A Video Game Intervention to Prevent Opioid Misuse Among Older Adolescents: Development and Preimplementation Study
Bibliographic record
Abstract
BACKGROUND: Opioid misuse and mental disorders are highly comorbid conditions. The ongoing substance misuse and mental health crises among adolescents in the United States underscores the importance of widely scalable substance misuse preventive interventions that also address mental health risks. Serious video games offer an engaging, widely scalable method for delivering and implementing preventive interventions. However, there are no video game interventions that focus on preventing opioid misuse among older adolescents, and there are limited existing video game interventions that address mental health. OBJECTIVE: This study aims to develop and conduct a formative evaluation of a video game intervention to prevent opioid misuse and promote mental health among adolescents aged 16-19 years (PlaySmart). We conducted formative work in preparation for a subsequent randomized controlled trial. METHODS: We conducted development and formative evaluation of PlaySmart in 3 phases (development, playtesting, and preimplementation) through individual interviews and focus groups with multiple stakeholders (adolescents: n=103; school-based health care providers: n=51; and addiction treatment providers: n=6). PlaySmart content development was informed by the health belief model, the theory of planned behavior, and social cognitive theory. User-centered design principles informed the approach to development and play testing. The Exploration, Preparation, Implementation, and Sustainability framework informed preimplementation activities. Thematic analysis was used to identify themes from interviews and focus groups that informed PlaySmart game content and approaches to future implementation of PlaySmart. RESULTS: We developed a novel video game PlaySmart for older adolescents that addresses the risk and protective factors for opioid misuse and mental health. Nine themes emerged from the focus groups that provided information regarding game content. Playtesting revealed areas of the game that required improvement, which were modified for the final game. Preimplementation focus groups identified potential barriers and facilitators for implementing PlaySmart in school settings. CONCLUSIONS: PlaySmart offers a promising digital intervention to address the current opioid and mental health crises among adolescents in a scalable manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".