How are different clusters of physical activity, sedentary, sleep, smoking, alcohol, and dietary behaviors associated with cardiometabolic health in older adults? A cross-sectional latent class analysis

Abstract

Background Studies to date that investigate combined impacts of health behaviors, have rarely examined device-based movement behaviors alongside other health behaviors, such as smoking, alcohol, and sleep, on cardiometabolic health markers. The aim of this study was to identify distinct classes based on device-assessed movement behaviors (prolonged sitting, standing, stepping, and sleeping) and self-reported health behaviors (diet quality, alcohol consumption, and smoking status), and assess associations with cardiometabolic health markers in older adults.

Methods The present study is a cross-sectional secondary analysis of data from the Mitchelstown Cohort Rescreen (MCR) Study (2015–2017). In total, 1,378 older adults (aged 55–74 years) participated in the study, of whom 355 with valid activPAL3 Micro data were included in the analytical sample. Seven health behaviors (prolonged sitting, standing, stepping, sleep, diet quality, alcohol consumption, and smoking status) were included in a latent class analysis to identify groups of participants based on their distinct health behaviors. One-class through to six-class solutions were obtained and the best fit solution (i.e., optimal number of classes) was identified using a combination of best fit statistics (e.g., log likelihood, Akaike’s information criteria) and interpretability of classes. Linear regression models were used to test associations of the derived classes with cardiometabolic health markers, including body mass index, body fat, fat mass, fat-free mass, glycated hemoglobin, fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, systolic and diastolic blood pressure.