Journal of Medical Internet Research

Effects of Virtual Reality on Pain, Anxiety, and Fear During Thyroid Fine-Needle Aspiration Biopsy: Open-Label Randomized Controlled Trial

2026-10-06T12:00:02-04:00

Background: Thyroid fine-needle aspiration biopsy (FNAB) is a commonly used diagnostic procedure in patients with suspected thyroid cancer; however, it may induce pain, anxiety, and fear during the procedure. Objective: This open-label randomized controlled trial aimed to evaluate the effect of virtual reality (VR) on pain as the primary outcome and anxiety and fear of pain as secondary outcomes in patients undergoing thyroid FNAB. Methods: The study was conducted between January 19, 2025, and April 30, 2025, at Gaziantep City Hospital, Türkiye. A total of 100 patients with suspected thyroid nodules were randomly assigned to either a VR intervention group (n=50) or a control group (n=50). Data were collected using a patient information form, the visual analog scale (VAS), the Beck Anxiety Inventory (BAI), and the Fear of Pain Questionnaire-III (FPQ-III). Results: After adjustment for baseline pain, previous thyroid mass diagnosis, and voice tone changes, the VR group had statistically significantly lower postintervention pain scores than the control group (adjusted mean 3.627 vs 4.493; F_1,95=4.021; P=.048; partial η²=0.041). However, the unadjusted between-group comparison for pain was not statistically significant (P=.12), and the unadjusted effect size was small, with a 95% CI that crossed 0 (Cohen d=−0.33, 95% CI −0.72 to 0.07). No statistically significant adjusted between-group differences were observed for anxiety (P=.48) or fear of pain (P=.07). Unadjusted standardized between-group effect sizes were also small for anxiety (d=−0.17) and fear of pain (d=−0.11). Conclusions: The adjusted analysis suggested a small reduction in procedural pain with VR; however, the between-group difference was not statistically significant in the unadjusted analysis and reached statistical significance only after adjustment for baseline pain and 2 nonprespecified covariates selected on the basis of observed baseline imbalance. Moreover, the observed adjusted effect (f=0.207) was smaller than the minimum effect size the trial was powered to detect (f=0.283). No statistically significant adjusted between-group effects were found for anxiety or fear of pain. Therefore, the potential analgesic effect of VR should be interpreted cautiously and confirmed in larger, adequately powered trials. Trial Registration: ClinicalTrials.gov NCT06792929; https://clinicaltrials.gov/study/NCT06792929

The United Kingdom’s New Blueprint for Regulating AI in Health Care

2026-10-05T16:15:14-04:00

Stage-Based Model of User Engagement Patterns in an Online Health Community for Cardiovascular Disease Management: Qualitative Interview Study

2026-10-05T16:15:14-04:00

Background: Online health communities (OHCs) provide vital peer support and health information to individuals managing chronic conditions. However, sustained user engagement remains challenging, with many users reducing activity over time despite the ongoing benefits these platforms offer. While some individuals may improve or become more knowledgeable, sustained engagement remains critical because ongoing participation fosters trust, peer support, and continuous access to evolving health information that may persist beyond initial recovery or learning. Hence, it is important to consider how community needs evolve as users’ health needs and information-seeking behaviors change to inform how we support users through different stages of their health and participation journeys. Objective: The aim of the study is to examine user engagement in a large OHC, identifying perceived stage-based behaviors, motivation, barriers, and design opportunities that could facilitate progression between stages and enhance long-term participation. Methods: We conducted semistructured interviews with 19 members of the American Heart Association Support Network Community. Participants were patients or survivors managing various cardiovascular diseases. Using narrative thematic analysis, we examined users’ perceived engagement motivation, behavior, challenges, and design opportunities across different stages of their community involvement. Results: This study highlighted 4 distinct engagement stages: discovery (crisis-driven initial engagement), exploration (navigation and orientation), commitment (active engagement and information management), and integration (sustained engagement and mentorship). Key barriers included information architecture complexity, concerns about misinformation, limited support for role transitions, and decreased participation as health management improved. Participants identified opportunities through which OHCs could increase long-term engagement, including adaptive recommendation systems, health information literacy programs, structured role transition support, and alternative engagement modalities, such as synchronous interactions and health tracking tools. Conclusions: User engagement in OHCs is dynamic and evolves with changes in health status, knowledge, and personal circumstances. Supporting sustained engagement requires stage-appropriate interventions, including personalized content delivery, health information literacy education, structured pathways for role transitions, and diversified engagement options. These findings provide actionable insights for designing OHCs that better support users throughout their health journey.

From Measurement Failure to Privacy Infrastructure: Reframing Contact Tracing Governance for the Next Pandemic

2026-10-05T16:00:18-04:00

No measurement, no understanding; no understanding, no control: this foundational scientific principle was exposed as a public health dysfunction by the COVID-19 pandemic. Transmission chains spread invisibly, and the contact histories, mobility patterns, and biosignals necessary for control were never systematically collected. Although sensors and digital technologies existed, the fundamental reason measurement failed was the absence of privacy infrastructure that would have enabled people to provide data with confidence. This failure had structural reasons. The object of measurement in infectious disease control is not a physical phenomenon but human beings, and measurement therefore enters the core of privacy: contact histories, social relationships, and bodily states. Because greater precision also deepens privacy intrusion, contact-tracing apps faced 2 failures: privacy-centered designs lost epidemiological utility, while utility-centered designs were rejected through public distrust. Neither achieved sufficient measurement. This Viewpoint reframes the problem. Privacy protection is not a constraint that impedes infectious disease control but the enabling condition upon which effective measurement depends. Existing regulations and technical methods have not been designed from this premise and have therefore failed to break the cycle of structural distrust. As an institutional approach to filling this gap, we present VRAIO (verifiable record of AI output), which integrates democratic rule-setting, metadata declaration, third-party verification, tamper-proof ledgers, and violation-deterrence incentives. Once privacy infrastructure is established, this foundational principle can operate freely in infectious disease control for the first time. It will enable high-resolution epidemiology and precision intervention, opening a new path for public health that reconciles infection control with individual autonomy and social freedom without relying on blanket social shutdowns.

10 Years of BRAVE Self-Help Service Delivery for Child and Adolescent Anxiety in Australia: Open Effectiveness-Implementation Trial

2026-10-05T16:00:10-04:00

Background: Self-directed digital mental health programs can increase access to evidence-based interventions for child anxiety. However, there has been little comprehensive investigation of real-world dissemination, especially using implementation science approaches. Objective: This study aimed to describe and comprehensively assess the implementation of the BRAVE Self-Help program for child and adolescent anxiety in Australia over a 10-year period, including implementation strategies during establishment and sustainability phases and implementation-effectiveness outcomes. Methods: This study was a large, open implementation-effectiveness trial involving Australian child and adolescent participants in the BRAVE Self-Help Program, conducted between January 1, 2015, and December 31, 2024. Implementation strategies were reported descriptively across program establishment and sustainability phases. Implementation and effectiveness outcomes were reported across 10 years, in line with the implementation outcomes framework (IOF) and taxonomy of implementation outcomes for digital interventions. Metrics included adoption, penetration, appropriateness, fidelity, feasibility, acceptability, and effectiveness. Results: Over the 10-year period, the BRAVE Self-Help program had 53,726 users, including 28,700 children (mean age 9.36, SD 1.40 years) and 25,026 adolescents (mean age 14.15, SD 1.62 years). Data revealed a wide variety of user characteristics in terms of age, gender, geographical location, baseline severity, and referral sources (community, health, and education settings). Improvement in anxiety symptoms from first to last interaction with the program was observed for participants in general, particularly among those with elevated anxiety at program registration (<.001; Cohen =0.54). Of all users completing the registration assessment, 38.72% (20,026/51,721) completed 3 or more of the 10 sessions, with consistent moderate to high satisfaction rates across all sessions. Conclusions: This study demonstrated that a self-help digital program for child and adolescent anxiety can be successfully disseminated nationally, and that it is feasible, acceptable, and effective for many young people. During a span of 10 years, the BRAVE Self-Help program for child and adolescent anxiety offered evidence-based support to more than 50,000 families. Notably, although progress through sessions was low for some young people, significant improvements could be made in as few as 3 sessions, and decisions to stop treatment occurred for many reasons, including treatment success.

AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review

2026-10-05T16:00:10-04:00

Background: Intrinsic capacity (IC) has become a central concept in healthy aging because it emphasizes functional ability across the aging trajectory rather than disease alone. However, current IC assessment primarily relies on episodic clinical evaluations, which are insufficient for continuous monitoring and early identification of functional decline. Recent advances in AI and digital health technologies have created new opportunities for objective, continuous, and real-world assessment of IC. However, existing evidence remains fragmented across AI-enabled devices, digital biomarkers (DBs), AI techniques, and IC domains. Objective: This scoping review aimed to systematically synthesize the current evidence on AI-based measurement tools for IC and to characterize the landscape of AI-enabled IC assessment using a 3D analytical framework integrating AI-enabled digital devices and systems, DBs, and AI techniques. Methods: A comprehensive search of PubMed, Embase, CINAHL, PsycINFO, the Cochrane Library, SinoMed, and China National Knowledge Infrastructure (CNKI) was conducted from database inception to July 2025 and updated on May 31, 2026, in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline. Studies investigating AI-based measurement tools applicable to one or more IC domains were included. Results: A total of 161 studies met the inclusion criteria. Research on AI-based measurement tools for IC has expanded rapidly since 2016, with studies conducted in 28 countries, predominantly the United States and China. Most studies focused on a single IC domain, with cognition accounting for the largest proportion. Eleven categories of AI-enabled digital devices and systems were identified, among which multimodal data acquisition devices, computer vision (CV) systems, and AI-driven health platforms were the most frequently reported. Twenty-one types of DBs were extracted and classified into 3 major categories, with gait parameters, digital task performance, physical activity features, speech and language features, and facial features representing the most commonly used biomarkers. Machine learning and deep learning were the predominant AI techniques, while CV and natural language processing played central roles in multimodal data interpretation. The distribution and maturity of evidence varied substantially across domains, with cognition and locomotor capacity representing the most developed areas, whereas vitality, hearing, and multidomain IC assessment remained comparatively underrepresented. Conclusions: This scoping review provides a 3D synthesis of AI-enabled digital devices and systems, DBs, and AI techniques across the 6 World Health Organization (WHO)–defined domains of IC. Unlike previous technology-, disease-, or domain-specific reviews, it compares evidence across the broader IC framework, identifying more developed areas, key evidence gaps, and priorities for standardization, external validation, and multidomain assessment. AI-based measurement tools may complement conventional assessment in community, primary care, and home settings, although their clinical translation will require robust validation, integration into care pathways, and implementation approaches that address the needs of older adults.

Efficacy of a Prescription-Based Mobile Digital Therapeutic as an Adjunct to Pharmacotherapy for the Acute Phase of Panic Disorder: Multicenter Randomized Controlled Trial

