AU - Botwey, Henry Ransford AU - Duke, David AU - Stettler, Christoph AU - Diem, Peter AU - Mougiakakou, Stavroula PY - 2016/05/11 TI - Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study JO - J Med Internet Res SP - e101 VL - 18 IS - 5 KW - diabetes mellitus, type 1 KW - carbohydrate counting KW - computer vision systems KW - food recognition KW - meal assessment KW - mobile phone KW - food volume estimation N2 - Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, ?Inselspital? (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital?s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user?s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals. UR - http://www.jmir.org/2016/5/e101/ UR - http://dx.doi.org/10.2196/jmir.5567 UR - http://www.ncbi.nlm.nih.gov/pubmed/27170498 ID - info:doi/10.2196/jmir.5567 ER - TY - JOUR AU - Torous, John AU - Kiang, V. Mathew AU - Lorme, Jeanette AU - Onnela, Jukka-Pekka PY - 2016/05/05 TI - New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research JO - JMIR Mental Health SP - e16 VL - 3 IS - 2 KW - mental health KW - schizophrenia KW - evaluation KW - smartphone KW - informatics N2 - Background: A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective: Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods: We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results: We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions: Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health. UR - http://mental.jmir.org/2016/2/e16/ UR - http://dx.doi.org/10.2196/mental.5165 UR - http://www.ncbi.nlm.nih.gov/pubmed/27150677 ID - info:doi/10.2196/mental.5165 ER - TY - JOUR AU - Schoot, S. Tessa AU - Weenk, Mariska AU - van de Belt, H. Tom AU - Engelen, JLPG Lucien AU - van Goor, Harry AU - Bredie, JH Sebastian PY - 2016/05/05 TI - A New Cuffless Device for Measuring Blood Pressure: A Real-Life Validation Study JO - J Med Internet Res SP - e85 VL - 18 IS - 5 KW - hypertension KW - cuffless blood pressure monitor KW - wearable device KW - cardiovascular risk management KW - patient empowerment N2 - Background: Cuffless blood pressure (BP) monitoring devices, based on pulse transit time, are being developed as an easy-to-use, more convenient, fast, and relatively cheap alternative to conventional BP measuring devices based on cuff occlusion. Thereby they may provide a great alternative to BP self-measurement. Objective: The objective of our study was to evaluate the performance of the first release of the Checkme Health Monitor (Viatom Technology), a cuffless BP monitor, in a real-life setting. Furthermore, we wanted to investigate whether the posture of the volunteer and the position of the device relative to the heart level would influence its outcomes. Methods: Study volunteers fell into 3 BP ranges: high (>160 mmHg), normal (130?160 mmHg), and low (<130 mmHg). All requirements for test environment, observer qualification, volunteer recruitment, and BP measurements were met according to the European Society of Hypertension International Protocol (ESH-IP) for the validation of BP measurement devices. After calibrating the Checkme device, we measured systolic BP with Checkme and a validated, oscillometric reference BP monitor (RM). Measurements were performed in randomized order both in supine and in sitting position, and with Checkme at and above heart level. Results: We recruited 52 volunteers, of whom we excluded 15 (12 due to calibration failure with Checkme, 3 due to a variety of reasons). The remaining 37 volunteers were divided into low (n=14), medium (n=13), and high (n=10) BP ranges. There were 18 men and 19 women, with a mean age of 54.1 (SD 14.5) years, and mean recruitment systolic BP of 141.7 (SD 24.7) mmHg. BP results obtained by RM and Checkme correlated well. In the supine position, the difference between the RM and Checkme was >5 mmHg in 17 of 37 volunteers (46%), of whom 9 of 37 (24%) had a difference >10 mmHg and 5 of 37 (14%) had a difference >15 mmHg. Conclusions: BP obtained with Checkme correlated well with RM BP, particularly in the position (supine) in which the device was calibrated. These preliminary results are promising for conducting further research on cuffless BP measurement in the clinical and outpatient settings. UR - http://www.jmir.org/2016/5/e85/ UR - http://dx.doi.org/10.2196/jmir.5414 UR - http://www.ncbi.nlm.nih.gov/pubmed/27150527 ID - info:doi/10.2196/jmir.5414 ER - TY - JOUR AU - Priest, Chad AU - Knopf, Amelia AU - Groves, Doyle AU - Carpenter, S. Janet AU - Furrey, Christopher AU - Krishnan, Anand AU - Miller, R. Wendy AU - Otte, L. Julie AU - Palakal, Mathew AU - Wiehe, Sarah AU - Wilson, Jeffrey PY - 2016/03/09 TI - Finding the Patient?s Voice Using Big Data: Analysis of Users? Health-Related Concerns in the ChaCha Question-and-Answer Service (2009?2012) JO - J Med Internet Res SP - e44 VL - 18 IS - 3 KW - social meda KW - health information seeking KW - adolescent KW - sexual health KW - patient engagement KW - ChaCha KW - big data KW - question-and-answer service KW - infodemiology KW - infoveillance N2 - Background: The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large. Big datasets from social media and question-and-answer services provide insight into the public?s health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods and may prove a useful starting point for public-engagement health research (infodemiology). Objective: The objective of our study was to describe user characteristics and health-related queries of the ChaCha question-and-answer platform, and discuss how these data may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large. Methods: We conducted a retrospective automated textual analysis of anonymous user-generated queries submitted to ChaCha between January 2009 and November 2012. A total of 2.004 billion queries were read, of which 3.50% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses. Results: Males and females submitted roughly equal numbers of health queries, but content differed by sex. Questions from females predominantly focused on pregnancy, menstruation, and vaginal health. Questions from males predominantly focused on body image, drug use, and sexuality. Adolescents aged 12?19 years submitted more queries than any other age group. Their queries were largely centered on sexual and reproductive health, and pregnancy in particular. Conclusions: The private nature of the ChaCha service provided a perfect environment for maximum frankness among users, especially among adolescents posing sensitive health questions. Adolescents? sexual health queries reveal knowledge gaps with serious, lifelong consequences. The nature of questions to the service provides opportunities for rapid understanding of health concerns and may lead to development of more effective tailored interventions. UR - http://www.jmir.org/2016/3/e44/ UR - http://dx.doi.org/10.2196/jmir.5033 UR - http://www.ncbi.nlm.nih.gov/pubmed/26960745 ID - info:doi/10.2196/jmir.5033 ER - TY - JOUR AU - Hamad, O. Eradah AU - Savundranayagam, Y. Marie AU - Holmes, D. Jeffrey AU - Kinsella, Anne Elizabeth AU - Johnson, M. Andrew PY - 2016/03/08 TI - Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds JO - J Med Internet Res SP - e60 VL - 18 IS - 3 KW - health care social media KW - Twitter feeds KW - health care tweets KW - mixed methods research KW - content analysis KW - coding KW - computer-aided content analysis KW - infodemiology KW - infoveillance KW - digital disease detection N2 - Background: Twitter?s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. Objective: The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. Methods: We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results: Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. Conclusions: We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts. UR - http://www.jmir.org/2016/3/e60/ UR - http://dx.doi.org/10.2196/jmir.5391 UR - http://www.ncbi.nlm.nih.gov/pubmed/26957477 ID - info:doi/10.2196/jmir.5391 ER - TY - JOUR AU - Baumel, Amit AU - Muench, Fred PY - 2016/01/13 TI - Heuristic Evaluation of Ehealth Interventions: Establishing Standards That Relate to the Therapeutic Process Perspective JO - JMIR Mental Health SP - e5 VL - 3 IS - 1 KW - eHealth KW - mHealth KW - digital health KW - mobile health KW - heuristics KW - evaluation KW - principles KW - therapeutic process UR - http://mental.jmir.org/2016/1/e5/ UR - http://dx.doi.org/10.2196/mental.4563 UR - http://www.ncbi.nlm.nih.gov/pubmed/26764209 ID - info:doi/10.2196/mental.4563 ER - TY - JOUR AU - Abroms, C. Lorien AU - Whittaker, Robyn AU - Free, Caroline AU - Mendel Van Alstyne, Judith AU - Schindler-Ruwisch, M. Jennifer PY - 2015/12/21 TI - Developing and Pretesting a Text Messaging Program for Health Behavior Change: Recommended Steps JO - JMIR mHealth uHealth SP - e107 VL - 3 IS - 4 KW - mHealth KW - telemedicine KW - SMS KW - text messaging KW - behavior change KW - behavior modification N2 - Background: A growing body of evidence demonstrates that text messaging-based programs (short message service [SMS]) on mobile phones can help people modify health behaviors. Most of these programs have consisted of automated and sometimes interactive text messages that guide a person through the process of behavior change. Objective: This paper provides guidance on how to develop text messaging programs aimed at changing health behaviors. Methods: Based on their collective experience in designing, developing, and evaluating text messaging programs and a review of the literature, the authors drafted the guide. One author initially drafted the guide and the others provided input and review. Results: Steps for developing a text messaging program include conducting formative research for insights into the target audience and health behavior, designing the text messaging program, pretesting the text messaging program concept and messages, and revising the text messaging program. Conclusions: The steps outlined in this guide may help in the development of SMS-based behavior change programs. UR - http://mhealth.jmir.org/2015/4/e107/ UR - http://dx.doi.org/10.2196/mhealth.4917 UR - http://www.ncbi.nlm.nih.gov/pubmed/26690917 ID - info:doi/10.2196/mhealth.4917 ER - TY - JOUR AU - Kuang, Jinqiu AU - Argo, Lauren AU - Stoddard, Greg AU - Bray, E. Bruce AU - Zeng-Treitler, Qing PY - 2015/12/17 TI - Assessing Pictograph Recognition: A Comparison of Crowdsourcing and Traditional Survey Approaches JO - J Med Internet Res SP - e281 VL - 17 IS - 12 KW - crowdsourcing KW - patient discharge summaries KW - Amazon Mechanical Turk KW - pictograph recognition KW - cardiovascular N2 - Background: Compared to traditional methods of participant recruitment, online crowdsourcing platforms provide a fast and low-cost alternative. Amazon Mechanical Turk (MTurk) is a large and well-known crowdsourcing service. It has developed into the leading platform for crowdsourcing recruitment. Objective: To explore the application of online crowdsourcing for health informatics research, specifically the testing of medical pictographs. Methods: A set of pictographs created for cardiovascular hospital discharge instructions was tested for recognition. This set of illustrations (n=486) was first tested through an in-person survey in a hospital setting (n=150) and then using online MTurk participants (n=150). We analyzed these survey results to determine their comparability. Results: Both the demographics and the pictograph recognition rates of online participants were different from those of the in-person participants. In the multivariable linear regression model comparing the 2 groups, the MTurk group scored significantly higher than the hospital sample after adjusting for potential demographic characteristics (adjusted mean difference 0.18, 95% CI 0.08-0.28, P<.001). The adjusted mean ratings were 2.95 (95% CI 2.89-3.02) for the in-person hospital sample and 3.14 (95% CI 3.07-3.20) for the online MTurk sample on a 4-point Likert scale (1=totally incorrect, 4=totally correct). Conclusions: The findings suggest that crowdsourcing is a viable complement to traditional in-person surveys, but it cannot replace them. UR - http://www.jmir.org/2015/12/e281/ UR - http://dx.doi.org/10.2196/jmir.4582 UR - http://www.ncbi.nlm.nih.gov/pubmed/26678085 ID - info:doi/10.2196/jmir.4582 ER - TY - JOUR AU - Parker, Brent AU - Rajapakshe, Rasika AU - Moldovan, Andrew AU - Araujo, Cynthia AU - Crook, Juanita PY - 2015/09/28 TI - An Internet-Based Means of Monitoring Quality of Life in Post-Prostate Radiation Treatment: A Prospective Cohort Study JO - JMIR Res Protoc SP - e115 VL - 4 IS - 3 KW - prostate cancer KW - radiation oncology KW - quality of life KW - patient-reported outcomes KW - Internet survey KW - prospective study N2 - Background: Widespread integration of the Internet has resulted in an increase in the feasibility of using Web-based technologies as a means of communicating with patients. It may be possible to develop secure and standardized systems that facilitate Internet-based patient-reported outcomes which could be used to improve patient care. Objective: This study investigates patient interest in participating in an online post-treatment disease outcomes and quality of life monitoring program developed specifically for patients who have received radiation treatment for prostate cancer at a regional oncology center. Methods: Patients treated for prostate cancer between 2007 and 2011 (N=1113) at the British Columbia Cancer Agency, Centre for the Southern Interior were invited by mail to participate in a standardized questionnaire related to their post-treatment health. Overall participation rates were calculated. In addition, demographics, access to broadband Internet services, and treatment modalities were compared between participants and nonparticipants. Results: Of the 1030 eligible invitees, 358 (358/1030, 34.7%) completed the online questionnaire. Participation rates were higher in individuals younger than age 60 when compared to those age 60 or older (42% vs 31%) and also for those living in urban areas compared with rural (37% vs 29%) and in those who received brachytherapy versus external beam radiotherapy (EBRT) (41% vs 31%). Better participation rates were seen in individuals who had access to Internet connectivity based on the different types of broadband services (DSL 35% for those with DSL connectivity vs 29% for those without DSL connectivity; cable 35% vs 32%; wireless 38% vs 26%). After adjusting for age, the model indicates that lack of access to wireless broadband connectivity, living in a rural area, and receiving EBRT were significant predictors of lower participation. Conclusions: This study demonstrates that participation rates vary in patient populations within the interior region of British Columbia, especially with older patients, those in rural areas, and those with limited access to quality Internet services. UR - http://www.researchprotocols.org/2015/3/e115/ UR - http://dx.doi.org/10.2196/resprot.3974 UR - http://www.ncbi.nlm.nih.gov/pubmed/26416584 ID - info:doi/10.2196/resprot.3974 ER - TY - JOUR AU - Fissler, Tim AU - Bientzle, Martina AU - Cress, Ulrike AU - Kimmerle, Joachim PY - 2015/09/08 TI - The Impact of Advice Seekers? Need Salience and Doctors? Communication Style on Attitude and Decision Making: A Web-Based Mammography Consultation Role Play JO - JMIR Cancer SP - e10 VL - 1 IS - 2 KW - communication style KW - needs KW - need salience KW - attitude KW - decision-making KW - mammography screening KW - online consultation N2 - Background: Patients and advice seekers come to a medical consultation with typical needs, and physicians require adequate communication skills in