https://www.jmir.org/issue/feed 2024-01-01T10:15:03-05:00 JMIR Publications [email protected] Open Journal Systems The leading peer-reviewed journal for digital medicine and health and health care in the internet age.  https://www.jmir.org/article/view/jmir_v27i1e72892 2025-05-09T16:32:09-04:00 Courtney Potts Carmen Kealy Jamie M McNulty Alba Madrid-Cagigal Thomas Wilson Maurice D Mulvenna Siobhan O'Neill Gary Donohoe Margaret M Barry

<strong>Background:</strong> Digital mental health interventions for young people offer a promising avenue for promoting mental well-being and addressing mental health issues in this population. <strong>Objective:</strong> This scoping review aims to explore the range of digital mental health interventions available for young people aged 16-25 years, with a particular focus on digital tool types, modalities, delivery formats, target populations, and study retention rates. <strong>Methods:</strong> The scoping review was conducted in 6 databases (PubMed, Web of Science, Scopus, MEDLINE, Cochrane Library, and PsychInfo). Studies were included if they were published from 2019 to 2024 in English, reported on a population of young people aged 16-25 years, and included validated mental health or well-being outcome measures. All types of digital interventions from promotion and prevention to treatment of mental health were included. <strong>Results:</strong> After screening 13,306 articles, 145 articles were included in the final review. The findings reveal a diverse landscape of studies, equally focusing on the prevention and promotion of mental health and the treatment of mental ill health, most commonly using cognitive behavioral therapy (63/145, 43.4%). The most common digital tools were apps (51/135, 37.8%), web-based resources (45/135, 33.3%), and websites (19/135, 14.1%). The results highlight the over emphasis on convenience sampling (140/145, 96.6%), with participants mainly recruited from universities or colleges, and a lack of representation from marginalized groups, including lesbian, gay, bisexual, transgender, and queer youth; those from socioeconomically deprived backgrounds; and those who are neurodivergent. Moreover, the focus on anxiety and depression leaves other mental health conditions underrepresented. Retention rates ranged from 16% to 100% and averaged 66% across all studies. <strong>Conclusions:</strong> There is a need for more research on mental health promotion and prevention measures among those aged younger than 25 years as young people are at increased risk of mental health issues. This includes exploring different intervention approaches and modalities beyond cognitive behavioral therapy and ensuring inclusivity in study populations. Standardizing intervention durations and incorporating long-term follow-up data could provide valuable insights into the efficacy and effectiveness of digital interventions. Future studies should aim for greater inclusivity, ensuring representation from marginalized groups to address the diverse mental health needs of young people effectively. By adopting these approaches, digital mental health interventions can become more accessible, engaging, and impactful for young people worldwide.

2025-05-09T16:32:09-04:00 https://www.jmir.org/article/view/jmir_v27i1e68648 2025-05-09T15:00:27-04:00 Maxi Pia Bretschneider Agnieszka Barbara Kolasińska Lenka Šomvárska Jan Klásek Jan Mareš Peter EH Schwarz

