Spring Mass Characteristics of the Fastest Men on Earth

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation

This study presents a novel approach to modeling the velocity-time curve in 100m sprinting by integrating machine learning algorithms. It critically addresses the limitations of traditional speed models, which often require extensive and intricate data collection, by proposing a more accessible and accurate method using fewer variables. The research utilized data from various international track events from 1987 to 2019. Two machine learning models, Random Forest (RF) and Neural Network (NN), were employed to predict the velocity-time curve, focusing on the acceleration phase of the sprint. The models were evaluated against the traditional exponential speed model using Mean Squared Error (MSE), with the NN model demonstrating superior performance. Additionally, the study explored the correlation between maximum velocity, the time of maximum velocity occurrence, the duration of the maximum speed phase, and the overall 100m sprint time. The findings indicate a strong negative correlation between maximum velocity and final time, offering new insights into the dynamics of sprinting performance. This research contributes significantly to the field of sports science, particularly in optimizing training and performance analysis in sprinting.

The influence of anthropometric parameters in track and field curve sprint

BACKGROUND

Poor information is available regarding real field data on the different factors that could have an influence on curve sprint and its association with anthropometric and strength parameters.

METHODS

We designed a crossover pilot-study that enrolled 14 track and field athletes of 200 and 400 m (8/14 men, age: 20.5±2.3 years, height: 1.73±0.06 m; body mass: 60.5±6.2 kg) that performed randomly in two different days assessment of anthropometric parameters, jump test by squat jump (SJ) and triple hop distance (THD), performance during a 20-m curve sprint (day 1), and assessment of 1RM for right and left limb on Bulgarian split squat (BSS) (day 2). The unpaired t test and Pearson's correlation were used for data analysis.

RESULTS

No statistical differences for anthropometric and strength parametric parameters between right and left lower limbs were observed. Twenty-meter curve sprints were negatively associated with body mass (P=0.0059, R=-0.7) and Body Mass Index (BMI; P=0.032, R=0.6). Moreover, a negative association was observed with SJ height (P=0.0025, R=-0.7), speed (P=0.0028; R=-0.7), strength (P=0.009, R=-0.7) and power (P=0.009, R=-0.7). Finally, 20-m curve sprint negatively correlated with right (P=0.0021, R=-0.7) and left (P<0.0001, R=-0.9) THD and 1 RM right (P=0.025, R=-0.6;) and left (P=0.0049, R=-0.7) BSS, respectively.

CONCLUSIONS

This pilot study demonstrated that 20-m curve sprint was negatively associated with body mass, BMI, vertical jump performance, THD and 1RM BSS. This information could be useful to coaches and sport scientists as a reference value to improve athlete performance for 200- and 400-m athletes.

Profiling elite male 100-m sprint performance: The role of maximum velocity and relative acceleration

PURPOSE

This study aimed to determine the accuracy of a 4 split time modelling method to generate velocity-time and velocity-distance variables in elite male 100-m sprinters and subsequently to assess the roles of key sprint parameters with respect to 100-m sprint performance. Additionally, this study aimed to assess the differences between faster and slower sprinters in key sprint variables that have not been assessed in previous work.

METHODS

Velocity-time and velocity-distance curves were generated using a mono-exponential function from 4 split times for 82 male sprinters during major athletics competitions. Key race variables-maximum velocity, the acceleration time constant (τ), and percentage of velocity lost (v_Loss)-were derived for each athlete. Athletes were divided into tertiles, based on 100-m time, with the first and third tertiles considered to be the faster and slower groups, respectively, to facilitate further analysis.

RESULTS

Modelled split times and velocities displayed excellent accuracy and close agreement with raw measures (range of mean bias was -0.2% to 0.2%, and range of intraclass correlation coefficients (ICCs) was 0.935 to 0.999) except for 10-m time (mean bias was 1.6% ± 1.3%, and the ICC was 0.600). The 100-m sprint performance time and all 20-m split times had a significant near-perfect negative correlation with maximum velocity (r ≥ -0.90) except for the 0 to 20-m split time, where a significantly large negative correlation was found (r = -0.57). The faster group had a significantly higher maximum velocity and τ (p < 0.001), and no significant difference was found for v_Loss (p = 0.085).

CONCLUSION

Coaches and researchers are encouraged to utilize the 4 split time method proposed in the current study to assess several key race variables that describe a sprinter's performance capacities, which can be subsequently used to further inform training.

