ECSS Paris 2023: OP-BM17
INTRODUCTION: Increasing step frequency typically improves running economy [1] and is associated with increased leg stiffness [2], which is often linked to shorter ground contact time [3]. Accordingly, reducing ground contact time may be considered an effective adjustment pattern when step frequency is increased. However, shortening ground contact time may increase the demand for the lower-limb stretch-shortening cycle (SSC) function, because runners must generate the same impulse in a shorter time window [4]. Thus, this study examined whether individual temporal adjustment patterns (stance vs. flight phase) in response to altered step frequency interact with SSC ability to determine the magnitude of running economy improvements. METHODS: Fifty-five endurance runners (35 males) completed measurement tests, including isokinetic ankle plantarflexion torques with and without countermovement, as well as squat, countermovement, and rebound jumps. These SSC-related functions were summarized using principal component analysis (PCA) into PC1 (jump performance), PC2 (simple isokinetic and slow countermovement plantarflexion torque), and PC3 (fast countermovement plantarflexion torque). Running kinematics and economy were measured at 11 km/h in females and 12 km/h in males under five step-frequency conditions ranging from -5% to +15% relative to baseline (5% increments). Linear mixed-effects models tested (1) associations of changes in duty factor (ground contact time relative to stride time) and leg stiffness with changes in running economy, and (2) interactions between duty factor and SSC-related functions (PC1–3) on running economy responses to altered step frequency. RESULTS: Running economy was optimized by increasing step frequency by 4.6% on average. A temporal adjustment pattern characterized by a relatively shorter flight phase (i.e., a higher duty factor) was associated with a reduced energy cost of running (β = -0.213, P = 0.005). Moreover, this effect depended on an interaction between duty factor and PC1 (β = 0.071, P = 0.023), indicating that runners with lower jump performance benefited more from shortening flight times than those with higher jump performance. In contrast, changes in leg stiffness showed a positive but non-significant association with those in energy cost (β = 0.396, P = 0.052), suggesting that increases in leg stiffness were likely detrimental to running economy. CONCLUSION: A temporal adjustment pattern that relatively shortens flight time rather than ground contact time was more effective for improving running economy during step-frequency increases. Furthermore, runners with lower jump performance gained greater benefits from shortening flight time. Collectively, temporal adjustment patterns in response to increased step-frequency are key determinants of running economy improvements, and their effectiveness may vary according to individual SSC ability. REFERENCES: [1] Connick et al. (2014) [2] Farley et al. (1996) [3] Morin et al. (2007) [4] Douglas et al. (2019)
Read CV Yuuri EiharaECSS Paris 2023: OP-BM17
INTRODUCTION: External resistance is widely used to manipulate running velocity and increase horizontal force production during sprint acceleration. It has been shown that both external resistance and speed influence hamstring and quadriceps activity (1). On the other hand, when comparisons are performed under velocity-matched conditions, only minor kinetic and kinematic differences are observed between resisted and unresisted sprinting (2), but little is known about how neuromuscular activation is affected. This study compared lower-limb muscle activation between resisted and unresisted sprinting performed at equivalent running velocities. METHODS: Thirty-seven elite athletes performed two trials under three conditions: a 40-m maximal sprint without resistance and two motorised resisted sprints corresponding to loads equivalent to 25% and 75% of body mass in a sled configuration. EMG activity was recorded in the quadriceps, hamstrings, and gluteal muscle groups. ANOVA and statistical parametric mapping (SPM) were used to analyse EMG activity during the stance and swing phases for specific steps selected according to running velocities corresponding to 20%, 40%, and 60% decreases in maximal unresisted velocity (VDEC). Comparisons were performed between unresisted and 25% resisted conditions at 20% VDEC, across all conditions at 40% VDEC, and between the 25% and 75% resisted conditions at 60% VDEC. RESULTS: No significant differences in EMG activity were observed at 40% and 60% VDEC between the resistance conditions. At high running velocity (20% VDEC), significant differences were found between the unresisted and 25% resisted condition during the swing phase. Greater activation for unresisted sprint was observed for the biceps femoris long head (90–100%), the rectus femoris (35–45%), the semimembranosus and the semitendinosus (55–85%), and the vastus lateralis (60–85%) (all p < 0.05). No effects were observed during the stance phase. CONCLUSION: In line with Macchi et al. (1), both resistance and running speed appear to influence lower-limb muscle activation. However, when running velocity was comparable, differences were limited and observed only at the highest speed during the swing phase. This suggests that velocity remains the primary determinant of neuromuscular demand, while resistance induces only minor, phase-specific adjustments. References Macchi et al. MSSE 2025 ;57:1530-1545. Da Silva et al. SJMSS 2025;35:e70174
Read CV Giuseppe RabitaECSS Paris 2023: OP-BM17
INTRODUCTION: Resisted sled sprinting (RSS) is widely used to enhance sprint acceleration and horizontal force production [1]. Current methods to individualize RSS loading typically rely on multiple-load load–velocity (L–v) profiling, which requires several resisted and unresisted maximal sprints [2,3]. While effective, this approach can be time-consuming, fatiguing, and impractical in high-performance environments. Simplified alternatives such as the 2-point method have shown validity in strength exercises, but their applicability to resisted sprinting remains unclear [4]. Therefore, this study aimed to examine and compare the concurrent validity of simplified 2-point and traditional multiple-point method to characterize individual resisted sprint load-velocity relationship and estimate maximal unloaded running velocity and maximal theoretical load. METHODS: Twenty-three international rugby sevens players were tested in two sessions separated by 48–72 h, performing in randomised order a multiple-load and a two-load resisted sprint protocol. The sprint L–v relationship was established using a multiple-point method (four loads corresponding to 25, 50, 75, and 100% of body mass [BM]) and a 2-point method (two loads at 25 and 75% BM). For each load, the peak velocity maintained for 2 s during maximal-effort sprinting was recorded and used to model the L–v relationship. Individual L–v relationships were obtained using full data from the multiple-point method and from both direct and indirect 2-point estimations. RESULTS: No significant differences were found between the multiple-point and 2-point methods for the estimation of L₀ and v₀. Trivial-to-small effect sizes (Cohen’s d ≤ 0.34), nearly perfect correlations (r ≥ 0.92), minor homoscedastic errors (r² ≤ 0.03), and no fixed or proportional bias according to ordinary least-products regression were observed between the standard method and both the direct and indirect 2-point methods. The level of agreement was consistent across participants, with low random error, indicating that the simplified 2-point method can provide comparable L–v profiling outcomes. CONCLUSION: In habituated athletes, the direct 2-point method is a practical tool for routinely assessing the L-v relationship during resisted sled sprinting and estimating both v0 and L0. This method offers several advantages over the standard multiple-load approach, including reduced testing time, lower fatigue, and decreased physical burden from unresisted sprints. Therefore, when appropriately implemented, the direct 2-point method can facilitate regular monitoring, estimation of maximal velocity from the L–v relationship, and individualized load prescription in applied sprint training settings. References 1. Petrakos G, Morin JB, Egan B. Sports Med. 2016;46(3):381-400. 2. Cross MR, Samozino P, Brown SR, Morin JB. Eur J Appl Physiol. 2018;118(3):563-571. 3. Samozino P, Rabita G, Dorel S, et al. Scand J Med Sci Sport. 2016;26(6):648-658. 4. García-Ramos A. Int J Sports Physiol Perform. 2023
Read CV Pedro Jimenez-ReyesECSS Paris 2023: OP-BM17