JMIR Publications

Recent Articles:

• 

  Consumer & Patient Education and...

  Dec 16, 2025

  Supporting Informed Vaccine Decision-Making and Communication in Pregnancy Through the Vaccines in Pregnancy Canada Intervention: Multimethod Co-Design Study

  View Authors View Abstract

  Authors List:

  • Eliana Castillo  
  • Andrea M Patey  
  • Medea Myers-Stewart  
  • Monica Santosh Surti  
  • Maria Castrellon Pardo  
  • Marcia Bruce  
  • Zaileen Jamal  
  • Madison Kennedy  
  • Julie A Bettinger  
  • Jessica Kaufman  
  • Margie Danchin  
  • Maoliosa Donald  

  Abstract:

  Background: Vaccination in pregnancy (VIP) protects pregnant individuals and their newborns; yet, uptake remains suboptimal. Pregnant individuals face unique decision-making challenges, and communication with their health care provider (HCP) is crucial for uptake. While there is extensive data on barriers to VIP, interventions applying evidence-based behavior change strategies and co-designed with end users are scarce. Our prior work indicated that a new Canadian intervention was needed. Objective: This study aimed to co-design a multicomponent intervention to support informed decision-making and vaccine communication in pregnancy. Methods: Our multimethod study followed the Double Diamond phases (ie, Discover, Define, Develop, and Deliver) and partnered with a diverse patient advisory council and a multidisciplinary team of HCPs. During the Discover and Define phases, our previous work, we explored gaps and barriers to VIP in Canada and defined the behavior change strategies to address those needs. During the Develop phase, we co-designed and conducted iterative prototyping of four intervention components: (1) a pregnancy-specific communication approach, (2) a skills course for HCPs, (3) a practice change plan, and (4) a website with evidence-based resources for patients and HCPs. We used online and in-person participatory co-design sessions and peer-to-peer, patient-oriented online focus groups and semistructured in-depth interviews. During the Deliver phase, we refined the intervention components through functionality and usability testing. Results: The Vaccines in Pregnancy Canada (VIP Canada) intervention consists of four integrated components: (1) DECIDE (Determine, Elicit, Consent, Interactive discussion, Deliver, and Empower): a patient-centered, pregnancy-specific communication approach for providers to deliver a clear vaccine recommendation while respecting autonomy. (2) Skills course for HCPs: 4 self-paced, online modules to learn the rationale for VIP and the DECIDE communication approach and 2 group sessions. Providers found the skills course clear, practical, and applicable across diverse clinical roles and settings. Feedback led to enhancements, including improved audio-visual synchronization, consistent closed captioning, and the addition of downloadable reference materials to support learning. (3) Practice change plan: an action plan HCPs make to integrate vaccine communication into their practice. (4) VIP Canada website: an evidence-based website with resources to support informed vaccine decision-making for patients and providers. Patient feedback informed iterative refinements to the layout and content of the website to enhance navigation, readability, and representation of diverse identities. Functionality and usability testing demonstrated that patients found the VIP Canada website visually appealing, easy to navigate, and supportive of informed decision-making. Conclusions: The VIP Canada is a promising intervention co-designed to drive behavior change by addressing key barriers to vaccine communication and informed decision-making around our patient partners’ and HCPs’ perspectives and lived experiences to bridge theoretical frameworks with real-world relevance. Next steps include a feasibility study for further refinement and a subsequent effectiveness study. Trial Registration:

  • J Med Internet Res 2025;27(1):e77446

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Knowledge Translation and...

  Dec 16, 2025

  Dashboards to Improve Extractability of Cardiovascular Indicators in a Learning Health Care System: Mixed Methods Study

  View Authors View Abstract

  Authors List:

  • Anna G M Zondag  
  • Karin R Jongsma  
  • Wouter W van Solinge  
  • Michiel L Bots  
  • Robin W M Vernooij  
  • Saskia Haitjema  
  • UCC-CVRM Study Group

  Abstract:

