Anthropometric and Physical Qualities of Elite Male Youth Rugby League Players

Rugby league is a collision team sport played at junior and senior levels worldwide, whereby players require highly developed anthropometric and physical qualities (i.e., speed, change of direction speed, aerobic capacity, muscular strength and power). Within junior levels, professional clubs and national governing bodies implement talent identification and development programmes to support the development of youth (i.e., 13-20 years) rugby league players into professional athletes. This review presents and critically appraises the anthropometric and physical qualities of elite male youth rugby league players aged between 13 and 20 years by age category, playing standard and playing position. Height, body mass, body composition, linear speed, change of direction speed, aerobic capacity, muscular strength and power characteristics are presented and demonstrate that qualities develop with age and differentiate between playing standard and playing position. This highlights the importance of anthropometric and physical qualities for the identification and development of youth rugby league players. However, factors such as maturity status, variability in development, longitudinal monitoring and career attainment should be considered to help understand, identify and develop the physical qualities of youth players. Further extensive research is required into the anthropometric and physical qualities of youth rugby league players, specifically considering national standardized testing batteries, links between physical qualities and match performance, together with intervention studies, to inform the physical development of youth rugby league players for talent identification and development purposes

Variations of training load, monotony, and strain and dose-response relationships with maximal aerobic speed, maximal oxygen uptake, and isokinetic strength in professional soccer players

This study aimed to identify variations in weekly training load, training monotony, and training strain across a 10-week period (during both, pre- and in-season phases); and to analyze the dose-response relationships between training markers and maximal aerobic speed (MAS), maximal oxygen uptake, and isokinetic strength. Twenty-seven professional soccer players (24.9±3.5 years old) were monitored across the 10-week period using global positioning system units. Players were also tested for maximal aerobic speed, maximal oxygen uptake, and isokinetic strength before and after 10 weeks of training. Large positive correlations were found between sum of training load and extension peak torque in the right lower limb (r = 0.57, 90%CI[0.15;0.82]) and the ratio agonist/antagonist in the right lower limb (r = 0.51, [0.06;0.78]). It was observed that loading measures fluctuated across the period of the study and that the load was meaningfully associated with changes in the fitness status of players. However, those magnitudes of correlations were small-to-large, suggesting that variations in fitness level cannot be exclusively explained by the accumulated load and loading profile

Physical fitness components associated with performance in a multiple-sprint test.

PURPOSE: The 5-m repeat-sprint test (5-m RST) measures resistance to fatigue after repeated bouts of short-duration, high-intensity activity. This study determined the components of fitness associated with performance in 5-m RSTs. METHODS: Speed (10-m and 40-m sprints), strength (bench press), agility, strength endurance (pull-ups and push-ups), and aerobic power (20-m shuttle-run test) were measured in male provincial- or national-level rugby (n = 110), hockey (n = 59), and soccer (n = 55) players. RESULTS: Subjects with either high (HI) or low (LO) resistance to fatigue in the 5-m RST differed in body mass (76.9 +/- 11.6 kg vs 102.1 +/- 18.9 kg, HI vs LO, respectively, P < .001), agility (14.55 +/- 0.41 seconds vs 15.56 +/- 0.30 seconds, P < .001), bench press (86 +/- 20 kg vs 114 +/- 33 kg, P = .03), pull-ups (13 +/- 4 vs 8 +/- 5, P = .02), push-ups (56 +/- 12 vs 39 +/- 13, P = .002), and 20-m shuttle-run test (20-m SRT; 133 +/- 11 vs 87 +/- 12 shuttles, P < .001). Body mass, strength, and aerobic power were the best predictors of 5-m RST performance: 5-m RST = -1.274(mass) + 0.756(1RM bench press) + 2.053(number of 20-m SRT shuttles) + 549.409 (R2 = .66). CONCLUSIONS: Performance in the 5-m RST is predicted best by a combination of factors including body mass, strength, and aerobic ability, rather than by any single component of fitness

Expanding the genotypic spectrum of CCBE1 mutations in Hennekam syndrome

Hennekam lymphangiectasia-lymphedema syndrome is an autosomal recessive disorder, with 25% of patients having mutations in CCBE1. We identified a family with two brothers presenting with primary lymphedema, and performed exome sequencing to determine the cause of their disease. Analysis of four family members showed that both affected brothers had the same rare compound heterozygous mutations in CCBE1. The presumed paternally inherited NM_133459.3:c.310G>A; p.(Asp104Asn), lies adjacent to other known pathogenic CCBE1 mutations, while the maternally inherited NM_133459.3:c.80T>C; p.(Leu27Pro) lies in the CCBE1 signal peptide, which has not previously been associated with disease. Functional analysis in a zebrafish model of lymphatic disease showed that both mutations lead to CCBE1 loss of function, confirming the pathogenicity of these variants and expanding the genotypic spectrum of lymphatic disorders. (c) 2016 Wiley Periodicals, Inc

How Much Rugby is Too Much? A Seven-Season Prospective Cohort Study of Match Exposure and Injury Risk in Professional Rugby Union Players.

