JMIR mHealth and uHealth最新文献

{"title":"Just-in-Time Adaptive Interventions to Reduce Sedentary Behavior and Promote Physical Activity in Patients With Chronic Disease: Scoping Review.","authors":"Junchen Guo, Furong Chen, Chan Chen, Yunyun Dai, Xianghua Xu, Yongyi Chen, Mengying Huang","doi":"10.2196/81378","DOIUrl":"10.2196/81378","url":null,"abstract":"<p><strong>Background: </strong>Patients with chronic diseases often struggle to maintain sufficient physical activity (PA) and reduce sedentary behavior in daily life. Just-in-time adaptive interventions (JITAIs), delivered through mobile health technologies such as mobile apps, wearable devices, sensors, and ecological momentary assessment, offer timely and personalized support based on individuals' changing needs. However, the evidence base for their use in promoting PA and reducing sedentary behavior among patients with chronic diseases remains fragmented.</p><p><strong>Objective: </strong>This review aimed to describe study characteristics and design features of JITAIs, summarize reported evaluation findings related to PA and sedentary behavior, and identify barriers and facilitators related to engagement and use of JITAIs.</p><p><strong>Methods: </strong>A comprehensive literature search was conducted in PubMed, Embase, Web of Science, CINAHL, and Scopus from database inception to June 2025. Two researchers independently screened the records, selected eligible studies, and performed data charting to ensure rigor and consistency.</p><p><strong>Results: </strong>A total of 14 studies were included in this review. Of these, 6 primarily targeted PA, 1 focused solely on sedentary behavior, and 5 addressed both PA and sedentary behavior. Of the remaining, 6 studies provided preliminary evidence supporting the positive effects of JITAIs in promoting PA among individuals with chronic diseases, while 3 studies suggested positive effects on reducing sedentary behavior. Barriers and facilitators influencing the use of JITAIs in this population were identified. Barriers included technological usability and literacy, burden and intrusiveness of the intervention, perceived value and acceptability, external and contextual barriers, and privacy and data security concerns. Facilitators included enhanced motivation and behavioral engagement, increased awareness and self-monitoring, usability and integration into daily life, timely and personalized support, and support from health care professionals and the care environment.</p><p><strong>Conclusions: </strong>This scoping review identified preliminary evidence suggesting that JITAIs may help reduce sedentary behavior and promote PA among patients with chronic diseases. However, more large-scale, high-quality randomized controlled trial studies are needed to strengthen evidence and generalizability.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e81378"},"PeriodicalIF":6.3,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13450892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148689123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility of Tablet-Based Eye Tracking for Early Screening of Poststroke Cognitive Impairment: Diagnostic Cohort Study.","authors":"Boxu Xie, Ronghui Ren, Yuting Zhang, Xuan Wang, Junli Zhen","doi":"10.2196/83194","DOIUrl":"10.2196/83194","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Poststroke cognitive impairment (PSCI) is a common and disabling complication after stroke; however, early screening remains challenging due to limited access to neuropsychological testing and the high cost of neuroimaging. Portable, tablet-based eye-tracking technology may offer a scalable, low-cost solution for early PSCI detection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the clinical utility of a tablet-based, AI-driven eye-tracking system for early screening of PSCI at 3 months after acute ischemic stroke. We sought to quantify oculomotor-cognitive associations and develop a practical nomogram for individualized risk prediction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We prospectively enrolled 142 hospitalized patients with acute cerebral infarction between May 2023 and October 2024, of whom 122 completed the 3-month follow-up and were included in the final analysis, along with 20 healthy community-dwelling controls. All patients underwent tablet-based eye tracking (visual paired comparison and antisaccade tasks) during the acute phase, as well as baseline and 3-month neuropsychological assessments. PSCI was defined using validated cutoffs. Multivariable logistic regression was used to identify independent predictors, and a nomogram was constructed. Internal validation was performed using bootstrap resampling (1000 samples).