JMIR mHealth and uHealth

Wearable-derived physiological features have been associated with disease risk, but most current studies focus on single conditions, limiting understanding of cross-disease patterns. This study adopts a trans-diagnostic approach to examine whether wearable data capture shared and condition-specific physiological signatures across multiple chronic conditions spanning physical and mental health, and then evaluates the utility of these features for disease classification. A total of 9,301 patients with at least 21 days of consecutive FitBit data from the All of Us Controlled Tier Dataset version 8 were analyzed. Disease subcohorts included cardiovascular disease (CVD), diabetes, obstructive sleep apnea (OSA), major depressive disorder (MDD), anxiety, bipolar disorder, and attention-deficit/ hyperactivity disorder (ADHD), chosen based on prevalence and relevance. Logistic regression and XGBoost models were fitted for each disease subcohort versus the control cohort. We found that compared to using just baseline demographic and lifestyle features, incorporating wearable-derived features enabled improved classification performance in all subcohorts for both models, except for ADHD where improvement was mainly observed for ROC-AUC in logistic regression model likely due to the smaller sample size in ADHD subcohort. The largest performance gains were observed in MDD (increase in ROC-AUC of 0.077 for Logistic regression, 0.071 for XGBoost; p < 0.001) and anxiety (increase in ROC-AUC of 0.077 for logistic regression, 0.108 for XGBoost; p < 0.001). This study provides one of the first comprehensive transdiagnostic evaluations of wearable-derived features for disease classification, highlighting their potential to enhance risk stratification in the real-world setting as a practical complement to clinical assessments and providing a foundation to explore more fine-grained wearable data. Author summaryWearable devices such as fitness trackers and smartwatches are becoming increasingly popular and affordable, providing continuous measurements of heart rate, physical activity, and sleep. Alongside the growing digitization of health records, this creates new opportunities for large-scale, real-world health studies. In this study, we analyzed wearable-derived physiological patterns across a range of chronic conditions spanning both physical and mental health to better understand how these signals relate to disease risk. We found that incorporating wearable-derived heart rate, activity and sleep features improved disease risk classification across several conditions, with particularly strong gains for major depressive disorder and anxiety. By examining how individual features contributed to model predictions, we also identified meaningful associations between physiological signals and disease risk. For example, both duration and day-to-day variation of deep and rapid eye movement (REM) sleep were associated with increased risk in certain conditions. Our study supports the development of real-time, automated tools to assess disease risk alongside clinical care.

Study objectives Consumer sleep technologies (CSTs) enable low-burden longitudinal sleep monitoring, and their output measures are often interpreted as equivalent to polysomnography (PSG) measures. We applied a measurement reliability-aware approach to determine whether CST-derived 'sleep' measures (1) are interchangeable or device-specific, (2) can reliably assess trait-like sleep characteristics of an individual, (3) can be reduced to latent principal components of sleep, and (4) can be used for classification and biomarker discovery. Methods Data from 74 older adults (20 people living with dementia [PLWD]) were collected at-home (upto 14 nights; Total=752nights) using four tools simultaneously: research-grade actigraphy (Axivity), a wearable (Withings Watch), a nearable (Withings Sleep Analyzer) and Sleep Diary, followed by one in-lab PSG assessment. We used repeated-measures correlation analyses, intraclass correlation coefficients (ICC), principal component analysis (PCA) and binary classification models to address our objectives. Results Single-night between-device correlations and correlations with PSG were moderate (0.3[&le;]r<0.7) for some duration- and timing-related measures, but other associations were weak (r<0.3). Seventy-one percent of sleep measures reached acceptable reliability (ICC[&ge;]0.7) within seven nights of aggregation, but the required aggregation window varied across measures, tools and between PLWD and Controls. Reliability-filtered PCA yielded stable and interpretable principal components, but Duration was the only component showing moderate between-device association. Principal components were successfully used to classify PLWD vs Controls but feature importance varied across devices. Conclusions Aggregation of CST derived measures across 7-14 nights, yielded reliable measures, most of which were device-specific, with duration being the only essential aspect transferable between devices.

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.

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.