  Background: Cardiovascular risk management (CVRM) guidelines have been developed for evaluation and management of all patients at higher cardiovascular risk, being either symptomatic or still asymptomatic. Although these exist already for long time, adherence varies. A learning healthcare system (LHS) could address adherence by continuously analyzing routine care data to inform and improve healthcare practice. Dashboards may be used to inform clinicians on the care provided and potentially improve structured registration of CVRM indicators in electronic health records (EHRs). Objective: Our aim was to evaluate whether the implementation of dashboards in our LHS has led to changes in the structured registration of cardiovascular indicators in patients with an increased risk of cardiovascular disease (CVD). Methods: In our mixed-methods study, patients who visited the UMC Utrecht between January 2022 and November 2023, the period in which the dashboard was implemented, were included. We assessed the extractability of the CVRM indicators (i.e., body mass index, blood pressure, smoking status, medical CVD history, lipid levels, glycated haemoglobin, haemoglobin, and the estimated glomerular filtration rate), stratified by department. We compared the extractability of the indicators with the extractability before the Utrecht Cardiovascular Cohort – Cardiovascular Risk Management (UCC-CVRM) LHS was initialized and with the period during which the UCC-CVRM was protocolized, yet without use of dashboards. To explain our quantitative findings and to gain a deeper understanding about how the dashboards were viewed and perceived, we conducted semi-structured interviews with clinicians and analyzed these thematically. Results: The extractability of CVRM indicators among 8941 first hospital visits remained low and stable during the period the dashboards were used. Compared to the protocolized UCC-CVRM, indicators were up to 45% less extractable meaning that CVRM indicators were less often registered in structured fields of the EHR. Interviews with clinicians (N=5) revealed that the low extractability could be attributed to unclear responsibility for CVRM, lack of harmonized agreements for registration in EHRs, perceived challenges related to the EHR system (e.g., some structured fields were not easily accessible), time constraints, and habits (e.g., maintaining habitual ways of working that are perceived to best suit their workflow). Conclusions: We found that dashboards did not improve registration of CVRM indicators in structured fields of the EHR. This was explained by perceived organizational, technical and operational issues. Our findings provide guidance on what aspects to consider for the extractability to be improved, which, in the end, will be beneficial for both clinical practice and scientific research using real-world data.

  • J Med Internet Res 2025;27(1):e71978

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  News and Perspectives

  Dec 16, 2025

  “Feasible but Fragile”: An Inflection Point for Artificial Intelligence in Mental Health Care

  View Authors View Abstract

  Authors List:

  • Kayleigh-Ann Clegg

  Abstract:

  • J Med Internet Res 2025;27(1):e89202

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Digital Health Reviews

  Dec 16, 2025

  Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues

  View Authors View Abstract

  Authors List:

  • Qiyang Zhang  
  • Renwen Zhang  
  • Yiying Xiong  
  • Yuan Sui  
  • Chang Tong  
  • Fu-Hung Lin  

  Abstract:

  Background: To date, there is no comprehensive paper that systematically synthesizes the effect of generative AI chatbot’s impact on mental health. Can generative AI chatbots help reduce our psychological distress? Objective: To comprehensively assess existing evidence, a systematic review and meta-analysis is essential to evaluate the overall effectiveness, identify gaps, and guide future research in this evolving field. This paper aims to: 1) synthesize current evidence on generative AI chatbot interventions targeting mental health issues, 2) quantify the effectiveness of these interventions via a meta-analysis of randomized controlled trials (RCTs), and examine key moderators of intervention effectiveness. Methods: This systematic review included 26 studies for narrative synthesis, out of which 12 randomized controlled trials were included in the meta-analysis. Results: The systematic synthesis revealed that 1) generative AI-chatbot interventions mostly took place in non-WEIRD countries (Western, Educated, Industrialized, Rich, and Democratic) and 2) there is a lack of studies focusing on young children and older adults. The meta-analysis showed a statistically significant effect (ES = 0.36, p = .039), which means that generative AI chatbots are, on average, effective in reducing negative mental health issues. Among moderators, we found statistically significant and higher effect sizes among interventions that have an active control group, conducted in WEIRD countries, recruited non-clinical populations, older age, majority female, non-personalized, with human assistance, and social-oriented. Conclusions: In conclusion, this comprehensive review has highlighted the potential of generative AI chatbots in addressing anxiety, depression, negative mood, and stress. The findings indicate that generative AI interventions are particularly beneficial in WEIRD countries, among non-clinical populations, older adults, and females. Human-assisted and social-oriented programs, as opposed to fully autonomous or task-oriented ones, demonstrate greater effectiveness. Meanwhile, non-personalized chatbots appear to yield more effective outcomes than personalized systems.

  • J Med Internet Res 2025;27(1):e78238

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Public (e)Health, Digital...

  Dec 15, 2025

  Accelerometer-Derived Rest-Activity Rhythm Amplitude, Genetic Predisposition, and the Risk of Ischemic Heart Disease: Observational and Mendelian...

  View Authors View Abstract

  Authors List:

  • Lele Wang  
  • Juying Zhang  
  • Xiong Xiao  
  • Xuewei Tang  
  • Jianxiang Tang  
  • Hao Xu  
  • Bing Guo  
  • Xing Zhao  

  Abstract:

  Background: The rest-activity rhythm amplitude (RARA), as a fundamental human behavior, has been linked to various health conditions. However, its causal relationship with ischemic heart disease (IHD), along with the potential modification by genetic predisposition, remains unclear. Objective: This study aimed to investigate the causal association between RARA and IHD using a triangulation approach that incorporated both observational and Mendelian randomization (MR) analyses, and to determine whether genetic predisposition modifies this relationship. Methods: First, a prospective cohort analysis was conducted among individuals who had no history of IHD before wearing wrist actigraphy between 2013 and 2015 in the UK Biobank. RARA was derived nonparametrically from accelerometer data worn for at least 7 days. Disrupted RARA was established as the lowest quintile of accelerometer-derived amplitude. Incident IHD was identified through medical records using ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes I20-25. Genetic predisposition was assessed with polygenic risk scores for IHD (IHD-PRS), which were categorized into “low IHD-PRS” (lowest quartile), “intermediate IHD-PRS” (second and third quartiles), and “high IHD-PRS” (highest quartile). Cox proportional hazards models were used to assess the association between RARA and incident IHD, as well as the modification effects of IHD-PRS. Second, we obtained RARA genome-wide association study data from the UK Biobank and IHD genome-wide association study data from FinnGen. A 2-sample MR using inverse-variance weighted methods was performed to examine the causality between them. Several other well-established methods, including random-effects and radial inverse-variance weighted method, Mendelian randomization pleiotropy residual sum and outlier, and maximum likelihood, were also performed for sensitivity analyses. Results: A total of 84,095 participants were followed up for a median of 7.90 (IQR 7.33-8.41) years. Overall, 3870 (4.60%) individuals developed IHD. Disrupted RARA was significantly associated with a higher risk of IHD (hazard ratio [HR] 1.20, 95% CI 1.12-1.30; P=.002). No significant modification effects by genetic predisposition on the multiplicative scale were found for this association (HR 0.92, 95% CI 0.76-1.11; P=.39 and HR 0.91, 95% CI 0.74-1.12; P=.37, respectively). The results remained consistent when we used the additive interaction scale to assess effect modification. Compared with participants with high RARA and low IHD-PRS (reference), those with disrupted RARA and high IHD-PRS had the highest risk of IHD (HR 2.63, 95% CI 2.29-3.02; P<.001), while those with disrupted RARA and low IHD-PRS had the smallest increased risk (HR 1.29, 95% CI 1.10-1.52; P<.001). The remaining groups showed intermediate risks in ascending order. MR results supported the observational findings (odds ratio [OR] 1.13, 95% CI 1.00-1.28; P=.047). This association was robust in our sensitivity MR analyses. Conclusions: The study suggests a potential causal relationship between RARA and IHD, independent of genetic predisposition, highlighting the significance of RARA for IHD prevention.

  • J Med Internet Res 2025;27(1):e79301

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  New Methods

  Dec 15, 2025

  Multimodal Data–Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure...