Application of Leg, Vertical, and Joint Stiffness in Running Performance: A Literature Overview

Stiffness, the resistance to deformation due to force, has been used to model the way in which the lower body responds to landing during cyclic motions such as running and jumping. Vertical, leg, and joint stiffness provide a useful model for investigating the store and release of potential elastic energy via the musculotendinous unit in the stretch-shortening cycle and may provide insight into sport performance. This review is aimed at assessing the effect of vertical, leg, and joint stiffness on running performance as such an investigation may provide greater insight into performance during this common form of locomotion. PubMed and SPORTDiscus databases were searched resulting in 92 publications on vertical, leg, and joint stiffness and running performance. Vertical stiffness increases with running velocity and stride frequency. Higher vertical stiffness differentiated elite runners from lower-performing athletes and was also associated with a lower oxygen cost. In contrast, leg stiffness remains relatively constant with increasing velocity and is not strongly related to the aerobic demand and fatigue. Hip and knee joint stiffness are reported to increase with velocity, and a lower ankle and higher knee joint stiffness are linked to a lower oxygen cost of running; however, no relationship with performance has yet been investigated. Theoretically, there is a desired “leg-spring” stiffness value at which potential elastic energy return is maximised and this is specific to the individual. It appears that higher “leg-spring” stiffness is desirable for running performance; however, more research is needed to investigate the relationship of all three lower limb joint springs as the hip joint is often neglected. There is still no clear answer how training could affect mechanical stiffness during running. Studies including muscle activation and separate analyses of local tissues (tendons) are needed to investigate mechanical stiffness as a global variable associated with sports performance.

Assessment of Sprint Parameters in Top Speed Interval in 100 m Sprint-A Pilot Study Under Field Conditions

Improving performances in sprinting requires feedback on sprint parameters such as step length and step time. However, these parameters from the top speed interval (TSI) are difficult to collect in a competition setting. Recent advances in tracking technology allows to provide positional data with high spatio-temporal resolution. This pilot study, therefore, aims to automatically obtain general sprint parameters, parameters characterizing, and derived from TSI from raw speed. In addition, we propose a method for obtaining the intra-cyclic speed amplitude in TSI. We analyzed 32 100 m-sprints of 7 male and 9 female athletes (18.9 ± 2.8 years; 100 m PB 10.55-12.41 s, respectively, 12.18-13.31 s). Spatio-temporal data was collected with a radio-based position detection system (RedFIR, Fraunhofer Institute, Germany). A general velocity curve was fitted to the overall speed curve (vbase), TSI (upper quintile of vbase values) was determined and a cosine term was added to vbase within TSI (vcycle) to capture the cyclic nature of speed. This allowed to derive TSI parameters including TSI amplitude from the fitted parameters of the cosine term. Results showed good approximation for vbase (error: 5.0 ± 1.0%) and for vcycle (2.0 ± 1.0%). For validation we compared spatio-temporal TSI parameters to criterion values from laser measurement (speed) and optoelectric systems (step time and step length) showing acceptable RMSEs for mean speed (0.08 m/s), for step time (0.004 s), and for step length (0.03 m). Top speed interval amplitude showed a significant difference between males (mean: 1.41 m/s) and females (mean: 0.71 m/s) and correlations showed its independence from other sprint parameters. Gender comparisons for validation revealed the expected differences. This pilot study investigated the feasibility of estimating sprint parameters from high-quality tracking data. The proposed method is independent of the data source and allows to automatically obtain general sprint parameters and TSI parameters, including TSI amplitude assessed here for the first time in a competition-like setting.

Bouncing behavior of sub-four minute milers

Elite middle distance runners present as a unique population in which to explore biomechanical phenomena in relation to running speed, as their training and racing spans a broad spectrum of paces. However, there have been no comprehensive investigations of running mechanics across speeds within this population. Here, we used the spring-mass model of running to explore global mechanical behavior across speeds in these runners. Ten elite-level 1500 m and mile runners (mean 1500 m best: 3:37.3 ± 3.6 s; mile: 3:54.6 ± 3.9 s) and ten highly trained 1500 m and mile runners (mean 1500 m best: 4:07.6 ± 3.7 s; mile: 4:27.4 ± 4.1 s) ran on a treadmill at 10 speeds where temporal measures were recorded. Spatiotemporal and spring-mass characteristics and their corresponding variation were calculated within and across speeds. All spatiotemporal measures changed with speed in both groups, but the changes were less substantial in the elites. The elite runners ran with greater approximated vertical forces (+ 0.16 BW) and steeper impact angles (+ 3.1°) across speeds. Moreover, the elites ran with greater leg and vertical stiffnesses (+ 2.1 kN/m and + 3.6 kN/m) across speeds. Neither group changed leg stiffness with increasing speeds, but both groups increased vertical stiffness (1.6 kN/m per km/h), and the elite runners more so (further + 0.4 kN/m per km/h). The elite runners also demonstrated lower variability in their spatiotemporal behavior across speeds. Together, these findings suggested that elite middle distance runners may have distinct global mechanical patterns across running speeds, where they behave as stiffer, less variable spring-mass systems compared to highly trained, but sub-elite counterparts.

