Pilot and Feasibility Projects

Abstract: Low-income rural communities face significant social risks that complicate the management of type 2 diabetes (T2D), including food insecurity, inadequate transportation, unstable housing, and persistent socioeconomic hardship. The region is home to a large Latino population, many of whom experience disproportionate burdens of T2D and face systemic barriers to care. These barriers contribute to poor disease control, increased complications, and higher healthcare costs. Community-clinical linkage (CCL) interventions offer a promising approach to bridging healthcare services with social support systems, but their implementation in rural areas remains challenging due to limited healthcare infrastructure and service availability. Mobile health clinics present a potential solution by delivering primary care directly to underserved populations and integrating CCL interventions to address both medical and social needs. This study, in collaboration with San Ysidro Health (SYHealth), a federally qualified health center serving Eastern San Diego County, aims to develop and test the feasibility of a culturally informed, bilingual CCL intervention within a mobile clinic setting. We will first engage a community advisory board and clinical stakeholders in a structured intervention mapping process to identify key educational, behavioral, and clinical components of the intervention. We will then conduct a single-arm feasibility trial with 25 Spanish- and English-speaking community members living with T2D, assessing intervention retention and adherence. We hypothesize that participants will demonstrate high engagement, with retention rates exceeding 80%. Findings from this study will inform scalable strategies to improve diabetes outcomes and reduce health disparities in rural, low-income populations.

Approximately 1 in 3 people with diabetes experience diabetes distress, which is associated with a range of adverse outcomes, including poorer glycemic management, greater prevalence and severity of diabetes complications, poorer adherence to diabetes self-management behaviors, and higher mortality relative to nondistressed adults with diabetes. Concurrently, racial and ethnic minoritized and low socioeconomic status (SES) populations experience a disproportionately high prevalence of diabetes and diabetes complications. Although national guidelines recommend diabetes self-management support and education programs for all people with diabetes, barriers at multiple levels limit access, particularly for racial and ethnic minoritized and low SES populations. Digital interventions and continuous glucose monitoring (CGM) have emerged as promising strategies to improve access and outcomes, and recent national guidelines recommend combining these two strategies. However, relatively few studies have researched a combined intervention approach among patients with T2D, particularly among those from racial and ethnic minoritized and low SES backgrounds.

To improve access to diabetes self-management support, our team developed a digital version of the evidencebased DECIDETM Self-Management Program, which is a 9-module problem-solving training curriculum for people with diabetes that improves clinical and behavioral outcomes. The overall objective of this proposal is to pilot and evaluate the feasibility and acceptability of an intervention that combines CGM with Digital DECIDE delivered over 9 weeks. We will recruit 20 adults with type 2 diabetes and diabetes distress from racial and ethnic minoritized and/or low SES backgrounds who receive care at Northwell Health, the largest healthcare provider in New York state. The aims of this study are: 1) to determine the feasibility and acceptability of an intervention pairing Digital DECIDE with CGMs and 2) to explore change in diabetes clinical and behavioral factors between baseline and 3-months post-intervention. Participants will be randomized to receive Digital DECIDE and CGM (n=10) or Digital DECIDE only (n=10). Feasibility will be assessed as rates of recruitment and retention, Digital DECIDE session completion, CGM initiation, and CGM maintenance. Acceptability will be assessed by qualitative interviews with patients at post-intervention. Hemoglobin A1c will be assessed at baseline and 3-months post-intervention, and validated questionnaires of diabetes distress and health-related knowledge, skills, and behaviors will be administered at baseline, 1-week post-intervention, and 3-months post-intervention. Feasibility measures will be reported using frequency distributions and percentages, and qualitative analysis will be used to code themes around acceptability. Diabetes clinical and behavioral outcomes will be reported as means and standard deviations at each timepoint for each arm. Change scores will be computed within each arm, and paired samples t-tests will explore pre-post differences. This work, and a future R01-level studies, will lead to the development of an intervention that has the potential to provide diabetes self-management support digitally, augmented by CGM, to improve diabetes outcomes in diverse, underserved adults with type 2 diabetes and diabetes distress.

Understanding the biopsychosocial health status of individuals with Latent Autoimmune Diabetes in Adulthood (LADA) is essential for developing appropriate interventions and reducing health disparities. There is some evidence that LADA may be more prevalent among U.S. Hispanic/Latino and Black individuals than non-Hispanic White individuals. LADA is often misdiagnosed as type 2 diabetes, leading to inappropriate treatment, medical mistrust, and suboptimal management, perhaps particularly among racial and ethnic minority groups. There is an emerging literature on the etiology and clinical characteristics of individuals with LADA. However, an understanding of the biopsychosocial health status and social determinants of health experienced by those with LADA is lacking. While up to 12% of adults diagnosed with diabetes are better characterized by LADA, it is relatively rare on a population level and thus difficult to study. We propose to fill this gap by using national VA electronic health record (EHR) data to characterize the biopsychosocial health status and examine the prevalence of social determinants of health (SDH) among Veterans diagnosed with LADA. We propose to develop an EHR phenotyping algorithm using ICD codes, expert consensus criteria, interviews with VA diabetes clinic endocrinologists, and natural language processing. We will validate this algorithm using manual chart review. Specifically, we will examine the following aims: Aim 1) Develop and validate an electronic health record algorithm to identify cases of LADA; Aim 2) Characterize the biopsychosocial health status of Veterans with LADA; and Exploratory Aim 1) Examine the association between social determinants of health and biopsychosocial health status factors among Veterans with LADA. Our resulting EHR algorithm will estimate the prevalence and incidence of LADA in the underserved VA population and can be applied to studies using EHR databases from other healthcare systems. Further, our results can support a needs case for larger NIH funding to further refine the algorithm, understand the unique experiences associated with LADA, and identify targets for tailored behavioral and psychosocial interventions.