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Interpretable Machine Learning Model for Predicting and Assessing the Risk of Diabetic Nephropathy: Prediction Model Study

Interpretable Machine Learning Model for Predicting and Assessing the Risk of Diabetic Nephropathy: Prediction Model Study

The pathogenesis of DN is attributed to a high-glucose milieu, oxidative stress, inflammation, and fibrosis, collectively contributing to substantial morphological changes in kidneys including thickening of glomerular basement membrane, glomerulosclerosis, tubular atrophy, interstitial inflammation, and renal fibrosis [1].

Yili Wen, Zhiqiang Wan, Huiling Ren, Xu Wang, Weijie Wang

JMIR Med Inform 2025;13:e64979


Empowering Caregiver Well-Being With the Adhera Caring Digital Program for Family Caregivers of Children Living With Type 1 Diabetes: Mixed Methods Feasibility Study

Empowering Caregiver Well-Being With the Adhera Caring Digital Program for Family Caregivers of Children Living With Type 1 Diabetes: Mixed Methods Feasibility Study

Inclusion criteria were (1) caregivers who are legal guardians of children living with T1 D under 18 years of age; (2) child’s T1 D diagnosis for at least three months; (3) use of continuous glucose monitor; (4) caregivers’ willingness to use the mobile solution and share data. Exclusion criteria included only one legal guardian per child could participate, prior participation in SS1, and incomplete or refusal to provide consent.

Antonio de Arriba Muñoz, Elisa Civitani Monzon, Maria Pilar Ferrer, Marta Ferrer-Lozano, Silvia Quer-Palomas, Joia Nuñez, Alba Xifra-Porxas, Francesca Aimée Mees Mlatiati, Ioannis Bilionis, Ricardo C Berrios, Luis Fernández-Luque

JMIR Pediatr Parent 2025;8:e66914


Community-Based Intelligent Blood Glucose Management for Older Adults With Type 2 Diabetes Based on the Health Belief Model: Randomized Controlled Trial

Community-Based Intelligent Blood Glucose Management for Older Adults With Type 2 Diabetes Based on the Health Belief Model: Randomized Controlled Trial

The inclusion criteria were as follows: (1) age 65 years and older; (2) type 2 diabetes mellitus: diabetes was diagnosed based on self-reports with verification (current treatment, medical records, confirmation from health care providers, fasting plasma glucose of ≥126 mg/d L, or symptoms of hyperglycemia with a plasma glucose level of ≥200 mg/d L); (3) informed consent and voluntary participation in the study; and (4) capability for self-care, no communication barriers, and no cognitive impairment.

Anqi Zhang, Jinsong Wang, Xiaojuan Wan, Ziyi Zhang, Shuhan Zhao, Shuo Bai, Yamin Miao, Shuang Yang, Xue Jiang

JMIR Mhealth Uhealth 2025;13:e60227


The Effect of a Mobile App (eMOM) on Self-Discovery and Psychological Factors in Persons With Gestational Diabetes: Mixed Methods Study

The Effect of a Mobile App (eMOM) on Self-Discovery and Psychological Factors in Persons With Gestational Diabetes: Mixed Methods Study

The e MOM app shows detailed glucose values (Figure 1 C) when tapping the glucose curve on the screen (Figures 1 A and 1 D). (A) Day view with nutrition and glucose filters selected, (B) detailed nutrition view, (C) detailed glucose view, (D) day view with physical activity (steps), and (E) week view with glucose and physical activity (minutes) selected.

Sini Määttänen, Saila Koivusalo, Hanna Ylinen, Seppo Heinonen, Mikko Kytö

JMIR Mhealth Uhealth 2025;13:e60855


Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study

Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study

During the first 3 days of app use, participants are instructed to eat normally but to test their glucose levels before every eating occasion. Reported preprandial glucose levels during this time are averaged to establish a baseline glucose threshold. After this initial phase, users are encouraged to observe their hunger sensations and test their preprandial glucose levels only when hungry, and to delay eating until their glucose levels fall below their personalized threshold (ie, glucose-guided eating).

Michelle R Jospe, Martin Kendall, Susan M Schembre, Melyssa Roy

JMIR Form Res 2025;9:e65368


High-Intensity Interval Training for Individuals With Isolated Impaired Fasting Glucose: Protocol for a Proof-of-Concept Randomized Controlled Trial

High-Intensity Interval Training for Individuals With Isolated Impaired Fasting Glucose: Protocol for a Proof-of-Concept Randomized Controlled Trial

CGM: continuous glucose monitoring; HIIT: high-intensity interval training; i-IFG: isolated impaired fasting glucose; OGTT: oral glucose tolerance test; REDCap: Research Electronic Data Capture. Potential participants will be identified using Emory’s electronic health care records system, known as “My Chart.”

Sathish Thirunavukkarasu, Thomas R Ziegler, Mary Beth Weber, Lisa Staimez, Felipe Lobelo, Mindy L Millard-Stafford, Michael D Schmidt, Aravind Venkatachalam, Ram Bajpai, Farah El Fil, Maria Prokou, Siya Kumar, Robyn J Tapp, Jonathan E Shaw, Francisco J Pasquel, Joe R Nocera

JMIR Res Protoc 2025;14:e59842


Now I can see it works!” Perspectives on Using a Nutrition-Focused Approach When Initiating Continuous Glucose Monitoring in People with Type 2 Diabetes: Qualitative Interview Study

Now I can see it works!” Perspectives on Using a Nutrition-Focused Approach When Initiating Continuous Glucose Monitoring in People with Type 2 Diabetes: Qualitative Interview Study

Moreover, continuous glucose monitoring (CGM) has also been shown to improve glycemic outcomes for people with T2 D [3]. CGM can provide a comprehensive assessment of the impact of foods and other behaviors on glucose in real time and over the course of time. People with T2 D may benefit from using CGM data to guide food choices that help achieve their desired glycemic goals, including time in range (TIR; percent time with glucose levels between 70-180 mg/d L).

Holly J Willis, Maren S G Henderson, Laura J Zibley, Meghan M JaKa

JMIR Diabetes 2025;10:e67636


The Effect of Effort During a Resistance Exercise Session on Glycemic Control in Individuals Living With Prediabetes or Type 2 Diabetes: Protocol for a Crossover Randomized Controlled Trial

The Effect of Effort During a Resistance Exercise Session on Glycemic Control in Individuals Living With Prediabetes or Type 2 Diabetes: Protocol for a Crossover Randomized Controlled Trial

Type 2 diabetes (T2 D) is a disease in which peripheral insulin resistance associated with pancreatic beta-cell dysfunction leads to chronically elevated blood glucose levels [1]. If left unchecked over long periods of time, high glucose levels lead to vascular complications such as increased risk of cardiovascular diseases, diabetic nephropathy, neuropathy, retinopathy, and lower limb amputation [2].

Marissa Ramirez, Maja Gebauer, Christine Mermier, Jonathan Peter Little, Luotao Lin, Gabriel Palley, Yu Yu Hsiao, Roberto Ivan Mota Alvidrez, Zach A Mang, Fabiano Trigueiro Amorim, Valmor Tricoli, Flavio De Castro Magalhaes

JMIR Res Protoc 2024;13:e63598


A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study

A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study

In total, 2 studies have addressed the importance of culturally sensitive apps for diabetes management to improve blood glucose in Asian people with diabetes [14,15]. They identified the gap in culturally tailored content and features that address specific dietary habits, lifestyle practices, and health beliefs of diverse ethnic populations.

Peeranuch LeSeure, Elizabeth Chin, Shelley Zhang

JMIR Diabetes 2024;9:e63393