Cardiovascular age is a powerful risk-communication tool that translates complex physiological data into an intuitive number, yet traditional estimates require clinical testing. Consumer wearables now estimate cardiorespiratory fitness age from photoplethysmography-derived heart rate data, enabling continuous, passive health monitoring, but whether such estimates capture substantive lifestyle variation has not been examined. We characterized Cardio Age, a wearable-derived cardiorespiratory fitness age estimate, in 442 Ultrahuman Ring users across a 12-month window ending February 2026, separating independent lifestyle correlates from direct or indirect algorithmic inputs. The mean Cardio Age gap (CA gap; mean Cardio Age minus chronological age) was -1.84{+/-}2.97 years, with 82.6% of participants exhibiting younger estimated cardiovascular ages. Independent lifestyle metrics with no algorithmic link to Cardio Age showed significant associations: sleep efficiency (r = -0.194, p < 0.001), rapid eye movement (REM) sleep (r = -0.203, p < 0.001), sleep duration (r = -0.200, p < 0.001), and daily steps (r = -0.145, p = 0.003). A monotonic body mass index (BMI) dose-response was observed, with underweight participants showing a mean CA gap of -3.73 years versus -0.52 for obese participants. Extreme-group comparisons revealed that users with the youngest cardiovascular ages slept 37 minutes longer, achieved 22 more minutes of REM sleep, and had 1.8% higher sleep efficiency than those with the oldest cardiovascular ages (all p < 0.05). Sustained improvers over 12 months showed a mean CA reduction of 3.24 years, accompanied by decreased resting heart rate (-0.8 bpm, p < 0.001) and increased estimated VO2 max (+1.3 mL/kg/min, p < 0.001), indicating that Cardio Age tracks physiological changes over time.

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 ([&ge;]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 [&ge;]10-hour and [&ge;]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.

Sudden behavioral outbursts in youth with autism spectrum disorder (ASD) are difficult to predict and create substantial caregiving burdens. Wearable physiological monitoring might enable prediction, but sustained use may be limited by tolerability. We evaluated adherence and data completeness in 40 youth with ASD over a two-week period across four device types (wristband, headband, adhesive chest patch, and finger ring) alongside caregiver-reported useability and comfort. Data completeness varied markedly by device, with the patch achieving the highest completeness ([~]80%), followed by the wristband ([~]60%), headband ([~]50%), and ring ([~]20%). In multivariate analyses, adherence was driven by the device form factor rather than participant-level clinical characteristics. Devices rated as more comfortable did not yield higher completeness, revealing a divergence between reported preference and actual use. These findings suggest that device choice is a key consideration for studies in ASD youths, highlighting the need for research into model stability across sensor types in neurodivergent populations.

Actigraphy is widely used for long-term sleep monitoring, but established sleep-wake scoring algorithms often require parameter tuning, which is commonly performed manually and can reduce reproducibility. In this study, a grid-search-based calibration framework is presented for established actigraphy algorithms and evaluate whether it can serve as a practical alternative to manual tuning. The method was evaluated using two datasets: a multi-subject polysomnography-validated actigraphy dataset and a self-collected dual-device dataset. In the polysomnography-validated dataset, grid-search optimization produced performance patterns similar to manual parameter selection, while slightly improving detection of sleep onset and sleep offset and yielding modest gains in wake-sensitive metrics. In the dual-device dataset, consensus and majority voting were useful for reducing the influence of brief wake episodes occurring within the main sleep period, including micro-awakenings that can fragment sleep predictions across individual algorithms. Overall, these findings show that grid-search can replace manual parameter tuning with a more explicit and reproducible procedure while providing small improvements in sleep timing estimation and benefiting ensemble-based handling of within-sleep wakefulness.

Background: Digital phenotyping using wearable devices and ecological momentary assessment (EMA) enables continuous, real-world monitoring of physiological and emotional states, but identifying high-risk stress states in real time remains challenging. We examined day-level associations between emotional distress and heart rate variability (HRV), and assessed whether daily physical activity modifies this relationship using longitudinal wearable and EMA data. Methods: The Smart Momentary Interactive Longitudinal Evaluation Study (SMILES) was a prospective cohort study conducted among STEM graduate students in the U.S. in 2025. Participants wore an Oura Ring Generation 3 continuously for five months and completed daily EMA surveys assessing emotional distress. The primary outcome was nightly HRV measured as the root mean square of successive differences and log-transformed for analysis. Quantile regression within a quadratic inference function framework was used to estimate associations at the 25th, 50th, and 75th percentiles of HRV, accounting for within-participant correlation and time-varying covariates. Findings: Thirty-one participants contributed 1,724 person-days of observation. High emotional distress was associated with lower HRV across the HRV distribution, with the strongest association observed at the lower HRV quantile ({beta} = -0.094, 95\% CI: [-0.111, -0.078]). A significant interaction between daily step count and emotional distress was observed across quantiles, such that higher physical activity was associated with higher HRV on high emotional distress days but not on low-to-moderate distress days. Interpretation: Integration of wearable-derived physiological data with EMA enables real-time identification of high-risk stress states in naturalistic settings. The observed buffering effect of physical activity during periods of elevated emotional distress suggests that wearable-guided, personalized just-in-time adaptive interventions, such as physical activity prompts, could be deployed to improve autonomic regulation and mental health.

