@Article{info:doi/10.2196/22124, author="Senteio, Charles and Murdock, Joseph Paul", title="The Efficacy of Health Information Technology in Supporting Health Equity for Black and Hispanic Patients With Chronic Diseases: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="4", volume="24", number="4", pages="e22124", keywords="chronic disease", keywords="minority health", keywords="technology assessment", keywords="biomedical", keywords="self-management", keywords="systematic review", keywords="mobile phone", abstract="Background: Racial inequity persists for chronic disease outcomes amid the proliferation of health information technology (HIT) designed to support patients in following recommended chronic disease self-management behaviors (ie, medication behavior, physical activity, and dietary behavior and attending follow-up appointments). Numerous interventions that use consumer-oriented HIT to support self-management have been evaluated, and some of the related literature has focused on racial minorities who experience disparate chronic disease outcomes. However, little is known about the efficacy of these interventions. Objective: This study aims to conduct a systematic review of the literature that describes the efficacy of consumer-oriented HIT interventions designed to support self-management involving African American and Hispanic patients with chronic diseases. Methods: We followed an a priori protocol using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-Equity 2012 Extension guidelines for systematic reviews that focus on health equity. Themes of interest included the inclusion and exclusion criteria. We identified 7 electronic databases, created search strings, and conducted the searches. We initially screened results based on titles and abstracts and then performed full-text screening. We then resolved conflicts and extracted relevant data from the included articles. Results: In total, there were 27 included articles. The mean sample size was 640 (SD 209.5), and 52\% (14/27) of the articles focused on African American participants, 15\% (4/27) of the articles focused on Hispanic participants, and 33\% (9/27) included both. Most articles addressed 3 of the 4 self-management behaviors: medication (17/27, 63\%), physical activity (17/27, 63\%), and diet (16/27, 59\%). Only 15\% (4/27) of the studies focused on follow-up appointment attendance. All the articles investigated HIT for use at home, whereas 7\% (2/27) included use in the hospital. Conclusions: This study addresses a key gap in research that has not sufficiently examined what technology designs and capabilities may be effective for underserved populations in promoting health behavior in concordance with recommendations. ", doi="10.2196/22124", url="https://www.jmir.org/2022/4/e22124", url="http://www.ncbi.nlm.nih.gov/pubmed/35377331" } @Article{info:doi/10.2196/33787, author="Smits, Merlijn and Kim, Mi Chan and van Goor, Harry and Ludden, S. Geke D.", title="From Digital Health to Digital Well-being: Systematic Scoping Review", journal="J Med Internet Res", year="2022", month="Apr", day="4", volume="24", number="4", pages="e33787", keywords="well-being", keywords="design", keywords="evaluation", keywords="technology assessment", keywords="digital health", keywords="eHealth", keywords="mHealth", keywords="telehealth", keywords="mobile phone", abstract="Background: Digital health refers to the proper use of technology for improving the health and well-being of people and enhancing the care of patients through the intelligent processing of clinical and genetic data. Despite increasing interest in well-being in both health care and technology, there is no clear understanding of what constitutes well-being, which leads to uncertainty in how to create well-being through digital health. In an effort to clarify this uncertainty, Brey developed a framework to define problems in technology for well-being using the following four categories: epistemological problem, scope problem, specification problem, and aggregation problem. Objective: This systematic scoping review aims to gain insights into how to define and address well-being in digital health. Methods: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Papers were identified from 6 databases and included if they addressed the design or evaluation of digital health and reported the enhancement of patient well-being as their purpose. These papers were divided into design and evaluation papers. We studied how the 4 problems in technology for well-being are considered per paper. Results: A total of 117 studies were eligible for analysis (n=46, 39.3\% design papers and n=71, 60.7\% evaluation papers). For the epistemological problem, the thematic analysis resulted in various definitions of well-being, which were grouped into the following seven values: healthy body, functional me, healthy mind, happy me, social me, self-managing me, and external conditions. Design papers mostly considered well-being as healthy body and self-managing me, whereas evaluation papers considered the values of healthy mind and happy me. Users were rarely involved in defining well-being. For the scope problem, patients with chronic care needs were commonly considered as the main users. Design papers also regularly involved other users, such as caregivers and relatives. These users were often not involved in evaluation papers. For the specification problem, most design and evaluation papers focused on the provision of care support through a digital platform. Design papers used numerous design methods, whereas evaluation papers mostly considered pre-post measurements and randomized controlled trials. For the aggregation problem, value conflicts were rarely described. Conclusions: Current practice has found pragmatic ways of circumventing or dealing with the problems of digital health for well-being. Major differences exist between the design and evaluation of digital health, particularly regarding their conceptualization of well-being and the types of users studied. In addition, we found that current methodologies for designing and evaluating digital health can be improved. For optimal digital health for well-being, multidisciplinary collaborations that move beyond the common dichotomy of design and evaluation are needed. ", doi="10.2196/33787", url="https://www.jmir.org/2022/4/e33787", url="http://www.ncbi.nlm.nih.gov/pubmed/35377328" } @Article{info:doi/10.2196/28901, author="Daskalaki, Elena and Parkinson, Anne and Brew-Sam, Nicola and Hossain, Zakir Md and O'Neal, David and Nolan, J. Christopher and Suominen, Hanna", title="The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e28901", keywords="type 1 diabetes", keywords="wearable sensors", keywords="big data", keywords="consumer health informatics", keywords="mobile health", keywords="survey", abstract="Background: Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter---glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters. Objective: The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management. Methods: A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts. Results: On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3\%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. Conclusions: Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions. ", doi="10.2196/28901", url="https://www.jmir.org/2022/4/e28901", url="http://www.ncbi.nlm.nih.gov/pubmed/35394448" } @Article{info:doi/10.2196/33307, author="Bernstein, E. Emily and Weingarden, Hilary and Wolfe, C. Emma and Hall, D. Margaret and Snorrason, Ivar and Wilhelm, Sabine", title="Human Support in App-Based Cognitive Behavioral Therapies for Emotional Disorders: Scoping Review", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e33307", keywords="digital health", keywords="mental health", keywords="cognitive behavioral therapy", keywords="coaching", keywords="guided", keywords="mobile app", keywords="emotional disorder", keywords="mobile phone", abstract="Background: Smartphone app--based therapies offer clear promise for reducing the gap in available mental health care for people at risk for or people with mental illness. To this end, as smartphone ownership has become widespread, app-based therapies have become increasingly common. However, the research on app-based therapies is lagging behind. In particular, although experts suggest that human support may be critical for increasing engagement and effectiveness, we have little systematic knowledge about the role that human support plays in app-based therapy. It is critical to address these open questions to optimally design and scale these interventions. Objective: The purpose of this study is to provide a scoping review of the use of human support or coaching in app-based cognitive behavioral therapy for emotional disorders, identify critical knowledge gaps, and offer recommendations for future research. Cognitive behavioral therapy is the most well-researched treatment for a wide range of concerns and is understood to be particularly well suited to digital implementations, given its structured, skill-based approach. Methods: We conducted systematic searches of 3 databases (PubMed, PsycINFO, and Embase). Broadly, eligible articles described a cognitive behavioral intervention delivered via smartphone app whose primary target was an emotional disorder or problem and included some level of human involvement or support (coaching). All records were reviewed by 2 authors. Information regarding the qualifications and training of coaches, stated purpose and content of the coaching, method and frequency of communication with users, and relationship between coaching and outcomes was recorded. Results: Of the 2940 titles returned by the searches, 64 (2.18\%) were eligible for inclusion. This review found significant heterogeneity across all of the dimensions of coaching considered as well as considerable missing information in the published articles. Moreover, few studies had qualitatively or quantitatively evaluated how the level of coaching impacts treatment engagement or outcomes. Although users tend to self-report that coaching improves their engagement and outcomes, there is limited and mixed supporting quantitative evidence at present. Conclusions: Digital mental health is a young but rapidly expanding field with great potential to improve the reach of evidence-based care. Researchers across the reviewed articles offered numerous approaches to encouraging and guiding users. However, with the relative infancy of these treatment approaches, this review found that the field has yet to develop standards or consensus for implementing coaching protocols, let alone those for measuring and reporting on the impact. We conclude that coaching remains a significant hole in the growing digital mental health literature and lay out recommendations for future data collection, reporting, experimentation, and analysis. ", doi="10.2196/33307", url="https://www.jmir.org/2022/4/e33307", url="http://www.ncbi.nlm.nih.gov/pubmed/35394434" } @Article{info:doi/10.2196/35554, author="Oh, Soyeon Sarah and Moon, Youn Jong and Chon, Doukyoung and Mita, Carol and Lawrence, A. Jourdyn and Park, Eun-Cheol and Kawachi, Ichiro", title="Effectiveness of Digital Interventions for Preventing Alcohol Consumption in Pregnancy: Systematic Review and Meta-analysis", journal="J Med Internet Res", year="2022", month="Apr", day="11", volume="24", number="4", pages="e35554", keywords="fetal alcohol spectrum disorders", keywords="fetal alcohol syndrome", keywords="digital health", keywords="pregnancy", keywords="alcohol consumption", keywords="text message", keywords="text messaging", keywords="alcohol", keywords="digital intervention", keywords="mother", keywords="systematic review", keywords="meta-analysis", keywords="mobile health", keywords="mHealth", keywords="computer-based intervention", keywords="internet-based intervention", abstract="Background: Alcohol consumption in pregnancy has been associated with serious fetal health risks and maternal complications. While previous systematic reviews of digital interventions during pregnancy have targeted smoking cessation and flu vaccine uptake, few studies have sought to evaluate their effectiveness in preventing alcohol consumption during pregnancy. Objective: This systematic review aims to assess (1) whether digital interventions are effective in preventing alcohol consumption during the pregnancy/pregnancy-planning period, and (2) the differential effectiveness of alternative digital intervention platforms (ie, computers, mobiles, and text messaging services). Methods: PubMed, Embase, CINAHL, and Web of Science were searched for studies with digital interventions aiming to prevent alcohol consumption among pregnant women or women planning to become pregnant. A random effects primary meta-analysis was conducted to estimate the combined effect size and extent to which different digital platforms were successful in preventing alcohol consumption in pregnancy. Results: Six studies were identified and included in the final review. The primary meta-analysis produced a sample-weighted odds ratio (OR) of 0.62 (95\% CI 0.42-0.91; P=.02) in favor of digital interventions decreasing the risk of alcohol consumption during pregnancy when compared to controls. Computer/internet-based interventions (OR 0.59, 95\% CI 0.38-0.93) were an effective platform for preventing alcohol consumption. Too few studies of text messaging (OR 0.29, 95\% CI 0.29-2.52) were available to draw a conclusion. Conclusions: Overall, our review highlights the potential for digital interventions to prevent alcohol consumption among pregnant women and women planning to become pregnant. Considering the advantages of digital interventions in promoting healthy behavioral changes, future research is necessary to understand how certain platforms may increase user engagement and intervention effectiveness to prevent women from consuming alcohol during their pregnancies. ", doi="10.2196/35554", url="https://www.jmir.org/2022/4/e35554", url="http://www.ncbi.nlm.nih.gov/pubmed/35404257" } @Article{info:doi/10.2196/29842, author="Wickersham, Alice and Barack, Tamara and Cross, Lauren and Downs, Johnny", title="Computerized Cognitive Behavioral Therapy for Treatment of Depression and Anxiety in Adolescents: Systematic Review and Meta-analysis", journal="J Med Internet Res", year="2022", month="Apr", day="11", volume="24", number="4", pages="e29842", keywords="adolescent", keywords="anxiety", keywords="depression", keywords="meta-analysis", abstract="Background: Depression and anxiety are major public health concerns among adolescents. Computerized cognitive behavioral therapy (cCBT) has emerged as a potential intervention, but its efficacy in adolescents remains unestablished. Objective: This review aimed to systematically review and meta-analyze findings on the efficacy of cCBT for the treatment of adolescent depression and anxiety. Methods: Embase, PsycINFO, and Ovid MEDLINE were systematically searched for randomized controlled trials in English, which investigated the efficacy of cCBT for reducing self-reported depression or anxiety in adolescents aged 11 to 19 years. Titles, abstracts, and full texts were screened for eligibility by 2 independent researchers (TB and LC). A random-effects meta-analysis was conducted to pool the effects of cCBT on depression and anxiety symptom scores compared with the control groups. Study quality was assessed using the Cochrane Collaboration Risk of Bias tool. Results: A total of 16 randomized controlled trials were eligible for inclusion in this review, of which 13 (81\%) were included in the meta-analysis. The quality of the studies was mixed, with 5 (31\%) studies rated as good overall, 2 (13\%) rated as fair, and 9 (56\%) rated as poor. Small but statistically significant effects of cCBT were detected, with cCBT conditions showing lower symptom scores at follow-up compared with control conditions for both anxiety (standardized mean difference ?0.21, 95\% CI ?0.33 to ?0.09; I2=36.2\%) and depression (standardized mean difference ?0.23, 95\% CI ?0.39 to ?0.07; I2=59.5\%). Secondary analyses suggested that cCBT may be comparable with alternative, active interventions (such as face-to-face therapy or treatment as usual). Conclusions: This meta-analysis reinforces the efficacy of cCBT for the treatment of anxiety and depression and is the first to examine this exclusively in adolescents. Future research could aim to identify the active components of these interventions toward optimizing their development and increasing the feasibility and acceptability of cCBT in this age group. Trial Registration: PROSPERO CRD42019141941; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=141941 ", doi="10.2196/29842", url="https://www.jmir.org/2022/4/e29842", url="http://www.ncbi.nlm.nih.gov/pubmed/35404263" } @Article{info:doi/10.2196/28867, author="Ploderer, Bernd and Rezaei Aghdam, Atae and Burns, Kara", title="Patient-Generated Health Photos and Videos Across Health and Well-being Contexts: Scoping Review", journal="J Med Internet Res", year="2022", month="Apr", day="12", volume="24", number="4", pages="e28867", keywords="patient engagement", keywords="patient-generated health data", keywords="consumer-generated health data", keywords="personal health information", keywords="patient empowerment", keywords="mobile phone", abstract="Background: Patient-generated health data are increasingly used to record health and well-being concerns and engage patients in clinical care. Patient-generated photographs and videos are accessible and meaningful to patients, making them especially relevant during the current COVID-19 pandemic. However, a systematic review of photos and videos used by patients across different areas of health and well-being is lacking. Objective: This review aims to synthesize the existing literature on the health and well-being contexts in which patient-generated photos and videos are used, the value gained by patients and health professionals, and the challenges experienced. Methods: Guided by a framework for scoping reviews, we searched eight health databases (CINAHL, Cochrane Library, Embase, PsycINFO, PubMed, MEDLINE, Scopus, and Web of Science) and one computing database (ACM), returning a total of 28,567 studies. After removing duplicates and screening based on the predefined inclusion criteria, we identified 110 relevant articles. Data were charted and articles were analyzed following an iterative thematic approach with the assistance of NVivo software (version 12; QSR International). Results: Patient-generated photos and videos are used across a wide range of health care services (39/110, 35.5\% articles), for example, to diagnose skin lesions, assess dietary intake, and reflect on personal experiences during therapy. In addition, patients use them to self-manage health and well-being concerns (33/110, 30\%) and to share personal health experiences via social media (36/110, 32.7\%). Photos and videos create significant value for health care (59/110, 53.6\%), where images support diagnosis, explanation, and treatment (functional value). They also provide value directly to patients through enhanced self-determination (39/110, 35.4\%), social (33/110, 30\%), and emotional support (21/110, 19.1\%). However, several challenges emerge when patients create, share, and examine photos and videos, such as limited accessibility (16/110, 14.5\%), incomplete image sets (23/110, 20.9\%), and misinformation through photos and videos shared on social media (17/110, 15.5\%). Conclusions: This review shows that photos and videos engage patients in meaningful ways across different health care activities (eg, diagnosis, treatment, and self-care) for various health conditions. Although photos and videos require effort to capture and involve challenges when patients want to use them in health care, they also engage and empower patients, generating unique value. This review highlights areas for future research and strategies for addressing these challenges. ", doi="10.2196/28867", url="https://www.jmir.org/2022/4/e28867", url="http://www.ncbi.nlm.nih.gov/pubmed/35412458" } @Article{info:doi/10.2196/33372, author="Chua, Valerie and Koh, Hean Jin and Koh, Gerald Choon Huat and Tyagi, Shilpa", title="The Willingness to Pay for Telemedicine Among Patients With Chronic Diseases: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="13", volume="24", number="4", pages="e33372", keywords="willingness to pay", keywords="telemedicine", keywords="chronic disease", keywords="patients", keywords="systematic review", keywords="mobile phone", abstract="Background: Telemedicine is