Shorter Ground Contact Time and Better Running Economy

Purpose:This study aimed to determine whether biomechanical characteristics such as ground-contact time, swing time, and stride length and frequency contribute to the exceptional running economy of East African runners.Methods:Seventeen elite long-distance runners (9 Eritrean, 8 European) performed an incremental maximal running test and 3 submaximal running bouts at 17, 19, and 21 km/h. During the tests, gas-exchange parameters were measured to determine maximal oxygen uptake (VO2max) and running economy (RE). In addition, ground-contact time, swing time, stride length, and stride frequency were measured.Results:The European runners had higher VO2max values than the Eritrean runners (77.2 ± 5.2 vs 73.5 ± 6.0 mL · kg−1 · min−1, P = .011, effect sizes [ES] = 0.65), although Eritrean runners were more economical at 19 km/h (191.4 ± 10.4 vs 205.9 ± 13.3 mL · kg−1 · min−1, P = .026, ES = 1.21). There were no differences between groups for ground-contact time, swing time, stride length, or stride frequency at any speed. Swing time was associated with running economy at 21 km/h in the Eritrean runners (r = .71, P = .033), but no other significant association was found between RE and biomechanical variables. Finally, best 10-km performance was significantly correlated with RE (r = –.57; P = .013).Conclusions:Eritrean runners have superior RE compared with elite European runners. This appears to offset their inferior VO2max. However, the current data suggest that their better RE does not have a biomechanical basis. Other factors, not measured in the current study, may contribute to this RE advantage.

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Long Ground Contact Time Enhances Running Economy at High-Intensity Running

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000538540.54101.51 ◽

2018 ◽

Vol 50 (5S) ◽

pp. 772

Author(s):

Fumiya TANJI ◽

Hayato OHNUMA ◽

Ryosuke ANDO ◽

Tatsuaki IKEDA ◽

Yasuhiro SUZUKI

Keyword(s):

High Intensity ◽

Contact Time ◽

Running Economy ◽

Ground Contact ◽

Ground Contact Time

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Attempting To Acutely Manipulate Ground Contact Time Imbalances Impairs Running Economy

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000675940.80736.bb ◽

2020 ◽

Vol 52 (7S) ◽

pp. 219-219

Author(s):

Dustin P. Joubert ◽

Broderick L. Dickerson ◽

Eric J. Jones ◽

Dani D. Willis

Keyword(s):

Contact Time ◽

Running Economy ◽

Ground Contact ◽

Ground Contact Time

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Associations between Well-Being State and Match External and Internal Load in Amateur Referees

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18063322 ◽

2021 ◽

Vol 18 (6) ◽

pp. 3322

Author(s):

Eñaut Ozaeta ◽

Javier Yanci ◽

Carlo Castagna ◽

Estibaliz Romaratezabala ◽

Daniel Castillo

Keyword(s):

External Load ◽

Contact Time ◽

Well Being ◽

Ground Contact ◽

Internal Load ◽

Match Play ◽

Power Meter ◽

Ground Contact Time ◽

Training Impulse ◽

External Loads

The main aim of this paper was to examine the association between prematch well-being status with match internal and external load in field (FR) and assistant (AR) soccer referees. Twenty-three FR and 46 AR participated in this study. The well-being state was assessed using the Hooper Scale and the match external and internal loads were monitored with Stryd Power Meter and heart monitors. While no significant differences were found in Hooper indices between match officials, FR registered higher external loads (p < 0.01; ES: 0.75 to 5.78), spent more time in zone 4 and zone 5, and recorded a greater training impulse (TRIMP) value (p < 0.01; ES: 1.35 to 1.62) than AR. Generally, no associations were found between the well-being variables and external loads for FR and AR. Additionally, no associations were found between the Hooper indices and internal loads for FR and AR. However, several relationships with different magnitudes were found between internal and external match loads, for FR, between power and speed with time spent in zone 2 (p < 0.05; r = −0.43), ground contact time with zone 2 and zone 3 (p < 0.05; r = 0.50 to 0.60) and power, speed, cadence and ground contact time correlated with time spent in zone 5 and TRIMP (p < 0.05 to 0.01; r = 0.42 to 0.64). Additionally, for AR, a relationship between speed and time in zone 1 was found (p < 0.05; r = −0.30; CL = 0.22). These results suggest that initial well-being state is not related to match officials’ performances during match play. In addition, the Stryd Power Meter can be a useful device to calculate the external load on soccer match officials.

