Web-Based Patient Self-Reported Outcome After Radiotherapy in Adolescents and Young Adults With Cancer: Survey on Acceptance of Digital Tools
Notice bibliographique
Résumé
BACKGROUND: eHealth and mobile health (mHealth) are an evolving trend in the medical field. The acceptance of digital tools is high, and the need is growing. OBJECTIVE: Young adults (18-40 years) confronted with a cancer diagnosis present unique needs and require special care. They often have a strong affinity and are familiar with modern technology. On that account, we implemented a web-based symptom and quality of life (QoL) assessment to address patients' attitudes and willingness to use mHealth tools. The study also aims to evaluate sociodemographic parameters that could influence patients' opinions. METHODS: A total of 380 young patients aged 18-40 treated with radiotherapy between 2002 and 2017 were included in the trial. We assessed QoL via the European Organization for Research and Treatment of Cancer-Core 30 (EORTC C30) questionnaire and added general questions about mHealth technology. The added questions inquired patients' opinions regarding general aspects, including technical advances in medicine, mobile and app assistance during cancer treatment, data transfer, and app-specific features. The survey was conducted for 12 months. Participation was voluntary and pseudonymized; prior written consent was obtained. RESULTS: We achieved a participation rate of 57.6% (219/380) and a completion rate of 50.2% (110/219). The median age was 33 years (range 18-40). Of all participants, 89.1% (98/110) considered new technologies in medicine as positive; 10.9% (12/110) answered with neutral. Nearly all patients (96.4%, 106/110) stated that they would send further data via a web-based platform. Of all, 96.4% (106/110) considered the provided pseudonymization of their data as safe. We further asked the patients if they would use a mobile app for symptom and QoL assessment similar to the present web-based system: 74.5% (82/110) answered with yes and 25.5% (28/110) said they would not use a mobile app in the future. We tested the willingness to use an app on several sociodemographic parameters, such as age, gender, education, health insurance status, and cancer-related parameters: tumor stage, time since radiation treatment, and treatment intention. None of these parameters correlated with app use in this group of young adults. Patients who were generally positive regarding using an app rated several possible functions of a future app. The 3 most requested features were appointment reminders (89.0%, 73/82), contact overview of all involved clinics and physicians (87%, 71/82), and making an appointment via app (78%, 64/82). CONCLUSIONS: eHealth and mHealth tools should be available as an integrated part of a comprehensive cancer care approach. It provides automated, thorough documentation of health parameters during therapy and follow-up for doctors, medical staff, and tumor patients to optimize treatment. With this study, we could show that young adults are the ideal patient population to use eHealth/mHealth tools. Such tools offer further digital support and improve the patients' need for constant QoL during cancer care.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».