Integrated, Cross-Entity Information on Preventive Measures for Bowel, Breast, and Prostate Cancer: Evaluation Study of the Web Application “Prevent-Take-Up”
Notice bibliographique
Résumé
Background: About 40% of cancers are preventable through evidence-based interventions; however, uptake remains suboptimal. Knowledge and acceptance of primary and secondary preventive measures in the general population is not sufficient. We hypothesized that a web-based tool providing comprehensive, easily accessible, and individualized information on preventive strategies for multiple tumor entities could support informed decisions. Objective: This study aimed to evaluate an interactive web application offering guideline-based, risk-adapted information on preventive measures for colorectal, breast, and prostate cancer. Methods: The content of web application was developed based on German S3 guidelines. Evaluation questions consisted of the system usability scale questionnaire and queries developed by the Prevent-Take-up consortium. An initial version was tested by two focus groups comprising general practitioners (GPs), specialists and patients, revised and then made publicly available in GPs' offices, hospitals, and pharmacies. We report on the evaluation of the revised web application. Data were collected from 2022-2023. The web application also gathers information regarding family and individual risk factors and offers personalized recommendations (eg, to seek further information). Personal data related to specific recommendations were not stored. Participants receiving a recommendation from the web application were asked to anonymously answer questions about the information provided and the website application's functionality using a 5-point Likert scale. As breast cancer mainly occurs in women and prostate cancer only in men, the questions regarding prevention had intrinsic sex specific items and hence all data were evaluated by sex using descriptive statistics. The main evaluation questions were (1) usability or user-friendliness of the web application for cross-entity cancer prevention and (2) motivation of users to seek further preventive advice by the information received. Results: Data from the first 101 users (62 female, 38 male, one unspecified; predominantly aged 50-70 y) showed high score regarding user-friendliness (female 47/62, 76%; male 25/38, 65%), question comprehensibility (female 54/62, 87%; male 32/38, 83%), and the relevance of recommendations (female 47/62, 76%; male 24/38, 63%). A total of 37/62 (59%) of female and 16/38 (44%) of male participants appreciated the web application's functionality; 29/62 (47%) of female and 14/38 (37%) of male participants reported increased knowledge about prevention and early detection of colorectal, breast, and prostate cancers. Additionally, 44/62 (71%) of female and 18/38 (47%) of male participants expressed willingness to follow-up on the web application's recommendations and seek more information from their GPs. Conclusions: Our web application for risk-adapted prevention across multiple cancers was rated as user-friendly by participants. Having used the web application, more female participants than males were willing to seek further information on prevention and early detection measures. Overall, our study demonstrates that a web application can be a useful tool to deliver integrated and individualized prevention recommendations.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».