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Compositional Data Analysis in Time-Use Epidemiology: What, Why, How
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 individu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177981/ https://www.ncbi.nlm.nih.gov/pubmed/32224966 http://dx.doi.org/10.3390/ijerph17072220 |
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author | Dumuid, Dorothea Pedišić, Željko Palarea-Albaladejo, Javier Martín-Fernández, Josep Antoni Hron, Karel Olds, Timothy |
author_facet | Dumuid, Dorothea Pedišić, Željko Palarea-Albaladejo, Javier Martín-Fernández, Josep Antoni Hron, Karel Olds, Timothy |
author_sort | Dumuid, Dorothea |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7177981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71779812020-04-28 Compositional Data Analysis in Time-Use Epidemiology: What, Why, How Dumuid, Dorothea Pedišić, Željko Palarea-Albaladejo, Javier Martín-Fernández, Josep Antoni Hron, Karel Olds, Timothy Int J Environ Res Public Health Article 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. MDPI 2020-03-26 2020-04 /pmc/articles/PMC7177981/ /pubmed/32224966 http://dx.doi.org/10.3390/ijerph17072220 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dumuid, Dorothea Pedišić, Željko Palarea-Albaladejo, Javier Martín-Fernández, Josep Antoni Hron, Karel Olds, Timothy Compositional Data Analysis in Time-Use Epidemiology: What, Why, How |
title | Compositional Data Analysis in Time-Use Epidemiology: What, Why, How |
title_full | Compositional Data Analysis in Time-Use Epidemiology: What, Why, How |
title_fullStr | Compositional Data Analysis in Time-Use Epidemiology: What, Why, How |
title_full_unstemmed | Compositional Data Analysis in Time-Use Epidemiology: What, Why, How |
title_short | Compositional Data Analysis in Time-Use Epidemiology: What, Why, How |
title_sort | compositional data analysis in time-use epidemiology: what, why, how |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177981/ https://www.ncbi.nlm.nih.gov/pubmed/32224966 http://dx.doi.org/10.3390/ijerph17072220 |
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