JMIR Diabetes

Continuous glucose monitoring (CGM) has transformed diabetes management and research by providing high-frequency data that address many of the limitations of hemoglobin A, enabling more precise clinical treatment targets and responsive trial endpoints. The richness and complexity of high-resolution time-series CGM data have spurred the development of numerous metrics for both clinical care and research applications. Beyond established metrics, there is a growing set of clinical, composite, and research-oriented measures that may support clinical decision support, intervention planning, risk stratification, and discovery-oriented research. This proliferation has created significant challenges in metric selection, interpretation, calculation, and standardization, particularly when metrics are applied across different devices, populations, software packages, and study designs.

Type 2 diabetes mellitus (T2DM) affects approximately 590 million people worldwide, and its management relies heavily on patient education. With the emergence of online health information and artificial intelligence (AI) large language models, patients are increasingly sourcing medical information independently.

Wearable technologies, including smart insoles and sensor-equipped footwear, enable continuous monitoring of key foot parameters such as plantar pressure and temperature in individuals at risk of diabetic foot ulcers (DFUs).

Diabetic foot ulcers (DFU) are serious complications of diabetes that contribute substantially to morbidity, mortality, and health care burden. Accurate and timely wound assessment is essential for effective DFU management; however, conventional assessment methods are limited by subjectivity, time constraints, and interobserver variability.

Digital therapeutics integrating continuous glucose monitoring (CGM) with personalized lifestyle coaching can enhance glycemic control in individuals with type 2 diabetes mellitus (T2DM). However, real-world evidence evaluating such multicomponent interventions—combining CGM with nutrition coaching, physiotherapy, and cognitive behavioral therapy within a unified digital platform—remains limited in South Asian populations.

Digital twin (DT) systems have emerged as a promising approach in health care, enabling real-time, patient-specific virtual modeling and personalized interventions. In diabetes care, DTs offer the potential to revolutionize glucose management, decision support, and therapy personalization through integration of real-time and longitudinal patient data.

Type 2 diabetes mellitus, a public health challenge, disproportionately impacts low- and middle-income countries (LMICs), accounting for 73% of global cases. Due to resource constraints, these nations have adopted task-shifting strategies using community health workers (CHWs). However, evidence on the effectiveness of training CHWs in diabetes management is limited and, at most, indirect due to the limited studies, variable training methods, and complex interventions that make it difficult to isolate training effects.

Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of glucose metabolism. Traditional static insulin regimens fail to adapt to individual variability in diet, physical activity, stress, and metabolic fluctuations, leading to suboptimal glycemic control. Reinforcement learning (RL) offers a promising alternative by enabling personalized, real-time insulin adjustments that improve the balance between hyperglycemia and hypoglycemia.

Prediabetes is common in the United States, and adverse social determinants of health (SDOH) are known to undermine diabetes prevention efforts. Chinese Americans experience a disproportionately high prevalence of prediabetes, yet the SDOH profiles of this population remain understudied.

The prevalence of diabetes in the United States necessitates investigations into how to better enable adults with type 2 diabetes mellitus (T2DM) to manage their health using easy-to-access and personally adaptable technologies. The ubiquity of digital content further justifies the need to consider the impact of different digital intervention modalities in diabetes self-care activities.

Diabetes mellitus is a chronic metabolic disorder marked by elevated blood glucose levels and has emerged as a global epidemic that requires management strategies for effective glycemic control through diet. In recent years, mobile apps have emerged as valuable tools for supporting self-management in chronic diseases such as diabetes, particularly for the nutritional aspects of the disease. However, the quality, accuracy, and adherence of these apps to established dietary guidelines remain underexplored and inconsistent.

Gestational diabetes mellitus (GDM) requires effective self-management to mitigate associated health risks. A comprehensive understanding of the multilevel factors influencing adherence is essential to designing effective, holistic support strategies.