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;At 3 months, out of 122 patients, 47 (38.5%) met PSCI criteria. Compared with patients with non-PSCI (n=75), patients with PSCI showed significantly prolonged correct saccade latency (median 322.96, IQR 209.45-445.59 ms vs 194.55, IQR 141.50-299.75 ms; Z=-4.03, P&lt;.001), increased uncorrected error rate (median 30.00%, IQR 15.00%-42.00% vs 5.00%, IQR 0.00%-28.00%; Z=-4.24, P&lt;.001), and reduced novelty preference ratio (median 1.44, IQR 0.97-1.70 vs 2.12, IQR 1.27-4.56; Z=-3.44, P=.001). Multivariable analysis identified 4 independent predictors of 3-month PSCI: older age (odds ratio [OR] 1.067 per year, 95% CI 1.009-1.129; P=.02), lower education level (OR 0.841 per year, 95% CI 0.708-0.999; P=.049), higher NIHSS (National Institutes of Health Stroke Scale) scores (OR 1.557 per point, 95% CI 1.075-2.256; P=.02), and prolonged correct saccade latency (OR 1.004 per ms, 95% CI 1.000-1.007; P=.04). A nomogram incorporating these 4 factors achieved good discriminative performance (area under the receiver operating characteristic curve 0.86, 95% CI 0.793-0.927) with satisfactory calibration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Age, education, admission NIHSS, and correct saccade latency were identified as possible independent predictors of 3-month PSCI in this cohort. The tablet-based eye-tracking system, when combined with clinical variables, may represent a feasible approach for early PSCI screening. A nomogram based on these variables demonstrated high accuracy and potential clinical utility for early PSCI identification. This approach may facilita","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e83194"},"PeriodicalIF":6.3,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Effectiveness of Virtual Emergency Care Models: Systematic Review.","authors":"Ravi Shankar, Linda Wang, Soon Hoe Ho, Mei Fong Liew, Satya Pavan Kumar Gollamudi, Tze Chin Wong, Serene Wong","doi":"10.2196/82143","DOIUrl":"10.2196/82143","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Virtual care technologies have rapidly expanded in emergency medicine, particularly following the COVID-19 pandemic. However, comprehensive economic evaluations of their cost-effectiveness remain fragmented across different clinical applications and health care settings, creating uncertainty for policymakers and health care administrators considering implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to systematically review and synthesize evidence on the cost-effectiveness of virtual emergency care models compared to traditional in-person emergency care across diverse clinical conditions, populations, and health care settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching 8 electronic databases (PubMed, Embase, Scopus, Web of Science, CINAHL, Cochrane Library, MEDLINE, and PsycINFO) from inception to February 2025. We included full economic evaluations comparing virtual emergency care interventions with usual care. Two reviewers independently screened studies, extracted data, and assessed quality using the Drummond checklist and Consensus Health Economic Criteria (CHEC) list. Evidence certainty was evaluated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. Given heterogeneity in interventions and methods, we conducted a narrative synthesis by virtual care modality and clinical application.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From 5817 identified references, 13 studies met inclusion criteria, representing diverse virtual care modalities across 6 countries (United States, Australia, Italy, Canada, Haiti, and Belgium). All included studies reported favorable economic outcomes for virtual emergency care. Video consultation was the most common modality (11/13 studies), achieving 31% to 73% reduction in patient transfers and cost savings of US $73 (AUD $105) to US $5118 per encounter. A total of 6 (46%) studies found virtual care to be dominant (both less costly and more effective). Incremental cost-effectiveness ratios ranged from US $1273 (€990) to US $108,363 per quality-adjusted life year, with most below accepted willingness-to-pay thresholds. Transfer avoidance was the primary economic driver, particularly in rural settings. Quality assessment revealed high methodological rigor (mean Drummond score 92.3%, SD 6.0%; mean CHEC score 95%, SD 4.2%). Using GRADE, evidence certainty was rated high for cost-effectiveness, moderate for transfer reduction and quality of life improvements, and low for emergency department length of stay and mortality benefits.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Virtual emergency care demonstrates strong and consistent cost-effectiveness across diverse clinical conditions, populations, and health care settings. The evidence particularly supports implementation for stroke care, pediatric emergencies, and rural/r","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e82143"},"PeriodicalIF":6.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13490934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adoption of App-Based Peer Support by Canadian Health Care Providers: Mixed Methods Organizational Implementation Study.","authors":"Sandra Moll, Melissa Parker, Peter Smith, Edward Sykes","doi":"10.2196/85658","DOIUrl":"10.2196/85658","url":null,"abstract":"<p><strong>Background: </strong>There is an urgent and critical need to support the mental health of health care providers, given high rates of stress and burnout. Although the issues are complex, digital access to information and support can help address the needs, as technology can facilitate on-demand links to private, customized resources, including peer support. Beyond Silence (McMaster University) is an evidence-informed mobile health platform co-designed with health care workers and grounded in prior evidence that mental health literacy and peer support can reduce stigma and facilitate earlier help-seeking.</p><p><strong>Objective: </strong>This study aimed to (1) explore how health care workers across diverse health care settings use the app and (2) identify opportunities and barriers to implementation.</p><p><strong>Methods: </strong>A multiple-case study framework, informed by the Consolidated Framework for Implementation Research (CFIR), was applied to capture 4 months of implementation across a purposive sample of 7 diverse Canadian health care organizations. Implementation within each organization was led by designated organizational champions who leveraged existing communication channels and standardized promotional materials to invite employees to voluntarily download and use the app. Implementation outcomes were assessed using app analytics (downloads and feature use) and semistructured baseline and follow-up interviews with organizational champions to explore contextual influences on uptake.</p><p><strong>Results: </strong>Approximately 1066 employees downloaded the app over the 4-month period, ranging from <2% to >45% of employees across the 7 organizations. Interviews with 28 organizational champions noted that there was good leadership support for the technology, aligning with their mission to address employee mental health. Barriers to use, however, included workplace culture surrounding mental health and help-seeking, lack of awareness about when and how to use the app, and infrastructure-related challenges, such as limited time and a lack of private spaces to download and use the technology.</p><p><strong>Conclusions: </strong>Effective implementation is a precondition for positive outcomes; therefore, strategies are needed to optimize technology implementation. Recommendations include evaluating organizational readiness, building mental health literacy, creating a multimodal communication and implementation plan, addressing technology requirements, and embedding the technology into organizational policies and practices. This study highlights key challenges in the implementation of the Beyond Silence peer support platform for health care workers, including slow adoption linked to mental health stigma, competing demands, and limited frontline engagement. Addressing these barriers will require innovative, trust-building strategies to support meaningful uptake and sustained use.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e85658"},"PeriodicalIF":6.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13440936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone-Based Physical Performance and Multidimensional Determinants of Self-Reported Knee Pain in Community-Dwelling Older Adults: Cross-Sectional Machine Learning and Network Analysis Study.","authors":"Ui-Jae Hwang, Peng Xia, Tianxiang Fan, Arnold Yl Wong, Oh-Yun Kwon, Siu-Ngor Fu","doi":"10.2196/92466","DOIUrl":"10.2196/92466","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Knee pain affects 22.9% of individuals aged 40 years and over globally and is associated with body function, activity, environmental, and personal factors described in the International Classification of Functioning, Disability and Health (ICF) model. Most prior studies examined isolated risk factors using conventional regression.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine self-reported knee pain in community-dwelling older adults by combining machine learning and partial-correlation network analysis with smartphone-based physical performance measurement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This cross-sectional study included 852 adults aged 60 years and older. Baseline assessment of 38 variables across 7 ICF-aligned domains was reduced to 21 predictors using prespecified, rule-based criteria (events per variable=14.95). Walking speed, sit-to-stand, gait knee flexion, and gait asymmetry were measured using a validated vision-based smartphone app. Missing data were addressed by multiple imputation (m=5) with Rubin rules. Six algorithms (logistic regression with elastic-net penalty as baseline, k-nearest neighbors, random forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, and support vector machine) were trained using nested 5×5 cross-validation, and the best model was interpreted using Shapley Additive Explanations and partial dependence plots. Partial-correlation network analysis examined the 10 top-ranked features and the knee-pain node, with bootstrap stability assessment and a sensitivity analysis excluding EuroQol 5-Dimension (EQ-5D) Pain/Discomfort.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Knee pain prevalence was 36.9% (314/852). Pooled area under the receiver operating characteristic curve values ranged from 0.692 to 0.723, with random forest highest (area under the receiver operating characteristic curve=0.723, 95% CI 0.689-0.757). The top Shapley Additive Explanations features were EQ-5D Pain/Discomfort, EQ-5D Utility, and house estate. In the 11-node network, only EQ-5D Pain/Discomfort (partial correlation=0.260) and house estate (0.158) had direct edges with knee pain. Sit-to-stand showed the highest strength centrality (0.679) without a direct edge, acting as a hub linking body function, body structure, mental health, and activity variables. Shapley Additive Explanations importance and network strength were weakly correlated (ρ=0.018). The sensitivity analysis preserved the pattern (best area under the receiver operating characteristic curve=0.724).