JMIR mHealth and uHealth

The postpartum period involves profound physical, emotional, and social changes, yet many women report fragmented, infant-centered care that leaves their own needs insufficiently addressed. Digital health tools, including mobile apps, wearables, telehealth, and online resources, are increasingly used by postpartum women to seek information, support, and reassurance; however, little is known about how women experience these tools in their everyday lives. This qualitative study employed thematic analysis to explore the perspectives of postpartum women on digital health. Postpartum women ([≤]12 months after birth) living in Alberta, Canada, were recruited through maternity clinics and targeted social media advertisements. Four virtual focus groups (4-6 participants in each; 18 participants overall) were conducted via Zoom using a semi-structured guide on postpartum healthcare experiences, use of digital tools (apps, wearables, telehealth, AI), and perceived barriers and facilitators to adoption. Sessions were audio-recorded, transcribed verbatim, and coded by multiple researchers. Thematic analysis identified 32 codes, organized into 12 subthemes and four overarching themes: navigating postpartum support networks; empowerment through digital health tools; conditions for acceptable digital health design; and when technology feels like a burden. Women appreciated multiple sources of support from midwives, public health nurses, peers, and online communities, but described care that quickly became infant-focused, leaving their own recovery and mental health under-addressed, particularly in rural settings. Digital tools helped mothers structure infant and self-care, track symptoms, and prepare for appointments, yet also created new forms of burden, including information overload, usability challenges, privacy concerns, and feelings of surveillance or pressure to perform. Participants emphasized personalization (flexible notifications, mother-focused content), embedded mental health support, integration with trusted providers, and co-designed, credible platforms endorsed by Canadian health systems. Overall, to be acceptable and effective, tools must center mothers needs and be embedded within a broader ecosystem of responsive, continuous care. Author summaryBecoming a parent is a major life change, and many women feel that support from the health system drops off once the baby is born. At the same time, new mothers are increasingly turning to mobile phone apps, wearable devices, online groups, and video visits to answer questions, track health, and feel less alone. We wanted to understand women lived experience with these digital tools after giving birth: what feels helpful, what feels burdensome, and what they would want in an ideal tool. Our research team, consisting of three PhD students, held four online focus group discussions (4-6 participants per group; 18 participants overall) with women in Alberta, Canada, who had given birth within the past year. They described digital tools as both empowering and exhausting. Apps and wearables helped them track feeding, sleep, and symptoms, organize daily life, and come better prepared for medical appointments. At the same time, constant tracking, frequent notifications, and unclear data practices could feel overwhelming, guilt-inducing, or intrusive. This study is an important first step in a larger co-design work. By listening closely to mothers stories, we gathered practical ideas about what a supportive postpartum app should (and should not) do. In future phases, we plan to work directly with postpartum women and frontline clinicians to turn these ideas into a user-friendly, trustworthy digital tool that supports both mothers and babies health.

Abstract Objective Wrist-worn accelerometers are common in large-scale epidemiological studies, but their ability to measure sedentary behaviour in free-living environments is unknown. We therefore aimed to evaluate the accuracy of openly-available methods to infer sedentary time from wrist-worn accelerometers. Methods We analysed data from 662 working-age adults in the SMART Work & Life study (20-70 years; mean age 45 years; 72% female) who concurrently wore wrist- and thigh-worn accelerometers for up to eight free-living days. Reference measurements of sedentary time were derived from the thigh accelerometer data using proprietary algorithms. Wrist accelerometer data were processed using widely used, publicly available activity recognition models. Performance was evaluated at 30-second epochs to generate per-participant metrics, alongside comparisons of mean daily sedentary time, mean daily number of prolonged sedentary bouts ([≥] 30 minutes) and proportion of sedentary time in prolonged bouts. Model performance was examined across subgroups defined by age, sex, body mass index, season, recruitment centre, and in sensitivity analyses restricted to daytime hours (08:00-22:00). Results The best performing machine learning model (Actinet) accurately classified sedentary time from wrist-worn accelerometer data with a mean per-participant accuracy of 0.87 and F1 score of 0.85. Cut point-based approaches demonstrated lower accuracy of 0.80 (F1 score of 0.79). The ActiNet machine learning model showed strong agreement in daily sedentary time, daily number of prolonged sedentary bouts and proportion of sedentary time in prolonged bouts, all within 10% of the free-living thigh reference. Findings were consistent across subgroups and in analyses restricted to daytime hours. Conclusion Wrist-worn accelerometers can provide accurate measurements of sedentary behaviour in free-living settings, when assessed using current machine learning models, particularly ActiNet. This work provides confidence in future epidemiological research to examine sedentary behaviour patterns from wrist-worn accelerometers and their associations with health outcomes.

