Assessing the User Experience of the EU Mobile App for Cancer Prevention: Mixed Methods Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».