Factors Affecting Levels of Health-Related Physical Fitness in Secondary School Students in Selangor, Malaysia
Notice bibliographique
Résumé
The purpose of this study was to measure health-related fitness of children based on different implementation levels of the physical education program. Another was to determine the effect of anthropometric and social factors on students’ health-related fitness. A total of 918 students’ age 13, 14, and 16 years old were selected from three different implementation levels program. The total score of the checklist questions was used as criteria in classifying implementation levels in Selangor schools. Heights and weights were measured, from which the BMI was calculated. Data concerning students’ family income were collected from school files. Data on student involvement in a variety of PA during and outside of school hours were gathered from information given by students (SKAF questionnaire). Tanner, self-reported assessment was used to estimate students’ stage of maturation. Length was considered as indicator of adolescent growth. While, students’ health-fitness was measured by a battery of health fitness tests. Effectiveness of these factors on students’ health-related fitness was determined by comparing the pre-post-health-fitness tests scores of students. Results indicated that children in the high-implementation-level have better-health fitness performance on both pre-test and post-test measurements than children in the low-implementation level. However, health- fitness performances that reflect significant differences were different among age groups. The older age groups generally performed better on overall fitness tests than did the younger age groups. Several covariates had strong relationships with pre and post-test fitness scores for different age groups such as; height, weight, BMI, maturity status, time spent in PA, race, and family income. Variations of health-related fitness performance between students involved in this study are most likely contributing to the different implementation levels. Thus, a well-programmed and supervised PE program can develop the health status of students at all levels of education
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».