Professor Ben Jones

In collision sports, the tackle has the highest injury incidence, and is key to a successful performance. Although the contact load of players has been measured using microtechnology, this has not been related to tackle technique. The aim of this study was to explore how PlayerLoad™ changes between different levels of tackling technique during a simulated tackle. Nineteen rugby union players performed twelve tackles on a tackle contact simulator (n = 228 tackles). Each tackle was recorded with a video-camera and each player wore a Catapult OptimEyeS5. Tackles were analysed using tackler proficiency criteria and split into three categories: Low scoring(≤5 Arbitrary units (AU), medium scoring(6 and 7AU) and high scoring tackles(≥8AU). High scoring tackles recorded a higher PlayerLoad™ at tackle completion. The PlayerLoad™ trace was also less variable in the high scoring tackles. The variability in the PlayerLoad™ trace may be a consequence of players not shortening their steps before contact. This reduced their ability to control their movement during the contact and post-contact phase of the tackle and increased the variability. Using the PlayerLoad™ trace in conjunction with subjective technique assessments offers coaches and practitioners insight into the physical-technical relationship of each tackle to optimise tackle skill training and match preparation.

© 2018, The Author(s). Background: Video analysis is a common tool used in rugby union research to describe match performance. Studies using video analysis range from broad statistical studies of commercial databases to in-depth case-studies of specific match events. The range of types of studies using video analysis in rugby union, and how different studies apply the methodology, can make it difficult to compare the results of studies and translate the findings to a real-world setting. In attempt to consolidate the information on video analysis in rugby, a critical review of the literature was performed. Main body: Ninety-two studies were identified. The studies were categorised based on the outcome of the study and the type of research question, sub-categorised as ‘what’ and ‘how’ studies. Each study was reviewed using a number of questions related to the application of video analysis in research. There was a large range in the sample sizes of the studies reviewed, with some of the studies being under-powered. Concerns were raised of the generalisability of some of the samples. One hundred percent of ‘how’ studies included at least one contextual variables in their analyses, with 86% of ‘how’ studies including two or more contextual variables. These findings show that the majority of studies describing how events occur in matches attempted to provide context to their findings. The majority of studies (93%) provided practical applications for their findings. Conclusion: The review raised concerns about the usefulness of the some of the findings to coaches and practitioners. To facilitate the transfer and adoption of research findings into practice, the authors recommend that the results of ‘what’ studies inform the research questions of ‘how’ studies, and the findings of ‘how’ studies provide the practical applications for coaches and practitioners.

Background: Rugby league is a popular collision sport among Australian adolescent and young adult men. Concussion is one of the more common injuries in rugby league. Few studies have examined concussion in youth rugby league. To examine medically diagnosed concussions from a single season within two elite-level pathway rugby league competitions by evaluating game play risk factors and conducting a video review of potential concussion signs. Methods: All players involved in the Queensland Rugby League's (QRL) under 18 years and under 20 years age group competitions during the 2019 season were included in this study. Data included all head injury assessments (HIAs) identified in real-time through the QRL injury surveillance system for these two QRL age group competitions. The purpose of this study was to (i) report the rates of HIAs and medically diagnosed concussions; (ii) examine video signs of potential concussion; (iii) review game play risk factors related to HIAs and concussions; and (iv) determine the number of days until a concussed player returned to match play and the number of subsequent games missed by concussed players. Results: There were 86 HIAs and 30 medically diagnosed concussions from the two competitions. The concussion incidence was 2.93 per 1000 player match hours in the under 18-year age group and 5.75 per 1000 player match hours in the under 20-year age group. Slow to stand was the most commonly observed video sign (78.6%; 22/28 concussions). Most concussed players (91%, 21/23) missed at least one subsequent game (M ​= ​1.4, SD ​= ​1.7, range ​= ​0–7 games), with the average days to return-to-play being 15.7 (SD ​= ​7.0, range ​= ​7–41 days). Conclusions: In elite-level pathway rugby league, the incidences of HIAs and medically diagnosed concussions were higher in the under 20 age group than the under 18 age group. Both age groups had lower incidences of HIAs and concussions than professional adult rugby league players. Return-to-play following concussion was similar across the two age groups and differed considerably compared to the elite level, with a longer time before return to play for the younger elite level development pathway players.

Background and Aims: To determine the effect of evolocumab treatment in patients with asymptomatic carotid artery stenosis ≥50% on carotid plaque morphology and composition, as determined by magnetic resonance imaging. Methods: We conducted a double-blind randomized controlled trial in patients with asymptomatic carotid artery plaque with ≥50% stenosis and low-density lipoprotein-associated cholesterol (LDL-C) ≥1.8 mmol/L, despite standard lipid-lowering therapy, with 12 months of evolocumab or placebo injection every two weeks. The primary endpoint was the between group difference in the absolute change from baseline in carotid plaque lipid-rich necrotic core (LRNC), assessed by carotid magnetic resonance. Results: Due to interrupted recruitment during the COVID-19 pandemic, 33 patients (36% female) were randomised, which was less than the target of 52. Mean age was 68.7 years (SD, 8.5) and baseline LDL-C 2.4 mmol/L (SD, 0.7). LDL-C was reduced with evolocumab to 0.8 mmol/L (SD, 0.5) vs 2.2 mmol/L (SD, 0.7) with placebo at 3 months (between group absolute difference -1.3 mmol/L [95% confidence interval [CI], -1.7 to -0.9], p < 0.001). Evolocumab treatment was associated with a favourable change in LRNC at 12 months of -16 mm

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The hydration status of rugby league players during competitive home match play was assessed throughout the 2008 Super League season. Fourteen players from 2 Super League clubs were monitored (72 observations). On arrival, 2 h prior to kick off, following normal prematch routines, players' body mass were measured following a urine void. Prematch fluid intake, urine output, and osmolality were assessed until kick off, with additional measurements at half time. Fluid intake was also monitored during match play for club B only, and final measurements of variables were made at the end of the match. Mean body mass loss per match was 1.28 ± 0.7 kg (club A, 1.15 kg; club B, 1.40 kg), which would equate to an average level of dehydration of 1.31% (mass loss, assumed to be water loss, expressed as a percentage of body mass), with considerable intra-individual coefficient of variation (CV, 47%). Mean fluid intake for club B was 0.64 ± 0.5 L during match play, while fluid loss was 2.0 ± 0.7 L, with considerable intra-individual CV (51% and 34%, respectively). Mean urine osmolality was 396 ± 252 mosm·kg-1 on arrival, 237 ± 177 mosm·kg-1 prematch, 315 ± 133 mosm·kg-1 at half time, and 489 ± 150 mosm·kg-1 postmatch. Body mass losses were primarily a consequence of body fluid losses not being completely balanced by fluid intake. Furthermore, these data show that there is large inter- and intra-individual variability of hydration across matches, highlighting the need for future assessment of individual relevance.

Talent development environments (TDEs) strive to develop junior athletes towards senior elite performance, however, are subject to a range of contextual factors influencing their operations. This study aimed to investigate the influence of contextual factors on efficiency and effectiveness across all English rugby union men's academies. Fourteen focus groups were conducted, one for each academy. Underpinned by pragmatic research philosophy, focus group discussions were analysed via reflexive thematic analysis. Analysis led to the generation of four themes to explain the impact of contextual factors: "multiple loosely connected concurrent environments", "regulation drives practice", "organisational influences" and "searching for bang for buck". Findings suggest complex interactions between a network of individuals and organisations, both internal and external to the structure of the talent system. In this context, it seemed inadequate to only consider the role of a single TDE. Overall, results reflect contextual and resource challenges constrain practice within English rugby union academies. In practice, we suggest the need to consider the tension of regulation to enhance minimum standards, against the increased autonomy that may result from flexibility of regulation to facilitate enhanced efficiency and effectiveness.

The purpose of this research was to waterproof a near-infrared spectroscopy device (PortaMon, Artinis Medical Systems) to enable NIR measurement during swim exercise. Candidate materials were initially tested for waterproof suitability by comparing light intensity values during phantom-based tissue assessment. Secondary assessment involved repeated isokinetic exercises ensuring reliability of the results obtained from the modified device. Tertiary assessment required analysis of the effect of water immersion and temperature upon device function. Initial testing revealed that merely covering the PortaMon light sources with waterproof materials considerably affected the NIR light intensities. Modifying a commercially available silicone covering through the addition of a polyvinyl chloride material (impermeable to NIR light transmission) produces an acceptable compromise. Bland-Altman analysis indicated that exercise-induced changes in tissue saturation index (TSI %) were within acceptable limits during laboratory exercise. Although water immersion had a small but significant effect upon NIR light intensity, this resulted in a negligible change in the measured TSI (%). We then tested the waterproof device in vivo illustrating oxygenation changes during a 100 m freestyle swim case study. Finally, a full study compared club level swimmers and triathletes. Significant changes in oxygenation profiles when comparing upper and lower extremities for the two groups were revealed, reflecting differences in swim biomechanics.

The Challenge Point Framework (CPF) guides practice design for optimal motor skill learning. The CPF’s use and prevalence has not been reported. This review’s aims are to–(i) identify research areas that use the CPF, (ii) determine the CPF’s prevalence across research areas and (iii) summarise applications of the CPF across research areas. A systematic scoping review, following modified PRISMA-ScR guidelines, was conducted. Papers referencing Guadagnoli and Lee’s (2004) “Challenge Point Framework” paper were reviewed against inclusion/exclusion criteria. Data from 100 included papers were analysed for (1) numerical; (2) thematic; and (3) descriptive summaries. Four themes were identified and common CPF applications were identified within each theme. CPF use has been viewed favourably whilst its limitations have been acknowledged (e.g., lack of practical application research).

Portable near-infrared spectroscopy (NIRS) devices were originally developed for use in exercise and sports science by Britton Chance in the 1990s (the RunMan and microRunman series). However, only recently with the development of more robust, and wireless systems, has the routine use in elite sport become possible. As with the medical use of NIRS, finding applications of the technology that are relevant to practitioners is the key issue. One option is to use NIRS to track exercise training-induced adaptations in muscle. Portable NIRS devices enable monitoring during the normal 'field' routine uses to assess fitness, such as repeat sprint shuttle tests. Knowledge about the acute physiological responses to these specific tests has practical applications within team sport training prescription, where development of both central and peripheral determinants of high-intensity intermittent exercise needs to be considered. The purpose of this study was to observe NIRS-detected parameters during a repeat sprint test. We used the PortaMon, a two wavelength spatially resolved NIR spectrometer manufactured by Artinis Inc., to assess NIR changes in the gastrocnemius muscle of both the left and right leg during high-intensity running. Six university standard rugby players were assessed (age 20 ± 1.5 years; height 183 ± 1.0 cm; weight 89.4 ± 5.8 kg; body fat 12.2 ± 3.0 %); the subjects completed nine repeated shuttle runs, which incorporated forward, backward and change of direction movements. Individual sprint time, total time to complete test, blood lactate response (BL), heart rate values (HR) and haemoglobin variables (ΔHHb, ΔtHb, ΔHbO2 and ΔTSI%) were measured. Total time to complete the test was 260 ± 20 s, final blood lactate was 14.3 ± 2.8 mM, and maximal HR 182 ± 5 bpm. NIRS variables displayed no differences between right and left legs. During the test, the group-averaged data showed a clear decrease in HbO2 (max. decrease 11.41 ± 4.95 μM), increase in HHb (max. increase 17.65 ± 4.48 μM) and drop in %TSI (max. drop-24.44 ± 4.63 %). tHb was largely unchanged. However, large interindividual differences were seen for all the NIRS parameters. In conclusion, this observational study suggests that a portable NIRS device is both robust and sensitive enough to detect haemoglobin changes during a high-intensity repeated shuttle run test. It therefore has the possibility to be used to assess exercise training-induced adaptations following a specific training protocol. However, it is at present unclear, given the individual variability, whether NIRS can be used to assess individual performance. We recommend that future studies report individual as well as group data. © 2013 Springer Science+Business Media New York.

Aims [D3,G40,K41.C16 diacid]exendin-4 (acyl-ExD3) and [F1,G40,K41.C16 diacid]exendin-4 (acyl-ExF1) are oppositely biased glucagon-like peptide-1 (GLP-1) receptor agonists that preferentially promote β-arrestin recruitment or G protein-induced signalling, respectively. The latter is more favourable in glycaemic control and induces a steeper reduction in body weight in diet-induced obese mice. Here, we compared the effects of G protein-biased agonist acyl-ExF1 to those of β-arrestin-biased agonist acyl-ExD3 on lipid metabolism in hyperlipidaemic mice. Materials and methods APOE*3-Leiden.CETP mice were treated with saline, acyl-ExD3 or acyl-ExF1 via intraperitoneal injections for 6 weeks or intracerebroventricular infusion for 18 days. Body weight and composition were monitored at regular intervals, as were plasma glucose, triglyceride and cholesterol levels. At endpoint, mice were injected with very low-density lipoprotein (VLDL)-like particles containing glycerol tri[3H]oleate to study triglyceride-derived fatty acid uptake by peripheral tissues including brown and white adipose tissue (BAT and WAT). Results Upon peripheral treatment, body weight gain was prevented and plasma glucose levels were reduced by acyl-ExF1, but circulating lipids were not affected by either acyl-ExF1 or acyl-ExD3. In contrast, central administration of either agonist strongly reduced plasma triglyceride and cholesterol levels, but did not affect glucose levels. Acyl-ExD3 and acyl-ExF1 increased [3H]oleate uptake by adipose tissue, reaching statistical significance for the uptake by BAT and WAT, respectively, compared to vehicle treatment. Conclusion The oppositely biased GLP-1 receptor agonists acyl-ExD3 and acyl-ExF1 do not differentially affect lipid metabolism in APOE*3-Leiden.CETP mice, while effects on glucose homeostasis and prevention of body weight gain are more pronounced upon peripheral acyl-ExF1 treatment.

This study examined the effects of Sprint Interval Cycling (SIT) on muscle oxygenation kinetics and performance during the 30-15 intermittent fitness test (IFT). Twenty-five women hockey players of Olympic standard were randomly selected into an experimental group (EXP) and a control group (CON). The EXP group performed six additional SIT sessions over six weeks in addition to their normal training program. To explore the potential training-induced change, EXP subjects additionally completed 5 x 30s maximal intensity cycle testing before and after training. During these tests near-infrared spectroscopy (NIRS) measured parameters; oxyhaemoglobin + oxymyoglobin (HbO2+ MbO2), tissue deoxyhaemoglobin + deoxymyoglobin (HHb+HMb), total tissue haemoglobin (tHb) and tissue oxygenation (TSI %) were taken. In the EXP group (5.34±0.14 to 5.50±0.14m.s

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The aim of this review was to consolidate and synthesise rugby union (RU) and rugby league (RL) studies on tackle and RU studies on ruck technique for rugby stakeholders. Forty-nine studies were identified (20 in RL and 29 in RU). RL studies primarily focussed on identifying factors that impact tackling ability. Leaner, fitter players, with greater lower body strength, tended to have more proficient tackle technique. Experience and level of play were positively associated with tackling ability. These findings highlight the importance of developing tackle technique and physical qualities to allow players to progress to higher levels. Research in RU mostly focussed on identifying tackle and ruck techniques associated with performance measures and injury outcomes. Eleven tackle techniques and five ball-carrier techniques were associated with both performance measures and injury outcomes. These findings support national injury prevention programmes that advocate that safe contact technique is also effective technique.

An amendment to this paper has been published and can be accessed via the original article.

Abstract

Cummins, C, Charlton, G, Naughton, M, Jones, B, Minahan, C, and Murphy, A. The validity of automated tackle detection in women's rugby league. J Strength Cond Res 36(7): 1951–1955, 2022—This study assessed the validity of microtechnology devices to automatically detect and differentiate tackles in elite women's rugby league match-play. Elite female players ( n = 17) wore a microtechnology device (OptimEye S5 device; Catapult Group International) during a representative match, which involved a total of 512 tackles of which 365 were defensive and 147 were attacking. Tackles automatically detected by Catapult's tackle detection algorithm and video-coded tackles were time synchronized. True positive, false negative and false positive events were utilized to calculate sensitivity (i.e., when a tackle occurred, did the algorithm correctly detect this event) and precision (i.e., when the algorithm reported a tackle, was this a true event based on video-coding). Of the 512 video-derived attacking and defensive tackle events, the algorithm was able to detect 389 tackles. The algorithm also produced 81 false positives and 123 false negatives. As such when a tackle occurred, the algorithm correctly identified 76.0% of these events. When the algorithm reported that a tackle occurred, this was an actual event in 82.8% of circumstances. Across all players, the algorithm was more sensitive to the detection of an attacking event (sensitivity: 78.2%) as opposed to a defensive event (sensitivity: 75.1%). The sensitivity and precision of the algorithm was higher for forwards (sensitivity: 81.8%; precision: 92.1%) when compared with backs (sensitivity: 64.5%; precision: 66.1%). Given that understanding the tackle demands of rugby league is imperative from both an injury-prevention and physical-conditioning perspective there is an opportunity to develop a specific algorithm for the detection of tackles within women's rugby league.

Growth and maturation affect long term physical performance, making the appraisal of athletic ability difficult. We sought to longitudinally track youth soccer players to assess the developmental trajectory of athletic performance over a 6-year period in an English Premier League academy. Age-specific z-scores were calculated for sprint and jump performance from a sample of male youth soccer players (n = 140). A case study approach was used to analyse the longitudinal curves of the six players with the longest tenure. The trajectories of the sprint times of players 1 and 3 were characterised by a marked difference in respective performance levels up until peak height velocity (PHV) when player 1 achieved a substantial increase in sprint speed and player 3 experienced a large decrease. Player 5 was consistently a better performer than player 2 until PHV when the sprint and jump performance of the former markedly decreased and he was overtaken by the latter. Fluctuations in players’ physical performance can occur quickly and in drastic fashion. Coaches must be aware that suppressed, or inflated, performance could be temporary and selection and deselection decisions should not be made based on information gathered over a short time period.

The aim of this study was to identify the changes in movement variability and movement velocity during a six-week training period using a resistance horizontal forward–backward task without (NOBALL) or with (BALL) the constraint of catching and throwing a rugby ball in the forward phase. Eleven elite male rugby union players (mean ± SD: age 25.5 ± 2.0 years, height 1.83 ± 0.06 m, body mass 95 ± 18 kg, rugby practice 14 ± 3 years) performed eight repetitions of NOBALL and BALL conditions once a week in a rotational flywheel device. Velocity was recorded by an attached rotary encoder while acceleration data were used to calculate sample entropy (SampEn), multiscale entropy, and the complexity index. SampEn showed no significant decrease for NOBALL (ES = -0.64 ± 1.02) and significant decrease for BALL (ES = -1.71 ± 1.16; p < 0.007) conditions. Additionally, movement velocity showed a significant increase for NOBALL (ES = 1.02 ± 1.05; p < 0.047) and significant increase for BALL (ES = 1.25 ± 1.08; p < 0.025) between weeks 1 and 6. The complexity index showed higher levels of complexity in the BALL condition, specifically in the first three weeks. Movement velocity and complex dynamics were adapted to the constraints of the task after a four-week training period. Entropy measures seem a promising processing signal technique to identify when these exercise tasks should be changed.