While wearable activity trackers (WATs) are widely used in the present day, with device ownership increasing, some individuals subsequently discontinue device use. Existing research primarily examines the initiation and maintenance of device use, with less focus on device discontinuation. Examining this phenomenon can provide valuable insight into human-computer interactions and habit reversal. Therefore, the current study examined the perceived emotional and behavioural impact of WAT discontinuation, alongside reasons for this action in former WAT users. Fifteen former WAT users (9 female, aged 23 to 56 years) who reported either full or partial device discontinuation were interviewed. Three themes and nine sub-themes were identified which detailed the impacts of device discontinuation. Participants reported a mindset shift around ones body image, exercise performance and exercise motivation. Device discontinuation removed numerical feedback provision which led to participants gaining bodily intuition and a sense of freedom. However, discontinuation also resulted in short-term negative emotions including frustration around the loss of external praise and envy in current WAT users. Current findings hold important implications around digital safety from user perspective, highlighting the need for guidance around healthy WAT use and vulnerable user profiles. More broadly, findings also raise the need for physical activity promotion whilst protecting individuals well-being.

Objective: We examined the link between cohabitation with a partner and nighttime smartphone use through the social control of health behavior theory. Background: Nighttime smartphone use is a behavioral risk factor for sleep problems. While previous research has predominantly focused on individual-level risks of sleep disturbances, the role of social context remains underexplored. Theoretical frameworks, specifically the Social Control of Health Behavior, suggest that social relationships regulate health-related behaviors; however, it is unclear how far this regulation extends to modern digital behaviors among couples. Method: We analyzed survey data from three waves of the SmartSleep Study (2018, 2020, and 2023; total N = 25,028), including a longitudinal follow-up subset (N = 1,003). We tested multivariate associations between living with a partner, changes in cohabitation status and frequent nighttime smartphone use by fitting generalized linear mixed-effects models. Additionally, we mapped the complex interplay between indicators of social integration, social support, smartphone use, and sleep quality using hierarchical clustering of non-linear correlations. Results: Cohabiting participants had lower odds of frequent nighttime smartphone use compared to those living alone (OR = 0.66; 95% CI: 0.61, 0.72). This lower risk was driven primarily by cohabitation with a partner (OR = 0.49; 95% CI: 0.36, 0.66). Longitudinal analysis supported these findings, showing that sustained cohabitation was associated with less frequent nighttime use (OR = 0.56; 95% CI: 0.38, 0.82). Clustering analysis revealed that indicators of social integration and support clustered with favorable sleep quality. Conclusion: Our findings suggest that the health-protective effects of cohabitation with a partner extend to digital behaviors. Consistent with social control of health behavior theory, the presence of a partner appears to reduce frequent nighttime smartphone use, highlighting the critical importance of considering social context when addressing digital health hygiene and promoting sleep.

Introduction: Managing gestational weight gain (GWG) is crucial for the health of mothers and their children. Mobile applications (apps) specifically designed for pregnancy are emerging as modalities to deliver accessible lifestyle intervention at a low-cost. However, current studies are varied in results and suffer from heterogeneity. Thus, we conducted this systematic review and meta-analysis to summarize the efficacy of mobile apps in managing GWG and investigate variables that may contribute to heterogeneity. Methodology: Seven databases were systematically searched up to 9 November, 2024. Only randomized controlled trials (RCTs) were included. Outcomes were excessive GWG and inadequate GWG according to the 2009 Institute of Medicine (IOM) guideline. Quality appraisal was performed using the Cochrane Risk of Bias 2 (RoB 2) tool. Random-effect model meta-analysis was conducted using odds ratio (OR) as the summary measure alongside their 95% confidence intervals (CI). Results and Discussion: Fifteen RCTs were included. Mobile apps led to a significant overall decrease in excessive GWG (OR: 0.71; 95% CI: 0.54 to 0.95; p-value: 0.02; I2: 60%). Subgroup analysis showed that social media apps, self-monitoring functionalities, and overweight/obese patients are associated with a significant reduction in excessive GWG. However, there was significant evidence of small-study bias in the analysis. Moreover, mobile apps also significantly increased inadequate GWG (OR: 1.51; 95% CI: 1.04 to 2.21; I2: 0%). Meta-regression did not reveal any significant finding. Conclusion: In conclusion, mobile app interventions are shown to be effective in preventing excessive GWG, particularly social media apps and those with self-monitoring functionalities. However, the reduction in excessive GWG may only be seen in overweight and obese patients and more studies are needed to ascertain this finding. Lastly, mobile apps are associated with an increased risk of inadequate GWG and strategies to combat inadequate GWG are needed.