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Knee pain in older adults was associated with variables spanning 7 ICF domains, with EQ-5D Pain/Discomfort and housing environment (lower-income rental estate residence) as direct correlates and sit-to-stand as a network hub. Combining machine learning and partial-correlation network analysis may inform multidisciplinary biopsychosocial assessment, pending confirmation in prospective and externally v","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e92466"},"PeriodicalIF":6.3,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Information-Driven Approach for the Early Health Technology Sustainability Assessment and the Frugal Design of the Internet of Medical Things: Exploratory Study of Wearable Activity Monitoring Devices.","authors":"Ernesto Quisbert-Trujillo, Nicolas Vuillerme","doi":"10.2196/88237","DOIUrl":"10.2196/88237","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Over the past decade, wearable activity monitoring (WAM) devices have become prevalent to improve quality of life and support the prevention and management of a wide range of health disorders. WAM devices based on the Internet of Medical Things (IoMT) paradigm provide a practical means of tracking physical activity, but their widespread adoption raises sustainability concerns. Meanwhile, health technology assessment and life cycle assessment are typically applied at advanced development stages, when empirical certainty about the final design and operating conditions is available, leaving little room for further improvements.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This exploratory work provides the empirical foundations for an information-driven approach addressing this paradox in the early evaluation and conception of wrist-worn step counters, for which evidence suggests overdimensioned and unsustainable electronic designs. Specifically, we identify optimal resource-performance trade-offs in critical electronic components of frugal smartbands based on the data they collect and the information they preserve for step counting. This approach accounts for uncertainties in device reliability, users' gait speeds, and material and energy consumption in final products.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a secondary analysis of an accelerometer dataset characterizing wrist motion in healthy individuals walking at different speeds. The original x-, y-, and z-axis signals were progressively downsampled using cubic spline interpolation and discretized to quantify the information preserved, first across sampling frequencies and then with respect to changes in motion velocity. We also preliminarily assessed the viability of the downsampled signals for step detection by estimating percentage errors in peak and valley counts. Based on this, we constructed and evaluated 4 frugal smartband design archetypes, linking energy consumption and sampling frequency for 4 widely used accelerometers and combining essential electronic components, implementing a suboptimal asynchronous first-in-first-out (FIFO) algorithm at different sampling rates. Finally, we evaluated the environmental impact and circularity of these components through a streamlined analysis focused on raw materials.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;About 70%-90% of the information is lost in signals downsampled at very low frequencies (2-5 Hz), whereas losses remain below 24% from 20 Hz onward. Substantial information loss also occurs when individuals walk briskly or jog (≥8 km/h), or walk below 8 km/h with sampling frequencies below 7 Hz or above 25 Hz. Step-counting accuracy is expected to be acceptable from approximately 11 Hz onward. Conversely, higher sampling rates rapidly saturate FIFO buffers and increase energy overhead, particularly when implemented in memory-dense components handling both processing and data transfer. Finally, gold and silver in transceivers ","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e88237"},"PeriodicalIF":6.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148663942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Acceptance and Barriers to IoT Usage in Health Care: Systematic Literature Review.","authors":"Zaenal Arifin, Putu Wuri Handayani","doi":"10.2196/81260","DOIUrl":"10.2196/81260","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The Internet of Things (IoT) represents a transformative paradigm in health care service delivery, offering unprecedented potential to enhance quality of care, operational efficiency, and patient outcomes through interconnected devices and real-time data analytics. Despite rapid adoption, implementation depends on patient acceptance as end users. While literature extensively documents technical and institutional perspectives, a comprehensive understanding of patient acceptance factors remains fragmented, with high technology abandonment rates. Systematic synthesis of patient perspectives is critically needed to inform user-centered design, effective implementation strategies, and supportive policy frameworks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic literature review aims to identify and synthesize factors influencing patient acceptance of IoT technology in health care services, barriers hindering adoption, and effective strategies for enhancing acceptance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched eight electronic databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, ScienceDirect, ACM Digital Library, ProQuest, and Google Scholar) for empirical studies published between January 2016 and December 2024. Inclusion criteria encompassed peer-reviewed empirical research examining patient perspectives on IoT technology in health care services, published in English or Indonesian. From 2537 initially identified papers, 62 studies met inclusion criteria after systematic screening