FightHPV: Design and Evaluation of a Mobile Game to Raise Awareness About Human Papillomavirus and Nudge People to Take Action Against Cervical Cancer
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) is the most common sexually transmitted infection globally. High-risk HPV types can cause cervical cancer, other anogenital cancer, and oropharyngeal cancer; low-risk HPV types can cause genital warts. Cervical cancer is highly preventable through HPV vaccination and screening; however, a lack of awareness and knowledge of HPV and these preventive strategies represents an important barrier to reducing the burden of the disease. The rapid development and widespread use of mobile technologies in the last few years present an opportunity to overcome this lack of knowledge and create new, effective, and modern health communication strategies. OBJECTIVE: This study aimed to describe the development of a mobile app called FightHPV, a game-based learning tool that educates mobile technology users about HPV, the disease risks associated with HPV infection, and existing preventive methods. METHODS: The first version of FightHPV was improved in a design-development-evaluation loop, which incorporated feedback from a beta testing study of 40 participants, a first focus group of 6 participants aged between 40 and 50 years and a second focus group of 23 participants aged between 16 and 18 years. Gameplay data from the beta testing study were collected using Google Analytics (Google), whereas feedback from focus groups was evaluated qualitatively. Of the 29 focus group participants, 26 returned self-administered questionnaires. HPV knowledge before and after playing the game was evaluated in the 22 participants from the second focus group who returned a questionnaire. RESULTS: FightHPV communicates concepts about HPV, associated diseases and their prevention by representing relationships among 14 characters in 6 episodes of 10 levels each, with each level being represented by a puzzle. Main concepts were reinforced with text explanations. Beta testing revealed that many players either failed or had to retry several times before succeeding at the more difficult levels in the game. It also revealed that players gave up at around level 47 of 60, which prompted the redesign of FightHPV to increase accessibility to all episodes. Focus group discussions led to several improvements in the user experience and dissemination of health information in the game, such as making all episodes available from the beginning of the game and rewriting the information in a more appealing way. Among the 26 focus group participants who returned a questionnaire, all stated that FightHPV is an appealing educational tool, 69% (18/26) reported that they liked the game, and 81% (21/26) stated that the game was challenging. We observed an increase in HPV knowledge after playing the game (P=.001). CONCLUSIONS: FightHPV was easy to access, use, and it increased awareness about HPV infection, its consequences, and preventive measures. FightHPV can be used to educate people to take action against HPV and cervical cancer.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".