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Twenty-Four-Hour Compositional Data Analysis in Healthcare: Clinical Potential and Future Directions

Author

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  • Cain Craig Truman Clark

    (College of Life Sciences, Birmingham City University, Birmingham B15 3TN, UK)

  • Clarice Maria de Lucena Martins

    (Laboratory for Integrative and Translational Research in Population Health, Research Centre in Physical Activity, Health and Leisure, University of Porto, 4200-450 Porto, Portugal)

Abstract

Compositional Data Analysis (CoDA) is a powerful statistical approach for analyzing 24 h time-use data, effectively addressing the interdependence of sleep, sedentary behavior, and physical activity. Unlike traditional methods that struggle with perfect multicollinearity, CoDA handles time use as proportions of a whole, providing biologically meaningful insights into how daily activity patterns relate to health. Applications in epidemiology have linked variations in time allocation between behaviors to key health outcomes, including adiposity, cardiometabolic health, cognitive function, fitness, quality of life, glycomics, clinical psychometrics, and mental well-being. Research consistently shows that reallocating time from sedentary behavior to sleep or moderate-to-vigorous physical activity (MVPA) improves health outcomes. Importantly, CoDA reveals that optimal activity patterns vary across populations, supporting the need for personalized, context-specific recommendations rather than one-size-fits-all guidelines. By overcoming challenges in implementation and interpretation, CoDA has the potential to transform healthcare analytics and deepen our understanding of lifestyle behaviors’ impact on health.

Suggested Citation

  • Cain Craig Truman Clark & Clarice Maria de Lucena Martins, 2025. "Twenty-Four-Hour Compositional Data Analysis in Healthcare: Clinical Potential and Future Directions," IJERPH, MDPI, vol. 22(7), pages 1-10, June.
    • Handle: RePEc:gam:jijerp:v:22:y:2025:i:7:p:1002-:d:1687265
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    References listed on IDEAS

    as
    1. Lin Zhou & Wei Liang & Yuxiu He & Yanping Duan & Ryan E. Rhodes & Hao Liu & Hongmei Liang & Xiaowei Shi & Jun Zhang & Yingzhe Cheng, 2022. "Relationship of 24-Hour Movement Behaviors with Weight Status and Body Composition in Chinese Primary School Children: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
    2. Simone A. Tomaz & Trina Hinkley & Rachel A. Jones & Estelle D. Watson & Rhian Twine & Kathleen Kahn & Shane A. Norris & Catherine E. Draper, 2020. "Screen Time and Sleep of Rural and Urban South African Preschool Children," IJERPH, MDPI, vol. 17(15), pages 1-12, July.
    3. Alexander R. Bennett & Jon Lundstrøm & Sayantani Chatterjee & Morten Thaysen-Andersen & Daniel Bojar, 2025. "Compositional data analysis enables statistical rigor in comparative glycomics," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
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