https://www.jmir.org/issue/feed 2025-01-01T11:30:03-05:00 JMIR Publications editor@jmir.org Open Journal Systems The leading peer-reviewed journal for digital medicine and health and health care in the internet age.  https://www.jmir.org/2026/1/e82414 2026-02-03T16:45:05-05:00 Ying-ying Li A-qian Hu Ling-li Yi Zi-xin Mao Qiu-yue Lü Juan Wang Wei Wei Yue-qi Huang Shu Huang Wen-jing Dai Meng-xuan Qiao Jia-jun Xu Qiang Wang Xiao-jing Li Fu-gang Luo Wei Deng Yu-zheng Hu Tao Li Wan-jun Guo

Background: Both Internet Gaming Disorder (IGD) and Internet Addiction (IA) have been associated with diverse psychopathological symptoms. However, how the two conditions relate to each other and which is more strongly associated with psychopathology remain unclear. Objective: This study aimed to examine the association between IGD and IA and to compare the strength of their associations with various types of psychopathological symptoms. Methods: This cross-sectional study surveyed three independent samples of Chinese adolescents: the first sample (S1) comprised 8,194 first-year undergraduates at a comprehensive university in Chengdu; the second sample (S2) comprised 1,720 students from a high school in Hangzhou; and the third sample (S3) comprised 551 inpatients aged 13–19 years recruited from two tertiary psychiatric hospitals in Hangzhou and Chengdu. IGD was defined as a score ≥ 22 on the Internet Gaming Disorder Scale–Short Form (IGDS9-SF), whereas IA was defined as a score ≥50 on Young’s 20-item Internet Addiction Test (IAT-20). Symptoms of depression, anxiety, psychoticism, paranoid ideation, and attention-deficit/hyperactivity were assessed using internationally validated scales including Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), psychoticism and paranoid Ideation Subscales of the Symptom Checklist 90 (absence for S2) and Adult ADHD Self-Report Scale (absence for S1), though online surveys in S1 (October 2020) and S3 (January 2022 to February 2025) and via an offline survey in S2 (March 2024). Results: Prevalence estimates (95% CIs) of IGD were 4.8% (4.3–5.2%) in S1, 15.8% (14.0–17.5%) in S2, and 32.3% (28.4–36.2%) in S3, whereas prevalence estimates of IA were consistently higher across samples, ranging from 7.3% (6.8–7.9%) in S1 and 18.8% (17.0–20.6%) in S2 to 45.9% (41.8–50.1%) in S3. IGDS9-SF and IAT-20 were moderately correlated (Pearson’s r = 0.51–0.57, all p < .001) and were associated with the severity of most psychopathological symptom domains, with consistently stronger associations observed for IAT-20 scores. In multivariate models including all psychopathological symptoms as independent variables, the coefficients of determination (R²s; 95% CIs) were consistently higher for IAT-20 than for IGDS9-SF in S1 (0.33 [0.30–0.35] vs. 0.13 [0.11–0.16]) and S2 (0.44 [0.39–0.49] vs. 0.23 [0.18–0.27]), with a similar but nonsignificant pattern observed in S3 (0.13 [0.06–0.26] vs. 0.06 [0.03–0.16]). Post hoc analyses indicated that psychopathological symptoms were generally more severe in individuals with IA, either alone or comorbid with IGD, than in those with IGD only. Conclusions: This study found that IGD and IA are distinct yet interrelated constructs, with IA showing consistently stronger associations with psychopathological symptom severity, and it extends prior work by comparing the associations of IGD and IA with psychopathological symptom severity across three independent adolescent samples. These findings underscore the importance of recognizing and addressing compulsive and problematic online behaviors that extend beyond gaming, highlighting the need to refine diagnostic frameworks and prioritize targeted clinical interventions.

