Samantha Kanny

Introduction: Social determinants of health (SDoH) are key risk factors impacting diabetes outcomes. The American Diabetes Association has identified five principal SDoH categories influencing diabetes outcomes: socioeconomic status, neighborhood and physical environment, food environment, healthcare, and social context. Currently, no concise and valid instruments exist to measure the burden of SDoH factors for individuals with diabetes. To fill this gap, we developed the Diabetes Index for Social Determinants of Health (DISDOH). Research design and methods: Participants with type 1 and type 2 diabetes were recruited from the regional Health Extension for Diabetes (HED) programme and a national crowdsourcing platform. Item development through a diabetes expert stakeholder group yielded a pool of 16 items, which were further refined through piloting with individuals living with diabetes. Principal component analysis was conducted with a sample of 440 HED participants, identifying a 5-factor solution. Confirmatory factor analysis supported this 5-factor solution using 215 individuals with type 1 and type 2 diabetes. Reliability and validity of DISDOH were also assessed. Results: The five DISDOH domains demonstrated acceptable internal consistency estimates: Domain 1, socioeconomic status (a=0.660); Domain 2, neighborhood and physical environment (a=0.812); Domain 3, food environment (a=0.801); Domain 4, health context (a=0.812); and Domain 5, social context (a=0.708). DISDOH showed strong convergent validity with related SDoH measures, and divergent validity was supported by weak or non-significant correlations with distinct constructs. Conclusions: DISDOH is the first validated, diabetes-specific SDoH assessment designed for brief, practical use in clinical and community settings. Unlike existing instruments, which are often lengthy and not tailored to diabetes management, DISDOH offers a concise yet comprehensive approach to identifying social risk factors that impact diabetes self-care and outcomes.

Approximately 11.6% of Americans have diabetes and South Carolina has one of the highest rates of adults with diabetes. Diabetes self-management programs have been observed to be effective in promoting weight loss and improving diabetes knowledge and self-care behaviors. The ability to keep vulnerable individuals in these programs is critical to helping the growing diabetic population. Utilizing machine learning is gaining popularity in healthcare settings. The objective of this study is to assess the effectiveness of several machine learning methods in predicting attrition from a diabetes self-management program, utilizing participant demographics and various evaluation measures. Data were collected from participants enrolled in Health Extension for Diabetes (HED). Descriptive statistics were used to examine HED participant demographics, while Mann-Whitney U tests and chi-square tests were used to examine relationships between demographics and pre-program evaluation measures. Through the various analyses, health-related measures – specifically the SF-12 quality of life scores, Distressed Communities Index (DCI) score, along with demographic factors (race, age, height, and educational attainment), and spatial variables (drive time to the nearest grocery store) emerged as influential predictors of attrition. However, the machine learning models showed poor overall performance, with AUC values ranging from 0.53 – 0.64 and F-1 scores between 0.19 – 0.36, indicating low predictive power. Among the models tested, XGBoost with downsampling yielded the highest AUC value (0.64) and a slightly higher F-1 score (0.36). To enhance model interpretability, SHAP (SHapley Additive exPlanations) was applied. While these models are not suitable for accurately predicting individual attrition risk in diabetes self-management programs, they identify potential factors influencing dropout rates. These findings underscore the difficulty for models to accurately predict health behavior outcomes, highlighting the need for future research to improve predictive modeling to better support patient engagement and retention.

