Clustering of healthy behaviours in Canadians - Protocol for a multiple behaviour analysis of data from the CLSA

Health behaviours such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for non-communicable chronic disease and play a central role in limiting health and life satisfaction. To date, however, health behaviours tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviours and often focused only on a given health behaviour without considering the co-occurrence of family, social, work and other behaviours of everyday life. Understanding how behaviours cluster, and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behaviour change. Using cross-sectional baseline data from the Canadian Longitudinal Study of Aging, we will perform a pre-defined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviours. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioural tendencies. Multinomial logistic regression will then be employed to model the relationships between clusters and demographic indicators, healthcare utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. Additionally, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviours based on the strength of relationships between variables. This study will help to inform the development of interventions tailored to sub-populations of adults (e.g., physically inactive smokers) defined by the multiple behaviours that describe their everyday life experience.