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Open AccessArticle

Compositional Data Analysis in Time-Use Epidemiology: What, Why, How

Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide 5001, Australia
Institute for Health and Sport, Victoria University, Melbourne 3000, Australia
Biomathematics and Statistics Scotland, EH9 3FD Edinburgh, Scotland, UK
Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain
Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 77146 Olomouc, Czech Republic
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(7), 2220; (registering DOI)
Received: 9 March 2020 / Revised: 20 March 2020 / Accepted: 23 March 2020 / Published: 26 March 2020
(This article belongs to the Section Health Behavior, Chronic Disease and Health Promotion)
In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-h time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-h time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9 ± 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way. View Full-Text
Keywords: compositional data; physical activity; sedentary behavior; sleep compositional data; physical activity; sedentary behavior; sleep
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Dumuid, D.; Pedišić, Ž.; Palarea-Albaladejo, J.; Martín-Fernández, J.A.; Hron, K.; Olds, T. Compositional Data Analysis in Time-Use Epidemiology: What, Why, How. Int. J. Environ. Res. Public Health 2020, 17, 2220.

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