Compositional data analysis for physical activity, sedentary time and sleep research
University of South Australia · The University of Adelaide · +13 more institutions
Abstract
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…
Citation impact
- FWCI
- 28.30
- Percentile
- 100%
- References
- 50
Authors
20Topics & keywords
- Compositional data
- Multicollinearity
- Physical activity
- Statistical inference
- Causal inference
- Statistics
- Econometrics
- Inference