The Influence of Healthy Lifestyle and Health Status on Body Mass Index (BMI) in Adults

Abstract

Background: With growing concerns for obesity and health comes the need to better understand factors that may affect body mass index (BMI). The aim of this research was to examine the influence of healthy lifestyle factors and health status indicators on BMI in adults. Methods: The Montana Behavioral Risk Factor Surveillance System (BRFSS, 2020) was used for this study. Seven healthy lifestyle variables were created indicating “high risk” and included physical activity, smoking, alcohol consumption, seatbelt use, visiting a dentist, health insurance, and sleep quantity. Nine health status variables were created indicating “poor” health and included self-rated health, heart disease, stroke, cancer, lung disease, depression, arthritis, kidney disease, and diabetes. Multiple linear regression was used to examine the effect of healthy lifestyle factors and health status indicators on BMI while controlling for sociodemographic variables. Results: The fully adjusted healthy lifestyles model showed high risk of physical activity (slope (b) = 1.72 kg/m2), seatbelt use (b = 1.04 kg/m2), and sleep quantity (b = 0.92 kg/m2) directly related and smoking (b = -2.14 kg/m2) and alcohol consumption (b = -0.81 kg/m2) indirectly related to BMI (all ps b = 1.84 kg/m2), depression (b = 1.54 kg/m2), arthritis (b = 1.02 kg/m2), and diabetes (b = 3.28 kg/m2) directly related to BMI (all ps p = .031) and indicated substantially greater mean BMI for those high risk for physical activity (b = 2.57 kg/m2, p = .003) among those with poor health status for diabetes, as compared to those high risk for physical activity (b = 1.33 kg/m2, p Conclusion: This study found that several healthy lifestyle factors and health status indicators influence BMI in adults. Health promotion specialists concerned with obesity should understand the influence that each healthy lifestyle factor has on relative body weight. Physical activity programming should in particular target those who have poor health status for diabetes in Montana.