RESUMEN

This study aimed to identify the similarities and differences among half-marathon runners in relation to their performance level. Forty-eight male runners were classified into 4 groups according to their performance level in a half-marathon (min): Group 1 (n = 11, < 70 min), Group 2 (n = 13, < 80 min), Group 3 (n = 13, < 90 min), Group 4 (n = 11, < 105 min). In two separate sessions, training-related, anthropometric, physiological, foot strike pattern and spatio-temporal variables were recorded. Significant differences (p<0.05) between groups (ES = 0.55-3.16) and correlations with performance were obtained (r = 0.34-0.92) in training-related (experience and running distance per week), anthropometric (mass, body mass index and sum of 6 skinfolds), physiological (VO2max, RCT and running economy), foot strike pattern and spatio-temporal variables (contact time, step rate and length). At standardized submaximal speeds (11, 13 and 15 km·h-1), no significant differences between groups were observed in step rate and length, neither in contact time when foot strike pattern was taken into account. In conclusion, apart from training-related, anthropometric and physiological variables, foot strike pattern and step length were the only biomechanical variables sensitive to half-marathon performance, which are essential to achieve high running speeds. However, when foot strike pattern and running speeds were controlled (submaximal test), the spatio-temporal variables were similar. This indicates that foot strike pattern and running speed are responsible for spatio-temporal differences among runners of different performance level.

Asunto(s)

RESUMEN

Concurrent plyometric and running training has the potential to improve running economy (RE) and performance through increasing muscle strength and power, but the possible effect on spatiotemporal parameters of running has not been studied yet. The aim of this study was to compare the effect of 8 weeks of concurrent plyometric and running training on spatiotemporal parameters and physiological variables of novice runners. Twenty-five male participants were randomly assigned into two training groups; running group (RG) (n = 11) and running + plyometric group (RPG) (n = 14). Both groups performed 8 weeks of running training programme, and only the RPG performed a concurrent plyometric training programme (two sessions per week). Anthropometric, physiological (VO2max, heart rate and RE) and spatiotemporal variables (contact and flight times, step rate and length) were registered before and after the intervention. In comparison to RG, the RPG reduced step rate and increased flight times at the same running speeds (P < .05) while contact times remained constant. Significant increases in pre- and post-training (P < .05) were found in RPG for squat jump and 5 bound test, while RG remained unchanged. Peak speed, ventilatory threshold (VT) speed and respiratory compensation threshold (RCT) speed increased (P < .05) for both groups, although peak speed and VO2max increased more in the RPG than in the RG. In conclusion, concurrent plyometric and running training entails a reduction in step rate, as well as increases in VT speed, RCT speed, peak speed and VO2max. Athletes could benefit from plyometric training in order to improve their strength, which would contribute to them attaining higher running speeds.

Asunto(s)

RESUMEN

Gómez-Molina, J, Ogueta-Alday, A, Stickley, C, Tobalina, JC, Cabrejas-Ugartondo, J, and García-López, J. Differences in spatiotemporal parameters between trained runners and untrained participants. J Strength Cond Res 31(8): 2169-2175, 2017-The aim of this study was to compare the spatiotemporal parameters of trained runners and untrained participants with the same foot strike pattern (rearfoot) during running at controlled speeds. Twenty-one participants were classified in 2 groups according to their training experience: Trained (n = 10, amateur runners with long distance training experience) and Untrained (n = 11, healthy untrained participants). Anthropometric variables were recorded, and the participants performed both a submaximal (between 9 and 15 km·h) and a graded exercise running test (from 6 km·h until exhaustion) on a treadmill. Physiological (V[Combining Dot Above]O2max, heart rate, running economy [RE], peak speed …) and biomechanical variables (contact and flight times, step rate, and length) were simultaneously registered. Trained runners showed higher step rate and shorter step length than the Untrained group at the same running speeds (between 4 and 7%, p ≤ 0.05) and at the same physiological intensities (between 7 and 11%, p ≤ 0.05). However, there were no differences in contact and flight times between groups. Significant differences (p ≤ 0.05) and large effect sizes (Cohen's d) between groups were found for body mass, sum of 6 skinfolds, V[Combining Dot Above]O2max, peak speed, and ventilatory threshold and respiratory compensation threshold speeds. The Trained group also showed a ∼7% better RE (ml·kg·km) than the Untrained group. In conclusion, adopting higher step rate and shorter step length may be an adaptive mechanism of the Trained group to reduce injury risk and possibly improve RE. However, contact and flight times were consistent regardless of training level.

Asunto(s)

RESUMEN

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO2max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance.