Compositional data analysis for physical activity, sedentary time and sleep research

• Published in: Statistical methods in medical research Vol. 27; no. 12; p. 3726
• Main Authors: Dumuid, Dorothea, Stanford, Tyman E, Martin-Fernández, Josep-Antoni, Pedišić, Željko, Maher, Carol A, Lewis, Lucy K, Hron, Karel, Katzmarzyk, Peter T, Chaput, Jean-Philippe, Fogelholm, Mikael, Hu, Gang, Lambert, Estelle V, Maia, José, Sarmiento, Olga L, Standage, Martyn, Barreira, Tiago V, Broyles, Stephanie T, Tudor-Locke, Catrine, Tremblay, Mark S, Olds, Timothy
• Format: Journal Article
• Language: English
• Published: England 01.12.2018
• Subjects: Child; Data Interpretation, Statistical; Exercise; Humans; Pediatric Obesity; Sedentary Behavior; Sleep; sleep; sedentary behaviour; multicollinearity; Compositional data analysis; physical activity
• ISSN: 1477-0334
• DOI: 10.1177/0962280217710835

The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children's daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.