Creating a Theoretically Grounded, Gamified Health App: Lessons From Developing the Cigbreak Smoking Cessation Mobile Phone Game
Bibliographic record
Abstract
BACKGROUND: Gaming techniques are increasingly recognized as effective methods for changing behavior and increasing user engagement with mobile phone apps. The rapid uptake of mobile phone games provides an unprecedented opportunity to reach large numbers of people and to influence a wide range of health-related behaviors. However, digital interventions are still nascent in the field of health care, and optimum gamified methods of achieving health behavior change are still being investigated. There is currently a lack of worked methodologies that app developers and health care professionals can follow to facilitate theoretically informed design of gamified health apps. OBJECTIVE: This study aimed to present a series of steps undertaken during the development of Cigbreak, a gamified smoking cessation health app. METHODS: A systematic and iterative approach was adopted by (1) forming an expert multidisciplinary design team, (2) defining the problem and establishing user preferences, (3) incorporating the evidence base, (4) integrating gamification, (5) adding behavior change techniques, (6) forming a logic model, and (7) user testing. A total of 10 focus groups were conducted with 73 smokers. RESULTS: Users found the app an engaging and motivating way to gain smoking cessation advice and a helpful distraction from smoking; 84% (62/73) of smokers said they would play again and recommend it to a friend. CONCLUSIONS: A dedicated gamified app to promote smoking cessation has the potential to modify smoking behavior and to deliver effective smoking cessation advice. Iterative, collaborative development using evidence-based behavior change techniques and gamification may help to make the game engaging and potentially effective. Gamified health apps developed in this way may have the potential to provide effective and low-cost health interventions in a wide range of clinical settings.
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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) | 0.000 | 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".