INTRODUCTION: Numerous studies have documented the incidence and nature of injuries in professional rugby union, but few have identified specific risk factors for injury in this population using appropriate statistical methods. In particular, little is known about the role of previous short-term or longer-term match exposures in current injury risk in this setting. OBJECTIVES: Our objective was to investigate the influence that match exposure has upon injury risk in rugby union. METHOD: We conducted a seven-season (2006/7-2012/13) prospective cohort study of time-loss injuries in 1253 English premiership professional players. Players' 12-month match exposure (number of matches a player was involved in for ≥20 min in the preceding 12 months) and 1-month match exposure (number of full-game equivalent [FGE] matches in preceding 30 days) were assessed as risk factors for injury using a nested frailty model and magnitude-based inferences. RESULTS: The 12-month match exposure was associated with injury risk in a non-linear fashion; players who had been involved in fewer than ≈15 or more than ≈35 matches over the preceding 12-month period were more susceptible to injury. Monthly match exposure was linearly associated with injury risk (hazard ratio [HR]: 1.14 per 2 standard deviation [3.2 FGE] increase, 90% confidence interval [CI] 1.08-1.20; likely harmful), although this effect was substantially attenuated for players in the upper quartile for 12-month match exposures (>28 matches). CONCLUSION: A player's accumulated (12-month) and recent (1-month) match exposure substantially influences their current injury risk. Careful attention should be paid to planning the workloads and monitoring the responses of players involved in: (1) a high (>≈35) number of matches in the previous year, (2) a low (<≈15) number of matches in the previous year, and (3) a low-moderate number of matches in previous year but who have played intensively in the recent past. These findings make a major contribution to evidence-based policy decisions regarding match workload limits in professional rugby union

Differential training loads and individual fitness responses to pre-season in professional rugby union players

We aimed to compare differentiated training loads (TL) between fitness responders and non-responders to an eight-week pre-season training period in a squad of thirty-five professional rugby union players. Differential TL were calculated by multiplying player’s perceptions of breathlessness (sRPE-B) and leg muscle exertion (sRPE-L) with training duration for each completed session. Performance-based fitness measures included the Yo-Yo Intermittent Recovery Test Level 1 (YYIRTL1), 10-, 20-, and 30-m linear sprint times, countermovement jump height (CMJ) and predicted one-repetition maximum back squat (P1RM Squat). The proportion of responders (≥ 75% chance that the observed change in fitness was > typical error and smallest worthwhile change) were 37%, 50%, 52%, 82% and 70% for YYIRTL1, 20/30-m, 10-m, CMJ and P1RM Squat, respectively. Weekly sRPE-B-TL was very likely higher in YYIRTL1 responders (mean difference = 18%; ±90% confidence limits 11%), likely lower in 20/30-m (19%; ±20%) and 10-m (18%; ±17%) responders, and likely higher in CMJ responders (15%; ±16%). All other comparisons were unclear. Weekly sRPE-B discriminate between rugby union players who respond to pre-season training when compared with players who do not. Our findings support the collection of differential ratings of perceived exertion and the use of individual response analysis in team-sport athletes

A three-season comparison of match performances among selected and unselected elite youth rugby league players

This is an author's accepted manuscript of an article published in Journal of Sports Sciences, 28 February 2014, available online: http:www.tandfonline.com/10.1080/02640414.2014.889838This study compared technical actions, movements, heart rates and perceptual responses of selected and unselected youth rugby league players during matches (under-15 to under-17). The players’ movements and heart rates were assessed using 5 Hz Global Positioning Systems (GPS), while their technical actions were analysed using video analysis. The maturity of each player was predicted before each season for statistical control. There were no differences (P > 0.05) between selected and unselected players in the under-15 or the under-17 age groups for any variables. However, in the under-16 group, the selected players (57.1 ± 11.9 min) played for longer than the unselected players (44.1 ± 12.3 min; P = 0.017; ES = 1.08 ± CI = 0.87), and covered more distance (5,181.0 ± 1063.5 m cf. 3942.6 ± 1,108.6m, respectively; P = 0.012; ES = 1.14 ± CI = 0.88) and high intensity distance (1,808.8 ± 369.3 m cf. 1,380.5 ± 367.7 m, respectively; P = 0.011; ES = 1.16 ± CI = 0.88). Although successful carries per minute was higher in the selected under-15 group, there were no other differences (P > 0.05) in match performance relative to playing minutes between groups. Controlling for maturity, the less mature, unselected players from the under-16 group performed more high-intensity running (P < 0.05). Our findings question the use of match- related measurements in differentiating between selected and unselected players, showing that later maturing players were unselected, even when performing greater high-intensity running during matches

Activity profiles of elite wheelchair rugby players during competition

To quantify the activity profiles of elite wheelchair rugby and establish classification-specific arbitrary speed zones. Additionally, indicators of fatigue during full matches were explored. Methods: Seventy-five elite wheelchair rugby players from eleven national teams were monitored using a radio-frequency based, indoor tracking system across two international tournaments. Players who participated in complete quarters (n = 75) and full matches (n = 25) were included and grouped by their International Wheelchair Rugby Federation functional classification: group I (0-0.5), II (1.0-1.5), III (2.0-2.5) and IV (3.0-3.5). Results: During a typical quarter, significant increases in total distance (m), relative distance (m·minˉ¹), and mean speed (m·sˉ¹) were associated with an increase in classification group (P < 0.001), with the exception of group III and IV. However, group IV players achieved significantly higher peak speeds (3.82 ± 0.31 m·sˉ¹) than groups I (2.99 ± 0.28 m·sˉ¹), II (3.44 ± 0.26 m·sˉ¹) and III (3.67 ± 0.32 m·sˉ¹). Groups I and II differed significantly in match intensity during very low/low speed zones and the number of high-intensity activities in comparison with groups III and IV (P < 0.001). Full match analysis revealed that activity profiles did not differ significantly between quarters. Conclusions: Notable differences in the volume of activity were displayed across the functional classification groups. However, the specific on-court requirements of defensive (I and II) and offensive (III and IV) match roles appeared to influence the intensity of match activities and consequently training prescription should be structured accordingly