JMIR Publications

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  Digital Health Reviews

  Apr 30, 2025

  Readdressing the Ongoing Challenge of Missing Data in Youth Ecological Momentary Assessment Studies: Meta-Analysis Update

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  Authors List:

  • Konstantin Drexl  
  • Vanisha Ralisa  
  • Joëlle Rosselet-Amoussou  
  • Cheng K Wen  
  • Sébastien Urben  
  • Kerstin Jessica Plessen  
  • Jennifer Glaus  

  Abstract:

  Background: Ecological momentary assessment (EMA) is pivotal in longitudinal health research in youth, but potential bias associated with nonparticipation, omitted reports, or dropout threatens its clinical validity. Previous meta-analytic evidence is inconsistent regarding specific determinants of missing data. Objective: This meta-analysis aimed to update and expand upon previous research by examining key participation metrics—acceptance, compliance, and retention—in youth EMA studies. In addition, it sought to identify potential moderators among sample and design characteristics, with the goal of better understanding and mitigating the impact of missing data. Methods: We used a bibliographic database search to identify EMA studies involving children and adolescents published from 2001 to November 2023. Eligible studies used mobile-delivered EMA protocols in samples with an average age up to 18 years. We conducted separate meta-analyses for acceptance, compliance, and retention rates, and performed meta-regressions to address sample and design characteristics. Furthermore, we extracted and pooled sample-level effect sizes related to correlates of response compliance. Risk of publication bias was assessed using funnel plots, regression tests, and sensitivity analyses targeting inflated compliance rates. Results: We identified 285 samples, including 17,441 participants aged 5 to 17.96 years (mean age 14.22, SD 2.24 years; mean percentage of female participants 55.7%). Pooled estimates were 67.27% (k=88, 95% CI 62.39-71.96) for acceptance, 71.97% (k=216, 95% CI 69.83-74.11) for compliance, and 96.57% (k=169, 95% CI 95.42-97.56) for retention. Despite overall poor moderation of participation metrics, acceptance rates decreased as the number of EMA items increased (log-transformed b=−0.115, SE 0.036; 95% CI −0.185 to −0.045; P=.001; R²=19.98), compliance rates declined by 0.8% per year of publication (SE 0.25, 95% CI −1.3 to −0.3; P=.002; R²=4.17), and retention rates dropped with increasing study duration (log-transformed b=−0.061, SE 0.015; 95% CI −0.091 to 0.032; P<.001; R²=10.06). The benefits of monetary incentives on response compliance diminished as the proportion of female participants increased (b=−0.002, SE 0.001; 95% CI −0.003 to −0.001; P=.003; R²=9.47). Within-sample analyses showed a small but significant effect indicating higher compliance in girls compared to boys (k=25; g=0.18; 95% CI 0.06-0.31; P=.003), but no significant age-related effects were found (k=14; z score=0.05; 95% CI −0.01 to 0.16). Conclusions: Despite a 5-fold increase in included effect sizes compared to the initial review, the variability in rates of missing data that one can expect based on specific sample and design characteristics remains substantial. The inconsistency in identifying robust moderators highlights the need for greater attention to missing data and its impact on study results. To eradicate any health-related bias in EMA studies, researchers should collectively increase transparent reporting practices, intensify primary methodological research, and involve participants’ perspectives on missing data. Clinical Trial: PROSPERO CRD42022376948; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022376948

  • J Med Internet Res 2025;27(1):e65710

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  Digital Health Reviews

  Apr 30, 2025

  Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis

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  Authors List:

  • Ling Wang  
  • Jinglin Li  
  • Boyang Zhuang  
  • Shasha Huang  
  • Meilin Fang  
  • Cunze Wang  
  • Wen Li  
  • Mohan Zhang  
  • Shurong Gong  

  Abstract:

