JMIR mHealth and uHealth最新文献

{"title":"Weight Loss and Engagement Among Active Users of a Dietitian-Led Digital Nutrition Platform: Retrospective Cohort Study.","authors":"Frederick G B Goddard, Jessica Garay, Francisco Teixeira Neves, Alexandra Ligeti, Liudmila Zhaunova, Manuela Abreu","doi":"10.2196/94732","DOIUrl":"10.2196/94732","url":null,"abstract":"<p><strong>Background: </strong>Approximately 2.5 billion adults worldwide were living with overweight or obesity as of 2022, highlighting the need for comprehensive weight management solutions. Digital nutrition platforms have emerged as an accessible solution that can be implemented at scale. Nutrium Care is an employer-sponsored program delivered via the Nutrium platform, facilitating dietitian-led nutrition counseling through consultations and in-app communication, while also enabling users to track food intake, water consumption, and physical activity.</p><p><strong>Objective: </strong>This study set out to characterize longitudinal weight changes among active users of the Nutrium Care program and estimate associations between program engagement and weight loss.</p><p><strong>Methods: </strong>This retrospective cohort study analyzed deidentified data from adult users with overweight or obesity enrolled in the Nutrium Care program. Users were included if they were taking action to manage their weight and had at least 3 months of follow-up data available; these users were referred to as active users. The primary outcome was weight loss after 3 and 6 months, estimated using piecewise linear mixed-effects models. To estimate the association between engagement and weight loss, more- and less-engaged users were matched on sociodemographic characteristics. Engagement was defined by the number of dietitian appointments, days of active platform use, and logging of food, water intake, and physical exercise.</p><p><strong>Results: </strong>The study included 10,288 active users, predominantly female (n=6887, 66.9%) and aged 18 to 49 years (n=9741, 94.7%), who contributed a median of 6 (IQR 4-9) months of data to the cohort. In the first 3 months, active users lost 2.0 kg (2.4% of baseline body weight; 95% CI 1.9-2.0 kg) on average, increasing to 2.3 kg (2.8%; 95% CI 2.2-2.4 kg) by 6 months. Higher program engagement was associated with more weight loss, with the strongest association estimated for at least 1 dietitian appointment every 2 months, corresponding to 0.8 kg (95% CI 0.7-1.0 kg) additional weight loss after 3 months.</p><p><strong>Conclusions: </strong>This retrospective cohort study addresses a critical research gap in hybrid nutrition interventions integrating professional dietitian support with an employer-sponsored digital nutrition program. Active users achieved weight loss, and regular contact with a dietitian was the strongest engagement factor associated with improved outcomes, supporting the potential role of professional support in digital weight management programs.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e94732"},"PeriodicalIF":6.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13577621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148976768","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":"Daily Reminders Increase Adherence to an Electronic Headache Diary (DMKG-App): Retrospective Longitudinal Cohort Study.","authors":"Ruth Ruscheweyh, Lars Neeb, Bianca Raffaelli, Torsten Kraya, Tobias Freilinger, Thomas Dresler, Gudrun Gossrau, Tim Patrick Jürgens, Victoria Ruschil, Jörg Scheidt, Yannic Siebenhaar","doi":"10.2196/73259","DOIUrl":"10.2196/73259","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The use of electronic headache diaries via smartphone apps has several advantages over paper and pencil diaries. Still, adherence is not always satisfactory. One potential strategy to improve adherence is the implementation of daily reminders, but their effect remains uncertain.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The DMKG-App (the headache diary app of the German Migraine and Headache Society, DMKG) sends daily reminders to encourage user adherence. Due to a technical problem, reminders failed for a 14-day period in early 2023. This provided a unique opportunity to evaluate the effect of daily reminders on adherence to the headache diary in the form of a retrospective longitudinal observational cohort study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The failure period was compared to 14-day prefailure and postfailure periods and to corresponding periods of the previous year. We evaluated effects on the number of daily entries, delay to make the daily entry, and attrition rate, using ANOVA and logistic regression (α&lt;.05). The effect of age, gender, headache frequency, and previous adherence was investigated using interaction analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The DMKG-App had 4236 active users before reminders failed (females 81.6%, 95% CI 80.5-82.8), age 39.8 (95% CI 39.4-40.2) years. During the failure period, the number of daily entries fell by 11.0% (95% CI 9.2-12.8). More specifically, entries per 14-day period significantly declined from 10.2 (95% CI 10.1-10.3) prefailure to 9.1 (95% CI 9.0-9.2) during the failure period, followed by a rise to 10.2 (95% CI 10.1-10.3) during the postfailure period (&lt;i&gt;F&lt;/i&gt;&lt;sub&gt;2,11498&lt;/sub&gt;=79.2; &lt;i&gt;P&lt;/i&gt;&lt;.001). The delay to make the daily entry increased by 46.3% (95% CI 45.8-46.8), from 1.90 (95% CI 1.89-1.90) days prefailure to 2.78 (95% CI 2.77-2.79) days during failure, and returned to 1.85 (95% CI 1.84-1.86) days postfailure (&lt;i&gt;F&lt;/i&gt;&lt;sub&gt;2,39&lt;/sub&gt;=155.4&lt;i&gt;; P&lt;/i&gt;&lt;.001). In addition, there was a significant rise in attrition rate by 32.2% (95% CI 8.1-61.7) (from 4.0%, 95% CI 3.4-4.6, in the prefailure period to 5.2%, 95% CI 4.6-5.9, in the failure period, returning to 3.9%, 95% CI 3.4-4.5, in the postfailure period, &lt;i&gt;z&lt;/i&gt;=3.10; &lt;i&gt;P&lt;/i&gt;=.002). Interaction analysis revealed that the effect of reminder failure was larger in males than in females (regarding entry delay), in persons with lower compared to higher headache frequencies (regarding number of daily entries and entry delay), and in persons with higher vs lower previous adherence rates (regarding number of daily entries, entry delay, and attrition). There was no interaction between age and reminder failure.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This is the first study investigating the effect of daily reminders in a digital headache diary app. Results suggest that reminders are an effective means to improve adherence. Males, persons with low headache frequencies, and those with high adherence during the presence of re","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e73259"},"PeriodicalIF":6.3,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13574331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148968790","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":"Accessing Germany's Electronic Patient Record Through Health Insurer-Provided Apps: Qualitative Interview Study.","authors":"Susann May, Manuela Marquardt, Gina Barzen, Frances Seifert, Mary Nyarko, Antonia Niemeyer, Sonja Gscheidle, Liane Schenk, Sebastian Spethmann, Felix Muehlensiepen","doi":"10.2196/95294","DOIUrl":"10.2196/95294","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Nationwide electronic health records (EHRs) are intended to strengthen patient empowerment and improve information continuity across health care settings. In Germany, the electronic patient record (elektronische Patientenakte [ePA]) transitioned from an opt-in to an opt-out model in 2025. Initial survey data suggest that technical complexity and access requirements may hinder engagement; yet, little is known about how users experience this access process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study explored users' experiences of setting up and authenticating access to insurer-provided ePA apps and examined the barriers affecting successful access.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a qualitative interview study with individuals covered by statutory health insurance. From 61 interviews conducted within the broader ePA4all project, 23 were selected because participants had attempted to access the ePA through an insurer-provided application and described at least 1 concrete action or event during setup or authentication. Semistructured telephone interviews were conducted between August and December 2025. Data were analyzed using Kuckartz's structured qualitative content analysis. The process phases were developed inductively from the material. All 23 interviews were independently coded by 2 researchers, with differences resolved through consensus. A second analytic step examined how barriers interacted and accumulated across the access pathway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The sample included 21 participants who had successfully accessed the ePA and 2 who had initiated but abandoned setup or authentication. Setup and authentication were experienced not as a single technical step but as an interdependent process comprising 5 phases: orientation, technical entry, verification, interruption, and transition to initial use. During orientation, incomplete or outdated information left requirements and procedural steps unclear. Technical entry required users to coordinate different apps, interfaces, and device requirements. Verification involved multiple credentials, identification services, and partly repeated security steps. Technical failures, administrative inconsistencies, postal procedures, and waiting periods interrupted progression and sometimes required users to repeat previously completed steps. After successful access, an unclear immediate benefit could limit the transition to initial use. Across phases, participants relied on digital