Using Trends in Biometric Data to Predict Interest in Enrolling in an Employer-Sponsored National Diabetes Prevention Program Focusing on Diet and Exercise: A Retrospective Cohort Study - Victoria Zigmont; Abigail Shoben; Gail Kaye; Steven Clinton; Randall Harris; Susan Olivo-Marston (Journal of Epidemiology and Public Health, 2024)
Background: Evidence-based lifestyle programs including the Diabetes Prevention Program can delay an individual’s risk of developing type 2 diabetes. Identifying which individuals are less likely to enroll in these programs and tailoring recruitment approaches to encourage participation among those with perceived barriers is an effective strategy to increase engagement in health promotion. This study aimed to identify the pre-enrollment differences in biometric trends between individuals with prediabetes who did and did not express interest in free worksite diabetes prevention programs.Subjects and Method: This retrospective cohort study was conducted among individuals in the Midwest enrolled in a private insurance plan from 2011 to 2014. Data was combined from annual biometric screenings and a health survey. Demographic characteristics were summarized for the study population (n=2,066). The dependent variable for this study was interest in the DPP, while the independent variables included body mass index, waist circumference, body weight, lipid measurements, and blood pressure. Linear mixed models with random intercepts were used to compare bio-metric trajectories for body mass index, waist circumference, body weight, lipid measurements (triglycerides and cholesterol), and blood pressure for the two groups.Results: No differences were observed in biometric trends for those who did and did not choose to enroll in the free worksite program.Conclusion: Examining pre-enrollment biometric trend data is a relatively novel approach to evaluating engagement in health programs. More research is needed to understand how this information can be used to identify an individual’s interest in enrolling in health programming.