JMIR mHealth and uHealth

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.

Objectives To identify stakeholder-perceived design tensions in a two-sided marketplace for reusable digital therapeutics (DTx) software components and to use these tensions to propose alternative marketplace concepts. Methods We conducted 24 semi-structured interviews with digital health researchers and professionals. Data were analysed using hybrid deductive-inductive codebook thematic analysis. The Magic Triangle provided the initial deductive structure. One researcher coded all transcripts; a second independently applied the developing codebook to five transcripts to refine definitions and consistency. Seventeen parent themes were synthesized into 12 design tensions, which informed three author-generated marketplace concepts. Results Participants described trade-offs concerning target users and host, component scope and customization, quality labels, verification, geographic scope, pricing, interoperability, platform launch, risks and market niche. The resulting concepts emphasized a regional startup ecosystem, a research-oriented hybrid marketplace or a global marketplace with stricter entry requirements. Discussion The concepts combine the tensions in different ways and highlight competing priorities in governance, openness, assurance, scalability and early platform growth. Conclusion Stakeholders identified recurring design choices for a DTx software-component marketplace. The concepts provide hypotheses for prototyping and evaluation; the study did not test technical feasibility, market demand, regulatory acceptability or effects on development cost or time.

Whether diverse populations outside Western settings are behaviourally ready to integrate wearable-derived data into clinical care remains poorly understood. This study examines sociotechnical determinants of wearable adoption and digital health data-sharing readiness in a large, highly diverse multinational population in Qatar, a rapidly digitising health ecosystem with advanced eHealth infrastructure. We conducted a cross-sectional community-based survey of 3,004 adults across Qatar, assessing wearable device use, behavioural engagement, and willingness to integrate wearable-generated data into healthcare workflows. Multivariable logistic regression identified independent predictors of wearable adoption. Wearable device use prevalence was 34.1%. Behavioural factors were the strongest independent predictors of adoption: daily exercisers had more than four times the odds of wearable use compared with rarely active participants, and willingness to share data with healthcare providers was independently associated with adoption after full adjustment. Notably, education level was not independently associated with wearable use, suggesting that behavioural readiness outweighs traditional socioeconomic indicators as a determinant of digital health engagement. Older age ([≥]56 years) and African ethnicity were associated with lower adoption odds, highlighting persistent digital inequities. These findings challenge the assumption that digital health equity is primarily an education or access problem, repositioning it as a behavioural engagement challenge. Health systems scaling remote monitoring programmes should prioritise identifying behaviourally engaged subpopulations rather than relying solely on demographic targeting. Targeted digital engagement strategies addressing older adults and underrepresented ethnic groups are essential for equitable implementation of digital medicine.

Introduction: The Diabetes Prevention Program Outcomes Study (DPPOS) is an established cohort of aging persons with pre-diabetes and type 2 diabetes with 25 years of median follow-up. In 2022 DPPOS added Alzheimer's disease (AD), and AD related dementias (ADRD) phenotyping using the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDSv3), which included a standardized neurological examination across 25 clinical sites, administered by clinical staff and interpreted centrally by clinicians. Methods: A DPPOS video-based asynchronous neurological examination (DPPOS-VANE) was developed iteratively through consensus from research clinicians and staff feedback to harmonize with UDSv3 to identify common neurological diagnoses aside from dementia including diabetic cranial neuropathies, stroke and parkinsonism. DPPOS-VANE was designed to be conducted without direct participant contact by the examiner, reproducible, and independent of clinical skills of PCs. An iPad camera recorded the video exam, comprised of assessments of extraocular and facial movements, visual fields, speech, gross motor strength, pronator drift, praxis and parkinsonism. A 10-minute training video demonstrated the examination step-by-step with scripts and instructions in English and Spanish. Site-specific performance review, feedback, and staff certification preceded central reading of video recordings by physicians. After two years of implementation, 1286 DPPOS-VANEs led to 1284 examination reviews. Of these, 1204 (93%) were completed by having the examiner follow the standard script. Overall, 1237 examinations (96%) were delivered as planned, 41 (3%) had minor errors but were still usable, and 6 (0.4%) had major deviations in exam technique; two additional recorded evaluations were not usable as recorded videos were inaccessible due to technical errors. Each examination was completed within 10-15 minutes. Each site on average completed 51.4 examinations (range 14-92). Discussion: Engaging 55 research staff across 25 sites and 3 physician-reviewers, this study is the first to demonstrate feasibility of a VANE as an efficient neurological examination model enabled by commonly used devices. Such a multisite standardized VANE represents a novel paradigm for large epidemiological studies.

Background. Maximal oxygen uptake (VO2max) is a leading marker of cardiorespiratory fitness and a strong predictor of all-cause mortality. Cardiopulmonary exercise testing (CPET) is the reference method but is resource-intensive, so consumer wearables estimate VO2max from passively collected signals; these estimates compress the fitness range, returning near-correct group averages while ranking individuals poorly. No peer-reviewed validation of a smart-ring VO2max estimate against CPET has been reported, and none in a South Asian cohort. Objective. To validate the Ultrahuman Ring AIR VO2max estimate against laboratory CPET, benchmark it against published prediction equations, and assess its generalization and construct validity. Methods. In a single-site paired ring-CPET cohort (N = 101; mean CPET peak VO2 43.3 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1}, SD 9.9), peak oxygen uptake was measured by treadmill or cycle-ergometer CPET, and the Ultrahuman Ring AIR estimate was computed from passively collected signals using a transparent ensemble based on published equations. Ensemble weights and calibration were selected on an 85-subject development set by an automated search minimizing a composite 5-fold cross-validated error criterion; the locked estimate was evaluated on a 16-subject held-out test set. The calibrated coefficients are proprietary. Agreement was quantified with mean absolute error (MAE), bias, Pearson r, regression slope and Lin's concordance correlation coefficient (CCC; bootstrap 95% CIs), and Bland-Altman limits of agreement. Separately, in 181,133 de-identified Ring users (no CPET reference), construct validity was assessed against ring-measured sleep, continuous glucose monitoring (n = 2,597), and a venous blood panel (n up to 15,203), adjusted for age, sex, and BMI, with lipoprotein(a) as a pre-specified negative control. Reporting followed TRIPOD and STARD. Results. With a self-reported fitness level provided, the estimate agreed with CPET peak VO2 at MAE 4.68 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1} (95% CI 3.93 to 5.49), Pearson r 0.79, CCC 0.79, and slope 0.71. The five published equations were worse on every metric (MAE 6.2 to 10.6, CCC 0.28 to 0.56, slope 0.32 to 0.42), each compressing the fitness range. On the held-out test set (n = 16), agreement held (r 0.84, slope 0.81, MAE essentially unchanged). Without the fitness input, full-cohort MAE was 5.16, still ahead of every published equation. At population scale, higher estimated fitness tracked a healthier profile on measurements the estimate does not use: better ring-measured sleep; higher continuous-glucose time in target range (79.6% versus 61.5%, top versus bottom decile; n = 222 and 399 of 2,597 users); and lower triglycerides, fasting glucose, and HOMA-IR (n up to 15,203 assayed per marker). These associations held after adjustment for age, sex, and BMI, whereas the pre-specified negative control lipoprotein(a) did not separate the deciles. Conclusions. The Ultrahuman Ring AIR VO2max estimate agreed with laboratory CPET substantially better than published prediction equations, held its agreement on held-out subjects, and ordered a large population along independent cardiometabolic gradients consistent with true fitness.

Background: Globally, at least 2.6 billion people need rehabilitation services and more than 2.5 billion people need assistive technology (AT). However, reliable data are lacking on population level need for rehabilitation services and assistive products (AP) in different settings for evidence-based policy and programme planning. This first study paper describes the development of the Functional Needs Assessment Tool (FNAT), a new survey tool developed to fill this data gap between 2018 and 2023. Objective: To develop a new multidomain tool to assess population-level functional difficulties and need for service and AP utilising both self-report and clinical assessment methodologies. Development stages: FNAT was developed based upon primary and secondary data analysis, existing survey tools and expert consultation through a series of four steps: Step 1 Inform, Step 2 Build, Step 3 Draft and Step 4 Develop. FNAT uses both self-reported and clinical assessment tools to estimate the prevalence of functional difficulties/impairment and the need for services and AP in the following seven domains: vision, hearing, mobility, communication, cognition, self-care and mental health. It uses a two-stage population-based assessment with data collection through a bespoke tablet-based mobile application and web-based platform. Discussion: FNAT is a new multi-domain modular tool developed to address data gaps by estimating prevalence of functional difficulties and service/AP needs in a population. Potential advantages and disadvantages were highlighted during the development stages, and the tool needs to be pilot tested to assess the feasibility of the methodology and the functionality of the tablet-based mobile data collection application.

Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease

Background: University students experience substantial psychological well-being and body-image concerns, while scalable, personalized digital support remains underexamined in Malaysia. Artificial intelligence chatbots may deliver repeated lifestyle guidance, but the incremental value of personalization over structured chatbot support is uncertain. Objectives: This study evaluated changes in psychological well-being and body appreciation following a 12 week personalized AI-powered lifestyle intervention, NExGEN, among Malaysian university students. Methods: A two-arm, controlled, quasi-experimental pre-post study allocated 140 students aged 18 to 35 years by matched blocks to NExGEN (n = 70) or a structured-prompt ChatGPT control (n = 70). NExGEN generated adaptive weekly lifestyle actions from a 47-item onboarding assessment, whereas control participants received standardized weekly prompts covering the same lifestyle domains. Psychological well-being and body appreciation were assessed at baseline and week 12 using the World Health Organization-Five Well-Being Index and Body Appreciation Scale-2. Intention-to-treat linear mixed models estimated adjusted within-group changes and between-group differences in change, with Holm adjustment for the co-primary outcomes. Results: Week-12 assessments were completed by 121 participants (86.43%). In NExGEN, psychological well-being improved by an adjusted 8.68 points (95% CI, 6.22 to 11.14), z = 6.91, p < .001, and body appreciation improved by 0.17 points (95% CI, 0.10 to 0.24), z = 4.82, p < .001. However, between-group differences in change were not statistically significant for psychological well-being (2.87 points; 95% CI, -0.48 to 6.23; z = 1.68; Holm-adjusted p = .093) or body appreciation (0.10 points; 95% CI, 0.00 to 0.19; z = 1.99; Holm-adjusted p = .093). Median platform logins were 68.00 in NExGEN and 58.50 in control; mean acceptability scores were 3.92 and 3.59, respectively. Conclusions: NExGEN participation was associated with significant within-group improvements in psychological well-being and body appreciation, but personalized guidance did not demonstrate superiority over structured chatbot guidance. Because allocation was quasi-experimental, causal attribution remains limited. Randomized component-level trials are needed to determine whether personalization provides incremental benefit.

We evaluated aggregated, privacy-preserving smartphone-detected nocturnal cough activity from the Sleep Cycle application as a potential syndromic surveillance signal in England. Weekly cough metrics from January 2023 to January 2026 were compared with UK Health Security Agency indicators: NHS 111 acute respiratory infection (ARI) triage calls, influenza and COVID-19 PCR positivity, and hospital admission rates for influenza, COVID-19, and respiratory syncytial virus. We evaluated total cough counts alongside two population-normalised metrics, coughs per user and coughs per hour of sleep, and assessed temporal relationships nationally and regionally using cross-correlation with prewhitening. The strongest and most consistent associations were observed for NHS 111 ARI triage calls, where population-normalised cough metrics showed raw national correlations of approximately 0.95 and retained prewhitened correlations above 0.55 at lag 0. This indicates that nocturnal cough activity closely tracks short-term variation in an established syndromic surveillance indicator, beyond shared seasonality, long-term trends, and autocorrelation. Similar near-contemporaneous patterns were observed across regions. Population-normalised cough metrics also showed epidemiologically plausible leading associations with pathogen-specific indicators: coughs per hour of sleep peaked one week before influenza PCR positivity, while both coughs per user and coughs per hour of sleep peaked one week before COVID-19 PCR positivity. Hospital-based indicators showed weaker and more heterogeneous relationships, but the normalised cough metrics still showed plausible temporal alignment with influenza and COVID-19 admissions, including contemporaneous associations with influenza admissions and short leading associations with COVID-19 admissions. In contrast, unnormalised total cough counts produced less stable and often non-interpretable lag structures, consistent with sensitivity to variation in observation volume. These findings suggest that passive, near-real-time nocturnal cough monitoring can provide a population-level signal of respiratory symptom burden, with greatest utility as a broad syndromic indicator that complements surveillance sources affected by healthcare-seeking behaviour, laboratory turnaround times, backfilling, and reporting delays.

To estimate the life expectancy gains that could be realised from increases in Queenslanders physical activity (PA) levels. Design Lifetable analysis Setting, Participants We modelled the 2025 Queensland population aged [&ge;]40 years. Modelled scenarios We applied two approaches. In the first, we estimated life expectancy differences between device-measured PA quartiles, with quartile1 representing the least active and quartile 4 the most active. In the second, we compared observed device-measured PA levels in Queensland with scenarios in which all individuals moved to either [&ge;]12,000 steps/day or [&le;]2,000 steps/day. We converted the steps per day by age group and PA quartile into equivalent daily minutes of moderate-intensity walking at 4.8 km/h. Additional scenarios were explored in sensitivity analyses. Main outcomes Changes in life expectancy, and total life-years gained over the lifetime of the modelled population. Benefits were also translated into minutes of life gained per additional hour walked. Results If all Queenslanders aged [&ge;]40 years were as active as the most active quartile, life expectancy at birth could be 88.3 years, an increase of 4.8 years above the life expectancy at observed activity levels. The life expectancy differences between individuals in the least active quartile and the most active quartile was 9.7 years. Achieving the activity level of the most active quartile would require individuals in the lowest activity quartile to undertake an additional 85.9 minutes/day of moderate-intensity walking, with each extra hour of PA associated with an average gain of approximately 3 hours (177 minutes) of life. In step-based modelling, life expectancy in the most active scenario (all achieving [&ge;]12,000 steps/day) was higher by {approx}7.1 years compared with the least active scenario (all at [&le;]2,000 steps/day). Conclusions Increasing PA could yield meaningful gains in life expectancy for Queenslanders, with the largest gains seen in least active individuals. Our findings strengthen the case for prioritising investment in PA -promoting programs and environments.

Background: Female chronic pelvic pain disorders (CPPDs) are highly prevalent and frequently accompanied by sleep disturbance and autonomic nervous system (ANS) dysregulation. Heart rate variability (HRV), a non-invasive index of ANS function, may provide an objective, physiological correlate of sleep health and can be monitored using wearable devices, enabling a continuous, scalable approach. Objectives: This study examined whether wearable-derived daily HRV metrics are associated with self-reported sleep disturbance in women with CPPD(s) compared with healthy controls, using epoch-level data and generalized additive models. Methods: We conducted a retrospective observational study using up to 90 days of data from a mobile health research app. Participants were 128 women with CPPD(s) and 63 demographically matched healthy controls, who completed a daily PROMIS-based 3-item sleep disturbance questionnaire and wore Fitbit devices that provided 5-minute HRV epochs. Primary predictors were high frequency (HF) and low frequency (LF) power and root mean square of successive differences (RMSSD), with group (CPPD vs control), daily pain severity, and menstrual status as covariates. We fit separate generalized additive mixed models (GAMMs) for each HRV metric with a nonlinear smooth term and an HRV x Group interaction. Results: Higher HF and RMSSD were associated with lower sleep disturbance scores, and these associations were stronger in controls than in the CPPD group (HF x group B {approx} -1.59, p < 0.00010; RMSSD x group B {approx} -0.58, p < 0.0001). LF showed a more complex pattern but also differed by group (B {approx} -0.531, p < 0.0001). HRV smooth terms were highly nonlinear, and models explained ~8-9% of deviance in sleep disturbances. Pain severity and menstrual bleeding were strongly associated with worse sleep. Conclusion: These findings indicate small but consistent associations between wearable-derived HRV metrics and daily sleep disturbances in women with CPPD(s) and healthy controls, with weaker associations in CPPD(s). Integrating continuous HRV with symptom tracking could support low-burden and multimodal monitoring of sleep health in chronic pelvic pain, but prospective validation is needed before HRV can be used for diagnostic or treatment response decision making.

Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settings. Nurses increasingly make autonomous clinical decisions using telephone, video, and online/digital systems, often with limited sensory information and under conditions of uncertainty. However, empirical understanding of how nurses make clinical decisions via remote reviews remains limited. Aim: To explore and understand how registered nurses (RNs) make clinical decisions about patient care via remote reviews. Methods: A convergent mixed-methods design was employed. Quantitative data (analytic quantitative sample N=53) were collected using validated questionnaires that measured decision-making processes, physician-nurse collaboration, decision-making stress, and perceived decision-making ability. Qualitative data (N=23) were generated through semi-structured interviews. Data collection took place between October 2024 and April 2025. Quantitative data were analysed using descriptive statistics, correlation, and multiple regression. Qualitative data were analysed using framework analysis. Integration was achieved through pillar-building and theory-driven synthesis and illustrated by joint display tables. Results: Most nurses demonstrated a flexible decision-making style, integrating analytical and intuitive reasoning. Both analytical and intuitive processes were positively associated with perceived decision-making ability. Physician-nurse collaboration emerged as a strong predictor of decision-making confidence, while decision-related stress was not a significant predictor. Qualitative findings identified three themes: characteristics of remote review; making adaptive decisions shaped by both internal and external constraints and enablers; and external influencing factors. The integrated findings informed a theory-informed ICE framework to illustrate how nurses make clinical decisions via remote reviews. Conclusion: Remote clinical decision-making is a dynamic cognitive-environmental process rather than a purely individual cognitive act. The ICE framework conceptualises this interaction, extending existing decision-making theories to digitally mediated care. Impact: Understanding remote decision-making supports training design, clinical governance, and the development of Artificial Intelligence-enhanced decision-support tools grounded in ecological bounded rationality. Patient or Public Contribution: Patient and public representatives contributed to stakeholder discussions that informed the development of the interview topic guide and the theoretical model. Patients or members of the public were not involved in recruitment, data collection, analysis, interpretation of findings, or preparation of the manuscript. Keywords: clinical decision-making, remote reviews, telehealth, nursing, mixed methods, ecological bounded rationality

The increasing deployment of artificial intelligence (AI) assistive systems across healthcare, education, and organisational domains necessitates a deeper understanding of dispositional factors shaping trust and acceptance. This study investigated the Big Five personality traits as predictors of trust in and acceptance of AI assistive systems among a large adult sample (N = 380) in Makurdi Benue State. Anchored in the Technology Acceptance Model (TAM) developed by Davis (1989), the study examined both direct and indirect pathways linking personality traits to AI acceptance through trust. Participants completed standardised measures of the Big Five Inventory, Trust in AI Scale, and AI Acceptance Scale. Data were analysed using structural equation modelling (SEM) with maximum likelihood estimation. The hypothesised model demonstrated good fit indices (CFI = .84, TLI = .82, RMSEA = .05). Openness to experience ({beta} = .34, p < .001) and agreeableness ({beta} = .27, p < .01) significantly predicted trust in AI systems, which in turn strongly predicted AI acceptance ({beta} = .62, p < .001). Neuroticism negatively predicted trust ({beta} = -.29, p < .001), while conscientiousness showed a modest positive direct effect on acceptance ({beta} = .18, p < .05). Extraversion was not a significant direct predictor but exerted an indirect effect through trust. Mediation analysis confirmed that trust significantly mediated the relationship between personality traits and AI acceptance. The findings underscore the centrality of dispositional traits in shaping technological trust formation and highlight the psychological architecture underlying human AI interaction. These results contribute to social psychological theory and provide empirical guidance for designing personality sensitive AI systems to enhance user adoption and sustained engagement.

Autonomic dysfunction is a common sequela of mild traumatic brain injury (mTBI). Physical activity progression is an integral component of mTBI rehabilitation, particularly in addressing autonomic dysfunction. However, clinicians often rely on point-in-time evaluation of orthostatic and exercise intolerance to guide activity recommendations. Commercially available wearable devices (e.g., Fitbits) provide an opportunity to evaluate heart rate response to activity in a real-world setting. Previous work has used physiologic (heart rate) and activity (step count) data to identify subgroups of adults with stroke that may be used to guide activity recommendations. This method may be useful to subgroup youth post-mTBI to identify those who have abnormal physiologic responses to activity. We aimed to identify subgroups using heart rate and step count data in adolescents presenting for specialty care after diagnosed mTBI. Eighty participants aged 13-18 within six months of mTBI diagnosis were recruited to wear a Fitbit Sense 2. Data from seven days and two nights collected within fourteen days of enrollment were included. A group-based steps per minute (SPM) threshold (25th percentile; 10 SPM) and individualized heart rate threshold (20% heart rate reserve (HRR)) were used to classify each minute of active daytime data into one of four quadrants: SPM>10 & HRR>20% (QI), SPM<10 & HRR>20% (QII), SPM<10 & HRR<20% (QIII), and SPM>10 & HRR<20% (QIV). We used percentage of minutes in each quadrant, mean steps per day, percentage of minutes with zero steps, mean SPM in QI, and resting heart rate in a k-means clustering algorithm to identify subgroups. We evaluated subgroup differences by clustering variables using Kruskal-Wallis tests. Sixty-one participants were included. Three subgroups emerged: Sedentary (n=12), Active (n=23), and Atypically Elevated Heart Rate (AEHR; n=26). Subgroups varied significantly on all clustering variables (p<0.01). The Active subgroup took a high number of steps per day, had lower sedentary time, and had the highest activity intensity (mean SPM in QI). The Sedentary subgroup took fewer steps per day compared to the Active subgroup, had high sedentary time, and showed the highest resting heart rate. The AEHR subgroup took fewer steps per day compared to the Active subgroup and had high sedentary time. The AEHR subgroup also spent a higher percentage of time with an atypically high heart rate response to low levels of activity compared to the other subgroups. Our findings suggest that data from wearable devices can identify subgroups of adolescents with mTBI with distinct physiologic/physical activity profiles, which may ultimately be used to inform personalized activity prescriptions. Future work should aim to understand how the identified subgroups relate to longitudinal outcomes.

Background Primary care in China lacks structured mental-health assessment, and the machine-learning models that could support such screening are typically developed on heavily selected samples. Cumulative inclusion and exclusion criteria, though usually treated as neutral data-cleaning steps, can create heterogeneity in predictive reliability among retained participants. Using the China Health and Retirement Longitudinal Study (CHARLS) 2011 baseline, we quantified how selection funnels distort epidemiological associations and inflate machine-learning metrics, and tested selective prediction as mitigation. Methods Using the CHARLS 2011 baseline with temporal external validation in CHARLS-2018, we built a four-level selection funnel (L0-L3), evaluated five classifiers with nested cross-validation and SMOTE, and compared model-embedded uncertainty with a decoupled predictor-selector framework; XGBoost cross-validation residuals drove risk stratification and classification and regression tree (CART) rules. Results Sample sizes fell from L0 n=17,705 to L3 n=4,256 (24.0%). The cancer-depression odds ratio attenuated from 1.78 (95% CI 1.32-2.41) to 1.39 (0.74-2.63), losing significance. AUC rose with selection but not after multiple-comparison correction, whereas calibration error increased for four of five models. Model-embedded uncertainty succeeded only for XGBoost; with the decoupled XGBoost residual selector, all five models achieved selective prediction at approximately 20% coverage (test AUC 0.90, 95% CI 0.85-0.95), abstaining on approximately 80% of cases for individual safety. Risk stratification was stable (residual Spearman correlations >0.95; multi-seed Jaccard 0.88), and CART rules used self-rated health, education, pain, and marital status. Conclusions The findings support a deployable primary-care triage pathway: a four-variable rule identifies patients suitable for algorithm-assisted scoring (approximately 20% coverage) and routes the remainder to human evaluation. Methodologically, cumulative selection bias produces a dual distortion: epidemiological associations are compressed and machine-learning metrics inflated. Selective prediction is limited mainly by uncertainty-indicator design. Performance metrics should be reported with selection level, coverage, and calibration trajectory. Decoupled selective prediction with CART rule extraction provides an actionable framework for quality-controlled, tiered-care deployment. Keywords: selective prediction, selection bias, CHARLS, depression, predictor-selector decoupling, uncertainty quantification, classification and regression tree, triage, clinical decision support, health management.

Objectives To assess the feasibility of using clinical data automatically extracted via the Swiss Personalized Health Network (SPHN) to complement or replace manually abstracted clinical data in the Swiss Paediatric Airway Cohort (SPAC). Materials and Methods We studied 1,075 SPAC participants enrolled between 2017-2023 at two Swiss children's hospitals. Clinical data were extracted from electronic health records via SPHN in Resource Description Framework format, transformed into visit-centered datasets, and compared with manually abstracted SPAC clinical data and parent-reported emergency department (ED) visits and hospitalizations from follow-up questionnaires. We assessed feasibility by identifying challenges in acquiring data and evaluated data quantity, completeness, and agreement between datasets. Results We obtained analysis-ready SPHN-derived datasets from two hospitals after 24 months. SPHN-derived data captured more pneumology outpatient visits than manual abstraction (Hospital A: 1,963 vs 1,049; Hospital B: 2,343 vs 1,010) and identified clinical events among children without follow-up questionnaires. Completeness of variables varied across hospitals and encounters, reflecting differences in local clinical documentation practices. SPHN-derived and manually abstracted data showed high agreement for structured clinical variables, including spirometry measurements (concordance correlation coefficient >0.99). Self-reported and SPHN-derived ED visits and hospitalizations showed high absolute agreement but moderate concordance. Discussion and Conclusion Automated extraction of routine clinical data increased the completeness of longitudinal information compared with manual abstraction, suggesting that SPHN-derived data can complement manual data collection in cohort studies. Broader use remains limited by heterogeneous clinical documentation practices and the substantial effort required to harmonize and transform extracted data into analysis-ready research datasets.

Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance. Keywords: preterm birth; cervical ultrasound; prediction model; external validation; deep learning

The apnea-hypopnea index (AHI), the conventional metric of obstructive sleep apnea (OSA) severity, is typically studied using scalar summaries of sleep architecture, such as the total time spent in each sleep stage. Although clinically interpretable, these summaries fail to capture the temporal organization of overnight sleep-stage sequences and may obscure stage-specific associations with OSA severity. Modeling the complete sleep-stage trajectory provides substantially richer temporal information; however, because total sleep duration varies across individuals, sleep-stage trajectories are observed over subject-specific domains, limiting the applicability of conventional functional regression methods that assume a common observation interval. We therefore applied Variable-Domain Functional Regression (VDFR) to overnight polysomnographic data from the APPLES study (n= 1,103), treating the epoch-by-epoch sleep-stage sequence as a continuous, variable-length functional predictor of AHI. We compared three levels of sleep-stage granularity: five stages (Wakefulness, N1, N2, N3, REM), three stages (Wakefulness, Non-REM, REM), and binary staging (Wakefulness vs. Sleep). Functional sleep-stage terms were significant across all staging granularities and model structures (all p-values [&le;]0.001). Wake, N1, and N2 were positively associated with AHI, whereas N3 and REM were negatively associated, with REM exhibiting the strongest association. These effects were attenuated under coarser staging representations, highlighting the importance of preserving fine-grained sleep architecture. To our knowledge, this is the first application of VDFR to overnight polysomnographic data in OSA, showing that accommodating subject-specific sleep durations enables the identification of stage-specific temporal associations with AHI severity that are attenuated or obscured by coarser staging and conventional scalar analyses.

