ECSS Paris 2023: CP-BM12
INTRODUCTION: The influence of anthropometry and running biomechanics on running economy in recreational female runners is unclear, with previous investigations on this topic involving small homogeneous groups of runners and only examining a limited set of anthropometric or biomechanical measurements [1]. As such, this study aims to investigate the relationships between anthropometry and lower extremity biomechanics with running economy within a homogenous sample of female runners. METHODS: Twenty-six female recreational runners (age 34.7 ± 9.4yrs), performed a standardised incremental running protocol on a force-sensing treadmill (4-minute stages at 8km/h, 10km/h, and 12km/h). During the final minute of each stage ground contact time (GCT), flight time, stride length (SL), stride frequency (SF), leg stiffness and vertical oscillation (VO) were collected and compared to respiratory gases to determine the submaximal (<1.00 respiratory exchange ratio) locomotory cost (LEc) of running. Whole body and segmental anthropometry were also obtained using dual-energy x-ray absorptiometry and correlated to LEc. RESULTS: Absolute LEc (ABSLEc) was positively associated with 8 out of 9 anthropometric variables in the first two stages of the running protocol and 7 out of 9 during the final stage. Only SL and VO out of the six biomechanical variables were positively associated with ABSLEc at 8 and 10km/h, respectively. Relative LEc (RELLEc) was positively correlated with body mass (BM) and height during the first stage. SL showed significant correlations with RELLEc at 8km/h (R=0.401; P=0.03). At faster velocities, significant positive and negative correlations were identified between GCT (R=542 (10km/h); R=0.451 (12km/h) and SF (R=-0.545 (10km/h); R=-0.407 (12km/h), respectively. Backward multiple linear regression analysis revealed four anthropometric measures (BM, bone mass, leg fat mass, and fat-free mass), plus VO at 12km/h, explained most variance in ABSLEc (R2=0.742 (8km/h); R2=0.704 (10km/h); R2=0.433 (12km/h). In addition, regression analysis suggested that BM, height, and SL explained variance in RELLEc at a slower velocity (R2=0.162, (8km/h). At faster running speeds, combined effect of GCT and SF explained a small variation in RELLEc (R2=0.137, (10km/h); R2=0.075, (12km/h). CONCLUSION: This study presents novel evidence that anthropometric and body composition factors explain a substantial proportion of the variance in running economy compared to running biomechanics alone among recreational female runners, maintaining a slim physique, minimising GCT, and increasing SF result in a lower LEc. These findings indicate that coaches and practitioners working with female recreational runners should optimise individual anthropometrics beneficial for running, while concurrently addressing specific spatiotemporal variables to enhance running economy, and distance running performance. 1) Van Hooren B et al., Sports Medicine, 2024
Read CV Billy SeningtonECSS Paris 2023: CP-BM12
INTRODUCTION: Foot posture is a risk factor for running-related injuries (RRIs). In a case-control study, the odds of RRIs were 20 times higher for highly pronated foot than for neutral foot [1]. Although differences in foot kinetics according to foot posture may be a contributing factor, it is unclear whether foot kinetics differ between the neutral and highly pronated foot during running. The purpose of the present study was to compare multi-segmental foot kinetics during running between neutral foot and highly pronated foot groups. METHODS: Nine females running with mid- and forefoot strike participated in this study. Foot posture was evaluated using the Foot Posture Index (FPI) [2]. Total score ranges from -12 to +12. Four participants were classified as neutral foot (0 to +5) and five participants were classified as highly pronated foot (+10 to +12) according to the FPI score. Participants were attached to reflective markers and plantar pressure sensor and asked to run at 3.3 m/s ± 10% on a 10-m runway. We synchronously collected plantar pressure, ground reaction force, and kinematics including shank, rear-, and forefoot during the running task. Ankle and midfoot moments were calculated from distal to proximal using the Newton-Euler equation. Joint power was calculated as the scalar product of the joint moment and angular velocity. Joint work was calculated as the time integration of the joint power. Independent t-test or Mann-Whitney U test was used to compare peak moment, peak power (positive and negative), and work (positive and negative) in