Importance: Problematic use of the internet (PUI) behaviors, including problematic gaming, social media use, smartphone use, and general internet use, have been increasingly studied worldwide. So far, it is unclear what the global prevalence of PUI is. Objective: To critically appraise existing systematic reviews and meta-analyses on the prevalence of PUI behaviors and generate aggregated global prevalence estimates across different manifestations and definitions. Data Sources: MEDLINE (Ovid), Embase (Ovid), Scopus, Web of Science, CINAHL, and the Cochrane Review Library were searched for relevant articles from database inception to the most recent available search prior to manuscript preparation. Searches targeted systematic reviews and meta-analyses reporting prevalence for PUI-related behaviors. Study Selection: Systematic reviews and meta-analyses of observational studies reporting prevalence estimates for problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, or sexting were included. Scoping reviews were retained for descriptive synthesis only. Data Extraction and Synthesis: An umbrella review methodology was used. Data extraction and methodological appraisal were conducted using AMSTAR-2 to assess the quality of included systematic reviews up to February 2026. Primary studies included in each review were extracted and pooled using random-effects meta-analysis. Analyses were conducted to estimate pooled prevalence with 95% confidence intervals (CIs) and heterogeneity across non-overlapping primary studies. Small-study effects were examined. Main Outcomes and Measures: Global pooled prevalence estimates for PUI behaviors, including problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, and sexting. Results: Eleven reviews, including 10 systematic reviews and 1 scoping review, met inclusion criteria, representing data from 3,145,428 individuals, of whom 3,030,023 were included in pooled prevalence analyses. Across regions, pooled prevalence estimates were 6% (95% CI, 5%-7%) for problematic gaming, 16% (95% CI, 15%-17%) for problematic internet use, 32% (95% CI, 28%-35%) for problematic smartphone use, and 23% (95% CI, 19%-28%) for problematic social media use. Substantial heterogeneity (I2 > 99%) was observed across primary studies, reflecting variation in study methodologies, sampled populations, and definitions of PUI behaviors. Conclusions and Relevance: PUI behaviors appear to affect a substantial proportion of the global population. However, methodological concerns were common, with 9 of 10 systematic reviews rated as having low or critically low confidence according to AMSTAR-2. Evidence remains concentrated in East Asia and Europe, and many reviews combine heterogeneous populations and sampling strategies. Additional high-quality epidemiological research, including studies in underrepresented regions, is needed to refine prevalence estimates, clarify risk factors, and support the development of standardized criteria for PUI behaviors.

Vayu is a mobile breathwork system comprising an iOS companion app and Apple Watch application that delivers slow, resonant breathing using screen-free haptic cues, HRV-adaptive pacing, and reflective journaling grounded in Patanjali's five states of mind. The watchOS component provides tactile phase guidance and real-time biometric sensing (heart rate, HRV), while the iOS interface supports analytics and personalized recommendations. In a 4-6-week naturalistic pilot involving 199 adults (ages 22-65) across Canada, the United States, and India, participants engaged in daily 5-10-minute sessions guided by on-wrist haptics. Average adherence was 4.1 +/- 2.3 sessions per week, with 71% of active users maintaining at least 3 sessions per week. By week four, perceived stress (PSS-10) decreased by 2.5 points, resting heart rate declined by 7.4 bpm, and HRV increased by a median of 28.6% relative to baseline, accompanied by mood improvements. No adverse events were reported. HRV metrics are derived from Apple Watch PPG-based proxies and interpreted as relative trends. These findings suggest Vayu is effective and well-tolerated, demonstrating strong engagement and early efficacy signals.

Wearable devices offer the ability to objectively characterize free-living physical activity; however, raw step-count data generated by commercial devices require systematic processing before they can support rigorous inference. We describe a transparent, reproducible standard operating procedure (SOP) for transforming epoch-level step-count data from commercial Garmin devices into participant-level analytic variables and demonstrate its application in the STRRIDE-PD Reunion study: a long-term follow-up of older adults originally enrolled in a supervised exercise intervention trial. This data pipeline standardizes timestamps, reconstructs daily epoch grids, infers wear time from observed step patterns, and applies a prespecified valid-day threshold ([≥]10 hours inferred wear time) to generate participant-level summaries. Among 67 participants (mean age 71.4 years, 65.7% women), the median valid-day count was 10 days, median average daily steps were 5,794, and participant-level estimates were identical across [≥]10-hour and [≥]6-hour valid-day thresholds. Wearable-derived step counts were significantly associated with 9 of 16 cardiometabolic and fitness outcomes, including cardiorespiratory fitness, body composition, and lipid profiles. By contrast, self-reported exercise - assessed via a frequency-by-duration composite ranked into deciles - was not significantly associated with any outcome. A regression calibration framework applied to the full sample quantified the attenuation underlying this discrepancy: the naive self-report model systematically underestimated associations relative to both the observed Garmin model and calibration-corrected estimates. These findings demonstrate that measurement approach is a determinant of scientific conclusions in physical activity research, and that reproducible wearable data pipelines are essential infrastructure for aging epidemiology.

