Landing-Takeoff Asymmetries Applied to Running Mechanics: A New Perspective for Performance

Influence of sports background on the bouncing mechanism of running

During running, the mechanical energy of the centre of mass of the body (COM) oscillates throughout the step like a spring-mass system, where part of its mechanical energy is stored during negative phases to be released during the following positive phases. This storage-release of energy improves muscle-tendon efficiency, which is related to lower-limb stiffness. This study explores the effect of sports background on the bouncing mechanism, by examining differences in stiffness and step spatiotemporal parameters between swimmers and football athletes. All athletes performed three consecutive running bouts on an instrumented treadmill at three different speeds (3.9, 4.4 and 5.0 m·s-1). The ground reaction forces were recorded. Vertical stiffness and step spatiotemporal parameters were analysed and compared using a two-way ANOVA. Vertical stiffness of football players was on average 21.0 ± 1.1% higher than swimmers. The modification of step spatiotemporal parameters also suggests a more elastic rebound by increasing the stretch of tendons relative to muscle within muscle-tendon units in football players. Compared to swimmers, they (1) decrease the effective contact time by 9.7 ± 2.4% and (2) decrease the duration of the push by 15.0 ± 6.4%, suggesting that background training adaptations influence spring-mass behaviour during running.

The Relationship Between Running Biomechanics and Running Economy: A Systematic Review and Meta-Analysis of Observational Studies

BACKGROUND

Running biomechanics is considered an important determinant of running economy (RE). However, studies examining associations between running biomechanics and RE report inconsistent findings.

OBJECTIVE

The aim of this systematic review was to determine associations between running biomechanics and RE and explore potential causes of inconsistency.

METHODS

Three databases were searched and monitored up to April 2023. Observational studies were included if they (i) examined associations between running biomechanics and RE, or (ii) compared running biomechanics between groups differing in RE, or (iii) compared RE between groups differing in running biomechanics during level, constant-speed, and submaximal running in healthy humans (18-65 years). Risk of bias was assessed using a modified tool for observational studies and considered in the results interpretation using GRADE. Meta-analyses were performed when two or more studies reported on the same outcome. Meta-regressions were used to explore heterogeneity with speed, coefficient of variation of height, mass, and age as continuous outcomes, and standardization of running shoes, oxygen versus energetic cost, and correction for resting oxygen or energy cost as categorical outcomes.

RESULTS

Fifty-one studies (n = 1115 participants) were included. Most spatiotemporal outcomes showed trivial and non-significant associations with RE: contact time r = - 0.02 (95% confidence interval [CI] - 0.15 to 0.12); flight time r = 0.11 (- 0.09 to 0.32); stride time r = 0.01 (- 0.8 to 0.50); duty factor r = - 0.06 (- 0.18 to 0.06); stride length r = 0.12 (- 0.15 to 0.38), and swing time r = 0.12 (- 0.13 to 0.36). A higher cadence showed a small significant association with a lower oxygen/energy cost (r = - 0.20 [- 0.35 to - 0.05]). A smaller vertical displacement and higher vertical and leg stiffness showed significant moderate associations with lower oxygen/energy cost (r = 0.35, - 0.31, - 0.28, respectively). Ankle, knee, and hip angles at initial contact, midstance or toe-off as well as their range of motion, peak vertical ground reaction force, mechanical work variables, and electromyographic activation were not significantly associated with RE, although potentially relevant trends were observed for some outcomes.

CONCLUSIONS

Running biomechanics can explain 4-12% of the between-individual variation in RE when considered in isolation, with this magnitude potentially increasing when combining different variables. Implications for athletes, coaches, wearable technology, and researchers are discussed in the review.

PROTOCOL REGISTRATION

https://doi.org/10.17605/OSF.IO/293 ND (OpenScience Framework).

The biomechanics of running and running styles: a synthesis

Running movements are parametrised using a wide variety of devices. Misleading interpretations can be avoided if the interdependencies and redundancies between biomechanical parameters are taken into account. In this synthetic review, commonly measured running parameters are discussed in relation to each other, culminating in a concise, yet comprehensive description of the full spectrum of running styles. Since the goal of running movements is to transport the body centre of mass (BCoM), and the BCoM trajectory can be derived from spatiotemporal parameters, we anticipate that different running styles are reflected in those spatiotemporal parameters. To this end, this review focuses on spatiotemporal parameters and their relationships with speed, ground reaction force and whole-body kinematics. Based on this evaluation, we submit that the full spectrum of running styles can be described by only two parameters, namely the step frequency and the duty factor (the ratio of stance time and stride time) as assessed at a given speed. These key parameters led to the conceptualisation of a so-called Dual-axis framework. This framework allows categorisation of distinctive running styles (coined 'Stick', 'Bounce', 'Push', 'Hop', and 'Sit') and provides a practical overview to guide future measurement and interpretation of running biomechanics.

Biomechanical, physiological and anthropometrical predictors of performance in recreational runners

Background

The maximal running speed (VMAX) determined on a graded treadmill test is well-recognized as a running performance predictor. However, few studies have assessed the variables that predict VMAX in recreationally active runners.

Methods

We used a mathematical procedure combining Fick's law and metabolic cost analysis to verify the relation between (1) VMAX versus anthropometric and physiological determinants of running performance and, (2) theoretical metabolic cost versus running biomechanical parameters. Linear multiple regression and bivariate correlation were applied. We aimed to verify the biomechanical, physiological, and anthropometrical determinants of VMAX in recreationally active runners. Fifteen recreationally active runners participated in this observational study. A Conconi and a stead-steady running test were applied using a heart rate monitor and a simple video camera to register the physiological and mechanical variables, respectively.

Results

Statistical analysis revealed that the speed at the second ventilatory threshold, theoretical metabolic cost, and fat-mass percentage confidently estimated the individual running performance as follows: VMAX = 58.632 + (-0.183 * fat percentage) + (-0.507 * heart rate percentage at second ventilatory threshold) + (7.959 * theoretical metabolic cost) (R2 = 0.62, p = 0.011, RMSE = 1.50 km.h-1). Likewise, the theoretical metabolic cost was significantly explained (R2 = 0.91, p = 0.004, RMSE = 0.013 a.u.) by the running spatiotemporal and elastic-related parameters (contact and aerial times, stride length and frequency, and vertical oscillation) as follows: theoretical metabolic cost = 10.421 + (4.282 * contact time) + (-3.795 * aerial time) + (-2.422 * stride length) + (-1.711 * stride frequency) + (0.107 * vertical oscillation).

Conclusion

Critical determinants of elastic mechanism, such as maximal vertical force and vertical and leg stiffness were unrelated to the metabolic economy. VMAX, a valuable marker of running performance, and its physiological and biomechanical determinants can be effectively evaluated using a heart rate monitor, treadmill, and a digital camera, which can be used in the design of training programs to recreationally active runners.

Kinematics and mechanical changes with step frequency at different running speeds

PURPOSE

Running at a given speed can be achieved by taking large steps at a low frequency or on the contrary by taking small steps at a high frequency. The consequences of a change in step frequency, at a fixed speed, affects the stiffness of the lower limb differently. In this study, we compared the running mechanics and kinematics at different imposed step frequencies (from 2 step s-1 to 3.6 step s-1) to understand the relationship between kinematic and kinetic parameters.

METHODS

Eight recreational male runners ran on a treadmill at 5 different speeds and 5 different step frequencies. The lower-limb segment motion and the ground reaction forces were recorded. Mechanical powers, general gait parameters, lower-limb movements and coordination were investigated.

RESULTS

At low step frequencies, in order to limit the magnitude of the ground reaction force, the vertical stiffness is reduced and thus runners deviate from an elastic rebound. At high step frequencies, the stiffness is increased and the elastic rebound is optimised in its ability to absorb and restore energy during the contact phase.

CONCLUSION

We studied the consequences of a change in step frequency on the bouncing mechanics of running. We showed that the lower limb stiffness and the intersegmental coordination of the lower-limb segments are affected by running step frequency rather than speed. The runner rather adapts their lower limb stiffness to match a step frequency for a given speed than the opposite.

Mechanical energy on anaerobic capacity during a supramaximal treadmill running in men: Is there influence between runners and active individuals?

