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Enregistrement W4405185206 · doi:10.2196/59478

A Novel Telehealth Exercise Program Designed for Rural Survivors of Cancer With Cancer-Related Fatigue: Single-Arm Feasibility Trial

2024· article· en· W4405185206 sur OpenAlexvenueno aff
Ryan J. Marker, Andrew J. Kittelson, Jared J. Scorsone, Ian A. Moran, John C. Quindry, Heather J. Leach

Notice bibliographique

RevueJMIR Cancer · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer survivorship and care
Établissements canadiensnon disponible
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Institute of General Medical SciencesNational Cancer Institute
Mots-clésPreprintTelehealthGerontologyCancer survivorMedicineCancerPhysical therapyTelemedicineHealth careComputer scienceWorld Wide WebPolitical science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Exercise interventions are among the best-known interventions for cancer-related fatigue (CRF). Rural survivors of cancer, however, report specific barriers to engaging in exercise programs and lack overall access to effective programs. OBJECTIVE: The purpose of this investigation was to assess the feasibility of a novel telehealth exercise program designed specifically for rural survivors of cancer with CRF. METHODS: A single-arm clinical trial of the BfitBwell Telehealth Program was performed. Based on an established clinical program, this adapted 12-week program addressed barriers previously reported by rural survivors by providing synchronous videoconference exercise sessions (2 per program), asynchronous exercise sessions using a personal training smartphone or internet app (3-5 per week), and regular symptom (CRF) monitoring using automated emailed surveys (every 2 weeks). Personalized exercise prescriptions containing aerobic and resistance activities were implemented by cancer exercise specialists. Symptom-triggered synchronous sessions were initiated for participants failing to improve in CRF, as identified by a reference chart of CRF improvements observed during a supervised exercise program. Eligible participants were adult survivors of any cancer diagnosis who had completed treatment with curative intent in the past 12 months or had no planned changes in treatment for the duration of the study, lived in a rural area, and were currently experiencing CRF. Feasibility was assessed by objective measures of recruitment, data collection, intervention acceptability and suitability, and preliminary evaluations of participant responses. CRF was the primary clinical outcome (assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue Scale [FACIT-Fatigue]) and was measured before, after, and 6 months after program completion. RESULTS: In total, 19 participants enrolled in the study, 16 initiated the exercise program, and 15 completed the program. A total of 14 participants were recruited through internet advertisements, and the total recruitment rate peaked at 5 participants per month. Participants completed 100% of initial and final assessments (30 assessments across all participants) and 93% (70/75 possible surveys across all participants) of emailed surveys and attended 97% (29/30 possible sessions across all participants) of synchronous exercise sessions. In total, 6 participants initiated symptom-triggered sessions, with 6 of 7 initiated sessions attended. The mean FACIT-Fatigue scores significantly improved (P=.001) by 11.2 (SD 6.8) points following the completion of the program. A total of 13 participants demonstrated at least a minimal clinically important difference in FACIT-Fatigue scores (≥ +3 points) at this time. FACIT-Fatigue scores did not significantly change from program completion to 6-month follow-up (n=13; mean change -1.1, SD 3.4 points; P=.29). CONCLUSIONS: Results from this investigation support the feasibility of the BfitBwell Telehealth Program and a subsequent efficacy trial. Novel program components also provide potential models for improving exercise program efficacy and efficiency through asynchronous exercise prescription and symptom monitoring. TRIAL REGISTRATION: ClinicalTrials.gov NCT04533165; https://clinicaltrials.gov/study/NCT04533165.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,785
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,065
Tête enseignante GPT0,389
Écart entre enseignants0,324 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2024
Routes d'admission1
Résumé présentoui

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