JMIR mHealth and uHealth

Objectives: To describe device-measured movement behaviours in a large sample of middle-aged and older Australian adults using complementary posture- and intensity-based accelerometers, examine variation across demographic groups, and identify behavioural phenotypes using clustering approaches Design: Cross-sectional analysis of the Australian Breakthrough Cancer (ABC) Study Accelerometer Sub-study (ACM). Methods: Participants from the ABC cohort completed seven days of simultaneous monitoring using a thigh-mounted activPAL and waist-mounted ActiGraph GT3X+. activPAL characterised posture-based behaviours (sitting, lying, standing, stepping, postural transitions, and sedentary accumulation), while ActiGraph characterised intensity-based activity (sedentary, light, and moderate-to-vigorous physical activity [MVPA]). Movement behaviours were summarised overall and by gender, age group, body mass index (BMI), and education. Behavioural phenotypes were identified using k-means clustering. Results: Among 4,238 ACM participants, 3,426 met inclusion criteria with valid data from both devices (1,711 females; 1,715 males). Participants accumulated substantially more time in sedentary and low-intensity behaviours than in MVPA. activPAL estimates indicated mean daily time of 379.3 min sitting, 282.2 min lying, 160.8 min standing, and 64.5 min stepping, with a mean of 5,185 steps/day. ActiGraph estimates indicated 584.0 min/day sedentary time, 290.9 min/day light-intensity activity, and 33.2 min/day MVPA. Considerable heterogeneity in movement behaviours was observed between individuals, whereas demographic differences were comparatively modest. Three behavioural phenotypes were identified: active/fragmented (24%), low activity (41%), and prolonged sedentary (35%). Notably, the low-activity and prolonged sedentary phenotypes were distinct, indicating that low overall movement and prolonged uninterrupted sitting represented different behavioural patterns. The prolonged sedentary phenotype was characterised by greater uninterrupted sitting time, lower stepping time, fewer steps, lower MVPA, and fewer sit-to-stand transitions. Conclusions: Movement behaviours in middle-aged to older Australian adults (40-74 yrs) were characterised by high sedentary time, low accumulation of MVPA, and substantial between-person heterogeneity. Distinct behavioural phenotypes highlighted differences in both movement volume and sedentary accumulation patterns, suggesting that movement behaviour is multidimensional and not adequately described by single summary measures alone. These findings may help inform our understanding of population movement patterns relevant to cancer and cardiometabolic disease prevention. Key words: Accelerometry, Motor Activity, Sedentary Behaviors, Cluster Analysis, Postural Allocation, Behavioural Phenotypes

Background. Alcohol use impacts sleep; however, sleep is also impacted by other 24-hour movement behaviors, including sedentary behavior (SB) and physical activity (PA). This study used compositional data analysis (CoDA) to simultaneously account for SB and PA and investigate within- and between-person associations between sleep and alcohol use behaviors. Methods. Participants were 21-44-year-old overweight/obese adults. Across 21 days, participants wore an activPAL monitor to assess sleep, PA, and SB hours, and completed morning surveys of past day alcohol use to assess drinks/day. CoDA identified the daily proportions of time spent in sleep, PA, and SB. Multilevel models investigated within- and between-person associations between sleep, demographic factors, and alcohol use. Differences in sleep, SB, and PA across drinking versus non-drinking days were investigated with individual MANOVAs for each participant. Results. Participants (N=91, Mage=30.7{+/-}6.5, 57% female, 75% White) reported drinking on 23% (n=437) of days, consumed 1 drink/day, and averaged 8.5{+/-}2.3 sleep hours, 7.4{+/-}2.8 SB hours, and 8.0{+/-}2.7 PA hours per day. Multilevel models indicated that participants slept 16.6 fewer minutes on drinking days (p=.046) each additional drink corresponded with 4.4 fewer minutes of sleep (p=.04). There was large between-person variability in time use allocations across drinking versus non-drinking days. For many the change was non-significant; however, three participants spent significantly less time sleeping on drinking days (1.16-4.74 fewer hours of sleep) and one participant spent significantly (2.62) more hours sleeping on drinking versus non-drinking days. Conclusions. Drinking days and greater alcohol consumption were associated with fewer minutes of sleep within-people; however, shifts towards PA and SB were inconsistent. Investigating contextual factors, such as the physical/social context of alcohol use, the time-of-day drinking occurs, the type of alcohol consumed, or the type of PA a person engages in could provide deeper insight into the conditions under which alcohol use detrimentally impacts sleep.

