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Young Adults With Type 1 Diabetes and Their Perspectives on Diabetes-Related Social Media: Qualitative Study

Young Adults With Type 1 Diabetes and Their Perspectives on Diabetes-Related Social Media: Qualitative Study

Young adulthood, generally defined as ages 18‐25 years, is a time of major transition with increasing developmental autonomy, which can make diabetes self-management more challenging for those people with T1 D[1]. Young adults struggle with attainment of target glycemic control, even in the current era of advanced diabetes technologies that generally reduce the burdens of T1 D self-care [2].

Tara Maxwell, Lillian Branka, Noa Asher, Persis Commissariat, Lori Laffel

JMIR Diabetes 2025;10:e69243

The Communities Organizing for Power Through Empathy (COPE) Community-Based Intervention to Improve Adult Mental Health During Disasters and Crises: Protocol for a Stepped-Wedge Cluster Randomized Trial

The Communities Organizing for Power Through Empathy (COPE) Community-Based Intervention to Improve Adult Mental Health During Disasters and Crises: Protocol for a Stepped-Wedge Cluster Randomized Trial

Studies have shown, however, that protective factors, including individual adaptive coping strategies [41-43], self-efficacy [44-46], and spirituality [47-50] interpersonal social support and relationships, and community or neighborhood cohesion [33,51], can reduce the likelihood of experiencing adverse mental health outcomes after a disaster.

Jennifer Scott, Tara Powell, Natasha M Lee-Johnson

JMIR Res Protoc 2025;14:e63723

Building and Beta-Testing Be Well Buddy Chatbot, a Secure, Credible and Trustworthy AI Chatbot That Will Not Misinform, Hallucinate or Stigmatize Substance Use Disorder: Development and Usability Study

Building and Beta-Testing Be Well Buddy Chatbot, a Secure, Credible and Trustworthy AI Chatbot That Will Not Misinform, Hallucinate or Stigmatize Substance Use Disorder: Development and Usability Study

Upon investigation, we determined that the system was retrieving messages from a different library within the Clinic Chat system focused on chronic illness self-management. We unlinked the libraries between the SUD and chronic conditions content to avoid this error in the future. These two errors were the only ones we documented when the system returned an incorrect response (ie, 2 of 426 or 99% level of system precision overall.

Adam Jerome Salyers, Sheana Bull, Joshva Silvasstar, Kevin Howell, Tara Wright, Farnoush Banaei-Kashani

JMIR Hum Factors 2025;12:e69144

Recruiting Medical, Dental, and Biomedical Students as First Responders in the Immediate Aftermath of the COVID-19 Pandemic: Prospective Follow-Up Study

Recruiting Medical, Dental, and Biomedical Students as First Responders in the Immediate Aftermath of the COVID-19 Pandemic: Prospective Follow-Up Study

The difference in self-reported confidence in performing BLS maneuvers was also assessed. Data curation and analysis were carried out using STATA/BE (version 17.0; Stata Corp LLC). Descriptive statistics were used to describe the evolution of the number of students at each step of the learning path. Given the sample size, parametric tests were used when appropriate. A P value of less than .05 was considered statistically significant.

Nicolas Schnetzler, Victor Taramarcaz, Tara Herren, Eric Golay, Simon Regard, François Mach, Amanta Nasution, Robert Larribau, Melanie Suppan, Eduardo Schiffer, Laurent Suppan

JMIR Med Educ 2025;11:e63018

Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

In a subanalysis among those who reported lower education (n=49), the average rating of the messages was higher on more days than the average ratings of true comparison messages (77% vs 23%; P The Adapt2 Quit intervention is based on the self-determination theory (SDT). SDT-based interventions support autonomous decisions and are designed to increase intrinsic motivation and self-regulation and they have been shown to improve motivation and cessation outcomes among those who smoke [19-24].