Actigraphy is a non-invasive and cost-effective method for monitoring behavioral rhythms under real-world conditions by collecting time-resolved measurements of locomotor activity, light exposure, and temperature. Although several open-source packages support specific aspects of actigraphy analysis, aspects such as preprocessing, metric calculation, and mathematical modeling are often distributed across separate software packages, limiting interoperability and increasing programming overhead. Here we introduce circStudio, a Python package that unifies actigraphy data processing and mathematical modeling of circadian rhythms within a single framework. Built from the pyActigraphy codebase and integrating circadian models from the Arcascope circadian package, circStudio provides flexible preprocessing tools, support for multiple actigraphy file formats through adaptor classes, standalone functions for computing commonly used actigraphy metrics, and implementations of several mathematical models of circadian rhythms. The package enables users to move efficiently from raw wearable data to physiologically interpretable circadian outputs. Ultimately, circStudio aims to facilitate reproducible workflows and to provide a flexible foundation for research applications across circadian biology, sleep science, and digital health.

Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very severe sleep apnea defined as an apnea-hypopnea index (AHI)[&ge;] 50 events/h, and to assess how training-set composition affects performance in this subgroup. MethodsWe analyzed 552 overnight recordings, 318 from the Sleep Lab Dataset and 234 from the Hospital Dataset. In the Hospital Dataset, 26.5% had very severe sleep apnea. We developed a deep learning model for sleep staging using RR intervals from wrist-worn photoplethysmography and three-axis accelerometry. Baseline performance was assessed by cross-validation under 5-stage and 4-stage staging. We examined night-level associations with AHI severity. We also compared the baseline model with an ablation model trained on the same number of recordings but with more Sleep Lab Dataset and lower-AHI Hospital Dataset recordings, evaluating both models in the very severe subgroup. ResultsIn 5-stage classification, Cohens kappa was 0.586 in the Sleep Lab Dataset and 0.446 in the Hospital Dataset. Under 4-stage staging, the gap narrowed, with kappa values of 0.632 and 0.525, respectively. In the Hospital Dataset, performance declined with increasing AHI severity. Among 62 recordings with very severe sleep apnea, reducing high-AHI representation in training lowered kappa from 0.365 to 0.303. ConclusionsWearable sleep staging performance declined across greater sleep apnea severity in this clinical cohort. Clinical utility may benefit from training data that better represent the target severity spectrum and from selecting staging granularity to match the intended use case. Statement of SignificanceRepeated laboratory polysomnography is impractical for long-term sleep apnea management. Wearable sleep staging could support scalable monitoring, yet its reliability in clinically severe sleep apnea has remained unclear. This study developed and evaluated a wearable sleep staging approach in both sleep-laboratory and hospital cohorts. The hospital cohort included many severe and very severe cases. Performance was lower in the hospital cohort and declined with greater sleep apnea severity. A coarser staging scheme reduced the gap between cohorts, and models trained without representative very severe cases performed worse in this target population. These findings highlight the value of severity-aware model development and motivate future multi-night home validation with reliability cues.

Time compositions of physical behaviours are associated with premature mortality, but the moderating role of sleep remains unclear. Using data from the UK Biobank accelerometry subsample, we examined associations of time reallocations between five device-measured physical behaviours (sleep, sedentary behaviour (SB), standing, light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA)) with all-cause, cardiovascular disease (CVD) and physical activity-related cancer mortality, and the potential effect modification by sleep duration and regularity. Compositional Cox regression was used to examine associations of behavioural reallocations with mortality. In 58,149 adults, 2,209 deaths occurred over a mean follow-up of 8.0 years. Among participants who meet sleep duration guidelines, reallocating 30 minutes from sleep to standing, LPA or MVPA was favourably associated with all-cause mortality with HRs of 0.86 (95%CI 0.79, 0.93), 0.87 (0.80, 0.95), and 0.80 (0.73, 0.87), respectively. Reallocating 30 minutes from sleep to SB, standing, or LPA was adversely associated with CVD risk (HRs 1.08 (1.02, 1.15), 1.10 (1.01, 1.20), and 1.11 (1.03, 1.20)) among those not meeting guidelines. Beneficial associations of reallocating SB to sleep were evident only amongst short (<7h/day) or regular (SRI>87.8) sleepers across mortality outcomes. Our findings support incorporating sleep characteristics into future personalised behavioural interventions design and behavioural targets.

