Background: Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviors tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behavior change. However, whether co-occurrence or co-variation-based approaches are better suited for this task remains relatively unknown.
Purpose: To compare the utility of co-occurrence vs. co-variation-based approaches for understanding the interconnectedness between multiple health-impacting behaviors.
Methods: Using baseline and follow-up data (N = 40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviors. We used cluster analysis to group individuals based on their behavioral tendencies across multiple behaviors and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioral correlations and compared regression analyses of clusters and individual behaviors predicting future health outcomes.
Results: Seven clusters were identified, with clusters differentiated by six of the seven health behaviors included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviors were generally small. In regression analyses individual behaviors accounted for more variance in health outcomes than clusters.
Conclusions: Co-occurrence-based approaches may be more suitable for identifying sub-groups for intervention targeting while co-variation approaches are more suitable for building an understanding of the relationships between health behaviors.
Keywords: CLSA; Cluster analysis; Health behaviors; Multiple behaviors.
Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. A better understanding of which behavioral combinations people engage in, and which behaviors are associated with each other, may provide new insights to support the development of interventions to promote multiple health behavior change. Using data with two time points (N = 40,268) from the Canadian Longitudinal Study of Aging, we grouped people into clusters based on their health behaviors and examined how these clusters are associated with demographic characteristics and health indicators. Seven clusters were identified with sociodemographic patterns evident across several clusters. Correlations between behaviors were generally small. We compared whether individual health behaviors, or groupings of people based on their health behaviors, were better predictors of future health outcomes. Individual behaviors were slightly better predictors of future health outcomes than clusters.
© The Author(s) 2023. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.