There is increasing interest in parent-child technoference: the interference with personal interactions caused by technology devices. This study examined the reliability and construct validity of the Technology Device Interference Scale (TDIS) to measure technoference in a sample of Canadian parents and children. Parents (n=883) and children (n=376) were recruited from clinical and community settings and completed the TDIS for their own and family member technoference over three timepoints (T1=2023, T2=2024, T3=2025). TDIS internal consistency, test-retest reliability, and construct validity were assessed using Cronbachs alpha, intraclass correlation coefficient, and confirmatory factor analysis, respectively. The TDIS showed good internal consistency and adequate to good construct validity when used by children to report on their own technoference (all >.70; CFI>.95, TLI>.95, RMSEA<.07) or their parents technoference (all >.70; CFI>.95, TLI>.90, RMSEA[&le;].11). The TDIS had low to acceptable internal consistency and poor model fit for parent report of their own technoference ( range: .63 - .66; CFI<.95, TLI[&le;].80, RMSEA[&ge;].14) or their childs technoference ( range: .56 - .63; CFI<.95, TLI[&le;].85, RMSEA[&ge;].11), factor loadings for the items "phone" and "tablet" were low (phone<.50, tablet<.50). Test-retest reliability was moderate for all reports. The TDIS demonstrated good reliability and construct validity of child-reported technoference. Parent-reported technoference demonstrated lower reliability and construct validity, with "phone" and "tablet" weakly associated with the other technological devices. Future studies should distinguish between handheld vs. non-handheld devices when investigating parent-reported technoference.

Background: Companies in the Health and Life Insurance space are increasingly turning to digital tools to promote healthier behaviours among their user base and reduce future health risks. This approach shifts insurers' role from passive underwriters to partners in health management. These tools, often smartphone or wearable-tracker-based, enable real-time monitoring of behaviours (such as physical activity or meditation), providing fruitful targets for behavioural change interventions. Gamification, a Behavioural Change Technique with rich theoretical backing, is increasingly used in this context; however, despite its theoretical promise, current evidence remains mixed, and makes it hard to disambiguate its effect compared to more isolated financial incentives, the extent to which initial effects may be sustained over time, and how such changes in behaviour potentially translate to downstream health risk reductions. Objective: This 9-month parallel-group, open-label Randomised Controlled Trial was designed to assess the causal impact of gamification in promoting health behaviours, independent of financial incentivisation. This was conducted in a real-world workplace setting, involving a cohort of participants using the YuLife Health and Wellbeing app, provided within an employer-sponsored group cover setting. Methods: For the purposes of the RCT, the app was adapted such that gamification features could be turned on or off in a controlled manner, and in-app rewards in the form of "YuCoin" were adjusted between treatment groups to account for the effect of financial incentives. Following a baseline phase involving acquisition of baseline step estimates and questionnaire data, 1,288 participants -- recruited from a number of companies partnered with YuLife, spanning various sectors -- were randomised to gamified versus non-gamified versions of the app using stratified block-randomisation, and evaluated at specific milestones over a 9-month period, to enable comparison of short-term to long-term outcomes. The primary outcomes assessed were absolute differences in mean daily step count and engagement with the YuLife app. The data were analysed using Linear Mixed-Effects Models (LMMs). Additionally, a Cox Proportional Hazards model fitted to UK Biobank data was used to map step differences directly onto downstream health risks, and reductions were evaluated using an LMM. Further secondary outcomes (such as smoking and alcohol consumption) were also evaluated using non-parametric statistics. Results: Compared with control, the gamified intervention was associated with greater mean daily steps throughout the study, with month / intervention interaction effects reaching one-sided 5% significance at months 3 ({beta}=473.84, p=0.027), 5 ({beta}=626.54, p=0.006), and 9 ({beta}=480.91, p=0.033). Additionally, strong seasonal effects were identified, with fewer steps in Autumn ({beta}{approx}-943.50, p<0.001) and Winter ({beta}{approx}-1,145.45, p<0.001) versus Summer; higher baseline activity was a strong predictor of later activity ({beta}{approx}0.85, p<0.001) and higher BMI was negatively associated with steps ({beta}{approx}-60.84 per unit, p<0.001). For app engagement, month / intervention interactions were positive and significant from Month 3 onwards (Month 3 {beta}=0.205, Month 5 {beta}=0.182, Month 7 {beta}=0.170, Month 9 {beta}=0.175, all p<0.001), effectively showing sustained engagement while main milestone terms indicated declines in the control arm. Sensitivity analyses demonstrated the potential for baseline step inflation due to novelty effects, motivating repeating the step count analyses under an alternative baseline definition; this showed similar results, but with interaction effects achieving one-sided significance over all study milestones. Predicted partial-hazard analyses showed progressively larger month / intervention reductions in hazard, reaching one-sided significance at months 5 (coef=-0.018, p=0.016) and 9 (coef=-0.026, p=0.002). No significant intervention effects were observed for other secondary outcomes (e.g. smoking, alcohol) following Bonferroni-Holm correction. Conclusions: Gamification elements can be an effective component in the context of digital interventions aiming to promote positive health behaviours, leading to improved engagement with the intervention and positive behavioural outcomes. Through progressive risk-reduction, even small but sustained improvements can be shown to meaningfully improve long-term health outcomes. Gamification is likely to add value to workplace health promotion initiatives, particularly for targeted short- to medium-term behavioural change interventions operating within a larger risk-management framework. Trial Pre-registration: https://osf.io/926pd

BackgroundCOCOA, Japans Bluetooth-based COVID-19 contact tracing app, was widely regarded as ineffective due to persistently low key-match counts. However, this assessment may have conflated two distinct phenomena: (1) a structurally suppressed positive registration rate caused by administrative friction in the HER-SYS linkage, and (2) genuine epidemiological inefficacy. ObjectiveTo empirically examine whether the correlation between individual COCOA key-match counts and regional COVID-19 case numbers depended on positive registration rate, using a unique longitudinal dataset from a single observer with a rigorously controlled behavioral pattern. MethodsThe corresponding author (S.N.) recorded daily key-match counts from his personal iPhone from January 10 to October 8, 2022, encompassing the Sixth Wave (January 10-April 20, 2022) and Seventh Wave (July 9-September 2, 2022). Daily reported COVID-19 cases in Tokyo were obtained from publicly available NHK data. Pearson correlation coefficients were calculated for each wave period separately. ResultsDuring the Sixth Wave, no meaningful correlation was observed between key-match counts and daily case numbers (r2 = 0.018, p = 0.059, n = 194). In contrast, during the Seventh Wave, a strong positive correlation emerged (r2 = 0.530, p < 0.001, n = 56). This correlation disappeared abruptly after September 12, 2022, coinciding with Japans revision of the mandatory full case reporting (Zenshu Todokedashi) policy, which substantially reduced positive registrations in COCOA. ConclusionsCOCOAs utility as an individual infection risk indicator was critically dependent on positive registration rate rather than app installation rate. These findings provide the first real-world empirical evidence supporting the threshold effect predicted by prior simulation studies, and offer important lessons for the design of digital tools in future pandemic preparedness.

Background: Older adults with vision impairment often experience barriers to using digital technology. The indirect associations between vision impairment and digital access and skills via digital self-efficacy and frustration among older adults remain largely unknown. Objective: This study aimed to 1) explore factors associated with digital access, skills, self-efficacy, and frustration among older adults with vision impairment; 2) examine associations between vision impairment and digital access, skills, self-efficacy, and frustration among older adults; and 3) examine whether digital self-efficacy and frustration may help explain associations between vision impairment and digital access and skills among older adults. Methods: This was a cross-sectional study using nationally representative data from the Health Information National Trends Survey (HINTS) 2024. Respondents aged 60 and older were included. Vision impairment was assessed using a self-reported item. Outcomes included self-reported digital access, skills, self-efficacy, and frustration. Survey-weighted multivariable logistic regression and generalized structural equation modeling were conducted, adjusting for age, sex, race/ethnicity, education, and the number of comorbidities. Results: Among 3,149 older adults (mean [SD] age, 70.7 [10.0] years; 45.6% female), 7.1% (n=223) reported vision impairment. Among older adults with vision impairment, 65.6% (95% CI, 53.5% to 75.9%) used the internet daily, and 79.5% (95% CI, 66.8% to 88.2%) used a smartphone in the past 12 months. In multivariable logistic regression analyses among older adults with vision impairment, older age was associated with lower odds of daily internet use (OR, 0.84; 95% CI, 0.79 to 0.90), smartphone use (OR, 0.85; 95% CI, 0.75 to 0.97), wearable device use (OR, 0.88; 95% CI, 0.79 to 0.97), and using the internet to send a message to a healthcare provider (OR, 0.87; 95% CI, 0.80 to 0.93). Older adults who self-identified as racial and ethnic minority groups (e.g., Black/African American, Hispanic) had lower odds of daily internet use (OR, 0.15; 95% CI, 0.05 to 0.50) and using the internet to send a message to a healthcare provider (OR, 0.17; 95% CI, 0.04 to 0.73) compared with Non-Hispanic White older adults. Vision impairment was associated with lower odds of daily internet use (OR, 0.60; 95% CI, 0.37 to 0.99) and digital self-efficacy (OR, 0.53; 95% CI, 0.32 to 0.86). Digital self-efficacy was associated with higher odds of daily internet use (OR, 2.95; 95% CI, 2.04 to 4.26). Generalized structural equation modeling identified an indirect association between vision impairment and daily internet use via digital self-efficacy (coefficient, -0.68; 95% CI, -1.24 to -0.12). Conclusions: Findings suggest that reduced digital self-efficacy may help explain the observed association between vision impairment and daily internet use among older adults. Interventions targeting digital self-efficacy, including accessible interface designs, personalized coaching, and peer support, may help bridge the digital divide among older adults with vision impairment.

