DIGITAL HEALTH

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.

Background: Mobile phone-based tools are increasingly used to collect data on non-communicable disease (NCD) risk factors, particularly in low-resource settings where traditional data collection systems face operational and infrastructural constraints. This study examined stakeholder perspectives on the use of enhanced mobile phone-based capabilities to support the collection of public health surveillance data on NCD risk factors in low-resource settings. Methods: An exploratory qualitative study was conducted between November 2022 and July 2023. Twenty in-depth interviews were conducted with public health specialists, ethicists, NCD researchers, health informaticians, and policy makers in Uganda. Thematic analysis was used to interpret the results. Results: Four themes emerged from the data, including benefits of using mobile phone capabilities for NCD risk factor data collection; ethical, legal, and social implications; perceived challenges of using such mobile phone capabilities; and proposed solutions to improve the utility of phone-based capabilities in data collection on NCD risk factors. Participants recognized the potential of mobile technologies to improve data collection efficiency and expand access to hard-to-reach populations. However, concerns emerged regarding inadequate informed consent, risks to privacy and confidentiality, unclear data ownership, and vulnerabilities created by inconsistent enforcement of data protection laws. Social concerns included low digital literacy, unequal access to mobile devices, and fear of stigmatization. Participants emphasized the need for transparent communication, robust data governance, and community engagement. Conclusion: Mobile phone-based systems can strengthen the collection of NCD risk factor data in low-resource settings; however, their benefits depend on addressing key ethical, legal, and social challenges. To ensure responsible deployment, digital health initiatives must prioritize participant autonomy, data protection, equity, and trust building. Integrating contextualized ethical, legal, and social considerations into design and policy frameworks will be essential to leveraging mobile technologies in ways that support inclusive and effective NCD prevention and control.

BackgroundShort-video platforms have become increasingly important sources of health information for the general public. However, the informational quality and dissemination patterns of content related to specific therapeutic modalities, such as enhanced external counterpulsation (EECP), remain insufficiently characterized. This study aimed to evaluate the informational quality of EECP-related videos on a short-video platform and to examine the relationship between content quality and user engagement. MethodsA cross-sectional content analysis was conducted on EECP-related short videos identified through keyword-based searches. Informational quality was independently assessed using four validated instruments: the Global Quality Scale (GQS), the Journal of the American Medical Association (JAMA) benchmark criteria, the modified DISCERN instrument (mDISCERN), and the Video Information and Quality Index (VIQI). Video characteristics and user engagement metrics were extracted and analyzed. ResultsOverall, EECP-related videos demonstrated low-to-moderate informational quality across all assessment tools. Longer video duration was consistently associated with higher informational quality scores. In contrast, user engagement metrics, including the number of likes and comments, showed weak or negative associations with informational quality. Compared with videos addressing other coronary heart disease treatments, EECP-related videos were less frequently represented and received lower overall engagement. ConclusionsEECP-related content on short-video platforms is characterized by limited visibility and modest informational quality, with a notable misalignment between user engagement and informational value. These findings suggest that clinically relevant but complex therapies such as EECP may be structurally disadvantaged in short-video health communication environments.

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.

