Cross-sectional and longitudinal associations between active commuting and patterns of movement behaviour during discretionary time: A compositional data analysis.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2019
Historique:
received: 23 10 2018
accepted: 25 04 2019
entrez: 17 8 2019
pubmed: 17 8 2019
medline: 10 3 2020
Statut: epublish

Résumé

Active living approaches seek to promote physical activity and reduce sedentary time across different domains, including through active travel. However, there is little information on how movement behaviours in different domains relate to each other. We used compositional data analysis to explore associations between active commuting and patterns of movement behaviour during discretionary time. We analysed cross-sectional and longitudinal data from the UK Biobank study. At baseline (2006-2010) and follow up (2009-2013) participants reported their mode of travel to work, dichotomised as active (walking, cycling or public transport) or inactive (car). Participants also reported activities performed during discretionary time, categorised as (i) screen time; (ii) walking for pleasure; and (iii) sport and do-it-yourself (DIY) activities, summed to produce a total. We applied compositional data analysis to test for associations between active commuting and the composition and total amount of discretionary time, using linear regression models adjusted for covariates. Adverse events were not investigated in this observational analysis. The survey response rate was 5.5%. In the cross-sectional analysis (n = 182,406; mean age = 52 years; 51% female), active commuters engaged in relatively less screen time than those who used inactive modes (coefficient -0.12, 95% confidence interval [CI] -0.13 to -0.11), equating to approximately 60 minutes less screen time per week. Similarly, in the longitudinal analysis (n = 4,323; mean age = 51 years; 49% female) there were relative reductions in screen time in those who used active modes at both time points compared with those who used inactive modes at both time points (coefficient -0.15, 95% confidence interval [CI] -0.24 to -0.06), equating to a difference between these commute groups of approximately 30 minutes per week at follow up. However, as exposures and outcomes were measured concurrently, reverse causation is possible. Active commuting was associated with a more favourable pattern of movement behaviour during discretionary time. Active commuters accumulated 30-60 minutes less screen time per week than those using inactive modes. Though modest, this could have a cumulative effect on health over time.

Sections du résumé

BACKGROUND
Active living approaches seek to promote physical activity and reduce sedentary time across different domains, including through active travel. However, there is little information on how movement behaviours in different domains relate to each other. We used compositional data analysis to explore associations between active commuting and patterns of movement behaviour during discretionary time.
METHODS AND FINDINGS
We analysed cross-sectional and longitudinal data from the UK Biobank study. At baseline (2006-2010) and follow up (2009-2013) participants reported their mode of travel to work, dichotomised as active (walking, cycling or public transport) or inactive (car). Participants also reported activities performed during discretionary time, categorised as (i) screen time; (ii) walking for pleasure; and (iii) sport and do-it-yourself (DIY) activities, summed to produce a total. We applied compositional data analysis to test for associations between active commuting and the composition and total amount of discretionary time, using linear regression models adjusted for covariates. Adverse events were not investigated in this observational analysis. The survey response rate was 5.5%. In the cross-sectional analysis (n = 182,406; mean age = 52 years; 51% female), active commuters engaged in relatively less screen time than those who used inactive modes (coefficient -0.12, 95% confidence interval [CI] -0.13 to -0.11), equating to approximately 60 minutes less screen time per week. Similarly, in the longitudinal analysis (n = 4,323; mean age = 51 years; 49% female) there were relative reductions in screen time in those who used active modes at both time points compared with those who used inactive modes at both time points (coefficient -0.15, 95% confidence interval [CI] -0.24 to -0.06), equating to a difference between these commute groups of approximately 30 minutes per week at follow up. However, as exposures and outcomes were measured concurrently, reverse causation is possible.
CONCLUSIONS
Active commuting was associated with a more favourable pattern of movement behaviour during discretionary time. Active commuters accumulated 30-60 minutes less screen time per week than those using inactive modes. Though modest, this could have a cumulative effect on health over time.

Identifiants

pubmed: 31419234
doi: 10.1371/journal.pone.0216650
pii: PONE-D-18-30730
pmc: PMC6697339
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0216650

Subventions

Organisme : Medical Research Council
ID : MR/K023187/1
Pays : United Kingdom
Organisme : British Heart Foundation
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12015/3
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12015/6
Pays : United Kingdom
Organisme : Cancer Research UK
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 087636/Z/08/Z
Pays : United Kingdom

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

JAMA. 2003 Apr 9;289(14):1785-91
pubmed: 12684356
Med Sci Sports Exerc. 2003 Aug;35(8):1381-95
pubmed: 12900694
Eur J Clin Nutr. 2003 Sep;57(9):1089-96
pubmed: 12947427
Lancet. 2007 Sep 22;370(9592):1078-88
pubmed: 17868817
Med Sci Sports Exerc. 2011 Aug;43(8):1575-81
pubmed: 21681120
Int J Epidemiol. 2011 Oct;40(5):1382-400
pubmed: 22039197
Lancet. 2012 Jul 21;380(9838):219-29
pubmed: 22818936
Int J Behav Nutr Phys Act. 2013 Feb 27;10:28
pubmed: 23445724
Am J Epidemiol. 2014 Feb 1;179(3):323-34
pubmed: 24318278
J Sci Med Sport. 2015 Jan;18(1):43-8
pubmed: 24602689
Med Sci Sports Exerc. 2014 Oct;46(10):1946-50
pubmed: 24674977
Int J Behav Nutr Phys Act. 2014 Oct 24;11:132
pubmed: 25344355
Ann Intern Med. 2015 Jan 20;162(2):123-32
pubmed: 25599350
Int J Behav Nutr Phys Act. 2015 Feb 28;12:30
pubmed: 25886356
Int J Behav Nutr Phys Act. 2015 Sep 30;12:121
pubmed: 26419654
Prev Med. 2015 Dec;81:339-44
pubmed: 26441297
PLoS One. 2015 Oct 13;10(10):e0139984
pubmed: 26461112
Prev Med. 2016 Feb;83:26-30
pubmed: 26656405
Int J Behav Nutr Phys Act. 2015 Dec 18;12:161
pubmed: 26682539
Lancet. 2016 Sep 24;388(10051):1302-10
pubmed: 27475271
Med Sci Sports Exerc. 2017 Jun;49(6):1111-1119
pubmed: 28106621
Med Sci Sports Exerc. 2017 Jul;49(7):1351-1358
pubmed: 28263284
Lancet Public Health. 2016 Dec;1(2):e46-e55
pubmed: 28299370
BMJ. 2017 Apr 19;357:j1456
pubmed: 28424154
Stat Methods Med Res. 2018 Dec;27(12):3726-3738
pubmed: 28555522
Int J Behav Nutr Phys Act. 2018 Mar 21;15(1):26
pubmed: 29562923
Eur J Epidemiol. 2018 Sep;33(9):811-829
pubmed: 29589226
Prev Med. 2018 Jul;112:61-69
pubmed: 29604327
Heart. 2018 Nov;104(21):1749-1755
pubmed: 29785956

Auteurs

Louise Foley (L)

MRC Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom.

Dorothea Dumuid (D)

School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia.

Andrew J Atkin (AJ)

School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, England, United Kingdom.

Katrien Wijndaele (K)

MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom.

David Ogilvie (D)

MRC Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom.

Timothy Olds (T)

School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH