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Journal Description

The Journal of Medical Internet Research (JMIR) is the pioneer open access eHealth journal, and is the flagship journal of JMIR Publications. It is a leading health services and digital health journal globally in terms of quality/visibility (Journal Impact Factor 6.0, Journal Citation Reports 2025 from Clarivate), ranking Q1 in both the 'Medical Informatics' and 'Health Care Sciences & Services' categories, and is also the largest journal in the field. The journal is ranked #1 on Google Scholar in the 'Medical Informatics' discipline. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth and informatics applications for patient education, prevention, population health and clinical care.

JMIR is indexed in all major literature indices including National Library of Medicine(NLM)/MEDLINE, Sherpa/Romeo, PubMed, PMCScopus, Psycinfo, Clarivate (which includes Web of Science (WoS)/ESCI/SCIE), EBSCO/EBSCO Essentials, DOAJ, GoOA and others. Journal of Medical Internet Research received a Scopus CiteScore of 11.7 (2024), placing it in the 92nd percentile (#12 of 153) as a Q1 journal in the field of Health Informatics. It is a selective journal complemented by almost 30 specialty JMIR sister journals, which have a broader scope, and which together receive over 10,000 submissions a year. 

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews). Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to a different journal but can simply transfer it between journals. 

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

As all JMIR journals, the journal encourages Open Science principles and strongly encourages publication of a protocol before data collection. Authors who have published a protocol in JMIR Research Protocols get a discount of 20% on the Article Processing Fee when publishing a subsequent results paper in any JMIR journal.

Be a widely cited leader in the digital health revolution and submit your paper today!

 

Recent Articles:

  • Source: Freepik; Copyright: wayhomestudio; URL: https://www.freepik.com/free-photo/woman-checks-results-fitness-training-smartwatch-listens-music-via-headphones-dressed-anorak-poses-blurred_19046120.htm; License: Licensed by JMIR.

    The Feasibility of Smartwatch Micro–Ecological Momentary Assessment for Tracking Eating Patterns of Malaysian Children and Adolescents in the South-East...

    Abstract:

    Background: Mobile phone ecological momentary assessment (EMA) methods are a well-established measure of eating and drinking behaviors, but compliance can be poor. Micro-EMA (μEMA), which collects information with a single tap response to brief questions on smartwatches, offers a novel application that may improve response rates. To our knowledge, there is no data evaluating μEMA to measure eating habits in children or in low-to-middle-income countries. Objective: In this study, we investigated the feasibility of micro-EMA to measure eating patterns in Malaysian children and adolescents. Methods: We invited 100 children and adolescents aged 7-18 years in Segamat, Malaysia, to participate in 2021-2022. Smartwatches were distributed to 83 children and adolescents who agreed to participate. Participants were asked to wear the smartwatch for 8 days and respond to 12 prompts per day, hourly, from 9AM to 8PM, asking for information on their meals, snacks, and drinks consumed. A questionnaire captured their experiences using the smartwatch and μEMA interface. Response rate (proportion of prompts responded to) assessed participants’ adherence. We explored associations between response rate with time of day, across days, age, and sex using multilevel binomial logistic regression modeling. Results: Eighty-two participants provided usable smartwatch data. The median number (IQR) of meals, drinks, and snacks per day was 2 (2-4), 3 (1-5), and 1 (0-2), respectively, on the first day of the study. The median response rate across the study was 68% (IQR 50-83). The response rate decreased across study days from 74% (68-78) on Day 1 to 40% (30-50) on Day 7 (odds ratio [OR] per study day 0.73, 95% CI 0.64-0.83). Response rate was lowest at the start of the day and highest between the hours of 12 PM and 2 PM. Female participants responded to more prompts than male participants (OR 1.72, 95% CI 1.03-2.86). There was no evidence of differential response by age (OR 0.73, 95% CI 0.41-1.28). Most participants (65%) rated their experience using the smartwatch positively, with 33% saying they were happy to participate in future studies using the smartwatch. For children that did not wear the smartwatch for the full study duration (n=22), discomfort was the most common complaint (41%). Conclusions: In this study of the feasibility of μEMA on smartwatches to measure eating in Malaysian children, we found the method was acceptable. However, response rates declined across study days, resulting in substantial missingness. Future studies (eg, through focus groups) should explore approaches to improving response to event prompts, trial alternative devices to increase children’s comfort, and evaluate revised protocols for reporting of intake events.

