Editorial: School-based nutrition and physical activity interventions among children and adolescents
Notice bibliographique
Résumé
Schools play a crucial role in shaping children's eating habits by providing access to healthy food options, implementing nutrition education programs, and fostering a culture that encourages healthy eating (1). Effective implementation strategies include integrating nutrition education into the curriculum, collaborating with local food suppliers to provide healthier meals, and promoting awareness through school-wide campaigns. By creating a supportive environment, schools can enhance student well-being and reinforce positive dietary habits (1). School-based interventions significantly improve students' food choices, encourage healthier eating patterns, and reduce the prevalence of diet-related health issues. The considerable influence of school environments on students' dietary habits highlights the need for healthy canteen interventions as a strategic approach to improve students' nutritional intake (1)(2)(3). Effective School-based interventions should focus on enhancing the availability and affordability of nutritious foods, increasing student participation in physical education (PE), and establishing social support systems to promote physical activity(PA)(4).The increasing prevalence of obesity, sedentary lifestyles, and unhealthy eating habits among children and adolescents is associated with chronic health issues (5)(6)(7). Recent data from the World Health Organization indicate that over 39 million children under the age of five were overweight or obese in 2020, underscoring the urgency of preventive interventions. Therefore, promoting healthy eating and sufficient PA among school-aged children is essential. This Research Topic presents the outcomes of 13 school-based studies designed to enhance dietary quality and PA levels in children and adolescents. Among these, four studies focused on nutrition and dietary patterns in children and adolescents, seven examined PA, and two addressed recruitment and outreach within a school-based pediatric obesity intervention, as well as health behaviors and health-related quality of life. Jha et al. conducted an interventional study to evaluate the impact of a health promotion initiative on the dietary behaviors of Indian adolescents, utilizing the theory of planned behavior (TPB) as a framework. TPB is particularly relevant in this context as it helps explain how attitudes, subjective norms, and perceived behavioral control influence dietary decisions and intentions to adopt healthier eating habits. The study found that the intervention successfully fostered a positive shift in adolescents' intentions to adopt healthier dietary practices. The study highlighted the effectiveness of model-based and construct-oriented intervention strategies in enhancing adolescents' commitment to healthier eating habits (8). Devine et al. explored the factors influencing food choices in school canteens, identifying barriers such as convenience, food placement, peer influence and food availability. The study proposed practical, cost-effective strategies, including menu planning, labeling and pricing adjustments, to promote healthier eating habits among secondary school students(9). Sezer et al. investigated the effects of socioeconomic status on the diet quality and snack preferences of adolescents from diverse backgrounds. The findings revealed that adolescents attending public schools had a lower tendency to choose healthy snacks than their peers who attended private schools. This disparity underscores socioeconomic status as a critical determinant of eating behaviors among adolescents. In addition, significant income differences between students attending private and public schools likely contributed to the higher frequency of snack consumption among students attending private schools. Thus, financial resources play a vital role in shaping dietary habits and preferences in this age group (10). A systematic review performed by Ahmed et al. evaluated the effects of the coronavirus disease 2019 (COVID-19) pandemic on school food programs in Canada, focusing on their delivery, adaptability, and resilience. These programs implemented various strategies to address the challenges posed by the pandemic, ensuring that vulnerable students continued to receive nutritious meals. Key initiatives included the distribution of prepared meals, food kits, and gift cards, which effectively enhanced food availability for pupils and their families. The study highlighted the importance of increased collaboration among community members, organizations, and stakeholders as a critical factor in maintaining food delivery and developing new methods for food distribution. However, the study also identified significant challenges related to the sustainability of these programs, particularly operating costs and funding (11). Forseth et al. evaluated two recruitment strategies for a pediatric obesity treatment trial targeting rural families, specifically focusing on school recruitment and participant enrollment rates. The opt-in approach, in which caregivers consented to have their child screened for eligibility, and the screen-first approach, in which all children were screened regardless of prior consent. The findings revealed that schools using the opt-in method were more successful in enrolling at least five families and implementing the intervention. In contrast, the screen-first approach resulted in higher overall participation rates (19). A study performed by Liu et al.examined the individual and combined effects of breakfast consumption, sedentary behavior, sleep, and PA on health-related quality of life (HRQoL) among Chinese high school students. It was found that students who used computers for two or more hours daily were more likely to report health issues in areas such as mobility, self-care, and daily activities. Additionally, lower PA levels were correlated with increased feelings of worry and sadness, as well as a lower visual analogue scale (VAS) score. Inadequate sleep (defined as less than 7 hours) and skipping breakfast were also associated with poorer HRQoL, particularly concerning pain, discomfort, and emotional well-being (20).
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 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 | 0,006 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,006 | 0,001 |
| Intégrité de la recherche | 0,017 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,012 |
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 ».