ECSS Paris 2023: OP-AP22
INTRODUCTION: Mechanical work is commonly used in biomechanics to explain changes in whole-body metabolic cost [1]. In a fatiguing run, stance phase joint work shifts from the ankle to knee, hypothesized to increase metabolic cost due to higher energy cost of contracting the quadriceps [2]. However, joint work does not reflect internal muscle contraction dynamics (e.g. muscle activation, contractile vs. series-elastic contributions, fiber lengths, etc.) that influence the local metabolic stress (LMS) of the muscles performing work. Therefore, the purpose of this study was to determine if changes in mechanical joint work and LMS of the muscles at the knee are correlated during a fatiguing run. METHODS: Ten recreational runners completed a 45 min treadmill run at 88% of their estimated maximal metabolic steady-state speed. Stance phase sagittal plane total knee joint work (Wtot = W+ + |W-|) was calculated from biomechanical data . Muscle oxygen saturation (%SmO2) slope of the rectus femoris (RF), representing LMS, was assessed via near-infrared spectroscopy. To account for initial exercise muscle oxygenation dynamics, data from 30 -100% of run duration were analyzed. Rating of perceived exertion (RPE; Borg scale) was collected at the start and end of run. RESULTS: RPE rose from 9 ± 2 to 16 ± 2, indicating a perceived effort of ‘very hard’ at the end. There was a moderate but not significant correlation between %SmO2 slope and percent change in total knee joint work (r = 0.362, p = 0.33). Post-hoc review of individual runner data showed that the group was evenly split between runners with a negative or positive %SmO2 slope. Of the 5 runners with a negative %SmO2 slope, 4 had a decrease in joint work (-2.5% average); and of the 5 runners with a positive %SmO2 slope, all had an increase in joint work (+6.6% average). CONCLUSION: A negative %SmO2 slope, indicating increased LMS, was not strongly correlated with the percent change in joint work performed at the knee, suggesting no strong linear relationship between the LMS of the RF and stance phase knee joint work. This suggests that changes in knee joint work should be interpreted with caution when drawing conclusions to the quadriceps metabolic demand. While joint work and %SmO2 slope were not significantly correlated, this suggests that more exploration into the relationship between muscles’ metabolic environment and mechanical work is warranted. These findings challenge the assumption that changes in knee joint work directly reflect quadriceps metabolic demand during fatigue; and suggest that muscle metabolic stress reflects a complex, integrated response – highlighting the need for interdisciplinary approaches. References 1. Riddick & Kuo. Sci Reports 2022; 12:645. 2. Sanno M., Willwacher, S., Epro, G., Bruggemann, G-P. Med Sci Sport Exerc. 2018. 2507-2517.
Read CV Evan DayECSS Paris 2023: OP-AP22
INTRODUCTION: Running economy, defined as the oxygen cost of running at a given submaximal speed, is a key determinant of performance in elite middle- long-distance runners. Strength training has demonstrated positive effects on improving running economy in this population. In contrast, evidence regarding the effects of speed-based training on running economy remains equivocal. METHODS: A systematic search was conducted across, PubMed, EBSCO, SPORTDiscus, and Medline for articles published up to November 2025. Studies were included if they met the following criteria: (1) population; competitive middle and long-distance runners (≥ 800m), aged ≥18 years, with minimum VO₂max values of ≥ 55 mL·kg-1·min-1 for males, and ≥ 45 mL·kg-1·min-1 for females; (2) intervention; a speed (i.e., acceleration, maximum-velocity, or speed endurance training) or strength training programme (i.e. maximal-, explosive-, reactive-, special- or combined-strength training) performed in addition to or partially replacing endurance training for ≥ 2 weeks, with ≥ 1 weekly sessions; (3) comparator; studies included a control group that performed endurance running training but did not receive any form of strength or speed training; (4) outcomes; running economy was recorded in at least one speed before and after the strength or speed training intervention, running economy expressed as either oxygen cost or energy cost; (5) study design; randomised and non-randomized controlled studies. Random-effects meta-analysis were conducted to examine the effects of maximal strength, reactive