2026-10-05T15:45:03-04:00

Background: Although pharmacotherapy is the primary treatment for patients with acute-phase panic disorder, its incomplete efficacy causes them to experience frequent panic attacks and severe anticipatory anxiety for a considerable period. A prescription-based mobile digital therapeutic (DTx) that integrates self-guided cognitive behavioral therapy (CBT), real-time symptom management, and lifestyle tracking can be used as an adjunct to pharmacotherapy for these patients, helping them achieve rapid symptom recovery. Objective: This study aimed to evaluate the efficacy of this adjunctive DTx in alleviating symptoms in patients with acute-phase panic disorder. Methods: In total, 66 acute-phase patients experiencing frequent panic attacks were recruited from 6 institutions and randomly divided into a group receiving pharmacotherapy combined with DTx or pharmacotherapy alone, participating in an 8-week multicenter single-blind trial. The DTx app included 3 major services: training service consisting of structured CBT modules, companion service of just-in-time modules for coping with panic attacks, and care service providing daily self-management tracking tools. The self-report scales used as efficacy indicators were administered via paper questionnaires during a total of 4 visits at baseline, week 2, week 4, and week 8. The primary end point, the change in the Panic Disorder Severity Scale-Self Report (PDSS-SR) score, and the secondary end points, such as changes in overall anxiety, depression, panic-related catastrophic cognitions, and fear of bodily sensations, were compared between the 2 groups. Adherence to the DTx was objectively assessed using device use metrics. Results: As 5 enrolled patients were excluded due to insufficient safety analysis or efficacy evaluation, the final analysis included 32 in the DTx group and 29 in the control group. The DTx group showed a significantly greater reduction in PDSS-SR scores at week 8 compared to the control group (mean 40.29%, SD 26.68% vs mean 17.61%, SD 30.37%; =.007). This therapeutic benefit appeared rapidly by week 2 (mean 26.66%, SD 22.03% vs mean 11.05%, SD 21.73%; =.02). The responder (≥40% PDSS-SR reduction) rate was also significantly higher in the DTx group (17/32, 53.12% vs 5/29, 17.24%; =.007). While changes in depression and somatic fears were transient or similar between groups, the DTx group showed significantly greater sustained improvements in overall anxiety and catastrophic cognitions. The overall mean adherence rate was 78.12%, with no significant difference between responders and nonresponders. Conclusions: The adjunctive use of our prescription-based DTx resulted in early and sustained symptom reductions across panic severity, overall anxiety, and catastrophic cognitions, suggesting that this multifunctional and self-guided DTx including just-in-time management and lifestyle tracking can serve as a practical complement to routine psychiatric care for acute-phase patients experiencing frequent panic attacks. This app is expected to present a new framework for the treatment of acute-phase panic disorder by enabling the incorporation of various symptomatic aspects in patients’ daily life into clinicians’ evaluations and guidance in the clinic. Trial Registration: Clinical Research Information Service KCT0010500; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=34277

Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis

2026-10-05T15:45:03-04:00

Background: Cervical degenerative diseases are a global public health issue, and their incidence is rising worldwide. Although an increasing number of studies on traditional machine learning (TML) and deep learning (DL) have been conducted in the detection and segmentation of cervical degenerative diseases and have reported promising task-specific results, the performance of these models has not yet been systematically analyzed. Objective: This systematic review and meta-analysis aimed to summarize and evaluate existing evidence on TML and DL approaches for diagnosing cervical degenerative diseases, thereby comprehensively guiding future research and clinical applications. Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive literature search was conducted on PubMed, Embase, the Cochrane Library, Web of Science, Scopus, and the Institute of Electrical and Electronics Engineers (IEEE Xplore) from January 2000 to June 2026, supplemented by backward and forward citation searching in Scopus. Studies evaluating TML and DL algorithms for diagnosing cervical degenerative diseases using medical imaging were included. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and the Quality Assessment of Diagnostic Accuracy Studies AI (QUADAS-AI) tool. For the primary diagnostic accuracy meta-analysis, data were synthesized using a bivariate mixed-effects logistic regression model. Sensitivity and specificity were summarized separately using random-effects meta-analysis with the Knapp-Hartung adjustment, and 95% prediction intervals (PIs) were reported. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Results: This systematic review included 30 studies, of which 21 involved a total of 25,301 patients included in the meta-analysis. The pooled sensitivity and specificity were 0.92 (95% CI 0.89‐0.96; 95% PI 0.80‐1.00) and 0.88 (95% CI 0.84‐0.91; 95% PI 0.72‐1.00), respectively. The positive likelihood ratio (LR) was 8.36 (95% CI 6.14‐11.36), and the negative LR was 0.07 (95% CI 0.04‐0.11). The area under the summary receiver operating characteristic (SROC) curve was 0.96 (95% CI 0.94‐0.97). Leave-one-out analyses did not materially alter the pooled estimates. High risk of bias was identified in 4 studies using QUADAS-2 and in 17 using QUADAS-AI. The overall certainty of evidence was rated as low according to the GRADE approach. Conclusions: TML and DL models demonstrated satisfactory diagnostic performance for cervical degenerative diseases, although external validation was limited. Unlike previously published reviews in this field, this study provides pooled estimates of the diagnostic performance of TML and DL for cervical degenerative diseases and indicates that, given between-study heterogeneity and low certainty of evidence, AI should currently be used as clinical decision support rather than an independent replacement for physicians.

