Journal of Medical Internet Research最新文献

Unlabelled: Many people use peer online forums to seek support for health-related problems. More research is needed to understand the impacts of forum use and how these are generated. However, there are significant ethical and practical challenges with the methods available to do the required research. We examine the key challenges associated with conducting each of the most commonly used online data collection methods: surveys, interviews, forum post analysis, and triangulation of these methods. Based on our learning from the Improving Peer Online Forums (iPOF) study, an interdisciplinary realist-informed mixed methods evaluation of peer online forums, we outline strategies that can be used to address key issues pertaining to assessing important outcomes, facilitating participation, validating participants (users who consent to take part in one or more parts of the study), protecting anonymity, gaining consent, managing risk, multistakeholder engagement, and triangulation. We share this learning to support researchers, reviewers, and ethics committees faced with deciding how best to address these challenges. We highlight the need for open, transparent discussion to ensure the research field keeps pace with evolving technology design and societal attitudes to online data use.

Background: Young and middle-aged adults are vulnerable to poor sleep quality. eHealth literacy, defined as the ability to effectively access and use digital health information, has been linked to improved health behaviors and may promote better sleep outcomes. However, its relationship with sleep quality remains unclear, especially across age groups. Age-related disparities in eHealth literacy may contribute to a digital health divide in sleep outcomes.

Objective: This study aimed to examine the relationship between eHealth literacy and sleep quality among adults aged 18 to 59 years in Shanghai, China, as well as explore age-stratified effects.

Methods: A cross-sectional study was conducted between October and December 2022 in 3 districts of Shanghai, with 7 community health service centers randomly selected. Participants were recruited through convenience sampling to complete an online survey. eHealth literacy was assessed using the eHealth Literacy Scale, and sleep quality was measured using the Pittsburgh Sleep Quality Index. Covariates included sociodemographic characteristics, health status, and health behaviors. Logistic regression models were applied to examine the relationship between eHealth literacy and sleep quality, with stratified analyses conducted by age (emerging adults [18-29 years], established adults [30-45 years], and middle-aged adults [46-59 years]).

Results: A total of 1810 participants completed the survey. The prevalence of poor sleep quality was 37.9% (686/1810). Participants with eHealth literacy scores in the 25th to 75th percentile range (odds ratio [OR] 1.594, 95% CI 1.216-2.089, P<.001) and below the 25th percentile (OR 1.584, 95% CI 1.149-2.182, P=.005) had a significantly higher likelihood of reporting poor sleep quality compared to those with scores above the 75th percentile. Age-stratified analysis indicated that this association was significant only among emerging adults (OR 2.491, 95% CI 1.133-5.479, P=.02 for scores between the 25th and 75th percentiles; OR 2.975, 95% CI 1.230-7.195, P=.02 for scores below the 25th percentile) and established adults (OR 1.439, 95% CI 1.001-2.067, P=.049 for scores between the 25th and 75th percentiles).

Conclusions: This study found that eHealth literacy was associated with sleep quality among younger participants but not middle-aged ones, highlighting the digital divide in sleep health. These findings suggest that enhancing eHealth literacy may serve as an effective strategy for improving sleep outcomes. However, to ensure equitable health outcomes, interventions should be tailored to address the age-specific needs and varying levels of digital access across different groups.

Background: With the increasing use of machine learning (ML)-based risk prediction models for venous thromboembolism (VTE) in patients, the quality and applicability of these models in practice and future research remain unknown. The prediction mechanism of ML and the number of selected factors have been research hotspots in VTE prediction.

Objective: This study aimed to systematically review the literature on the predictive value of ML for VTE.

Methods: PubMed, Web of Science, MEDLINE, Embase, CINAHL, and Cochrane Library databases were searched for studies published up to March 26, 2025. Studies that developed and validated an ML model for VTE prediction in the patient population and were published in English were eligible, and studies with duplicate data were excluded. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias in the included studies. Meta-analyses were performed to evaluate the C-index, sensitivity, and specificity.

Results: A total of 27 studies with 596,092 patients reported the assessment value of ML models for predicting VTE. The risk of bias assessment yielded 18 (67%) studies with a high risk of bias, 8 (30%) with an unclear risk of bias, and 1 (4%) with a low risk of bias. The pooled sensitivity and specificity were 0.79 (95% CI 0.78-0.80) and 0.82 (95% CI 0.81-0.82), respectively. The positive likelihood ratio was 5.02 (95% CI 3.81-6.60), the negative likelihood ratio was 0.27 (95% CI 0.22-0.33), and the diagnostic odds ratio was 20.14 (95% CI 13.69-29.63; P<.001). A random-effects model was leveraged for meta-analysis of the C-index, which was 0.84 (95% CI 0.80-0.88). The most significant predictors for VTE were age, D-dimer level, and VTE history.

Conclusions: ML has been shown to effectively predict VTE in patients. However, a high risk of bias was identified in most of the included studies (18/27, 67%), primarily due to shortcomings in handling missing data and reporting the study design. Consequently, future research must prioritize external validation and address methodological rigor to facilitate the translation of these models into routine clinical practice.

Background: Although the integration of self-monitored patient data into mental health care offers potential for advancing personalized approaches, its application in clinical practice remains largely underexplored. Capturing individuals' mental health outside the therapy room using experience sampling methodology (ESM) may bridge this gap by supporting shared decision-making and personalized interventions.

Objective: This qualitative study investigated the perspectives of German mental health professionals regarding prototypes of ESM data visualizations designed for integration into a digital mental health tool.

Methods: Semistructured interviews were conducted with clinicians on their perceptions of such visualizations in routine care.

Results: Using reflexive thematic analysis, 3 key findings emerged (1) ESM and ESM data visualizations were seen as valuable tools for enhancing patient motivation and engagement over the course of treatment; (2) simplicity and clarity of visual formats, particularly line graphs, were preferred for usability; and (3) practical concerns, such as workflow integration challenges centered on time constraints (psychotherapy session duration 50 min) and need for patient psychoeducation materials, influenced perceived utility. Challenges, including the risk of cognitive overload from dense data representations (eg, ESM mood-in-context visualizations), were raised.

Conclusions: These findings underline the importance of designing digital tools that align with clinical needs while addressing potential barriers to implementation by exploring the opportunities and challenges associated with ESM visualizations.