Rules of nature's Formula Run: Muscle mechanics during late stance is the key to explaining maximum running speed

The maximum running speed of legged animals is one evident factor for evolutionary selection-for predators and prey. Therefore, it has been studied across the entire size range of animals, from the smallest mites to the largest elephants, and even beyond to extinct dinosaurs. A recent analysis of the relation between animal mass (size) and maximum running speed showed that there seems to be an optimal range of body masses in which the highest terrestrial running speeds occur. However, the conclusion drawn from that analysis-namely, that maximum speed is limited by the fatigue of white muscle fibres in the acceleration of the body mass to some theoretically possible maximum speed-was based on coarse reasoning on metabolic grounds, which neglected important biomechanical factors and basic muscle-metabolic parameters. Here, we propose a generic biomechanical model to investigate the allometry of the maximum speed of legged running. The model incorporates biomechanically important concepts: the ground reaction force being counteracted by air drag, the leg with its gearing of both a muscle into a leg length change and the muscle into the ground reaction force, as well as the maximum muscle contraction velocity, which includes muscle-tendon dynamics, and the muscle inertia-with all of them scaling with body mass. Put together, these concepts' characteristics and their interactions provide a mechanistic explanation for the allometry of maximum legged running speed. This accompanies the offering of an explanation for the empirically found, overall maximum in speed: In animals bigger than a cheetah or pronghorn, the time that any leg-extending muscle needs to settle, starting from being isometric at about midstance, at the concentric contraction speed required for running at highest speeds becomes too long to be attainable within the time period of a leg moving from midstance to lift-off. Based on our biomechanical model, we, thus, suggest considering the overall speed maximum to indicate muscle inertia being functionally significant in animal locomotion. Furthermore, the model renders possible insights into biological design principles such as differences in the leg concept between cats and spiders, and the relevance of multi-leg (mammals: four, insects: six, spiders: eight) body designs and emerging gaits. Moreover, we expose a completely new consideration regarding the muscles' metabolic energy consumption, both during acceleration to maximum speed and in steady-state locomotion.

Time to Differentiate Postactivation "Potentiation" from "Performance Enhancement" in the Strength and Conditioning Community

Coaches and athletes in elite sports are constantly seeking to use innovative and advanced training strategies to efficiently improve strength/power performance in already highly-trained individuals. In this regard, high-intensity conditioning contractions have become a popular means to induce acute improvements primarily in muscle contractile properties, which are supposed to translate to subsequent power performances. This performance-enhancing physiological mechanism has previously been called postactivation potentiation (PAP). However, in contrast to the traditional mechanistic understanding of PAP that is based on electrically-evoked twitch properties, an increasing number of studies used the term PAP while referring to acute performance enhancements, even if physiological measures of PAP were not directly assessed. In this current opinion article, we compare the two main approaches (i.e., mechanistic vs. performance) used in the literature to describe PAP effects. We additionally discuss potential misconceptions in the general use of the term PAP. Studies showed that mechanistic and performance-related PAP approaches have different characteristics in terms of the applied research field (basic vs. applied), effective conditioning contractions (e.g., stimulated vs. voluntary), verification (lab-based vs. field tests), effects (twitch peak force vs. maximal voluntary strength), occurrence (consistent vs. inconsistent), and time course (largest effect immediately after vs. ~ 7 min after the conditioning contraction). Moreover, cross-sectional studies revealed inconsistent and trivial-to-large-sized associations between selected measures of mechanistic (e.g., twitch peak force) vs. performance-related PAP approaches (e.g., jump height). In an attempt to avoid misconceptions related to the two different PAP approaches, we propose to use two different terms. Postactivation potentiation should only be used to indicate the increase in muscular force/torque production during an electrically-evoked twitch. In contrast, postactivation performance enhancement (PAPE) should be used to refer to the enhancement of measures of maximal strength, power, and speed following conditioning contractions. The implementation of this terminology would help to better differentiate between mechanistic and performance-related PAP approaches. This is important from a physiological point of view, but also when it comes to aggregating findings from PAP studies, e.g., in the form of meta-analyses, and translating these findings to the field of strength and conditioning.

