Technology-Based Prehabilitation for Cancer Patients Before Elective Treatment: A Protocol for a Scoping Review (Preprint)
Notice bibliographique
Résumé
Background: Advances in cancer treatment have improved survival rates; however, patients continue to experience significant treatment-related side effects, leading to reduced quality of life. Prehabilitation is an intervention that occurs before treatment and can improve patients' functional capacity, recovery, and well-being through exercise, nutrition, and psychological support. Typical hospital-based prehabilitation is not accessible to all patients due to geographical, socioeconomic, and time-related barriers. Technology-based approaches, including eHealth and mobile health (mHealth) interventions, may overcome these barriers by enabling remote, patient-centered delivery. However, the current evidence base is heterogeneous and lacks synthesis regarding feasibility, acceptability, and outcomes. Objective: This protocol for a scoping review aims to outline how we will systematically map and synthesize the evidence on technology-based prehabilitation interventions for people with cancer to identify intervention designs, assess feasibility and accessibility, and highlight knowledge gaps to guide future research and practice. Methods: The review will follow the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A 3-step search strategy will be applied across multiple databases and gray literature sources. Eligible studies will include adults (aged ≥18 years) with a cancer diagnosis who are scheduled for elective treatment (surgery, radiotherapy, chemotherapy, immunotherapy, or hormone therapy). Interventions must involve eHealth or mHealth approaches supporting unimodal or multimodal prehabilitation activities such as exercise, nutrition, psychological support, or lifestyle modification. Outcomes of interest include functional fitness, quality of life, psychological well-being, treatment preparedness, recovery, adherence, and feasibility. Two independent reviewers will conduct title, abstract, and full-text screening, with disagreements resolved through discussion or consultation with a third reviewer. Data will be charted and presented in tables and figures and as a narrative synthesis. Critical appraisal using JBI tools will contextualize methodological quality but not exclude studies. Risk of bias will be assessed using the Cochrane Risk of Bias Tool for Randomized Trials version 2 (RoB 2) and Risk of Bias in Non-Randomized Studies of Interventions version 2 (ROBINS-I V2) tool. This will not be used to exclude studies, but to determine the quality of articles included. Results: The search strategy has been pilot tested and finalized. Database searches are scheduled to commence in March 2026, with study selection and screening anticipated to be completed by April 2026. Data analysis and synthesis are expected to begin in May 2026, and final results will be available by October 2026. Conclusions: This protocol outlines a rigorous and transparent approach to mapping the current evidence on technology-based prehabilitation in cancer care. By systematically characterizing intervention features, outcome domains, and evidence gaps, the review will provide an up-to-date evidence map to guide future research priorities, inform clinical implementation, and support the development of more standardized and inclusive prehabilitation pathways.
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 | 0,099 | 0,129 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,005 |
| Méta-épidémiologie (sens large) | 0,009 | 0,012 |
| Bibliométrie | 0,013 | 0,012 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,005 | 0,008 |
| Intégrité de la recherche | 0,010 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,122 | 0,027 |
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 ».