While the public perceives e-cigarettes as less harmful than combustible tobacco, little is known about their specific health concerns regarding vaping. We demonstrate a data-driven strategy to discover the public's health concerns about vaping e-cigarettes expressed on social media. We obtained all public posts from the largest e-cigarette-related subreddit, r/electronic_cigarette, from its inception on September 17, 2008, through April 1, 2022 (N = 10,403,433). We identified health concerns attributed to vaping by (a) selecting all cause phrases containing "cause" and its inflections, (b) calculating the empirical frequency ratio of words and bi-grams occurring in these phrases relative to random phrases, (c) retaining the 10% of words with the greatest empirical frequency of occurring in cause phrases, and (d) annotating this sample for health-relevant concerns and their subjects. In total, 76,342 posts contained cause phrases, with increased volume over time. Of the 425 words most strongly associated with cause phrases compared to random phrases, 53.4% (95%CI, 48.7-58.1) were identified as health-relevant. The top health-related concern was lipoid pneumonia, cited in 5.9% (95%CI, 5.0-6.8) of all cause phrases, followed by pneumonia (4.1%; 95%CI, 3.3-4.9), and nausea (2.7%;95%CI, 2.0-3.4). The top health concern subjects were respiratory, representing 23.7% (95%CI, 18.5-29.5) of all cause phrases, followed by gastrointestinal (12.7%; 95%CI, 8.8-17.2) and cardiovascular (8.5%; 95%CI, 5.3-12.3) concerns. Other subjects included neurological, dermatological, oral health, sexual health, psychiatric, oncologic, addiction, and sleep concerns. Because our strategy relies on data-driven techniques, our analysis can be integrated into routine social media monitoring and applied across different types of social media and text data, potentially leading to more timely identification of emerging concerns and a broader understanding across platforms. As a result, experts can craft messaging that accounts for current perceptions held by the public using our method.

Background: Marijuana is among the most widely used controlled substances, with use rising as risk perception declines. Despite growing acceptance, concerns remain regarding its effects on cardiovascular and autonomic physiology, sleep, and risk of cannabis use disorder. Although laboratory and epidemiological studies have characterized these associations, objective evidence from real-world settings is limited. Methods We studied cardiac autonomic function, sleep, and marijuana use patterns in 16,965 consumer-wearable device users, contributing a total of 5,226,739 person-days of data with or without self-reported use of marijuana. Linear mixed models related day-level use to nocturnal cardiac autonomic function (resting heart rate [RHR], heart rate variability [HRV]) and sleep (duration, fragmentation, architecture) at the within- and between-person levels. Binomial mixed models were used to estimate use probability across 72 weeks of marijuana tracking. Results: Members reported marijuana use on a median of 25.1% of logged days (IQR, 7.1-62.6%). Within individuals, use was associated with lower RHR (-0.12bpm; 99.5% CI: [-0.14, -0.10]) and lower HRV (-0.47ms [-0.53, -0.42]). Sleep was longer (+7.60min [7.37, 7.83]) but more fragmented, with more disturbances (+0.47 [0.45, 0.48]), greater wake time (+1.79min [1.70, 1.89]), and altered wearable-estimated sleep architecture. Daily use probability declined over 72 weeks (-11.1% [-11.5, -10.8]). Conclusions: Marijuana use was associated with altered nocturnal cardiac autonomic physiology and longer but more disrupted sleep. Marijuana use declined after commencing tracking, raising the possibility that longitudinal self-monitoring may influence use. These findings extend laboratory evidence on marijuana use to real-world settings and demonstrate the potential of consumer wearables for studying substance use and its physiological correlates at scale.