ABSTRACT Background: Onset of cancer is linked to psychological distress and cancer is prevalent in older adults. Yet, the association to suicide is scarcely examined. The aim of this study was to assess whether cancer diagnosed in older adults is associated with suicide incidence. Methods: All older adults (65+ years) who lived in Austria in the years 2014-2021 (n=2,175,134) were followed. Of these, 223,932 were diagnosed with a new cancer. We used non-parametric survival models with inverse-probability-treatment weights to compare risk ratios (relative risk) and risk differences (absolute risk) of older adults with and without cancer. Results: Out of 2,158 suicide deaths, 442 (20.5%; 83.7% males) occurred among older adults with a new cancer diagnosis. The incidence rate was 74 among those with a new cancer diagnosis versus 23 per 100,000 person-years among those with no new cancer. One year after being diagnosed, older adults with a new cancer had a 4 times higher relative risk of dying by suicide compared to those without. The risk was highest within the first three months after diagnosis and for cancers with a poor prognosis (disseminated disease; lung, oesophagus, stomach, liver, pancreas, and brain cancers). The absolute risk of dying by suicide within 5 years after cancer diagnosis was 0.18% versus to 0.11% among those with no new cancer. Discussion: Older adults who received a new cancer diagnosis had elevated suicide risks. Provision of support to cope with mental distress should be considered at cancer diagnosis, especially for older adults with a poor prognosis.

INTRODUCTION: The Diabetes Prevention Program (DPP) was a randomized clinical trial designed to prevent type 2 diabetes (T2D) in adults with prediabetes. The DPP Outcomes Study (DPPOS) is the 30-year follow-up of this cohort, focusing on T2D, prediabetes, and related complications. Cognitive assessments began in 2009 and expanded in 2022 to examine cognitive impairment, including Alzheimer's disease (AD) and AD related dementias (ADRD), in the surviving cohort. To support these aims, the National Alzheimer's Coordinating Center Uniform Data Set version 3 (NACC-UDSv3), the standardized framework used by Alzheimer's Disease Research Centers, was implemented in DPPOS in 2022 to enable data sharing with NACC. These forms were complemented by cognitive tests administered in DPPOS. We aimed to integrate the NACC-UDSv3 into the existing longitudinal DPPOS framework while maintaining fidelity to its structure and developing automated reports to streamline cognitive outcomes adjudication. METHODS: Items from the 16 NACC-UDSv3 data forms were compared with those already collected within DPPOS to integrate overlapping similar items, add missing NACC-UDSv3 items, and create a dataset harmonized with NACC-UDSv3. Forms were adapted for electronic data capture (EDC) using the MIDAS (Multimodal Integrated Data Acquisition System, George Washington University). Automated reports integrated current and prior neuropsychological scores to support adjudications. In the first wave of the DPPOS-AD/ADRD study, 1561 cognitive adjudications were successfully completed using the harmonized DPPOS and NACC-UDSv3 data implemented into MIDAS. DISCUSSION: The DPPOS-AD/ADRD project demonstrated that NACC-UDSv3 can be successfully integrated into a long-standing longitudinal cohort not originally designed for AD/ADRD research. The harmonization, electronic capture, and automated adjudication processes may provide a practical framework for other cohorts seeking to incorporate NACC-UDSv3 to align with national AD/ADRD research standards.