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.

BackgroundCaring for people with dementia can impose a considerable psychological burden on caregivers, yet access to caregiver support in Malaysia remains limited. The World Health Organizations iSupport for Dementia program provides dementia education via textual, e-learning format. However, a culturally adapted Malaysian version has not been available. ObjectiveThis study aimed to develop and gather user feedback on a culturally adapted, multimedia version of iSupport tailored for Malaysia (iSupport-Malaysia). MethodsGuided by a four-phase cultural adaptation framework, the generic iSupport content was translated into Bahasa Malaysia, adapted to local customs, and transformed into multimedia lessons on an e-learning platform. A mixed-methods design was used to explore user perceptions and evaluate usability through four homogeneous focus group discussions and 15 individual usability test sessions with informal caregivers (FG: n=9; UT: n=9) and healthcare professionals (FG: n=11; UT: n=6). Focus groups examined aesthetics, ease of use, clarity, cultural relevance, comprehensiveness, and satisfaction. Usability testing involved Think Aloud tasks, post-test questionnaires, and brief interviews. Qualitative data was analysed thematically, and descriptive statistics summarised usability performance. ResultsiSupport-Malaysia demonstrated good usability (M=74.3{+/-}18.0), with most tasks completed without assistance. Strengths included interactive learning activities, peer discussion features, and flexible self-paced learning. Content was viewed as culturally appropriate, credible, and useful. Suggested improvements included enhancing visual aesthetics, shortening videos, refining quizzes, and increasing practical relevance. ConclusionUser insights indicate that iSupport-Malaysia is usable and culturally appropriate. These findings will inform refinement of the platform prior to the pilot feasibility study and provide recommendations for future multimedia-based caregiver interventions.

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.

More than one third of adults with diabetes can experience diabetes distress due to the demands of daily self-care. As a cognitive therapy, mindfulness can alleviate diabetes distress but face-to-face programmes can be difficult to access and pay for, and apps lack personalisation and feedback. Virtual reality (VR) may support mindfulness practice, but no VR app tailored to people experiencing diabetes distress exists. We interviewed mindfulness practitioners and conducted co-design workshops (using focus groups, questionnaires, artistic methods, generative artificial intelligence tools and prioritization techniques) with adults with type 2 diabetes to gather perspectives on designing a VR mindfulness app. We analysed data using descriptive statistics and the framework approach. Most participants preferred a simple design and layout to use the virtual environment to practice mindfulness, with customisable design options and interactive features that were culturally appropriate. We identified new design features, functionality, and content that informed a software design specific documentation to build a prototype VR mindfulness app for people experiencing diabetes distress. Further research should include more diverse populations to elicit detailed specifications for software design and include safety features to minimise risk when using VR technologies to practice mindfulness.

Hypertension, a prevalent cardiovascular condition, requires effective management of multimodal health risk factors. This study examines the effectiveness of a digital health tool designed for hypertension management and explores user perspectives on its utility. We analyse a cohort of 5,136 participants who used the digital tool, which provides continuous blood pressure monitoring, real-time feedback, and personalized health recommendations. Our results show that users achieve significant reduction in their blood pressure values and this reduction is positively correlated with the duration for which users report their blood pressure values. Additionally, we obtain high retention rates even after one year of using the digital tool. User feedback was collected through an online survey revealing high satisfaction rates. Participants highlighted the tools ease of use, and felt less anxious. Overall, our study demonstrates the potential of digital health tools in enhancing hypertension management and highlights the importance of user-centred design in developing effective health interventions.

Digital health tools may facilitate care continuum. However, enhancement of digital aid is imperative to prevent information gaps or redundancies, as well as to facilitate support of flexible care plans. The study presents Health Circuit, a digital health tool with an adaptive case management approach and analyses its healthcare impact, as well as its usability (SUS) and acceptability (NPS) by healthcare professionals and patients. In 2018-19, an initial prototype of Health Circuit was tested in a cluster randomized clinical pilot (n=100) in patients with high risk for hospitalization (Study I). In 2021, a pilot version of Health Circuit was evaluated in 104 high risk patients undergoing prehabilitation before major surgery (Study II). In study I, Health Circuit resulted in reduction of emergency room visits [4 (13%) vs 7 (44%)] and enhanced patients empowerment (p<0.0001) and showed good acceptability/usability scores (NPS 31 and SUS 54/100). In Study II, NPS scored 40 and SUS 85/100. The acceptance rate was also high (mean score of 8.4/10). Health Circuit showed potential for healthcare value generation, good both acceptability and usability despite being a prototype system, prompting the need for testing a completed system in real-world scenarios.