Background Worldwide use of systemic menopausal hormone therapy (MHT) declined sharply after 2002 following reports of increased breast cancer and cardiovascular risks. Evidence that the benefit-risk balance depends on initiation timing and treatment patterns has renewed clinical interest in MHT recently. A comprehensive characterisation of utilisation patterns is thus needed to assess alignment with guidelines. We describe systemic MHT use among women aged [≥]45 years in France between 2011 and 2024. Methods In this descriptive nationwide study using the French National Health Data System (SNDS), we included 21,983,503 women aged [≥]45 years affiliated with the main health insurance scheme between 2011 and 2024. Systemic MHT was identified from reimbursements for drugs containing systemic oestrogen. Users had at least two reimbursements for systemic oestrogen (unopposed or with a progestogen) within 12 months, and initiators had no reimbursements in the preceding five years. Annual prevalence, initiation and discontinuation proportions were estimated overall and by age group. Results We identified 1,250,037 systemic MHT users and 578,668 initiators between 2011 and 2024. Annual prevalence decreased from 4.3% in 2011 to 2.0% in 2022, then stabilised, with a slight increase after 2022 among women aged 45-59 years. Prevalence was consistently highest among women aged 55-59 years. Initiation declined from 0.5% of women in 2011 to 0.2% in 2022, before increasing to 0.3% in 2024, and was highest among women aged 50-54 years. Discontinuation was highest among women aged 45-49 years, peaking in 2019, and remained stable in older age groups. Median age at initiation was 52 (Interquartile Range [IQR] 50-55) years, and median treatment duration was 2.1 (IQR 0.6-6.1) years. Most initiators (79.3%) started combined oestrogen-progestogen therapy. Oestradiol was used almost exclusively (98.5%), mainly transdermally (74.2%). Micronised progesterone was the predominant progestogen, used exclusively by 62.9% of combined therapy initiators, followed by dydrogesterone (9.2%). Conclusion After prolonged declining use, systemic MHT initiation increased modestly after 2022. Prescribing appeared broadly aligned with current recommendations favouring transdermal oestradiol combined with micronised progesterone, for relatively short durations. These findings provide a benchmark for public health authorities to monitor alignment between prescribing patterns and guidelines and inform future decisions.

Background: Passively collected smartphone and wearable data, combined with genomic information, may enable scalable early identification of youth who are at risk for substance use disorder. However, heterogeneous data collected in naturalistic settings limit researcher control over data quality, requiring substantial preprocessing. Data cleaning and feature engineering decisions can meaningfully influence analytic outcomes yet are often minimally described or omitted in published work. Objectives: We provide rationales and recommendations for common preprocessing challenges in passive sensing research, including establishing inclusion criteria when "outliers" may reflect true behavioral variability, analyzing differences across smartphone operating systems, handling observations collected outside protocol windows, and engineering features for machine learning applications. Methods: We illustrate principled approaches to these challenges using smartphone and Fitbit data from the Adolescent Brain Cognitive Development Study (ABCD). The sample was drawn from the available half of the year-4 follow-up (data release 5.1; N = 4,754; ages 13-14). Results: Extreme outlying observations were concentrated among low-engagement participants with few observed days. Smartphone usage among Apple users appeared under-recorded relative to Android users when compared with self-report measures. Fitbit-derived behavior differed systematically between observations collected within versus outside protocol windows. We also identified potential errors in the study's default preprocessing pipeline. Conclusion: We recommend prioritizing person-level inclusion criteria that preserve meaningful behavioral variability, examining temporal distributions rather than assuming observations are exchangeable, and thoroughly investigating and reporting data quality issues. Although illustrated using the ABCD Study, these principles broadly apply to research using smartphones, wearables, and other digital data sources.