order to address those needs effectively. It is largely unclear, however, to what extent advice seekers? attitudes toward a medical procedure or their resulting decisions are influenced by a physician?s communication that ignores or explicitly takes these needs into account. Objective: This experimental study tested how advice seekers? salient needs and doctor?s communication styles influenced advice seekers? attitudes toward mammography screening and their decision whether or not to participate in this procedure. Methods: One hundred women (age range 20-47 years, mean 25.22, SD 4.71) participated in an interactive role play of an online consultation. During the consultation, a fictitious, program-controlled physician provided information about advantages and disadvantages of mammography screening. The physician either merely communicated factual medical information or made additional comments using a communication style oriented toward advice seekers? typical needs for clarity and well-being. Orthogonal to this experimental treatment, participants? personal needs for clarity and for well-being were either made salient before or after the consultation with a needs questionnaire. We also measured all participants? attitudes toward mammography screening and their hypothetical decisions whether or not to participate before and after the experiment. Results: As assumed, the participants expressed strong needs for clarity (mean 4.57, SD 0.42) and for well-being (mean 4.21, SD 0.54) on 5-point Likert scales. Making these needs salient or not revealed significant interaction effects with the physician?s communication style regarding participants? attitude change (F1,92=7.23, P=.009, ?2=.073) and decision making (F1,92=4.43, P=.038, ?2=.046). Those participants whose needs were made salient before the consultation responded to the physician?s communication style, while participants without salient needs did not. When the physician used a need-oriented communication style, those participants with salient needs had a more positive attitude toward mammography after the consultation than before (mean 0.13, SD 0.54), while they changed their attitude in a negative direction when confronted with a purely fact-oriented communication style (mean ?0.35, SD 0.80). The same applied to decision modification (need-oriented: mean 0.10, SD 0.99; fact-oriented: mean ?0.30, SD 0.88). Conclusions: The findings underline the importance of communicating in a need-oriented style with patients and advice seekers who are aware of their personal needs. Ignoring the needs of those people appears to be particularly problematic. So physicians? sensitivity for advice seekers? currently relevant needs is essential. UR - http://cancer.jmir.org/2015/2/e10/ UR - http://dx.doi.org/10.2196/cancer.4279 UR - http://www.ncbi.nlm.nih.gov/pubmed/28410160 ID - info:doi/10.2196/cancer.4279 ER - TY - JOUR AU - Park, Albert AU - Hartzler, L. Andrea AU - Huh, Jina AU - McDonald, W. David AU - Pratt, Wanda PY - 2015/08/31 TI - Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text JO - J Med Internet Res SP - e212 VL - 17 IS - 8 KW - UMLS KW - natural language processing KW - automatic data processing KW - quantitative evaluation KW - information extraction N2 - Background: The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. Objective: The primary objective of this study is to explore an alternative approach?using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. Methods: Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap?s commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. Results: From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap?s mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively. Conclusions: We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text. UR - http://www.jmir.org/2015/8/e212/ UR - http://dx.doi.org/10.2196/jmir.4612 UR - http://www.ncbi.nlm.nih.gov/pubmed/26323337 ID - info:doi/10.2196/jmir.4612 ER - TY - JOUR AU - Ji, Xiaonan AU - Yen, Po-Yin PY - 2015/08/31 TI - Using MEDLINE Elemental Similarity to Assist in the Article Screening Process for Systematic Reviews JO - JMIR Med Inform SP - e28 VL - 3 IS - 3 KW - systematic review KW - evidence-based medicine KW - automatic document classification KW - relevance feedback N2 - Background: Systematic reviews and their implementation in practice provide high quality evidence for clinical practice but are both time and labor intensive due to the large number of articles. Automatic text classification has proven to be instrumental in identifying relevant articles for systematic reviews. Existing approaches use machine learning model training to generate classification algorithms for the article screening process but have limitations. Objective: We applied a network approach to assist in the article screening process for systematic reviews using predetermined article relationships (similarity). The article similarity metric is calculated using the MEDLINE elements title (TI), abstract (AB), medical subject heading (MH), author (AU), and publication type (PT). We used an article network to illustrate the concept of article relationships. Using the concept, each article can be modeled as a node in the network and the relationship between 2 articles is modeled as an edge connecting them. The purpose of our study was to use the article relationship to facilitate an interactive article recommendation process. Methods: We used 15 completed systematic reviews produced by the Drug Effectiveness Review Project and demonstrated the use of article networks to assist article recommendation. We evaluated the predictive performance of MEDLINE elements and compared our approach with existing machine learning model training approaches. The performance was measured by work saved over sampling at 95% recall (WSS95) and the F-measure (F1). We also used repeated analysis over variance and Hommel?s multiple comparison adjustment to demonstrate statistical evidence. Results: We found that although there is no significant difference across elements (except AU), TI and AB have better predictive capability in general. Collaborative elements bring performance improvement in both F1 and WSS95. With our approach, a simple combination of TI+AB+PT could achieve a WSS95 performance of 37%, which is competitive to traditional machine learning model training approaches (23%-41% WSS95). Conclusions: We demonstrated a new approach to assist in labor intensive systematic reviews. Predictive ability of different elements (both single and composited) was explored. Without using model training approaches, we established a generalizable method that can achieve a competitive performance. UR - http://medinform.jmir.org/2015/3/e28/ UR - http://dx.doi.org/10.2196/medinform.3982 UR - http://www.ncbi.nlm.nih.gov/pubmed/26323593 ID - info:doi/10.2196/medinform.3982 ER - TY - JOUR AU - Cole-Lewis, Heather AU - Varghese, Arun AU - Sanders, Amy AU - Schwarz, Mary AU - Pugatch, Jillian AU - Augustson, Erik PY - 2015/08/25 TI - Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning JO - J Med Internet Res SP - e208 VL - 17 IS - 8 KW - social media KW - Twitter KW - e-cigarette KW - machine learning N2 - Background: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public?s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. UR - http://www.jmir.org/2015/8/e208/ UR - http://dx.doi.org/10.2196/jmir.4392 UR - http://www.ncbi.nlm.nih.gov/pubmed/26307512 ID - info:doi/10.2196/jmir.4392 ER - TY - JOUR AU - Lewis, Lorchan Thomas AU - Wyatt, C. Jeremy PY - 2015/08/19 TI - App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps JO - J Med Internet Res SP - e200 VL - 17 IS - 8 KW - mHealth KW - medical app KW - mobile phone KW - metric KW - risk assessment KW - medical informatics apps KW - population impact KW - mobile health KW - patient safety KW - mobile app N2 - Background: One factor when assessing the quality of mobile apps is quantifying the impact of a given app on a population. There is currently no metric which can be used to compare the population impact of a mobile app across different health care disciplines. Objective: The objective of this study is to create a novel metric to characterize the impact of a mobile app on a population. Methods: We developed the simple novel metric, app usage factor (AUF), defined as the logarithm of the product of the number of active users of a mobile app with the median number of daily uses of the app. The behavior of this metric was modeled using simulated modeling in Python, a general-purpose programming language. Three simulations were conducted to explore the temporal and numerical stability of our metric and a simulated app ecosystem model using a simulated dataset of 20,000 apps. Results: Simulations confirmed the metric was stable between predicted usage limits and remained stable at extremes of these limits. Analysis of a simulated dataset of 20,000 apps calculated an average value for the app usage factor of 4.90 (SD 0.78). A temporal simulation showed that the metric remained stable over time and suitable limits for its use were identified. Conclusions: A key component when assessing app risk and potential harm is understanding the potential population impact of each mobile app. Our metric has many potential uses for a wide range of stakeholders in the app ecosystem, including users, regulators, developers, and health care professionals. Furthermore, this metric forms part of the overall estimate of risk and potential for harm or benefit posed by a mobile medical app. We identify the merits and limitations of this metric, as well as potential avenues for future validation and research. UR - http://www.jmir.org/2015/8/e200/ UR - http://dx.doi.org/10.2196/jmir.4284 UR - http://www.ncbi.nlm.nih.gov/pubmed/26290093 ID - info:doi/10.2196/jmir.4284 ER - TY - JOUR AU - Turnbull, E. Alison AU - O'Connor, L. Cristi AU - Lau, Bryan AU - Halpern, D. Scott AU - Needham, M. Dale PY - 2015/07/29 TI - Allowing Physicians to Choose the Value of Compensation for Participation in a Web-Based Survey: Randomized Controlled Trial JO - J Med Internet Res SP - e189 VL - 17 IS - 7 KW - data collection KW - monetary incentives KW - cash KW - physicians KW - electronic questionnaire KW - survey design KW - response rate N2 - Background: Survey response rates among physicians are declining, and determining an appropriate level of compensation to motivate participation poses a major challenge. Objective: To estimate the effect of permitting intensive care physicians to select their preferred level of compensation for completing a short Web-based survey on physician (1) response rate, (2) survey completion rate, (3) time to response, and (4) time spent completing the survey. Methods: A total of 1850 US intensivists from an existing database were randomized to receive a survey invitation email with or without an Amazon.com incentive available to the first 100 respondents. The incentive could be instantly redeemed for an amount chosen by the respondent, up to a maximum of US $50. Results: The overall response rate was 35.90% (630/1755). Among the 35.4% (111/314) of eligible participants choosing the incentive, 80.2% (89/111) selected the maximum value. Among intensivists offered an incentive, the response was 6.0% higher (95% CI 1.5-10.5, P=.01), survey completion was marginally greater (807/859, 94.0% vs 892/991, 90.0%; P=.06), and the median number of days to survey response was shorter (0.8, interquartile range [IQR] 0.2-14.4 vs 6.6, IQR 0.3-22.3; P=.001), with no difference in time spent completing the survey. Conclusions: Permitting intensive care physicians to determine compensation level for completing a short Web-based survey modestly increased response rate and substantially decreased response time without decreasing the time spent on survey completion. UR - http://www.jmir.org/2015/7/e189/ UR - http://dx.doi.org/10.2196/jmir.3898 UR - http://www.ncbi.nlm.nih.gov/pubmed/26223821 ID - info:doi/10.2196/jmir.3898 ER - TY - JOUR AU - Perez, L. Susan AU - Paterniti, A. Debora AU - Wilson, Machelle AU - Bell, A. Robert AU - Chan, Shan Man AU - Villareal, C. Chloe AU - Nguyen, Huy Hien AU - Kravitz, L. Richard PY - 2015/07/20 TI - Characterizing the Processes for Navigating Internet Health Information Using Real-Time Observations: A Mixed-Methods Approach JO - J Med Internet Res SP - e173 VL - 17 IS - 7 KW - dual processing KW - information seeking KW - Internet search KW - health information N2 - Background: Little is known about the processes people use to find health-related information on the Internet or the individual characteristics that shape selection of information-seeking approaches. Objective: Our aim was to describe the processes by which users navigate the Internet for information about a hypothetical acute illness and to identify individual characteristics predictive of their information-seeking strategies. Methods: Study participants were recruited from public settings and agencies. Interested individuals were screened for eligibility using an online questionnaire. Participants listened to one of two clinical scenarios?consistent with influenza or bacterial meningitis?and then conducted an Internet search. Screen-capture video software captured Internet search mouse clicks and keystrokes. Each step of the search was coded as hypothesis testing (etiology), evidence gathering (symptoms), or action/treatment seeking (behavior). The coded steps were used to form a step-by-step pattern of each participant?s information-seeking process. A total of 78 Internet health information seekers ranging from 21-35 years of age and who experienced barriers to accessing health care services participated. Results: We identified 27 unique patterns of information seeking, which were grouped into four overarching classifications based on the number of steps taken during the search, whether a pattern consisted of developing a hypothesis and exploring symptoms before ending the search or searching an action/treatment, and whether a pattern ended with action/treatment seeking. Applying dual-processing theory, we categorized the four overarching pattern classifications as either System 1 (41%, 32/78), unconscious, rapid, automatic, and high capacity processing; or System 2 (59%, 46/78), conscious, slow, and deliberative processing. Using multivariate regression, we found that System 2 processing was associated with higher education and younger age. Conclusions: We identified and classified two approaches to processing Internet health information. System 2 processing, a methodical approach, most resembles the strategies for information processing that have been found in other studies to be associated with higher-quality decisions. We conclude that the quality of Internet health-information seeking could be improved through consumer education on methodical Internet navigation strategies and the incorporation of decision aids into health information websites. UR - http://www.jmir.org/2015/7/e173/ UR - http://dx.doi.org/10.2196/jmir.3945 UR - http://www.ncbi.nlm.nih.gov/pubmed/26194787 ID - info:doi/10.2196/jmir.3945 ER - TY - JOUR AU - Saeb, Sohrab AU - Zhang, Mi AU - Karr, J. Christopher AU - Schueller, M. Stephen AU - Corden, E. Marya AU - Kording, P. Konrad AU - Mohr, C. David PY - 2015/07/15 TI - Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study JO - J Med Internet Res SP - e175 VL - 17 IS - 7 KW - depression KW - mobile health (mHealth) KW - activities of daily living KW - cluster analysis KW - classification N2 - Background: Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results: A number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r=-.63, P=.005), normalized entropy (mobility between favorite locations; r=-.58, P=.012), and location variance (GPS mobility independent of location; r=-.58, P=.012). Phone usage features, usage duration, and usage frequency were also correlated (r=.54, P=.011, and r=.52, P=.