Rare diseases affect millions worldwide and are associated with long diagnostic delays, limited access to treatments, and substantial challenges in daily care and coordination. Digital health technologies, including mobile apps, telehealth, and data-sharing platforms, offer opportunities to improve care and quality of life for people living with rare diseases. As these tools rapidly expand, this study examines the needs, expectations, and conditions for successful adoption of patient-centered digital solutions among individuals living with rare diseases and their families. Using a mixed-methods design, we surveyed 149 patients and caregivers, and conducted follow-up focus groups with 15 participants. Our findings highlight the essential role of digital tools in supporting people with rare diseases and their families. Key priorities include centralized health data, support for patient-generated data, and improved communication and information exchange with clinicians. Participants strongly emphasized the value of telehealth to reduce travel and simplify daily life, as well as patient-centered tools for diagnosis and emergency situations. Future digital solutions should integrate system-wide data, incorporate AI, and provide support during stressful situations, ultimately reducing patient burden despite persistent structural challenges. Respondents expressed strong interest in technologies that place patients at the center of care and improve coordination across providers. Overall, our study identifies actionable targets for innovation and highlights technological, regulatory, and resource-related barriers that must be addressed to advance patient-centered digital solutions for rare diseases and guide future research and policy development. Author SummaryPeople living with rare diseases often wait years for a diagnosis and struggle with complex, fragmented care. Digital health technologies could help address these challenges, but only if they are designed around patients real needs. To better understand these needs, we surveyed 149 patients and caregivers in Quebec and held follow-up discussions with 15 participants. They emphasized the importance of centralized access to health information, better communication with clinicians, and tools that support patient-generated data. Telehealth was especially valued because it reduces travel and simplifies everyday life. Our findings show that people with rare diseases want digital solutions that reduce their daily burden, improve coordination across providers, and support them during stressful moments such as emergency visits or the diagnostic process. This work provides practical guidance for designing patient-centered digital tools and highlights system-level barriers that must be addressed to ensure these innovations truly benefit the rare disease community.

Objective: Develop a methodology to implement action plans that mitigate the negative impacts associated with the EHR implementation project and evaluate their effectiveness in reducing these issues. Methods: The research involved the development of mitigation plans for the potential negative impacts of implementing an electronic health record system, ensuring their execution and subsequently analyzing the effectiveness of the method. Results: Findings confirmed that 19.3% of 264 identified impacts were resolved through 52 plans before Go Live. During Go Live, the remaining 213 impacts were addressed through 337 plans. Six months later, 190 impacts were confirmed, and the plans were considered effective or partially effective in 80.5% of cases. Conclusions: Effective governance, a multidisciplinary methodology, and well-planned and executed actions increase the likelihood of success for health technology projects.

BackgroundCardiovascular disease remains the leading cause of global morbidity and mortality. The original My Heart Counts smartphone application demonstrated the feasibility of large-scale, fully digital recruitment and trial conduct, but was limited by platform exclusivity and the need for human experts to create text-based behavioral interventions. MethodsThe next-generation My Heart Counts smartphone application is a prospective, observational cohort study with an embedded randomized crossover trial, evaluating personalized text-based coaching prompts, available in both English and Spanish. All study and trial operations will be conducted via the My Heart Counts smartphone application, re-designed using the open-source Stanford Spezi framework to support iOS, with a planned Android release in 2027. The target enrollment is N=15,000 adults across the United States and United Kingdom. The study establishes a comprehensive digital biobank by synthesizing passive mobile health data (steps, flights climbed, heart rate, sleep, workouts), raw sensor data (e.g., accelerometry), longitudinal clinical surveys, active tasks (6-minute walk test and 12-minute Cooper run test), electrocardiograms (ECG), and electronic health record (EHR) data integrated via HL7 FHIR protocols. The embedded trial evaluates the effect of text-based coaching prompts generated by a large language model (LLM) grounded in the Transtheoretical Model of Change on daily physical activity, as compared to generic prompts. Planned AnalysisThe primary endpoint of the randomized crossover trial is change in daily step count between LLM-driven and generic text-based intervention arms, analyzed using mixed-effects models. Secondary endpoints include change in mean active minutes and calorie burn over each intervention week. Other analyses include the changes in submaximal (6-minute walk test) and maximal (Cooper 12-minute run test) cardiorespiratory fitness, changes to sensor-derived biomarkers (e.g., sleep quality, resting heart rate, and heart rate variability), and association of sensor-derived biomarkers with EHR-confirmed clinical outcomes. ConclusionsBy utilizing autonomous, LLM-driven coaching, modular software design, and cross-platform accessibility, our smartphone application-based study will provide a scalable model for inclusive and decentralized preventive care of patients with cardiovascular disease. Trial StatusRecruitment commenced in March 2026 and is ongoing.

