Adolescents’ physical activity: cross-national comparisons of levels, distributions and disparities across 52 countries
Notice bibliographique
Résumé
Abstract Background Despite global concerns regarding physical inactivity, limited cross-national evidence exists to compare adolescents’ physical activity participation. We analysed data from 52 high- and low-middle income countries, with activity undertaken inside and outside of school in 2015. We investigated gender and socioeconomic disparities, and additionally examined correlations with country-level indices of physical education (PE) curriculum time allocation, wealth, and income inequality. Methods We compared adolescents’ reported activity levels inside and outside of school using nationally representative cross-sectional data from 52 high- and low-middle income countries ( N = 347,935)—the Programme for International Student Assessment (PISA) in 2015. Students reported average attendance (days/week) in PE classes, and the days/week engaged in moderate activity (MPA) and vigorous activity (VPA) outside of school. We also compared gender and socioeconomic disparities, and additionally examined correlations with purported determinants—country-level estimates of PE curriculum time allocation, wealth, and income inequality. Results Average activity levels differed substantially both between and within regions, with potentially important differences in distributions identified—such as a bimodal distribution in the U.S. and Canada in PE. Males were more active than females, as were those from households with higher rather than lower household wealth; these disparities were modest for PE, but higher for moderate and vigorous activity outside school—there was strong evidence for heterogeneity in the magnitude of these disparities (e.g., I 2 > 95% for gender differences across all countries). PE class attendance was positively correlated with PE curriculum time allocation (rho = 0.36); activity outcomes were inconsistently associated with country-level wealth and income inequality. Conclusions Our findings reveal extensive cross-country differences in adolescents’ physical activity; in turn, these highlight policy areas that could ultimately improve global adolescent health, such as the incorporation of minimum country-level PE classes, and the targeting of gender and socioeconomic disparities in activity conducted outside of school. Our findings also highlight the utility of educational databases such as PISA for use in global population health research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».