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ?5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants? PHQ-9 scores obtained an average error of 23.5%. Conclusions: Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. UR - http://www.jmir.org/2015/7/e175/ UR - http://dx.doi.org/10.2196/jmir.4273 UR - http://www.ncbi.nlm.nih.gov/pubmed/26180009 ID - info:doi/10.2196/jmir.4273 ER - TY - JOUR AU - Mohr, C. David AU - Schueller, M. Stephen AU - Riley, T. William AU - Brown, Hendricks C. AU - Cuijpers, Pim AU - Duan, Naihua AU - Kwasny, J. Mary AU - Stiles-Shields, Colleen AU - Cheung, Ken PY - 2015/07/08 TI - Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies JO - J Med Internet Res SP - e166 VL - 17 IS - 7 KW - mHealth KW - eHealth KW - clinical trials KW - methodology UR - http://www.jmir.org/2015/7/e166/ UR - http://dx.doi.org/10.2196/jmir.4391 UR - http://www.ncbi.nlm.nih.gov/pubmed/26155878 ID - info:doi/10.2196/jmir.4391 ER - TY - JOUR AU - Soong, Andrea AU - Chen, Cen Julia AU - Borzekowski, LG Dina PY - 2015/06/24 TI - Using Ecological Momentary Assessment to Study Tobacco Behavior in Urban India: There?s an App for That JO - JMIR Res Protoc SP - e76 VL - 4 IS - 2 KW - ecological momentary assessment KW - tobacco control KW - cell phones KW - mobile phones KW - mHealth KW - telemedicine KW - smoking N2 - Background: Ecological momentary assessment (EMA) uses real-time data collection to assess participants? behaviors and environments. This paper explores the strengths and limitations of using EMA to examine social and environmental exposure to tobacco in urban India among older adolescents and adults. Objective: Objectives of this study were (1) to describe the methods used in an EMA study of tobacco use in urban India using a mobile phone app for data collection, (2) to determine the feasibility of using EMA in the chosen setting by drawing on participant completion and compliance rates with the study protocol, and (3) to provide recommendations on implementing mobile phone EMA research in India and other low- and middle-income countries. Methods: Via mobile phones and the Internet, this study used two EMA surveys: (1) a momentary survey, sent multiple times per day at random to participants, which asked about their real-time tobacco use (smoked and smokeless) and exposure to pro- and antitobacco messaging in their location, and 2) an end-of-day survey sent at the end of each study day. Trained participants, from Hyderabad and Kolkata, India, reported on their social and environmental exposure to tobacco over 10 consecutive days. This feasibility study examined participant compliance, exploring factors related to the successful completion of surveys and the validity of EMA data. Results: The sample included 205 participants, the majority of whom were male (135/205, 65.9%). Almost half smoked less than daily (56/205, 27.3%) or daily (43/205, 21.0%), and 4.4% (9/205) used smokeless tobacco products. Participants completed and returned 46.87% and 73.02% of momentary and end-of-day surveys, respectively. Significant predictors of momentary survey completion included employment and completion of end-of-day surveys. End-of-day survey completion was only significantly predicted by momentary survey completion. Conclusions: This first study of EMA in India offers promising results, although more research is needed on how to increase compliance. End-of-day survey completion, which has a lower research burden, may be the more appropriate approach to understanding behaviors such as tobacco use within vulnerable populations in challenging locations. Compliance may also be improved by increasing the number of study visits, compliance checks, or opportunities for retraining participants before and during data collection. UR - http://www.researchprotocols.org/2015/2/e76/ UR - http://dx.doi.org/10.2196/resprot.4408 UR - http://www.ncbi.nlm.nih.gov/pubmed/26109369 ID - info:doi/10.2196/resprot.4408 ER - TY - JOUR AU - Kortum, Philip AU - Peres, Camille S. PY - 2015/06/05 TI - Evaluation of Home Health Care Devices: Remote Usability Assessment JO - JMIR Human Factors SP - e10 VL - 2 IS - 1 KW - health care evaluation mechanisms KW - human-computer interaction design and evaluation methods KW - patient satisfaction KW - usability testing N2 - Background: An increasing amount of health care is now performed in a home setting, away from the hospital. While there is growing anecdotal evidence about the difficulty patients and caregivers have using increasingly complex health care devices in the home, there has been little systematic scientific study to quantify the global nature of home health care device usability in the field. Research has tended to focus on a handful of devices, making it difficult to gain a broad view of the usability of home-care devices in general. Objective: The objective of this paper is to describe a remote usability assessment method using the System Usability Scale (SUS), and to report on the usability of a broad range of health care devices using this metric. Methods: A total of 271 participants selected and rated up to 10 home health care devices of their choice using the SUS, which scores usability from 0 (unusable) to 100 (highly usable). Participants rated a total of 455 devices in their own home without an experimenter present. Results: Usability scores ranged from 98 (oxygen masks) to 59 (home hormone test kits). An analysis conducted on devices that had at least 10 ratings showed that the effect of device on SUS scores was significant (P<.001), and that the usability of these devices was on the low end when compared with other commonly used items in the home, such as microwave ovens and telephones. Conclusions: A large database of usability scores for home health care devices collected using this remote methodology would be beneficial for physicians, patients, and their caregivers. UR - http://humanfactors.jmir.org/2015/1/e10/ UR - http://dx.doi.org/10.2196/humanfactors.4570 UR - http://www.ncbi.nlm.nih.gov/pubmed/27025664 ID - info:doi/10.2196/humanfactors.4570 ER - TY - JOUR AU - Brown III, William AU - Ibitoye, Mobolaji AU - Bakken, Suzanne AU - Schnall, Rebecca AU - Balán, Iván AU - Frasca, Timothy AU - Carballo-Diéguez, Alex PY - 2015/06/04 TI - Cartographic Analysis of Antennas and Towers: A Novel Approach to Improving the Implementation and Data Transmission of mHealth Tools on Mobile Networks JO - JMIR mHealth uHealth SP - e63 VL - 3 IS - 2 KW - cartographic analysis KW - mHealth KW - mobile health KW - antenna KW - short message service KW - text messaging KW - SMS KW - wireless KW - HIV N2 - Background: Most mHealth tools such as short message service (SMS), mobile apps, wireless pill counters, and ingestible wireless monitors use mobile antennas to communicate. Limited signal availability, often due to poor antenna infrastructure, negatively impacts the implementation of mHealth tools and remote data collection. Assessing the antenna infrastructure prior to starting a study can help mitigate this problem. Currently, there are no studies that detail whether and how the antenna infrastructure of a study site or area is assessed. Objective: To address this literature gap, we analyze and discuss the use of a cartographic analysis of antennas and towers (CAAT) for mobile communications for geographically assessing mobile antenna and tower infrastructure and identifying signal availability for mobile devices prior to the implementation of an SMS-based mHealth pilot study. Methods: An alpha test of the SMS system was performed using 11 site staff. A CAAT for the study area?s mobile network was performed after the alpha test and pre-implementation of the pilot study. The pilot study used a convenience sample of 11 high-risk men who have sex with men who were given human immunodeficiency virus test kits for testing nonmonogamous sexual partners before intercourse. Product use and sexual behavior were tracked through SMS. Message frequency analyses were performed on the SMS text messages, and SMS sent/received frequencies of 11 staff and 11 pilot study participants were compared. Results: The CAAT helped us to successfully identify strengths and weaknesses in mobile service capacity within a 3-mile radius from the epicenters of four New York City boroughs. During the alpha test, before CAAT, 1176/1202 (97.84%) text messages were sent to staff, of which 26/1176 (2.21%) failed. After the CAAT, 2934 messages were sent to pilot study participants and none failed. Conclusions: The CAAT effectively illustrated the research area?s mobile infrastructure and signal availability, which allowed us to improve study setup and sent message success rates. The SMS messages were sent and received with a lower fail rate than those reported in previous studies. UR - http://mhealth.jmir.org/2015/2/e63/ UR - http://dx.doi.org/10.2196/mhealth.3941 UR - http://www.ncbi.nlm.nih.gov/pubmed/26043766 ID - info:doi/10.2196/mhealth.3941 ER - TY - JOUR AU - Runge, K. Shannon AU - Craig, M. Benjamin AU - Jim, S. Heather PY - 2015/06/02 TI - Word Recall: Cognitive Performance Within Internet Surveys JO - JMIR Mental Health SP - e20 VL - 2 IS - 2 KW - cognition KW - online surveys KW - episodic memory KW - Health and Retirement Study KW - Women?s Health Valuation Study N2 - Background: The use of online surveys for data collection has increased exponentially, yet it is often unclear whether interview-based cognitive assessments (such as face-to-face or telephonic word recall tasks) can be adapted for use in application-based research settings. Objective: The objective of the current study was to compare and characterize the results of online word recall tasks to those of the Health and Retirement Study (HRS) and determine the feasibility and reliability of incorporating word recall tasks into application-based cognitive assessments. Methods: The results of the online immediate and delayed word recall assessment, included within the Women?s Health and Valuation (WHV) study, were compared to the results of the immediate and delayed recall tasks of Waves 5-11 (2000-2012) of the HRS. Results: Performance on the WHV immediate and delayed tasks demonstrated strong concordance with performance on the HRS tasks (?c=.79, 95% CI 0.67-0.91), despite significant differences between study populations (P<.001) and study design. Sociodemographic characteristics and self-reported memory demonstrated similar relationships with performance on both the HRS and WHV tasks. Conclusions: The key finding of this study is that the HRS word recall tasks performed similarly when used as an online cognitive assessment in the WHV. Online administration of cognitive tests, which has the potential to significantly reduce participant and administrative burden, should be considered in future research studies and health assessments. UR - http://mental.jmir.org/2015/2/e20/ UR - http://dx.doi.org/10.2196/mental.3969 UR - http://www.ncbi.nlm.nih.gov/pubmed/26543924 ID - info:doi/10.2196/mental.3969 ER - TY - JOUR AU - Vermeulen, Joan AU - Neyens, CL Jacques AU - Spreeuwenberg, D. Marieke AU - van Rossum, Erik AU - Boessen, BCG April AU - Sipers, Walther AU - de Witte, P. Luc PY - 2015/05/27 TI - The Relationship Between Balance Measured With a Modified Bathroom Scale and Falls and Disability in Older Adults: A 6-Month Follow-Up Study JO - J Med Internet Res SP - e131 VL - 17 IS - 5 KW - telemonitoring KW - balance KW - bathroom scale KW - older adults KW - falls KW - disability KW - validity N2 - Background: There are indications that older adults who suffer from poor balance have an increased risk for adverse health outcomes, such as falls and disability. Monitoring the development of balance over time enables early detection of balance decline, which can identify older adults who could benefit from interventions aimed at prevention of these adverse outcomes. An innovative and easy-to-use device that can be used by older adults for home-based monitoring of balance is a modified bathroom scale. Objective: The objective of this paper is to study the relationship between balance scores obtained with a modified bathroom scale and falls and disability in a sample of older adults. Methods: For this 6-month follow-up study, participants were recruited via physiotherapists working in a nursing home, geriatricians, exercise classes, and at an event about health for older adults. Inclusion criteria were being aged 65 years or older, being able to stand on a bathroom scale independently, and able to provide informed consent. A total of 41 nursing home patients and 139 community-dwelling older adults stepped onto the modified bathroom scale three consecutive times at baseline to measure their balance. Their mean balance scores on a scale from 0 to 16 were calculated?higher scores indicated better balance. Questionnaires were used to study falls and disability at baseline and after 6 months of follow-up. The cross-sectional relationship between balance and falls and disability at baseline was studied using t tests and Spearman rank correlations. Univariate and multivariate logistic regression analyses were conducted to study the relationship between balance measured at baseline and falls and disability development after 6 months of follow-up. Results: A total of 128 participants with complete datasets?25.8% (33/128) male?and a mean age of 75.33 years (SD 6.26) were included in the analyses of this study. Balance scores of participants who reported at baseline that they had fallen at least once in the past 6 months were lower compared to nonfallers?8.9 and 11.2, respectively (P<.001). The correlation between mean balance score and disability sum-score at baseline was -.51 (P<.001). No significant associations were found between balance at baseline and falls after 6 months of follow-up. Baseline balance scores were significantly associated with the development of disability after 6 months of follow-up in the univariate analysis?odds ratio (OR) 0.86 (95% CI 0.76-0.98)?but not in the multivariate analysis when correcting for age, gender, baseline disability, and falls at follow-up?OR 0.94 (95% CI 0.79-1.11). Conclusions: There is a cross-sectional relationship between balance measured by a modified bathroom scale and falls and disability in older adults. Despite this cross-sectional relationship, longitudinal data showed that balance scores have no predictive value for falls and might only have limited predictive value for disability development after 6 months of follow-up. UR - http://www.jmir.org/2015/5/e131/ UR - http://dx.doi.org/10.2196/jmir.3802 UR - http://www.ncbi.nlm.nih.gov/pubmed/26018423 ID - info:doi/10.2196/jmir.3802 ER - TY - JOUR AU - Waterlander, Elzeline Wilma AU - Jiang, Yannan AU - Steenhuis, Margaretha Ingrid Hendrika AU - Ni Mhurchu, Cliona PY - 2015/04/28 TI - Using a 3D Virtual Supermarket to Measure Food Purchase Behavior: A Validation Study JO - J Med Internet Res SP - e107 VL - 17 IS - 4 KW - virtual reality KW - user-computer interface KW - software validation KW - nutrition policy KW - food KW - behavior KW - public health N2 - Background: There is increasing recognition that supermarkets are an important environment for health-promoting interventions such as fiscal food policies or front-of-pack nutrition labeling. However, due to the complexities of undertaking such research in the real world, well-designed randomized controlled trials on these kinds of interventions are