Abstract

The COVID-19 pandemic in 2020 has resulted in widespread training disruption in many sports. Some athletes have access to facilities and equipment, while others have limited or no access, severely limiting their training practices. A primary concern is that the maintenance of key physical qualities (e. g. strength, power, high-speed running ability, acceleration, deceleration and change of direction), game-specific contact skills (e. g. tackling) and decision-making ability, are challenged, impacting performance and injury risk on resumption of training and competition. In extended periods of reduced training, without targeted intervention, changes in body composition and function can be profound. However, there are strategies that can dramatically mitigate potential losses, including resistance training to failure with lighter loads, plyometric training, exposure to high-speed running to ensure appropriate hamstring conditioning, and nutritional intervention. Athletes may require psychological support given the challenges associated with isolation and a change in regular training routine. While training restrictions may result in a decrease in some physical and psychological qualities, athletes can return in a positive state following an enforced period of rest and recovery. On return to training, the focus should be on progression of all aspects of training, taking into account the status of individual athletes.

ABSTRACT

Oftentimes, the various coaching staff, sport science, and medical practitioners of a sports club require a single, holistic indication of an athlete's athleticism. Currently, there is no consensus on how this is best defined, and thus, a total score of athleticism (TSA) may provide one such method. The TSA is derived from the average of Z-scores (or T-scores in the case of small samples) from a sport-specific testing battery, ensuring athletes are judged across all the relevant fitness capacities that best define the physical demands of competition. To aid readers in using the TSA, this article also details how it is computed in EXCEL.

© 2019 Informa UK Limited, trading as Taylor & Francis Group. Instruction: Effective playing time in soccer is typically < 60 min per game and while players may reposition themselves when the ball is out of play, it is likely the physical demand decreases during this period. Therefore, if this period is included in data when quantifying match demands, it may under-report the physical requirements of soccer players. This study investigated an alternative method for quantifying external workload called ball in play (BiP), which analyses the data excluding stoppages, and thus potentially offers a more insightful analysis of match demands.Methods: Whole match demands as typically recorded via GPS, were compared to those based on BiP, and maximum BiP, with the latter representing worst case scenario phases of play. The 25-elite male youth soccer players (age: 17.9 ± 0.6 years; height: 174.8 ± 6.2 cm; body mass: 66.3 ± 8.1 kg) who participated in this study were also categorised in to positional groups (defender, midfielder, and forward) to assess differences in positional demands.Results: While no differences were noted based on position, whole match metrics were significantly lower than mean and maximum BiP metrics (p < 0.05). There was also a significant difference for maximum BiP outputs across different in-play durations, when comparing 30-60 seconds, 60–90 seconds, and > 90 seconds.Conclusion: This data allows practitioners to gain a deeper understanding of the physical demands imposed on players and plan sessions using targets that better represent match demands.

OBJECTIVES: To examine the interactions between SARS-CoV-2 positive players and other players during rugby league matches and determine within-match SARS-CoV-2 transmission risk. METHODS: Four Super League matches in which SARS-CoV-2 positive players were subsequently found to have participated were analysed. Players were identified as increased-risk contacts, and player interactions and proximities were analysed by video footage and global positioning system (GPS) data. The primary outcome was new positive cases of SARS-CoV-2 within 14 days of the match in increased-risk contacts and other players participating in the matches. RESULTS: Out of 136 total players, there were 8 SARS-CoV-2 positive players, 28 players identified as increased-risk contacts and 100 other players in the matches. Increased-risk contacts and other players were involved in 11.4±9.0 (maximum 32) and 4.0±5.2 (maximum 23) tackles, respectively. From GPS data, increased-risk contacts and other players were within 2 m of SARS-CoV-2 positive players on 10.4±18.0 (maximum 88) and 12.5±20.7 (maximum 121) occasions, totalling 65.7±137.7 (maximum 689) and 89.5±169.4 (maximum 1003) s, respectively. Within 14 days of the match, one increased-risk contact and five players returned positive SARS-CoV-2 reverse transcriptase PCR (RT-PCR) tests, and 27 increased-risk contacts and 95 other participants returned negative SARS-CoV-2 RT-PCR tests. Positive cases were most likely traced to social interactions, car sharing and wider community transmission and not linked to in-match transmission. CONCLUSION: Despite tackle involvements and close proximity interactions with SARS-CoV-2 positive players, in-match SARS-CoV-2 transmission was not confirmed. While larger datasets are needed, these findings suggest rugby presents a lower risk of viral transmission than previously predicted.

BACKGROUND: Collisions (i.e. tackles, ball carries, and collisions) in the rugby league have the potential to increase injury risk, delay recovery, and influence individual and team performance. Understanding the collision demands of the rugby league may enable practitioners to optimise player health, recovery, and performance. OBJECTIVE: The aim of this review was to (1) characterise the dose of collisions experienced within senior male rugby league match-play and training, (2) systematically and critically evaluate the methods used to describe the relative and absolute frequency and intensity of collisions, and (3) provide recommendations on collision monitoring. METHODS: A systematic search of electronic databases (PubMed, SPORTDiscus, Scopus, and Web of Science) using keywords was undertaken. A meta-analysis provided a pooled mean of collision frequency or intensity metrics on comparable data sets from at least two studies. RESULTS: Forty-three articles addressing the absolute (n) or relative collision frequency (n min-1) or intensity of senior male rugby league collisions were included. Meta-analysis of video-based studies identified that forwards completed approximately twice the number of tackles per game than backs (n = 24.6 vs 12.8), whilst ball carry frequency remained similar between backs and forwards (n = 11.4 vs 11.2). Variable findings were observed at the subgroup level with a limited number of studies suggesting wide-running forwards, outside backs, and hit-up forwards complete similar ball carries whilst tackling frequency differed. For microtechnology, at the team level, players complete an average of 32.7 collisions per match. Limited data suggested hit-up and wide-running forwards complete the most collisions per match, when compared to adjustables and outside backs. Relative to playing time, forwards (n min-1 = 0.44) complete a far greater frequency of collision than backs (n min-1 = 0.16), with data suggesting hit-up forwards undertake more than adjustables, and outside backs. Studies investigating g force intensity zones utilised five unique intensity schemes with zones ranging from 2-3 g to 13-16 g. Given the disparity between device setups and zone classification systems between studies, further analyses were inappropriate. It is recommended that practitioners independently validate microtechnology against video to establish criterion validity. CONCLUSIONS: Video- and microtechnology-based methods have been utilised to quantify collisions in the rugby league with differential collision profiles observed between forward and back positional groups, and their distinct subgroups. The ball carry demands of forwards and backs were similar, whilst tackle demands were greater for forwards than backs. Microtechnology has been used inconsistently to quantify collision frequency and intensity. Despite widespread popularity, a number of the microtechnology devices have yet to be appropriately validated. Limitations exist in using microtechnology to quantify collision intensity, including the lack of consistency and limited validation. Future directions include application of machine learning approaches to differentiate types of collisions in microtechnology datasets.

PURPOSE: The aim of this study was to describe the physical demands during U18 elite basketball games according to the game quarter and to identify a smaller subset of variables and threshold scores that distinguish players' physical performance in each quarter. METHODS: Data was collected from ninety-four players who participated in the study (age: 17.4 ± 0.74 years; height: 199.0 ± 0.1 cm; body mass: 87.1 ± 13.1 kg) competing in the Euroleague Basketball Next Generation Tournament. Players' movements during the games were measured using a portable local positioning system (LPS) (WIMU PRO®, Realtrack Systems SL, Almería, Spain) and included relative distance (total distance / playing duration), relative distance in established speed zones, high-intensity running (18.1-24.0 km·h-1) and sprinting (> 24.1 km·h-1). player load, peak speed (km·h-1) and peak acceleration (m·s-2) number of total accelerations and total decelerations, high intensity accelerations (> 2 m·s-2) and decelerations (< -2 m·s-2). RESULTS: There was an overall decrease in distance covered, player load, number of high intensity accelerations and decelerations between the first and last quarter of the games in all playing positions. A classification tree analysis showed that the first quarter had much influence of distance covered (above 69.0 meters), distance covered <6.0 km·h-1 and accelerations (> 2 m·s-2), whereas the fourth quarter performance had much influence of distance covered (below 69.0) and distance covered 12.1-18.0 km·h-1. CONCLUSIONS: A significant reduction in physical demands occurs during basketball, especially between first and last quarter for players in all playing positions during basketball games of under 18 elite players.

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. 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.

© 2018, © 2018 Cardiff Metropolitan University. Previous research on performance indicators in rugby league has suggested that dimension reduction techniques should be utilised when analysing sporting data sets with a large number of variables. Forty-five rugby league team performance indicators, from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, were reduced to 10 orthogonal principal components with standardised team scores produced for each component. Forced-entry logistic (match outcome) and linear (point’s difference) regression models were used alongside exhaustive chi-square automatic interaction detection decision trees to determine how well each principle component predicted success. The 10 principal components explained 81.8% of the variance in point’s difference and classified match outcome correctly ~90% of the time. Results suggested that if a team increased “amount of possession” and “making quick ground” component scores, they were more likely to win (β = 15.6, OR = 10.1 and β = 7.8, OR = 13.3) respectively. Decision trees revealed that “making quick ground” was an important predictor of match outcome followed by “quick play” and “amount of possession”. The use of PCA provided a useful guide on how teams can increase their chances of success by improving performances on a collection of variables, instead of analysing variables in isolation.