<strong>Background:</strong> Effective diabetes management requires a multimodal approach involving lifestyle changes, pharmacological treatment, and continuous patient education. Self-management demands can be overwhelming for patients, leading to lowered motivation, poor adherence, and compromised therapeutic outcomes. In this context, digital health apps are emerging as vital tools to provide personalized support and enhance diabetes management and clinical outcomes. <strong>Objective:</strong> This study evaluated the impact of the digital health application Vitadio on glycemic control in patients with type 2 diabetes mellitus (T2DM). Secondary objectives included evaluating its effects on cardiometabolic parameters (weight, BMI, waist circumference, blood pressure, and heart rate) and self-reported measures of diabetes distress and self-management. <strong>Methods:</strong> In this 6-month, 2-arm, multicenter, unblinded randomized controlled trial, patients aged 18 years or older diagnosed with T2DM were randomly assigned (1:1) to an intervention group (IG) receiving standard diabetes care reinforced by the digital health app Vitadio or to a control group (CG) provided solely with standard diabetes care. Vitadio provided a mobile-based self-management support tool featuring educational modules, motivational messages, peer support, personalized goal setting, and health monitoring. The personal consultant was available in the app to provide technical support for app-related issues. The primary outcome, assessed in the intention-to-treat population, was a change in glycated hemoglobin (HbA_1c) levels at 6 months. Secondary outcomes included changes in cardiometabolic measures and self-reported outcomes. Data were collected in 2 study centers: diabetologist practice in Dessau-Roßlau and the University of Dresden. <strong>Results:</strong> Between November 2022 and June 2023, a total of 276 patients were screened for eligibility, with 149 randomized to in intervention group (IG; n=73) and a control group (CG; n=76). The majority of participants were male (91/149, 61%). The dropout rate at month 6 was 19% (121/149). While both groups achieved significant HbA_1c reduction at 6 months (IG: mean –0.8, SD 0.9%, <i>P</i>&lt;.001; CG: mean –0.3, SD 0.7%, <i>P</i>=.001), the primary confirmatory analysis revealed statistically significant advantage of the IG (adjusted mean difference: –0.53%, SD 0.15, 95% CI –0.24 to –0.82; <i>P</i>&lt;.001; effect size [Cohen <i>d</i>]=0.67, 95% CI 0.33-1). Significant between-group differences in favor of the IG were also observed for weight loss (<i>P</i>=.002), BMI (<i>P</i>=.001) and systolic blood pressure (<i>P</i>&lt;.03). In addition, Vitadio users experienced greater reduction in diabetes-related distress (<i>P</i>&lt;.03) and obtained more pronounced improvements in self-care practices in the areas of general diet (<i>P</i>&lt;.001), specific diet (<i>P</i>&lt;.03), and exercise (<i>P</i>&lt;.03). <strong>Conclusions:</strong> This trial provides evidence for the superior efficacy of Vitadio in lowering the HbA_1c levels in T2DM patients compared to standard care. In addition, Vitadio contributed to improvements in cardiometabolic health, reduced diabetes-related distress, and enhanced self-management, highlighting its potential as an accessible digital tool for comprehensive diabetes management. <strong>Trial Registration:</strong> German Clinical Trials Registry DRKS00027405; https://drks.de/search/de/trial/DRKS00027405.

2025-05-09T15:00:27-04:00 https://www.jmir.org/article/view/jmir_v27i1e72186 2025-05-09T14:30:05-04:00 Qi Chwen Ong Chin-Siang Ang Nai Ming Lai Rifat Atun Josip Car

Not applicable.

2025-05-09T14:30:05-04:00 https://www.jmir.org/article/view/jmir_v27i1e73190 2025-05-09T14:15:03-04:00 Yongsheng Zhang Hongyu Zhang Dawei Wang Na Li Haoyue Lv Guang Zhang

<strong>Background:</strong> Diabetes has emerged as a critical global public health crisis. Prediabetes, as the transitional phase with 5%-10% annual progression to diabetes, offers a critical window for intervention. The lack of a 5-year risk prediction model for diabetes progression among Chinese individuals with prediabetes limits clinical decision-making support. <strong>Objective:</strong> This study aimed to develop and validate a machine learning–based 5-year risk prediction model of progression from prediabetes to diabetes for the Chinese population and establish an interactive web-based platform to facilitate high-risk patients identifying and early targeted interventions, ultimately reducing diabetes incidence and health care burdens. <strong>Methods:</strong> A retrospective cohort study was conducted on 2 prediabetes cohorts from 2 Chinese medical centers (primary cohort: n=6578 and external validation cohort: n=2333) tracking from 2019 to 2024. Participants meeting the American Diabetes Association (ADA) criteria (prediabetes: hemoglobin A1c [HbA1c] level of 5.7%-6.4%; diabetes: HbA1c level of ≥6.5%) were identified. A total of 42 variables (demographics, physical measures, and hematologic biomarkers) were collected using standardized protocols. Patients were split into the training (70%) and test (30%) sets randomly in the primary cohort. Significant predictors were selected on the training set using recursive feature elimination methods, followed by prediction model development using 7 machine learning algorithms (logistic regression, random forest, support vector machine, multilayer perceptron, extreme gradient boosting machine, light gradient boosting machine, and categorical boosting machine [CatBoost]), optimized through grid search and 5-fold cross-validation. Model performance was assessed using the receiver operating characteristic curve, the precision-recall curves, accuracy, sensitivity, and specificity as well as multiple other metrics on both the test set and the external test set. <strong>Results:</strong> During the follow-up of 5 years, 2610 (41.6%) participants and 760 (35.2%) participants progressed from prediabetes to diabetes, with mean annual progression rates of 8.34% and 7.04% in the primary cohort and the external cohort, respectively. Using 14 features selected using the recursive feature elimination-logistic algorithm, the CatBoost model achieved optimal performance in the test set and the external test set with an area under the receiver operating characteristic curve of 0.819 and 0.807, respectively. It also showed the best discrimination performance on the accuracy, negative predictive value (NPV), and F1-scores as well as the calibration performances in both the test set and the external test set. Then the Shapley Additive Explanations (SHAP) analysis highlighted the top 6 predictors (FBG, HDL, ALT/AST, BMI, age, and MONO), enabling targeted modification of these risk factors to reduce diabetes incidence. <strong>Conclusions:</strong> We developed a 5-year risk prediction model of progression from prediabetes to diabetes for the Chinese population, with the CatBoost model showing the best predictive performance, which could effectively identify individuals at high risk of diabetes.

