Sex differences in muscle morphology between male and female sprinters.

There is a marked difference between males and females in sprint running performance, yet a comprehensive investigation of sex differences in the muscle morphology of sprinters, which could explain the performance differences, remains to be completed. This study compared muscle volumes of 23 individual leg muscles and 5 functional muscle groups, assessed with 3 T magnetic resonance imaging, between male (n = 31) and female (n = 22) sprinters, as well as subgroups of elite males (EM, n = 5), elite females (EF, n = 5), and performance-matched (to elite females) males (PMMEF, n = 6). Differences in muscle volume distribution between EM, EF, and unathletic male (UM) controls were also assessed. For the full cohorts, male sprinters were more muscular than their female counterparts, but the differences were nonuniform and anatomically variable, with the largest differences in the hip extensors and flexors. However, among elite sprinters the sex differences in the volume of the functional muscle groups were almost uniform (absolute volume +47-53%), and the muscle volume distribution of EM was more similar to EF than to UM (P < 0.039). For PMMEF, relative hip extensor volume, but not stature or percent body fat, differentiated for performance (PMMEF and EF < EM) rather than sex. In conclusion, although the full cohorts of sprinters showed a marked sex difference in the amount and distribution of muscle mass, elite sprinters appeared to be selected for a common muscle distribution phenotype that for these elite subgroups was a stronger effect than that of sex. Relative hip extensor muscle volume, rather than stature, percent body fat, or total relative muscle volume, appeared to be the primary determinant of the sex difference in performance.NEW & NOTEWORTHY We present novel evidence suggesting muscle volume, specifically relative hip extensor volume, may be a primary deterministic variable for the sex difference in sprint performance, such that with matched sprint times, male and female sprinters may be expected to have equivalent muscle morphology. We highlight striking similarities in distribution of leg muscle mass between elite male and female sprinters and provide evidence for the existence of a muscular distribution phenotype specific to elite sprinters, irrespective of sex.

The Muscle Morphology of Elite Female Sprint Running.

INTRODUCTION: A paucity of research exists examining the importance of muscle morphological and functional characteristics for elite female sprint performance. PURPOSE: This study aimed to compare lower body muscle volumes and vertical jumping power between elite and subelite female sprinters and assess the relationships of these characteristics with sprint race and acceleration performance. METHODS: Five elite (100 m seasons best [SBE 100 ], 11.16 ± 0.06 s) and 17 subelite (SBE 100 , 11.84 ± 0.42 s) female sprinters underwent: 3T magnetic resonance imaging to determine the volume of 23 individual leg muscles/compartments and five functional muscle groups; countermovement jump and 30 m acceleration tests. RESULTS: Total absolute lower body muscle volume was higher in elite versus subelite sprinters (+15%). Elite females exhibited greater muscle volume of the hip flexors (absolute, +28%; relative [to body mass], +19%), hip extensors (absolute, +22%; relative, +14%), and knee extensors (absolute, +21%), demonstrating pronounced anatomically specific muscularity, with relative hip flexor volume alone explaining 48% of sprint performance variability. The relative volume of five individual muscles (sartorius, gluteus maximus, adductor magnus, vastus lateralis, illiopsoas) were both distinct between groups (elite > subelite) and related to SBE 100 ( r = 0.553-0.639), with the combination of the sartorius (41%) and the adductor magnus (17%) explaining 58% of the variance in SBE 100 . Elite female sprinters demonstrated greater absolute countermovement jump power versus subelite, and absolute and relative power were related to both SBE 100 ( r = -0.520 to -0.741) and acceleration performance ( r = 0.569 to 0.808). CONCLUSIONS: This investigation illustrates the distinctive, anatomically specific muscle volume distribution that facilitates elite sprint running in females, and emphasizes the importance of hip flexor and extensor relative muscle volume.

The influence of swing leg technique on maximum running speed.

The motion of the swing leg of elite sprinters at maximum speed is markedly different from that of slower sprinters, but the mechanisms by which this difference influences performance are unknown. The aim of this study was to establish whether and, if so, how the motion of the swing leg influences maximum achievable running speed using computer simulation. A seven-segment planar computer model was constructed to simulate the stance phase of sprinting. Optimisation was used to maximise the running speed of the model using two different swing leg techniques, one representative of an elite sprint athlete, and the other of a sub-elite athlete. The maximum speed of the model increased when using the swing leg technique of the elite athlete compared with the technique of the sub-elite athlete (10.2 m s-1 vs 9.3 m s-1). This improvement in performance was due to greater horizontal displacement of the mass centre during stance (0.861 m vs 0.814 m), and an increase in average vertical ground force of 51 N (0.06 bodyweights). The increase in vertical force was due to a larger impact peak caused by more negative vertical momentum of the stance leg at touchdown, and subsequently greater torques in the joints of the stance leg which were placed in faster eccentric conditions and at angles closer to optimum during the first half of stance. It is likely that force increases in early stance associated with swing leg technique contribute to the asymmetrical vertical ground reaction force traces observed in elite sprinters.

The correlation of force-velocity-power relationship of a whole-body movement with 20 m and 60 m sprint performance.

Sprinting ability is important for successful performance in sports. The aim of this study was to examine the correlation between force-velocity-power relationship of a whole-body movement and sprint performance. Twelve male participants performed maximal squat jumps with additional loads ranging from 0% to 100% body weight to obtain force-velocity profiles. The mean force and velocity were calculated during the push-off phase for each jump, which resulted in a force-velocity curve. The theoretical maximal force (F0), theoretical maximal velocity (V0) and theoretical maximum power (P0) were computed via extrapolation of the force and velocity data. In the second session, participants performed two 60 m sprints and the time to cover 20 m (t20), time to cover 60 m (t60), and maximum sprint velocity (Vmax) were calculated from the best 60 m trial. Correlation analyses revealed strong and significant correlations between V0 and t20 (r = -0.60), V0 and t60 (r = -0.60), P0 and t20 (r = -0.75) and P0 and t60 (r = -0.78). Multiple linear regression indicated that P0 explained 56%, 61% and 60% of the variability in t20, t60 and Vmax, respectively. Our results emphasise the importance of developing power production capabilities to improve sprint performance.

Neuromechanics of Middle-Distance Running Fatigue: A Key Role of the Plantarflexors?

PURPOSE: This study aimed to investigate the changes in lower limb kinematics, kinetics, and muscle activation during a high-intensity run to fatigue (HIRF). METHODS: Eighteen male and female competitive middle-distance runners performed a HIRF on an instrumented treadmill at a constant but unsustainable middle-distance speed (~3 min) based on a preceding maximum oxygen uptake (V˙O2max) test. Three-dimensional kinematics and kinetics were collected and compared between the start, 33%, 67%, and the end of the HIRF. In addition, the activation of eight lower limb muscles of each leg was measured with surface EMG (sEMG). RESULTS: Time to exhaustion was 181 ± 42 s. By the end of the HIRF (i.e., vs the start), ground contact time increased (+4.0%), whereas flight time (-3.2%), peak vertical ground reaction force (-6.1%), and vertical impulse (-4.1%) decreased (all P < 0.05), and joint angles at initial contact became more (dorsi)flexed (ankle, +1.9°; knee, +2.1°; hip, +3.6°; all P < 0.05). During stance, by the end of the HIRF: peak ankle plantarflexion moment decreased by 0.4 N·m·kg-1 (-9.0%), whereas peak knee extension moment increased by 0.24 N·m·kg-1 (+10.3%); similarly, positive ankle plantarflexion work decreased by 0.19 J·kg-1 (-13.9%), whereas positive knee extension work increased by 0.09 J·kg-1 (+33.3%; both P < 0.05) with no change in positive hip extension work. Hip extensor surface EMG amplitude increased during the late swing phase (+20.9-37.3%; P < 0.05). CONCLUSION: Running at a constant middle-distance pace led primarily to the fatigue of the plantarflexors with a compensatory increase in positive work done at the knee. Improving the fatigue resistance of the plantarflexors might be beneficial for middle-distance running performance.

