Diabetes Spectrum最新文献

Diabetes is a major cause of end-stage kidney disease (ESKD). Glycemic management is challenging in this population, and A1C, commonly used for monitoring glycemic control, is unreliable. Continuous glucose monitoring indices can be used for glycemic monitoring in people with ESKD. Dipeptidyl peptidase 4 inhibitors, incretin mimetic agents (glucagon-like peptide 1 and glucose-dependent insulinotropic peptide receptor agonists), and insulin using an automated insulin delivery system are preferred to manage diabetes in people with ESKD on hemodialysis.

This article discusses the implementation and impact of autonomous artificial intelligence (AI) systems for diabetic eye disease testing at the Johns Hopkins Medicine health system, highlighting improvements in screening rates, access to care, and health equity for underserved populations. The AI technology has been effective in both adult and pediatric populations and has reduced disparities and increased follow-up with eye care professionals. While considering the challenges and successes of this approach, this article also highlights the potential long-term impact of AI systems in improving visual health outcomes for people with diabetes in diverse health care settings.

Objective: Management of type 1 diabetes during illness confers risk for serious harm because sick days happen infrequently, management is complex and multistep, and errors can result in diabetic ketoacidosis and hospitalization. Simulation offers a safe, experiential learning environment in which to practice skills with the goal of identifying and closing knowledge gaps. We hypothesized that simulation of sick-day scenarios with patients and caregivers via video conference could be used to assess knowledge of sick-day management for type 1 diabetes and determine the availability of necessary supplies at home.

Research design and methods: Our study included development of performance-based, scripted sick-day scenarios; selection and training of content experts as simulation facilitators; piloting and iterative revision of scenarios; and implementation of simulation sessions. Five distinct scenarios were created for either insulin injections or continuous insulin pump therapy. Each participant was block randomized to receive three of the five scenarios. Participant performance was measured by categorizing actions as most desired, less desired, or incorrect.

Results: Ten caregivers and two young adults with type 1 diabetes participated in 12 simulations, representing 14 individuals with type 1 diabetes. The most common category of less desired and incorrect processes chosen by participants was ketone management.

Conclusion: Simulation-based training for sick-day management is a feasible, interactive form of education for people with type 1 diabetes and caregivers. Video conferences allowed firsthand observation of supplies and resources available to families at home. Participant understanding of sick-day management will inform future interventions to address challenges with ketone management and recognition of diabetes technology issues when caring for an ill child with type 1 diabetes.

People with type 1 diabetes are living longer and, by 2030, one-fifth of the U.S. population will be older adults ≥65 years of age. This qualitative study addressed the scarcity of research on the experiences of aging people with type 1 diabetes. Nineteen older adults (mean age 65.63 ± 6.27 years, mean duration of diabetes 36.97 ± 18.94 years) were interviewed about their disease management challenges and gaps in available resources. Identified themes included positive and negative changes over the life span resulting from 1) diabetes-related psychological health, 2) hypoglycemia unawareness, and 3) health care barriers. Responses also suggested that a lack of support may lead to poorer health outcomes and greater diabetes distress. Additional research is needed to better understand these issues and to inform the creation or tailoring of public policies to support older adults with type 1 diabetes.

Objective: Diabetes distress (DD) is the negative emotional toll of living with or caring for type 1 diabetes, encompassing the pervasiveness of its daily demands. We recently developed Remedy to Diabetes Distress (R2D2), a screen-to-treat intervention for families of school-aged children with type 1 diabetes. This article reports on the use of crowdsourcing methods to design the intervention and capture the voice of parent stakeholders.

Research design and methods: We recruited 41 parents of 8- to 12-year-olds with type 1 diabetes. In a secure online social network, we posted study questions to participants, who could respond to our questions and view and comment on other participants' responses (i.e., participants could interact as a "crowd"). An iterative approach allowed our questions to move from broad to specific, enabling parent input on treatment content.

Results: Using a conceptual model of DD, we examined responses and applied codes to distill qualitative categories. We established substantial agreement on identified categories, with fidelity at κ = 0.615. In addition to providing broad feedback (e.g., on preferred language for common phrases used in type 1 diabetes interventions), parents also provided feedback on specific treatment components and the overall tone of the intervention. Parents highlighted benefit-finding and positive talk/self-talk as pertinent for incorporation into treatment.

Conclusion: This study illustrates the use of crowdsourcing methods to capture the voices of parents of school-aged children with type 1 diabetes when designing a DD intervention. The next phase of R2D2 will be a small pre-trial to evaluate the feasibility and acceptability of the intervention, followed by a proof-of-concept randomized controlled trial (RCT), during which family voices will continue to be incorporated.

This article is adapted from the address Dr. Neumiller delivered as the recipient of the American Diabetes Association's Outstanding Educator in Diabetes Award for 2024. He delivered the address in June 2024 during the Association's 84th Scientific Sessions.