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Compositional Data Analysis in Physical Activity and Health Research. Looking for the Right Balance

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Advances in Compositional Data Analysis

Abstract

The time spent in different types of sleep, sedentary behaviour and physical activity during a day represents compositional data. Previous use of compositional analysis of daily activity data through orthonormal log-ratio coordinates in the form of pivot coordinates of four components (sleep, sedentary behaviour, light physical activity, moderate to vigorous physical activity) has facilitated critical comparison with and the resolution of contradicting reports of the influence of daily behaviour on health which had arisen from the use of standard statistical analysis. For example, there has been a long-standing debate about whether the effects of time spent sedentary are independent of the time spent in moderate to vigorous activity. However, daily activity may be usefully broken down into more categories, and other compositional analysis approaches may be relevant. Here we demonstrate the use of log-ratio balances to evaluate the association between daily activity broken into six components (sleep, short periods of sedentary behaviour, long periods of sedentary behaviour, standing, slow walking and fast walking) from a nationally representative sample of adults with health markers in obesity, lipidemia and glycemia. The balanced approach to compositional analysis of daily behaviour patterns offers a way to focus the statistical assessment and modelling on variables, representing meaningful contrasts of interest between behaviours. This is fully consistent with the view that actual effects on health are associated with exchanges between some classes of daily behaviours.

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Acknowledgements

We would like to thank the participants of the AusDiab study. We would also like to acknowledge the help of and helpful discussions with Elisabeth Winkler, Neville Owen and David Dunstan. This work was partly supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division [DM and JPA] and by the Spanish Ministry of Science, Innovation and Universities under the project CODAMET (RTI2018-095518-B-C21, 2019–2021) [JPA].

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Authors and Affiliations

  1. School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK

    Duncan E. McGregor, Philippa M. Dall & Sebastien F. M. Chastin

  2. Biomathematics and Statistics Scotland, Edinburgh, UK

    Duncan E. McGregor & Javier Palarea-Albaladejo

  3. Department of Movement and Sport Sciences, Ghent University, Ghent, Belgium

    Sebastien F. M. Chastin

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  1. Duncan E. McGregor
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  2. Philippa M. Dall
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  3. Javier Palarea-Albaladejo
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  4. Sebastien F. M. Chastin
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Correspondence to Sebastien F. M. Chastin .

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Editors and Affiliations

  1. Institute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria

    Peter Filzmoser

  2. Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czech Republic

    Karel Hron

  3. Department of Computer Science, Applied Mathematics and Statistics, University of Girona, Girona, Spain

    Josep Antoni Martín-Fernández

  4. Biomathematics and Statistics Scotland, Edinburgh, UK

    Javier Palarea-Albaladejo

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McGregor, D.E., Dall, P.M., Palarea-Albaladejo, J., Chastin, S.F.M. (2021). Compositional Data Analysis in Physical Activity and Health Research. Looking for the Right Balance. In: Filzmoser, P., Hron, K., Martín-Fernández, J.A., Palarea-Albaladejo, J. (eds) Advances in Compositional Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71175-7_19

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