Evidence map›Paper›PMID 30841921›Full record

SynthesisBMC public health2019

Distinct trajectories of physical activity and related factors during the life course in the general population: a systematic review.

Irinja Lounassalo, Kasper Salin, Anna Kankaanpää, Mirja Hirvensalo, Sanna Palomäki, Asko Tolvanen, Xiaolin Yang, Tuija H Tammelin

Abstract readSystematic Review
In one paragraph

Synthesis in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
120citing papers in PubMed, 8 pooled it
–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

120 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Trial
  10. Trial
  11. Trial
  12. Trial
  13. Review
  14. Article
  15. Observational
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

60 more citing papers are in PubMed but not listed here.

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

8 authors.

Irinja LounassaloFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland. irinja.lounassalo@jyu.fi.ORCID http://orcid.org/0000-0002-3185-4485
Kasper SalinFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Anna KankaanpääLIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland.
Mirja HirvensaloFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Sanna PalomäkiFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Asko TolvanenMethodology Center for Human Sciences, University of Jyväskylä, Jyväskylä, Finland.
Xiaolin YangLIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland.
Tuija H TammelinLIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland.

Funding

Finnish Ministry of Education and Culture Grant 83/626/2014
6 · The paper itself

Abstract

backgroundIn recent years, researchers have begun applying a trajectory approach to identify homogeneous subgroups of physical activity (PA) in heterogeneous populations. This study systematically reviewed the articles identifying longitudinal PA trajectory classes and the related factors (e.g., determinants, predictors, and outcomes) in the general population during different life phases.

methodsThe included studies used finite mixture models for identifying trajectories of PA, exercise, or sport participation. Three electronic databases, PubMed (Medline), Web of Science, and CINAHL, were searched from the year 2000 to 13 February 2018. The study was conducted according to the PRISMA recommendations.

resultsTwenty-seven articles were included and organized into three age group: youngest (eleven articles), middle (eight articles), and oldest (eight articles). The youngest group consisted mainly of youth, the middle group of adults and the oldest group of late middle-aged and older adults. Most commonly, three or four trajectory classes were reported. Several trajectories describing a decline in PA were reported, especially in the youngest group, whereas trajectories of consistently increasing PA were observed in the middle and oldest group. While the proportion of persistently physically inactive individuals increased with age, the proportion was relatively high at all ages. Generally, male gender, being Caucasian, non-smoking, having low television viewing time, higher socioeconomic status, no chronic illnesses, and family support for PA were associated either with persistent or increasing PA.

conclusionsThe reviewed articles identified various PA subgroups, indicating that finite mixture modeling can yield new information on the complexity of PA behavior compared to studying population mean PA level only. The studies also provided novel information how different factors relate to changes in PA during life course. The recognition of the PA subgroups and their determinants is important for the more precise targeting of PA promotion and PA interventions.

trial registrationPROSPERO registration number: CRD42018088120 .

Indexed as

ExerciseHealth StatusAge FactorsHealth BehaviorHumansLongitudinal StudiesRacial GroupsRecreationSex FactorsSocioeconomic FactorsExerciseFinite mixture modelLongitudinalPhysical activityProspectiveReviewSport participationTrajectory

Identifiers

PMID30841921
PMCPMC6404287

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.