This study verified whether mechanical variables influence the anaerobic capacity outcome on treadmill running and whether these likely influences were dependent of running experience. Seventeen physical active and 18 amateur runners, males, performed a graded exercise test and constant load exhaustive running efforts at 115% of intensity associated to maximal oxygen consumption. During the constant load were determined the metabolic responses (i.e., gas exchange and blood lactate) to estimate the energetic contribution and anaerobic capacity as well as kinematic responses. The runners showed higher anaerobic capacity (16.6%; p = 0.005), but lesser time to exercise failure (-18.8%; p = 0.03) than active subjects. In addition, the stride length (21.4%; p = 0.00001), contact phase duration (-11.3%; p = 0.005), and vertical work (-29.9%; p = 0.015). For actives, the anaerobic capacity did not correlate significantly with any physiologic, kinematic, and mechanical variables and no regression model was fitted using the stepwise multiple regression, while to runners the anaerobic capacity was significantly correlated with phosphagen energetic contribution (r = 0.47; p = 0.047), external power (r = -0.51; p = 0.031), total work (r = -0.54; p = 0.020), external work (r = -0.62; p = 0.006), vertical work (r = -0.63; p = 0.008), and horizontal work (r = -0.61; p = 0.008), and the vertical work and phosphagen energetic contribution presented a coefficient of determination of 62% (p = 0.001). Based on findings, it is possible to assume that for active subjects, the mechanical variables have no influence over the anaerobic capacity, however, for experienced runners, the vertical work and phosphagen energetic contribution have relevant effect over anaerobic capacity output.

Effects of flywheel resistance training on the running economy of young male well-trained distance runners

The study aimed to investigate the effect of flywheel accentuated eccentric loading (AEL) training on the running economy (RE) of young male well-trained distance runners. Twenty-two runners participated and were randomly assigned to the flywheel (FG, n = 12) and the control group (CG, n = 10). Traditional endurance training was performed in both groups three times a week for 6-week, while traditional resistance and flywheel AEL training was added to the CG and FG respectively. Subjects performed the incremental exercise test, squat jump, and countermovement jump (CMJ) before and after training. The results showed that 1) the RE at 65% of peak oxygen consumption (VO₂peak), 75% VO₂peak, and 85% VO₂peak improved significantly after 6 weeks of training (p < 0.01, Effect size (ES) = 0.76; p < 0.01, ES = 1.04; p < 0.01, ES = 1.85) in FG, and the RE of 85% VO₂peak in FG was significantly lower than CG (p < 0.05, ES = 0.30); 2) in post-training, both squat jump (p < 0.01, ES = 0.73) and CMJ (p < 0.01, ES = 1.15) performance, eccentric utilization ratio (p < 0.04, ES = 0.44), the rate of force development (RFD) of squat jump (p < 0.05, ES = 0.46), and CMJ_RFD (p < 0.01, ES = 0.66) were significantly improved in FG. And there are no significant differents in CG group because it was maintain training for our participants. Our findings showed that 1) flywheel AEL training improves the muscles' explosive strength and other neuromuscular functions, and improves the athlete's running economy under 65%, 75%, and 85% VO₂peak, which potentially increases endurance performance. 2) Flywheel AEL training can improve the height, RFD, and the eccentric utilization ratio of squat jump and CMJ, and other lower limb elastic potential energy indicators of the young male, well-trained distance runners.

Running-style modulation: Effects of stance-time and flight-time instructions on duty factor and cadence

BACKGROUND

The duty factor (reflecting the ratio of stance to flight time) is an important variable related to running performance, economy, and injury risk. According to the dual-axis model, the duty factor and the cadence are sufficient to describe an individual's running style at a certain speed. To test this model, one should be able to modulate both variables independently. While acoustic pacing is an established method for cadence modulation, no such method is available for duty-factor modulation.

RESEARCH QUESTIONS

Can people modulate their duty factor based on verbal instructions to change either their stance or flight time without changing their cadence? And, if so, which instruction is most effective?

METHODS

Twelve participants ran on an instrumented treadmill and completed four training blocks starting with a baseline trial and ending with a performance trial in which they followed verbal instructions to both increase and decrease their stance and flight time. Acoustic pacing at their preferred cadence was present during the first part of each trial. We calculated the duty factor and cadence for paced and non-paced parts of each trial, assessed the effectiveness of the instructions aimed at changing the duty factor, and examined the effects of instructions and acoustic pacing on cadence using Bayesian statistics.

RESULTS

The duty factor changed in intended directions with verbal instructions to increase and decrease the stance and flight time (18.04 ≤ BF₁₀ ≤ 4954.42), without differences between the instructions or during and after acoustic pacing. The instructions and acoustic pacing did not result in a consistent change in cadence (0.40 ≤ BF₁₀ ≤ 2.59).

