%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65551 %T Current Technological Advances in Dysphagia Screening: Systematic Scoping Review %A Wong,Duo Wai-Chi %A Wang,Jiao %A Cheung,Sophia Ming-Yan %A Lai,Derek Ka-Hei %A Chiu,Armstrong Tat-San %A Pu,Dai %A Cheung,James Chung-Wai %A Kwok,Timothy Chi-Yui %+ Department of Biomedical Engineering, Faculty of Engineering, Hong Kong Polytechnic University, GH137, GH Wing, 1/F, Department of Biomedical Engineering,, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, 999077, China (Hong Kong), 852 27667673, james.chungwai.cheung@polyu.edu.hk %K digital health %K computer-aided diagnosis %K computational deglutition %K machine learning %K deep learning %K artificial intelligence %K AI %K swallowing disorder %K aspiration %D 2025 %7 5.5.2025 %9 Review %J J Med Internet Res %G English %X Background: Dysphagia affects more than half of older adults with dementia and is associated with a 10-fold increase in mortality. The development of accessible, objective, and reliable screening tools is crucial for early detection and management. Objective: This systematic scoping review aimed to (1) examine the current state of the art in artificial intelligence (AI) and sensor-based technologies for dysphagia screening, (2) evaluate the performance of these AI-based screening tools, and (3) assess the methodological quality and rigor of studies on AI-based dysphagia screening tools. Methods: We conducted a systematic literature search across CINAHL, Embase, PubMed, and Web of Science from inception to July 4, 2024, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. In total, 2 independent researchers conducted the search, screening, and data extraction. Eligibility criteria included original studies using sensor-based instruments with AI to identify individuals with dysphagia or unsafe swallow events. We excluded studies on pediatric, infant, or postextubation dysphagia, as well as those using non–sensor-based assessments or diagnostic tools. We used a modified Quality Assessment of Diagnostic Accuracy Studies–2 tool to assess methodological quality, adding a “model” domain for AI-specific evaluation. Data were synthesized narratively. Results: This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls). In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per–swallow event classification. Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources. In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality. Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%). Performance varied widely—accuracy ranged from 71.2% to 99%, area under the receiver operating characteristic curve ranged from 0.77 to 0.977, and sensitivity ranged from 63.6% to 100%. Multimodal systems generally outperformed unimodal systems. The methodological quality assessment revealed a risk of bias, particularly in patient selection (unclear in 18/24, 75% of the studies), index test (unclear in 23/24, 96% of the studies), and modeling (high risk in 13/24, 54% of the studies). Notably, no studies conducted external validation or domain adaptation testing, raising concerns about real-world applicability. Conclusions: This review provides a comprehensive overview of technological advancements in AI and sensor-based dysphagia screening. While these developments show promise for continuous long-term tele-swallowing assessments, significant methodological limitations were identified. Future studies can explore how each modality can target specific anatomical regions and manifestations of dysphagia. This detailed understanding of how different modalities address various aspects of dysphagia can significantly benefit multimodal systems, enabling them to better handle the multifaceted nature of dysphagia conditions. %M 40324167 %R 10.2196/65551 %U https://www.jmir.org/2025/1/e65551 %U https://doi.org/10.2196/65551 %U http://www.ncbi.nlm.nih.gov/pubmed/40324167 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 11 %N %P e64083 %T A Digital Home-Based Health Care Center for Remote Monitoring of Side Effects During Breast Cancer Therapy: Prospective, Single-Arm, Monocentric Feasibility Study %A Huebner,Hanna %A Wurmthaler,Lena A %A Goossens,Chloë %A Ernst,Mathias %A Mocker,Alexander %A Krückel,Annika %A Kallert,Maximilian %A Geck,Jürgen %A Limpert,Milena %A Seitz,Katharina %A Ruebner,Matthias %A Kreis,Philipp %A Heindl,Felix %A Hörner,Manuel %A Volz,Bernhard %A Roth,Eduard %A Hack,Carolin C %A Beckmann,Matthias W %A Uhrig,Sabrina %A Fasching,Peter A %K breast cancer %K digital medicine %K telehealth %K remote monitoring %K cyclin-dependent kinase 4/6 inhibitor %K CDK4/6 inhibitor %K mobile phone %D 2025 %7 2.5.2025 %9 %J JMIR Cancer %G English %X Background: The introduction of oral anticancer therapies has, at least partially, shifted treatment from clinician-supervised hospital care to patient-managed home regimens. However, patients with breast cancer receiving oral cyclin-dependent kinase 4/6 inhibitor therapy still require regular hospital visits to monitor side effects. Telemonitoring has the potential to reduce hospital visits while maintaining quality care. Objective: This study aims to develop a digital home-based health care center (DHHC) for acquiring electrocardiograms (ECGs), white blood cell (WBC) counts, side effect photo documentation, and patient-reported quality of life (QoL) data. Methods: The DHHC was set up using an Apple Watch Series 6 (ECG measurements), a HemoCue WBC DIFF Analyzer (WBC counts), an iPhone SE (QoL assessments and photo documentation), a TP-Link M7350-4G Wi-Fi router, and a Raspberry Pi 4 Model B. A custom-built app stored and synchronized remotely collected data with the clinic. The feasibility and acceptance of the DHHC among patients with breast cancer undergoing cyclin-dependent kinase 4/6 inhibitor therapy were evaluated in a prospective, single-arm, monocentric study. Patients (n=76) monitored side effects—ECGs, WBC counts, photo documentation, and QoL—at 3 predefined time points: study inclusion (on-site), day 14 (remote), and day 28 (remote). After the study completion, patients completed a comprehensive questionnaire on user perception and feasibility. Adherence to scheduled visits, the success rate of the data transfer, user perception and feasibility, and the clinical relevance of remote measurements were evaluated. Results: Mean adherence to the planned remote visits was 63% on day 14 and 37% on day 28. ECG measurements were performed most frequently (day 14: 57/76, 75%; day 28: 31/76, 41%). The primary patient-reported reason for nonadherence was device malfunction. The expected versus the received data transfer per patient was as follows: ECGs: 3 versus 3.04 (SD 1.9); WBC counts: 3 versus 2.14 (SD 1.14); QoL questionnaires: 3 versus 2.5 (SD 1.14); and photo documentation: 6 versus 4.4 (SD 3.36). Among patients, 81% (55/68) found ECG measurements easy, 82% (55/67) found photo documentation easy, and 48% (33/69) found WBC measurements easy. Additionally, 61% (40/66) of patients felt comfortable with self-monitoring and 79% (54/68) were willing to integrate remote monitoring into their future cancer care. Therapy-induced decreased neutrophil count was successfully detected (P<.001; mean baseline: 4.3, SD 2.2, ×109/L; on-treatment: 1.8, SD 0.8, ×109/L). All-grade neutropenia and corrected QT interval prolongations were detected in 80% (55/68) and 2% (1/42) of patients, respectively. Conclusions: Adherence to scheduled remote visits was moderate, with nonadherence primarily attributed to device-related complications, which may have also affected the success rate of data transfer. Overall, patients considered remote monitoring useful and feasible. The prevalence of reported adverse events was comparable to existing literature, suggesting clinical potential. This initial feasibility study highlights the potential of the DHHC. %R 10.2196/64083 %U https://cancer.jmir.org/2025/1/e64083 %U https://doi.org/10.2196/64083 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 8 %N %P e69052 %T Detecting Older Adults’ Behavior Changes During Adverse External Events Using Ambient Sensing: Longitudinal Observational Study %A Fritz,Roschelle %A Cook,Diane %K internet of things %K digital phenotyping %K chronic disease %K COVID-19 %K air pollution %D 2025 %7 1.5.2025 %9 %J JMIR Nursing %G English %X Background: Older adults manage multiple impacts on health, including chronic conditions and adverse external events. Smart homes are positioned to have a positive impact on older adults’ health by (1) allowing new understandings of behavior change so risks associated with external events can be assessed, (2) quantifying the impact of social determinants on health, and (3) designing interventions that respond appropriately to detected behavior changes. Information derived from smart home sensors can provide objective data about behavior changes to support a learning health care system. In this paper, we introduce a smart home capable of detecting behavior changes that occur during adverse external events like pandemics and wildfires. Objective: Examine digital markers collected before and during 2 events (the COVID-19 pandemic and wildfires) to determine whether clinically relevant behavior changes can be observed and targeted upstream interventions suggested. Methods: Secondary analysis of historic ambient sensor data collected on 39 adults managing one or more chronic conditions was performed. Interrupted time series analysis was used to extract behavior markers related to external events. Comparisons were made to examine differences between exposures using machine learning classifiers. Results: Behavior changes were detected for 2 adverse external events (the COVID-19 pandemic and wildfire smoke) initially and over time. However, the direction and magnitude of change differed between participants and events. Significant pandemic-related behavior changes ranked by impact included a decrease in time (3.8 hours/day) spent out of home, an increase in restless sleep (946.74%), and a decrease in indoor activity (38.89%). Although participants exhibited less restless sleep during exposure to wildfire smoke (120%), they also decreased their indoor activity (114.29%). Sleep duration trended downward during the pandemic shutdown. Time out of home and sleep duration gradually decreased while exposed to wildfire smoke. Behavior trends differed across exposures. In total, two key discoveries were made: (1) using retrospective analysis, the smart home was capable of detecting behavior changes related to 2 external events; and (2) older adults’ sleep efficiency, time out of home, and overall activity levels changed while experiencing external events. These behavior markers can inform future sensor-based monitoring research and clinical application. Conclusions: Sensor-based findings could support individualized interventions aimed at sustaining the health of older adults during events like pandemics and wildfires. Creating care plans that directly respond to sensor-derived health information, like adding guided indoor exercise, web-based socialization sessions, and mental health–promoting activities, would have practical impacts on wellness. The smart home’s novel, evidence-based information could inform future management of chronic conditions, allowing nurses to understand patients’ health-related behaviors between the care points so timely, individualized interventions are possible. %R 10.2196/69052 %U https://nursing.jmir.org/2025/1/e69052 %U https://doi.org/10.2196/69052 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e67950 %T Efficacy of an Intelligent and Integrated Older Adult Care Model on Quality of Life Among Home-Dwelling Older Adults: Randomized Controlled Trial %A Guo,Rongrong %A Zhang,Jiwen %A Yang,Fangyu %A Wu,Ying %+ School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China, 86 13910789837, helenywu@vip.163.com %K efficacy %K home care %K integrated care %K intelligent %K elderly people %K quality of life %K mobile phone %D 2025 %7 21.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Integrated care models enhanced by the clinical decision support system offer innovative approaches to managing the growing global burden of older adult care. However, their efficacy remains uncertain. Objective: This study aimed to evaluate the efficacy of an intelligent and integrated older adult care model, termed the SMART (Sensors and scales [receptor], a Mobile phone autonomous response system [central nervous system in the spinal cord], a Remote cloud management center [central nervous system in the brain], and a Total care system [effector]) system, in improving the quality of life (QOL) for home-dwelling older adults. Methods: In this stratified randomized controlled trial, we consecutively recruited older adults aged 65 years or older from November 1, 2020, to December 31, 2020. Eligible participants were randomly allocated 1:1 to either the SMART group, receiving routine discharge instructions and personalized integrated care interventions across 11 domains (decreased or lost self-care ability, falls, delirium, dysphagia, incontinence, constipation, urinary retention, cognitive decline, depression, impaired skin integrity, and common diseases) generated by the SMART system, or the usual care group, receiving only routine discharge instructions. The intervention lasted for 3 months. The primary end point was the percent change in QOL from baseline to the 3-month follow-up, assessed using the World Health Organization Quality of Life Instrument - Older Adults Module. Secondary end points included functional status at the 3-month follow-up and percent changes in health self-management ability, social support, and confidence in avoiding falling from baseline to the 3-month follow-up. Data were analyzed following the intention-to-treat principle, using covariance or logistic regression models, as appropriate. Subgroup and sensitivity analyses were conducted to assess result consistency and robustness. Results: In total, 94 participants were recruited, with 48 assigned to the SMART group. The personalized and integrated care by the SMART system significantly improved the QOL among the older adults, with an estimated intervention difference of 11.97% (95% CI 7.2%-16.74%, P<.001), and social support and health self-management ability as well, with estimated intervention differences of 6.75% (95% CI 3.19%-10.3%, P<.001) and 4.95% (95% CI 0.11%-10%, P=.003), respectively, while insignificantly improving in the Modified Falls Efficacy Scale score. Similarly, the SMART system had a 66% reduction in instrumental activities of daily living disability (odds ratio [OR] 0.34, 95% CI 0.11-0.83, P=.02). However, the SMART system did not significantly affect activities of daily living disability or the Modified Falls Efficacy Scale score. The subgroup and sensitivity analyses confirmed the robustness of the findings. Conclusions: The personalized and integrated older adult care by the SMART system demonstrated significant efficacy in improving QOL, health self-management ability, and social support, while reducing instrumental activities of daily living disability among home-dwelling older adults. Trial Registration: Chinese Clinical Trial Registry ChiCTR-IOR-17010368; https://tinyurl.com/2zax24xr %M 40258267 %R 10.2196/67950 %U https://www.jmir.org/2025/1/e67950 %U https://doi.org/10.2196/67950 %U http://www.ncbi.nlm.nih.gov/pubmed/40258267 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63090 %T Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study %A Kriara,Lito %A Dondelinger,Frank %A Capezzuto,Luca %A Bernasconi,Corrado %A Lipsmeier,Florian %A Galati,Adriano %A Lindemann,Michael %+ , F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, CH-4070, Switzerland, 41 61 687 10 20, lito.kriara@roche.com %K Floodlight Open %K multiple sclerosis %K smartphone %K sensors %K mobile phone %K wearable electronic devices %K digital health %K equivalence %K device equivalence %K cognition %K gait %K upper extremity function %K hand motor function %K balance %K digital biomarker %K variability %K mHealth %K mobile health %K autoimmune disease %K motor %K digital assessment %D 2025 %7 3.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Floodlight Open is a global, open-access, fully remote, digital-only study designed to understand the drivers and barriers in deployment and persistence of use of a smartphone app for measuring functional impairment in a naturalistic setting and broad study population. Objective: This study aims to assess measurement equivalence properties of the Floodlight Open app across operating system (OS) platforms, OS versions, and smartphone device models. Methods: Floodlight Open enrolled adult participants with and without self-declared multiple sclerosis (MS). The study used the Floodlight Open app, a “bring-your-own-device” (BYOD) solution that remotely measured MS-related functional ability via smartphone sensor–based active tests. Measurement equivalence was assessed in all evaluable participants by comparing the performance on the 6 active tests (ie, tests requiring active input from the user) included in the app across OS platforms (iOS vs Android), OS versions (iOS versions 11-15 and separately Android versions 8-10; comparing each OS version with the other OS versions pooled together), and device models (comparing each device model with all remaining device models pooled together). The tests in scope were Information Processing Speed, Information Processing Speed Digit-Digit (measuring reaction speed), Pinching Test (PT), Static Balance Test, U-Turn Test, and 2-Minute Walk Test. Group differences were assessed by permutation test for the mean difference after adjusting for age, sex, and self-declared MS disease status. Results: Overall, 1976 participants using 206 different device models were included in the analysis. Differences in test performance between subgroups were very small or small, with percent differences generally being ≤5% on the Information Processing Speed, Information Processing Speed Digit-Digit, U-Turn Test, and 2-Minute Walk Test; <20% on the PT; and <30% on the Static Balance Test. No statistically significant differences were observed between OS platforms other than on the PT (P<.001). Similarly, differences across iOS or Android versions were nonsignificant after correcting for multiple comparisons using false discovery rate correction (all adjusted P>.05). Comparing the different device models revealed a statistically significant difference only on the PT for 4 out of 17 models (adjusted P≤.001-.03). Conclusions: Consistent with the hypothesis that smartphone sensor–based measurements obtained with different devices are equivalent, this study showed no evidence of a systematic lack of measurement equivalence across OS platforms, OS versions, and device models on 6 active tests included in the Floodlight Open app. These results are compatible with the use of smartphone-based tests in a bring-your-own-device setting, but more formal tests of equivalence would be needed. %M 40179369 %R 10.2196/63090 %U https://www.jmir.org/2025/1/e63090 %U https://doi.org/10.2196/63090 %U http://www.ncbi.nlm.nih.gov/pubmed/40179369 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e60437 %T Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility Study %A Janes,William E %A Marchal,Noah %A Song,Xing %A Popescu,Mihail %A Mosa,Abu Saleh Mohammad %A Earwood,Juliana H %A Jones,Vovanti %A Skubic,Marjorie %+ Department of Occupational Therapy, College of Health Science, University of Missouri, 802 Clark Hall, Columbia, MO, 65211, United States, 1 5738824183, janesw@health.missouri.edu %K amyotrophic lateral sclerosis %K machine learning %K precision health %K ALS %K health monitoring %K electronic health record %K EHR %K federated approach %K in-home sensor data %D 2025 %7 12.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Amyotrophic lateral sclerosis (ALS) leads to rapid physiological and functional decline before causing untimely death. Current best-practice approaches to interdisciplinary care are unable to provide adequate monitoring of patients’ health. Passive in-home sensor systems enable 24×7 health monitoring. Combining sensor data with outcomes extracted from the electronic health record (EHR) through a supervised machine learning algorithm may enable health care providers to predict and ultimately slow decline among people living with ALS. Objective: This study aims to describe a federated approach to assimilating sensor and EHR data in a machine learning algorithm to predict decline among people living with ALS. Methods: Sensor systems have been continuously deployed in the homes of 4 participants for up to 330 days. Sensors include bed, gait, and motion sensors. Sensor data are subjected to a multidimensional streaming clustering algorithm to detect changes in health status. Specific health outcomes are identified in the EHR and extracted via the REDCap (Research Electronic Data Capture; Vanderbilt University) Fast Healthcare Interoperability Resource directly into a secure database. Results: As of this writing (fall 2024), machine learning algorithms are currently in development to predict those health outcomes from sensor-detected changes in health status. This methodology paper presents preliminary results from one participant as a proof of concept. The participant experienced several notable changes in activity, fluctuations in heart rate and respiration rate, and reductions in gait speed. Data collection will continue through 2025 with a growing sample. Conclusions: The system described in this paper enables tracking the health status of people living with ALS at unprecedented levels of granularity. Combined with tightly integrated EHR data, we anticipate building predictive models that can identify opportunities for health care services before adverse events occur. We anticipate that this system will improve and extend the lives of people living with ALS. International Registered Report Identifier (IRRID): DERR1-10.2196/60437 %M 40073394 %R 10.2196/60437 %U https://www.researchprotocols.org/2025/1/e60437 %U https://doi.org/10.2196/60437 %U http://www.ncbi.nlm.nih.gov/pubmed/40073394 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67715 %T Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study %A Nejadshamsi,Shayan %A Karami,Vania %A Ghourchian,Negar %A Armanfard,Narges %A Bergman,Howard %A Grad,Roland %A Wilchesky,Machelle %A Khanassov,Vladimir %A Vedel,Isabelle %A Abbasgholizadeh Rahimi,Samira %+ Family Medicine Department, Faculty of Medicine and Health Sciences, McGill University, 5858 Côte des Negies, Montreal, QC, H3S 1Z1, Canada, 1 5143987375, samira.rahimi@mcgill.ca %K depression %K classification %K machine learning %K artificial intelligence %K older adults %D 2025 %7 3.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. Objective: This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. Methods: In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. Results: A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” Conclusions: The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study. %M 40053734 %R 10.2196/67715 %U https://aging.jmir.org/2025/1/e67715 %U https://doi.org/10.2196/67715 %U http://www.ncbi.nlm.nih.gov/pubmed/40053734 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59921 %T Impact of the Smarter Safer Homes Solution on Quality of Life and Health Outcomes in Older People Living in Their Own Homes: Randomized Controlled Trial %A Lu,Wei %A Silvera-Tawil,David %A Yoon,Hwan-Jin %A Higgins,Liesel %A Zhang,Qing %A Karunanithi,Mohanraj %A Bomke,Julia %A Byrnes,Joshua %A Hewitt,Jennifer %A Smallbon,Vanessa %A Freyne,Jill %A Prabhu,Deepa %A Varnfield,Marlien %+ , Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 7, Surgical Treatment and Rehabilitation Services, 296 Herston Road, Brisbane, QLD, 4029, Australia, 61 732533603, marlien.varnfield@csiro.au %K randomized controlled trial %K digital health %K eHealth %K smart home %K sensor %K health monitoring %K home monitoring %K aged care %K aging in place %K older adult %K quality of life %D 2025 %7 22.