DIGITAL HEALTH

Background Digital legacy (the digital information available about someone following their death) has increasing societal importance as personal assets and interactions become increasingly digitized. Healthcare professionals often have a limited understanding of how to address digital legacy in practice, and there is a lack of interdisciplinary networks to improve education, research, and professional development in digital legacy. Objective This paper describes the development of an interdisciplinary initiative designed to build research capacity and develop consensus-based recommendations for integrating digital legacy into palliative care. Method Over 12-months, we conducted interdisciplinary engagement activities with diverse stakeholders, including clinicians, designers, and sociologists. We used a modified World Cafe method to facilitate dialogue and capture feedback on how memories are digitally curated, the management of digital estates, and intergenerational perspectives on digital legacy. Results We identified eight core recommendations for research and policy, including promoting digital legacy education, supporting policy development, and broadening the scope of interdisciplinary research. Our discussions highlighted the complexity of modern digital estates and the need for legal and ethical frameworks to protect individual rights. Conclusions The Network demonstrates that interdisciplinary collaboratives can address important issues relating to digital legacy, which provides a foundation to conduct collaborative research that improves the management of digital legacies in society.

Background Stroke remains one of the leading causes of mortality and long-term disability worldwide, with low- and middle-income countries bearing a disproportionate share of the global disease burden. In India, delays in risk identification, fragmented referral pathways, and limited continuity of preventive care present significant challenges, particularly in rural communities. As a frontline health worker Accredited Social Health Activists (ASHAs) are strategically positioned to support community-based stroke prevention; however, existing workflows are frequently constrained by multi-tasking, predominantly paper-based documentation and fragmented digital systems. Advances in mobile health, artificial intelligence along with digital health ecosystem provided by Ayushman Bharat Digital Mission (ABDM) provide an opportunity to strengthen community healthcare through integrated digital platforms. Objective This protocol describes the design, system architecture, and prospective evaluation framework of ASHA Assist India, an integrated AI-assisted mobile health platform intended to support community-based stroke prevention by connecting citizens, ASHA workers, Primary Health Centres (PHCs), and higher levels of healthcare facilities within a unified digital ecosystem. Methods ASHA Assist India has been designed as a modular, cloud-based digital health platform supporting standardized data collection, longitudinal health monitoring, referral management, and AI-assisted clinical decision support. The proposed system comprises four user-facing applications corresponding to citizens, ASHA workers, PHCs, and referral hospitals, integrated through a centralized backend providing authentication, secure data management, interoperability, analytics, and notification services. The AI framework includes three planned analytical modules: (i) population-level stroke risk stratification, (ii) longitudinal stroke risk prediction, and (iii) acute stroke symptom recognition. A prospective implementation study is planned to evaluate platform usability, feasibility, workflow integration, implementation outcomes, and operational performance within routine community healthcare settings. Future validation of the AI modules will be conducted using prospectively collected longitudinal datasets. Expected Impact The proposed platform aims to strengthen community-based stroke prevention by improving digital workflow integration, facilitating coordinated referral pathways, and supporting longitudinal monitoring through the existing healthcare providers at health and wellness centres like ASHA, Community Health Officers (CHOs), ANM, etc. Beyond stroke prevention, the modular architecture is intended to provide a scalable framework for future digital health programmes addressing multiple non-communicable diseases within primary healthcare systems. Publication of this protocol establishes a transparent implementation and evaluation framework that may guide future research, digital health innovation, and implementation science in resource-constrained settings.

Introduction: Computerized cognitive training (CCT) is a promising and innovative solution to improve the quality of life for those experiencing age-related cognitive decline. The comprehension of instructions for CCT plays a crucial role in determining technology engagement. This study delves into the relationship between the presentation modes of CCT serious games instructions, their comprehension, and the resulting acceptability among older adults (aged over 65) without any known cognitive impairments. Methodology: In a within-subjects experimental design, two types of CCT instructions were submitted to 128 older participants (mean age 71.5, 70% female): without visual cues and with visual cues. This approach was complemented by a study of the influence of self-efficacy and technology-related anxiety on the acceptability of the games. Results: Instructions without salient visual cues were more acceptable for a complex functional game. Additionally, individuals with lower confidence in their cognitive abilities were less receptive to cognitive training, except for a highly familiar game. Conclusion: The study highlights that older individuals may prefer simpler instructions for complex functional games, suggesting a preference for reduced cognitive load. It also shows the subtle role of self-efficacy in technology acceptance, except for the most familiar games, with higher cognitive self-confidence linked to greater acceptability. It emphasizes the importance of metacognition and self-efficacy in engagement when CCT involves mobilizing cognitive resources. It points the need for simple and personalized instructions to improve acceptance of CCT, and to contribute to the development of tailor-made interventions for older people.

Digital mental health tools are increasingly promoted as scalable supports for young people, yet implementation remains inconsistent, particularly for marginalised youth. Acceptability and usability are key determinants of successful adoption, but little is known about how these factors shape engagement across diverse youth populations. The aim of the study was to examine the acceptability, usability, and implementation potential of 11 evidence?based digital mental health tools among marginalised young people across the Republic of Ireland (ROI) and Northern Ireland (NI). A mixed?methods design integrated baseline surveys (n = 38), a two?week trial of digital tools delivered through a co?designed Google Site, online workshops/individual interviews (n = 22), and a final usability and engagement survey (n = 24). Usability was assessed using the System Usability Scale (SUS), engagement using the Twente Engagement with E?Health Technologies Scale (TWEETS), and mental wellbeing using the Short Warwick-Edinburgh Mental Well?Being Scale (SWEMWBS). Qualitative data were analysed thematically and mapped to the Consolidated Framework for Implementation Research (CFIR). Only two tools exceeded the SUS usability benchmark. Engagement was moderate overall, with one tool achieving the highest engagement despite lower usability. SWEMWBS scores indicated moderate baseline mental wellbeing. Thematic analysis identified five acceptability themes: credibility and trust; accessibility and ease of use; positive content supporting emotional regulation; personalisation and self?monitoring; and engagement and habit formation. CFIR analysis highlighted usability, institutional trust, cultural relevance, and emotional needs as core implementation determinants. Digital literacy was high and supported engagement, and usability remained a critical gateway to implementation. Designers and commissioners of digital mental health tools should ensure that supports are simple, trustworthy, culturally relevant, and youth?centred to enable adoption among marginalised young people. Implementation strategies are needed that will co?design with diverse youth communities and prioritise youth work settings as well as governance clarity.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Patients with a brain tumour receive evidence-based clinical care in Australia but a focus on supportive care, including social connection, is often deficient. Digital health platforms hold promise to support these patients and their carers. Existing platforms often lack end-user co-design, evidence-based development and rigorous evaluation. Recognising this unmet need, we co-designed Brain Tumours Online, a digital supportive care platform to streamline access to educational resources, symptom management tools, and peer support for patients, carers, and healthcare professionals. In this article, we present our evaluation approach for Brain Tumours Online to advance methodological thinking in the evaluation of multi-faceted, co-designed digital health platforms. In contrast to standardised procedures in clinical trials, digital health interventions such as supportive care platforms are more complex due to their interactive nature, no prescriptive protocols for usage and the dynamic content of web-based information. Thus, traditional evaluation approaches often fall short in evaluating such multi-faceted digital health supportive care platforms. To address these challenges, we developed a bespoke, logic-modelling based evaluation approach to assess the usability, engagement, impact, and economic value of our platform. Our pragmatic but rigourous evaluation approach required the adaptation of existing evaluation frameworks, subject-matter, and lived experience expert knowledge. Our implementation science and co-design approach are shared in different papers. Our study outcomes will also be shared in a separate paper. In the current paper, we share our approach to the evaluation of Brain Tumours Online and provide insights that may be of value for other researchers interested in the nuances of trialing multi-faceted digital health supportive care platforms.