  View Authors View Abstract

  Authors List:

  • Yijun Wang  
  • Yaoling Wang  
  • Yujie Luan  
  • Menglei Hao  
  • Wenyuan Yin  
  • Bing Wu  
  • Tongjian Zhu  
  • Jiajun Zhu  
  • Bowen Zhou  
  • Long Tang  
  • Jun Wang  
  • Jinhui Wu  

  Abstract:

  Background: Heart failure with preserved ejection fraction (HFpEF) and unstable angina (UA) often coexist in clinical practice, constituting a high-risk cardiovascular phenotype with a markedly increased incidence of major adverse cardiovascular events (MACEs). Identification of high-risk patients within this population is crucial for reducing complications, improving outcomes, and guiding clinical decision-making. Objective: This study aimed to develop and externally validate predictive models based on machine learning (ML) algorithms to estimate the risk of MACEs in patients with coexisting UA and HFpEF, and to construct an online risk calculator to support individualized prevention strategies. Methods: This multicenter cohort study included 4459 patients with both HFpEF and UA admitted to seven hospitals across eastern, central, and western China between January 1, 2015, and December 31, 2021. Patients were divided into the derivation cohort (n=2923) and external validation cohort (n=1536) based on geographic regions. Clinical, laboratory, and imaging data were extracted from electronic medical records. Key predictors were identified using a hybrid feature selection method combining LASSO and Boruta algorithms. A total of 33 survival models were developed, including a variety of ML algorithms and survival analysis models. The model with the best C-index performance was deployed as a web-based risk calculator. Additionally, we assessed other performance indicators of the best-performing model, including the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, Brier scores, calibration curves and decision curve analysis. Results: Using a combination of LASSO regression and the Boruta algorithm, seven key predictors were identified: Diabetes mellitus, Blood platelet count, triglyceride, systemic inflammatory response index (SIRI), TyG-BMI index, NT-proBNP and atherogenic index of plasma (AIP). The surv.xgboost.cox model was used to predict MACEs in patients with UA and HFpEF due to its superior C-index. The model demonstrated the following performance metrics in the external validation cohort: a C-index of 0.788, C/D AUC of 0.81, and AUC values at 20-, 30-, and 40-month of 0.809 (95% CI: 0.745–0.873), 0.784 (95% CI: 0.745–0.824), and 0.807 (95% CI: 0.776–0.838), respectively.The model exhibited satisfactory calibration and clinical utility in predicting 40-month MACEs. Model interpretability was enhanced using SurvSHAP(t) to provide global and individual explanations. Furthermore, we converted the surv.xgboost.cox-based model into a publicly available tool for predicting 40-month MACEs, providing estimated probabilities based on the predictive indicators entered. Conclusions: We developed a surv.xgboost.cox-based predictive model for MACEs in patients with the dual phenotype of HFpEF and UA. We implemented this model as a web-based calculator to facilitate clinical application. Clinical Trial: The study was registered in the Chinese Clinical Trial Registry (Registration No. ChiCTR2400080282).

  • J Med Internet Res 2025;27(1):e78402

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Digital Health Reviews

  Dec 15, 2025

  Mobile Phone Messaging–Based Interventions to Improve Physical Activity in Patients With Cancer: Systematic Review and Meta-Analysis

  View Authors View Abstract

  Authors List:

  • Xueyan Cheng  
  • Mu-Hsing Ho  
  • Chun Kit Chan  
  • Denise Shuk Ting Cheung  

  Abstract:

