JMIR Diabetes

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.

One in four Veterans who receive care through the Veterans Health Administration (VHA) has type 2 diabetes (T2D). Dietary carbohydrate restriction can promote weight loss and improve blood glucose control, but Veterans taking certain medications (e.g., insulin) may experience serious complications (e.g., hypoglycemia) without adequate support and monitoring.

Insulin therapy is crucial for type 2 diabetes mellitus management, with increasing usage in Indonesia, and its effectiveness is well-established. However, prescribing insulin poses various challenges that can impact the effectiveness of insulin. Patient education is crucial for the successful implementation of insulin therapy. Proper insulin use remains insufficient in Indonesia.

Basal rates (BR) adjustment is crucial for managing Type 1 Diabetes Mellitus (T1DM), accounting for 30% to 50% of Total Daily Insulin (TDI) needs. All current Closed Loop systems revert to the user’s usual pump BR (known as manual mode) in the event of closed-loop failure. Further, those in low and middle-income countries (LMICs) and those without suitable health insurance, access to Closed Loop remains relatively low. Accurately adjusting the BR remains challenging, leading to hyperglycaemia or hypoglycaemia, and research on optimizing the BR is limited.

To encourage insulin dose self-titration by adults living with type 2 diabetes, we developed an innovative bilingual toolkit comprised of a personalized action plan and educational videos.

Managing Type 1 Diabetes (T1D) requires maintaining target blood glucose levels through precise diet and insulin dosing. Predicting postprandial glycaemic responses (PPGRs) based solely on carbohydrate content is limited by factors like meal composition, individual physiology, and lifestyle. Continuous glucose monitors (CGMs) provide insights into these responses, revealing significant individual variability. The statistical clustering method propsed here balances the number of clusters formed and the glycaemic variability of the PPGRs within each cluster to offer a clustering technique on which treatment decisions could be based.

Diabetes self-management plays a major role in controlling blood sugar levels and avoiding chronic complications. Meanwhile, AI tools such as ChatGPT are becoming increasingly available to patients and are often used for disease management advice. Frontline caregivers must be aware of these tools’ strengths and weaknesses to ensure their safe use.