  • AI-generated image, in response to the request "Asian man showing his smartwatch his his female psychiatrist" (Generator: Google Pixel Studio December 21, 2025; Requestor: Adam Charles Frank). Source: Created with Google Pixel Studio; Copyright: N/A (AI Generated Image); URL: https://www.jmir.org/2026/1/e85033; License: Public Domain (CC0).

    Interactions of Technology and Obsessive-Compulsive Disorder Symptomatology in Adults: Qualitative Interview Study

    Abstract:

    Background: Obsessive-Compulsive Disorder (OCD) affects 1–3% of the population and is marked by intrusive obsessions and compulsive behaviors that impair daily functioning. As digital technologies have become ubiquitous, their features may interact with OCD symptom dimensions in ways that both exacerbate and alleviate symptoms. While case reports and clinical anecdotes suggest such interactions, systematic investigation of patients’ lived experiences with technology remains limited. Objective: This study aimed to explore how individuals with OCD perceive and navigate their interactions with modern technologies, and to identify how specific features of technology may contribute to, reinforce, or relieve obsessive-compulsive symptom cycles. Methods: We conducted semi-structured interviews (n=24) with adults self-reporting a diagnosis of OCD, recruited through online OCD communities and advocacy networks. Interviews were conducted via HIPAA-compliant Zoom between May and December 2024 (median duration: 51 minutes). Transcripts were coded in Dedoose (v9.2.22) using a constructivist grounded theory approach. Coding proceeded iteratively through open and focused coding, with theoretical saturation reached after 15 interviews. Constant comparison and analytic memoing guided the development of a conceptual framework linking technology features to OCD symptom dimensions. Results: Participants (median age 26, range 20–64; 67% female; 29% male; 4% non-binary) described technology as both a trigger for and a coping tool against OCD symptoms. Analysis produced four central technology-related categories: (1) information-provision platforms (e.g., social media, search engines, large language models, etc) that triggered disturbing-thought obsessions and enabled compulsive checking and reassurance-seeking; (2) gamification/quantification features (e.g., streaks, progress bars, tracking metrics) that reinforced “not-just-right” and symmetry-based compulsions; (3) notifications that provoked urges to clear, check, and maintain control, spanning both disturbing-thought and symmetry domains; and (4) user interfaces whose complexity and customizability elicited compulsive ordering, avoidance behaviors, and digital overwhelm. Conclusions: This study characterizes how interactions between OCD and digital technologies manifest across established symptom domains, most notably disturbing-thought and “not-just-right” categories. Participants overwhelmingly experienced compulsive checking, reassurance-seeking, and ordering behaviors reinforced by features such as information-provision, gamification, notifications, and user interfaces. These findings highlight the clinical relevance of technology-related compulsions and suggest value in their systematic assessment, incorporation into psychoeducation, and consideration in digital design.

  • Source: Pixabay; Copyright: ArtsyBee; URL: https://pixabay.com/photos/e-learning-training-school-online-3734521/; License: Licensed by JMIR.

    Key Components and Barriers in Web-Based Suicide Prevention Gatekeeper Training: Systematic Narrative Review

    Abstract:

    Background: Gatekeeper training programs (GTPs) are a key component of contemporary suicide prevention strategies, equipping community members and non–mental health professionals with the skills to identify, engage with, and refer individuals at risk of suicide. Increasingly, these programs are delivered via the web, offering a compelling alternative to in-person training through greater scalability, flexibility, and cost-effectiveness. However, little consensus exists regarding the design, modes of delivery, and implementation strategies of web-based GTPs. Further, there is a limited understanding of which components affect their usability and engagement. Objective: This systematic narrative review aims to identify the key components—including facilitators and barriers—of web-based GTPs. Methods: We systematically searched web-based databases (CINAHL, Embase, MEDLINE, PsycINFO, and Web of Science) to identify peer-reviewed articles published between 2000 and 2025 that involved web-based GTPs. After screening, 59 studies met the inclusion criteria and were analyzed using content analysis to identify key components and barriers affecting the delivery and receipt of web-based GTPs. Results: Results were organized under 3 categories: design, content, and pedagogy. Key design considerations emphasized accessibility for diverse learning styles and digital literacy levels, customizability for different user groups, privacy protection, and the long-term sustainability of training content and delivery platforms. Core training content covered four domains: (1) suicide-related knowledge (eg, prevalence, myths, and at-risk groups), (2) gatekeeping skills (eg, understanding risk factors, recognizing warning signs, problem-solving and safety planning), (3) resource awareness (eg, available local resources and referral procedures), and (4) general mental health education (eg, mental fitness, mindfulness, and self-care strategies for gatekeepers). In terms of pedagogy, the reviewed studies used a wide range of strategies that comprised interactive learning activities (eg, simulation, practice exercises), periodic knowledge checks (eg, quizzes), and reinforcement mechanisms (eg, booster sessions). Additionally, fostering a sense of community (eg, online support spaces or discussion forums) and promoting trainees’ autonomy (eg, self-paced training) were highlighted as key components of training delivery. Conclusions: Web-based GTPs represent a promising avenue for expanding access to suicide prevention training. Their effectiveness may be strengthened through the integration of frameworks tailored to web-based learning environments, as well as interactive and user-centered design elements that support learning and retention. Future research should examine the acceptability, feasibility, and sustainability of these programs, while also refining their adaptation for diverse populations. In this regard, co-design approaches could facilitate the tailoring of such programs to the needs and specificities of their target populations. Overall, enhancing the design and delivery of web-based GTPs may ultimately improve their contribution to suicide prevention efforts.

  • Source: Freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/child-with-social-media-addiction_26527546.htm; License: Licensed by JMIR.

    The Effectiveness of the Headspace App for Improving Sleep: Randomized Controlled Trial

    Abstract:

    Background: Improving sleep is critical for optimizing short-term and long-term health. Although in-person meditation training has been shown to impact sleep positively, there is a gap in our understanding of whether apps that teach self-guided meditation are also effective. Objective: We tested whether Headspace improves sleep quality, tiredness, sleep duration, and sleep efficiency. Methods: Staff employees (n = 135; aged M = 38.1, SD = 10.9; 75.0% female; 59.3% non-Hispanic White; 27.1% Hispanic) from a university in California’s San Joaquin Valley participated in the study. Participants were randomized to complete 10 minutes of daily meditation via the Headspace app for eight weeks or waitlist control. Sleep assessments were taken for four consecutive days at baseline, and then for four-day bursts two, five, and eight weeks after randomization. Sleep quality and subjective sleep duration was assessed each morning with a sleep diary, tiredness was assessed throughout the day using ecological momentary assessment, and objective sleep duration and efficiency was measured using a Fitbit Charge 2. Results: Both subjective and objective sleep outcomes improved. For subjective sleep outcomes, multilevel modeling revealed that those in the Headspace condition, compared to the control group, reported better sleep quality at sessions 2 (B = 0.48, SE = 0.12, P < .001), 5 (B = 0.91, SE = 0.13, P < .001), and 8 (B = 0.69, SE = 0.15, P < .001) compared to baseline, and a decrease in tiredness at session 5 (B = -0.58, SE = 0.19, P = .001) compared to baseline, but not at sessions 2 or 8. For objective sleep outcomes, those in the Headspace condition compared to the control group had longer sleep durations at session 5 (B = 23.96, SE = 12.19, P = .049) compared to baseline, but not at sessions 2 or 8. There were no significant effects for sleep efficiency. Conclusions: The current study continues adding to the ever-developing field of mHealth apps by demonstrating that Headspace can positively impact sleep quality, tiredness, and duration. Clinical Trial: ClinicalTrials.gov NCT03652168; https://clinicaltrials.gov/study/NCT03652168?cond=NCT03652168&rank=1

  • Communicative behaviors in an internet-based intervention for individuals with autism. Source: Image created by the authors (Britta Westerberg); Copyright: The Authors; URL: https://www.jmir.org/2026/1/e76527/; License: Creative Commons Attribution (CC-BY).

