JMIR mHealth and uHealth

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.

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.

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.

Objective. To evaluate the perceived usability, acceptability, and user experience (rather than the clinical effectiveness) of Boora, an AI-assisted, human-supervised digital platform prototype for longitudinal overweight and obesity care, among users and health professionals in Brazilian primary care. Design. Convergent mixed-methods formative evaluation. Perceived usability was measured with the System Usability Scale (SUS) and summarised descriptively; semi-structured interviews conducted after hands-on use were analysed with codebook thematic analysis (Braun and Clarke); the two strands were integrated through a joint display. Qualitative reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Setting. Primary health care network of Ananindeua, Para, within the Brazilian Unified Health System (January to February 2026). Participants. Fifteen adults with overweight or obesity (BMI at least 25 kg/m2, confirmed via electronic health records) who used the patient application on their own smartphones for 24 hours, and eight primary care professionals (nurses, physicians, and a dietitian) who used the professional dashboard for approximately 20 minutes on predefined tasks with synthetic data. Main outcome measures. SUS scores and qualitative themes addressing usability, acceptability, perceived usefulness, barriers, and perceived clinical and workflow fit. Results. Boora showed good perceived usability in both cohorts (users mean 76.5, SD 10.3; professionals mean 77.5, SD 4.6; both above the SUS normative average of 68). Four themes emerged per cohort. Users valued an accessible interface and visible progress but described daily logging burden, fragile anticipated engagement, and digital-literacy and accessibility barriers. Professionals valued a clear interface and the prospect of panel-managed, proactive follow-up, while requiring training, AI governance, protected time, and interoperability with the national record. Integration indicated that the disengagement users anticipated was the risk professionals perceived the dashboard could help identify, whereas the educational AI assistant was the weakest and most ambiguous component for both groups. Conclusions. Boora was perceived as usable and acceptable, with perceived value concentrated in human-supervised, longitudinal follow-up rather than autonomous self-tracking or AI advice. These findings concern perceived usability and acceptability, not clinical effectiveness or sustained engagement. Real-world adoption would depend on accessibility refinements, electronic-record integration, and clear AI governance aligned with the principles of Brazil's proposed risk-based AI framework and the LGPD.

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.

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.

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.

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.

Background: Regularity of sleep patterns over time has increasingly gained traction as an important axis of sleep health. Since sleep habits are under some degree of behavioral control, understanding such patterns in naturalistic settings is particularly important. We quantified sleep variability and tested the hypothesis that regularity correlates with physical activity, resting heart rate (rHR), and heart rate variability (HRV). Methods: We analyzed real-world digital health data from over 81,000 participants (over 18 million nights) who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep, activity, and heart rate data to the study. Variability was quantified using the standard deviation (SD) computed from total sleep time (TST), sleep start time (S-start), end time (S-end), and midpoint time (MP), as well as the Sleep Regularity Index (SRI). Results: The SD-based variability metrics correlated with one another (R values 0.74-0.92), and with the SRI metric (R values 0.62-0.64). More consistent sleep, by any metric, was associated with more activity and better rHR and HRV. The most consistent tertile for TST variability had higher median TST (6.9 vs 5.9 hours), more daily exercise (32.8 vs 20.4 minutes), lower rHR (62.4 vs 65.6 beats per minute), and higher HRV (40.6 vs 37.3), all p<1e-100. The findings were similar when variability was defined by S-start SD, S-end SD, MP SD, or SRI. Conclusion: Sleep consistency metrics are highly correlated with each other, and consistency by any metric was associated with more activity, lower rHR, and higher HRV. While causality cannot be established, the results of this large, naturalistic observational cohort are consistent with the growing literature on the potential positive health associations of sleep consistency.

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.

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.

In Mongolia, an average of 65,000 women become pregnant each year, and about 59,500 babies are born. Although the number of pregnancies is decreasing by 8-12 percent each year, the level of fetal monitor usage remains high. The capital's maternity hospital currently has 27 fetal monitors in use, and an average of 30-35 calls are recorded per month. However, there is a lack of research on the use of fetal monitors, the causes and influencing factors of damage, and the organization of technical services. Therefore, this topic was chosen to determine the usage status of fetal monitors, the causes of malfunctions, and ways to improve them. Purpose To study the causes and factors affecting possible damage and injury during the use of fetal monitors, and to identify ways to reduce them. Materials and methods A one-time study was conducted on 10 MT-610 fetal monitors that were put into operation in 2019 at the Urgo Maternity Hospital in the capital. Data were collected and processed using document analysis methods from the technical passports and call logs of these devices. The factors contributing to common failures were identified using focus group interviews with the engineers and technicians responsible for the equipment. Results This study found that fetal monitor failures are caused by improper use, lack of regular calibration, electrical fluctuations, ambient temperature and humidity, and insufficient medical staff skills, training, and knowledge of how to use the device, all of which contribute to failures and measurement errors. It is also observed that when a replacement part is needed for a monitor that frequently breaks, the monitor is more likely to break again if it is used as a replacement from a previously broken monitor. Therefore, training doctors and nurses who replace spare parts on their use has been observed to significantly reduce future breakdowns. Conclusion According to the study results, the breakdowns and failures of fetal monitoring devices are mainly related to internal system failures, unstable power supply, and wear and tear of accessories and mechanical parts. The highest percentage of device failures indicates the need for special attention to the reliability of the device's basic functions. Additionally, the high percentage of accessory and printer failures indicates the need for proper use and monitoring of the entire device. In addition to technical factors, human misuse, lack of maintenance, and environmental influences also play a significant role in damage. Therefore, it is concluded that to ensure the reliable operation of fetal monitors, it is necessary to perform regular maintenance, stabilize the power supply, improve the quality of accessories, and increase the knowledge and skills of medical staff. Keywords: Fetal Monitoring, Equipment Failure, Risk Factors

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.

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.

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.

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.