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This study aimed to 1) develop a consensus (≥70% agreement between experts) on injury risk factors specific to women playing rugby league, 2) establish the importance of the identified injury risk factors and the feasibility of mitigating these risk factors and 3) establish context specific barriers to injury risk management. Aim 1: A Delphi panel, consisting of 12 experts in rugby league and injury (e.g., physiotherapists, research scientists) were asked to identify injury risk factors specific to women playing rugby league. Aim 2: seven coaches of women's rugby league teams were asked to rate each risk factor that achieved consensus by their importance and feasibility to manage. Aim 3: Coaches reported barriers which restrict injury risk factor mitigation. Of the 53 injury risk factors which achieved consensus, the five injury risk factors with the highest combination of importance and feasibility ratings were: "poor tackle technique", "a lack of pre-season intensity", "training session are too short", "the current medical standards", and "limited access to physiotherapists". Following the identification of injury risk factors, their feasibility to manage and context specific barriers, this study proposes three constraint driven, integrated solutions which may reduce the barriers which limit injury risk factor management.

This study examined the relative contribution of exercise duration and intensity to team-sport athlete's training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load [sRPE-TL], individualised training impulse, total distance, BodyLoad™; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad™). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC

This systematic review aimed to identify and summarise associations between currently identified contextual factors and match running in senior male professional rugby league. Eligible articles included at least one contextual factor and used GPS to measure at least one displacement variable within competitive senior, male, professional rugby league matches. Of the 15 included studies, the identified contextual factors were grouped into factors related to individual characteristics (n = 3), match result (n = 4), team strength (n = 2), opposition strength (n = 3), match conditions (n = 6), technical and tactical demands (n = 6), spatial and temporal characteristics (n = 7), and nutrition (n = 1). Speed was the most commonly reported measure of match running (100%), followed by distance (47%), and acceleration (20%). Inconsistencies were found between studies for most contextual factors on match running. Higher speeds were generally associated with higher fitness, encountered earlier in the match and whilst defending. All 15 studies utilised a univariate approach to quantify associations of a contextual factor. The inconsistencies found in the associations of given contextual factors highlight the complex and multi-faceted nature of match running. Therefore, practitioners should consider contextual factors when analysing and interpreting GPS data.

Establishing dose-response relationships between training load and fatigue can help the planning of training. The aim was to establish the relative importance of external training load measurements to relate to the musculoskeletal response on a group and individual player level. Sixteen elite male rugby league players were monitored across three seasons. Two to seven day exponential weighted averages (EWMA) were calculated for total distance, and individualised speed thresholds (via 30-15 Intermittent Fitness Test) derived from global positioning systems. The sit and reach, dorsiflexion lunge, and adductor squeeze tests represented the musculoskeletal response. Partial least squares and repeated measures correlation analyses established the relative importance of training load measures and then investigated their relationship to the collective musculoskeletal response for individual players through the construction of latent variables. On a group level, 2 and 3 day EWMA total distance had the highest relative importance to the collective musculoskeletal response (p < 0.0001). However, the magnitude of relationships on a group (r value = 0.20) and individual (r value = 0.06) level were trivial to small. The lack of variability in the musculoskeletal response over time suggest practitioners adopting such measures to understand acute musculoskeletal fatigue responses should do so with caution.

To quantify the peak movement and contact demands of National Rugby League (NRL) and European Super League (ESL) competition players were tracked during 10 NRL (166 files) and 10 ESL (143 files) matches using microtechnology devices. The peak 1- to 5-min periods were then calculated for average match speed (m·min−1), and acceleration (m·s−2) when 0, 1, 2, and ≥3 collisions per min occurred. Linear mixed effect models and Cohen’s effect size statistic (± 90%CI) were used to determine the differences in movement profiles when collisions occurred. Compared to no collision periods, as frequency of collisions per minute increased, there were progressive reductions in running speed for most positional groups. The addition of 1 or more collisions per min resulted in average effect size reductions in match speed of −0.14 for NRL forwards, −0.89 for NRL backs, −0.48 for ESL forwards, and −2.41 for ESL backs. ESL forwards had the highest frequency of peak periods involving 3 or more collisions per min, 22% of all periods, followed by NRL forwards (14%), NRL backs (10%) and ESL backs (8%). This study highlights the peak movement and collision demands of professional rugby league competition and allows practitioners to develop training drills that reflect worst case scenarios.

Understanding the locomotor characteristics of competition can help rugby league (RL) coaches optimise training prescription. To date, no research exists on the locomotor characteristics of women's RL. The aim was to compare whole match and peak locomotor characteristics of women's RL competition at international (RL World Cup [WRLWC]) and domestic level (Super League [WSL]). Microtechnology data were collected from 58 players from 3-WSL clubs and 1-WRLWC team. Participants were classified into forwards (

This systematic review and meta-analysis aimed to determine differences in body compositionn between playing standard and age in male rugby union and rugby league athletes. The MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines for design, implementation, and reporting were followed. Studies were required to be in male rugby union or league and have body composition as the primary or secondary outcome. Data was required to be presented separately for positional groups and body composition presented as whole-body. A systematic search was performed in PubMed, Cochrane Library, MEDLINE, SPORTDiscus, and CINHAHL via EBSCOhost. 57 studies were included for meta-analysis. Results highlighted significantly higher fat-free mass in senior elite than senior sub-elite or junior elite athletes for all RU and RL forwards. Small and non-significant differences were found in fat mass between rugby union playing standards and age categories. Rugby league senior elite forwards had less fat mass than junior elite forwards. Practitioners should prioritise training and nutritional strategies that maximise fat-free mass development, especially in junior elite cohorts.

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Academy rugby league competition is an important step along the pathway to professional status, but little is known about injury at this level of the game. The aim of this research was to establish the nature, incidence and burden of injury in English academy rugby league. Using an observational prospective cohort study design, and a time-loss injury definition, the injury outcomes of three professional rugby league academies were recorded during the 2017 season. A total of 87 injuries occurred in 59 matches for an overall injury incidence of 85 (95%CI 67-103) injuries per 1000 hours played. The mean severity of injury was 22 ± 19 days resulting in an overall injury burden of 1898 (95%CI 1813-1983) days lost per 1000 hours. The tackle event was the most common cause of injury (77% of all injuries). Forwards sustained a greater proportion of injuries than backs (forwards 67% vs. backs 33% of injuries). Concussion (13 (6-20) per 1000 hours) and ankle sprains (11 (4-17) per 1000 hours) were the most commonly diagnosed injuries. The shoulder joint was the most commonly injured site (17 (9-25) per 1000 hours). The incidence of injury for academy rugby league is similar to senior professional rugby league.

The aim of this study was to investigate the differences and long-term reliability in perceptual, metabolic, and neuromuscular responses to velocity loss resistance training protocols. Using a repeated, counterbalanced, crossover design, twelve team-sport athletes completed 5-sets of barbell back-squats at a load corresponding to a mean concentric velocity of ~0.70 m·s-1. On different days, repetitions were performed until a 10%, 20% or 30% velocity loss was attained, with outcome measures collected after each set. Sessions were repeated after four-weeks. There were substantial between-protocol differences in post-set differential ratings of perceived exertion (dRPE, i.e., breathlessness and leg muscles, AU) and blood lactate concentration (B[La], mmol·L-1), such that 30%>20%>10% by small to large magnitudes. Differences in post-set countermovement jump (CMJ) variables were small for most variables, such that 30%

© 2018 Informa UK Limited, trading as Taylor & Francis Group Limited research has compared the physical qualities of adolescent rugby union (RU) players across differing playing standards. This study therefore compared the physical qualities of academy and school Under-18 RU players. One-hundred and eighty-four (professional regional academy, n = 55 school, n = 129) male RU players underwent a physical testing battery to quantify height, body mass, strength (bench press and pull-up), speed (10, 20 and 40 m), 10 m momentum (calculated; 10 m velocity * body mass) and a proxy measure of aerobic fitness (Yo-Yo Intermittent Recovery Test Level 1; IRTL1). The practical significance of differences between playing levels were assessed using magnitude-based inferences. Academy players were taller (very likely small), heavier (likely moderate) and stronger (bench press possibly large; pull-up plus body mass likely small) than school players. Academy players were faster than school players over 20 and 40 m (possibly and likely small), although differences in 10 m speed were not apparent (possibly trivial). Academy players displayed greater 10 m momentum (likely moderate) and greater IRTL1 performance (likely small) than school players. These findings suggest that body size, strength, running momentum, 40 m speed and aerobic fitness contribute to a higher playing standard in adolescent rugby union.

The aim was to compare the physical characteristics of under-18 academy and schoolboy rugby union competition by position (forwards and backs). Using a microsensor unit, match characteristics were recorded in 66 players. Locomotor characteristics were assessed by maximum sprint speed (MSS) and total, walking, jogging, striding and sprinting distances. The slow component (

This study investigated the change in body composition and bone mineral content (BMC) of senior rugby league players between 2008 and 2014. Twelve male professional rugby league players (age, 24.6±4.0 years; stature, 183.4±8.4 cm) received a DXA scan during pre-season in 2008 and 2014. Between 2008 and 2014, very likely increases in leg lean mass, total trunk and leg BMC, and a likely increase in arm BMC and possible increases in body mass, total and trunk fat mass, and total, trunk and arm lean mass were observed. Unlikely decreases and unclear changes in leg and arm fat mass were also found. Large negative correlations were observed between age and body mass (r=-0.72), lean mass (r=-0.70), fat mass (r=-0.61), and BMC (r=-0.84) change. Three participants (19.1 ± 1.6 years) increased lean mass by 7.0 – 9.3 kg. Younger players had the largest increases in lean mass during this period, although an older player (30 year-old) still increased lean mass. Differences in body composition change were also observed for participants of the same age, thus contextual factors should be considered. This study demonstrates the individuality of body composition changes in senior professional rugby players, while considering the potential change in young athletes.

This study examined the validity of a tool that assesses tackle and ruck technique in training and established reference data for tackle, ball-carry and ruck technique at different levels of play in rugby union. One hundred and thirty-one amateur rugby union players; 37 senior, 51 first-grade academy and 43 second-grade academy players, participated in a two-on-two contact drill. The drill was filmed and the players' tackle, ball-carry and ruck technique were assessed using standardized technical criteria.

The aims of this study were (a) to compare players' physical demands between different playing positions in elite U18 basketball games and (b) to identify different clusters of performance. Data were collected from 94 male subjects (age: 17.4 ± 0.7 years), competing in a Euroleague Basketball Tournament. Guards covered a greater relative distance than centres and forwards (small to moderate effect). Forwards and guards had more peak accelerations, high accelerations and high decelerations than centres (moderate to large effects). A cluster analysis allowed to classify all cases into three different groups (Lower, Medium and Higher activity demands), containing 37.4%, 52.8% and 9.8% of the cases, respectively. The high accelerations, high decelerations, peak accelerations and total distance covered were the variables that most contributed to classify the players into the new groups. The percentage of cases distributed in the clusters according to playing position, game type (worst vs worst, mixed opposition, best vs best) and team were different. Centres have lower physical demands specially related with the number of accelerations and decelerations at high intensity and the peak acceleration when compared with guards. Each team has a different activity profile, that does not seem to influence the tournament outcome.

This study assessed the influence of training load, exposure to match play and sleep duration on two daily wellbeing measures in youth athletes. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on the previous day’s training loads (TL) and self-reported sleep duration (sleep) every day for 13 weeks (n = 2727). Linear mixed models assessed the effect of TL, exposure to match play and sleep on DWB and PRS. An increase in TL had a most likely small effect on muscle soreness (d = −0.43;± 0.10) and PRS (d = −0.37;± 0.09). Match play had a likely small additive effect on muscle soreness (d = −0.26;± 0.09) and PRS (d = −0.25;± 0.08). An increase in sleep had a most likely moderate effect on sleep quality (d = 0.80;± 0.14); a most likely small effect on DWB (d = 0.45;± 0.09) and fatigue (d = 0.42;± 0.11); and a likely small effect on PRS (d = 0.25;± 0.09). All other effects were trivial or did not reach the pre-determined threshold for practical significance. The influence of sleep on multiple DWB subscales and the PRS suggests that practitioners should consider the recovery of an athlete alongside the training stress imposed when considering deviations in wellbeing measures.

The most frequently occurring contact events in rugby union are the tackle and ruck. The ability repeatedly to engage and win the tackle and ruck has been associated with team success. To win the tackle and ruck, players have to perform specific techniques. These techniques have not been studied at the highest level of rugby union. Therefore, the purpose of this study was to identify technical determinants of tackle and ruck performance at the highest level of rugby union. A total of 4479 tackle and 2914 ruck events were coded for the Six Nations and Championship competitions. Relative risk ratio (RR), the ratio of the probability of an outcome occurring when a characteristic was observed (versus the non-observed characteristic), was determined using multinomial logistic regression. Executing front-on tackles reduced the likelihood of offloads and tackle breaks in both competitions (Six Nations RR 3.0 Behind tackle, 95% confidence interval [95% CI]: 1.9–4.6, effect size [ES] = large, P