Journal of Medical Internet Research

Digital Illness Narratives of Young Chinese Adults With Diabetes on RedNote: Qualitative Narrative Analysis

2026-06-24T18:00:21-04:00

Background: Chronic illness disrupts everyday routines, social roles, and sense of self, particularly among young individuals undergoing identity formation. With the expansion of digital media, social platforms have become key sites where patients narrate illness experiences, negotiate stigma, and seek support. However, such processes remain underexplored in non-Western, collectivist contexts. Objective: This study examines how young Chinese individuals with diabetes construct illness narratives and negotiate identity in digital environments. Methods: This study uses a narrative analysis approach, combining inductive thematic coding with culturally and critically informed interpretation. A total of 303 narrative posts were collected from RedNote, a Chinese social media platform characterized by diary-like user-generated content. The dataset includes both text-based and video-based posts, capturing longitudinal and first-person accounts of living with diabetes. Results: In total, 4 distinct narrative types were identified. The chaos narrative captures experiences of cognitive dissonance, emotional breakdown, and disruption of daily routines following diagnosis, often accompanied by guilt toward family members and anxiety over future uncertainty. The stigma narrative reflects social withdrawal, concealment of illness, and perceived discrimination in intimate relationships and employment contexts, highlighting the role of externally imposed social judgment. The resilience narrative illustrates processes of self-acceptance, disciplined self-management, and the integration of illness into everyday life through routinized practices such as blood glucose monitoring and dietary regulation. The solidarity narrative emphasizes the importance of familial care and digitally mediated peer support, where users exchange practical knowledge, emotional encouragement, and collective identity markers, transforming isolation into shared experience. Across these narratives, illness is not only experienced as disruption but also actively reinterpreted through culturally embedded values such as familial responsibility and collective belonging. Conclusions: This study advances illness narrative research by demonstrating how digital platforms mediate culturally specific forms of meaning-making among young patients with chronic illness. It extends the concept of biographical disruption by conceptualizing it as a dynamic and relational process shaped by digital storytelling, familial expectations, and peer interaction. The findings highlight the importance of culturally sensitive and platform-aware approaches to health communication and digital patient support.

Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review

2026-06-24T17:30:17-04:00

Background: Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasingly investigated for various medical applications as a complementary or even alternative modality to traditional medical devices. The adoption of deep learning techniques in recent literature is motivated by their superior performance compared to classical machine learning methods. However, ethical and regulatory concerns regarding the black-box nature of these models have limited their integration into clinical workflows. Consequently, explainable artificial intelligence (XAI) has recently been used to address this issue by generating explanations for opaque model outputs. Ideally, medical XAI systems aim to provide human-understandable, clinically grounded explanations essential for enhanced AI trustworthiness and, thereby, facilitate adoption into real-world clinical settings. Objective: We conduct a systematic literature review of XAI methods applied for explaining deep learning techniques in audio-based voice and speech clinical applications. We aim to identify what XAI methods have been used to explain the decisions of deep learning voice and speech AI systems in health care, as well as XAI-informed insights. Additionally, we aim to contextualize these findings with respect to clinical applicability and stakeholder relevance. Lastly, we identify opportunities and recommendations for future clinical audio XAI design. Methods: We used PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Six electronic databases (IEEE Xplore, ACM Digital Library, Scopus, PubMed, Web of Science, and Nature) were searched for papers published between January 2015 and February 2025. Eligible studies applied explainability or interpretability methods to deep learning models for voice or speech audio in health care contexts. Risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool). The results were thematically synthesized across explainability categories, input representations, clinical domains, validation strategies, and stakeholder considerations. Results: A total of 30 studies met the inclusion criteria. These studies used a range of explainability approaches, including gradient-based methods, perturbation-based techniques, surrogate model–based methods, model-internal representation analyses, concept-based detectors, and attention-based explanations. Applications spanned diverse clinical domains, including voice disorders, neurodegenerative diseases, psychiatric conditions, and traumatic brain injury. Overall, results indicate that most studies relied primarily on qualitative interpretation of explainability outputs, with limited quantitative validation of explanation consistency across external datasets. Furthermore, none of the included studies explicitly conducted human-in-the-loop evaluations with relevant stakeholders, highlighting a substantial gap in stakeholder alignment. Conclusions: Current XAI practices in clinical voice and speech analysis are limited by insufficient validation, lack of domain-specific design, and misalignment with clinical stakeholder needs. This review highlights opportunities for developing validated, audio-aware, and stakeholder-centered XAI approaches to support trustworthy clinical deployment. Interpretation of these findings should consider limitations related to single-reviewer study selection, potential high-risk of bias, and the repeated use of benchmark datasets.