  Background: Large language models (LLMs) have flourished and gradually become an important research and application direction in the medical field. However, due to the high degree of specialization, complexity, and specificity of medicine, which results in extremely high accuracy requirements, controversy remains about whether LLMs can be used in the medical field. More studies have evaluated the performance of various types of LLMs in medicine, but the conclusions are inconsistent. Objective: This study uses a network meta-analysis (NMA) to assess the accuracy of LLMs when answering clinical research questions to provide high-level evidence-based evidence for its future development and application in the medical field. Methods: In this systematic review and NMA, we searched PubMed, Embase, Web of Science, and Scopus from inception until October 14, 2024. Studies on the accuracy of LLMs when answering clinical research questions were included and screened by reading published reports. The systematic review and NMA were conducted to compare the accuracy of different LLMs when answering clinical research questions, including objective questions, open-ended questions, top 1 diagnosis, top 3 diagnosis, top 5 diagnosis, and triage and classification. The NMA was performed using Bayesian frequency theory methods. Indirect intercomparisons between programs were performed using a grading scale. A larger surface under the cumulative ranking curve (SUCRA) value indicates a higher ranking of the corresponding LLM accuracy. Results: The systematic review and NMA examined 168 articles encompassing 35,896 questions and 3063 clinical cases. Of the 168 studies, 40 (23.8%) were considered to have a low risk of bias, 128 (76.2%) had a moderate risk, and none were rated as having a high risk. ChatGPT-4o (SUCRA=0.9207) demonstrated strong performance in terms of accuracy for objective questions, followed by Aeyeconsult (SUCRA=0.9187) and ChatGPT-4 (SUCRA=0.8087). ChatGPT-4 (SUCRA=0.8708) excelled at answering open-ended questions. In terms of accuracy for top 1 diagnosis and top 3 diagnosis of clinical cases, human experts (SUCRA=0.9001 and SUCRA=0.7126, respectively) ranked the highest, while Claude 3 Opus (SUCRA=0.9672) performed well at the top 5 diagnosis. Gemini (SUCRA=0.9649) had the highest rated SUCRA value for accuracy in the area of triage and classification. Conclusions: Our study indicates that ChatGPT-4o has an advantage when answering objective questions. For open-ended questions, ChatGPT-4 may be more credible. Humans are more accurate at the top 1 diagnosis and top 3 diagnosis. Claude 3 Opus performs better at the top 5 diagnosis, while for triage and classification, Gemini is more advantageous. This analysis offers valuable insights for clinicians and medical practitioners, empowering them to effectively leverage LLMs for improved decision-making in learning, diagnosis, and management of various clinical scenarios. Trial Registration: PROSPERO CRD42024558245; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024558245

  • J Med Internet Res 2025;27(1):e64486

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  Research Instruments, Questionnaires,...

  Apr 30, 2025

  Potential Harms of Feedback After Web-Based Depression Screening: Secondary Analysis of Negative Effects in the Randomized Controlled DISCOVER Trial

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  Authors List:

  • Franziska Sikorski  
  • Bernd Löwe  
  • Anne Daubmann  
  • Sebastian Kohlmann  

  Abstract:

  Background: Web-based depression screening followed by automated feedback of results is frequently used and promoted by mental health care providers. However, criticism points to potential associated harms. Systematic empirical evidence on postulated negative effects is missing. Objective: We aimed to examine whether automated feedback after web-based depression screening is associated with misdiagnosis, mistreatment, deterioration in depression severity, deterioration in emotional response to symptoms, and deterioration in suicidal ideation at 1 and 6 months after screening. Methods: This is a secondary analysis of the German-wide, web-based, randomized controlled DISCOVER trial. Affected but undiagnosed individuals screening positive for depression (9-item Patient Health Questionnaire [PHQ-9] ≥10 points) were randomized 1:1:1 to receive nontailored feedback, tailored feedback, or no feedback on their screening result. Misdiagnosis and mistreatment were operationalized as having received a depression diagnosis by a health professional and as having started guideline-based depression treatment since screening (self-report), respectively, while not having met the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-V) criteria of a major depressive disorder at baseline (Structured Clinical Interview for DSM-V Disorders). Deterioration in depression severity was defined as a pre-post change of ≥4.4 points in the PHQ-9, deterioration in emotional response to symptoms as a pre-post change of ≥3.1 points in a composite scale of the Brief Illness Perception Questionnaire, and deterioration in suicidal ideation as a pre-post change of ≥1 point in the PHQ-9 suicide item. Outcome rates were compared between each feedback arm and the no feedback arm in terms of relative risks (RRs). Results: In the per protocol sample of 948 participants (n=685, 72% female; mean age of 37.3, SD 14.1 years), there was no difference in rates of misdiagnosis (ranging from 3.5% to 4.9% across all study arms), mistreatment (7.2%-8.3%), deterioration in depression severity (2%-6.8%), deterioration in emotional response (0.7%-2.9%), and deterioration in suicidal ideation at 6 months (6.8%-13.1%) between the feedback arms and the no feedback arm (RRs ranging from 0.46 to 1.96; P values ≥.13). The rate for deterioration in suicidal ideation at 1 month was increased in the nontailored feedback arm (RR 1.92; P=.01) but not in the tailored feedback arm (RR 1.26; P=.43), with rates of 12.3%, 8.1%, and 6.4% in the nontailored, tailored, and no feedback arms, respectively. All but 1 sensitivity analyses as well as subgroup analyses for false-positive screens supported the findings. Conclusions: The results indicate that feedback after web-based depression screening is not associated with negative effects such as misdiagnosis, mistreatment, and deterioration in depression severity or in emotional response to symptoms. However, it cannot be ruled out that nontailored feedback may increase the risk of deterioration in suicidal ideation. Robust prospective research on negative effects and particularly suicidal ideation is needed and should inform current practice. Trial Registration: ClinicalTrials.gov NCT04633096; https://clinicaltrials.gov/study/NCT04633096; Open Science Framework 10.17605/OSF.IO/TZYRD; https://osf.io/tzyrd

  • J Med Internet Res 2025;27(1):e59476

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  Artificial Intelligence

  Apr 30, 2025

  Harnessing an Artificial Intelligence–Based Large Language Model With Personal Health Record Capability for Personalized Information Support in Postsurgery...

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  Authors List:

  • Ting-ting Yang  
  • Hong-xia Zheng  
  • Sha Cao  
  • Mei-ling Jing  
  • Ju Hu  
  • Yan Zuo  
  • Qing-yong Chen  
  • Jian-jun Zhang  

  Abstract:

  Background: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have improved survival rates, effective management during recovery remains challenging. Traditional informational support systems often provide generic guidance that does not account for individualized medical histories or psychosocial factors. Recently, artificial intelligence (AI)–based large language models (LLM) tools have emerged as promising interventions to deliver personalized health information to post-MI patients. Objective: We aim to explore the user experiences and perceptions of an AI-based LLM tool (iflyhealth) with integrated personal health record functionality in post-MI care, assess how patients and their family members engaged with the tool during recovery, identify the perceived benefits and challenges of using the technology, and to understand the factors promoting or hindering continued use. Methods: A purposive sample of 20 participants (12 users and 8 nonusers) who underwent MI surgery within the previous 6 months was recruited between July and August 2024. Data were collected through semistructured, face-to-face interviews conducted in a private setting, using an interview guide to address participants’ first impressions, usage patterns, and reasons for adoption or nonadoption of the iflyhealth app. The interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi method. Results: Four key themes revealed included: (1) participants’ experiences varied based on digital literacy, prior exposure to health technologies, and individual recovery needs; (2) users appreciated the app’s enhanced accessibility to professional health information, personalized advice tailored to their clinical conditions, and the tool’s responsiveness to health status changes; (3) challenges such as difficulties with digital literacy, usability concerns, and data privacy issues were significant barriers; and (4) nonusers and those who discontinued use primarily cited complexity of the interface and perceived limited relevance of the advice as major deterrents. Conclusions: iflyhealth, an LLM AI app with a built-in personal health record functionality, shows significant potential in assisting post-MI patients. The main benefits reported by iflyhealth users include improved access to personalized health information and an enhanced ability to respond to changing health conditions. However, challenges such as digital literacy, usability, and privacy and security concerns persist. Overcoming the barriers may further enhance the use of the iflyhealth app, which can play an important role in patient-centered, personalized post-MI management. Trial Registration:

  • J Med Internet Res 2025;27(1):e68762

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  Extended Reality, Virtual Reality and...

  Apr 30, 2025

  The Influence of Previous Experience on Virtual Reality Adoption in Medical Rehabilitation and Overcoming Knowledge Gaps Among Health Care Professionals:...

