Optimizing and Projecting Lifestyle Behavioral Interventions to Sustain Long-term Health Behaviors and Outcomes in Focus Populations

About This Grant

Ninety percent of the $4.5 trillion in annual healthcare expenditures in the United States can be attributed to chronic conditions.1,2 Many of these often co-occurring conditions, including obesity, diabetes, hypertension and heart disease, can be prevented or improved by lifestyle change interventions (LCIs) such as diet and exercise.3-8 However, African Americans (AA)9-26 and rural residents regardless of race,27-34 who have higher risks of developing multiple chronic conditions (multimorbidity),35,36 often do not participate in LCIs due to barriers specific to them. They therefore do not realize the benefit of reducing multimorbidity at lower costs, with significant cost implications for payers and LCI implementers. To address these barriers and respond to the SIP25-004 Project 1 requirements, we will pursue three aims that will collectively inform policymakers and implementers on the LCI type and dose that maximize desired policy goals at the payers’ available budget level. We will focus on LCIs targeting AA or rural residents or consisting of a sufficient number of such participants. In Aim 1, we will conduct updated meta-analyses on the dose-response relationships between these LCIs and long-term (6 months+) behavior changes (physical activity, diet), improvement in cardiometabolic indicators (body mass index, blood pressure, serum sugar levels), and prevention of multimorbidity (obesity, diabetes, hypertension, heart disease). This aim will help us understand the effective LCI doses that may facilitate LCI adoption in these populations specifically. In Aim 2, we will estimate LCI implementation costs and key factors associated with these costs, either by collecting cost information directly from the published results (if available), or through direct measurement by estimating the unit costs and quantities of labor and material inputs. This aim can help identify opportunities to lower implementation costs. Finally, in Aim 3 we will combine the outcome information (from Aim 1) and the cost information (from Aim 2) to conduct a new cost-effectiveness analysis of LCIs in achieving several desired policy goals. Specifically, we will use the validated Future Elderly Model37 to project multimorbidity onset over a lifetime in a nationally representative sample of AA and rural residents and estimate the cost effectiveness of each LCI in reducing multimorbidity, increasing quality-adjusted life years, decreasing healthcare expenditures, and increasing productivity gains from improved health. Finally, to ensure continued relevance of our research, we will provide an online impact analysis dashboard for policymakers and LCI implementers to visualize cost- effectiveness at various implementation costs and LCI types and doses. Throughout, we will engage community stakeholders to help translate and disseminate our findings beyond academic audiences to encourage adoption of cost-effective LCIs. Our research will inform the optimum allocation of resources to alleviate the burden of multimorbidity and reduce cost for private, state, and federal payers as well as LCI implementers that serve AA and rural residents.