increasingly being leveraged, as the need for remote access to health care has been driven by the rising chronic disease incidence and the COVID-19 pandemic. It is also important to understand patients' willingness to pay (WTP) for telemedicine and the factors contributing toward it, as this knowledge may inform health policy planning processes, such as resource allocation or the development of a pricing strategy for telemedicine services. Currently, most of the published literature is focused on cost-effectiveness analysis findings, which guide health care financing from the health system's perspective. However, there is limited exploration of the WTP from a patient's perspective, despite it being pertinent to the sustainability of telemedicine interventions. Objective: To address this gap in research, this study aims to conduct a systematic review to describe the WTP for telemedicine interventions and to identify the factors influencing WTP among patients with chronic diseases in high-income settings. Methods: We systematically searched 4 databases (PubMed, PsycINFO, Embase, and EconLit). A total of 2 authors were involved in the appraisal. Studies were included if they reported the WTP amounts or identified the factors associated with patients' WTP, involved patients aged ?18 years who were diagnosed with chronic diseases, and were from high-income settings. Results: A total of 11 studies from 7 countries met this study's inclusion criteria. The proportion of people willing to pay for telemedicine ranged from 19\% to 70\% across the studies, whereas the values for WTP amounts ranged from US \$0.89 to US \$821.25. We found a statistically significant correlation of age and distance to a preferred health facility with the WTP for telemedicine. Higher age was associated with a lower WTP, whereas longer travel distance was associated with a higher WTP. Conclusions: On the basis of our findings, the following are recommendations that may enhance the WTP: exposure to the telemedicine intervention before assessing the WTP, the lowering of telemedicine costs, and the provision of patient education to raise awareness on telemedicine's benefits and address patients' concerns. In addition, we recommend that future research be directed at standardizing the reporting of WTP studies with the adoption of a common metric for WTP amounts, which may facilitate the generalization of findings and effect estimates. ", doi="10.2196/33372", url="https://www.jmir.org/2022/4/e33372", url="http://www.ncbi.nlm.nih.gov/pubmed/35416779" } @Article{info:doi/10.2196/35178, author="Brobbin, Eileen and Deluca, Paolo and Hemrage, Sofia and Drummond, Colin", title="Accuracy of Wearable Transdermal Alcohol Sensors: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="14", volume="24", number="4", pages="e35178", keywords="alcohol consumption", keywords="alcohol detection", keywords="alcohol monitoring", keywords="alcohol treatment", keywords="digital technology", keywords="ecologic momentary assessment", keywords="transdermal alcohol sensors", keywords="wearables", keywords="mobile phone", abstract="Background: There are a range of wearable transdermal alcohol sensors that are available and are being developed. These devices have the potential to monitor alcohol consumption continuously over extended periods in an objective manner, overcoming some of the limitations of other alcohol measurement methods (blood, breath, and urine). Objective: The objective of our systematic review was to assess wearable transdermal alcohol sensor accuracy. Methods: A systematic search of the CINAHL, Embase, Google Scholar, MEDLINE, PsycINFO, PubMed, and Scopus bibliographic databases was conducted in February 2021. In total, 2 team members (EB and SH) independently screened studies for inclusion, extracted data, and assessed the risk of bias. The methodological quality of each study was appraised using the Mixed Methods Appraisal Tool. The primary outcome was transdermal alcohol sensor accuracy. The data were presented as a narrative synthesis. Results: We identified and analyzed 32 studies. Study designs included laboratory, ambulatory, and mixed designs, as well as randomized controlled trials; the length of time for which the device was worn ranged from days to weeks; and the analyzed sample sizes ranged from 1 to 250. The results for transdermal alcohol concentration data from various transdermal alcohol sensors were generally found to positively correlate with breath alcohol concentration, blood alcohol concentration, and self-report (moderate to large correlations). However, there were some discrepancies between study reports; for example, WrisTAS sensitivity ranged from 24\% to 85.6\%, and specificity ranged from 67.5\% to 92.94\%. Higher malfunctions were reported with the BACtrack prototype (16\%-38\%) and WrisTAS (8\%) than with SCRAM (2\%); however, the former devices also reported a reduced time lag for peak transdermal alcohol concentration values when compared with SCRAM. It was also found that many companies were developing new models of wearable transdermal alcohol sensors. Conclusions: As shown, there is a lack of consistency in the studies on wearable transdermal alcohol sensor accuracy regarding study procedures and analyses of findings, thus making it difficult to draw direct comparisons between them. This needs to be considered in future research, and there needs to be an increase in studies directly comparing different transdermal alcohol sensors. There is also a lack of research investigating the accuracy of transdermal alcohol sensors as a tool for monitoring alcohol consumption in clinical populations and use over extended periods. Although there is some preliminary evidence suggesting the accuracy of these devices, this needs to be further investigated in clinical populations. Trial Registration: PROSPERO CRD42021231027; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=231027 ", doi="10.2196/35178", url="https://www.jmir.org/2022/4/e35178", url="http://www.ncbi.nlm.nih.gov/pubmed/35436239" } @Article{info:doi/10.2196/31889, author="Seiler, Jessie and Libby, E. Tanya and Jackson, Emahlea and Lingappa, JR and Evans, WD", title="Social Media--Based Interventions for Health Behavior Change in Low- and Middle-Income Countries: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="14", volume="24", number="4", pages="e31889", keywords="social media", keywords="behavior change", keywords="low- and middle-income countries", keywords="mobile phone", abstract="Background: Despite the wealth of evidence regarding effective health behavior change techniques using digital interventions to focus on residents of high-income countries, there is limited information of a similar nature for low- and middle-income countries. Objective: The aim of this review is to identify and describe the available literature on effective social media--based behavior change interventions within low- and middle-income countries. Methods: This systematic review was conducted in accordance with the 2009 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, Embase, Elsevier, CINAHL, PsycInfo, and Global Index Medicus, and the final search was conducted on April 6, 2021. We excluded studies published before 2000 because of the subject matter. We included studies that evaluated interventions conducted at least partly on a social media platform. Results: We identified 1832 studies, of which 108 (5.89\%) passed title-abstract review and were evaluated by full-text review. In all, 30.6\% (33/108) were included in the final analysis. Although 22 studies concluded that the social media intervention was effective, only 13 quantified the level of social media engagement, of which, few used theory (n=8) or a conceptual model (n=5) of behavior change. Conclusions: We identified gaps in the settings of interventions, types and sectors of interventions, length of follow-up, evaluation techniques, use of theoretical and conceptual models, and discussions of the privacy implications of social media use. Trial Registration: PROSPERO CRD42020223572; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=223572 ", doi="10.2196/31889", url="https://www.jmir.org/2022/4/e31889", url="http://www.ncbi.nlm.nih.gov/pubmed/35436220" } @Article{info:doi/10.2196/33537, author="Olaye, M. Iredia and Belovsky, P. Mia and Bataille, Lauren and Cheng, Royce and Ciger, Ali and Fortuna, L. Karen and Izmailova, S. Elena and McCall, Debbe and Miller, J. Christopher and Muehlhausen, Willie and Northcott, A. Carrie and Rodriguez-Chavez, R. Isaac and Pratap, Abhishek and Vandendriessche, Benjamin and Zisman-Ilani, Yaara and Bakker, P. Jessie", title="Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="14", volume="24", number="4", pages="e33537", keywords="digital medicine", keywords="digital measures", keywords="adherence", keywords="compliance", keywords="mobile phone", abstract="Background: Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. Objective: We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. Methods: We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). Results: Our PubMed search terms identified 940 manuscripts; 100 (10.6\%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30\% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92\% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. Conclusions: We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports. ", doi="10.2196/33537", url="https://www.jmir.org/2022/4/e33537", url="http://www.ncbi.nlm.nih.gov/pubmed/35436221" } @Article{info:doi/10.2196/35465, author="Dhombres, Ferdinand and Bonnard, Jules and Bailly, K{\'e}vin and Maurice, Paul and Papageorghiou, T. Aris and Jouannic, Jean-Marie", title="Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="20", volume="24", number="4", pages="e35465", keywords="artificial intelligence", keywords="systematic review", keywords="knowledge bases", keywords="machine learning", keywords="obstetrics", keywords="gynaecology", keywords="perinatology", keywords="medical informatics", abstract="Background: The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. Objective: The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. Methods: The PubMed database was searched for citations indexed with ``artificial intelligence'' and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: ``obstetrics''; ``gynecology''; ``reproductive techniques, assisted''; or ``pregnancy.'' All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. Results: The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41\%, 27/66), gynecology (3\%, 2/66), assisted reproductive medicine (33\%, 22/66), early pregnancy (2\%, 1/66), and fetal medicine (21\%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53\%, 35/66), hypothesis generation (42\%, 28/66), or software development (3\%, 2/66). Validation was performed on one data set (86\%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82\%, 54/66) remain out of the scope of the usual OB/GYN journals. Conclusions: In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered. ", doi="10.2196/35465", url="https://www.jmir.org/2022/4/e35465", url="http://www.ncbi.nlm.nih.gov/pubmed/35297766" } @Article{info:doi/10.2196/33320, author="Dupont, Charl{\`e}ss and Smets, Tinne and Monnet, Fanny and Pivodic, Lara and De Vleminck, Aline and Van Audenhove, Chantal and Van den Block, Lieve", title="Publicly Available, Interactive Web-Based Tools to Support Advance Care Planning: Systematic Review", journal="J Med Internet Res", year="2022", month="Apr", day="20", volume="24", number="4", pages="e33320", keywords="advance care planning", keywords="systematic review", keywords="web-based tools", keywords="health communication", keywords="quality of online content", abstract="Background: There is an increasing number of interactive web-based advance care planning (ACP) support tools, which are web-based aids in any format encouraging reflection, communication, and processing of publicly available information, most of which cannot be found in the peer-reviewed literature. Objective: This study aims to conduct a systematic review of web-based ACP support tools to describe the characteristics, readability, and quality of content and investigate whether and how they are evaluated. Methods: We systematically searched the web-based gray literature databases OpenGrey, ClinicalTrials.gov, ProQuest, British Library, Grey Literature in the Netherlands, and Health Services Research Projects in Progress, as well as Google and app stores, and consulted experts using the following eligibility criteria: web-based, designed for the general population, accessible to everyone, interactive (encouraging reflection, communication, and processing of information), and in English or Dutch. The quality of content was evaluated using the Quality Evaluation Scoring Tool (score 0-28---a higher score indicates better quality). To synthesize the characteristics of the ACP tools, readability and quality of content, and whether and how they were evaluated, we used 4 data extraction tables. Results: A total of 30 tools met the eligibility criteria, including 15 (50\%) websites, 10 (33\%) web-based portals, 3 (10\%) apps, and 2 (7\%) with a combination of formats. Of the 30 tools, 24 (80\%) mentioned a clear aim, including 7 (23\%) that supported reflection or communication, 8 (27\%) that supported people in making decisions, 7 (23\%) that provided support to document decisions, and 2 (7\%) that aimed to achieve all these aims. Of the 30 tools, 7 (23\%) provided information on the development, all of which were developed in collaboration with health care professionals, and 3 (10\%) with end users. Quality scores ranged between 11 and 28, with most of the lower-scoring tools not referring to information sources. Conclusions: A variety of ACP support tools are available on the web, varying in the quality of content. In the future, users should be involved in the development process of ACP support tools, and the content should be substantiated by scientific evidence. Trial Registration: PROSPERO CRD42020184112; https://tinyurl.com/mruf8b43 ", doi="10.2196/33320", url="https://www.jmir.org/2022/4/e33320", url="http://www.ncbi.nlm.nih.gov/pubmed/35442207" } @Article{info:doi/10.2196/35940, author="Imai, Hissei and Tajika, Aran and Narita, Hisashi and Yoshinaga, Naoki and Kimura, Kenichi and Nakamura, Hideki and Takeshima, Nozomi and Hayasaka, Yu and Ogawa, Yusuke and Furukawa, Toshi", title="Unguided Computer-Assisted Self-Help Interventions Without Human Contact in Patients With Obsessive-Compulsive Disorder: Systematic Review and Meta-analysis", journal="J Med Internet Res", year="2022", month="Apr", day="21", volume="24", number="4", pages="e35940", keywords="randomized controlled trial", keywords="RCT", keywords="information technology", keywords="psychotherapy", keywords="treatment adherence", keywords="anxiety disorder", keywords="anxiety", keywords="OCD", keywords="obsessive-compulsive disorder", keywords="systematic review", keywords="meta-analysis", keywords="mental health", keywords="computer-assisted", keywords="therapy", keywords="efficacy", keywords="acceptability", keywords="eHealth", keywords="mental illness", abstract="Background: Computer-assisted treatment may reduce therapist contact and costs and promote client participation. This meta-analysis examined the efficacy and acceptability of an unguided computer-assisted therapy in patients with obsessive-compulsive disorder (OCD) compared with a waiting list or attention placebo. Objective: This study aimed to evaluate the effectiveness and adherence of computer-assisted self-help treatment without human contact in patients with OCD using a systematic review and meta-analysis approach. Methods: Randomized controlled trials with participants primarily diagnosed with OCD by health professionals with clinically significant OCD symptoms as measured with validated scales were included. The interventions included self-help treatment through the internet, computers, and smartphones. We excluded interventions that used human contact. We conducted a search on PubMed, Cochrane Central Register of Controlled Trials, EMBASE, World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov, as well as the reference lists of the included studies. The risk of bias was evaluated using version 2 of the Cochrane risk-of-bias tool for randomized trials. We calculated the standardized mean differences for continuous outcomes and risk ratios for dichotomous outcomes. The primary outcomes were short-term improvement of OCD symptoms measured by validated scales and dropout for any reason. Results: We included 11 randomized controlled trials with a total of 983 participants. The results indicated that unguided computer-assisted self-help therapy was significantly more effective than a waiting list or psychological placebo (standard mean difference ?0.47, 95\% CI ?0.73 to ?0.22). Unguided computer-assisted self-help therapy had more dropouts for any reason than waiting list or psychological placebo (risk ratio 1.98, 95\% CI 1.21 to 3.23). However, the quality of evidence was very low because of the risk of bias and inconsistent results among the included studies. The subgroup analysis showed that exposure response and prevention and an intervention duration of more than 4 weeks strengthen the efficacy without worsening acceptability. Only a few studies have examined the interaction between participants and systems, and no study has used gamification. Most researchers only used text-based interventions, and no study has used a mobile device. The overall risk of bias of the included studies was high and the heterogeneity of results was moderate to considerable. Conclusions: Unguided computer-assisted self-help therapy for OCD is effective compared with waiting lists or psychological placebo. An exposure response and prevention component and intervention duration of more than 4 weeks may strengthen the efficacy without worsening the acceptability of the therapy. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021264644; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=264644 ", doi="10.2196/35940", url="https://www.jmir.org/2022/4/e35940", url="http://www.ncbi.nlm.nih.gov/pubmed/35451993" } @Article{info:doi/10.2196/34061, author="Rahman, Obaidur Md and Yamaji, Noyuri and Nagamatsu, Yasuko and Ota, Erika", title="Effects of mHealth Interventions on Improving Antenatal Care Visits and Skilled Delivery Care in Low- and Middle-Income Countries: Systematic Review and Meta-analysis", journal="J Med Internet Res", year="2022", month="Apr", day="22", volume="24", number="4", pages="e34061", keywords="mobile health", keywords="ANC", keywords="skilled delivery care", keywords="LMICs", keywords="systematic review and meta-analysis", abstract="Background: The poor coverage of essential maternal services, such as antenatal care (ANC) and skilled delivery care utilization, accounts for higher maternal and infant mortality in low- and middle-income countries (LMICs). Although mobile health (mHealth) interventions could potentially improve the service utilization in resource-limited settings, their effectiveness remains unclear. Objective: This review aimed to summarize the effect of mHealth interventions on improving the uptake of ANC visits, skilled birth attendance at the time of delivery, and facility delivery among pregnant women in LMICs. Methods: We conducted a comprehensive search on 9 electronic databases and other resources from inception to October 2020. We included individual randomized controlled trials and cluster randomized controlled trials that assessed the effectiveness of mHealth interventions for improving perinatal health care utilization among healthy pregnant women in LMICs. We performed a random-effects meta-analysis and estimated the pooled effect size by using risk ratios (RRs) with 95\% CIs. In addition, 2 reviewers independently assessed the risk of bias of the included studies by using the Cochrane risk of bias tool and the certainty of the evidence by using the Grading of Recommendation, Assessment, Development and Evaluation approach. Results: A total of 9 studies (10 articles) that randomized 10,348 pregnant women (n=6254, 60.44\% in the intervention group; n=4094, 39.56\% in the control group) were included in this synthesis. The pooled estimates showed a positive effect of mHealth interventions on improving 4 or more ANC visit utilizations among pregnant women in LMICs, irrespective of the direction of interventions (1-way communications: RR 2.14, 95\% CI 1.76-2.60, I2=36\%, 2 studies, moderate certainty; 2-way communications: RR 1.17, 95\% CI 1.08-1.27, I2=59\%, 3 studies, low certainty). Only 2-way mHealth interventions were effective in improving the use of skilled birth attendance during delivery (RR 1.23, 95\% CI 1.14-1.33, I2=0\%, 2 studies, moderate certainty), but the effects were unclear for 1-way mHealth interventions (RR 1.04, 95\% CI 0.97-1.10, I2=73\%, 3 studies, very low certainty) when compared with standard care. For facility delivery, the interventions were effective in settings where fewer pregnant women used facility delivery (RR 1.68, 95\% CI 1.30-2.19, I2=36\%, 2 studies, moderate certainty); however, the effects were unclear in settings where most pregnant women already used facility delivery (RR 1.01, 95\% CI 0.97-1.04, I2=0\%, 1 study, low certainty). Conclusions: mHealth interventions may contribute to improving ANC and skilled delivery care utilization among pregnant women in LMICs. However, more studies are required to improve their reproducibility and efficiency or strengthen the evidence of different forms of mHealth interventions because of the considerable heterogeneity observed in the meta-analyses. Trial Registration: PROSPERO CRD42020210813; https://tinyurl.com/2n7ny9a7 ", doi="10.2196/34061", url="https://www.jmir.org/2022/4/e34061", url="http://www.ncbi.nlm.nih.gov/pubmed/35451987" } @Article{info:doi/10.2196/35120, author="Amagai, Saki and Pila, Sarah and Kaat, J. Aaron and Nowinski, J. Cindy and Gershon, C. Richard", title="Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review", journal="J Med Internet Res", year="2022", month="Apr", day="26", volume="24", number="4", pages="e35120", keywords="mobile phone", keywords="mHealth", keywords="retention", keywords="engagement", abstract="Background: Mobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant dropout or attrition, which may impact the representativeness of the sample and the effectiveness of the study. Therefore, it is imperative for researchers to understand what makes participants stay with mHealth apps or studies using mHealth apps. Objective: This study aimed to review the current peer-reviewed research literature to identify the notable factors and strategies used in adult participant engagement and retention. Methods: We conducted a systematic search of PubMed, MEDLINE, and PsycINFO databases for mHealth studies that evaluated and assessed issues or strategies to improve the engagement and retention of adults from 2015 to 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Notable themes were identified and narratively compared among different studies. A binomial regression model was generated to examine the factors affecting retention. Results: Of the 389 identified studies, 62 (15.9\%) were included in this review. Overall, most studies were partially successful in maintaining participant engagement. Factors related to particular elements of the app (eg, feedback, appropriate reminders, and in-app support from peers or coaches) and research strategies (eg, compensation and niche samples) that promote retention were identified. Factors that obstructed retention were also identified (eg, lack of support features, technical difficulties, and usefulness of the app). The regression model results showed that a participant is more likely to drop out than to be retained. Conclusions: Retaining participants is an omnipresent challenge in mHealth studies. The insights from this review can help inform future studies about the factors and strategies to improve participant retention. ", doi="10.2196/35120", url="https://www.jmir.org/2022/4/e35120", url="http://www.ncbi.nlm.nih.gov/pubmed/35471414" } @Article{info:doi/10.2196/30503, author="Su, Zhaohui and Bentley, L. Barry and McDonnell, Dean and Ahmad, Junaid and He, Jiguang and Shi, Feng and Takeuchi, Kazuaki and Cheshmehzangi, Ali and da Veiga, Pereira Claudimar", title="6G and Artificial Intelligence Technologies for Dementia Care: Literature Review and Practical Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="27", volume="24", number="4", pages="e30503", keywords="COVID-19", keywords="6G", keywords="digital health", keywords="artificial intelligence", keywords="dementia", keywords="first-perspective health solutions", abstract="Background: The dementia epidemic is progressing fast. As the world's older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients' health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients' health care needs and improve their quality of life. Objective: This study aims to investigate ways in which 6G and AI technologies could elevate dementia care to address this study gap. Methods: A literature review was conducted in databases such as PubMed, Scopus, and PsycINFO. The search focused on three themes: dementia, 6G, and AI technologies. The initial search was conducted on April 25, 2021, complemented by relevant articles identified via a follow-up search on November 11, 2021, and Google Scholar alerts. Results: The findings of the study were analyzed in terms of the interplay between people with dementia's unique health challenges and the promising capabilities of health technologies, with in-depth and comprehensive analyses of advanced technology-based solutions that could address key dementia care needs, ranging from impairments in memory (eg, Egocentric Live 4D Perception), speech (eg, Project Relate), motor (eg, Avatar Robot Caf{\'e}), cognitive (eg, Affectiva), to social interactions (eg, social robots). Conclusions: To live is to grow old. Yet dementia is neither a proper way to live nor a natural aging process. By identifying advanced health solutions powered by 6G and AI opportunities, our study sheds light on the imperative of leveraging the potential of advanced technologies to elevate dementia patients' will to live, enrich their daily activities, and help them engage in societies across shapes and forms. ", doi="10.2196/30503", url="https://www.jmir.org/2022/4/e30503", url="http://www.ncbi.nlm.nih.gov/pubmed/35475733" } @Article{info:doi/10.2196/36463, author="Zhang, Yan and Kim, Yeolib", title="Consumers' Evaluation of Web-Based Health Information Quality: Meta-analysis", journal="J Med Internet Res", year="2022", month="Apr", day="28", volume="24", number="4", pages="e36463", keywords="online health information", keywords="information quality", keywords="credibility", keywords="trust", keywords="consumer health information behavior", keywords="meta-analysis", abstract="Background: The internet has become a major source of health information for general consumers. Web-based health information quality varies widely across websites and applications. It is critical to understand the factors that shape consumers' evaluation of web-based health information quality and the role that it plays in their appraisal and use of health information and information systems. Objective: This paper aimed to identify the antecedents and consequences of consumers' evaluation of web-based health information quality as a means to consolidate the related research stream and to inform future studies on web-based health information quality. Methods: We systematically searched 10 databases, examined reference lists, and conducted manual searches. Empirical studies that investigated consumers' evaluation of web-based health information quality, credibility, or trust and their respective relationships with antecedents or consequences were included. Results: We included 147 studies reported in 136 papers in the analysis. Among the antecedents of web-based health information quality, system navigability ($\rho$=0.56), aesthetics ($\rho$=0.49), and ease of understanding ($\rho$=0.49) had the strongest relationships with web-based health information quality. The strongest consequences of web-based health information quality were consumers' intentions to use health information systems ($\rho$=0.58) and satisfaction with health information ($\rho$=0.46). Web-based health information quality relationships were moderated by numerous cultural dimensions, research designs, and publication moderators. Conclusions: Consumers largely rely on peripheral cues and less on cues that require more information processing (eg, content comprehensiveness) to determine web-based health information quality. Surprisingly, the relationships between individual differences and web-based health information quality are trivial. Web-based health information quality has stronger effects on cognitive appraisals and behavioral intentions than on behavior. Despite efforts to include various moderators, a substantial amount of variance is still unexplained, indicating a need to study additional moderators. This meta-analysis provides broad and consistent evidence for web-based health information quality relationships that have been fractured and incongruent in empirical studies. ", doi="10.2196/36463", url="https://www.jmir.org/2022/4/e36463", url="http://www.ncbi.nlm.nih.gov/pubmed/35482390" } @Article{info:doi/10.2196/35595, author="Brunner, Melissa and Rietdijk, Rachael and Togher, Leanne", title="Training Resources Targeting Social Media Skills to Inform Rehabilitation for People Who Have an Acquired Brain Injury: Scoping Review", journal="J Med Internet Res", year="2022", month="Apr", day="28", volume="24", number="4", pages="e35595", keywords="brain injury", keywords="social media", keywords="training", keywords="social communication", keywords="scoping review", abstract="Background: In 2020 and 2021, people increasingly used the internet to connect socially and professionally. However, people with an acquired brain injury (ABI) experience challenges in using social media, and rehabilitation professionals have reported feeling underprepared to support them in its use. To date, no review of social media skills training to inform ABI rehabilitation has been conducted. Objective: This scoping review aimed to examine research on interventions addressing social media skills and safety, with a focus on people living with health conditions; free web-based resources for the general public on social media skills training; and currently available online support groups for people with ABI. Methods: An integrative scoping review was conducted, with a systematic search strategy applied in March and November 2020 across OvidSP (MEDLINE, AMED, PsycINFO, and Embase), Scopus, Web of Science, CINAHL, Google Scholar, Google, and Facebook. The data collected were critically appraised and synthesized to describe the key content and features of social media training resources. Results: This review identified 47 peer-reviewed academic articles, 48 social media training websites, and 120 online support groups for people with ABI. A key recommendation was interactive training with practical components addressing cybersafety, how to use platforms, and how to connect with others. However, no social media training resources that were relevant and accessible for people with ABI were identified. Conclusions: Training resources to support people with ABI in safely using social media are limited. The key content to be addressed and the features to be incorporated into web-based social media training were determined, including the need for interactive training that is co-designed and safe and incorporates practical components that support people with ABI. These findings can be used to inform the development of web-based evidence-based support for people with ABI who may be vulnerable when participating in social media. ", doi="10.2196/35595", url="https://www.jmir.org/2022/4/e35595", url="http://www.ncbi.nlm.nih.gov/pubmed/35482369" } @Article{info:doi/10.2196/29841, author="Hussain-Shamsy, Neesha and McMillan, Ian and Cook, Sheridan and Furfaro-Argier, Alyssa and Sadler, Andrea and Delos-Reyes, Faith and Wasserman, Lori and Bhatia, Sacha and Martin, Danielle and Seto, Emily and Vigod, N. Simone and Zaheer, Juveria and Agarwal, Payal and Mukerji, Geetha", title="Operationalizing and Evaluating Synchronous Virtual Group Health Interventions: Wide-Scale Implementation at a Tertiary Care Academic Hospital", journal="J Med Internet Res", year="2022", month="Apr", day="7", volume="24", number="4", pages="e29841", keywords="virtual care", keywords="group therapy", keywords="patient education", keywords="videoconferencing", keywords="sustainability", keywords="innovation", keywords="health systems", keywords="health promotion", keywords="patient portal", keywords="electronic medical records", keywords="health service delivery", keywords="video call", doi="10.2196/29841", url="https://www.jmir.org/2022/4/e29841", url="http://www.ncbi.nlm.nih.gov/pubmed/35389350" } @Article{info:doi/10.2196/36804, author="Basch, H. Corey and Basch, E. Charles and Hillyer, C. Grace and Meleo-Erwin, C. Zoe", title="Social Media, Public Health, and Community Mitigation of COVID-19: Challenges, Risks, and Benefits", journal="J Med Internet Res", year="2022", month="Apr", day="12", volume="24", number="4", pages="e36804", keywords="COVID-19 pandemic", keywords="social media", keywords="misinformation", keywords="disinformation", keywords="COVID-19", keywords="pandemic", keywords="infodemiology", keywords="health literacy", keywords="health information", keywords="public health", keywords="COVID risk", keywords="information seeking", doi="10.2196/36804", url="https://www.jmir.org/2022/4/e36804", url="http://www.ncbi.nlm.nih.gov/pubmed/35380539" } @Article{info:doi/10.2196/35037, author="Roy, Joy and Levy, R. Deborah and Senathirajah, Yalini", title="Defining Telehealth for Research, Implementation, and Equity", journal="J Med Internet Res", year="2022", month="Apr", day="13", volume="24", number="4", pages="e35037", keywords="telehealth", keywords="telemedicine", keywords="standards", keywords="health equity", keywords="public health", keywords="digital health", keywords="delivery of health care", doi="10.2196/35037", url="https://www.jmir.org/2022/4/e35037", url="http://www.ncbi.nlm.nih.gov/pubmed/35416778" } @Article{info:doi/10.2196/33167, author="Kogetsu, Atsushi and Kato, Kazuto", title="Framework and Practical Guidance for the Ethical Use of Electronic Methods for Communication With Participants in Medical Research", journal="J Med Internet Res", year="2022", month="Apr", day="20", volume="24", number="4", pages="e33167", keywords="online communication", keywords="electronic methods", keywords="online recruitment", keywords="electronic informed consent", keywords="e-IC", keywords="digital consent", keywords="online consent", keywords="data communication", keywords="digital health", doi="10.2196/33167", url="https://www.jmir.org/2022/4/e33167", url="http://www.ncbi.nlm.nih.gov/pubmed/35442208" } @Article{info:doi/10.2196/36338, author="Krishnamurti, Tamar and Birru Talabi, Mehret and Callegari, S. Lisa and Kazmerski, M. Traci and Borrero, Sonya", title="A Framework for Femtech: Guiding Principles for Developing Digital Reproductive Health Tools in the United States", journal="J Med Internet Res", year="2022", month="Apr", day="28", volume="24", number="4", pages="e36338", keywords="United States", keywords="North America", keywords="femtech", keywords="mHealth", keywords="health equity", keywords="pregnancy", keywords="women's health", keywords="preterm birth", keywords="contraception", keywords="family planning", keywords="reproductive care", keywords="sterilization", keywords="cystic fibrosis", keywords="rheumatic disease", keywords="eHealth", keywords="mobile health", keywords="reproductive health", keywords="digital health", keywords="health technology", keywords="health outcomes", doi="10.2196/36338", url="https://www.jmir.org/2022/4/e36338", url="http://www.ncbi.nlm.nih.gov/pubmed/35482371" } @Article{info:doi/10.2196/28291, author="Fundingsland Jr, Lauritz Edwin and Fike, Joseph and Calvano, Joshua and Beach, Jeffrey and Lai, Deborah and He, Shuhan", title="Methodological Guidelines for Systematic Assessments of Health Care Websites Using Web Analytics: Tutorial", journal="J Med Internet Res", year="2022", month="Apr", day="15", volume="24", number="4", pages="e28291", keywords="Google Analytics", keywords="website usability", keywords="conversion rate", keywords="website engagement", keywords="user demographics", keywords="website traffic", keywords="website content", keywords="internet browsers", keywords="healthcare websites", keywords="web analytics", keywords="healthcare industry", keywords="usability", doi="10.2196/28291", url="https://www.jmir.org/2022/4/e28291", url="http://www.ncbi.nlm.nih.gov/pubmed/35436216" } @Article{info:doi/10.2196/26339, author="Olson, Jenny and Hadjiconstantinou, Michelle and Luff, Carly and Watts, Karen and Watson, Natasha and Miller, Venus and Schofield, Deborah and Khunti, Kamlesh and Davies, J. Melanie and Calginari, Sara", title="From the United Kingdom to Australia---Adapting a Web-Based Self-management Education Program to Support the Management of Type 2 Diabetes: Tutorial", journal="J Med Internet Res", year="2022", month="Apr", day="20", volume="24", number="4", pages="e26339", keywords="diabetes mellitus", keywords="type 2", keywords="technology", keywords="self-management", doi="10.2196/26339", url="https://www.jmir.org/2022/4/e26339", url="http://www.ncbi.nlm.nih.gov/pubmed/35442198" } @Article{info:doi/10.2196/27900, author="Sourander, Andre and Ristkari, Terja and Kurki, Marjo and Gilbert, Sonja and Hinkka-Yli-Salom{\"a}ki, Susanna and Kinnunen, Malin and Pulkki-R{\aa}back, Laura and McGrath, J. Patrick", title="Effectiveness of an Internet-Based and Telephone-Assisted Training for Parents of 4-Year-Old Children With Disruptive Behavior: Implementation Research", journal="J Med Internet Res", year="2022", month="Apr", day="4", volume="24", number="4", pages="e27900", keywords="parent training", keywords="early intervention", keywords="implementation", keywords="disruptive behavior", keywords="behavior problems", keywords="preschool children", keywords="internet-assisted", keywords="child mental health", keywords="mental health", keywords="behavior", keywords="intervention", keywords="children", keywords="parents", abstract="Background: There is a lack of effectiveness studies when digital parent training programs are implemented in real-world practice. The efficacy of the internet-based and telephone-assisted Finnish Strongest Families Smart Website (SFSW) parent training intervention on the disruptive behavior of 4-year-old children was studied in a randomized controlled trial setting in Southwest Finland between 2011 and 2013. After that, the intervention was implemented nationwide in child health clinics from 2015 onwards. Objective: The main aim of this study was to compare the treatment characteristics and effectiveness of the SFSW parent training intervention between the families who received the intervention when it was implemented as a normal practice in child health clinics and the families who received the same intervention during the randomized controlled trial. Methods: The implementation group comprised 600 families who were recruited in the SFSW intervention between January 2015 and May 2017 in real-world implementation. The RCT intervention group comprised 232 families who were recruited between October 2011 and November 2013. The same demographic and child and parent measures were collected from both study groups and were compared using linear mixed-effect models for repeated measurements. The child psychopathology and functioning level were measured using the Child Behavior Checklist (CBCL) version 1.5-5 for preschool children, the Inventory of Callous-Unemotional Traits (ICU), and a modified version of the Barkley Home Situations Questionnaire. Parenting skills were measured using the 31-item Parenting Scale and the shorter 21-item Depression, Anxiety and Stress Scale (DASS-21). The estimated child and parent outcomes were adjusted for CBCL externalizing scores at baseline, maternal education, duration of the behavior problems, and paternal age. The baseline measurements of each outcome were used as covariates. Results: The implementation group was more likely to complete the intervention than the RCT intervention group (514/600, 85.7\% vs 176/232, 75.9\%, respectively; P<.001). There were no significant differences between the implementation and RCT intervention groups with regard to child measures, including CBCL externalizing score (--0.2, 95\% CI --1.3 to 1.6; P=.83), total score (--0.7, 95\% CI --3.0 to 4.5; P=.70), internalizing score (--0.3, 95\% CI --1.0 to 1.6; P=.64), and ICU total score (--0.4, 95\% Cl --1.9 to 1.2; P=.64). No significant difference was detected in the Parenting Scale total score (0.0, 95\% Cl --0.1 to 0.1; P=.50), while DASS-21 total score differed nearly significantly (2.5, 95\% Cl 