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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.

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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.

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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.

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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.

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The main purpose of this study was to assess the acute effects of small changes in crank length (assumable by competitive cyclists) on metabolic cost and pedalling technique during submaximal cycling. Twelve amateur road cyclists performed three sets of submaximal pedalling (150, 200 and 250 W) at a constant cadence (91.3 ± 0.8 rpm) in a randomised order with three commonly used crank lengths, preferred (172.5-175 mm), +5 mm and -5 mm. Energy cost of pedalling, kinetic and kinematic variables were simultaneously registered. Changes in crank length had no significant effect on heart rate (144 ± 13, 145 ± 12 and 145 ± 13 bpm, respectively) and gross efficiency (GE) (20.4 ± 2.1, 20.1 ± 2.2 and 20.3 ± 2.4%, respectively). A longer crank induced a significant (P < 0.05) reduction of positive impulse proportion (PIP) (0.9-1.9%) due to a greater maximum (1.0-2.3 N · m) and minimum torque (1.0-2.2 N · m). At the same time, the maximum flexion and range of motion of the hip and knee joints were significantly increased (1.8-3.4° and P < 0.05), whereas the ankle joint was not affected. In conclusion, the biomechanical changes due to a longer crank did not alter the metabolic cost of pedalling, although they could have long-term adverse effects. Therefore, in case of doubt between two lengths, the shorter one might be recommended.

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The purpose of this study was to compare the pedalling technique in road cyclists of different competitive levels. Eleven professional, thirteen elite and fourteen club cyclists were assessed at the beginning of their competition season. Cyclists' anthropometric characteristics and bike measurements were recorded. Three sets of pedalling (200, 250 and 300 W) on a cycle ergometer that simulated their habitual cycling posture were performed at a constant cadence (~90 rpm), while kinetic and kinematic variables were registered. The results showed no differences on the main anthropometric variables and bike measurements. Professional cyclists obtained higher positive impulse proportion (1.5-3.3% and P < 0.05), mainly due to a lower resistive torque during the upstroke (15.4-28.7% and P < 0.05). They also showed a higher ankle range of movement (ROM, 1.1-4.0° and P < 0.05). Significant correlations (P < 0.05) were found between the cyclists' body mass and the kinetic variables of pedalling: positive impulse proportion (r = -0.59 to -0.61), minimum (r = -0.59 to -0.63) and maximum torques (r = 0.35-0.47). In conclusion, professional cyclists had better pedalling technique than elite and club cyclists, because they opted for enhancing pulling force at the recovery phase to sustain the same power output. This technique depended on cycling experience and level of expertise.

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PURPOSE: This study aimed to analyze the influence of foot strike pattern on running economy and biomechanical characteristics in subelite runners with a similar performance level. METHODS: Twenty subelite long-distance runners participated and were divided into two groups according to their foot strike pattern: rearfoot (RF, n = 10) and midfoot (MF, n = 10) strikers. Anthropometric characteristics were measured (height, body mass, body mass index, skinfolds, circumferences, and lengths); physiological (VO2max, anaerobic threshold, and running economy) and biomechanical characteristics (contact and flight times, step rate, and step length) were registered during both incremental and submaximal tests on a treadmill. RESULTS: There were no significant intergroup differences in anthropometrics, VO2max, or anaerobic threshold measures. RF strikers were 5.4%, 9.3%, and 5.0% more economical than MF at submaximal speeds (11, 13, and 15 km·h respectively, although the difference was not significant at 15 km·h, P = 0.07). Step rate and step length were not different between groups, but RF showed longer contact time (P < 0.01) and shorter flight time (P < 0.01) than MF at all running speeds. CONCLUSIONS: The present study showed that habitually rearfoot striking runners are more economical than midfoot strikers. Foot strike pattern affected both contact and flight times, which may explain the differences in running economy.

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The purposes of this study were to analyze the validity and reliability of 2 photocell mats and to probe the possible influence of the type of mat (contact vs. photocell) on vertical jump height estimated from flight time. In 2 separate studies, 89 and 92 physical students performed 3 countermovement jumps that were simultaneously registered by a Force Plate (gold standard method), 2 photocell mats (SportJump System Pro and ErgoJump Plus), and a contact mat (SportJump-v1.0). The first study showed that the 2 photocell mats underestimated the vertical jump height (1.3 ± 0.2 cm and 5.9 ± 5.2 cm, respectively), but only SportJump System Pro showed a high correlation with the Force Plate (r = 0.999 and 0.676, respectively) and good intraday reliability (coefficient of variation = 2.98 and 15.94%, intraclass correlation coefficients = 0.95-0.97 and 0.45-0.57, respectively). The second study demonstrated a strong correlation (r = 0.994) between the 2 technologies (contact vs. photocell mats) with differences in vertical jump height of 2.0 ± 0.8 cm (95% confidence interval = 1.9-2.1 cm), which depended on both flight time and subjects' body mass. In conclusion, SportJump System Pro was a valid and reliable device. The new devices to measure vertical jump height from flight time should be validated. The type of mat (contact vs. photocell) affected approximately 6% the vertical jump height (approximately 2 cm in this study), which should be considered in further studies. The use of validated photocell mats instead of the contact mats was recommended.

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The purpose of this study was to validate a new method to measure contact and flight times during treadmill running and to test its reliability and sensitivity. Fifteen well-trained runners performed 7 sets of running at different speeds (from 10 to 22 km·h). Contact and flight times were simultaneously recorded by a high-speed video system (gold standard method) and a new method based on laser technology (SportJump System Pro). Athletes were classified according to their foot strike pattern (rearfoot vs. midfoot and forefoot). The new method overestimated the contact time and underestimated the flight time with respect to the gold standard method (p < 0.001). However, relationships and intraclass correlation coefficients (ICCs) between both systems were very strong (r and ICC > 0.99, p < 0.001). Contact time differences between the 2 systems depended on running speed (p < 0.001) but not on foot strike pattern or runners' body mass. This allowed to correct the differences in contact time and flight time. The new method was sensitive for detecting small differences in contact time (<20 ms) when the running speed increased and when the type of foot strike patterns changed. Additionally, a low intraindividual step variability (coefficient of variation = 2.0 ± 0.5%) and high intra- (ICC = 0.998) and interobserver (ICC = 0.977) reliability were shown. In conclusion, the new method was validated, being reliable and sensitive for detecting small differences in contact and flight times during treadmill running. Therefore, it could be used to compare biomechanical variables between groups in cross-sectional studies and to verify the influence of some independent variables (i.e., training, running economy, or performance) on running biomechanics.

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