Background: Shiftwork disrupts natural sleep-wake cycles, alters light exposure patterns, and contributes to circadian misalignment. Detrimental health consequences associated with shift work include elevated risk for metabolic disorders, cardiovascular disease, cancer and all-cause mortality. Healthcare workers have one of the highest rates of shift work exposure, yet there are relatively few non-pharmacological interventions (with good evidence) developed to improve sleep outcomes in this population. Objective: A pre-post pilot interventional study assessed the acceptability and perceived effectiveness of commercial noise-masking earbuds on improving subjective sleep characteristics among National Health Service (NHS) healthcare staff working fast rotating shifts. Methods: Noise-masking sleep earbuds (Kokoon NightBuds) were worn for a pilot six-week intervention by twenty-seven NHS nurses (aged 26-43 years, 88.9% female) working fast rotating shifts from the EClocker Study. Sensors inside the earbuds were paired with a smartphone app to monitor sleep. An audio library in the smartphone app delivered personalised relaxation exercises and sleep techniques drawn from cognitive behavioural therapy for insomnia (CBT-I). A pre-post two-week monitoring period with daily smartphone-based Experience Sampling Methods (ESM) captured perceived daily sleep patterns. Acceptability and perceived effectiveness of the earbuds in promoting better sleep outcomes was assessed. Results: Use of the noise-masking sleep earbuds over a six-week period was associated with positive sleep improvement trends and elicited promising acceptability. Almost two thirds of NHS fast rotating shift nurses (63%) subjectively reported reductions in general sleep disturbance symptoms (PSQI Global), one in four experienced perceived sleep quality improvements (SQ; 25.9%), one in five reported sleeping longer (TST; 22.2%), and a third perceived falling asleep faster (SOL; 33.3%), had better sleep efficiency (SE; 33.3%) and improved daytime dysfunction (33.3%) (PSQI subcomponent scores). Sleep diaries (CSD) collected daily using smartphone-based ESM also demonstrated small improvements post-sleep earbud use; nurses reported sleeping an average 18 minutes longer (TST) and fell asleep more easily, on average 11 minutes faster (SOL). Sleep earbuds were generally well tolerated; 56% of nurses reported the earbuds as (somewhat to very) helpful, 52% reported (somewhat to strongly) falling asleep more easily (SOL), 44% felt (somewhat to strongly) their sleep quality was improved (SQ) and 30% agreed (somewhat to strongly) they slept longer (TST) and had less disturbed sleep. Conclusions: To our knowledge, this is the first study in Europe to pilot noise-masking earbuds as a potential non-pharmacological aid to improve sleep-wake behaviours or mitigate fatigue for healthcare staff. Preliminary results showed promising acceptability and (small) perceived sleep improvement trends following a targeted six-week earbud intervention in NHS fast rotating shift nurses.

Digital mental health tools are increasingly promoted as scalable supports for young people, yet implementation remains inconsistent, particularly for marginalised youth. Acceptability and usability are key determinants of successful adoption, but little is known about how these factors shape engagement across diverse youth populations. The aim of the study was to examine the acceptability, usability, and implementation potential of 11 evidence?based digital mental health tools among marginalised young people across the Republic of Ireland (ROI) and Northern Ireland (NI). A mixed?methods design integrated baseline surveys (n = 38), a two?week trial of digital tools delivered through a co?designed Google Site, online workshops/individual interviews (n = 22), and a final usability and engagement survey (n = 24). Usability was assessed using the System Usability Scale (SUS), engagement using the Twente Engagement with E?Health Technologies Scale (TWEETS), and mental wellbeing using the Short Warwick-Edinburgh Mental Well?Being Scale (SWEMWBS). Qualitative data were analysed thematically and mapped to the Consolidated Framework for Implementation Research (CFIR). Only two tools exceeded the SUS usability benchmark. Engagement was moderate overall, with one tool achieving the highest engagement despite lower usability. SWEMWBS scores indicated moderate baseline mental wellbeing. Thematic analysis identified five acceptability themes: credibility and trust; accessibility and ease of use; positive content supporting emotional regulation; personalisation and self?monitoring; and engagement and habit formation. CFIR analysis highlighted usability, institutional trust, cultural relevance, and emotional needs as core implementation determinants. Digital literacy was high and supported engagement, and usability remained a critical gateway to implementation. Designers and commissioners of digital mental health tools should ensure that supports are simple, trustworthy, culturally relevant, and youth?centred to enable adoption among marginalised young people. Implementation strategies are needed that will co?design with diverse youth communities and prioritise youth work settings as well as governance clarity.