    Communicative Behaviors in an Internet-Based Intervention for Individuals With Autism: Mixed Methods Analysis

    Abstract:

    Background: To meet the needs of individuals diagnosed with autism, internet-based interventions have been developed with a variety of objectives. A deeper understanding of the mechanisms of change may help tailor interventions to individual needs. The communicative behaviors of individuals with autism participating in text-based internet-based interventions remain largely unexplored, as do their potential relations to clinical outcomes. An improved understanding of participants’ behaviors may help therapists better tailor support, promote engagement, and enhance treatment outcomes. Objective: This study aimed to explore the communicative behaviors of individuals with autism participating in an internet-based intervention and to examine whether different behavioral patterns were associated with treatment outcomes or treatment adherence. Methods: Messages from 34 participants enrolled in an 18-week internet-based cognitive behavioral therapy program were analyzed using abductive qualitative content analysis. Correlational analyses were used to examine the relationships between qualitative categories and change scores on outcome measures and rates of module completion. Results: Fourteen behavioral categories were identified and grouped into three overarching domains: (1) “This is me,” which encompasses the participants’ narratives on identity, personality, autistic functioning, current and past circumstances, and worldview; (2) “Working with the treatment,” which included statements related to engagement with the treatment process; and (3) “I struggle,” which comprised of past and present negative experiences and challenges. Correlational analyses revealed associations between several behavioral categories and improvements in quality of life and treatment adherence. Conclusions: The findings highlight the importance of self-narrative formulation among individuals with autism and suggest that certain communicative behaviors—particularly those involving identity reflection and recognition of treatment-related gains—were positively associated with therapeutic outcomes. The findings enhance our understanding of how individuals with autism engage in internet-based cognitive behavioral therapy and may serve as a valuable source of information for therapists when guiding expectations regarding client outcomes and identifying participants who may benefit from additional support. Trial Registration: ClinicalTrials.gov NCT03570372; https://clinicaltrials.gov/study/NCT03570372

  • Source: Freepik; Copyright: wayhomestudio; URL: https://www.freepik.com/free-photo/cropped-shot-view-woman-s-hands-holding-mobile-phone_10271842.htm; License: Licensed by JMIR.

    Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

    Abstract:

    Background: Adolescents are particularly vulnerable to mental health disorders, with over 75% of lifetime cases emerging before age 25. Yet most young people with significant symptoms do not seek support. Digital phenotyping, leveraging active (self-reported) and passive (sensor-based) data from smartphones, offers a scalable, low-burden approach for early risk detection. Despite this potential, its application in school-going adolescents from general (non-clinical) populations remains limited, leaving a critical gap in community-based prevention efforts. Objective: This study evaluated the feasibility of using a smartphone app to predict mental health risks in non-clinical adolescents by integrating active and passive data streams within a machine learning framework. We examined the utility of this approach for identifying risks related to internalising and externalising difficulties, eating disorders, insomnia, and suicidal ideation. Methods: Participants (N=103; mean age 16.1) from three UK secondary schools used the Mindcraft app for 14 days, providing daily self-reports (e.g., mood, sleep, loneliness) and continuous passive sensor data (e.g., location, step count, app usage). We developed a deep learning model incorporating contrastive pretraining with triplet margin loss to stabilise user-specific behavioural patterns, followed by supervised fine-tuning for binary classification of four mental health outcomes: SDQ-high risk, insomnia, suicidal ideation, and eating disorder. Performance was assessed using leave-one-subject-out cross-validation, with balanced accuracy as the primary metric. Comparative analyses were conducted using CatBoost and MLP models without pretraining. Feature importance was assessed using SHAP values, and associations between key digital features and clinical scales were analysed. Results: Integration of active and passive data outperformed single-modality models, achieving mean balanced accuracies of 0.71 for SDQ-high risk, 0.67 for insomnia, 0.77 for suicidal ideation, and 0.70 for eating disorder. The contrastive learning approach improved representation stability and predictive robustness. SHAP analysis highlighted clinically relevant features, such as negative thinking and location entropy, underscoring the complementary value of combining subjective and objective data. Correlation analyses confirmed meaningful associations between key digital features and mental health outcomes. Performance in an independent external validation cohort (N=45) achieved balanced accuracies of 0.63–0.72 across outcomes, suggesting generalisability to new settings. Conclusions: This study demonstrates the feasibility and utility of smartphone-based digital phenotyping for predicting mental health risks in non-clinical, school-going adolescents. By integrating active and passive data with advanced machine modelling techniques, this approach shows promise for early detection and scalable intervention strategies in community settings.

  • Source: Freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/medium-shot-woman-holding-menstrual-cup_27615776.htm; License: Licensed by JMIR.

    Effects of Digital Health Interventions to Promote Safer Sex Behaviors Among Youth: Systematic Review and Bayesian Network Meta-Analysis

    Abstract:

    Background: Youth aged 15-24 carry a disproportionate HIV/STIs burden. Recent years different modalities of digital health interventions (DHIs) have been explored to promote safer sex behaviors among youth, but their comparative effectiveness across modalities and relative to non-digital interventions (NDIs) remains unclear. Objective: To compare DHI modalities on safer sex behaviors and HIV/STIs incidence, rank modalities using Bayesian network meta-analysis (NMA), and position their effectiveness relative to NDIs. Methods: A systematic review and Bayesian NMA of randomized controlled trials (RCTs) were conducted by comprehensively searching PubMed, EMBASE, Web of Science, and Cochrane Library (inception to November 2025). Eligible studies were those enrolled youth aged 15-24 years and evaluated mobile app-based (MAI), telecommunication-based (TCI), static web-based (SWI), or interactive online (IOI)—with an NDI or another DHI. Primary outcomes were condom use at last sexual contact, consistent condom use, and proportion of condom use, secondary outcomes included condom use self-efficacy, number of sexual partners, and STI incidence (including HIV). Risk of bias was assessed with RoB 2 and certainty of evidence with GRADE/CINeMA. Bayesian random-effects NMAs estimated odds ratios (ORs) with 95% credible intervals (CrIs), complementary frequentist NMAs provided 95% confidence intervals and 95% prediction intervals. Results: Twenty-four RCTs (20,134 participants) were included, forming treatment networks across five intervention types. TCI was the only intervention that significantly improved condom use at last sex compared to NDI (OR = 1.13, 95% CrI: 1.02-1.26). For consistent condom use, SWI and IOI outperformed TCI (SWI vs TCI: OR = 1.77, 95% CrI: 1.03-3.06; IOI vs TCI: OR = 1.68, 95% CrI: 1.02-2.76). For the proportion of condom use, IOI outperformed SWI (OR = 1.34, 95% CrI: 1.01-1.80), and MAI ranked highest in probability rankings, though estimates lacked precision. For STI incidence, NDI was associated with fewer STIs than SWI (OR = 0.61, 95% CrI 0.46-0.82). Conclusions: This is the first NMA to compare the effectiveness of DHIs on condom use and HIV/STI outcomes among youth populations. It demonstrates that the impact of DHIs on HIV prevention varies substantially by intervention modality and outcome type. While TCI demonstrates the most consistent improvement in condom use at last sex, SWI and IOI may be more effective for promoting consistent condom use, though estimates remain imprecise. However, wide prediction intervals and low-certainty evidence suggest that self-reported behavioral changes may not translate into reductions in HIV/STIs incidents without integration with offline services and broader structural support. Future trials might consider including standardized outcome indicators, and longer follow-up to generate more precise estimates of effectiveness of DHIs and guide generalization of youth-centered digital HIV/STIs prevention. Clinical Trial: PROSPERO CRD42024527317; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024527317

  • URL: https://unsplash.com/photos/person-holding-a-smartphone-with-a-green-screen-qBFyprJYWi4
License link: https://unsplash.com/license

Notes: Edited by the Holly Health to include the home screen of the health coaching app used in the study. Source: Unsplash/The Authors; Copyright: Vitaly Gariev/The Authors; URL: https://unsplash.com/photos/person-holding-a-smartphone-with-a-green-screen-qBFyprJYWi4; License: Licensed by the authors.

