Back to Search Start Over

Does vibration superimposed on low-level isometric contraction alter motor unit recruitment strategy?

Authors :
Xu, L.
Negro, Francesco
Xu, Yu
Rabotti, C.
Schep, Goof
Farina, Dario
Mischi, M.
Xu, L.
Negro, Francesco
Xu, Yu
Rabotti, C.
Schep, Goof
Farina, Dario
Mischi, M.
Source :
Journal of Neural Engineering vol.15 (2018) date: 2018-09-11 nr.6 [ISSN 1741-2560]
Publication Year :
2018

Abstract

OBJECTIVE: Beneficial effects, including improved muscle strength and power performance, have been observed during vibration exercise (VE) and partially ascribed to a specific reflex mechanism referred to as Tonic vibration reflex (TVR). TVR involves motor unit (MU) activation synchronized and un-synchronized with the vibration cycle; this suggests VE to alter the temporal MU recruitment strategy. However, the effects of VE on MU recruitment remain poorly understood. This study aims to elucidate the influence of VE on MU recruitment indirectly, by investigating the effects of low-intensity VE on muscle activation. APPROACH: Twenty volunteers performed isometric contractions on the biceps brachii of the right arm at a baseline (low) force equal to 30% of the maximum voluntary contraction without vibration (control) and with vibration at 20, 30, 40, and 55 Hz. Three vibration amplitudes were employed at 12.5%, 25%, and 50% of the baseline. Mean muscle-fiber conduction velocity (mCV), mean frequency (MF), and root mean square (RMS) value were estimated from surface electromyography as indicators of the alteration in MU recruitment strategies. MAIN RESULTS: The mCV estimates during VE were significantly (p < 0.05) higher compared to the control condition. Furthermore, six VE conditions produced significantly larger RMS values compared to control condition. The estimated MF did not show any consistent trend. SIGNIFICANCE: These results suggest that vibration superimposed on low-level isometric contraction alters the MU recruitment strategy, activating larger and faster MUs.

Details

Database :
OAIster
Journal :
Journal of Neural Engineering vol.15 (2018) date: 2018-09-11 nr.6 [ISSN 1741-2560]
Notes :
Xu, L.
Publication Type :
Electronic Resource
Accession number :
edsoai.on1119603858
Document Type :
Electronic Resource