Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea
Notice bibliographique
Résumé
BACKGROUND: Childhood obesity has emerged as a major health issue due to the rapid growth in the prevalence of obesity among young children worldwide. Establishing healthy eating habits and lifestyles in early childhood may help children gain appropriate weight and further improve their health outcomes later in life. OBJECTIVE: This study aims to classify clusters of young children according to their eating habits and identify the features of each cluster as they relate to childhood obesity. METHODS: A total of 1280 children were selected from the Panel Study on Korean Children. Data on their eating habits (eating speed, mealtime regularity, consistency of food amount, and balanced eating), sleep hours per day, outdoor activity hours per day, and BMI were obtained. We performed a cluster analysis on the children's eating habits using k-means methods. We conducted ANOVA and chi-square analyses to identify differences in the children's BMI, sleep hours, physical activity, and the characteristics of their parents and family by cluster. RESULTS: At both ages (ages 5 and 6 years), we identified 4 clusters based on the children's eating habits. Cluster 1 was characterized by a fast eating speed (fast eaters); cluster 2 by a slow eating speed (slow eaters); cluster 3 by irregular eating habits (poor eaters); and cluster 4 by a balanced diet, regular mealtimes, and consistent food amounts (healthy eaters). Slow eaters tended to have the lowest BMI (P<.001), and a low proportion had overweight and obesity at the age of 5 years (P=.03) and 1 year later (P=.005). There was a significant difference in sleep time (P=.01) and mother's education level (P=.03) at the age of 5 years. Moreover, there was a significant difference in sleep time (P=.03) and the father's education level (P=.02) at the age of 6 years. CONCLUSIONS: Efforts to establish healthy eating habits in early childhood may contribute to the prevention of obesity in children. Specifically, providing dietary guidance on a child's eating speed can help prevent childhood obesity. This research suggests that lifestyle modification could be a viable target to decrease the risk of childhood obesity and promote the development of healthy children. Additionally, we propose that future studies examine long-term changes in obesity resulting from lifestyle modifications in children from families with low educational levels.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».