BackgroundUniversity students often struggle to maintain healthy sleep, physical activity, and screen usage due to academic pressures and irregular schedules. Ecological momentary assessments (EMAs) and interventions (EMIs) offer real-time, context-aware opportunities to monitor and promote healthier behaviors. This pilot study aimed to evaluate the feasibility of a hybrid study design combining continuous monitoring with sequential randomized controlled trials (RCTs) evaluating EMIs targeting movement behaviors among university students. MethodsThe MOVE@NUS pilot study (September 2024 - January 2025) embedded three sequential RCTs, each targeting one behavior: sleep, physical activity, or screen time. For each RCT, participants were randomized on a 1:1:1 schedule (control, intervention 1, intervention 2). Eligible participants were first-year undergraduates, aged 18-25 years, who regularly used an iPhone and an Apple Watch. Smartwatches (primary) and smartphones (supplementary) passively and continuously tracked behaviors. EMAs (eight 3-day bursts) and web-based surveys captured self-reported behaviors and participant experience. All assessments were self-administered, and no provider assistance was involved. ResultsOf 229 students who met screening criteria, 65 enrolled (mean age 20.4 {+/-} 1.5 years; 53.8% female). Questionnaire completion was high (baseline: 100.0%, midway: 89.2%, endpoint: 86.2%). EMA engagement decreased from 88.7% (first burst) to 49.2% (final burst). Passively monitored data were obtained from 62 participants (95.4%) with a mean tracking duration of 67.8 days (range: 11 to 114). Data completeness was highest for passively captured measures of physical activity, while more participant-dependent measures, such as manually uploading screen time screenshots, showed greater attrition. Overall satisfaction was 78.9% for sleep, 70.6% for physical activity, and 60.0% for screen time. ConclusionsThis hybrid study design is feasible and acceptable among university students, with successful integration of self-reports and passive tracking. Variations in engagement and data completeness highlight areas for optimization in future large-scale digital cohort studies. Trial registrationClinicalTrials.gov ID NCT06597890 First Posted: 2024-09-19.

Cardiac rehabilitation is critical for secondary prevention, yet long-term adherence remains low. We present CUOREMA, a new personalized mobile health system integrating self-monitoring diaries, wearable data, virtual coaching, and reinforcement learning-enhanced adaptive interventions to support lifestyle change during and after outpatient cardiac rehabilitation. In a six-month two-center feasibility study (N = 53, Switzerland and France), we evaluated usability, engagement patterns, and preliminary health-related outcomes. Attrition was high: only 19\% of participants used the app on more than 100 days, and questionnaire response rates declined from 55\% at baseline to 13\% at six months. Despite these limitations, exploratory data-driven analysis revealed three distinct engagement clusters (dropout, sporadic, and consistent), which were further supported by matching patterns in app component usage, medication diary adoption, and smartwatch wearing time. Engagement clusters were not associated with demographic factors; instead, psychological themes of patients' personal goals suggested that intrinsic motivation characterized sustained users, whereas extrinsic motivation predominated among early dropouts. User experience was rated positively, and validated questionnaire scores showed no deterioration over time. One center demonstrated a statistically significant improvement in 6-minute walking test performance, though the study was not powered to detect clinical outcomes and selective dropout cannot be ruled out. These findings highlight engagement variability as a central challenge in digital cardiac rehabilitation and suggest that tailoring interventions to individual motivational profiles may improve long-term adherence.

Background: Public services are increasingly delivered through digital platforms. Although digital health may improve access and scalability, they may also widen inequalities for people who lack reliable access, confidence, skills, affordability or trust. Objective: This study examined the prevalence of self-reported digital exclusion among UK veterans and assessed its association with depression, anxiety and loneliness. Methods: A cross-sectional online survey was conducted between July 2025 and March 2026. Participants were UK Armed Forces veterans and resident in the UK. The survey collected sociodemographic, military service, digital access and health data. Self-reported digital exclusion was defined as reporting feeling excluded or disadvantaged due to lack of digital access or skills. Probable depression, anxiety and loneliness were assessed using the PHQ-2, GAD-2 and three-item UCLA Loneliness Scale, respectively. Associations between digital exclusion and each outcome were examined using adjusted multivariable logistic regression. Results: Of 1,911 responses received, 1,607 were included after data quality exclusions. Among participants with valid responses to the primary digital exclusion item, 553 (41.7%) reported digital exclusion. Digital exclusion was more common among females, younger veterans and those with lower household income. Probable depression, anxiety and loneliness were more prevalent among digitally excluded participants than among non-excluded participants. In adjusted models, self-reported digital exclusion was associated with higher odds of probable depression (AOR 1.38; 95% CI 1.04 to 1.83; p=0.028), probable anxiety (AOR 1.63, 95% CI 1.23 to 2.16; p<0.001), and probable loneliness (AOR 1.85; 95% CI 1.43 to 2.40; p<0.001). Conclusion: More than two-fifths of veterans with valid exposure data reported digital exclusion, despite high reported device access and confidence. Self-reported digital exclusion was associated with poorer mental health and loneliness, although causality cannot be inferred from these cross-sectional data. Digital-first services for veterans should include routine digital needs screening, targeted support and clear non-digital routes to care.

