JMIR Diabetes

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.

The escalating rates of obesity and T2DM in Saudi Arabia highlight the impending burden of Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) and nonalcoholic steatohepatitis.

Despite efforts to raise glycemic targets and reduce modifiable risk factors, hypoglycemia continues to impact a large number of long-term care (LTC) residents living with diabetes mellitus and remains one of the leading causes of hospitalization in this cohort. Effective, sustainable practice strategies to monitor and maintain glycemic control in LTC are lacking. We describe the stepwise approach used by 2 LTC homes switching from traditional fingerstick testing to a continuous glucose monitoring (CGM) system as part of a quality improvement initiative to reduce nursing workload and address hypoglycemia. This was an exploratory pilot project. A working group was established at each of the 2 participating LTC homes, including representatives from management and direct care staff. Kickoff meetings were held with key direct care staff to discuss the limitations of current monitoring practices and potential solutions. The following interventions were agreed upon and implemented by the working groups: (1) initiation of structured glucose monitoring for residents using CGM (FreeStyle Libre 2), requiring scanning of sensors 4 times per day; (2) provision of staff education and training on CGM by a diabetes expert; and (3) scheduling of interdisciplinary rounds as needed to optimize diabetes management. System changes were gradually introduced in a stepwise manner over a 3-month period (intervention phase), during which the LTC homes progressed from traditional fingerstick testing to point-of-care sensor readings and then to full use of the CGM software platform. Hypoglycemia was defined as a glucose reading of ≤4mmol/L. Glucose readings were collected from 38 residents living with diabetes mellitus and receiving insulin in the 6 months before the start of the intervention phase (baseline evaluation) and in the 6 months after the end of the intervention phase (post-launch evaluation). All hypoglycemic readings detected by a sensor at a point-of-care test were validated using a fingerstick test. Nursing workload associated with glucose testing was assessed in an anonymous survey of nursing staff at baseline and post-launch. The approach resulted in a 40% reduction in nursing time required to complete a glucose reading (from 5.1 minutes per test at baseline to 3.1 minutes per test at the post-launch evaluation). The frequency of glucose monitoring increased from a total of 19,438 glucose readings in the baseline evaluation to 35,971 point-of-care sensor scans in the post-launch evaluation. The number of detected hypoglycemic events increased 12-fold, from 88 in the baseline evaluation to 1049 in the post-launch evaluation. Hypoglycemic events continue to impact a large number of LTC residents living with diabetes mellitus. CGM can improve the detection of hypoglycemic events while decreasing nursing workload. A gradual transition to CGM can help overcome underlying barriers and concerns and ensure a sustainable approach.