Patient Experiences with Diabetes Technology

Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja Karnani

JMIR Diabetes 2022 (Jul 05); 7(3):e32366

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Community Health Worker-Led mHealth-Enabled Diabetes Self-management Education and Support Intervention in Rural Latino Adults: Single-Arm Feasibility Trial

Shiyu Li, Zenong Yin, Janna Lesser, Chengdong Li, Byeong Yeob Choi, Deborah Parra-Medina, Belinda Flores, Brittany Dennis, Jing Wang

JMIR Diabetes 2022 (May 30); 7(2):e37534

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Implementing the Digital Diabetes Questionnaire as a Clinical Tool in Routine Diabetes Care: Focus Group Discussions With Patients and Health Care Professionals

Maria Svedbo Engström, Unn-Britt Johansson, Janeth Leksell, Ebba Linder, Katarina Eeg-Olofsson

JMIR Diabetes 2022 (May 25); 7(2):e34561

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An Innovative, Paradigm-Shifting Lifestyle Intervention to Reduce Glucose Excursions With the Use of Continuous Glucose Monitoring to Educate, Motivate, and Activate Adults With Newly Diagnosed Type 2 Diabetes: Pilot Feasibility Study

Tamara K Oser, Mark Cucuzzella, Marilyn Stasinopoulos, Matthew Moncrief, Anthony McCall, Daniel J Cox

JMIR Diabetes 2022 (Feb 23); 7(1):e34465

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