BackgroundLarge Language Model (LLM) chatbots are increasingly used for exercise and fitness topics, yet users experience with these tools remains understudied. MethodsThis study is a national survey of U.S. adults who have used an LLM chatbot for exercise-related topics in the past month. Participants answered questions about the exercise-related topics for which they used LLM chatbots, their perceptions of these chatbots value for exercise-related questions, and how chatbot use had changed their exercise behaviors and use of other exercise-related resources. ResultsParticipants (n=258) were majority male (n=138, 53.5%) and white (n=146, 56.6%) with a mean age of 41.7 (SD=14.9) years. The most endorsed topics for LLM chatbot use were making an exercise plan (n=137, 53.1%), nutrition related to exercise (n=132, 51.2%), advice on amount of exercise (n=122, 47.3%), specific exercises to try (n=120, 46.5%), and motivation or emotional support for exercise (n=112, 43.4%). On average, participants endorsed high trust (M=4.0, SD=0.7; on 1-5 scale) and a moderate emotional bond (M=3.0, SD=1.3) with LLM chatbots. Most participants (n=140, 54.3%) reported that they increased their exercise due to LLM chatbot use (M=55.6 minutes increase). Some participants reported increases in use of other resources; e.g., gyms (26.4%), wearable technology (23.3%), and exercise questions to their healthcare providers (25.6%). Those who increased exercise with LLM chatbot use reported significantly higher trust (M=4.1 vs M=3.9) and emotional bond (M=3.2 vs M=2.6) with chatbots and more use for motivation/emotional support (70.5% vs 29.5%) compared to those who did not. Many participants also used LLM chatbots for nutrition and weight-related questions. DiscussionLLM chatbots may meaningfully impact exercise-related behavior and resource use, warranting more rigorous causal research.

Smartphone use among medical students has become pervasive. While existing literature links excessive smartphone use to poor sleep quality, the specific behavioral pattern most strongly associated with sleep disruption remains insufficiently characterized. This study investigated whether the timing of smartphone cessation relative to sleep onset is more strongly associated with poor sleep quality than total daily screen time among medical students in Punjab, Pakistan, and examined the moderating role of exam period status. A cross-sectional anonymous online survey was conducted among medical students across Punjab, Pakistan (May 2026). Sleep quality was assessed using items informed by Pittsburgh Sleep Quality Index (PSQI) response formats. Descriptive statistics, chi-square tests, and binary logistic regression were applied to 369 eligible responses, reported in accordance with STROBE guidelines. Of 369 respondents (49.9% female, 48.2% male), 74.8% reported using smartphones 6 or more hours daily and 61.2% used their smartphone until falling asleep. Overall, 75.7% reported poor sleep quality. Students using smartphones until sleep onset had 95.1% poor sleep quality compared to 44.8% in those who ceased use before sleeping (p<0.001). In logistic regression with both variables entered simultaneously, bedtime use until sleep onset remained independently associated with poor sleep quality (OR 15.3, 95% CI 5.7-41.2, p<0.001), while total daily screen time lost significance (OR 1.8, 95% CI 0.7-4.7, p=0.228). Outside exam periods, 99.0% of students using smartphones until sleep onset reported poor sleep quality versus 24.2% of those who stopped before sleeping, a difference of 74.8 percentage points (p<0.001). During exam periods, no significant association was observed (p=0.075), suggesting exam-related stress may attenuate the bedtime behavior effect. Hostel-dwelling students showed the highest prevalence of bedtime smartphone use, with 79.0% using smartphones until sleep onset compared to 23.2% of family-living students (p<0.001). Bedtime smartphone use until sleep onset is more strongly associated with poor sleep quality than total daily screen time among Pakistani medical students. Medical institutions should consider integrating targeted digital wellness education specifically addressing bedtime cessation timing into student health programs, with particular attention to hostel-dwelling students.

Existing interfaces modulate user experience through visual, auditory, and haptic channels, but direct physiological modulation, which programmatically alters a user's internal state, remains largely underexplored. We present a wearable sonophoresis patch that uses low-frequency acoustic stimulation to deliver psychoactive substances transdermally, and evaluate its potential for programmable physiological modulation in HCI. We tested this in a double-blinded study (N=26) delivering 100 mg caffeine versus sham control, recording physiological signals during rest and a sustained attention task (SART). The planned comparison for heart rate standard deviation during rest was significant (HR-SD p=0.025, d=1.48), with the caffeine group showing suppressed HR~SD consistent with sympathetic activation. Mean heart rate at rest was not significant (p=0.365), but exploratory analyses during the cognitive task revealed significant cardiovascular divergence: heart rate (p=0.003) and heart rate standard deviation (p=0.027) both moved in directions consistent with systemic caffeine delivery, with effects emerging within minutes of device activation and a sustained group effect across all task rounds (p<0.001). These results provide indirect evidence that wearable sonophoresis can deliver substances to modulate user physiology, opening the design space for on-skin chemical interfaces that adapt delivery in real time to change the user's physiological state on demand.