Breaking the athletics world record in the 100 and 400 meters: an alternative method for assessment

BACKGROUND

The top 10 athletes in the International Association of Athletic Federations in 100-m and 400-m ranking for each sex were assessed for their history of race times before achieving their personal record (PR). The main goal of this study was to create a new method for optimal performance improvement rate assessment for coaches and athletes aiming the World Record.

METHODS

The difference between PR ('current' season) and the best race time in the last season was defined as the first season improvement rate (1-SIR), whereas the average improvement rate in the last and preceding seasons was the multi-season improvement rate (M-SIR). 1-SIR and M-SIR were calculated for each athlete.

RESULTS

The sex comparison for the 100 m event showed a significant difference in the M-SIR in favor of women. No statistical differences were identified for the 400 m event, with a trivial effect in both 1-SIR and M-SIR.

CONCLUSIONS

As a practical applicability, graph plots were designed to help verifying the improvement rate of athletes and to evaluate whether a long-term training strategy induced an acceptable performance improvement or whether some adjustments needed and check within the plots if the improvement rate is within the average of the top-10 athletes of their event.

Time-course of running treadmill adaptation in novice treadmill runners

Studies on running biomechanics and energetics are usually conducted on a treadmill. To ensure that locomotion on a treadmill is comparable to locomotion overground, participants need to be expert in the use of the device. This study aimed to identify the number and duration of sessions needed to obtain stable measurements for spatiotemporal and metabolic parameters in unexperienced treadmill runners. Fourteen male recreational runners performed three 15-min treadmill running trials in different days at a submaximal speed. Spatiotemporal and metabolic parameters were registered at minutes: 5, 10, 15 and their within-trial and between-trial changes were analysed using a two-way repeated measures ANOVA and Bonferroni post-hoc test. Within-trial differences were found in step frequency (decreased over time), Step Length and Contact Time (increased), reaching stability at different time points. Ventilator parameters increased, reaching stability after 5-10 min, while heart rate increased progressively over time. The only between-trial differences were an increase in step length and a decrease in step frequency at min 1, between trials 1 and 3. In conclusion, at least three running trials of 15 min are required to familiarize with the device. The last 5 min of the third trial can be regarded as stable measurements.

How to run 50% faster without external energy

Technological innovations may enable next-generation running shoes to provide unprecedented mobility. But how could a running shoe increase the speed of motion without providing external energy? We found that the top speed of running may be increased more than 50% using a catapult-like exoskeleton device, which does not provide external energy. Our finding uncovers the hidden potential of human performance augmentation via unpowered robotic exoskeletons. Our result may lead to a new-generation of augmentation devices developed for sports, rescue operations, and law enforcement, where humans could benefit from increased speed of motion.

Prosthetic model, but not stiffness or height, affects maximum running velocity in athletes with unilateral transtibial amputations

The running-specific prosthetic (RSP) configuration used by athletes with transtibial amputations (TTAs) likely affects performance. Athletes with unilateral TTAs are prescribed C- or J-shaped RSPs with a manufacturer-recommended stiffness category based on body mass and activity level, and height based on unaffected leg and residual limb length. We determined how 15 different RSP model, stiffness, and height configurations affect maximum running velocity (v_max) and the underlying biomechanics. Ten athletes with unilateral TTAs ran at 3 m/s to v_max on a force-measuring treadmill. v_max was 3.8-10.7% faster when athletes used J-shaped versus C-shaped RSP models (p < 0.05), but was not affected by stiffness category, actual stiffness (kN/m), or height (p = 0.72, p = 0.37, and p = 0.11, respectively). v_max differences were explained by vertical ground reaction forces (vGRFs), stride kinematics, leg stiffness, and symmetry. While controlling for velocity, use of J-shaped versus C-shaped RSPs resulted in greater stance average vGRFs, slower step frequencies, and longer step lengths (p < 0.05). Stance average vGRFs were less asymmetric using J-shaped versus C-shaped RSPs (p < 0.05). Contact time and leg stiffness were more asymmetric using the RSP model that elicited the fastest v_max (p < 0.05). Thus, RSP geometry (J-shape versus C-shape), but not stiffness or height, affects v_max in athletes with unilateral TTAs.

How Do Spatiotemporal Parameters and Lower-Body Stiffness Change with Increased Running Velocity? A Comparison Between Novice and Elite Level Runners

This study aimed to examine the effect of running velocity on spatiotemporal parameters and lower-body stiffness of endurance runners, and the influence of the performance level on those adaptations. Twenty-two male runners (novice [NR], n = 12, and elite runners [ER], n = 10) performed an incremental running test with a total of 5 different running velocities (10, 12, 14, 16, 18 km/h). Each condition lasted 1 min (30 s acclimatization period, and 30 s recording period). Spatiotemporal parameters were measured using the OptoGait system. Vertical (Kvert) and leg (Kleg) stiffness were calculated according to the sine-wave method. A repeated measures ANOVA (2 x 5, group x velocities) revealed significant adaptations (p < 0.05) to increased velocity in all spatiotemporal parameters and Kvert in both NR and ER. ER showed a greater flight time (FT) and step angle (at 18 km/h) (p < 0.05), longer step length (SL) and lower step frequency (SF) (p < 0.05), whereas no between-group differences were found in contact time (CT) nor in the sub-phases during CT at any speed (p ≥ 0.05). ER also showed lower Kvert values at every running velocity (p < 0.05), and no differences in Kleg (p ≥ 0.05). In conclusion, lower SF and Kvert and, thereby, longer FT and SL, seem to be the main spatiotemporal characteristics of high-level runners compared to their low-level counterparts.

World-Class Male Sprinters and High Hurdlers Have Similar Start and Initial Acceleration Techniques

The effect of the inclusion of a high hurdle 13.72 m after the start line on elite sprint start and initial acceleration technique has yet to be investigated or understood. This highly novel study addresses that lack of information in an exceptional manner, through detailed biomechanical analysis of the world's best sprint and hurdle athletes, with data collected in situ at the 2018 IAAF World Indoor Championships, held in Birmingham, UK. High speed videos (150 Hz) were compared for eight sprinters and seven hurdlers for the start and initial acceleration phase of the finals of the men's 60 m and 60 m hurdles. Temporal and kinematic data were supplemented by vector coding analysis to investigate mechanisms by which these world-class athletes translate their centres of mass (CM) up to the fourth touchdown post-block exit. The sprinters and hurdlers coordinated their lower limb and trunk movement in a similar manner throughout the start and initial acceleration phases, which contributes new conceptual understanding of the mechanisms that underpin start and initial acceleration performance. Differences between groups were initiated from block set-up, with the hurdlers utilising a larger block spacing, but with the front block nearer to the start line than sprinters. Even after accounting for stature, the biggest differences in the raising of the CM occurred during the block phase, with hurdlers greater than sprinters (difference in vertical CM displacement scaled to stature = -0.037, very large effect size). Subsequent flight phases showed the biggest differences in the translation of the CM, in part due to longer flight times in the hurdlers, whilst the techniques of the two groups generally converged during the ground contact phases of initial acceleration. In highlighting that similar techniques are used by world-class sprinters and hurdlers, despite differing task constraints, this study has provided invaluable insights for scientists, coaches, and athletes, that will inform further developments in understanding and practice across both sprints and hurdles.

PREDICTION OF 100 METERS SPRINT PERFORMANCE BASED ON FIELD TEST

ABSTRACT Introduction: The 100-meter dash (100 m) event holds particular appeal. Coaches and researchers seek to understand the determinants of performance in this task. Although information has been produced over the years, it is not fully applied by coaches who generally assess the success of employed training methods through objective field tests, such as 60 m dash test performance. Objective: Investigate 100 m performance based on 60 m performance. Methods: Two hundred and forty six men and 153 women divided into two subgroups were evaluated for estimation (Fvalidation; n=123 and Mvalidation; n=204) and validation of predictive models (Fcross-validation; n=30 and Mcross-validation; n=42) for 100 m dash performance (time take to cover 100 m). Partial time was measured based on the 100 m distance marked previously every 10 meters from the starting line on both sides of the track. The predictive models were based on the interval in the 60 meters with a time interval of 10-10 m. Results: Magnitude of correlation was very high. High coefficients of determination and differences of no statistical significance (p <.001) were found between the criteria and predicted values. The predictive equations presented constant error values below 0.001s; total absolute error of 0.12s; 0.10s for Mvalidation and Fvalidation, respectively, and 1.13% and 0.85% of total relative error for Mvalidation and Fvalidation, respectively. Bland-Altman analysis showed an increase in the level of concordance between the criteria and predicted values of Fvalidation and Mvalidation. Similar responses were found when the proposed models were applied to Fcross-validation and Mcross-validation. Conclusion: The estimation models were able to accurately predict 100 m performance based on 60 m performance. Level of evidence: II; Diagnostic studies - Investigating a diagnostic test.