SIGNIFICANCE

Runners can change their duty factor through verbal instructions pertaining to stance or flight time, without clear concomitant effects on cadence. Running styles can thus be altered with verbal instructions to change stance or flight time for duty-factor modulation, optionally combined with acoustic pacing to prescribe cadence.

Sex-specific differences in biomechanics among runners: A systematic review with meta-analysis

Patellofemoral disorders are more common in female runners compared to their male counterparts. Differences in biomechanical characteristics between groups of runners could provide insight into the causes of higher rates of injury in female versus male runners, which would be useful to physical therapists and athletic trainers in development of individualized injury prevention programs. This review compares the differences in biomechanical characteristics between female and male runners. Electronic databases, including PubMed, Scopus, Web of Science, and Embase were searched in December 2021 for studies evaluating sex-specific differences in lower limb mechanics of healthy participants during running. Two independent reviewers determined the inclusion and quality of each research paper. Meta-analyses were used where possible. A total of 13 studies were selected. Means and standard deviations of reported data were retrieved from each selected paper for comparison of results. Three biomechanical variables, including dynamics, muscle activation, and kinematics, were compared between female and male runners. However, no differences were found in kinetic variables or muscle activation between groups due to insufficient data available from the selected studies. Meta-analyses of kinematic variables revealed that female runners exhibited significantly greater hip flexion angle, hip adduction angle, and hip internal rotation angle, but smaller knee flexion angle compared to male runners during running. We found significant differences in kinematic variables between female and male runners, which could influence the training advice of physical therapists and athletic trainers who work with runners, and inform the development of injury prevention programs.

Augmented Cooper test: Biomechanical contributions to endurance performance

Running mechanics are modifiable with training and adopting an economical running technique can improve running economy and hence performance. While field measurement of running economy is cumbersome, running mechanics can be assessed accurately and conveniently using wearable inertial measurement units (IMUs). In this work, we extended this wearables-based approach to the Cooper test, by assessing the relative contribution of running biomechanics to the endurance performance. Furthermore, we explored different methods of estimating the distance covered in the Cooper test using a wearable global navigation satellite system (GNSS) receiver. Thirty-three runners (18 highly trained and 15 recreational) performed an incremental laboratory treadmill test to measure their maximum aerobic speed (MAS) and speed at the second ventilatory threshold (sVT2). They completed a 12-minute Cooper running test with foot-worm IMUs and a chest-worn GNSS-IMU on a running track 1–2 weeks later. Using the GNSS receiver, an accurate estimation of the 12-minute distance was obtained (accuracy of 16.5 m and precision of 1.1%). Using this distance, we showed a reliable estimation [R² > 0.9, RMSE ϵ (0.07, 0.25) km/h] of the MAS and sVT2. Biomechanical metrics were extracted using validated algorithm and their association with endurance performance was estimated. Additionally, the high-/low-performance runners were compared using pairwise statistical testing. All performance variables, MAS, sVT2, and average speed during Cooper test, were predicted with an acceptable error (R² ≥ 0.65, RMSE ≤ 1.80 kmh⁻¹) using only the biomechanical metrics. The most relevant metrics were used to develop a biomechanical profile representing the running technique and its temporal evolution with acute fatigue, identifying different profiles for runners with highest and lowest endurance performance. This profile could potentially be used in standardized functional capacity measurements to improve personalization of training and rehabilitation programs.

Running-Induced Fatigue Changes the Structure of Motor Variability in Novice Runners

Understanding the effects of fatigue is a central issue in the context of endurance sports. Given the popularity of running, there are numerous novices among runners. Therefore, understanding the effects of fatigue in novice runners is an important issue. Various studies have drawn conclusions about the control of certain variables by analyzing motor variability. One variable that plays a crucial role during running is the center of mass (CoM), as it reflects the movement of the whole body in a simplified way. Therefore, the aim of this study was to analyze the effects of fatigue on the motor variability structure that stabilizes the CoM trajectory in novice runners. To do so, the uncontrolled manifold approach was applied to a 3D whole-body model using the CoM as the result variable. It was found that motor variability increased with fatigue (UCMꓕ). However, the UCMRatio did not change. This indicates that the control of the CoM decreased, whereas the stability was not affected. The decreases in control were correlated with the degree of exhaustion, as indicated by the Borg scale (during breaking and flight phase). It can be summarized that running-induced fatigue increases the step-to-step variability in novice runners and affects the control of their CoM.

