Development and Promotion of an mHealth App for Adolescents Based on the European Code Against Cancer: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Mobile health technologies, underpinned by scientific evidence and ethical standards, exhibit considerable promise and potential in actively engaging consumers and patients while also assisting health care providers in delivering cancer prevention and care services. The WASABY mobile app was conceived as an innovative, evidence-based mobile health tool aimed at disseminating age-appropriate messages from the European Code Against Cancer (ECAC) to adolescents across Europe. OBJECTIVE: This study aims to assess the outcomes of the design, development, and promotion of the WASABY app through a 3-pronged evaluation framework that encompasses data on social media promotion, app store traffic, and user engagement. METHODS: The WASABY app's content, cocreated with cancer-focused civil society organizations across 6 European countries, drew upon scientific evidence from the ECAC. The app's 10 modules were designed using the health belief model and a gamification conceptual framework characterized by spaced repetition learning techniques, refined through 2 rounds of testing. To evaluate the effectiveness of the app, we conducted a retrospective cohort study using the WASABY app's user database registered from February 4 to June 30, 2021, using a 3-pronged assessment framework: social media promotion, app store traffic, and user engagement. Descriptive statistics and association analyses explored the relationship between sociodemographic variables and user performance analytics. RESULTS: After extensive promotion on various social media platforms and subsequent traffic to the Apple App and Google Play stores, a sample of 748 users aged between 14 and 19 years was included in the study cohort. The selected sample exhibited a mean age of 16.08 (SD 1.28) years and was characterized by a predominant representation of female users (499/748, 66.7%). Most app users identified themselves as nonsmokers (689/748, 92.1%), reported either no or infrequent alcohol consumption (432/748, 57.8% and 250/748, 33.4%, respectively), and indicated being physically active for 1 to 5 hours per week (505/748, 67.5%). In aggregate, the app's content garnered substantial interest, as evidenced by 40.8% (305/748) of users visiting each of the 10 individual modules. Notably, sex and smoking habits emerged as predictors of app completion rates; specifically, male and smoking users demonstrated a decreased likelihood of successfully completing the app's content (odds ratio 0.878, 95% CI 0.809-0.954 and odds ratio 0.835, 95% CI 0.735-0.949, respectively). CONCLUSIONS: The development and promotion of the WASABY app presents a valuable case study, illustrating the effective dissemination of evidence-based recommendations on cancer prevention within the ECAC through an innovative mobile app aimed at European adolescents. The data derived from this study provide insightful findings for the implementation of Europe's Beating Cancer Plan, particularly the creation of the EU Mobile App for Cancer Prevention.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".