2025-05-09T14:15:03-04:00 https://www.jmir.org/article/view/jmir_v27i1e60889 2025-05-08T13:00:29-04:00 Siqing Chen Chenchen Wang Albert Ko Carol Ewing Garber Edward Giovannucci Yuting Yang Matthew Stults-Kolehmainen Lili Yang

Background: Mobile health (mHealth) provides health information through electronic devices, even at home. The escalating prevalence of sedentary behaviors among older adults, which leads to increased adverse health outcomes, underscores the pressing need for a comprehensive understanding of the effectiveness of mHealth interventions. Objective: This study aims to examine the effectiveness of mHealth interventions in the sitting time of older adults (age 55 years). Methods: A systematic review and meta-analysis of randomized controlled trials was conducted to evaluate the effects of mHealth interventions on total sitting time during waking hours, excluding sleep. A literature search was conducted using multiple databases, including PubMed, Embase, Web of Science, and Cochrane, covering articles published from the inception of each database through October 2023. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were explicitly applied to structure this report. Methodological quality was assessed using the Cochrane Risk of Bias (ROB 2) tool for randomized controlled trials and the Methodological Index for Non-Randomized Studies (MINORS) tool for nonrandomized studies. Two independent reviewers screened the studies, extracted data, and assessed methodological quality using established criteria. Meta-analyses were performed using Review Manager (version 5.4; Cochrane Collaboration). Results: Ten studies were identified, of which 3 were included in the meta-analysis, while the remaining 7 were assessed exclusively in the systematic review. The interventions predominantly took place in community settings (n=3) and home-based settings (n=3). Three studies aimed to decrease sedentary behavior and 7 aimed to increase physical activity. The interventions were primarily conducted once daily (n=7) via mobile devices such as smartphones (n=7) and typically involved a single intervention delivered at different time intervals, such as every 15, 20, or 30 minutes (n=4). The interventions typically lasted 12 weeks (n=4) and used objective assessment tools, such as the ActiGraph GT3X+ (n=8). The included studies applied the habit formation theory (n=1), the self-efficacy theory (n=1), the social cognitive theory (n=1), and the social-ecological theory (n=1) as frameworks. Additionally, behavior change techniques, including “goal setting,” “problem-solving,” “action planning,” and “review behavior goal(s)” (n=6), were used. Meta-analysis of the 3 studies included showed a significant decrease in sedentary behavior with mHealth interventions compared with conventional or no interventions (weighted mean difference [WMD]=59.1 min/d, 95% CI 99.1 to 20.2; <i>P</i>=.003). Conclusions: mHealth interventions effectively reduce sitting time in older adults. Strategies using interventions with specific frequencies and durations, dedicated mobile monitoring devices, and behavior change techniques showed the potential to reduce sedentary behavior among older adults. These results also underscore the potential of mHealth as a key tool for promoting the well-being of older adults through technology-driven public health efforts. Clinical Trial: PROSPERO CRD42023443926; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023443926

2025-05-08T13:00:29-04:00 https://www.jmir.org/article/view/jmir_v27i1e58024 2025-05-08T13:00:03-04:00 Anna-Carlotta Zarski Karina Bernstein Harald Baumeister Dirk Lehr Stella Wernicke Ann-Marie Küchler Fanny Kählke Kai Spiegelhalder David Daniel Ebert