sagittal plane at ankle and midfoot between two groups (α = 0.05). RESULTS: The FPI scores of the neutral foot and the highly pronated foot groups were 0.5 ± 0.6 and 10.4 ± 0.5, respectively. There were no differences in demographics, running speed, and stance time between groups. The neutral foot showed significantly larger peak positive power and negative work at ankle compared to the highly pronated foot (8.77 ± 2.26 W/kg vs. 5.99 ± 1.00 W/kg; p = 0.04, -0.50 ± 0.13 J/kg vs. -0.31 ± 0.11 J/kg; p = 0.05). CONCLUSION: The neutral foot demonstrated large power absorption and efficient power generation at ankle. The neutral foot showed a greater peak dorsiflexion angle at ankle than the pronated foot during gait [3]. Thus, the increase of dorsiflexion angular velocity in the neutral foot appears to result in large negative work. The large positive power in the neutral foot could be explained by the increase of plantarflexion angular velocity at rearfoot. The neutral foot has a higher arch stiffness than the pronated foot [4], which could promote efficient propulsive force generation at ankle by elevating the arch immediately. This study emphasizes the need to consider changes in foot kinetics associated with foot posture. [1] Pérez-Morcillo, A. et al., Clin Biomech 61: 217–221, 2019 [2] Redmond, A. C et al., Clin Biomech 21: 89-98, 2006 [3] Marouvo, J. et al., Appl Sci 11: 7077, 2021 [4] Zifchock, R. A. et al., Foot Ankle Int 25: 367–372, 2006
Read CV Tomohito NakatsugawaECSS Paris 2023: CP-BM12
INTRODUCTION: Sprint performance is enhanced when the bodys muscular system functions as an integrated unit, with particular emphasis on the lower limb muscles that drive the sprinting action. Therefore, it is essential to evaluate lower limb muscle activity during sprinting. Surface electromyography has been widely used to measure lower limb muscle activities during sprinting (Kuitunen et al. 2002, Kakehata et al. 2022); however, this technique is limited by its inability to measure deep muscle activities and distinguish between muscle crosstalk. To address these limitations, T2-weighted magnetic resonance imaging (MRI) can be used as an alternative method for assessing lower limb muscle activity during sprinting (Yoshimoto et al. 2022). This study aimed to determine the distribution of thigh muscle activity during sprinting using T2-weighted MRI. METHODS: Twelve male sprinters (age: 20.3±1.5 years, body height: 173.8±6.0 cm, body mass: 66.9±4.8 kg, 100-m personal best time: 10.89±0.39 sec) completed a repeated sprint protocol consisting of three 60-m sprints, each performed at maximal effort, with 2-min rest intervals. Before and after the sprint protocol, 15 axial T2-weighted images, covering a total of 30 cm of the thigh (i.e., 15 cm proximal and distal to the mid-thigh), were acquired using a 3T MRI system. The distribution of thigh muscle activity was assessed based on pre-post changes in T2 values for 13 thigh muscles, and site-specific differences in the T2 value changes within each muscle. A paired t-test and one-way repeated ANOVA with a Bonferroni post-hoc test were conducted to determine the pre-post changes and site-specific differences. RESULTS: T2 values significantly increased in the rectus femoris, sartorius, adductor longus, adductor magnus, gracilis, semimembranosus, semitendinosus, and both the long and short heads of the biceps femoris after the sprint protocol (corrected P<0.05). Site-specific differences were observed, with a greater increase in T2 in the proximal region of the long head of the biceps femoris compared to the middle and distal regions (P<0.05). In contrast, T2 values increased more in the middle and distal regions of the semitendinosus than in its proximal region (P<0.05). T2 changes were significantly higher in the middle region of the semimembranosus than in the distal region (P<0.001). CONCLUSION: Our findings indicate that sprinting induces non-uniform muscle activation in the thigh, with certain regions demonstrating greater involvement. Notably, the site-specific differences observed in the hamstrings indicate varying regional muscle engagement. These results highlight the importance of evaluating both whole-muscle and regional activation patterns using T2, providing valuable insights for enhancing sprint performance and preventing sports injuries. References: [1] Kuitunen et al. Med Sci Sports Exerc 34:166-173, 2002. [2] Kakehata et al. Med Sci Sports Exerc 54:1002-1012, 2022. [3] Yoshimoto et al. Int J Sports Physiol Perform 17:774-779, 2022.
Read CV Haruto AraiECSS Paris 2023: CP-BM12