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Single-Leg Jump Performance Before and After Exercise in Healthy and Anterior Cruciate Ligament Reconstructed Individuals

Journal of Sport Rehabilitation ◽

10.1123/jsr.2019-0159 ◽

2020 ◽

Vol 29 (7) ◽

pp. 879-885

Author(s):

Haley Bookbinder ◽

Lindsay V. Slater ◽

Austin Simpson ◽

Jay Hertel ◽

Joseph M. Hart

Keyword(s):

Anterior Cruciate Ligament ◽

Repeated Measures ◽

Contact Time ◽

Cruciate Ligament ◽

Ground Contact ◽

Jump Height ◽

Before And After ◽

Healthy Control ◽

Ground Contact Time ◽

Anterior Cruciate

Context: Many clinicians measure lower-extremity symmetry after anterior cruciate ligament reconstruction (ACLR); however, testing is completed in a rested state rather than postexercise. Testing postexercise may better model conditions under which injury occurs. Objective: To compare changes in single-leg performance in healthy and individuals with history of ACLR before and after exercise. Design: Repeated-measures case-control. Setting: Laboratory. Patients: Fifty-two subjects (25 control and 27 ACLR). Intervention: Thirty minutes of exercise. Main Outcome Measures: Limb symmetry and involved limb performance (nondominant for healthy) for single-leg hop, ground contact time, and jump height during the 4-jump test. Cohen d effect sizes were calculated for all differences identified using a repeated-measures analysis of variance. Results: Healthy controls hopped farther than ACLR before (d = 0.65; confidence interval [CI], 0.09 to 1.20) and after exercise (d = 0.60; CI, 0.04 to 1.15). Those with ACLR had longer ground contact time on the reconstructed limb compared with the uninvolved limb after exercise (d = 0.53; CI, −0.02 to 1.09), and the reconstructed limb had greater ground contact time compared with the healthy control limb after exercise (d = 0.38; CI, −0.21 to 0.73). ACLR were less symmetrical than healthy before (d = 0.38; CI, 0.17 to 0.93) and after exercise (d = 0.84; CI, 0.28 to 1.41), and the reconstructed limb demonstrated decreased jump height compared with the healthy control limbs before (d = 0.75; CI, 0.19 to 1.31) and after exercise (d = 0.79; CI, 0.23 to 1.36). Conclusions: ACLR became more symmetric, which may be from adaptations of the reconstructed limb after exercise. Changes in performance and symmetry may provide additional information regarding adaptations to exercise after reconstruction.

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Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds

10.31236/osf.io/9vhwd ◽

2021 ◽

Author(s):

Ryan Alcantara ◽

Evan Day ◽

Michael Hahn ◽

Alena Grabowski

Keyword(s):

Risk Factors ◽

Contact Time ◽

Injury Risk ◽

Stress Fractures ◽

Whole Body ◽

Accelerometer Data ◽

Ground Contact ◽

Wearable Sensor ◽

Ground Contact Time ◽

Injury Risk Factors

Background. Stress fractures are injuries caused by repetitive loading during activities such as running. The application of advanced analytical methods such as machine learning to data from multiple wearable sensors has allowed for predictions of biomechanical variables associated with running-related injuries like stress fractures. However, it is unclear if data from a single wearable sensor can accurately estimate variables that characterize external loading during running such as peak vertical ground reaction force (vGRF), vertical impulse, and ground contact time. Predicting these biomechanical variables with a single wearable sensor could allow researchers, clinicians, and coaches to longitudinally monitor biomechanical running-related injury risk factors without expensive force-measuring equipment.Purpose. We quantified the accuracy of applying quantile regression forest (QRF) and linear regression (LR) models to sacral-mounted accelerometer data to predict peak vGRF, vertical impulse, and ground contact time across a range of running speeds.Methods. Thirty-seven collegiate cross country runners (24 females, 13 males) ran on a force-measuring treadmill at 3.8 – 5.4 m/s while wearing an accelerometer clipped posteriorly to the waistband of their running shorts. We cross-validated QRF and LR models by training them on acceleration data, running speed, step frequency, and body mass as predictor variables. Trained models were then used to predict peak vGRF, vertical impulse, and contact time. We compared predicted values to those calculated from a force-measuring treadmill on a subset of data (n = 9) withheld during model training. We quantified prediction accuracy by calculating the root mean square error (RMSE) and mean absolute percentage error (MAPE).Results. The QRF model predicted peak vGRF with a RMSE of 0.150 body weights (BW) and MAPE ± SD of 4.27 ± 2.85%, predicted vertical impulse with a RMSE of 0.004 BW*s and MAPE of 0.80 ± 0.91%, and predicted contact time with a RMSE of 0.011 s and MAPE of 4.68 ± 3.00%. The LR model predicted peak vGRF with a RMSE of 0.139 BW and MAPE of 4.04 ± 2.57%, predicted vertical impulse with a RMSE of 0.002 BW*s and MAPE of 0.50 ± 0.42%, and predicted contact time with a RMSE of 0.008 s and MAPE of 3.50 ± 2.27%. There were no statistically significant differences between QRF and LR model prediction MAPE for peak vGRF (p = 0.549) or vertical impulse (p = 0.073), but the LR model’s MAPE for contact time was significantly lower than the QRF model’s MAPE (p = 0.0497).Conclusions. Our findings indicate that the QRF and LR models can accurately predict peak vGRF, vertical impulse, and contact time (MAPE < 5%) from a single sacral-mounted accelerometer across a range of running speeds. These findings may be beneficial for researchers, clinicians, or coaches seeking to monitor running-related injury risk factors without force-measuring equipment.

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