Dr Sean Scantlebury

Background: There is growing concern that exposure to head acceleration events may be associated with potential long-term health consequences. Rugby league is a contact sport involving a high number of collisions, and therefore has a high risk of head accelerations. It is therefore important to quantify head acceleration exposure in rugby league. Instrumented mouthguards (iMGs) are a validated means for quantifying head acceleration events (HAEs) and have been implemented within men’s rugby league, however HAEs are yet to be quantified within women’s rugby league. Accordingly, this study implemented iMGs across teams participating in the Women’s Super League competition, with the aim of describing HAEs during matches. Methodology: Seven elite women’s rugby league teams were provided with iMGs, resulting in the collection of iMG data from 84 players, across 116 player matches. In-vivo HAEs were approximated using linear and angular kinematics measured by accelerometers and gyroscopes embedded within iMGs. Peak linear acceleration (PLA; g) and peak angular acceleration (PAA; rad/s2) were calculated to approximate the magnitude of each HAE. Validated machine learning classification algorithms were used to remove false positive events from the dataset. Results: Across 116 player matches, 1389 HAEs were recorded. The median (IQR) number of HAEs per player match was 7 (3 to 18) HAEs per player match. The median (IQR) HAE magnitude was 12 (8.6 to 18.1) g and 982 (657 to 1,723) rad/s2, for PLA and PAA, respectively. Towards the higher end of magnitudes, the 95th percentile magnitude was 36.8 g and 3,740 rad/s2. Conclusion: For the first time, HAEs have been quantified in women’s rugby league matches. Overall, the number of head accelerations per player match is lower than previously reported in men’s rugby league, while the distribution of HAE magnitudes also seems lower.

Background: Given the high injury rate of netball, injury prevention is a focus within the sport and for governing bodies. To support the development of appropriate injury mitigation strategies further consideration of the risk factors for injury is needed. Therefore, this study aimed to establish consensus on injury risk factors (RFs) in women’s netball via a combined systematic review and Delphi method approach. 6 Methodology: A systematic search of databases (PubMed, Scopus, MEDLINE, SPORTDiscus, CINAHL) was conducted from inception until June 2023. Twenty-four risk factors (RFs) were extracted from 17 studies and combined with a three-round Delphi approach to achieve consensus. In round-one, experts listed perceived RFs for injury in netball which were combined with the RFs identified via the systematic review. In rounds-two and round-three, experts rated their level of agreement with each risk factor on a 5-point Likert scale (1-strongly disagree to 5-strongly agree). Consensus was defined as >80% agreement (with <10% in disagreement). In round-three, experts also rated the priority for mitigating the risk factor (1- very low to 5-very high). Results: Nineteen experts participated in round-one and round-two, and sixteen participated in round- three (response rate 84%). One-hundred and nine RFs for injury were identified by the systematic review and experts combined. Sixty-one RFs reached consensus, categorised into five groups: ‘individual characteristics’ (n = 22), ‘lifestyle’ (n = 11), ‘training and competition’ (n = 14), ‘sport science and medical provision’ (n = 6) and ‘facilities and equipment’ (n = 8). ‘Poor landing technique/ mechanics’ had a median (interquartile range) mitigation priority rating of 5(1), while all others had median ratings of 3-4.5. Conclusion: This study identifies a range of RFs for injury, provides focus areas for injury prevention and highlights the importance of a multi-disciplinary approach to injury mitigation in netball. Future research is needed to investigate the priority and feasibility of the mitigation of the risk factors in specific environments to support tailored injury prevention strategies.

Understanding the physical qualities of athletes can lead to improved training prescription, monitoring, and ranking. Consequently, testing and profiling athletes is an important aspect of strength and conditioning. However, results can often be difficult to interpret because of the wide range of available tests and outcome variables, the diverse forms of technology used, and the varying levels of standardization implemented. Furthermore, physical qualities can easily be misrepresented without careful consideration if fundamental scientific principles are not followed. This review discusses how to develop impactful testing batteries so that practitioners can maximize their understanding of athletic development while helping to monitor changes in performance to better individualize and support training. It also provides recommendations on the selection of tests and their outcome measures; considerations for the proper interpretation, setup, and standardization of testing protocols; methods to maximize testing information; and techniques to enhance visualization and interpretation.

The popularity of female rugby is increasing rapidly around the world. However, the underpinning female-specific sports science evidence base has not increased concurrently with the game’s growth. The need to increase scientific literature supporting female rugby is paramount due to the unique considerations for female rugby players (e.g., the menstrual cycle, concussion response, increased risk of anterior cruciate ligament injury). Injury, concussion occurrence, risk factors, mechanisms and management have emerged as primary research priorities for female rugby. This is supported by the limited research available in senior female rugby which has established concussion and lower extremity injury (e.g., anterior cruciate ligament injuries) to have the highest injury incidence with the tackle being the most prevalent injury mechanism. The burden associated with concussion and anterior cruciate ligament injuries highlights the importance of identifying injury risk factors specific to females who play rugby as well as strategies to mitigate them. Therefore, this chapter provides three constraint-driven solutions including (1) increasing player and coach education, (2) improving the physical development of youth female rugby players and (3) increasing the minimum standards of provision to reduce the barriers which restrict the management of injury risk factors specific to females who play rugby.

Women’s rugby union is growing in popularity and participation internationally. However, there are still large gaps in the female evidence base, which means training and development are informed by male research despite the distinct physical and physiological differences between sexes. Information on the physical qualities and match demands of women’s rugby union players is crucial for both researchers and practitioners to aid the development of women’s rugby union. The Celtic Challenge competition was created to help develop women’s rugby in Scotland, Ireland, and Wales, and to provide their international players with an alternative elite domestic league to England’s Premier 15s. This study aimed to profile the physical qualities, movement demands and match characteristics of elite women’s domestic rugby in the British Isles and to compare the profile of the Premier 15s and the inaugural Celtic Challenge. Secondary fitness testing, GPS and video match data was collected from three teams in the Celtic Challenge and three teams in the 2022/23 season of the Premier 15s. Premier 15s players were found to be significantly older, more experienced, and faster than Celtic Challenge players during fitness testing and match play. The match characteristics of the two competitions were similar, aside from the number of mauls, which there were significantly more of in the Premier 15s. Findings suggest that Premier 15s players are faster than their CC counterparts; however, despite tactical differences, match characteristics are similar between the two competitions. 

Different talent development (TDE) environments exhibit varying training practices in the rugby league talent identity and development systems (TIDS), which may influence rates of talent development and subsequent productivity of each TDE. This study aimed to compare physical qualities and rates of physical development between different rugby league TDEs within the same TIDS, alongside differences between groups of TDEs based on their level of productivity. A sample of 261 youth rugby league players from six academy teams (i.e., TDEs) within the professional TIDS were tested as part of a league-wide fitness testing battery for measures of anthropometrics, strength, power, speed, and cardiovascular fitness. Linear mixed models revealed medium, significant differences in maximum sprint velocity at the beginning of the season (η 2  = 0.05, p = 0.03) and large, significant differences in the development of prone Yo-Yo IR1 distance over time (η 2  = 0.14–0.18, p < 0.001) between TDEs. No significant differences between groups of TDEs based on their productivity were found. These findings indicate that possible variability in the practices of TDEs mostly leads to small or trivial differences in physical qualities and physical development. Differences in physical qualities and physical development do not appear to relate to the productivity of TDEs, therefore TDEs should focus on holistic development to maximise productivity.

ABSTRACT

This study aimed to describe the characteristics of contact and head acceleration event (HAE) exposure in an international women's rugby union team, across an international tournament, encompassing match and training contexts. Using a retrospective case study design, the contact and HAE exposure of 28 women's rugby union players were assessed using video analysis and instrumented mouthguards (iMGs). In a three‐week tournament, three matches and 16 training sessions were coded using consensus operational definitions, and synchronized with iMG data. Exposure duration was recorded for each player, facilitating analysis of contact frequency, and HAE incidence per player hour. The probability of contact events to result in HAEs was reported. Training accounted for 71% (forwards) and 81% (backs) of weekly contact count. Forwards had a greater contact frequency than backs during matches (58.0 ± 10.5 vs. 21.3 ± 8.6 events per player hour). The probability for an HAE was greater in matches than training, with large inter‐individual variability observed. During matches and training, the tackle event accounted for 82% and 71% of HAEs ≥ 25g, and 79% and 78% of HAEs ≥ 1.5 krad/s2 respectively. Training contact exposure should be routinely monitored and managed for players who accumulate more HAEs, considering the substantial contribution of training to weekly cumulative exposure. Strategies to reduce HAEs should be focused on the tackle area, given that the majority of HAEs are attributed to the ball carrier and tackler.

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.

Background: Head acceleration events (HAEs) are a source of concern across sport due to potential negative long-term brain health in athletes exposed to them. Tackle height is highlighted as a possible factor for risk mitigation in rugby codes. This study aimed to identify the probability of the ball-carrier and tackler receiving a HAE for a given tackle height and estimate the potential impact of changes in tackle height. Methodology: A prospective observational cohort study was conducted during the men’s elite rugby league Super League 2023 season (12 teams, 94 players, 702 player matches). HAEs recorded from instrumented mouthguards were linked to ball-carries and tackles confirmed via video. Events were then labelled by tackle height (i.e., contact on ball-carrier; head/neck, shoulder, upper torso, abdomen, shorts, upper leg and lower leg). Only initial collision HAEs were analysed. Ordinal mixed-effects regression models provided exceedance probabilities for peak linear acceleration (recorded, >10g, >25g, >40g, >55g and >70g) and peak angular acceleration (recorded, >1000rads/s2, >2000 rads/s2, >3000 rads/s2, >4000 rads/s2, and >5000 rads/s2). Differences in initial HAEs were simulated across a range of tackle height distributions using the probabilities and the total number of tackles across the season. Results: The probability of a ball-carrier and tackler recording an initial HAE were 13.4% and 24.2%. The greatest exceedance probabilities for the ball-carrier were initial impact to the head/neck: 35.5% recorded, 4.0% >25g, 13.6% >2000 rads/s2. For other impact locations, ball-carrier HAE probability was 20% at all tackle heights except impact to the ball-carriers head/neck (12.2%). The highest probability for the tackler was contact with the shorts (recorded; 30.9%, >25g; 3.0%, >2000 rads/s2; 11.7%). When 40% of tackles were redistributed from the shoulder to lower parts of the body evenly, the estimated number of HAEs reduced from 40,292 to 35,358. Conclusion: The probability of receiving a HAE for the tackler and ball-carrier differs by overall probability and tackle height. Consequently, simulating the redistribution of tackles below the line of the shoulder suggests there could be a lower number of initial HAE observed across a season.

The purpose of this study was to investigate the influence of maturity status on the physical characteristics of youth female soccer players. One hundred fifty-seven players from 3 elite soccer academies in England completed assessments of anthropometry, strength (isometric midthigh pull), lower-body power (countermovement jump [CMJ]), aerobic capacity (Yo-Yo intermittent recovery test level 1), change of direction (CoD: 505-left/right), and speed (10 and 30 m). Each player was classified into 1 of 6 maturity groups based on their estimated years from peak height velocity (YPHV). Magnitude-based inferences were used to assess for the practical significance between consecutive groups. Speed, CoD time, CMJ, and aerobic capacity were all possibly most likely better in more mature players. However, there was a likely difference in relative peak force between maturity groups −0.5 YPHV (27.13 ± 4.24 N·Kg−1) and 0.5 YPHV (24.62 ± 3.70 N·Kg−1), which was associated with a likely difference in 10-m sprint time (−0.5 YPHV: 2.00 ± 0.12 vs. 0.5 YPHV 2.08 ± 0.16 seconds) and unclear changes in CMJ and CoD time. Findings provide novel comparative data for this cohort relative to maturity status and can be used by strength and conditioning coaches to inform the design of training programs for youth female soccer players. Strength and conditioning coaches should be aware that youth female soccer players may experience a decrease in relative strength around peak height velocity, which may impact upon the speed, CoD time, and CMJ of players.

INTRODUCTION Research within Rugby league (RL) tackle investigations using video analysis has often used two sources of variables. The exception being King et al (2010) who described the characteristics of the RL tackle event such as number of tacklers and tackle height of the first tackler. However, the majority of investigations have either adopted technical variables from rugby union (RU) tackle variables (Sperenza et al., 2017) or technical criteria from coaching cues (Gabbett, 2008). In doing so, content validity and relevance to RL could be questioned (O’Donoghue, 2014). The aim of this study was to adopt a 5 stage process to determine tackle variables which are valid and reliable for RL research METHODS A 5 stage process was undertaken based upon recommendations by O’Donoghue (2014). STAGE 1 involved a synthesis of literature and examined phases of the tackle, variables describing the tackle descriptions of these variables research. A draft variable list was then developed before the start of STAGE 2. To achieve content validity and relevancy, STAGE 2 formed an expert group of practitioners to critique the previously formed draft variable list and develop new phases, variables and descriptors. STAGE 3 refined the variable list based upon the practitioner consultation. STAGE 4 established an expert group agreement in the refined variable list. Finally, STAGE 5 tested intra and inter-reliability of the list using Kappa statistics (McHugh, 2012). RESULTS The agreed variable list comprised of 6 phases including defensive start point, pre-contact, initial contact, post-contact and play the ball phases. Within the phases 66 variables were determined. The intra- and inter-reliability testing resulted in at least moderate agreement (>0.7) (McHugh, 2012) of all phases. DISCUSSION Due to possessing both strong relevance to an RL tackle and demonstrating good levels of reliability, researchers can be confident that the variables within the list are valid for research purposes (O’Donoghue, 2014). In addition, the rigorous 5 stage process of validating the content of the variable list should be used when determining different variables within different sports and actions for research purposes. In doing so, researchers can be confident that they are valid in use and thus can be used consistently for research purposes. Furthermore, the findings show that although there are similarities between a RU and RL tackle, clear differences exist and therefore justifies the need for specific RL variables during tackle research.

Abstract

Sawczuk, T, Jones, B, Scantlebury, S, and Till, K. Influence of perceptions of sleep on well-being in youth athletes. J Strength Cond Res 35(4): 1066–1073, 2021—To date, most research considering well-being questionnaires has only considered the training stress imposed on the athlete, without evaluating the questionnaire's relationship with a measure of recovery (e.g., sleep). This study aimed to assess the influence of sleep duration (Sduration), sleep quality (Squality), and sleep index (Sindex; Sduration × Squality) on well-being in youth athletes, while accounting for the known training stressors of training load and exposure to match play. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily questionnaire including well-being (DWBno-sleep; fatigue, muscle soreness, stress, and mood) measures, Perceived Recovery Status Scale (PRS), the previous day's training loads, Sduration, and Squality every day for 13 weeks. Linear mixed models assessed the impact of Sduration, Squality, and Sindex on DWBno-sleep, its individual subscales, and PRS. Sduration had a small effect on DWBno-sleep (d = 0.31; ±0.09), fatigue (d = 0.42; ±0.11), and PRS (d = 0.25; ±0.09). Squality had a small effect on DWBno-sleep (d = 0.47; ±0.08), fatigue (d = 0.53; ±0.11), stress (d = 0.35; ±0.07), mood (d = 0.41; ±0.09), and PRS (d = 0.37; ±0.08). Sindex had a small effect on DWBno-sleep (d = 0.44; ±0.08), fatigue (d = 0.55; ±0.11), stress (d = 0.29; ±0.07), mood (d = 0.37; ±0.09), and PRS (d = 0.36; ±0.09). The results indicate that an athlete's perceptions of sleep are associated with deviations in well-being measures and should be used as an input to the monitoring process rather than as part of the outcome well-being score. The sleep index is suggested as a potential input because it provides information on both the duration and quality of the sleep experienced.

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.

A plethora of research exists examining the physical qualities of rugby league players. However, no research has investigated practitioners’ insights into the use, analysis and perceptions of such fitness testing data that is vital for applying research into practice. Therefore, this study aimed to examine practitioners’ (coaches and strength & conditioning [S&C] coaches) perceptions and challenges of using fitness testing and the development of physical qualities. Twenty-four rugby league practitioners were purposefully sampled and completed a semi-structured interview. Interviews were transcribed and thematically analysed identifying five themes (it’s important, but it’s not everything; monitoring; evaluation and decision making; motivation; and other external challenges). The theme of “it’s important, but it’s not everything” emerged as a fundamental issue with regard fitness testing and the use of such data and that physical data alone does not inform coaches decisions. There appears conflicts between coaches and S&C coaches’ perceptions and use of fitness data, identifying complexities of supporting players in multidisciplinary teams. Collectively, the findings highlight the multifaceted nature of academy rugby league and suggest that practitioners should utilise fitness testing to inform player evaluations, positively influence training and assist with decision making. Moreover, practitioners should understand the combination of factors that influence fitness testing and work collaboratively to enhance talent development strategies.

The quantification of internal (i.e., the physical stress imposed on the athlete) and external (i.e., distance covered) training load is viewed as essential to determine whether an athlete is adapting to a training programme, whilst minimising the risk of injury and overreaching. Although research has established correlations between internal measures of training load (i.e., session rating of perceived exertion [s-RPE] vs. summated heart rate zone method; Borrensen & Lambert, 2008, International Journal of Sports physiologi and performance, 3, 16-30) limited research exists comparing internal and external methods in team sports. The aim of this study was to establish the accuracy of s-RPE to quantify internal and external training load in adolescent rugby and hockey. Following institutional ethics approval, 22 youth sport (rugby & hockey) athletes were monitored across 125 training sessions (64 rugby & 61 hockey). External training load was monitored using a microtechnology unit to determine total distance and PlayerLoad, whilst internal loads were monitored using heart rate (summated heart rate zones) and s-RPE. Pearson correlation coefficients and 90% confidence intervals were calculated. Fishers r to z transformation compared the correlations between rugby and hockey. For summated HR zones and s-RPE, a large correlation (r=0.58, 90% CI: 0.43-0.70) was found for rugby with a very large correlation (r=0.75 90% CI: 0.64 to 0.83) for hockey. In rugby, large correlations were found between s-RPE and PlayerLoad (r= 0.64, 90% CI: 0.50 to 0.75), and total distance (r= 0.66 90% CI: 0.52 to 0.76). In hockey, large and moderate correlations were found between s-RPE and PlayerLoad (r= 0.55 90% CI: 0.39 to 0.69) and total distance (r= 0.42, 90% CI: 0.23 to 0.58) respectively. No significant differences were found between the correlations of internal and external measures between sports. The large and moderate correlations found between measures of total distance & PlayerLoad to s-RPE appear to support the theory that the individuals internal load is influenced by the external load they are exposed to highlighting the need for future research within this area. Furthermore, the large correlations found between s-RPE and the summated heart rate zones method highlights the potential for s-RPE to be used as an efficient technique in quantifying internal training load within adolescent rugby and hockey athletes. This suggests coaches can confidently monitor the internal training load of their athletes using s-RPE methods when HR technology is not available.

Purpose: To assess the relationships between training load, sleep duration and three daily wellbeing, recovery and fatigue measures in youth athletes. Methods: Fifty-two youth athletes completed three maximal countermovement jumps (CMJ), a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on their previous day's training loads (training) and self-reported sleep duration (sleep) on four weekdays over a seven week period. Partial correlations, linear mixed models and magnitude-based inferences were used to assess the relationships between the predictor variables (training; sleep) and the dependent variables (CMJ; DWB; PRS). Results: There was no relationship between CMJ and training (r=-0.09; ±0.06) or sleep (r=0.01; ±0.06). The DWB was correlated with sleep (r=0.28; ±0.05, small), but not training (r=-0.05; ±0.06). The PRS was correlated with training (r=-0.23; ±0.05, small), but not sleep (r=0.12; ±0.06). The DWB was sensitive to low sleep(d=-0.33; ±0.11) relative to moderate, PRS was sensitive to high (d=-0.36; ±0.11) and low (d=0.29; ±0.17) training relative to moderate. Conclusions: The PRS is a simple tool to monitor the training response, but DWB may provide a greater understanding of the athlete's overall wellbeing. The CMJ was not associated with the training or sleep response in this population.

The monitoring of training load is important to ensure athletes are adapting optimally to a training stimulus. Before quanti ca- tion of training load can take place, coaches must be con dent that the tools available are accurate. We aimed to quantify the within-participant correlation between the session rating of perceived exertion (s-RPE) and summated heart rate zone (sHRz) methods of monitoring internal training load. Training load (s-RPE and heart rate) data were collected for rugby, soc- cer and eld hockey eld-based training sessions over a 14- week in-season period. A total of 397 sessions were monitored (rugby n = 170, soccer n = 114 and eld hockey n = 113). With- in-subject correlations between s-RPE and sHRz were quanti- ed for each sport using a general linear model. Large correla- tions between s-RPE and the sHRz method were found for rugby (r = 0.68; 95 % CI 0.59–0.75) and eld hockey (r = 0.60; 95 % CI 0.47–0.71) with a very large correlation found for soccer (r = 0.72; 95 % CI 0.62–0.80). No signi cant di erences were found between the correlations for each sport. The very large and large correlations found between s-RPE and the sHRz meth- ods support the use of s-RPE in quantifying internal training load in youth sport.

This study aims to establish the validity and reliability of the prone Yo-YoIRL1 in elite female rugby league players (part one) and determine the anthropometric and physical characteristics contributing to 15m prone Yo-YoIRL1 performance (part two). Part one, 21 subjects completed one Yo-YoIRL1, one 20m and two 15m prone Yo-YoIRL1 tests over four sessions, with 7–14 days in-between. Part two, ten subjects completed a testing battery, including body mass, height, dual-energy x-ray absorptiometry, isometric mid-thigh pull, isometric bench-press, 10m and 20m sprints and an incremental treadmill test (V˙O2max). The 15m prone YoYoIRL1 demonstrated poor reliability with a typical error of 68m (21%) and a smallest worthwhile change of 54m (9%). Validity analysis found the prone versions of the YoYoIRL1 were not sensitive measures of intermittent running performance. Both prone YoYoIRL1 test distances demonstrated large mean bias (76% and -37% respectively) and typical error of the estimate (19% and 21%, respectively) in comparison to the YoYoIRL1. Body mass (r = -0.89), lean mass (r = -0.64), body fat % (r = -0.68), V˙O2max (l∙min-1) (r = -0.64), IMTP (r = -0.69), IBP (r = -0.15), 10m (r = -0.77) and 20m (r = -0.72) momentum displayed large negative relationships with 15m prone Yo-YoIRL1 performance. Due to the poor validity of the 20m prone YoYoIRL1, the poor validity and reliability of the 15m prone YoYoIRL1, and the anthropometric and physical characteristics which negatively impact performance, practitioners should reconsider the use of the prone YoYoIRL1 test to monitor high intensity intermittent running performance.

Objectives Identify the frequency, propensity, and factors related to tackle events which result in contact with the head in elite-level women's rugby league. Design Prospective video analysis study. Methods Video footage from 59 Women's Super League matches were analysed (n = 14,378 tackle events). All tackle events were coded as no head contact or head contact. Other independent variables included: area contacting head, impacted player, concussion outcome, penalty outcome, round of competition, time in match and team standard. Results There were 83.0 ± 20.0 (propensity 304.0/1000 tackle events) head contacts per match. The propensity of head contact was significantly greater for the tackler than ball-carrier (178.5 vs. 125.7/1000 tackle events; incident rate ratio 1.42, 95 % confidence interval 1.34 to 1.50). Head contacts occurring from an arm, shoulder, and head occurred significantly more than any other contact type. The propensity of concussions was 2.7/1000 head contacts. There was no significant influence of team standard or time in match on the propensity of head contacts. Conclusions The observed head contacts can inform interventions, primarily focusing on the tackler not contacting the ball-carrier's head. The tackler's head should also be appropriately positioned to avoid contact with the ball-carrier's knee (highest propensity for concussion). The findings are consistent with other research in men's rugby. Law modifications and/or enforcement (reducing the number of un-penalised head contacts), concurrent with coaching interventions (optimising head placement or reducing the head being contacted) may help minimise head contact risk factors for women's rugby league.

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing standard (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and standards. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

To alleviate issues arising from the over/under prescription of training load, coaches must ensure that desired athlete responses to training are being achieved. The present study aimed to assess the level of agreement between the coach intended (pre-session) and observed (post-session) rating of perceived exertion (RPE), with athlete RPE during different training intensities (easy, moderate, hard). Coach intended RPE was taken prior to all field based training sessions over an 8 week in-season period. Following training, all coaches and athletes, whom were participants in hockey, netball, rugby and soccer were asked to provide an RPE measure for the completed session. Sessions were then classified based on the coaches intended RPE, with a total of 28, 125 and 66 easy, moderate and hard training sessions collected respectively. A univariate analysis of variance was used to calculate within-participant correlations between coach intended/observed RPE and athlete RPE. Moderate correlations were found between coach intended and athlete RPE for sessions intended to be moderate and hard whilst a small correlation was found for sessions intended to be easy. The level of agreement between coach and athlete RPE improved following training with coaches altering their RPE to align with those of the athlete. Despite this, moderate and small differences between coach observed and athlete RPE persisted for sessions intended to be easy and moderate respectively. Coaches should therefore incorporate strategies to monitor training load to increase the accuracy of training periodisation and reduce potential over/under prescription of training.

Introduction Research within Rugby league (RL) tackle investigations using video analysis has often used two sources of variables. The exception being King et al (2010) who described the characteristics of the RL tackle event such as number of tacklers and tackle height of the first tackler. However, the majority of investigations have either adopted technical variables from rugby union (RU) tackle variables (Sperenza et al., 2017) or technical criteria from coaching cues (Gabbett, 2008). In doing so, content validity and relevance to RL could be questioned (O’Donoghue, 2014). The aim of this study was to adopt a 5 stage process to determine tackle variables which are valid and reliable for RL research. Method A 5 stage process was undertaken based upon recommendations by O’Donoghue (2014). STAGE 1 involved a synthesis of literature and examined phases of the tackle, variables describing the tackle descriptions of these variables research. A draft variable list was then developed before the start of STAGE 2. To achieve content validity and relevancy, STAGE 2 formed an expert group of practitioners to critique the previously formed draft variable list and develop new phases, variables and descriptors. STAGE 3 refined the variable list based upon the practitioner consultation. STAGE 4 established an expert group agreement in the refined variable list. Finally, STAGE 5 tested intra and inter-reliability of the list using Kappa statistics (McHugh, 2012). Results The agreed variable list comprised of 6 phases including defensive start point, pre-contact, initial contact, post-contact and play the ball phases. Within the phases 66 variables were determined. The intra- and inter-reliability testing resulted in at least moderate agreement (>0.7) (McHugh, 2012) of all phases. Discussion Due to possessing both strong relevance to an RL tackle and demonstrating good levels of reliability, researchers can be confident that the variables within the list are valid for research purposes (O’Donoghue, 2014). In addition, the rigorous 5 stage process of validating the content of the variable list should be used when determining different variables within different sports and actions for research purposes. In doing so, researchers can be confident that they are valid in use and thus can be used consistently for research purposes. Furthermore, the findings show that although there are similarities between a RU and RL tackle, clear differences exist and therefore justifies the need for specific RL variables during tackle research.

ABSTRACT

The structure of a talent identification and development system (TIDS), in terms of its starting, entry, and exit points is an important consideration for sporting organisations. Early talent identification decisions can be ineffective due to unpredictable and individually variable talent development. Physical qualities are a key contributor to performance in rugby league. Therefore, understanding physical development differences between age groups can inform the structure of the rugby league TIDS by highlighting key phases of development. Between‐player variability in physical development must also be considered to understand the generalisability of age‐group trends. Consequently, this study aimed to compare rates of physical development between annual age groups (i.e., U15, 16, 17, 18) in 261 youth rugby league players from multiple clubs, considering individual differences in development rates. Latent growth curve analysis was used to model rates of physical development for size (i.e., height, mass), strength, power, speed, and cardiovascular fitness in each age group. Results showed that U15s had significantly faster rates of development for body size and strength qualities compared with all older age groups, with large between‐player variability. No differences were apparent between age groups for power, speed, or cardiovascular fitness. These findings suggest that early talent identification and (de)selection decisions may ignore the potential development of body size and strength qualities, which occurs at individually variable rates. Such findings can inform the structure and design of the rugby league TIDS by highlighting expected rates of physical development based on players' age groups.

Different talent development (TDE) environments exhibit varying training practices in the rugby league talent identity and development systems (TIDS), which may influence rates of talent development and subsequent productivity of each TDE. This study aimed to compare physical qualities and rates of physical development between different rugby league TDEs within the same TIDS, alongside differences between groups of TDEs based on their level of productivity. A sample of 261 youth rugby league players from six academy teams (i.e., TDEs) within the professional TIDS were tested as part of a league-wide fitness testing battery for measures of anthropometrics, strength, power, speed, and cardiovascular fitness. Linear mixed models revealed medium, significant differences in maximum sprint velocity at the beginning of the season (η 2  = 0.05, p = 0.03) and large, significant differences in the development of prone Yo-Yo IR1 distance over time (η 2  = 0.14–0.18, p < 0.001) between TDEs. No significant differences between groups of TDEs based on their productivity were found. These findings indicate that possible variability in the practices of TDEs mostly leads to small or trivial differences in physical qualities and physical development. Differences in physical qualities and physical development do not appear to relate to the productivity of TDEs, therefore TDEs should focus on holistic development to maximise productivity.

Instrumented mouthguards (iMGs) are a novel technology being used within rugby to quantify head acceleration events. Understanding practitioners' perceptions of the barriers and facilitators to their use is important to support implementation and adoption. This study assessed men's and women's rugby union and league iMG managers' perceptions of staff and player interest in the technology, data and barriers to use. Forty‐six iMG managers (men's rugby union and league n = 20 and n = 9 and women's rugby union and league n = 7 and n = 10) completed an 18‐question survey. Perceived interest in data varied across staff roles with medical staff being reported as having the most interest. The iMG devices were perceived as easy to use but uncomfortable. Several uses of data were identified, including medical applications, player monitoring and player welfare. The comfort, size and fit of the iMG were reported as the major barriers to player use. Time constraints and a lack of understanding of data were barriers to engagement with the data. Continued education on how iMG data can be used is required to increase player and staff buy‐in, alongside improving comfort of the devices. Studies undertaken with iMGs investigating player performance and welfare outcomes will make data more useful and increase engagement.

Women’s rugby (rugby league, rugby union and rugby sevens) has recently grown in participation and professionalisation. There is under-representation of women-only cohorts within applied sport science and medicine research and within the women’s rugby evidence base. The aims of this article are: Part 1: to undertake a systematic-scoping review of the applied sport science and medicine of women’s rugby, and Part 2: to develop a consensus statement on future research priorities. This article will be designed in two parts: Part 1: a systematic-scoping review, and Part 2: a three-round Delphi consensus method. For Part 1, systematic searches of three electronic databases (PubMed (MEDLINE), Scopus, SPORTDiscus (EBSCOhost)) will be performed from the earliest record. These databases will be searched to identify any sport science and medicine themed studies within women’s rugby. The Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews will be adhered to. Part 2 involves a three-round Delphi consensus method to identify future research priorities. Identified experts in women’s rugby will be provided with overall findings from Part 1 to inform decision-making. Participants will then be asked to provide a list of research priority areas. Over the three rounds, priority areas achieving consensus (≥70% agreement) will be identified. This study has received institutional ethical approval. When complete, the manuscript will be submitted for publication in a peer-reviewed journal. The findings of this article will have relevance for a wide range of stakeholders in women’s rugby, including policymakers and governing bodies.

Objectives: In part 1, the objective was to undertake a systematic scoping review of applied sports science and sports medicine in women’s rugby, and in part 2 to develop a consensus statement on future research priorities. Design: In part 1, a systematic search of PubMed (MEDLINE), Scopus and SPORTDiscus (EBSCOhost) was undertaken from the earliest records to January 2021. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, the PRISMA extension for Scoping Reviews, and the PRISMA extension protocols were followed. In part 2, 31 international experts in women’s rugby (ie, elite players, sports scientists, medical clinicians, sports administrators) participated in a three-round Delphi consensus method. These experts reviewed the findings from part 1 and subsequently provided a list of priority research topics in women’s rugby. Research topics were grouped into expert-based themes and expert-based subthemes via content analysis. Expert-based themes and expert-based subthemes were ranked from very low to very high research priority on a 1–5 Likert scale. Consensus was defined by ≥70% agreement. The median research priority agreement and IQR were calculated for each expert-based theme and subtheme. Data sources: PubMed (MEDLINE), Scopus and SPORTDiscus (EBSCOhost). Eligibility criteria for selecting studies: Studies were eligible for inclusion if they investigated applied sports science or sports medicine in women’s rugby. Results: In part 1, the systematic scoping review identified 123 studies, which were categorised into six sports science and sports medicine evidence-based themes: injury (n=48), physical performance (n=32), match characteristics (n=26), fatigue and recovery (n=6), nutrition (n=6), and psychology (n=5). In part 2, the Delphi method resulted in three expert-based themes achieving consensus on future research priority in women’s rugby: injury (5.0 (1.0)), female health (4.0 (1.0)) and physical performance (4.0 (1.0)). Summary/Conclusion: This two-part systematic scoping review and Delphi consensus is the first study to summarise the applied sports science and sports medicine evidence base in women’s rugby and establish future research priorities. The summary tables from part 1 provide valuable reference information for researchers and practitioners. The three expert-based themes that achieved consensus in part 2 (injury, female health and physical performance) provide clear direction and guidance on future research priorities in women’s rugby. The findings of this two-part study facilitate efficient and coordinated use of scientific resources towards high-priority research themes relevant to a wide range of stakeholders in women’s rugby.

This study investigated the seasonal change in physical performance of 113 (Under 10: U10 (n=20), U12 (n=30), U14 (n=31) and U16 (n=32)) elite youth female soccer players. Players completed testing pre-, mid- and post-season, including speed (10 and 30m sprint), change of direction (CoD; 505 test), power (Countermovement jump, CMJ), strength (isometric midthigh pull) and aerobic capacity (YoYo Intermittent Recovery Test Level 1; YYIRL1).

Purpose: To evaluate the relative importance and predictive ability of salivary immunoglobulin A (s-IgA) measures with regards to upper respiratory illness (URI) in youth athletes. Methods: Over a 38-week period, 22 youth athletes (age = 16.8 [0.5] y) provided daily symptoms of URI and 15 fortnightly passive drool saliva samples, from which s-IgA concentration and secretion rate were measured. Kernel-smoothed bootstrapping generated a balanced data set with simulated data points. The random forest algorithm was used to evaluate the relative importance (RI) and predictive ability of s-IgA concentration and secretion rate with regards to URI symptoms present on the day of saliva sampling (URIday), within 2 weeks of sampling (URI2wk), and within 4 weeks of sampling (URI4wk). Results: The percentage deviation from average healthy s-IgA concentration was the most important feature for URIday (median RI 1.74, interquartile range 1.41–2.07). The average healthy s-IgA secretion rate was the most important feature for URI4wk (median RI 0.94, interquartile range 0.79–1.13). No feature was clearly more important than any other when URI symptoms were identified within 2 weeks of sampling. The values for median area under the curve were 0.68, 0.63, and 0.65 for URIday, URI2wk, and URI4wk, respectively. Conclusions: The RI values suggest that the percentage deviation from average healthy s-IgA concentration may be used to evaluate the short-term risk of URI, while the average healthy s-IgA secretion rate may be used to evaluate the long-term risk. However, the results show that neither s-IgA concentration nor secretion rate can be used to accurately predict URI onset within a 4-week window in youth athletes.

The development of a youth team sport athlete is a complex process. This paper outlines challenges which may restrict the optimal balance between training and recovery and provide solutions to help practitioners overcome these challenges. To facilitate positive youth athletic development, training aims must be aligned between stakeholders to synchronise periods of intensified training and recovery. Within- and between-athlete variations in weekly training load must be managed and practitioners should attempt to ensure the intended load of training equals the load perceived by the athlete. Furthermore, practitioners should be cognizant of the athletes’ non-sport related stressors to enable both academic and sporting pursuits. Whilst each of these challenges adds intricacy, they may be overcome through collaboration, monitoring and if necessary, the modification of the athletes’ training load.

This study aimed to identify and compare the training frequency and intensity (via session rating of perceived exertion load (sRPE load)) of representative and non-representative late adolescent athletes. Thirty-six team sport athletes completed a web-based questionnaire daily over an 8-month period, reporting their training/match activities from the previous day. Athletes were categorised as representative (academy/county/international) or non-representative (club/school) depending on the highest level of their sport they participated. Mean weekly frequencies and sRPE load of different training/match activities were quantified for each athlete across five school terms. Mann-Whitney U tests established the significance of differences and effect sizes between playing standards for mean weekly frequencies and mean sRPE load. Within-athlete weekly sRPE loads were highly variable for both playing standards however representative level athletes participated in significantly more activity outside of school compared to non-representative athletes during November to December (effect size; 0.43 – club technical training; 0.36 – club matches), January to February (effect size; 0.78 – club technical training; 0.75 – club matches) and February to March (effect size; 0.63 – club technical training; 0.44 – club matches). Therefore, club and school coaches must ensure that all elements of representative athlete's training schedules are coordinated and flexible to promote positive adaptions to training such as skill & physical development and prevent maladaptive responses such as overuse injury and non-functional overreaching. A cooperative and malleable training schedule between club/school coaches and the athlete will allow the athlete to perform on multiple fronts whilst also being able to meet the demands of additional stressors such as schoolwork.