Patient narratives can reveal aspects of health that standardized measures miss, yet collecting open-ended responses at population scale remains difficult. We evaluated whether speech-to-text (STT) could enable richer responses in online health surveys without the resource demands of interviews. Across four surveys involving people with post-COVID-19 condition, healthy adults, people who engage in sex work, and older adults, participants could answer open-ended questions by typing or using STT. We compared response length, lexical diversity, stopword proportion, and total unique content words, alongside STT uptake and participant feedback. Quantitative comparisons were made using a mixed-effects model including modality (STT vs typed) as fixed effect and a random intercept per participant. STT uptake varied markedly across populations (0.7-80.0%). Spoken responses were estimated to contain more characters than typed responses in two studies, by 245.0 and 891.8 characters on average (both p<0.001), and contained more unique content words, despite lower lexical diversity and higher stopword proportions. Participants generally valued STT for its convenience and spontaneity, but uptake was constrained by context, privacy, interface design and individual preferences. STT may enable population-scale collection of richer health narratives, provided implementation is tailored to the study population and research context.

Background: Approximately 2.5 billion people require assistive products (APs), yet access remains limited partly because of shortages of trained personnel. eHealth tools may help strengthen workforce capacity and translate evidence into practice. The World Health Organization's Training in Assistive Products (TAP) program is an open-access eHealth tool designed to strengthen AP provision. However, its impact on learner outcomes remains insufficiently evaluated. This study assessed the usability and acceptability of the TAP module and changes in self-efficacy in mobility aid provision. Methods: We conducted a quasi-experimental pre-post study with health science students who completed the WHO TAP "Introduction to Assistive Products" module. We assessed usability using the System Usability Scale, acceptability using the Technology Acceptance Model and Theoretical Framework of Acceptability, and self-efficacy using an adapted version of Bandura's Multidimensional Scales of Perceived Self-Efficacy. We analyzed data using descriptive statistics and paired-samples t-tests. Results: Of 53 participants, 33 completed both assessments. Usability exceeded established benchmarks (76.97 +/- 9.66), indicating a positive user experience. Acceptability findings demonstrated favourable responses across domains, with high ratings for ease of use and intervention coherence, and comparatively lower burden scores. Mean self-efficacy increased across all competencies from 2.32 +/- 0.58 before the module to 4.23 +/- 0.36 afterward (p < 0.001), reflecting substantial gains in confidence in AP provision. Conclusion: The eHealth TAP module demonstrated above-average usability and favourable acceptability, while significantly improving self-efficacy in AP provision. These findings highlight the potential for eHealth training implementation to strengthen workforce capacity and bridge training gaps in global health.