© 2018 European College of Sport Science The purpose of the study was to examine the perspectives of both academics and practitioners in relation to forming applied collaborative sport science research within team sports. Ninety-three participants who had previously engaged in collaborative research partnerships within team sports completed an online survey which focused on motivations and barriers for forming collaborations using blinded sliding scale (0–100) and rank order list. Research collaborations were mainly formed to improve the team performance (Academic: 73.6 ± 23.3; Practitioner: 84.3 ± 16.0; effect size (ES = 0.54), small). Academics ranked journal articles’ importance significantly higher than practitioners did (Academic: Mrank = 53.9; Practitioner: 36.0; z = −3.18, p = .001, p < q). However, practitioners rated one-to-one communication as more preferential (Academic: Mrank = 41.3; Practitioner 56.1; z = −2.62, p = .009, p < q). Some potential barriers were found in terms of staff buy in (Academic: 70.0 ± 25.5; Practitioner: 56.8 ± 27.3; ES = 0.50, small) and funding (Academic: 68.0 ± 24.9; Practitioner: 67.5 ± 28.0; ES = 0.02, trivial). Both groups revealed low motivation for invasive mechanistic research (Academic: 36.3 ± 24.2; Practitioner: 36.4 ± 27.5; ES = 0.01, trivial), with practitioners have a preference towards ‘fast’ type research. There was a general agreement between academics and practitioners for forming research collaborations. Some potential barriers still exist (e.g. staff buy in and funding), with practitioners preferring ‘fast’ informal research dissemination compared to the ‘slow’ quality control approach of academics.

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. 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. Senior level players scored significantly higher in all three assessments; tackle technique senior vs academy 1st (p < 0.01, effect size (ES) = 0.7, moderate), senior vs academy 2nd (p < 0.01, ES = 0.7, moderate); ball-carry technique senior vs academy 1st (p < 0.01, ES = 0.6, moderate), senior vs academy 2nd (p < 0.01, ES = 0.8, moderate); ruck technique senior vs academy 1st (p < 0.01, ES = 0.7, moderate), senior vs academy 2nd (p < 0.01, ES = 0.4, small). These findings emphasize the importance of developing contact technique to allow players to progress to higher levels, and provide validity to an assessment tool which can facilitate this process.

Identifying and understanding the fatigue response of youth rugby players following training and competition is necessary to avoid chronic fatigue, underperformance, and injury. There is an abundance of monitoring tools that have been used in youth rugby cohorts to describe the fatigue response. Tests typically include assessment of lower- and upper-body neuromuscular function, biochemical markers, and athlete self-reported measures. Researchers in youth rugby have typically assessed fatigue measures following training, single matches, and during periods of congested fixtures. The fatigue response following training depends upon the training volume and design (e.g., non-contact vs. contact). Fatigue is present for up to 72 hours following single matches and has the potential for accumulation during periods of congested fixtures. Despite the limited number of studies investigating fatigue and recovery in youth rugby players, this chapter presents the current findings from the literature with the aim to provide an understanding of how youth rugby players recover from competition. This information can help to guide training prescription and recovery methods when youth athletes may be most vulnerable to performance decrements or negative health consequences.

This study investigated the relationship between physical qualities and contact technique proficiency in rugby union players. Thirty-eight (n = 38) male academy rugby union players participated in the study. Physical measures of anthropometry, functional mobility, strength endurance, strength, power, speed, agility, and anaerobic and aerobic endurance were assessed. Tackler, ball-carrier, and ruck technique were assessed using video analysis of a standardised two-on-two contact drill. Seven physical qualities were moderately associated with tackler technique; Push-ups (r2 = 0.2; β = 0.04; p = 0.005; ES = 0.26), Sit-ups (r2 = 0.2; β = 0.08; p = 0.004; ES = 0.27), Relative 1RM Bench Press (r2 = 0.2; β = 2.32; p = 0.003; ES = 0.29), Broad Jump (r2 = 0.2; β = 0.03; p = 0.009; ES = 0.22), Agility (r2 = 0.2; β = −0.47; p = 0.019; ES = 0.19), 40m-Speed with Ball (r2 = 0.1; β = 0.93; p = 0.027; ES = 0.16) and Functional Mobility (r2 = 0.2; β = 0.16; p = 0.007; ES = 0.25). There was a large association between ball-carrier technique and Medicine Ball Throw (r2 = 0.3; β = 1.13; p = 0.001; ES = 0.37), and a moderate association between ruck technique and agility without (r2 = 0.2; β = −0.75; p = 0.005; ES = 0.21) and with (r2 = 0.2; β = −0.55; p = 0.015; ES = 0.29) the ball. The findings demonstrate the important contribution of physical strength and conditioning to contact technique in rugby union players. Contact technique training should be accompanied with physical strength and conditioning, as improvements in physical qualities may serve as foundational components to underpin improvements in technique.

Objectives: Effective tackle technique is associated with reduced injury risks and improved performance in contact. Injury prevention programmes aim to provide players with knowledge of effective technique. However, little is known of the impact of this knowledge on a player's technique in the tackle. This study aimed to determine the association between knowledge of proper tackle technique and tackle technique proficiency in training. Methods: Fifty-three rugby union players participated in a tackle contact drill and, thereafter, completed a questionnaire. The drill was filmed, and the players' tackle and ball-carry technique were assessed using standardised technical proficiency. In the questionnaire, the players were asked to rate the importance of each tackle and ball-carry technique on a 5-point Likert scale, for both injury prevention and performance tackle outcomes. Linear regression was performed to assess the relationship between the knowledge of the importance of proper tackle technique and tackle technique proficiency during the drill. Results: No association was found between players' knowledge of the importance of proper technique and tackle contact technique in training for both injury prevention and performance. Conclusion: The lack of association between players' knowledge and actual tackle contact technique reveals the gap between the knowledge of safe and effective techniques and the knowledge of how to execute the said techniques.

Objective: The tackle is the most injurious event in rugby league and carries the greatest risk of concussion. This study aims to replicate previous research conducted in professional men's rugby league by examining the association between selected tackle characteristics and head impact events (HIEs) in women's professional rugby league. Methods: We reviewed and coded 83 tackles resulting in an HIE and every tackle (6,318 tackles) that did not result in an HIE for three seasons (2018–2020) of the National Rugby League Women's (NRLW) competition. Tackle height, body position of the tackler and ball carrier, as well as the location of head contact with the other player's body were evaluated. Propensity of each situation that caused an HIE was calculated as HIEs per 1,000 tackles. Results: The propensity for tacklers to sustain an HIE was 6.60 per 1,000 tackles (95% CI: 4.87–8.92), similar to that of the ball carrier (6.13 per 1,000 tackles, 95% CI: 4.48–8.38). The greatest risk of an HIE to either the tackler or ball carrier occurred when head proximity was above the sternum (21.66 per 1,000 tackles, 95% CI: 16.55–28.35). HIEs were most common following impacts between two heads (287.23 HIEs per 1,000 tackles, 95% CI: 196.98–418.84). The lowest propensity for both tackler (2.65 per 1,000 tackles, 95% CI: 0.85–8.20) and ball carrier HIEs (1.77 per 1,000 tackles, 95% CI: 0.44–7.06) occurred when the head was in proximity to the opponent's shoulder and arm. No body position (upright, bent or unbalanced/off feet) was associated with an increased propensity of HIE to either tackler or ball carrier. Conclusions: In the NRLW competition, tacklers and ball carriers have a similar risk of sustaining an HIE during a tackle, differing from men's NRL players, where tacklers have a higher risk of HIEs. Further studies involving larger samples need to validate these findings. However, our results indicate that injury prevention initiatives in women's rugby league should focus on how the ball carrier engages in contact during the tackle as well as how the tackler executes the tackle.

This review and meta‐analysis aimed to describe the current rugby‐7s injury epidemiological literature by examining injury data from both sexes, all levels of play, and their associated risk factors. Studies published up until March 2024 were included. These studies were retrieved from six databases using search terms related to rugby‐7s or sevens, tackle, collision, collision sport, injury, athlete, incidence rate, mechanism, and risk factor. Only peer‐reviewed original studies using prospective or retrospective cohort designs with a clearly defined rugby‐7s sample were considered. Included studies needed to report one injury outcome variable. Non‐English and qualitative studies; reviews, conference papers, and abstracts were excluded. Twenty studies were included. The meta‐analysis used the DerSimonian–Laird continuous random‐effects method to calculate the pooled estimated means and 95% confidence interval. The estimated mean injury incidence rate for men was 108.5/1000 player‐hours (95% CI: 85.9–131.0) and 76.1/1000 player‐hours (95% CI: 48.7–103.5) for women. The estimated mean severity for men was 33.9 days (95% CI: 20.7–47.0) and 44.2 days (95% CI: 32.1–56.3) for women. Significantly more match injuries occurred in the second half of matches, were acute, located at the lower limb, diagnosed as joint/ligament, and resulted from being tackled. Fatigue, player fitness, and previous injuries were associated with an increased risk of injury. There were no statistically significant differences between women's and men's injury profiles. However, the inherent cultural and gendered factors which divide the two sports should not be ignored. The findings from this review will help pave the way forward beyond the foundational stages of injury prevention research in rugby‐7s.

Objectives: To provide normative reference values for the Sport Concussion Assessment Tool – 5th Edition for elite-level male rugby league players. Design: A descriptive cross-sectional study. Methods: Baseline Sport Concussion Assessment Tool – 5th Edition scores were obtained from 1005 National Rugby League players during the 2018 and 2019 preseasons. Normative values were calculated for the Standardized Assessment of Concussion, Symptom Evaluation (i.e., severity and number), and the Modified Balance Error Scoring System for each group and in total. Players self-identified their cultural heritage or ethnicity to be ‘Pasifika (Pacific Islander) or Maori’ (n = 243; 24.2 %) or ‘Indigenous Australian’ (n = 82; 8.2 %). Those who identified as any other race, ethnicity, or cultural heritage were combined into a single group (n = 680; 67.7 %). Results: In total the median Standardized Assessment of Concussion score was 27 (interquartile range = 25–28), the median symptom severity was 0 (interquartile range = 0–2), the median symptom number was 0 (interquartile range = 0–1), and the median the Modified Balance Error Scoring System error score was 3 (interquartile range = 1–5). Reporting 4 of 22 symptoms and 6 of 132 on the total severity score was uncommon. There was no significant difference between the cultural heritage or ethnicity groups for Standardized Assessment of Concussion scores, symptom severity or number, or Modified Balance Error Scoring System errors (p-values > 0.05). Conclusions: This normative data will assist with the clinical interpretation of Sport Concussion Assessment Tool – 5th Edition scores following a concussion in the National Rugby League.

The role of glucose dependent insulinotropic polypeptide (GIP) signalling in metabolic homeostasis is a hot area of research. The study of GIP receptor (GIPR) expression and trafficking, vital aspects in understanding GIPR physiology and pharmacology, has been hindered by the lack of validated GIPR antibodies, meaning that studies have relied on the presence of Gipr mRNA to determine GIPR expression in mouse and human tissue.1-3 The development of fluorophore-conjugated GIPR agonists has provided some insights into endogenous GIPR distribution and behaviour3, 4 but offers only indirect evidence of receptor localisation and may be misleading in some contexts, for example, if agonists dissociate from receptors after entering the endocytic pathway.

The purpose of this study was to compare tackle and ruck frequencies between pool and knockout matches during the Men's International World Rugby Sevens Series and also determine which technical determinants increase the likelihood of tackle success within each stage of the tournament. Video analysis of all matches during the 2018/2019 International Men's Rugby Sevens World Series was conducted (n = 449 matches). This equated to 21226 tackle contact events and 6345 rucks events. Each tackle event was further coded for tackle descriptors (type of tackle, direction of contact and point of body contact) and tackle outcomes (successful and unsuccessful). No differences were found between the mean tackles per match of pool and knockout stages (pool 47.5, 95% CI 46.5–48.6 vs. knockout 46.9, 95% CI 45.7–48.0). There was a significant difference (p < 0.001) in mean rucks per match between pool and knockout stages (pool 14.8, 95% CI 14.2–15.4 vs. knockout 13.3, 95% CI 12.7–13.9). In conclusion, tackle frequencies per match remained consistent across the series and between the different competition stages and match halves. Ruck frequencies on the other hand decreased from the first tournament to latter parts of the series, and fewer rucks were observed in the knockout stage of the tournaments. The frequency and higher likelihood of tackle success for arm tackles in Sevens highlights a unique demand of Sevens, which strengthens the argument for Sevens‐specific tackle training and coaching.

The conversion rate of junior athletes to senior performance is typically used to judge efficiency in talent development. This study aimed to calculate the conversion rate of English academy rugby union players conferred with 'high-potential' 'England Academy Player' status, distinguishing them from their age-matched peers, from academy selection into the Premiership, the elite domestic league. Conversion was calculated overall and between academies, considering when players were conferred high-potential status. In total, 3127 male players were sampled. Players conferred with high-potential status early (prior to achieving a senior contract) accounted for 68.03% of elite debuts and had increased odds of converting compared to other player sub-populations (OR: 5.82, 95% CI: 4.60-7.37). Regression analysis inferred acquiring high-potential status at younger ages increased odds of a Premiership appearance by 2.501. However, delimiting to players achieving senior contracts, players conferred 'high-potential' after accomplishing a senior contract had a greater relative conversion (46.34%) compared to early status players (40.27%). Academy conversion rates and contribution to net development considerably varied (14.63-45.28% and 2.72-12.84%). Findings suggest that whilst high-potential status influences progression, other important factors influence player development. Results demonstrate the need to consider how the specific context of each academy within the talent system influences player development.

Objectives There is renewed interest in targeting the glucose-dependent insulinotropic polypeptide receptor (GIPR) for treatment of obesity and type 2 diabetes. G-protein coupled receptor desensitisation is suggested to reduce the long-term efficacy of glucagon-like-peptide 1 receptor (GLP-1R) agonists and may similarly affect the efficacy of GIPR agonists. We explored the extent of pancreatic GIPR functional desensitisation with sustained agonist exposure. Methods A long-acting GIPR agonist, GIP108, was used to probe the effect of sustained agonist exposure on cAMP responses in dispersed pancreatic islets using live cell imaging, with rechallenge cAMP responses after prior agonist treatment used to quantify functional desensitisation. Receptor internalisation and β-arrestin-2 activation were investigated in vitro using imaging-based assays. Pancreatic mouse GIPR desensitisation was assessed in vivo via intraperitoneal glucose tolerance testing. Results GIP108 treatment led to weight loss and improved glucose homeostasis in mice. Prolonged exposure to GIPR agonists produced homologous functional GIPR desensitisation in isolated islets. GIP108 pre-treatment in vivo also reduced the subsequent anti-hyperglycaemic response to GIP re-challenge. GIPR showed minimal agonist-induced internalisation or β-arrestin-2 activation. Conclusions Although GIP108 chronic treatment improved glucose tolerance, it also resulted in partial desensitisation of the pancreatic islet GIPR. This suggests that ligands with reduced desensitisation tendency might lead to improved in vivo efficacy. Understanding whether pancreatic GIPR desensitisation affects the long-term benefits of GIPR agonists in humans is vital to design effective metabolic pharmacotherapies.

Objectives To investigate the network of stakeholders involved in rugby union research across the globe. Methods Using author affiliations listed on scientific publications, we identified the organisations that contributed to rugby union research from 1977 to 2022 and examine collaboration through coauthorship indicators. We determined the locations and sectors of identified organisations and constructed a collaboration network. Network metrics, including degree centrality and betweenness centrality, are computed to identify influential organisations and measure intersector collaboration. Results There is an increase in scientific knowledge creation and collaboration between organisations for rugby union research over time. Among the sectors, the university, professional sports team and sports governing body sectors exhibit the highest intersectoral and intrasectoral density. Predominantly, influential actors are located in England, Australia, France, New Zealand, Ireland and South Africa. Australian Catholic University, Leeds Beckett University, Stellenbosch University, Swansea University, University College London and the University of Cape Town emerge as influential actors between 2016 and 2022. Conclusions Our study underscores the ongoing growth of scientific knowledge generation in rugby union, primarily led by organisations in tier 1 rugby-playing nations within the university sector. Intersectoral collaboration with sports governing bodies plays a crucial role, acting as a broker between sectors. However, the overall collaboration landscape between and within sectors is low. These results highlight an opportunity for improved collaboration opportunities, as the organisations driving knowledge creation have been identified.

Trial matches are frequently used for team preparation in rugby league competitions, making it essential to understand the demands experienced to assess their specificity to actual competition. Consequently, this study aimed to compare the activity demands between pre-season trial matches and early in-season rugby league matches. Following a repeated-measures observational design, 39 semi-professional, male rugby league players from two clubs were monitored using microsensors during two trial matches and the first two in-season matches across two consecutive seasons. Total distance, average speed, peak speed, absolute and relative high-speed running (HSR; > 18 km · h-1) and low-speed running (LSR; < 18 km · h-1) distance, as well as absolute and relative impacts, accelerations (total and high-intensity > 3 m · s-2), and decelerations (total and high-intensity < -3 m · s-2) were measured. Linear mixed models and Cohen's d effect sizes were used to compare variables between match types. Playing duration was greater for in-season matches (p < 0.001, d = 0.64). Likewise, higher (p < 0.001, d = 0.45-0.70) activity volumes were evident during in-season matches indicated via total distance, HSR distance, LSR distance, total accelerations, high-intensity accelerations, total decelerations, and high-intensity decelerations. Regarding activity intensities, a higher average speed (p = 0.008, d = 0.31) and relative LSR distance (p = 0.005, d = 0.31) only were encountered during in-season matches. Despite players completing less volume, the average activity intensities and impact demands were mostly similar between trial and early in-season matches. These findings indicate trial matches might impose suitable activity stimuli to assist players in preparing for early in-season activity intensities.

Background: In rugby league (RL), the ability to repeatedly engage in the tackle, whether as a ball carrier or tackler, is essential for team success and player performance. It is also the leading cause of injury, with over 90% of total injuries occurring during the tackle in professional and amateur cohorts. To effectively reduce the risk of injury and optimise performance, establishing the extent of the ‘problem’, through injury surveillance or descriptive performance studies is required. Objective: The purpose of this narrative synthesis was to systematically search and synthesise tackle injury epidemiology and tackle performance frequency in RL. To achieve this objective, a systematic review was conducted. Methods: The search was limited to English-only articles published between January 1995 and October 2018. Based on the search criteria, a total of 53 studies were found: 32 focused on tackle injury epidemiology (nine cases studies) and 21 focused on tackle frequency. Results: In general, over 600 tackles may occur during an RL match. Tackle injury frequencies (both overall and time-loss injuries) ranged between 47%-94% at the professional level, and between 38%-96% for the lower levels of play. A greater proportion of injuries occurring in professional RL are severe time-loss injuries when compared to lower levels of play. Most time-loss and overall injuries occur to players who are tackled, i.e., ball carriers, across all levels of play. Conclusion: This narrative synthesis will facilitate tackle injury prevention and performance research in RL, and act as a reference document for coaches and practitioners.

Background: The risk of concussion at the elite level of rugby league has been extensively evaluated. However, there has been very little concussion research conducted at the semi-elite level. Purpose: To examine cases of medically diagnosed concussion from a single season of adult men's semi-elite rugby league. Methods: A retrospective review of the 2019 Queensland Cup season head injury assessment surveillance program was completed. All Head Injury Assessment (HIA) cases, including cases of medically diagnosed concussion were retrospectively video reviewed and game play characteristic variables along with video signs of concussion were coded. This data was combined with the return to play data to form the research database. Results: There were 132 players removed for HIAs in 170 games. There were 36 players medically diagnosed with concussions, which equates to an incidence rate was 6.11 concussions per 1000 player match hours, or one concussion every 4.7 matches. All concussions occurred in a tackle event, where the player was struck in the head/face. Possible balance disturbance was the most commonly observed video sign (97.2 ​%; 35/36), with slow to stand also commonly observed in concussed players (91.7 ​%; 33/36). Most concussed players (63.9 ​%; 23/36) did not miss a game following the concussion. Conclusion: This is one of the first studies to review video footage of concussions in sub-elite rugby league. These findings build on the growing body of video analysis research in rugby league and suggest that the retrospective review of the video of incidents may offer insights into modifiable risk factors that may help reduce concussion in rugby league.

OBJECTIVES: This study analysed the overall sentiment of attitudes, opinions, views and emotions expressed in posts on X related to red-carded and yellow-carded tackles during the 2019 Rugby World Cup (RWC). METHODS: Sentiment analysis was conducted on posts on X about red or yellow cards issued at the 2019 RWC. Posts were classified as 'agree', 'disagree' and 'neutral'. The frequency of posts, red cards, yellow cards, all injuries, tackle injuries and total number of tackles per match were also synced to the 45-match playing schedule. RESULTS: Five tackle-related red cards were issued during the 2019 RWC, and 15 tackle-related yellow cards, with 337 and 302 posts identified for each card decision, respectively. For red cards, 42% of posts (n=158/377) agreed with the referee's decision, 19% (n=71/377) disagreed and 40% were neutral. For yellow cards, 24% (n=73/302) agreed with the referee's decision, 33% (n=99/302) disagreed and 43% were neutral. CONCLUSIONS: For red cards, posts were 2.2 times more likely to agree with the referee's decision than disagree. Posts that agreed with a red card decision were also more likely to be shared (reposted) than posts that disagreed with a red card decision. In contrast, sentiments expressed for yellow card decisions were mixed. This may be related to interpreting the degree of danger and whether mitigation is applied. Within the ecosystem of rugby, sharing sentiments on social media plays a powerful role in creating a positive player welfare narrative.

Abstract

Abstract

Participation in sport offers numerous physiological, psychological, and social benefits, yet injury remains an inherent risk, particularly in collision-based sports. Increasing scrutiny surrounds these sports, especially for youth, with inconsistency in the age for introducing deliberate contact (e.g., body checking, tackle) and debate regarding proposals for banning high-risk actions to reduce injuries. This article explores the policies and controversies regarding how, and when, physical contact is introduced in sports. Current policies vary significantly across sports, sexes, and national jurisdictions, leading to inconsistent implementation and outcomes. We outline arguments for both delaying and lowering the contact introduction age, including implications for participation rates, skill acquisition, and injury risk. Raising the age may reduce injury history and cumulative head impacts, while earlier, progressive contact training may enhance technical competence. Growth, maturation and size discrepancies further complicate such policy decisions. Evidence supports multimodal approaches, including training guidelines (e.g., reduced contact in practices), neuromuscular training, and rule modifications, to enhance safety without compromising play. Weight-based categorisation and bio-banding (grouping players by attributes associated with growth and/or maturation instead of chronological age) strategies show potential for injury-risk reduction but lack comprehensive evaluation. Despite polarised opinions, developing sport-specific recommendations on best practices for contact introduction remains critical to ensuring athlete welfare and sustainable participation in collision sports.

Abstract

Dual agonists targeting glucagon-like peptide-1 receptor (GLP1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR) are breakthrough treatments for patients with type 2 diabetes and obesity. Compared to GLP1R agonists, dual agonists show superior efficacy for glucose lowering and weight reduction. However, delineation of dual agonist cell targets remains challenging. Here, we develop and test daLUXendin and daLUXendin+, non-lipidated and lipidated fluorescent GLP1R/GIPR dual agonist probes, and use them to visualize cellular targets. daLUXendins are potent GLP1R/GIPR dual agonists that advantageously show less functional selectivity for mouse GLP1R over mouse GIPR. daLUXendins label rodent and human pancreatic islet cells, with a signal intensity of β cells > α cells = δ cells. Systemic administration of daLUXendin strongly labels GLP1R + and GIPR + neurons in circumventricular organs characterized by an incomplete blood–brain barrier but does not penetrate the brain beyond labelling seen with single (ant)agonists. At the single-molecule level, daLUXendin targets endogenous GLP1R–GIPR nanodomains, which differ in organization and composition from those targeted by a single agonist. daLUXendins reveal dual agonist targets in the pancreas and brain and exclude a role for brain penetration in determining the superior efficacy of dual agonists, shedding new light on different modes of action of dual agonists versus single agonists.

Abstract

Abstract

Glucagon-like peptide-1 receptor (GLP-1R) agonists (GLP-1RAs) ameliorate mitochondrial health by increasing mitochondrial turnover in metabolically relevant tissues. Mitochondrial adaptation to metabolic stress is crucial to maintain pancreatic β-cell function and prevent type 2 diabetes (T2D) progression. While the GLP-1R is well-known to stimulate cAMP production leading to Protein Kinase A (PKA) and Exchange Protein Activated by cyclic AMP 2 (Epac2) activation, there is a lack of understanding of the molecular mechanisms linking GLP-1R signalling with mitochondrial and β-cell functional adaptation. Here, we present a comprehensive study in β-cell lines and primary islets that demonstrates that, following GLP-1RA stimulation, GLP-1R-positive endosomes associate with the endoplasmic reticulum (ER) membrane contact site (MCS) tether VAPB at ER-mitochondria MCSs (ERMCSs), where active GLP-1R engages with SPHKAP, an A-kinase anchoring protein (AKAP) previously linked to T2D and adiposity risk in genome-wide association studies (GWAS). The inter-organelle complex formed by endosomal GLP-1R, ER VAPB and SPHKAP triggers a pool of ERMCS-localised cAMP/PKA signalling via the formation of a PKA-RIα biomolecular condensate which leads to changes in mitochondrial contact site and cristae organising system (MICOS) complex phosphorylation, mitochondrial remodelling, and β-cell functional adaptation, with important consequences for the regulation of β-cell insulin secretion and survival to stress.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective therapies for type 2 diabetes (T2D) and obesity, yet patient responses are variable, with GLP1R gene variation potentially linked to therapeutic outcomes. A GLP1R natural missense variant, A316T, protects against T2D and cardiovascular disease. Here, we generated and characterized a human GLP1R A316T mouse model. Human GLP1R A316T/A316T mice displayed lower fasting blood glucose versus wild-type littermates even under metabolic stress, as well as slower weight gain and alterations in islet cytoarchitecture, glucagon secretion, and liver metabolism under a high-fat, high-sucrose diet. This was however associated with blunted responses to pharmacological GLP-1RAs in vivo. Further investigations in β cell models demonstrated that human GLP1R A316T exhibits characteristics of constitutive activation but dampened GLP-1RA responses. Results are further supported by cryo-EM analyses and molecular dynamics simulations of GLP-1R A316T structure, collectively demonstrating that the A316T variant governs basal GLP-1R activity and pharmacological responses to GLP-1R–targeting therapies.

Abstract

Internalisation of G protein-coupled receptors (GPCRs) can contribute to altered cellular responses by directing signalling from non-canonical locations, such as endosomes. If signalling processes are locally constrained, active receptors in different subcellular locations could produce different downstream effects. This phenomenon may be relevant to the optimal targeting of the glucagon-like peptide-1 receptor (GLP-1R), a type 2 diabetes and obesity target GPCR for which several ligands with varying internalisation tendency have been discovered. To investigate, we compared the signalling localisation effects of two prototypical GLP-1RAs with opposite signal bias and effects on GLP-1R trafficking: exendin-asp3 (ExD3), a full agonist that drives rapid internalisation, and exendin-phe1 (ExF1), which shows much slower internalisation. After using bioorthogonal labelling and fluorescent agonist conjugates to verify the divergent trafficking patterns of ExF1 and ExD3 in β-cell lines and primary pancreatic islets, we used live cell biosensors to monitor signalling at different subcellular locations. This revealed that cAMP/PKA/ERK signalling in β-cells is in fact distributed widely across the cell over short- (<5 min) and medium-term (up to 60 min) stimulation at pharmacological (>10 pM) concentrations, with no major differences in signal localisation that could be linked to internalised versus cell surface-bound GLP-1R. Moreover, washout experiments highlighted that, whilst fast-internalising ExD3 shows much greater accumulation and binding to GLP-1R in endosomes than slow-internalising ExF1, it is a rather inefficient driver of both cAMP production in β-cells and insulin secretion from perfused rat pancreata. These data provide a greater understanding of the cellular effects of biased GLP-1R agonism.