  Background: Despite the benefits of physical activity for improving cancer-related outcomes, the majority of patients with cancer fail to meet physical activity guidelines. Mobile phone messaging is a scalable approach for promoting physical activity, but its effect on improving physical activity among cancer patients has not been reviewed. Objective: To systematically evaluate the effects of mobile phone messaging-based interventions in promoting physical activity among patients with cancer. Methods: A systematic search in eight English and Chinese databases (PubMed, EMBASE, Web of Science, MEDLINE, the Cochrane Library, Scopus, Wanfang and China National Knowledge Infrastructure) was performed. Randomised controlled trials that examined the effect of mobile phone messaging-based interventions on improving physical activity among cancer patients were included. Potential sources of substantial heterogeneity were investigated by subgroup analysis based on participants’ characteristics, mobile phone messaging regimens and physical activity estimates. Random effects models were used to estimate the overall effect size. Risk of bias was assessed by two independent reviewers using the revised Cochrane Collaboration’s risk of bias tool. Sensitivity analyses were performed through leave-one-out analyses, removal of outliers, and inclusion of only studies with low or some risk of bias. Potential publication bias was explored. Results: Thirteen studies involving 777 individuals were included in this review. At post-intervention, mobile phone messaging-based interventions significantly improved objective PA with a small effect size (SMD=0.37, 95% CI: 0.10 to 0.64, P = 0.007, I2=0%), but not self-reported PA (SMD=0.20, 95% CI: -0.07 to 0.47, P = 0.15, I2=56%) or step count (SMD=0.27, 95% CI: -0.19 to 0.73, P = 0.25, I2=69%). Interventions that adopted more behaviour change techniques and targeted patients who have completed active cancer treatment significantly improved step count. At follow-up, the effect of mobile phone messaging on self-reported physical activity, objective physical activity, and step count was found to be insignificant. Nine studies showed low or some risk of bias. Sensitivity analyses and trim-and-fill tests confirmed relatively stable effects of mobile phone messaging. No potential publication bias was identified. Conclusions: Mobile phone messaging-based interventions show promise as a scalable intervention to modestly improve objective PA in cancer patients, though effects vary, with limited impact on self-reported PA or step count. Evidence for sustained long-term benefit remains limited, highlighting the need for rigorously designed trials with extended follow-up. Clinical Trial: PROSPERO CRD42024557519; crd.york.ac.uk/PROSPERO/view/CRD42024557519

  • J Med Internet Res 2025;27(1):e73934

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  eHealth Literacy / Digital Literacy

  Dec 15, 2025

  Development and Validation of a Revised Multidimensional Digital Health Literacy Scale: Secondary Analysis Using Cross-Sectional Data From the 2022...

  View Authors View Abstract

  Authors List:

  • Ihoghosa Iyamu  
  • Pierce Gorun  
  • Hsiu-Ju Chang  
  • Rodrigo Sierra-Rosales  
  • Devon Haag  
  • Heather Pedersen  
  • Sofia Bartlett  
  • Nathan Lachowsky  
  • Geoffrey McKee  
  • Catherine Worthington  
  • Troy Grennan  
  • Lorie Donelle  
  • Daniel Grace  
  • Mark Gilbert  

  Abstract:

  Background: Digital technologies are reshaping health care, making digital health literacy (DHL) a critical competency for navigating online health information. Although widely conceived and measured as a unidimensional measure of DHL, the literature increasingly supports a multidimensional framing of the eHealth Literacy Scale (eHEALS). Studies propose alternative factor structures that can better inform population-level interventions, but these studies have not accounted for the ordinal nature of eHEALS response data. Objective: This study aimed to identify and validate an alternate multidimensional structure of eHEALS accounting for its ordinal response scale. Methods: Data were drawn from the 2022 GetCheckedOnline community survey of consenting English-speaking British Columbia residents aged ≥16 years who reported sexual activity in the past 12 months. Participants were recruited through geo-targeted digital advertisements, community outreach, and in-person recruitment at public events, and community locations. DHL was measured using eHEALS, with responses collected on a 5-point Likert scale. Descriptive statistics summarized eHEALS responses using means, medians, and IQRs. Exploratory and confirmatory factor analyses were used to assess the scale’s structure using polychoric correlations and standard model fit indices. Reliability and validity were evaluated using polychoric ordinal alpha, average variance extracted, and composite reliability, with missing data addressed via multiple imputation. Results: Overall, 1657 participants met inclusion criteria with a mean age of 33.0 (SD 11.77, 95% CI 32.4-33.6) years. Among these 47.3% (95% CI 44.9%-49.7%) identified as women, 30.4% (95% CI 28.1%-32.6%) identified as racialized minorities, 80.5% (95% CI 78.5%–82.3%) reported easy internet access, and 32.2% (95% CI 30.0%-34.5%) had a bachelor’s degree or higher. Across eHEALS items, median scores were 4.0 (IQR 1.0-2.0) with excellent internal consistency (polychoric ordinal α=.92). Exploratory factor analysis supported a 3-factor solution explaining 65.7% of the variance, demonstrated through confirmatory factor analysis (χ²₁₇=71.7, P<.001, root-mean-square error of approximation=0.059, standardized root-mean-square residual=0.026, comparative fit index=0.969, Tucker-Lewis Index=0.948). The final model included Information Navigation (standardized loadings=0.765-0.917), Resource Appraisal (0.825-0.892), and Confidence in Use (0.803 for both items), with composite reliability (0.784-0.900), and average variance extracted (0.503-0.738) supporting construct validity. Conclusions: This study confirms a multidimensional structure of eHEALS, identifying Information Navigation, Resource Appraisal, and Confidence in Use as key dimensions of DHL. This revised model enhances measurement precision, enabling more accurate identification of populations with limited DHL and informing the development of targeted, equity-oriented interventions. Future research should aim to confirm this multidimensional structure in more diverse populations and explore how distinct DHL domains influence access to digital health services in various contexts. Additionally, ongoing scale development must adapt to account for the role of emerging technologies, including artificial intelligence and social media algorithms in health care.

  • J Med Internet Res 2025;27(1):e78008

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Cost-Effectiveness and Economics

  Dec 15, 2025

  Cost-Utility Analysis and Value-Based Pricing of Digital Therapeutics for Pulmonary Rehabilitation in Chronic Respiratory Disease: Economic Evaluation Based...

  View Authors View Abstract

  Authors List:

  • Hyeonjung Park  
  • Minseol Jang  
  • Daehyun Kim  
  • Chul Kim  
  • Jun Hyeong Song  
  • Ju Hyun Oh  
  • Chin Kook Rhee  
  • Jae Ha Lee  
  • Hee-Eun Choi  
  • Hae Sun Suh  

  Abstract:

  Background: Pulmonary rehabilitation, a nonpharmacological treatment for chronic respiratory diseases, is underused due to limited access and time constraints. In a randomized controlled trial, the digital therapeutics (DTx) demonstrated superior efficacy to standard treatment. However, evidence on the cost-effectiveness of DTx and appropriate pricing strategies remains limited. Objective: This study aimed to evaluate the cost-effectiveness of DTx through cost-utility analysis and to explore a value-based price for its implementation. Methods: An economic evaluation was based on an 8-week randomized controlled trial involving 84 participants assigned to either the DTx group or standard treatment group. Costs were estimated from a health care system perspective. Quality-adjusted life years (QALYs) were estimated by using mapping algorithms from the chronic obstructive pulmonary disease assessment test to EQ-5D-3L. Cost-utility analysis was conducted to estimate the incremental cost-utility ratio (ICUR), which represents the additional cost per QALY gained. The willingness-to-pay threshold was set at US $19,410 per QALY. Sensitivity analyses included probabilistic sensitivity analysis, deterministic sensitivity analysis, and subgroup and scenario analyses, including a 1-year Markov model. Results: Compared with standard treatment, DTx increased QALY by 0.0096 at an additional cost of US $85.33, resulting in an ICUR of US $8890 per QALY gained. The maximum value-based price for an 8-week DTx program was estimated at US $192. In probabilistic sensitivity analysis, DTx had a 60.2% probability of being cost-effective at the set willingness-to-pay threshold, with 88.6% of iterations in the northeast quadrant falling below the threshold. The deterministic sensitivity analysis showed that ICURs remained below the willingness-to-pay threshold under all tested assumptions, with the maximum ICUR (US $15,644/QALY) also staying below the threshold. Subgroup analysis confirmed cost-effectiveness in both older adults (≥65 y) and non–older adults (<65 y) populations, and in both chronic obstructive pulmonary disease and interstitial lung disease groups. The 1-year Markov model estimated an ICUR of US $4398 per QALY. Conclusions: DTx for pulmonary rehabilitation demonstrated the cost-effectiveness compared with standard treatment. These findings support its potential for improving outcomes in patients with chronic respiratory disease and provide a pricing framework to facilitate its integration into health care systems.