2026-02-03T16:45:05-05:00 https://www.jmir.org/2026/1/e79671 2026-02-03T16:30:09-05:00 Jun Tao Ellie Pavlick Amaris Grondin Josue D Bustamante Harrison Martin Hannah Parent Natalie Fenn Alexi Almonte Amanda Maguire-Wilkerson Mofan Gu Jack Rusley Bryce K Perler Tyler Wray Amy S Nunn Philip A Chan

Background: The HIV epidemic in the United States disproportionately impacts gay, bisexual, and other men who have sex with men (MSM). Despite the effectiveness of HIV pre-exposure prophylaxis (PrEP) in preventing HIV acquisition, uptake among MSM remains suboptimal. Motivational interviewing (MI) has demonstrated efficacy at increasing PrEP uptake among MSM but is resource-intensive, limiting scalability. The use of artificial intelligence (AI), particularly large language models with conversational agents (i.e., “chatbots”) such as ChatGPT, may offer a scalable approach to delivering MI-based counseling for PrEP and HIV prevention. Objective: This study aimed to describe the development of an AI-based chatbot and evaluate its ability to provide MI-aligned education about PrEP and HIV prevention. Methods: The Chatbot for HIV Prevention and Action (CHIA) was built on a GPT-4o base model embedded with a validated knowledge database on HIV and PrEP in English and Spanish. CHIA was fine-tuned through training on a large MI dataset and prompt engineering. Use of the AutoGen multi-agent framework enabled CHIA to integrate two agents, the PrEP Counselor Agent and the Assistant Agent, which specialized in providing MI-based counseling and handling function calls (e.g., assessment of HIV risk), respectively. During internal testing from March 10-April 28, 2025, we systematically evaluated CHIA’s performance in English and Spanish using a set of five-point Likert scales to measure accuracy, conciseness, up-to-dateness, trustworthiness, and alignment with aspects of the MI spirit (e.g., collaboration, autonomy support) and MI-consistent behaviors (e.g., affirmation, open-ended questions). Descriptive statistics and independent samples t tests were used to analyze the data. Results: A total of 305 responses, including 140 English responses and 165 Spanish responses, were collected during the internal testing period. Overall, CHIA demonstrated strong performance across both languages, receiving the highest combined scores in the general response quality metrics including up-to-dateness (mean 4.6, SD 0.8), trustworthiness (mean 4.5, SD 0.9), accuracy (mean 4.4, SD 0.9), and conciseness (mean 4.2, SD 1.1). CHIA generally received higher combined scores for metrics that assessed alignment with the MI spirit (i.e. empathy, evocation, autonomy support, and collaboration) and lower combined scores for MI-consistent behaviors (i.e. affirmation, open-ended questions, and reflections). Spanish responses had significantly lower mean scores than English responses across nearly all MI-based metrics. Conclusions: These findings highlight the potential of AI-based chatbots including CHIA as a scalable tool for delivering MI-aligned counseling in English and Spanish to promote HIV prevention and PrEP uptake.

2026-02-03T16:30:09-05:00 https://www.jmir.org/2026/1/e88495 2026-02-03T16:01:03-05:00 Jia-Yan Pan Jonas Rafi 2026-02-03T16:01:03-05:00 https://www.jmir.org/2026/1/e82170 2026-02-03T16:00:25-05:00 Xueyao Cai Weidong Li Wenjun Shi Yuchen Cai Jianda Zhou

<strong>Background:</strong> While artificial intelligence (AI) holds significant promise for health care, excessive trust in these tools may unintentionally delay patients from seeking professional care, particularly among patients with chronic illnesses. However, the behavioral dynamics underlying this phenomenon remain poorly understood. <strong>Objective:</strong> This study aims to quantify the influence of AI trust on health care delays through integrated survey-based mediation analysis and real-world research, and to simulate intervention efficacy using agent-based modeling (ABM). <strong>Methods:</strong> A cross-sectional online survey was conducted in China from December 2024 to May 2025. Participants were recruited via convenience sampling on social media (WeChat and QQ) and hospital portals. The survey included a 21-item questionnaire measuring AI trust (5-point Likert scale), AI usage frequency (6-point scale), chronic disease status (physician-diagnosed, binary), and self-reported health care delay (binary). Responses with completion time &lt;90 seconds, logical inconsistencies, missing values, or duplicates were excluded. Analyses included descriptive statistics, multivariable logistic regression (α=.05), mediation analysis with nonparametric bootstrapping (500 iterations), and moderation testing. Subsequently, an ABM simulated 2460 agents within a small-world network over 14 days to model behavioral feedback and test 3 interventions: broadcast messaging, behavioral reward, and network rewiring. <strong>Results:</strong> The final sample included 2460 adults (mean age 34.46, SD 11.62 years; n=1345, 54.7% female). Higher AI trust was associated with increased odds of delays (odds ratio [OR] 1.09, 95% CI 1.00-1.18; <i>P</i>=.04), with usage frequency partially mediating this relationship (indirect OR 1.24, 95% CI 1.20-1.29; <i>P</i>&lt;.001). Chronic disease status amplified the delay odds (OR 1.42, 95% CI 1.09-1.86; <i>P</i>=.01). The ABM demonstrated a bidirectional trust erosion loop, with population delay rates declining from 10.6% to 9.5% as mean AI trust decreased from 1.91 to 1.52. Interventions simulation found broadcast messaging most effective in reducing delay odds (OR 0.94, 95% CI 0.94-0.95; <i>P</i>&lt;.001), whereas network rewiring increased odds (OR 1.04, 95% CI 1.04-1.05; <i>P</i>&lt;.001), suggesting a “trust polarization” effect. <strong>Conclusions:</strong> This study reveals a nuanced relationship between AI trust and delayed health care–seeking. While trust in AI enhances engagement, it can also lead to delayed care, particularly among patients with chronic conditions or frequent AI users. Integrating survey data with ABM highlights how AI trust and delay behaviors can strengthen one another over time. Our findings indicate that AI health tools should prioritize calibrated decision support rather than full automation to balance autonomy, odds, and decision quality in digital health. Unlike previous studies that focus solely on static associations, this research emphasizes the dynamic interactions between AI trust and delay behaviors.

2026-02-03T16:00:25-05:00 https://www.jmir.org/2026/1/e78245 2026-02-03T16:00:04-05:00 Jingjing Chen Jianjuan Zhao Huiyu Qiu Yanhui Liu Yunqi Zhang Qicheng Sun Yan Yi Hongying Tang Jing Zhao Bin Xu Qiong Zhang Ge Yang Hui Li Junjie Liu Zhongzhou Yang Shaolin Liang Yanping Li Jing Fu

<strong>Background:</strong> Accurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet challenging for effective ovarian stimulation. Currently, there is a lack of a comprehensive model capable of simultaneously forecasting the number of oocytes retrieved (NOR) and assessing the risk of early-onset moderate-to-severe ovarian hyperstimulation syndrome (OHSS). <strong>Objective:</strong> This study aimed to establish an integrated mode capable of forecasting the NOR and assessing the risk of early-onset moderate-to-severe OHSS across varying starting doses of FSH. <strong>Methods:</strong> This prognostic study included patients undergoing their first ovarian stimulation cycles at 2 independent in vitro fertilization clinics. Automated classifiers were used for variable selection. Machine learning models (11 for NOR and 11 for OHSS) were developed and validated using internal (n=6401) and external (n=3805) datasets. Shapley additive explanation was applied for variable interpretation. The best-performing models were incorporated into a web-based prediction tool. <strong>Results:</strong> For NOR prediction, 17 variables were selected, with the gradient boosting regressor achieving the highest performance (internal dataset: <i>R</i>²=0.7978; external dataset: <i>R</i>²=0.7924). For OHSS prediction, 19 variables were identified, and the LightGBM model demonstrated superior performance (internal dataset: area under the receiver operating characteristic curve=0.7588; external dataset: area under the receiver operating characteristic curve=0.7287). Shapley additive explanation analysis highlighted the FSH starting dose to BMI ratio and baseline antral follicle count as key predictors for NOR and OHSS, respectively. Dose-response curves were generated to visualize predicted outcomes with varying FSH starting doses. The models were implemented in a user-friendly, research-oriented online prototype, individualized ovarian stimulation guide (InOvaSGuide). <strong>Conclusions:</strong> This study introduces an integrated framework for predicting NOR and early-onset moderate-to-severe OHSS risk across different FSH doses. Future prospective evaluation is needed before clinical implementation.