Chinese Mobile Health Apps for Preventing and Managing Pelvic Floor Dysfunction: Quality Assessment and Content Analysis

2026-06-24T17:00:24-04:00

Background: Pelvic floor dysfunction (PFD) is a highly prevalent health problem, encompassing urinary incontinence, emptying disorders of the bladder, fecal incontinence, emptying disorders of the bowel, pelvic organ prolapse, sexual dysfunction, and chronic pelvic pain. Mobile health (mHealth) interventions delivered through apps can provide remote health services to improve patient compliance and enhance treatment effectiveness. Although apps for preventing and managing PFD have been developed and used, the features and quality of these apps in China have not been systematically examined. Objective: This study aimed to systematically summarize the functions and evaluate the quality of the existing mHealth apps for preventing and managing all kinds of PFD, such as urinary incontinence, fecal incontinence, and chronic pelvic pain. Methods: We systematically searched for potential PFD apps on the Apple App Store, Huawei AppGallery, and VIVO App Store. Apps were included if they were free, designed for preventing or managing PFD, in the Chinese language, could be downloaded and run on Android, Harmony, or iOS operating systems (OS), and incorporated elements of preventing and managing PFD. We excluded apps that were intended for use by health care providers and not relevant to PFD. Apps that met the inclusion criteria were downloaded and included for final analysis. The user version of the Mobile App Rating Scale (uMARS) was used to assess the apps’ quality and summarize the apps’ functionality according to guidelines. Results: Of the 3897 apps screened, 46 apps that met the inclusion criteria were included in the final analysis. All apps were developed by corporations. More than half of the apps had download counts exceeding 10,000, and 24 (52.2%) apps scored 4 or higher in app stores. Furthermore, nearly half of the apps (n=21, 45.7%) had been updated within the past month at the time of retrieval. The overall uMARS scores ranged from 2.29 to 4.50, with a mean uMARS score of 3.46 (SD 0.50), which is considered acceptable quality. Based on uMARS scores, 15.2% (n=7) were rated as poor quality, 65.2% (n=30) as acceptable, and 19.6% (n=9) as good quality. More than half of the apps provided the functions of exercise (n=44, 95.7%), personal information recording (n=31, 67.4%), and health education (n=28, 60.9%). Only 5 apps provided 5 or more functions. Conclusions: The apps for PFD revealed acceptable quality, and the majority provided exercise, personal information recording, and health education functions. However, many apps lacked comprehensive functionalities and did not provide immediate feedback or high-quality educational information. Health care providers should follow international guidelines to create high-quality, evidence-based, multifunctional apps for PFD. Future studies should explore the effects of the apps and real-world user feedback data in clinical settings.

