JMIR Diabetes

Effective diabetes management requires precise glycemic control to prevent both hypoglycemia and hyperglycemia, yet existing machine learning (ML) and reinforcement learning (RL) approaches often fail to balance multiple competing objectives. Traditional RL-based glucose regulation systems primarily focus on single-objective optimization, overlooking critical factors such as minimizing insulin overuse, reducing glycemic variability, and ensuring patient safety. Furthermore, these approaches typically rely on centralized data processing, raising significant privacy concerns due to the sensitive nature of healthcare data. There is a critical need for a decentralized, privacy-preserving framework that can personalize blood glucose regulation while addressing the multi-objective nature of diabetes management.

Young adults (YA) with type 1 diabetes (T1D) often struggle with self-management and achieving target glycemic control and, thus, may benefit from additional support during this challenging developmental life stage. They are also some of the highest utilizers of social media (SM), which may have some benefits to young people with T1D.

Continuous glucose monitoring (CGM) is used to assess glycemic trends and guide therapeutic changes for people with diabetes. We aimed to increase patient access to this tool by equipping primary care physicians (PCPs) to accurately interpret and integrate CGM into their practice via a multidisciplinary team approach.

Diabetic foot problems are among the most debilitating complications of diabetes mellitus. The prevalence of diabetes mellitus and its complications, notably diabetic foot ulcers (DFUs), continues to rise, challenging healthcare despite advancements in medicine. Traditional detection methods for DFUs face scalability issues due to inefficiencies in time and practical application, leading to high recurrence and amputation rates alongside substantial healthcare costs. Human Medical Thermography presents a viable solution, offering an inexpensive, portable method without ionizing radiation, which could significantly enhance disease monitoring and detection, including DFUs.

Diabetes mellitus type 2 (T2DM) is a metabolic disease that affects over 38 million adults in the United States. T2DM disproportionately affects Hispanic adults in the U.S.

Digital health solutions (DHS) are technologies with the potential to improve patient outcomes as well as change the way care is delivered. The value of DHS for people with diabetes is not well understood, nor is it clear how to quantify this value.

Adherence to Type 1 Diabetes Mellitus (T1DM) treatment regimens decreases during adolescence. While comorbid depression and health insurance disparities are individually known to potentiate this risk, use of technological devices for T1DM appears to be protective.

Diabetes-related lower extremity complications, such as foot ulceration and amputation, are on the rise, currently affecting nearly 131 million people worldwide. Methods for early detection of individuals at high risk remain elusive due to heterogeneity in clinical trajectories, barriers in patient-provider communication, and competing demands for clinician time during clinic visits. While data-driven diabetic polyneuropathy algorithms exist, high-performing, clinically useful tools to assess risk are needed to improve clinical care.

Numerous barriers to moderate to vigorous physical activity exist for youths with type 1 diabetes (T1D). The virtual exercise games for youth with T1D (ExerT1D) intervention implement synchronous support of moderate to vigorous physical activity including T1D peers and role models.

Diabetes management involves a large degree of data collection and self care in order to accurately administer insulin. Several mobile apps are available that allow people to track and record various factors that influence their blood sugar levels. Existing diabetes apps offer features that enable integrations with various devices that streamline diabetes management, such as continuous glucose metres (CGM), insulin pumps or regular activity trackers. While this reduces tracking burden on the users, research highlighted several issues with diabetes apps, including issues with reliability and trustworthiness. As pumps and CGM are safety-critical systems – where issues can result in serious harm or fatalities – it is important to understand what issues and vulnerabilities could be introduced by relying on popular diabetes apps as an interface for interacting with such devices.

This research letter presents a cross-sectional analysis comparing the agreement of artificial intelligence models and nephrologists in responding to common patient questions about diabetic nephropathy.

Mobile health (mHealth) is a low-cost method to improve health for patients with diabetes seeking care in safety-net emergency departments, resulting in improved medication adherence and self-management. Additions of social support to mHealth interventions could further enhance diabetes self-management by increasing the gains and the postintervention maintenance.