  • J Med Internet Res 2025;27(1):e73739

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Artificial Intelligence

  Dec 12, 2025

  Stakeholder Criteria for Trust in Artificial Intelligence–Based Computer Perception Tools in Health Care: Qualitative Interview Study

  View Authors View Abstract

  Authors List:

  • Ansh Rai  
  • Meghan E Hurley  
  • John Herrington  
  • Eric A Storch  
  • Casey J Zampella  
  • Julia Parrish-Morris  
  • Anika Sonig  
  • Gabriel Lázaro-Muñoz  
  • Kristin Kostick-Quenet  

  Abstract:

  Background: Computer perception (CP) technologies hold significant promise for advancing precision mental health care systems, given their ability to leverage algorithmic analysis of continuous, passive sensing data from wearables and smartphones (eg, behavioral activity, geolocation, vocal features, and ambient environmental data) to infer clinically meaningful behavioral and physiological states. However, successful implementation critically depends on cultivating well-founded stakeholder trust. Objective: This study aims to investigate, across adolescents, caregivers, clinicians, and developers, the contingencies under which CP technologies are deemed trustworthy in health care. Methods: We conducted 80 semistructured interviews with a purposive sample of adolescents (n=20) diagnosed with autism, Tourette syndrome, anxiety, obsessive-compulsive disorder, or attention-deficit/hyperactivity disorder and their caregivers (n=20); practicing clinicians across psychiatry, psychology, and pediatrics (n=20); and CP system developers (n=20). Interview transcripts were coded by 2 independent coders and analyzed using multistage, inductive thematic content analysis to identify prominent themes. Results: Across stakeholder groups, 5 core criteria emerged as prerequisites for trust in CP outputs: (1) epistemic alignment—consistency between system outputs, personal experience, and existing diagnostic frameworks; (2) demonstrable rigor—training on representative data and validation in real-world contexts; (3) explainability—transparent communication of input variables, thresholds, and decision logic; (4) sensitivity to complexity—the capacity to accommodate heterogeneity and comorbidity in symptom expression; and (5) a nonsubstitutive role—technologies must augment, rather than supplant, clinical judgment. A novel and cautionary finding was that epistemic alignment—whether outputs affirmed participants’ preexisting beliefs, diagnostic expectations, or internal states—was a dominant factor in determining whether the tool was perceived as trustworthy. Participants also expressed relational trust, placing confidence in CP systems based on endorsements from respected peers, academic institutions, or regulatory agencies. However, both trust strategies raise significant concerns: confirmation bias may lead users to overvalue outputs that align with their assumptions, while surrogate trust may be misapplied in the absence of robust performance validation. Conclusions: This study advances empirical understanding of how trust is formed and calibrated around artificial intelligence–based CP technologies. While trust is commonly framed as a function of technical performance, our findings show that it is deeply shaped by cognitive heuristics, social relationships, and alignment with entrenched epistemologies. These dynamics can facilitate intuitive verification but may also constrain the transformative potential of CP systems by reinforcing existing beliefs. To address this, we recommend a dual strategy: (1) embedding CP tools within institutional frameworks that uphold rigorous validation, ethical oversight, and transparent design; and (2) providing clinicians with training and interface designs that support critical appraisal and minimize susceptibility to cognitive bias. Recalibrating trust to reflect actual system capacities—rather than familiarity or endorsement—is essential for ethically sound and clinically meaningful integration of CP technologies.

  • J Med Internet Res 2025;27(1):e78757

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  eHealth Literacy / Digital Literacy

  Dec 12, 2025

  Support Strategies and Interventions for eHealth Inclusion: Scoping Review

  View Authors View Abstract

  Authors List:

  • Wieke E Bouwes  
  • Nynke D Scherpbier  
  • Eveline Hage  
  • Luc A R Tjalma  
  • Esther I Metting  

  Abstract:

  Background: Policymakers increasingly promote eHealth as a way to improve healthcare efficiency. However, digitalization risks excluding individuals and groups who cannot fully engage with eHealth—for example, due to limited digital literacy or restricted access to resources. Targeted support, such as skills training, personalized guidance, or system-level initiatives, may help, but evidence on how such support is organized and whether it is effective remains limited. Objective: This scoping review aimed to map proposed strategies to promote eHealth inclusion, identify concrete support interventions, and report evidence on their outcomes Methods: This scoping review followed Arksey and O’Malley’s framework and the PRISMA-ScR guidelines. We searched PubMed, Scopus, Web of Science, and Embase for peer-reviewed studies published from 2014 onwards. We included empirical studies reporting on support to enhance eHealth inclusion. In total, 40 studies met the criteria: 19 examined support strategies and 21 evaluated targeted interventions. Strategies and interventions were categorized by actors at the micro level (interpersonal, such as family members, friends, or peers), meso level (organizations, such as healthcare organizations or community organizations), and macro level (policy or system). Results: Support strategies and interventions addressed a range of eHealth types, including video consultations, mobile health applications, and patient portals. Strategy studies often emphasized interpersonal support from family, friends, or peers, whereas interventions more often involved healthcare providers. Intervention outcomes, as identified during analysis, were grouped into adoption, use, skills, and attitudes. Adoption-focused interventions led by healthcare organizations showed limited effectiveness. Interventions targeting use partly demonstrated positive effects, such as increased completion of video visits, whereas outcomes related to attitudes were mixed. Nearly all multi-actor interventions—combining efforts across micro, meso, and macro levels—effectively improved eHealth skills, including digital and eHealth literacy. Examples include programs linking healthcare providers with community organizations, and initiatives pairing students with older adults, both of which improved these skills. Regional differences were also observed: healthcare providers played a dominant role in studies from the United States, community organizations were more prominent in African contexts, and multi-actor approaches were common in European studies. Conclusions: Overall, interventions yielded mixed results, but multi-actor collaborations frequently improved eHealth skills. These findings underscore the value of combining interpersonal, organizational, and policy-level efforts when designing support structures. For healthcare organizations, initiatives led solely by healthcare actors may suffice for promoting the use of specific applications (such as video consultations), but they seem insufficient for fostering broader eHealth literacy. Future research should address the sustainability and scalability of multi-actor interventions and how health system contexts and cultural factors shape their outcomes. Clinical Trial: Open Science Framework (OSF) 0.17605/OSF.IO/YRWFD; https://osf.io/6qmcj/overview

  • J Med Internet Res 2025;27(1):e79760

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  New Methods

  Dec 12, 2025

  Lumbar Acceleration Gait Estimation: “Step-by-Step” Algorithm Updates and Improvements

  View Authors View Abstract

  Authors List:

  • Lukas Adamowicz  
  • Wenyi Lin  
  • F Isik Karahanoglu  
  • Xuemei Cai  
  • Mar Santamaria  
  • Charmaine Demanuele  
  • Junrui Di  

  Abstract:

  Background: Digital health technologies, such as accelerometry, offer low participant burden and provide quantitative metrics with ease of deployment, making them increasingly popular for gait monitoring. Remote gait monitoring delivers quantifiable, continuous health measures over extended periods, surpassing the limited insights from single clinic or laboratory visits and offering a more comprehensive health perspective. Numerous gait algorithm implementations, inspired by prior research, aim to standardize these metrics across devices. The SciKit Digital Health (SKDH) package exemplifies this as a device-agnostic framework. Objective: This study introduces a series of literature-informed enhancements to the SKDH gait algorithm, improving its performance against reference standards and reducing the need for manual parameter adjustments across diverse populations. Methods: A block-wise refinement process was undertaken, examining each algorithmic component for potential enhancements and evaluating their cumulative impact on the complete gait algorithm and the metrics generated. Results: Using data from healthy adult and pediatric participants, the novel gait event estimation method reduced the mean absolute error by more than 50% compared with its predecessor. Following the updates, the intraclass correlation values for final gait metric concordance with the in-laboratory reference improved markedly, from 0.50-0.74 to 0.81-0.90. Additionally, the systematic bias observed in the previous version’s gait speed estimation was rectified, narrowing the difference from the reference from 0.065-0.230 to 0.00-0.03 m/s. Conclusions: The findings from this study provide robust evidence supporting the validity of the enhancements made to the gait algorithm. They demonstrate that a single lumbar accelerometer can capture gait characteristics with high accuracy and reliability across various speeds and age groups.

  • J Med Internet Res 2025;27(1):e72831

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.