<strong>Background:</strong> Internet-based cognitive behavioral therapy for insomnia (iCBT-I) provides flexibility but requires significant time and includes potentially challenging components such as sleep restriction therapy. This raises questions about its incremental effectiveness compared to less demanding minimal interventions such as sleep hygiene psychoeducation. <strong>Objective:</strong> This study aimed to assess the incremental efficacy of self-guided iCBT-I with optional on-demand feedback for university students with insomnia compared to a single session of digital psychoeducation on sleep hygiene. <strong>Methods:</strong> In a randomized controlled trial, 90 students with insomnia (Insomnia Severity Index ≥10) were randomly allocated to self-help–based iCBT-I (45/90, 50%) or one session of digital sleep hygiene psychoeducation with stimulus control instructions (active control group [aCG]: 45/90, 50%). The self-help–based iCBT-I consisted of 6 sessions on psychoeducation, sleep restriction, and stimulus control, including written feedback on demand from an eCoach. Assessments occurred at baseline (T1), 8 weeks after treatment (T2), and at a 6-month follow-up (T3) via web-based self-assessment and diagnostic telephone interviews. The primary outcome was insomnia severity at T2. Analyses of covariance were conducted in an intention-to-treat sample. Secondary outcomes included diagnoses of insomnia and major depression, sleep quality, sleep efficiency, worrying, recovery experiences, recovery activities, presenteeism, procrastination, cognitive irritation, and recuperation in sleep. <strong>Results:</strong> There was no difference in insomnia severity at T2 between the iCBT-I group (mean 11.27, SD 5.21) and aCG group (mean 12.36, SD 4.16; <i>F</i>_1,989.03=1.12<i>; P</i>=.29; <i>d</i>=–0.26; 95% CI 0.68 to 0.17). A significant difference emerged at T3 (iCBT-I: mean 9.43, SD 5.36; aCG: mean 12.44, SD 5.39; <i>F</i>_1,426.15=4.72; <i>P</i>=.03), favoring iCBT-I with a medium effect (<i>d</i>=–0.57; 95% CI 1.07 to –0.06). Most secondary outcomes revealed no significant differences between the groups. In total, 51% (23/45) of participants in the iCBT-I group completed all 6 sessions, and 69% (31/45) completed the 4 core sessions. <strong>Conclusions:</strong> In the short term, students might benefit from low-intensity, easily accessible digital sleep hygiene psychoeducation or iCBT-I. However, it appears that iCBT-I offers superiority over sleep hygiene psychoeducation in the long term. <strong>Trial Registration:</strong> German Clinical Trials Register DRKS00017737; https://drks.de/search/de/trial/DRKS00017737

2025-05-08T13:00:03-04:00 https://www.jmir.org/article/view/jmir_v27i1e63576 2025-05-08T10:45:24-04:00 Tanja Schiffelers Kaya Kapteijns Laura Hochstenbach Bas Kietselaer Esther Talboom-Kamp Marieke Spreeuwenberg

<strong>Background:</strong> The health care sector faces increasing pressure, with demand outpacing supply and multiple challenges in accessibility, affordability, and quality. The current organization of health care systems is unsustainable—exacerbated by labor shortages and escalating expenditures in Europe, particularly the Netherlands. To address these issues, hospitals are increasingly adopting digital transformation strategies. This digital transformation involves the systematic implementation of digital technologies and processes. To achieve high-quality hybrid care, hospitals must integrate digital health care seamlessly into existing workflows. However, there is no definitive strategy for implementing these transformations. <strong>Objective:</strong> This study examines how Dutch hospitals organize their digital transformation, the strategies they employ, and the best practices they follow, to provide evidence-based recommendations for hospitals embarking on similar initiatives. <strong>Methods:</strong> A qualitative multicase study was conducted using purposive sampling. A total of 11 Dutch hospitals were invited, and 8 participated. Professionals—project or program managers of digital care, or advisors in policy, management, strategy, or related positions—from these hospitals took part in semistructured interviews. Topics included digital transformation strategies, organizational structures, barriers and facilitators, and lessons learned. All interviews were recorded, transcribed verbatim, and analyzed using directed content analysis. <strong>Results:</strong> Although hospitals organize their digital transformation in different ways and with different teams or departments, they encounter similar facilitators and barriers. Inspired by the Consolidated Framework for Implementation Research, the ExpandNet Scaling Up framework, and the Hybrid Health Care Quality Assessment, these factors were grouped into the following categories: the structure of the digital program, cultural factors within the organization, financial factors (internal or external), political factors (internal or external), patient needs, resources and skills, and technical factors. <strong>Conclusions:</strong> Despite variations in implementation, hospitals share key challenges and enablers in digital transformation. Common factors—such as organizational culture, financial resources, and technical infrastructure—may serve as foundational elements for effective digital transformation in hospital care.

2025-05-08T10:45:24-04:00 https://www.jmir.org/article/view/jmir_v27i1e63712 2025-05-07T17:30:07-04:00 Elizabeth A Williams Michelle D Martin-Pozo Alexis H Yu Krystyna Daniels Madeline Marks April O'Connor Elizabeth J Phillips

Background: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are genetically mediated life-threatening reactions that in adults are usually caused by a medication. These genetic associations promise an opportunity for pre-prescription screening and prevention. However, the study of genetic risk and long-term sequelae of SJS/TEN across racially diverse populations has been hampered by many factors that include its rarity, social disparities, and trust in healthcare and providers, which impact access to hospital and clinic-based research studies. Objective: To explore the utility of multiple social media and web-based search tools with the goal of increasing numbers, diversity, and inclusivity of study enrollment. Methods: The SJS survivor study is a community-based retrospective cohort study which remotely recruits drug-induced SJS/TEN survivors in the United States to help determine genetic risk and long-term outcomes. Baseline recruitment included advertisements on the SJS Foundation website and American Burn Association newsletter. Social media ads were later introduced by Vanderbilt University Medical Center (VUMC) Facebook and Instagram accounts, where posts were created using flyers and 60-second SJS/TEN survivor video vignettes. Finally, a national Google Ad campaign was launched. We measured the change in registration of both study interest and effectiveness of implementation of specific social media and web-based search tools before and after their implementation. Results: Since the introduction of social media and Google Ads in March 2022, we report a 48.6% increase in enrollment overall and a 289.5% increase in participation interest. We noticed the ads were inclusive to all age groups and saw a more even age distribution of enrolled participants from 18 through 74 years old was seen, with an average of 15% enrolled into each age category. The most significant increase in both enrollment and diversity of responses came from Google Ads with a total of 201 expressions of interest, 33% of which self-identified as non-White and 56 participants enrolled. Conclusions: Social media and web-based search tools differ in their enrollment effectiveness. In this community-based study, social media and web-based strategies increased numbers, diversity, and inclusion of enrollment and show promise as tools to increase both diversity and enrollment in rare diseases such as SJS/TEN.

2025-05-07T17:30:07-04:00 https://www.jmir.org/article/view/jmir_v27i1e66432 2025-05-07T16:30:02-04:00 Ahmer Adnan Rebecca Eilish Irvine Allison Williams Matthew Harris Grazia Antonacci

<strong>Background:</strong> Mobile health apps (MHAs) are increasingly used in modern health care provision. The technology acceptance model (TAM) is the most widely used framework for predicting health care technology acceptance. Since the advent of this model in 1989, technology has made generational advancements, and extensions of this model have been implemented. <strong>Objective:</strong> This systematic review aimed to re-examine TAM models to establish their validity for predicting the acceptance of modern MHAs, reviewing relevant core and extended constructs, and the relationships between them. <strong>Methods:</strong> In this systematic review, MEDLINE, Embase, Global Health, APA PsycINFO, CINAHL, and Scopus databases were searched on March 8, 2024, with no time constraints, for studies assessing the use of TAM-based frameworks for MHA acceptance. Studies eligible for data extraction were required to be peer-reviewed, English-language, primary research articles evaluating MHAs with health-related utility, using TAM as the primary technology acceptance evaluation framework, and reporting app use data. Data were extracted and grouped into 5 extended TAM construct themes. Quality assessment was conducted using the Joanna Briggs Institute (JBI) tools. For cross-sectional methodologies (9/14, 64%), the JBI checklist for analytical cross-sectional studies was used. For non–cross-sectional studies (5/14, 36%), the JBI checklist most relevant to the specific study design was used. For mixed methods studies (1/14, 7%), the JBI checklist for qualitative studies was applied, in addition to the JBI checklist most suited to the quantitative design. A subsequent narrative synthesis was conducted in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. <strong>Results:</strong> A total of 2790 records were identified, and 14 were included. Furthermore, 10 studies validated the efficacy of TAM and its extensions for the assessment of MHAs. Relationships between core TAM constructs (perceived usefulness, perceived ease of use, and behavioral intention) were validated. Extended TAM constructs were grouped into 5 themes: health risk, application factors, social factors, digital literacy, and trust. Digital literacy, trust, and application factor extended construct themes had significant predictive capacity. Application factors had the strongest MHA acceptance predictive capabilities. Perceived usefulness and extended constructs related to social factors, design aesthetics, and personalization were more influential for those from deprived socioeconomic backgrounds. <strong>Conclusions:</strong> TAM is an effective framework for evaluating MHA acceptance. While original TAM constructs wield significant predictive capacity, the incorporation of social and clinical context-specific extended TAM constructs can enhance the model’s predictive capabilities. This review’s findings can be applied to optimize MHAs’ user engagement and minimize health care inequalities. Our findings also underscore the necessity of adapting TAM and other acceptability frameworks as the technological and social landscape evolves. <strong>Trial Registration:</strong> PROSPERO CRD42024532974; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024532974

