Evidence map›Paper›PMID 31419234›Full record

ArticlePloS one2019

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

Louise Foley, Dorothea Dumuid, Andrew J Atkin, Katrien Wijndaele, David Ogilvie, Timothy Olds

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Compositional Data Analysis in Time-Use Epidemiology: What, Why, How.International journal of environmental research and public health · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Louise FoleyMRC Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom.ORCID 0000-0003-3028-7340
Dorothea DumuidSchool of Health Sciences, University of South Australia, Adelaide, South Australia, Australia.ORCID 0000-0003-3057-0963
Andrew J AtkinSchool of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, England, United Kingdom.ORCID 0000-0002-3819-3448
Katrien WijndaeleMRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom.
David OgilvieMRC Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom.
Timothy OldsSchool of Health Sciences, University of South Australia, Adelaide, South Australia, Australia.ORCID 0000-0001-6894-5519

Funding

British Heart FoundationCancer Research UKDepartment of HealthMedical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MC_UU_12015/3Medical Research Council MC_UU_12015/6Medical Research Council MR/K023187/1Wellcome Trust 087636/Z/08/Z
6 · The paper itself

Abstract

backgroundActive 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

findingsWe 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.

conclusionsActive 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.

Indexed as

ExerciseSedentary BehaviorTransportationAdultAgedBicyclingCross-Sectional StudiesData AnalysisFemaleHumansLinear ModelsLongitudinal StudiesMaleMiddle AgedScreen TimeSurveys and Questionnaires

Identifiers

PMID31419234
PMCPMC6697339

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.