Feasibility, User Acceptance, and Outcomes of Using a Cancer Prehabilitation App for Exercise: Pilot Cohort Study
Notice bibliographique
Résumé
Background: The efficacy of cancer prehabilitation programs is supported by international reviews and meta-analyses. Technology has been deployed in cancer prehabilitation to address challenges such as access or limited resources. This study evaluated the feasibility, user acceptance, safety, and program outcomes of a newly developed mobile app for cancer prehabilitation. The app integrates with Singapore's existing health care mobile app, Health Buddy, and provides instructional videos for prescribed exercises. Objective: The objectives of this study were to investigate the feasibility, user experience, safety, and outcomes of a mobile app for cancer prehabilitation within a hospital-associated, home-based, multimodal cancer prehabilitation program. Methods: This retrospective study analyzed the records of patients enrolled in the cancer prehabilitation program from September 1, 2022, to March 30, 2023. Patients who participated in the prehabilitation program (n=63) were categorized into 2 groups: those prescribed the app (n=41) and those who were not (n=22). There was further subgroup analysis of those who were prescribed: app users (n=25) versus those who were non-app users (n=16). Demographics, Fried Frailty Phenotype, prehabilitation duration, app use, and functional outcome measures (6-minute walk test [6MWT], 30-second sit-to-stand test [STS], timed up and go test [TUG], and Hospital Anxiety and Depression Scale [HADS]) were collected. Compliance was determined by the completion of prescribed exercises and the accuracy of executing these exercises, with a high compliance rate considered to be at 80% or more. Baseline characteristics and preoperative outcomes were compared between the groups. User satisfaction was assessed through surveys among app users (n=25). Results: Among 63 patients, 41 (65.1%) patients were prescribed the app, of which 22 (34.9%) patients were users. No significant differences in preoperative functional improvements were observed between app users and nonusers (6MWT: P=.60; STS: P=.81; TUG: P=.53; HADS: P=.36), or between those prescribed and not prescribed the app (6MWT: P=.94; STS: P=.26; TUG: P=.39; HADS: P=.62). However, high compliance rates (80%) were observed among app users. Patient satisfaction with the app was high (>90%), with positive feedback on ease of use and technical reliability. Baseline measures revealed significantly lower functional scores and higher mean frailty scores in the nonprescribed group. Conclusions: This preliminary study demonstrates the acceptability, feasibility, and safety of Singapore's first smartphone app for exercise prescription in cancer prehabilitation. Lower baseline functional outcome measures and a higher mean frailty score in the unprescribed group have implications for the selection process and patient participation. Further studies should include strategies to enhance patients' readiness for technology, sustainability, and effectiveness in older patients.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».