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Patterns of health behaviour associated with active travel: a compositional data analysis

BACKGROUND: Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doi...

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Autores principales: Foley, Louise, Dumuid, Dorothea, Atkin, Andrew J., Olds, Timothy, Ogilvie, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861598/
https://www.ncbi.nlm.nih.gov/pubmed/29562923
http://dx.doi.org/10.1186/s12966-018-0662-8
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author Foley, Louise
Dumuid, Dorothea
Atkin, Andrew J.
Olds, Timothy
Ogilvie, David
author_facet Foley, Louise
Dumuid, Dorothea
Atkin, Andrew J.
Olds, Timothy
Ogilvie, David
author_sort Foley, Louise
collection PubMed
description BACKGROUND: Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. METHODS: We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. RESULTS: 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. CONCLUSIONS: Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12966-018-0662-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-58615982018-03-26 Patterns of health behaviour associated with active travel: a compositional data analysis Foley, Louise Dumuid, Dorothea Atkin, Andrew J. Olds, Timothy Ogilvie, David Int J Behav Nutr Phys Act Research BACKGROUND: Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. METHODS: We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. RESULTS: 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. CONCLUSIONS: Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12966-018-0662-8) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-21 /pmc/articles/PMC5861598/ /pubmed/29562923 http://dx.doi.org/10.1186/s12966-018-0662-8 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Foley, Louise
Dumuid, Dorothea
Atkin, Andrew J.
Olds, Timothy
Ogilvie, David
Patterns of health behaviour associated with active travel: a compositional data analysis
title Patterns of health behaviour associated with active travel: a compositional data analysis
title_full Patterns of health behaviour associated with active travel: a compositional data analysis
title_fullStr Patterns of health behaviour associated with active travel: a compositional data analysis
title_full_unstemmed Patterns of health behaviour associated with active travel: a compositional data analysis
title_short Patterns of health behaviour associated with active travel: a compositional data analysis
title_sort patterns of health behaviour associated with active travel: a compositional data analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861598/
https://www.ncbi.nlm.nih.gov/pubmed/29562923
http://dx.doi.org/10.1186/s12966-018-0662-8
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