What’s Cooking? A Content and Quality Analysis of Food Preparation Mobile Applications (P16-050-19)
Notice bibliographique
Résumé
To assess the nutrition content and overall quality of mobile apps for children that focus on food preparation. These apps are games that require the user to cook, prepare and decorate virtual foods. A systematic search of the Canadian Apple App and Google Play Stores was conducted using 16 unique search terms related to nutrition, education, and children. Apps were included for analysis if they were rated appropriate for children, if the app had been updated since January 2016 and was in English. App titles, developers and descriptions were screened to identify apps eligible for analysis. App content was assessed by classifying foods according to the Canadian Food Guide categorizations. App quality was evaluated using the Mobile Application Rating Scale (MARS), which ranges from 1 (lowest quality) to 5 (highest quality). All screening and analysis were conducted by two independent reviewers with a third reviewer to resolve disagreements. A total of 2575 unique apps were identified. After screening, 142 were included in the analysis. Apps were most likely to contain the following foods: Dairy products (73%), candy/frozen desserts (71%), refined grains (68%) fruits (61%), lean meats (52%), desserts/baked goods (49%), vegetables (44%), sugar-sweetened beverages (38%) and processed meats (33%). Apps were least likely to include fish (14%), plant-based proteins (10%), and whole grains (4%). Although the appearance of fruits in apps was high, in 55% of apps fruit was shown in combination with desserts, chocolate and candy, and rarely on their own (7%). Apps were more likely to include sweet, high-sugar foods and/or sugar-sweetened beverages (86%) over savoury, high-sodium foods (40%). No app directly provided nutrition information and only 3% of apps included healthy eating messages. The mean MARS score was 3.6 (range 2.5–4.8), indicating moderate quality overall. Children’s food preparation games were moderate quality and include many foods that are not recommended by dietary guidelines. Given the popularity of these games, collaborations between app developers and nutritionists could enhance the quality and content of food preparation apps by incorporating a variety of foods recommended by current guidelines and healthy eating messages. Ontario Research Excellence Fund.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».