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Record W3038202117 · doi:10.3390/ijerph17134773

Levels of Physical Activity during School Hours in Children and Adolescents: A Systematic Review

2020· review· en· W3038202117 on OpenAlexaboutno aff
Alberto Grao‐Cruces, María J. Velázquez-Romero, Fernando Rodríguez‐Rodríguez

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsScopusMedicinePhysical activityPhysical therapyQuarter (Canadian coin)Web of scienceDemographyPediatricsPsychologyGerontologyMEDLINEMeta-analysisInternal medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND: This systematic review determines the levels of physical activity (PA) during school hours in children and adolescents. METHODS: Studies carried out from January 1987 to December 2019 were retrieved from four databases (Web of Science, Pubmed, Scopus and SportDiscus). The 29 selected studies were cross-sectional, long-term and case studies. RESULTS: Most of them used accelerometers and showed that male and female children accumulated a mean of between 14 and 68 min of moderate-to-vigorous PA (MVPA) during school hours (3%-22% of this daily segment), and male and female adolescents accumulated a mean of between 13 and 28 min of MVPA during this daily segment (3%-8% of the school hours). Less than a quarter of children and adolescents reached the recommended 30 min of MVPA during school hours, with notable differences between sexes. CONCLUSIONS: These results suggest that the levels of PA during school hours are not enough, and consequently, schools should develop strategies for helping children and adolescents reach the school PA recommendation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0100.012
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.421
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations84
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of Environmental Research and Public HealthSame topicObesity, Physical Activity, DietFrench-language works237,207