Gamification and Oral Health in Children and Adolescents: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Oral health is a determinant of overall well-being and quality of life. Individual behaviors, such as oral hygiene and dietary habits, play a central role in oral health. Motivation is a crucial factor in promoting behavior change, and gamification offers a means to boost health-related knowledge and encourage positive health behaviors. OBJECTIVE: This study aims to evaluate the impact of gamification and its mechanisms on oral health care of children and adolescents. METHODS: A systematic search covered multiple databases: PubMed/MEDLINE, PsycINFO, the Cochrane Library, ScienceDirect, and LILACS. Gray literature, conference proceedings, and WHOQOL internet resources were considered. Studies from January 2013 to December 2022 were included, except for PubMed/MEDLINE, which was searched until January 2023. A total of 15 studies were selected following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The eligibility criteria were peer-reviewed, full-text, and empirical research related to gamification in oral health care, reports of impact, and oral health care outcomes. The exclusion criteria encompassed duplicate articles; unavailable full texts; nonoriginal articles; and non-digital game-related, non-oral health-related, and protocol studies. Selected studies were scrutinized for gamification mechanisms and outcomes. Two main questions were raised: "Does gamification in oral health care impact oral health?" and "Does oral health care gamification enhance health promotion and literacy?" The PICO (Patient, Intervention, Comparison, Outcome) framework guided the scoping review. RESULTS: Initially, 617 records were obtained from 5 databases and gray literature sources. After applying exclusion criteria, 15 records were selected. Sample size in the selected studies ranged from 34 to 190 children and adolescents. A substantial portion (11/15, 73%) of the studies discussed oral self-care apps supported by evidence-based oral health. The most clearly defined data in the apps were "brushing time" (11/11, 100%) and "daily amount brushing" (10/11, 91%). Most studies (11/15, 73%) mentioned oral health care behavior change techniques and included "prompt intention formation" (11/26, 42%), "providing instructions" (11/26, 42%), "providing information on the behavior-health link" (10/26, 38%), "providing information on consequences" (9/26, 35%), "modeling or demonstrating behavior" (9/26, 35%), "providing feedback on performance" (8/26, 31%), and "providing contingent rewards" (8/26, 31%). Furthermore, 80% (12/15) of the studies identified game design elements incorporating gamification features in oral hygiene applications. The most prevalent gamification features were "ideological incentives" (10/12, 83%) and "goals" (9/16, 56%), which were found in user-specific and challenge categories, respectively. CONCLUSIONS: Gamification in oral health care shows potential as an innovative approach to promote positive health behaviors. Most studies reported evidence-based oral health and incorporated oral health care behavior change techniques.
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 | 0,011 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».