ECSS Paris 2023: OP-BM18
INTRODUCTION: Adventure running requires carrying additional loads during competition. This load transportation impacts biomechanical parameters like spatio-temporal and coordination variables. In this study we proposed to evaluate the effect of loads of 7% and 15% of body mass and different velocities (9, 12 and 15 km/h) on stride time (ST) and frequency (SF), contact time (CT), stride length (SL), flight time (FT), duty factor (DF), bilateral coordination consistency (BCC), bilateral coordination accuracy (BCA) and phase coordination index (PCI). METHODS: Twelve male athletes at the national level participated in three tests with random loads on different days. Kinematic data were collected by a video camera (CASIO - EXFH25) recording at 100hz perpendicularly placed 2m from the treadmill. The spatiotemporal data analyses were realized using the software Kinovea® (v. 0.8.27) and plotted into a spreadsheet. A Generalized Linear Model (SPSS v. 20.0) was used to evaluate kinematic parameters between load and velocity conditions with a p-value of < 0.05. RESULTS: Extra load conditions of 7% and 15% did not significantly change the running kinematics (p>0.05), and there were no interactions between loads and velocities (p>0.05). Regarding coordination variables, %BCC (p=0.06), % of BCA (p=0.24), and % of PCI (p=0.06), the additional load did not affect the coordination and symmetry of the athletes, except for velocities parameters (p < 0.05), which is expected according to literature. CONCLUSION: The novel finding of the present research states that running with 7 or 15% body mass as additional load does not seem to influence kinematic variables in adventure runners besides those for velocity parameters, which is expected according to the literature. Although there is a relative increase in processes that regulate attentional mechanisms during slow walking (1), these mechanisms do not seem to impair the lower limb’s kinematics investigated during fast walking or running (1, 2), revealing that these aspects may behave similarly with additional loads. Also, compensatory cortical function and supraspinal input effect with extra load may help athletes keep stable running. Some limitations may be considered in the study: i) there is an invariant gait pattern in a treadmill compared to overground conditions (3) and ii) the time used to analyze the kinematic parameters. 1 - Plotnik, M., et al. (2013). Effects of walking speed on asymmetry and bilateral coordination of gait. Gait & posture, 38(4), 864-869. 2 - Hafer, J. F., et al. (2016). Changes in coordination and its variability with an increase in running cadence. Journal of sports sciences, 34(15), 1388-1395. 3 - Hollman, J. H., et al. (2016). A comparison of variability in spatiotemporal gait parameters between treadmill and overground walking conditions. Gait & posture, 43, 204-209.
Read CV Alex De Oliveira FagundesECSS Paris 2023: OP-BM18
INTRODUCTION: A mathematical model describing the force-velocity-endurance (FoVE) relationship has recently been proposed. This model provides a framework to understand the force production capacity of an athlete during locomotion as a function of movement velocity and effort duration [1]. It has recently been evidenced that FoVE profile during endurance event can be determined from training and race data recorded by cycling power meters [2]. It might be possible to use a similar approach in trail running. Using GNSS data, the runners velocity can be assessed. In addition, the force needed to raise the runners center of mass is proportional to the sinus of the angle of inclination (alpha), the body mass (m), and the gravitational constant (g = 9.81 m.s⁻²), according to the equation F = m.g.sin(alpha). This study aims to investigate the goodness of fit and validity of in situ FoVE profiles determined for trail runners from GNSS data. METHODS: FoVE profiles of 18 well trained trail runners (16 M, 2 F; age :35.8) were computed based on GNSS data preceding from 3 month a world-class ultra-trail (166km 10000m D+). The maximum mean velocity (V) was calculated for every 2% gradient between -20 to 20 % for 3 to 20 min duration (1 min increment). Gradient was then expressed in force normalized to body mass: F = m.g.sin(alpha). The record velocity for each gradient and duration conditions was used to fit the 4 parameters (D’, F0c, V0c and C) of the FoVE model: F(V,t) = D’/t + (F0c × (V0c − V))/(V0c + V×C). An incremental test with VO2 measure was performed with a 12% gradient on treadmill which allow to determine the velocity at anaerobic threshold (AeT). This velocity was then compared to the critical velocity at a force corresponding to at 12% gradient calculated from the FoVE profile with a correlation approach. RESULTS: FoVE profile was computed based on 103 (± 45) training sessions. The goodness of fit of the FoVE model fitting presented a median r² = 0.93 (all > 0.82). AeT velocity in 12% gradient was 2.54 ± 0.34 m.s-1. Critical velocity at a force corresponding to a 12% gradient was 2.49 ± 0.34 m.s-1. Correlation between AeT and 12%-critical velocity from the FoVE model was significant (r²=0.74; RMSE = 0.18 m.s-1 or 7%; p<0.001) and ICC was 0.86. CONCLUSION: These findings suggest that in situ FoVE profile can be determined from GNSS data in trail running. The observed correlations with physiological measures confirm a practical validity of this approach. In addition, the FoVE profiling makes it possible to model the entire critical force-velocity relationship of a runner. In other words, it is possible to know his critical speed for any slope condition. This has the potential to become a valuable tool for trail runners and coaches to modify training session or proposed pacing strategies. [1] B. Morel et al, ECSS Congress (2024) [2] Y. Bertron, M. Bowen, P. Leo, P. Samozino, F. Hintzy, B. Morel, SportRχiv (2022) 1–10.
Read CV Clément DelhayeECSS Paris 2023: OP-BM18
INTRODUCTION: Downhill running (DR) causes important mechanical stress like increased electromyographic signal (EMG) of knee extensors, high ground reaction forces and muscle soft-tissue vibrations (STV) (1). STV are known to accelerate fatigue onset and generate muscle damage (2). Compression garments (CG) seem to be effective in reducing acute and delayed neuromuscular alterations, when used as a preventive strategy during running (3). It has been hypothesized that the protective effects of CG were likely due to a significant modification of muscle activation, and to a reduction in STV (4). This study aimed to evaluate the influence of CGs on the evolution of heel impacts, STV and muscle activation during exhausting DR. METHODS: Twenty healthy active men running at least once a week performed a DR bout to exhaustion, at 55% V02max on a -15% slope, wearing CG on one randomly selected leg and standardized sport shoes. Participants were fitted with EMG sensors on compressed (COMP) and control (CONT) Vastii Laterali (VL), tri-axial accelerometers on the heel cup of the shoes and the VL. All signals were recorded during 30-sec every 3-min until exhaustion. The second recording was considered to be the subjects state of "freshness" (FRESH), while the last one represented the state of "fatigue"(FAT). Continuous wavelet transform was used to enable the assessment of magnitude and frequency of the acceleration and EMG signal. Statistical non-Parametric Mapping (SnPM) and 2-factor ANOVA with repeated measures were performed to compare continuous and discrete means of COMP and CONT during the DR bout. RESULTS: No difference was observed for the heel impacts between COMP and CONT but total magnitude of STV of VL (TMV) was significantly reduced in COMP (406,3±35 UA) compared to CONT (535±62 UA, p=0,016) just as the median frequency (MF) (CONT : 45,2±7,6 Hz, COMP : 38±5,8 Hz; p=0,012). SnPM analysis showed a significant reduction in STV above 150Hz for COMP. The evolution from FRESH to FAT showed a significant increase of TMV of VL (432±180 UA vs 510±210; p=0,004) and MF (41,1±6,7 HZ to 42,2±6,8 Hz; p=0,034). SnPM showed a significant increase of heel impacts and STV of VL with fatigue above 150Hz (also between 70-110Hz for VL). No interaction compression/fatigue was detected. No significant differences were found for the evolution of surface EMG signals. CONCLUSION: Analysis of the evolution of shocks and STV parameters during DR indicates that fatigue leads to a significant increase in TMV in particular at high frequencies, while wearing CGs leads to a significant reduction of the same parameters during the run. The use of CG during downhill running might contribute to the “mechanical protection” of the muscle and reduce neuromuscular alterations without modifying muscle activation. REFERENCES 1) Vernillo & al., 2017 2) Ritteweger & al., 2000 3) Play & al., 2022 4) Ehrström & al., 2018
Read CV Robin GassierECSS Paris 2023: OP-BM18