Enhancing Physician Resilience to Generative AI: Multilevel Framework for Shared Authority, Verification, and Skill Preservation

2026-06-24T16:45:14-04:00

As generative artificial intelligence (AI), particularly large language model–based tools, is increasingly integrated into diagnosis, triage, decision support, and treatment planning, it offers potential gains in efficiency and information access. However, real-world deployment also introduces important risks, including hallucinations, miscalibrated confidence, automation bias, and increased verification burden on physicians. This burden may divert attention from independent clinical reasoning, contribute to deskilling, and increase vulnerability when models fail silently or perform poorly in unfamiliar clinical contexts. Existing AI governance frameworks emphasize data quality, transparency, accountability, and ethical deployment, but pay less attention to physician-facing resilience, defined in this paper as the capacity to sustain independent and safe clinical judgment when collaborating with generative AI. In this viewpoint, we propose a multilevel governance framework organized around 3 coordinated domains: cognitive workload shaping, clinical authority governance and allocation, and organizational safety governance and accountability. Together, these domains aim to reduce verification burden, preserve physician decisional authority, and align institutional oversight with safe and context-sensitive AI use. The framework includes mechanisms such as risk-sensitive verification triggers, bounded delegation, structured interprofessional review, and organizational monitoring to support safe clinical integration while minimizing avoidable workflow disruption. At the same time, implementation may be limited by workflow friction, alert fatigue, variable institutional resources, and the need for ongoing monitoring and recalibration to ensure that safeguards remain clinically useful rather than burdensome. Accordingly, this paper outlines a structured governance framework to guide safer integration of generative AI into clinical care and inform future evaluation across specialties, workflows, and institutional settings.

Dietary Inflammatory Index and Depressive Symptoms in Chinese University Students Leveraging an Intelligent Ordering System: 3-Year Longitudinal Prospective Cohort Study

2026-06-24T16:00:03-04:00

Background: Depression is a major global cause of disability, and depressive symptoms are highly prevalent and increasing among Chinese university students. Mounting evidence confirms that inflammation plays a key role in the pathogenesis of depression, and dietary inflammatory potential regulates systemic inflammation to influence depressive symptom development. However, existing research is limited by cross-sectional designs, recall bias from self-reported dietary surveys, and a lack of long-term prospective cohort evidence on the diet-inflammation-mental health pathway in Chinese university students. Objective: This study aimed to examine the longitudinal association between Dietary Inflammatory Index (DII) and the incidence of depressive symptoms in Chinese university students, and to explore subgroup differences by family relationship and socioeconomic status. Methods: A 3-year longitudinal prospective cohort study was conducted among 5314 students from a university in Shanghai, China. Eligible participants met the criteria of ≥86 days of annual campus cafeteria dining and at least 1 breakfast, lunch, and dinner in campus canteens per quarter; students with abnormal monthly energy intake, excessive food consumption, or incomplete 3-year dietary/psychological data were excluded. Dietary data were continuously collected via the Intelligent Ordering System (IOS) from April 2020 to March 2023 to calculate DII scores. Depressive symptoms were annually assessed using the Beck Depression Inventory-II from March 2021 to March 2023. Mixed-effects logistic regression (α=.05) was used to analyze the association, with subgroup analyses stratified by family relationship and poverty status. Results: The baseline prevalence of depressive symptoms was 10.75% (571/5314; male: 261/2679, 9.74%; female: 310/2635, 11.76%). After adjusting for covariates, compared with the highest DII quartile (most proinflammatory diet), lower DII quartiles (more anti-inflammatory or low proinflammatory diets) were associated with a reduced risk of incident depressive symptoms in participants without depressive symptoms at baseline: Q1 (odds ratio [OR] 0.27, 95% CI 0.16-0.47), Q2 (OR 0.52, 95% CI 0.33-0.84), and Q3 (OR 0.26, 95% CI 0.16-0.42). Subgroup analyses showed this protective effect was only significant in students with harmonious family relationships and non–poverty-stricken students; no significant association between DII and depressive symptom improvement was found in participants with baseline depressive symptoms. Conclusions: This study is among the first to prospectively examine dietary inflammatory potential and depressive symptoms in university students using long-term objective dietary monitoring. Unlike studies relying on self-reported dietary surveys, this study used an automated and precise campus-based IOS to continuously capture real-world dietary behaviors over 3 years. The findings indicate that sustained anti-inflammatory dietary patterns are associated with a lower risk of depressive symptoms among Chinese university students, although this protective effect was weaker in students experiencing family discord or socioeconomic disadvantage. These findings provide new longitudinal evidence for the diet-inflammation-mental health relationship and support integrated campus interventions combining dietary guidance with psychosocial support.