lacking. The Virtual Supermarket is a 3-dimensional computerized research environment designed to enable experimental studies in a supermarket setting without the complexity or costs normally associated with undertaking such research. Objective: The primary objective was to validate the Virtual Supermarket by comparing virtual and real-life food purchasing behavior. A secondary objective was to obtain participant feedback on perceived sense of ?presence? (the subjective experience of being in one place or environment even if physically located in another) in the Virtual Supermarket. Methods: Eligible main household shoppers (New Zealand adults aged ?18 years) were asked to conduct 3 shopping occasions in the Virtual Supermarket over 3 consecutive weeks, complete the validated Presence Questionnaire Items Stems, and collect their real supermarket grocery till receipts for that same period. Proportional expenditure (NZ$) and the proportion of products purchased over 18 major food groups were compared between the virtual and real supermarkets. Data were analyzed using repeated measures mixed models. Results: A total of 123 participants consented to take part in the study. In total, 69.9% (86/123) completed 1 shop in the Virtual Supermarket, 64.2% (79/123) completed 2 shops, 60.2% (74/123) completed 3 shops, and 48.8% (60/123) returned their real supermarket till receipts. The 4 food groups with the highest relative expenditures were the same for the virtual and real supermarkets: fresh fruit and vegetables (virtual estimate: 14.3%; real: 17.4%), bread and bakery (virtual: 10.0%; real: 8.2%), dairy (virtual: 19.1%; real: 12.6%), and meat and fish (virtual: 16.5%; real: 16.8%). Significant differences in proportional expenditures were observed for 6 food groups, with largest differences (virtual ? real) for dairy (in expenditure 6.5%, P<.001; in items 2.2%, P=.04) and fresh fruit and vegetables (in expenditure: ?3.1%, P=.04; in items: 5.9%, P=.002). There was no trend of overspending in the Virtual Supermarket and participants experienced a medium-to-high presence (88%, 73/83 scored medium; 8%, 7/83 scored high). Conclusions: Shopping patterns in the Virtual Supermarket were comparable to those in real life. Overall, the Virtual Supermarket is a valid tool to measure food purchasing behavior. Nevertheless, it is important to improve the functionality of some food categories, in particular fruit and vegetables and dairy. The results of this validation will assist in making further improvements to the software and with optimization of the internal and external validity of this innovative methodology. UR - http://www.jmir.org/2015/4/e107/ UR - http://dx.doi.org/10.2196/jmir.3774 UR - http://www.ncbi.nlm.nih.gov/pubmed/25921185 ID - info:doi/10.2196/jmir.3774 ER - TY - JOUR AU - Suominen, Hanna AU - Zhou, Liyuan AU - Hanlen, Leif AU - Ferraro, Gabriela PY - 2015/04/27 TI - Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations JO - JMIR Med Inform SP - e19 VL - 3 IS - 2 KW - computer systems evaluation KW - data collection KW - information extraction KW - nursing records KW - patient handoff KW - records as topic KW - speech recognition software N2 - Background: Over a tenth of preventable adverse events in health care are caused by failures in information flow. These failures are tangible in clinical handover; regardless of good verbal handover, from two-thirds to all of this information is lost after 3-5 shifts if notes are taken by hand, or not at all. Speech recognition and information extraction provide a way to fill out a handover form for clinical proofing and sign-off. Objective: The objective of the study was to provide a recorded spoken handover, annotated verbatim transcriptions, and evaluations to support research in spoken and written natural language processing for filling out a clinical handover form. This dataset is based on synthetic patient profiles, thereby avoiding ethical and legal restrictions, while maintaining efficacy for research in speech-to-text conversion and information extraction, based on realistic clinical scenarios. We also introduce a Web app to demonstrate the system design and workflow. Methods: We experiment with Dragon Medical 11.0 for speech recognition and CRF++ for information extraction. To compute features for information extraction, we also apply CoreNLP, MetaMap, and Ontoserver. Our evaluation uses cross-validation techniques to measure processing correctness. Results: The data provided were a simulation of nursing handover, as recorded using a mobile device, built from simulated patient records and handover scripts, spoken by an Australian registered nurse. Speech recognition recognized 5276 of 7277 words in our 100 test documents correctly. We considered 50 mutually exclusive categories in information extraction and achieved the F1 (ie, the harmonic mean of Precision and Recall) of 0.86 in the category for irrelevant text and the macro-averaged F1 of 0.70 over the remaining 35 nonempty categories of the form in our 101 test documents. Conclusions: The significance of this study hinges on opening our data, together with the related performance benchmarks and some processing software, to the research and development community for studying clinical documentation and language-processing. The data are used in the CLEFeHealth 2015 evaluation laboratory for a shared task on speech recognition. UR - http://medinform.jmir.org/2015/2/e19/ UR - http://dx.doi.org/10.2196/medinform.4321 UR - http://www.ncbi.nlm.nih.gov/pubmed/25917752 ID - info:doi/10.2196/medinform.4321 ER - TY - JOUR AU - Wang, Yi-Chia AU - Kraut, E. Robert AU - Levine, M. John PY - 2015/04/20 TI - Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support JO - J Med Internet Res SP - e99 VL - 17 IS - 4 KW - social support KW - health communication KW - self-disclosure KW - social media KW - support groups KW - emotions KW - natural language processing N2 - Background: Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. Objective: The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Methods: Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Results: Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=?.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=?.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Conclusions: Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities. UR - http://www.jmir.org/2015/4/e99/ UR - http://dx.doi.org/10.2196/jmir.3558 UR - http://www.ncbi.nlm.nih.gov/pubmed/25896033 ID - info:doi/10.2196/jmir.3558 ER - TY - JOUR AU - Kassam-Adams, Nancy AU - Marsac, L. Meghan AU - Kohser, L. Kristen AU - Kenardy, A. Justin AU - March, Sonja AU - Winston, K. Flaura PY - 2015/04/15 TI - A New Method for Assessing Content Validity in Model-Based Creation and Iteration of eHealth Interventions JO - J Med Internet Res SP - e95 VL - 17 IS - 4 KW - telemedicine KW - methods KW - stress disorders, post-traumatic KW - child KW - secondary prevention N2 - Background: The advent of eHealth interventions to address psychological concerns and health behaviors has created new opportunities, including the ability to optimize the effectiveness of intervention activities and then deliver these activities consistently to a large number of individuals in need. Given that eHealth interventions grounded in a well-delineated theoretical model for change are more likely to be effective and that eHealth interventions can be costly to develop, assuring the match of final intervention content and activities to the underlying model is a key step. We propose to apply the concept of ?content validity? as a crucial checkpoint to evaluate the extent to which proposed intervention activities in an eHealth intervention program are valid (eg, relevant and likely to be effective) for the specific mechanism of change that each is intended to target and the intended target population for the intervention. Objective: The aims of this paper are to define content validity as it applies to model-based eHealth intervention development, to present a feasible method for assessing content validity in this context, and to describe the implementation of this new method during the development of a Web-based intervention for children. Methods: We designed a practical 5-step method for assessing content validity in eHealth interventions that includes defining key intervention targets, delineating intervention activity-target pairings, identifying experts and using a survey tool to gather expert ratings of the relevance of each activity to its intended target, its likely effectiveness in achieving the intended target, and its appropriateness with a specific intended audience, and then using quantitative and qualitative results to identify intervention activities that may need modification. We applied this method during our development of the Coping Coach Web-based intervention for school-age children. Results: In the evaluation of Coping Coach content validity, 15 experts from five countries rated each of 15 intervention activity-target pairings. Based on quantitative indices, content validity was excellent for relevance and good for likely effectiveness and age-appropriateness. Two intervention activities had item-level indicators that suggested the need for further review and potential revision by the development team. Conclusions: This project demonstrated that assessment of content validity can be straightforward and feasible to implement and that results of this assessment provide useful information for ongoing development and iterations of new eHealth interventions, complementing other sources of information (eg, user feedback, effectiveness evaluations). This approach can be utilized at one or more points during the development process to guide ongoing optimization of eHealth interventions. UR - http://www.jmir.org/2015/4/e95/ UR - http://dx.doi.org/10.2196/jmir.3811 UR - http://www.ncbi.nlm.nih.gov/pubmed/25881584 ID - info:doi/10.2196/jmir.3811 ER - TY - JOUR AU - Yamada, C. Kosuke AU - Inoue, Satoshi AU - Sakamoto, Yuichiro PY - 2015/02/27 TI - An Effective Support System of Emergency Medical Services With Tablet Computers JO - JMIR mHealth uHealth SP - e23 VL - 3 IS - 1 KW - emergency medical services KW - EMS KW - EMS communication systems KW - prehospital KW - ambulance KW - tablet computers KW - cloud computing N2 - Background: There were over 5,000,000 ambulance dispatches during 2010 in Japan, and the time for transportation has been increasing, it took over 37 minutes from dispatch to the hospitals. A way to reduce transportation time by ambulance is to shorten the time of searching for an appropriate facility/hospital during the prehospital phase. Although the information system of medical institutions and emergency medical service (EMS) was established in 2003 in Saga Prefecture, Japan, it has not been utilized efficiently. The Saga Prefectural Government renewed the previous system in an effort to make it the real-time support system that can efficiently manage emergency demand and acceptance for the first time in Japan in April 2011. Objective: The objective of this study was to evaluate if the new system promotes efficient emergency transportation for critically ill patients and provides valuable epidemiological data. Methods: The new system has provided both emergency personnel in the ambulance, or at the scene, and the medical staff in each hospital to be able to share up-to-date information about available hospitals by means of cloud computing. All 55 ambulances in Saga are equipped with tablet computers through third generation/long term evolution networks. When the emergency personnel arrive on the scene and discern the type of patient?s illness, they can search for an appropriate facility/hospital with their tablet computer based on the patient?s symptoms and available medical specialists. Data were collected prospectively over a three-year period from April 1, 2011 to March 31, 2013. Results: The transportation time by ambulance in Saga was shortened for the first time since the statistics were first kept in 1999; the mean time was 34.3 minutes in 2010 (based on administrative statistics) and 33.9 minutes (95% CI 33.6-34.1) in 2011. The ratio of transportation to the tertiary care facilities in Saga has decreased by 3.12% from the year before, 32.7% in 2010 (regional average) and 29.58% (9085/30,709) in 2011. The system entry completion rate by the emergency personnel was 100.00% (93,110/93,110) and by the medical staff was 46.11% (14,159/30,709) to 47.57% (14,639/30,772) over a three-year period. Finally, the new system reduced the operational costs by 40,000,000 yen (about $400,000 US dollars) a year. Conclusions: The transportation time by ambulance was shorter following the implementation of the tablet computer in the current support system of EMS in Saga Prefecture, Japan. The cloud computing reduced the cost of the EMS system. UR - http://mhealth.jmir.org/2015/1/e23/ UR - http://dx.doi.org/10.2196/mhealth.3293 UR - http://www.ncbi.nlm.nih.gov/pubmed/25803096 ID - info:doi/10.2196/mhealth.3293 ER - TY - JOUR AU - Morrison, Cecily AU - Doherty, Gavin PY - 2014/11/13 TI - Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data JO - J Med Internet Res SP - e252 VL - 16 IS - 11 KW - engagement KW - log-data analysis KW - data visualisation KW - Web-based interventions N2 - Background: Engagement has emerged as a significant cross-cutting concern within the development of Web-based interventions. There have been calls to institute a more rigorous approach to the design of Web-based interventions, to increase both the quantity and quality of engagement. One approach would be to use log-data to better understand the process of engagement and patterns of use. However, an important challenge lies in organizing log-data for productive analysis. Objective: Our aim was to conduct an initial exploration of the use of visualizations of log-data to enhance understanding of engagement with Web-based interventions. Methods: We applied exploratory sequential data analysis to highlight sequential aspects of the log data, such as time or module number, to provide insights into engagement. After applying a number of processing steps, a range of visualizations were generated from the log-data. We then examined the usefulness of these visualizations for understanding the engagement of individual users and the engagement of cohorts of users. The visualizations created are illustrated with two datasets drawn from studies using the SilverCloud Platform: (1) a small, detailed dataset with interviews (n=19) and (2) a large dataset (n=326) with 44,838 logged events. Results: We present four exploratory visualizations of user engagement with a Web-based intervention, including Navigation Graph, Stripe Graph, Start?Finish Graph, and Next Action Heat Map. The first represents individual usage and the last three, specific aspects of cohort usage. We provide examples of each with a discussion of salient features. Conclusions: Log-data analysis through data visualization is an alternative way of exploring user engagement with Web-based interventions, which can yield different insights than more commonly used summative measures. We describe how understanding the process of engagement through visualizations can support the development and evaluation of Web-based interventions. Specifically, we show how visualizations can (1) allow inspection of content or feature usage in a temporal relationship to the overall program at different levels of granularity, (2) detect different patterns of use to consider personalization in the design process, (3) detect usability issues, (4) enable exploratory analysis to support the design