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Early Detection of Type 1 Diabetes in First-Degree Relatives in Saudi Arabia (VISION-T1D): Protocol for a Pilot Implementation Study

Iman S Algadi, Yazed AlRuthia, Muhammad H Mujammami, Khaled Hani Aburisheh, Metib Alotaibi, Sharifah Al Issa, Amal A Al-Saif, David Seftel, Cheng-Ting Tsai, Reem A Al Khalifah

JMIR Res Protoc 2025;14:e70575

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Identifying Intersecting Factors Associated With Suicidal Thoughts and Behaviors Among Transgender and Gender Diverse Adults: Preliminary Conditional Inference Tree Analysis

Amelia M Stanton, Lauren A Trichtinger, Norik Kirakosian, Simon M Li, Katherine E Kabel, Kiyan Irani, Alexandra H Bettis, Conall O’Cleirigh, Richard T Liu, Qimin Liu

J Med Internet Res 2025;27:e65452

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Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study

Daniel Sumsion, Elijah Davis, Marta Fernandes, Ruoqi Wei, Rebecca Milde, Jet Malou Veltink, Wan-Yee Kong, Yiwen Xiong, Samvrit Rao, Tara Westover, Lydia Petersen, Niels Turley, Arjun Singh, Stephanie Buss, Shibani Mukerji, Sahar Zafar, Sudeshna Das, Valdery Moura Junior, Manohar Ghanta, Aditya Gupta, Jennifer Kim, Katie Stone, Emmanuel Mignot, Dennis Hwang, Lynn Marie Trotti, Gari D Clifford, Umakanth Katwa, Robert Thomas, M Brandon Westover, Haoqi Sun

JMIR Med Inform 2025;13:e64113

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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

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

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AI Applications for Chronic Condition Self-Management: Scoping Review

ML algorithms predict a hypoglycemia event in the next 24 h using self-monitored blood glucose and medication information. Prediction accuracy was over 90% in models using RF or SVM. RF and SVM models had a 91.7% sensitivity and 69.5% specificity. After incorporating medication information, the sensitivity and specificity were over 90%. The DL model predicts daily glucose levels based on patient health data, including glucose levels from the day before, diet, physical activity, and weight.

Misun Hwang, Yaguang Zheng, Youmin Cho, Yun Jiang

J Med Internet Res 2025;27:e59632

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Anticipated Acceptability of Blended Learning Among Lay Health Care Workers in Malawi: Qualitative Analysis Guided by the Technology Acceptance Model

Ethical clearance was provided by the University of North Carolina at Chapel Hill institutional review board (#20‐1810), the Malawi National Health Sciences Research Committee (#20/06/2566) and the Baylor College of Medicine institutional review board (H-48800). Interviewers obtained written informed consent from all participants before starting the IDIs, reminding participants that their participation was voluntary and could be withdrawn at any time.

Tiwonge E Mbeya-Munkhondya, Caroline J Meek, Mtisunge Mphande, Tapiwa A Tembo, Mike J Chitani, Milenka Jean-Baptiste, Caroline Kumbuyo, Dhrutika Vansia, Katherine R Simon, Sarah E Rutstein, Victor Mwapasa, Vivian Go, Maria H Kim, Nora E Rosenberg