JMIR Diabetes

Older adults with diabetes frequently access their electronic health record (EHR) notes but often report difficulty understanding medical jargon and nonspecific self-care instructions. To address this communication gap, we developed SEE-Diabetes (Support-Engage-Empower-Diabetes), a patient-centered, EHR-integrated diabetes self-management support tool designed to embed tailored educational statements within the Assessment and Plan section of clinical notes.

Sulphonylureas are commonly prescribed for managing type 2 diabetes, yet treatment responses vary significantly among individuals. Although advances in machine learning (ML) may enhance predictive capabilities compared to traditional statistical methods, their practical utility in real-world clinical environments remains uncertain.

In the past decade, telehealth has transformed healthcare delivery by allowing patients more rapid and convenient access to necessary care without the cost and logistical challenges of traveling to a healthcare facility. Telehealth services can benefit patients with type 2 diabetes mellitus (T2DM) amidst a growing epidemic of T2DM in the United States that affects people of all ages and races. In 2020, 33 million people were diagnosed with this chronic disease, with the number expected to rise 50% by 2040. Telehealth facilitates regular contact between patients and their providers, especially when there are geographic barriers and time constraints prohibiting physical interaction, at little or no added cost to the patient and at their convenience.

Closed-loop insulin delivery is the new standard of care for patients with type 1 diabetes (T1D). However, in France, its implementation remains predominantely hospital based. Expanding access to this treatment through alternative care models looks essential.

Diabetes prediction requires accurate, privacy-preserving, and scalable solutions. Traditional machine learning (ML) models rely on centralized data, posing risks to data privacy and regulatory compliance. Moreover, healthcare settings are highly heterogeneous, with diverse participants, hospitals, clinics, and wearables, producing non-IID data and operating under varied computational constraints. Learning in isolation at individual institutions limits model generalizability and effectiveness. Collaborative federated learning (FL) enables institutions to jointly train models without sharing raw data, but current approaches often struggle with heterogeneity, security threats, and system coordination.

Existing qualitative research in peer support interventions has largely focused on the recipients of support, rather than those delivering support. Exploring the perspectives of both roles may provide a holistic understanding of the peer support experience.

In our study, the Sibio KS1 CKM device captured β-Hydroxybutyrate (BHB) dynamics during exogenous ketosis but revealed a gradual decline day-to-day BHB concentrations over 14 days in both KE and PBO groups, likely reflecting sensor drift.

Diabetic kidney disease (DKD) is a major complication of diabetes and the leading cause of end-stage renal disease globally. Artificial intelligence (AI) technologies have shown increasing potential in DKD research for early detection, risk prediction, and disease management. However, the landscape of AI applications in this field remains incompletely mapped, especially in terms of collaboration networks, thematic evolution, and clinical translation.

Gestational Diabetes Mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (GenAI) and large language models (LLMs) have shown promise in healthcare, their application in GDM management remains underexplored.

Continuous Glucose Monitors (CGM) reduce the burden of glycemic monitoring and improve glycemic control, quality of life, and healthcare utilization. Despite expanded insurance coverage and adoption, barriers remain especially in primary care. Existing research largely evaluates specific populations or interventions, leaving limited insight into broader primary care experience.

Many mobile applications exist for diabetes self-management; however, most target Western populations and lack dietary content relevant to Asian contexts. Our mobile application addresses this gap by providing self-care tools and a database of regionally relevant foods.

Primary care diabetes management lacks objective, scalable methods for continuous physical activity surveillance. Bioelectrical impedance analysis (BIA), routinely collected in diabetes care, offers untapped potential as an automated digital biomarker but requires validation for behavioral phenotyping.