Youth athletes frequently participate in multiple sports or for multiple teams within the same sport. To optimise player development and minimise undesirable training outcomes (e.g., overuse injuries), practitioners must be cognizant of an athlete's training load within and outside of their practice. The present study aimed to establish the validity of a 24-hour (s-RPE24) and 72-hour (s-RPE72) recall of session rating of perceived exertion (s-RPE) against the criterion measure of s-RPE collected 30 minutes' post training (s-RPE30). Thirty-eight adolescent athletes provided a s-RPE30 following the first field based training session of the week. Approximately 24 hours later subjects were asked to recall the intensity and duration of the previous days training. The following week subjects once again provided a s-RPE30 measure post training before recalling the intensity and duration of the session approximately 72 hours later. A nearly perfect correlation (0.98 [0.97 - 0.99]) was found between s-RPE30 and s-RPE24, with a small typical error of estimate (TEE; 8.3% [6.9 - 10.5]) and trivial mean bias (-1.1% [-2.8 - 0.6]). Despite a large correlation between s-RPE30 and s-RPE72 (0.73 [0.59 - 0.82]) and a trivial mean bias (-0.2% [-6.8 - 6.8]) there was a large typical error of estimate (TEE; 35.3% [29.6 - 43.9]). s-RPE24 provides a valid measure of retrospectively quantifying s-RPE, however the large error associated with s-RPE72 suggests it is not a suitable method for monitoring training load in youth athletes.

Video analysis research into the rugby league tackle typically uses technical criteria from coaching cues or tackle variables from rugby union. As such, the content validity and relevance could be questioned. A video analysis framework which establishes appropriate variables for rugby league is therefore required. The aim of this study was to adopt a 5-stage process to establish a video analysis framework for the rugby league tackle, which was content valid, relevant and reliable. The 5-stage process included 1) creation of draft variable list (video analysis framework), using available rugby tackle research, 2) expert group recruitment and critique, 3) refinement of video analysis framework to establish content validity, 4) response process validity task and agreement within expert group, 5) intra- and inter-reliability testing using Kappa statistics. The agreed video analysis framework comprised 6 phases including; tackle event, defensive start point, pre-contact, initial contact, post-contact and play the ball. Within the identified phases, 63 variables were established. The intra- and inter-reliability testing resulted in strong agreement (>0.81-1.0) within all phases. The 5-stage process allowed for the creation of a valid, relevant and reliable video analysis framework. The video analysis framework can be used in rugby league tackle research, categorising complex tackle events such as injurious or optimal tackles, improving both player welfare and performance. Furthermore, the application of the video analysis framework to future rugby league research will increase the coherence and usefulness of research findings.

To compare the probability of tackle success (the tackler preventing the ball‐carrier and ball from progressing towards the tackler try‐line) when contacting the ball‐carrier at different heights (shoulder, mid‐torso and legs) for different types of tackles (active, passive, smother and arm) while accounting for other tackler situational factors within seven playing levels. Video footage of 271 male rugby union matches were analysed across seven playing groups (Under [U] 12, n = 25 matches; U14, n = 35; U16, n = 39; U18 Amateur n = 39; U18 Elite n = 38; Senior Amateur, n = 40 and Senior Elite, n = 50) across England, New Zealand, South Africa, Portugal and USA (a total of 51,106 tackles). A multi‐level logistic regression model with tackle success as the outcome variable and first point of contact and type of tackle as the explanatory variables were computed. Included in the model as cofounders were the situational variables tackle direction, tackle sequence, number of players in the tackle and attacker intention. Post‐estimation marginal effects were used to calculate the probabilities (expressed as a percentage %) of tackle success for each interaction between tackle type (active shoulder, smother, passive shoulder and arm) and the first point of contact (shoulder, mid‐torso and legs). The probability of tackle success in relation to where the ball‐carrier is contacted varied by tackle type and within each age group. The probabilities (Pr) for contacting the shoulder versus mid‐torso at the senior levels (elite and amateur) did not differ in relation to tackle success (for instance, for active shoulder tackles within senior elite; shoulder Pr 86% 95% CI 82–89 and mid‐torso Pr 82% 95% CI 77–86), whereas at the junior levels, contacting the shoulder had a higher probability than other points of contact. Active shoulder tackles had the highest probability of tackle success across the different playing levels across the different contact heights, whereas arm tackles had the lowest probability (for instance, for mid‐torso tackles within senior elite, active Pr 82% 95% CI 77–86 vs. arm Pr 69% 95% CI 64–75). Coaches and practitioners can use this information to improve tackle training design and planning within the different age groups and facilitate player development.

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, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively.. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60–70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.

This study aimed to establish consensus on injury risk factors in netball via a combined systematic review and Delphi method approach. A systematic search of databases (PubMed, Scopus, MEDLINE, SPORTDiscus, CINAHL) was conducted from inception until June 2023. Twenty-four risk factors were extracted from 17 studies and combined with a three-round Delphi approach to achieve consensus. In round-one, experts listed perceived risk factors for injury in netball which were combined with the risk factors identified via the systematic review. In round-two and round-three, experts rated their level of agreement with each risk factor on a 5-point Likert scale (1-strongly disagree to 5-strongly agree). Consensus was defined as 80% agreement (with <10% in disagreement). In round-three, experts also rated the priority for mitigating the risk factor (1-very low to 5-very high). Nineteen experts participated in round-one and round-two, and sixteen participated in round-three (response rate 84%). One-hundred and nine risk factors for injury were identified by the systematic review and experts combined. Sixty-one risk factors reached consensus, categorised into five groups: ‘individual characteristics’ (n=22), ‘lifestyle’ (n=11), ‘training and competition’ (n=14), ‘sport science and medical provision’ (n=6) and ‘facilities and equipment’ (n=8). ‘Poor landing technique/mechanics’ had a median (interquartile range) mitigation priority rating of 5(1), while all others had median ratings of 3-4.5. This study identifies a range of risk factors for injury, provides focus areas for injury prevention, and highlights the importance of a multi-disciplinary approach to injury mitigation in netball.

Purpose: To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis. Methods: Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups. Results: Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = −1.83) were identified between U18 academy and international backs within PC1. Conclusion: Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.

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.

This study quantified and compared the collision and non-collision match characteristics across age categories (i.e. U12, U14, U16, U18, Senior) for both amateur and elite playing standards from Tier 1 rugby union nations (i.e. England, South Africa, New Zealand). Two-hundred and one male matches (5911 min ball-in-play) were coded using computerised notational analysis, including 193,708 match characteristics (e.g. 83,688 collisions, 33,052 tackles, 13,299 rucks, 1006 mauls, 2681 scrums, 2923 lineouts, 44,879 passes, 5568 kicks). Generalised linear mixed models with post-hoc comparisons and cluster analysis compared the match characteristics by age category and playing standard. Overall significant differences (p < 0.001) between age category and playing standard were found for the frequency of match characteristics, and tackle and ruck activity. The frequency of characteristics increased with age category and playing standard except for scrums and tries that were the lowest at the senior level. For the tackle, the percentage of successful tackles, frequency of active shoulder, sequential and simultaneous tackles increased with age and playing standard. For ruck activity, the number of attackers and defenders were lower in U18 and senior than younger age categories. Cluster analysis demonstrated clear differences in all and collision match characteristics and activity by age category and playing standard. These findings provide the most comprehensive quantification and comparison of collision and non-collision activity in rugby union demonstrating increased frequency and type of collision activity with increasing age and playing standard. These findings have implications for policy to ensure the safe development of rugby union players throughout the world.

