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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dorothea Dumuid & Željko Pedišić & Javier Palarea-Albaladejo & Josep Antoni Martín-Fernández & Karel Hron & Timothy Olds, 2020. "Compositional Data Analysis in Time-Use Epidemiology: What, Why, How," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2220-:d:337319
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    References listed on IDEAS

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    1. Duncan E. McGregor & Valerie Carson & Javier Palarea-Albaladejo & Philippa M. Dall & Mark S. Tremblay & Sebastien F. M. Chastin, 2018. "Compositional Analysis of the Associations between 24-h Movement Behaviours and Health Indicators among Adults and Older Adults from the Canadian Health Measure Survey," IJERPH, MDPI, vol. 15(8), pages 1-14, August.
    2. Louise Foley & Dorothea Dumuid & Andrew J Atkin & Katrien Wijndaele & David Ogilvie & Timothy Olds, 2019. "Cross-sectional and longitudinal associations between active commuting and patterns of movement behaviour during discretionary time: A compositional data analysis," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    3. K. Hron & P. Filzmoser & K. Thompson, 2012. "Linear regression with compositional explanatory variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1115-1128, November.
    4. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
    5. Toby Hunt & Marie T. Williams & Timothy S. Olds & Dorothea Dumuid, 2018. "Patterns of Time Use across the Chronic Obstructive Pulmonary Disease Severity Spectrum," IJERPH, MDPI, vol. 15(3), pages 1-12, March.
    6. Nikola Štefelová & Jan Dygrýn & Karel Hron & Aleš Gába & Lukáš Rubín & Javier Palarea-Albaladejo, 2018. "Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data," IJERPH, MDPI, vol. 15(10), pages 1-18, October.
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    Cited by:

    1. Christine W. St. Laurent & Sarah Burkart & Katrina Rodheim & Robert Marcotte & Rebecca M. C. Spencer, 2020. "Cross-Sectional Associations of 24-Hour Sedentary Time, Physical Activity, and Sleep Duration Compositions with Sleep Quality and Habits in Preschoolers," IJERPH, MDPI, vol. 17(19), pages 1-13, September.
    2. Wenyi Lin & Jingjing Zou & Chongzhi Di & Dorothy D. Sears & Cheryl L. Rock & Loki Natarajan, 2023. "Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 309-329, July.
    3. Wissem Benaissa & Fatiha Saidi & Khadidja Rahmoun, 2025. "Utilizing data mining techniques for the design of structural and mechanical properties of ABX3 perovskites," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 98(7), pages 1-20, July.
    4. Ryan Donald Burns & Timothy A. Brusseau & Yang Bai & Wonwoo Byun, 2021. "Segmented School Physical Activity and Weight Status in Children: Application of Compositional Data Analysis," IJERPH, MDPI, vol. 18(6), pages 1-11, March.
    5. Meiyuan Chen & Terence Chua & Zhi Shen & Lee Yong Tay & Xiaozan Wang & Michael Chia, 2022. "The Associations between 24-Hour Movement Behaviours and Quality of Life in Preschoolers: A Compositional Analysis of Cross-Sectional Data from 2018–2021," IJERPH, MDPI, vol. 19(22), pages 1-19, November.
    6. Nikola Štefelová & Javier Palarea-Albaladejo & Karel Hron & Aleš Gába & Jan Dygrýn, 2024. "Compositional PLS biplot based on pivoting balances: an application to explore the association between 24-h movement behaviours and adiposity," Computational Statistics, Springer, vol. 39(2), pages 835-863, April.

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