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: An increasingly aging population, accompanied by a shortage of residential aged care homes and workforce and consumer feedback, has driven a growing interest in enabling older people to age in place through home-based care. In this context, smart home technologies for remote health monitoring have gained popularity for supporting older people to live in their own homes. Objective: This study aims to investigate the impact of smart home monitoring on multiple outcomes, including quality of life, activities of daily living, and depressive symptoms among older people living in their own homes over a 12-month period. Methods: We conducted an open-label, parallel-group randomized controlled trial. The control group continued to receive their existing care from aged care service providers. Meanwhile, the intervention group, in addition to receiving their usual aged care services, had their activities of daily living monitored using a smart home platform. Surveys including the Adult Social Care Outcomes Toolkit (ASCOT), EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), Katz Index of Independence in Activities of Daily Living (Katz ADL), Lawton Instrumental Activities of Daily Living Scale (IADL), and Geriatric Depression Scale (GDS) were conducted at baseline and 6 and 12 months from baseline. Linear mixed-effects models were used to compare the difference between the intervention and control groups, with the ASCOT as the primary outcome measure. Results: Data from 130 participants were used in the analysis, with no significant differences in baseline characteristics between the control group (n=61) and the intervention group (n=69). In comparison to the control group, the intervention group had a higher ASCOT score at the 6-month assessment (mean difference 0.045, 95% CI 0.001 to 0.089; Cohen d=0.377). However, this difference did not persist at the 12-month assessment (mean difference 0.031, 95% CI –0.014 to 0.076; Cohen d=0.259). There were no significant differences in EQ-5D-5L, Katz ADL, IADL, and GDS observed between the intervention and control groups at the 6-month and 12-month assessments. Conclusions: The study demonstrates that smart home monitoring can improve social care–related quality of life for older people living in their own homes. However, the improvement was not sustained over the long term. The lack of statistically significant findings and diminished long-term improvements may be attributed to the influence of the COVID-19 pandemic during the later stage of the trial. Further research with a larger sample size is needed to evaluate the effect of smart home monitoring on broader quality-of-life measures. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618000829213; https://tinyurl.com/2n6a75em International Registered Report Identifier (IRRID): RR2-10.2196/31970 %M 39608020 %R 10.2196/59921 %U https://www.jmir.org/2025/1/e59921 %U https://doi.org/10.2196/59921 %U http://www.ncbi.nlm.nih.gov/pubmed/39608020 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 7 %N %P e58094 %T Unobtrusive Nighttime Movement Monitoring to Support Nursing Home Continence Care: Algorithm Development and Validation Study %A Strauven,Hannelore %A Wang,Chunzhuo %A Hallez,Hans %A Vanden Abeele,Vero %A Vanrumste,Bart %K nursing home %K agitation %K incontinence %K accelerometer %K unobtrusive %K enuresis %K sensor technology %D 2024 %7 24.12.2024 %9 %J JMIR Nursing %G English %X Background: The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of NH residents’ movements and, more specifically, the agitation possibly associated with voiding events. Objective: This study aims to explore the application of an unobtrusive sensor system to monitor nighttime movement, integrated into a care bed with accelerometer sensors connected to a pressure-redistributing care mattress. Methods: A total of 6 participants followed a 7-step protocol. The obtained dataset was segmented into 20-second windows with a 50% overlap. Each window was labeled with 1 of the 4 chosen activity classes: in bed, agitation, turn, and out of bed. A total of 1416 features were selected and analyzed with an XGBoost algorithm. At last, the model was validated using leave one subject out cross-validation (LOSOCV). Results: The trained model attained a trustworthy overall F1-score of 79.56% for all classes and, more specifically, an F1-score of 79.67% for the class “Agitation.” Conclusions: The results from this study provide promising insights in unobtrusive nighttime movement monitoring. The study underscores the potential to enhance the quality of care for NH residents through a machine learning model based on data from accelerometers connected to a viscoelastic care mattress, thereby driving progress in the field of continence care and artificial intelligence–supported health care for older adults. %R 10.2196/58094 %U https://nursing.jmir.org/2024/1/e58094 %U https://doi.org/10.2196/58094 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60892 %T Accuracy, Reproducibility, and Responsiveness to Treatment of Home Spirometry in Cystic Fibrosis: Multicenter, Retrospective, Observational Study %A Oppelaar,Martinus C %A van Helvoort,Hanneke AC %A Bannier,Michiel AGE %A Reijers,Monique HE %A van der Vaart,Hester %A van der Meer,Renske %A Altenburg,Josje %A Conemans,Lennart %A Rottier,Bart L %A Nuijsink,Marianne %A van den Wijngaart,Lara S %A Merkus,Peter JFM %A Roukema,Jolt %+ Department of Pediatric Pulmonology, Amalia Children's Hospital, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6500 HB, Netherlands, +31 243614430, marc.oppelaar@radboudumc.nl %K telemonitoring %K digital health %K telespirometry %K remote monitoring %K cystic fibrosis %K pediatrics %K reliability %K mobile phone %K hereditary %K chronic pulmonary inflammation %K pulmonary infections %K morbidity %K mortality %K chronic respiratory disease %D 2024 %7 3.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Portable spirometers are increasingly used to measure lung function at home, but doubts about the accuracy of these devices persist. These doubts stand in the way of the digital transition of chronic respiratory disease care, hence there is a need to address the accuracy of home spirometry in routine care across multiple settings and ages. Objective: This study aimed to assess the accuracy, reproducibility, and responsiveness to the treatment of home spirometry in long-term pediatric and adult cystic fibrosis care. Methods: This retrospective observational study was carried out in 5 Dutch cystic fibrosis centers. Home spirometry outcomes (forced expiratory volume in one second [FEV1], and forced vital capacity [FVC]) for 601 anonymized users were collected during 3 years. For 81 users, data on clinic spirometry and elexacaftor/tezacaftor/ivacaftor (ETI) use were available. Accuracy was assessed using Bland-Altman plots for paired clinic-home measurements on the same day and within 7 days of each other (nearest neighbor). Intratest reproducibility was assessed using the American Thoracic Society/European Respiratory Society repeatability criteria, the coefficient of variation, and spirometry quality grades. Responsiveness was measured by the percentage change in home spirometry outcomes after the start of ETI. Results: Bland-Altman analysis was performed for 86 same-day clinic-home spirometry pairs and for 263 nearest neighbor clinic-home spirometry pairs (n=81). For both sets and for both FEV1 and FVC, no heteroscedasticity was present and hence the mean bias was expressed as an absolute value. Overall, home spirometry was significantly lower than clinic spirometry (mean ΔFEV1clinic-home 0.13 L, 95% CI 0.10 to 0.19; mean ΔFVCclinic-home 0.20 L, 95% CI 0.14 to 0.25) and remained lower than clinic spirometry independent of age and experience. One-way ANOVA with post hoc comparisons showed significantly lower differences in clinic-home spirometry in adults than in children (Δmean 0.11, 95% CI –0.20 to –0.01) and teenagers (Δmean 0.14, 95% CI –0.25 to –0.02). For reproducibility analyses, 2669 unique measurement days of 311 individuals were included. Overall, 87.3% (2331/2669) of FEV1 measurements and 74.3% (1985/2669) of FVC measurements met reproducibility criteria. Kruskal-Wallis with pairwise comparison demonstrated that for both FVC and FEV1, coefficient of variation was significantly lower in adults than in children and teenagers. A total of 5104 unique home measurements were graded. Grade E was given to 2435 tests as only one home measurement was performed. Of the remaining 2669 tests, 43.8% (1168/2669) and 43.6% (1163/2669) received grade A and B, respectively. The median percentage change in FEV1 from baseline after initiation of ETI was 19.2% after 7-14 days and remained stable thereafter (n=33). Conclusions: Home spirometry is feasible but not equal to clinic spirometry. Home spirometry can confirm whether lung functions remain stable, but the context of measurement and personal trends are more relevant than absolute outcomes. %M 39626236 %R 10.2196/60892 %U https://www.jmir.org/2024/1/e60892 %U https://doi.org/10.2196/60892 %U http://www.ncbi.nlm.nih.gov/pubmed/39626236 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58892 %T AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review %A Chan,Pin Zhong %A Jin,Eric %A Jansson,Miia %A Chew,Han Shi Jocelyn %+ Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore, 65 65168687, jocelyn.chew.hs@nus.edu.sg %K artificial intelligence %K blood glucose %K diabetes %K noninvasive %K self-monitoring %K machine learning %K scoping review %K monitoring %K management %K health informatics %K deep learning %K accuracy %K heterogeneity %K mobile phone %D 2024 %7 19.11.2024 %9 Review %J J Med Internet Res %G English %X Background: Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience. Objective: This review aimed to map the use cases of artificial intelligence (AI) in NIBGM. Methods: A systematic scoping review was conducted according to the Arksey O’Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI. Results: A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data. Conclusions: Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management. %M 39561353 %R 10.2196/58892 %U https://www.jmir.org/2024/1/e58892 %U https://doi.org/10.2196/58892 %U http://www.ncbi.nlm.nih.gov/pubmed/39561353 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e54735 %T Using a Quality-Controlled Dataset From ViSi Mobile Monitoring for Analyzing Posture Patterns of Hospitalized Patients: Retrospective Observational Study %A Huang,Emily J %A Chen,Yuexin %A Clark,Clancy J %K posture monitoring %K ViSi mobile %K wearable device %K inpatient %K quality control %K observational study %K monitoring data %K inpatient monitoring %K wearables %K posture %D 2024 %7 6.11.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: ViSi Mobile has the capability of monitoring a patient’s posture continuously during hospitalization. Analysis of ViSi telemetry data enables researchers and health care providers to quantify an individual patient’s movement and investigate collective patterns of many patients. However, erroneous values can exist in routinely collected ViSi telemetry data. Data must be scrutinized to remove erroneous records before statistical analysis. Objective: The objectives of this study were to (1) develop a data cleaning procedure for a 1-year inpatient ViSi posture dataset, (2) consolidate posture codes into categories, (3) derive concise summary statistics from the continuous monitoring data, and (4) study types of patient posture habits using summary statistics of posture duration and transition frequency. Methods: This study examined the 2019 inpatient ViSi posture records from Atrium Health Wake Forest Baptist Medical Center. First, 2 types of errors, record overlap and time inconsistency, were identified. An automated procedure was designed to search all records for these errors. A data cleaning procedure removed erroneous records. Second, data preprocessing was conducted. Each patient’s categorical time series was simplified by consolidating the 185 ViSi codes into 5 categories (Lying, Reclined, Upright, Unknown, User-defined). A majority vote process was applied to remove bursts of short duration. Third, statistical analysis was conducted. For each patient, summary statistics were generated to measure average time duration of each posture and rate of posture transitions during the whole day and separately during daytime and nighttime. A k-means clustering analysis was performed to divide the patients into subgroups objectively. Results: The analysis used a sample of 690 patients, with a median of 3 days of extensive ViSi monitoring per patient. The median of posture durations was 10.2 hours/day for Lying, 8.0 hours/day for Reclined, and 2.5 hours/day for Upright. Lying had similar percentages of patients in low and high durations. Reclined showed a decrease in patients for higher durations. Upright had its peak at 0‐2 hours, with a decrease for higher durations. Scatter plots showed that patients could be divided into several subgroups with different posture habits. This was reinforced by the k-means analysis, which identified an active subgroup and two sedentary ones with different resting styles. Conclusions: Using a 1-year ViSi dataset from routine inpatient monitoring, we derived summary statistics of posture duration and posture transitions for each patient and analyzed the summary statistics to identify patterns in the patient population. This analysis revealed several types of patient posture habits. Before analysis, we also developed methodology to clean and preprocess routinely collected inpatient ViSi monitoring data, which is a major contribution of this study. The procedure developed for data cleaning and preprocessing can have broad application to other monitoring systems used in hospitals. %R 10.2196/54735 %U https://mhealth.jmir.org/2024/1/e54735 %U https://doi.org/10.2196/54735 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50461 %T Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis %A Kallio,Johanna %A Kinnula,Atte %A Mäkelä,Satu-Marja %A Järvinen,Sari %A Räsänen,Pauli %A Hosio,Simo %A Bordallo López,Miguel %+ VTT Technical Research Centre of Finland Ltd, Kaitoväylä 1, Oulu, 90571, Finland, 358 50 527 0180, johanna.kallio@vtt.fi %K field trial %K behavioral research %K sensor data %K machine learning %K pervasive technology %K stakeholder engagement %K qualitative coding %K mobile phone %D 2024 %7 31.10.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Pervasive technologies are used to investigate various phenomena outside the laboratory setting, providing valuable insights into real-world human behavior and interaction with the environment. However, conducting longitudinal field trials in natural settings remains challenging due to factors such as low recruitment success and high dropout rates due to participation burden or data quality issues with wireless sensing in changing environments. Objective: This study gathers insights and lessons from 3 real-world longitudinal field studies assessing human behavior and derives factors that impacted their research success. We aim to categorize challenges, observe how they were managed, and offer recommendations for designing and conducting studies involving human participants and pervasive technology in natural settings. Methods: We developed a qualitative coding framework to categorize and address the unique challenges encountered in real-life studies related to influential factor identification, stakeholder management, data harvesting and management, and analysis and interpretation. We applied inductive reasoning to identify issues and related mitigation actions in 3 separate field studies carried out between 2018 and 2022. These 3 field studies relied on gathering annotated sensor data. The topics involved stress and environmental assessment in an office and a school, collecting self-reports and wrist device and environmental sensor data from 27 participants for 3.5 to 7 months; work activity recognition at a construction site, collecting observations and wearable sensor data from 15 participants for 3 months; and stress recognition in location-independent knowledge work, collecting self-reports and computer use data from 57 participants for 2 to 5 months. Our key extension for the coding framework used a stakeholder identification method to identify the type and role of the involved stakeholder groups, evaluating the nature and degree of their involvement and influence on the field trial success. Results: Our analysis identifies 17 key lessons related to planning, implementing, and managing a longitudinal, sensor-based field study on human behavior. The findings highlight the importance of recognizing different stakeholder groups, including those not directly involved but whose areas of responsibility are impacted by the study and therefore have the power to influence it. In general, customizing communication strategies to engage stakeholders on their terms and addressing their concerns and expectations is essential, while planning for dropouts, offering incentives for participants, conducting field tests to identify problems, and using tools for quality assurance are relevant for successful outcomes. Conclusions: Our findings suggest that field trial implementation should include additional effort to clarify the expectations of stakeholders and to communicate with them throughout the process. Our framework provides a structured approach that can be adopted by other researchers in the field, facilitating robust and comparable studies across different contexts. Constantly managing the possible challenges will lead to better success in longitudinal field trials and developing future technology-based solutions. %M 39481098 %R 10.2196/50461 %U https://www.jmir.org/2024/1/e50461 %U https://doi.org/10.2196/50461 %U http://www.ncbi.nlm.nih.gov/pubmed/39481098 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e52383 %T Sensors for Smoking Detection in Epidemiological Research: Scoping Review %A Favara,Giuliana %A Barchitta,Martina %A Maugeri,Andrea %A Magnano San Lio,Roberta %A Agodi,Antonella %+ Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, Via Santa Sofia 87, Catania, 95123, Italy, 39 0953782183, agodia@unict.it %K smoking %K tobacco smoke %K smoke exposure %K cigarette smoking %K wearable sensor %K public health %D 2024 %7 30.10.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The use of wearable sensors is being explored as a challenging way to accurately identify smoking behaviors by measuring physiological and environmental factors in real-life settings. Although they hold potential benefits for aiding smoking cessation, no single wearable device currently achieves high accuracy in detecting smoking events. Furthermore, it is crucial to emphasize that this area of study is dynamic and requires ongoing updates. Objective: This scoping review aims to map the scientific literature for identifying the main sensors developed or used for tobacco smoke detection, with a specific focus on wearable sensors, as well as describe their key features and categorize them by type. Methods: According to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol, an electronic search was conducted on the PubMed, MEDLINE, and Web of Science databases, using the following keywords: (“biosensors” OR “biosensor” OR “sensors” OR “sensor” OR “wearable”) AND (“smoking” OR “smoke”). Results: Among a total of 37 studies included in this scoping review published between 2012 and March 2024, 16 described sensors based on wearable bands, 15 described multisensory systems, and 6 described other strategies to detect tobacco smoke exposure. Included studies provided details about the design or application of wearable sensors based on an elastic band to detect different aspects of tobacco smoke exposure (eg, arm, wrist, and finger movements, and lighting events). Some studies proposed a system composed of different sensor modalities (eg, Personal Automatic Cigarette Tracker [PACT], PACT 2.0, and AutoSense). Conclusions: Our scoping review has revealed both the obstacles and opportunities linked to wearable devices, offering valuable insights for future research initiatives. Tackling the recognized challenges and delving into potential avenues for enhancement could elevate wearable devices into even more effective tools for aiding smoking cessation. In this context, continuous research is essential to fine-tune and optimize these devices, guaranteeing their practicality and reliability in real-world applications. %M 39476379 %R 10.2196/52383 %U https://mhealth.jmir.org/2024/1/e52383 %U https://doi.org/10.2196/52383 %U http://www.ncbi.nlm.nih.gov/pubmed/39476379 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e60496 %T Current State of Connected Sensor Technologies Used During Rehabilitation Care: Protocol for a Scoping Review %A Rauzi,Michelle R %A Akay,Rachael B %A Balakrishnan,Swapna %A Piper,Christi %A Gobert,Denise %A Flach,Alicia %+ Denver/Seattle Center of Innovation for Veteran-centered and Value Driven Care, Rocky Mountain Regional VA Medical Center, 1700 N Wheeling St, Aurora, CO, 80045, United States, 1 303 724 9590, michelle.rauzi@cuanschutz.edu %K connected sensor technology %K digital health %K rehabilitation %K rehabilitation care %K remote monitoring %K telehealth %K mHealth %K mobile health %K wearables %K wearable technology %D 2024 %7 24.10.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations. Objective: This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions. Methods: The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness. Results: We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024. Conclusions: Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. Trial Registration: Open Science Framework jys53; https://osf.io/jys53 International Registered Report Identifier (IRRID): DERR1-10.2196/60496 %M 39446418 %R 10.2196/60496 %U https://www.researchprotocols.org/2024/1/e60496 %U https://doi.org/10.2196/60496 %U http://www.ncbi.nlm.nih.gov/pubmed/39446418 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e57703 %T Managing Patients With COVID-19 in Armenia Using a Remote Monitoring System: Descriptive Study %A Musheghyan,Lusine %A Harutyunyan,Nika M %A Sikder,Abu %A Reid,Mark W %A Zhao,Daniel %A Lulejian,Armine %A Dickhoner,James W %A Andonian,Nicole T %A Aslanyan,Lusine %A Petrosyan,Varduhi %A Sargsyan,Zhanna %A Shekherdimian,Shant %A Dorian,Alina %A Espinoza,Juan C %+ Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, 225 E. Chicago Avenue, BOX 205, Chicago, IL, 60611-2605, United States, 1 3125037603, jespinozasalomon@luriechildrens.org %K COVID-19 %K remote patient monitoring %K Armenia %K web platform %K home oxygen therapy %K pandemic %K global health care %K low and middle-income countries %K health care infrastructure %K Yerevan %K home monitoring %K resource-constrained %D 2024 %7 30.9.