competence, prior experience, persistence, and self-efficacy to navigate the process. Difficulties were also reported by participants with high affinity for technology. Barriers could accumulate across phases: insufficient orientation increased subsequent coordination work, failures produced interruptions and repeated attempts, and prolonged effort contributed to frustration or disengagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Access to the opt-out ePA through insurer-p","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e95294"},"PeriodicalIF":6.3,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13620427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148968674","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 Apps for Preventing Adolescent Health Problems Among Health Care Professionals: Systematic Search and Quality Assessment.","authors":"Elodie Million, Marijn Kuijper, Carole Bautista, Lola Pirat, Bruno Falissard, Béatrice Lognos, François Carbonnel","doi":"10.2196/83908","DOIUrl":"10.2196/83908","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health care professionals must consider multiple dimensions of prevention when consulting with adolescents. Identifying risky behaviors early in adolescence is crucial for reducing both morbidity and mortality. General practitioners are increasingly eager to incorporate digital tools for prevention into their consultations with adolescents; however, the relevance and clinical validity of these digital tools are not always established or well-known. Consequently, primary care professionals require guidance and support in selecting relevant mobile health (mHealth) tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study is to identify relevant and useful digital apps to help primary care professionals detect at-risk adolescents across all recommended areas of prevention: orthopedics, mental health, substance abuse, risk behaviors, sexual health, vaccinations, social relationships, and nutrition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A systematic review of smartphone apps, with an analysis of content quality, was carried out by 4 researchers using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. The App Store and Google Play Store platforms were surveyed. The inclusion criteria were as follows: free of charge, date of last update, availability in French or English, relevance of the preventive approach to adolescents, and scientific validation. Four health care professionals assessed the apps: 2 selected the apps relevant to health care professionals, then 3 analyzed these apps using the French version of the Mobile App Rating Scale (MARS-F). Intraclass correlation coefficient, model (2,1) (2-way random effects, absolute agreement, single measures); standard error of measurement; and mean absolute error were also calculated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 976 apps were identified, 49 of which had disappeared from the platforms prior to analysis. Nine apps were retained. Seven (0.72%) were included after evaluation using the MARS-F: 2 on mental health and 5 on sexual health (including 3 on contraception only). The mean MARS-F interrater score ranged from 2.5/5 to 3.8/5. The global MARS-F score demonstrated a pooled SD of 0.60 and an intraclass correlation coefficient (2,1) of 0.0003, resulting in a calculated standard error of measurement of 0.60. The average discrepancy between raters was a mean absolute error of 0.53.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;No similar studies have been identified in the literature that specifically focus on mobile apps designed to support health care professionals in delivering preventive care to adolescents. Of the 8 areas of prevention identified as relevant for adolescents, only 3 are addressed by the apps validated through our methodology (5 focus on sexual health). Consequently, current apps are insufficient to support health care professionals in their overall preventive work with adolescents. Such a review should be conducted sy","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e83908"},"PeriodicalIF":6.3,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13568167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944714","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":"Radiographic Knee Osteoarthritis Identification Using Wearable Plantar Pressure: Secondary Cross-Sectional Analysis of Interpretable Framework Integrating Biomechanical Descriptors and the Multiscale Synergy Index.","authors":"Guanyu Xin, Hongbo Yao, Jingyuan Qiao, Huilin Yao, Siyuan Li, Lu Liu, Jingyi Zhang, Ke Qin, Lin Shu","doi":"10.2196/101026","DOIUrl":"10.2196/101026","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Radiographic knee osteoarthritis (ROA) is associated with abnormal plantar loading and altered gait coordination. Insole-based plantar-pressure sensing offers a practical wearable approach to ROA identification, but existing methods