Development and Alpha Testing of QuitIT: An Interactive Video Game to Enhance Skills for Coping With Smoking Urges
Bibliographic record
Abstract
BACKGROUND: Despite many efforts at developing relapse prevention interventions, most smokers relapse to tobacco use within a few months after quitting. Interactive games offer a novel strategy for helping people develop the skills required for successful tobacco cessation. OBJECTIVE: The objective of our study was to develop a video game that enables smokers to practice strategies for coping with smoking urges and maintaining smoking abstinence. Our team of game designers and clinical psychologists are creating a video game that integrates the principles of smoking behavior change and relapse prevention. We have reported the results of expert and end-user feedback on an alpha version of the game. METHODS: The alpha version of the game consisted of a smoking cue scenario often encountered by smokers. We recruited 5 experts in tobacco cessation research and 20 current and former smokers, who each played through the scenario. Mixed methods were used to gather feedback on the relevance of cessation content and usability of the game modality. RESULTS: End-users rated the interface from 3.0 to 4.6/5 in terms of ease of use and from 2.9 to 4.1/5 in terms of helpfulness of cessation content. Qualitative themes showed several user suggestions for improving the user interface, pacing, and diversity of the game characters. In addition, the users confirmed a high degree of game immersion, identification with the characters and situations, and appreciation for the multiple opportunities to practice coping strategies. CONCLUSIONS: This study highlights the procedures for translating behavioral principles into a game dynamic and shows that our prototype has a strong potential for engaging smokers. A video game modality exemplifies problem-based learning strategies for tobacco cessation and is an innovative step in behavioral management of tobacco use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".