Evidence map›Paper›PMID 30630517›Full record

ArticleThe international journal of behavioral nutrition and physical activity2019

Daily domain-specific time-use composition of physical behaviors and blood pressure.

Nidhi Gupta, Mette Korshøj, Dorothea Dumuid, Pieter Coenen, Karen Allesøe, Andreas Holtermann

Open access · goldAbstract read
In one paragraph

Article in The international journal of behavioral nutrition and physical activity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% of its field
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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Machine learning in physical activity, sedentary, and sleep behavior research.Journal of activity, sedentary and sleep behaviors · 2024
    Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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 at 4 institutions in 2 countries.

Nidhi GuptaNational Research Centre for the Working Environment, Copenhagen, Denmark. ngu@nfa.dk.ORCID 0000-0002-0035-9342
Mette KorshøjNational Research Centre for the Working Environment, Copenhagen, Denmark.
Dorothea DumuidAlliance for Research in Exercise, Nutrition, and Activity (ARENA), School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
Pieter CoenenDepartment of Public and Occupational Health, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
Karen AllesøeCenter for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark.
Andreas HoltermannNational Research Centre for the Working Environment, Copenhagen, Denmark.
National Research Centre for the Working Environment · DKFrederiksberg Hospital · DKUniversity of South Australia · AUUniversity of Southern Denmark · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious research has shown contrasting effects on hypertension for occupational and leisure-time physical behaviors-physical activity and sedentary behavior and time in bed. However, (a) none of these studies have addressed the compositional property of the physical behaviors and (b) most knowledge on the association between domain-specific physical behaviors and hypertension relies upon self-reported physical behaviors information primarily on white-collar worker study samples. We aimed to be the first to disentangle the relationship between technically measured 24-h time-use behaviors in work and leisure domains and blood pressure among blue-collar workers using a compositional data analysis approach.

methodsWorkers (n = 669) wore accelerometers to measure daily minutes of work and leisure sedentary time, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA), and time in bed which were isometrically log-transformed. Cross-sectional linear association between time-use composition and systolic (SBP) and diastolic (DBP) blood pressure were determined using compositional isotemporal substitutions models.

resultsThe time-use composition at the work and leisure domains was significantly associated with SBP (F = 4.98, p < 0.001) and DBP (F = 2.91, p = 0.008). Reallocating sedentary time to remaining behaviors within each domain-work and leisure-was favorably associated with SBP. Similar results were observed when reallocating time in bed from the remaining leisure behaviors. Results for reallocating time to/from MVPA and LPA at both domains were non-significant. Results regarding all physical behaviors for DBP were generally non-significant.

conclusionsTime-use composition of physical behaviors at work and leisure is associated with blood pressure among blue-collar workers. At both domains, reallocating sedentary time to remaining behaviors, especially to time in bed at leisure may reduce blood pressure. Our results, based on a compositional data approach, can be used to better design accurate and comprehensive time-use recommendations both at work and leisure for high-risk groups like blue-collar workers.

Indexed as

Blood PressureLeisure ActivitiesSedentary BehaviorWorkAccelerometryAdultCross-Sectional StudiesExerciseFemaleHumansHypertensionMaleMiddle AgedOccupationsSelf ReportAccelerometryDomainPhysical activitySedentary behaviorsSleep

Identifiers

PMID30630517
PMCPMC6327498
OpenAlexW2909608300

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.