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has imposed immense stress on global health care systems, especially in low- and middle-income countries (LMICs). Armenia, a middle-income country in the Caucasus region, contended with the pandemic and a concurrent war, resulting in significant demand on its already strained health care infrastructure. The COVID@home program was a multi-institution, international collaboration to address critical hospital bed shortages by implementing a home-based oxygen therapy and remote monitoring program. Objective: The objective of this study was to describe the program protocol and clinical outcomes of implementing an early discharge program in Armenia through a collaboration of partner institutions, which can inform the future implementation of COVID-19 remote home monitoring programs, particularly in LMICs or low-resource settings. Methods: Seven hospitals in Yerevan participated in the COVID@home program. A web app based on OpenMRS was developed to facilitate data capture and care coordination. Patients meeting eligibility criteria were enrolled during hospitalization and monitored daily while on oxygen at home. Program evaluation relied on data extraction from (1) eligibility and enrollment forms, (2) daily monitoring forms, and (3) discharge forms. Results: Over 11 months, 439 patients were screened, and 221 patients were managed and discharged. Around 94% (n=208) of participants safely discontinued oxygen therapy at home, with a median home monitoring duration of 26 (IQR 15-45 days; mean 32.33, SD 25.29) days. Women (median 28.5, mean 35.25 days) had similar length of stay to men (median 26, mean 32.21 days; P=.75). Despite challenges in data collection and entry, the program demonstrated feasibility and safety, with a mortality rate below 1% and low re-admission rate. Opportunities for operational and data quality improvements were identified. Conclusions: This study contributes practical evidence on the implementation and outcomes of a remote monitoring program in Armenia, offering insights into managing patients with COVID-19 in resource-constrained settings. The COVID@home program’s success provides a model for remote patient care, potentially alleviating strain on health care resources in LMICs. Policymakers can draw from these findings to inform the development of adaptable health care solutions during public health crises, emphasizing the need for innovative approaches in resource-limited environments. %M 39348686 %R 10.2196/57703 %U https://publichealth.jmir.org/2024/1/e57703 %U https://doi.org/10.2196/57703 %U http://www.ncbi.nlm.nih.gov/pubmed/39348686 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e52582 %T Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review %A Jeyasingh-Jacob,Julian %A Crook-Rumsey,Mark %A Shah,Harshvi %A Joseph,Theresita %A Abulikemu,Subati %A Daniels,Sarah %A Sharp,David J %A Haar,Shlomi %+ Department of Brain Sciences, Imperial College London, Sir Michael Uren Research Hub, London, W12 0BZ, United Kingdom, 44 20 759 48064, s.haar@imperial.ac.uk %K markerless motion capture %K motion analysis %K movement analysis %K motion %K neurodegeneration %K neurodegenerative %K systematic review %K movement %K body tracking %K tracking %K monitoring %K clinical decision making %K decision %K decision making %K dementia %K neurodegenerative disease %K mild cognitive impairment %K Parkinson's disease %K tool %K mobility %D 2024 %7 6.8.2024 %9 Review %J JMIR Aging %G English %X Background: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia. Objective: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease. Methods: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review.  Results: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models. Conclusions: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world. %R 10.2196/52582 %U https://aging.jmir.org/2024/1/e52582 %U https://doi.org/10.2196/52582 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e46903 %T Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights %A Sahu,Kirti Sundar %A Dubin,Joel A %A Majowicz,Shannon E %A Liu,Sam %A Morita,Plinio P %+ School of Public Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 31372, plinio.morita@uwaterloo.ca %K population-level health indicators %K internet of things %K public health surveillance %K mobility %K risk factors %K chronic diseases %K chronic %K risk %K surveillance %K mobility %K movement %K sensor %K population %D 2024 %7 20.3.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google’s GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. Objective: This study investigates in-home mobility data from ecobee’s smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google’s residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. Methods: Motion sensor data were acquired from the ecobee “Donate Your Data” initiative via Google’s BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces—Ontario, Quebec, Alberta, and British Columbia—during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. Results: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google’s data set. Examination of Google’s daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. Conclusions: This study’s findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google’s out-of-house residential mobility data and ecobee’s in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts. %M 38506901 %R 10.2196/46903 %U https://publichealth.jmir.org/2024/1/e46903 %U https://doi.org/10.2196/46903 %U http://www.ncbi.nlm.nih.gov/pubmed/38506901 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 6 %N %P e43777 %T Remote Monitoring of Physiology in People Living With Dementia: An Observational Cohort Study %A David,Michael C B %A Kolanko,Magdalena %A Del Giovane,Martina %A Lai,Helen %A True,Jessica %A Beal,Emily %A Li,Lucia M %A Nilforooshan,Ramin %A Barnaghi,Payam %A Malhotra,Paresh A %A Rostill,Helen %A Wingfield,David %A Wilson,Danielle %A Daniels,Sarah %A Sharp,David J %A Scott,Gregory %+ UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, 9th Floor, Sir Michael Uren Hub, 86 Wood Lane, London, W12 0BZ, United Kingdom, 44 (0)207 594 9010, gregory.scott99@ic.ac.uk %K dementia %K remote monitoring %K physiology %K Internet of Things %K alerts %K monitoring %K technology %K detection %K blood pressure %K support %K feasibility %K system %K quality of life %D 2023 %7 9.3.2023 %9 Original Paper %J JMIR Aging %G English %X Background: Internet of Things (IoT) technology enables physiological measurements to be recorded at home from people living with dementia and monitored remotely. However, measurements from people with dementia in this context have not been previously studied. We report on the distribution of physiological measurements from 82 people with dementia over approximately 2 years. Objective: Our objective was to characterize the physiology of people with dementia when measured in the context of their own homes. We also wanted to explore the possible use of an alerts-based system for detecting health deterioration and discuss the potential applications and limitations of this kind of system. Methods: We performed a longitudinal community-based cohort study of people with dementia using “Minder,” our IoT remote monitoring platform. All people with dementia received a blood pressure machine for systolic and diastolic blood pressure, a pulse oximeter measuring oxygen saturation and heart rate, body weight scales, and a thermometer, and were asked to use each device once a day at any time. Timings, distributions, and abnormalities in measurements were examined, including the rate of significant abnormalities (“alerts”) defined by various standardized criteria. We used our own study criteria for alerts and compared them with the National Early Warning Score 2 criteria. Results: A total of 82 people with dementia, with a mean age of 80.4 (SD 7.8) years, recorded 147,203 measurements over 958,000 participant-hours. The median percentage of days when any participant took any measurements (ie, any device) was 56.2% (IQR 33.2%-83.7%, range 2.3%-100%). Reassuringly, engagement of people with dementia with the system did not wane with time, reflected in there being no change in the weekly number of measurements with respect to time (1-sample t-test on slopes of linear fit, P=.45). A total of 45% of people with dementia met criteria for hypertension. People with dementia with α-synuclein–related dementia had lower systolic blood pressure; 30% had clinically significant weight loss. Depending on the criteria used, 3.03%-9.46% of measurements generated alerts, at 0.066-0.233 per day per person with dementia. We also report 4 case studies, highlighting the potential benefits and challenges of remote physiological monitoring in people with dementia. These include case studies of people with dementia developing acute infections and one of a person with dementia developing symptomatic bradycardia while taking donepezil. Conclusions: We present findings from a study of the physiology of people with dementia recorded remotely on a large scale. People with dementia and their carers showed acceptable compliance throughout, supporting the feasibility of the system. Our findings inform the development of technologies, care pathways, and policies for IoT-based remote monitoring. We show how IoT-based monitoring could improve the management of acute and chronic comorbidities in this clinically vulnerable group. Future randomized trials are required to establish if a system like this has measurable long-term benefits on health and quality of life outcomes. %M 36892931 %R 10.2196/43777 %U https://aging.jmir.org/2023/1/e43777 %U https://doi.org/10.2196/43777 %U http://www.ncbi.nlm.nih.gov/pubmed/36892931 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 6 %N %P e41322 %T Proactive and Ongoing Analysis and Management of Ethical Concerns in the Development, Evaluation, and Implementation of Smart Homes for Older Adults With Frailty %A Wang,Rosalie H %A Tannou,Thomas %A Bier,Nathalie %A Couture,Mélanie %A Aubry,Régis %+ Department of Occupational Science and Occupational Therapy, Temerty Faculty of Medicine, University of Toronto, 160-500 University Ave, Toronto, ON, M5G 1V7, Canada, 1 416 946 8566, rosalie.wang@utoronto.ca %K ethics %K older adults %K frailty %K smart home %K assistive technology %K aging in place %K ethical concerns %K implementation %K bioethics %K technology ethics %K autonomy %K privacy %K security %K informed consent %K support ecosystem %D 2023 %7 9.3.2023 %9 Viewpoint %J JMIR Aging %G English %X Successful adoption and sustained use of smart home technology can support the aging in place of older adults with frailty. However, the expansion of this technology has been limited, particularly by a lack of ethical considerations surrounding its application. This can ultimately prevent older adults and members of their support ecosystems from benefiting from the technology. This paper has 2 aims in the effort to facilitate adoption and sustained use: to assert that proactive and ongoing analysis and management of ethical concerns are crucial to the successful development, evaluation, and implementation of smart homes for older adults with frailty and to present recommendations to create a framework, resources, and tools to manage ethical concerns with the collaboration of older adults; members of their support ecosystems; and the research, technical development, clinical, and industry communities. To support our assertion, we reviewed intersecting concepts from bioethics, specifically principlism and ethics of care, and from technology ethics that are salient to smart homes in the management of frailty in older adults. We focused on 6 conceptual domains that can lead to ethical tensions and of which proper analysis is essential: privacy and security, individual and relational autonomy, informed consent and supported decision-making, social inclusion and isolation, stigma and discrimination, and equity of access. To facilitate the proactive and ongoing analysis and management of ethical concerns, we recommended collaboration to develop a framework with 4 proposed elements: a set of conceptual domains as discussed in this paper, along with a tool consisting of reflective questions to guide ethical deliberation throughout the project phases; resources comprising strategies and guidance for the planning and reporting of ethical analysis throughout the project phases; training resources to support leadership, literacy, and competency in project teams for the analysis and management of ethical concerns; and training resources for older adults with frailty, their support ecosystems, and the public to support their awareness and participation in teams and ethical analysis processes. Older adults with frailty require nuanced consideration when incorporating technology into their care because of their complex health and social status and vulnerability. Smart homes may have a greater likelihood of accommodating users and their contexts with committed and comprehensive analysis, anticipation, and management of ethical concerns that reflect the unique circumstances of these users. Smart home technology may then achieve its desired individual, societal, and economic outcomes and serve as a solution to support health; well-being; and responsible, high-quality care. %M 36892912 %R 10.2196/41322 %U https://aging.jmir.org/2023/1/e41322 %U https://doi.org/10.2196/41322 %U http://www.ncbi.nlm.nih.gov/pubmed/36892912 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e35312 %T Perspectives of Patients With Orthopedic Trauma on Fully Automated Digital Physical Activity Measurement at Home: Cross-sectional Survey Study %A Scherer,Julian %A Yogarasa,Vithush %A Rauer,Thomas %A Pape,Hans-Christoph %A Heining,Sandro-Michael %+ Department of Traumatology, University Hospital of Zurich, Raemistr. 100, Zurich, 8091, Switzerland, 41 44 255 1111, julian.scherer@usz.ch %K digital %K survey %K telehealth %K follow-up %K orthopedic trauma %K trauma %K attitude %K physical activity %K rehabilitation %K surveillance %K surgical procedure %K patients %K orthopedic %D 2023 %7 9.2.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The automated digital surveillance of physical activity at home after surgical procedures could facilitate the monitoring of postoperative follow-up, reduce costs, and enhance patients’ satisfaction. Data on the willingness of patients with orthopedic trauma to undergo automated home surveillance postoperatively are lacking. Objective: The aims of this study were to assess whether patients with orthopedic trauma would be generally willing to use the proposed automated digital home surveillance system and determine what advantages and disadvantages the system could bring with it. Methods: Between June 2021 and October 2021, a survey among outpatients with orthopedic trauma who were treated at a European level 1 trauma center was conducted. The only inclusion criterion was an age of at least 16 years. The paper questionnaire first described the possibility of fully automated movement and motion detection (via cameras or sensors) at home without any action required from the patient. The questionnaire then asked for the participants’ demographics and presented 6 specific questions on the study topic. Results: In total, we included 201 patients whose mean age was 46.9 (SD 18.6) years. Most of the assessed patients (124/201, 61.7%) were male. Almost half of the patients (83/201, 41.3%) were aged between 30 and 55 years. The most stated occupation was a nine-to-five job (62/199, 30.8%). The majority of the participants (120/201, 59.7%) could imagine using the proposed measurement system, with no significant differences among the genders. An insignificant higher number of younger patients stated that they would use the automated surveillance system. No significant difference was seen among different occupations (P=.41). Significantly more young patients were using smartphones (P=.004) or electronic devices with a camera (P=.008). Less than half of the surveyed patients (95/201, 47.3%) stated that they were using tracking apps. The most stated advantages were fewer physician visits (110/201, 54.7%) and less effort (102/201, 50.7%), whereas the most prevalent disadvantage was the missing physician-patient contact (144/201, 71.6%). Significantly more patients with a part-time job or a nine-to-five job stated that data analysis contributes to medical progress (P=.047). Conclusions: Most of the assessed participants (120/201, 59.7%) stated that they would use the automated digital measurement system to observe their postoperative follow-up and recovery. The proposed system could be used to reduce costs and ease hospital capacity issues. In order to successfully implement such systems, patients’ concerns must be addressed, and further studies on the feasibility of these systems are needed. %M 36757791 %R 10.2196/35312 %U https://formative.jmir.org/2023/1/e35312 %U https://doi.org/10.2196/35312 %U http://www.ncbi.nlm.nih.gov/pubmed/36757791 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 3 %P e38211 %T An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study %A Bijlani,Nivedita %A Nilforooshan,Ramin %A Kouchaki,Samaneh %+ Centre for Vision, Speech and Signal Processing, University of Surrey, 388 Stag Hill, Guildford, GU2 7XH, United Kingdom, 44 1483 300 800, n.bijlani@surrey.ac.uk %K contextual matrix profile %K multidimensional anomaly detection %K outlier detection %K sensor-based remote health monitoring %K dementia %K unsupervised learning %D 2022 %7 19.9.2022 %9 Original Paper %J JMIR Aging %G English %X Background: Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability. Objective: This study aims to develop an online, lightweight unsupervised learning–based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations. Methods: We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. Results: The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. Conclusions: To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios. %M 36121687 %R 10.2196/38211 %U https://aging.jmir.org/2022/3/e38211 %U https://doi.org/10.2196/38211 %U http://www.ncbi.nlm.nih.gov/pubmed/36121687 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 8 %P e40181 %T Self-management Interventions for People With Parkinson Disease: Scoping Review %A Milne-Ives,Madison %A Carroll,Camille %A Meinert,Edward %+ Centre for Health Technology, University of Plymouth, 6 Kirkby Place, Room 2, Plymouth, PL4 6DN, United Kingdom, 44 01752600600, edward.meinert@plymouth.ac.uk %K Parkinson disease %K self-management %K self-care %K home nursing %K self-efficacy %K quality of life %K signs and symptoms %K health behaviour %D 2022 %7 5.8.2022 %9 Review %J J Med Internet Res %G English %X Background: Parkinson disease can impose substantial distress and costs on patients, their families and caregivers, and health care systems. To address these burdens for families and health care systems, there is a need to better support patient self-management. To achieve this, an overview of the current state of the literature on self-management is needed to identify what is being done, how well it is working, and what might be missing. Objective: The aim of this scoping review was to provide an overview of the current body of research on self-management interventions for people with Parkinson disease and identify any knowledge gaps. Methods: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and Population, Intervention, Comparator, Outcome, and Study type frameworks were used to structure the methodology of the review. Due to time and resource constraints, 1 reviewer systematically searched 4 databases (PubMed, Ovid, Scopus, and Web of Science) for the evaluations of self-management interventions for Parkinson disease published in English. The references were screened using the EndNote X9 citation management software, titles and abstracts were manually reviewed, and studies were selected for inclusion based on the eligibility criteria. Data were extracted into a pre-established form and synthesized in a descriptive analysis. Results: There was variation among the studies on study design, sample size, intervention type, and outcomes measured. The randomized controlled trials had the strongest evidence of effectiveness: 5 out of 8 randomized controlled trials found a significant difference between groups favoring the intervention on their primary outcome, and the remaining 3 had significant effects on at least some of the secondary outcomes. The 2 interventions included in the review that targeted mental health outcomes both found significant changes over time, and the 3 algorithms evaluated performed well. The remaining studies examined patient perceptions, acceptability, and cost-effectiveness and found generally positive results. Conclusions: This scoping review identified a wide variety of interventions designed to support various aspects of self-management for people with Parkinson disease. The studies all generally reported positive results, and although the strength of the evidence varied, it suggests that self-management interventions are promising for improving the care and outcomes of people with Parkinson disease. However, the research tended to focus on the motor aspects of Parkinson disease, with few nonmotor or holistic interventions, and there was a lack of evaluation of cost-effectiveness. This research will be important to providing self-management interventions that meet the varied and diverse needs of people with Parkinson disease and determining which interventions are worth promoting for widespread adoption. %M 35930315 %R 10.2196/40181 %U https://www.jmir.org/2022/8/e40181 %U https://doi.org/10.2196/40181 %U http://www.ncbi.nlm.nih.gov/pubmed/35930315 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e39887 %T Utilizing Real-time Technology to Assess the Impact of Home Environmental Exposures on Asthma Symptoms: Protocol for an Observational Pilot Study %A Nyenhuis,Sharmilee %A Cramer,Emily %A Grande,Matthew %A Huntington-Moskos,Luz %A Krueger,Kathryn %A Bimbi,Olivia %A Polivka,Barbara %A Eldeirawi,Kamal %+ Section of Allergy and Immunology, Department of Pediatrics, University of Chicago, 5841 S. Maryland Ave., MC 5042, Chicago, IL, 60637, United States, 1 773 834 7121, snyenhuis@bsd.uchicago.edu %K asthma %K home environment %K ecologic momentary assessment %K air quality %K spirometry %D 2022 %7 2.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: It is estimated that over 60% of adults with asthma have uncontrolled symptoms, representing a substantial health and economic impact. The effects of the home environment and exposure to volatile organic compounds (VOCs) and fine particulate matter (PM2.5) on adults with asthma remain unknown. In addition, methods currently used to assess the home environment do not capture real-time data on potentially modifiable environmental exposures or their effect on asthma symptoms. Objective: The aims of this study are to (1) determine the feasibility and usability of ecological momentary assessment (EMA) to assess self-report residential environmental exposures and asthma symptoms, home monitoring of objective environmental exposures (total VOCs and PM2.5), and lung function in terms of forced expiratory volume in 1 second (FEV1%); (2) assess the frequency and level of residential environmental exposures (eg, disinfectants/cleaners, secondhand smoke) via self-reported data and home monitoring objective measures; (3) assess the level of asthma control as indicated by self-reported asthma symptoms and lung function; and (4) explore associations of self-reported and objective measures of residential environmental exposures with self-reported and objective measures of asthma control. Methods: We will recruit 50 adults with asthma who have completed our online Global COVID-19 Asthma Study, indicated willingness to be contacted for future studies, reported high use of disinfectant/cleaning products, and have asthma that is not well controlled. Participants will receive an indoor air quality monitor and a home spirometer to measure VOCs, PM2.5, and FEV1%, respectively. EMA data will be collected using a personal smartphone and EMA software platform. Participants will be sent scheduled and random EMA notifications to assess asthma symptoms, environmental exposures, lung function, and mitigation strategies. After the 14-day data collection period, participants will respond to survey items related to acceptability, appropriateness, and feasibility. Results: This study was funded in March 2021. We pilot tested our procedures and began recruitment in April 2022. The anticipated completion of the study is 2023. Conclusions: Findings from this feasibility study will support a powered study to address the impact of home environmental exposures on asthma symptoms and develop tailored, home-based asthma interventions that are responsive to the changing home environment and home routines. Trial Registration: ClinicalTrials.gov NCT05224076; https://clinicaltrials.gov/ct2/show/NCT05224076 International Registered Report Identifier (IRRID): DERR1-10.2196/39887 %M 35916686 %R 10.2196/39887 %U https://www.researchprotocols.org/2022/8/e39887 %U https://doi.org/10.2196/39887 %U http://www.ncbi.nlm.nih.gov/pubmed/35916686 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 2 %P e34239 %T Decision-making Factors Toward the Adoption of Smart Home Sensors by Older Adults in Singapore: Mixed Methods Study %A Cao,Yuanyuan %A Erdt,Mojisola %A Robert,Caroline %A Naharudin,Nurhazimah Binte %A Lee,Shan Qi %A Theng,Yin-Leng %+ Centre for Healthy and Sustainable Cities, Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore, 637718, Singapore, yycao@ntu.edu.sg %K aging in place %K health care systems and management %K telehealth %K assistive technology %K assisted living facilities %D 2022 %7 24.6.2022 %9 Original Paper %J JMIR Aging %G English %X Background: An increasing aging population has become a pressing problem in many countries. Smart systems and intelligent technologies support aging in place, thereby alleviating the strain on health care systems. Objective: This study aims to identify decision-making factors involved in the adoption of smart home sensors (SHS) by older adults in Singapore. Methods: The study involved 3 phases: as an intervention, SHS were installed in older adults’ homes (N=42) for 4 to 5 weeks; in-depth semistructured interviews were conducted with 18 older adults, 2 center managers, 1 family caregiver, and 1 volunteer to understand the factors involved in the decision-making process toward adoption of SHS; and follow-up feedback was collected from 42 older adult participants to understand the reasons for adopting or not adopting SHS. Results: Of the 42 participants, 31 (74%) adopted SHS after the intervention, whereas 11 (26%) did not adopt SHS. The reasons for not adopting SHS ranged from privacy concerns to a lack of family support. Some participants did not fully understand SHS functionality and did not perceive the benefits of using SHS. From the interviews, we found that the decision-making process toward the adoption of SHS technology involved intrinsic factors, such as understanding the technology and perceiving its usefulness and benefits, and more extrinsic factors, such as considering affordability and care support from the community. Conclusions: We found that training and a strong support ecosystem could empower older adults in their decision to adopt technology. We advise the consideration of human values and involvement of older adults in the design process to build user-centric assistive technology. %M 35749213 %R 10.2196/34239 %U https://aging.jmir.org/2022/2/e34239 %U https://doi.org/10.2196/34239 %U http://www.ncbi.nlm.nih.gov/pubmed/35749213 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 2 %P e35325 %T Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study %A Zolnoori,Maryam %A Vergez,Sasha %A Kostic,Zoran %A Jonnalagadda,Siddhartha Reddy %A V McDonald,Margaret %A Bowles,Kathryn K H %A Topaz,Maxim %+ School of Nursing, Columbia University, 390 Fort Washington Avenue, New York, NY, 10033, United States, 1 317 515 1950, mz2825@cumc.columbia.edu %K patients %K HHC %K communications %K nurse %K audio recording %K device %D 2022 %7 11.5.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Patients’ spontaneous speech can act as a biomarker for identifying pathological entities, such as mental illness. Despite this potential, audio recording patients’ spontaneous speech is not part of clinical workflows, and health care organizations often do not have dedicated policies regarding the audio recording of clinical encounters. No previous studies have investigated the best practical approach for integrating audio recording of patient-clinician encounters into clinical workflows, particularly in the home health care (HHC) setting. Objective: This study aimed to evaluate the functionality and usability of several audio-recording devices for the audio recording of patient-nurse verbal communications in the HHC settings and elicit HHC stakeholder (patients and nurses) perspectives about the facilitators of and barriers to integrating audio recordings into clinical workflows. Methods: This study was conducted at a large urban HHC agency located in New York, United States. We evaluated the usability and functionality of 7 audio-recording devices in a laboratory (controlled) setting. A total of 3 devices—Saramonic Blink500, Sony ICD-TX6, and Black Vox 365—were further evaluated in a clinical setting (patients’ homes) by HHC nurses who completed the System Usability Scale questionnaire and participated in a short, structured interview to elicit feedback about each device. We also evaluated the accuracy of the automatic transcription of audio-recorded encounters for the 3 devices using the Amazon Web Service Transcribe. Word error rate was used to measure the accuracy of automated speech transcription. To understand the facilitators of and barriers to integrating audio recording of encounters into clinical workflows, we conducted semistructured interviews with 3 HHC nurses and 10 HHC patients. Thematic analysis was used to analyze the transcribed interviews. Results: Saramonic Blink500 received the best overall evaluation score. The System Usability Scale score and word error rate for Saramonic Blink500 were 65% and 26%, respectively, and nurses found it easier to approach patients using this device than with the other 2 devices. Overall, patients found the process of audio recording to be satisfactory and convenient, with minimal impact on their communication with nurses. Although, in general, nurses also found the process easy to learn and satisfactory, they suggested that the audio recording of HHC encounters can affect their communication patterns. In addition, nurses were not aware of the potential to use audio-recorded encounters to improve health care services. Nurses also indicated that they would need to involve their managers to determine how audio recordings could be integrated into their clinical workflows and for any ongoing use of audio recordings during patient care management. Conclusions: This study established the feasibility of audio recording HHC patient-nurse encounters. Training HHC nurses about the importance of the audio-recording process and the support of clinical managers are essential factors for successful implementation. %M 35544296 %R 10.2196/35325 %U https://humanfactors.jmir.org/2022/2/e35325 %U https://doi.org/10.2196/35325 %U http://www.ncbi.nlm.nih.gov/pubmed/35544296 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 2 %P e31486 %T Stakeholder Perspectives on In-home Passive Remote Monitoring to Support Aging in Place in the Province of New Brunswick, Canada: Rapid Qualitative Investigation %A Read,Emily A %A Gagnon,Danie A %A Donelle,Lorie %A Ledoux,Kathleen %A Warner,Grace %A Hiebert,Brad %A Sharma,Ridhi %+ Faculty of Nursing, University of New Brunswick, 55 Lutz Street, Moncton, NB, E1C 0L2, Canada, 1 506 431 3017, Emily.Read@unb.ca %K aging in place %K home care %K older adults %K passive remote monitoring %D 2022 %7 11.5.2022 %9 Original Paper %J JMIR Aging %G English %X Background: The province of New Brunswick (NB) has one of the oldest populations in Canada, providing an opportunity to develop and test innovative strategies to address the unique health challenges faced by older adults. Passive remote monitoring technology has the potential to support independent living among older adults. Limited research has examined the benefits of and barriers to the adoption of this technology among community-dwelling older adults. Objective: This study aimed to explore perceptions of in-home passive remote monitoring technology designed to support aging in place from the perspective of older adults, their family or friend caregivers, social workers, and government decision-makers in the province of NB, Canada. Methods: Between October 2018 and March 2020, a rapid qualitative investigation of 28 one-on-one interviews was conducted in person or via telephone. Participants included 2 home support services clients and 11 family or friend caregivers who had used passive remote monitoring technology in their homes; 8 social workers who had worked as case managers for home support services clients; and 7 individuals who were key government decision-makers in the adoption, policy development, and use of the technology in the province of NB. The interviews focused on the following topics: decision to adopt the passive remote monitoring system, barriers to adopting the passive remote monitoring system, benefits of the passive remote monitoring system, impact on client health outcomes, and privacy concerns. The interviews were audio recorded, transcribed, and analyzed by a team of 6 researchers. Data analysis was conducted using a rapid assessment process approach that included matrix analysis. Results: Participants reported that the use of the remote monitoring system allowed older adults to live at home longer and provided caregiver relief. Stakeholders were invested in meeting the home support (home care) needs of older adults. However, when it came to the use of remote monitoring, there was a lack of consensus about which clients it was well-suited for and the role that social workers should play in informing clients and caregivers about the service (role ambiguity, gatekeeping, and perceived conflicts of interest). Conclusions: Our findings highlight many benefits and challenges of the adoption of passive remote monitoring for clients, their family or friend caregivers, and public provincial health and social services systems. Passive remote monitoring is a valuable tool that can provide support to older adults and their family or friend caregivers when it is a good fit with client needs. Further work is needed in NB to increase public and social workers’ awareness of the service and its benefits. %M 35544304 %R 10.2196/31486 %U https://aging.jmir.org/2022/2/e31486 %U https://doi.org/10.2196/31486 %U http://www.ncbi.nlm.nih.gov/pubmed/35544304 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 5 %P e35277 %T Development of an Internet of Things Technology Platform (the NEX System) to Support Older Adults to Live Independently: Protocol for a Development and Usability Study %A Timon,Claire M %A Heffernan,Emma %A Kilcullen,Sophia M %A Lee,Hyowon %A Hopper,Louise %A Quinn,Joe %A McDonald,David %A Gallagher,Pamela %A Smeaton,Alan F %A Moran,Kieran %A Hussey,Pamela %A Murphy,Catriona %+ Centre for eIntegrated Care, School of Nursing, Psychotherapy and Community Health, Dublin City University, Glasnevin, Dublin, D09 NR58, Ireland, 353 17006811, claire.timon@dcu.ie %K independent living %K older adults %K Internet of Things %K wearable electronic devices %K activities of daily living %K mobile phone %D 2022 %7 5.5.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: In a rapidly aging population, new and efficient ways of providing health and social support to older adults are required that not only preserve independence but also maintain quality of life and safety. Objective: The NEX project aims to develop an integrated Internet of Things system coupled with artificial intelligence to offer unobtrusive health and wellness monitoring to support older adults living independently in their home environment. The primary objective of this study is to develop and evaluate the technical performance and user acceptability of the NEX system. The secondary objective is to apply machine learning algorithms to the data collected via the NEX system to identify and eventually predict changes in the routines of older adults in their own home environment. Methods: The NEX project commenced in December 2019 and is expected to be completed by August 2022. Mixed methods research (web-based surveys and focus groups) was conducted with 426 participants, including older adults (aged ≥60 years), family caregivers, health care professionals, and home care workers, to inform the development of the NEX system (phase 1). The primary outcome will be evaluated in 2 successive trials (the Friendly trial [phase 2] and the Action Research Cycle trial [phase 3]). The secondary objective will be explored in the Action Research Cycle trial (phase 3). For the Friendly trial, 7 older adult participants aged ≥60 years and living alone in their own homes for a 10-week period were enrolled. A total of 30 older adult participants aged ≥60 years and living alone in their own homes will be recruited for a 10-week data collection period (phase 3). Results: Phase 1 of the project (n=426) was completed in December 2020, and phase 2 (n=7 participants for a 10-week pilot study) was completed in September 2021. The expected completion date for the third project phase (30 participants for the 10-week usability study) is June 2022. Conclusions: The NEX project has considered the specific everyday needs of older adults and other stakeholders, which have contributed to the design of the integrated system. The innovation of the NEX system lies in the use of Internet of Things technologies and artificial intelligence to identify and predict changes in the routines of older adults. The findings of this project will contribute to the eHealth research agenda, focusing on the improvement of health care provision and patient support in home and community environments. International Registered Report Identifier (IRRID): DERR1-10.2196/35277 %M 35511224 %R 10.2196/35277 %U https://www.researchprotocols.org/2022/5/e35277 %U https://doi.org/10.2196/35277 %U http://www.ncbi.nlm.nih.gov/pubmed/35511224 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 2 %P e28222 %T Using GPS Tracking to Investigate Outdoor Navigation Patterns in Patients With Alzheimer Disease: Cross-sectional Study %A Puthusseryppady,Vaisakh %A Morrissey,Sol %A Aung,Min Hane %A Coughlan,Gillian %A Patel,Martyn %A Hornberger,Michael %+ Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, United Kingdom, 44 1603 59 7139, m.hornberger@uea.ac.uk %K Alzheimer disease %K dementia %K spatial disorientation %K getting lost %K outdoor navigation %K risk factors %K environmental %K GPS tracking %K community %K mobile phone %D 2022 %7 21.4.2022 %9 Original Paper %J JMIR Aging %G English %X Background: Spatial disorientation is one of the earliest and most distressing symptoms seen in patients with Alzheimer disease (AD) and can lead to them getting lost in the community. Although it is a prevalent problem worldwide and is associated with various negative consequences, very little is known about the extent to which outdoor navigation patterns of patients with AD explain why spatial disorientation occurs for them even in familiar surroundings. Objective: This study aims to understand the outdoor navigation patterns of patients with AD in different conditions (alone vs accompanied; disoriented vs not disoriented during the study) and investigate whether patients with AD experienced spatial disorientation when navigating through environments with a high outdoor landmark density and complex road network structure (road intersection density, intersection complexity, and orientation entropy). Methods: We investigated the outdoor navigation patterns of community-dwelling patients with AD (n=15) and age-matched healthy controls (n=18) over a 2-week period using GPS tracking and trajectory mining analytical techniques. Here, for the patients, the occurrence of any spatial disorientation behavior during this tracking period was recorded. We also used a spatial buffer methodology to capture the outdoor landmark density and features of the road network in the environments that the participants visited during the tracking period. Results: The patients with AD had outdoor navigation patterns similar to those of the controls when they were accompanied; however, when they were alone, they had significantly fewer outings per day (total outings: P<.001; day outings: P=.003; night outings: P<.001), lower time spent moving per outing (P=.001), lower total distance covered per outing (P=.009), lower walking distance per outing (P=.02), and lower mean distance from home per outing (P=.004). Our results did not identify any mobility risk factors for spatial disorientation. We also found that the environments visited by patients who experienced disorientation versus those who maintained their orientation during the tracking period did not significantly differ in outdoor landmark density (P=.60) or road network structure (road intersection density: P=.43; intersection complexity: P=.45; orientation entropy: P=.89). Conclusions: Our findings suggest that when alone, patients with AD restrict the spatial and temporal extent of their outdoor navigation in the community to successfully reduce their perceived risk of spatial disorientation. Implications of this work highlight the importance for future research to identify which of these individuals may be at an actual high risk for spatial disorientation as well as to explore the implementation of health care measures to help maintain a balance between patients’ right to safety and autonomy when making outings alone in the community. %M 35451965 %R 10.2196/28222 %U https://aging.jmir.org/2022/2/e28222 %U https://doi.org/10.2196/28222 %U http://www.ncbi.nlm.nih.gov/pubmed/35451965 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 2 %P e28260 %T Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study %A Shahid,Zahraa Khais %A Saguna,Saguna %A Åhlund,Christer %+ Division of Computer Science, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Forskargatan 1, Skellefteå, 931 77, Sweden, 46 704741624, zahraa.shahid@ltu.se %K Activities of daily living %K smart homes %K elderly care %K anomaly detection %K IoT devices %K smart device %K elderly %K sensors %K digital sensors %K Internet of things %D 2022 %7 11.4.2022 %9 Original Paper %J JMIR Aging %G English %X Background: One of the main challenges of health monitoring systems is the support of older persons in living independently in their homes and with relatives. Smart homes equipped with internet of things devices can allow older persons to live longer in their homes. Previous surveys used to identify sensor-based data sets in human activity recognition systems have been limited by the use of public data set characteristics, data collected in a controlled environment, and a limited number of older participants. Objective: The objective of our study is to build a model that can learn the daily routines of older persons, detect deviations in daily living behavior, and notify these anomalies in near real-time to relatives. Methods: We extracted features from large-scale sensor data by calculating the time duration and frequency of visits. Anomalies were detected using a parametric statistical approach, unusually short or long durations being detected by estimating the mean (μ) and standard deviation (σ) over hourly time windows (80 to 355 days) for different apartments. The confidence level is at least 75% of the tested values within two (σ) from the mean. An anomaly was triggered where the actual duration was outside the limits of 2 standard deviations (μ−2σ, μ+2σ), activity nonoccurrence, or absence of activity. Results: The patterns detected from sensor data matched the routines self-reported by users. Our system observed approximately 1000 meals and bathroom activities and notifications sent to 9 apartments between July and August 2020. A service evaluation of received notifications showed a positive user experience, an average score of 4 being received on a 1 to 5 Likert-like scale. One was poor, two fair, three good, four very good, and five excellent. Our approach considered more than 75% of the observed meal activities were normal. This figure, in reality, was 93%, normal observed meal activities of all participants falling within 2 standard deviations of the mean. Conclusions: In this research, we developed, implemented, and evaluated a real-time monitoring system of older participants in an uncontrolled environment, with off-the-shelf sensors and internet of things devices being used in the homes of older persons. We also developed an SMS-based notification service and conducted user evaluations. This service acts as an extension of the health/social care services operated by the municipality of Skellefteå provided to older persons and relatives. %M 35404260 %R 10.2196/28260 %U https://aging.jmir.org/2022/2/e28260 %U https://doi.org/10.2196/28260 %U http://www.ncbi.nlm.nih.gov/pubmed/35404260 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e31448 %T Multisensory Home-Monitoring in Individuals With Stable Chronic Obstructive Pulmonary Disease and Asthma: Usability Study of the CAir-Desk %A Kohlbrenner,Dario %A Clarenbach,Christian F %A Ivankay,Adam %A Zimmerli,Lukas %A Gross,Christoph S %A Kuhn,Manuel %A Brunschwiler,Thomas %+ Department of Pulmonology, University Hospital Zurich, Raemistrasse 100, Zurich, 8091, Switzerland, 41 43 253 08 42, dario.kohlbrenner@usz.ch %K home monitoring %K digital health %K respiratory disease %K usability %K feasibility %K adherence %K disease management %K chronic disease %K patient monitoring %D 2022 %7 16.2.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Research integrating multisensory home-monitoring in respiratory disease is scarce. Therefore, we created a novel multisensory home-monitoring device tailored for long-term respiratory disease management (named the CAir-Desk). We hypothesize that recent technological accomplishments can be integrated into a multisensory participant-driven platform. We also believe that this platform could improve chronic disease management and be accessible to large groups at an acceptable cost. Objective: This study aimed to report on user adherence and acceptance as well as system functionality of the CAir-Desk in a sample of participants with stable chronic obstructive pulmonary disease (COPD) or asthma. Methods: We conducted an observational usability study. Participants took part in 4 weeks of home-monitoring with the CAir-Desk. The CAir-Desk recorded data from all participants on symptom burden, physical activity, spirometry, and environmental air quality; data on sputum production, and nocturnal cough were only recorded for participants who experienced symptoms. After the study period, participants reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. We used descriptive statistics and visualizations to display results. Results: Ten participants, 5 with COPD and 5 with asthma took part in this study. They completed symptom burden questionnaires on a median of 96% (25th percentile 14%, 75th percentile 96%), spirometry recordings on 55% (20%, 94%), wrist-worn physical activity recordings on 100% (97%, 100%), arm-worn physical activity recordings on 45% (13%, 63%), nocturnal cough recordings on 34% (9%, 54%), sputum recordings on 5% (3%, 12%), and environmental air quality recordings on 100% (99%, 100%) of the study days. The participants indicated that the measurements consumed a median of 13 (10, 15) min daily, and that they preferred the wrist-worn physical activity monitor to the arm-worn physical activity monitor. Conclusions: The CAir-Desk showed favorable technical performance and was well-accepted by our sample of participants with stable COPD and asthma. The obtained insights were used in a redesign of the CAir-Desk, which is currently applied in a randomized controlled trial including an interventional program. %M 35171107 %R 10.2196/31448 %U https://humanfactors.jmir.org/2022/1/e31448 %U https://doi.org/10.2196/31448 %U http://www.ncbi.nlm.nih.gov/pubmed/35171107 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e32713 %T The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Postacute Sequelae of SARS CoV-2 %A Corman,Benjamin Harris Peterson %A Rajupet,Sritha %A Ye,Fan %A Schoenfeld,Elinor Randi %+ Department of Electrical and Computer Engineering, College of Engineering and Applied Science, Stony Brook University, Light Engineering Building, Room 217, Stony Brook, NY, 11794-2350, United States, 1 631 632 8393, fan.ye@stonybrook.edu %K SARS CoV-2 %K COVID-19 %K post-acute sequelae of SARS CoV-2 (PASC) %K post-COVID %K long COVID %K continuous sensing %K passive monitoring %K wearable sensors %K contactless sensors %K vital sign monitoring %D 2022 %7 26.1.2022 %9 Viewpoint %J J Med Internet Res %G English %X Amid the COVID-19 pandemic, it has been reported that greater than 35% of patients with confirmed or suspected COVID-19 develop postacute sequelae of SARS CoV-2 (PASC). PASC is still a disease for which preliminary medical data are being collected—mostly measurements collected during hospital or clinical visits—and pathophysiological understanding is yet in its infancy. The disease is notable for its prevalence and its variable symptom presentation, and as such, management plans could be more holistically made if health care providers had access to unobtrusive home-based wearable and contactless continuous physiologic and physical sensor data. Such between-hospital or between-clinic data can quantitatively elucidate a majority of the temporal evolution of PASC symptoms. Although not universally of comparable accuracy to gold standard medical devices, home-deployed sensors offer great insights into the development and progression of PASC. Suitable sensors include those providing vital signs and activity measurements that correlate directly or by proxy to documented PASC symptoms. Such continuous, home-based data can give care providers contextualized information from which symptom exacerbation or relieving factors may be classified. Such data can also improve the collective academic understanding of PASC by providing temporally and activity-associated symptom cataloging. In this viewpoint, we make a case for the utilization of home-based continuous sensing that can serve as a foundation from which medical professionals and engineers may develop and pursue long-term mitigation strategies for PASC. %M 34932496 %R 10.2196/32713 %U https://www.jmir.org/2022/1/e32713 %U https://doi.org/10.2196/32713 %U http://www.ncbi.nlm.nih.gov/pubmed/34932496 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 1 %P e31970 %T The Smarter Safer Homes Solution to Support Older People Living in Their Own Homes Through Enhanced Care Models: Protocol for a Stratified Randomized Controlled Trial %A Zhang,Qing %A Varnfield,Marlien %A Higgins,Liesel %A Smallbon,Vanessa %A Bomke,Julia %A O'Dwyer,John %A Byrnes,Joshua M %A Sum,Melissa %A Hewitt,Jennifer %A Lu,Wei %A Karunanithi,Mohanraj %+ Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical, Treatment and Rehabilitation Service-STARS level 7, 296 Herston Road, Herston, 4029, Australia, 61 732533630, Qing.Zhang@csiro.au %K smart home %K aged care %K objective activity of daily living %K randomized trial %K wireless sensor network %K older adults %K care %K methodology %K platform %K benefit %K utilization %K support %K self-management %K digital health %D 2022 %7 24.1.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: An aging population, accompanied by the prevalence of age-related diseases, presents a significant burden to health systems. This is exacerbated by an increasing shortage of aged care staff due to the existing workforce entering their retirement and fewer young people being attracted to work in aged care. In line with consumer preferences and potential cost-efficiencies, government and aged care providers are increasingly seeking options to move care and support to the community or home as opposed to residential care facilities. However, compared to residential care, home environments may provide limited opportunity for monitoring patients’ progression/decline in functioning and therefore limited opportunity to provide timely intervention. To address this, the Smarter Safer Homes (SSH) platform was designed to enable self-monitoring and/or management, and to provide aged care providers with support to deliver their services. The platform uses open Internet of Things communication protocols to easily incorporate commercially available sensors into the system. Objective: Our research aims to detail the benefits of utilizing the SSH platform as a service in its own right as well as a complementary service to more traditional/historical service offerings in aged care. This work is anticipated to validate the capacity and benefits of the SSH platform to enable older people to self-manage and aged care service providers to support their clients to live functionally and independently in their own homes for as long as possible. Methods: This study was designed as a single-blinded, stratified, 12-month randomized controlled trial with participants recruited from three aged care providers in Queensland, Australia. The study aimed to recruit 200 people, including 145 people from metropolitan areas and 55 from regional areas. Participants were randomized to the intervention group (having the SSH platform installed in their homes to assist age care service providers in monitoring and providing timely support) and the control group (receiving their usual aged care services from providers). Data on community care, health and social-related quality of life, health service utilization, caregiver burden, and user experience of both groups were collected at the start, middle (6 months), and end of the trial (12 months). Results: The trial recruited its first participant in April 2019 and data collection of the last participant was completed in November 2020. The trial eventually recruited 195 participants, with 98 participants allocated to the intervention group and 97 participants allocated to the control group. The study also received participants’ health service data from government data resources in June 2021. Conclusions: A crisis is looming to support the aging population. Digital solutions such as the SSH platform have the potential to address this crisis and support aged care in the home and community. The outcomes of this study could improve and support the delivery of aged care services and provide better quality of life to older Australians in various geographical locations. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618000829213; https://tinyurl.com/2n6a75em International Registered Report Identifier (IRRID): DERR1-10.2196/31970 %M 35072640 %R 10.2196/31970 %U https://www.researchprotocols.org/2022/1/e31970 %U https://doi.org/10.2196/31970 %U http://www.ncbi.nlm.nih.gov/pubmed/35072640 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e32362 %T Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project %A Thivel,David %A Corteval,Alice %A Favreau,Jean-Marie %A Bergeret,Emmanuel %A Samalin,Ludovic %A Costes,Frédéric %A Toumani,Farouk %A Dualé,Christian %A Pereira,Bruno %A Eschalier,Alain %A Fearnbach,Nicole %A Duclos,Martine %A Tournadre,Anne %+ Clermont Auvergne University, 3 rue de la chabarde, Aubiere, 63170, France, 33 0770398975, david.thivel@uca.fr %K indicator %K fine body motion %K movement behaviors %K decomposition %K structuration %K sequencing %D 2022 %7 14.1.2022 %9 Viewpoint %J J Med Internet Res %G English %X Methods to measure physical activity and sedentary behaviors typically quantify the amount of time devoted to these activities. Among patients with chronic diseases, these methods can provide interesting behavioral information, but generally do not capture detailed body motion and fine movement behaviors. Fine detection of motion may provide additional information about functional decline that is of clinical interest in chronic diseases. This perspective paper highlights the need for more developed and sophisticated tools to better identify and track the decomposition, structuration, and sequencing of the daily movements of humans. The primary goal is to provide a reliable and useful clinical diagnostic and predictive indicator of the stage and evolution of chronic diseases, in order to prevent related comorbidities and complications among patients. %M 35029537 %R 10.2196/32362 %U https://www.jmir.org/2022/1/e32362 %U https://doi.org/10.2196/32362 %U http://www.ncbi.nlm.nih.gov/pubmed/35029537 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e28022 %T Older Adults’ Loneliness, Social Isolation, and Physical Information and Communication Technology in the Era of Ambient Assisted Living: A Systematic Literature Review %A Latikka,Rita %A Rubio-Hernández,Rosana %A Lohan,Elena Simona %A Rantala,Juho %A Nieto Fernández,Fernando %A Laitinen,Arto %A Oksanen,Atte %+ Faculty of Social Sciences, Tampere University, Kalevantie 4, Tampere, 33100, Finland, 358 504313178, rita.latikka@tuni.fi %K loneliness %K social isolation %K older adults %K physical information and communication technology %K systematic literature review %D 2021 %7 30.12.2021 %9 Review %J J Med Internet Res %G English %X Background: Loneliness and social isolation can have severe effects on human health and well-being. Partial solutions to combat these circumstances in demographically aging societies have been sought from the field of information and communication technology (ICT). Objective: This systematic literature review investigates the research conducted on older adults’ loneliness and social isolation, and physical ICTs, namely robots, wearables, and smart homes, in the era of ambient assisted living (AAL). The aim is to gain insight into how technology can help overcome loneliness and social isolation other than by fostering social communication with people and what the main open-ended challenges according to the reviewed studies are. Methods: The data were collected from 7 bibliographic databases. A preliminary search resulted in 1271 entries that were screened based on predefined inclusion criteria. The characteristics of the selected studies were coded, and the results were summarized to answer our research questions. Results: The final data set consisted of 23 empirical studies. We found out that ICT solutions such as smart homes can help detect and predict loneliness and social isolation, and technologies such as robotic pets and some other social robots can help alleviate loneliness to some extent. The main open-ended challenges across studies relate to the need for more robust study samples and study designs. Further, the reviewed studies report technology- and topic-specific open-ended challenges. Conclusions: Technology can help assess older adults’ loneliness and social isolation, and alleviate loneliness without direct interaction with other people. The results are highly relevant in the COVID-19 era, where various social restrictions have been introduced all over the world, and the amount of research literature in this regard has increased recently. %M 34967760 %R 10.2196/28022 %U https://www.jmir.org/2021/12/e28022 %U https://doi.org/10.2196/28022 %U http://www.ncbi.nlm.nih.gov/pubmed/34967760 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e30135 %T Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation %A Hsu,Yu-Cheng %A Wang,Hailiang %A Zhao,Yang %A Chen,Frank %A Tsui,Kwok-Leung %+ School of Public Health (Shenzhen), Sun Yat-sen University, Room 111, Unit 1, Gezhi Garden 3#, No. 132, East Outer Ring Road, Guangzhou Higher Education Mega Center, Guangzhou, 510000, China, 86 020 83226383, zhaoy393@mail.sysu.edu.cn %K fall risk %K balance %K activity recognition %K automatic framework %K community-dwelling elderly %D 2021 %7 20.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results: The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring. %M 34932008 %R 10.2196/30135 %U https://www.jmir.org/2021/12/e30135 %U https://doi.org/10.2196/30135 %U http://www.ncbi.nlm.nih.gov/pubmed/34932008 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 4 %P e29744 %T Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality? %A Pech,Marion %A Sauzeon,Helene %A Yebda,Thinhinane %A Benois-Pineau,Jenny %A Amieva,Helene %+ Medical Research Unit 1219, Bordeaux Population Health Research Center, Inserm, University of Bordeaux, 146 Rue Léo Saignant, Bordeaux, 33076 Cedex, France, 33 667455145, marion.pech@u-bordeaux.fr %K elderly people %K new technologies %K fall %K acceptability %K digital divide %K aging %K falls %K fall prevention %K detection %K geriatrics %K barriers %K technology acceptance %K home care %K seniors %D 2021 %7 9.12.2021 %9 Viewpoint %J JMIR Aging %G English %X There is an exponential increase in the range of digital products and devices promoting aging in place, in particular, devices aiming at preventing or detecting falls. However, their deployment is still limited and only few studies have been carried out in population-based settings owing to the technological challenges that remain to be overcome and the barriers that are specific to the users themselves, such as the generational digital divide and acceptability factors specific to the older adult population. To date, scarce studies consider these factors. To capitalize technological progress, the future step should be to better consider these factors and to deploy, in a broader and more ecological way, these technologies designed for older adults receiving home care to assess their effectiveness in real life. %M 34889755 %R 10.2196/29744 %U https://aging.jmir.org/2021/4/e29744 %U https://doi.org/10.2196/29744 %U http://www.ncbi.nlm.nih.gov/pubmed/34889755 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e25227 %T “A Question of Trust” and “a Leap of Faith”—Study Participants’ Perspectives on Consent, Privacy, and Trust in Smart Home Research: Qualitative Study %A Kennedy,Mari-Rose %A Huxtable,Richard %A Birchley,Giles %A Ives,Jonathan %A Craddock,Ian %+ Centre for Ethics in Medicine, University of Bristol, Bristol Medical School, 39 Whatley Road, Bristol, BS8 2PS, United Kingdom, 44 117 331 4512, mari-rose.kennedy@bristol.ac.uk %K smart homes %K assistive technology %K research ethics %K informed consent %K privacy %K anonymization %K trust %D 2021 %7 26.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Ubiquitous, smart technology has the potential to assist humans in numerous ways, including with health and social care. COVID-19 has notably hastened the move to remotely delivering many health services. A variety of stakeholders are involved in the process of developing technology. Where stakeholders are research participants, this poses practical and ethical challenges, particularly if the research is conducted in people’s homes. Researchers must observe prima facie ethical obligations linked to participants’ interests in having their autonomy and privacy respected. Objective: This study aims to explore the ethical considerations around consent, privacy, anonymization, and data sharing with participants involved in SPHERE (Sensor Platform for Healthcare in a Residential Environment), a project for developing smart technology for monitoring health behaviors at home. Participants’ unique insights from being part of this unusual experiment offer valuable perspectives on how to properly approach informed consent for similar smart home research in the future. Methods: Semistructured qualitative interviews were conducted with 7 households (16 individual participants) recruited from SPHERE. Purposive sampling was used to invite participants from a range of household types and ages. Interviews were conducted in participants’ homes or on-site at the University of Bristol. Interviews were digitally recorded, transcribed verbatim, and analyzed using an inductive thematic approach. Results: Four themes were identified—motivation for participating; transparency, understanding, and consent; privacy, anonymity, and data use; and trust in research. Motivations to participate in SPHERE stemmed from an altruistic desire to support research directed toward the public good. Participants were satisfied with the consent process despite reporting some difficulties—recalling and understanding the information received, the timing and amount of information provision, and sometimes finding the information to be abstract. Participants were satisfied that privacy was assured and judged that the goals of the research compensated for threats to privacy. Participants trusted SPHERE. The factors that were relevant to developing and maintaining this trust were the trustworthiness of the research team, the provision of necessary information, participants’ control over their participation, and positive prior experiences of research involvement. Conclusions: This study offers valuable insights into the perspectives of participants in smart home research on important ethical considerations around consent and privacy. The findings may have practical implications for future research regarding the types of information researchers should convey, the extent to which anonymity can be assured, and the long-term duty of care owed to the participants who place trust in researchers not only on the basis of this information but also because of their institutional affiliation. This study highlights important ethical implications. Although autonomy matters, trust appears to matter the most. Therefore, researchers should be alert to the need to foster and maintain trust, particularly as failing to do so might have deleterious effects on future research. %M 34842551 %R 10.2196/25227 %U https://mhealth.jmir.org/2021/11/e25227 %U https://doi.org/10.2196/25227 %U http://www.ncbi.nlm.nih.gov/pubmed/34842551 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e29001 %T Factors Associated With Behavioral and Psychological Symptoms of Dementia: Prospective Observational Study Using Actigraphy %A Cho,Eunhee %A Kim,Sujin %A Hwang,Sinwoo %A Kwon,Eunji %A Heo,Seok-Jae %A Lee,Jun Hong %A Ye,Byoung Seok %A Kang,Bada %+ Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 222283274, bdkang@yuhs.ac %K behavioral and psychological symptoms %K dementia %K older adults %K actigraphy %K sleep %K activity %K risk factors %D 2021 %7 29.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors—circadian rhythm–related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states—on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures. Objective: The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model. Methods: A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing. Results: Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day’s sleep and nighttime behaviors (odds ratio [OR] 0.9, 95% CI 0.8-1.0; P=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95% CI 0.0-0.5; P=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes. Conclusions: BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care. %M 34714244 %R 10.2196/29001 %U https://www.jmir.org/2021/10/e29001 %U https://doi.org/10.2196/29001 %U http://www.ncbi.nlm.nih.gov/pubmed/34714244 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e28622 %T Reach Outcomes and Costs of Different Physician Referral Strategies for a Weight Management Program Among Rural Primary Care Patients: Type 3 Hybrid Effectiveness-Implementation Trial %A Porter,Gwenndolyn %A Michaud,Tzeyu L %A Schwab,Robert J %A Hill,Jennie L %A Estabrooks,Paul A %+ Department of Health Promotion, University of Nebraska Medical Center, 984365 Nebraska Medical Center, Omaha, NE, 68198, United States, 1 4025591082, gwenndolyn.porter@unmc.edu %K weight management %K rural %K RE-AIM %K hybrid effectiveness-implementation %K primary care %K obesity %K physicians %K digital health %K health technology %K mobile phone %D 2021 %7 20.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Rural residents are at high risk for obesity; however, little resources exist to address this disproportional burden of disease. Primary care may provide an opportunity to connect primary care patients with overweight and obesity to effective weight management programming. Objective: The purpose of this study is to examine the utility of different physician referral and engagement processes for improving the reach of an evidence-based and technology-delivered weight management program with counseling support for rural primary care patients. Methods: A total of 5 rural primary care physicians were randomly assigned a sequence of four referral strategies: point-of-care (POC) referral with active telephone follow-up (ATF); POC referral, no ATF; a population health registry–derived letter referral with ATF; and letter referral, no ATF. For registry-derived referrals, physicians screened a list of patients with BMI ≥25 and approved patients for participation to receive a personalized referral letter via mail. Results: Out of a potential 991 referrals, 573 (57.8%) referrals were made over 16 weeks, and 98 (9.9%) patients were enrolled in the program (58/98, 59.2% female). Differences based on letter (485/991, 48.9%) versus POC (506/991, 51.1%) referrals were identified for completion (100% vs 7%; P<.001) and for proportion screened (36% vs 12%; P<.001) but not for proportion enrolled (12% vs 8%; P=.10). Patients receiving ATF were more likely to be screened (47% vs 7%; P<.001) and enrolled (15% vs 7%; P<.001) than those not receiving ATF. On the basis of the number of referrals made in each condition, we found variations in the proportion and number of enrollees (POC with ATF: 27/190, 50%; POC no ATF: 14/316, 41%; letter ATF: 30/199; 15.1%; letter no ATF: 27/286, 9.4%). Across all conditions, participants were representative of the racial and ethnic characteristics of the region (60% female, P=.15; 94% White individuals, P=.60; 94% non-Hispanic, P=.19). Recruitment costs totaled US $6192, and the overall recruitment cost per enrolled participant was US $63. Cost per enrolled participant ranged from POC with ATF (US $47), registry-derived letter without ATF (US $52), and POC without ATF (US $56) to registry-derived letter with ATF (US $91). Conclusions: Letter referral with ATF appears to be the best option for enrolling a large number of patients in a digitally delivered weight management program; however, POC with ATF and letters without ATF yielded similar numbers at a lower cost. The best referral option is likely dependent on the best fit with clinical resources. Trial Registration: ClinicalTrials.gov NCT03690557; http://clinicaltrials.gov/ct2/show/NCT03690557 %M 34668873 %R 10.2196/28622 %U https://formative.jmir.org/2021/10/e28622 %U https://doi.org/10.2196/28622 %U http://www.ncbi.nlm.nih.gov/pubmed/34668873 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 8 %P e14552 %T Use of Home-Based Connected Devices in Patients With Cystic Fibrosis for the Early Detection and Treatment of Pulmonary Exacerbations: Protocol for a Qualitative Study %A Morsa,Maxime %A Perrin,Amélie %A David,Valérie %A Rault,Gilles %A Le Roux,Enora %A Alberti,Corinne %A Gagnayre,Rémi %A Pougheon Bertrand,Dominique %+ The Health Education and Practices Laboratory (LEPS UR 3412), Sorbonne Paris North University, 74 rue Marcel Cachin, Bobigny, 93017, France, 33 177 194 691, maxime.morsa@univ-paris13.fr %K cystic fibrosis %K pulmonary exacerbation %K connected devices %K patient education %K self-management %D 2021 %7 18.8.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Early detection of pulmonary exacerbations (PEx) in patients with cystic fibrosis (CF) is important to quickly trigger treatment and reduce respiratory damage. We hypothesized that using home-based and wearable connected devices (CDs) and educating patients to react in case of abnormal variations in a set of parameters would allow patients to detect and manage their PEx early with their care team. Objective: This qualitative study aimed to assess the feasibility and appropriate conditions of a new PEx management process from the users’ point of view by analyzing the experience of patients and of CF center teams regarding the education program, the use of CDs, and the relationship between the patient and the care team during PEx management. Methods: We have been conducting a multicenter pilot study involving 36 patients with CF aged ≥12 years. The intervention was divided into 3 phases. In phase 1 (3 months), patients were equipped with CDs, and their parameters were collected on 3 nonconsecutive days each week. Phase 2 involved the development of a “React to PEx” educational program aimed at providing patients with a personalized action plan. A training session to the educational program was organized for the physicians. Physicians then determined the patients’ personalized alert thresholds by reviewing the data collected during phase 1 and their patients’ clinical history. In phase 3 (12 months), patients were educated by the physician during a clinic visit, and their action plan for reacting in timely fashion to their PEx signs was defined. Education and action plans were revised during clinic visits. At the end of the project, the patients’ experience was collected during semistructured interviews with a researcher as part of the qualitative study. The experience of CF teams was collected during focus groups using a semistructured guide once all their patients had finished the study. The interviews and focus groups were recorded and transcribed verbatim to be analyzed. Data from educational sessions were collected throughout the educational program to be put into perspective with the learnings reported by patients. Analyses are being led by 2 researchers using NVivo (QSR International). Results: The study received the favorable reception of the Committee for the Protection of Persons (CPP NORTH WEST III) on June 10, 2017 (#2017-A00723-50). Out of the 36 patients included in phase 1, 27 were educated and entered phase 3. We completed collection of all data from the patients and care providers. Qualitative analysis will provide a better understanding of users’ experience on the conditions of data collection, how useful CDs are for detecting PEx, how useful the PEx action plan is for reacting quickly, what patients learned about PEx management, and the conditions for this PEx management to be sustainable in routine care. Conclusions: This study will open new perspectives for further research into the implementation of an optimal PEx care process in the organization of care teams in order to support patient self-management. Trial Registration: ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/ct2/show/results/NCT03304028 International Registered Report Identifier (IRRID): DERR1-10.2196/14552 %M 34406124 %R 10.2196/14552 %U https://www.researchprotocols.org/2021/8/e14552 %U https://doi.org/10.2196/14552 %U http://www.ncbi.nlm.nih.gov/pubmed/34406124 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 3 %P e27047 %T Smart Home Sensing and Monitoring in Households With Dementia: User-Centered Design Approach %A Tiersen,Federico %A Batey,Philippa %A Harrison,Matthew J C %A Naar,Lenny %A Serban,Alina-Irina %A Daniels,Sarah J C %A Calvo,Rafael A %+ Dyson School of Design Engineering, Imperial College London, 25 Exhibition Road, South Kensington, London, SW7 2DB, United Kingdom, 44 (0)20 7594 8888, federico.tiersen16@imperial.ac.uk %K assistive technology %K independent living %K internet of things %K remote monitoring %K dementia %K human centered design %K user-centered design %K patient-centered care %K smart home %K digital health %D 2021 %7 11.8.2021 %9 Original Paper %J JMIR Aging %G English %X Background: As life expectancy grows, so do the challenges of caring for an aging population. Older adults, including people with dementia, want to live independently and feel in control of their lives for as long as possible. Assistive technologies powered by artificial intelligence and internet of things devices are being proposed to provide living environments that support the users’ safety, psychological, and medical needs through remote monitoring and interventions. Objective: This study investigates the functional, psychosocial, and environmental needs of people living with dementia, their caregivers, clinicians, and health and social care service providers toward the design and implementation of smart home systems. Methods: We used an iterative user-centered design approach comprising 9 substudies. First, semistructured interviews (9 people with dementia, 9 caregivers, and 10 academic and clinical staff) and workshops (35 pairs of people with dementia and caregivers, and 12 health and social care clinicians) were conducted to define the needs of people with dementia, home caregivers, and professional stakeholders in both daily activities and technology-specific interactions. Then, the spectrum of needs identified was represented via patient–caregiver personas and discussed with stakeholders in a workshop (14 occupational therapists; 4 National Health Service pathway directors; and 6 researchers in occupational therapy, neuropsychiatry, and engineering) and 2 focus groups with managers of health care services (n=8), eliciting opportunities for innovative care technologies and public health strategies. Finally, these design opportunities were discussed in semistructured interviews with participants of a smart home trial involving environmental sensors, physiological measurement devices, smartwatches, and tablet-based chatbots and cognitive assessment puzzles (10 caregivers and 2 people with dementia). A thematic analysis revealed factors that motivate household members to use these technologies. Results: Outcomes of these activities include a qualitative and quantitative analysis of patient, caregiver, and clinician needs and the identification of challenges and opportunities for the design and implementation of remote monitoring systems in public health pathways. Conclusions: Participatory design methods supported the triangulation of stakeholder perspectives to aid the development of more patient-centered interventions and their translation to clinical practice and public health strategy. We discuss the implications and limitations of our findings, the value and the applicability of our methodology, and directions for future research. %M 34383672 %R 10.2196/27047 %U https://aging.jmir.org/2021/3/e27047 %U https://doi.org/10.2196/27047 %U http://www.ncbi.nlm.nih.gov/pubmed/34383672 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 7 %P e27327 %T Medication Adherence Reminder System for Virtual Home Assistants: Mixed Methods Evaluation Study %A Corbett,Cynthia F %A Combs,Elizabeth M %A Chandarana,Peyton S %A Stringfellow,Isabel %A Worthy,Karen %A Nguyen,Thien %A Wright,Pamela J %A O'Kane,Jason M %+ College of Nursing, University of South Carolina, 1601 Greene St, Columbia, SC, 29208, United States, 1 8035766275, corbett@sc.edu %K medication adherence %K medication %K virtual home assistants %K virtual assistant %K public health %K health care costs %K Echo device %K device usability %K digital health %K mobile phone %D 2021 %7 13.7.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Medication nonadherence is a global public health challenge that results in suboptimal health outcomes and increases health care costs. Forgetting to take medicines is one of the most common reasons for unintentional medication nonadherence. Research findings indicate that voice-activated virtual home assistants, such as Amazon Echo and Google Home devices, may be useful in promoting medication adherence. Objective: This study aims to create a medication adherence app (skill), MedBuddy, for Amazon Echo devices and measure the use, usability, and usefulness of this medication-taking reminder skill. Methods: A single-group, mixed methods, cohort feasibility study was conducted with women who took oral contraceptives (N=25). Participants were undergraduate students (age: mean 21.8 years, SD 6.2) at an urban university in the Southeast United States. Participants were given an Amazon Echo Dot with MedBuddy—a new medication reminder skill for Echo devices created by our team—attached to their study account, which they used for 60 days. Participants self-reported their baseline and poststudy medication adherence. MedBuddy use was objectively evaluated by tracking participants’ interactions with MedBuddy through Amazon Alexa. The usability and usefulness of MedBuddy were evaluated through a poststudy interview in which participants responded to both quantitative and qualitative questions. Results: Participants’ interactions with MedBuddy, as tracked through Amazon Alexa, only occurred on half of the study days (mean 50.97, SD 29.5). At study end, participants reported missing their medication less in the past 1 and 6 months compared with baseline (χ21=0.9 and χ21=0.4, respectively; McNemar test: P<.001 for both). However, there was no significant difference in participants’ reported adherence to consistently taking medication within the same 2-hour time frame every day in the past 1 or 6 months at the end of the study compared with baseline (χ21=3.5 and χ21=0.4, respectively; McNemar test: P=.63 and P=.07, respectively). Overall feedback about usability was positive, and participants provided constructive feedback about the skill’s features that could be improved. Participants’ evaluation of MedBuddy’s usefulness was overwhelmingly positive—most (15/23, 65%) said that they would continue using MedBuddy as a medication reminder if provided with the opportunity and that they would recommend it to others. MedBuddy features that participants enjoyed were an external prompt separate from their phone, the ability to hear the reminder prompt from a separate room, multiple reminders, and verbal responses to prompts. Conclusions: The findings of this feasibility study indicate that the MedBuddy medication reminder skill may be useful in promoting medication adherence. However, the skill could benefit from further usability enhancements. %M 34255669 %R 10.2196/27327 %U https://formative.jmir.org/2021/7/e27327 %U https://doi.org/10.2196/27327 %U http://www.ncbi.nlm.nih.gov/pubmed/34255669 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e25529 %T Frequency of Self-Weighing and Weight Change: Cohort Study With 10,000 Smart Scale Users %A Vuorinen,Anna-Leena %A Helander,Elina %A Pietilä,Julia %A Korhonen,Ilkka %+ VTT Technical Research Centre of Finland, P.O. Box 1300, Tampere, FI-33101, Finland, 358 408485966, anna-leena.vuorinen@vtt.fi %K self-monitoring %K self-weighing %K weight change, weight loss, normal weight, overweight, obese, temporal weight change %D 2021 %7 28.6.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Frequent self-weighing is associated with successful weight loss and weight maintenance during and after weight loss interventions. Less is known about self-weighing behaviors and associated weight change in free-living settings. Objective: This study aimed to investigate the association between the frequency of self-weighing and changes in body weight in a large international cohort of smart scale users. Methods: This was an observational cohort study with 10,000 randomly selected smart scale users who had used the scale for at least 1 year. Longitudinal weight measurement data were analyzed. The association between the frequency of self-weighing and weight change over the follow-up was investigated among normal weight, overweight, and obese users using Pearson’s correlation coefficient and linear models. The association between the frequency of self-weighing and temporal weight change was analyzed using linear mixed effects models. Results: The eligible sample consisted of 9768 participants (6515/9768, 66.7% men; mean age 41.5 years; mean BMI 26.8 kg/m2). Of the participants, 4003 (4003/9768, 41.0%), 3748 (3748/9768, 38.4%), and 2017 (2017/9768, 20.6%) were normal weight, overweight, and obese, respectively. During the mean follow-up time of 1085 days, the mean weight change was –0.59 kg, and the mean percentage of days with a self-weigh was 39.98%, which equals 2.8 self-weighs per week. The percentage of self-weighing days correlated inversely with weight change, r=–0.111 (P<.001). Among normal weight, overweight, and obese individuals, the correlations were r=–0.100 (P<.001), r=–0.125 (P<.001), and r=–0.148 (P<.001), respectively. Of all participants, 72.5% (7085/9768) had at least one period of ≥30 days without weight measurements. During the break, weight increased, and weight gains were more pronounced among overweight and obese individuals: 0.58 kg in the normal weight group, 0.93 kg in the overweight group, and 1.37 kg in the obese group (P<.001). Conclusions: Frequent self-weighing was associated with favorable weight loss outcomes also in an uncontrolled, free-living setting, regardless of specific weight loss interventions. The beneficial associations of regular self-weighing were more pronounced for overweight or obese individuals. %M 34075879 %R 10.2196/25529 %U https://www.jmir.org/2021/6/e25529 %U https://doi.org/10.2196/25529 %U http://www.ncbi.nlm.nih.gov/pubmed/34075879 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e24666 %T Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study %A Schütz,Narayan %A Saner,Hugo %A Botros,Angela %A Pais,Bruno %A Santschi,Valérie %A Buluschek,Philipp %A Gatica-Perez,Daniel %A Urwyler,Prabitha %A Müri,René M %A Nef,Tobias %+ Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern, Switzerland, 41 31 632 75 79, tobias.nef@artorg.unibe.ch %K sleep restlessness %K telemonitoring %K digital biomarkers %K contactless sensing %K pervasive computing %K home-monitoring %K older adults %K toss and turns %K sleep monitoring %K body movements in bed %D 2021 %7 11.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective: In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods: We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results: Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions: Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options. %M 34114966 %R 10.2196/24666 %U https://mhealth.jmir.org/2021/6/e24666 %U https://doi.org/10.2196/24666 %U http://www.ncbi.nlm.nih.gov/pubmed/34114966 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 1 %P e26259 %T A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population %A Gardner,Mark %A Randhawa,Sharmil %A Malouf,Gordon %A Reynolds,Karen %+ Medical Device Research Institute, Flinders University, 1284 South Road, Clovelly Park, 5042, Australia, 61 0448121126, mark.gardner@sydney.edu.au %K heart rate %K ballistocardiography %K sleep apnea %K positive airway pressure %K gyroscope %K Kalman filter %D 2021 %7 5.5.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: Obstructive sleep apnea (OSA) is a condition in which a person’s airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR). Objective: This study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device. Methods: A low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation. Results: Before the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error. Conclusions: These results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible. %M 33949952 %R 10.2196/26259 %U https://cardio.jmir.org/2021/1/e26259 %U https://doi.org/10.2196/26259 %U http://www.ncbi.nlm.nih.gov/pubmed/33949952 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 2 %P e26875 %T Requirements for Unobtrusive Monitoring to Support Home-Based Dementia Care: Qualitative Study Among Formal and Informal Caregivers %A Wrede,Christian %A Braakman-Jansen,Annemarie %A van Gemert-Pijnen,Lisette %+ Centre for eHealth and Wellbeing Research, Department of Psychology, Health & Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands, 31 (0)53 489 7537, c.wrede@utwente.nl %K in-home monitoring %K ambient assisted living %K assistive technologies %K dementia %K home care %K informal care %K aging in place %D 2021 %7 12.4.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Due to a growing shortage in residential care, people with dementia will increasingly be encouraged to live at home for longer. Although people with dementia prefer extended independent living, this also puts more pressure on both their informal and formal care networks. To support (in)formal caregivers of people with dementia, there is growing interest in unobtrusive contactless in-home monitoring technologies that allow caregivers to remotely monitor the lifestyle, health, and safety of their care recipients. Despite their potential, these solutions will only be viable if they meet the expectations and needs of formal and informal caregivers of people with dementia. Objective: The objective of this study was to explore the expected benefits, barriers, needs, and requirements toward unobtrusive in-home monitoring from the perspective of formal and informal caregivers of community-dwelling people with dementia. Methods: A combination of semistructured interviews and focus groups was used to collect data among informal (n=19) and formal (n=16) caregivers of people with dementia. Both sets of participants were presented with examples of unobtrusive in-home monitoring followed by questions addressing expected benefits, barriers, and needs. Relevant in-home monitoring goals were identified using a previously developed topic list. Interviews and focus groups were transcribed and inductively analyzed. Requirements for unobtrusive in-home monitoring were elicited based on the procedure of van Velsen and Bergvall-Kåreborn. Results: Formal and informal caregivers saw unobtrusive in-home monitoring as a support tool that should particularly be used to monitor (the risk of) falls, day and night rhythm, personal hygiene, nocturnal restlessness, and eating and drinking behavior. Generally, (in)formal caregivers reported cross-checking self-care information, extended independent living, objective communication, prevention and proactive measures, emotional reassurance, and personalized and optimized care as the key benefits of unobtrusive in-home monitoring. Main concerns centered around privacy, information overload, and ethical concerns related to dehumanizing care. Furthermore, 16 requirements for unobtrusive in-home monitoring were generated that specified desired functions, how the technology should communicate with the user, which services surrounding the technology were seen as needed, and how the technology should be integrated into the existing work context. Conclusions: Despite the presence of barriers, formal and informal caregivers of people with dementia generally saw value in unobtrusive in-home monitoring, and felt that these systems could contribute to a shift from reactive to more proactive and less obtrusive care. However, the full potential of unobtrusive in-home monitoring can only unfold if relevant concerns are considered. Our requirements can inform the development of more acceptable and goal-directed in-home monitoring technologies to support home-based dementia care. %M 33843596 %R 10.2196/26875 %U https://aging.jmir.org/2021/2/e26875 %U https://doi.org/10.2196/26875 %U http://www.ncbi.nlm.nih.gov/pubmed/33843596 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e22613 %T Acceptance of Technologies for Aging in Place: A Conceptual Model %A Jaschinski,Christina %A Ben Allouch,Somaya %A Peters,Oscar %A Cachucho,Ricardo %A van Dijk,Jan A G M %+ Research Group Technology, Health & Care, Saxion University of Applied Sciences, M.H. Tromplaan 28, Enschede, 7513AB, Netherlands, 31 6 57 81 46 14, c.jaschinski@saxion.nl %K ambient assisted living %K assistive technology %K healthy aging %K technology adoption %K theory of planned behavior %K structural equation modeling %D 2021 %7 31.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Older adults want to preserve their health and autonomy and stay in their own home environment for as long as possible. This is also of interest to policy makers who try to cope with growing staff shortages and increasing health care expenses. Ambient assisted living (AAL) technologies can support the desire for independence and aging in place. However, the implementation of these technologies is much slower than expected. This has been attributed to the lack of focus on user acceptance and user needs. Objective: The aim of this study is to develop a theoretically grounded understanding of the acceptance of AAL technologies among older adults and to compare the relative importance of different acceptance factors. Methods: A conceptual model of AAL acceptance was developed using the theory of planned behavior as a theoretical starting point. A web-based survey of 1296 older adults was conducted in the Netherlands to validate the theoretical model. Structural equation modeling was used to analyze the hypothesized relationships. Results: Our conceptual model showed a good fit with the observed data (root mean square error of approximation 0.04; standardized root mean square residual 0.06; comparative fit index 0.93; Tucker-Lewis index 0.92) and explained 69% of the variance in intention to use. All but 2 of the hypothesized paths were significant at the P<.001 level. Overall, older adults were relatively open to the idea of using AAL technologies in the future (mean 3.34, SD 0.73). Conclusions: This study contributes to a more user-centered and theoretically grounded discourse in AAL research. Understanding the underlying behavioral, normative, and control beliefs that contribute to the decision to use or reject AAL technologies helps developers to make informed design decisions based on users’ needs and concerns. These insights on acceptance factors can be valuable for the broader field of eHealth development and implementation. %M 33787505 %R 10.2196/22613 %U https://www.jmir.org/2021/3/e22613 %U https://doi.org/10.2196/22613 %U http://www.ncbi.nlm.nih.gov/pubmed/33787505 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e24339 %T Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study %A Hendriks,Maartje M S %A van Lotringen,Jaap H %A Vos-van der Hulst,Marije %A Keijsers,Noël L W %+ Department of Research, Sint Maartenskliniek, PO Box 9011, Nijmegen, 6500 GM, Netherlands, 31 243659149, maa.hendriks@maartenskliniek.nl %K continuous sleep monitoring device %K bed sensor technology %K mHealth %K nocturnal heart rate %K nocturnal respiratory rate %K nocturnal movement activity %K neurological disorders %K incomplete spinal cord injury %K stroke %K inpatient rehabilitation %K clinical application %D 2021 %7 8.