Feasibility and Usefulness of Evidence-Based Gaming to Deliver Health Messages to Tweens in a Classroom Setting
Notice bibliographique
Résumé
Background Our interdisciplinary team developed a publicly available online game—Eat and Move as I Like (EAMAIL)—for tweens based on the MyPlate evidence-based representation of the Dietary Guidelines. Objective We aimed to test the feasibility of using EAMAIL in a classroom setting to promote engagement and self-awareness and motivate healthier diet behaviors in tweens. Methods Teachers in one middle school offered EAMAIL on school Chromebooks (institutional review board–approved). The researcher introduced EAMAIL’s login instructions, including nonidentifiable usernames, basic demographics, and home zip codes. Children were instructed to enter EAMAIL’s Story Mode, which had 5 MyPlate-food group levels; children caught healthy foods in color-matching buckets and avoided sweets. Each level delivers informational and motivational messages, asking users to report liking or disliking food groups and making dietary improvements on 7-point facial hedonic scales (from Love it to It’s okay to Hate it). At game completion, children rated the game based on whether it made them want to eat better and play again. Aligned with the Design, Play, and Experience Framework, the researcher made observations to assess child engagement, feelings about the game and the messages, and the motivation to make dietary improvements. Children were encouraged to complete the Story Mode before advancing to Free Play Mode, which had greater game challenges and 15-second interruptions every 2 to 3 minutes to deliver physical activity and health messages. Finally, each child completed a 13-item online survey to assess game-playing experiences, the desire to play again, new knowledge learning, and whether the game motivated healthier behaviors. Results EAMAIL was administered to five 30-minute classes involving 54 children (age: mean 11.6 years; female: 75%; White: 58%) and 105 users, and 1187 games were played. By the highest user level reached in Story Mode, 10% of users completed level 1 (Grains), 14% completed level 2 (Vegetables), 11% completed level 3 (Healthier Protein), 17% completed level 4 (Fruits), 15% completed level 5 (Dairy), and 31% completed all levels. Across users’ highest levels, Healthier Protein, on average, was the most liked, and Vegetables was the least liked. Most reported at least Like it to eating more fruits and vegetables (82%), vegetables (73%), healthier protein (79%), fruits (84%), and dairy (80%). All users responded to end-game questions; 64% reported at least Like it to “The game made me want to eat better” and “I would like to play the game again.” These responses were unchanged for most users who completed Story Mode and entered Free Play Mode (n=24); 6 reported worse and 3 reported better. From the postgame online survey, somewhat agreed to strongly agreed was reported by 76% of children with regard to learning about healthy eating and by 50% with regard to the game being fun, the game having positive attributes (pace, challenge, and flow), and whether they would share their game experiences. Researcher observations were consistent with children’s online responses. Conclusions EAMAIL appears feasible for teaching tweens in classroom settings about MyPlate, encouraging self-reflection, and motivating healthier eating, with Story Mode maximizing health promotion messages and engagement. Conflicts of Interest None declared.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 |
| 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 ».