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  Authors List:

  • Melina Schreiter  
  • Jasmin Hennrich  
  • Anna Lina Wolf  
  • Torsten Eymann  

  Abstract:

  Background: Virtual reality (VR) technologies in health care, particularly in medical rehabilitation, have demonstrated effectiveness by enabling patient remobilization in virtual environments, offering real-time feedback, enhancing physical function and quality of life, and allowing patients to exercise autonomously. Nevertheless, VR technologies are facing slow adoption in routine rehabilitative practice due to health care professionals’ concerns regarding data security, lack of time, or perceived cost. Objective: This study aimed to explore how previous experience with VR technologies influences health care professionals’ decisions to adopt or reject these technologies in medical rehabilitation. Methods: We conducted 23 semistructured interviews with health care professionals from different rehabilitative fields in Germany, whom we grouped into VR-experienced “innovators” and nonexperienced “laggards” according to their level of innovativeness. When analyzing the interviews, we applied qualitative content analysis techniques and derived 56 preliminary categories from the transcripts. Results: We merged the preliminary categories into 26 adoption and rejection factors, which were grouped under the 4 overarching categories of the diffusion of innovation theory by Rogers. In addition to the pure identification of context-specific influencing factors, we were able to identify differences between these factors concerning the two different adopter groups. VR-experienced innovators exhibited key characteristics such as openness to new technologies, solution-oriented thinking, and opinion leadership, whereas nonexperienced laggards focused on barriers and relied on top-down knowledge transfer. Despite these differences, both groups agreed on the factors that promote the adoption of VR technologies. Our results indicate that addressing the unique needs of both groups is crucial for wider VR acceptance in health care. Conclusions: This study demonstrates the importance of distinguishing between VR-experienced and nonexperienced health care professionals, providing actionable insights for developing adopter-specific communication strategies to overcome barriers and foster broader diffusion of VR technologies in the health care sector.

  • J Med Internet Res 2025;27(1):e62649

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  Digital Health Reviews

  Apr 30, 2025

  Mobile Health Interventions for Modifying Indigenous Maternal and Child–Health Related Behaviors: Systematic Review

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  Authors List:

  • Sana Ishaque  
  • Ola Ela  
  • Anna Dowling  
  • Chris Rissel  
  • Karla Canuto  
  • Kerry Hall  
  • Niranjan Bidargaddi  
  • Annette Briley  
  • Claire T Roberts  
  • Billie Bonevski  

  Abstract:

  Background: Mobile health (mHealth) interventions promoting healthy lifestyle changes offer an adaptable and inexpensive method for accessing health information but require cultural appropriateness and suitability for acceptance and effectiveness in Indigenous populations. No systematic review on effective mHealth interventions for Indigenous women during pregnancy and the early childhood years has been conducted. Objective: This review evaluated the effectiveness of mHealth interventions promoting healthy behaviors for Indigenous mothers and children from conception to 5 years post partum. It also aimed to explore the observed effectiveness differences based on participant engagement, intervention design, and provision of context. Further, the review explored if the interventions were co-designed. Methods: A systematic search of 5 databases was conducted: SCOPUS, MEDLINE, CINAHL, PsycINFO, and ProQuest (Dissertation or Thesis). Studies were included if they were either a randomized controlled trial, pre-post comparison, or a cohort study using mHealth with Indigenous women for maternal and child health following a preregistered PROSPERO protocol (CRD42023395710). HealthInfoNet was searched for gray literature and the reference lists of included studies were hand searched. The initial title and abstract screen for eligibility were performed by 1 reviewer. A full-text screen of eligible studies and a quality appraisal of included studies was performed by 2 reviewers independently. The appraisal tools used were the Mixed Methods Quality Appraisal Tool and the Centre of Excellence in Aboriginal Chronic Disease Knowledge Translation and Exchange (CREATE). A descriptive synthesis of the extracted data was performed. Results: Of the 663 articles screened, only 3 met the eligibility criteria. Each paper evaluated a different mHealth intervention: Remote Prenatal Education; the SMS Parent Action Intervention (two-way text messaging); and the Screening, Brief Intervention and Referral to Treatment (SBIRT) eCHECKUP To Go (web-based screening and intervention). Statistically significant changes were reported in some outcomes, including an increase in the parental participation rate in face-to-face prenatal education; increased rate of breastfeeding initiation and exclusive breastfeeding (2-12 months); improved overall children’s behavior related to sleep, diet, physical activity, screen time, and intake of sugary beverages; improved individual children’s behavior related to physical activity and sleep; and decrease in alcohol drinks per week and binge drinking episodes per 2 weeks due to time effect. However, no study provided a sample size calculation for the reported significant outcomes. Also, due to the small number of included studies and each study evaluating a different intervention, it was not possible to combine results to ascertain if the participant engagement, intervention design, or community context had any impact on the effectiveness. Conclusions: Due to the lack of sample size calculation, it was not possible to establish whether differences in the effectiveness were due to the interventions or a type I statistical error. Therefore, caution is required in the interpretation of these findings. Trial Registration: PROSPERO CRD42023395710; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023395710

