Impact of the Mobile Game FightHPV on Cervical Cancer Screening Attendance: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The wide availability of mobile phones has made it easy to disseminate health-related information and make it accessible. With gamification, mobile apps can nudge people to make informed health choices, including attending cervical cancer screening. OBJECTIVE: This matched retrospective cohort study examined the association between exposure to the FightHPV mobile app gamified educational content and having a cervical exam in the following year. METHODS: Women aged 20 to 69 years who signed an electronic consent form after downloading the FightHPV app in 2017 (intervention group) were matched 1:6 with women of the same age and with the same screening history (reference group) in 2015. To estimate the impact of exposure to the FightHPV app, we estimated cumulative incidence and hazard ratios (HRs) with 95% CIs. We used data from the Norwegian Cervical Cancer Screening Program database and Statistics Norway to determine screening participation and outcomes, respectively. RESULTS: We matched 3860 women in the control group to 658 women in the intervention group; 6 months after enrollment, 29.6% (195/658) of the women in the intervention group and 15.21% (587/3860) of those in the reference group underwent a cervical exam (P<.01). Women exposed to the FightHPV app were 2 times more likely to attend screening (adjusted HR 2.3, 95% CI 2.0-2.7), during which they were 13 times more likely to be diagnosed with high-grade abnormality (adjusted HR 12.7, 95% CI 5.0-32.5) than the women in the reference group. CONCLUSIONS: Exposure to the FightHPV app significantly increased cervical cancer screening attendance across the various analyses and improved detection of women with high risk for cervical cancer. For the first time, we demonstrated the effectiveness of gamification combined with mobile technology in cancer prevention by empowering women to make active health-related decisions. Gamification can significantly improve the understanding of complicated scientific concepts behind interventions and increase the acceptance of proposed cancer control measures.
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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 | 0.000 | 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) | 0.007 | 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".