of statistical queries to summarize the data, (5) provide new opportunities for real-time evaluation, and (6) examine assumptions about interactivity that underlie many summative measures in this field. UR - http://www.jmir.org/2014/11/e252/ UR - http://dx.doi.org/10.2196/jmir.3575 UR - http://www.ncbi.nlm.nih.gov/pubmed/25406097 ID - info:doi/10.2196/jmir.3575 ER - TY - JOUR AU - Fagundo, Beatriz Ana AU - Via, Esther AU - Sánchez, Isabel AU - Jiménez-Murcia, Susana AU - Forcano, Laura AU - Soriano-Mas, Carles AU - Giner-Bartolomé, Cristina AU - Santamaría, J. Juan AU - Ben-Moussa, Maher AU - Konstantas, Dimitri AU - Lam, Tony AU - Lucas, Mikkel AU - Nielsen, Jeppe AU - Lems, Peter AU - Cardoner, Narcís AU - Menchón, M. Jose AU - de la Torre, Rafael AU - Fernandez-Aranda, Fernando PY - 2014/08/12 TI - Physiological and Brain Activity After a Combined Cognitive Behavioral Treatment Plus Video Game Therapy for Emotional Regulation in Bulimia Nervosa: A Case Report JO - J Med Internet Res SP - e183 VL - 16 IS - 8 KW - eating disorders KW - bulimia nervosa KW - emotional regulation KW - impulsivity KW - video game therapy KW - neuroimaging KW - fMRI N2 - Background: PlayMancer is a video game designed to increase emotional regulation and reduce general impulsive behaviors, by training to decrease arousal and improve decision-making and planning. We have previously demonstrated the usefulness of PlayMancer in reducing impulsivity and improving emotional regulation in bulimia nervosa (BN) patients. However, whether these improvements are actually translated into brain changes remains unclear. Objective: The aim of this case study was to report on a 28-year-old Spanish woman with BN, and to examine changes in physiological variables and brain activity after a combined treatment of video game therapy (VGT) and cognitive behavioral therapy (CBT). Methods: Ten VGT sessions were carried out on a weekly basis. Anxiety, physiological, and impulsivity measurements were recorded. The patient was scanned in a 1.5-T magnetic resonance scanner, prior to and after the 10-week VGT/CBT combined treatment, using two paradigms: (1) an emotional face-matching task, and (2) a multi-source interference task (MSIT). Results: Upon completing the treatment, a decrease in average heart rate was observed. The functional magnetic resonance imaging (fMRI) results indicated a post-treatment reduction in reaction time along with high accuracy. The patient engaged areas typically active in healthy controls, although the cluster extension of the active areas decreased after the combined treatment. Conclusions: These results suggest a global improvement in emotional regulation and impulsivity control after the VGT therapy in BN, demonstrated by both physiological and neural changes. These promising results suggest that a combined treatment of CBT and VGT might lead to functional cerebral changes that ultimately translate into better cognitive and emotional performances. UR - http://www.jmir.org/2014/8/e183/ UR - http://dx.doi.org/10.2196/jmir.3243 UR - http://www.ncbi.nlm.nih.gov/pubmed/25116416 ID - info:doi/10.2196/jmir.3243 ER - TY - JOUR AU - Crutzen, Rik PY - 2014/08/05 TI - The Behavioral Intervention Technology Model and Intervention Mapping: The Best of Both Worlds JO - J Med Internet Res SP - e188 VL - 16 IS - 8 KW - mHealth KW - eHealth KW - behavioral intervention technology KW - intervention mapping UR - http://www.jmir.org/2014/8/e188/ UR - http://dx.doi.org/10.2196/jmir.3620 UR - http://www.ncbi.nlm.nih.gov/pubmed/25095730 ID - info:doi/10.2196/jmir.3620 ER - TY - JOUR AU - Schueller, M. Stephen AU - Begale, Mark AU - Penedo, J. Frank AU - Mohr, C. David PY - 2014/07/30 TI - Purple: A Modular System for Developing and Deploying Behavioral Intervention Technologies JO - J Med Internet Res SP - e181 VL - 16 IS - 7 KW - software tools KW - software engineering KW - open source KW - evaluation methodology KW - Internet intervention KW - mobile intervention KW - mobile health UR - http://www.jmir.org/2014/7/e181/ UR - http://dx.doi.org/10.2196/jmir.3376 UR - http://www.ncbi.nlm.nih.gov/pubmed/25079298 ID - info:doi/10.2196/jmir.3376 ER - TY - JOUR AU - Clemente, Miriam AU - Rey, Beatriz AU - Rodriguez-Pujadas, Aina AU - Breton-Lopez, Juani AU - Barros-Loscertales, Alfonso AU - Baños, M. Rosa AU - Botella, Cristina AU - Alcañiz, Mariano AU - Avila, Cesar PY - 2014/06/27 TI - A Functional Magnetic Resonance Imaging Assessment of Small Animals? Phobia Using Virtual Reality as a Stimulus JO - JMIR Serious Games SP - e6 VL - 2 IS - 1 KW - neuroimaging KW - patient assessment KW - virtual reality KW - phobia N2 - Background: To date, still images or videos of real animals have been used in functional magnetic resonance imaging protocols to evaluate the brain activations associated with small animals? phobia. Objective: The objective of our study was to evaluate the brain activations associated with small animals? phobia through the use of virtual environments. This context will have the added benefit of allowing the subject to move and interact with the environment, giving the subject the illusion of being there. Methods: We have analyzed the brain activation in a group of phobic people while they navigated in a virtual environment that included the small animals that were the object of their phobia. Results: We have found brain activation mainly in the left occipital inferior lobe (P<.05 corrected, cluster size=36), related to the enhanced visual attention to the phobic stimuli; and in the superior frontal gyrus (P<.005 uncorrected, cluster size=13), which is an area that has been previously related to the feeling of self-awareness. Conclusions: In our opinion, these results demonstrate that virtual stimulus can enhance brain activations consistent with previous studies with still images, but in an environment closer to the real situation the subject would face in their daily lives. UR - http://games.jmir.org/2014/1/e6/ UR - http://dx.doi.org/10.2196/games.2836 UR - http://www.ncbi.nlm.nih.gov/pubmed/25654753 ID - info:doi/10.2196/games.2836 ER - TY - JOUR AU - Mohr, C. David AU - Schueller, M. Stephen AU - Montague, Enid AU - Burns, Nicole Michelle AU - Rashidi, Parisa PY - 2014/06/05 TI - The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions JO - J Med Internet Res SP - e146 VL - 16 IS - 6 KW - mhealth KW - ehealth KW - behavioral intervention technology UR - http://www.jmir.org/2014/6/e146/ UR - http://dx.doi.org/10.2196/jmir.3077 UR - http://www.ncbi.nlm.nih.gov/pubmed/24905070 ID - info:doi/10.2196/jmir.3077 ER - TY - JOUR AU - Stanczyk, Nicola AU - Bolman, Catherine AU - van Adrichem, Mathieu AU - Candel, Math AU - Muris, Jean AU - de Vries, Hein PY - 2014/03/03 TI - Comparison of Text and Video Computer-Tailored Interventions for Smoking Cessation: Randomized Controlled Trial JO - J Med Internet Res SP - e69 VL - 16 IS - 3 KW - smoking cessation KW - multiple computer tailoring KW - delivery strategy KW - educational level KW - text messages KW - video messages N2 - Background: A wide range of effective smoking cessation interventions have been developed to help smokers to quit. Smoking rates remain high, especially among people with a lower level of education. Multiple tailoring adapted to the individual?s readiness to quit and the use of visual messaging may increase smoking cessation. Objective: The results of video and text computer tailoring were compared with the results of a control condition. Main effects and differential effects for subgroups with different educational levels and different levels of readiness to quit were assessed. Methods: During a blind randomized controlled trial, smokers willing to quit within 6 months were assigned to a video computer tailoring group with video messages (n=670), a text computer tailoring group with text messages (n=708), or to a control condition with short generic text advice (n=721). After 6 months, effects on 7-day point prevalence abstinence and prolonged abstinence were assessed using logistic regression analyses. Analyses were conducted in 2 samples: (1) respondents (as randomly assigned) who filled in the baseline questionnaire and completed the first session of the program, and (2) a subsample of sample 1, excluding respondents who did not adhere to at least one further intervention session. In primary analyses, we used a negative scenario in which respondents lost to follow-up were classified as smokers. Complete case analysis and multiple imputation analyses were considered as secondary analyses. Results: In sample 1, the negative scenario analyses revealed that video computer tailoring was more effective in increasing 7-day point prevalence abstinence than the control condition (OR 1.45, 95% CI 1.09-1.94, P=.01). Video computer tailoring also resulted in significantly higher prolonged abstinence rates than controls among smokers with a low (ready to quit within 4-6 months) readiness to quit (OR 5.13, 95% CI 1.76-14.92, P=.003). Analyses of sample 2 showed similar results, although text computer tailoring was also more effective than control in realizing 7-day point prevalence abstinence. No differential effects were found for level of education. Complete case analyses and multiple imputation yielded similar results. Conclusions: In all analyses, video computer tailoring was effective in realizing smoking cessation. Furthermore, video computer tailoring was especially successful for smokers with a low readiness to quit smoking. Text computer tailoring was only effective for sample 2. Results suggest that video-based messages with personalized feedback adapted to the smoker?s motivation to quit might be effective in increasing abstinence rates for smokers with diverse educational levels. Trial Registration: Netherlands Trial Register: NTR3102; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3102 (Archived by WebCite at http://www.webcitation.org/6NS8xhzUV). UR - http://www.jmir.org/2014/3/e69/ UR - http://dx.doi.org/10.2196/jmir.3016 UR - http://www.ncbi.nlm.nih.gov/pubmed/24589938 ID - info:doi/10.2196/jmir.3016 ER - TY - JOUR AU - Baker, B. Timothy AU - Gustafson, H. David AU - Shah, Dhavan PY - 2014/02/19 TI - How Can Research Keep Up With eHealth? Ten Strategies for Increasing the Timeliness and Usefulness of eHealth Research JO - J Med Internet Res SP - e36 VL - 16 IS - 2 KW - social media KW - Internet KW - randomized clinical trials KW - experimental designs KW - research techniques KW - patient education KW - patient engagement KW - health communication KW - telemedicine N2 - Background: eHealth interventions appear and change so quickly that they challenge the way we conduct research. By the time a randomized trial of a new intervention is published, technological improvements and clinical discoveries may make the intervention dated and unappealing. This and the spate of health-related apps and websites may lead consumers, patients, and caregivers to use interventions that lack evidence of efficacy. Objective: This paper aims to offer strategies for increasing the speed and usefulness of eHealth research. Methods: The paper describes two types of strategies based on the authors? own research and the research literature: those that improve the efficiency of eHealth research, and those that improve its quality. Results: Efficiency strategies include: (1) think small: conduct small studies that can target discrete but significant questions and thereby speed knowledge acquisition; (2) use efficient designs: use such methods as fractional-factorial and quasi-experimental designs and surrogate endpoints, and experimentally modify and evaluate interventions and delivery systems already in use; (3) study universals: focus on timeless behavioral, psychological, and cognitive principles and systems; (4) anticipate the next big thing: listen to voices outside normal practice and connect different perspectives for new insights; (5) improve information delivery systems: researchers should apply their communications expertise to enhance inter-researcher communication, which could synergistically accelerate progress and capitalize upon the availability of ?big data?; and (6) develop models, including mediators and moderators: valid models are remarkably generative, and tests of moderation and mediation should elucidate boundary conditions of effects and treatment mechanisms. Quality strategies include: (1) continuous quality improvement: researchers need to borrow engineering practices such as the continuous enhancement of interventions to incorporate clinical and technological progress; (2) help consumers identify quality: consumers, clinicians, and others all need to easily identify quality, suggesting the need to efficiently and publicly index intervention quality; (3) reduce the costs of care: concern with health care costs can drive intervention adoption and use and lead to novel intervention effects (eg, reduced falls in the elderly); and (4) deeply understand users: a rigorous evaluation of the consumer?s needs is a key starting point for intervention development. Conclusions: The challenges of distinguishing and distributing scientifically validated interventions are formidable. The strategies described are meant to spur discussion and further thinking, which are important, given the potential of eHealth interventions to help patients and families. UR - http://www.jmir.org/2014/2/e36/ UR - http://dx.doi.org/10.2196/jmir.2925 UR - http://www.ncbi.nlm.nih.gov/pubmed/24554442 ID - info:doi/10.2196/jmir.2925 ER - TY - JOUR AU - Friederichs, Stijn AU - Bolman, Catherine AU - Oenema, Anke AU - Guyaux, Janneke AU - Lechner, Lilian PY - 2014/02/13 TI - Motivational Interviewing in a Web-Based Physical Activity Intervention With an Avatar: Randomized Controlled Trial JO - J Med Internet Res SP - e48 VL - 16 IS - 2 KW - motivational interviewing KW - physical activity KW - Internet KW - avatar N2 - Background: Developing Web-based physical activity (PA) interventions based on motivational interviewing (MI) could increase the availability and reach of MI techniques for PA promotion. Integrating an avatar in such an intervention could lead to more positive appreciation and higher efficacy of the intervention, compared to an intervention that is purely text-based. Objective: The present study aims to determine whether a Web-based PA intervention based on MI with an avatar results in more positive appreciation and higher effectiveness of the intervention, when compared to an intervention that is purely text-based. Methods: A three-arm randomized controlled trial was conducted, containing the following research conditions: (1) a Web-based PA intervention based on MI with an avatar, (2) a content-identical intervention without an avatar, and (3) a control condition that received no intervention. Measurements included PA behavior and process variables, measured at baseline, directly following the intervention and 1 month post intervention. Results: Both interventions significantly increased self-reported PA at 1 month, compared to the control condition (betaAVATARvsCONTROL=.39, P=.011; betaTEXTvsCONTROL=.44, P=.006). No distinctions were found regarding intervention effect on PA between both interventions. Similarly, the results of the process evaluation did not indicate any significant differences between both interventions. Due to the limited relational skills of the avatar in this study, it probably did not succeed in forming a stronger relationship with the user, over and above text alone. Conclusions: The findings suggest that avatars that do not strengthen the social relationship with the user do not enhance the intervention impact. Future research should determine