搜索结果：Frontiers in digital health

Breastfeeding is widely recognized as one of the most cost-effective public health interventions for improving maternal and child health outcomes. Nevertheless, breastfeeding indicators remain suboptimal worldwide despite strong international recommendations. In recent years, digital technologies have emerged as tools to support breastfeeding promotion, education, and continuity. However, the evidence on digital and multimedia breastfeeding interventions is heterogeneous and scattered across disciplines, limiting a comprehensive understanding of their scope and effectiveness. For the purposes of this review, "digital resources" refers broadly to digital platforms and technologies used to deliver breastfeeding-related information or support; "interactive multimedia tools" refers to resources integrating two or more media formats (e.g., text, audio, video, graphics) with user interaction; and "digital interventions" is used as an umbrella term encompassing both concepts. To systematically map and synthesize available evidence on digital resources and interactive multimedia tools used to promote and support breastfeeding, describing their characteristics, implementation contexts, target populations, reported outcomes, and limitations. A scoping review was conducted following the Arksey and O'Malley methodological framework and reported in accordance with PRISMA-ScR guidelines. The methodological approach was also aligned with selected recommendations from the Joanna Briggs Institute for scoping reviews. Searches were carried out in PubMed, the Virtual Health Library (VHL), Google Scholar, and the AI-powered tool Consensus between April 2023 and July 2024. Peer-reviewed publications in English and Spanish from the last 10 years addressing digital resources or interactive multimedia tools for breastfeeding promotion or support were included. Data were extracted and synthesized using a descriptive analytical approach. A total of 23 studies published between 2019 and 2024 were included. The review identified a range of digital interventions, including social media platforms, mobile health (mHealth) applications, web-based resources, educational videos, telemedicine services, and multimedia materials. Most studies targeted pregnant women and breastfeeding mothers, often in contexts of social or economic vulnerability. Overall, digital interventions were associated with increased breastfeeding knowledge, improved maternal self-efficacy, enhanced access to information and peer support, and favorable perceptions. However, evidence regarding breastfeeding duration and exclusivity was inconsistent, and substantial variability was observed in intervention design, implementation strategies, and outcome measurement. Studies from both high-income countries (HICs) and low- and middle-income countries (LMICs) were identified, with social media campaigns and low-cost mobile approaches appearing particularly relevant in resource-constrained contexts. Digital resources and interactive multimedia tools represent promising complementary strategies for breastfeeding promotion and support. This scoping review highlights both the potential benefits and the heterogeneity of existing digital interventions, emphasizing the need for standardized, theory-informed, and context-sensitive approaches to strengthen evidence-based practice and future research in digital breastfeeding support.

Enteric infectious diseases claim more than 1 million lives annually and are among the top ten causes of death in children younger than 5 years. Remarkable global investment has been dedicated to enteric infectious disease prevention and control; however, the shifting global health landscape is testing the continuance of progress. To evaluate the current status and guide future interventions, we present the latest epidemiological estimates of enteric infectious diseases from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 and assess progress towards the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) mortality target of fewer than 20 deaths per 100 000 children younger than 5 years by 2025. We quantified the incidence, mortality, and disability-adjusted life-years (DALYs) of enteric infectious diseases by age, sex, and year across 204 countries and territories from 1990 to 2023. In GBD 2023, the following were considered under the category of enteric infectious diseases: diarrhoeal diseases, enteric fever (typhoid and paratyphoid), invasive non-typhoidal Salmonella spp (iNTS) infections, and other intestinal infectious diseases. We also examined 15 aetiologies contributing to diarrhoeal diseases. Incidence and prevalence were estimated with DisMod-MR (version 2.1), a Bayesian meta-regression tool, drawing on data from systematic reviews, population-based surveys, claims data, and hospital sources. Cause-specific mortality was modelled with Cause of Death Ensemble Modelling based on data from sources including vital registration, mortality surveillance, verbal autopsy, and minimally invasive tissue sampling. Years of life lost and years lived with disability were computed and combined to derive DALYs. For aetiology-specific estimation, population-attributable fractions (PAFs) for 15 pathogens were derived with a counterfactual framework. Point estimates and 95% uncertainty intervals (UIs) were generated from 250 draws from the posterior distribution. In 2023, enteric infectious diseases resulted in an estimated 1·27 million (95% UI 0·963-1·68) deaths globally, declining from 3·69 million (3·04-4·56) in 1990. The global age-standardised mortality rate (ASMR) decreased from 74·1 (62·0-92·9) per 100 000 population to 16·4 (12·6-21·3) per 100 000 population during the same period. Diarrhoeal diseases accounted for most deaths in 2023 (1·11 million [0·811-1·54]), followed by enteric fever and iNTS. South Asia and sub-Saharan Africa remained the most affected regions in 2023, with 599 000 (441 000-882 000) and 501 000 (373 000-648 000) deaths due to enteric infectious diseases, respectively, predominantly from diarrhoeal disease. Rotavirus was the leading cause of all-age diarrhoeal disease deaths (PAF 16·3% [12·0-21·5]), followed by norovirus (10·2% [2·4-17·0]) and Shigella spp (9·3% [5·4-15·2]). Among children younger than 5 years, PAFs of deaths due to diarrhoeal diseases were 40·2% (32·5-48·5) for rotavirus, 24·0% (15·1-36·7) for Shigella spp, and 23·4% (13·7-34·3) for adenovirus. Across 204 countries and territories, 141 met the GAPPD mortality target in 2023. The driving aetiologies among countries that did not meet the target in 2023 varied slightly by GBD super-region, but the highest or second-highest number of deaths in children younger than 5 years were consistently attributed to rotavirus. Astrovirus and sapovirus, newly included in GBD 2023, were responsible for 24 600 (6290-49 000) and 18 800 (4650-44 400) deaths, respectively, in 2023, mainly in children younger than 5 years. Our findings show that mortality and ASMRs of enteric infectious diseases declined substantially between 1990 and 2023. This decline is consistent with the expansion of public health measures and broader socioeconomic development. However, the burden in 2023 remains considerably high, with the highest mortality concentrated in sub-Saharan Africa and south Asia. Considering that more than a quarter of all countries had yet to meet the GAPPD mortality target in 2023, sustained efforts are needed to address the persistent burden in affected countries and to adapt to the changing global health landscape. Gates Foundation.

The increasing reliance on health information technology (HIT) has introduced new and often unforeseen risks to patient safety in complex healthcare systems. Many HIT-related safety problems emerge only after systems are embedded in routine clinical practice and are difficult to identify using prospective or purely quantitative methods. Incident reports provide valuable insights into real-world failures, but systematic methodologies for analysing HIT-related incidents remain underdeveloped. This article aims to describe and formalise a qualitative, multi-framework methodology for analysing health information technology-related patient safety incidents, based on retrospective incident report data. The methodology integrates multiple data sources, including incident reporting systems, existing incident databases, and supplementary interview-derived narratives. HIT-related incidents are identified through a structured screening process combining keyword-based searches and manual narrative review. Analysis is conducted using complementary deductive and inductive approaches, including established patient safety classification systems, HIT-specific frameworks, workflow-based analysis, and thematic analysis. Structured coding procedures, independent review, consensus-building, and reflexive practices are employed to enhance analytical rigour. Findings are systematically translated into preventive and corrective strategies grounded in sociotechnical principles. The proposed methodology enables systematic identification and characterisation of HIT-related patient safety incidents, capturing sociotechnical mechanisms, contributing factors, and outcomes that are not readily identified through single analytical frameworks. By combining multiple perspectives, the approach supports analysis of low-frequency, high-impact events, workflow disruptions, and system-level failures, and facilitates the development of context-sensitive preventive and corrective strategies. This multi-framework qualitative methodology provides a structured, transferable approach to learning from HIT-related patient safety incidents in complex healthcare systems. The framework supports researchers, clinicians, and safety analysts in understanding how digital systems fail in real-world practice and offers a robust foundation for improving the safety and resilience of digital healthcare.

Health information technology (HIT) is now integral to healthcare delivery, supporting clinical documentation, prescribing, diagnostics, and care coordination. Although these technologies offer substantial benefits, they have also introduced new patient safety risks that are often difficult to anticipate, detect, or manage. Many HIT-related safety problems arise not from isolated technical failures or individual mistakes, but from complex interactions between digital systems, clinical work practices, organisational structures, and governance arrangements. Traditional patient safety models that focus on discrete errors or linear causality are therefore insufficient for explaining how digital risks emerge and persist in practice. This article develops a sociotechnical theory of HIT-related risk grounded in patient safety science and sociotechnical systems theory. The theory is informed by empirical insights from incident-based research on HIT-related safety problems and synthesises evidence from real-world incident narratives. It adopts a conceptual, theory-building approach informed by purposive, iterative engagement with the relevant literature on health IT safety, sociotechnical systems, and resilience-oriented patient safety frameworks. Rather than analysing a single dataset, the paper identifies recurring mechanisms through which digital risks arise, remain hidden, propagate across contexts, and become recoverable or not. The proposed theory conceptualises HIT-related risk as a dynamic process involving four interrelated mechanisms: risk emergence, risk concealment, risk propagation, and recoverability. Risks emerge through misalignments between system design, configuration, and clinical workflows; they are concealed by automation, information fragmentation, and adaptive workarounds; they propagate through tightly coupled digital infrastructures and shared dependencies; and their recoverability depends on organisational capacity for detection, escalation, and learning. Together, these mechanisms explain why HIT-related incidents may affect multiple patients or services, why attribution to individual error is misleading, and why safety problems may persist despite corrective efforts. By reframing HIT-related incidents as manifestations of system-level vulnerabilities rather than isolated failures, this sociotechnical theory provides a coherent explanatory framework for understanding digital patient safety. It highlights how risks can evolve silently within routine practice, vary in visibility and scale, and emphasises the importance of organisational learning, governance, and resilience in managing digital safety risks.

Health systems contribute to an important part of planetary boundaries overshoot, the effect of its rapid digitalization being however not well known. DYNAMIC is a Tanzanian digital health project, aimed at improving quality of care for sick children at primary care level through the provision of a tablet-based clinical decision support algorithm for clinicians. We evaluated the raw material resources and energy required by this intervention, as well as the environmental impacts, to inform strategies for improving its sustainability. Additional resources, including IT equipment, diagnostics and medicines used by the health intervention compared to usual care, were quantified. A life cycle assessment was conducted to calculate greenhouse gas emissions, fossil energy and mineral resources use, and damages to ecosystems and human health. GHG emissions of the intervention in 40 health facilities over one year, allowing to attend 90,992 children, were 20.6 tons of CO2-eq per year (PY). Medical supplies were the main source of emissions (13 tons), followed by digital supplies (5 tons), and logistics (2.6 tons). Fossil energy and mineral resources used were 380 GigaJoules and 77.9 kg deprived PY respectively. Damage on human health was 0.062 DALY, and on ecosystems 12,385 Potentially Disappeared Fraction of Species per m2 of land PY. The two-third decrease in antibiotic prescription as a result of the DYNAMIC project could reduce 14.5 tons of CO2-eq emissions. The digital component of the DYNAMIC health intervention increased its carbon footprint by a third, the main drivers remaining however the increase in medicines and medical devices use. Three quarters of the overall emissions could however be saved thanks to the antibiotic stewardship effect of the intervention. This shows that the rapid digitalization of health systems could accelerate their dependency on fossil fuels and other raw material. This negative effect on the environmental should be systematically evaluated to know if it is at least compensated by a benefit in terms of medical supplies savings. Promoting local, eco-friendly production of essential medical supplies and synergizing digital health interventions to use a shared IT infrastructure are also essential strategies for preserving resources and protecting the environment.

Culturally and religiously responsive mental mobile health (mHealth) apps may improve access to and acceptability of mental health support among migrant communities; however, evidence to inform their design remains limited. This formative study investigated mental health perceptions, digital health information-seeking, and mental mHealth app use among first-generation Arabic-speaking migrants in Australia, with the aim of informing culturally adapted mental mHealth app design. An online survey was conducted among 219 first-generation Arabic-speaking migrants in Australia (aged 18-75 years), recruited from non-clinical community settings. The survey assessed attitudes toward mental health, awareness and use of mental mHealth apps, acceptance of app-based support, and desired features. Open-ended questions provided qualitative insights into cultural and religious preferences. Strong cultural and religious influences on mental health perceptions were observed, including high agreement regarding the role of divine will and religious practices. While most participants (76.3%) used the internet to seek mental health information, awareness (45.7%) and use (6.4%) of mental mHealth apps were low. Participants expressed high acceptance of mental mHealth apps that are free, user-friendly, confidential, and professionally developed. Highly valued features included culturally informed behavioural activation, mindfulness and religious practices (such as Dua'a and Tadabbur), and educational content incorporating Quranic verses and prophetic narratives. Information on crisis services and local multicultural mental health providers was also considered essential. Qualitative findings supported the inclusion of faith-based community features and religious motivational content, with several participants emphasising the importance of optional rather than mandatory religious elements. There is a clear demand for mental mHealth apps tailored to the cultural and religious needs of first-generation Arabic-speaking migrants in Australia. Formative evidence from this study highlights the importance of culturally and religiously congruent design, practical support features, confidentiality, and flexibility to accommodate individual preferences when developing mental mHealth interventions.