Sprint running: from fundamental mechanics to practice-a review

In this review, we examine the literature in light of the mechanical principles that govern linear accelerated running. While the scientific literature concerning sprint mechanics is comprehensive, these principles of fundamental mechanics present some pitfalls which can (and does) lead to misinterpretations of findings. Various models of sprint mechanics, most of which build on the spring-mass paradigm, are discussed with reference to both the insight they provide and their limitations. Although much research confirms that sprinters to some extent behave like a spring-mass system with regard to gross kinematics (step length, step rate, ground contact time, and lower limb deformation), the laws of motion, supported by empirical evidence, show that applying the spring-mass model for accelerated running has flaws. It is essential to appreciate that models are pre-set interpretations of reality; finding that a model describes the motor behaviour well is not proof of the mechanism behind the model. Recent efforts to relate sprinting mechanics to metabolic demands are promising, but have the same limitation of being model based. Furthermore, a large proportion of recent literature focuses on the interaction between total and horizontal (end-goal) force. We argue that this approach has limitations concerning fundamental sprinting mechanics. Moreover, power analysis based on isolated end-goal force is flawed. In closing, some prominent practical concepts and didactics in sprint running are discussed in light of the mechanical principles presented. Ultimately, whereas the basic principles of sprinting are relatively simple, the way an athlete manages the mechanical constraints and opportunities is far more complex.

How does the slope gradient affect spatiotemporal parameters during running? Influence of athletic level and vertical and leg stiffness

BACKGROUND

The current evidence leaves certain questions unanswered, including whether well-trained athletes adapt to different slope gradients in the same way as amateurs, and whether stiffness influences spatiotemporal adaptations during uphill running.

RESEARCH QUESTION

This study aimed to determine the effect of different slope gradients (0%-11%) on spatiotemporal gait characteristics during running, taking into account the influence of athletic level, vertical and leg stiffness.

METHODS

Male endurance runners (12 amateurs, 10 highly-trained) performed a running test on a motorized treadmill. The running velocity was set at 12 km/h, and participants completed six different running conditions (0, 3, 5, 7, 9 and 11% gradients). Spatiotemporal parameters were measured using the OptoGait system. Vertical (Kvert) and leg (Kleg) stiffness were calculated according to the sine-wave method.

RESULTS

A 2 (amateur; highly-trained) × 6 (running conditions) ANOVA found no significant between-group differences in spatiotemporal parameters at any gradient (P ≥ 0.05); however, significant Kvert and Kleg differences (P < 0.05) were found within both groups with increasing gradients. Stepwise linear regression analysis showed that Kleg was strongly associated with contact time (R²  = 0.797, P < 0.001), whereas Kvert was associated with spatiotemporal adaptations to different slope gradients (R²  = 0.547, P = 0.002).

SIGNIFICANCE

An increased slope gradient (0-11%) at a given running velocity (12 km.h^-1) caused spatiotemporal adaptations (i.e., increased CT and SF and decreased FT, SL and SA) regardless of the athletic level of the runner, although a non-significant trend differentiated the adaptations between the amateur and highly-trained groups. The results also indicated that leg stiffness plays a key role in the characteristics of spatiotemporal gait during level running, whereas vertical stiffness is strongly associated with spatiotemporal adaptations when running uphill.

