Effects of playing surface on physical, physiological and perceptual responses to a repeated sprint ability test: natural grass versus artificial turf

Purpose: The effect of playing surface on physical performance during a repeated sprint ability (RSA) test, and the mechanisms for any potential playing-surface-dependent effects on RSA performance, is equivocal. The purpose of this study was to investigate the effect of natural grass (NG) and artificial turf (AT) on physical performance, ratings of perceived exertion (RPE), feeling scale (FS) and blood biomarkers related to anaerobic contribution [lactate (Lac)], muscle damage [creatine kinase (CK) and lactate dehydrogenase (LDH)], inflammation [c-reactive protein (CRP)] and immune function [neutrophils (NEU), lymphocytes (LYM) and monocytes (MON)] in response to a RSA test. Methods: Nine male professional football players from the same regional team completed two sessions of RSA testing (6 × 30 s interspersed by 35 s recovery) on NG and AT in a randomized order. During the RSA test, total (sum of distances) and peak (highest distance covered in a single repetition) distance covered were determined using a measuring tape and the decrement in sprinting performance from the first to the last repetition was calculated. Before and after the RSA test, RPE, FS, and blood [Lac], [CK], [LDH], [CRP], [NEU], [LYM] and [MON] were recorded in both NG and AT conditions. Results: Although physical performance declined during the RSA blocks on both surfaces (p=0.001), the distance covered declined more on NG (15%) compared to AT [11%; p=0.04, ES=-0.34, 95% CI (-1.21, 0.56)] with a higher total distance covered (+6 ± 2%) on AT [p=0.018, ES=1.15, 95% CI (0.16, 2.04)]. In addition, lower RPE [p=0.04, ES=-0.49, 95% CI (-1.36, 0.42)] and blood [Lac], [NEU] and [LYM] [p=0.03; ES=-0.80, 95% CI (-1.67, 0.14); ES=-0.16, 95% CI (-1.03, 0.72) and ES=-0.94, 95% CI (-1.82, 0.02), respectively)] and more positive feelings [p=0.02, ES=0.81, 95%CI (-0.13, 1.69)] were observed following the RSA test performed on AT compared to NG. No differences were observed in the remaining physical and blood markers. Conclusion: These findings suggest that RSA performance is enhanced on AT compared to NG. This effect was accompanied by lower fatigue perception and blood [Lac], [NEU] and [LYM], and a more pleasurable feeling. These observations might have implications for physical performance in intermittent team sports athletes who train and compete on different playing surfaces

Effects of pomegranate juice supplementation on oxidative stress biomarkers following weightlifting exercise

The aim of this study was to test the hypothesis that pomegranate juice supplementation would blunt acute and delayed oxidative stress responses after a weightlifting training session. Nine elite weightlifters (21.0 ±1 years) performed two Olympic-Weightlifting sessions after ingesting either the placebo or pomegranate juice supplements. Venous blood samples were collected at rest and 3 min and 48 h after each session. Compared to the placebo condition, pomegranate juice supplementation attenuated the increase in malondialdehyde (-12.5%; p < 0.01) and enhanced the enzymatic (+8.6% for catalase and +6.8% for glutathione peroxidase; p < 0.05) and non-enzymatic (+12.6% for uric acid and +5.7% for total bilirubin; p < 0.01) antioxidant responses shortly (3 min) after completion of the training session. Additionally, during the 48 h recovery period, pomegranate juice supplementation accelerated (p < 0.05) the recovery kinetics of the malondialdehyde (5.6%) and the enzymatic antioxidant defenses compared to the placebo condition (9 to 10%). In conclusion, supplementation with pomegranate juice has the potential to attenuate oxidative stress by enhancing antioxidant responses assessed acutely and up to 48 h following an intensive weightlifting training session. Therefore, elite weightlifters might benefit from blunted oxidative stress responses following intensive weightlifting sessions, which could have implications for recovery between training sessions

Performance of classical k-means, simplified kmlShape using DTW and simplified kmlShape with <i>λ</i> = 0.1 on Trace dataset.

<p>Performance of classical k-means, simplified kmlShape using DTW and simplified kmlShape with <i>λ</i> = 0.1 on Trace dataset.</p

Trace trajectories.

<p>(a) real means (b) means found using a classical method; (c) means found using kmlShape with <i>λ</i> = 0.1. kmlShape found the real means.</p

Performance of classical k-means, simplified kmlShape using DTW and simplified kmlShape with <i>λ</i> = 1 on CBF dataset.

<p>Performance of classical k-means, simplified kmlShape using DTW and simplified kmlShape with <i>λ</i> = 1 on CBF dataset.</p

Performance of classical k-means, kmlShape, and simplified kmlShape with medium datasets (mean value ± standard deviation).

<p>Performance of classical k-means, kmlShape, and simplified kmlShape with medium datasets (mean value ± standard deviation).</p

The impact of using the classical distance, the classical mean, the Fr´echet distance and the Fr´echet mean.

<p>The impact of using the classical distance, the classical mean, the Fr´echet distance and the Fr´echet mean.</p

CBF trajectories.

<p>(a) real means (b) means found using a classical method; (c) means foung using kmlShape with DTW (d) means found using kmlShape with <i>λ</i> = 0.1. kmlShape found the real means.</p

LH trajectories, in 3 clusters.

<p>(a) with a classical method; (b) with kmlShape. kmlShape shows typical trajectories with two peaks that are not found with the classical method.</p

Steps for data partitioning with kmlShape the final complexity is in <i>O</i>(<i>nt</i>).

<p>Steps for data partitioning with kmlShape the final complexity is in <i>O</i>(<i>nt</i>).</p