Physical inactivity remains a persistent global health challenge despite long-standing evidence that regular physical activity (PA) reduces chronic disease risk, cognitive decline and premature mortality. In parallel, digital health technologies have expanded rapidly, yet it remains unclear how distinct platform types have emerged, diffused and been differentially adopted in clinical vs. non-clinical populations. We conducted a large-scale bibliometric trend analysis of technology-supported PA interventions indexed in Scopus and SportDiscus, covering records published from 1953 to 2025. Using an active-learning screening workflow with title/abstract screening, sensitivity checks and consensus adjudication, we identified 2,981 eligible studies published between 1988 and 2025 across 757 journals and 63 countries. Studies were coded by population (clinical vs. non-clinical), platform cluster, and author-reported PA outcome direction abstracted from the publication record. Publications increased markedly after 2008, with smartphone/mHealth and wearable sensors becoming the dominant platform clusters in the 2010s and early 2020s. In the smoothed overall trajectories, publication activity reached its highest level around 2022, followed by a contraction concentrated mainly in mature clinical platform clusters. Non-clinical studies generally adopted newer platforms earlier, whereas clinical studies showed a recurrent lag before converging for more established, accessible technologies. A distinct population-level reversal was visible in mature platforms: non-clinical studies led early smartphone, wearable and web-based uptake, clinical studies became more prominent around the COVID-19 period, and non-clinical studies again predominated by 2025 for smartphone/mHealth, wearable sensors and web applications. Multi-component designs were common, with the strongest network backbone centred on smartphone, wearable and web-based combinations. Reported PA improvement signals were common across both population strata, but these findings reflect patterns in published study reporting rather than comparative effectiveness. By providing a platform-centred, cross-population and temporally resolved map of technology-enabled PA interventions, this study identifies mature technology backbones, emerging areas of experimentation, and recurrent translational gaps between clinical and non-clinical contexts. The findings support more theory-informed and implementation-aware intervention design while underscoring that bibliometric prominence should not be equated with real-world efficacy.

Mobile health (mHealth) technological innovations are now widely being promoted as a scalable solution to the rising problem of obesity. Unfortunately, there is a dearth of empirical research on the extent to which mHealth utilization is associated with acceptance disparities in emerging economies. This work explores the associations among socioeconomic status (SES), urban-rural differences, mHealth utilization, and sustained physical activity in obesity management in Indonesia. The paper also assesses urban-rural variation in the associations among SES, mHealth utilization, and sustained physical activity. Quantitative assessment was used to examine the direct and indirect associations between SES, mHealth and sustained physical activity among 1,204 overweight and obese respondents in Indonesia. Multi-group SEM (structural equation modelling) was performed to assess differences between urban and rural cohorts and to examine group differences between the two samples. The empirical outcomes indicate that (1) SES is significantly associated with mHealth utilization and sustained physical activity, (2) mHealth utilization is significantly related to sustained physical activity for obesity control, and (3) Structural differences exist between urban and rural groups in the strength and significance of the associations among SES, mHealth utilization, and sustained physical activity among overweight and obese adults. The empirical outcomes align with earlier publications that discovered a substantial linkage between SES and mHealth utilization. The outcomes showed a positive correlation between SES and sustained physical activity, differing from an earlier work. This may be attributed to contextual differences: prior cohorts with higher wealth engaged in sedentary lifestyles, while here the link of SES-exercise was positive due to greater access to resources. Additionally, while earlier research has suggested mHealth utilization h as a psychosocial mediator, our outcomes indicate geographic disparity, where the benefits of technology are linked to urban infrastructure. It implies that mHealth utilization may be more prevalent in high-SES Indonesian cities, potentially widening health disparity gap.

The application of game elements to engage participants and improve data collection for clinical trials is relatively novel, with limited research around the impact of gamification in clinical research. This article explores published literature and surveys from patients and clinical sites. A targeted literature review was completed in November 2025 to identify published articles (≤10 years) on the application of gamification in clinical trials. Synthesized findings informed the design of two surveys of US adults (n = 1,044 from UserTesting.com) and clinical trial sites (n = 311) on their perceptions of gamification and acceptance in clinical trials. Both were ∼5-minute online surveys utilizing five open- and closed-ended questions. Twenty-four articles were focused on the application of gaming design and mechanics to non-gaming activities. Three primary areas identified were education (n = 3), health outcomes measures (n = 7), and patient engagement (n = 14). Eighteen studies reported an advantage of gamification, including positive impacts on health outcomes measures (n = 5) and patient engagement (n = 11). Survey respondents (adults) were most familiar with computer games (62%), stating a preference for participating in trials that included gamified cell phone applications, with the ability to customize application elements as the most important. From a thematic analysis of respondents' comments, potential impacts on human behavior and performance (33%) were the most prevalent concerns. Data (26%; including concerns about privacy, integrity, and security), and software (22%; including adaptability to account for ability and skill variation, satisfaction, user experience, controls, customization, and personalization) were also key areas of concern for patients. Key perceived benefits included improvements in experience (31%) and engagement (24%). Site respondents were most familiar with managing gamified clinical trials with gamified cell phone applications (30%) and would prefer to manage trials that included these elements vs. traditional trials. Notifications, education, and training were the most important gamification elements for site respondents. Potential advantages of gamification include increased engagement, trial education, adherence to protocols, and enjoyment of the clinical trial experience, which may increase retention and data completeness. Further research is required to better understand the potential impact of gamification on scores of how patients feel or function.

The large-scale deployment of digital health solutions requires robust operational frameworks capable of coordinating heterogeneous settings, diverse stakeholders, and complex technical infrastructures. However, actionable guidance for executing federated, multinational eHealth pilots remains limited in the implementation literature. Using a mixed-methods approach, including iterative focus groups, co-creation sessions, and a Delphi study, we developed and refined a practice-derived management framework over four years within the GATEKEEPER project (EU Horizon 2020, Grant Agreement No. 857223). The study involved a federation of four European large-scale pilots. A panel of 23 experts ranked 45 best practices across six operational domains: Engagement, Intervention, Monitoring and Control, Planning, Recruitment, and Other. The resulting framework integrates a structured definition of operative key performance indicators, standardised reporting and analysis tools, and a Business Intelligence dashboard to support real-time monitoring and decision-making across the preparation, deployment, and running phases of large-scale pilots. Among the ranked best practices, usability testing, user-centred digital tool design, and active recruitment strategies emerged as top priorities across pilot sites. This management framework addresses a critical gap in implementation science by offering actionable, consensus-validated guidance for coordinating large, distributed, multi-site digital health deployments. The GATEKEEPER experience demonstrates how structured operational governance and shared performance monitoring can support the execution of complex eHealth pilots, with insights that may inform future large-scale initiatives seeking sustainable and patient-centred digital health integration.

Maintaining exercise and medication habits is crucial for older adults, but conventional reminder-based digital interventions often produce only transient effects. We conducted a mixed-methods pilot feasibility study comparing a reminder-only intervention (Experiment 1, N = 10) with a healthcare community app combining reminders and avatar-based virtual social interaction (Experiment 2, N = 10) in older adult care facilities over 4-week baseline and 4-week intervention periods. Primary outcomes were daily step counts and medication adherence. The Reminder group showed a transient increase in step count in Week 1 (+16%) that declined to below baseline by Week 4 (-10.3%). In contrast, the Reminder + Support group maintained substantial step count improvements throughout the intervention (Week 4: +58.4%), with statistically significant differences at all time points. Medication adherence showed similar trends. A between-group comparison (Mann-Whitney U = 11.0, p < 0.05) supported greater efficacy of the Reminder + Support intervention. Qualitative data confirmed higher satisfaction and stronger social motivation in the Reminder + Support group. These preliminary findings suggest that integrating avatar-based social interaction with reminder functions may support more sustained health behavior change in older adults than reminders alone, warranting confirmation in larger randomized trials.

