Neuromuscular, Psychological, and Sleep Predictors of Cancer-Related Fatigue in Cancer Patients.
Fatigability
Modelling
Multidimensional
Quality of life
Supportive care
Journal
Clinical breast cancer
ISSN: 1938-0666
Titre abrégé: Clin Breast Cancer
Pays: United States
ID NLM: 100898731
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
received:
07
05
2020
revised:
03
11
2020
accepted:
03
12
2020
pubmed:
11
1
2021
medline:
27
1
2022
entrez:
10
1
2021
Statut:
ppublish
Résumé
Cancer-related fatigue (CRF) is the most reported side effect of cancer and its treatments. This distressing sense of exhaustion critically impairs quality of life and can persist for years after treatment completion. Mechanisms of CRF are multidimensional (eg, physical, psychological, or behavioral), suggesting the need for a complex assessment. Nevertheless, CRF remains assessed mainly with 1-dimensional questionnaires. The purpose of this study was to test whether neuromuscular parameters enhance a model including well-known predictors of CRF. Forty-five participants with cancer history completed self-assessment questionnaires about quality of life, CRF, sleep disturbances, and emotional symptoms. They also completed a 5-minute handgrip fatiguing test composed of 60 maximal voluntary contractions to assess neuromuscular fatigability. Hierarchical linear regression analyses were performed to determine whether the neuromuscular fatigability threshold improved the FA12 score prediction beyond that provided by anxiety/depression and sleep disturbances. The hierarchical linear regression analysis evidenced that a model including anxiety/depression, sleep disturbances, and neuromuscular fatigability explained 56% of CRF variance. In addition, the results suggest that the mechanisms leading to CRF may be different from one person to another. Results revealed that sleep disturbances, emotional symptoms, and neuromuscular fatigability were the most important CRF predictors in cancer patients. This information could be useful for healthcare professionals offering tailored, individual support to patients with CRF.
Sections du résumé
BACKGROUND
Cancer-related fatigue (CRF) is the most reported side effect of cancer and its treatments. This distressing sense of exhaustion critically impairs quality of life and can persist for years after treatment completion. Mechanisms of CRF are multidimensional (eg, physical, psychological, or behavioral), suggesting the need for a complex assessment. Nevertheless, CRF remains assessed mainly with 1-dimensional questionnaires. The purpose of this study was to test whether neuromuscular parameters enhance a model including well-known predictors of CRF.
PATIENTS AND METHODS
Forty-five participants with cancer history completed self-assessment questionnaires about quality of life, CRF, sleep disturbances, and emotional symptoms. They also completed a 5-minute handgrip fatiguing test composed of 60 maximal voluntary contractions to assess neuromuscular fatigability. Hierarchical linear regression analyses were performed to determine whether the neuromuscular fatigability threshold improved the FA12 score prediction beyond that provided by anxiety/depression and sleep disturbances.
RESULTS
The hierarchical linear regression analysis evidenced that a model including anxiety/depression, sleep disturbances, and neuromuscular fatigability explained 56% of CRF variance. In addition, the results suggest that the mechanisms leading to CRF may be different from one person to another.
CONCLUSION
Results revealed that sleep disturbances, emotional symptoms, and neuromuscular fatigability were the most important CRF predictors in cancer patients. This information could be useful for healthcare professionals offering tailored, individual support to patients with CRF.
Identifiants
pubmed: 33422432
pii: S1526-8209(20)30329-3
doi: 10.1016/j.clbc.2020.12.002
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
425-432Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.