Modelling 5-km Running Performance on Level and Hilly Terrains in Recreational Runners

Incline and level running on treadmills have been extensively studied due to their different cardiorespiratory and biomechanical acute responses. However, there are no studies examining the performance determinants of outdoor running on hilly terrains. We aimed to investigate the influence of anthropometrics, muscle strength, and cardiorespiratory and gait spatiotemporal parameters during level (0%) and inclined (+7%) running on performance in level and hilly 5-km races. Twenty male recreational runners completed two 5-km outdoor running tests (0% vs. +7% and −7%), and two submaximal (10 km·h−1) and incremental treadmill tests at 0 and 7% slopes, after complete laboratory evaluations. The velocity at maximal oxygen consumption (VO2max) evaluated at 7% incline and level treadmill running were the best performance predictors under both hilly (R2 = 0.72; p < 0.05) and level (R2 = 0.85; p < 0.01) conditions, respectively. Inclusion of ventilatory and submaximal heart rate data improved the predictive models up to 100%. Conversely, none of the parameters evaluated in one condition contributed to the other condition. The spatiotemporal parameters and the runners’ strength levels were not associated to outdoor performances. These results indicate that the vVO2max evaluated at similar slopes in the lab can be used to predict 5-km running performances on both level and hilly terrains.

Transfer of strength training to running mechanics, energetics, and efficiency

To examine the effects of increased strength on mechanical work, the metabolic cost of transport (Cost), and mechanical efficiency (ME) during running. Fourteen physically active men (22.0 ± 2.0 years, 79.3 ± 11.1 kg) were randomized to a strength-training group (SG, n = 7), who participated in a maximal strength training protocol lasting 8 weeks, and a control group (CG, n = 7), which did not perform any training intervention. Metabolic and kinematic data were collected simultaneously while running at a constant speed (2.78 m·s^-1). The ME was defined as the ratio between mechanical power (P_mec) and metabolic power (P_met). The repeated measures two-way ANOVA did not show any significant interaction between groups, despite some large effect sizes (d): internal work (W_int, p = 0.265, d = -1.37), external work (W_ext, p = 0.888, d = 0.21), total work (W_tot, p = 0.931, d = -0.17), P_mec (p = 0.917, d = -0.17), step length (SL, p = 0.941, d = 0.24), step frequency (SF, p = 0.814, d = -0.18), contact time (CT, p = 0.120, d = -0.79), aerial time (AT, p = 0.266, d = 1.12), P_met (p = 0.088, d = 0.85), and ME (p = 0.329, d = 0.54). The exception was a significant decrease in Cost (p = 0.047, d = 0.84) in SG. The paired t-test and Wilcoxon test only detected intragroup differences (pre- vs. post-training) for SG, showing a higher CT (p = 0.041), and a lower Cost (p = 0.003) and P_met (p = 0.004). The results indicate that improved neuromuscular factors related to strength training may be responsible for the higher metabolic economy of running after 8 weeks of intervention. However, this process was unable to alter running mechanics in order to indicate a significant improvement in ME.

A Multivariate Polynomial Regression to Reconstruct Ground Contact and Flight Times Based on a Sine Wave Model for Vertical Ground Reaction Force and Measured Effective Timings

Effective contact (tce) and flight (tfe) times, instead of ground contact (tc) and flight (tf) times, are usually collected outside the laboratory using inertial sensors. Unfortunately, tce and tfe cannot be related to tc and tf because the exact shape of vertical ground reaction force is unknown. However, using a sine wave approximation for vertical force, tce and tc as well as tfe and tf could be related. Indeed, under this approximation, a transcendental equation was obtained and solved numerically over a tce x tfe grid. Then, a multivariate polynomial regression was applied to the numerical outcome. In order to reach a root-mean-square error of 0.5 ms, the final model was given by an eighth-order polynomial. As a direct application, this model was applied to experimentally measured tce values. Then, reconstructed tc (using the model) was compared to corresponding experimental ground truth. A systematic bias of 35 ms was depicted, demonstrating that ground truth tc values were larger than reconstructed ones. Nonetheless, error in the reconstruction of tc from tce was coming from the sine wave approximation, while the polynomial regression did not introduce further error. The presented model could be added to algorithms within sports watches to provide robust estimations of tc and tf in real time, which would allow coaches and practitioners to better evaluate running performance and to prevent running-related injuries.