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective: The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods: Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results: In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=−2.1, P=.04) and movement activity (t18=−1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. Conclusions: It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants. %M 33555268 %R 10.2196/24339 %U http://mhealth.jmir.org/2021/2/e24339/ %U https://doi.org/10.2196/24339 %U http://www.ncbi.nlm.nih.gov/pubmed/33555268 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 1 %P e17501 %T Development and Feasibility of a Family-Based Health Behavior Intervention Using Intelligent Personal Assistants: Randomized Controlled Trial %A Carlin,Angela %A Logue,Caomhan %A Flynn,Jonathan %A Murphy,Marie H %A Gallagher,Alison M %+ Centre for Exercise Medicine, Physical Activity and Health, Sport and Exercise Sciences Research Institute, Ulster University, Shore Road, Newtownabbey, United Kingdom, 44 2871675037, a.carlin1@ulster.ac.uk %K children %K parent %K physical activity %K healthy eating %K technology %K mobile phone %D 2021 %7 28.1.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Intelligent personal assistants such as Amazon Echo and Google Home have become increasingly integrated into the home setting and, therefore, may facilitate behavior change via novel interactions or as an adjunct to conventional interventions. However, little is currently known about their potential role in this context. Objective: This feasibility study aims to develop the Intelligent Personal Assistant Project (IPAP) and assess the acceptability and feasibility of this technology for promoting and maintaining physical activity and other health-related behaviors in both parents and children. Methods: This pilot feasibility study was conducted in 2 phases. For phase 1, families who were attending a community-based weight management project were invited to participate, whereas phase 2 recruited families not currently receiving any additional intervention. Families were randomly allocated to either the intervention group (received a smart speaker for use in the family home) or the control group. The IPAP intervention aimed to promote positive health behaviors in the family setting through utilization of the functions of a smart speaker and its linked intelligent personal assistant. Data were collected on recruitment, retention, outcome measures, intervention acceptability, device interactions, and usage. Results: In total, 26 families with at least one child aged 5 to 12 years were recruited, with 23 families retained at follow-up. Across phase 1 of the intervention, families interacted with the intelligent personal assistant a total of 65 times. Although device interactions across phase 2 of the intervention were much higher (312 times), only 10.9% (34/312) of interactions were coded as relevant (related to diet, physical activity or well-being). Focus groups highlighted that the families found the devices acceptable and easy to use and felt that the prompts or reminders were useful in prompting healthier behaviors. Some further intervention refinements in relation to the timing of prompts and integrating feedback alongside the devices were suggested by families. Conclusions: Using intelligent personal assistants to deliver health-related messages and information within the home is feasible, with high levels of engagement reported by participating families. This novel feasibility study highlights important methodological considerations that should inform future trials testing the effectiveness of intelligent personal assistants in promoting positive health-related behaviors. Trial Registration: ISRCTN Registry ISRCTN16792534; http://www.isrctn.com/ISRCTN16792534 %M 33507155 %R 10.2196/17501 %U http://formative.jmir.org/2021/1/e17501/ %U https://doi.org/10.2196/17501 %U http://www.ncbi.nlm.nih.gov/pubmed/33507155 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 1 %P e23381 %T Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study %A Vollmer Dahlke,Deborah %A Lee,Shinduk %A Smith,Matthew Lee %A Shubert,Tiffany %A Popovich,Stephen %A Ory,Marcia G %+ DVD Associates, LLC, 8402 Silver Mountain CV, Austin, TX, 78737, United States, 1 512 699 4493, deborahvd@gmail.com %K wearables %K falls alert technology %K falls %K caregivers %K care recipients %D 2021 %7 27.1.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Wearable technology for fall alerts among older adult care recipients is one of the more frequently studied areas of technology, given the concerning consequences of falls among this population. Falls are quite prevalent in later life. While there is a growing amount of literature on older adults’ acceptance of technology, less is known about how caregivers’ attitudes toward technology can impact care recipients’ use of such technology. Objective: The objective of our study was to examine associations between caregivers’ attitudes toward technology for caregiving and care recipients’ use of fall alert wearables. Methods: This study examined data collected with an online survey from 626 caregivers for adults 50 years and older. Adapted from the technology acceptance model, a structural equation model tested the following prespecified hypotheses: (1) higher perceived usefulness of technologies for caregiving would predict higher perceived value of and greater interest in technologies for caregiving; (2) higher perceived value of technologies for caregiving would predict greater interest in technologies for caregiving; and (3) greater interest in technologies for caregiving would predict greater use of fall alert wearables among care recipients. Additionally, we included demographic factors (eg, caregivers’ and care recipients’ ages) and caregiving context (eg, caregiver type and caregiving situation) as important predictors of care recipients’ use of fall alert wearables. Results: Of 626 total respondents, 548 (87.5%) with all valid responses were included in this study. Among care recipients, 28% used fall alert wearables. The final model had a good to fair model fit: a confirmatory factor index of 0.93, a standardized root mean square residual of 0.049, and root mean square error of approximation of 0.066. Caregivers’ perceived usefulness of technology was positively associated with their attitudes toward using technology in caregiving (b=.70, P<.001) and interest in using technology for caregiving (b=.22, P=.003). Greater perceived value of using technology in caregiving predicted greater interest in using technology for caregiving (b=.65, P<.001). Greater interest in using technology for caregiving was associated with greater likelihood of care recipients using fall alert wearables (b=.27, P<.001). The caregiver type had the strongest inverse relationship with care recipients’ use of fall alert wearables (unpaid vs paid caregiver) (b=–.33, P<.001). Conclusions: This study underscores the importance of caregivers’ attitudes in care recipients’ technology use for falls management. Raising awareness and improving perception about technologies for caregiving may help caregivers and care recipients adopt and better utilize technologies that can promote independence and enhance safety. %M 33502320 %R 10.2196/23381 %U http://aging.jmir.org/2021/1/e23381/ %U https://doi.org/10.2196/23381 %U http://www.ncbi.nlm.nih.gov/pubmed/33502320 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e19625 %T Determining the Intellectual Structure and Academic Trends of Smart Home Health Care Research: Coword and Topic Analyses %A Kang,Hyo-Jin %A Han,Jieun %A Kwon,Gyu Hyun %+ Graduate School of Technology and Innovation Management, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea, 82 2 2220 2414, ghkwon@hanyang.ac.kr %K smart home %K smart home health care %K coword analysis %K topic analysis %K intellectual structure %K academic trends %D 2021 %7 21.1.2021 %9 Review %J J Med Internet Res %G English %X Background: With the rapid development of information and communication technologies, smart homes are being investigated as effective solutions for home health care. The increasing academic attention on smart home health care has primarily been on the development and application of smart home technologies. However, comprehensive studies examining the general landscape of diverse research areas for smart home health care are still lacking. Objective: This study aims to determine the intellectual structure of smart home health care in a time series by conducting a coword analysis and topic analysis. Specifically, it investigates (1) the intellectual basis of smart home health care through overall academic status, (2) the intellectual foci through influential keywords and their evolutions, and (3) intellectual trends through primary topics and their evolutions. Methods: Analyses were conducted in 5 steps: (1) data retrieval from article databases (Web of Science, Scopus, and PubMed) and the initial dataset preparation of 6080 abstracts from the year 2000 to the first half of 2019; (2) data preprocessing and refinement extraction of 25,563 words; (3) a descriptive analysis of the overall academic status and period division (ie, 4 stages of 3-year blocks); (4) coword analysis based on word co-occurrence networks for the intellectual foci; and (5) topic analysis for the intellectual trends based on latent Dirichlet allocation (LDA) topic modeling, word-topic networks, and researcher workshops. Results: First, regarding the intellectual basis of smart home health care, recent academic interest and predominant journals and research domains were verified. Second, to determine the intellectual foci, primary keywords were identified and classified according to the degree of their centrality values. Third, 5 themes pertaining to the topic evolution emerged: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care. Based on these findings, the pattern of technology diffusion in smart home health care research was determined as the adaptation of technologies, the proliferation of application areas, and an extension into system design and service experiences. Conclusions: The research findings provide academic and practical value in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by identifying its multifaceted intellectual structure in a time series. Second, they can help clinicians discern the development and dispersion level of their respective disciplines. Third, the pattern of technology diffusion in smart home health care could help scholars comprehend current and future research trends and identify research opportunities based on upcoming research waves of newly adapted technologies in smart home health care. %M 33475514 %R 10.2196/19625 %U http://www.jmir.org/2021/1/e19625/ %U https://doi.org/10.2196/19625 %U http://www.ncbi.nlm.nih.gov/pubmed/33475514 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e18806 %T Application of In-Home Monitoring Data to Transition Decisions in Continuing Care Retirement Communities: Usability Study %A Wild,Katherine %A Sharma,Nicole %A Mattek,Nora %A Karlawish,Jason %A Riley,Thomas %A Kaye,Jeffrey %+ Department of Neurology, Oregon Center for Aging and Technology, Oregon Health and Science University, Portland, OR, United States, 1 503 494 6975, wildk@ohsu.edu %K technology %K remote sensing technology %K care transition %D 2021 %7 13.1.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Continuous in-home monitoring of older adults can provide rich and sensitive data capturing subtle behavioral and cognitive changes. Our previous work has identified multiple metrics that describe meaningful trends in daily activities over time. The continuous, multidomain nature of this technology may also serve to inform caregivers of the need for higher levels of care to maintain the health and safety of at-risk older adults. Accordingly, care decisions can be based on objective, systematically assessed real-time data. Objective: This study deployed a suite of in-home monitoring technologies to detect changing levels of care needs in residents of independent living units in 7 retirement communities and to assess the efficacy of computer-based tools in informing decisions regarding care transitions. Methods: Continuous activity data were presented via an interactive, web-based tool to the staff identified in each facility who were involved in decisions regarding transitions in care among residents. Comparisons were planned between outcomes for residents whose data were shared and those whose data were not made available to the staff. Staff use of the data dashboard was monitored throughout the study, and exit interviews with the staff were conducted to explicate staff interaction with the data platform. Residents were sent weekly self-report questionnaires to document any health- or care-related changes. Results: During the study period, 30 of the 95 residents (32%) reported at least one incidence of new or increased provision of care; 6 residents made a permanent move to a higher level of care within their communities. Despite initial enthusiasm and an iterative process of refinement of measures and modes of data presentation based on staff input, actual inspection and therefore the use of resident data were well below expectation. In total, 11 of the 25 staff participants (44%) logged in to the activity dashboard throughout the study. Survey data and in-depth interviews provided insight into the mismatch between intended and actual use. Conclusions: Most continuous in-home monitoring technology acceptance models focus on perceived usefulness and ease of use and equate the intent to use technology with actual use. Our experience suggests otherwise. We found that multiple intervening variables exist between perceived usefulness, intent to use, and actual use. Ethical, institutional, and social factors are considered in their roles as determinants of use. %M 33439144 %R 10.2196/18806 %U https://www.jmir.org/2021/1/e18806 %U https://doi.org/10.2196/18806 %U http://www.ncbi.nlm.nih.gov/pubmed/33439144 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e24157 %T Issues Associated With the Management and Governance of Sensor Data and Information to Assist Aging in Place: Focus Group Study With Health Care Professionals %A Hunter,Inga %A Elers,Phoebe %A Lockhart,Caroline %A Guesgen,Hans %A Singh,Amardeep %A Whiddett,Dick %+ School of Management, Massey University, Private Bag 11 222, Palmerston North, 4442, New Zealand, 64 6 350 5701, r.j.whiddett@massey.ac.nz %K smart home %K home monitoring technology %K aging in place %K information governance %K information management %K older people %K support network %K aging %K elderly health %D 2020 %7 2.12.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smart home and telemonitoring technologies have often been suggested to assist health care workers in supporting older people to age in place. However, there is limited research examining diverse information needs of different groups of health care workers and their access to appropriate information technologies. Objective: The aim of this study was to investigate the issues associated with using technologies that connect older people to their health care providers to support aging in place and enhance older people’s health and well-being. Methods: Seven focus group discussions were conducted comprising 44 health care professionals who provided clinic-based or in-home services to community-dwelling older people. Participants were asked about their information needs and how technology could help them support older people to age in place. The recordings of the sessions were transcribed and thematically analyzed. Results: The perspectives varied between the respondents who worked in primary care clinics and those who worked in community-based services. Three overarching themes were identified. The first theme was “access to technology and systems,” which examined the different levels of technology in use and the problems that various groups of health care professionals had in accessing information about their patients. Primary care professionals had access to good internal information systems but they experienced poor integration with other health care providers. The community-based teams had poor access to technology. The second theme was “collecting and sharing of information,” which focused on how technology might be used to provide them with more information about their patients. Primary care teams were interested in telemonitoring for specific clinical indicators but they wanted the information to be preprocessed. Community-based teams were more concerned about gaining information on the patients’ social environment. The third theme was that all respondents identified similar “barriers to uptake”: cost and funding issues, usability of systems by older people, and information security and privacy concerns. Conclusions: The participants perceived the potential benefits of technologies, but they were concerned that the information they received should be preprocessed and integrated with current information systems and tailored to the older people’s unique and changing situations. Several management and governance issues were identified, which needed to be resolved to enable the widespread integration of these technologies into the health care system. The disconnected nature of the current information architecture means that there is no clear way for sensor data from telemonitoring and smart home devices to be integrated with other patient information. Furthermore, cost, privacy, security, and usability barriers also need to be resolved. This study highlights the importance and the complexity of management and governance of systems to collect and disseminate such information. Further research into the requirements of all stakeholder groups and how the information can be processed and disseminated is required. %M 33263551 %R 10.2196/24157 %U https://mhealth.jmir.org/2020/12/e24157 %U https://doi.org/10.2196/24157 %U http://www.ncbi.nlm.nih.gov/pubmed/33263551 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e21016 %T Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study %A Sahu,Kirti Sundar %A Oetomo,Arlene %A Morita,Plinio Pelegrini %+ School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 31372, plinio.morita@uwaterloo.ca %K disruptive technology %K health information systems %K public health surveillance %K health behavior %K Internet of Things %K cell phone %K mobile phone %D 2020 %7 20.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. Objective: The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an individual’s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. Methods: We conducted a pilot study with a sample size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators; sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. Results: The results showed a significant Spearman rank correlation coefficient of 0.8 (P<.001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. Conclusions: The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home. %M 33216001 %R 10.2196/21016 %U https://mhealth.jmir.org/2020/11/e21016 %U https://doi.org/10.2196/21016 %U http://www.ncbi.nlm.nih.gov/pubmed/33216001 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e21209 %T Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance %A Jalali,Niloofar %A Sahu,Kirti Sundar %A Oetomo,Arlene %A Morita,Plinio Pelegrini %+ School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 31372, plinio.morita@uwaterloo.ca %K public health %K IoT %K anomaly detection %K behavioral monitoring %K deep learning %K variational autoencoder %K LSTM %D 2020 %7 13.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective: The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods: From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results: The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions: This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment. %M 33185562 %R 10.2196/21209 %U http://mhealth.jmir.org/2020/11/e21209/ %U https://doi.org/10.2196/21209 %U http://www.ncbi.nlm.nih.gov/pubmed/33185562 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 11 %P e20215 %T Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report %A Lussier,Maxime %A Aboujaoudé,Aline %A Couture,Mélanie %A Moreau,Maxim %A Laliberté,Catherine %A Giroux,Sylvain %A Pigot,Hélène %A Gaboury,Sébastien %A Bouchard,Kévin %A Belchior,Patricia %A Bottari,Carolina %A Paré,Guy %A Consel,Charles %A Bier,Nathalie %+ Research Center of Institut universitaire de gériatrie de Montréal, Integrated Health and Social Services University Network for South-Central Montreal, 4545 chemin Queen-Mary, Montreal, QC, H3W 1W6, Canada, 1 514 340 3540, lussier.maxime@gmail.com %K activities of daily living %K aging %K Alzheimer disease %K ambient assisted living %K health care %K technology assessment %K health %K remote sensing technology %D 2020 %7 13.11.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Many older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level. Objective: The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation. Methods: This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity. Results: Over time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation. Conclusions: Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse’s questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making. %M 33185555 %R 10.2196/20215 %U https://medinform.jmir.org/2020/11/e20215 %U https://doi.org/10.2196/20215 %U http://www.ncbi.nlm.nih.gov/pubmed/33185555 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 3 %N 2 %P e21964 %T Use of an Internet-of-Things Smart Home System for Healthy Aging in Older Adults in Residential Settings: Pilot Feasibility Study %A Choi,Yong K %A Thompson,Hilaire J %A Demiris,George %+ Department of Public Health Sciences, School of Medicine, University of California Davis, 4610 X Street, Suite 2301, Sacramento, CA, 95817, United States, 1 916 734 6083, ygchoi@ucdavis.edu %K Internet of Things %K smart home %K independent living %K aging %K healthy aging %D 2020 %7 10.11.2020 %9 Original Paper %J JMIR Aging %G English %X Background: The Internet-of-Things (IoT) technologies can create smart residences that integrate technology within the home to enhance residents’ safety as well as monitor their health and wellness. However, there has been little research on real-world testing of IoT smart home devices with older adults, and the feasibility and acceptance of such tools have not been systematically examined. Objective: This study aims to conduct a pilot study to investigate the feasibility of using IoT smart home devices in the actual residences of older adults to facilitate healthy aging. Methods: We conducted a 2-month feasibility study on community-dwelling older adults. Participants chose among different IoT devices to be installed and deployed within their homes. The IoT devices tested varied depending on the participant’s preference: a door and window sensor, a multipurpose sensor (motion, temperature, luminosity, and humidity), a voice-operated smart speaker, and an internet protocol (IP) video camera. Results: We recruited a total of 37 older adults for this study, with 35 (95%) successfully completing all procedures in the 2-month study. The average age of the sample was 78 (SD 9) years and primarily comprised women (29/37, 78%), those who were educated (31/37, 86%; bachelor’s degree or higher), and those affected by chronic conditions (33/37, 89%). The most widely chosen devices among the participants were multipurpose sensors and smart speakers. An IP camera was a significantly unpopular choice among participants in both phases. The participant feedback suggests that perceived privacy concerns, perceived usefulness, and curiosity to technology were strong factors when considering which device to have installed in their home. Conclusions: Overall, our deployment results revealed that the use of IoT smart home devices is feasible in actual residences of older adults. These findings may inform the follow-up assessment of IoT technologies and their impact on health-related outcomes and advance our understanding of the role of IoT home-based monitoring technologies to promote successful aging-in-place for older adults. Future trials should consider older adults’ preferences for the different types of smart home devices to be installed in real-world residential settings. %M 33170128 %R 10.2196/21964 %U http://aging.jmir.org/2020/2/e21964/ %U https://doi.org/10.2196/21964 %U http://www.ncbi.nlm.nih.gov/pubmed/33170128 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 11 %P e23943 %T Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis %A Fritz,Roschelle L %A Wilson,Marian %A Dermody,Gordana %A Schmitter-Edgecombe,Maureen %A Cook,Diane J %+ College of Nursing, Washington State University, 14204 NE Salmon Creek Avenue, Vancouver, WA, 98686-9600, United States, 1 3605469623, shelly.fritz@wsu.edu %K pain %K remote monitoring %K sensors %K smart homes %K multiple methods %D 2020 %7 6.11.