Assessing the User Experience of the EU Mobile App for Cancer Prevention: Mixed Methods Study
Bibliographic record
Abstract
Background: In 2022, nearly 20 million new cancer cases and 9.7 million deaths occurred globally. Europe, comprising under 10% of the world's population, accounted for over 22% of cases and 20% of deaths, reflecting an aging population, lifestyle risk factors, and extensive screening. With 40% of cancers preventable through modifiable risk factor interventions, effective prevention is essential. The European Code Against Cancer provides evidence-based guidelines that drive health initiatives across Europe. Supported by Europe's Beating Cancer Plan and the EU4Health program, the EU Mobile App for Cancer Prevention was developed to disseminate these recommendations. However, its effectiveness depends on usability across populations with varying digital and health literacy; this study evaluates the app's usability among diverse European populations. Objective: This study aimed to identify enablers, barriers, and user requirements for the use and maintenance of the English version of the EU Mobile App for Cancer Prevention, focusing on how usability varied across individuals with different levels of digital health literacy and diverse sociodemographic backgrounds. In addition, user feedback on mock wireframes-visual representations of the app's interface and functionality-was gathered to evaluate usability and ease of use, providing insights for tailoring the app design to a broader population. Methods: We conducted a mixed methods study in 7 European countries with 76 adults aged 19-84 years recruited via purposive quota sampling. Participants completed quantitative usability testing using mock wireframes to perform 10 predefined tasks simulating core app functionalities (eg, profile setup and health goal tracking). We recorded task completion time, success rates, self-reported confidence, and perceived difficulty. Digital health literacy was assessed using the eHealth Literacy Scale (eHEALS) scale. Qualitative data were collected through focus group discussions guided by a semistructured interview guide, and transcripts were analyzed via thematic content analysis. Statistical analyses included descriptive statistics and 1-way ANOVA to explore group differences. Results: Overall task completion rates ranged from 75% to 98%, with a median of 86%, indicating general usability. However, usability varied by age, education, and digital health literacy: younger participants and those with higher education and literacy levels reported greater confidence and lower difficulty, whereas older adults and lower-literacy users experienced more challenges. Qualitative analysis identified key themes affecting usability: the need for accessibility (multilingual support and simple language), user-centric design (age-friendly interfaces and intuitive navigation), ethical concerns (data privacy and security), and motivational features (gamification and personalized health goals). Conclusions: The app is generally usable across diverse populations but requires streamlined interfaces and design adaptations to accommodate varying digital health literacy. Ensuring robust data privacy practices is essential for fostering user trust, and integrating motivational elements may enhance sustained engagement. Future work will involve piloting the finalized app to evaluate its real-world uptake and impact on cancer prevention behaviors.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".