Background: Children and young people (CYP) are particularly affected by mental health problems. Mobile apps provide a scalable and accessible approach to adolescent mental health support, and schools are well-positioned to address multiple risk factors and deliver large-scale interventions. By combining active (self-reported) and passive (sensor-derived) data, mobile apps can model mental states and deliver context-aware support. Artificial Intelligence (AI) enables adaptive, context-aware recommendations tailored to each user. However, there is limited research on AI-based mental health interventions in community CYP. MindCraft is a mobile app designed to monitor adolescents mental health using active and passive data and provide AI-informed recommendations ("nudges"). This study aims to investigate the effectiveness of personalised AI nudges delivered through MindCraft on improving mental health outcomes among adolescents in schools in the United Kingdom. Methods: The study is a three-arm RCT using a prospective cohort of secondary school students aged 14-19. Following informed consent, participants complete a baseline online assessment at school and download MindCraft. The primary outcome is the Strengths and Difficulties Questionnaire global and subscale scores. Secondary outcomes include the Eating Disorders Diagnostic Scale, the Sleep Condition Indicator Questionnaire, the Self-Injurious Thoughts and Behaviours Interview, the Self-Efficacy Questionnaire for Children and the World Health Organisation-Five Well-Being Index. Participants are randomised to: (1) an AI-informed intervention group receiving personalised nudges, (2) an active control receiving non-personalised nudges, or (3) a control group with self-monitoring only. Participants use the app for four weeks, with follow-up at one month. Repeated-measures analyses will assess changes across time points. Discussion: We hypothesise that AI nudges will have a greater positive effect on mental health outcomes at one month than general nudges and self-monitoring. Our findings will provide key evidence on the effectiveness of personalised mobile AI recommendations for adolescents mental health and inform school-based mental health prevention and early intervention. This study will contribute evidence on the ethical, acceptable, and scalable integration of AI-enabled digital mental health tools within public health and educational systems, with implications for the design of future digital public health interventions and policies supporting their safe integration in schools.

Background: Mental health issues are prevalent among men, yet help-seeking remains low due to stigma, masculinity norms and access barriers. Digital mental health (DMH) screening questionnaires offer opportunities for early detection, but their uptake among men is limited. Objective: This study explored the barriers and facilitators influencing mens willingness to use DMH screening questionnaires, with the aim of informing user-centered design that supports early detection and engagement. Methods: This interpretive qualitative study was conducted through semi-structured interviews with 17 purposively sampled Estonian men (aged 20-54) in a highly digitalized context until data saturation was reached. Thematic analysis followed a mixed deductive-inductive approach: deductive codes were derived from theoretical frameworks (Technology Acceptance Model, Health Belief Model, User-Centered Design, Behavioral Design), while inductive themes emerged from participants responses across the three research questions, including their evaluations of four screening questionnaire (PHQ-2, PHQ-9, EEK-2, WHO-5). Results: Key barriers included data privacy fears, distrust of digital solutions, lengthy questionnaires, and poor user experience (UX). Facilitators were anonymity, institutional trust, short (5-10 min) questionnaires, mobile-optimized design, personalized feedback, and clear next steps. As main contribution, four archetypes were identified: Skeptic, Self-Manager, Explorer, and Situational Seeker. They reflected distinct patterns across privacy concerns, institutional trust, user experience preferences, and help-seeking orientations. Skeptics were characterized by low institutional trust, high concern about data misuse, and a preference for anonymous, low-friction interactions, often delaying help-seeking. In contrast, Self-Managers emphasized autonomy, transparency, and evidence-based support, engaging in structured self-monitoring and purposeful help-seeking. Explorers showed openness to experimentation and engagement, particularly when supported by intuitive, interactive, and visually clear UX, while data sharing depended on perceived value. Situational Seekers demonstrated episodic engagement patterns, where trust, data-sharing, and help-seeking were highly context-dependent, preferring fast, low-effort interactions when needed. Conclusions: Mens uptake of DMH screening questionnaires is influenced by a combination of social, psychological, and usability factors. Effective design should integrate anonymity, institutional credibility, and user-centered features to support engagement and early mental health detection. Personalized, actionable feedback with transparency, user control, and clear next-step guidance emerged as key drivers of sustained engagement, while poor usability and lack of meaningful feedback led to disengagement. Importantly, the proposed archetypes capture how these factors co-occur in dynamic, context-dependent user profiles, offering a more actionable alternative to one-size-fits-all and demographic approaches for designing DMH questionnaires tailored to male users.