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Advances in Compositional Data Analysis pp 383–404Cite as

Compositional Data Analysis in Time-Use Epidemiology

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Abstract

How we allocate time to activities impacts our health. Daily times spent in activities are interrelated because they compete for time-shares within a finite 24 h window. If more time is spent in one activity, time must be taken from one or more of the remaining activities to maintain the fixed total of 24 h. Thus, time-use data have a relative nature and can be analysed accordingly using compositional data analysis. In this chapter, we demonstrate exploratory and cross-sectional inferential analyses of an eight-part time-use composition using data from the Longitudinal Study of Australian Children (n = 2224, 52% boys, mean age = 34 months, standard deviation = 3). For inferential analyses, time-use compositions are expressed as a specific choice of balance coordinates to separate between types of activities. Considering the balance coordinates as explanatory variables, we explore the relationship between children’s time-use composition and their socio-emotional health. Subsequently, we consider the balance coordinates as dependent variables and explore the relationship between parental perception of neighbourhood liveability and their child’s time-use composition.

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Acknowledgements

This chapter uses unit record data from Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC). The LSAC study was conducted in partnership with the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in the paper are those of the authors and should not be attributed to DSS, AIFS or the ABS.

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Authors and Affiliations

  1. Allied Health & Human Performance, Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, 5001, Australia

    Dorothea Dumuid

  2. Institute for Health and Sport, Victoria University, Melbourne, 3000, Australia

    Željko Pedišić

  3. Biomathematics and Statistics Scotland, Edinburgh, EH9 3FD, Scotland, UK

    Javier Palarea-Albaladejo

  4. Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003, Girona, Spain

    Josep Antoni Martín-Fernández

  5. Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 77146, Olomouc, Czech Republic

    Karel Hron

  6. Allied Health & Human Performance, Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, 5001, Australia

    Timothy Olds

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  1. Dorothea Dumuid
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Correspondence to Dorothea Dumuid .

Editor information

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

    Prof. Dr. Josep Antoni Martín-Fernández

  4. Biomathematics and Statistics Scotland, Edinburgh, UK

    Javier Palarea-Albaladejo

Ethics declarations

D.D. was supported by the National Health and Medical Research Council Early Career Fellowship (1162166) and the National Heart Foundation of Australia Postgraduate Fellowship (102084). J.P.A. and J.A.M.F. were supported by the Spanish Ministry of Science, Innovation and Universities under the project CODAMET (RTI2018-095518-B-C21, 2019-2021). J.P.A. was partly supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division. K.H. was funded by a research grant from the Czech Science Foundation no. 18-09188S.

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Dumuid, D., Pedišić, Ž., Palarea-Albaladejo, J., Martín-Fernández, J.A., Hron, K., Olds, T. (2021). Compositional Data Analysis in Time-Use Epidemiology. 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_20

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