The Muscle Morphology of Elite Sprint Running.

PURPOSE: This study aimed to investigate the differences in muscle volumes and strength between male elite sprinters, sub-elite sprinters, and untrained controls and to assess the relationships of muscle volumes and strength with sprint performance. METHODS: Five elite sprinters (100-m season's best equivalent [SBE100], 10.10 ± 0.07 s), 26 sub-elite sprinters (SBE100, 10.80 ± 0.30 s), and 11 untrained control participants underwent 1) 3-T magnetic resonance imaging scans to determine the volume of 23 individual lower limb muscles/compartments and 5 functional muscle groups and 2) isometric strength assessment of lower body muscle groups. RESULTS: Total lower body muscularity was distinct between the groups (controls < sub-elite +20% < elite +48%). The hip extensors exhibited the largest muscle group differences/relationships (elite, +32% absolute and +15% relative [per kg] volume, vs sub-elite explaining 31%-48% of the variability in SBE100), whereas the plantarflexors showed no differences between sprint groups. Individual muscle differences showed pronounced anatomical specificity (elite vs sub-elite absolute volume range, +57% to -9%). Three hip muscles were consistently larger in elite vs sub-elite (tensor fasciae latae, sartorius, and gluteus maximus; absolute, +45%-57%; relative volume, +25%-37%), and gluteus maximus volume alone explained 34%-44% of the variance in SBE100. The isometric strength of several muscle groups was greater in both sprint groups than controls but similar for the sprint groups and not related to SBE100. CONCLUSIONS: These findings demonstrate the pronounced inhomogeneity and anatomically specific muscularity required for fast sprinting and provides novel, robust evidence that greater hip extensor and gluteus maximus volumes discriminate between elite and sub-elite sprinters and are strongly associated with sprinting performance.

The Anthropometry of Economical Running.

The influence of anthropometry and body composition on running economy is unclear, with previous investigations involving small relatively homogeneous groups of runners and limited anthropometric/composition measurements. PURPOSE: To comprehensively investigate the relationships of anthropometry and body composition with running economy within a large heterogeneous sample of runners. METHODS: Eighty-five runners (males [M], n = 45; females [F], n = 40), of diverse competitive standard, performed a discontinuous protocol of incremental treadmill running (4-min stages, 1 km·h increments) to establish locomotory energy cost (LEc) of running at submaximal speeds (averaged across 10-12 km·h; the highest common speed < lactate turnpoint). Measurements of anthropometry, including segment lengths, perimeters, masses and moments of inertia, and body composition were obtained using tape-based measurements and dual-energy x-ray absorptiometry. RESULTS: Absolute LEc (ABSLEc, kcal·km) was positively correlated with 21 (of 27) absolute anthropometric variables in both male and female cohorts. Multiple-regression analyses revealed that one variable (mean perimeter z score) explained 49.4% (M) and 68.9% (F) of the variance in ABSLEc. Relative LEc (RELLEc, kcal·kg·km) was also correlated with five (M) and seven (F) normalized anthropometric variables, and regression analyses explained 31.6% (M; percentage bone mass and normalized hip perimeter) and 33.3% (F, normalized forearm perimeter) of the variance in RELLEc. CONCLUSIONS: These findings provide novel and robust evidence that anthropometry and body composition variables, predominantly indicative of relative slenderness, explain a considerable proportion of the variance in running economy (i.e., more slender, lower energy cost). We, therefore, recommend that runners and coaches are attentive to relative slenderness in selecting and training athletes with the aim of enhancing running economy, and improving distance running performance.

Is There an Optimal Speed for Economical Running?

