Identifying Video Game Preferences Among Adults Interested in Quitting Smoking Cigarettes: Survey Study
Bibliographic record
Abstract
BACKGROUND: Smoking is the most prevalent cause of morbidity and mortality in the United States. Although most individuals who smoke express a desire to quit smoking, only a small percentage are successful. Serious games have become popular in health sectors as a potential avenue for delivering a scalable treatment that is both accessible and engaging for the smoking population. Several smoking cessation games have already been developed, but these games feature a broad range of gameplay elements and are not necessarily based on existing video game preferences in the general or smoking population. OBJECTIVE: To better inform treatment development, this study aims to evaluate video game genre preferences among treatment-seeking individuals who smoke (N=473). METHODS: Participants responded to a screening survey to enroll in a larger, serious game intervention for smoking cessation. During this screening survey, participants were asked to disclose their favorite video games, which resulted in 277 unique game titles. These titles were coded for genre categories based on publisher listings and game features. The genres were then analyzed for the frequency of reporting overall and across age groups. RESULTS: Action, Role-Playing, and Action-Adventure were the most reported genres among adults aged ≤34 years; Action, Action-Adventure, and Logic were the most reported genres among adults aged 35-44 years; and Logic and Action were the most reported genres among adults aged ≥45 years. CONCLUSIONS: These data indicate that treatment-seeking individuals who smoke have different game preferences across age groups, and the data provide novel information to inform the development of future serious games targeting the smoking population that are tailored to the preferences of their age group. TRIAL REGISTRATION: ClinicalTrials.gov NCT03929003; https://clinicaltrials.gov/ct2/show/NCT03929003.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".