BackgroundIndividuals with severe mobility impairments (SMI) often experience significant psychological distress and chronic pain. Virtual walking (VW) presents an innovative rehabilitation approach to improve mood and alleviate pain. This study aimed to develop a home-based VW system with integrated mood and symptom tracking and to report on its feasibility and usability in a user study with individuals with SMI. MethodsA multidisciplinary, iterative frame-work guided the systems development. Following initial contextual research and design iterations, a user study was conducted with 11 participants with SMI. A repeated measures pre-post design was employed. Feasibility and usability were primarily assessed through post-study qualitative interviews, analyzed via content analysis. Changes in mood and symptoms were measured immediately before and after each session. Momentary mood was captured using an in-virtual reality (in-VR) two-dimensional (2D) affect grid, while embedded single-item state ratings were used to track anxiety, depressed mood, and pain. Daily mood changes and symptom trajectories were analyzed using logistic regression and generalized estimating equations (GEE), respectively. ResultsContextual research guided the system design towards enhancing accessibility, ergonomics, and therapeutic engagement. The final VW system featured three core modules: locomotion, multi-sensory feedback, and mood/symptom tracking. Qualitative analysis of the user study revealed high acceptance for the VW system, alongside challenges related to content variety and hardware ergonomics. Each intervention session was significantly associated with an immediate positive mood shift (odds ratio (OR) = 1.83), as measured by the affect grid. Furthermore, GEE models revealed a significant reduction in self-reported depression and anxiety symptoms over the intervention period (all P < 0.01). ConclusionsThis study confirms the feasibility and acceptability of the novel VW system for home-based use by individuals with SMI. The preliminary evidence suggests the system has high potential as a tool for improving mood and alleviating psychological distress. Future large-scale randomized controlled trials are warranted to establish its clinical efficacy. Trial registration numberNCT07073144-07/17/2025.

Artificial intelligence (AI) is increasingly integrated into healthcare delivery, yet patient acceptance in resource constrained settings remains incompletely characterized. This study assessed attitudes toward AI supported care among patients attending hospitals in three Jordanian governorates (Amman, Balqa, Irbid) and examined demographic and digital literacy correlates of acceptance. In a cross sectional survey (n = 500 complete questionnaires), participants rated exposure to AI in healthcare and five attitudinal domains, namely perceived usefulness or performance expectancy, trust and transparency, privacy and perceived risks, empathy and human interaction, and readiness or behavioral intention, using 25 items on 5 point Likert scales. Patients expressed conditional optimism: empathy and human interaction was most strongly endorsed (M = 4.33, SD = 0.58), alongside relatively high perceived usefulness (M = 3.97, SD = 0.68), while trust and transparency (M = 3.57, SD = 0.74) and readiness (M = 3.66, SD = 0.90) were moderate to high; privacy and risk concerns were moderate (M = 3.51, SD = 0.77) and self reported exposure was lowest (M = 2.57, SD = 1.07). The highest agreement item indicated preference for AI to work alongside physicians rather than be relied on alone (M = 4.47, SD = 0.81). Trust and transparency and perceived usefulness were positively associated with readiness (r = 0.48 and r = 0.44, respectively; p <.001), while privacy and perceived risks were negatively correlated with trust and usefulness. In multivariable regression adjusting for gender, age group, education, prior AI health app or device use, and self rated digital skill, lower educational attainment (less than high school and high school) predicted reduced readiness, whereas higher digital skill predicted increased readiness (R2 = 0.101). These findings suggest that implementation strategies in Jordan should emphasize human involvement alongside AI, transparent communication and governance, and interventions that build digital confidence and reduce readiness gaps linked to education. Author summaryAI is increasingly used in healthcare, for example to support diagnosis, triage, and treatment decisions. Whether these tools are accepted by patients depends not only on how well they work, but also on whether patients trust them, understand how they are used, and feel their privacy is protected. Evidence on patient views in middle income and resource constrained settings is still limited. We surveyed 500 patients attending hospitals in three Jordanian governorates to understand how they view AI supported care. Patients generally expected AI to be useful, but they strongly preferred that clinicians remain actively involved and that AI supports rather than replaces physicians. Trust and perceived usefulness were closely linked to willingness to accept AI enabled care, while privacy concerns were present and shaped trust. Readiness to accept AI was lower among participants with lower educational attainment and higher among those with greater self rated digital skill. These findings suggest that successful implementation in Jordan should prioritize transparent communication, strong privacy safeguards, and human centered workflows, while also strengthening digital confidence to avoid widening gaps in acceptance.