With the rise of artificial intelligence (AI) and its application within industries, there is no doubt that someday AI will be one of the key players in medical diagnoses, assessments and treatments. With the involvement of AI in health care and medicine comes concerns pertaining to its application, more specifically its impact on both patients and medical professionals. To further expand on the discussion, using ethics of care, literature and a systematic review, we will address the impact of allowing AI to guide clinicians with medical procedures and decisions. We will then argue that the impact of allowing AI to guide clinicians with medical procedures and decisions can hinder patient-clinician relationships, concluding with a discussion on the future of patient care and how ethics of care can be used to investigate issues within AI in medicine.

Withdrawal StatementThis article has been withdrawn by medRxiv because it was submitted with false information.

This paper describes eHEALS.com.br, a web-based platform that automates the administration of the Brazilian eHealth Literacy Scale (eHEALS-Br). The system collects responses online, scores users in real time, and provides personalized feedback based on five levels of digital health literacy. A systematic literature review was conducted to map existing instruments and identify gaps related to automation, temporal control, and inclusion. The platform architecture combines a React and TypeScript frontend with a Node.js and MongoDB backend, deployed on a secure virtual private server. In a pilot study with 12 participants, the automated eHEALS-Br scale demon-strated excellent internal consistency (Cronbachs alpha = 0.929), and additional questionnaire dimensions also showed good reliability. The results demonstrate the feasibility of automating eHEALS-Br, supporting both population studies and individualized clinical use, and reinforcing the potential of digital health tools for monitoring and improving health literacy in Brazil.

IntroductionMindfulness can positively impact physical and mental health, but face-to-face programmes are limited by poor accessibility, availability, and cost. Virtual reality (VR) offers immersive audio-visual environments that could improve mindfulness practice. AimTo evaluate commercially available VR apps related to mindfulness. MethodsApp stores and relevant online platforms were searched for VR apps related to mindfulness. Results were screened against eligibility criteria and relevant data extracted. Six raters used the Mobile Application Rating Scale (MARS) to assess the quality of VR apps. ResultsFive VR apps related to mindfulness were included i.e., Headspace XR, Hoame, Innerworld, Maloka and TRIPP. These provided access to meditative and mindfulness sessions, guided by virtual instructors in some cases, and situated in a range of virtual landscapes accompanied by sound or music. TRIPP received the highest average MARS score (4.1), followed by Hoame (3.8), Maloka (3.6), Headspace XR (3.4) and Innerworld (3.3). Most VR apps scored the highest on functionality (3.4 to 4.2), while the information category scored the lowest (3.1 to 3.7). The intra-class correlation was moderate. ConclusionThis review provides important insights into VR apps related to mindfulness such as their availability and quality. Only five VR apps were identified related to mindfulness practice with an overall moderate MARS quality score (3.62/5.00). These may provide a convenient and immersive way to access and engage in regular mindfulness practice, particularly for novices. Rigorous scientific research should assess the effectiveness of these VR apps in improving physical and mental health through immersive mindfulness practice.

ObjectiveThe study sought to investigate the relationship between attitude towards digital health technology and age, gender and frequency of use of digital health technology and to consider whether age, gender and frequency of use present potential barriers to accessing future healthcare in the UK. Differences in technological affinity are likely to lead to differences in the adoption of digital health technology and subsequent inequalities in healthcare between older and younger people and between men and women. DesignThe study represents an example of a technology adoption study employing a survey-based cross sectional correlational design. Attitude towards digital health technology was measured using the 20 item Digital Health Scale. Age, gender, frequency of use of health technology and employment status data were gathered using a demographics questionnaire. The opportunity sample (N = 247) included volunteer participants aged 16-84 years (M = 31.7, SD = 19.35, 156 females and 91 males). ResultsResults indicated a significant negative correlation between age and positive attitude towards digital health technology (r = -0.24, p < .01). Gender differences in attitudes towards digital health technology were non-significant (p > .05). Significant differences in frequency of use were also found, where occasional and frequent use resulted in more positive attitudes than never having used digital health technology (p < 0.05) and participants reporting frequent use were significantly older than those reporting never or occasional use (p < .05) ConclusionFindings identified age, but not gender, as a significant factor in attitude towards digital health technology, suggesting that continued and increased reliance on digital technology in healthcare may lead to age, but not gender, related inequalities in access to healthcare in the UK. That frequent users of digital health technology were also older, highlights the greater demand for healthcare services by older individuals and is further evidence for the potential of digital healthcare to lead to age related inequalities in access to and provision of healthcare. Recommendations for successful application of digital healthcare technology are considered in the light of these findings.