Background Wearable heart rate (HR) monitors collect minute-level HR continuously across days, yet these data are commonly reduced to scalars such as resting HR (RHR) and heart rate reserve (HRR), defined as the difference between maximum HR and RHR. This discards two dimensions of within-day HR dynamics: how HR range varies across the day and the magnitude and duration of individual HR rises and declines. We introduce diurnal heart rate range (dHRR), a time-of-day-specific measure of HR range across repeated days, and heart rate excursions (HREs), individual HR rises and declines characterized by amplitude and duration. Methods We analyzed minute-level HR from 750 participants in the Baltimore Longitudinal Study of Aging (mean [SD] age, 66 [12] years). We estimated the 5th, 50th, and 95th HR percentiles at each minute of the 24-hour cycle across repeated days, defining diurnal HR (dHR) as the median curve and dHRR as the 95th minus 5th percentile curve. A moving-average algorithm segmented HR into HREs. Function-on-scalar regression modeled dHR and dHRR by age, sex,and BMI across the day; local polynomial regression examined age and sex differences in HRR and HREs. Results Conventional HRR decreased with age, indicating a smaller overall observed HR range. Age-related differences in dHR varied substantially across the day: between ages 40 and 80, fitted dHR was approximately 2 bpm lower at 3:00 AM but 8 bpm lower at 6:00 PM. dHRR showed that this contraction was not uniform across the day: the evening peak near 6:00 PM at younger ages was largely absent by age 70, while the morning peak shifted from approximately 9:00 AM toward noon. At the event level, HREs showed two parallel age-related changes: between ages 40 and 80, mean excursion amplitude decreased from approximately 40 to 32 bpm, while mean excursion duration increased from approximately 90 to 104 minutes. Conclusions Aging was characterized by a smaller global HR range, compression and reorganization of HR range across the day, and smaller, longer-lasting HREs. dHRR and HREs provide complementary views of the diurnal organization and event-level dynamics of wearable HR and are implemented in the open-source ihr R package.

Wearable (accelerometer) data and machine-learning allow objective assessment of the amount of daily physical activity. However, wearable-derived human activity is subject to measurement error. No studies have corrected the dose-response association between physical activity and survival time to chronic diseases, including cardiovascular disease (CVD). The objective is to estimate the measurement error-corrected association between CVD events and multiple measures of daily duration of light and total physical activity, derived from machine-learning and conventional accelerometer-processing methods. Our method combined an accelerated failure time model, spline, and simulation-extrapolation (SIMEX). The method recovered the true dose-response non-linear association in simulated data, while the naive model failed to capture it due to substantial attenuation. Application to the UK Biobank accelerometer cohort also showed an increased protective association of total physical activity after SIMEX correction (Time Ratio [TR] = 1.56, 95% CI: 1.28-1.82 vs. TR = 1.38, 95% CI: 1.24-1.54 for SIMEX-corrected vs. uncorrected dose-response association between the 95th and 5th percentiles of total activity), with a similar increase for light physical activity. Sensitivity analysis indicates that the female population experiences a substantially larger protective association after SIMEX correction than males. Dose-response survival analysis is a widely used analytical method in physical activity epidemiology and benefits from measurement error correction.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Introduction: Extreme heat increasingly threatens older adults, particularly those with chronic conditions, yet generic heat-health advice may not be sufficiently timely or relevant to individual needs. This feasibility study describes a prototype and assesses the feasibility of a location-triggered, disease-specific heatwave short message service (SMS) intervention tailored to common heat-vulnerability conditions, compared with generic heatwave SMS advice. Methods: Mixed-methods feasibility study comprising a parallel two-arm 1:1 randomised controlled trial and post-heatwave focus groups. Community-dwelling Australians aged [&ge;]65 years in New South Wales, Victoria or South Australia with at least one eligible chronic condition (cardiovascular diseases, respiratory conditions, diabetes, and chronic kidney diseases) and a smartphone were recruited in summer 2026. Based on an initial codesign, participants received a 'prepare' SMS after enrolment and, when Bureau of Meteorology heatwave warnings were triggered, messages before, during and after heatwaves. Control participants received generic 'standard care' heat-health advice; intervention participants received condition-tailored messages and could request additional information via SMS codes. Outcomes were collected via baseline and post-heatwave surveys and thematic analysis of focus groups. Results: Seventy-three participants enrolled (36 control; 37 intervention); attrition was 9.6%. Intervention engagement was strong: 61% requested additional information, with frequent free-text replies and multi-condition requests indicating preference for more conversational interaction. Eight participants were heatwave-exposed and completed post-heatwave surveys (4 per arm), with a high usability score (median of 85/100). Among these 8 participants, 7 reported adopting heat-protective health behaviours; the most common were drinking more water (6/7). More total actions were reported in the intervention group (11 vs 8). No adverse effects were reported. Conclusion: A location-triggered, disease-tailored heatwave SMS system for older adults with chronic conditions was feasible, acceptable and highly usable, with high engagement and no harms. Findings support a larger trial and suggest benefits from tailored messaging.

Introduction: Chronic pelvic pain disorders (CPPDs) affect up to one in four reproductive-age women and are characterized by fluctuating, heterogeneous symptoms that complicate engagement in regular physical exercise. Standard programs rarely adjust to daily symptom variability. WorkoutCPP is a pilot series of N-of-1 trials evaluating the feasibility of an adaptive mobile-health (mHealth) intervention using reinforcement learning (RL) to personalize exercise for individuals with CPPDs. Methods and Analysis: This pilot study consists of a series of N-of-1 trials employing a randomized ABAB or BABA design, in which 45 participants (aged 18-55 years) with CPPDs each serve as their own control. Each participant completes one baseline week followed by four alternating 2-week intervention phases (A = generic exercise guidance; B = adaptive RL-generated recommendations), totaling 9 weeks. The RL agent integrated into the ehive app suggests daily exercise recommendations based on participant-reported pain and prior exercise behavior. Primary outcomes are exercise recommendation adherence rate and participant retention rate; safety and additional feasibility indicators are also monitored. Secondary outcomes assess algorithm interpretability. Exploratory outcomes assess contextual sensitivity of the RL algorithm and changes in pain and behavioral engagement. Ethics and Dissemination: Ethical approval was granted by the Icahn School of Medicine IRB (Protocol # STUDY-23-00721). Electronic informed consent will be obtained. Findings will be shared via peer-reviewed publications, conferences, and de-identified data summaries. Registration: This study protocol was publicly registered on the Open Science Framework (OSF) Registries (URL: https://osf.io/d9f36) prior to participant enrollment, to support international open-science data transparency and reproducibility standards for mobile health adaptive intervention trials. The study was subsequently registered on ClinicalTrials.gov (NCT07810218), consistent with ICMJE recommendations for trials informing future confirmatory research. All documented study procedures match the baseline human subjects tracking mechanisms approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board (Protocol # STUDY-23-00721).

Background: Estimating nutrient intake from meal photographs with vision-language models (VLMs) is now practical, but patient photographs often cannot leave the institution. We developed PLANS (Personal-device Local Assessment of Nutrients System), in which a small open VLM runs entirely on a personal device. Methods: Open VLMs were compared on two image sets with reference values (NutriImage, 887 Japanese meals; SNAPMe, 1,134 US meals), and the selected model (Qwen3-VL-4B, 4-bit) was run on an iPad Air without network access. Energy (primary outcome), protein, fat, carbohydrate and salt were evaluated by median absolute percentage error (MedAPE) and Spearman rank correlation (rho). The effect of supplying the food weight, including errors in it, was examined. Results: Energy estimates on the iPad were identical to those of a desktop-computer run for 84-86% of images (other values, 75-87%). MedAPE on the iPad was 37.0% and 36.5% for energy (26-28% of images within +/-20%), 43-65% for the macronutrients and 68-101% for salt (rho 0.51-0.85). On the desktop computer, supplying the true weight reduced MedAPE of the 4B model on NutriImage from 37.0% to 32.5% for energy and from 100.0% to 66.8% for salt; energy MedAPE stayed below the base value for declared weights of 0.5-1.25 times the true weight or rounded to 50 g, but rose markedly when the container weight was included. Conclusions: An offline VLM on a tablet reproduced server-side energy accuracy and is a candidate tool for dietary counselling where photographs cannot leave the institution; given the wide limits of agreement, its usefulness in practice remains to be examined.