typically use either conventional biomechanical descriptors or end-to-end temporal models. An interpretable subject-level framework that captures both local abnormalities and distributed coordination is, therefore, needed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop an interpretable subject-level framework that combines conventional biomechanical descriptors with coordination-level plantar information to identify ROA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In secondary cross-sectional analysis, using a previously reported Pearl River Osteoarthritis Cohort plantar-pressure dataset from our group, we analyzed 92 participants (43 with ROA and 49 without ROA; mean age 63.0, SD 8.4 y). The source cohort was recruited from clinical and nearby community sources at Zhujiang Hospital of Southern Medical University, Guangzhou, China, between January 2022 and February 2023. We developed an interpretable participant-level classification framework integrating conventional biomechanical descriptors with a coordination-level Plantar Synergy Index (PSI). Participant-wise stratified split allocated 72 participants for 5-fold cross-validation, and 20 for locked independent evaluation. Feature selection used Fisher-score ranking (k=50). All point estimates are accompanied by 2000-iteration bootstrap 95% CIs, and paired model comparisons used DeLong tests. All statistical tests used α=.05. Additional analyses examined feature stability, phase-resolved PSI patterns, robustness to perturbation and design variation, and comparisons with representative end-to-end temporal baselines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The fusion model achieved the best primary classification performance on the independent test set, with an &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-score of 0.842 (95% CI 0.615-1.000) and accuracy of 0.850 (95% CI 0.700-1.000). It also showed balanced performance across additional metrics, with area under the receiver operating characteristic curve (AUROC) of 0.818 (95% CI 0.576-1.000), precision of 0.800 (95% CI 0.538-1.000), recall of 0.889 (95% CI 0.636-1.000), and specificity of 0.818 (95% CI 0.571-1.000). DeLong testing showed that fusion outperformed PSI-only at the AUROC level (&lt;i&gt;P&lt;/i&gt;=.02). Stable retained descriptors were dominated by biomechanical features, with a reproducible PSI component. Phase-resolved analyses suggested distributed stance-phase coordination differences. Robustness and baseline comparisons supported the stability of the framework, but the wide CIs indicate that the findings remain preliminary.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study introduces an interpretable participant-level representation that integrates local biomechanical loading with multiscale interregional tempo","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e101026"},"PeriodicalIF":6.3,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13566073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944637","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":"Continuous Temperature Monitoring via Wearable Devices for Fever or Infection Management in Acute Care Hospitals: Scoping Review of Clinical Implementation and Data Analytics.","authors":"Yuwei Liu, Qimeng Zhao, Ka Li, Dawn Dowding","doi":"10.2196/89773","DOIUrl":"10.2196/89773","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Infections are a major health concern in hospitalized patients. Fever is an early sign of infection, making temperature monitoring essential for infection surveillance. Wearable devices are increasingly being explored for continuous temperature monitoring in acute care hospitals, but how temperature data from wearables are monitored, presented, and used to support fever or infection management in clinical practice remains poorly understood.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to map existing evidence on the use of temperature data from wearables for fever or infection management in acute care hospitals, focusing on wearables' characteristics; data transmission, storage, and presentation strategies; data preprocessing and analytic approaches; and the maturity of wearables' clinical integration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched MEDLINE, Embase, Web of Science, CINAHL, and IEEE Xplore for publications from January 2013 to July 2026 using the population, concept, context (PCC) framework. Primary research studies using wearables to monitor patients' body temperature in acute care hospitals for fever or infection management, published in English or Chinese, were eligible. Temperature data analyses were classified according to the descriptive, diagnostic, predictive, and prescriptive analytics framework. The clinical integration of wearables was assessed using a tailored maturity framework modified from the World Health Organization's stages of maturity for digital health interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We