BackgroundAlthough there has been an increase in the availability of mobile health (mHealth) tools globally and their potential benefits for both healthcare providers and patients, the adoption of mHealth is still relatively low. Additionally, only a limited number of studies have investigated the intention of individuals to download mHealth apps. ObjectiveWe conducted a study to explore peoples inclination towards using a health app. MethodsWe conducted the study in Nigeria using a discrete choice experiment. The study had a sample size of 2800 participants who were presented with two different attributes and levels. These attributes were price ($20 = N 17932.80 [at a currency exchange rate of $1= N896.64], and free subscription option) and data protection (with options of data protection vs no data protection). The participants were randomly assigned to the different attribute and level options. For the analysis, we used the conditional logistic model. ResultsAccording to the results of the study, the likelihood of downloading a mHealth app is significantly higher when the app is offered for free. The study also found that users tend to ignore data protection specifications, and instead prioritize free subscription offers while showing reluctance towards apps that come with a price tag. ConclusionsThe use of mobile health (mHealth) tools has a high potential in reducing healthcare costs and enhancing the efficacy of traditional health interventions and therapies. The major driving forces behind the increasing adoption of mHealth apps in the future are cost reduction and the establishment of sound business models. It is crucial to establish reliable standards for mHealth apps, which can include information about pricing and legislation regarding data protection, to ensure that potential consumers can make informed decisions.

Heart rate variability (HRV) is the fluctuation in the time interval between consecutive heartbeats, the measurement of which is a non-invasive method for assessing the autonomic status. The autonomic nervous system plays an important role in physiological situations, and in various pathological processes such as in cardiovascular diseases and viral infections. This study examined the cardiac autonomic responses, as measured by HRV before, after, and during coronavirus disease. In this study, we used beat interval data extracted from the Welltory app from 14 eligible subjects (9 men and 5 women) with a mean age (SD) of 44 (8.7) years. HRV analysis was performed through an assessment of time-domain indices (SDNN and RMSSD). Group analysis did not reveal any statistical difference between HRV metrics before, during, and after COVID-19. However, HRV at the individual level showed a statistically significant individual change during COVID-19 in some users. These data further support the usefulness of using individual-level HRV tracking for the detection of early diseases inclusive of COVID-19.

Smartwatches facilitate low-burden rapid-access micro-interactions, making them ideal for Experience Sampling Methods (ESMs). Despite the Apple Watch being the most popular smartwatch in the U.S., it has yet to be utilized in ESM studies due to a lack of accessible frameworks that enable deployment without technical expertise. We developed DOSE, an open-source ESM framework tailored for the Apple Watch. It includes tools and documentation that allow researchers to configure surveys, build custom apps, deploy studies, and stream data to servers without programming skills. We evaluated the frameworks feasibility in a 28-day field study with 18 participants (mean age = 55.3 {+/-} 9.2). Results showed reliable prompt delivery and high response rates (>80% overall), with median interaction times under 10 seconds. Participants demonstrated increasing efficiency in responses over time. These findings establish the DOSE framework as a practical, scalable solution for Apple Watch-based ESMs and a foundation for future smartwatch research.

BackgroundThe German electronic health record (EHR) aims to enhance patient care and reduce costs, but users often worry about data security. In this article, we propose and test communication strategies to mitigate privacy concerns and increase EHR uploads. ObjectiveWe explore whether presenting a privacy fact sheet (PFS) before users must decide whether to upload medical reports in the EHR increases their willingness to do so. Our study examines the effects of framing and length of PFS on this decision. MethodsIn an online user study with 227 German participants, we used a realistic EHR click dummy and varied the PFS in terms of length (short vs. long) and framing (EHR-centered vs. patient-centered). ResultsThe results show that a PFS has a positive effect on uploading (OR 4.276, P=.015). Although there was no effect regarding the length of a PFS, a patient-centered framing increased uploads compared to an EHR-centered framing (OR 4.043, P=.003). ConclusionDisplaying PFSs at the beginning of an upload process is a cost-effective intervention to boost EHR adoption and increase uploads of medical reports. While the length of a PFS did not influence behavior, PFSs are maximally effective if they frame information in a way that emphasizes how users can exert control over their data. Willingness to upload medical data is key to the success of the EHR, including better treatments and lower costs.