Objectives To compare match injury incidence, severity and burden in men's and women's elite rugby league. Design A prospective cohort epidemiological study. Methods Time loss match injury data were collected from all men's (11,301 exposure hours) and women's (5,244 exposure hours) Super League clubs. Results Injury incidence and burden were not different between men and women (mean [95 % CI]; 54 [45 to 65] vs. 60 [49 to 74] per 1000 match-hours; p = 0.39, and 2332 [1844 to 2951] vs. 1951 [1560 to 2440] days lost per 1000 match-hours; p = 0.26). However, injury severity was greater for men than women (42 [35–50] vs. 35 [29 to 42]; p = 0.01). Lower limbs accounted for 54 % and 52 % of injuries for men and women, with the head/face the most frequently injured location due to concussion (12 [10 to 15] and 10 [8 to 14] per 1000 match-hours for men and women). Injuries to the knee had the greatest burden for men and women (708 [268–1868] and 863 [320–2328] days lost per 1000 match-hours). Being tackled was the most common injury mechanism for men and women (28 % and 38 %) with greater burden (p < 0.01) than other injury mechanisms. Conclusions Male and female rugby league players have similar injury incidence and burden; however, injury severity was higher in men. Head/face injuries have the highest injury incidence and knee injuries have the highest burden. These injuries should be the focus for prevention initiatives at a league (via laws), player, and coach level, with equal and specific focus for both men's and women's rugby league players.

Objectives Report two-years of training injury data in senior and academy professional rugby league. Design Prospective cohort study. Method Match and training time-loss injuries and exposure data were recorded from two-seasons of the European Super League competition. Eleven/12 (2021) and 12/12 (2022) senior and 8/12 (2021) and 12/12 (2022) academy teams participated. Training injuries are described in detail and overall match injuries referred to for comparison only. Results 224,000 training exposure hours were recorded with 293 injuries at the senior (mean [95 % confidence interval]; 3[2–3] per 1000 h) and 268 academy level (2 [2–3] per 1000 h), accounting for 31 % and 40 % of all injuries (i.e., matches and training). The severity of training injuries (senior: 35 [30–39], academy: 36 [30–42] days-lost) was similar to match injuries. Lower-limb injuries had the greatest injury incidence at both levels (senior: 1.85 [1.61–2.12], academy: 1.28 [1.08–1.51] per 1000 h). Head injuries at the academy level had greater severity (35 [25–45] vs. 18 [12–14] days-lost; p < 0.01) and burden (17 [16–18] vs. 4[4–5] days-lost per 1000 h; p = 0.02) than senior level. At the senior level, the incidence of contact injuries was lower than non-contact injuries (risk ratio: 0.29 [0.09–0.88], p = 0.02). Conclusion Training injuries accounted for about a third of injuries, with similar injury severity to match-play. Within training there is a higher rate of non-contact vs. contact injuries. Whilst current injury prevention interventions target matches, these data highlight the importance of collecting high quality training injury data to develop and evaluate injury prevention strategies in training

Player profiling can aid talent identification and development by highlighting strengths and weaknesses, and evaluation training interventions. However, there is currently no consensus in rugby league on the qualities, skills, and characteristics (i.e., factors) which should be profiled, or the methods to use to assess these factors. Consequently, the aims of this two-part study were to 1) establish the most common factors and methods for profiling rugby league players, through a systematic scoping review, and 2) develop consensus on the factors and methods experts believe should be used when profiling rugby league players. In Part 1, a systematic scoping review of studies profiling rugby league players was conducted according to the PRISMA guideline for Scoping Reviews. In Part 2, a panel of 32 experts were invited to participate in a sequential three-round Delphi consensus, used to identify the factors that they believed should be profiled in rugby league players and associated methods of assessment. Part 1 identified 370 studies, which assessed varying numbers of factors from five higher order themes; physical (n=247, 67%), health-related (n=129, 35%), other (n=60, 16%; e.g., playing experience, level of education), technical-tactical (n=58, 16%), and psychological (n=25, 7%). Only 3% of these studies featured female participants (n=11). In Part 2, 120 factors were initially identified, of which 85 reached consensus (≥70% agreement). This included 22 physical, 22 psychological, 20 technical-tactical, 15 health-related, and six player information factors. Collectively, these findings evidence the multidimensional nature of talent in rugby league, highlighting a range of factors across several domains that should be considered when identifying and monitoring talent in the sport. Furthermore, technical-tactical and psychological factors were identified as areas for future research, due to the large number of factors which reached consensus in these areas and the comparatively low amount of research conducted in them.

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.

The provision of instantaneous visual kinematic feedback has been shown to improve physical performance and psychological traits. However, this research has only investigated changes across a single set of exercise in adolescent males. Therefore, the aim of this study was to assess the effects of visual kinematic feedback on kinematic outputs during multiple sets of the jump squat in adolescent female athletes. In addition, motivation and competitiveness were also assessed. Eleven adolescent female athletes volunteered to take part in this study. In a randomised-crossover study design, subjects either were or were not provided peak concentric velocity using visual feedback during three sets of six repetitions of the jump squat. A linear position transducer measured peak concentric velocity of each repetition across the three sets, while motivation and competitiveness were measured before and after exercise. Magnitude-based inferences were used to assess changes between conditions, with mean peak concentric velocity (mean ±90%CI: 0.23 ±0.04m·s-1; ES ±90%CI: 2.73 ±0.44; percent ±90%CI: 10.3 ±1.8) and power (mean ±90%CI: 330 ±53W; ES ±90%CI: 2.87 ±0.52; percent ±90%CI: 16.5 ±3.2) almost certainly greater when feedback was provided. Furthermore, motivation almost certainly improved (ES ±90%CI: 2.81 ±0.63) when feedback was provided, while competitiveness was almost certainly greater (ES ±90%CI: 4.88 ±0.58) following the provision of kinematic feedback. Findings from this study demonstrate that providing adolescent female athletes visual kinematic information while completing plyometric exercise is beneficial for performance and can enhance psychological responses across multiple sets. Consequently, practitioners are advised to utilise kinematic feedback during training to enhance training quality and improve motivation and competitiveness.

Background: Athlete exposure to contact could be a risk factor for injury. Governing bodies should provide guidelines preventing overexposure to contact. Objectives: Describe the current contact load practices and perceptions of contact load requirements within men’s and women’s rugby league to allow the Rugby Football League (RFL) to develop contact load guidelines. Methods: Participants (n=450 players, n=46 coaching staff, n=32 performance staff, n=23 medical staff) completed an online survey of 27 items, assessing the current contact load practices and perceptions within four categories: “current contact load practices” (n=12 items), “perceptions of required contact load” (n = 6 items), “monitoring of contact load” (n=3 items), and “the relationship between contact load and recovery” (n=6 items). Results: During men’s Super League pre-season, full contact and controlled contact training was typically undertaken for 15-30 minutes per week, and wrestling training for 15-45 minutes per week. During the in-season, these three training types were all typically undertaken for 15-30 mins per week. In women’s Super League, all training modalities were undertaken for up to 30 minutes per week in the pre- and in-season periods. Both men’s and women’s Super League players and staff perceived 15-30 minutes of full contact training per week was enough to prepare players for the physical demands of rugby league, but a higher duration may be required to prepare for the technical contact demands. Conclusion: Men’s and women’s Super League clubs currently undertake more contact training during pre-season than in-season, which was planned by coaches and is deemed adequate to prepare players for the demands of rugby league. This study provides data to develop contact load guidelines to improve player welfare whilst not impacting performance.

