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Funding Information: This research was made possible using the data or biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA data set [Baseline Tracking Dataset version 3.4 and Comprehensive Dataset version 4.0.], under Application Number [19CA012]. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. Peer review documents for the CIHR grant application are available in Multimedia Appendix 4. ZVA is supported by a CIHR Doctoral Award: Frederick Banting and Charles Best Canada Graduate Scholarship. SLB is supported by a CIHR-Strategy for Patient-Oriented Research Mentoring Chair (SMC-151518) and an FRQS (Fonds de Recherche du Québec–Santé) Chair (251618). MM is funded by a Marie-Sklodowska-Curie Fellowship (grant agreement 713567) at the ADAPT (Artificial Intelligence–Driven Digital Content Technology) Science Foundation Ireland Research Centre at Trinity College. PB was supported by Université du Québec à Montréal, Institut Universitaire de Santé Mentale de Montréal, and by a salary award from FRQS. The opinions expressed in this manuscript are the authors' own and do not reflect the views of the CLSA. Funding Information: This research was made possible using the data or biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA data set [Baseline Tracking Dataset version 3.4 and Comprehensive Dataset version 4.0.], under Application Number [19CA012]. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. Peer review documents for the CIHR grant application are available in Multimedia Appendix 4. ZVA is supported by a CIHR Doctoral Award: Frederick Banting and Charles Best Canada Graduate Scholarship. SLB is supported by a CIHR-Strategy for Patient-Oriented Research Mentoring Chair (SMC-151518) and an FRQS (Fonds de Recherche du Qu?bec-Sant?) Chair (251618). MM is funded by a Marie-Sklodowska-Curie Fellowship (grant agreement 713567) at the ADAPT (Artificial Intelligence-Driven Digital Content Technology) Science Foundation Ireland Research Centre at Trinity College. PB was supported by Universit? du Qu?bec ? Montr?al, Institut Universitaire de Sant? Mentale de Montr?al, and by a salary award from FRQS. The opinions expressed in this manuscript are the authors' own and do not reflect the views of the CLSA. Publisher Copyright: © 2021 JMIR Publications Inc. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. ; Background: 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. Objective: 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. Methods: 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. Results: 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). Conclusions: 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. ; publishersversion ; published