    The Impact of a Health Coaching App on the Subjective Well-Being of Individuals With Multimorbidity: Mixed Methods Study

    Abstract:

    Background: Multimorbidity, the coexistence of two or more chronic conditions, is associated with poor wellbeing. Health coaching apps offer cost-effective and accessible support. However, there is a lack of evidence of the impact of health coaching apps on individuals with multimorbidity. Objective: The study aimed to assess the impact and acceptability of a health coaching app (the Holly Health (HH) app) on the subjective wellbeing (SWB) of adults with multimorbidity. Methods: An explanatory-sequential mixed methods design, with quantitative secondary data analysis in the first phase, and qualitative interviews in the second phase. In the quantitative phase (n=565), pre-and post-SWB (ONS4) scores from existing app users with multimorbidity were analysed using Bayesian growth curve modelling to assess the impact of HH. In the qualitative phase (n=22), data was collected via semi-structured interviews and analysed using reflexive thematic analysis. Mechanisms of action that supported SWB were categorised using the Multi-level Leisure Mechanisms Framework. Results: There was a significant increase in life satisfaction (Coef.=0.71, 95% HDI=0.52-0.89), worthwhileness (Coef.=0.62, 95% HDI=0.43-0.81), and happiness (Coef.=0.74, 95% HDI=0.54-0.92) and a decrease in anxiety (Coef.=-0.50, 95% HDI=-0.74-(-0.25)) before and after using the HH app. Eight acceptable app features activated five mechanisms of action, including behavioural, psychological, and social mechanisms. Three additional factors influenced the acceptability of the health coaching app: type of chronic condition, availability of time, and the use of other support tools. Conclusions: The study demonstrates that health coaching apps could be effective and acceptable support tools for individuals with multimorbidity. This study contributes to understanding why health coaching apps support SWB and could be used to inform the development of future digital health interventions in multimorbidity.

  • Source: Freepik; Copyright: wirestock; URL: https://www.freepik.com/free-photo/high-angle-shot-female-rolling-joint_17991523.htm; License: Licensed by JMIR.

    Transformer-Based Topic Modeling: Characterizing Cannabis Product Adverse Experiences Self-Reported as Requiring Medical Attention on Reddit

    Abstract:

    This study uses keyword filtering, a transformer-based algorithm, and inductive content coding to identify and characterize cannabis adverse experiences as discussed on the social media platform Reddit and reports a total of 1177 self-reported adverse experiences requiring medical attention.

  • Source: Freepik; Copyright: pressfoto; URL: https://www.freepik.com/free-photo/portrait-handsome-asian-student_5576720.htm; License: Licensed by JMIR.

    Effect of Digital Health Interventions on College Students’ Lifestyle Behaviors: Systematic Review

    Abstract:

