Adapting an Efficacious Peer-Delivered Physical Activity Program for Survivors of Breast Cancer for Web Platform Delivery: Protocol for a 2-Phase Study
Notice bibliographique
Résumé
BACKGROUND: Interventions promoting physical activity (PA) among survivors of cancer improve their functioning, reduce fatigue, and offer other benefits in cancer recovery and risk reduction for future cancer. There is a need for interventions that can be implemented on a wider scale than that is possible in research settings. We have previously demonstrated that a 3-month peer-delivered PA program (Moving Forward Together [MFT]) significantly increased the moderate to vigorous PA (MVPA) of survivors of breast cancer. OBJECTIVE: Our goal is to scale up the MFT program by adapting an existing peer mentoring web platform, Mentor1to1. InquistHealth's web platform (Mentor1to1) has demonstrated efficacy in peer mentoring for chronic disease management. We will partner with InquisitHealth to adapt their web platform for MFT. The adaptation will allow for automating key resource-intensive components such as matching survivors with a coach via the web-based peer mentoring platform and collecting key indexes to prepare for large-scale implementation. The aim is to streamline intervention delivery, assure fidelity, and improve survivor outcomes. METHODS: In phase 1 of this 2-phase study, we will interview 4 peer mentors or coaches with experience in delivering MFT and use their feedback to create Mentor1to1 web platform adapted for MFT (webMFT). Next, another 4 coaches will participate in rapid, iterative user-centered testing of webMFT. In phase 2, we will conduct a randomized controlled trial by recruiting and training 10 to 12 coaches from cancer organizations to deliver webMFT to 56 survivors of breast cancer, who will be assigned to receive either webMFT or MVPA tracking (control) for 3 months. We will assess effectiveness with survivors' accelerometer-measured MVPA and self-reported psychosocial well-being at baseline and 3 months. We will assess implementation outcomes, including acceptability, feasibility, and program costs from the perspective of survivors, coaches, and collaborating organizations, as guided by the expanded Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. RESULTS: As of September 2023, phase 1 of the study was completed, and 61 survivors were enrolled in phase 2. Using newer technologies for enhanced intervention delivery, program management, and automated data collection has the exciting promise of facilitating effective implementation by organizations with limited resources. Adapting evidence-based MFT to a customized web platform and collecting data at multiple levels (coaches, survivors, and organizations) along with costs will provide a strong foundation for a robust multisite implementation trial to increase MVPA and its benefits among many more survivors of breast cancer. CONCLUSIONS: The quantitative and qualitative data collected from survivors of cancer, coaches, and organizations will be analyzed to inform a future larger-scale trial of peer mentoring for PA delivered by cancer care organizations to survivors. TRIAL REGISTRATION: ClinicalTrials.gov NCT05409664; https://clinicaltrials.gov/study/NCT05409664. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52494.
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 | 0,026 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,007 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,012 |
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 ».