Background: LLMs enable patient-facing conversational agents, creating a pathway toward digital twins that capture older adults' lived experiences and behavioral responses across time. A central barrier is personality drift---inconsistent trait expression across repeated interactions---which undermines reliability of generated trajectories and intervention-response simulation in geriatric care. Objective: To develop ELDER-SIM, a multi-role elderly-care conversational platform for building personality-stable digital twin agents, and to propose a psychometric validation framework for quantifying personality consistency in LLM-based agents. Methods: ELDER-SIM was implemented via n8n workflow orchestration with local LLM inference (Ollama/vLLM), integrating (1) Big Five (OCEAN) trait specifications, (2) a Cognitive Conceptualization Diagram (CCD) grounded in Beck's CBT framework, and (3) a MySQL-based long-term memory module. Ablation studies across four conditions---Baseline, +Memory, +CCD, and +LoRA (fine-tuned on 19,717 instruction pairs from CHARLS)---were evaluated via Cronbach's $\alpha$, ICC, and role discrimination accuracy. Results: Personality measurement reliability was acceptable to excellent across conditions (Cronbach's : 0.70-0.94), with consistently high test-retest stability (ICC: 0.85- 2 0.96). Role discrimination improved stepwise from 83.3% (Baseline) to 88.9% (+Memory), 94.4% (+CCD), and 97.2% (+LoRA). CCD produced the largest gain in internal consistency (mean 0.702[-&gt;]0.892), while LoRA achieved the highest overall internal consistency ( 0.940) and ICC (0.958). Conclusions: ELDER-SIM provides a psychometrically validated approach for constructing personality-consistent elderly digital twin agents. Structured cognitive modeling and domain adaptation reduce personality drift, supporting reliable longitudinal simulation for elderly mental health care and reproducible in silico evaluation before clinical deployment.

BackgroundThe electronic health record (EHR) is integral to improving healthcare efficiency and quality. Its successful implementation hinges on patient willingness to use it, particularly in Germany where concerns about data security and privacy significantly influence usage intention. Little is known, however, about how specific characteristics of medical data influence patients intention to use the EHR. ObjectiveThis study aims to validate the Privacy Calculus Model (PCM) in the EHR context and to assess how personal and disease characteristics, namely disease-related stigma and disease time course, affect PCM predictions. MethodsAn online survey was conducted to empirically validate the PCM for EHR, incorporating a case vignette varying in disease-related stigma (high/low) and time course (acute/chronic), with 241 German participants. The data were analyzed using SEM-PLS. ResultsThe model explains R{superscript 2}=71.8% of the variance in intention to use. The intention to use is influenced by perceived benefits, data privacy concerns, trust in the provider, and social norms. However, only the diseases time course, not stigma, affects this intention. For acute diseases, perceived benefits and social norms are influential, whereas for chronic diseases, perceived benefits, privacy concerns, and trust in the provider influence intention. ConclusionsThe PCM validation for EHRs reveals that personal and disease characteristics shape usage intention in Germany. This suggests the need for tailored EHR adoption strategies that address specific needs and concerns of patients with different disease types. Such strategies could lead to a more successful and widespread implementation of EHRs, especially in privacy-conscious contexts.

Large language models (LLMs) like ChatGPT often exhibit Type 1 thinking--fast, intuitive reasoning that relies on familiar patterns--which can be dangerously simplistic in complex medical or ethical scenarios requiring more deliberate analysis. In our recent explorations, we observed that LLMs frequently default to well-known answers, failing to recognize nuances or twists in presented situations. For instance, when faced with modified versions of the classic "Surgeons Dilemma" or medical ethics cases where typical dilemmas were resolved, LLMs still reverted to standard responses, overlooking critical details. Even models designed for enhanced analytical reasoning, such as ChatGPT-o1, did not consistently overcome these limitations. This suggests that despite advancements toward fostering Type 2 thinking, LLMs remain heavily influenced by familiar patterns ingrained during training. As LLMs are increasingly integrated into clinical practice, it is crucial to acknowledge and address these shortcomings to ensure reliable and contextually appropriate AI assistance in medical decision-making.

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.

BackgroundDiagnosis and treatment of progressive eye diseases require effective monitoring of visual structure and function. However, current methods for measuring visual function are limited in accuracy, reliability, and usability. We propose a novel approach that leverages virtual reality (VR) technology to overcome these challenges, enabling more comprehensive measurements of visual function endpoints. ResultsWe developed VisualR, a low-cost VR-based application that consists of a smartphone app and a simple VR headset. The virtual environment allows precise control of visual stimuli, full control over the field of view, separating visual input to the left and right eyes, controlling visual angles and blocking background visual noise. We developed novel tests for metamorphopsia, contrast sensitivity and reading speed that can be performed by following simple instructions and providing verbal responses to visual cues, without the need for expert supervision. The smartphone app does not require an internet connection, handles all data processing and image display, and all data is stored locally and owned by the user. ConclusionVisualR demonstrates the feasibility of building a visual function test device using consumer-grade hardware. Eventually, this technology could be used to measure visual function endpoints during clinical development of new treatments and to support disease diagnosis and monitoring. We open-sourced the application code and provide guidelines for creating reliable and user-friendly VR-based tests. We believe VR can open a new paradigm in visual function testing, and invite the wider community to build upon our work.