Abstract Background: A critical challenge for large-scale multi-country population health studies is the ability to collect consistent data across many sites and time periods and ensure that the data collected are valid and comparable. The use of mobile digital devices coupled with data collection platforms can address this challenge. We developed a fit-for-purpose digital data collection platform for the South Asia Biobank study. Objective: To describe the process by which a digital platform was designed, developed and deployed across four countries in South Asia; to demonstrate how the platform enabled field research teams located across these countries to collect non-communicable diseases epidemiological data consistently. Methods: A user-centred design approach was employed for the development of the digital platform to address the dynamic nature of study requirements. This approach uses 5-step iterative loops that, with each iteration, produce a usable prototype version of the software that was then tested by potential users of the platform. Qualitative interviews and quantitative system usability assessments were conducted, and findings utilised as input for the start of the next iterative loop. The process was completed when a working version of the software was developed for the use in the study. Results: Over the course of four iterative loops, the platform was progressively built and tested to ensure its functionality met the requirements of the study. Detailed feedback was collected from key stakeholders and incorporated into the platform with each new version of the applications. The platform leverages advances in mobile and medical device technology along with software integration capabilities to enable efficient and consistent data collection, along with the ability to review data quality and make improvements to the data collection process in real-time. The successful deployment of the data platform has enabled collection of comprehensive baseline data from 205,536 participants in four South Asian countries. Conclusions: Using user-centred design principles, it is possible to develop and deploy a comprehensive digital surveillance data management platform that allows consistent and high-quality data collection in population health studies in remote settings. To the best of our knowledge, this is the first platform that enables the integrated capture of health assessment data from a wide variety of medical equipment that is tailored for deployment in a range of LMIC settings.

The openEHR standard provides an open, vendor-neutral architecture for clinical data repositories (CDRs), yet its real-world deployment has not been systematically documented. We conducted a dual-perspective survey combining a vendor survey of openEHR CDR providers with a community survey of openEHR practitioners. Eleven vendor organisations reported deployments across 22 countries and over 100 institutions and health regions. A complementary community survey (n=29, 17 countries) provided context on regulatory environments, adoption drivers, and barriers. Combined, the surveys cover 28 countries, 26 of them with a reported openEHR CDR deployment. Three findings emerge: openEHR has achieved national-scale presence through two distinct channels. Through vendor-market convergence, openEHR-based systems cover the majority of regional health authorities without a national mandate, including 19 of 21 Swedish regions, 3 of 4 Norwegian health regions, and 16 of 21 Finnish wellbeing services counties. Through national health record adoption, governments have built or procured national systems on openEHR as their technical foundation, including Ireland, Malta, Greece, Jamaica and Slovenia. Across Europe, this constitutes an openEHR-based interoperability infrastructure already in place across multiple EU member states. We identified no country in which openEHR is named in binding national regulation, creating structural fragility and an unrealised opportunity for alignment with the European Health Data Space (EHDS). Second, 61% of deployments serve primary use only, and 12% support both primary and secondary use. Third, lack of openEHR-specific knowledge is the most consistent adoption barrier across all geographies and deployment tiers. Adoption is driven by practitioner need and innovation, not by regulatory mandate.

Introduction: Extreme heat increasingly threatens older adults, particularly those with chronic conditions, yet generic heat-health advice may not be sufficiently timely or relevant to individual needs. This feasibility study describes a prototype and assesses the feasibility of a location-triggered, disease-specific heatwave short message service (SMS) intervention tailored to common heat-vulnerability conditions, compared with generic heatwave SMS advice. Methods: Mixed-methods feasibility study comprising a parallel two-arm 1:1 randomised controlled trial and post-heatwave focus groups. Community-dwelling Australians aged [≥]65 years in New South Wales, Victoria or South Australia with at least one eligible chronic condition (cardiovascular diseases, respiratory conditions, diabetes, and chronic kidney diseases) and a smartphone were recruited in summer 2026. Based on an initial codesign, participants received a 'prepare' SMS after enrolment and, when Bureau of Meteorology heatwave warnings were triggered, messages before, during and after heatwaves. Control participants received generic 'standard care' heat-health advice; intervention participants received condition-tailored messages and could request additional information via SMS codes. Outcomes were collected via baseline and post-heatwave surveys and thematic analysis of focus groups. Results: Seventy-three participants enrolled (36 control; 37 intervention); attrition was 9.6%. Intervention engagement was strong: 61% requested additional information, with frequent free-text replies and multi-condition requests indicating preference for more conversational interaction. Eight participants were heatwave-exposed and completed post-heatwave surveys (4 per arm), with a high usability score (median of 85/100). Among these 8 participants, 7 reported adopting heat-protective health behaviours; the most common were drinking more water (6/7). More total actions were reported in the intervention group (11 vs 8). No adverse effects were reported. Conclusion: A location-triggered, disease-tailored heatwave SMS system for older adults with chronic conditions was feasible, acceptable and highly usable, with high engagement and no harms. Findings support a larger trial and suggest benefits from tailored messaging.

Visual wayfinding is essential for safe navigation but remains poorly characterized in people with ultra-low vision (ULV). Because assessing complex environments in the real world carries safety risks, this study utilized a calibrated virtual reality (VR) platform to safely quantify navigation. Participants with ULV, normal vision (NV), and simulated ULV (sULV) completed tasks across three environments (street crossing, cafeteria, and metro station) of increasing complexity to determine which metrics best capture task difficulty. Navigation metrics included motion onset latency, walking speed, path efficiency, and turn deviation derived from head position data. Participants with ULV showed longer onset latency, slower walking speed, reduced path efficiency, and greater turn deviation compared with NV, while sULV showed intermediate performance. These metrics successfully reflected increasing task difficulty across environments, with the metro station posing the greatest challenge. Path efficiency consistently detected differences between environments across groups, whereas turn deviation provided insight into complex tasks. Findings indicate that diverse virtual environments capture distinct aspects of navigation that cannot be safely studied in the real world, and trajectory-based metrics capture navigation behavior more effectively than conventional measures. VR-based assessment offers a useful approach for evaluating functional navigation and guiding rehabilitation strategies in profound vision loss.

TikTok has become a significant source of health information, and concern has grown about AI-generated content (henceforth, 'AIGC') as a vehicle for health misinformation. Where AIGC presents realistic-appearing people giving health advice, disclosure labels are the viewer's only reliable cue that what they are watching is synthetic. This research letter compares AI label metadata across 128,016 mental health-related TikTok videos and 4,924 videos from a network of 50 profiles posting exclusively AI-generated mental health content to evaluate how much content reaches audiences undisclosed. In a keywords-based collection, fewer than a percent of TikTok videos about mental health carried an AI label, but in profiles containing purely AI-generated content, just over 9 in 10 videos (90.23%) were neither labelled by the creator nor identified by TikTok's automatic detection. Additionally, in the keyword collection, automatic detection produced the majority of labels, while in confirmed AI-generated content from 50 profiles, it accounted for just three of the 481 labelled videos. These findings highlight the challenging landscape of AI disclosure and labelling and raise questions about where automatic detection is failing.

Introduction: SkinScan3D (SS3D) is a novel, artificial intelligence-enabled device that provides objective three-dimensional measurements for monitoring Kaposi Sarcoma (KS) lesions. Prior to launching a clinical trial of the device, we obtained end-user perspectives to guide device refinement. Methods: Between April and May 2025, we conducted six focus group discussions and 28 in-depth interviews with patients, healthcare providers, and community representatives in Kenya and Uganda. Participants viewed a demonstration video and handled the SS3D prototype. Data were analyzed using hybrid deductive-inductive thematic analysis informed by the Health Information Technology Usability Evaluation Model and the Consolidated Framework for Implementation Research. Results: Qualitative findings were synthesized into a conceptual framework for SS3D adoption with two interconnected themes: 1) experiences and context, and 2) device perceptions and implementation factors. Participants' receptivity to the device was first shaped by experiences with medical technologies and the broader sociocultural context, including trust in providers, health beliefs, and gender preferences. After interacting with the prototype, participants viewed the SS3D as intuitive, accurate, and potentially capable of improving the objectivity and efficiency of KS lesion monitoring. They identified concerns related to safety, infection prevention, data security, affordability, maintenance, and workflow integration. Successful implementation was perceived to depend on device refinement, supportive organizational factors, including leadership engagement, provider training, maintenance capacity, and patient education to address misconceptions about the device. Participants proposed hardware, software, connectivity, and training refinements to support safe integration into routine clinical care. Conclusion: End users demonstrated overall satisfaction and receptivity to the SS3D, given potential benefits for both patients and providers. We identified targeted refinements to optimize the device's functionality and integration into the oncology environment to improve its fit with the local context.

Background: Adverse events (AEs) after hospitalization are common and disproportionately affect adults with multiple chronic conditions (MCC). Capturing patient-reported symptoms and self-assessed health may enable earlier detection of post-discharge AEs. Objective: To identify and test user requirements for an automated remote monitoring system to enhance AE surveillance during the transition home following discharge. Methods: We conducted a mixed-methods study using an iterative, user-centered design approach. Semi-structured interviews with patients and clinicians informed system requirements, followed by real-world field testing in 20 patients who used the system for up to 7 days after discharge. The prototype leveraged interoperable electronic health record data services, delivered automated post-discharge check-ins using a combined questionnaire assessing new or worsening symptoms and patient-reported outcomes (PROs), provided risk-stratified health advice (when and with whom to initiate contact), and escalated high-risk symptoms to clinicians in real-time. Descriptive statistics assessed feasibility and utilization; conventional content analysis identified user needs and implementation considerations. Results: Thirty-seven patients with MCC and 23 clinicians participated. Key requirements for patients included clear communication of personalized risk based on red-flag symptoms, and actionable guidance aligned with discharge instructions. Key requirements for clinicians included explicit delineation of responsibility across inpatient and outpatient setting, and selective escalation to minimize burden. Field testing patients completed 60% of the combined questionnaires. Seven patients received Level 2 or Level 3 health advice after reporting new or worsening symptoms. Three patients triggered Level 3 alerts, resulting in one-time, secure escalation emails to clinicians. Four of the 7 patients who received Level 2 or 3 health advice had chart-confirmed emergency department visits within 1 week of discharge. Patients found the system understandable and helpful, while clinicians noted challenges interpreting PRO trends. Conclusions: These observations support the feasibility and acceptability among patients and clinicians of collecting patient-reported symptoms and PROs during the early post-discharge period. Future iterations should prioritize clear risk communication, role clarity, and interpretable patient-reported data. Formal validation is required to assess predictive performance and clinical utility of symptom-based escalation for post-discharge AE surveillance.

Objective. Formal large language model (LLM) evaluations score isolated prompts, but clinicians and health-informatics researchers meet model failures inside multi-step workflows where erroneous output can alter procedures or contaminate documents. We present TRACE (Tracking Reliability of AI-generated Conversational Evidence), a practitioner-audit framework for evaluating the downstream workflow reliability of conversational AI. Materials and Methods. A method paper with an empirical demonstration: 45 documentation-positive incidents recorded by one clinician-informatician across scholarly, clinical informatics, and clinical-adjacent workflows over seven weeks, coded with a consequence-based severity rubric, an error definition, a taxonomy crosswalk, and a Response-Audit Scorecard. Three reviewer-authors independently coded a 16-incident subsample; three vendor-blinded AI comparators applied the taxonomy to all 45 incidents. Results. Four categories tied as most frequent: verification failure, factual numerical error, tool-behavior misunderstanding, and citation or reference formatting (n=7 each). Four workflow-harm patterns recurred: procedural propagation, documentary contamination, trust-calibration disruption, and user-borne corrective burden, and one incident carried an estimated $2500 impact. Category agreement across three human reviewer-authors was low (Fleiss {kappa}=0.155), whereas three AI comparators agreed substantially (Fleiss {kappa}=0.632), suggesting taxonomy legibility under standardized conditions even where human judgment diverged. Discussion. Category assignment is comparatively legible, whereas severity and claimed-verification remain judgment-dependent. The claimed-verification gap is a measurable failure mode distinct from hallucination, sycophancy, and over-refusal. Conclusion. Practitioner audits with structured response scoring complement benchmarks by documenting workflow harm as an applied evaluation unit for clinical informatics and public-health work; this is a pilot that motivates, not estimates, error rates or cross-model comparisons.

Language discordance can impede community-based research and health communication where trained interpreters are limited. Although multimodal artificial intelligence systems can provide real-time spoken translation, performance with under-resourced languages during spontaneous field interactions remains poorly characterized. We evaluated ChatGPT-4o during bidirectional English-Nepali voice translation in a community setting near Dhulikhel Hospital, Nepal. In this cross-sectional field study, 30 primarily Nepali-speaking adults were recruited by convenience sampling. ChatGPT-4o mediated conversations using standardized English questions and spontaneous Nepali responses. A bilingual Nepali-English reviewer assessed 485 translated utterances using a 3-point accuracy scale and an inductively developed framework for translation and conversational deviations. Of 485 translations, 282 (58.1%) received the highest accuracy rating, 134 (27.6%) a moderate rating, and 69 (14.2%) the lowest. Mean accuracy was higher for English-to-Nepali than Nepali-to-English translation (2.63 {+/-} 0.53 vs 2.23 {+/-} 0.86); 63 of 69 low-accuracy translations (91.3%) occurred in the Nepali-to-English direction. Among 329 deviation tags, the most frequent were distortion of intended meaning (17.1%), overly formal or unnatural phrasing (14.7%), omission (14.2%), and addition of content (11.5%). Some fluent outputs substantially altered meaning or introduced information not expressed by the speaker. ChatGPT-4o demonstrated potential for real-time English-Nepali communication but also produced errors that could alter interpretation of participant responses. Accuracy was lower and more variable for Nepali-to-English translation; however, translation direction was confounded with input type because Nepali inputs were spontaneous and English inputs standardized, limiting conclusions about directional performance. These findings support cautious use for low-stakes conversational exchange and human verification when errors could affect research validity, clinical decisions, or participant understanding. As multimodal AI evolves, performance should be reevaluated across languages, real-world conditions, and model versions, with bilingual oversight and community partnership remaining central to responsible use.

Background: In two large studies conducted in Bangladesh, our recently developed artificial intelligence (AI)-based models for assessing dehydration severity in children under five years (DHAKA models) and patients over age five (NIRUDAK models) were significantly more accurate and reliable than the WHO IMCI and IMAI guidelines for diarrhea management. We incorporated these models into a novel mobile health (mHealth) clinical decision support tool (CDST), called FluidCalc, with the potential to improve acute diarrhea management by frontline health workers worldwide. Our objective was to assess the barriers and facilitators to uptake and use of our mHealth CDST in both a low-resource setting (Tanzania) and high-resource setting (United States (US)) among healthcare providers and stakeholders. Methods: Qualitative data were collected through focus group discussions (FGDs) with healthcare providers and in-depth interviews (IDIs) with stakeholders and policymakers from February - July 2025 in Tanzania and February - March 2026 in the US. The Consolidated Framework for Implementation Research (CFIR) was used to guide discussions and elicit participant feedback. Audio recordings were transcribed and translated from Swahili to English where applicable, and data were analyzed using framework matrix analysis. Results: 35 providers from different cadres participated in FGDs, and 13 stakeholders participated in IDIs. Facilitators to implementation included FluidCalc's simplicity, ease of use, and offline functionality. Participants reported that the app could streamline clinical workflows, promote adherence to diarrhea management guidelines, facilitate task shifting, support antibiotic stewardship, and reduce errors in fluid rehydration calculations. FluidCalc was also viewed as a valuable teaching tool, and for supporting less experienced healthcare providers and trainees, and as useful during diarrheal disease outbreaks. Perceived barriers included the need for reliable digital infrastructure, including access to mobile devices, internet connectivity, and dependable electricity and lengthy institutional approval processes. Endorsement and approval from the Ministry of Health and health facility leadership were perceived as essential for successful implementation. Conclusion: Healthcare providers and stakeholders believe FluidCalc has the potential to improve care for patients with acute diarrhea in both high- and low-resource settings. Addressing identified barriers and ensuring reliable digital health infrastructure are needed to support effective integration into patient care.

Seizure diaries are one of the most useful sources of information in the management of epilepsy, however patient engagement with them can be sporadic. Sustained participation with seizure diaries affects the completeness and reliability of self-reported data, so it is vital to be able to measure engagement. To facilitate this, we create a multidimensional engagement metric with which to characterize how patients interact with their seizure diary. We utilise data from the Helpilepsy, a seizure diary application, common features found in application engagement metrics in business settings, and well understood clinical features to do this. Clustering is then performed to isolate different user groups based on how engaged they are, and these groups are studied to understand what drives the differences in engagement. We found three groups emerge from the clustering: low, medium and highly engaged users. Investigating these groups further, we put together a ``profile" for highly-engaged users. We find that they tend to be older at the point of diagnosis, and have had epilepsy for longer than the other users. We also find they tend to have had more medications, have higher doses of common anti-seizure medications, and they have more medications typically given to those with refractory epilepsy. The implications for e-diary design are that more attention should be given to those newer to epilepsy in the onboarding phase. Also, engagement is not necessarily based on just the upload of seizures, with other features of an e-diary being important to be filled in.

Abstract Introduction: Cardiac rehabilitation (CR) is key for secondary prevention; however, participation remains low due to persistent barriers. Identifying strategies used by high-performing programs may inform approaches to improve patient engagement and outcomes. Purpose: To identify strategies associated with improved participation and adherence in CR programs from the perspective of leaders in high-performing sites. Methods: Semi-structured interviews were conducted with 10 CR leaders from urban, suburban, and rural programs ranked in the top 10% on at least two objective performance measures (e.g., participation and adherence rates) but moderate or low on others. Data were analyzed using thematic analysis to identify strategies associated with high performance. Results: Programs with high participation and adherence rates consistently implemented proactive, patient-centered strategies to address barriers. Individualized care approaches tailored to language, culture, health literacy, and age were commonly used to improve engagement among Hispanic, Black, and older adult populations. High-performing programs addressed structural barriers such as insurance and transportation through flexible scheduling, community partnerships, and targeted outreach. Strong coordination with referring providers and effective transitions from inpatient to outpatient care were associated with higher enrollment and sustained participation. Additional strategies included staff development through ongoing education, use of digital tools for patient tracking, and implementation of virtual and hybrid CR models. Integration of psychological support further enhanced patient engagement. Conclusion: High-performing CR programs employ coordinated, patient-centered, and system-level strategies associated with improved participation and adherence. These findings provide actionable approaches to enhance accessibility and improve programs and patients outcomes in CR across diverse settings. Keywords: Cardiac rehabilitation; participation; adherence; health disparities; implementation strategies

Disseminating study results to participants is an ethical responsibility for researchers but remains uncommon in low- and middle-income countries, and participants preferences for receiving study results are poorly understood. This study examined study result dissemination preferences among pregnant women in a phase III malaria prevention trial in Papua New Guinea (PNG). Participants completed an interviewer-administered questionnaire (survey) assessing their interest in and motivation for receiving trial results and preferences for dissemination methods and content. Associations between participants characteristics and dissemination preferences were explored using multivariable logistic regression analysis. Of 1172 trial participants, 96.0% (1125/1172) completed the survey, and of these 99.6% (1121/1125) wanted to learn about the trial results. The main motivation factors driving participants interest were an acknowledgment of their contribution to research (51.7%; n=579) and a better understanding of the study (45.0%; n=505). Most participants (78.9%; n=884) wanted to learn about the trial findings through written summary and a group meeting with other participants at the nearest clinic (31.1%, n=349). Multivariable regression analysis indicated that participants from rural/peri-urban clinics were more likely to choose non-electronic media dissemination approaches such as a group meeting as compared to urban-dwelling participants. Frequently selected items (>50% of participants) for content included information regarding good results of the study, purpose of the study, medical treatment advances, results specific to me, and how study was conducted. There was heterogenicity in the preference for dissemination content: compared to urban clinics rural clinics are less likely to want to learn about how and why study was conducted and medical and scientific advances. Overall, the majority wanted to learn about trial results, highlighting the importance of integrating dissemination into research activities in PNG. Variation in preferences for mode and content of dissemination between study clinics suggests that dissemination activities could be tailored to local context and preferences.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.