Objectives: Substantial research has considered the factors contributing to effective talent systems, including environmental features and resources. This study aimed to quantitatively explore associations between academy resources and outcome effectiveness in the English male Premiership rugby talent system. Design: This study utilised a retrospective analysis of archival data. We compared academy resources (human, financial, contextual) between 2016/17 and 2019/20 seasons, with academy outcomes between 2020/21 and 2023/24. Academy outcomes were operationalised as number of Premiership players developed and Premiership appearances subsequently made. Thirteen of fourteen possible academies were included in analysis. Methods: Cost effectiveness was considered by dividing academy total financial investment (inflation adjusted) by number of developed Premiership players, with one-way ANOVA analysis comparing differences between academies. To explore the possible influence of academy resources on academy outcomes, three sets of multiple linear regression models were constructed. Results: Cost of development did not significantly differ between academies (F(12, 30) = 1.740, p = 0.107, Eta

2

The quantification and development of physical qualities of youth rugby players is vital to support athlete preparation and long-term development. This chapter summarises and presents the research quantifying the physical qualities and their development of male youth rugby union and rugby league players and compares between age grades and playing positions, whilst considering the effect on career attainment and coaches’ perspectives. A range of research presents the physical qualities of youth rugby players, including stature, body mass, body fat percentage, muscular strength, muscular power, linear speed, change of direction, and aerobic capacity. Differences are apparent by age grade and position. However, the research has several limitations, including the presentation of small sample sizes and lack of consistency in the measures used. Future research should consider the use of national standardised testing batteries due to the inconsistency in testing methods and small samples limiting the reporting of positional differences. Practitioners can use the results from this review to evaluate the physical qualities of youth rugby players to enhance training prescription and goal setting.

Identifying and understanding the fatigue response of youth rugby players following training and competition is paramount to avoid chronic fatigue, underperformance and injury. Fatigue resulting from rugby participation may last for up to 72 hours and the potential for its accumulation during periods of congested fixtures exists, although it can depend on contextual and player-related factors (e.g., time between matches, playing level). The fatigue response following training depends upon the training volume and content (e.g., (non)inclusion of contact). As such, practitioners seek to monitoring players’ fatigue primarily through assessments of lower- and upper-body neuromuscular function, and athlete self-reported measures. However, the successful implementation of a fatigue monitoring system depends on several factors related to the needs of the environment, and the collection, analysis and visualisation of the data. This chapter presents (1) an overview of surrogate measures of fatigue, (2) the current findings from the literature related to the fatigue response to youth rugby training and competition and (3) practical considerations for the successful development of a fatigue monitoring system for young rugby players.

Objectives The purpose of this study was to investigate head kinematic variables in elite men’s and women’s rugby union and their ability to predict player removal for an off-field (HIA1) head injury assessment. Methods Instrumented mouthguard (iMG) data were collected for 250 men and 132 women from 1865 and 807 player-matches, respectively, and synchronised to video-coded match footage. Head peak resultant linear acceleration (PLA), peak resultant angular acceleration (PAA) and peak change in angular velocity (dPAV) were extracted from each head acceleration event (HAE). HAEs were linked to documented HIA1 events, with ten logistical regression models for men and women, using a random subset of non-case HAEs, calculated to identify kinematic variables associated with HIA1 events. Receiver operating characteristic curves (ROC) were used to describe thresholds for HIA1 removal. Results Increases in PLA and dPAV were significantly associated with an increasing likelihood of HIA1 removal in the men’s game, with an OR ranging from 1.05–1.12 and 1.13–1.18, respectively. The optimal values to maximise for both sensitivity and specificity for detecting an HIA1 were 1.96 krad⋅s−2, 24.29 g and 14.75 rad⋅s−1 for PAA, PLA and dPAV, respectively. Only one model had any significant variable associated with increasing the likelihood of a HIA1 removal in the women’s game—PAA with an OR of 8.51 (1.23–58.66). The optimal values for sensitivity and specificity for women were 2.01 krad⋅s−2, 25.98 g and 15.38 rad⋅s−1 for PAA, PLA and dPAV, respectively. Conclusion PLA and dPAV were predictive of men’s HIA1 events. Further HIA1 data are needed to understand the role of head kinematic variables in the women’s game. The calculated spectrum of sensitivity and specificity of iMG alerts for HIA1 removals in men and women present a starting point for further discussion about using iMGs as an additional trigger in the existing HIA process.

Objective To examine the likelihood of head acceleration events (HAEs) as a function of previously identified risk factors: match time, player status (starter or substitute) and pitch location in elite-level men’s and women’s rugby union matches. Methods Instrumented mouthguard data were collected from 179 and 107 players in the men’s and women’s games and synchronised to video-coded match footage. Head peak resultant linear acceleration (PLA) and peak resultant angular acceleration were extracted from each HAE. Field location was determined for HAEs linked to a tackle, carry or ruck. HAE incidence was calculated per player hour across PLA recording thresholds with 95% CIs estimated. Propensity was calculated as the percentage of contact events that caused HAEs across PLA recording thresholds, with a 95% CI estimated. Significance was assessed by non-overlapping 95% CIs. Results 29 099 and 6277 HAEs were collected from 1214 and 577 player-matches in the men’s and women’s games. No significant differences in match quarter HAE incidence or propensity were found. Substitutes had higher HAE incidence than starters at lower PLA recording thresholds for men but similar HAE propensity. HAEs were more likely to occur in field locations with high contact event occurrence. Conclusion Strategies to reduce HAE incidence need not consider match time or status as a substitute or starter as HAE rates are similar throughout matches, without differences in propensity between starters and substitutes. HAE incidence is proportional to contact frequency, and strategies that reduce either frequency or propensity for contact to cause head contact may be explored.

As a coach, coach educator and researcher, Wilbur contributed significantly to the field rugby science, both as an author and reviewer. The aim of this research note, structured as a ‘short report’, is to highlight some of Wilbur's many work achievements.

Objectives: This study investigated the disclosure and reasons for non-disclosure of possible concussions and their symptoms in National Rugby League Women's (NRLW) Premiership players in Australia. Design: Cross sectional survey. Methods: During the 2022 NRLW season, NRLW players were invited to participate in a voluntary, anonymous, online survey exploring (i) player demographics, (ii) rugby playing history, (iii) concussion disclosure, and (iv) instances of, and reasons for, non-disclosure of possible concussions to medical staff during the past two seasons. Logistic regression analyses were used to identify reasons for non-disclosure of possible concussions in NRLW players. Results: Of the 132 eligible participants, 86 players responded to the survey and 63 % (n = 54/86) reported that they always disclosed a possible concussion during the past two seasons. A substantial number of NRLW players surveyed (n = 32/86, 37 %) did not disclose a possible concussion to their team or medical staff on one or more occasions. Sixty-three players (73 %) always reported symptoms during a medical assessment. Twenty-three players (27 %) did not disclose their symptoms during a medical assessment, primarily during or after a game or training session (n = 12/23, 52 %). Of the players who did not disclose their possible concussion symptoms, the two main reasons for non-disclosure were ‘not wanting to be ruled out of the game or training session’ (n = 8/23,35 %) and not being ‘sure if the symptoms were related to concussion’ (n = 8/23, 35 %). Most surveyed players (n = 74/86, 86 %) reported attending mandatory concussion education sessions at their respective clubs. Conclusion: We found high rates of non-disclosure amongst NRLW players, which is inconsistent with previous research suggesting that women are more aware of their symptoms than men and more likely to disclose their concussions. Not wanting to be ruled out of the game or training session and being unsure if the symptoms were related to concussion were the two most common reasons for nondisclosure. Concussion education initiatives could promote a supportive culture fostering disclosure amongst all stakeholders to ensure optimal player welfare.

© 2019 The use of unstable surfaces during resistance training has demonstrated a maintenance or reduction on force production. However, the use of unstable surface on force variability has not been assessed using non-linear methods that may be better suited to detect changes in movement variability throughout a given movement. Consequently, this study compared the use of stable vs unstable surfaces on force variability during bilateral squats performed with an inertial flywheel device (Eccoteck, Byomedic System SCP, Spain). Twenty healthy men (mean ± SD: age 22.9 ± 2.9 years, height 1.81 ± 0.7 m, body mass 76.4 ± 7.6 kg and 1RM back squat 110.9 ± 19.7 kg) with a minimum of four years in resistance training performed six sets of six repetitions of squats at maximal concentric effort with one minute rest between sets. Force output on the vertical axes was measured using a strain gauge and the results were processed using non-linear sample entropy (SampEn). Results showed no differences for any of the dependent variables between stable and unstable conditions. SampEn showed no differences between conditions (chi-squared = 0.048 P = 0.827), while Forcemean and SampEn presented a small correlation (r = 0.184; p < 0.01). No changes in entropy were found over the course of the series. Together, these results suggest that the structure of force variability between stable and unstable surfaces are similar. This lack of difference between surfaces may be due to postural and anticipatory adjustments. Consequently, by introducing unstable surfaces to the flywheel bilateral squat exercise, practitioners may not observe changes in Forcemean and force variability when compared to stable surface training suggesting that increased training volumes or intensity may be required during unstable environments to cause a desired training stimulus.