included 29 publications from 26 studies, published between 2018 and 2026. Eighteen wearable devices were identified, monitoring temperature at the axilla, chest, wrist, or upper arm. Continuous data streams were predominantly transmitted in real time, while 5 studies used non-real-time batch uploads, periodic synchronization, or device-memory downloads. Only 7 studies presented wearable temperature data in clinical settings, displaying data on mobile devices and/or centralized monitoring stations; only 1 study reported enabling alerts through electronic health records (EHRs). Invalid sensor data filtering methods varied across studies, such as physiological thresholds, firmware quality scores, and statistical outlier detection. Temperature data were most frequently analyzed by descriptive analytics (n=19) to depict the frequency, timing, and duration of fever episodes, followed by diagnostic analytics (n=6) to identify risk factors or distinguish causes of fever, and predictive analytics (n=9) to forecast impending fever or infections. None of the included studies used prescriptive analytics. For clinical integration, most studies were at the clinical validation stage (n=9) or clinical research stage (n=17), corresponding to an early stage of maturity; 3 studies reached the routine clinical practice stage, and none progressed to multicenter implementation or full integration stag","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e89773"},"PeriodicalIF":6.3,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944513","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":"Objective Smartphone Screen Time Monitoring Among Caregivers of Preschool-Aged Children: Reliability Study of Monitoring Duration.","authors":"Joshua Culverhouse, Taylor Adair, Meghan Restino, Heidi M Weeks, Jenny S Radesky, R Glenn Weaver, Sarah Burkart, Hannah Parker, Olivia L Finnegan, Anthony J Holmes, Keagan Kiely, Rahul Ghosal, Srihari Nelakuditi, Michael W Beets, Bridget Armstrong","doi":"10.2196/82846","DOIUrl":"10.2196/82846","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Reliable measurement of smartphone screen time is important for research examining associations between digital media use and health outcomes. Although objective monitoring tools reduce bias associated with self-report, the number of monitoring days required to reliably estimate typical smartphone use remains unclear. This question is particularly important for study design because longer monitoring periods increase participant burden and the likelihood of missing data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to determine the minimum number of valid monitoring days within a 7-day measurement period required to reliably estimate average daily smartphone screen time during the same 7-day period among primary caregivers of preschool-aged children using 2 objective methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This secondary analysis included caregivers participating in 2 observational studies conducted in the Eastern United States. Android users installed Chronicle, a passive sensing application that records raw Android UsageEvents data, which were preprocessed into smartphone session events (pickups where the device was unlocked). iOS users submitted daily screenshots of Apple Screen Time summaries. Reliability analyses included 100 Android Chronicle participants and 129 iOS Screenshot participants with at least 7 consecutive complete days of data. The primary outcome was day-level total smartphone screen time. For Android Chronicle, this was defined as the summed duration of device-unlock sessions across each calendar day, whereas for iOS Screenshot, this was defined as the daily total reported by Apple Screen Time. One-way random-effects intraclass correlation coefficients (ICC [1,1]) with 95% bootstrap CIs (500 resamples) were calculated separately for Android and iOS data. The Spearman-Brown prophecy formula was used to estimate the number of monitoring days required to achieve target reliability thresholds (ICC≥0.70, 0.80, and 0.90). Sensitivity analyses using the combined durations of sessions and glances produced similar conservative monitoring-day recommendations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Single-day ICCs were 0.69 (95% CI 0.63-0.76) for Android Chronicle and 0.71 (95% CI 0.65-0.77) for iOS Screenshot data. Point-estimate projections indicated that 2 monitoring days were sufficient to achieve an ICC≥0.80 for both data sources. Using the more conservative criterion that the lower bound of the projected 95% CI exceeded 0.80, 3 monitoring days were required for both Android and iOS data. Screen time was significantly lower on weekends than on weekdays, suggesting that monitoring periods should include at least 1 weekend day.