Feasibility and Acceptability of a Mobile Game to Support Smoking Cessation: Repeated Measures Study
Bibliographic record
Abstract
BACKGROUND: Approximately half of smokers attempt to quit, but 90% of these attempts fail. Video game-based interventions have the potential to address common barriers to evidence-based smoking cessation treatment, including high cost, lack of health care access, and low engagement. OBJECTIVE: The purpose of this study was to evaluate the feasibility and acceptability of a video game-based smoking cessation intervention called Inspired and its impact on the 7-day smoking point prevalence at the 30-day follow-up. METHODS: US adults (n=48) were recruited via the web to use Inspired on their smartphones for 7 weeks. The object of the game was to defend a healing tree against attackers. Levels of the game were unlocked twice daily when participants self-reported the number of cigarettes they smoked since the previous entry. Completion of the levels awarded players in-game currency, which could strengthen in-game abilities. Participants received additional in-game rewards to aid gameplay by submitting either smoking self-reports only or self-reports indicating abstinence, determined through random assignment. In addition, participants completed a web-based survey at intake, week 4, week 7, and the 30-day follow-up. RESULTS: Of the 48 participants, who had an average age of 39.8 (SD 10.7) years, 27 (56%) were female, 4 (8%) Hispanic, 37 (77%) White, and 27 (56%) employed; 26 (54%) earned <US $40,000 a year; and 14 (29%) lived in nonurban areas (eg, rural and suburban). There were no significant differences between the groups, so all outcome data were combined. Participants averaged 20.6 (SD 15.3) days of gameplay and reached a mean highest game level of 10.7 (SD 8.4), although there was a high degree of variability. Participants reported abstinence on 31.4% (SD 38.2%) of all cessation phase reports and averaged 5.4 (SD 9.8) consecutive abstinent smoke reports. For every 1 SD increase in the highest level achieved, there was a 27% increase in the percentage of abstinent samples and a 405% increase in longest continuous abstinence. At the 30-day follow-up, 23% (11/48) of the participants reported having not taken a cigarette puff in the prior 7 days and 31% (15/48) had spent at least 24 hours without smoking in the prior 14 days. On an 11-point scale, participants rated the intervention moderately favorably: if they had to do it again, they would use Inspired to help them quit (mean 6.4, SD 3.4), and Inspired was helpful in their current attempt to quit (mean 5.4, SD 3.6). CONCLUSIONS: These results support the acceptability of Inspired. Although high dropout rates prevent conclusions on feasibility, a subset of the participants responded favorably. Scalable and accessible video game-based smoking cessation interventions could be the key to addressing the foremost cause of preventable morbidity and mortality in the United States. 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 | 0.000 |
| 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) | 0.001 | 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".