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Record W2972839295 · doi:10.1111/apa.15005

Three times as much physical education reduced the risk of children being overweight or obese after 5 years

2019· article· en· W2972839295 on OpenAlexaff
Petra Kühr, Rodrigo Antunes Lima, Anders Grøntved, Niels Wedderkopp, Heidi Klakk

Bibliographic record

VenueActa Paediatrica · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of New Brunswick
FundersEgmont FondenTrygFondenIMK Almene FondNordea-fondenSyddansk UniversitetCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorGigtforeningenHjerteforeningen
KeywordsMedicineOverweightPediatricsObesityGerontologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Abstract Aim We evaluated the effect that increasing physical education lessons from 1.5 to 4.5 hours per week for 5 years had on the body mass index (BMI) and waist circumferences of children aged 5‐11 years at inclusion. Methods From 2008 to 2013, six intervention schools in Svendborg, Denmark, delivered 4.5 hours of physical education lessons per week to 750 children. Meanwhile, four matched control schools gave 549 children the standard 1.5 hours of physical education lessons per week. Measurements were taken at baseline and yearly for 5 years. Of the 1299 children, 81 joined the schools after 2008. Results At baseline, the percentage of overweight children was 12% in the intervention schools and 13% in the control schools, whereas 15% and 19% were abdominal obese, respectively. After 5 years, the respective risks of remaining abdominal obese or overweight were 43% and 51% in the intervention schools and 78% and 84% in the control schools. Mean BMI increased 0.450 kg/m 2 more in the control group over the five‐year period. The intervention was not effective in decreasing the average waist circumference. Conclusion Three times as much physical education lessons per week, for 5 years, effectively decreased BMI and the likelihood of remaining overweight or obese.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.004
GPT teacher head0.242
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2019
Admission routes1
Has abstractyes

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