  • J Med Internet Res 2025;27(1):e57019

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  Digital Health Reviews

  Apr 30, 2025

  Effects of Virtual Reality–Based Interventions on Preoperative Anxiety in Patients Undergoing Elective Surgery With Anesthesia: Systematic Review and...

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  Authors List:

  • Huiyuan Li  
  • Pak Lung Chiu  
  • Defi Efendi  
  • Haiying Huang  
  • Ka Yan Ko  
  • Cho Lee Wong  

  Abstract:

  Background: Preoperative anxiety is a common yet often neglected problem for patients undergoing surgery. Virtual reality (VR)–based intervention is a promising alternative with benefits for managing preoperative anxiety. However, the components of VR-based intervention and its effectiveness on preoperative anxiety in patients undergoing elective surgery with anesthesia remain unclear. Objective: This study aimed to identify the major components (ie, device, medium, format, and duration) of VR-based interventions and summarize evidence regarding their effectiveness in reducing preoperative anxiety in patients undergoing elective surgery with anesthesia. Methods: Allied and Complementary Medicine, Chinese University of Hong Kong Full Text Journals, CINAHL via EBSCOhost, Cochrane Library, Joanna Briggs Institute EBP Database, EMBASE, MEDLINE via OvidSP, PubMed, PsychINFO, Scopus, China Journal Net, and WanFang Data Chinese Dissertations Database were searched from inception to February 2025. Randomized controlled trials (RCTs) of VR-based interventions for patients undergoing elective surgery with anesthesia were included. The Cochrane Collaboration’s tool was used for risk of bias assessment. A random effect model was used for pooling the results. Results: A total of 35 RCTs with 3341 patients (female: n=1474, 44.1%) were included in this review, of which 29 RCTs were included for meta-analysis. Compared with usual care, VR-based interventions showed substantial benefits in decreasing preoperative anxiety in patients undergoing elective surgery (standardized mean difference [SMD] 0.65, 95% CI 0.37-0.92; P<.001). Regarding the subgroup analysis, VR-based intervention showed significant but moderate effects on preoperative anxiety in the pediatric population (SMD 0.77, 95% CI 0.32-1.22; P<.001) compared to the adult population (SMD 0.58, 95% CI 0.23-0.93; P=.001). The distraction approach showed more significant effects (SMD 0.73, 95% CI 0.24-1.21; P=.004) on preoperative anxiety than the exposure approach (SMD 0.61, 95% CI 0.27-0.95; P<.001). Conclusions: Patients undergoing elective surgery with anesthesia may benefit from VR as a novel alternative to reduce preoperative anxiety, especially pediatric patients via the distraction approach. However, more rigorous research is needed to confirm VR’s effectiveness.

  • J Med Internet Res 2025;27(1):e55291

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  Mobile Health (mhealth)

  Apr 29, 2025

  Real-World Mobile Health Implementation and Patient Safety: Multicenter Qualitative Study

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  Authors List:

  • Jing Jing Su  
  • Michelle Hui Shan Chan  
  • Gabriela Lima de Melo Ghisi  
  • Rick Yiu Cho Kwan  
  • Arkers Kwan Ching Wong  
  • Rose Lin  
  • Jerry Wing Fai Yeung  
  • Qijun He  
  • Garyfallia Pepera  
  • Ladislav Batalik  

  Abstract:

  Background: Mobile health (mHealth) is increasingly being used in contemporary health care provision owing to its portability, accessibility, ability to facilitate communication, improved interprofessional collaboration, and benefits for health outcomes. However, there is limited discourse on patient safety in real-world mHealth implementation, especially as care settings extend beyond traditional center-based technology usage to home-based care. Objective: This study aimed to explore health care professionals’ perspectives on the safety aspects of mHealth integration in real-world service provision, focusing on Hong Kong Special Administrative Region (SAR) and Wuhan city in mainland China. In Hong Kong SAR, real-world mHealth care provision is largely managed by the Hospital Authority, which has released various mobile apps for home-based care, such as Stoma Care, Hip Fracture, and HA Go. In contrast, mHealth care provision in Wuhan is institutionally directed, with individual hospitals or departments using consultation apps, WeChat mini-programs, and the WeChat Official Accounts Platform (a subapp within the WeChat ecosystem). Methods: A multicenter qualitative study design was used. A total of 27 participants, including 22 nurses and 5 physicians, from 2 different health care systems were interviewed individually. Thematic analysis was used to analyze the data. Results: The mean age of the participants was 32.19 (SD 3.74) years, and the mean working experience was 8.04 (SD 4.05) years. Most participants were female (20/27, 74%). Nearly half of the participants had a bachelor’s degree (13/27, 48%), some had a master’s degree (9/27, 33%), and few had a diploma degree (3/27, 11%) or a doctoral degree (2/27, 7%). Four themes emerged from the data analysis. Considering the current uncertainties surrounding mHealth implementation, participants emphasized “liability” concerns when discussing patient safety. They emphasized the need for “change management,” which includes appropriate referral processes, adequate resources and funding, informed mHealth usage, and efficient working processes. They cautioned about the risks in providing mHealth information without ensuring understanding, appreciated the current regulations available, and identified additional regulations that should be considered to ensure information security. Conclusions: As health care systems increasingly adopt mHealth solutions globally to enhance both patient care and operational efficiency, it becomes crucial to understand the implications for patient safety in these new care models. Health care professionals recognized the importance of patient safety in making mHealth usage reliable and sustainable. The promotion of mHealth should be accompanied by the standardization of mHealth services with institutional, health care system, and policy-level support. This includes fostering mHealth acceptance among health care professionals to encourage appropriate referrals, accommodate changes, ensure patient comprehension, and proactively identify and address threats to information security.

  • J Med Internet Res 2025;27(1):e71086

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  E-Health / Health Services Research...

  Apr 29, 2025

  Telehealth Initiative to Enhance Primary Care Access in Brazil (UBS+Digital Project): Multicenter Prospective Study

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  Authors List:

  • Celina de Almeida Lamas  
  • Patrícia Gabriela Santana Alves  
  • Luciano Nader de Araújo  
  • Ana Beatriz de Souza Paes  
  • Ana Claudia Cielo  
  • Luciana Maciel de Almeida Lopes  
  • André Longo Araújo de Melo  
  • Thais Yokoyama  
  • Clarice Pagani Savastano  
  • Paula Gobi Scudeller  
  • Carlos Carvalho  
  • Digital Primary Health Unit HCFMUSP Study Group

  Abstract:

  Background: Brazil faces significant inequities in health care access, particularly in remote communities. The Brazilian Unified Health System is struggling to deliver adequate health care to its vast population. Telehealth, regulated in Brazil starting in 2022, emerged as a solution to improve access and quality of care. Thus, the Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, in partnership with the Agência Brasileira de Apoio à Gestão do Sistema Único de Saúde, created the Unidade Básica de Saúde (UBS)+Digital project, which aimed to mitigate the lack of medical care in remote areas of Brazil by providing teleconsultation in primary health units (PHUs) across the country. Through teletraining and digital health strategies, the initiative enabled health care professionals to provide remote assistance, improving access to medical care. Objective: To describe the implementation and results of the UBS+Digital project, a telehealth initiative focused on training health care professionals, providing teleconsultations, and monitoring key performance indicators among PHUs in Brazil. Methods: The study examined 15 Brazilian PHUs using a multicenter, prospective design. Data were collected through anonymous surveys of patients and physicians, which were recorded in the REDCap (Research Electronic Data Capture) database. PHUs were selected based on criteria such as the absence of an on-site physician and existing technological infrastructure. Synchronous and asynchronous training was provided, focusing on digital health and teleconsultation skills. In loco training included workshops and community events to share experiences and foster local engagement. A community of practice facilitated ongoing knowledge exchange. Teleconsultations followed the person-centered clinical method and Calgary-Cambridge methodology. Key performance indicators were monitored by a dashboard to guide continuous improvement. The transition of operations was managed based on physician availability and project duration. Microcosting analysis assessed the project’s economic impact using Brazilian guidelines, with statistical analysis performed using Jamovi software. Results: From March to November 2023, the project conducted 6312 telehealth sessions. A total of 342 professionals were trained, including participants from all three training modalities that were implemented. The Net Promoter Score for teleconsultations was 97, indicating excellent service quality. Of the teleconsultations, 65.3% (4009/6140) were prescheduled, and 34.7% (2130/6140) were on demand, depending on the family health team organization. Teleconsultations resolved 85% (5219/6140) of cases, with 15% (921/6140) requiring in-person referrals or emergency care. The average absenteeism rate was 15% (1083/7223), and consultation durations were between 15 and 20 minutes, suggesting potential adjustments in scheduling. Conclusions: The results highlight the effectiveness of telehealth programs in primary care settings with limited medical professionals. The UBS+Digital project demonstrated that telehealth can enhance health care access, presenting a pioneering model within the Brazilian Unified Health System for digital primary care.

