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

Dorothea Dumuid, Željko Pedišić, Javier Palarea-Albaladejo, Josep Antoni Martín-Fernández, Karel Hron and Timothy Olds
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Dorothea Dumuid: Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide 5001, Australia
Željko Pedišić: Institute for Health and Sport, Victoria University, Melbourne 3000, Australia
Javier Palarea-Albaladejo: Biomathematics and Statistics Scotland, EH9 3FD Edinburgh, Scotland, UK
Josep Antoni Martín-Fernández: Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain
Karel Hron: Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 77146 Olomouc, Czech Republic
Timothy Olds: Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide 5001, Australia

IJERPH, 2020, vol. 17, issue 7, 1-17

Abstract: 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.

Keywords: compositional data; physical activity; sedentary behavior; sleep (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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