Effects of Semisupervised Exercise Training on Health Outcomes in People With Lung or Head and Neck Cancer: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Lung or head and neck cancers are known for their high prevalence and mortality rates. Chemotherapy and radiotherapy are usually recommended as cancer treatment for these malignancies; however, they can negatively impact both the physical and mental status of patients. Hence, it is reasonable to consider resistance and aerobic exercise training to prevent these negative health outcomes. Further, several factors prevent patients from attending outpatient exercise training programs, and, therefore, a semisupervised home-based exercise training program may be seen as a well-accepted alternative. OBJECTIVE: The aim of this study will be to investigate the effects of a semisupervised home-based exercise training program on physical performance, body composition, and self-reported outcomes; changes in the initial cancer treatment dose prescribed; number of hospitalizations at 3, 6, and 9 months; and 12-month survival in people with primary lung or head and neck cancer. METHODS: Participants will be randomly allocated to the training group (TG) or control group (CG). The TG will undergo semisupervised home-based resistance and aerobic exercise training throughout their cancer treatment. The resistance training will be performed using elastic bands (TheraBand) twice a week. The aerobic training (ie, brisk walk) will be performed for at least 20 minutes per day outdoors. The equipment and tools used during the training sessions will be provided. This intervention will start the week before treatment commencement, will be performed throughout the duration of the treatment, and will continue for 2 weeks after treatment completion. The CG will undergo usual care (ie, cancer treatment with no formal exercise prescription). Assessments will take place 2 weeks before the beginning of the usual cancer treatment and 2 weeks after treatment completion. The measures of physical function (peripheral muscle strength, functional exercise capacity, and physical activity), body composition, and self-reported outcomes (symptoms of anxiety and depression, health-related quality of life, and symptoms related to the disease and treatment) will be collected. We will report on any change in the initial cancer treatment dose prescribed; number of hospitalizations at 3, 6, and 9 months; and 12-month survival. RESULTS: In February 2021, the clinical trial registration was approved. Recruitment and data collection for the trial are ongoing (as of April 2023, 20 participants had already been randomized), and findings of this study are likely to be published late in 2024. CONCLUSIONS: This exercise training as a complementary treatment for patients with cancer is likely to promote positive effects on the health outcomes assessed, over and above any change in the CG, and prevent the reduction of initial cancer treatment dose prescribed. If these positive effects are shown, they will likely impact long-term outcomes such as hospitalizations and 12-month survival. TRIAL REGISTRATION: Brazilian Clinical Trials Registry (ReBEC) RBR-5cyvzh9; https://ensaiosclinicos.gov.br/rg/RBR-5cyvzh9. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43547.
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 | 0,032 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,004 |
| Méta-épidémiologie (sens large) | 0,017 | 0,009 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,010 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,070 | 0,011 |
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 ».