A comparison of standard and compositional data analysis in studies addressing group differences in sedentary behavior and physical activity

Data on time spent in physical activity, sedentary behavior and sleep during a day is compositional in nature, i.e. they add up to a constant value. Compositional data have fundamentally different properties from unconstrained data in real space, and require other analytical procedures, referred to...

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Bibliographic Details
Published in:The international journal of behavioral nutrition and physical activity 2018-06, Vol.15 (1), p.53-53, Article 53
Main Authors: Gupta, Nidhi, Mathiassen, Svend Erik, Mateu-Figueras, Glòria, Heiden, Marina, Hallman, David M, Jørgensen, Marie Birk, Holtermann, Andreas
Format: Article
Language:English
Subjects:
Accelerometry
Accelerometry - statistics & numerical data
Adult
Age
Age (Psychology)
Age Factors
Age groups
Analysis
Behavior
CoDA
CoDA-based MANOVA
Cross-Sectional Studies
Data analysis
Denmark
Exercise
Female
gender
Gender differences
Health behavior
Health-Promoting Work
Humans
Hälsofrämjande arbete
Isometric log-ratio
Leisure
Male
MANOVA
Methodology
Middle Aged
Mortality
Sedentary Behavior
Sex
Sex Factors
Sexes
Sleep
Statistical analysis
Studies
Surveys and Questionnaires
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Summary:Data on time spent in physical activity, sedentary behavior and sleep during a day is compositional in nature, i.e. they add up to a constant value. Compositional data have fundamentally different properties from unconstrained data in real space, and require other analytical procedures, referred to as compositional data analysis (CoDA). Most physical activity and sedentary behavior studies, however, still apply analytical procedures adapted to data in real space, which can lead to misleading results. The present study describes a comparison of time spent sedentary and in physical activity between age groups and sexes, and investigates the extent to which results obtained by CoDA differ from those obtained using standard analytical procedures. Time spent sedentary, standing, and in physical activity (walking/running/stair climbing/cycling) during work and leisure was determined for 1-4 days among 677 blue-collar workers using accelerometry. Differences between sexes and age groups were tested using MANOVA, using both a standard and a CoDA approach based on isometric log-ratio transformed data. When determining differences between sexes for different activities time at work, the effect size using standard analysis (η  = 0.045, p 
ISSN:1479-5868
1479-5868
DOI:10.1186/s12966-018-0685-1