2026-02-03T16:00:04-05:00 https://www.jmir.org/2026/1/e91456 2026-02-03T15:30:10-05:00 Catharine Solomon

2026-02-03T15:30:10-05:00 https://www.jmir.org/2026/1/e84532 2026-02-02T12:00:04-05:00 Xite Zheng Lu Xing Haitao Lu Shimeng Hao Fen Liu

<strong>Background:</strong> Traditional stroke rehabilitation is facing challenges, and virtual reality (VR)–based rehabilitation is a promising solution. However, results from studies focusing on VR-based stroke rehabilitation remain inconsistent, largely due to the use of noncustomized interventions in previous trials. <strong>Objective:</strong> To enhance rehabilitation services and inform the development of patient-centered VR rehabilitation systems, this study aimed to (1) explore the experiences and unmet needs of survivors of stroke during current hospital rehabilitation, and (2) examine their perspectives on the use of VR technology in poststroke rehabilitation. <strong>Methods:</strong> We conducted a qualitative thematic analysis based on descriptive phenomenology between January and July 2025 at the China Rehabilitation Research Center. Adult patients with a clinical diagnosis of stroke within the past 18 months were eligible. A total of 21 survivors of stroke (mean age, 52.7, SD 17.3 y; men, n=17) were included. Data were collected through face-to-face semistructured interviews, complemented by a short questionnaire on sociodemographic, clinical, and technology-use characteristics. All interviews were audio-recorded, transcribed verbatim, and analyzed using a thematic approach, with thematic saturation used to determine the sample size. <strong>Results:</strong> After a stroke, patients experience significant physical and psychological changes. On the one hand, the sudden loss of abilities alters their perceived roles within the family and society; on the other hand, the sharp contrast between their desire for recovery and their current recovery limitations creates substantial psychological pressure. Accepting their condition and rebuilding confidence is a long-term process. Traditional rehabilitation is commonly described as burdensome, monotonous, and lacking continuity after discharge. Although patients desire a better rehabilitation approach and improved outcomes, attitudes toward VR-based rehabilitation vary. Some view VR as a convenient tool, while others express no interest or perceived need for technology-based rehabilitation. Patients indicated that serious games should be diversified to meet different individual and training needs, and should incorporate clearer feedback mechanisms, appropriate scoring systems, adjustable difficulty levels, and progressive game chapters. Functional expectations for VR systems included family involvement, access to personal rehabilitation data, telerehabilitation support, safety monitoring, and technical support. <strong>Conclusions:</strong> Stroke rehabilitation services in China require improvement in the appeal of rehabilitation content, patient self-management, and continuity of care. Although patients desire better rehabilitation approaches and outcomes, the effective integration of VR technology must account for factors, such as personal characteristics and preferences, as well as socioeconomic status. Unlike previous studies that primarily examined user experiences with digital technologies or compared rehabilitation outcomes, our research contributes to the literature by linking the challenges and patient needs in conventional rehabilitation with concrete directions for the future design of VR rehabilitation. These insights deepen current understanding of how VR technologies can be meaningfully integrated into stroke care and provide a roadmap for developing patient-centered and culturally responsive VR solutions.