Patients with Systemic Lupus Erythematosus (SLE) and Sj&#xf6;gren's disease (SjD) often report disrupted sleep, excessive fatigue, and decreased physical activity. Symptom assessment and their impact on daily life relies heavily on subjective measures, which are limited. Investigating novel digital biomarkers could facilitate continuous monitoring of symptoms such as sleep disturbances and reduced physical capacity to better capture disease impact and therapy effects. To evaluate passive sensing for identifying activity, sleep, and breathing patterns that distinguish SLE and SjD phenotypes in the home environment. 29 SLE, 29 SjD and 37 demographic-matched healthy participants were recruited in a 6-month in-home study. We investigated a set of digital measures collected from a wrist worn actigraphy device (ActiGraph CentrePoint Insight Watch) to measure physical activity and a wall mounted non-contact radio wave device (Emerald) to measure sleep staging and breathing signals during sleep. In addition, self-reported eDiaries and clinical assessments of disease activity were administered. We performed a disease profiling analysis by exploring how eDiary and digital data differ by cohort. We also investigated how ActiGraph and Emerald data correlate with eDiary and disease activity. Results from ActiGraph and Emerald data suggest that SLE participants exhibited lower physical activity, poorer sleep quality, and higher breathing rate and breathing variability when compared to healthy participants. Similarly to SLE participants, SjD participants showed a reduction in physical activity with an earlier peak activity time, but no differences were recorded in sleep and breathing. Overall, many of the digital measurements of physical activity, sleep, and breathing had weak correlation with self-reported symptoms captured via eDiary and both higher breathing rate and breathing variability were associated with higher SLE disease activity. Data obtained from digital health devices indicates that physical activity is disrupted in patients with SLE and SjD, while sleep and breathing patterns are also impaired in those with SLE. These results align with the known SLE and SJD symptoms that affect physical activity and sleep and provide initial support for the importance of using passive sensing to understand quality of life in individuals living with chronic autoimmune diseases.

The Human Phenotype Ontology (HPO) provides a unified framework cataloguing over 17,500 phenotypic abnormalities across more than 8,600 rare diseases, defining hierarchical relationships between them. For example, classifying missing arms and missing legs as both abnormalities of the limb. This structure enables phenome-wide analyses, including the prioritisation of phenotypes as candidates for gene therapy. However, the HPO currently lacks sufficient metadata describing the clinical severity of these phenotypes. Manual expert curation at this scale would be prohibitively labour-intensive, creating a need for automated approaches to systematically annotate phenotypic severity. GPT-4, a large language model (LLM) developed by OpenAI, was employed to annotate the severity of all phenotypic abnormalities catalogued in the HPO. Severity was operationalised using nine clinical characteristics: congenital onset, reduced fertility, sensory impairments, impaired mobility, immunodeficiency, physical malformations, cancer, intellectual disability, and death. Each characteristic was further qualified by frequency of occurrence across four levels: never, rarely, often, and always. To assess annotation quality, GPT-4's outputs were benchmarked against ground-truth labels embedded within the HPO itself. For instance, phenotypes residing in the "Cancer" HPO branch were expected to be annotated as cancer-causing. A novel severity scoring system was then developed that integrates both the nature of each clinical characteristic and its frequency of occurrence. Benchmarking demonstrated strong performance across all clinical characteristics, with true positive recall rates ranging from 89% to 100% (mean = 97%). This indicates that GPT-4 can replicate expert-level curation with high fidelity. The resulting severity scoring system produced quantitative severity metrics for phenotypic abnormalities across the HPO, incorporating both the type and frequency of associated clinical characteristics. These findings demonstrate that LLMs can automate the large-scale curation of clinical metadata with a high degree of accuracy, substantially reducing the burden of manual expert annotation. The severity metrics generated here provide a foundation for systematically ranking human phenotypes by their impact on health and quality of life, enabling more principled prioritisation of targets for therapeutic intervention, particularly in the context of rare diseases where evidence is sparse and resources for curation are limited. Future work may extend this framework to incorporate additional clinical dimensions or validate annotations against independent clinical datasets.

Anxiety disorders are among the most prevalent mental health problems worldwide, and access to effective treatment is not always available. Preventive interventions need to be scalable and cost-effective, which can be achieved through communication and information technologies. However, recruiting participants for digital prevention trials remains a major methodological challenge. To evaluate the performance of different approaches to recruiting participants for a digital preventive intervention for anxiety (the prevANS trial), and to assess participants' motivations for enrolling in the trial. Descriptive analyses were conducted to evaluate the performance of each recruitment strategy (number of potential participants attracted per week). Quantitative data were obtained from website records of individuals initiating the online screening process while each strategy was active, and self-reported information on how participants learned about the study. Baseline group differences between the intervention and control groups were examined using chi-square and Mann-Whitney U-tests. Reflexive inductive thematic analysis was used to analyze qualitative data on participants' main motivations for enrolling, collected through an open-ended survey question. Over a 26-month recruitment period, 6,017 individuals initiated screening and 1,054 participants were enrolled (17.5% conversion rate). The most effective strategies for attracting potential participants were social media and university dissemination. Self-reported data also indicated that word of mouth had a notable impact on recruitment. The final sample was mainly composed of women and highly educated participants, and the intervention and control groups were balanced across all variables except for age. Thematic analysis revealed three main motivations for enrollment: helping others, health related issues, and own benefits. Recruitment strategies should be tailored to the target population, as their performance may vary across groups. Involving users through co-design and co-creation can enhance both the intervention and the identification of effective recruitment channels in digital trials.