Background: The disconnect between episodic in-clinic assessments and patients goals to improve activity in daily life presents a challenge in rehabilitation. Wearable sensors are promising for addressing this disconnect but are not part of routine practice. The purpose of this study was to develop and test a digital health solution that integrated wearable sensor technology into outpatient rehabilitation. Methods: Using an iterative user-centered design approach, a digital solution was built that integrated data from a wrist sensor into the electronic health record (EHR). The feasibility, usability, and resources required of the solution were evaluated in a 9-month proof-of-concept study in outpatient rehabilitation. Results: 17 clinicians and 38 patients (median monitoring duration: 42 days) participated. Patient adherence to daily data syncing was a median of 68.6% and was not significantly correlated with digital technology self-efficacy (r = 0.03, p = 0.86). Recent sensor data ([&le;] 3 days old) were available in the EHR for 90.2% (193/214) of clinic visits, and clinicians viewed these data in 81.9% (158/193) of these visits. Overall user-friendliness on the System Usability Scale received a median rating of "good" from both patients and clinicians. Resources included costs to build the data transfer pipeline, a 24-month build timeline, and ongoing technical support. Conclusions: By combining user-centered design principles with dedicated resources, we built a feasible and usable digital health solution to bridge the gap between brief clinic visits and daily life in outpatient rehabilitation. These results and future refinements will facilitate broader-scale implementation of sensor technology in rehabilitation.

Limited evidence on access to assistive technologies among older adults may obscure significant inequities and hinder the development of effective policies aimed at reducing unmet need. This secondary analysis of the 2021-2025 Autonomy survey conducted in France used data from 8,024 respondents aged 60 and above on access to 54 assistive products across nine functional domains, 34 home modifications across 5 areas of the home and 24 activities requiring human help. Using survey weights, use, unmet need, total need, met need, undermet need and coverage were calculated across the total population and for vulnerable subgroups. Reasons for non-use were examined among those not using needed assistive products. We estimated that 83.2% (81.8%, 84.5%) of the French older population use assistive technology. Use was largest for assistive products for seeing, reading and for everyday life, home modifications in the bathing area and human help for undertaking less frequent tasks. Coverage was low for assistive products for communication and for transfers, home modifications in the kitchen and bathing area, as well as human help for running errands and using electronic devices. Use and unmet need were more than five times greater than the total population for subgroups with functional limitations. Cost was the biggest barrier to access. While the majority of total need for assistive technologies corresponded to use, important gaps in coverage were identified that merit attention to support an autonomous life among older populations.

BackgroundDietary assessment is the cornerstone of clinical management and research studies evaluating diet and health. Traditional methods such as food diaries and 24-hour recalls can be burdensome, prone to recall bias, and difficult to adhere to. Image-based dietary assessment using vision-language models (VLMs) offers a potential solution. ObjectiveOur goal was to benchmark state-of-the-art VLMs for automated food recognition, weight estimation, and calorie estimation using Googles Nutrition5k dataset. MethodsWe evaluated 3,229 food images using ten approaches: proprietary VLMs (Gemini 2.0 Flash, 2.5 Flash, 3.0 Flash, and 3.1 Flash Lite; GPT- 4o, GPT-4o-mini, and GPT-5 Mini; and Claude Haiku 4.5), an open-source VLM (Qwen2-VL-7B), and a commercial food recognition API (FatSecret). We assessed calorie and weight estimation using Lins Concordance Correlation Coefficient (CCC) and component detection using Jaccard similarity. ResultsGemini 3.0 Flash achieved the best calorie estimation (CCC 0.767, MAE 80.7 kcal), while Gemini 3.1 Flash Lite offered very comparable accuracy (CCC 0.754) with the highest ingredient recognition (Jaccard 0.655) at the lowest cost among top-performing models ($0.59/1K images). Among earlier-generation models, Gemini 2.0 Flash remained competitive (CCC 0.742, Jaccard 0.621) at a fraction of the cost ($0.10/1K images). A human validation study in which four annotators reviewed 440 images revealed systematic omissions in the original Nutrition5k labels. After correction, the extrapolated ingredient-overlap score for Gemini 2.0 Flash increased from 0.62 to an estimated 0.82, suggesting that raw Jaccard scores substantially underestimate true model performance. ConclusionsCurrent VLMs can perform automated dietary assessment with reasonable accuracy from single overhead photographs. Our results inform model selection for dietary assessment applications and highlight remaining challenges in calorie estimation and component detection for complex, multi-item meals.

Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.