Gamified Physical-Digital Smoking Cessation Intervention for Young Adults: Mixed Methods Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Smoking remains a leading cause of death worldwide, with young adults particularly at risk due to the lack of targeted cessation initiatives. While mobile apps show promise in supporting smoking cessation, they primarily target smokers already motivated enough to install them, highlighting the need for interventions that reach those who are not yet ready to take that step. OBJECTIVE: This paper focuses on designing and evaluating Smokwit, a digital smoking cessation intervention aimed at young adults during the act of smoking. Smokwit seeks to investigate the early stages of smoking cessation (precontemplation and contemplation) that are important yet rarely investigated. METHODS: The paper is based on the design science research methodology where a digital intervention-Smokwit-was designed and evaluated in the wild using a mixed method approach combining quantitative results of a quasi-experiment with qualitative insights from users and experts. More specifically, Smokwit is a novel gamified ambient intervention that integrates a connected ashtray with a mobile app. The ashtray aims to trigger processes of change, in particular consciousness raising and social liberation (as part of the transtheoretical model of change) by provoking curiosity, self-reflection, and ad-hoc peer discussions among smokers. The linked mobile app is designed to reinforce this goal by providing smoking cessation self-help material and coaching possibilities. We evaluated the effectiveness of this intervention through a 3-month field study designed as a quasi-experiment with a treatment and control group (n=46). A qualitative analysis with users (n=10) and smoking cessation experts (n=7) provides insights into the type of interactions that happened within and outside the system as well as practical implications for smoking cessation organizations. RESULTS: The qualitative findings revealed that the intervention promoted smokers' self-reflection, peer discussions, and mobile app interactions. Furthermore, the quantitative analysis uncovered a possible trend toward increased readiness to quit among smokers in the treatment group compared to the control group; however, this did not reach conventional levels of statistical significance (b=1.33; z=1.91; P=.06). CONCLUSIONS: Smokwit provides encouraging insights into how to design a bottom-up digital intervention that targets young adults at an opportune moment to support them on their smoking cessation journey.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".