Shared sleeping quarters are commonplace in contexts such as athletes at major sporting events, academic dormitories, and military barracks, yet mismatched sleep preferences can undermine rest and ultimately, human behaviour and performance. We introduce the Roommate Sleep Preference Questionnaire (ROOMPREF), a brief eight-question survey capturing preferences for noise, lighting, and temperature tolerances, snoring behaviour, and chronotype. Responses feed into a free, web-based clustering tool built in Python, which flags preference conflicts, and implements adaptive K-Means clustering within sex-chronotype subgroups. A post-cluster swapping algorithm further mitigates residual mismatches, enhancing the room-matching process. The resource includes distribution charts, group summaries, and optional automated room allocations, with downloadable CSV outputs. We demonstrate its application in a pilot cohort, highlighting its potential to improve sleep outcomes across various use-cases. This free resource has the potential to alleviate mismatched rooming partners, resulting in enhanced sleep and wellbeing outcomes.

ObjectiveLong term monitoring of obstructive sleep apnea (OSA) severity may be relevant for several clinical applications. We developed a method for estimating the apnea-hypopnea index (AHI) using wrist-worn, reflective photoplethysmography (PPG). ApproachA neural network was developed to detect respiratory events using PPG and PPG-derived sleep stages as input. The development database encompassed retrospective data from three polysomnographic datasets (N=3111), including a dataset with concurrent reflective PPG recordings from a wrist-worn device (N=969). The model was pre-trained with (transmissive) finger-PPG signals from all overnight recordings and then fine-tuned to wrist-PPG characteristics using transfer learning. Validation was performed on the test portion of the development set and on a fourth, external hold-out dataset containing both wrist-PPG and PSG data (N=171). Performance was evaluated in terms of AHI estimation accuracy and OSA severity classification. Main ResultsThe fine-tuned wrist-PPG model demonstrated strong agreement with the PSG-derived gold-standard AHI, achieving intra-class correlation coefficients of 0.87 in the test portion of the development set and 0.91 in the external hold-out validation set. Diagnostic performance was high, with accuracies above 80% for all severity thresholds. SignificanceThe study highlights the potential of reflective PPG-based AHI estimation, achieving high estimation performance in comparison with PSG. These measurements can be performed with relatively comfortable sensors integrated in convenient wrist-worn wearables, enabling long-term assessment of sleep disordered breathing, both in a diagnostic phase, and during therapy follow-up.

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.

BackgroundEmotion regulation--the ability to respond to and restore equilibrium after emotional perturbations--is central to mental health. Yet objective measurement remains limited to lab-based studies with group-level results, while consumer wearables focus on physical activity-related metrics rather than emotional dynamics. ObjectiveWe aimed to develop computational models that extract personalized, interpretable emotion regulation parameters from continuous heart rate variability (HRV) data collected via consumer wearables during everyday life, and validate these parameters against self-reported anxiety symptoms. MethodsWe analyzed 4 weeks of continuous HRV data from N = 49 healthy adults wearing Samsung Galaxy Active 2 smartwatches. We derived a continuous autonomic balance signal and developed three computational modeling approaches of increasing sophistication: (1) a static sympathetic load metric, (2) an Ornstein-Uhlenbeck (OU) dynamical systems model capturing continuous restoration dynamics, and (3) a discrete-state Markov transition model--the React & Rebound model-- capturing reactivity and rebound dynamics. All models were estimated using joint hierarchical Bayesian models that simultaneously extract subject-specific parameters from HRV time series and estimate their association with Generalized Anxiety Disorder 7-item scale (GAD-7) scores. The validity of extracted parameters was evaluated against anxiety symptom severity. ResultsStatic sympathetic load correlated modestly with GAD-7 (r = 0.39, R2 = 0.16). The OU model captured 69% of variance (R2 = 0.69), and the React & Rebound model captured 60% (R2 = 0.60) with substantially fewer parameters. Both models revealed that anxiety symptom severity is associated with the interaction between activation and restoration parameters--not either alone. Fast rebound appeared protective even for highly reactive individuals, who scored comparably to low-reactivity groups when restoration was rapid (Cohens d = 1.17 between highest- and lowest-risk quadrants). In the OU model, the interaction effect was specific to GAD-7 scores versus PHQ-9 and ISI scores; in the React & Rebound model, the interaction was credible across all three symptom measures. Both models were unchanged after controlling for physical activity ({Delta}R2 < 0.002). ConclusionsComputational models can extract interpretable emotion regulation parameters from naturalistic wearable data. The React & Rebound model yields two personalized parameters--reactivity and rebound--that are strongly associated with anxiety symptoms and define meaningful autonomic profiles. These parameters bridge autonomic dynamics measurable via consumer devices to neural circuit models of emotion regulation, with implications for characterizing individual autonomic profiles via consumer wearables.