Healthcare systems, including the Veterans Health Administration (VHA), are facing tremendous growth in virtual care technologies that are intended to foster connections between patients, informal caregivers, and healthcare team members. The adoption of some of these virtual care technologies was accelerated by the onset of the COVID-19 pandemic, emphasizing the need to more comprehensively and rigorously assess the impacts of such technologies. An encompassing framework that represents the universe of outcomes relevant to virtual care technologies and can inform related measurement is necessary to understand such impacts in depth. Such a framework can inform how, where, and when these technologies may have the most impact, and by extension, contribute the most value. This article describes the participatory and literature-based development of a Value Framework reflecting the potential value of virtual care technologies for VHA healthcare stakeholder groups and the VHA healthcare system. We pursued a combination of participatory co-design approaches involving key stakeholders representing different domains of expertise in VHA and completed a targeted scoping review of 96 prior randomized clinical trials funded by VHA to identify and describe outcomes related to virtual care technologies. Findings from these activities were synthesized and used to inform the Value Framework's organization. The Framework is comprised of five primary value categories reflective of healthcare's Quintuple Aim: 1. Experiences of Care, 2. Access to and Utilization of Quality Healthcare, 3. Population Health, 4. Costs, and 5. Equity. Each of these primary value categories includes subcategories and sets of distinct outcomes related to the adoption and use of virtual care technologies. Since its development, VHA has adopted this Value Framework to inform efforts in evaluating and communicating the impacts of its portfolio of virtual care technologies. The Value Framework may offer researchers and healthcare organizations a tool that can support the development of a cumulative evidence base regarding the value of virtual care technologies.

As many as three in four older adults live with chronic pain, and osteoarthritis is a leading cause of chronic pain in older adults. Knee and hip osteoarthritis being the most common forms of the condition. Osteoarthritis symptoms are worsened by low levels of physical activity, excess sustained sedentary time, weight gain, and social isolation, ultimately impairing quality of life. Data from several pilot trials have demonstrated the feasibility, acceptability, and potential benefit of a unique remote group-mediated behavioral intervention rooted in social cognitive and self-determination theories that targets three domains of behavior change: (1) dietary behavior change with a focus on weight loss via caloric restriction alongside diet quality, satiety, and reduced inflammation, (2) increased physical activity, and (3) decreased sitting via the accumulation of steps in frequent bouts throughout the day (i.e., daylong movement). Herein we describe the protocol for a Stage-II parallel randomized controlled trial examining the efficacy of 6 months of a remotely delivered group-mediated daylong movement and weight loss intervention in older adults with obesity and chronic knee or hip osteoarthritic pain. Outcomes of interest include daily steps (primary outcome) and pain interference, body weight, and physical function (secondary outcomes). We will also explore intervention effects on long-term behavior change over 12 months following the intervention and whether changes in steps, body weight, pain catastrophizing, or pain self-efficacy mediate intervention effects on pain interference, if present. Low-active older adults (N&#x2009;=&#x2009;200) with chronic osteoarthritic hip and/or knee pain and obesity will be randomly assigned to the daylong movement and weight loss intervention or to an enhanced usual care control. All participants will receive the same self-monitoring technologies to account for any effect of basic device provision on activity and diet behavior. The results of this trial will inform future real-world efficacy and effectiveness trials of a package well-suited to broad scale delivery.