and full-text evaluation. Quality assessment was conducted using the Mixed Methods Appraisal Tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The 62 included studies represented diverse geographic contexts (Asia, Europe, North America, and the Middle East) and methodological approaches. Quality assessment revealed 45 (73%) studies of good-to-excellent quality. Perceived usefulness emerged as the strongest acceptance facilitator, identified in 55 of 62 (89%) studies, followed by perceived ease of use in 47 (76%) studies, and trust and security in 42 (68%) studies. Cost-effectiveness was identified in 32 (52%) studies as an important consideration. Primary barriers included data security concerns in 26 (42%) studies, privacy issues in 24 (39%) studies, lack of digital literacy in 22 (36%) studies, and resistance to change in 20 (32%) studies. Interoperability issues and high costs were identified in 19 (31%) studies and 18 (29%) studies, respectively. User-centered design was the most frequently recommended enhancement strategy in 20 (32%) studies, followed by user-friendly interface development in 19 (31%) studies, digital literacy programs in 18 (29%) studies, and health care professional involvement in 15 (24%) studies. Digital literacy functioned as a significant moderator, and trust served as both direct predictor and ","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e81260"},"PeriodicalIF":6.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148664005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Symptom Dynamics and Profiles of Subgroups at Risk for Major Depression and Suicidal Ideation Among Korean Adults: 2-Week Ecological Momentary Assessment Study.","authors":"Kyungmi Chung, Jin Young Park","doi":"10.2196/80332","DOIUrl":"10.2196/80332","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Traditional clinical assessments in psychiatric research or clinical practice rely on global retrospective self-report depression measures, which do not adequately capture intra- and interindividual variability in depressive symptoms over time and across contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to (1) assess the sensitivity of mobile ecological momentary assessment (EMA) for monitoring depressive symptoms compared with traditional depression scales, (2) investigate changes in depressive symptoms and recall consistency observed between the first week (FW) and the second week (SW), and (3) identify subgroups at higher risk for depression and suicidal ideation, and characterize their sociodemographic, psychological, and psychiatric profiles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Participants' self-reports were collected once daily for 14 consecutive days via a mobile app-based EMA to monitor the presence of 20 depressive symptoms based on a 24-hour recall period, thereby capturing naturalistic severity and variability. Baseline questionnaires measured sociodemographic characteristics, digital sensitivity, and personality traits. On day 14, postquestionnaires were administered to assess their clinical symptoms. In addition, modified depression scales were administered on day 7 for 1-week recall, and day 14 for both 1-week and 2-week recalls. For cluster analysis, 3 active EMA-derived features were included: mean symptom severity, within-person symptom variability, and frequency of suicidal ideation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Generalized Linear Mixed Model analysis (model 1) revealed that traditional 2-week recall explained only 35.5% of the variance in daily symptom presence (odds ratio 0.666, 95% CI 0.659-0.674; P&lt;.001). Additional Generalized Linear Mixed Model analysis (model 2) identified a robust interaction, indicating that the consistency between weekly retrospective recall and daily EMA differed significantly between FW and SW (F1, 81876=124.550; P&lt;.001). While overall symptom severity scores significantly decreased from FW to SW across both assessment methods (Cohen d=0.10-0.34; P&lt;.001), the estimated mean of the probability of symptom reporting showed a contrasting upward trend from 0.853 to 0.908. In cluster analysis, 695 participants (244 males and 451 females; aged 19-73 years, mean 36.14, SD 10.71 years) who completed at least 7 EMA sessions over a 2-week period were classified into three distinct clusters: (1) no or low risk (n=445, 64.0%), (2) moderate risk (n=223, 32.1%), and (3) high risk (n=27, 3.9%). While depressive symptom severity, frequency of suicidal ideation, and psychiatric profiles progressively increased across clusters (cluster 1&lt;2&lt;3), the highest symptom variability was observed in cluster 2 (cluster 1&lt;3&lt;2).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrated that mobile EMA effectively reduced recall bias in assessing depressive symptoms and revealed symptom ","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e80332"},"PeriodicalIF":6.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable Devices for Monitoring and Management of Comorbid Obstructive Sleep Apnea and Hypertension: Scoping Review.","authors":"Hongyi Wang, Anran Cheng, Yusheng Li, Haining Zhang, Shun Fan, Rong Shen, Wei Zhang, Huanan Li, Jingui Wang","doi":"10.2196/84506","DOIUrl":"10.2196/84506","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;As an established driver of hypertension, obstructive sleep apnea (OSA) generates a significant cardiovascular burden when both disorders are present. This intersection not only compromises nocturnal hemodynamics but also hampers long-term clinical management. Yet, current health care delivery rarely integrates the simultaneous and continuous tracking of these dual burdens. While wearable technology provides a noninvasive, pragmatic toolset for synchronized physiological monitoring, research remains largely siloed within single-disease frameworks. Consequently, clinical evidence supporting wearable applications specifically for comorbid populations remains sparse.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;In this scoping review, we summarized the current applications of wearable technology for comorbid OSA and hypertension. The analysis primarily outlines device categories, monitored physiological indicators, prevalent clinical scenarios, and existing challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The search for relevant literature spanned PubMed, Web of Science, Embase, and IEEE Xplore, covering the period from January 2015 to February 2026. Eligible studies included adults and used wearable or portable technologies to objectively track sleep- or respiratory-related indicators and cardiovascular/blood pressure metrics. Study selection, data charting, and evidence synthesis were conducted using a 2-reviewer process and descriptive and narrative approaches.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our initial search yielded 739 records. Following title and abstract screening, we reviewed 54 full texts, ultimately finalizing a cohort of 13 eligible studies. Published between 2015 and 2026 across 9 countries, these articles capture data from 5596 participants. Most were observational studies and device validation studies. Evaluated device types included wrist-worn devices, fingertip contact devices, patch and single-lead devices, and multiparameter portable monitoring systems. The clinical applications mainly focused on screening and risk stratification for comorbid OSA and hypertension, monitoring of abnormal nocturnal blood pressure and hemodynamic changes, and cardiovascular risk assessment and remote longitudinal management. However, research remains sparse, and different devices vary greatly in reference standards, diagnostic thresholds, and validation pathways; therefore, their clinical translational value still requires further validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Wearable devices may complement traditional assessment by providing continuous nocturnal and longitudinal data. However, at present, they are more suitable as auxiliary monitoring and risk warning tools in comorbidity management and cannot yet replace standard sleep studies or standard blood pressure monitoring. Future research should further shift from feasibility validation to large-sample, prospective, multicenter clinical studies in comorbid populations.&lt;/","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e84506"},"PeriodicalIF":6.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: Scoping Review.","authors":"Shifali Sharma, Aswin Kumar Janakiraman, Lujie Karen Chen","doi":"10.2196/76632","DOIUrl":"10.2196/76632","url":null,"abstract":"<p><strong>Background: </strong>Stress, as commonly recognized, is an integral part of modern life and can significantly affect both mental and physical health. While substantial advancements have been made in measuring physical fitness through wearable devices, the detection and assessment of mental stress remain in their early stages.</p><p><strong>Objective: </strong>The objective of this paper is to review recent studies of wearable-based stress detection in naturalistic settings, with a specific focus on characterizing machine learning frameworks inspired by the model card approach.</p><p><strong>Methods: </strong>This review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. A total of 353 articles were identified through searches in databases such as PubMed, MEDLINE, ScienceDirect, IEEE, ACM Digital Library, Web of Science, and Embase. Studies were considered eligible if they collected data from healthy adults in naturalistic settings using wearable devices and used machine learning models for stress detection.</p><p><strong>Results: </strong>A total of 34 articles met the eligibility criteria, including 11 conference papers, 22 journal articles, and 1 preprint published between 2017 and 2024. From these studies, we analyzed key machine learning modeling decisions such as problem formulation, ground truth determination, and machine learning algorithms. Additionally, we examined the major contributions of each study, focusing on the challenges they addressed and the solutions they proposed. Based on these findings, we proposed a model card framework for reporting machine learning-based, wearable-based stress detection.</p><p><strong>Conclusions: </strong>This scoping review highlights recent trends in machine learning models for stress detection and measurement using wearable signals. It underscores the need for improved standardization in reporting practices for datasets and key machine learning decisions, as well as the importance of addressing critical challenges associated with data collection in real-world settings. We hope this review will support and strengthen ongoing research efforts, promote knowledge sharing, and promote collaboration among researchers-ultimately advancing the field as a community.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e76632"},"PeriodicalIF":6.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}