Browsing Faculty of Medicine, Dentistry and Life Sciences by Subjects
Now showing items 1-4 of 4
Carbohydrate and caffeine improves high intensity running of elite rugby league interchange players during simulated match playCarbohydrate and caffeine improves high intensity running of elite rugby league interchange players during simulated match play
The discriminant validity of standardised testing battery and its ability to differentiate anthropometric and physical characteristics between youth, academy and senior professional rugby league playersPurpose: To assess a standardised testing battery’s ability to differentiate anthropometric and physical qualities between youth, academy and senior rugby league players, and determine the discriminant validity of the battery. Methods: A total of 729 rugby league players from multiple clubs within England categorised as youth (n = 235), academy (n = 362) and senior (n = 132) players completed a standardised testing battery that included the assessment of anthropometric and physical characteristics during preseason. Data was analysed using magnitude-based inferences and discriminant analysis. Results: Academy players were most likely taller and heavier than youth players (effect size (ES) = 0.64 to 1.21), with possibly to most likely superior CMJ, medicine ball throw and prone Yo-Yo IR1 performance (ES = 0.23 to 1.00). Senior players were likely to most likely taller and heavier (ES = 0.32 to 1.84), with possibly to most likely superior 10 and 20 m sprint times, CMJ, CoD, medicine ball throw and prone Yo-Yo IR1 compared to youth and academy (ES = -0.60 to 2.06). The magnitude of difference appeared to be influenced by playing position. For the most part, the battery possessed discriminant validity with an accuracy of 72.2%. Conclusion: The standardised testing battery differentiates anthropometric and physical qualities of youth, academy and senior players as a group and, in most instances, within positional groups. Furthermore, the battery is able to discriminate between playing standards with good accuracy and might be included in future assessments and rugby league talent identification.
Factors affecting the anthropometric and physical characteristics of elite academy rugby league players: a multi-club study.Purpose: To investigate the factors affecting the anthropometric and physical characteristics of elite academy rugby league players. Methods: One hundred and ninety-seven elite academy rugby league players (age = 17.3 ± 1.0 years) from five Super League clubs completed measures of anthropometric and physical characteristics during a competitive season. The interaction between, and influence of contextual factors on characteristics was assessed using linear mixed modelling. Results: Associations were observed between several anthropometric and physical characteristics. All physical characteristics improved during preseason and continued to improve until mid-season where thereafter 10 m sprint (η2 = 0.20 cf. 0.25), CMJ (η2 = 0.28 cf. 0.30) and prone Yo-Yo Intermittent Recovery Test (Yo-Yo IR) (η2 = 0.22 cf. 0.54) performance declined. Second (η2 = 0.17) and third (η2 = 0.16) years were heavier than first years, whilst third years had slower 10 m sprint times (η2 = 0.22). Large positional variability was observed for body mass, 20 m sprint time, medicine ball throw, countermovement jump, and prone Yo-Yo IR1. Compared to bottom-ranked teams, top demonstrated superior 20 m (η2 = -0.22) and prone Yo-Yo IR1 (η2 = 0.26) performance whilst middle-ranked teams reported higher CMJ height (η2 = 0.26) and prone Yo-Yo IR1 distance (η2 = 0.20), but slower 20 m sprint times (η2 = 0.20). Conclusion: These findings offer practitioners designing training programmes for academy rugby league players insight into the relationships between anthropometric and physical characteristics and how they are influenced by playing year, league ranking, position and season phase.