This paper focuses on forecasting minute-by-minute stress, anxiety, and affective states using wearable sensor data. It addresses mental health as a growing concern and the limitations of traditional assessment methods. A time-series machine learning framework was developed using electrodermal activity (EDA) and heart rate variability (HRV) features from the WESAD dataset. Models were trained and evaluated for minute-by-minute prediction of self-reported psychological states. Both classification (stress, anxiety) and regression models (affect) were explored comparing time-series and static approaches. Findings support the feasibility of real-time, personalized mental health monitoring using wearable devices and their potential for timely interventions in clinical or remote settings. The paper demonstrates how temporal modeling can enhance emotional state prediction and inform future research and system development.

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.

Background: Improvements in health technology offer opportunities for remote disease screening, diagnosis and monitoring. The Withings Sleep Analyzer (WSA), an under mattress ballistocardiograph sensor able to detect body movement, breathing, and cardiac ejection is a promising technology for the non-invasive detection and monitoring of neurodegenerative diseases. InSleep46 aims to evaluate whether the WSA is able to detect preclinical Alzheimer's disease in members of the 1946 British Birth cohort, now in their late 70s. Objectives: To assess feasibility of deployment of a remote sleep, circadian and physiological monitoring device in a population of older adults. Participants: 356 participants from the Insight 46 neuroimaging sub-study (1946 British Birth Cohort), all born in one week in March 1946. Methods: We describe remote recruitment, device installation, and troubleshooting protocols. Feasibility analysis examined participant characteristics associated with recruitment and successful device set-up using logistic regression. Troubleshooting events for device installation and maintenance were recorded over a mean 14-month follow-up period. Results: During the feasibility analysis period, 263 (74%) participants, mean (SD) age 77 years (0.47) agreed to take part, of whom 245 (93%) successfully set up the WSA. Recruitment and successful set up of the WSA were not dependent on cognitive ability, socioeconomic position, or educational attainment. 162 (62%) of recruited individuals required [&ge;]1 troubleshooting call (mean 2.3 per participant, range 0-16). 603 calls were required in total. Conclusion: Deployment of a remote sleep and physiological monitoring device in an older adult population is feasible. Most participants required individualised assistance to set up the device. For the technology to be widely implemented, the set up must be accessible, with dedicated support available.

Abstract Background: Insufficient physical activity, excessive sedentary time, and suboptimal sleep are linked to premature mortality and chronic disease and may contribute to social inequalities in health, but most evidence is self-reported. Device-measured, nationally representative data capturing the full 24-hour movement spectrum remain scarce, particularly among older adults. This study examined social, economic, and environmental disparities in 24-hour movement behaviours in the 2021-23 English Longitudinal Study of Ageing (ELSA) accelerometry sub-study. Methods: A subset of 5,382 ELSA participants (71.9%) was invited to wear an Axivity AX3 wrist accelerometer for eight days, with 4,354 (80.9%) agreeing. Raw data were processed using machine learning to derive step count, sleep duration, moderate-to-vigorous and light physical activity, sedentary time, and time in bed. Cross-sectional associations with sex, age, education, marital status, wealth, and urbanicity were examined using linear regression. Findings: Data from 3,648 participants (mean age 68.5 {+/-} 9.3 years; 44.3% men) were included in wear time analyses (median 6.6 days, IQR 6.0-6.9), with 3,161 (86.7%) having sufficient wear time for movement behaviour analyses. Older, unmarried, or lower education/wealth participants were less active, more sedentary, and slept less. Rural participants were more active than urban participants. Women accumulated fewer steps and less moderate-to-vigorous physical activity and sedentary time, but more light activity and longer sleep than men. Interpretation: Social, economic, and environmental disparities exist across the full 24-hour movement spectrum, highlighting population groups for targeted interventions. Follow-up data will clarify how 24-hour movement behaviours influence healthy ageing and contribute to social inequalities in health.