Hydrolyzed Collagen Supplementation on Lower Body Stiffness in Recreational Triathletes

Background: Myotendinous stiffness is related to the collagen content of the muscle and tendon, and can be estimated during running by changes in vertical stiffness (kvert) and the resulting modifications of the spatiotemporal parameters (on-off ground asymmetry and landing-takeoff asymmetry). Supplementation with amino acids found in collagen, such as proline, glycine, and hydroxyl proline, combined with ascorbic acid, improve collagen synthesis and potentially result in improved mechanical strength and stiffness. Objectives: To determine if hydrolyzed collagen (HC) supplementation increases kvert and improves the spatiotemporal parameters during running in recreational triathletes. Methods: Nine active males (weight; 68.4 ± 5.7 kg, height; 171.8 ± 5.4 cm, age; 32.5 ± 4.1 years; Vo2max; 53.15 ± 2.19 mL/kg/min) were randomly distributed into a collagen group (CollG, n = 5) and a control group (CG, n = 4). Participants were supplemented for 4 weeks with 15g HC (CollG) or 15g placebo (CG; maltodextrin), 3 times per week. One hour after supplementation, the participants of both groups were asked to perform four repetitions of short sprints to further stimulate collagen synthesis. The ground reaction forces were recorded during running at 4.44 m s-1, 5.55 m.s-1, and 6.66 m.s-1 for assessment of kvert and the spatiotemporal step parameters. Results: Both groups increased kvert with speed (4.44 - 6.66 m s-1) from 24.8 ± 2.7 to 53.7 ± 16.5 N/m and from 25.1 to 49.8 N/m in the CollG and CG, respectively (P < 0.0001); however, there were no differences between groups before and after the supplementation period. As a consequence, the spatiotemporal parameters of running were also similar between groups. Conclusions: Four weeks of HC supplementation does not improve the bouncing mechanism of running in recreational triathletes.

Stride-to-Stride Variability of the Center of Mass in Male Trained Runners After an Exhaustive Run: A Three Dimensional Movement Variability Analysis With a Subject-Specific Anthropometric Model

The motion of the human body can be described by the motion of its center of mass (CoM). Since the trajectory of the CoM is a crucial variable during running, one can assume that trained runners would try to keep their CoM trajectory constant from stride to stride. However, when exposed to fatigue, runners might have to adapt certain biomechanical parameters. The Uncontrolled Manifold approach (UCM) and the Tolerance, Noise, and Covariation (TNC) approach are used to analyze changes in movement variability while considering the overall task of keeping a certain task relevant variable constant. The purpose of this study was to investigate if and how runners adjust their CoM trajectory during a run to fatigue at a constant speed on a treadmill and how fatigue affects the variability of the CoM trajectory. Additionally, the results obtained with the TNC approach were compared to the results obtained with the UCM analysis in an earlier study on the same dataset. Therefore, two TNC analyses were conducted to assess effects of fatigue on the CoM trajectory from two viewpoints: one analyzing the CoM with respect to a lab coordinate system (PVlab) and another one analyzing the CoM with respect to the right foot (PVfoot). Full body kinematics of 13 healthy young athletes were captured in a rested and in a fatigued state and an anthropometric model was used to calculate the CoM based on the joint angles. Variability was quantified by the coefficient of variation of the length of the position vector of the CoM and by the components Tolerance, Noise, and Covariation which were analyzed both in 3D and the projections in the vertical, anterior-posterior and medio-lateral coordinate axes. Concerning PVlab we found that runners increased their stride-to-stride variability in medio-lateral direction (1%). Concerning PVfoot we found that runners lowered their CoM (4 mm) and increased their stride-to-stride variability in the absorption phase in both 3D and in the vertical direction. Although we identified statistically relevant differences between the two running states, we have to point out that the effects were small (CV ≤ 1%) and must be interpreted cautiously.

