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Theory-Based Social Media Intervention for Nonmedical Use of Prescription Opioids in Young Adults: Protocol for a Randomized Controlled Trial

Theory-Based Social Media Intervention for Nonmedical Use of Prescription Opioids in Young Adults: Protocol for a Randomized Controlled Trial

Tam and his colleagues’ previous research on NMUPO in college students has indicated several additional factors that reduce risk for NMUPO, such as psychological resilience, positive coping, outcome expectancies, and self-esteem [10,19-21]. Importantly, psychosocial factors robustly contribute to NMUPO.

Cheuk Chi Tam, Sean D Young, Sayward Harrison, Xiaoming Li, Alain H Litwin

JMIR Res Protoc 2025;14:e65847

Stakeholder Consensus on an Interdisciplinary Terminology to Enable the Development and Uptake of Medication Adherence Technologies Across Health Systems: Web-Based Real-Time Delphi Study

Stakeholder Consensus on an Interdisciplinary Terminology to Enable the Development and Uptake of Medication Adherence Technologies Across Health Systems: Web-Based Real-Time Delphi Study

Addressing the prevalent low adherence to medication is likely to improve the use of limited resources and population health [4]. However, the sustainable implementation of health technologies in routine practice is lagging, in part due to the lack of accessibility to users in real-life settings [5]. Many repositories have been developed to improve accessibility and, thus, facilitate uptake of digital health [6], including digital apps [7] and medication adherence interventions [8].

Alexandra Lelia Dima, Urska Nabergoj Makovec, Janette Ribaut, Frederik Haupenthal, Pilar Barnestein-Fonseca, Catherine Goetzinger, Sean Grant, Cristina Jácome, Dins Smits, Ivana Tadic, Job van Boven, Ioanna Tsiligianni, Maria Teresa Herdeiro, Fátima Roque, European Network to Advance Best Practices and Technology on Medication Adherence (ENABLE)

J Med Internet Res 2025;27:e59738

Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models

Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models

This evaluation procedure contains detailed guidelines to assess Chat GPT’s response to open-ended questions and validate the robustness of these responses using a sentence dropout method. This 2-staged approach, to our knowledge, is the first comprehensive strategy aimed at better understanding LLM responses and their implications for medical misinformation.

Troy Zada, Natalie Tam, Francois Barnard, Marlize Van Sittert, Venkat Bhat, Sirisha Rambhatla

JMIR Form Res 2025;9:e66207

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review

Summary of methods of studies that use machine learning to identify cardiac scars from electrocardiogram (ECG) data. Within the study design, N refers to the number of ECGs or vectorcardiograms included in the study, and n scar refers to the number of cases with confirmed scarring using a method other than ECG. The number of sources of study population, data sources, is included. Scar modality refers to the method used to confirm cardiac scarring.

Julia Handra, Hannah James, Ashery Mbilinyi, Ashley Moller-Hansen, Callum O'Riley, Jason Andrade, Marc Deyell, Cameron Hague, Nathaniel Hawkins, Kendall Ho, Ricky Hu, Jonathon Leipsic, Roger Tam

JMIR Cardio 2024;8:e60697

The Effect of the Mediterranean Diet–Integrated Gamified Home-Based Cognitive-Nutritional (GAHOCON) Training Programme for Older People With Cognitive Frailty: Pilot Randomized Controlled Trial

The Effect of the Mediterranean Diet–Integrated Gamified Home-Based Cognitive-Nutritional (GAHOCON) Training Programme for Older People With Cognitive Frailty: Pilot Randomized Controlled Trial

Cognitive training refers to interventions designed to enhance domain-specific cognitive functions through repeated practice of theoretically driven skills and strategies [7]. Recently, cognitive training has transitioned from face-to-face formats to computerized platforms, offering improved cost-effectiveness, accessibility, and the ability to tailor content and difficulty levels to individual participants [8].

Rick Yiu Cho Kwan, Queenie Pui Sze Law, Jenny Tsun Yee Tsang, Siu Hin Lam, Kam To Wang, Olive Shuk Kan Sin, Daphne Sze Ki Cheung

JMIR Rehabil Assist Technol 2024;11:e60155

Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning–Enhanced Approaches

Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning–Enhanced Approaches

Surging emergency department (ED) visits lead to overcrowding in the ED setting, contributing to adverse patient outcomes, staffing challenges, and health system constraints [1]. Challenges in maintaining ED capacity are estimated to cost millions in health care expenditures [2].

Daniel Raff, Kurtis Stewart, Michelle Christie Yang, Jessie Shang, Sonya Cressman, Roger Tam, Jessica Wong, Martin C Tammemägi, Kendall Ho

Interact J Med Res 2024;13:e56729

The Effect of Walking on Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis

The Effect of Walking on Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis

In one study, participants’ mean age was 16.8 years, and the mean ages of the participants in the remaining studies ranged from 30.4 to 84.7 years. The intervention duration ranged from 10 days to 18 months, with 44 (59%) studies having 3 to 6 months of intervention.

Zijun Xu, Xiaoxiang Zheng, Hanyue Ding, Dexing Zhang, Peter Man-Hin Cheung, Zuyao Yang, King Wa Tam, Weiju Zhou, Dicken Cheong-Chun Chan, Wenyue Wang, Samuel Yeung-Shan Wong

JMIR Public Health Surveill 2024;10:e48355

Investigating the Interrelationships Among Mental Health, Substance Use Disorders, and Suicidal Ideation Among Lesbian, Gay, and Bisexual Adults in the United States: Population-Based Statewide Survey Study

Investigating the Interrelationships Among Mental Health, Substance Use Disorders, and Suicidal Ideation Among Lesbian, Gay, and Bisexual Adults in the United States: Population-Based Statewide Survey Study

To this end, comprehensive interventions and policies targeting the underlying causative factors—discrimination and minority stress—are paramount to curtailing the prevalence of mental health issues, substance use disorders, and suicidal ideation among LGB individuals. Bridging this research gap, this study is dedicated to an in-depth exploration of the interrelationships among mental health, substance use disorders, and suicidal ideation among LGB adults within the United States.

Alex Siu Wing Chan, Hon Lon Tam, Florence Kwai Ching Wong, Gordon Wong, Lok Man Leung, Jacqueline Mei Chi Ho, Patrick Ming Kuen Tang, Elsie Yan

JMIR Public Health Surveill 2024;10:e48776

Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation

Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation

The last name of author GKD: Dimitriadish Has been revised to: Dimitriadis The correction will appear in the online version of the paper on the JMIR Publications website on June 7, 2024, together with the publication of this correction notice. Because this was made after submission to Pub Med, Pub Med Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.

Han Shi Jocelyn Chew, Nicholas WS Chew, Shaun Seh Ern Loong, Su Lin Lim, Wai San Wilson Tam, Yip Han Chin, Ariana M Chao, Georgios K Dimitriadis, Yujia Gao, Jimmy Bok Yan So, Asim Shabbir, Kee Yuan Ngiam

J Med Internet Res 2024;26:e62767

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation

For instance, participants enrolled in conventional weight management programs typically attend multiple face-to-face sessions at designated facilities, which could be burdensome and inconvenient as one needs to schedule appointments and travel to the facility that may be beyond one’s usual mobility pattern.

Han Shi Jocelyn Chew, Nicholas WS Chew, Shaun Seh Ern Loong, Su Lin Lim, Wai San Wilson Tam, Yip Han Chin, Ariana M Chao, Georgios K Dimitriadish, Yujia Gao, Jimmy Bok Yan So, Asim Shabbir, Kee Yuan Ngiam

J Med Internet Res 2024;26:e46036