JMIR Diabetes

For adults living with type 1 diabetes (T1D), diabetes-specific mental health support is limited. Peer support and digital health platforms are promising strategies for delivering this support to this population, particularly those from geographically marginalized communities. Mobile apps, in particular, can enhance self-management and deliver support. This paper describes the iterative co-design and development process of a novel mobile app for use in a pilot trial, T1D REACHOUT (REACHOUT), that aims to reduce the diabetes distress, a core facet of diabetes-specific mental health, of adults with T1D. An initial think tank and 6 focus groups were conducted with adults with T1D to better understand their support needs and identify platform requirements. Following this, we partnered with adults living with T1D, the “end users,” to iteratively co-design the REACHOUT app, enhancing usability and ensuring relevance. Adapting the open-source Rocket.Chat platform to user-defined requirements, we deployed the app in a single cohort pilot study. A network analysis of messages exchanged during the pilot study was performed to explore trends and patterns and demonstrate implementation feasibility. Pilot study outcomes informed further refinement before implementation in a randomized controlled trial. The implementation of the REACHOUT app features 6 key components identified in 6 initial focus groups: a 24/7 chatroom (a customized group messaging function with threads), topic-specific discussion boards, a peer supporter library, peer supporter profiles for a user-driven matching process, small group virtual sessions, and direct (one-to-one) messaging. Forty-six participants were encouraged to use any or all of the features as frequently as desired over a 6-month period during the pilot study. During this time, 179 private small groups were created, and 10,410 messages were sent, including 1389 chat room messages and 7116 direct messages; among these were 3446 messages exchanged between participants and their self-selected peer supporters. Key factors for successful implementation included (1) the co-design process involving comprehensive user engagement and (2) the opportunities realized through hybrid development. These findings offer generalizable lessons for mobile health research teams developing similar app-based interventions.

Digital health interventions, including artificial intelligence (AI)-driven solutions, offer promise for type 2 diabetes mellitus (T2DM) and prediabetes management through enhanced self-management, adherence, and personalization. However, engagement challenges and barriers, particularly among young adults and diverse populations, persist. Existing reviews emphasize clinical outcomes while neglecting engagement factors crucial to intervention success. This review highlights engagement barriers and facilitators, offering insights into improving digital health solutions for diabetes management.

Patients with diabetes carry a 1.5- to 2-fold higher risk of community-acquired pneumonia (CAP) and experience more severe outcomes, yet the mechanisms that integrate metabolic dysregulation, pathogen shifts, and novel cell death pathways remain fragmented.

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Culturally and linguistically diverse (CaLD) populations are at a higher risk of developing prediabetes; however, the effectiveness and implementation of digital health interventions for prediabetes management in this population are not well understood.

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Sulphonylureas are commonly prescribed for managing type 2 diabetes, yet treatment responses vary significantly among individuals. Although advances in machine learning (ML) may enhance predictive capabilities compared to traditional statistical methods, their practical utility in real-world clinical environments remains uncertain.

In the past decade, telehealth has transformed healthcare delivery by allowing patients more rapid and convenient access to necessary care without the cost and logistical challenges of traveling to a healthcare facility. Telehealth services can benefit patients with type 2 diabetes mellitus (T2DM) amidst a growing epidemic of T2DM in the United States that affects people of all ages and races. In 2020, 33 million people were diagnosed with this chronic disease, with the number expected to rise 50% by 2040. Telehealth facilitates regular contact between patients and their providers, especially when there are geographic barriers and time constraints prohibiting physical interaction, at little or no added cost to the patient and at their convenience.

Closed-loop insulin delivery is the new standard of care for patients with type 1 diabetes (T1D). However, in France, its implementation remains predominantely hospital based. Expanding access to this treatment through alternative care models looks essential.

Diabetes prediction requires accurate, privacy-preserving, and scalable solutions. Traditional machine learning (ML) models rely on centralized data, posing risks to data privacy and regulatory compliance. Moreover, healthcare settings are highly heterogeneous, with diverse participants, hospitals, clinics, and wearables, producing non-IID data and operating under varied computational constraints. Learning in isolation at individual institutions limits model generalizability and effectiveness. Collaborative federated learning (FL) enables institutions to jointly train models without sharing raw data, but current approaches often struggle with heterogeneity, security threats, and system coordination.

Existing qualitative research in peer support interventions has largely focused on the recipients of support, rather than those delivering support. Exploring the perspectives of both roles may provide a holistic understanding of the peer support experience.