Abstract MP029: Sleep Duration and Sedentary Behavior as Predictors of Cardiovascular Disease in Mid-Life

Abstract

Introduction: Sedentary behavior and short sleep duration are independently associated with cardiovascular disease (CVD), but there is limited research on the interaction between sedentary behavior and sleep duration on CVD. Hypothesis: Short sleep duration combined with greater sedentary behavior will be significantly associated with CVD. Methods: Middle-aged (40-65), community-dwelling adults were recruited to participate in a healthy aging study. Participants completed questionnaires on demographics, medical history, and habitual sleep duration. Sleep duration was categorized as < 6 vs. ≥ 6 hours. Sedentary behavior, measured with the International Physical Activity Questionnaire, was defined as sitting in hours/day dichotomized at the 75 th percentile (< 9 vs. ≥ 9 hrs). CVD was defined as reported diagnosis of one or more of five conditions including coronary artery disease, congestive heart failure, myocardial infarction, arrhythmias, and stroke. Of the total sample of 770 individuals, 731 who had complete data on sleep, sitting, and CVD were included in the analysis. We conducted logistic regression models predicting CVD from sleep duration and sitting time groups adjusting sequentially for demographics (age, sex, education level) and CVD risk factors (reported high blood pressure and diabetes) (see Table). Results: CVD was present in 21% of the sample (n = 162). Participants who reported < 6 hrs sleep duration and sitting < 9 hrs/day were at significantly greater risk for CVD events compared to participants obtaining ≥ 6 hrs sleep duration and sitting < 9 hrs/day in the demographic model (see Table). Adjustment for CVD risk factors attenuated the relationship to borderline significance. Conclusions: Contrary to prediction, short sleep duration combined with short to moderate sitting time was associated with CVD events. Interpretation of the results should be done cautiously given our analysis was limited by a small sample size and a lack of adjustment for other potential CVD risk factors.