JMIR Diabetes

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.

Gestational diabetes mellitus and type 2 diabetes mellitus impose psychosocial burdens on pregnant individuals. As there is less evidence about the experience and management of psychosocial burdens of diabetes mellitus during pregnancy, we sought to identify these psychosocial burdens and understand how a novel smartphone app may alleviate them. The app was designed to provide supportive, educational, motivational, and logistical support content, delivered through interactive messages.

Background: Diabetic ketoacidosis (DKA) represents a significant and potentially life-threatening complication of diabetes, predominantly observed in individuals with type 1 diabetes (T1D). Studies have documented suboptimal adherence to diabetes management among children and adolescents, evidenced by deficient ketone monitoring practices.

Type 2 diabetes mellitus affects over 500 million people globally, with 10-20% requiring surgery. Diabetes patients are at increased risk for perioperative complications, including prolonged hospital stays and higher mortality, primarily due to perioperative hyperglycemia. Managing blood glucose during the perioperative period is challenging, conventional monitoring is often inadequate to detect rapid fluctuations. Clinical decision support systems (CDSS) are emerging tools to improve perioperative diabetes management by providing real-time glucose data and medication recommendations. This viewpoint examines the role of CDSS in perioperative diabetes care, highlighting their benefits and limitations. CDSS can help manage blood glucose more effectively, preventing both hyperglycemia and hypoglycemia. However, technical and integration challenges, along with clinician acceptance, remain significant barriers.

The use of digital health technology (DHT) in diabetes self-care is increasing, making electronic health (eHealth) literacy an important factor to consider among people with type 1 diabetes. There are very few studies investigating eHealth literacy among adults with type 1 diabetes, highlighting the need to explore this area further.

Type 2 diabetes mellitus (T2DM) has seen a continuous rise in prevalence in recent years, and a similar trend has been observed in the increased availability of glucose-lowering drugs. There is a need to understand the variation in treatment response to these drugs to be able to predict people who will respond well or poorly to a drug. Electronic health records, clinical trials, and observational studies provide a huge amount of data to explore predictors of drug response. The use of artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) techniques, has the capacity to improve the prediction of treatment response in patients. AI can assist in the analysis of vast datasets to identify patterns and may provide valuable information on selecting an effective drug. Predicting an individual’s response to a drug can aid in treatment selection, optimizing therapy, exploring new therapeutic options, and personalised medicine. This Viewpoint highlights the growing evidence supporting the potential of AI-based methods to predict drug response with accuracy. Furthermore, the methods highlight a trend toward using ensemble methods as preferred models in drug response prediction studies.

School-partnered interventions may improve health outcomes for children with type 1 diabetes, though there is limited evidence to support their effectiveness and sustainability. Family, school, or health system factors may interfere with intervention usability and implementation.

Technologies such as mobile apps, continuous glucose monitors (CGMs), and activity trackers are available to support adults with diabetes, but it is not clear how they are used together for diabetes self-management.