Ariana Kamberi, Benjamin Weitz, Julie Flahive, Julianna Eve, Reem Najjar, Tara Liaghat, Daniel Ford, Peter Lindenauer, Sharina Person, Thomas K Houston, Megan E Gauvey-Kern, Jackie Lobien, Rajani S Sadasivam

JMIR Res Protoc 2025;14:e63693

Co-Designing a Web-Based and Tablet App to Evaluate Clinical Outcomes of Early Psychosis Service Users in a Learning Health Care Network: User-Centered Design Workshop and Pilot Study

Co-Designing a Web-Based and Tablet App to Evaluate Clinical Outcomes of Early Psychosis Service Users in a Learning Health Care Network: User-Centered Design Workshop and Pilot Study

The MCSI is a 14-item, self-report scale which measures the frequency of psychiatric symptoms, including symptoms of mood, psychosis, cognition, forgetfulness, and risk to self and others. Respondents indicate frequency of symptoms over the past 30 days on a 0 to 4 scale of “not at all” to “at least every day.” Total scores range between 0 and 56, with higher scores indicating higher frequency and number of psychiatric symptoms.

Kathleen E Burch, Valerie L Tryon, Katherine M Pierce, Laura M Tully, Sabrina Ereshefsky, Mark Savill, Leigh Smith, Adam B Wilcox, Christopher Komei Hakusui, Viviana E Padilla, Amanda P McNamara, Merissa Kado-Walton, Andrew J Padovani, Chelyah Miller, Madison J Miles, Nitasha Sharma, Khanh Linh H Nguyen, Yi Zhang, Tara A Niendam

JMIR Hum Factors 2025;12:e65889

Developing and Assessing a Scalable Digital Health Tool for Pretest Genetic Education in Patients With Early-Onset Colorectal Cancer: Mixed Methods Design

Developing and Assessing a Scalable Digital Health Tool for Pretest Genetic Education in Patients With Early-Onset Colorectal Cancer: Mixed Methods Design

Providers’ self-reported practice characteristics included the frequency of communication with patients about genetic risk, referrals to GT, working with patients with early-onset CRC, and working with ethnic/racial minority patients. At the end of each module (n=5), participants were asked 2 questions: whether the information was easy to understand and whether it was helpful, using a 5-point Likert scale ranging from 1=strongly disagree to 5=strongly agree.

Jessica N Rivera Rivera, Moran Snir, Emilie Simmons, Tara Schmidlen, Misha Sholeh, Melinda Leigh Maconi, Carley Geiss, Hayden Fulton, Laura Barton, Brian D Gonzalez, Jennifer Permuth, Susan Vadaparampil

JMIR Cancer 2025;11:e59464

Delivering a Group-Based Quality of Life Intervention to Young Adult Cancer Survivors via a Web Platform: Feasibility Trial

Delivering a Group-Based Quality of Life Intervention to Young Adult Cancer Survivors via a Web Platform: Feasibility Trial

Usability testing participants also self-reported demographic and medical information. Audio recordings of the usability testing sessions were transcribed verbatim by a third-party service (GMR Transcription Services, Inc.). Each transcript was reviewed by at least 2 reviewers using an analytic approach similar to Gale and colleagues’ [29] rapid qualitative analytic method to identify actionable feedback.

Rina S Fox, Tara K Torres, Terry A Badger, Emmanuel Katsanis, DerShung Yang, Stacy D Sanford, David E Victorson, Betina Yanez, Frank J Penedo, Michael H Antoni, Laura B Oswald

JMIR Cancer 2024;10:e58014

Rank Ordered Design Attributes for Health Care Dashboards Including Artificial Intelligence: Usability Study

Rank Ordered Design Attributes for Health Care Dashboards Including Artificial Intelligence: Usability Study

Beyond the number and location of the participants, this study is limited in scope as it is based on self-reporting by the public for what they consider a positive dashboard design. It does not measure whether a better dashboard design, for example, results in a better actual health care decision. Beyond these limitations, this study is important as it attempts to quantify the top 15 elements of health care dashboard design which is undocumented in this context in previous literature.

Melina Malkani, Eesha Madan, Dillon Malkani, Arav Madan, Neel Singh, Tara Bamji, Harman Sabharwal

Online J Public Health Inform 2024;16:e58277