Does neuromuscular fatigue generated by trail running modify foot‐ground impact and soft tissue vibrations?

Abstract

The purpose of the study was to assess the influence of a preceding mountain ultramarathon on the impact between the foot and the ground and the resulting soft tissue vibrations (STV). Two sessions of measurements were performed on 52 trail runners, before and just after mountain trail running races of various distances (from 40 to 171 km). Triaxial accelerometers were used to quantify the foot-ground impact (FGI) and STV of both gastrocnemius medialis (GAS) and vastus lateralis (VL) muscles during level treadmill running at 10 km•h −1 . A continuous wavelet transform was used to analyze the acceleration signals in the time-frequency domain, and the maps of coefficients as well as the frequency and damping properties of STV were computed. Fatigue was assessed from isometric maximal voluntary contraction force loss of knee extensors (KE) and plantar flexors (PF) after each race. Statistical nonParametric Mapping and linear mixed models were used to compare the means between the data obtained before and after the races. FGI amplitude and GAS STV were not modified after the race, while VL STV amplitude, frequency and damping significantly decreased whatever the running distance. A significant force loss was observed for the PF (26 ± 14%) and KE (27 ± 16%), but this was not correlated to the changes observed in STV. These results might reveal a protection mechanism of the muscles, indicating that biomechanical and/or physiological adaptations may occur in mountain ultramarathons to limit STV and muscle damage of knee extensors.Trial registration: ClinicalTrials.gov identifier: NCT04025138.. Highlights. Trail running races with distances from 40 to 171 km induced the same alterations of soft-tissue vibrations. Due to the hilly characteristics of trail running, only the vastus lateralis soft-tissue vibrations were affected by the races. . Vastus lateralis vibration amplitude, frequency and damping coefficient were reduced after trail running races. These modifications can arise from a protection mechanism and/or modification in the muscle properties. . Neuromuscular fatigue quantified with loss of maximal isometric force production is not predictive of soft-tissue vibration modifications.

Soft Tissue Vibrations in Running: A Narrative Review

“…From the time representation of the acceleration signal (Fig. 1 b), one can compute the peak acceleration [ 14 , 22 , 32 , 33 , 38 ], the mean standard deviation [ 27 ], or the root mean square acceleration [ 9 ]. A temporal analysis of passive marker displacement allows the calculation of the maximum displacement of the soft tissue [ 28 , 29 ].…”

“…The frequency content of the vibration signal changes over time as higher frequencies are more damped than lower ones [ 41 ], which has led authors to employ time–frequency analyses such as continuous wavelet transforms (CWT) [ 33 , 37 , 40 , 42 , 43 ] or empirical mode decomposition-based method (EMD) [ 41 ]. The difference between these two methods is that CWT works on all the frequency range, while EMD extracts intrinsic mode functions.…”

“…3 c). By choosing the wavelets or mode functions analysed, mainly between 6 and 130 Hz [ 32 , 33 , 37 , 40 ] (the frequencies below and above corresponding to movement and noise, respectively), it is possible to calculate different indicators for different time and frequency ranges. To illustrate the major advantage of time–frequency analysis over the frequency analysis, one can consider a signal with a first frequency with high power during a short time period, and a second frequency with low power for a long time.…”

“…3 c) as a descriptive variable during running. Instead, maps are used to extract variables [ 32 , 33 , 37 , 42 ]. First, by integrating the map in function of time, one can compute an equivalent of the nFT and obtain the same information as that obtained with FFT analysis.…”

“…3 b), these methods are a compromise between the time and frequency resolution and thus, are often used in parallel of frequency analysis. Second, by integrating the map in function of the frequency [ 15 ] or by summing the wavelets power [ 6 , 31 , 33 , 37 , 40 ], one can compute the overall amplitude of the vibration in the time domain. The result of this computation depends on the frequency range analysed, and/or of the wavelets summed.…”