2026-02-02T12:00:04-05:00 https://www.jmir.org/2026/1/e82773 2026-02-02T11:45:03-05:00 Kristine Aasness Fjeldstad Anne Therese Tveter Eivor Rasmussen Lena Olden Sissel Nyheim Thalita Blanck Rikke Munk Killingmo Ingvild Kjeken

<strong>Background:</strong> People with hand osteoarthritis represent a large patient group with limited access to recommended treatment. In recent years, there has been a notable shift in health care delivery, with increased use of digital technologies. The Happy Hands app (The University Information Technology Center [USIT]) is a digital self-management intervention developed to provide evidence-based treatment for people with hand osteoarthritis, with the goal of empowering them to self-manage their disease. Participants’ experiences and perceptions of using this digital intervention are crucial for the adoption and continued use of the Happy Hands app. <strong>Objective:</strong> The objective of this qualitative study was to explore participants’ experience with using the Happy Hands app, focusing on whether and how it empowered them to self-manage their hand osteoarthritis. <strong>Methods:</strong> The study is embedded within a randomized controlled trial (RCT). The participants were recruited from the intervention group in the RCT, who got access to the Happy Hands app. The 12-week self-management intervention included a hand exercise program and informational videos about hand osteoarthritis. Focus groups were conducted in various geographical areas in Norway. The focus groups were transcribed verbatim, coded, and analyzed inductively using reflexive thematic analysis. <strong>Results:</strong> Seven focus groups, with a total of 26 participants, were recruited from both specialist and primary health care. The mean age was 67 years. Three themes were developed from the analysis. The first theme, “Being acknowledged,” highlights the essential role of recognition for people with hand osteoarthritis. It suggests that the Happy Hands app provided participants with a sense of validation and support. The second theme, “Changed perception of hand osteoarthritis,” indicates that participants gained insights and knowledge about their condition. This new understanding empowered them to make more informed decisions about their care, fostering a sense of hope and motivation by demonstrating that effective measures are available to manage the disease. The third theme, “Changing habits with the Happy Hands app,” describes how participants developed new habits after using the self-management intervention delivered through the app. The exercise program was experienced as motivating, flexible, well-structured, and committing. Some challenges were reported, including experiencing pain during or after exercising. The new habits included performing hand exercises and implementing ergonomic working methods, which were tailored to meet the individual needs and integrated into the participants’ daily lives and routines. <strong>Conclusions:</strong> The findings suggest that the Happy Hands app is a valuable tool for supporting people with hand osteoarthritis in managing their disease by helping them integrate hand osteoarthritis management into their daily lives. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT05568875; https://clinicaltrials.gov/study/NCT05568875

2026-02-02T11:45:03-05:00 https://www.jmir.org/2026/1/e75591 2026-02-02T11:30:10-05:00 Yosefa Birati Roy Tzemah-Shahar

Background: The transformation of digital health technologies has reshaped how healthcare is delivered, particularly in primary care. However, despite the advantages of these innovations, older adults remain among the most resistant users. Traditional technology adoption models may not fully capture the complexity of this reluctance, which is shaped not only by usability challenges but also by emotional, psychological, and identity-related concerns. Innovation Resistance Theory (IRT) offers a complementary framework focused on understanding barriers to adoption rather than solely on facilitators. Objective: To map and synthesize evidence on older adults’ resistance to digital health technologies in primary care through the lens of IRT, and to examine how empirically observed resistance factors align with, extend, or refine IRT’s functional and psychological barriers. Methods: A scoping review combined with concept-driven thematic synthesis was conducted. Empirical studies published between 2014 and 2025 were identified through systematic searches across five databases: PubMed, CINAHL, Ovid Medline, Web of Science, and Scopus. Inclusion criteria focused on studies examining barriers or resistance to digital health use among older adults aged 60 and above in primary care settings. The search was guided by terms related to “older adults”, “digital health”, “eHealth”, “telemedicine”, and “technology resistance”. After screening and reviewing the full texts, data were extracted into a structured matrix, and findings were organized according to the five dimensions of the IRT: usage, value, risk, tradition, and image barriers. Results: Of 4,976 identified records, seventeen studies met the inclusion criteria. Functional barriers included usability challenges, interface complexity, and age-related impairments. Psychological resistance was frequently linked to emotional discomfort, symbolic misalignment, and concerns about the loss of relational care. Value and risk concerns included distrust in diagnostics accuracy, concerns regarding privacy and data security, and skepticism about care quality. Traditional preferences for face-to-face interactions and generational digital divides further reinforced image-based resistance. A key finding was the interaction between barriers, where low self-efficacy and technology anxiety create feedback loops that reinforce avoidance behaviors. Conclusions: Resistance to digital health among older adults is not simply a lack of adoption but a complex, emotionally grounded process involving functional, psychological, and identity-based barriers. Interventions must go beyond technical usability to rebuild emotional trust, preserve the relational aspects of care, and align digital solutions with the values and expectations of older adults. Innovation Resistance Theory offers a comprehensive framework for understanding these multifaceted dynamics and serves as a valuable guide for policy development, user-centered design, and future research Clinical Trial: None

2026-02-02T11:30:10-05:00 https://www.jmir.org/2026/1/e71960 2026-02-02T11:30:04-05:00 Julia Adams Jonathan Davies Prai Wattanatakulchat Julieta Galante Felicity Miller Simon D'Alfonso Nicholas T Van Dam

<strong>Background:</strong> Meditation apps are increasingly popular, yet there is limited understanding of how much users actually engage with them. While meditation apps show promise for supporting mental health, engagement in real-world settings appears to be notably low. The patterns of app use and the factors that influence usage remain relatively unclear. <strong>Objective:</strong> This study aims to examine the extent of meditation app use and the factors associated with user engagement. <strong>Methods:</strong> We conducted a cross-sectional survey of 536 recent meditation app users across 5 English-speaking countries. Engagement data were collected via self-report and app-verified screenshots. Assessed factors included user characteristics (age, education, income, sex, country, personality, self-efficacy, readiness and expectations for change, self-compassion, and quality of life), mental health (distress, well-being, life satisfaction, anxiety, depression, support, and stress), and app-related elements (therapeutic alliance, appeal, functionality, aesthetics, information, quality, and perceived impact). The 4 outcome variables representing engagement were app-verified minutes, self-reported minutes, app-verified minutes per year (adjusted for app download date), and self-reported minutes per year (adjusted for app download date). Associations between app use and variables of interest were examined using correlations. Factors with significant associations were then included in multivariable regression models to identify those most strongly associated with engagement. <strong>Results:</strong> Age (ρ=0.13-0.15, PP^FDR, where FDR is false discovery rate), expectations for sleep (ρ=0.12-0.33, P^FDR&lt;.05), and expectations for thriving (ρ=0.12-0.18, P^FDR&lt;.05) were associated with all outcome measures except adjusted objective minutes. Readiness to change was associated with all outcome measures (ρ=0.24-0.33, P^FDR&lt;.05). Among app factors, appeal (ρ=0.18-0.23, P^FDR&lt;.05) and perceived impact (ρ=0.23-0.32, P^FDR&lt;.05) were associated with all outcome measures except adjusted self-report minutes, while perceived quality (r=0.28-0.51, P^FDR&lt;.05) was associated with all outcome measures. Robust linear regressions showed that greater readiness to change (β=0.005-0.026, <i>P</i>=.006-.02), higher education level (β=0.029-0.540, <i>P</i>&lt;.001), and higher openness (β=0.004-0.010, <i>P</i>=.008-.03) were associated with increased engagement. Additionally, greater expectations for sleep (β=0.004-0.009, <i>P</i>=.02-.04), greater expectation match (β=0.023, <i>P</i>=.03), and higher perceived app quality (β=0.008-0.042, <i>P</i>=.001-.01) were uniquely associated with increased engagement. <strong>Conclusions:</strong> Most individuals who download meditation apps engage minimally. Our findings suggest that users who are more educated, open to new experiences, and hold strong beliefs in the effectiveness of meditation apps are more likely to use them regularly. Longitudinal studies are needed to examine patterns of use and strengthen causal inferences.

2026-02-02T11:30:04-05:00