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Compositional Data Analysis in Time-Use Epidemiology: What, Why, How
D. Dumuid, Ž. Pedišić, J. Palarea-Albaladejo, JA. Martín-Fernández, K. Hron, T. Olds,
Language English Country Switzerland
Document type Journal Article, Research Support, Non-U.S. Gov't
NLK
Free Medical Journals
from 2004
PubMed Central
from 2005
Europe PubMed Central
from 2005
ProQuest Central
from 2009-01-01
Open Access Digital Library
from 2004-01-01
Open Access Digital Library
from 2005-01-01
Medline Complete (EBSCOhost)
from 2008-12-01
Health & Medicine (ProQuest)
from 2009-01-01
Public Health Database (ProQuest)
from 2009-01-01
ROAD: Directory of Open Access Scholarly Resources
from 2004
- MeSH
- Adiposity MeSH
- Data Analysis * MeSH
- Activities of Daily Living MeSH
- Exercise * MeSH
- Child MeSH
- Cohort Studies MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Sedentary Behavior * MeSH
- Sleep MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Australia MeSH
In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-h time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-h time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9 ± 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way.
Biomathematics and Statistics Scotland EH9 3FD Edinburgh Scotland UK
Institute for Health and Sport Victoria University Melbourne 3000 Australia
References provided by Crossref.org
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