Serious Games Supporting the Prevention and Treatment of Alcohol and Drug Consumption in Youth: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: The consumption of alcohol and drugs, particularly in adolescents and young adults, has increased worldwide in the last several years, representing a significant public health challenge. Serious games have the potential to support preventive and treatment interventions for substance use, facilitating the acquisition of relevant knowledge and the motivation for changes in attitudes and behaviors regarding substance consumption. OBJECTIVE: This scoping review aims to analyze a set of 7 relevant characteristics of current serious games designed to support the prevention and treatment of alcohol and drug consumption in adolescents and young adults-the substance addressed, the type of intervention, the theoretical basis, the computational techniques used, the mechanism for data security and privacy, the evaluation procedure followed, and the main results obtained. METHODS: The review was performed by following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Data were retrieved from January 2010 to May 2022, using PubMed, Scopus (Elsevier), IEEE Xplore, and ACM Digital as data sources. The eligibility criteria included studies that described serious games designed to support the prevention or treatment of alcohol and drug consumption, targeted a population aged between 12 and 30 years, and included an evaluation procedure. Authors (JMM and IEEC) individually screened the titles and abstracts, and then full articles were reviewed for a final inclusion decision. RESULTS: A total of 629 records were obtained, and 29 (4.6%) fulfilled the inclusion criteria. Most of the serious games (14/29, 48%) were focused on the prevention or treatment of alcohol use. The type of intervention that was the most supported was prevention (18/29, 62%), and most studies mentioned the theory, theoretical construct, or therapeutic technique used as a foundation (22/29, 76%). Most of the studies only provided information about the platform for execution (23/29, 79%), and few described the use of computational techniques, such as virtual reality or motion-based interaction (5/29, 17%). A small set of studies (10/29, 34%) explicitly mentioned how data security and privacy were addressed. Most of the reported evaluation protocols were pilot studies (11/29, 38%), followed by randomized controlled trials (10/29, 34%), and the reported results were positive in terms of acceptability, usability, and efficacy. However, more research is needed to assess long-term effects. CONCLUSIONS: Given the increasing interest in the use of serious games as digital interventions to support the prevention or treatment of substance use, knowing their main features is highly important. This review highlights whether and how current serious games incorporate 7 key features that are useful to consider for the further development of the area.
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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 | 0,000 | 0,000 |
| 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 ».