ENABLE—App-Based Digital Capture and Intervention of Patient-Reported Quality of Life, Adverse Events, and Treatment Satisfaction in Breast Cancer: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: In recent years, breast cancer treatment has taken the path toward personalized medicine. Based on individual tumor biology, therapy tailored to the particular subtype of cancer is increasingly being used. The aim here is to find the most suitable therapy for the disease. However, the success of therapy depends to a large extent on the patient's adherence to treatment. This, in turn, depends on how the therapy is tolerated and how the treatment team cares for the patient. Patient-centered care seeks to identify and address the individual needs of each patient and to find the best form of care for that person. OBJECTIVE: In order to improve comprehensive oncological care of patients with breast cancer, the ENABLE trial digitally recorded the health-related quality of life (HRQoL), adverse events (AEs), and patient satisfaction using a mobile smartphone app. The trial provided individualized responses to reported AEs and offered assistance. Additionally, it assessed the impact of a patient-reported outcome-based intervention across various therapy settings. METHODS: Patients with breast cancer were eligible to participate in the study before neoadjuvant, adjuvant, postneoadjuvant, or palliative systemic therapy against breast cancer was initiated at the Heidelberg, Mannheim, and Tuebingen, Germany, university hospitals. After 1:1 randomization into an intervention and a control group, HRQoL assessments were performed at six fixed time points during the therapy using validated questionnaires. In the intervention group, HRQoL was also assessed briefly every week using a visual analog scale (EQ-VAS). In cases of significant deterioration, therapy-associated side effects were assessed in a graduated manner, recommendations were sent to the patient, and the treatment team was informed. Additionally, the app served as an "eHealth companion" for education, training, and organizational support during therapy. RESULTS: Recruitment started in March 2021; follow-up was completed in February 2024. In total, 606 patients were enrolled, and 592 patients participated in the study. Enrollment was completed in September 2023, and the last visit was in February 2024. The first results are expected to be published in Q2 2025. CONCLUSIONS: Participation in the intervention group is expected to improve treatment satisfaction, adherence, detection, and timely treatment of critical AEs. The close-meshed, weekly, brief HRQoL assessment will also be tested as a screening tool to detect relevant side effects during therapy. The study offers a more objective HRQoL assessment across treatment strategies. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00025611; https://drks.de/search/en/trial/DRKS00025611. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69855.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,027 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,010 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,009 |
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 ».