The influence of running speed and sex on running economy is unclear and may have been confounded by measurements of oxygen cost that do not account for known differences in substrate metabolism, across a limited range of speeds, and differences in performance standard. Therefore, this study assessed the energy cost of running over a wide range of speeds in high-level and recreational runners to investigate the effect of speed (in absolute and relative terms) and sex (men vs women of equivalent performance standard) on running economy. To determine the energy cost (kcal · kg-1 · km-1) of submaximal running, speed at lactate turn point (sLTP), and maximal rate of oxygen uptake, 92 healthy runners (high-level men, n = 14; high-level women, n = 10; recreational men, n = 35; recreational women, n = 33) completed a discontinuous incremental treadmill test. There were no sex-specific differences in the energy cost of running for the recreational or high-level runners when compared at absolute or relative running speeds (P > .05). The absolute and relative speed-energy cost relationships for the high-level runners demonstrated a curvilinear U shape with a nadir reflecting the most economical speed at 13 km/h or 70% sLTP. The high-level runners were more economical than the recreational runners at all absolute and relative running speeds (P < .05). These findings demonstrate that there is an optimal speed for economical running, there is no sex-specific difference, and high-level endurance runners exhibit better running economy than recreational endurance runners.

Running Technique is an Important Component of Running Economy and Performance.

Despite an intuitive relationship between technique and both running economy (RE) and performance, and the diverse techniques used by runners to achieve forward locomotion, the objective importance of overall technique and the key components therein remain to be elucidated. PURPOSE: This study aimed to determine the relationship between individual and combined kinematic measures of technique with both RE and performance. METHODS: Ninety-seven endurance runners (47 females) of diverse competitive standards performed a discontinuous protocol of incremental treadmill running (4-min stages, 1-km·h increments). Measurements included three-dimensional full-body kinematics, respiratory gases to determine energy cost, and velocity of lactate turn point. Five categories of kinematic measures (vertical oscillation, braking, posture, stride parameters, and lower limb angles) and locomotory energy cost (LEc) were averaged across 10-12 km·h (the highest common velocity < velocity of lactate turn point). Performance was measured as season's best (SB) time converted to a sex-specific z-score. RESULTS: Numerous kinematic variables were correlated with RE and performance (LEc, 19 variables; SB time, 11 variables). Regression analysis found three variables (pelvis vertical oscillation during ground contact normalized to height, minimum knee joint angle during ground contact, and minimum horizontal pelvis velocity) explained 39% of LEc variability. In addition, four variables (minimum horizontal pelvis velocity, shank touchdown angle, duty factor, and trunk forward lean) combined to explain 31% of the variability in performance (SB time). CONCLUSIONS: This study provides novel and robust evidence that technique explains a substantial proportion of the variance in RE and performance. We recommend that runners and coaches are attentive to specific aspects of stride parameters and lower limb angles in part to optimize pelvis movement, and ultimately enhance performance.

A kinematic algorithm to identify gait events during running at different speeds and with different footstrike types.

Although a number of algorithms exist for estimating ground contact events (GCEs) from kinematic data during running, they are typically only applicable to heelstrike running, or have only been evaluated at a single running speed. The purpose of this study was to investigate the accuracy of four kinematics-based algorithms to estimate GCEs over a range of running speeds and footstrike types. Subjects ran over a force platform at a range of speeds; kinetic and kinematic data was captured at 1000Hz, and kinematic data was downsampled to 250Hz. A windowing process initially identified reduced time windows containing touchdown and toe-off. Algorithms based on acceleration and jerk signals of the foot markers were used to estimate touchdown (2 algorithms), toe-off (2 algorithms), and ground contact time (GCT) (4 algorithms), and compared to synchronous 'gold standard' force platform data. An algorithm utilising the vertical acceleration peak of either the heel or first metatarsal marker (whichever appeared first) for touchdown, and the vertical jerk peak of the hallux marker for toe-off, resulted in the lowest offsets (+3.1ms, 95% Confidence Interval (CI): -11.8 to +18.1ms; and +2.1ms, CI: -8.1 to +12.2ms respectively). This method also resulted in the smallest offset in GCT (-1.1ms, CI: -18.6 to +16.4ms). Offsets in GCE and GCT estimates from all algorithms were typically negatively correlated to running speed, with offsets decreasing as speed increased. Assessing GCEs and GCT using this method may be useful when a force platform is unavailable or impractical.

