4.6 Article

Age-related difference in muscle metabolism patterns during upper limb's encircling exercise: a near-infrared spectroscopy study

期刊

BIOMEDICAL OPTICS EXPRESS
卷 13, 期 9, 页码 4737-4751

出版社

Optica Publishing Group
DOI: 10.1364/BOE.462551

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资金

  1. National Key Research and Development Program of China [2020YFC2004300, 2020YFC2004302, 2020YFC2004301, 2020YFC2004303, 2020YFC2004304]
  2. National Natural Science Foundation of China [32000980, 82071970, 2171533]
  3. Basic and Applied Basic Research Foundation of Guangdong Province [2019A1515110427,2020B1515120014]
  4. Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project [2021B1515020064]
  5. Key Laboratory Program of Guangdong Higher Education Institutes [2020KSYS001]
  6. Sci-ence and Technology Program of Guangzhou [202103000032]
  7. Science and Technology Innovation Project of Jianghan University [2021kjzx008]

向作者/读者索取更多资源

This study investigated the aging effect on muscle metabolism patterns during upper limb's exercise and found that older subjects showed stronger muscle hemodynamic metabolism, higher complexity and stronger interlimb coupling of muscle oxygenation signals.
Aging is usually accompanied by decrease in limb motor function and change in muscle metabolism patterns. However, few studies have investigated the aging effect on muscle hemodynamics of the upper extremity. This study aims to explore the aging effect on muscle metabolism patterns during upper limb's exercise. Twelve middle-aged and elderly subjects and 12 young subjects were recruited, and muscle oxygenation signals from these subjects' biceps brachii muscles were collected during active and passive upper limb's encircling exercise with near-infrared spectroscopy (LAIRS). The old group showed stronger muscle hemodynamic metabolism than the young group. The multiscale fuzzy approximate entropy and multiscale transfer entropy analyses indicated higher complexity and stronger interlimb coupling of the muscle oxygenation signals for the old group. Based on the selected muscle metabolism features, the constructed support vector machine model showed a high accuracy rate for classifying the two groups of subjects: 91.6% for the passive mode and 87.5% for the active mode. Our results proved the specific muscle metabolism patterns in the upper limb's exercise for old subjects, promoting the understanding of the aging effect on muscle hemodynamics.

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