    Background: College students undergo a critical transition from adolescence to adulthood, during which lifestyle behaviors such as physical activity, sedentary behavior, diet, and sleep are key determinants of long-term health. Digital health interventions (DHIs) are increasingly recognized as a promising strategy for improving these behaviors among college students. Objective: This systematic review aims to evaluate the effectiveness and applicability of DHIs targeting lifestyle behaviors among college students by analyzing intervention objectives, modalities, functionalities, outcomes, and other key characteristics. Methods: In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, multiple scientific databases, including Scopus, Web of Science, PubMed, MEDLINE, PsycINFO, SPORTDiscus, ProQuest Central, APA PsycArticles, ERIC, and Academic Search Premier, were searched for studies published between January 2010 and December 2025 (initial search: August 5, 2025; updated search: December 27, 2025). The inclusion criteria were original empirical studies on DHIs targeting lifestyle behaviors (physical activity, sedentary behavior, diet, and sleep) among college students, published in English. Studies focusing on nondigital interventions, lacking sufficient methodological details, or not reporting lifestyle behavior–related outcomes were excluded. Quality assessment was conducted in 2 stages: all studies were first evaluated using the Mixed Methods Appraisal Tool (2018 version), followed by Risk of Bias 2 for randomized controlled trials and Joanna Briggs Institute critical appraisal tools for nonrandomized studies. A narrative synthesis was used to present and synthesize the findings. Results: A total of 2998 records were retrieved, of which 46 publications met the inclusion criteria. These included 30 (65%) studies related to physical activity, 26 (57%) studies to diet, 10 (22%) studies related to sedentary behavior, and 6 (13%) studies related to sleep. This review enabled an examination of the effects of DHIs on college students’ lifestyle behaviors. DHIs primarily used mobile apps, web-based platforms, and mobile communication technologies, with core functionalities such as education, guidance, monitoring, and prompting. DHIs were more effective in improving physical activity and diet; however, evidence for reducing sedentary behavior and improving sleep remained limited. Of the 46 studies, 31 (67%) reported positive effects, with larger sample sizes and intervention durations of 8-16 weeks being associated with more favorable outcomes. Conclusions: This review focuses on college students, addressing a gap in the literature that often centers on general adult populations. Unlike previous reviews that focus on a single behavior, this study integrates multiple lifestyle behaviors and evaluates DHIs across diverse modalities and functionalities. These contributions help refine future DHIs for college students and inform health promotion strategies in higher education. Although DHIs show potential for improving lifestyle behaviors, evidence of their long-term effectiveness remains limited. Future interventions should prioritize multibehavior integration, interactivity, and population-differentiated design to enhance precision, sustainability, and equity. This study has several limitations, including issues related to sample representativeness, intervention refinement, and methodological rigor. Trial Registration: PROSPERO CRD420251119078; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251119078

  • AI generated image in response to the prompt "Create a photorealistic, publication-ready image (or a realistic 3D render) suitable for the Journal of Medical Internet Research (JMIR), depicting a young adult (18–19 years old) representing youth digital behavior. The scene shows the individual seated at a clean, organized desk in a bright indoor environment with soft natural daylight, using a computer while wearing headphones. The person displays noticeable but mild fatigue, such as a slightly slouched posture or tired eyes, without exaggerated facial expressions. Add a minimal, semi-transparent digital interface overlay indicating very prolonged screen time (e.g., extended daily usage) and a simple stress or cognitive load line graph, symbolizing problematic internet use and psychological strain. The overall aesthetic should be neutral, clinical, and professional, with no dramatic props, no dystopian or dark atmosphere, and no cartoon or anime elements. Aspect ratio 4:3, final size 1000×750 pixels, sharp focus, realistic lighting, designed to meet academic journal illustration standards.". Source: DALL·E; Copyright: N/A (AI-generated image); URL: https://www.jmir.org/2026/1/e82414/; License: Public Domain (CC0).

    Comparing the Associations of Internet Addiction and Internet Gaming Disorder With Psychopathological Symptoms: Cross-Sectional Study of Three Independent...

    Abstract:

    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.

  • Source: freepik; Copyright: h9images; URL: https://www.freepik.com/free-photo/red-ribbon-mobile-phone-with-white-background-hiv-aids-ribbon-awareness_20112213.htm; License: Licensed by JMIR.

    Evaluation of an Artificial Intelligence Conversational Chatbot to Enhance HIV Preexposure Prophylaxis Uptake: Development and Usability Internal Testing

    Abstract:

    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.

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    Date Submitted: Feb 3, 2026

    Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026

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    Date Submitted: Feb 5, 2026

    Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026

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    Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026

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    Date Submitted: Feb 4, 2026

    Open Peer Review Period: Feb 5, 2026 - Apr 2, 2026

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