Managing diabetes-related conditions is time-intensive and cognitively demanding for patients and caregivers, requiring ongoing glucose monitoring, dietary regulation, physical activity planning, and continuous lifestyle adaptation. With the emergence of large language models (LLMs), patients have increasingly turned to these tools for information, guidance, and support. However, there is limited empirical understanding of which diabetes-related medical tasks patients delegate to LLMs and what their experiences are. To address this gap, we combined qualitative thematic analysis with LLM-assisted analysis to examine patient attitudes and real-world use cases in using LLMs for diabetes-related tasks. Our analysis identified diverse application areas, ranging from clinical interpretation to nutrition and diet support, and disease management amongst others. LLMs functioned not only as information sources, but as interpretive, analytical, decision-support, emotional, and logistical aids supporting patients self-management. Last, we discuss implications for integrating LLMs into patients self-management support ecosystems and identify areas that require support and safeguards.

BackgroundThe COVID-19 pandemic significantly disrupted healthcare services globally, particularly in low-resource settings. This study explores the impact of the pandemic on physiotherapy services in Nepal. MethodsA cross-sectional study was conducted. Qualitative data were collected through semi-structured interviews with 12 physiotherapists, while quantitative data were gathered from an onsite survey of 29 health facilities at six different districts of Province III of Nepal. Inductive thematic analysis approach was used to analyze the qualitative data, and descriptive statistics were used for the closed ended questions. ResultsThe findings were categorized into sub-themes under two major themes: i) Pandemic effect on physiotherapy services and patient care and ii) Adaptation, innovation and collaboration. The study revealed a significant disruption in physiotherapy services with a notable decline in patient flow and service availability. Most patients, especially those with disabilities and post-operative needs, experienced worsening conditions due to limited access to care. There was an increased recognition of the role of physiotherapy in acute respiratory care and post-COVID-19 recovery. Tele-rehabilitation was explored as an alternative care method but faced challenges in implementation. More than half (62.07%) of the centers reported uninterrupted physiotherapy services, whereas almost one third (31.03%) experienced service suspension. Most centers (89.7%) had personal protective equipment available, and majority (86.2%) of the physiotherapists worked in multidisciplinary team: fever clinics, triage, emergency care, respiratory physical therapy, and nursing and administrative support were among the expanded roles. Several centers (37.9%) used virtual care with telephone consultation serving as the primary modality. Virtual service was mostly absent in centers where in-person services persisted. ConclusionThe COVID-19 pandemic significantly impacted physiotherapy services in Nepal, leading to service disruptions and compromised patient care. It highlighted the need to further incorporate physiotherapy into the healthcare system and enhance rehabilitation services to improve continued patient care.

AimTo outline the opportunities and barriers when using hairdressing salons as a novel site for enhancing cardiovascular risk factor assessment and management in women. MethodsA process evaluation nested within a cluster-randomised trial, Hairdressers for Health. The trial evaluated a nudge intervention advising women [&ge;]45years attending hairdressing salons to undertake a Heart Health Check with their General Practitioner. The UK Medical Research Council process evaluation framework was used to guide the design, data collection and analysis. Nineteen interviews were conducted with nine hairdressers, nine study participants and a project officer. Thematic analysis assessed recruitment, reach, acceptability, and adoption. Characteristics of the salons and participants were analysed using descriptive statistics. ResultsRecruitment of the planned 88 metropolitan and 28 regional salons for the trial was challenging, requiring resource-intensive face-to-face visits. The nudge intervention was well accepted by participants, and salons were perceived to be an appropriate setting to effectively reach women. Adoption of the study by salons was limited with only 54 of the 116 salons recruiting participants (total recruited 239, range 1-22 participants per salon). Barriers to participant recruitment included technological constraints while using a decentralised online recruitment and data collection platform, client preferences and privacy concerns. Established hairdresser-client relationships in smaller salons facilitated greater client participation and was perceived as a good mechanism for health promotion. ConclusionsCardiovascular health prevention messaging for women in salons was acceptable to hairdressers and clients. Designing the study to make better use of hairdresser-client personal relationships may have improved project implementation. Trial RegistrationACTRN12621001740886