Lower limb stiffness testing in athletic performance: a critical review

Stiffness describes the resistance of a body to deformation. In regard to athletic performance, a stiffer leg-spring would be expected to augment performance by increasing utilisation of elastic energy. Two-dimensional spring-mass and torsional spring models can be applied to model whole-body (vertical and/or leg stiffness) and joint stiffness. Various tasks have been used to characterise stiffness, including hopping, gait, jumping, sledge ergometry and change of direction tasks. Appropriate levels of reliability have been reported in most tasks, although they vary between investigations. Vertical stiffness has demonstrated the strongest reliability across tasks and may be more sensitive to changes in high-velocity running performance than leg stiffness. Joint stiffness demonstrates the weakest reliability, with ankle stiffness more reliable than knee stiffness. Determination of stiffness has typically necessitated force plate analyses; however, validated field-based equations permit determination of whole-body stiffness without force plates. Vertical, leg and joint stiffness measures have all demonstrated relationships with performance measures. Greater stiffness is typically demonstrated with increasing intensity (i.e., running velocity or hopping frequency). Greater stiffness is observed in athletes regularly subjecting the limb to high ground reaction forces (i.e., sprinters). Careful consideration should be given to the most appropriate assessment of stiffness on a team/individual basis.

Effects of a Body-Weight Supporting Kite on Sprint Running Kinematics in Well-Trained Sprinters

Data of elite sprinters indicate that faster athletes realize shorter ground contact times compared with slower individuals. Furthermore, the importance of the so-called "front side mechanics" for elite sprint performance is frequently emphasized by researchers and coaches. Recently, it was demonstrated that using a body-weight supporting kite during full-effort sprints in highly trained sprinters leads to a reduction in ground contact time. The aim of this study was to investigate possible negative effects of this body-weight supporting device on sprint running kinematics, which was not clarified in previous studies. Eleven well-trained Austrian sprinters performed flying 20-m sprints under 2 conditions: (a) free sprint (FS); and (b) body-weight supported sprint (BWS). Sprint cycle characteristics were recorded during the high-speed phase by a 16 camera 3D-system (Vicon), an optical acquisition system (Optojump-next), and a high-speed camera. Paired sample t-tests and Cohen's d effect size were used to determine differences between sprinting conditions. Compared with FS, BWS caused a decrease in ground contact time by 5.6% and an increase in air time by 5.5% (both p < 0.001), whereas stride length and rate remained unchanged. Furthermore, a reduced hip joint extension at and after take-off, an increased maximal hip joint flexion (i.e., high knee position), and a smaller horizontal distance of the touchdown to the center of gravity could be observed (all p < 0.01). These results indicate no negative effects on front side mechanics during BWS and that sprinting with a body-weight supporting kite seems to be a highly specific method to reduce ground contact time in well-trained sprinters.

On the Existence of Step-To-Step Breakpoint Transitions in Accelerated Sprinting

Accelerated running is characterised by a continuous change of kinematics from one step to the next. It has been argued that breakpoints in the step-to-step transitions may occur, and that these breakpoints are an essential characteristic of dynamics during accelerated running. We examined this notion by comparing a continuous exponential curve fit (indicating continuity, i.e., smooth transitions) with linear piecewise fitting (indicating breakpoint). We recorded the kinematics of 24 well trained sprinters during a 25 m sprint run with start from competition starting blocks. Kinematic data were collected for 24 anatomical landmarks in 3D, and the location of centre of mass (CoM) was calculated from this data set. The step-to-step development of seven variables (four related to CoM position, and ground contact time, aerial time and step length) were analysed by curve fitting. In most individual sprints (in total, 41 sprints were successfully recorded) no breakpoints were identified for the variables investigated. However, for the mean results (i.e., the mean curve for all athletes) breakpoints were identified for the development of vertical CoM position, angle of acceleration and distance between support surface and CoM. It must be noted that for these variables the exponential fit showed high correlations (r²>0.99). No relationship was found between the occurrences of breakpoints for different variables as investigated using odds ratios (Mantel-Haenszel Chi-square statistic). It is concluded that although breakpoints regularly appear during accelerated running, these are not the rule and thereby unlikely a fundamental characteristic, but more likely an expression of imperfection of performance.

How 100-m event analyses improve our understanding of world-class men's and women's sprint performance

This study aimed to compare the force (F)-velocity (v)-power (P)-time (t) relationships of female and male world-class sprinters. A total of 100 distance-time curves (50 women and 50 men) were computed from international 100-m finals, to determine the acceleration and deceleration phases of each race: (a) mechanical variables describing the velocity, force, and power output; and (b) F-P-v relationships and associated maximal power output, theoretical force and velocity produced by each athlete (P_max , F₀ , and V₀ ). The results showed that the maximal sprint velocity (V_max ) and mean power output (W/kg) developed over the entire 100 m strongly influenced 100-m performance (r > -0.80; P ≤ 0.001). With the exception of mean force (N/kg) developed during the acceleration phase or during the entire 100 m, all of the mechanicals variables observed over the race were greater in men. Shorter acceleration and longer deceleration in women may explain both their lower V_max and their greater decrease in velocity, and in turn their lower performance level, which can be explained by their higher V₀ and its correlation with performance. This highlights the importance of the capability to keep applying horizontal force to the ground at high velocities.