2025-05-07T16:30:02-04:00 https://www.jmir.org/article/view/jmir_v27i1e63066 2025-05-07T15:30:03-04:00 Shuo Zhou Enid Kawala Kagoya Alexandra Coria Alyssa Beck Jessica Evert Marina Haque Molly M Lamb Amy RL Rule Lisa Umphrey

Background: Global health activities (GHAs) can potentially reduce health disparities by facilitating resource sharing, promoting medical education and professional development worldwide, and enhancing collaboration among high-income countries (HICs) and low- and middle-income countries (LMICs). However, the COVID-19 pandemic disrupted in-person GHAs due to strict infection control and travel restrictions. To ensure the continuity of GHAs and further address health inequity, virtual GHAs (VGHAs) are gaining traction. Objective: Our research aimed to understand how people perceive the benefits and challenges of VGHAs, analyze and compare whether HIC and LMIC respondents have different perceptions of virtual and in-person GHAs, and summarize suggestions for improvement to inform the future development of VGHAs. Methods: We conducted a cross-sectional web-based survey during the COVID-19 pandemic in early 2022. Eligible participants were adult students, trainees, or professionals who participated in, created, taught, or facilitated GHAs. We thematically analyzed participants’ free-text responses regarding their perceptions of the benefits and challenges of virtual and in-person GHAs. The patterns differed depending on whether respondents were from HICs or LMICs; thus, we compared frequencies of each theme between the 2 groups. Results: A total of 154 respondents from 34 countries were included in the analysis. Key benefits of VGHAs were improved access to global health resources or content, reduced cost, easier scheduling and planning, expanded remote participation, and wider participation and reach. The themes that emerged as challenges of VGHAs included a lack of infrastructure to engage virtually, being less motivated and engaged, a lack of in-person and hands-on experience, and challenges with virtual communication and collaboration. LMIC respondents, compared to HIC counterparts, were more likely to identify reduced cost (26/67, 39% LMIC compared to 20/87, 23% HIC; <i>χ</i>²₁=4.5; <i>P</i>=.03) and expanding knowledge, experience, or skills (15/67, 22% LMIC compared to 8/87, 9% HIC; <i>χ</i>²₁=5.2; <i>P</i>=.02) as benefits of VGHAs, lack of infrastructure to engage virtually as a challenge of VGHAs (38/67, 57% LMIC compared to 31/87, 36% HIC; <i>χ</i>²₁=6.8; <i>P</i>=.009), and to suggest improving the content to be more interesting and relevant (6/67, 9% LMIC compared to 1/87, 1% HIC; <i>χ</i>²₁=5.3, <i>P</i>=.02). In contrast, HIC respondents were more likely to identify fostering continuity of relationship or activities (28/87, 32% HIC compared to 6/67, 9% LMIC; <i>χ</i>²₁=11.9; <i>P</i><.001) as a benefit of VGHAs and being less engaged and motivated to participate virtually (43/87, 49% HIC compared to 19/67, 28% LMIC; <i>χ</i>²₁=7.0; <i>P</i>=.008) as a challenge of VGHAs. Conclusions: Our findings add to the existing literature by understanding how GHA participants from HICs and LMICs perceive the benefits and challenges of VGHAs differently. These data help elucidate what makes VGHAs acceptable to global health partners and suggest improvements to ensure partner needs are served equitably within the partnership.

2025-05-07T15:30:03-04:00