Background: In the United States, 38.4 million people have been diagnosed with diabetes, and it continues to rise. The increasing rate of diabetes has become a significant public health challenge due, in part, to the association between diabetes and decreased levels of physical and emotional well-being. Currently, there are few assessments of the impact of diabetes self-management programs on individuals with diabetes quality of life and social vulnerability. This study examined pre- to post-program quality of life outcomes for participants in a community-based diabetes-self management and support (DSMS) program and assessed the association between the change in quality of life pre- to post-program and social vulnerability. Methods: Health Extension for Diabetes (HED) is a 4-month, community-based DSMS program delivered in the Southeast region of the United States. HED includes standardized education and personalized support to help participants manage their diabetes. The 12-Item Short Form Health Survey (SF-12) was utilized to assess participants’ physical and mental quality of life pre- and post-program participation. The Centers for Disease Control and Prevention’s (CDC) Social Vulnerability Index (SVI) was used to determine individuals’ social vulnerability level (low: 0–0.25, low-to-moderate: 0.2501–0.5, moderate-to-high: 0.501–0.75, high: 0.7501–1.0). Wilcoxon sign-ranked tests assessed changes in SF-12 pre- and post-HED and linear regressions examined the association between quality of life and social vulnerability level. Results: SF-12 scores indicated significant positive changes in physical and mental quality of life for all program participants (N = 1,006). All SVI subgroups were observed to have significant improvements in physical health scores. Individuals with moderate-to-high and high SVI scores showed significant improvement in mental health scores, while individuals with low and low-to-moderate SVI scores did not. Conclusion: Participants of the community-based diabetes self-management and support program experienced improvements in quality of life across varying levels of social vulnerability, as measured by the SVI. While integrating upstream social determinants of health considerations into DSMS program design and delivery addresses health disparities, future research should consider the implementation of more general mental health resources to address the psychological burden associated with living with chronic disease.

Background: Diabetes is a complex, prevalent condition. Effective diabetes self‐management requires knowledge, skills, and strategies to prevent or delay the onset of diabetes‐related complications. This study evaluated the effectiveness of delivery methods, online versus in‐person, for the 4‐month‐long Health Extension for Diabetes (HED) program, a self‐management support program. Methodology: A quasiexperimental comparative study design assessed whether online HED delivery was as effective as in‐person delivery and whether group differences affected health outcomes. Multivariate analysis of covariance (MANCOVA) was conducted to assess changes in participants’ weight, BMI, HbA1c, diabetes self‐efficacy, knowledge, and self‐management behaviors. This study included a total of 1018 participants ( o nline = 613; i n‐ p erson = 405). The online program was delivered synchronously via video or phone conferencing platforms, while in‐person sessions occurred at a community location. Survey and biometric data were collected at baseline and upon program completion to evaluate pre‐ to postprogram changes. Results: Online HED participants were mostly White non‐Hispanic, significantly younger, more educated, and had higher income than in‐person participants. Regardless of the delivery modality, positive changes pre‐ to postprogram were seen across all biometric and diabetes outcome measures. Significant differences between delivery modalities were observed for diabetes knowledge, with individuals in online groups demonstrating significantly higher knowledge scores at both assessment points. For weight and BMI, both modalities showed significant improvements. Conclusion: Online and in‐person HED modalities effectively improved diabetes self‐management, reaching two different demographic groups. Demographic differences between groups underscore the importance of offering multiple participation modalities to ensure accessibility and engagement across diverse populations. The HED program model, using a community‐clinical linkage through Cooperative Extension, is an effective model for improving health outcomes of participants enrolled in both online and in‐person DSMS programs. Future research should focus on how different participant characteristics influence engagement and long‐term diabetes outcomes across delivery modalities.

Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.

Sport participation provides a direct means to attain health-enhancing physical activity; however, sport participation declines during adolescence, and over 85% of adolescent females fail to meet the recommended 60 min of moderate-vigorous physical activity daily. Given the importance of overcoming barriers to sport and increasing equity in women’s sports, the purpose of this systematic review was to identify factors associated with sport participation among adolescent girls and operationalize those factors into theoretical constructs to guide future research. Six databases were systematically searched, and 36 records were included for review. Factors impacting girls’ sport participation were categorized as personal, peer, family, socioeconomic, environmental, or other factors. Of these categories, personal factors, including self perceptions and desirable personal outcomes related to sport, were most frequently associated with sport participation. Most research on girls’ sport participation lacks theoretical framework, so to aid future studies, this review categorized important participatory factors into the constructs of the theory of planned behavior. Future research would benefit from theory-driven prospective approaches to make clear and consistent predictions about factors impacting sport participation, as well as mixed-method approaches aimed to provide more robust understanding of girls’ experiences with and perceptions of factors impacting their participation in sports.