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Among caregivers of preschool-aged children, relatively short objective monitoring periods may provide reliable estimates of average daily smartphone screen time within a 7-day monitoring period. Two monitoring days were sufficient under point-","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e82846"},"PeriodicalIF":6.3,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148930065","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":"Mobile-First Web Access and Captioned Video in Francophone Cardiology Education: Multicountry Ecological Learning Analytics Study.","authors":"Thomas Roussel, Elodie Benhaim, Louis Perrard, Benoit Merat, Shamir Vally, Remi Girerd, Pierre Deharo, Louis-Marie Desroche","doi":"10.2196/90345","DOIUrl":"10.2196/90345","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobile health (mHealth) and online video are increasingly central to cardiology education and point-of-care decision support. However, little is known about how simple design choices, such as mobile-first web layouts and captioned videos, translate into real-world practice across countries with different income levels.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This exploratory ecological study used routinely collected, cross-platform learning analytics from a francophone cardiology mHealth initiative to (1) describe how mobile web access and caption-enabled YouTube viewing varied across World Bank income groups and (2) examine whether greater reliance on mobile access was associated with poorer engagement on the website or on YouTube.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed country-level analytics from the École Numérique de Cardiologie (ENC; Saint-Denis) mobile-optimized website and its companion YouTube channel (YouTube, LLC [Google LLC]) over a two-year window (September 2023 to September 2025). Countries were grouped as high-, middle-, or low-income (World Bank, three-level classification). Country-level metrics included mobile device session share; website bounce rate; time on page; and YouTube average view duration, audience retention, and intentional views. Caption-related and demographic YouTube metrics were available only as income-group aggregates and were therefore reported descriptively as between-group contrasts; country-level inferential analyses were restricted to country-level variables. Reporting followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Thirty-four countries contributed data: 13/34 (38%) high-income, 14/34 (41%) middle-income, and 7/34 (21%) low-income. Caption-enabled watch time was 18.8% in high-income countries (HICs), compared with 38.7% in middle-income countries (MICs) and 60.9% in low-income countries (LICs), representing a caption equity gap (CEG) of 42.1% between low- and high-income settings. Median website mobile share rose with decreasing income (36.5%, 63.3%, and 81.4%, respectively; Jonckheere-Terpstra &lt;i&gt;P=&lt;/i&gt;.01). Across income groups, higher caption-enabled watch time coincided with a higher share of intentional views. At the country level, greater reliance on mobile access was not associated with higher bounce rate or shorter time on page, and Spearman correlations between mobile share and YouTube engagement metrics were small and nonsignificant (all |ρ|≤0.28; all &lt;i&gt;P&lt;/i&gt;≥.18).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this multicountry, francophone, mHealth learning analytics case study, mobile web access and captioned video were used most intensively in lower-income settings, and greater reliance on mobile access was not associated with measurable penalties in basic engagement metrics. These findings support treating mobile-optimized design and systematic captioning as core, low-cost, access-","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e90345"},"PeriodicalIF":6.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912112","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":"Intensive Longitudinal Data Collection Methods in Health Research: Tutorial for Selecting Measures and Designing a Sampling Protocol.","authors":"Jimikaye Courtney, Genevieve Dunton","doi":"10.2196/81290","DOIUrl":"10.2196/81290","url":null,"abstract":"<p><strong>Unlabelled: </strong>Intensive longitudinal data (ILD) include frequent and dense repeated measures captured over acute timescales (eg, every second, hour, or day) that are used to investigate within-person processes both within and across days. ILD are collected via wearable sensor data, ecological momentary assessments, or daily diaries and provide unique insights into within-person processes under ecologically valid conditions that can strengthen understanding of temporal relationships among variables and potential causal processes, while also informing the development of just-in-time adaptive interventions. Advancements in mobile and sensor technology have facilitated an explosion of ILD studies that have outpaced formal training in ILD study design. When designing ILD studies, researchers need to make careful decisions about the frequency (ie, how often) and