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing level (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and levels. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

Abstract

This is the first study to assess longitudinal changes in anthropometric, physiological, and physical qualities of international women’s rugby league players. Thirteen forwards and 11 backs were tested three times over a 10-month period. Assessments included: standing height and body mass, body composition measured by dual x-ray absorptiometry (DXA), a blood panel, resting metabolic rate (RMR) assessed by indirect calorimetry, aerobic capacity (i.e., VLO 2 max) evaluated by an incremental treadmill test, and isometric force production measured by a force plate. During the pre-season phase, lean mass increased significantly by ∼2% for backs (testing point 1: 47 kg; testing point 2: 48 kg) and forwards (testing point 1: 50 kg; testing point 2: 51 kg) (p = ≤ 0.05). Backs significantly increased their VLO 2 max by 22% from testing point 1 (40 ml·kg −1 ·min −1 ) to testing point 3 (49 ml·kg −1 ·min −1 ) (p = ≤ 0.04). The VLO 2 max of forwards increased by 10% from testing point 1 (41 ml·kg −1 ·min −1 ) to testing point 3 (45 ml·kg −1 ·min −1 ), however this change was not significant (p = ≥ 0.05). Body mass (values represent the range of means across the three testing points) (backs: 68 kg; forwards: 77-78 kg), fat mass percentage (backs: 25-26%; forwards: 30-31%), resting metabolic rate (backs: 7 MJ·day −1 ; forwards: 7 MJ·day −1 ), isometric mid-thigh pull (backs: 2106-2180 N; forwards: 2155-2241 N), isometric bench press (backs: 799-822 N; forwards: 999-1024 N), isometric prone row (backs: 625-628 N; forwards: 667-678 N) and bloods (backs: ferritin 21-29 ug/L, haemoglobin 137-140 g/L, iron 17-21 umol/L, transferrin 3 g/L, transferring saturation 23-28%; forwards: ferritin 31-33 ug/L, haemoglobin 141-145 g/L, iron 20-23 umol/L, transferrin 3 g/L, transferrin saturation 26-31%) did not change (p = ≥ 0.05). This study provides novel longitudinal data which can be used to better prepare women rugby league players for the unique demands of their sport, underpinning female athlete health.

© 2017 Thomas Sawczuk, Ben Jones, Sean Scantlebury, Jonathan Weakley, Dale Read, Nessan Costello, Joshua David Darrall-Jones, Keith Stokes, and Kevin Till This study aimed to evaluate the between-day reliability and usefulness of a fitness testing battery in a group of youth sport athletes. Fifty-nine youth sport athletes (age = 17.3 ± 0.7 years) undertook a fitness testing battery including the isometric mid-thigh pull, counter-movement jump, 5–40 m sprint splits, and the 5–0-5 change of direction test on two occasions separated by 7 days. Usefulness was assessed by comparing the reliability (typical error) to the smallest worthwhile change. The typical error was 5.5% for isometric mid-thigh pull and 3.8% for counter-movement jump. The typical error values were 2.7, 2.5, 2.2, 2.2, and 1.8% for the 5, 10, 20, 30, and 40 m sprint splits, and 4.1% (left) and 5.4% (right) for the 5–0-5 tests. The smallest worthwhile change ranged from 1.1 to 6.1%. All tests were identified as having “good” or “acceptable” reliability. The isometric mid-thigh pull and counter-movement jump had “good” usefulness, all other tests had “marginal” usefulness.

The importance of contributors that can result in negative player outcomes in sport and the feasibility and barriers to modifying these to optimise player health and well-being have yet to be established. Within rugby codes (rugby league, rugby union and rugby sevens), within male and female cohorts across playing levels (full-time senior, part-time senior, age grade), this project aims to develop a consensus on contributors to negative biopsychosocial outcomes in rugby players (known as the CoNBO study) and establish stakeholder perceived importance of the identified contributors and barriers to their management. This project will consist of three parts; part 1: a systematic review, part 2: a three-round expert Delphi study and part 3: stakeholder rating of feasibility and barriers to management. Within part 1, systematic searches of electronic databases (PubMed, Scopus, MEDLINE, SPORTDiscus, CINAHL) will be performed. The systematic review protocol is registered with PROSPERO. Studies will be searched to identify physical, psychological and/or social factors resulting in negative player outcomes in rugby. Part 2 will consist of a three-round expert Delphi consensus study to establish additional physical, psychological and/or social factors that result in negative player outcomes in rugby and their importance. In part 3, stakeholders (eg, coaches, chief executive officers and players) will provide perceptions of the feasibility and barriers to modifying the identified factors within their setting. On completion, several manuscripts will be submitted for publication in peer-reviewed journals. The findings of this project have worldwide relevance for stakeholders in the rugby codes. PROSPERO registration number CRD42022346751.

Objective Within women’s rugby league (n=12 teams), we (1) identified modifiers for head-to-head contacts informed by sport partners (eg, players, coaches, match officials); (2) compared head-to-head contact and concussion rates to the previous two seasons following a one-season tackle technique coaching intervention and (3) explored barriers and enablers of the intervention. Methods A multi-method design was used. Part 1: Mitigation strategies were identified by sport partners reviewing footage of head-to-head contacts, informing the development of a coach-targeted tackle technique intervention. Part 2 evaluated the intervention, comparing head-to-head contact and concussion incidence rates (IRs). Interviews with coaches and players (n=6) explored barriers and enablers to effective implementation and compliance with the intervention. Results Sport partners reported tacklers were more responsible for head-to-head contacts and lowering the tackle height was the most frequently suggested mitigation strategy preintervention and postintervention. Head-to-head contact rates were significantly lower during the intervention than preintervention (IR 59; 95% CI 56 to 62 vs IR 28; 95% CI 25 to 30/1000 tackle events); however, concussion rates showed no difference. Perceived barriers to the intervention included underdeveloped physical and technical foundations of players, lack of knowledge and understanding of the intervention and its purpose, and the environmental context and lack of resources in women’s rugby league. Beliefs about the consequences of the tackle and concussion were perceived as barriers and enablers. Conclusions Head-to-head contact rates were significantly lower; however, concussion rates did not decrease following a tackle technique coaching intervention. Reduced head-to-head contacts are potentially due to an increased focus on head injury reduction and increased player/coach awareness and support.

This is the first study to assess longitudinal changes in anthropometric, physiological, and physical qualities of international women’s rugby league players. Thirteen forwards and 11 backs were tested three times over a 10-month period. Assessments included: standing height and body mass, body composition measured by dual x-ray absorptiometry (DXA), a blood panel, resting metabolic rate (RMR) assessed by indirect calorimetry, aerobic capacity (i.e.,V˙O2max) evaluated by an incremental treadmill test, and isometric force production measured by a force plate. During the pre-season phase, lean mass increased significantly by ~2% for backs (testing point 1: 47 kg; testing point 2: 48 kg) and forwards (testing point 1: 50 kg; testing point 2: 51 kg) (p = ≤ 0.05). Backs significantly increased their V˙O2max by 22% from testing point 1 (40 ml kg-1 min-1) to testing point 3 (49 ml kg-1 min-1) (p = ≤ 0.04). The V˙O2max of forwards increased by 10% from testing point 1 (41 ml kg-1 min-1) to testing point 3 (45 ml kg-1 min-1), however this change was not significant (p = ≥ 0.05). Body mass (values represent the range of means across the three testing points) (backs: 68 kg; forwards: 77–78 kg), fat mass percentage (backs: 25–26%; forwards: 30–31%), resting metabolic rate (backs: 7 MJ day-1; forwards: 7 MJ day-1), isometric mid-thigh pull (backs: 2106–2180 N; forwards: 2155–2241 N), isometric bench press (backs: 799–822 N; forwards: 999–1024 N), isometric prone row (backs: 625–628 N; forwards: 667–678 N) and bloods (backs: ferritin 21–29 ug/L, haemoglobin 137–140 g/L, iron 17–21 umol/L, transferrin 3 g/L, transferring saturation 23–28%; forwards: ferritin 31–33 ug/L, haemoglobin 141–145 g/L, iron 20–23 umol/L, transferrin 3 g/L, transferrin saturation 26–31%) did not change (p = ≥ 0.05). This study provides novel longitudinal data which can be used to better prepare women rugby league players for the unique demands of their sport, underpinning female athlete health.