Effects of jump training on physical fitness and athletic performance in endurance runners: A meta-analysis

This systematic review and meta-analysis aimed to assess the effects of jump training (JT) on measures of physical fitness and athletic performances in endurance runners. Controlled studies which involved healthy endurance runners, of any age and sex, were considered. A random-effects model was used to calculate effect sizes (ES; Hedge's g). Means and standard deviations of outcomes were converted to ES with alongside 95% confidence intervals (95%CI). Twenty-one moderate-to-high quality studies were included in the meta-analysis, and these included 511 participants. The main analyses revealed a significant moderate improvement in time-trial performance (i.e. distances between 2.0 and 5.0 km; ES = 0.88), without enhancements in maximal oxygen consumption (VO₂max), velocity at VO₂max, velocity at submaximal lactate levels, heart rate at submaximal velocities, stride rate at submaximal velocities, stiffness, total body mass or maximal strength performance. However, significant small-to-moderate improvements were noted for jump performance, rate of force development, sprint performance, reactive strength, and running economy (ES = 0.36-0.73; p < 0.001 to 0.031; I² = 0.0% to 49.3%). JT is effective in improving physical fitness and athletic performance in endurance runners. Improvements in time-trial performance after JT may be mediated through improvements in force generating capabilities and running economy.

Improving spring-mass parameter estimation in running using nonlinear regression methods

Runners are commonly modeled as spring-mass systems, but the traditional calculations of these models rely on discrete observations during the gait cycle (e.g. maximal vertical force) and simplifying assumptions (e.g. leg length), challenging the predicative capacity and generalizability of observations. We present a method to model runners as spring-mass systems using nonlinear regression (NLR) and the full vertical ground reaction force (vGRF) time series without additional inputs and fewer traditional parameter assumptions. We derived and validated a time-dependent vGRF function characterized by four spring-mass parameters - stiffness, touchdown angle, leg length and contact time - using a sinusoidal approximation. Next, we compared the NLR-estimated spring-mass parameters with traditional calculations in runners. The mixed-effect NLR method (ME NLR) modeled the observed vGRF best (RMSE:155 N) compared with a conventional sinusoid approximation (RMSE: 230 N). Against the conventional methods, its estimations provided similar stiffness approximations (-0.2±0.6 kN m^-1) with moderately steeper angles (1.2±0.7 deg), longer legs (+4.2±2.3 cm) and shorter effective contact times (-12±4 ms). Together, these vGRF-driven system parameters more closely approximated the observed vertical impulses (observed: 214.8 N s; ME NLR: 209.0 N s; traditional: 223.6 N s). Finally, we generated spring-mass simulations from traditional and ME NLR parameter estimates to assess the predicative capacity of each method to model stable running systems. In 6/7 subjects, ME NLR parameters generated models that ran with equal or greater stability than traditional estimates. ME NLR modeling of the vGRF in running is therefore a useful tool to assess runners holistically as spring-mass systems with fewer measurement sources or anthropometric assumptions. Furthermore, its utility as statistical framework lends itself to more complex mixed-effects modeling to explore research questions in running.

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO₂/min (11.1%, r = 0.97) and 144 (149) mlO₂/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET.

Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter

Equations predicting stride frequency (SF) and duty factor (DF) solely based on running speed have been proposed. However, for a given speed, kinematics vary depending on the global running pattern (GRP), i.e., the overall individual movement while running, which depends on the vertical oscillation of the head, antero-posterior motion of the elbows, vertical pelvis position at ground contact, antero-posterior foot position at ground contact, and strike pattern. Hence, we first verified the validity of the aforementioned equations while accounting for GRP. Kinematics during three 50-m runs on a track (n = 20) were used with curve fitting and linear mixed effects models. The percentage of explained variance was increased by ≥133% for DF when taking into account GRP. GRP was negatively related to DF (p = 0.004) but not to SF (p = 0.08), invalidating DF equation. Second, we assessed which parameters among anthropometric characteristics, sex, training volume, and GRP could relate to SF and DF in addition to speed, using kinematic data during five 30-s runs on a treadmill (n = 54). SF and DF linearly increased and quadratically decreased with speed (p < 0.001), respectively. However, on an individual level, SF was best described using a second-order polynomial equation. SF and DF showed a non-negligible percentage of variance explained by random effects (≥28%). Age and height were positively and negatively related to SF (p ≤ 0.05), respectively, while GRP was negatively related to DF (p < 0.001), making them key parameters to estimate SF and DF, respectively, in addition to speed.

Physiological Predictors of Maximal Incremental Running Performance

Purpose

The aim of this study was to verify whether physiological components [vertical jumps (Squat Jump - SJ and Countermovement Jump - CMJ), eccentric utilization ratio (EUR) of vertical jumps, running economy (RE), metabolic cost (C _MET ), first and second ventilatory threshold (VT₁ and VT₂) maximal oxygen uptake (VO_2MAX)] can predict maximal endurance running performance.