timing (ie, when) of measurements. Decisions about the frequency and timing of measurement are influenced by issues such as variability across individuals and constructs, the purpose of the assessment, concerns about recall biases, saliency, or missing information, and participant needs. The interpretation of study results, causal inferences, and the predictive value of ILD are also impacted by decisions related to the timing of assessments, temporal lags between measures, how a \"day\" is defined, and data aggregation choices. Due to the increased interest in and adoption of ILD studies, and a lack of formal training, researchers can benefit from guidance on how to design ILD studies. Therefore, this paper aims to provide practical guidance to researchers on how to plan ILD studies using a step-by-step decision-making tutorial with applied health behavior research examples examining phenomena (eg, physical activity and alcohol use) that vary over acute time scales.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e81290"},"PeriodicalIF":6.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912152","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":"A Smartphone-Based Ecological Momentary Intervention for Workplace Mental Health: Randomized Controlled Trial.","authors":"Sisi Li, Lik Hang Lincoln Lo, Chiu Yi Charlie Lau, Megan Lam, Meanne Chan","doi":"10.2196/72435","DOIUrl":"10.2196/72435","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Work-related stress has been widely associated with an increased risk of various mental disorders and poor mental well-being. The fast-growing mobile health services industry has provided new opportunities for workplace mental health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This randomized controlled trial examined the effectiveness of &lt;i&gt;Neurum&lt;/i&gt; (Neurum Limited), a smartphone-based intervention tool featuring ecological momentary assessments and interventions that aims to reduce workplace stress in real-time and real-world settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 201 working adults were recruited for a 4-week smartphone-based intervention that incorporated cognitive behavioral therapy, mindfulness exercises, and self-regulation exercises delivered on &lt;i&gt;Neurum&lt;/i&gt;. A simple randomization procedure was used. Participants in the intervention group were encouraged to log mood journals, complete mental health exercises, and provide user feedback whenever applicable. The key outcome was measured by the Depression, Anxiety, and Stress Scale-21 items (DASS-21; Cronbach α=0.87).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The intervention group consisted of 102 participants, while the control group consisted of 99 participants. More participants dropped out from the intervention group (n=21) than from the control group (n=2; &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;1&lt;/sub&gt;=18.259; &lt;i&gt;P&lt;/i&gt;&lt;.001). The final sample consisted of 178 participants (male: 85/178, 47.8%; female: 93/178, 52.2%; mean age of 34.65, SD 7.67 y). Analyses revealed that after the 4-week intervention, the DASS-21 scores decreased in the intervention group (mean difference [MD]&lt;sub&gt;post-pre intervention&lt;/sub&gt;=-14.518) but increased in the control group (MD&lt;sub&gt;post-pre intervention&lt;/sub&gt;=3.319; &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1,176&lt;/sub&gt;=59.358, &lt;i&gt;P&lt;/i&gt;&lt;.001; &lt;i&gt;η&lt;sup&gt;2&lt;/sup&gt;&lt;/i&gt;=0.252). This effect was largely led by stress reduction (&lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1,176&lt;/sub&gt;=64.679, &lt;i&gt;P&lt;/i&gt;&lt;.001; for the intervention group, MD&lt;sub&gt;post-pre intervention&lt;/sub&gt;=-6.692, while for the control group, MD&lt;sub&gt;post-pre intervention&lt;/sub&gt;=2.000). On average, participants completed 6.27 (SD 9.4) exercises and provided 9.74 (SD 18.2) mood journal logs, with a daily engagement of 4.95 (SD 6.89) minutes. However, the associations between the changes in DASS-21 scores and the numbers of exercises or mood journal logs did not reach statistical significance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study primarily established the effectiveness of &lt;i&gt;Neurum&lt;/i&gt; in alleviating depression, anxiety, and stress symptoms in noninstitutionalized working adults, with a satisfactory user retention rate. Despite potential health-related culture differences, &lt;i&gt;Neurum&lt;/i&gt; contributed to evidence-based digital health in nonclinical settings for timely needs and general accessibility as an alternative to traditional, face-to-face, and high-cost mental health services. Future directions involving a personalized approach in online ment","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 ","pages":"e72435"},"PeriodicalIF":6.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912183","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}