Implementing change in organizations is challenging, and a key factor in success is the perception of the implementers. While many studies report on implementers' perceptions as barriers or facilitators for implementing innovations, they often do not examine how these perceptions change over time. We aimed to evaluate changes in perceptions among nurses and psychiatrists in emergency departments (EDs) regarding the implementation of telepsychiatry (live video) for involuntary hospitalization. This study utilized quantitative and qualitative methods, administering an online questionnaire to nurses and psychiatrists in eight EDs in Israel before and after telepsychiatry implementation. The questionnaire included: 1. Background characteristics (i.e., role and seniority); 2. SHEMESH questionnaire ("Organizational Readiness to Change Assessment") comprising 11 items about the evidence for telepsychiatry, its feasibility, its effectiveness compared to face-to-face evaluation, and items about how supportive the ED environment for implementing changes in practice; 3. Two open-ended questions about how telepsychiatry would fit patients and the challenges in using telepsychiatry for involuntary commitment. Quantitative data were analyzed using descriptive statistics, t-tests, and regression models, while qualitative data were analyzed and categorized. 344 participants completed the questionnaire (54% from the pre-implementation phase and 46% from the telepsychiatry phase). Both phases showed high scores on the overall Evidence construct, indicating that telepsychiatry is evidence-based, with an increase from the pre-implementation to the telepsychiatry phase. High scores on the overall Context construct were also observed, with no difference between phases. A regression model identified study phase, psychiatrists, and management team as predictors of higher Evidence and Context scores. Respondents' comments in the pre-implementation phase focused on concerns about feasibility, while comments in the telepsychiatry phase focused on patient cooperation and agitated patients. Questions about professionalism arose in both phases. Overall, negative responses decreased significantly during the telepsychiatry phase, while perceptions of telepsychiatry's fit for patients increased, though not statistically significantly. Ongoing use of an innovation over time can result in a favorable change in implementers' perceptions as they grow accustomed to the innovation and appreciate its advantages, such as increased efficiency and improved results. ClinicalTrials.gov identifier: NCT05771545, March 15, 2023.

Wellness tourism is among the fastest-growing segments of the global health economy, yet its development in Central Asian heritage regions remains constrained by fragmented service delivery, limited digital infrastructure, and a shortage of evidence-based planning tools. In this Perspective, we argue that advancing wellness tourism in such regions requires coupling econometric diagnosis of revenue drivers with the design of a digital platform that operationalizes those drivers, and we illustrate this dual approach using Bukhara, Uzbekistan-a UNESCO World Heritage Site rich in thermal springs, therapeutic hot sands, and mineral-rich muds. Drawing on panel data from 12 wellness facilities observed over 2021-2024, a weighted least squares model identifies three revenue determinants: client base size, service breadth, and qualified staffing. Client base expansion and qualified staffing emerge as the strongest positive determinants, while service breadth shows a paradoxical negative effect, suggesting that resource dispersion outweighs portfolio benefits in this setting. Revenue projections indicate substantial sectoral growth by 2030, with nature-oriented sanatoriums leading in relative terms. Building on these patterns, we propose the "Wellness Bukhara Voucher System"-a digitally integrated platform connecting disparate facilities through standardized vouchers, QR-code authentication, automated analytics, and a public-private partnership financial model. The platform addresses the diversification paradox through "network specialization," allowing each facility to deepen its core competencies while the system as a whole expands service breadth via cross-referrals. We discuss infrastructure, stakeholder, regulatory, and privacy conditions for viable deployment, and argue that this perspective offers a transferable model for heritage regions seeking to convert natural healing assets into digitally coordinated wellness economies.

Language offers a low-burden and scalable pathway for digital anxiety screening, particularly in telehealth or repeated-monitoring settings where spontaneous speech may already be available. This study introduces a contrastive autobiographical recall framework that uses short positive and negative personal memories to capture within person affective shifts in language. By modelling how the same individual expresses emotionally distinct experiences, the proposed approach aims to identify anxiety-related linguistic patterns that may not be captured from a single static text representation. A total of 156 participants completed a 5-7 minute spontaneous speech task involving positive and negative autobiographical memories. Anxiety status was defined using HAM-A scores, yielding non-anxious (n&#x2009;=&#x2009;101) and anxious (n&#x2009;=&#x2009;55) groups. Transcripts were segmented using Qwen-2.5-7B-Instruct as a deterministic constrained extractor, preserving only verbatim positive and negative spans alongside the complete transcript. Positive, negative, and complete narratives were encoded with frozen BERT model and combined with a contrast vector capturing within-person affective shift. Performance was evaluated using a leakage-safe leave-one-out cross-validation pipeline. The proposed pipeline achieved 70% accuracy and 0.67 macro-F1 across leaveone-out folds, with stronger performance for non-anxious participants than anxious participants. Bootstrap confidence intervals were 0.62-0.77 for accuracy and 0.59-0.75 for macro-F1. Ablation analysis showed that the full composite representation provided the best balanced performance and strongest anxious-class detection. The method also outperformed BERT-based and lexicon-based baseline models. These findings suggest that short autobiographical speech can provide a useful complementary signal for digital anxiety screening when modelled with contextual embeddings and within-person affective contrast. Latent-space augmentation supported learning in this small cohort without altering participant-authored language. However, anxious-class sensitivity was moderate, and HAM-A labels should be interpreted as screening rather than diagnostic labels. Further validation in larger and more diverse clinical cohorts is needed.