Evidence map›Paper›PMID 32315605›Full record

ArticleJournal of sport and health science2021

Sensor-based physical activity, sedentary time, and reported cell phone screen time: A hierarchy of correlates in youth.

Pedro B Júdice, João P Magalhães, Gil B Rosa, Duarte Henriques-Neto, Megan Hetherington-Rauth, Luís B Sardinha

Abstract read
In one paragraph

Article in Journal of sport and health science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Machine learning used to study risk factors for chronic diseases: A scoping review.Canadian journal of public health = Revue canadienne de sante publique · 2026
    Article
  3. Article
  4. Observational
  5. Article
  6. Sleep Time, Physical Activity, and Screen Time among Montana American Indian Youth.International journal of environmental research and public health · 2023
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys.International journal of environmental research and public health · 2021
    Article
  12. 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.

Pedro B JúdiceExercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon 1499-002, Portugal; Faculty of Physical Education and Sport, Lusofona University, Lisbon 1749-024, Portugal. Electronic address: pedrojudice@fmh.ulisboa.pt.
João P MagalhãesExercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon 1499-002, Portugal.
Gil B RosaExercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon 1499-002, Portugal.
Duarte Henriques-NetoExercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon 1499-002, Portugal.
Megan Hetherington-RauthExercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon 1499-002, Portugal.
Luís B SardinhaExercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon 1499-002, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvidence on correlates relies on subjective metrics and fails to include correlates across all levels of the ecologic model. We determined which correlates best predict sensor-based physical activity (PA), sedentary time (ST), and self-reported cell phone screen time (CST) in a large sample of youth, while considering a multiplicity of correlates.

methodsUsing sensor-based accelerometry, we assessed the PA and ST of 2179 youths. A χ

resultsAmong youth 10-14 years old, the correlates for being inactive consisted of being a girl, not having sport facilities in the neighborhood, and not perceiving the neighborhood as a safe place, whereas in the youth 15-18 years old, the correlate for being inactive was not performing sports (9.7% chance of being active). The correlates for predicting high ST in the younger group was not performing sports (55.8% chance for high ST), and in the older group, the correlates were not owning a pet, perceiving the neighborhood as safe, and having inactive parents (63.7% chance for high ST). In the younger group, the greatest chances of having high CST were among those who were in the last elementary school years, who were girls, and who did not have friends in the neighborhood (73.1% chance for high CST), whereas in the older group, the greatest chance for having high CST was among those who were girls and had a TV in the bedroom (74.3% chance for high CST).

conclusionTo counteract ST and boost MVPA among youths, a specific focus on girls, the promotion of sport participation and facilities, neighborhood safety, and involvement of family must be prioritized.

Indexed as

AccelerometryExerciseScreen TimeSedentary BehaviorAdolescentAge FactorsAlgorithmsBody CompositionCell PhoneChildFamily CharacteristicsFemaleFriendsHumansMaleParentsAdolescentsEnvironmentObjectiveSedentary behaviorSocioecologic model

Identifiers

PMID32315605
PMCPMC7856564

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.