Beyond AI Literacy: Understanding Health Care Workforce AI Enablement in the Generative AI Era

2026-10-05T15:30:16-04:00

The rapid diffusion of generative AI is transforming health care delivery, education, administration, and research. In response, health care organizations have invested heavily in AI literacy initiatives and workforce development programs. Existing frameworks primarily focus on whether health care professionals understand AI and whether they can engage with AI effectively. However, health care practice increasingly reveals that individuals with similar levels of AI literacy and AI engagement often contribute very differently to AI-enabled work and organizational adoption. Some professionals primarily use AI to improve their own work, whereas others facilitate AI adoption, coordinate stakeholders, and integrate AI into routine practice. This observation suggests that current perspectives may overlook an important dimension of workforce AI enablement. In this Viewpoint, we argue that AI literacy and AI engagement alone provide an incomplete explanation of how health care organizations realize the benefits of AI. Drawing upon literature from AI literacy, fluency theory, human-AI interaction, innovation diffusion, implementation science, and health care workforce development, we propose the health care workforce AI enablement matrix (HWAEM). HWAEM conceptualizes workforce AI enablement through 2 complementary capabilities: AI fluency and AI harnessing. AI fluency refers to the capability to engage with AI effectively, appropriately, and responsibly across professional contexts, whereas AI harnessing refers to the capability to identify opportunities for AI-enabled improvement, mobilize stakeholders, facilitate adoption, and integrate AI into collective work practices. The interaction of these capabilities generates 4 workforce profiles: AI novices, AI practitioners, AI facilitators, and AI leaders. Through HWAEM, this Viewpoint argues that health care workforce AI enablement is better understood through the complementary capabilities of AI fluency and AI harnessing than through AI literacy and AI engagement alone. We illustrate how these profiles manifest in health care practice and discuss implications for workforce development and AI implementation. HWAEM offers a new perspective for understanding health care workforce preparedness in the generative AI era and provides a foundation for future empirical research.

Clinician Experiences With Tele-Emergency Care: Qualitative Study

2026-10-05T15:15:13-04:00

Background: Emergency departments (EDs) face persistent challenges related to overcrowding, boarding, ambulatory care access barriers, and workforce strain, contributing to compromised patient care and high rates of physician burnout. Virtual care has emerged as a potential strategy to alleviate pressure on emergency care systems. In 2020, the Veterans Health Administration (VA) launched the national Tele-Emergency Care (TEC) program, in which patients who call a call center can be connected to an emergency medicine clinician by phone or video. Although virtual care may help address ED capacity and clinician burnout, the perspectives of emergency medicine–trained clinicians remain limited. Objective: The aim of this study is to examine the experiences of emergency medicine clinicians participating in VA’s TEC program. Methods: As part of a national mixed methods evaluation of TEC, we conducted semistructured interviews with clinicians delivering emergency care through TEC between February 2025 and June 2025. Participants (n=15) were recruited via multistage purposeful sampling from 4 of 18 regional TEC programs that varied in geography, volume, duration, and operational models. Interviews explored experiences of providing care in a virtual environment, including perceived benefits and challenges. We performed a descriptive qualitative analysis. Results: We interviewed 14 physicians and 1 nurse practitioner with formal emergency medicine training. Interviewees described four primary themes: (1) clinical decision-making in a virtual environment; (2) development of the provider-patient relationship; (3) clinician job satisfaction and professional well-being; and (4) challenges. Participants reported that TEC provided perceived opportunities to avoid ED referrals, more focused patient interactions, and improved job satisfaction related to flexible virtual shifts. Reported challenges included filling primary care gaps and performing care coordination tasks. Conclusions: TEC represents an emerging model of emergency care delivery that clinicians perceive may expand access, prevent avoidable ED visits, and support clinician well-being while also introducing distinct clinical and operational challenges. Our findings can inform the implementation of similar emergency telehealth services in other health systems.