strength, combined strength, and speed on running economy. RESULTS: The pooled analysis demonstrated that reactive strength, maximum strength and combined strength training, elicited moderate, statistically significant improvements in running economy, whereas speed-based training did not show a consistent or significant effect. Seventeen studies (350 participants; 312 males, 38 females) were included, with participants aged 20.2 – 40.3 years (mean 28.8 years), and VO₂max range was 50.5 – 70.4 mL·kg-1·min-1. Interventions lasted 4 to 12 weeks with one to three weekly sessions. Reactive strength training showed a robust moderate improvement in run economy (SMD = -0.47; 95%, p = 0.003), whereas maximal strength (SMD = -0.69; p = 0.048) and combined strength training (SMD = -0.46; p = 0.029 demonstrated moderate but non-robust effects. Speed training showed a non-significant, and highly heterogeneous effect (SMD = -0.45; 95% CI: -2.32; 1.41, p = 0.633; I² = 93.1%). CONCLUSION: Reactive, maximal, and combine strength training elicit moderate improvements in running economy in well-trained middle long-distance runners, whereas speed-based training does not currently demonstrate a consistent effect. These findings highlight the importance of appropriately, dosed individualised strength training in this population and underscore in need for high-quality research to clarify the role of speed training, for improvements in running economy.
Read CV Lee Van HaeftenECSS Paris 2023: OP-AP22
INTRODUCTION: World-class sprinters generate horizontal power outputs of up to 36 W/kg during acceleration and vertical ground reaction forces of > 4.5x body mass to reach speeds of more than 12 m/s. Sprint performance is therefore tightly governed by lower-limb neuromuscular capacities. Despite this mechanistic link, empirical studies frequently report inconsistent associations between isolated strength and power tests and sprint performance [1,2]. This suggests that group-level interpretation of single test metrics may obscure meaningful individual neuromuscular characteristics, while inter-individual variability may reflect distinct neuromuscular performance profiles (NPP) within elite sprinters. Accordingly, this study aimed to explore NPP to improve test interpretation and identify profile-specific performance indicators. METHODS: 36 male (100m PR: 10.06 –10.78 s) and 28 female (100m PR: 11.40–12.03 s) elite sprinters completed two trials of countermovement jump (CMJ), drop jump (DJ) and run-specific hip- and ankle isometric push tests [3] on a dual force plate system. A matrix of biomechanical features (64 subjects x 24 features) was constructed, with metrics averaged across trials and normalized to body mass. Unsupervised machine learning was applied to identify NPP, with cluster quality evaluated across combinations of dimensionality reduction and clustering algorithms. Between-cluster differences were quantified using one-way ANOVA with significance defined as P < 0.05 and effect-size calculated as eta_p^2. RESULTS: Combining principal component analysis (PCA) with K-means clustering resulted in the highest cluster quality (Silhouette = 0.45, Davies–Bouldin = 0.82, Calinski–Harabasz = 75.87). PCA revealed five factors with an Eigenvalue > 1, explaining 86.96% of variance in the dataset. Three distinct NPP were identified: (1) high force / high restitution profile, characterized by rapid force production and high stiffness; (2) low velocity / high impulse profile, relying on prolonged, concentric-dominant force application; and (3) a low stiffness / low power profile, with low rate of force development. Clusters differed significantly in CMJ jump height, DJ reactive strength index and ankle and hip-push-test relative peak vertical force (p<0.001; eta_p^2= [0.36, 0.54]). CONCLUSION: Even within a homogenous cohort of elite sprinters, three distinct NPP were evident, highlighting meaningful inter-cluster differences in physical capacities. Acknowledging these distinct NPP may help explaining previously reported inconsistent associations between isolated metrics and sprint performance, while enabling researcher and coaches to identify cluster-specific performance patterns and establish NPP-specific benchmarks to guide training and rehabilitation. REFERENCES: 1) Healy et al. (2019) J Strength Cond Res 2) Loturco et al. (2019) J Strength Cond Res 3) Ryan et al. (2025) Phys Ther Sport
Read CV Jonas KleinECSS Paris 2023: OP-AP22