BackgroundRecruitment remains a major barrier to timely clinical trial completion. Trialshub is an LLM-powered, chat-based platform intended to help users identify relevant trials and connect with coordinators to streamline recruitment workflows. ObjectiveTo evaluate the perceived usability and operational value of Trialshub, and identify implementation considerations for real-world deployment. MethodsA usability test was conducted at Morehouse School of Medicine for the Trialshub application. Purposively selected participants included clinical research coordinators and individuals with and without clinical trial search experience. Participants completed a pre-test survey assessing demographics, digital health information behaviors, and familiarity with AI tools, followed by a moderated usability session using a Trialshub prototype. Users completed scenario-based tasks (locating a breast cancer trial, reviewing results, and initiating coordinator contact) using a think-aloud protocol. Task ratings, screen recordings, and transcribed feedback were analyzed descriptively and thematically, and reported. ResultsParticipants reported high comfort with using digital tools and moderate-to-high familiarity with AI. Trialshubs chat-first design, guided prompts, and checklist-style eligibility display were perceived as intuitive and reduced cognitive load. Fast access to trials and the coordinator-contact workflow were viewed positively. Key usability issues included uncertainty at step transitions, insufficient cues for selecting results and next actions, and inconsistent system reliability (loading delays, errors, and broken trial detail pages). Participants also noted redundant questioning due to limited conversational memory, requested improved filtering/sorting, and clearer calls-to-action. All participants indicated that Trialshub has strong potential to meaningfully improve clinical trial processes. ConclusionsTrialshub shows promise for improving trial discovery and recruitment workflows, with identified design implications for real-world deployment.

Background: Safety is a critical concern in behavioral health crisis units (BHCUs), where environmental risks (e.g., ligature points) can lead to injury to self or others. However, limited research has examined how perceived safety influences facility selection among patients and care partners, or how these perceptions align with AI-driven safety risk assessments in such environments. Method: To address these gaps, a nationwide discrete choice online survey was conducted using image-based scenarios of BHCU environments, where participants selected preferred facilities based on a range of attributes, including environmental safety risks (e.g., ligature points). Additionally, participants identified safety risks in survey images, which were compared with outputs from an AI-driven tool developed and trained to detect environmental risks by experts. Quantitative analysis using conditional logit models examined the influence of attributes on facility choice, while spatial comparisons of annotated images and heatmaps assessed participant and AI-identified risk alignments. Results: Findings revealed that the higher frequency of safety risks in images significantly reduced the likelihood of facility selection (p < .001, OR {approx} 1.28), highlighting the importance of perceived safety in user decision-making. While there was notable alignment between heatmaps generated by participants and AI, key differences emerged, suggesting that participant safety perception was influenced by features not fully captured by AI, such as the type of materials or unknown, out-of-label safety risks in facility images. Conclusions: Despite these limitations, results highlighted the value of integrating AI-driven assistive tools for non-expert user safety risk assessment to support decision-making for safer BHCU environments.

BackgroundFalls are a major health concern among older adults, leading to injury, reduced independence, and increased healthcare cost. Reactive balance training can reduce fall risk, but barriers such as fear, joint discomfort, and harness burden limit its use - barriers that aquatic training may help overcome. ObjectiveTo design an aquatic reactive balance training (AquaReBal) program for older adults, integrating end-user perspectives to enhance safety, accessibility, and engagement. MethodsUsing a participatory design approach, we engaged older adult partners, physiotherapists, and researchers in iterative phases including literature review, stakeholder consultations, practical pool sessions, and feedback meetings. Data were collected through online meetings, surveys, and real-time observations, following the Guidance for Reporting Involvement of Patients and the Public 2 framework. ResultsThree older adult partners and a multidisciplinary team co-designed the AquaReBal protocol through two participatory design sessions, one practical pool session, and two internal team sessions. Key recommendations from partners included using a vest instead of a hip belt for perturbations, addressing pool depth visibility, and creating an introductory package with practical information for participants. Partners emphasized safety, instructor support, and social engagement as critical for adherence and satisfaction. ConclusionThe co-design process enabled the development of an AquaReBal protocol tailored to older adults needs and preferences, demonstrating potential for broader implementation.

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.

BackgroundDigital health plays an indispensable role in facilitating data analysis and use for enhancing healthcare delivery across health settings. However, there is scant information on the extent to which digital health influences the improvement of primary health services delivery through data use. This study examined the determinants that influence the use of digital health to improve health service delivery in council hospitals in Tanzania. MethodsA cross-sectional design was employed in six regions, involving 12 council hospitals. We used a self-administered questionnaire to collect data from 203 members of hospital quality improvement teams. Descriptive analysis was used to determine the frequency, proportion, and mean of responses, while bootstrapping analysis was conducted to test the statistically significant influence of digital health factors on data use for improving health service delivery. ResultsResults show moderate agreement on data compatibility for planning and decision-making, with 40.4% of respondents agreeing it supports ordering commodities, 43.8% for staff allocation, and 38.4% for planning. However, dissatisfaction was higher for user-friendliness (47.8%), reliability (up to 65.5%), and usefulness (up to 63.5%). Overall, 50.2% (M=2.74{+/-}0.87) disagreed that digital systems effectively support data use. Structural model analysis confirmed significant positive influence of usefulness ({beta}=0.199, p<0.001) and access to quality data ({beta}=0.729, p<0.001) on data use, which strongly impacted service delivery ({beta}=0.593, p<0.001), despite some factors showing no direct influence. ConclusionThe study finds that current digital health initiatives only modestly improve the user-friendliness, reliability, and usefulness of data systems, partly due to fragmented, non-interoperable platforms that burden data management. However, compatibility, usability, reliability, and usefulness of digital tools significantly enhance access to quality data and data-driven decisions. The study recommends strengthening and integrating existing systems and providing continuous digital health training to institutionalize data-informed decision-making.

BackgroundGenerative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language models (LLMs) can generate draft responses to patient messages using electronic health record (EHR) data. This could mitigate increased workload related to high message volumes. While effectiveness and feasibility of these GenAI tools have been studied in the United States, evidence from non-English contexts is scarce, particularly regarding user experience. ObjectiveThis study evaluated the effectiveness, feasibility and barriers and facilitators of implementing Epics Augmented Response Technology (Art) GenAI tool (Epic Systems Corporation, Verona, WI, USA) in a Dutch academic healthcare setting among a broad range of end users. It explored healthcare professionals (HCP) usage metrics, expectations, and early user experiences. MethodsWe conducted a hybrid type 1 effectiveness-implementation design. HCPs of four clinical departments (dermatology, medical oncology, otorhinolaryngology, and pulmonology) participated in a six-month study. Effectiveness of Art was assessed using efficiency indicators from Epic (including all InBasket users in the hospital) and survey scales measuring well-being and clinical efficiency at three time points: PRE, POST-1 (1 month), and POST-2 (4 months). Feasibility of Art was evaluated through adoption indicators from Epic and survey scales on use and usability. Barriers and facilitators of Art implementation were collected through the survey and thematized using the NASSS framework (Nonadoption, Abandonment, Scale-up, Spread and Sustainability). Results237 unique HCPs generated a total of 8,410 drafts. Review and drafting times were similar for users with and without Art, indicating minimal differences. Perceived clinical efficiency declined significantly from PRE to POST-2, while well-being remained unchanged. Adoption was initially high but decreased over time, averaging 16.7% across departments. Usability and intention-to-use scores also declined significantly. Oualitative findings highlighted time savings, well-structured drafts, and patient-centered language as facilitators. Reported barriers included limited impact on time, low practical utility, content inaccuracies, and style misalignment. ConclusionsThis evaluation of a GenAI tool for patient-provider communication in a non-English academic hospital revealed mixed perceptions of effectiveness and feasibility. High initial expectations contrasted with limited perceived impact on time-savings, well-being and clinical efficiency, alongside declining adoption and usability. Barriers and facilitators revealed contrasting views. These findings underscore the need for a workflow for the handling of user feedback, guidance on clinical responsibilities, along with clear communication about the tools purpose and limitations to manage expectations. Additionally, establishing consensus on a set of quality indicators and their thresholds that indicate when a GenAI tool is sufficiently robust will be critical for responsible scaling of GenAI in clinical practice.

Structured Abstract (for clinical articles and laboratory investigations)O_ST_ABSObjectiveC_ST_ABSEvaluating and monitoring patients with cervical spondylotic myelopathy (CSM) remains a challenge due to limited tools for assessing objective neurological disability longitudinally and in the home environment. Given their prevalence and low cost, mobile health (mHealth), and specifically smartphone technologies offer a promising approach to fill this gap. This study explored stakeholder perspectives on the role of mHealth in CSM monitoring to inform development of a smartphone-based assessment application. MethodsWe conducted semi-structured interviews with 15 patients with CSM and 14 healthcare providers (spine surgeons, physical therapists, and occupational therapists). Interviews explored current assessment practices, perceived limitations, and attitudes toward mHealth integration. Data were analyzed using thematic analysis. ResultsTwo major themes emerged from provider interviews: (1) diagnosing and monitoring CSM is challenging due to limitations in current tools, and (2) mHealth presents significant opportunities but requires thoughtful integration. Providers described current methods and technologies, clinical signs and symptoms, and challenges evaluating patients. Current tools were viewed as inadequate for precision medicine, with inter-rater variability and inability to capture real-world function. Within the second theme, providers identified ways mHealth could improve care, challenges for integration, and practical implementation considerations. Patients expressed strong interest in objective, longitudinal monitoring of gait, dexterity, and daily function. ConclusionsStakeholders recognized substantial potential for mHealth to address unmet needs in CSM assessment. Successful implementation will require intuitive design, electronic medical record integration, and attention to accessibility. These findings provide a foundation for user-centered development of digital health tools in CSM care.

BackgroundPerinatal mood and anxiety disorders are the most common complications of pregnancy. Given the limited mental health resources, there is a need for novel treatment approaches. Though smartphone applications can increase access to evidence-based care, recent research highlights notable limitations, including varying quality and unclear effectiveness. Blended hybrid care models, which integrate synchronous telehealth services with asynchronous modalities (such as mobile apps), have emerged as an alternative. This pilot study evaluates one such model, the Digital Clinic, to determine its potential to bridge this critical treatment gap and compare outcomes to that of non-peripartum patients in the clinic. MethodsPregnant and postpartum women referred for anxiety and depression received 8 weeks of synchronous, virtual, evidence-based CBT from a trained clinician. This treatment was complemented by the asynchronous use of the mindLAMP app, providing digital phenotyping, psychoeducation, and CBT skills, with the support of a Digital Navigator. The efficacy of the intervention was evaluated by comparing GAD-7 and PHQ-9 scores from intake to the end of treatment. ResultsThis secondary analysis included 13 peripartum women from a larger sample of 224 clinic patients. At intake, they reported a mean PHQ-9 score of 9.4 (SD=3.9) and a mean GAD-7 score of 11.69 (SD=6.0). After 8 weeks, participants reported statistically significant decreases of 4.14 points on the GAD-7 (p<.01) and 3.92 points on the PHQ-9 (p<.01). Effect sizes for these reductions were 0.74 (95% CI: 0.20, 1.28) for GAD-7 and 1.10 (95% CI: 0.29, 1.90) for PHQ-9. ConclusionA novel blended hybrid care model, the Digital Clinic, was successful in reducing depression and anxiety among pregnant and postpartum women. This novel approach to maternal mental health shows promise for delivering accessible, effective, evidence-based care to peripartum patients in real-world settings. Future work should further validate its effectiveness with larger, more diverse patient populations with moderate to severe disease.