whether Web-based PA interventions based on MI could benefit from inclusion of a virtual coach capable of more complex relational skills than used in the current study, such as responding in gesture to the user?s state and input. Trial Registration: Dutch Trial Register trial number: NTR3147; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3147 (Archived by WebCite at http://www.webcitation.org/6NCbwdUJX). UR - http://www.jmir.org/2014/2/e48/ UR - http://dx.doi.org/10.2196/jmir.2974 UR - http://www.ncbi.nlm.nih.gov/pubmed/24550153 ID - info:doi/10.2196/jmir.2974 ER - TY - JOUR AU - Masalski, Marcin AU - Grysi?ski, Tomasz AU - Kr?cicki, Tomasz PY - 2014/01/15 TI - Biological Calibration for Web-Based Hearing Tests: Evaluation of the Methods JO - J Med Internet Res SP - e11 VL - 16 IS - 1 KW - pure-tone audiometry KW - computer-assisted instruction KW - self-examination N2 - Background: Online hearing tests conducted in home settings on a personal computer (PC) require prior calibration. Biological calibration consists of approximating the reference sound level via the hearing threshold of a person with normal hearing. Objective: The objective of this study was to identify the error of the proposed methods of biological calibration, their duration, and the subjective difficulty in conducting these tests via PC. Methods: Seven methods have been proposed for measuring the calibration coefficients. All measurements were performed in reference to the hearing threshold of a normal-hearing person. Three methods were proposed for determining the reference sound level on the basis of these calibration coefficients. Methods were compared for the estimated error, duration, and difficulty of the calibration. Web-based self-assessed measurements of the calibration coefficients were carried out in 3 series: (1) at a otolaryngology clinic, (2) at the participant?s home, and (3) again at the clinic. Additionally, in series 1 and 3, pure-tone audiometry was conducted and series 3 was followed by an offline questionnaire concerning the difficulty of the calibration. Participants were recruited offline from coworkers of the Department and Clinic of Otolaryngology, Wroclaw Medical University, Poland. Results: All 25 participants, aged 22-35 years (median 27) completed all tests and filled in the questionnaire. The smallest standard deviation of the calibration coefficient in the test-retest measurement was obtained at the level of 3.87 dB (95% CI 3.52-4.29) for the modulated signal presented in accordance with the rules of Bekesy?s audiometry. The method is characterized by moderate duration time and a relatively simple procedure. The simplest and shortest method was the method of self-adjustment of the sound volume to the barely audible level. In the test-retest measurement, the deviation of this method equaled 4.97 dB (95% CI 4.53-5.51). Among methods determining the reference sound level, the levels determined independently for each frequency revealed the smallest error. The estimated standard deviations of the difference in the hearing threshold between the examination conducted on a biologically calibrated PC and pure-tone audiometry varied from 7.27 dB (95% CI 6.71-7.93) to 10.38 dB (95% CI 9.11-12.03), depending on the calibration method. Conclusions: In this study, an analysis of biological calibration was performed and the presented results included calibration error, calibration time, and calibration difficulty. These values determine potential applications of Web-based hearing tests conducted in home settings and are decisive factors when selecting the calibration method. If there are no substantial time limitations, it is advisable to use Bekesy method and determine the reference sound level independently at each frequency because this approach is characterized by the lowest error. UR - http://www.jmir.org/2014/1/e11/ UR - http://dx.doi.org/10.2196/jmir.2798 UR - http://www.ncbi.nlm.nih.gov/pubmed/24429353 ID - info:doi/10.2196/jmir.2798 ER - TY - JOUR AU - Aalbers, Teun AU - Baars, E. Maria A. AU - Olde Rikkert, M. Marcel G. AU - Kessels, C. Roy P. PY - 2013/12/03 TI - Puzzling With Online Games (BAM-COG): Reliability, Validity, and Feasibility of an Online Self-Monitor for Cognitive Performance in Aging Adults JO - J Med Internet Res SP - e270 VL - 15 IS - 12 KW - cognitive testing KW - brain aging KW - games KW - validity KW - reliability KW - self-monitoring KW - Internet KW - eHealth N2 - Background: Online interventions are aiming increasingly at cognitive outcome measures but so far no easy and fast self-monitors for cognition have been validated or proven reliable and feasible. Objective: This study examines a new instrument called the Brain Aging Monitor?Cognitive Assessment Battery (BAM-COG) for its alternate forms reliability, face and content validity, and convergent and divergent validity. Also, reference values are provided. Methods: The BAM-COG consists of four easily accessible, short, yet challenging puzzle games that have been developed to measure working memory (?Conveyer Belt?), visuospatial short-term memory (?Sunshine?), episodic recognition memory (?Viewpoint?), and planning (?Papyrinth?). A total of 641 participants were recruited for this study. Of these, 397 adults, 40 years and older (mean 54.9, SD 9.6), were eligible for analysis. Study participants played all games three times with 14 days in between sets. Face and content validity were based on expert opinion. Alternate forms reliability (AFR) was measured by comparing scores on different versions of the BAM-COG and expressed with an intraclass correlation (ICC: two-way mixed; consistency at 95%). Convergent validity (CV) was provided by comparing BAM-COG scores to gold-standard paper-and-pencil and computer-assisted cognitive assessment. Divergent validity (DV) was measured by comparing BAM-COG scores to the National Adult Reading Test IQ (NART-IQ) estimate. Both CV and DV are expressed as Spearman rho correlation coefficients. Results: Three out of four games showed adequate results on AFR, CV, and DV measures. The games Conveyer Belt, Sunshine, and Papyrinth have AFR ICCs of .420, .426, and .645 respectively. Also, these games had good to very good CV correlations: rho=.577 (P=.001), rho=.669 (P<.001), and rho=.400 (P=.04), respectively. Last, as expected, DV correlations were low: rho=?.029 (P=.44), rho=?.029 (P=.45), and rho=?.134 (P=.28) respectively. The game Viewpoint provided less desirable results with an AFR ICC of .167, CV rho=.202 (P=.15), and DV rho=?.162 (P=.21). Conclusions: This study provides evidence for the use of the BAM-COG test battery as a feasible, reliable, and valid tool to monitor cognitive performance in healthy adults in an online setting. Three out of four games have good psychometric characteristics to measure working memory, visuospatial short-term memory, and planning capacity. UR - http://www.jmir.org/2013/12/e270/ UR - http://dx.doi.org/10.2196/jmir.2860 UR - http://www.ncbi.nlm.nih.gov/pubmed/24300212 ID - info:doi/10.2196/jmir.2860 ER - TY - JOUR AU - Shortreed, M. Susan AU - Bogart, Andy AU - McClure, B. Jennifer PY - 2013/11/21 TI - Using Multiple Imputations to Accommodate Time-Outs in Online Interventions JO - J Med Internet Res SP - e252 VL - 15 IS - 11 KW - online interventions KW - engagement KW - time spent online KW - multiple imputations KW - automatic time-out KW - smoking cessation KW - utilization KW - behavioral research KW - Internet N2 - Background: Accurately estimating the period of time that individuals are exposed to online intervention content is important for understanding program engagement. This can be calculated from time-stamped data reflecting navigation to and from individual webpages. Prolonged periods of inactivity are commonly handled with a time-out feature and assigned a prespecified exposure duration. Unfortunately, this practice can lead to biased results describing program exposure. Objective: The aim of the study was to describe how multiple imputations can be used to better account for the time spent viewing webpages that result in a prolonged period of inactivity or a time-out. Methods: To illustrate this method, we present data on time-outs collected from the Q2 randomized smoking cessation trial. For this analysis, we evaluate the effects on intervention exposure of receiving content written in a prescriptive versus motivational tone. Using multiple imputations, we created five complete datasets in which the time spent viewing webpages that resulted in a time-out were replaced with values estimated with imputation models. We calculated standard errors using Rubin?s formulas to account for the variability due to the imputations. We also illustrate how current methods of accounting for time-outs (excluding timed-out page views or assigning an arbitrary viewing time) can influence conclusions about participant engagement. Results: A total of 63.00% (1175/1865) of participants accessed the online intervention in the Q2 trial. Of the 6592 unique page views, 683 (10.36%, 683/6592) resulted in a time-out. The median time spent viewing webpages that did not result in a time-out was 1.07 minutes. Assuming participants did not spend any time viewing a webpage that resulted in a time-out, no difference between the two message tones was observed (ratio of mean time online: 0.87, 95% CI 0.75-1.02). Assigning 30 minutes of viewing time to all page views that resulted in a time-out concludes that participants who received content in a motivational tone spent less time viewing content (ratio of mean time online: 0.86, 95% CI 0.77-0.98) than those participants who received content in a prescriptive tone. Using multiple imputations to account for time-outs concludes that there is no difference in participant engagement between the two message tones (ratio of mean time online: 0.87; 95% CI 0.75-1.01). Conclusions: The analytic technique chosen can significantly affect conclusions about online intervention engagement. We propose a standardized methodology in which time spent viewing webpages that result in a time-out is treated as missing information and corrected with multiple imputations. Trial Registration: Clinicaltrials.gov NCT00992264; http://clinicaltrials.gov/ct2/show/NCT00992264 (Archived by WebCite at http://www.webcitation.org/6Kw5m8EkP). UR - http://www.jmir.org/2013/11/e252/ UR - http://dx.doi.org/10.2196/jmir.2781 UR - http://www.ncbi.nlm.nih.gov/pubmed/24263289 ID - info:doi/10.2196/jmir.2781 ER - TY - JOUR AU - Diamantidis, Jonas Clarissa AU - Fink, Wanda AU - Yang, Shiming AU - Zuckerman, R. Marni AU - Ginsberg, Jennifer AU - Hu, Peter AU - Xiao, Yan AU - Fink, C. Jeffrey PY - 2013/11/15 TI - Directed Use of the Internet for Health Information by Patients With Chronic Kidney Disease: Prospective Cohort Study JO - J Med Internet Res SP - e251 VL - 15 IS - 11 KW - chronic kidney disease KW - health information technology KW - patient safety N2 - Background: Health information technology has become common in the care of patients with chronic diseases; however, there are few such applications employed in kidney disease. Objective: The aim of the study was to evaluate the use of a website providing disease-specific safety information by patients with predialysis chronic kidney disease. Methods: As part of the Safe Kidney Care (SKC) study, an educational website was designed to provide information on safety concerns in chronic kidney disease. Phase I study participants were provided a medical alert accessory with a unique ID number, the Safe Kidney Care website, and an in-person tutorial on the use of the Internet and accessing the SKC website at baseline. Participants were asked to visit the website and enter their unique ID as frequently as they desired over the next 365 days or until their annual follow-up visit, whichever occurred first. Participants? visits and dwell times on specific safety modules were tracked using embedded webpage PHP scripts linked to a MySQL database, enabling the collection of website usage statistics. Results: Of 108 Phase I participants, 28.7% (31/108) visited the website from 1-6 times during the observation period (median follow-up 365 days). Median access time was 7 minutes per visit (range <1-46) and 13 minutes per person (range <1-123). The three most frequently visited pages were ?Renal function calculator?, ?Pills to avoid?, and ?Foods to avoid?. High school education and frequent Internet use were significantly associated with website entry (P=.02 and P=.03, respectively). Conclusions: Preliminary results show general interest in a Web-based platform designed to improve patient safety in chronic kidney disease. Trial Registration: Clinicaltrials.gov NCT01407367; http://clinicaltrials.gov/show/NCT01407367 (Archived by WebCite at http://www.webcitation.org/6KvxFKA6M). UR - http://www.jmir.org/2013/11/e251/ UR - http://dx.doi.org/10.2196/jmir.2848 UR - http://www.ncbi.nlm.nih.gov/pubmed/24240617 ID - info:doi/10.2196/jmir.2848 ER - TY - JOUR AU - Gilbert, Mark AU - Hottes, Salway Travis AU - Kerr, Thomas AU - Taylor, Darlene AU - Fairley, K. Christopher AU - Lester, Richard AU - Wong, Tom AU - Trussler, Terry AU - Marchand, Rick AU - Shoveller, Jean AU - Ogilvie, Gina PY - 2013/11/14 TI - Factors Associated With Intention to Use Internet-Based Testing for Sexually Transmitted Infections Among Men Who Have Sex With Men JO - J Med Internet Res SP - e254 VL - 15 IS - 11 KW - homosexuality KW - male KW - Internet KW - testing KW - human immunodeficiency virus KW - sexually transmitted infection KW - health equity KW - patient acceptance of health care N2 - Background: Internet-based testing programs are being increasingly used to reduce testing barriers for individuals at higher risk of infection, yet the population impact and potential for exacerbation of existing health inequities of these programs are not well understood. Objective: We used a large online sample of men who have sex with men (MSM) in Canada to measure acceptability of Internet-based testing and perceived advantages and disadvantages of this testing approach. Methods: We asked participants of the 2011/2012 Sex Now Survey (a serial online survey of gay and bisexual men in Canada) whether they intended to use Internet-based testing and their perceived benefits and disadvantages of use. We examined whether intention to use was associated with explanatory variables spanning (A) sociodemographics, (B) Internet and technology usage, (C) sexually transmitted infections (STI)/ human immunodeficiency virus (HIV) and risk, and (D) health care access and testing, using multivariable logistic regression (variable selection using Bayesian information criterion). Results: Overall, intention to use was high (5678/7938, 71.53%) among participants with little variation by participant characteristics. In our final model, we retained the variables related to (B) Internet and technology usage: use of Internet to cruise for sex partners (adjusted odds ratio [AOR] 1.46, 95% CI 1.25-1.70), use of Internet to search for sexual health information (AOR 1.36, 95% CI 1.23-1.51), and mobile phone usage (AOR 1.19, 95% 1.13-1.24). We also retained the variables for (D) health care access and testing: not ?out? to primary care provider (AOR 1.24, 95% CI 1.10-1.41), delayed/avoided testing due to privacy concerns (AOR 1.77, 95% CI 1.49-2.11), and delayed/avoided testing due to access issues (AOR 1.65, 95% CI 1.40-1.95). Finally, we retained the variable being HIV positive (AOR 0.56, 95% CI 0.46-0.68) or HIV status unknown (AOR 0.89, 95% CI 0.77-1.01), age <30 years (AOR 1.41, 95% CI 1.22-1.62), and identifying as bisexual (AOR 1.18, 95% CI 1.04-1.34) or straight/other (AOR 0.67, 95% CI 