This study aimed to clarify the kinematic factors for the cause and effect of hitting hurdles during the initial phase of a 110-m hurdle run. Nine experienced male hurdlers participated in this study (body height: 1.74 ± 0.04 m, body mass: 67.4 ± 5.9 kg, age: 20.2 ± 1.4 years, personal best: 15.21 ± 0.47 s, seasonal best: 15.33 ± 0.55 s). Hurdlers undertook 12 trials of the initial phase of hurdling from the start to the second hurdle landing. Dual-sided sagittal plane motion was obtained from images from two high-speed cameras operating at 120 Hz. One ‘hit’ trial which had the largest horizontal displacement of markers fixed on the hurdle and one ‘non-hit’ trial which had the fastest time of hurdle clearance were extracted for each participant. Kinematic variables were compared between the two trials. Significantly lower height of the whole-body centre of mass at the take-off was found as a possible cause of hitting hurdles, caused by insufficient swing-up of the lead leg thigh. In contrast to conventional understanding, take-off velocity, take-off distance and the take-off angle were comparable between the ‘hit’ trial and ‘non-hit’ trial. Regarding the effect of hitting hurdles, it was observed that running velocity during hurdling was not substantially reduced. However, several characteristic movements were identified that might induce inefficient motion to re-accelerate running velocity during the following landing steps.
The present study aimed to establish a more robust, reliable statistical model of touchdown times based on the data of elite 110 m hurdlers to precisely predict performance based on touchdown times. We obtained 151 data (race time: 13.65 ± 0.33 s, range of race time: 12.91 s– 14.47 s) from several previous studies. Regression equations were developed to predict each touchdown time (times from the start signal to the instants of the leading leg landing after clearing 1st to 10th hurdles) from the race time. To avoid overtraining for each regression equation, data were split into training and testing data sets in accordance with a leave–one–out cross-validation. From the results of cross-validation, the agreement and generalization were compared between the present study model and the existing model. As a result, the proposed predictive equations showed a good agreement and generalization (R2 = 0.527–0.981, MSE = 0.0015–0.0028, MAE = 0.019–0.033) compared to that of existing equations (R2 = 0.481–0.979, MSE = 0.0017–0.0039, MAE = 0.034–0.063). Therefore, it can be assumed that the proposed predictive equations are a more robust, reliable model than the existing model. The touchdown times needed for coaches and elite hurdlers to set their target records will be accurately understood using the model of this study. Therefore, this study model would help to improve training interventions and race evaluations.
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