The effect of increasing strength and approach velocity on triple jump performance.

The triple jump is an athletic event comprising three phases in which the optimal phase ratio (the proportion of each phase to the total distance jumped) is unknown. This study used a planar whole body torque-driven computer simulation model of the ground contact parts of all three phases of the triple jump to investigate the effect of strength and approach velocity on optimal performance. The strength and approach velocity of the simulation model were each increased by up to 30% in 10% increments from baseline data collected from a national standard triple jumper. Increasing strength always resulted in an increased overall jump distance. Increasing approach velocity also typically resulted in an increased overall jump distance but there was a point past which increasing approach velocity without increasing strength did not lead to an increase in overall jump distance. Increasing both strength and approach velocity by 10%, 20%, and 30% led to roughly equivalent increases in overall jump distances. Distances ranged from 14.05m with baseline strength and approach velocity, up to 18.49m with 30% increases in both. Optimal phase ratios were either hop-dominated or balanced, and typically became more balanced when the strength of the model was increased by a greater percentage than its approach velocity. The range of triple jump distances that resulted from the optimisation process suggests that strength and approach velocity are of great importance for triple jump performance.

Optimisation of phase ratio in the triple jump using computer simulation.

The triple jump is an athletic event comprising three phases in which the optimal proportion of each phase to the total distance jumped, termed the phase ratio, is unknown. This study used a whole-body torque-driven computer simulation model of all three phases of the triple jump to investigate optimal technique. The technique of the simulation model was optimised by varying torque generator activation parameters using a Genetic Algorithm in order to maximise total jump distance, resulting in a hop-dominated technique (35.7%:30.8%:33.6%) and a distance of 14.05m. Optimisations were then run with penalties forcing the model to adopt hop and jump phases of 33%, 34%, 35%, 36%, and 37% of the optimised distance, resulting in total distances of: 13.79m, 13.87m, 13.95m, 14.05m, and 14.02m; and 14.01m, 14.02m, 13.97m, 13.84m, and 13.67m respectively. These results indicate that in this subject-specific case there is a plateau in optimum technique encompassing balanced and hop-dominated techniques, but that a jump-dominated technique is associated with a decrease in performance. Hop-dominated techniques are associated with higher forces than jump-dominated techniques; therefore optimal phase ratio may be related to a combination of strength and approach velocity.

Trade-offs between horizontal and vertical velocities during triple jumping and the effect on phase distances.

The triple jump is an athletic event involving three ground contact phases during which athletes must trade off the maintenance of horizontal velocity against the generation of vertical velocity. Previous studies have indicated that individual athletes have a linear relationship between the loss in horizontal velocity and the gain in vertical velocity during each phase. This study used computer simulation to investigate the effects of constraining the takeoff velocities in the hop phase on the velocity trade-offs in this and subsequent phases. Kinematic data were obtained from an entire triple jump using a Vicon automatic motion capture system, and strength and anthropometric data were collected from the triple jumper. A planar 13-segment torque-driven subject-specific computer simulation model was used to maximise the distance of each phase by varying torque generator activation timings using a genetic algorithm. Vertical takeoff velocities in the hop phase were constrained to be 100%, ±10%, ±20%, and ±30% of the performance velocity, and subsequent phases were optimised with initial conditions calculated from the takeoff of the previous phase and with no constraints on takeoff velocity. The results showed that the loss in horizontal velocity during each contact phase was strongly related to the vertical takeoff velocity (R(2)=0.83) in that phase rather than the overall gain in vertical velocity as found in previous studies. Maximum overall distances were achieved with step phases which were 30% of the total distance of the triple jump confirming the results of experimental studies on elite triple jumpers.