Background: Voluntary behaviors and socio-economic factors, such as social jetlag and shift work, can lead to insufficient or disrupted sleep, resulting in drowsiness among active individuals. In occupational and driving contexts, drowsiness poses a serious safety risk by impairing alertness, slowing reaction times, and increasing the likelihood of accidents. Developing automatic and easy-to-implement tools for drowsiness detection or prediction is essential in the management of sleepy patient or in high-risk environments where sustained vigilance is critical. Objective: This #Study aims to validate continuous or predictive methods for assessing drowsiness using automated analysis of a limited number of electroencephalogram (EEG) channels. Methods: Designed as a single-center, non-randomized, single-group #Study, this investigation will evaluate drowsiness and cognitive performance in forty healthy volunteers exposed to two sleep deprivation conditions simulating real-world occupational scenarios. The primary outcome will be the Objective Sleepiness Scale (OSS) and its automated analysis, with a focus on its ability to measure objective wakefulness as assessed by the Maintenance of Wakefulness Test (MWT). Secondary outcomes will include multimodal resting-state EEG markers, subjective and objective sleepiness measures, performance on a simulated driving task, attention, executive function and vigilance assessments, as well as sleep quality, sleep quantity, and mind-wandering. The influence of sociodemographic and clinical variables on the measurement and prediction of drowsiness will also be systematically examined. Results: Subject recruitment began in March 2023 and was completed in May 2025, with a database lock and the start of data analysis in June 2025. No data have yet been reported. Conclusions: By validating these novel EEG-based measures, this #Study aims to lay the groundwork for proactive strategies for drowsiness management in occupational, transportation, and clinical settings. Clinical Trial: ClinicalTrials.gov NCT05453643; https://clinicaltrials.gov/#Study/NCT05453643?id=NCT05453643&rank=1

Fibromyalgia is a prevalent musculoskeletal pain condition that causes major personal, social, and societal burden. Pharmacological therapies often provide only limited benefit, making multimodal approaches and self-management the cornerstones of care. Such strategies, spanning lifestyle modification, physical activity, psychoeducation, and cognitive-behavioral approaches, target the biopsychosocial complexity of fibromyalgia and promote sustainable coping. In parallel, digital health technologies are transforming how these interventions can be delivered and coordinated in the form of digital therapeutics. This viewpoint draws on a multiphase investigation to appraise the current and future landscape of fibromyalgia self-management in the digital era. Its objective is to present an evidence-based framework and recommendations to guide the development of a mobile health self-management program for patients with fibromyalgia. In phase 1, we conducted a review of international guidelines and randomized controlled trial–based systematic reviews addressing nondigital self-management interventions for fibromyalgia and related nociplastic pain conditions. In phase 2, we analyzed the content and certification status of currently available mobile and virtual health applications for fibromyalgia. In phase 3, we convened a multidisciplinary focus group of rheumatologists, patients, and digital health developers to identify priorities for translating evidence-based self-management content into mobile health formats. Collectively, we suggest that effective digital self-management for fibromyalgia should evolve beyond single-domain interventions toward validated, personalized, and interactive multimodal platforms. Virtual care may increasingly function at the point of care, linking monitoring, education, and behavioral support in one continuum.

Background: Climate change–induced international migration has the potential to negatively impact the health and well-being of displaced populations. Pregnancy often serves as a point of entry into the healthcare system for migrant women; however, these women often face reduced access to maternal healthcare services compared to non-migrants. In the context of climate-related international migration, these disparities may be further exacerbated, increasing the risk of maternal morbidity and adverse perinatal outcomes. While the intersections between climate change, migration, and health are increasingly acknowledged, literature specifically focused on climate-related international migrant women—particularly during the perinatal period—remains limited and dispersed. Thus, there is a growing need for #Research and synthesized data on climate change, population movements, and the perinatal healthcare needs of childbearing women. Objective: To examine and describe the scope and nature of available evidence on maternal health and care for international climate-related migrant women, from pregnancy through the postpartum period. Methods: We will conduct a scoping review following the JBI methodology. A tailored search strategy using key terms related to climate change, migration, women, and perinatal healthcare will be applied to four databases: Embase, CINAHL, PsycINFO, and Ovid MEDLINE, without restriction on publication date. Relevant grey literature sources will also be searched and considered for inclusion. Only literature published in English, French, Portuguese or Spanish will be included. Two reviewers will independently screen full-text records based on predefined inclusion criteria and extract the relevant data. Results: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2000 flow diagram. We anticipate finalizing the manuscript for this work in 2026. Conclusions: Considering vulnerability factors related to migration status is essential to improve access to integrated perinatal healthcare and to reduce health inequities among immigrant women. This review will provide valuable insights to tailor interventions to the social and cultural needs of climate-affected migrant women during the perinatal period. Clinical Trial: Open Science Framework 10.17605/OSF.IO/CS4ZM; https://osf.io/vhbma

Background: Generative artificial intelligence (AI) is increasingly used in medical education #mededu, including AI-based virtual patients to improve interview skills. However, how much AI-based assessment (ABA) differs from human-based assessment (HBA) remains unclear. Objective: This study aimed to compare the quality of clinical interview assessments generated by an ABA (ChatGPT-o1 Pro (ABA-o1) and ChatGPT-5 Pro (ABA-5)) with those provided by an HBA conducted by clinical instructors in an AI-based virtual patient setting. We also examined whether AI reduced evaluation time and assessed agreement across participants with different levels of clinical experience. Methods: A standardized case of leg weakness was implemented in an AI-based virtual patient. Seven participants (two medical students, three residents, two attending physicians) each conducted an interview with the AI-patient, and transcripts were scored using the 25-item Master Interview Rating Scale (0–125). Three evaluation strategies were compared. First, ChatGPT-o1 Pro and ChatGPT-5 Pro scored each transcript five times with different random seeds to test case specificity. Processing time was logged automatically. Second, five blinded clinical instructors independently rated each transcript once using the same rubric, after completing a webinar to standardize scoring. Third, reliability metrics were applied. For AI, intraclass correlation coefficients (ICCs) quantified repeatability. For humans, ICC(2,1) was calculated. Agreement was quantified with Pearson r, Lin concordance correlation coefficient (CCC), Bland-Altman limits of agreement (LoA), Cronbach α, and ICC. Time efficiency was expressed as mean minutes per transcript and relative percentage reduction. Results: Mean interview scores were similar across methods: ABA-o1 52.1 (SD 6.9), ABA-5 53.2 (SD 6.8), HBA 53.7 (SD 6.8). Agreement with HBA was strong (r=0.90; CCC 0.88) with minimal bias (mean bias: ABA-o1 0.4, ABA-5 1.5; LoA: ABA-o1 –4.9 to 5.7, ABA-5 –8.6 to 11.7). Cronbach α was 0.81 (ABA-o1), 0.83 (ABA-5), and 0.80 (HBA); ICC(3,1) 0.77 (ABA-o1) and 0.82 (ABA-5); ICC(2,1) 0.38 (HBA). The coefficient of variation for ABA was about half that of HBA (6.6% vs 13.9%). Processing time for five runs was 4 min 19 s (ABA-o1) and 3 min 20 s (ABA-5) vs 10 min 16 s for physicians. Conclusions: ABA-o1 and ABA-5 produced scores closely matching HBA while demonstrating superior consistency and reliability. In the setting of virtual interview transcripts, these findings suggest that ABA may serve as a valid, rapid, and scalable alternative to HBA, reducing per-assessment time by over half. Applied strategically, AI-based scoring could enable timely feedback, improve efficiency, and reduce faculty workload. Further research is needed to confirm generalizability across broader settings.

Background: Allogeneic hematopoietic stem cell transplant (HCT) is a complex but essential treatment for malignant and nonmalignant conditions, requiring strict posttransplant adherence to immunosuppressant medications to prevent complications such as graft-versus-host disease. Adolescents and young adults undergoing HCT face unique challenges, including balancing growing independence with ongoing reliance on care partners, often parents. Medication adherence in this group is often suboptimal, and few interventions address adolescent and young adult–care partner dyads. To address this gap, we aim to develop a mobile health (mHealth) app that engages both the patients and care partner to improve adherence. Objective: As formative research for early-stage intervention development, this study aimed to (1) explore current HCT medication adherence strategies and challenges; (2) understand attitudes toward digital technology, including dyadic perspectives on app use to support adherence; and (3) assess adolescent and young adult–care partner relationships, including views on care partner involvement. This process was intended to inform the design of a relevant, user-centered mHealth app. Methods: Eligible participants included adolescents and young adult patients aged 12-39 years and primary care partners, such as parents, involved in medication management. Participants were recruited from a large academic medical center through direct outreach and electronic health records. Data collection involved 2 focus groups (6 dyads and 2 additional adolescents and young adults), 4 individual interviews (2 patients and 2 care partners), and 6 dyadic interviews. Semistructured sessions (in person or virtual) gathered feedback on medication adherence practices and app design preferences. All sessions were audio recorded with consent and professionally transcribed. Qualitative data were analyzed systematically: transcripts were deidentified, coded using both inductive and deductive strategies, and themes were refined through team consensus. Patterns were organized into major themes, and representative quotations were selected to illustrate findings. Data management was facilitated by NVivo (version 13; Lumivero) software. Results: We included 28 participants (15 adolescents and young adults and 13 care partners). The median age of adolescents and young adults was 18 (range 13-39) years and 53% (8/15) were female. Adolescents and young adults were 47% (7/15) White, 40% (6/15) Black, and 13% (2/15) mixed race. Care partners’ median age was 48 (range 36-72) years, with 92% (12/13) female and 77% (10/13) White. Three principal themes emerged: (1) existing reminders and organizational tools are often insufficient for consistent adherence; (2) adherence barriers are multifaceted, often involving autonomy vs care partner support; and (3) both adolescents and young adults and care partners showed strong interest in a dyadic digital health intervention to foster collaboration and support shared adherence goals. Conclusions: This formative study highlights the complex dynamics of medication adherence in adolescent and young adult–care partner dyads and supports the need for a dyadic mHealth app to enhance adherence, collaboration, and relationship quality.

Background: Polycystic ovary syndrome (PCOS) is a common chronic hormonal condition affecting 8%-13% of women and individuals assigned female at birth. Symptoms may include subfertility, menstrual, skin, and metabolic problems, with long-term health risks including diabetes and cardiovascular disease. PCOS has a significant negative impact on mental health, quality of life, and well-being. We explored proof of concept for a web-based self-management support intervention, “Hope PCOS,” designed to reduce anxiety and depression and increase positive well-being for women living with PCOS. Objective: We aim to pilot the intervention to test #feasibility for web-based recruitment and delivery, acceptability, and potential to reduce anxiety and depression and increase positive well-being. Methods: Women with PCOS were recruited via social media with support from a patient advocacy charity and offered places on a 6-session cohort of the intervention. In a pre-post design, participants reported depression (Patient Health Questionnaire 9-Items), anxiety (Generalized Anxiety Disorder 7-Items), well-being (Warwick-Edinburgh Mental Wellbeing Scale), hope (SHS [State Hope Scale]), and gratitude (GQ-6 [Gratitude Questionnaire]) at baseline and 6 weeks. All participants who accessed 3 or more sessions were invited to a follow-up qualitative interview to explore user experience. Data from 8 interviews were thematically analyzed, and pre-post data were explored with descriptive statistics. Results: A total of 63 eligible women responded and were given access to the intervention. Three withdrew, leaving a baseline sample of 60, aged 20-58 (median 30, IQR 25-36) years. Further, 48 of the 60 started, of whom 46% (22/48) completed at least 3 sessions, and 29% (14/48) completed all 6. Additionally, 8 women (aged 25-38, median 29, IQR 26-35) years who completed between 3 and 6 sessions reported acceptability and experiences in exit interviews, including prioritizing self-care, developing a self-management mindset, setting motivating goals, improved mental health, self-compassion, reduced shame, openness about PCOS, preparedness for future health concerns, and continuing practice to consolidate behavior change. Furthermore, 11 women aged 25-43 (median 31, IQR 27-37) years, who completed 1-6 sessions (median 6, IQR 6-6), completed pre- and postintervention outcomes. Descriptive quantitative analysis indicated decreases in anxiety and depression and increases in hope agency, hope pathways, and gratitude. There was a meaningful (≥3 points) increase in well-being. Among patients with baseline and follow-up data, 73% (8/11) met clinical caseness for depression at baseline and 36% (4/11) post intervention. Conclusions: We explored proof of concept. Web-based recruitment and delivery online were feasible. We detected early signs of acceptability and potential benefits for anxiety, depression, and positive well-being that warrant testing in a controlled trial. Future research should assess the #feasibility of a randomized controlled trial to evaluate effectiveness, acceptability, and cost-effectiveness. Trial Registration:

Background: Spinal cord injury (SCI) may, and often does, profoundly reshape daily life, altering physical abilities, social roles and personal identities. While assistive technologies, including assistive robotics, are often framed as solutions to re-establish independence, their adoption is shaped by practical, emotional and social considerations as well as functional qualities. Individuals with SCI, their relatives and healthcare professionals need to navigate complex dynamics when encountering assistive robotics. Understanding how assistive technologies are perceived and positioned in everyday life may help developers and designers create assistive robotics that are meaningful and useful for intended users. Objective: The aim of this qualitative #Study was to explore how individuals with SCI, relatives and healthcare professionals working with SCI patients perceive and describe the possibilities and limitations of assistive robotics. The #Study sought to understand the factors that influence the intention to use assistive robotics among individuals with spinal cord injuries. Methods: We used a qualitative approach, conducting semi-structured interviews and participatory workshops in Sweden and S#Pain. In total, the #Study involved 18 interview participants with SCI, 21 workshop participants with SCI, 12 relatives and 26 healthcare professionals. The interviews and workshops elicited reflections on participants’ experiences, expectations and concerns regarding assistive robotics in general and SRLs in particular. Data were analysed using reflexive thematic analysis, with a focus on interpreting the meanings embedded in participants’ narratives. Results: The analysis revealed that participants’ engagement with assistive robotics was influenced by expectations of technological benefits and by practical constraints in everyday life. The main barriers identified were practical constraints, including the subthemes ‘navigating a changing reality’, ‘difficulties with awareness and access’ and ‘concerns about costs’; and interaction with robots, including ‘doubts about meaningfulness’, ‘uncertainty regarding reliability and safety’, ‘uneasiness about competence’ and ‘apprehension of social norms’. Participants’ visions of enhanced self-efficacy through assistive robotics were described as important enablers of the intention to use and motivation to try assistive robotics. Shared expectations and concerns about future technologies (technological imaginaries) also influenced how participants’ talked about assistive robotics. Conclusions: Rather than presenting assistive robotics as an inevitable progression towards greater autonomy, this #Study highlights the complexities and contingencies that shape how individuals relate to assistive robotics in general and SRLs in particular. Participants’ responses illustrate that robotic assistance is not merely a question of technological feasibility but is deeply entangled with embodied experiences, shifting identities and evolving social relations. While visions of independence through assistive robotics remain compelling among participants, sociotechnical imaginaries coexist with concerns about meaningful engagement, reliability, safety, competence and social norms, as well as challenges related to transition periods, costs and limited awareness and access to assistive robotics.

Background: artificial intelligence (#AI) (AI) is increasingly applied to #healthcare, yet concerns about fairness persist, particularly in relation to sociodemographic disparities. Prior studies suggest that socioeconomic status (SES) and sex may influence AI model performance, potentially affecting groups that are historically underserved or understudied. Objective: This study aimed to (1) assess algorithmic bias in AI-driven dementia prediction models based on SES and sex (biological sex); (2) compare the utility of an individual-level SES measure (HOUSES Index) versus an area-level measure (Area Deprivation Index, ADI) for bias detection; and (3) evaluate the effectiveness of an oversampling technique (Synthetic Minority Oversampling Technique, SMOTE-NC) for bias mitigation. Methods: This study utilized data from two population-based cohorts: the Mayo Clinic Study on Aging (MCSA; N=3,041) and the Rochester Epidemiology Project (REP; N=19,572). Four AI models (Random Forest, Logistic Regression, Support Vector Machine, and Naïve Bayes) were trained using a 5-year observation window of structured EHR data to predict dementia onset within the subsequent 1-year window. Fairness and model performance were assessed using the balanced error rate (BER) across intersecting SES-sex subgroups. The SMOTE-NC algorithm was applied to the training data to balance the representation of SES groups. Results: Across both cohorts, the population with lower SES generally exhibited higher BERs (worse performance) than high SES groups, confirming the presence of bias. In the MCSA cohort, males with high SES, as indicated by the HOUSES Index, consistently exhibit the lowest BERs across all evaluated models. Balancing the training data based on a specific SES measure showed a trend toward reducing the BER disparity when evaluated using that same measure. However, this targeted improvement demonstrated non-universal benefits; in some cases, it exacerbated disparities when evaluated using other, un-balanced SES measures. This pattern suggests that fairness interventions are not universally beneficial across different definitions of the protected attribute. While the balancing approach improved fairness in model performance for lower SES groups, it often came at the cost of a slight reduction in overall model performance. However, an exception was observed in the MCSA cohort when balancing based on the HOUSES Index using LR, SVM and NB, where both the high and low SES groups performances improved. Conclusions: This #research highlights the importance of incorporating sociodemographic context into AI modeling in #healthcare. The choice of SES measure may lead to different assessments of algorithmic bias. The HOUSES Index, as a validated individual-level SES measure, may be more effective for bias mitigation than area-level measures. Future AI development should integrate bias mitigation strategies to ensure models do not reinforce existing disparities in #health outcomes.

Background: As communication technology advances and the #Digital divide grows, a deeper understanding of the influence of different information sources on #Vaccine uptake by generation can inform targeted #PublicHealth intervention in times of future crisis. While the #COVID19 #coronavirus pandemic highlighted the role of media sources on the decision to receive #Vaccines, no studies have focused on the impact of type and number of information sources in a population-based sample in California. Objective: In this study, we examined associations between Californians’ self-reported most relied upon #COVID19 #coronavirus information sources, categorized by type and measured as a count, and their #COVID19 #coronavirus #Vaccination status using data collected for the 2022 California Health Interview Survey (CHIS). To address differences in information preferences and #Vaccine uptake by age, we also tested for potential effect modification of the relationship between relied upon #COVID19 #coronavirus information sources and #Vaccination status by generational membership (e.g. Generation Z, Millennials, Generation X, Baby Boomers, Silent Generation). Methods: We conducted a secondary analysis of cross-sectional data from the 2022 California Health Interview Survey. #Vaccine status (any/none) was modeled as a function of information sources (or count) controlling for important socio-demographic and health confounding variables. Interaction terms of information sources (or count) by generational status were added to the models to test effect modification, and if significant, the models were stratified by generation. All analysis was survey-weighted to account for the complex survey sampling design. Results: Compared to relying on traditional news media for #COVID19 #coronavirus information, relying on “word of mouth” (OR=0.6), #SocialMedia (OR=0.62), and doctors (OR=0.41) for #COVID19 #coronavirus information was associated with lower odds of being #Vaccinated for #COVID19 #coronavirus. A dose-response relationship was identified with each additional information source associated with 9% higher odds of being #Vaccinated for #COVID19 #coronavirus. In stratified models, #SocialMedia, compared to traditional news media, was associated with lower odds of #Vaccination for Generation X, Baby Boomers, and the Silent Generation. Conclusions: Health information preferences, especially for traditional news media, are associated with #COVID19 #coronavirus #Vaccine uptake and the information sources differ by generation. These findings provide information for shareholders interested in #Vaccine hesitancy, health informatics, messaging strategies, health literacy, and future health information outreach programs during #Epidemics or pandemics. Dissemination of #PublicHealth information should include multiple information sources to reach all individual preferences across different generations.

Background: Leveraging #Digital technologies in healthcare is recognised as essential for effective and efficient services. However, significant challenges in implementing these technologies in #Stroke #Rehabilitation practice remain, and research on their influence is limited. Objective: This #Study aimed to explore the current influence of #Digital technologies on #Stroke #Rehabilitation practices and consider how these technologies could shape the future landscape of #Rehabilitation for multidisciplinary healthcare professionals in post-#Stroke #Rehabilitation. Methods: A qualitative, exploratory design was used. Data were collected from 12 experienced multidisciplinary healthcare professionals from two Norwegian #Rehabilitation settings via semi-structured interviews, which were analysed using reflexive thematic analysis. Data analysis was guided by Social Practice Theory. Results: The 12 participants included experienced physiotherapists, occupational therapists, speech therapists, nurses, physicians and social workers. Three main themes were generated: 1) Outsourcing information about and to #Stroke survivors: Coordination and continuity within and across services, with sub-themes on follow-up, inter-service collaborations, and user-centred approaches 2) Navigating ambivalence of remaining human relations in #Digital psychosocial support conversations, highlighting multidisciplinary challenges in building relational depth and addressing sensitive topics, and 3) Enhancing #Digital supplements for assessment and engagement in motor #Rehabilitation, with sub-themes on progress monitoring and motor skills exercises. Overall, using #Digital technologies in specialised #Stroke #Rehabilitation practices was seen as an adjunct to practices. While #Digital technologies influenced #Rehabilitation practices, ambivalence and challenges were noted particularly in #Digitalising multidisciplinary psychological support and exercise programs. Systems for sharing medical records and goal-setting apps, which enhance coordination and involve #Stroke survivors, were emphasised as future #Digital technologies shaping #Stroke #Rehabilitation. Conclusions: Healthcare professionals used various technologies in their daily specialist practices, as well as for coordination and follow-up of #Stroke survivors after referrals to community services. This #Study identified several organisational processes, roles, standards, and rules that can act as barriers or drivers to implementing #Digital technologies in practice. Viewing and leveraging familiar #Digital technologies as supplements to existing practices, rather than as a singular solution for all areas of specialised #Stroke #Rehabilitation, offers significant potential for quality improvement. These findings provide valuable insights for technology developers, healthcare personnel, and user groups in specialised neurological #Rehabilitation settings.

Artificial intelligence (AI) is revolutionizing medical education #mededu by enabling personalized, interactive, and efficient learning experiences. Powered by technologies such as natural language processing, machine learning, and generative models, AI supports a wide range of educational tasks from literature synthesis and virtual simulations to curriculum design, automated assessments, and academic writing. These innovations enhance clinical reasoning, streamline administrative processes, and optimize learner feedback across diverse educational settings. However, the integration of AI also presents critical challenges, including ethical concerns, data privacy risks, algorithmic bias, and unequal access in low-resource environments. Ensuring the responsible and equitable use of AI in medical education #mededu requires robust digital literacy, high-quality data, and collaborative regulatory frameworks. By embracing interdisciplinary collaboration and ethical integration, AI holds the potential to advance medical training while preserving humanistic values and improving global health education outcomes.

Background: Ageist beliefs tend to take root in one’s formative years and persist into adulthood. As future older adults, children must be instilled with more positive attitudes towards aging. Despite being a critical medium for analyzing age stereotypes, nursery rhymes have evaded the attention of the gerontological community. Previous research focuses predominantly on stereotypes held by children or on age stereotypes that proliferate in children’s storybooks. An analysis of nursery rhymes can facilitate an understanding of the ways in which age stereotypes held by children are perpetuated or subverted by these cultural artifacts. Objective: To gain insight into the ways in which children are being socialized to view old age, this study examines the depictions of old age in nursery rhymes. Our content analysis of nursery rhymes is grounded in the following research questions: What are the prevailing stereotypes associated with old age in nursery rhymes? Are these stereotypes primarily positive or negative? How are older characters being treated in the nursery rhymes? Insights from this study can provide insights that inform interventions to counteract negative portrayals of old age in nursery rhymes, thus paving the way for a more age-friendly society. Methods: To create a comprehensive dataset of nursery rhymes, we gathered material from four websites: BBC Nursery Rhymes and Songs, the Nursery Rhyme Collections, All Nursery Rhymes and NurseryRhymes.org. A web-ingestion tool was used to compile the dataset. In total, 735 unique nursery rhymes were retrieved. To identify rhymes related to old age, we conducted a search using various age-related terms (e.g., old, elder, grandfather, grandma). This search yielded a total of 85 nursery rhymes. Upon applying our exclusion criteria (e.g., we removed rhymes where ‘old’ was used to refer to old buildings), 34 rhymes were retained. Both inductive and deductive approaches guided our qualitative content analysis. Results: Old age was mentioned in 5% (N=34) of the 735 nursery rhymes. Among the rhymes retained for analysis, 50% contained negative age stereotypes. The remaining rhymes featured about an equal mix of positive (26%) and neutral age stereotypes (24%). Examples of negative stereotypes include being frail, mentally impaired, unkempt, incompetent and uncooperative. Examples of positive stereotypes include being wise, intelligent, affectionate, nurturing and joyful. Conclusions: In the context of an aging population, it is paramount that society is led by people who embrace a less pessimistic outlook of aging. Although nursery rhymes may seem like mere tales not to be taken seriously, they nonetheless remain powerful tools capable of molding thought processes. Our study sets the stage for telling children accurate and nuanced stories about older adults to inculcate healthy attitudes towards aging.

Background: Commercial wearable devices allow for continuous heart rate (HR) monitoring in daily life. Their accuracy under ecologically valid conditions, however, remains insufficiently independently tested, especially during irregular activity, cognitive stress, and variable climates. Objective: The present study evaluated the HR accuracy of ten commercially available wearables under controlled variations in physical activity, cognitive stress, and temperature. We hypothesized that physical activity irregularity, cognitive stress, and thermal climate conditions would affect measurement accuracy. Methods: Forty-five healthy adults (21–68 years, mean ± SD: 34 ± 12) completed a standardized protocol in climate-controlled chambers simulating neutral (23°C), hot (36°C), and cold (10°C) conditions. Tasks included rest, cognitive stress (Montreal Imaging Stress Task), steady walking, and intermittent walking. Each of ten devices (Fitbit Charge 6, Fitbit Inspire 3, Garmin Vivosmart 5, Garmin Vivoactive 5, Apple Watch SE, Google Pixel Watch 2, Polar Ignite 3, Polar Pacer, Xiaomi Watch 2, Oura Ring Gen 3) was compared against ECG-derived HR from a Zephyr BioHarness chest strap. Accuracy was assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), repeated-measures concordance correlation coefficient (CCC), and Bland–Altman analysis. Results: Significant variability across devices was observed. Fitbit Charge 6 (MAE 4.5 bpm, MAPE 5.5%, CCC 0.93) and Google Pixel Watch 2 (MAE 4.9 bpm, MAPE 6.7%, CCC 0.87) showed strong agreement with the gold standard. In contrast, Fitbit Inspire 3, Polar Ignite 3, Polar Pacer, and Oura Ring displayed larger errors (MAE 9–14 bpm, MAPE 11–16%) and lower CCC values (0.45–0.66). The climate conditions did not significantly affect the measurement accuracy of the test devices. The activity type, however, did have a significant effect: intermittent walking increased errors for multiple devices. Conclusions: Wearable HR measurement accuracy is device-specific and context-dependent. Moderate climates did not impair performance, but irregular movement reduced accuracy. Fitbit Charge 6 and Google Pixel Watch 2 demonstrated highest reliability, supporting their use in health and sports monitoring. Careful device selection and context-aware interpretation remain critical for applied and clinical applications.

Background: Trust is increasingly recognized as a cornerstone for the successful implementation of digital health initiatives - from mobile applications to the use of AI in medicine - yet it remains underrepresented in educational curricula. In the course of our research on trust in digital health and health data sharing, we found a gap in teaching resources designed to support students in conducting structured trust analyses. Digitization introduces new complexities into trust relationships, as interactions become increasingly mediated by digital tools. To prepare future professionals for these challenges, students must go beyond technical expertise and develop a critical understanding of how trust functions within digital systems - especially in the health sector. Objective: To address this gap, we developed and tested the first Trust Analysis Canvas for Teaching (TACT), a tool designed to guide students in conducting trust analyses of case studies in digital public health and medicine. Given the cross-cutting nature of trust-related issues, TACT was also designed to be accessible to students across a range of disciplinary backgrounds and academic levels, while remaining visually intuitive and engaging. Methods: Grounded in conceptual research on trust in health systems and health data sharing, we: (1) developed the canvas content and reviewed it with two trust researchers; (2) tested and iteratively refined the tool with 23 students (3 BSc, 14 MSc, 6 PhD) from diverse disciplines and academic levels through in-person and online focus groups at the Universities of Zurich and Bern, using digital public health and medicine case studies; (3) collaborated with a graphic designer to optimize its visual layout; and (4) translated the final canvas into French, Italian, German, and Spanish to enhance accessibility and broader usability. Results: The resulting canvas comprises 16 guiding questions organized around six core themes, designed to support students in conducting trust analyses of case studies in digital public health and medicine. Conclusions: TACT enables students to engage with the complex concept of trust in a guided and structured manner. It promotes participatory learning by encouraging active problem-solving and critical thinking, supporting students from diverse disciplinary backgrounds and academic levels in analyzing trust relationships within the context of digital public health and medicine.

Background: Obesity is a significant global public health concern. Primary prevention and health promotion to encourage positive health behavior to address obesity could be delivered via mobile health (#mHealth), but evidence of apps improving health outcomes over sufficient time frames to be clinically meaningful is limited. #mHealth interventions for physical activity, healthy eating, and weight loss typically prioritize intention as the primary driver of behavior. This may limit their impact, as intention does not consistently translate into behavior. Objective: This review updates a previous systematic review on the effectiveness of mobile apps for health behavior change while narrowing the scope to weight management interventions to enable a more focused analysis. Our primary objective is to investigate the effectiveness of #mHealth apps in improving health behaviors with respect to physical activity and healthy eating and to explore the inclusion of behavioral theories and behavior change techniques and the evidence for their effectiveness. Methods: This #Protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis #Protocols) checklist, and the review will be structured using the PRISMA 2020 statement. Nine databases (PubMed, EMBASE, CINAHL, APA PsycINFO, Cochrane Library, SPORTDiscus, SCOPUS, Web of Science, and Science Direct) will be searched for studies reporting evaluation of the impact of #mHealth interventions on weight loss, healthy eating, or physical activity outcomes. EndNote 21 software will be used for deduplication and initial screening, followed by manual title and abstract screening, and then full-text screening by 2 independent reviewers. Data regarding the studies, intervention characteristics, their theoretical basis (eg, use of behavior change frameworks such as the COM-B [Capability, Opportunity, Motivation-Behavior] model or the social cognitive theory), evaluation methods, and outcomes will be extracted into a predetermined form. A meta-analysis will be conducted on eligible studies (reporting control group comparisons) to synthesize evidence of their effectiveness, and the remaining quantitative data will be descriptively analyzed. Results: The review is expected to start in December 2025 and to be submitted for publication by the end of 2026. Conclusions: This review will synthesize evidence on the theoretical basis underpinning #mHealth interventions for enhancing physical activity, healthy eating, and weight loss and generate new insights into how particular behavior change techniques can best support intended outcomes. This will help guide the development of more impactful mobile-based interventions to support healthy behaviors that are better able to reduce the risk factors for chronic health conditions. Trial Registration: PROSPERO CRD42024602819; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024602819

Background: Medication adherence is vital for older adults living with type 2 diabetes mellitus (T2DM), but it remains low and needs improvement. Current interventions have limited effectiveness, while video-based interventions show promising potential for enhancing adherence. Objective: To evaluate the impact of the “Diabetes Little Helper” video intervention, developed based on the information–motivation–behavioral skills model, on improving medication adherence in older adults living with T2DM in Henan. Methods: This parallel-group randomized controlled trial was conducted in 2 hospitals in Zhengzhou, involving 68 patients from each hospital. The intervention group (IG) received standard care plus the video intervention for one month, while the control group (CG) received only standard care. The primary outcome was medication adherence, and secondary outcomes included medication knowledge, attitude, behavior, belief, and social support. Data were collected at baseline, postintervention, and at 3-month follow-up. Intention-to-treat analysis and the last observation carried forward method were applied for missing data, with the generalized estimating equation model used for effect assessment (P

Date Submitted: Feb 16, 2026. Open Peer Review Period: Feb 17, 2026 - Apr 14, 2026.

Date Submitted: Feb 16, 2026. Open Peer Review Period: Feb 17, 2026 - Apr 14, 2026.

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