JMIR Diabetes

Emerging technologies, medical devices, apps, sensors, and informatics to help people with diabetes

Editor-in-Chief:

Ricardo Correa, MD, EdD (Co-Editor-in-Chief), Cleveland Clinic, United States

Sheyu Li, MD (Co-Editor-in-Chief), West China Hospital, Sichuan University, China


Impact Factor 2.6 CiteScore 4.7

JMIR Diabetes (JD, ISSN 2371-4379) focuses on technologies, medical devices, apps, engineering, informatics and patient education for diabetes prevention, self-management, care, and cure, to help people with diabetes. JMIR Diabetes may consider papers that do not have a digital health component but represent a significant innovation for diabetes prevention and care.

JMIR Diabetes publishes original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews) covering, for example, wearable devices and trackers, mobile apps, glucose monitoring (including emerging technologies such as Google contact lens), medical devices for insulin and metabolic peptide delivery, closed loop systems and artificial pancreas, telemedicine, web-based diabetes education and elearning, innovations for patient self-management and "quantified self", diabetes-specific EHR improvements, clinical or consumer-focused software, diabetes epidemiology and surveillance, crowdsourcing and quantified self-based research data, new sensors and actuators to be applied to diabetes.

As an Open Access journal, JMIR Diabetes is read by clinicians and patients alike and has (as all JMIR Publications journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies, as well as on diabetes prevention and epidemiology.

JMIR Diabetes is indexed in PubMed, PubMed Central, DOAJ, Scopus and the Web of Science™ (ESCI).

JMIR Diabetes received an inaugural Journal Impact Factor of 2.6 according to the latest release of the Journal Citation Reports from Clarivate, 2025.

With a CiteScore of 4.7 (2024), JMIR Diabetes is a Q2 journal in the field of Health Informatiion Management, according to Scopus data.

 

 

Recent Articles

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Sensors for Glucose Monitoring and Diabetes Management

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.

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Artificial Intelligence in Diabetes Care and Prevention

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.

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Exercise and Diet Tracking for Diabetes Patients

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.

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Diabetes Reviews and Scoping Studies

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.

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Diabetes Self-Management

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.

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Artificial Intelligence in Diabetes Care and Prevention

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.

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Diabetes Education and Elearning for Patients

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.

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Apps, Mobile, Wearables for Diabetes

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.

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Research Letter

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.

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Diabetes Self-Management

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.

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Diabetes Self-Management

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.

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Patient Experiences with Diabetes Technology

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.

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