1 School of Psychology, University of Ottawa, Ottawa, ON, Canada.
2 Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
3 Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, QC, Canada.
4 Montreal Behavioural Medicine Centre, Le Centre intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada.
5 Department of Physical Activity Sciences, University of Quebec in Montreal, Montreal, QC, Canada.
6 Research Center of the Montreal Mental Health University Institute, Montreal, QC, Canada.
7 Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.
8 Department of Food and Nutrition Sciences, Laval University, Quebec City, QC, Canada.
9 Department of Med

<strong>Background:</strong> Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life.

<strong>Objective:</strong> The aim of this study is to understand how behaviors 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 behavior changes.

<strong>Methods:</strong> Using cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables.

<strong>Results:</strong> Baseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H).

<strong>Conclusions:</strong> This study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences.

<strong>International registered report identifier (irrid):</strong> DERR1-10.2196/24887.