Development of Virtual Reality Health Literacy: Delphi Expert Consensus Study

2026-06-24T15:30:18-04:00

Background: Virtual reality (VR) is a promising tool in health care, offering immersive and interactive environments that can enhance patient education, rehabilitation, and mental health interventions. However, effective patient engagement with head-mounted display (HMD)–based immersive VR depends on a combination of functional competencies and readiness-related determinants that have not yet been systematically defined. Objective: This study aimed to conceptualize an initial framework of VR health literacy, focused on HMD-based immersive VR in clinical settings, and to achieve expert consensus on its definition, domains, and subdomains. Methods: A 3-phase modified Delphi study was conducted between January and April 2024, including a literature review in MEDLINE (via PubMed) and Embase (2017-2023) informed by scoping review methodology, a multidisciplinary expert panel formation, and 2 online survey rounds, in which panelists rated each subdomain on a 4-point necessity scale and provided open-ended feedback, with 15 experts from the health care, VR, and health literacy fields. Consensus was defined using IQRs and agreement thresholds; items with moderate consensus were further evaluated through structured internal deliberation. Results: A total of 15 experts participated in Round 1, and 13 continued to Round 2 (retention rate, 87%). An initial structure of 7 candidate domains with 23 subdomains was iteratively refined across the two rounds based on consensus levels, expert panel feedback, and internal deliberation, with consensus thresholds applied as guiding criteria rather than automatic exclusion rules; subdomains with moderate consensus were further evaluated through structured internal deliberation to determine theoretical necessity within the framework. The final framework comprised 5 domains and 14 subdomains: performance expectancy (perceived usefulness of VR for health management; expectations of future VR benefits); effort expectancy (perceived immersion or embodiment; perceived interactivity and responsiveness; understanding of VR-related terms); facilitating conditions (access to VR devices and platforms; digital knowledge and confidence; technical proficiency with VR devices; digital self-efficacy); attitudes toward VR (awareness of VR in health contexts; interest in VR technology; problem-solving ability using VR content); and behavioral intention (intention to use VR technology or services; willingness to engage with VR for health). Conclusions: This study presents an initial consensus-based framework of VR health literacy for HMD-based immersive VR in clinical settings. Developed through a multidisciplinary Delphi process, the framework combines operational competencies with engagement-related determinants to provide both theoretical clarity and practical use, offering guidance for clinicians, educators, and policymakers to design and implement VR interventions that are accessible, equitable, and effective in health care contexts.

The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review

2026-06-24T15:30:18-04:00

Background: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet, access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)–enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. Objective: This systematic review aimed to synthesize empirical evidence on how AI-enabled technologies are used for self-directed stress management. We mapped the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. Methods: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)–compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed, MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). Results: Of 3008 records identified, 35 studies met the inclusion criteria. The methodological quality of included studies was critically appraised using the Mixed Methods Appraisal Tool (version 2018). Findings illustrated that AI-supported stress management can operate through 5 core functions, including psychological intervention, behavioral support, psychoeducation, companionship, and emotional support, and stress monitoring, detection, and triage. Across the reviewed studies, these functions supported self-directed stress management by helping users identify stress, regulate responses, and engage in coping outside formal clinical care. Conclusions: AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks. Trial Registration: PROSPERO CRD420251135780; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251135780

Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study

2026-06-24T15:30:18-04:00

Background: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing research using various video data sources and analytical approaches, no comprehensive framework exists to classify these technologies. Objective: This scoping review aimed to develop and validate a taxonomy for AI technologies in vision-based epilepsy monitoring and to characterize visual AI approaches in epilepsy care. Methods: Using an extended taxonomy development framework, we developed the taxonomy in 5 iterative cycles, drawing on theory and practice. We conducted a scoping review, market analysis, and applicability evaluation with market-ready solutions. We searched Scopus, Web of Science, and PubMed, including MeSH (Medical Subject Headings) terms; the final search was completed on January 16, 2026. We included primary studies from 2013 onward on AI-based or machine learning–based monitoring or prediction of epileptic seizures in humans using visual data. We excluded reviews, non-English publications, nonepilepsy studies, studies focused only on electroencephalography or wearables, animal studies, and pre-2013 publications. Evidence was charted through narrative and tabular synthesis and descriptive frequency analysis. In line with scoping review guidance, we did not conduct a meta-analysis or critical appraisal. To assess validity and practical relevance, 9 domain experts evaluated the taxonomy using a Delphi technique. Results: We included 40 original studies. Study analysis yielded 16 dimensions, including data acquisition source, tracking target, image processing, classifier type, performance metrics, environment, seizure classification, data privacy, and user interface. Expert feedback added 4 further dimensions, including communication mode and information purpose. The final taxonomy comprises 23 dimensions with 102 characteristics. The review identified structural evidence gaps across settings, evaluation maturity, and reporting practices. Detection and classification in stationary settings predominated, whereas predictive approaches and real-time feedback were limited. Deep learning detection methods were common, but performance reporting was inconsistent, and patient-facing functionalities were limited. Privacy safeguards and standardized metrics were often incompletely reported, reducing comparability and maturity assessment. The taxonomy translates these patterns into guidance for benchmarking, procurement evaluation, user interface, and explainable AI design. We synthesized 5 main findings and 10 implications for research and practice. Key challenges concern standardization, seizure prediction, and real-time applicability. Conclusions: Vision-based AI technologies for epilepsy monitoring are still dominated by proof-of-concept and pilot evaluations, indicating a gap between technical feasibility and deployment-ready systems. This scoping review presents an implementation-oriented taxonomy integrating application context, system architecture, visual analysis, AI models, performance reporting, and feedback design into a single classification framework. Unlike prior work that mainly maps methods or data sources, the taxonomy provides a shared structure for consistent system-level characterization and comparison across studies and emerging solutions. It may support benchmarking, implementation-focused evaluation, procurement, and translation into clinical and home settings.

Accessibility of Digital Financial Applications for People With Visual Impairment: Scoping Review

2026-06-23T18:00:27-04:00

Background: Routine financial activities are now conducted primarily through digital channels. Many such systems remain inaccessible to more than 2.2 billion people globally living with vision impairment, limiting independent financial management. Constrained access can create financial strain and social disadvantage, reducing access to health-enabling resources, and contributing to avoidable health inequities. Objective: This scoping review maps evidence on the accessibility of digital financial services for individuals with visual impairment (VI) as a digital determinant of health. We synthesized barriers and facilitators, characterized study designs, settings, and populations, and identified evidence gaps to inform inclusive design, digital health research priorities, and policy. Methods: A scoping review was conducted using the Joanna Briggs Institute framework and reported in line with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eight databases (PubMed, MEDLINE, CINAHL, Scopus, Web of Science, Business Source Complete, ProQuest, and IEEE Xplore) were searched for peer-reviewed papers in English published between 1995 and 2026. Searches featured controlled vocabulary and free-text terms structured in 3 conceptual blocks (VI, digital financial services, and accessibility or usability). A random sample of 20% of titles, abstracts, full texts, and included studies was independently screened or charted by 2 reviewers to calibrate decisions; the remainder were screened and charted by a single reviewer. Data were charted using a standardized extraction form, and results were synthesized descriptively and thematically. Results: Twenty-three studies met the inclusion criteria. Studies were conducted across 12 countries, with the largest number from India (n=7), Indonesia (n=2), Thailand (n=2), and the United States (n=2). Study designs included qualitative studies (n=6), mixed methods studies (n=1), cross-sectional studies (n=4), nonrandomized experimental studies (n=2), and technical or design-focused evaluations (n=6). One study was a large population survey (n=19,136), and the remaining studies with human participants had sample sizes ranging from 4 to 36 participants. Accessibility barriers were reported across all platform types, with authentication-related barriers described in 18 studies and screen reader incompatibility in 17 studies. Reported barriers included reliance on sighted assistance for tasks such as login, verification, and payments, compromising privacy and independence. Facilitators included assistive technology support, logical navigation order, nonvisual feedback mechanisms, and accessible authentication alternatives. Evidence mapping revealed recurrent barrier patterns across Android, iOS, and web platforms. No longitudinal or intervention-based evaluations were identified. Conclusions: This review provides a focused synthesis of accessibility evidence at the intersection of digital financial services and VI, a domain addressed by neither prior digital accessibility reviews nor financial inclusion for people with disabilities. Authentication methods, interface labeling, and navigation were identified as persistent cross-platform accessibility barriers. The findings carry implications for financial technology developers, accessibility auditors, and policymakers implementing accessibility legislation and extend the digital determinants of health framework by demonstrating how inaccessible financial technology may compound health inequities.

Reddit Discussions During the 2022 Mpox Outbreak: Observational Analysis of Sentiment, Topics, and Audience Engagement

2026-06-23T17:15:15-04:00

Background: Public health crises often reshape online discourse by amplifying uncertainty, frustration, stigma, and misinformation, with important implications for risk communication. Objective: This study examines these dynamics on Reddit (Reddit Inc) during a recent outbreak, using Mpox as a case study. Methods: We analyzed sentiment, topical themes, and audience engagement in posts and comments drawn from 4 Mpox-related subreddits. Using natural language processing methods, we applied sentiment analysis and latent Dirichlet allocation to classify 1169 posts and 6571 comments (from July 21, 2021, to July 16, 2025) into sentiment categories and 9 distinct topics. Of the 1169 posts, 611 (52.3%) were neutral, 370 (31.6%) were negative, and 188 (16.1%) were positive. Among comments, 2825 of 6571 (43%) were neutral, 1962 (29.9%) were negative, and 1784 (27.1%) were positive. We then used Kruskal-Wallis tests, Dunn post hoc comparisons, and Vargha-Delaney A to assess relationships among sentiment, topic, and engagement metrics. Results: Engagement differed significantly by sentiment (<.001) and topic (<.001). Negative posts had higher median scores (median 7, IQR 2-27) than positive ones (median 5, IQR 2-16; score=6.02; adjusted <.001; Vargha-Delaney A=0.55). Posts about systemic public health failures (Topic 4) received lower median scores (median 4, IQR 1.75-14.25) than other topics. Topic 9 accounted for 980 of 6571 (14.9%) comments, dominating discussions regardless of original post topic. Positive posts generated 284 of 922 (30.8%) positive comments, whereas negative posts received 526 of 1615 (32.6%) negative comments. Comments on positive posts had higher sentiment scores (Vargha-Delaney A=0.550), whereas comments on negative posts had lower sentiment scores (Vargha-Delaney A=0.463). Topic-level differences in comment sentiment were also observed: comments responding to posts on scientific- and policy-related debates (Topic 8) were more positive (Vargha-Delaney A=0.531), whereas those on systemic failures (Topic 4) were more negative (Vargha-Delaney A=0.478). Conclusions: Overall, the findings highlight how audience reactions can amplify emotionally charged narratives and reframe technical information into socially and politically charged debates. These insights can inform public health communication strategies by anticipating likely audience responses, mitigating stigma and misinformation, and fostering constructive dialogue during health crises.