Methods

Twenty male runners performed maximal vertical jumps, submaximal running at constant speeds, and maximal incremental running test. Before, we measured anthropometric parameters (body mass and height) and registered the training history and volume. SJ and CMJ tests were evaluated prior to running tests. Initially, the oxygen uptake (VO₂) was collected at rest in the orthostatic position for 6 min. Soon after, a 10-min warm-up was performed on the treadmill at 10 km⋅h^-1, followed by two 5-min treadmill rectangular tests at 12 and 16 km⋅h^-1 monitored by a gas analyzer. After that, the runners performed a maximal incremental test, where the VT₁, VT₂, and VO_2MAX were evaluated, as well as the maximum running speed (vVO_2MAX). Thus, RE and C _MET were calculated with data obtained during rectangular running tests. Multivariate stepwise regression analyses were conducted to measure the relationship between independent variables (height and power of SJ and CMJ, EUR; RE and C _MET 12 and 16 km⋅h^-1 _; VT₁, VT₂, and VO_2MAX), as predictors of maximal running performance (vVO_2MAX), with significance level at α = 0.05.

Results

We found that VO_2MAX and RE at 16 km⋅h^-1 predict 81% of performance (vVO_2MAX) of endurance runners (p < 0.001).

Conclusion

The main predictors of the maximal incremental running test performance were VO_2MAX and RE.

Factors Affecting Training and Physical Performance in Recreational Endurance Runners

Endurance running has become an immensely popular sporting activity, with millions of recreational runners around the world. Despite the great popularity of endurance running as a recreational activity during leisure time, there is no consensus on the best practice for recreational runners to effectively train to reach their individual objectives and improve physical performance in a healthy manner. Moreover, there are lots of anecdotal data without scientific support, while most scientific evidence on endurance running was developed from studies observing both recreational and professional athletes of different levels. Further, the transference of all this information to only recreational runners is difficult due to differences in the genetic predisposition for endurance running, the time available for training, and physical, psychological, and physiological characteristics. Therefore, the aim of this review is to present a selection of scientific evidence regarding endurance running to provide training guidelines to be used by recreational runners and their coaches. The review will focus on some key aspects of the training process, such as periodization, training methods and monitoring, performance prediction, running technique, and prevention and management of injuries associated with endurance running.

Running Stride Length And Rate Are Changed And Mechanical Efficiency Is Preserved After Cycling In Middle-Level Triathletes

Although cycling impairs the subsequent metabolic cost and performance of running in some triathletes, the consequences on mechanical efficiency (Eff) and kinetic and potential energy fluctuations of the body center of mass are still unknown. The aim of this study was to investigate the effects of previous cycling on the cost-of-transport, Eff, mechanical energy fluctuations (W_tot), spring stiffness (K_leg and K_vert) and spatiotemporal parameters. Fourteen middle-level triathletes (mean ± SD: maximal oxygen uptake, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{{\rm{V}}}$$\end{document}V˙O_2max = 65.3 ± 2.7 ml.kg⁻¹.min⁻¹, age = 30 ± 5 years, practice time = 6.8 ± 3.0 years) performed four tests. Two maximal oxygen uptake tests on a cycle ergometer and treadmill, and two submaximal 20-minute running tests (14 km.h⁻¹) with (prior-cycling) and without (control) a previous submaximal 30-minute cycling test. No differences were observed between the control and post-cycling groups in Eff or W_tot. The Eff remains unchanged between conditions. On the other hand, the K_vert (20.2 vs 24.4 kN.m⁻¹) and K_leg (7.1 vs 8.2 kN.m⁻¹, p < 0.05) were lower and the cost-of-transport was higher (p = 0.018, 3.71 vs 3.31 J.kg⁻¹.m⁻¹) when running was preceded by cycling. Significantly higher stride frequency (p < 0.05, 1.46 vs 1.43 Hz) and lower stride length (p < 0.05, 2.60 vs 2.65 m) were observed in the running after cycling condition in comparison with control condition. Mechanical adjustments were needed to maintain the Eff, even resulting in an impaired metabolic cost after cycling performed at moderate intensity. These findings are compatible with the concept that specific adjustments in spatiotemporal parameters preserve the Eff when running is preceded by cycling in middle-level triathletes, though the cost-of-transport increased.