0.0-5.1; P=.05), indicating better improvement in the implementation group. Conclusions: The internet-based and telephone-assisted SFSW parent training intervention was effectively implemented in real-world settings. These findings have implications for addressing the unmet needs of children with disruptive behavior problems. Our initiative could also provide a quick socially distanced solution for the considerable mental health impact of the COVID-19 pandemic. Trial Registration: ClinicalTrials.gov NCT01750996; https://clinicaltrials.gov/ct2/show/NCT01750996 International Registered Report Identifier (IRRID): RR2-10.1186/1471-2458-13-985 ", doi="10.2196/27900", url="https://www.jmir.org/2022/4/e27900", url="http://www.ncbi.nlm.nih.gov/pubmed/35377332" } @Article{info:doi/10.2196/28504, author="Ozluk, Pelin and Cobb, Rebecca and Hoots, Alyson and Sylwestrzak, Malgorzata", title="Association Between Mobile App Use and Caregivers' Support System, Time Spent on Caregiving, and Perceived Well-being: Survey Study From a Large Employer", journal="J Med Internet Res", year="2022", month="Apr", day="11", volume="24", number="4", pages="e28504", keywords="caregiving", keywords="mobile app", keywords="mobile phone", abstract="Background: Mobile technology to address caregiver needs has been on the rise. There is limited evidence of effectiveness of such technologies on caregiver experiences. Objective: This study evaluates the effectiveness of ianacare, a mobile app, among employees of a large employer. ianacare mobilizes personal social circles to help with everyday tasks. Through the use of ianacare, we evaluate the associations between coordinating caregiving tasks among a caregiver's personal support network and outcomes related to the caregiver's support system, time use, perceived productivity, and perceived health and well-being. Caregiver tasks include tasks such as meal preparation, respite care, pet care, and transportation. Time use is the measure of a caregiver's time spent on caregiving tasks and how much time they had to take off from work to attend planned or unplanned caregiving tasks. Methods: We conducted 2 surveys to assess within-participant changes in outcomes for the unpaid, employed, caregivers after 6 weeks of using the mobile app (n=176) between March 30, 2020, and May 11, 2020. The surveys contained questions in three domains: the caregiver's support system, time use and perceived productivity, and perceived health and well-being. The results of the linear probability models are presented below. Results: App use was significantly associated with decreasing the probability of doing most caregiving tasks alone by 9.1\% points (SE 0.04; P=.01) and increasing the probability of at least one person helping the primary caregiver by 8.0\% points (SE 0.035; P=.02). App use was also associated with improving the time use of the primary caregiver who took significantly less time off work to attend to caregiving duties by 12.5\% points (SE 0.04; P=.003) and decreased the probability of spending more than 30 hours weekly on caregiving by 9.1\% points (SE 0.04; P=.02). Additional findings on the positive impact of the app included a decrease in the probability of reporting feeling overwhelmed by caregiving tasks by 12.5\% points (SE 0.04; P=.003) and a decrease in the probability of reporting negative health effects by 6.8\% points (SE 0.04; P=.07) because of caregiving. Although subjects reported that COVID-19 increased their stress attributed to caregiving and prevented them from requesting help for some caregiving tasks, using the app was still associated with improvements in receiving help and lessening of the negative effects of caregiving on the caregivers. Conclusions: App use was associated with improvements in 7 of 11 caregiver outcomes across three main categories: their support system, time spent on caregiving, and perceived health and well-being. These findings provide encouraging evidence that the mobile app can significantly reduce caregiver burden by leveraging a caregiver's support network despite the additional challenges brought by COVID-19 on caregivers. ", doi="10.2196/28504", url="https://www.jmir.org/2022/4/e28504", url="http://www.ncbi.nlm.nih.gov/pubmed/35404266" } @Article{info:doi/10.2196/26438, author="Korpilahti-Leino, Tarja and Luntamo, Terhi and Ristkari, Terja and Hinkka-Yli-Salom{\"a}ki, Susanna and Pulkki-R{\aa}back, Laura and Waris, Otto and Matinolli, Hanna-Maria and Sinokki, Atte and Mori, Yuko and Fukaya, Mami and Yamada, Yuko and Sourander, Andre", title="Single-Session, Internet-Based Cognitive Behavioral Therapy to Improve Parenting Skills to Help Children Cope With Anxiety During the COVID-19 Pandemic: Feasibility Study", journal="J Med Internet Res", year="2022", month="Apr", day="13", volume="24", number="4", pages="e26438", keywords="adolescent", keywords="anxiety", keywords="child", keywords="cognitive behavioral therapy", keywords="coping", keywords="COVID-19", keywords="Internet", keywords="mental health", keywords="parents", keywords="web-based", abstract="Background: The COVID-19 pandemic has had a major impact on families' daily routines and psychosocial well-being, and technology has played a key role in providing socially distanced health care services. Objective: The first objective of this paper was to describe the content and delivery of a single-session, internet-based cognitive behavioral therapy (iCBT) intervention, which has been developed to help parents cope with children's anxiety and manage daily situations with their children. The second objective was to report user adherence and satisfaction among the first participants who completed the intervention. Methods: The Let's Cope Together intervention has been developed by our research group. It combines evidence-based CBT elements, such as psychoeducation and skills to manage anxiety, with parent training programs that strengthen how parents interact with their child and handle daily situations. A pre-post design was used to examine user satisfaction and the skills the parents learned. Participants were recruited using advertisements, media activity, day care centers, and schools and asked about background characteristics, emotional symptoms, and parenting practices before they underwent the iCBT. After they completed the 7 themes, they were asked what new parenting skills they had learned from the iCBT and how satisfied they were with the program. Results: Of the 602 participants who filled in the baseline survey, 196 (32.6\%) completed the program's 7 themes, and 189 (31.4\%) completed the postintervention survey. Most (138/189, 73.0\%) of the participants who completed the postintervention survey were satisfied with the program and had learned skills that eased both their anxiety (141/189, 74.6\%) and their children's anxiety (157/189, 83.1\%). The majority (157/189, 83.1\%) reported that they learned how to organize their daily routines better, and just over one-half (100/189, 53.0\%) reported that the program improved how they planned each day with their children. Conclusions: The single-session iCBT helped parents to face the psychological demands of the COVID-19 pandemic. Future studies should determine how the participation rate and adherence can be optimized in digital, universal interventions. This will help to determine what kinds of programs should be developed, including their content and delivery. ", doi="10.2196/26438", url="https://www.jmir.org/2022/4/e26438", url="http://www.ncbi.nlm.nih.gov/pubmed/35138265" } @Article{info:doi/10.2196/35614, author="Mattila, Elina and Horgan, Graham and Palmeira, L. Ant{\'o}nio and O'Driscoll, Ruairi and Stubbs, James R. and Heitmann, L. Berit and Marques, M. Marta", title="Evaluation of the Immediate Effects of Web-Based Intervention Modules for Goals, Planning, and Coping Planning on Physical Activity: Secondary Analysis of a Randomized Controlled Trial on Weight Loss Maintenance", journal="J Med Internet Res", year="2022", month="Apr", day="14", volume="24", number="4", pages="e35614", keywords="digital intervention", keywords="Fitbit", keywords="weight", keywords="weight loss maintenance", keywords="physical activity", keywords="fitness", keywords="exercise", keywords="goal setting", keywords="action planning", keywords="coping planning", keywords="control trial", keywords="secondary analysis", keywords="RCT", keywords="randomized controlled trial", keywords="long-term effect", keywords="short-term effect", keywords="immediate effect", keywords="sustained effect", abstract="Background: The use of digital interventions can be accurately monitored via log files. However, monitoring engagement with intervention goals or enactment of the actual behaviors targeted by the intervention is more difficult and is usually evaluated based on pre-post measurements in a controlled trial. Objective: The objective of this paper is to evaluate if engaging with 2 digital intervention modules focusing on (1) physical activity goals and action plans and (2) coping with barriers has immediate effects on the actual physical activity behavior. Methods: The NoHoW Toolkit (TK), a digital intervention developed to support long-term weight loss maintenance, was evaluated in a 2 x 2 factorial randomized controlled trial. The TK contained various modules based on behavioral self-regulation and motivation theories, as well as contextual emotion regulation approaches, and involved continuous tracking of weight and physical activity through connected commercial devices (Fitbit Aria and Charge 2). Of the 4 trial arms, 2 had access to 2 modules directly targeting physical activity: a module for goal setting and action planning (Goal) and a module for identifying barriers and coping planning (Barriers). Module visits and completion were determined based on TK log files and time spent in the module web page. Seven physical activity metrics (steps; activity; energy expenditure; fairly active, very active and total active minutes; and distance) were compared before and after visiting and completing the modules to examine whether the modules had immediate or sustained effects on physical activity. Immediate effect was determined based on 7-day windows before and after the visit, and sustained effects were evaluated for 1 to 8 weeks after module completion. Results: Out of the 811 participants, 498 (61.4\%) visited the Goal module and 406 (50.1\%) visited the Barriers module. The Barriers module had an immediate effect on very active and total active minutes (very active minutes: before median 24.2, IQR 10.4-43.0 vs after median 24.9, IQR 10.0-46.3; P=.047; total active minutes: before median 45.1, IQR 22.9-74.9 vs after median 46.9, IQR 22.4-78.4; P=.03). The differences were larger when only completed Barriers modules were considered. The Barriers module completion was also associated with sustained effects in fairly active and total active minutes for most of the 8 weeks following module completion and for 3 weeks in very active minutes. Conclusions: The Barriers module had small, significant, immediate, and sustained effects on active minutes measured by a wrist-worn activity tracker. Future interventions should pay attention to assessing barriers and planning coping mechanisms to overcome them. Trial Registration: ISRCTN Registry ISRCTN88405328; https://www.isrctn.com/ISRCTN88405328 ", doi="10.2196/35614", url="https://www.jmir.org/2022/4/e35614", url="http://www.ncbi.nlm.nih.gov/pubmed/35436232" } @Article{info:doi/10.2196/30138, author="Ahlers, Joachim and Baumgartner, Christian and Augsburger, Mareike and Wenger, Andreas and Malischnig, Doris and Boumparis, Nikolaos and Berger, Thomas and Stark, Lars and Ebert, D. David and Haug, Severin and Schaub, P. Michael", title="Cannabis Use in Adults Who Screen Positive for Attention Deficit/Hyperactivity Disorder: CANreduce 2.0 Randomized Controlled Trial Subgroup Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="20", volume="24", number="4", pages="e30138", keywords="attention deficit/hyperactivity disorder", keywords="ADHD", keywords="cannabis", keywords="cannabis use disorder", keywords="CANreduce", keywords="web-based self-help tool", keywords="online tool", keywords="online health", keywords="mental health", keywords="digital health", keywords="anxiety", keywords="depression", abstract="Background: Prevalence rates for lifetime cannabis use and cannabis use disorder are much higher in people with attention deficit/hyperactivity disorder than in those without. CANreduce 2.0 is an intervention that is generally effective at reducing cannabis use in cannabis misusers. This self-guided web-based intervention (6-week duration) consists of modules grounded in motivational interviewing and cognitive behavioral therapy. Objective: We aimed to evaluate whether the CANreduce 2.0 intervention affects cannabis use patterns and symptom severity in adults who screen positive for attention deficit/hyperactivity disorder more than in those who do not. Methods: We performed a secondary analysis of data from a previous study with the inclusion criterion of cannabis use at least once weekly over the last 30 days. Adults with and without attention deficit/hyperactivity disorder (based on the Adult Attention deficit/hyperactivity disorder Self-Report screener) who were enrolled to the active intervention arms of CANreduce 2.0 were compared regarding the number of days cannabis was used in the preceding 30 days, the cannabis use disorder identification test score (CUDIT) and the severity of dependence scale score (SDS) at baseline and the 3-month follow-up. Secondary outcomes were Generalized Anxiety Disorder score, Center for Epidemiological Studies Depression scale score, retention, intervention adherence, and safety. Results: Both adults with (n=94) and without (n=273) positive attention-deficit/hyperactivity disorder screening reported significantly reduced frequency (reduction in consumption days: with: mean 11.53, SD 9.28, P<.001; without: mean 8.53, SD 9.4, P<.001) and severity of cannabis use (SDS: with: mean 3.57, SD 3.65, P<.001; without: mean 2.47, SD 3.39, P<.001; CUDIT: with: mean 6.38, SD 5.96, P<.001; without: mean 5.33, SD 6.05, P<.001), as well as anxiety (with: mean 4.31, SD 4.71, P<.001; without: mean 1.84, SD 4.22, P<.001) and depression (with: mean 10.25, SD 10.54; without: mean 4.39, SD 10.22, P<.001). Those who screened positive for attention deficit/hyperactivity disorder also reported significantly decreased attention deficit/hyperactivity disorder scores (mean 4.65, SD 4.44, P<.001). There were no significant differences in change in use (P=.08), dependence (P=.95), use disorder (P=.85), attention deficit/hyperactivity disorder status (P=.84), depression (P=.84), or anxiety (P=.26) between baseline and final follow-up, dependent on positive attention-deficit/hyperactivity disorder screening. Attention deficit/hyperactivity disorder symptom severity at baseline was not associated with reduced cannabis use frequency or severity but was linked to greater reductions in depression (Spearman $\rho$=.33) and anxiety (Spearman $\rho$=.28). Individuals with positive attention deficit/hyperactivity disorder screening were significantly less likely to fill out the consumption diary (P=.02), but the association between continuous attention deficit/hyperactivity disorder symptom severity and retention (Spearman $\rho$=?0.10, P=.13) was nonsignificant. There also was no significant intergroup difference in the number of completed modules (with: mean 2.10, SD 2.33; without: mean 2.36, SD 2.36, P=.34), and there was no association with attention deficit/hyperactivity disorder symptom severity (Spearman $\rho$=?0.09; P=.43). The same was true for the rate of adverse effects (P=.33). Conclusions: Cannabis users screening positive for attention deficit/hyperactivity disorder may benefit from CANreduce 2.0 to decrease the frequency and severity of cannabis dependence and attenuate symptoms of depression and attention deficit/hyperactivity disorder-related symptoms. This web-based program's advantages include its accessibility for remote users and a personalized counselling option that may contribute to increased adherence and motivation to change among program users. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 11086185; http://www.isrctn.com/ISRCTN11086185 ", doi="10.2196/30138", url="https://www.jmir.org/2022/4/e30138", url="http://www.ncbi.nlm.nih.gov/pubmed/35442196" } @Article{info:doi/10.2196/27387, author="Campbell, Benjamin and Heitner, Jesse and Amos Mwelelo, Peter and Fogel, Alexis and Mujumdar, Vaidehi and Adams, V. Lisa and Boniface, Respicious and Su, Yanfang", title="Impact of SMS Text Messaging Reminders on Helmet Use Among Motorcycle Drivers in Dar es Salaam, Tanzania: Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Apr", day="7", volume="24", number="4", pages="e27387", keywords="road traffic injury", keywords="behavior change", keywords="SMS reminders", keywords="mobile health", keywords="vehicle safety", keywords="mHealth", keywords="SMS", keywords="traffic injuries", keywords="transportation", keywords="public transportation", keywords="safety", keywords="automotive", keywords="automotive safety", abstract="Background: Road traffic injury is a pressing public health issue in Tanzania. Increasing helmet use among motorcycle drivers can help reduce the burden due to road traffic injuries in the country. Helmet adherence can be supported through mobile health interventions. Objective: The aim of this study is to evaluate the comparative impact of two different types of SMS text messaging reminders on motorcycle helmet use. Methods: Participants were 391 commercial motorcycle taxi drivers in Dar es Salaam, Tanzania. Participants were randomized into three groups, each receiving a different set of messages: (1) social norming messages aimed at emphasizing society's positive stance on helmet wearing, (2) fear appeal messages that emphasized the dangers of riding without a helmet, and (3) control group messages, which included basic road safety messages unrelated to helmet use. Every participant received the control messages. Adherence to helmet use was evaluated by self-report through surveys conducted at baseline, 3 weeks, and 6 weeks. Results: At 6 weeks, the odds of self-reporting consistent helmet use were estimated to be 1.58 times higher in the social norming group than in the control group (P=.04), though this difference was not significant after accounting for multiple testing. There was little difference between fear appeal and control group recipients (odds ratio 1.03, P=.47). Subgroup analysis suggests that both fear appeal and social norming message types might have been associated with increased helmet use among participants who did not consistently wear helmets at baseline (odds ratio 1.66 and odds ratio 1.84, respectively), but this was not significant (P=.11 and P=.07, respectively). Among those who were consistent wearers at baseline, the social norming messages performed better than the fear appeal messages, and this difference reached traditional significance (P=.03), but was not significant after accounting for multiple testing. Conclusions: The use of SMS text messaging reminders may improve helmet use among motorcycle drivers when framed as social norming messages. Given that nearly half of the drivers in our sample did not consistently wear their helmets on every trip, strategies to increase consistent usage could greatly benefit public safety. Trial Registration: ClinicalTrials.gov NCT02120742; https://clinicaltrials.gov/ct2/show/NCT02120742 ", doi="10.2196/27387", url="https://www.jmir.org/2022/4/e27387", url="http://www.ncbi.nlm.nih.gov/pubmed/35389364" } @Article{info:doi/10.2196/29380, author="Kim, Heon Ho and Kim, Youngin and Michaelides, Andreas and Park, Rang Yu", title="Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study", journal="J Med Internet Res", year="2022", month="Apr", day="15", volume="24", number="4", pages="e29380", keywords="clustering", keywords="mobile health", keywords="weight loss", keywords="weight management", keywords="behavior management", keywords="time series analysis", keywords="mHealth", keywords="obesity", keywords="outcomes", keywords="machine learning", keywords="mobile app", keywords="adherence", keywords="prediction", keywords="mobile phone", abstract="Background: In obesity management, whether patients lose ?5\% of their initial weight is a critical factor in clinical outcomes. However, evaluations that take only this approach are unable to identify and distinguish between individuals whose weight changes vary and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight changes through a mobile-based intervention for obesity can facilitate understanding of an individual's behavior and weight changes from a longitudinal perspective. Objective: The aim of this study is to use a machine learning approach to examine weight loss trajectories and explore factors related to behavioral and app use characteristics that induce weight loss. Methods: We used the lifelog data of 13,140 individuals enrolled in a 16-week obesity management program on the health care app Noom in the United States from August 8, 2013, to August 8, 2019. We performed k-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify use factors determining clustering assignment, we longitudinally compared weekly use statistics with effect size on a weekly basis. Results: The initial average BMI value for the participants was 33.6 (SD 5.9) kg/m2, and it ultimately reached 31.6 (SD 5.7) kg/m2. Using the weight log data, we identified five clusters: cluster 1 (sharp decrease) showed the highest proportion of participants who reduced their weight by >5\% (7296/11,295, 64.59\%), followed by cluster 2 (moderate decrease). In each comparison between clusters 1 and 3 (yo-yo) and clusters 2 and 3, although the effect size of the difference in average meal record adherence and average weight record adherence was not significant in the first week, it peaked within the initial 8 weeks (Cohen d>0.35) and decreased after that. Conclusions: Using a machine learning approach and clustering shape-based time series similarities, we identified 5 weight loss trajectories in a mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as potential predictors of these trajectories. ", doi="10.2196/29380", url="https://www.jmir.org/2022/4/e29380", url="http://www.ncbi.nlm.nih.gov/pubmed/35436211" } @Article{info:doi/10.2196/32825, author="McBeth, John and Dixon, G. William and Moore, Mary Susan and Hellman, Bruce and James, Ben and Kyle, D. Simon and Lunt, Mark and Cordingley, Lis and Yimer, Birlie Belay and Druce, L. Katie", title="Sleep Disturbance and Quality of Life in Rheumatoid Arthritis: Prospective mHealth Study", journal="J Med Internet Res", year="2022", month="Apr", day="22", volume="24", number="4", pages="e32825", keywords="mobile health", keywords="sleep", keywords="rheumatoid arthritis", keywords="pain", keywords="fatigue", keywords="mood", keywords="sleep disturbance", keywords="HRQoL", keywords="quality of life", keywords="health-related quality of life", keywords="QoL", keywords="sleep efficiency", keywords="WHOQoL-BREF", keywords="mobile phone", abstract="Background: Sleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies. Objective: This mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL. Methods: The quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social). Results: Of the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants' scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points ($\beta$=1.11, 95\% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF. Conclusions: Optimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA. ", doi="10.2196/32825", url="https://www.jmir.org/2022/4/e32825", url="http://www.ncbi.nlm.nih.gov/pubmed/35451978" } @Article{info:doi/10.2196/36489, author="Li, Ang and Jiao, Dongdong and Zhu, Tingshao", title="Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e36489", keywords="stigma", keywords="cybersuicide", keywords="livestreamed suicide", keywords="linguistic analysis", keywords="social media", abstract="Background: The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience's attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited. Objective: This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics. Methods: A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide. Results: In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide ($\chi$21=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow ($\chi$21=28.9; P<.001), false representation ($\chi$21=144.4; P<.001), weak and pathetic ($\chi$21=20.4; P<.001), glorified and normalized ($\chi$21=177.6; P<.001), and immoral ($\chi$21=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85. Conclusions: The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide. ", doi="10.2196/36489", url="https://www.jmir.org/2022/4/e36489", url="http://www.ncbi.nlm.nih.gov/pubmed/35394437" } @Article{info:doi/10.2196/30218, author="Turmaine, Kathleen and Dumas, Agn{\`e}s and Chevreul, Karine and ", title="Conditions for the Successful Integration of an eHealth Tool ``StopBlues'' Into Community-Based Interventions in France: Results From a Multiple Correspondence Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="22", volume="24", number="4", pages="e30218", keywords="eHealth", keywords="internet-based intervention", keywords="community participation", keywords="health promotion", keywords="prevention", keywords="mental health", abstract="Background: For over a decade, digital health has held promise for enabling broader access to health information, education, and services for the general population at a lower cost. However, recent studies have shown mixed results leading to a certain disappointment regarding the benefits of eHealth technologies. In this context, community-based health promotion represents an interesting and efficient conceptual framework that could help increase the adoption of digital health solutions and facilitate their evaluation. Objective: To understand how the local implementation of the promotion of an eHealth tool, StopBlues (SB), aimed at preventing psychological distress and suicide, varied according to local contexts and if the implementation was related to the use of the tool. Methods: The study was nested within a cluster-randomized controlled trial that was conducted to evaluate the effectiveness of the promotion, with before and after observation (NCT03565562). Data from questionnaires, observations, and institutional sources were collected in 27 localities where SB was implemented. A multiple correspondence analysis was performed to assess the relations between context, type of implementation and promotion, and use of the tool. Results: Three distinct promotion patterns emerged according to the profiles of the localities that were associated with specific SB utilization rates. From highest to lowest utilization rates, they are listed as follows: the privileged urban localities, investing in health that implemented a high-intensity and digital promotion, demonstrating a greater capacity to take ownership of the project; the urban, but less privileged localities that, in spite of having relatively little experience in health policy implementation, managed to implement a traditional and high-intensity promotion; and the rural localities, with little experience in addressing health issues, that implemented low-intensity promotion but could not overcome the challenges associated with their local context. Conclusions: These findings indicate the substantial influence of local context on the reception of digital tools. The urban and socioeconomic status profiles of the localities, along with their investment and pre-existing experience in health, appear to be critical for shaping the promotion and implementation of eHealth tools in terms of intensity and use of digital communication. The more digital channels used, the higher the utilization rates, ultimately leading to the overall success of the intervention. International Registered Report Identifier (IRRID): RR2-10.1186/s13063-020-04464-2 ", doi="10.2196/30218", url="https://www.jmir.org/2022/4/e30218", url="http://www.ncbi.nlm.nih.gov/pubmed/35451977" } @Article{info:doi/10.2196/29258, author="Wiklund, Tobias and Molander, Peter and Lindner, Philip and Andersson, Gerhard and Gerdle, Bj{\"o}rn and Dragioti, Elena", title="Internet-Delivered Cognitive Behavioral Therapy for Insomnia Comorbid With Chronic Pain: Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Apr", day="29", volume="24", number="4", pages="e29258", keywords="insomnia", keywords="chronic pain", keywords="comorbid", keywords="CBT-i", keywords="RCT", keywords="web-based CBT", keywords="pain", keywords="online health", keywords="online treatment", keywords="digital health", keywords="mental health", keywords="rehabilitation", abstract="Background: Patients with chronic pain often experience insomnia symptoms. Pain initiates, maintains, and exacerbates insomnia symptoms, and vice versa, indicating a complex situation with an additional burden for these patients. Hence, the evaluation of insomnia-related interventions for patients with chronic pain is important. Objective: This randomized controlled trial examined the effectiveness of internet-based cognitive behavioral therapy for insomnia (ICBT-i) for reducing insomnia severity and other sleep- and pain-related parameters in patients with chronic pain. Participants were recruited from the Swedish Quality Registry for Pain Rehabilitation. Methods: We included 54 patients (mean age 49.3, SD 12.3 years) who were randomly assigned to the ICBT-i condition and 24 to an active control condition (applied relaxation). Both treatment conditions were delivered via the internet. The Insomnia Severity Index (ISI), a sleep diary, and a battery of anxiety, depression, and pain-related parameter measurements were assessed at baseline, after treatment, and at a 6-month follow-up (only ISI, anxiety, depression, and pain-related parameters). For the ISI and sleep diary, we also recorded weekly measurements during the 5-week treatment. Negative effects were also monitored and reported. Results: Results showed a significant immediate interaction effect (time by treatment) on the ISI and other sleep parameters, namely, sleep efficiency, sleep onset latency, early morning awakenings, and wake time after sleep onset. Participants in the applied relaxation group reported no significant immediate improvements, but both groups exhibited a time effect for anxiety and depression at the 6-month follow-up. No significant improvements on pain-related parameters were found. At the 6-month follow-up, both the ICBT-i and applied relaxation groups had similar sleep parameters. For both treatment arms, increased stress was the most frequently reported negative effect. Conclusions: In patients with chronic pain, brief ICBT-i leads to a more rapid decline in insomnia symptoms than does applied relaxation. As these results are unique, further research is needed to investigate the effect of ICBT-i on a larger sample size of people with chronic pain. Using both treatments might lead to an even better outcome in patients with comorbid insomnia and chronic pain. Trial Registration: ClinicalTrials.gov NCT03425942; https://clinicaltrials.gov/ct2/show/NCT03425942 ", doi="10.2196/29258", url="https://www.jmir.org/2022/4/e29258", url="http://www.ncbi.nlm.nih.gov/pubmed/35486418" } @Article{info:doi/10.2196/29781, author="Hajek, Andr{\'e} and K{\"o}nig, Hans-Helmut", title="Frequency and Correlates of Online Consultations With Doctors or Therapists in Middle-Aged and Older Adults: Nationally Representative Cross-sectional Study", journal="J Med Internet Res", year="2022", month="Apr", day="7", volume="24", number="4", pages="e29781", keywords="online consultations", keywords="doctor", keywords="therapists", keywords="telehealth", keywords="COVID-19", keywords="SARS-CoV-2", keywords="digital health", abstract="Background: A few studies have identified the frequency and correlates of online consultations with doctors or therapists. However, there is a lack of studies using nationally representative data from middle-aged and older adults in Germany. Objective: This study aims to determine the frequency and correlates of online consultations with doctors or therapists in Germany. Methods: For this study, cross-sectional data were taken from the nationally representative German Ageing Survey (DEAS; n=3067 in the analytical sample; age range 46-98 years). As part of the DEAS, a short survey was conducted between June 8 and July 22, 2020, examining the everyday life and living conditions among these middle-aged and older individuals during the COVID-19 pandemic. The frequency of online consultations with doctors or therapists served as the dependent variable (daily, several times a week, once a week, 1-3 times a month, less often, and never). Multiple logistic regressions were performed. Results: In sum, 10.02\% (381/3806) of individuals with access to the internet had online consultations with doctors or therapists. Multiple logistic regressions showed that the likelihood of using online consultations with doctors or therapists (compared with those never using such services) was positively associated with higher education (compared with medium education; odds ratio [OR] 1.31, 95\% CI 1.01-1.70), living with a partner in the same household (compared with single; OR 1.53, 95\% CI 1.05-2.22), poorer self-rated health (OR 1.42, 95\% CI 1.16-1.74), increased loneliness (OR 1.45, 95\% CI 1.10-1.90), and increased satisfaction with life (OR 1.30, 95\% CI 1.03-1.64). Conclusions: Study findings suggest that a non-negligible proportion of about 1 out of 10 individuals aged 46 years and over had online consultations with doctors or therapists. However, compared with other countries, this proportion remains small. Knowledge about the correlates of (non)use may assist in identifying corresponding individuals. In times of reshaping the health care system, these efforts in online consultations with doctors or therapists may contribute to addressing patient needs. Moreover, increased use of such services may reduce the risk of getting infected with SARS-CoV-2 by reducing social contact. ", doi="10.2196/29781", url="https://www.jmir.org/2022/4/e29781", url="http://www.ncbi.nlm.nih.gov/pubmed/35389360" } @Article{info:doi/10.2196/32570, author="Dang, Stuti and Muralidhar, Kiranmayee and Li, Shirley and Tang, Fei and Mintzer, Michael and Ruiz, Jorge and Valencia, Marcos Willy", title="Gap in Willingness and Access to Video Visit Use Among Older High-risk Veterans: Cross-sectional Study", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e32570", keywords="high-risk veterans", keywords="older adults", keywords="telemedicine", keywords="video visits", keywords="health disparities", keywords="Area Deprivation Index", keywords="mobile phone", abstract="Background: The recent shift to video care has exacerbated disparities in health care access, especially among high-need, high-risk (HNHR) adults. Developing data-driven approaches to improve access to care necessitates a deeper understanding of HNHR adults' attitudes toward telemedicine and technology access. Objective: This study aims to identify the willingness, access, and ability of HNHR veterans to use telemedicine for health care. Methods: WWe designed a questionnaire conducted via mail or telephone or in person. Among HNHR veterans who were identified using predictive modeling with national Veterans Affairs data, we assessed willingness to use video visits for health care, access to necessary equipment, and comfort with using technology. We evaluated physical health, including frailty, physical function, performance of activities of daily living (ADL) and instrumental ADL (IADL); mental health; and social needs, including Area Deprivation Index, transportation, social support, and social isolation. Results: The average age of the 602 HNHR veteran respondents was 70.6 (SD 9.2; range 39-100) years; 99.7\% (600/602) of the respondents were male, 61\% (367/602) were White, 36\% (217/602) were African American, 17.3\% (104/602) were Hispanic, 31.2\% (188/602) held at least an associate degree, and 48.2\% (290/602) were confident filling medical forms. Of the 602 respondents, 327 (54.3\%) reported willingness for video visits, whereas 275 (45.7\%) were unwilling. Willing veterans were younger (P<.001) and more likely to have an associate degree (P=.002), be health literate (P<.001), live in socioeconomically advantaged neighborhoods (P=.048), be independent in IADLs (P=.02), and be in better physical health (P=.04). A higher number of those willing were able to use the internet and email (P<.001). Of the willing veterans, 75.8\% (248/327) had a video-capable device. Those with video-capable technology were younger (P=.004), had higher health literacy (P=.01), were less likely to be African American (P=.007), were more independent in ADLs (P=.005) and IADLs (P=.04), and were more adept at using the internet and email than those without the needed technology (P<.001). Age, confidence in filling forms, general health, and internet use were significantly associated with willingness to use video visits. Conclusions: Approximately half of the HNHR respondents were unwilling for video visits and a quarter of those willing lacked requisite technology. The gap between those willing and without requisite technology is greater among older, less health literate, African American veterans; those with worse physical health; and those living in more socioeconomically disadvantaged neighborhoods. Our study highlights that HNHR veterans have complex needs, which risk being exacerbated by the video care shift. Although technology holds vast potential to improve health care access, certain vulnerable populations are less likely to engage, or have access to, technology. Therefore, targeted interventions are needed to address this inequity, especially among HNHR older adults. ", doi="10.2196/32570", url="https://www.jmir.org/2022/4/e32570", url="http://www.ncbi.nlm.nih.gov/pubmed/35394440" } @Article{info:doi/10.2196/29492, author="Aronoff-Spencer, Eliah and McComsey, Melanie and Chih, Ming-Yuan and Hubenko, Alexandra and Baker, Corey and Kim, John and Ahern, K. David and Gibbons, Christopher Michael and Cafazzo, A. Joseph and Nyakairu, Pia and Vanderpool, C. Robin and Mullett, W. Timothy and Hesse, W. Bradford", title="Designing a Framework for Remote Cancer Care Through Community Co-design: Participatory Development Study", journal="J Med Internet Res", year="2022", month="Apr", day="12", volume="24", number="4", pages="e29492", keywords="cancer care", keywords="distress screening", keywords="human-centered design", keywords="participatory design", keywords="Appalachia", keywords="mobile phone", abstract="Background: Recent shifts to telemedicine and remote patient monitoring demonstrate the potential for new technology to transform health systems; yet, methods to design for inclusion and resilience are lacking. Objective: The aim of this study is to design and implement a participatory framework to produce effective health care solutions through co-design with diverse stakeholders. Methods: We developed a design framework to cocreate solutions to locally prioritized health and communication problems focused on cancer care. The framework is premised on the framing and discovery of problems through community engagement and lead-user innovation with the hypothesis that diversity and inclusion in the co-design process generate more innovative and resilient solutions. Discovery, design, and development were implemented through structured phases with design studios at various locations in urban and rural Kentucky, including Appalachia, each building from prior work. In the final design studio, working prototypes were developed and tested. Outputs were assessed using the System Usability Scale as well as semistructured user feedback. Results: We co-designed, developed, and tested a mobile app (myPath) and service model for distress surveillance and cancer care coordination following the LAUNCH (Linking and Amplifying User-Centered Networks through Connected Health) framework. The problem of awareness, navigation, and communication through cancer care was selected by the community after framing areas for opportunity based on significant geographic disparities in cancer and health burden resource and broadband access. The codeveloped digital myPath app showed the highest perceived combined usability (mean 81.9, SD 15.2) compared with the current gold standard of distress management for patients with cancer, the paper-based National Comprehensive Cancer Network Distress Thermometer (mean 74.2, SD 15.8). Testing of the System Usability Scale subscales showed that the myPath app had significantly better usability than the paper Distress Thermometer (t63=2.611; P=.01), whereas learnability did not differ between the instruments (t63=--0.311; P=.76). Notable differences by patient and provider scoring and feedback were found. Conclusions: Participatory problem definition and community-based co-design, design-with methods, may produce more acceptable and effective solutions than traditional design-for approaches. ", doi="10.2196/29492", url="https://www.jmir.org/2022/4/e29492", url="http://www.ncbi.nlm.nih.gov/pubmed/35412457" } @Article{info:doi/10.2196/33656, author="Crespi, Elizabeth and Hardesty, J. Jeffrey and Nian, Qinghua and Sinamo, Joshua and Welding, Kevin and Kennedy, David Ryan and Cohen, E. Joanna", title="Agreement Between Self-reports and Photos to Assess e-Cigarette Device and Liquid Characteristics in Wave 1 of the Vaping and Patterns of e-Cigarette Use Research Study: Web-Based Longitudinal Cohort Study", journal="J Med Internet Res", year="2022", month="Apr", day="27", volume="24", number="4", pages="e33656", keywords="tobacco", keywords="e-cigarette", keywords="methodology", keywords="internet", keywords="photo", keywords="survey", keywords="self-report", abstract="Background: e-Cigarette device and liquid characteristics are highly customizable; these characteristics impact nicotine delivery and exposure to toxic constituents. It is critical to understand optimal methods for measuring these characteristics to accurately assess their impacts on user behavior and health. Objective: To inform future survey development, we assessed the agreement between responses from survey participants (self-reports) and photos uploaded by participants and the quantity of usable data derived from each approach. Methods: Adult regular e-cigarette users (?5 days per week) aged ?21 years (N=1209) were asked questions about and submitted photos of their most used e-cigarette device (1209/1209, 100\%) and liquid (1132/1209, 93.63\%). Device variables assessed included brand, model, reusability, refillability, display, and adjustable power. Liquid variables included brand, flavor, nicotine concentration, nicotine formulation, and bottle size. For each variable, percentage agreement was calculated where self-report and photo data were available. Krippendorff $\alpha$ and intraclass correlation coefficient (ICC) were calculated for categorical and continuous variables, respectively. Results were stratified by device (disposable, reusable with disposable pods or cartridges, and reusable with refillable pods, cartridges, or tanks) and liquid (customized and noncustomized) type. The sample size for each calculation ranged from 3.89\% (47/1209; model of disposable devices) to 95.12\% (1150/1209; device reusability). Results: Percentage agreement between photos and self-reports was substantial to very high across device and liquid types for all variables except nicotine concentration. These results are consistent with Krippendorff $\alpha$ calculations, except where prevalence bias was suspected. ICC results for nicotine concentration and bottle size were lower than percentage agreement, likely because ICC accounts for the level of disagreement between values. Agreement varied by device and liquid type. For example, percentage agreement for device brand was higher among users of reusable devices (94\%) than among users of disposable devices (75\%). Low percentage agreement may result from poor participant knowledge of characteristics, user modifications of devices inconsistent with manufacturer-intended use, inaccurate or incomplete information on websites, or photo submissions that are not a participant's most used device or liquid. The number of excluded values (eg, self-report was ``don't know'' or no photo submitted) differed between self-reports and photos; for questions asked to participants, self-reports had more usable data than photos for all variables except device model and nicotine formulation. Conclusions: Photos and self-reports yield data of similar accuracy for most variables assessed in this study: device brand, device model, reusability, adjustable power, display, refillability, liquid brand, flavor, and bottle size. Self-reports provided more data for all variables except device model and nicotine formulation. Using these approaches simultaneously may optimize data quantity and quality. Future research should examine how to assess nicotine concentration and variables not included in this study (eg, wattage and resistance) and the resource requirements of these approaches. ", doi="10.2196/33656", url="https://www.jmir.org/2022/4/e33656", url="http://www.ncbi.nlm.nih.gov/pubmed/35475727" } @Article{info:doi/10.2196/34015, author="Hart, Alexander and Reis, Dorota and Prestele, Elisabeth and Jacobson, C. Nicholas", title="Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment", journal="J Med Internet Res", year="2022", month="Apr", day="28", volume="24", number="4", pages="e34015", keywords="digital biomarkers", keywords="machine learning", keywords="ecological momentary assessment", keywords="smartphone sensors", keywords="internal states", keywords="paradata", keywords="accelerometer", keywords="gyroscope", keywords="mood", keywords="mobile phone", abstract="Background: Sensors embedded in smartphones allow for the passive momentary quantification of people's states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants' moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals' work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals' self-reported states at later measurement occasions. The mean R2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3\%) had an R2 of ?0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41\% to 51.38\%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals' daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference. ", doi="10.2196/34015", url="https://www.jmir.org/2022/4/e34015", url="http://www.ncbi.nlm.nih.gov/pubmed/35482397" } @Article{info:doi/10.2196/37127, author="Yang, Eunjin and Lee, Hee Kyung", title="The Moderating Effects of Disability on Mobile Internet Use Among Older Adults: Population-Based Cross-sectional Study", journal="J Med Internet Res", year="2022", month="Apr", day="4", volume="24", number="4", pages="e37127", keywords="older adults", keywords="people with disabilities", keywords="digital divide", keywords="mobile phone use", abstract="Background: The preferred devices to access the internet are changing from personal computers to mobile devices, and the number of older adults with or without disabilities is rapidly increasing in an aging society. However, little is known about the moderating effects of disability on mobile internet use among older adults. Objective: This study aimed to examine the levels of mobile internet use and factors associated with this use among older adults according to their disabilities. In addition, moderating effects of disability on mobile internet use were investigated. Methods: This study consisted of a secondary data analysis using the 2020 Digital Divide Survey conducted in South Korea. The single inclusion criterion was participants being aged 55 years or older; accordingly, 2243 people without disabilities and 1386 people with disabilities were included in the study. Multiple regression analyses considering complex sample designs were conducted to identify mobile internet use factors and to test the moderating effects of disability on mobile internet use. Results: Older adults with disabilities used mobile internet less than older adults without disabilities. However, disability status had moderating effects on the relationships between mobile internet use and (1) operational skills regarding mobile devices (B=0.31, P=.004), (2) internet use skills (B=1.46, P<.001), (3) motivation to use digital devices (B=0.46, P=.01), and (4) attitude toward new technology (B=0.50, P=.002). The results revealed that these positive relationships were stronger among older adults with disabilities than among adults without disabilities. Conclusions: Although older adults and people with disabilities are considered vulnerable populations regarding technology adoption, disability creates a stronger association between several determinants and actual mobile internet use. Therefore, policy makers and practitioners should pay attention to older adults with disabilities to deliver appropriate information-literacy education. Older adults with disabilities could be the primary beneficiaries of mobile services and new technology. ", doi="10.2196/37127", url="https://www.jmir.org/2022/4/e37127", url="http://www.ncbi.nlm.nih.gov/pubmed/35377329" } @Article{info:doi/10.2196/34513, author="Yuan, Jing and Au, Rhoda and Karjadi, Cody and Ang, Fang Ting and Devine, Sherral and Auerbach, Sanford and DeCarli, Charles and Libon, J. David and Mez, Jesse and Lin, Honghuang", title="Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study)", journal="J Med Internet Res", year="2022", month="Apr", day="15", volume="24", number="4", pages="e34513", keywords="Clock Drawing Test", keywords="digital", keywords="neuropsychological test", keywords="cognitive", keywords="technology", keywords="Boston Process Approach", keywords="neurology", keywords="Framingham Heart Study", keywords="dementia", keywords="Alzheimer", abstract="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. ", doi="10.2196/34513", url="https://www.jmir.org/2022/4/e34513", url="http://www.ncbi.nlm.nih.gov/pubmed/35436225" } @Article{info:doi/10.2196/35788, author="Golder, Su and Stevens, Robin and O'Connor, Karen and James, Richard and Gonzalez-Hernandez, Graciela", title="Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review", journal="J Med Internet Res", year="2022", month="Apr", day="29", volume="24", number="4", pages="e35788", keywords="twitter", keywords="social media", keywords="race", keywords="ethnicity", abstract="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. ", doi="10.2196/35788", url="https://www.jmir.org/2022/4/e35788", url="http://www.ncbi.nlm.nih.gov/pubmed/35486433" } @Article{info:doi/10.2196/35013, author="Costa, Silva Thiago Bulh{\~o}es da and Shinoda, Lucas and Moreno, Alfredo Ramon and Krieger, E. Jose and Gutierrez, Marco", title="Blockchain-Based Architecture Design for Personal Health Record: Development and Usability Study", journal="J Med Internet Res", year="2022", month="Apr", day="13", volume="24", number="4", pages="e35013", keywords="electronic health record", keywords="personal health record", keywords="blockchain", keywords="smart contract", abstract="Background: The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users. Objective: To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works. Methods: We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network. Results: With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 103 tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 101 seconds, while that only read have delays close to 100 seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 102 tps to 101 tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values. Conclusions: To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production. ", doi="10.2196/35013", url="https://www.jmir.org/2022/4/e35013", url="http://www.ncbi.nlm.nih.gov/pubmed/35416782" } @Article{info:doi/10.2196/26307, author="Zhang, Lili and Vashisht, Himanshu and Nethra, Alekhya and Slattery, Brian and Ward, Tomas", title="Differences in Learning and Persistency Characterizing Behavior in Chronic Pain for the Iowa Gambling Task: Web-Based Laboratory-in-the-Field Study", journal="J Med Internet Res", year="2022", month="Apr", day="6", volume="24", number="4", pages="e26307", keywords="chronic pain", keywords="decision-making", keywords="computational modeling", keywords="Iowa Gambling Task", keywords="lab-in-the-field experiment", abstract="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. ", doi="10.2196/26307", url="https://www.jmir.org/2022/4/e26307", url="http://www.ncbi.nlm.nih.gov/pubmed/35384855" } @Article{info:doi/10.2196/35058, author="Chua, Ling Wei and Ooi, Leng Sim and Chan, Han Gene Wai and Lau, Ching Tang and Liaw, Ying Sok", title="The Effect of a Sepsis Interprofessional Education Using Virtual Patient Telesimulation on Sepsis Team Care in Clinical Practice: Mixed Methods Study", journal="J Med Internet Res", year="2022", month="Apr", day="18", volume="24", number="4", pages="e35058", keywords="sepsis", keywords="interprofessional education", keywords="team training", keywords="nurse-physician communication", keywords="simulation", keywords="telesimulation", abstract="Background: Improving interprofessional communication and collaboration is necessary to facilitate the early identification and treatment of patients with sepsis. Preparing undergraduate medical and nursing students for the knowledge and skills required to assess, escalate, and manage patients with sepsis is crucial for their entry into clinical practice. However, the COVID-19 pandemic and social distancing measures have created the need for interactive distance learning to support collaborative learning. Objective: This study aimed to evaluate the effect of sepsis interprofessional education on medical and nursing students' sepsis knowledge, team communication skills, and skill use in clinical practice. Methods: A mixed methods design using a 1-group pretest-posttest design and focus group discussions was used. This study involved 415 undergraduate medical and nursing students from a university in Singapore. After a baseline evaluation of the participants' sepsis knowledge and team communication skills, they underwent didactic e-learning followed by virtual telesimulation on early recognition and management of sepsis and team communication strategies. The participants' sepsis knowledge and team communication skills were evaluated immediately and 2 months after the telesimulation. In total, 4 focus group discussions were conducted using a purposive sample of 18 medical and nursing students to explore their transfer of learning to clinical practice. Results: Compared with the baseline scores, both the medical and nursing students demonstrated a significant improvement in sepsis knowledge (P<.001) and team communication skills (P<.001) in immediate posttest scores. At the 2-month follow-up, the nursing students continued to have statistically significantly higher sepsis knowledge (P<.001) and communication scores (P<.001) than the pretest scores, whereas the medical students had no significant changes in test scores between the 2-month follow-up and pretest time points (P=.99). A total of three themes emerged from the qualitative findings: greater understanding of each other's roles, application of mental models in clinical practice, and theory-practice gaps. The sepsis interprofessional education---particularly the use of virtual telesimulation---fostered participants' understanding and appreciation of each other's interprofessional roles when caring for patients with sepsis. Despite noting some incongruities with the real-world clinical practice and not encountering many sepsis scenarios in clinical settings, participants shared the application of mental models using interprofessional communication strategies and the patient assessment framework in their daily clinical practice. Conclusions: Although the study did not show long-term knowledge retention, the use of virtual telesimulation played a critical role in facilitating the application of mental models for learning transfer and therefore could serve as a promising education modality for sepsis training. For a greater clinical effect, future studies could complement virtual telesimulation with a mannequin-based simulation and provide more evidence on the long-term retention of sepsis knowledge and clinical skills performance. ", doi="10.2196/35058", url="https://www.jmir.org/2022/4/e35058", url="http://www.ncbi.nlm.nih.gov/pubmed/35436237" } @Article{info:doi/10.2196/29455, author="Tardini, Elisa and Zhang, Xinhua and Canahuate, Guadalupe and Wentzel, Andrew and Mohamed, R. Abdallah S. and Van Dijk, Lisanne and Fuller, D. Clifton and Marai, Elisabeta G.", title="Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad", journal="J Med Internet Res", year="2022", month="Apr", day="20", volume="24", number="4", pages="e29455", keywords="digital twin dyad", keywords="reinforcement learning", keywords="head and neck cancer", abstract="Background: Currently, selection of patients for sequential versus concurrent chemotherapy and radiation regimens lacks evidentiary support and it is based on locally optimal decisions for each step. Objective: We aim to optimize the multistep treatment of patients with head and neck cancer and predict multiple patient survival and toxicity outcomes, and we develop, apply, and evaluate a first application of deep Q-learning (DQL) and simulation to this problem. Methods: The treatment decision DQL digital twin and the patient's digital twin were created, trained, and evaluated on a data set of 536 patients with oropharyngeal squamous cell carcinoma with the goal of, respectively, determining the optimal treatment decisions with respect to survival and toxicity metrics and predicting the outcomes of the optimal treatment on the patient. Of the data set of 536 patients, the models were trained on a subset of 402 (75\%) patients (split randomly) and evaluated on a separate set of 134 (25\%) patients. Training and evaluation of the digital twin dyad was completed in August 2020. The data set includes 3-step sequential treatment decisions and complete relevant history of the patient cohort treated at MD Anderson Cancer Center between 2005 and 2013, with radiomics analysis performed for the segmented primary tumor volumes. Results: On the test set, we found mean 87.35\% (SD 11.15\%) and median 90.85\% (IQR 13.56\%) accuracies in treatment outcome prediction, matching the clinicians' outcomes and improving the (predicted) survival rate by +3.73\% (95\% CI --0.75\% to 8.96\%) and the dysphagia rate by +0.75\% (95\% CI --4.48\% to 6.72\%) when following DQL treatment decisions. Conclusions: Given the prediction accuracy and predicted improvement regarding the medically relevant outcomes yielded by this approach, this digital twin dyad of the patient-physician dynamic treatment problem has the potential of aiding physicians in determining the optimal course of treatment and in assessing its outcomes. ", doi="10.2196/29455", url="https://www.jmir.org/2022/4/e29455", url="http://www.ncbi.nlm.nih.gov/pubmed/35442211" } @Article{info:doi/10.2196/30260, author="Hoag, A. Jennifer and Karst, Jeffrey and Bingen, Kristin and Palou-Torres, Akasha and Yan, Ke", title="Distracting Through Procedural Pain and Distress Using Virtual Reality and Guided Imagery in Pediatric, Adolescent, and Young Adult Patients: Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Apr", day="18", volume="24", number="4", pages="e30260", keywords="virtual reality", keywords="procedural", keywords="pain", keywords="anxiety", keywords="pediatric", keywords="guided imagery", abstract="Background: Children with acute and chronic illness undergo frequent, painful, and distressing procedures. Objective: This randomized controlled trial was used to evaluate the effectiveness of guided imagery (GI) versus virtual reality (VR) on the procedural pain and state anxiety of children and young adults undergoing unsedated procedures. We explored the role of trait anxiety and pain catastrophizing in intervention response. Methods: Children and young adults were recruited from the hematology, oncology, and blood and marrow transplant clinics at a children's hospital. Each study participant completed the GI and VR intervention during separate but consecutive unsedated procedures. Self-report measures of pain and anxiety were completed before and after the procedures. Results: A total of 50 participants (median age 13 years) completed both interventions. GI and VR performed similarly in the management of procedural pain. Those with high pain catastrophizing reported experiencing less nervousness about pain during procedures that used VR than those using GI. State anxiety declined pre- to postprocedure in both interventions; however, the decrease reached the level of significance during the VR intervention only. Those with high trait anxiety had less pain during GI. Conclusions: In our sample, VR worked as well as GI to manage the pain and distress associated with common procedures experienced by children with acute or chronic illnesses. Children who are primed for pain based on beliefs about pain or because of their history of chronic pain had a better response to VR. GI was a better intervention for those with high trait anxiety. Trial Registration: ClinicalTrials.gov NCT04892160; https://clinicaltrials.gov/ct2/show/NCT04892160 ", doi="10.2196/30260", url="https://www.jmir.org/2022/4/e30260", url="http://www.ncbi.nlm.nih.gov/pubmed/35436209" } @Article{info:doi/10.2196/34072, author="Langnickel, Lisa and Podorskaja, Daria and Fluck, Juliane", title="Pre2Pub---Tracking the Path From Preprint to Journal Article: Algorithm Development and Validation", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e34072", keywords="preprints", keywords="information retrieval", keywords="COVID-19", keywords="metadata", keywords="BERT", keywords="Bidirectional Encoder Representations from Transformers", abstract="Background: The current COVID-19 crisis underscores the importance of preprints, as they allow for rapid communication of research results without delay in review. To fully integrate this type of publication into library information systems, we developed preview: a publicly available, central search engine for COVID-19--related preprints, which clearly distinguishes this source from peer-reviewed publications. The relationship between the preprint version and its corresponding journal version should be stored as metadata in both versions so that duplicates can be easily identified and information overload for researchers is reduced. Objective: In this work, we investigated the extent to which the relationship information between preprint and corresponding journal publication is present in the published metadata, how it can be further completed, and how it can be used in preVIEW to identify already republished preprints and filter those duplicates in search results. Methods: We first analyzed the information content available at the preprint servers themselves and the information that can be retrieved via Crossref. Moreover, we developed the algorithm Pre2Pub to find the corresponding reviewed article for each preprint. We integrated the results of those different resources into our search engine preVIEW, presented the information in the result set overview, and added filter options accordingly. Results: Preprints have found their place in publication workflows; however, the link from a preprint to its corresponding journal publication is not completely covered in the metadata of the preprint servers or in Crossref. Our algorithm Pre2Pub is able to find approximately 16\% more related journal articles with a precision of 99.27\%. We also integrate this information in a transparent way within preVIEW so that researchers can use it in their search. Conclusions: Relationships between the preprint version and its journal version is valuable information that can help researchers finding only previously unknown information in preprints. As long as there is no transparent and complete way to store this relationship in metadata, the Pre2Pub algorithm is a suitable extension to retrieve this information. ", doi="10.2196/34072", url="https://www.jmir.org/2022/4/e34072", url="http://www.ncbi.nlm.nih.gov/pubmed/35285808" } @Article{info:doi/10.2196/29982, author="Park, Yeongjun James and Hsu, Tzu-Chun and Hu, Jiun-Ruey and Chen, Chun-Yuan and Hsu, Wan-Ting and Lee, Matthew and Ho, Joshua and Lee, Chien-Chang", title="Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach", journal="J Med Internet Res", year="2022", month="Apr", day="13", volume="24", number="4", pages="e29982", keywords="sepsis", keywords="mortality", keywords="machine learning", keywords="SuperLearner", abstract="Background: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge--based logistic regression approach. Objective: The aim of this study is to examine the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compare it with that of the conventional context knowledge--based logistic regression approach. Methods: We examined inpatient admissions for sepsis in the US National Inpatient Sample using hospitalizations in 2010-2013 as the training data set. We developed four ML models to predict in-hospital mortality: logistic regression with least absolute shrinkage and selection operator regularization, random forest, gradient-boosted decision tree, and deep neural network. To estimate their performance, we compared our models with the Super Learner model. Using hospitalizations in 2014 as the testing data set, we examined the models' area under the receiver operating characteristic curve (AUC), confusion matrix results, and net reclassification improvement. Results: Hospitalizations of 923,759 adults were included in the analysis. Compared with the reference logistic regression (AUC: 0.786, 95\% CI 0.783-0.788), all ML models showed superior discriminative ability (P<.001), including logistic regression with least absolute shrinkage and selection operator regularization (AUC: 0.878, 95\% CI 0.876-0.879), random forest (AUC: 0.878, 95\% CI 0.877-0.880), xgboost (AUC: 0.888, 95\% CI 0.886-0.889), and neural network (AUC: 0.893, 95\% CI 0.891-0.895). All 4 ML models showed higher sensitivity, specificity, positive predictive value, and negative predictive value compared with the reference logistic regression model (P<.001). We obtained similar results from the Super Learner model (AUC: 0.883, 95\% CI 0.881-0.885). Conclusions: ML approaches can improve sensitivity, specificity, positive predictive value, negative predictive value, discrimination, and calibration in predicting in-hospital mortality in patients hospitalized with sepsis in the United States. These models need further validation and could be applied to develop more accurate models to compare risk-standardized mortality rates across hospitals and geographic regions, paving the way for research and policy initiatives studying disparities in sepsis care. ", doi="10.2196/29982", url="https://www.jmir.org/2022/4/e29982", url="http://www.ncbi.nlm.nih.gov/pubmed/35416785" } @Article{info:doi/10.2196/31825, author="Sharma, Videha and Eleftheriou, Iliada and van der Veer, N. Sabine and Brass, Andrew and Augustine, Titus and Ainsworth, John", title="Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study", journal="J Med Internet Res", year="2022", month="Apr", day="21", volume="24", number="4", pages="e31825", keywords="digital transformation", keywords="health information exchange", keywords="interoperability", keywords="medical informatics", keywords="data journey modelling", keywords="kidney transplantation", abstract="Background: Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. Objective: To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. Methods: An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. Results: Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. Conclusions: Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation. ", doi="10.2196/31825", url="https://www.jmir.org/2022/4/e31825", url="http://www.ncbi.nlm.nih.gov/pubmed/35451983" } @Article{info:doi/10.2196/31659, author="Saifee, Hasnain Danish and Hudnall, Matthew and Raja, Uzma", title="Physician Gender, Patient Risk, and Web-Based Reviews: Longitudinal Study of the Relationship Between Physicians' Gender and Their Web-Based Reviews", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e31659", keywords="web-based physician reviews", keywords="gender", keywords="gender bias", keywords="patient perception", keywords="Alabama", keywords="patient risk", abstract="Background: Web-based reviews of physicians have become exceedingly popular among health care consumers since the early 2010s. A factor that can potentially influence these reviews is the gender of the physician, because the physician's gender has been found to influence patient-physician communication. Our study is among the first to conduct a rigorous longitudinal analysis to study the effects of the gender of physicians on their reviews, after accounting for several important clinical factors, including patient risk, physician specialty, and temporal factors, using time fixed effects. In addition, this study is among the first to study the possible gender bias in web-based reviews using statewide data from Alabama, a predominantly rural state with high Medicaid and Medicare use. Objective: This study conducts a longitudinal empirical investigation of the relationship between physician gender and their web-based reviews using data across the state of Alabama, after accounting for patient risk and temporal effects. Methods: We created a unique data set by combining data from web-based physician reviews from the popular physician review website, RateMDs, and clinical data from the Center for Medicare and Medicaid Services for the state of Alabama. We used longitudinal econometric specifications to conduct an econometric analysis, while controlling for several important clinical and review characteristics across four rating dimensions (helpfulness, knowledge, staff, and punctuality). The overall rating and these four rating dimensions from RateMDs were used as the dependent variables, and physician gender was the key explanatory variable in our panel regression models. Results: The panel used to conduct the main econometric analysis included 1093 physicians. After controlling for several clinical and review factors, the physician random effects specifications showed that male physicians receive better web-based ratings than female physicians. Coefficients and corresponding SEs and P values of the binary variable GenderFemale (1 for female physicians and 0 otherwise) with different rating variables as outcomes were as follows: OverallRating (coefficient --0.194, SE 0.060; P=.001), HelpfulnessRating (coefficient --0.221, SE 0.069; P=.001), KnowledgeRating (coefficient --0.230, SE 0.065; P<.001), StaffRating (coefficient --0.123, SE 0.062; P=.049), and PunctualityRating (coefficient --0.200, SE 0.067; P=.003). The negative coefficients indicate a bias toward male physicians versus female physicians for aforementioned rating variables. Conclusions: This study found that female physicians receive lower web-based ratings than male physicians even after accounting for several clinical characteristics associated with the physicians and temporal effects. Although the magnitude of the coefficients of GenderFemale was relatively small, they were statistically significant. This study provides support to the findings on gender bias in the existing health care literature. We contribute to the existing literature by conducting a study using data across the state of Alabama and using a longitudinal econometric analysis, along with incorporating important clinical and review controls associated with the physicians. ", doi="10.2196/31659", url="https://www.jmir.org/2022/4/e31659", url="http://www.ncbi.nlm.nih.gov/pubmed/35394435" } @Article{info:doi/10.2196/29408, author="Luo, Xueyan and Xu, Wei and Ming, Wai-Kit and Jiang, Xinchan and Yuan, Quan and Lai, Han and Huang, Chunji and Zhong, Xiaoni", title="Cost-Effectiveness of Mobile Health--Based Integrated Care for Atrial Fibrillation: Model Development and Data Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="19", volume="24", number="4", pages="e29408", keywords="mobile health", keywords="integrated care", keywords="ABC pathway", keywords="atrial fibrillation", keywords="model-based", keywords="cost-effectiveness", keywords="health economic evaluation", abstract="Background: Mobile health (mHealth) technology is increasingly used in disease management. Using mHealth tools to integrate and streamline care has improved clinical outcomes of patients with atrial fibrillation (AF). Objective: The aim of this study was to investigate the potential clinical and health economic outcomes of mHealth-based integrated care for AF from the perspective of a public health care provider in China. Methods: A Markov model was designed to compare outcomes of mHealth-based care and usual care in a hypothetical cohort of patients with AF in China. The time horizon was 30 years with monthly cycles. Model outcomes measured were direct medical cost, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed to examine the robustness of the base-case results. Results: In the base-case analysis, mHealth-based care gained higher QALYs of 0.0730 with an incurred cost of US \$1090. Using US \$33,438 per QALY (three times the gross domestic product) as the willingness-to-pay threshold, mHealth-based care was cost-effective, with an ICER of US \$14,936 per QALY. In one-way sensitivity analysis, no influential factor with a threshold value was identified. In probabilistic sensitivity analysis, mHealth-based care was accepted as cost-effective in 92.33\% of 10,000 iterations. Conclusions: This study assessed the expected cost-effectiveness of applying mHealth-based integrated care for AF according to a model-based health economic evaluation. The exploration suggested the potential cost-effective use of mHealth apps in streamlining and integrating care via the Atrial fibrillation Better Care (ABC) pathway for AF in China. Future economic evaluation alongside randomized clinical trials is highly warranted to verify the suggestion and investigate affecting factors such as geographical variations in patient characteristics, identification of subgroups, and constraints on local implementation. ", doi="10.2196/29408", url="https://www.jmir.org/2022/4/e29408", url="http://www.ncbi.nlm.nih.gov/pubmed/35438646" } @Article{info:doi/10.2196/30236, author="Treskes, Willem Roderick and van den Akker-van Marle, Elske M. and van Winden, Louise and van Keulen, Nicole and van der Velde, Tjeerd Enno and Beeres, Saskia and Atsma, Douwe and Schalij, Jan Martin", title="The Box---eHealth in the Outpatient Clinic Follow-up of Patients With Acute Myocardial Infarction: Cost-Utility Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="25", volume="24", number="4", pages="e30236", keywords="smart technology", keywords="myocardial infarction", keywords="cost-utility", keywords="outpatients", keywords="cost-effectiveness", keywords="eHealth", keywords="remote monitoring", keywords="cost of care", keywords="quality of life", abstract="Background: Smartphone compatible wearables have been released on the consumers market, enabling remote monitoring. Remote monitoring is often named as a tool to reduce the cost of care. Objective: The primary purpose of this paper is to describe a cost-utility analysis of an eHealth intervention compared to regular follow-up in patients with acute myocardial infarction (AMI). Methods: In this trial, of which clinical results have been published previously, patients with an AMI were randomized in a 1:1 fashion between an eHealth intervention and regular follow-up. The remote monitoring intervention consisted of a blood pressure monitor, weight scale, electrocardiogram device, and step counter. Furthermore, two in-office outpatient clinic visits were replaced by e-visits. The control group received regular care. The differences in mean costs and quality of life per patient between both groups during one-year follow-up were calculated. Results: Mean costs per patient were {\texteuro}2417{\textpm}2043 (US \$2657{\textpm}2246) for the intervention and {\texteuro}2888{\textpm}2961 (US \$3175{\textpm}3255) for the control group. This yielded a cost reduction of {\texteuro}471 (US \$518) per patient. This difference was not statistically significant (95\% CI --{\texteuro}275 to {\texteuro}1217; P=.22, US \$--302 to \$1338). The average quality-adjusted life years in the first year of follow-up was 0.74 for the intervention group and 0.69 for the control (difference --0.05, 95\% CI --0.09 to --0.01; P=.01). Conclusions: eHealth in the outpatient clinic setting for patients who suffered from AMI is likely to be cost-effective compared to regular follow-up. Further research should be done to corroborate these findings in other patient populations and different care settings. Trial Registration: ClinicalTrials.gov NCT02976376; https://clinicaltrials.gov/ct2/show/NCT02976376 International Registered Report Identifier (IRRID): RR2-10.2196/resprot.8038 ", doi="10.2196/30236", url="https://www.jmir.org/2022/4/e30236", url="http://www.ncbi.nlm.nih.gov/pubmed/35468091" } @Article{info:doi/10.2196/30898, author="Ye, Jiancheng and Wang, Zidan and Hai, Jiarui", title="Social Networking Service, Patient-Generated Health Data, and Population Health Informatics: National Cross-sectional Study of Patterns and Implications of Leveraging Digital Technologies to Support Mental Health and Well-being", journal="J Med Internet Res", year="2022", month="Apr", day="29", volume="24", number="4", pages="e30898", keywords="patient-generated health data", keywords="social network", keywords="population health informatics", keywords="mental health", keywords="social determinants of health", keywords="health data sharing", keywords="technology acceptability", keywords="mobile phone", keywords="mobile health", abstract="Background: The emerging health technologies and digital services provide effective ways of collecting health information and gathering patient-generated health data (PGHD), which provide a more holistic view of a patient's health and quality of life over time, increase visibility into a patient's adherence to a treatment plan or study protocol, and enable timely intervention before a costly care episode. Objective: Through a national cross-sectional survey in the United States, we aimed to describe and compare the characteristics of populations with and without mental health issues (depression or anxiety disorders), including physical health, sleep, and alcohol use. We also examined the patterns of social networking service use, PGHD, and attitudes toward health information sharing and activities among the participants, which provided nationally representative estimates. Methods: We drew data from the 2019 Health Information National Trends Survey of the National Cancer Institute. The participants were divided into 2 groups according to mental health status. Then, we described and compared the characteristics of the social determinants of health, health status, sleeping and drinking behaviors, and patterns of social networking service use and health information data sharing between the 2 groups. Multivariable logistic regression models were applied to assess the predictors of mental health. All the analyses were weighted to provide nationally representative estimates. Results: Participants with mental health issues were significantly more likely to be younger, White, female, and lower-income; have a history of chronic diseases; and be less capable of taking care of their own health. Regarding behavioral health, they slept <6 hours on average, had worse sleep quality, and consumed more alcohol. In addition, they were more likely to visit and share health information on social networking sites, write online diary blogs, participate in online forums or support groups, and watch health-related videos. Conclusions: This study illustrates that individuals with mental health issues have inequitable social determinants of health, poor physical health, and poor behavioral health. However, they are more likely to use social networking platforms and services, share their health information, and actively engage with PGHD. Leveraging these digital technologies and services could be beneficial for developing tailored and effective strategies for self-monitoring and self-management. ", doi="10.2196/30898", url="https://www.jmir.org/2022/4/e30898", url="http://www.ncbi.nlm.nih.gov/pubmed/35486428" } @Article{info:doi/10.2196/32630, author="Ni{\ss}en, Marcia and R{\"u}egger, Dominik and Stieger, Mirjam and Fl{\"u}ckiger, Christoph and Allemand, Mathias and v Wangenheim, Florian and Kowatsch, Tobias", title="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", journal="J Med Internet Res", year="2022", month="Apr", day="27", volume="24", number="4", pages="e32630", keywords="chatbot", keywords="conversational agent", keywords="social roles", keywords="interpersonal closeness", keywords="social role theory", keywords="working alliance", keywords="design", keywords="persona", keywords="digital health intervention", keywords="web-based experiment", abstract="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, $\eta$p2=0.115) and age (P<.001, $\eta$p2=0.192) and a significant interaction effect of persona and age (P=.01, $\eta$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, $\eta$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. ", doi="10.2196/32630", url="https://www.jmir.org/2022/4/e32630", url="http://www.ncbi.nlm.nih.gov/pubmed/35475761" } @Article{info:doi/10.2196/29088, author="Zenun Franco, Rodrigo and Fallaize, Rosalind and Weech, Michelle and Hwang, Faustina and Lovegrove, A. Julie", title="Effectiveness of Web-Based Personalized Nutrition Advice for Adults Using the eNutri Web App: Evidence From the EatWellUK Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Apr", day="25", volume="24", number="4", pages="e29088", keywords="personalized nutrition", keywords="web-based", keywords="nutrition app", keywords="app", keywords="dietary intervention", keywords="eNutri", keywords="precision nutrition", keywords="mHealth", keywords="healthy eating index", keywords="diet quality scores", keywords="FFQ", keywords="food frequency questionnaire", keywords="EatWellUK", abstract="Background: Evidence suggests that eating behaviors and adherence to dietary guidelines can be improved using nutrition-related apps. Apps delivering personalized nutrition (PN) advice to users can provide individual support at scale with relatively low cost. Objective: This study aims to investigate the effectiveness of a mobile web app (eNutri) that delivers automated PN advice for improving diet quality, relative to general population food-based dietary guidelines. Methods: Nondiseased UK adults (aged >18 years) were randomized to PN advice or control advice (population-based healthy eating guidelines) in a 12-week controlled, parallel, single-blinded dietary intervention, which was delivered on the web. Dietary intake was assessed using the eNutri Food Frequency Questionnaire (FFQ). An 11-item US modified Alternative Healthy Eating Index (m-AHEI), which aligned with UK dietary and nutritional recommendations, was used to derive the automated PN advice. The primary outcome was a change in diet quality (m-AHEI) at 12 weeks. Participant surveys evaluated the PN report (week 12) and longer-term impact of the PN advice (mean 5.9, SD 0.65 months, after completion of the study). Results: Following the baseline FFQ, 210 participants completed at least 1 additional FFQ, and 23 outliers were excluded for unfeasible dietary intakes. The mean interval between FFQs was 10.8 weeks. A total of 96 participants were included in the PN group (mean age 43.5, SD 15.9 years; mean BMI 24.8, SD 4.4 kg/m2) and 91 in the control group (mean age 42.8, SD 14.0 years; mean BMI 24.2, SD 4.4 kg/m2). Compared with that in the control group, the overall m-AHEI score increased by 3.5 out of 100 (95\% CI 1.19-5.78) in the PN group, which was equivalent to an increase of 6.1\% (P=.003). Specifically, the m-AHEI components nuts and legumes and red and processed meat showed significant improvements in the PN group (P=.04). At follow-up, 64\% (27/42) of PN participants agreed that, compared with baseline, they were still following some (any) of the advice received and 31\% (13/42) were still motivated to improve their diet. Conclusions: These findings suggest that the eNutri app is an effective web-based tool for the automated delivery of PN advice. Furthermore, eNutri was demonstrated to improve short-term diet quality and increase engagement in healthy eating behaviors in UK adults, as compared with population-based healthy eating guidelines. This work represents an important landmark in the field of automatically delivered web-based personalized dietary interventions. Trial Registration: ClinicalTrials.gov NCT03250858; https://clinicaltrials.gov/ct2/show/NCT03250858 ", doi="10.2196/29088", url="https://www.jmir.org/2022/4/e29088", url="http://www.ncbi.nlm.nih.gov/pubmed/35468093" } @Article{info:doi/10.2196/17180, author="Manning Hutson, Michelle and Hosking, M. Sarah and Mantalvanos, Soula and Berk, Michael and Pasco, Julie and Dunning, Trisha", title="What Injured Workers With Complex Claims Look For in Online Communities: Netnographic Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="7", volume="24", number="4", pages="e17180", keywords="work-related injury", keywords="care coordination", keywords="case management", keywords="netnography", abstract="Background: Improved understanding of social constructs around injury may help insurance case managers to understand how best to support people after injury. Objective: This study sought to explore what people who sustain work-related injuries may seek from online communities. The study highlights potential opportunities for improved engagement with insurance case management practice. Methods: An observational netnographic analysis was undertaken on anonymous, publicly available messages posted on Australian message boards. All research data were drawn from anonymous, online communities. A person (author SM) with experience of making a claim through an Australian workers' compensation system and online engagement was involved in study conception, design, and analysis. Data were analyzed using NVivo12 in an iterative, multistage process including coding, journaling, and member checking. A total of 141 people were engaged in discussion across 47 threads housed on 4 Australian forums. Results: In this qualitative study, themes emerged from the data, describing how injured workers use online communities to help make decisions, get support, and solve problems. The key motivators for action and engagement were seeking information, connection, or justice. Establishment of relationships was a key mediator of each of these parameters. Conclusions: Some work-related injuries may involve medical and medicolegal complexity as well as changed lifestyle and routine during convalescence and recovery. The mechanism used by some injured workers to seek information and problem solve suggests a capacity for self-management and self-care after work-related injury. Netnography provides information on a community that may not regularly engage with research because of the complexity of their situation and their vulnerability. ", doi="10.2196/17180", url="https://www.jmir.org/2022/4/e17180", url="http://www.ncbi.nlm.nih.gov/pubmed/35389358" } @Article{info:doi/10.2196/34253, author="Hawkes, E. Rhiannon and Miles, M. Lisa and French, P. David", title="Fidelity to Program Specification of the National Health Service Digital Diabetes Prevention Program Behavior Change Technique Content and Underpinning Theory: Document Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="27", volume="24", number="4", pages="e34253", keywords="diabetes prevention", keywords="digital interventions", keywords="behavior change", keywords="fidelity", abstract="Background: The National Health Service (NHS) Diabetes Prevention Program is a behavior change intervention for adults in England who are identified as being at high risk of developing type 2 diabetes. The face-to-face service was launched in 2016, followed by a digital service (NHS Digital Diabetes Prevention Program [NHS-DDPP]) in 2019. A total of 4 service providers were commissioned to deliver the NHS-DDPP and were required to deliver the digital service in line with a program specification detailing the key intervention content. The fidelity of the behavior change content in the digital service (ie, the extent to which the program is delivered as intended) is currently unknown. Digital interventions may allow higher fidelity as staff do not have to be trained to deliver all intervention content. Assessing fidelity of the intervention design is particularly important to establish the planned behavior change content in the NHS-DDPP and the extent to which this adheres to the program specification. This is the first known independent assessment of design fidelity in a large-scale digital behavior change intervention. Objective: This study aims to assess the fidelity of the behavior change content in each of the 4 NHS-DDPP providers' intervention designs to the full program specification. Methods: We conducted a document review of each provider's NHS-DDPP intervention design, along with interviews with program developers employed by the 4 digital providers (n=6). Providers' intervention design documents and interview transcripts were coded for behavior change techniques (BCTs; ie, the active ingredients of the intervention) using the Behavior Change Technique Taxonomy version 1 and underpinning theory using the Theory Coding Scheme framework. The BCTs identified in each digital provider's intervention design were compared with the 19 BCTs included in the program specification. Results: Of the 19 BCTs specified in the program specification, the 4 providers planned to deliver 16 (84\%), 17 (89\%), 16 (84\%), and 16 (84\%) BCTs, respectively. An additional 41 unspecified BCTs were included in at least one of the 4 digital providers' intervention designs. By contrast, inconsistent use of the underpinning theory was apparent across providers, and none of the providers had produced a logic model to explain how their programs were expected to work. All providers linked some of their planned BCTs to theoretical constructs; however, justification for the inclusion of other BCTs was not described. Conclusions: The fidelity of BCT content in the NHS-DDPP was higher than that previously documented for the face-to-face service. Thus, if service users engage with the NHS-DDPP, this should increase the effectiveness of the program. However, given that a clear theoretical underpinning supports the translation of BCTs in intervention designs to intervention delivery, the absence of a logic model describing the constructs to be targeted by specific BCTs is potentially problematic. ", doi="10.2196/34253", url="https://www.jmir.org/2022/4/e34253", url="http://www.ncbi.nlm.nih.gov/pubmed/35476035" } @Article{info:doi/10.2196/28114, author="Nam, Seojin and Kim, Donghun and Jung, Woojin and Zhu, Yongjun", title="Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="22", volume="24", number="4", pages="e28114", keywords="deep learning", keywords="scientometric analysis", keywords="research publications", keywords="research landscape", keywords="research collaboration", keywords="knowledge diffusion", abstract="Background: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. Objective: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. Methods: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. Results: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. Conclusions: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work. ", doi="10.2196/28114", url="https://www.jmir.org/2022/4/e28114", url="http://www.ncbi.nlm.nih.gov/pubmed/35451980" } @Article{info:doi/10.2196/32776, author="Yuan, Junyi and Wang, Sufen and Pan, Changqing", title="Mechanism of Impact of Big Data Resources on Medical Collaborative Networks From the Perspective of Transaction Efficiency of Medical Services: Survey Study", journal="J Med Internet Res", year="2022", month="Apr", day="21", volume="24", number="4", pages="e32776", keywords="medical collaborative networks", keywords="big data resources", keywords="transaction efficiency", abstract="Background: The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained. Objective: This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs. Methods: This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact. Results: Sharing of diagnosis and treatment data ($\beta$=.222; P=.03) and sharing of medical research data ($\beta$=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems ($\beta$=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions. Conclusions: This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs. ", doi="10.2196/32776", url="https://www.jmir.org/2022/4/e32776", url="http://www.ncbi.nlm.nih.gov/pubmed/35318187" } @Article{info:doi/10.2196/16141, author="van Lieshout, Jan and Lacroix, Joyca and van Halteren, Aart and Teichert, Martina", title="Effectiveness of a Pharmacist-Led Web-Based Medication Adherence Tool With Patient-Centered Communication: Results of a Clustered Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Apr", day="7", volume="24", number="4", pages="e16141", keywords="medication adherence", keywords="improvement", keywords="intervention", keywords="web-based", keywords="tailored intervention", keywords="patient centered", keywords="barriers", keywords="primary care", keywords="cardiovascular diseases", keywords="diabetes", abstract="Background: Growing numbers of people use medication for chronic conditions; nonadherence is common, leading to poor disease control. A web-based tool to identify an increased risk for nonadherence with related potential individual barriers might facilitate tailored interventions and improve adherence. Objective: This study aims to assess the effectiveness of a newly developed tool aimed at improving medication adherence. Methods: We performed a cluster randomized controlled trial in patients initiating cardiovascular or oral blood glucose--lowering medication. Participants were recruited from community pharmacies. They completed an online questionnaire comprising assessments of their risk for medication nonadherence and subsequently of barriers to adherence. In pharmacies belonging to the intervention group, individual barriers displayed in a graphical profile on a tablet were discussed by pharmacists and patients with high nonadherence risk in face-to-face meetings and shared with their general practitioners and practice nurses. Tailored interventions were initiated by pharmacists. Barriers of control patients were not presented nor discussed and these patients received usual care. The primary outcome was the effectiveness of the intervention on medication adherence at 8 months' follow-up between patients with an increased nonadherence risk from the intervention and control groups, calculated from dispensing data. Results: Data from 492 participants in 15 community pharmacies were available for analyses (intervention 253, 7 pharmacies; control 239, 8 pharmacies). The intervention had no effect on medication adherence (B=--0.01; 95\% CI --0.59 to 0.57; P=.96), nor in the post hoc per-protocol analysis (B=0.19; 95\% CI --0.50 to 0.89; P=.58). Conclusions: This study showed no effectiveness of a risk stratification and tailored intervention addressing personal barriers for medication adherence. Various potential explanations for lack of effectiveness were identified. These explanations relate, for instance, to high medication adherence in the control group, study power, and fidelity. Process evaluation should elicit possible improvements and inform the redesign of intervention and implementation. Trial Registration: The Netherlands National Trial Register NTR5186; https://tinyurl.com/5d8w99hk ", doi="10.2196/16141", url="https://www.jmir.org/2022/4/e16141", url="http://www.ncbi.nlm.nih.gov/pubmed/35389359" } @Article{info:doi/10.2196/34321, author="Tahamtan, Iman and Potnis, Devendra and Mohammadi, Ehsan and Singh, Vandana and Miller, E. Laura", title="The Mutual Influence of the World Health Organization (WHO) and Twitter Users During COVID-19: Network Agenda-Setting Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="26", volume="24", number="4", pages="e34321", keywords="COVID-19", keywords="agenda setting", keywords="network agenda setting", keywords="Twitter", keywords="social media", keywords="public opinion", keywords="content analysis", keywords="public health", keywords="WHO", abstract="Background: Little is known about the role of the World Health Organization (WHO) in communicating with the public on social media during a global health emergency. More specifically, there is no study about the relationship between the agendas of the WHO and Twitter users during the COVID-19 pandemic. Objective: This study utilizes the network agenda-setting model to investigate the mutual relationship between the agenda of the WHO's official Twitter account and the agenda of 7.5 million of its Twitter followers regarding COVID-19. Methods: Content analysis was applied to 7090 tweets posted by the WHO on Twitter from January 1, 2020, to July 31, 2020, to identify the topics of tweets. The quadratic assignment procedure (QAP) was used to investigate the relationship between the WHO agenda network and the agenda network of the 6 Twitter user categories, including ``health care professionals,'' ``academics,'' ``politicians,'' ``print and electronic media,'' ``legal professionals,'' and the ``private sector.'' Additionally, 98 Granger causality statistical tests were performed to determine which topic in the WHO agenda had an effect on the corresponding topic in each Twitter user category and vice versa. Results: Content analysis revealed 7 topics that reflect the WHO agenda related to the COVID-19 pandemic, including ``prevention,'' ``solidarity,'' ``charity,'' ``teamwork,'' ``ill-effect,'' ``surveillance,'' and ``credibility.'' Results of the QAP showed significant and strong correlations between the WHO agenda network and the agenda network of each Twitter user category. These results provide evidence that WHO had an overall effect on different types of Twitter users on the identified topics. For instance, the Granger causality tests indicated that the WHO tweets influenced politicians and print and electronic media about ``surveillance.'' The WHO tweets also influenced academics and the private sector about ``credibility'' and print and electronic media about ``ill-effect.'' Additionally, Twitter users affected some topics in the WHO. For instance, WHO followers affected ``charity'' and ``prevention'' in the WHO. Conclusions: This paper extends theorizing on agenda setting by providing empirical evidence that agenda-setting effects vary by topic and types of Twitter users. Although prior studies showed that network agenda setting is a ``one-way'' model, the novel findings of this research confirm a ``2-way'' or ``multiway'' effect of agenda setting on social media due to the interactions between the content creators and audiences. The WHO can determine which topics should be promoted on social media during different phases of a pandemic and collaborate with other public health gatekeepers to collectively make them salient in the public. ", doi="10.2196/34321", url="https://www.jmir.org/2022/4/e34321", url="http://www.ncbi.nlm.nih.gov/pubmed/35275836" } @Article{info:doi/10.2196/35786, author="Boender, Sonia Tamara and Louis-Ferdinand, Noah and Duschek, Gideon", title="Digital Visual Communication for Public Health: Design Proposal for a Vaccinated Emoji", journal="J Med Internet Res", year="2022", month="Apr", day="7", volume="24", number="4", pages="e35786", keywords="vaccination", keywords="emoji", keywords="design", keywords="science communication", keywords="infodemic management", keywords="vaccine confidence", keywords="digital communication", doi="10.2196/35786", url="https://www.jmir.org/2022/4/e35786", url="http://www.ncbi.nlm.nih.gov/pubmed/35389363" } @Article{info:doi/10.2196/33680, author="Gunasekeran, Visva Dinesh and Chew, Alton and Chandrasekar, K. Eeshwar and Rajendram, Priyanka and Kandarpa, Vasundhara and Rajendram, Mallika and Chia, Audrey and Smith, Helen and Leong, Kit Choon", title="The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review", journal="J Med Internet Res", year="2022", month="Apr", day="11", volume="24", number="4", pages="e33680", keywords="digital health", keywords="social media", keywords="big data", keywords="population health", keywords="blockchain", keywords="COVID-19", keywords="review", keywords="benefit", keywords="challenge", keywords="public health", abstract="Background: ?Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions. Objective: This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature. Methods: ?We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health. Results: ?The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy. Conclusions: ?Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health. ", doi="10.2196/33680", url="https://www.jmir.org/2022/4/e33680", url="http://www.ncbi.nlm.nih.gov/pubmed/35129456" } @Article{info:doi/10.2196/36830, author="Chen, Yen-Pin and Chen, Yi-Ying and Yang, Kai-Chou and Lai, Feipei and Huang, Chien-Hua and Chen, Yun-Nung and Tu, Yi-Chin", title="The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media", journal="J Med Internet Res", year="2022", month="Apr", day="26", volume="24", number="4", pages="e36830", keywords="misinformation", keywords="vaccine hesitancy", keywords="vaccination", keywords="infodemic", keywords="infodemiology", keywords="COVID-19", keywords="public immunity", keywords="social media", keywords="fake news", abstract="Background: Vaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals' decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan. Objective: In this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions. Methods: We collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data. Results: A total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient ($\beta$=0.98, P=.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient ($\beta$=--3.21, P=.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3\%. Conclusions: There was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age. ", doi="10.2196/36830", url="https://www.jmir.org/2022/4/e36830", url="http://www.ncbi.nlm.nih.gov/pubmed/35380546" }