0.50-0.90). The greatest perceived benefits of Internet-based testing were privacy (2249/8388, 26.81%), general convenience (1701/8388, 20.28%), and being able to test at any time (1048/8388, 12.49%). The greatest perceived drawbacks were the inability to see a doctor or nurse (1507/8388, 17.97%), wanting to talk to someone about results (1430/8388, 17.97%), not wanting online results (1084/8388, 12.92%), and low trust (973/8388, 11.60%). Conclusions: The high and wide-ranging intention to use that we observed suggests Internet-based testing has the potential to reach into all subgroups of MSM and may be particularly appealing to those facing current barriers to accessing STI/HIV testing and who are more comfortable with technology. These findings will be used to inform the promotion and further evaluation of an Internet-based testing program currently under development in British Columbia, Canada. UR - http://www.jmir.org/2013/11/e254/ UR - http://dx.doi.org/10.2196/jmir.2888 UR - http://www.ncbi.nlm.nih.gov/pubmed/24240644 ID - info:doi/10.2196/jmir.2888 ER - TY - JOUR AU - Kitsiou, Spyros AU - Paré, Guy AU - Jaana, Mirou PY - 2013/07/23 TI - Systematic Reviews and Meta-Analyses of Home Telemonitoring Interventions for Patients With Chronic Diseases: A Critical Assessment of Their Methodological Quality JO - J Med Internet Res SP - e150 VL - 15 IS - 7 KW - meta-analysis as topic KW - systematic review as topic KW - home telemonitoring KW - telehealth KW - telemetry KW - quality assessment KW - risk of bias KW - chronic diseases KW - heart failure KW - diabetes KW - hypertension KW - pulmonary disease N2 - Background: Systematic reviews and meta-analyses of home telemonitoring interventions for patients with chronic diseases have increased over the past decade and become increasingly important to a wide range of clinicians, policy makers, and other health care stakeholders. While a few criticisms about their methodological rigor and synthesis approaches have recently appeared, no formal appraisal of their quality has been conducted yet. Objective: The primary aim of this critical review was to evaluate the methodology, quality, and reporting characteristics of prior reviews that have investigated the effects of home telemonitoring interventions in the context of chronic diseases. Methods: Ovid MEDLINE, the Database of Abstract of Reviews of Effects (DARE), and Health Technology Assessment Database (HTA) of the Cochrane Library were electronically searched to find relevant systematic reviews, published between January 1966 and December 2012. Potential reviews were screened and assessed for inclusion independently by three reviewers. Data pertaining to the methods used were extracted from each included review and examined for accuracy by two reviewers. A validated quality assessment instrument, R-AMSTAR, was used as a framework to guide the assessment process. Results: Twenty-four reviews, nine of which were meta-analyses, were identified from more than 200 citations. The bibliographic search revealed that the number of published reviews has increased substantially over the years in this area and although most reviews focus on studying the effects of home telemonitoring on patients with congestive heart failure, researcher interest has extended to other chronic diseases as well, such as diabetes, hypertension, chronic obstructive pulmonary disease, and asthma. Nevertheless, an important number of these reviews appear to lack optimal scientific rigor due to intrinsic methodological issues. Also, the overall quality of reviews does not appear to have improved over time. While several criteria were met satisfactorily by either all or nearly all reviews, such as the establishment of an a priori design with inclusion and exclusion criteria, use of electronic searches on multiple databases, and reporting of studies characteristics, there were other important areas that needed improvement. Duplicate data extraction, manual searches of highly relevant journals, inclusion of gray and non-English literature, assessment of the methodological quality of included studies and quality of evidence were key methodological procedures that were performed infrequently. Furthermore, certain methodological limitations identified in the synthesis of study results have affected the results and conclusions of some reviews. Conclusions: Despite the availability of methodological guidelines that can be utilized to guide the proper conduct of systematic reviews and meta-analyses and eliminate potential risks of bias, this knowledge has not yet been fully integrated in the area of home telemonitoring. Further efforts should be made to improve the design, conduct, reporting, and publication of systematic reviews and meta-analyses in this area. UR - http://www.jmir.org/2013/7/e150/ UR - http://dx.doi.org/10.2196/jmir.2770 UR - http://www.ncbi.nlm.nih.gov/pubmed/23880072 ID - info:doi/10.2196/jmir.2770 ER - TY - JOUR AU - Aztiria, Asier AU - Farhadi, Golnaz AU - Aghajan, Hamid PY - 2013/06/18 TI - User Behavior Shift Detection in Ambient Assisted Living Environments JO - JMIR Mhealth Uhealth SP - e6 VL - 1 IS - 1 KW - shift detection KW - intelligent environments KW - disease detection UR - http://mhealth.jmir.org/2013/1/e6/ UR - http://dx.doi.org/10.2196/mhealth.2536 UR - http://www.ncbi.nlm.nih.gov/pubmed/25100679 ID - info:doi/10.2196/mhealth.2536 ER - TY - JOUR AU - Masalski, Marcin AU - Kr?cicki, Tomasz PY - 2013/04/12 TI - Self-Test Web-Based Pure-Tone Audiometry: Validity Evaluation and Measurement Error Analysis JO - J Med Internet Res SP - e71 VL - 15 IS - 4 KW - pure tone audiometry KW - computer-assisted instruction KW - self-examination N2 - Background: Potential methods of application of self-administered Web-based pure-tone audiometry conducted at home on a PC with a sound card and ordinary headphones depend on the value of measurement error in such tests. Objective: The aim of this research was to determine the measurement error of the hearing threshold determined in the way described above and to identify and analyze factors influencing its value. Methods: The evaluation of the hearing threshold was made in three series: (1) tests on a clinical audiometer, (2) self-tests done on a specially calibrated computer under the supervision of an audiologist, and (3) self-tests conducted at home. The research was carried out on the group of 51 participants selected from patients of an audiology outpatient clinic. From the group of 51 patients examined in the first two series, the third series was self-administered at home by 37 subjects (73%). Results: The average difference between the value of the hearing threshold determined in series 1 and in series 2 was -1.54dB with standard deviation of 7.88dB and a Pearson correlation coefficient of .90. Between the first and third series, these values were -1.35dB±10.66dB and .84, respectively. In series 3, the standard deviation was most influenced by the error connected with the procedure of hearing threshold identification (6.64dB), calibration error (6.19dB), and additionally at the frequency of 250Hz by frequency nonlinearity error (7.28dB). Conclusions: The obtained results confirm the possibility of applying Web-based pure-tone audiometry in screening tests. In the future, modifications of the method leading to the decrease in measurement error can broaden the scope of Web-based pure-tone audiometry application. UR - http://www.jmir.org/2013/4/e71/ UR - http://dx.doi.org/10.2196/jmir.2222 UR - http://www.ncbi.nlm.nih.gov/pubmed/23583917 ID - info:doi/10.2196/jmir.2222 ER - TY - JOUR AU - Kaonga, Nina Nadi AU - Labrique, Alain AU - Mechael, Patricia AU - Akosah, Eric AU - Ohemeng-Dapaah, Seth AU - Sakyi Baah, Joseph AU - Kodie, Richmond AU - Kanter, S. Andrew AU - Levine, Orin PY - 2013/04/03 TI - Using Social Networking to Understand Social Networks: Analysis of a Mobile Phone Closed User Group Used by a Ghanaian Health Team JO - J Med Internet Res SP - e74 VL - 15 IS - 4 KW - mobile health KW - electronic health KW - telehealth KW - sociology KW - social network analysis KW - rural health KW - global health KW - evaluation research KW - Ghana N2 - Background: The network structure of an organization influences how well or poorly an organization communicates and manages its resources. In the Millennium Villages Project site in Bonsaaso, Ghana, a mobile phone closed user group has been introduced for use by the Bonsaaso Millennium Villages Project Health Team and other key individuals. No assessment on the benefits or barriers of the use of the closed user group had been carried out. Objective: The purpose of this research was to make the case for the use of social network analysis methods to be applied in health systems research?specifically related to mobile health. Methods: This study used mobile phone voice records of, conducted interviews with, and reviewed call journals kept by a mobile phone closed user group consisting of the Bonsaaso Millennium Villages Project Health Team. Social network analysis methodology complemented by a qualitative component was used. Monthly voice data of the closed user group from Airtel Bharti Ghana were analyzed using UCINET and visual depictions of the network were created using NetDraw. Interviews and call journals kept by informants were analyzed using NVivo. Results: The methodology was successful in helping identify effective organizational structure. Members of the Health Management Team were the more central players in the network, rather than the Community Health Nurses (who might have been expected to be central). Conclusions: Social network analysis methodology can be used to determine the most productive structure for an organization or team, identify gaps in communication, identify key actors with greatest influence, and more. In conclusion, this methodology can be a useful analytical tool, especially in the context of mobile health, health services, and operational and managerial research. UR - http://www.jmir.org/2013/4/e74/ UR - http://dx.doi.org/10.2196/jmir.2332 UR - http://www.ncbi.nlm.nih.gov/pubmed/23552721 ID - info:doi/10.2196/jmir.2332 ER - TY - JOUR AU - Gao, Xiaoli AU - Hamzah, SH AU - Yiu, Yung Cynthia Kar AU - McGrath, Colman AU - King, M. Nigel PY - 2013/02/22 TI - Dental Fear and Anxiety in Children and Adolescents: Qualitative Study Using YouTube JO - J Med Internet Res SP - e29 VL - 15 IS - 2 KW - dental fear KW - dental anxiety KW - children KW - adolescents KW - qualitative research KW - Internet social media N2 - Background: Dental fear and anxiety (DFA) refers to the fear of and anxiety towards going to the dentist. It exists in a considerable proportion of children and adolescents and is a major dilemma in pediatric dental practice. As an Internet social medium with increasing popularity, the video-sharing website YouTube offers a useful data source for understanding health behaviors and perceptions of the public. Objective: Using YouTube as a platform, this qualitative study aimed to examine the manifestations, impacts, and origins of DFA in children and adolescents from the public?s perspective. Methods: To retrieve relevant information, we searched YouTube using the keywords ?dental fear?, ?dental anxiety?, and ?dental phobia?. Videos in English expressing a layperson?s views or experience on children?s or adolescent?s DFA were selected for this study. A video was excluded if it had poor audiovisual quality, was irrelevant, was pure advertisement or entertainment, or contained only the views of professionals. After the screen, we transcribed 27 videos involving 32 children and adolescents, which were reviewed by a panel of 3 investigators, including a layperson with no formal dental training. Inductive thematic analysis was applied for coding and interpreting the data. Results: The videos revealed multiple manifestations and impacts of DFA, including immediate physical reactions (eg, crying, screaming, and shivering), psychological responses (eg, worry, upset, panic, helplessness, insecurity, resentment, and hatred), and uncooperativeness in dental treatment. Testimonials from children, adolescents, and their parents suggested diverse origins of DFA, namely personal experience (eg, irregular dental visits and influence of parents or peers), dentists and dental auxiliaries (eg, bad manner, lack of clinical skills, and improper work ethic), dental settings (eg, dental chair and sounds), and dental procedures (eg, injections, pain, discomfort, and aesthetic concerns). Conclusions: This qualitative study suggests that DFA in children and adolescents has multifaceted manifestations, impacts, and origins, some of which only became apparent when using Internet social media. Our findings support the value of infodemiological studies using Internet social media to gain a better understanding of health issues. UR - http://www.jmir.org/2013/2/e29/ UR - http://dx.doi.org/10.2196/jmir.2290 UR - http://www.ncbi.nlm.nih.gov/pubmed/23435094 ID - info:doi/10.2196/jmir.2290 ER - TY - JOUR AU - Dallery, Jesse AU - Cassidy, N. Rachel AU - Raiff, R. Bethany PY - 2013/02/08 TI - Single-Case Experimental Designs to Evaluate Novel Technology-Based Health Interventions JO - J Med Internet Res SP - e22 VL - 15 IS - 2 KW - Research design KW - technology KW - mHealth KW - single-case design KW - preliminary efficacy UR - http://www.jmir.org/2013/2/e22/ UR - http://dx.doi.org/10.2196/jmir.2227 UR - http://www.ncbi.nlm.nih.gov/pubmed/23399668 ID - info:doi/10.2196/jmir.2227 ER - TY - JOUR AU - Heselmans, Annemie AU - Aertgeerts, Bert AU - Donceel, Peter AU - Van de Velde, Stijn AU - Vanbrabant, Peter AU - Ramaekers, Dirk PY - 2013/01/17 TI - Human Computation as a New Method for Evidence-Based Knowledge Transfer in Web-Based Guideline Development Groups: Proof of Concept Randomized Controlled Trial JO - J Med Internet Res SP - e8 VL - 15 IS - 1 KW - clinical practice guidelines KW - evidence-based medicine KW - guideline development KW - consensus methods KW - human computation KW - games with a purpose N2 - Background: Guideline developers use different consensus methods to develop evidence-based clinical practice guidelines. Previous research suggests that existing guideline development techniques are subject to methodological problems and are logistically demanding. Guideline developers welcome new methods that facilitate a methodologically sound decision-making process. Systems that aggregate knowledge while participants play a game are one class of human computation applications. Researchers have already proven that these games with a purpose are effective in building common sense knowledge databases. Objective: We aimed to evaluate the feasibility of a new consensus method based on human computation techniques compared to an informal face-to-face consensus method. Methods: We set up a randomized design to study 2 different methods for guideline development within a group of advanced students completing a master of nursing and obstetrics. Students who participated in the trial were enrolled in an evidence-based health care course. We compared the Web-based method of human-based computation (HC) with an informal face-to-face consensus method (IC). We used 4 clinical scenarios of lower back pain as the subject of the consensus process. These scenarios concerned the following topics: (1) medical imaging, (2) therapeutic options, (3) drugs use, and (4) sick leave. Outcomes were expressed as the amount of group (dis)agreement and the concordance of answers with clinical evidence. We estimated within-group and between-group effect sizes by calculating Cohen?s d. We calculated within-group effect sizes as the absolute difference