Quantification of physical contact and its influence on simulated performance and recovery in rugby players.The aim of this thesis was to investigate the influence of physical collisions on internal (physiological and perceptual) and external (locomotive and accelerometer) load during simulated rugby league performance and fatigue responses in the days after. Chapter 4 examined the influence of physical contact type on internal and external load using a traditional soft tackle bag and custom-built tackle sled. Using a traditional tackle bag to simulate physical collisions resulted in likely faster sprint to contact speed (16.1 ± 1.5 c.f. 14.8 ± 1.1 km.h -1 ) but possibly lower overall high-speed running distance (27.7 ± 2.4 c.f. 28.4 ± 2.6 m.min-1 ). Also, the heavier tackle sled likely increased time at 91-100% HRpeak (12:58 ± 13:21 c.f. 6:44 ± 8:06 min:s) and resulted in greater lower limb fatigue reflected by the likely larger decrease in countermovement jump (CMJ) performance (5.9 ± 4.9 c.f. 2.6 ± 5.4%). Also of note was the variation in number of tackles detected using the automatic tackle detection feature compared to the actual number in the match simulation. During the Bag and Sled simulations ~53 and ~59 tackles were detected compared to 48 performed. The purpose of Chapter 5 was to investigate the influence of sprint to contact speed and contact type on automatic tackle detection using microtechnology. Repetitions were divided into three speed categories; walking, jogging and striding (1, 2.5 and 4 m.s -1 ) and four conditions: i) no contact standing upright (NCST), ii) no contact dropping to the ground in a prone position (NCGR), iii) contact with the tackle bag and remaining upright (CST), iv) contact with the tackle bag and going to ground (CGR). Similar tackle detection accuracy was observed between NCGR and CST conditions with one tackle observed in 41 and 43% of trials, respectively. While CGR resulted in the greatest frequency of correct tackle detection (62%), during 16% of trials two tackles were detected. During NCST, there were no tackles detected and 100% accuracy. The PlayerLoadTM results demonstrated that the metric can detect differences in movement speed, the inclusion of physical contact and changes in orientation during short periods of activity (8-10 s). In Chapter 6 the rugby league movement simulation protocol for interchange players (RLMSP-i) was modified to include a tackle shield collision to investigate the reliability of PlayerLoadTM metrics to quantify collision load. The coefficient of variation (%CV) for locomotive metrics ranged from 1.3 to 14.4%, with greatest variability observed for high-speed running distance (8.0 and 14.4% for Bouts 1 and 2, respectively). Accelerometer metrics CV% were 4.4 to 10.0%, while internal load markers were 4.8 to 13.7%. All variables presented a CV% less than the calculated moderate change during one or both bouts of the match simulation except from high-speed distance (m.min-1 ), %HRpeak and RPE (AU). The aim of Chapter 7 was to investigate the influence of contact type on external load metrics including PlayerLoadTM derivatives whilst controlling for total running distance. Participants were randomly assigned to one group to complete the match simulation with either a tackle shield (n = 10), tackle bag (n = 7) or no-contact (n = 10). Total PlayerLoadTM, PlayerLoadTM 2D (AU), PlayerLoadTM slow (AU) and PlayerLoadTM slow-ratio (%) were analysed from the accelerometer in addition to high- and low-speed running and sprint speed. Total PlayerLoadTM was likely lower for the Bag group compared to the Run group (498 c.f. 460 AU), with no clear differences between the other groups. 3 PlayerLoadTM slow for the Shield group (167 ± 26 AU) was very likely greater than both the Bag (133 ± 11 AU) and Run groups (128 ± 20 AU) but no clear difference was observed between the Bag and Run groups. No differences were observed in PlayerLoadTM 2D between any groups. High-speed running distance was likely lower in the Shield group (1056 ± 225 m) compared to the Bag group (1326 ± 245 m) and very likely lower compared to the Run group (1318 ± 175 m). Total PlayerLoadTM is not sensitive to contact type during simulated rugby league activity but does reflect greater high-speed running distance during a rugby league match simulation. However, PlayerLoadTM slow can detect the types of contact and might be preferred for quantifying match and training loads associated with physical contact. The purpose of the final empirical chapter (Chapter 8) was to determine the influence of contact type on in neuromuscular, perceptual and biochemical parameters associated with exercise-induced muscle damage. The participants were again assigned to one of three groups to complete the match simulation with a tackle shield (n = 6), tackle bag (n = 7) or no contact (n = 7). In addition to internal and external load measured during the match simulation, venous blood, muscle function and soreness measures were collected immediately (+0), +24 and +72 hours after the match simulation. Upper body neuromuscular performance and knee flexion torque likely decreased in the Shield group +0 and +72 hours after the simulation compared to the other groups while CMJ power likely decreased more in the Run group. All three groups demonstrated a very likely increase in IL-6 and IL-10 concentration immediately after the match simulation, but differences between the groups were unclear and values returned to baseline +24 hours after the simulation. In conclusion, current automatic tackle detection metric should be used with caution, particularly in training sessions where physical contact is replicated. Instead PlayerLoadTM and associated derivatives from the embedded accelerometer can provide a useful measure of contact-specific load during training and competitive matches. Physical contact type affected external load by modifying a participant’s running strategy during simulated match performance, thereby influencing site-specific fatigue during and after a simulated rugby league match. However, regardless of contact type, large increases in cytokine and leukocyte concentration are apparent with a return to basal values 24 hours after. Therefore it is not recommended to use such biomarkers in applied settings to quantify the magnitude of muscle damage specifically associated with physical contact.