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. Objective: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. Methods: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. Results: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. Conclusions: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance. %M 33105099 %R 10.2196/23943 %U http://www.jmir.org/2020/11/e23943/ %U https://doi.org/10.2196/23943 %U http://www.ncbi.nlm.nih.gov/pubmed/33105099 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e19068 %T Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study %A Evers,Luc JW %A Raykov,Yordan P %A Krijthe,Jesse H %A Silva de Lima,Ana Lígia %A Badawy,Reham %A Claes,Kasper %A Heskes,Tom M %A Little,Max A %A Meinders,Marjan J %A Bloem,Bastiaan R %+ Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Reinier Postlaan 4, Nijmegen, Netherlands, 31 3616600, luc.evers@radboudumc.nl %K digital biomarkers %K remote patient monitoring %K wearable sensors %K real-life gait %K Parkinson disease %K biomarker %K patient monitoring %K wearables %K gait %D 2020 %7 9.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. Objective: This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. Methods: The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. Results: From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. Conclusions: We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders. %M 33034562 %R 10.2196/19068 %U https://www.jmir.org/2020/10/e19068 %U https://doi.org/10.2196/19068 %U http://www.ncbi.nlm.nih.gov/pubmed/33034562 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e19732 %T Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study %A Kim,Ben %A McKay,Sandra M %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K frailty %K mobile health %K wearables %K physical activity %K home care %K prediction %K predictive modeling, older adults %K activities of daily living, sleep %D 2020 %7 3.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population. Objective: The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods: From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty. Results: A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions: We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services. %M 32880582 %R 10.2196/19732 %U https://www.jmir.org/2020/9/e19732 %U https://doi.org/10.2196/19732 %U http://www.ncbi.nlm.nih.gov/pubmed/32880582 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e16414 %T Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial %A Gatsios,Dimitris %A Antonini,Angelo %A Gentile,Giovanni %A Marcante,Andrea %A Pellicano,Clelia %A Macchiusi,Lucia %A Assogna,Francesca %A Spalletta,Gianfranco %A Gage,Heather %A Touray,Morro %A Timotijevic,Lada %A Hodgkins,Charo %A Chondrogiorgi,Maria %A Rigas,George %A Fotiadis,Dimitrios I %A Konitsiotis,Spyridon %+ Department of Neurology, Medical School, University of Ioannina, University Campus of Ioannina, Ioannina, 45110, Greece, 30 2651007261, d.gatsios@uoi.gr %K Parkinson's disease %K determinants of compliance %K clinically meaningful data %K ecological validity %D 2020 %7 29.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. Objective: We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. Methods: PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored. Results: Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. Conclusions: We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. Trial Registration: ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-018-2767-4 %M 32442154 %R 10.2196/16414 %U https://mhealth.jmir.org/2020/6/e16414 %U https://doi.org/10.2196/16414 %U http://www.ncbi.nlm.nih.gov/pubmed/32442154 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e15923 %T Overview of Policies, Guidelines, and Standards for Active Assisted Living Data Exchange: Thematic Analysis %A Fadrique,Laura X %A Rahman,Dia %A Vaillancourt,Hélène %A Boissonneault,Paul %A Donovska,Tania %A Morita,Plinio P %+ School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567, plinio.morita@uwaterloo.ca %K ambient assisted living %K active assisted living %K AAL %K Internet of Things %K aging well %K aging in place %K elderly %K geriatrics %K standards %K policies %K health care %K ambient intelligence %K domotics %K ubiquitous health %K wearable %D 2020 %7 22.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A primary concern for governments and health care systems is the rapid growth of the aging population. To provide a better quality of life for the elderly, researchers have explored the use of wearables, sensors, actuators, and mobile health technologies. The term AAL can be referred to as active assisted living or ambient assisted living, with both sometimes used interchangeably. AAL technologies describes systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for those who need assistance at any stage of their lives. Objective: The aim of this study was to understand the standards and policy guidelines that companies use in the creation of AAL technologies and to highlight the gap between available technologies, standards, and policies and what should be available for use. Methods: A literature review was conducted to identify critical standards and frameworks related to AAL. Interviews with 15 different stakeholders across Canada were carried out to complement this review. The results from interviews were coded using a thematic analysis and then presented in two workshops about standards, policies, and governance to identify future steps and opportunities regarding AAL. Results: Our study showed that the base technology, standards, and policies necessary for the creation of AAL technology are not the primary problem causing disparity between existing and accessible technologies; instead nontechnical issues and integration between existing technologies present the most significant issue. A total of five themes have been identified for further analysis: (1) end user and purpose; (2) accessibility; (3) interoperability; (4) data sharing; and (5) privacy and security. Conclusions: Interoperability is currently the biggest challenge for the future of data sharing related to AAL technology. Additionally, the majority of stakeholders consider privacy and security to be the main concerns related to data sharing in the AAL scope. Further research is necessary to explore each identified gap in detail. %M 32568090 %R 10.2196/15923 %U https://mhealth.jmir.org/2020/6/e15923 %U https://doi.org/10.2196/15923 %U http://www.ncbi.nlm.nih.gov/pubmed/32568090 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 6 %P e16371 %T Feasibility of In-Home Sensor Monitoring to Detect Mild Cognitive Impairment in Aging Military Veterans: Prospective Observational Study %A Seelye,Adriana %A Leese,Mira Isabelle %A Dorociak,Katherine %A Bouranis,Nicole %A Mattek,Nora %A Sharma,Nicole %A Beattie,Zachary %A Riley,Thomas %A Lee,Jonathan %A Cosgrove,Kevin %A Fleming,Nicole %A Klinger,Jessica %A Ferguson,John %A Lamberty,Greg John %A Kaye,Jeffrey %+ Minneapolis Veterans Affairs Health Care System, 1 Veterans Dr, Minneapolis, MN, , United States, 1 6127252000, adriana.seelye@va.gov %K aging %K mild cognitive impairment %K activities of daily living %K technology %D 2020 %7 8.6.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Aging military veterans are an important and growing population who are at an elevated risk for developing mild cognitive impairment (MCI) and Alzheimer dementia, which emerge insidiously and progress gradually. Traditional clinic-based assessments are administered infrequently, making these visits less ideal to capture the earliest signals of cognitive and daily functioning decline in older adults. Objective: This study aimed to evaluate the feasibility of a novel ecologically valid assessment approach that integrates passive in-home and mobile technologies to assess instrumental activities of daily living (IADLs) that are not well captured by clinic-based assessment methods in an aging military veteran sample. Methods: Participants included 30 community-dwelling military veterans, classified as healthy controls (mean age 72.8, SD 4.9 years; n=15) or MCI (mean age 74.3, SD 6.0 years; n=15) using the Clinical Dementia Rating Scale. Participants were in relatively good health (mean modified Cumulative Illness Rating Scale score 23.1, SD 2.9) without evidence of depression (mean Geriatrics Depression Scale score 1.3, SD 1.6) or anxiety (mean generalized anxiety disorder questionnaire 1.3, SD 1.3) on self-report measures. Participants were clinically assessed at baseline and 12 months later with health and daily function questionnaires and neuropsychological testing. Daily computer use, medication taking, and physical activity and sleep data were collected via passive computer monitoring software, an instrumented pillbox, and a fitness tracker watch in participants’ environments for 12 months between clinical study visits. Results: Enrollment began in October 2018 and continued until the study groups were filled in January 2019. A total of 201 people called to participate following public posting and focused mailings. Most common exclusionary criteria included nonveteran status 11.4% (23/201), living too far from the study site 9.4% (19/201), and having exclusionary health concerns 17.9% (36/201). Five people have withdrawn from the study: 2 with unanticipated health conditions, 2 living in a vacation home for more than half of the year, and 1 who saw no direct benefit from the research study. At baseline, MCI participants had lower Montreal Cognitive Assessment (P<.001) and higher Functional Activities Questionnaire (P=.04) scores than healthy controls. Over seven months, research personnel visited participants’ homes a total of 73 times for technology maintenance. Technology maintenance visits were more prevalent for MCI participants (P=.04) than healthy controls. Conclusions: Installation and longitudinal deployment of a passive in-home IADL monitoring platform with an older adult military veteran sample was feasible. Knowledge gained from this pilot study will be used to help develop acceptable and effective home-based assessment tools that can be used to passively monitor cognition and daily functioning in older adult samples. %M 32310138 %R 10.2196/16371 %U https://formative.jmir.org/2020/6/e16371 %U https://doi.org/10.2196/16371 %U http://www.ncbi.nlm.nih.gov/pubmed/32310138 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e17930 %T Perceptions About Technologies That Help Community-Dwelling Older Adults Remain at Home: Qualitative Study %A Verloo,Henk %A Kampel,Thomas %A Vidal,Nicole %A Pereira,Filipa %+ School of Health Sciences, HES-SO Valais/Wallis, 5, Chemin de l’Agasse, Sion, 1950, Switzerland, 41 276036752, henk.verloo@netplus.ch %K technology %K gerontechnology %K photo-elicitation %K informal caregivers %K cognitive impairment %K professional caregivers %K interviews %K focus groups %K content analysis %K physical impairment %K frailty %D 2020 %7 4.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The population of Europe is aging rapidly. Most community-dwelling older adults (CDOAs) want to remain in their homes, particularly those experiencing functional decline. Politicians and academics repeatedly praise technological instruments for being the preferred solution for helping older adults with deteriorating health to remain at home. Objective: This study aimed to understand the perceptions of CDOAs and their informal caregivers (ICs) and professional caregivers (PCs) about technologies that can help keep older adults at home. Methods: This qualitative study used personal interviews, focus groups, and photo-elicitation interviews to better understand the perceptions of a convenience sample of 68 CDOAs, 21 ICs, and 32 PCs. Results: A fraction of CDOAs did not perceive technological instruments to be a very useful means of helping them remain at home. However, the ICs and PCs were more positive. The CDOAs preferred and were more willing to adopt technologies related to their mobility and safety and those that would help slow down their cognitive decline. The ICs preferred technological aids that assist in the activities of daily living as well as safety-related technologies for detecting falls and helping to locate disoriented older adults. The PCs preferred integrated communication and information systems to improve collaboration between all stakeholders, housing equipped with technologies to manage complex care, high-performance ancillary equipment to transfer people with reduced mobility, and surveillance systems to ensure safety at home. Conclusions: Although our study reports that CDOAs have limited interest in innovative technologies to help them remain at home, their technological skills will undoubtedly improve in the future, as will those of ICs and PCs. Technological tools will play an increasingly important role in home health care. %M 32496197 %R 10.2196/17930 %U http://www.jmir.org/2020/6/e17930/ %U https://doi.org/10.2196/17930 %U http://www.ncbi.nlm.nih.gov/pubmed/32496197 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 5 %P e16854 %T Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study %A Rawtaer,Iris %A Mahendran,Rathi %A Kua,Ee Heok %A Tan,Hwee Pink %A Tan,Hwee Xian %A Lee,Tih-Shih %A Ng,Tze Pin %+ Department of Psychiatry, Sengkang General Hospital, Singhealth Duke NUS Academic Medical Centre, 110 Sengkang East Way, Singapore, 544886, Singapore, 65 69302288, iris.rawtaer@singhealth.com.sg %K dementia %K neurocognitive disorder %K pattern recognition, automated/methods %K internet of things %K early diagnosis %D 2020 %7 5.5.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection. Objective: The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively. Methods: We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered. Results: Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system. Conclusions: We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information. %M 32369031 %R 10.2196/16854 %U https://www.jmir.org/2020/5/e16854 %U https://doi.org/10.2196/16854 %U http://www.ncbi.nlm.nih.gov/pubmed/32369031 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9795 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThe objective of this study is to explore individual, household and population-level health indicators collected in the home via smart thermostats. The study’s approach is to (a) identify if it is possible to isolate specific user behaviours using the motion and thermostat sensor data, and (b) develop Remote Monitoring of healthy behaviours at population level. Furthermore, this study is interested in identifying if observed patterns will suffer variations. As a result, it will be possible to understand human behaviours and consequently understand lifestyle habits of a person or a group of people.IntroductionPublic health surveillance relies on surveys and/or self-reported data collection, both of which require manpower, time commitment, and financial resources from public health agencies and participants. The survey results can quickly become outdated due to fast-paced changes in our society. The health habits of Canadians have rapidly evolved with technology and research indicates we are becoming a sedentary society, thus the levels of physical activity (PA) are very important population level health indicators. We will present a novel method to gather data at a granular level in near real-time, with minimal effort from participants. Simple thermostats are found in nearly every house in Canada, and smart thermostats enable efficient temperature adjustment, saving energy costs by adjusting according to human activity. Thermostats are ubiquitous in Canadian homes and the current expansion of smart thermostats make them an ideal data source over traditional methods. Utilizing technology that can be deployed at a population level will enable vast granular data collection beyond capabilities of traditional surveys. In this project UbiLab1 is exploring the use of the zero-effort technology using sensor data collected by smart thermostats and other associated sensors to develop an innovative health surveillance platform and monitor an individual’s health at the household level as well as health indicators at population level. Utilizing the smart wi-fi thermostat, we able to report on PA, sedentary behaviour, and sleep patterns at the household level. The thermostat and remote sensors (RS) contain temperature and motion sensors, which can be used to monitor activity in the home (i.e. lack of travel indicates sedentary behaviour), as well as sleep characteristics. This is beneficial as no action is required from participants, allowing individuals to go about their lives unperturbed. This powerful system will be able to deliver real-time health insights to public health professionals.MethodsZero-effort-technologies2 represent the future of ambient assisted living (AAL), in which sensors gather data generated by the person without conscious effort by the user. Such data could be integrated with other technologies to give the system the ability to tackle unsolved remote monitoring issues challenged the traditional data collection method barriers. For example, when the RS is placed in the bedroom, they can provide insights on sleep duration and quality. This addresses the challenges of declining participant engagement, low response rates in surveys and focus groups, and technical barriers to wearable technology. This eliminates recall bias, common when asking participants to quantify the amount of PA and types of behaviours they engaged in. Using the motion data, we can quantify the amount of PA in the home to determine individual levels of PA. The UbiLab partnered with ecobee3, a Canadian smart wi-fi thermostat company, leveraging data from over 10,000 households in North-America collected through the Donate Your Data (DYD)4 program. A small pilot study (n = 8) was done to validate the use of motion sensor readings of movement between rooms through a cross comparison with Fitbit5 step data. And the DYD dataset was analyzed for patterns using Python6, pandas7, Elasticsearch8, and Kibana8. This method will enable the delivery of personalized insights to monitor individual- and population-level health behaviours.ResultsPhysical Activity, Sedentary Behaviour and Sleep (PASS) indicators9 are measured through surveys (i.e. Canadian Health Measures Survey and Canadian Community Housing Survey) administered by Statistics Canada. Using this technology public health agencies will enable to collect novel health indicators, monitor health in real-time and deliver health insights to Canadians to increase health literacy. A positive association between Fitbit and ecobee data was found (Spearman’s Correlation coefficient = 0.7, p > 0.001) from 380 person hours from the pilot study. Indicators (sleep, interrupted sleep, daily indoor activity, sedentary) based on the PASS Indicators Framework from the Public Health Agency of Canada (PHAC)2 were measured using DYD data. Single occupant ecobee households in Canada averaged 7.2 hours of sleep in 24-hours, 2.1 hours of interrupted sleep, were active for 85 minutes daily, and spent 4.44 hours being sedentary. Recently, we have improved data collection adding Fitbit Charge 2 HRs, to capture sleep and heart rate not previously possible with the Fitbit Zip. Adding more sensors functionality is crucial for algorithm modifications, this includes collecting additional data via the Samsung SmartThings Hub10; presence, light usage, and luminance. ecobee is sharing participants and data from their own study, increasing variability within data. We have improved our data storage and analysis process, moving the big data architecture from python to Elasticsearch for real-time data streaming and analysis. We are also actively collaborating with PHAC and improving our algorithm and analysis process using their feedback.ConclusionsThis is a key opportunity to innovate traditional data collection methods, empowering patients through education and leveraging technology infrastructures to enable healthcare and policy decisions to be made with relevant and real-time data. Lessons learned at the individual and community health levels will be shared with community members and researchers. Implications include understanding short-term impacts with minimal effort and new health policies at the community level. Increased awareness and improvement can help to better physical activity, sleep and sedentary behaviour which may lead to improvements in overall health and wellbeing.References1. Waterloo U of. Ubilab. https://uwaterloo.ca/ubiquitous-health-technology-lab/.2. Public Health Agency of Canada - Canada.ca. https://www.canada.ca/en/public-health.html. Accessed October 26, 2018.3. ecobee | Smart Home Technology |. https://www.ecobee.com/. Accessed October 26, 2018.4. Donate your Data | Smart WiFi Thermostats by ecobee. https://www.ecobee.com/donateyourdata/. Accessed September 21, 2017.5. Fitbit Official Site for Activity Trackers & More. https://www.fitbit.com/en-ca/home. Accessed September 21, 2017.6. Welcome to Python.org. https://www.python.org/. Accessed November 22, 2017.7. Python Data Analysis Library — pandas: Python Data Analysis Library. https://pandas.pydata.org/. Accessed January 14, 2018.8. Elasticsearch. https://www.elastic.co/. Accessed October 26, 2018.9. Physical Activity, Sedentary Behaviour and Sleep (PASS) Indicator Framework for surveillance - Canada.ca. https://www.canada.ca/en/services/health/monitoring-surveillance/physical-activity-sedentary-behaviour-sleep.html. Accessed January 14, 2018.10. Samsung. Samsung Smart thing hub. 2018. https://www.smartthings.com/products/smartthings-hub. %R 10.5210/ojphi.v11i1.9795 %U %U https://doi.org/10.5210/ojphi.v11i1.9795 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 4 %N 3 %P e4275 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2012 %7 ..2012 %9 %J Online J Public Health Inform %G English %X This article provides a possible methodology to facilitate communication between open source and propriety systems using interoperability principles and a simple flexible text format. %M 23569647 %R 10.5210/ojphi.v4i3.4275 %U %U https://doi.org/10.5210/ojphi.v4i3.4275 %U http://www.ncbi.nlm.nih.gov/pubmed/23569647