Serious Games Supporting the Prevention and Treatment of Alcohol and Drug Consumption in Youth: Scoping Review
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".