9.58 and 10.49: Nearing the Citius End for 100 m?

Human upper performance limits in the 100-m sprint remain the subject of much debate. The aim of this commentary is to highlight the vulnerabilities of prognoses from historical trends by shedding light on the mechanical and physiological limitations associated with human sprint performance. Several conditions work against the athlete with increasing sprint velocity; air resistance and braking impulse in each stride increase while ground-contact time typically decreases with increasing running velocity. Moreover, muscle-force production declines with increasing speed of contraction. Individual stature (leg length) strongly limits stride length such that conditioning of senior sprinters with optimized technique mainly must be targeted to enhance stride frequency. More muscle mass means more power and thereby greater ground-reaction forces in sprinting. However, as the athlete gets heavier, the energy cost of accelerating that mass also increases. This probably explains why body-mass index among world-class sprinters shows low variability and averages 23.7 ± 1.5 and 20.4 ± 1.4 for male and female sprinters, respectively. Performance development of world-class athletes indicates that ~8% improvement from the age of 18 represents the current maximum trainability of sprint performance. However, drug abuse is a huge confounding factor associated with such analyses, and available evidence suggests that we are already very close to “the citius end” of 100-m sprint performance.

Sprint mechanics in world-class athletes: a new insight into the limits of human locomotion

The objective of this study was to characterize the mechanics of maximal running sprint acceleration in high-level athletes. Four elite (100-m best time 9.95-10.29 s) and five sub-elite (10.40-10.60 s) sprinters performed seven sprints in overground conditions. A single virtual 40-m sprint was reconstructed and kinetics parameters were calculated for each step using a force platform system and video analyses. Anteroposterior force (FY), power (PY), and the ratio of the horizontal force component to the resultant (total) force (RF, which reflects the orientation of the resultant ground reaction force for each support phase) were computed as a function of velocity (V). FY-V, RF-V, and PY-V relationships were well described by significant linear (mean R(2) of 0.892 ± 0.049 and 0.950 ± 0.023) and quadratic (mean R(2) = 0.732 ± 0.114) models, respectively. The current study allows a better understanding of the mechanics of the sprint acceleration notably by modeling the relationships between the forward velocity and the main mechanical key variables of the sprint. As these findings partly concern world-class sprinters tested in overground conditions, they give new insights into some aspects of the biomechanical limits of human locomotion.

Are running speeds maximized with simple-spring stance mechanics?

Are the fastest running speeds achieved using the simple-spring stance mechanics predicted by the classic spring-mass model? We hypothesized that a passive, linear-spring model would not account for the running mechanics that maximize ground force application and speed. We tested this hypothesis by comparing patterns of ground force application across athletic specialization (competitive sprinters vs. athlete nonsprinters, n = 7 each) and running speed (top speeds vs. slower ones). Vertical ground reaction forces at 5.0 and 7.0 m/s, and individual top speeds ( n = 797 total footfalls) were acquired while subjects ran on a custom, high-speed force treadmill. The goodness of fit between measured vertical force vs. time waveform patterns and the patterns predicted by the spring-mass model were assessed using the R2 statistic (where an R2 of 1.00 = perfect fit). As hypothesized, the force application patterns of the competitive sprinters deviated significantly more from the simple-spring pattern than those of the athlete, nonsprinters across the three test speeds ( R2 <0.85 vs. R2 ≥ 0.91, respectively), and deviated most at top speed ( R2 = 0.78 ± 0.02). Sprinters attained faster top speeds than nonsprinters (10.4 ± 0.3 vs. 8.7 ± 0.3 m/s) by applying greater vertical forces during the first half (2.65 ± 0.05 vs. 2.21 ± 0.05 body wt), but not the second half (1.71 ± 0.04 vs. 1.73 ± 0.04 body wt) of the stance phase. We conclude that a passive, simple-spring model has limited application to sprint running performance because the swiftest runners use an asymmetrical pattern of force application to maximize ground reaction forces and attain faster speeds.