  • J Med Internet Res 2025;27(1):e68434

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  Digital Mental Health Interventions,...

  Apr 28, 2025

  Social Media Activities With Different Content Characteristics and Adolescent Mental Health: Cross-Sectional Survey Study

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  Authors List:

  • Chia-chen Yang  
  • Paul Hunhoff  
  • Yen Lee  
  • Jonah Abrell  

  Abstract:

  Background: Adolescent mental health concerns are rising in the United States, with social media often cited as a contributing factor, although research findings remain mixed. A key limitation is the simplistic view of social media use, which fails to consistently predict well-being. Scholars call for a more nuanced framework and a better understanding of how social media use influences adolescent mental health through various psychosocial mechanisms. Objective: Using the Multidimensional Model of Social Media Use, we explored how 4 activities with various content characteristics (intimate directed communication, intimate broadcasting, positive broadcasting, and positive content consumption) are associated with depression and anxiety through 3 psychosocial mediators: social support, approval anxiety, and social comparison. Methods: Cross-sectional survey data were collected through Qualtrics’ panel service from a sample of adolescents whose gender and racial or ethnic distributions were nationally representative (N=2105; mean age 15.39, SD 1.82 years). Participants passed attention checks to ensure data validity. Measures included 9 validated scales (Cronbach α=0.83-0.91): 4 social media activities (intimate directed communication, intimate broadcasting, positive broadcasting, and positive content consumption), 3 mediators (social support, approval anxiety, and social comparison), and 2 mental health outcomes (depression and anxiety). Using Mplus, we performed 2-step structural equation modeling. Confirmatory factor analysis established scale validity, and path analysis tested the hypothesized and exploratory associations between social media activities, mediators, and mental health, controlling for demographic covariates and the amount of phone use. Model fit criteria (comparative fit index and Tucker-Lewis index were close to or greater than 0.95; root mean square error of approximation was less than 0.08) were met. Significance was determined using a false discovery rate control, with the familywise type 1 error rate set at 0.05. Results: Our findings showed that positive broadcasting was associated with lower depression (β=–.14; P<.001) and anxiety (β=–.06; P=.03), mainly through the direct paths. The other 3 activities were related to more mental health problems. Specifically, intimate directed communication was associated with greater depression (β=.06; P=.03) and anxiety (β=.06; P=.04); intimate broadcasting was associated with greater anxiety (β=.07; P=.02); and positive content consumption was related to higher depression (β=.13; P<.001). Approval anxiety or social comparison played a salient role in these total effects. Conclusions: The findings highlight the importance of distinguishing social media activities when assessing risks and benefits. Intimate directed communication, intimate broadcasting, and positive content consumption became risk factors for increased anxiety and depression through approval anxiety, social comparison, or both. Positive broadcasting was related to better mental health because of its direct associations with lower depression and anxiety.

  • J Med Internet Res 2025;27(1):e73098

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  Viewpoints and Perspectives

  Apr 28, 2025

  A System Model and Requirements for Transformation to Human-Centric Digital Health

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  • Pekka Ruotsalainen  
  • Bernd Blobel  

  Abstract:

  Digital transformation is widely understood as a process where technology is used to modify an organization’s products and services and to create new ones. It is rapidly advancing in all sectors of society. Researchers have shown that it is a multidimensional process determined by human decisions based on ideologies, ideas, beliefs, goals, and the ways in which technology is used. In health care and health, the end result of digital transformation is digital health. In this study, a detailed literature review covering 560 research articles published in major journals was performed, followed by an analysis of ideas, beliefs, and goals guiding digital transformation and their possible consequences for privacy, human rights, dignity, and autonomy in health care and health. Results of literature analyses demonstrated that from the point of view of privacy, dignity, and human rights, the current laws, regulations, and system architectures have major weaknesses. One possible model of digital health is based on the dominant ideas and goals of the business world related to the digital economy and neoliberalism, including privatization of health care services, monetization and commodification of health data, and personal responsibility for health. These ideas represent meaningful risks to human rights, privacy, dignity, and autonomy. In this paper, we present an alternative solution for digital health called human-centric digital health (HCDH). Using system thinking and system modeling methods, we developed a system model for HCDH. It uses 5 views (ideas, health data, principles, regulation, and organizational and technical innovations) to align with human rights and values and support dignity, privacy, and autonomy. To make HCDH future proof, extensions to human rights, the adoption of the principle of restricted informational ownership of health data, and the development of new duties, responsibilities, and laws are needed. Finally, we developed a system-oriented, architecture-centric, ontology-based, and policy-driven approach to represent and manage HCDH ecosystems.

  • J Med Internet Res 2025;27(1):e68661

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  Electronic/Mobile Data Capture,...

  Apr 28, 2025

  Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

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  Authors List:

  • Imogen E Leaning  
  • Andrea Costanzo  
  • Raj Jagesar  
  • Lianne M Reus  
  • Pieter Jelle Visser  
  • Martien J H Kas  
  • Christian F Beckmann  
  • Henricus G Ruhé  
  • Andre F Marquand  

  Abstract:

  Background: Brain-related disorders are characterized by observable behavioral symptoms, for example, social withdrawal. Smartphones can passively collect behavioral data reflecting digital activities such as communication app usage and calls. These data are collected objectively in real time, avoiding recall bias, and may, therefore, be a useful tool for measuring behaviors related to social functioning. Despite promising clinical utility, analyzing smartphone data is challenging as datasets often include a range of temporal features prone to missingness. Objective: Hidden Markov models (HMMs) provide interpretable, lower-dimensional temporal representations of data, allowing for missingness. This study aimed to investigate the HMM as a method for modeling smartphone time series data. Methods: We applied an HMM to an aggregate dataset of smartphone measures designed to assess phone-related social functioning in healthy controls (HCs) and participants with schizophrenia, Alzheimer disease (AD), and memory complaints. We trained the HMM on a subset of HCs (91/348, 26.1%) and selected a model with socially active and inactive states. Then, we generated hidden state sequences per participant and calculated their “total dwell time,” that is, the percentage of time spent in the socially active state. Linear regression models were used to compare the total dwell time to social and clinical measures in a subset of participants with available measures, and logistic regression was used to compare total dwell times between diagnostic groups and HCs. We primarily reported results from a 2-state HMM but also verified results in HMMs with more hidden states and trained on the whole participant dataset. Results: We identified lower total dwell times in participants with AD (26/257, 10.1%) versus withheld HCs (156/257, 60.7%; odds ratio 0.95, 95% CI 0.92-0.97; false discovery rate [FDR]–corrected P<.001), as well as in participants with memory complaints (57/257, 22.2%; odds ratio 0.97, 95% CI 0.96-0.99; FDR-corrected P=.004). The result in the AD group was very robust across HMM variations, whereas the result in the memory complaints group was less robust. We also observed an interaction between the AD group and total dwell time when predicting social functioning (FDR-corrected P=.02). No significant relationships regarding total dwell time were identified for participants with schizophrenia (18/257, 7%; P>.99). Conclusions: We found the HMM to be a practical, interpretable method for digital phenotyping analysis, providing an objective phenotype that is a possible indicator of social functioning.

  • J Med Internet Res 2025;27(1):e64007

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