Compositional data analysis for physical activity, sedentary time and sleep research
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F18%3A73589109" target="_blank" >RIV/61989592:15310/18:73589109 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1177/0962280217710835" target="_blank" >http://dx.doi.org/10.1177/0962280217710835</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/0962280217710835" target="_blank" >10.1177/0962280217710835</a>
Alternative languages
Result language
angličtina
Original language name
Compositional data analysis for physical activity, sedentary time and sleep research
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
STATISTICAL METHODS IN MEDICAL RESEARCH
ISSN
0962-2802
e-ISSN
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Volume of the periodical
27
Issue of the periodical within the volume
12
Country of publishing house
GB - UNITED KINGDOM
Number of pages
13
Pages from-to
3726-3738
UT code for WoS article
000452307300013
EID of the result in the Scopus database
2-s2.0-85041863757