Fatigue-induced changes in electromyographic activity after repeated racing turns: a pilot study

Eur J Appl Physiol. 2024 Dec 21. doi: 10.1007/s00421-024-05691-0. Online ahead of print.

Abstract

Purpose: Alpine skiing races are physically demanding events characterized by numerous repeated near-maximal activations of the lower limb muscles. Although this type of task is known to induce neuromuscular fatigue, electromyographic activity (EMG) adaptations after repeated maximal-intensity skiing have not been previously investigated.

Methods: Six skiers completed a 6-turns section with (FAT) and without performing 30 giant slalom (GS) turns (CONT). Isometric knee extensors maximal force (Fmax) was measured before and immediately after both conditions. On-snow EMG activity of VM, VL, RF, BF, SMST and GM muscles were compared between conditions for both the outside (OL) and inside (IL) legs using turn-averaged EMG amplitude (RMSOL and RMSIL) and EMG mean power frequency (MPFOL and MPFIL). EMG time-frequency maps were also computed and compared between conditions using statistical parametric mapping (SPM) analysis.

Results: Fmax was significantly lower after FAT (-20.1%, p < 0.001), but did not change after CONT. RMSOL was lower in FAT for BF (-26.8%, p = 0.020). RMSIL was lower in FAT for VM (-24.7%, p = 0.036) and GM (-27.3%, p = 0.021). MPFOL was lower in FAT for VM (-8.2%, p = 0.028), VL (-11.3%, p = 0.025), RF (-13.1%, p = 0.007), SMST (-9.3%, p = 0.004) and GM (-7.4%, p = 0.034). MPFIL was lower in FAT for VM (-13.0%, p = 0.016) and RF (-11.1%, p = 0.034). SPM analysis indicated that the initiation phase of the turn was specifically affected.

Conclusion: Thirty GS turns led to a substantial decrease in Fmax and altered motor command, as indicated by reduced EMG frequency content, specifically in the initiation phase of the turn. The present pilot data highlight the importance of characterizing neuromuscular fatigue in competitive GS skiing.

Keywords: EMG; Maximal force; Performance; SPM; Time–frequency analysis; Wavelet.