between the outcome value at round 3 and the baseline outcome value, divided by the pooled standard deviation. We calculated between-group effect sizes as the absolute difference between the mean change in outcome value across rounds in HC and the mean change in outcome value across rounds in IC, divided by the pooled standard deviation. We analyzed statistical significance of within-group changes between round 1 and round 3 using the Wilcoxon signed rank test. We assessed the differences between the HC and IC groups using Mann-Whitney U tests. We used a Bonferroni adjusted alpha level of .025 in all statistical tests. We performed a thematic analysis to explore participants? arguments during group discussion. Participants completed a satisfaction survey at the end of the consensus process. Results: Of the 135 students completing a master of nursing and obstetrics, 120 participated in the experiment. We formed 8 HC groups (n=64) and 7 IC groups (n=56). The between-group comparison demonstrated that the human computation groups obtained a greater improvement in evidence scores compared to the IC groups, although the difference was not statistically significant. The between-group effect size was 0.56 (P=.30) for the medical imaging scenario, 0.07 (P=.97) for the therapeutic options scenario, and 0.89 (P=.11) for the drug use scenario. We found no significant differences in improvement in the degree of agreement between HC and IC groups. Between-group comparisons revealed that the HC groups showed greater improvement in degree of agreement for the medical imaging scenario (d=0.46, P=.37) and the drug use scenario (d=0.31, P=.59). Very few evidence arguments (6%) were quoted during informal group discussions. Conclusions: Overall, the use of the IC method was appropriate as long as the evidence supported participants? beliefs or usual practice, or when the availability of the evidence was sparse. However, when some controversy about the evidence existed, the HC method outperformed the IC method. The findings of our study illustrate the importance of the choice of the consensus method in guideline development. Human computation could be an acceptable methodology for guideline development specifically for scenarios in which the evidence shows no resonance with participants? beliefs. Future research is needed to confirm the results of this study and to establish practical significance in a controlled setting of multidisciplinary guideline panels during real-life guideline development. UR - http://www.jmir.org/2013/1/e8/ UR - http://dx.doi.org/10.2196/jmir.2055 UR - http://www.ncbi.nlm.nih.gov/pubmed/23328663 ID - info:doi/10.2196/jmir.2055 ER - TY - JOUR AU - Mantokoudis, Georgios AU - Dubach, Patrick AU - Pfiffner, Flurin AU - Kompis, Martin AU - Caversaccio, Marco AU - Senn, Pascal PY - 2012/07/16 TI - Speech Perception Benefits of Internet Versus Conventional Telephony for Hearing-Impaired Individuals JO - J Med Internet Res SP - e102 VL - 14 IS - 4 KW - VoIP KW - Internet telephony KW - hearing impaired KW - communication N2 - Background: Telephone communication is a challenge for many hearing-impaired individuals. One important technical reason for this difficulty is the restricted frequency range (0.3?3.4 kHz) of conventional landline telephones. Internet telephony (voice over Internet protocol [VoIP]) is transmitted with a larger frequency range (0.1?8 kHz) and therefore includes more frequencies relevant to speech perception. According to a recently published, laboratory-based study, the theoretical advantage of ideal VoIP conditions over conventional telephone quality has translated into improved speech perception by hearing-impaired individuals. However, the speech perception benefits of nonideal VoIP network conditions, which may occur in daily life, have not been explored. VoIP use cannot be recommended to hearing-impaired individuals before its potential under more realistic conditions has been examined. Objective: To compare realistic VoIP network conditions, under which digital data packets may be lost, with ideal conventional telephone quality with respect to their impact on speech perception by hearing-impaired individuals. Methods: We assessed speech perception using standardized test material presented under simulated VoIP conditions with increasing digital data packet loss (from 0% to 20%) and compared with simulated ideal conventional telephone quality. We monaurally tested 10 adult users of cochlear implants, 10 adult users of hearing aids, and 10 normal-hearing adults in the free sound field, both in quiet and with background noise. Results: Across all participant groups, mean speech perception scores using VoIP with 0%, 5%, and 10% packet loss were 15.2% (range 0%?53%), 10.6% (4%?46%), and 8.8% (7%?33%) higher, respectively, than with ideal conventional telephone quality. Speech perception did not differ between VoIP with 20% packet loss and conventional telephone quality. The maximum benefits were observed under ideal VoIP conditions without packet loss and were 36% (P = .001) for cochlear implant users, 18% (P = .002) for hearing aid users, and 53% (P = .001) for normal-hearing adults. With a packet loss of 10%, the maximum benefits were 30% (P = .002) for cochlear implant users, 6% (P = .38) for hearing aid users, and 33% (P = .002) for normal-hearing adults. Conclusions: VoIP offers a speech perception benefit over conventional telephone quality, even when mild or moderate packet loss scenarios are created in the laboratory. VoIP, therefore, has the potential to significantly improve telecommunication abilities for the large community of hearing-impaired individuals. UR - http://www.jmir.org/2012/4/e102/ UR - http://dx.doi.org/10.2196/jmir.1818 UR - http://www.ncbi.nlm.nih.gov/pubmed/22805169 ID - info:doi/10.2196/jmir.1818 ER - TY - JOUR AU - Daugherty, L. Bethany AU - Schap, E. TusaRebecca AU - Ettienne-Gittens, Reynolette AU - Zhu, M. Fengqing AU - Bosch, Marc AU - Delp, J. Edward AU - Ebert, S. David AU - Kerr, A. Deborah AU - Boushey, J. Carol PY - 2012/04/13 TI - Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents JO - J Med Internet Res SP - e58 VL - 14 IS - 2 KW - Mobile telephone food record KW - dietary assessment KW - technology KW - image analysis KW - volume estimation N2 - Background: The development of a mobile telephone food record has the potential to ameliorate much of the burden associated with current methods of dietary assessment. When using the mobile telephone food record, respondents capture an image of their foods and beverages before and after eating. Methods of image analysis and volume estimation allow for automatic identification and volume estimation of foods. To obtain a suitable image, all foods and beverages and a fiducial marker must be included in the image. Objective: To evaluate a defined set of skills among adolescents and adults when using the mobile telephone food record to capture images and to compare the perceptions and preferences between adults and adolescents regarding their use of the mobile telephone food record. Methods: We recruited 135 volunteers (78 adolescents, 57 adults) to use the mobile telephone food record for one or two meals under controlled conditions. Volunteers received instruction for using the mobile telephone food record prior to their first meal, captured images of foods and beverages before and after eating, and participated in a feedback session. We used chi-square for comparisons of the set of skills, preferences, and perceptions between the adults and adolescents, and McNemar test for comparisons within the adolescents and adults. Results: Adults were more likely than adolescents to include all foods and beverages in the before and after images, but both age groups had difficulty including the entire fiducial marker. Compared with adolescents, significantly more adults had to capture more than one image before (38% vs 58%, P = .03) and after (25% vs 50%, P = .008) meal session 1 to obtain a suitable image. Despite being less efficient when using the mobile telephone food record, adults were more likely than adolescents to perceive remembering to capture images as easy (P < .001). Conclusions: A majority of both age groups were able to follow the defined set of skills; however, adults were less efficient when using the mobile telephone food record. Additional interactive training will likely be necessary for all users to provide extra practice in capturing images before entering a free-living situation. These results will inform age-specific development of the mobile telephone food record that may translate to a more accurate method of dietary assessment. UR - http://www.jmir.org/2012/2/e58/ UR - http://dx.doi.org/10.2196/jmir.1967 UR - http://www.ncbi.nlm.nih.gov/pubmed/22504018 ID - info:doi/10.2196/jmir.1967 ER - TY - JOUR AU - Gupta, Samir AU - Wan, T. Flora AU - Newton, David AU - Bhattacharyya, K. Onil AU - Chignell, H. Mark AU - Straus, E. Sharon PY - 2011/12/08 TI - WikiBuild: A New Online Collaboration Process For Multistakeholder Tool Development and Consensus Building JO - J Med Internet Res SP - e108 VL - 13 IS - 4 KW - Consensus KW - focus groups KW - user-computer interface KW - Web 2.0 KW - asthma KW - self-care N2 - Background: Production of media such as patient education tools requires methods that can integrate multiple stakeholder perspectives. Existing consensus techniques are poorly suited to design of visual media, can be expensive and logistically demanding, and are subject to caveats arising from group dynamics such as participant hierarchies. Objective: Our objective was to develop a method that enables multistakeholder tool building while averting these difficulties. Methods: We developed a wiki-inspired method and tested this through the collaborative design of an asthma action plan (AAP). In the development stage, we developed the Web-based tool by (1) establishing AAP content and format options, (2) building a Web-based application capable of representing each content and format permutation, (3) testing this tool among stakeholders, and (4) revising this tool based on stakeholder feedback. In the wiki stage, groups of participants used the revised tool in three separate 1-week ?wiki? periods during which each group collaboratively authored an AAP by making multiple online selections. Results: In the development stage, we recruited 16 participants (9/16 male) (4 pulmonologists, 4 primary care physicians, 3 certified asthma educators, and 5 patients) for system testing. The mean System Usability Scale (SUS) score for the tool used in testing was 72.2 (SD 10.2). In the wiki stage, we recruited 41 participants (15/41 male) (9 pulmonologists, 6 primary care physicians, 5 certified asthma educators, and 21 patients) from diverse locations. The mean SUS score for the revised tool was 75.9 (SD 19.6). Users made 872, 466, and 599 successful changes to the AAP in weeks 1, 2, and 3, respectively. The site was used actively for a mean of 32.0 hours per week, of which 3.1 hours per week (9.7%) constituted synchronous multiuser use (2?4 users at the same time). Participants averaged 23 (SD 33) minutes of login time and made 7.7 (SD 15) changes to the AAP per day. Among participants, 28/35 (80%) were satisfied with the final AAP, and only 3/34 (9%) perceived interstakeholder group hierarchies. Conclusion: Use of a wiki-inspired method allowed for effective collaborative design of content and format aspects of an AAP while minimizing logistical requirements, maximizing geographical representation, and mitigating hierarchical group dynamics. Our method faced unique software and hardware challenges, and raises certain questions regarding its effect on group functioning. Potential uses of our method are broad, and further studies are required. UR - http://www.jmir.org/2011/4/e108/ UR - http://dx.doi.org/10.2196/jmir.1833 UR - http://www.ncbi.nlm.nih.gov/pubmed/22155694 ID - info:doi/10.2196/jmir.1833 ER - TY - JOUR AU - Bekhuis, Tanja AU - Kreinacke, Marcos AU - Spallek, Heiko AU - Song, Mei AU - O'Donnell, A. Jean PY - 2011/11/23 TI - Using Natural Language Processing to Enable In-depth Analysis of Clinical Messages Posted to an Internet Mailing List: A Feasibility Study JO - J Med Internet Res SP - e98 VL - 13 IS - 4 KW - Dentistry KW - dental informatics KW - clinical research informatics KW - natural language processing KW - information storage and retrieval KW - electronic mail KW - information-seeking behavior N2 - Background: An Internet mailing list may be characterized as a virtual community of practice that serves as an information hub with easy access to expert advice and opportunities for social networking. We are interested in mining messages posted to a list for dental practitioners to identify clinical topics. Once we understand the topical domain, we can study dentists? real information needs and the nature of their shared expertise, and can avoid delivering useless content at the point of care in future informatics applications. However, a necessary first step involves developing procedures to identify messages that are worth studying given our resources for planned, labor-intensive research. Objectives: The primary objective of this study was to develop a workflow for finding a manageable number of clinically relevant messages from a much larger corpus of messages posted to an Internet mailing list, and to demonstrate the potential usefulness of our procedures for investigators by retrieving a set of messages tailored to the research question of a qualitative research team. Methods: We mined 14,576 messages posted to an Internet mailing list from April 2008 to May 2009. The list has about 450 subscribers, mostly dentists from North America interested in clinical practice. After extensive preprocessing, we used the Natural Language Toolkit to identify clinical phrases and keywords in the messages. Two academic dentists classified collocated phrases in an iterative, consensus-based process to describe the topics discussed by dental practitioners who subscribe to the list. We then consulted with qualitative researchers regarding their research question to develop a plan for targeted retrieval. We used selected phrases and keywords as search strings to identify clinically relevant messages and delivered the messages in a reusable database. Results: About half of the subscribers (245/450, 54.4%) posted messages. Natural language processing (NLP) yielded 279,193 clinically relevant tokens or processed words (19% of all tokens). Of these, 2.02% (5634 unique tokens) represent the vocabulary for dental practitioners. Based on pointwise mutual information score and clinical relevance, 325 collocated phrases (eg, fistula filled obturation and herpes zoster) with 108 keywords (eg, mercury) were classified into 13 broad categories with subcategories. In the demonstration, we identified 305 relevant messages (2.1% of all messages) over 10 selected categories with instances of collocated phrases, and 299 messages (2.1%) with instances of phrases or keywords for the category systemic disease. Conclusions: A workflow with a sequence of machine-based steps and human classification of NLP-discovered phrases can support researchers who need to identify relevant messages in a much larger corpus. Discovered phrases and keywords are useful search strings to aid targeted retrieval. We demonstrate the potential value of our procedures for qualitative researchers by retrieving a manageable set of messages concerning systemic and oral disease. UR - http://www.jmir.org/2011/4/e98/ UR - http://dx.doi.org/10.2196/jmir.1799 UR - http://www.ncbi.nlm.nih.gov/pubmed/22112583 ID - info:doi/10.2196/jmir.1799 ER -