Objectives To describe and compare the incidence and propensity of head acceleration events (HAEs) using instrumented mouthguards (iMG) by playing position in a season of English elite-level men’s and women’s rugby union matches. Methods iMG data were collected for 255 men and 133 women from 1,865 and 807 player-matches, respectively, and synchronised to video-coded match footage. Head peak resultant linear acceleration (PLA) and peak resultant angular acceleration (PAA) were extracted from each HAE. Mean incidence and propensity values were calculated across different recording thresholds for forwards and backs in addition to positional groups (front row, second row, back row, half backs, centres, back three) with 95% confidence intervals (CI) estimated. Significance was determined based on 95% CI not overlapping across recording thresholds. Results For both men and women, HAE incidence was twice as high for forwards than backs across the majority of recording thresholds. HAE incidence and propensity were significantly lower in the women’s game compared to the men’s game. Back-row and front-row players had the highest incidence across all HAE thresholds for men’s forwards, while women’s forward positional groups and men’s and women’s back positional groups were similar. Tackles and carries exhibited a greater propensity to result in HAE for forward positional groups and the back three in the men’s game, and back row in the women’s game. Conclusion These data offer valuable benchmark and comparative data for future research, HAE mitigation strategies, and management of HAE exposure in elite rugby players. Positional-specific differences in HAE incidence and propensity should be considered in future mitigation strategies.

This study aimed to quantify the variability of physical, technical, and subjective task-load demands in small-sided games (SSGs), and the effect of manipulating of pitch size and player numbers in SSG in adolescent rugby union (RU) players. Twenty-six subjects completed six conditions in a crossover study design. In each condition subjects played 4 × 3-min periods of an SSG. Games were completed with either 4 × 4, 6 × 6 or 12 × 12 players on either a small (W: 25 m, L: 30 m), medium (W: 30 m, L: 40 m), or large (W: 35 m, L: 50 m) sized pitch. Match demands were assessed using global navigation satellite systems, heart rate (HR) monitors, ratings of perceived exertion, National Aeronautical Space Association task-load index and video analysis. Statistical analysis comprised of typical error, coefficient of variation (CV) and intra-class correlations to assess variability, and the use of linear mixed effects modelling to assess differences between conditions. A range of variability was observed in technical (CV = 25.00% to 52.38%), physical (CV = 4.12% to 51.18%) and subjective task-loads (CV = 7.65% to 17.14%) between identical games. Reducing player numbers increased physical demands such as m/min (ES range = 0.45 to 1.45), technical exposures such as total involvements (ES range = 0.04 to 0.63) and effort, physical and temporal task-loads. Increasing pitch size caused greater movement demands such as m/min (ES range = 0.11 to 0.79), but did not change the technical demands.

Player movement in rugby league is complex, being spatiotemporal and multifaceted. Modeling this complexity to provide robust measures of player activity and load has proved difficult, with important aspects of player movement yet to be considered. These include the influence of time-varying covariates on player activity and the combination of different dimensions of player movement. Few studies have simultaneously categorized player activity into different activity states and investigated factors influencing the transition between states, or compared player activity and load profiles between matches and training. This study applied hidden Markov models (HMMs)-a data-driven, multivariate approach-to rugby league training and match GPS data to i) demonstrate how HMMs can combine multiple variables in a data-driven way to effectively categorize player movement states, ii) investigate the influence of two time-varying covariates, score difference and elapsed match time on player activity states, and iii) compare player activity and load profiles within and between training and match modalities. HMMs were fitted to player GPS, accelerometer and heart rate data of one English Super League team across 60 training sessions and 35 matches. Distinct activity states were detected for both matches and training, with transitions between states in matches influenced by score difference and elapsed time and clear differences in activity and load profiles between training and matches. HMMs can model the complexity of player movement to effectively profile player activity and load in rugby league and have the potential to facilitate new research across several sports.

Quantifying the highest intensity of competition (the maximal intensity period [MIP]) for varying durations in team sports has been used to identify training targets to inform the preparation of players. However, its usefulness has recently been questioned since it may still underestimate the training intensity required to produce specific physiological adaptations. Within this conceptual review, we aimed to: (i) describe the methods used to determine the MIP; (ii) compare the data obtained using MIP or whole-match analysis, considering the influence of different contextual factors; (iii) rationalise the use of the MIP in team sports practice and (iv) provide limitations and future directions in the area. Different methods are used to determine the MIP, with MIP values far greater than those derived from averaging across the whole match, although they could be affected by contextual factors that should be considered in practice. Additionally, while the MIP might be utilised during sport-specific drills, it is inappropriate to inform the intensity of interval-based, repeated sprint and linear speed training modes. Lastly, MIP does not consider any variable of internal load, a major limitation when informing training practice. In conclusion, practitioners should be aware of the potential use or misuse of the MIP.

Objective To establish a consensus on the structure and process of healthcare services for patients with concussion in England to facilitate better healthcare quality and patient outcome. Design This consensus study followed the modified Delphi methodology with five phases: participant identification, item development, two rounds of voting and a meeting to finalise the consensus statements. The predefined threshold for agreement was set at ≥70%. Setting Specialist outpatient services. Participants Members of the UK Head Injury Network were invited to participate. The network consists of clinical specialists in head injury practising in emergency medicine, neurology, neuropsychology, neurosurgery, paediatric medicine, rehabilitation medicine and sports and exercise medicine in England. Primary outcome measure A consensus statement on the structure and process of specialist outpatient care for patients with concussion in England. Results 55 items were voted on in the first round. 29 items were removed following the first voting round and 3 items were removed following the second voting round. Items were modified where appropriate. A final 18 statements reached consensus covering 3 main topics in specialist healthcare services for concussion; care pathway to structured follow-up, prognosis and measures of recovery, and provision of outpatient clinics. Conclusions This work presents statements on how the healthcare services for patients with concussion in England could be redesigned to meet their health needs. Future work will seek to implement these into the clinical pathway.

ABSTRACT

There is no empirical evidence that soft‐padded headgear is protective against head injury risk in rugby. However, studies that have assessed purported protective effects have not accounted for rates of contact. The aim of this study was to compare head injury rates while considering tackle‐event exposure in players with and without headgear. In the 2018 and 2019 professional men's SuperRugby season, video analysts recorded headgear use, playing position, match time and head injury assessments (proxy for head injury risk) for each player. Tackle‐event involvements for each player were obtained from third‐party video analysis provider. Tackle‐related head injury rates were calculated per 1000 h (incidence) and per 1000 tackle‐events (propensity), and compared between headgear and non‐headgear wearers using incidence rate ratios (IRRs) with 95% confidence intervals and Poisson regression models. Players wearing headgear were involved in more tackles per match than players without headgear (IRR 1.07, 95% CI 1.05–1.09). Head injury incidence (IRR: 1.78 95% CI: 1.11–2.70) and propensity (IRR: 1.66 95% CI: 1.04–2.52) were higher in players wearing headgear. However, statistical models found no difference in this risk between positional groups. A lack of protective effect is consistent with previous studies and could be explained by World Rugby's headgear design regulations while increased risk may be a result of greater injury susceptibility. As World Rugby's headgear regulations change and further advancements in headgear is made, it is important to continue to examine their effect on head injury risk at an individual level.

The primary aim of this thesis was to evaluate the dietary intake, energy expenditure and energy balance of young professional male rugby league players across the season. Twenty participants from one European Super League U19 academy were recruited. Prior to assessing ‘free-living’ dietary intake, study 1 investigated the validity of a traditional and contemporary dietary assessment tool to measure the total energy intake of young professional rugby league players (n =12). Findings highlighted a small and moderate mean bias for under-reporting by Snap- N-Send a very likely higher (4.96 (0.97) MJ; ES =0.30 (0.07); p =0.0021) total energy expenditure across a five-day microcycle matched for physical activity demands. Findings highlighted the large resting metabolic rates (11.63 (2.46) MJ.day-1), total energy expenditures (18.93 (3.18) MJ.day-1) and moderate physical activity levels (1.6 (0.2)) of players. Players were in a self-reported negative energy balance (-1.63 (1.73) MJ.day-1; p =0.233) and lost body mass (-0.65 (0.78) kg; p =0.076). To support a desired increase in player body mass, case study 1 established the large energy requirements (resting metabolic rate: 14.7 MJ.day-1; total energy expenditure: 22.4 MJ.day-1; physical activity level: 1.5) of one young professional rugby league player across a two-week pre-season microcycle, using the Behaviour Change Wheel to design and implement a behavioural intervention over a twelve week period. Study 4 Findings highlighted the large resting metabolic rates (10.26 (1.49) MJ.day-1), total energy expenditures (16.15 (0.77) MJ.day-1) and moderate physical activity levels (1.6 (0.2)) of players. Players were in a self-reported negative energy balance (-0.70 (1.11) MJ.day-1; p =0.145) and lost body mass (-0.3 (0.6) kg; p =0.222). Sub-analysis within study 4 (n = 5), demonstrated an almost certainly higher total energy expenditure across a pre-season (study 3) vs. in-season microcycle (0.02 MJ.kg.BM- 1; ES =1.14 (0.41); p =0.004). Study 5 and study 6 utilised data collected within study 3 and 4 to investigate the validity of wearable technology or Snap-N-Send to measure the total energy expenditure or total energy intake of young professional rugby league players across ‘true’ free-living conditions (n = 8), respectively. To support appropriate energy intakes following match-play, case study 2 investigated three-day changes in resting metabolic rate following a competitive young professional RL match (n = 5). Findings demonstrated almost certainly, most likely and likely increases in resting metabolic rate at 24 (2.38 (1.02) MJ.day-1; ES =1.06 (0.43); p =0.006), 48 (1.44 (0.93) MJ.day-1; ES =0.62 (0.38); p =0.025) and 72 hours (0.94 (0.78) MJ.day-1;ES =0.40 (0.32); p =0.055) after baseline. In conclusion, studies presented within this thesis establish the large resting and total energy requirements of young professional male rugby league players across the season, which are increased in the days following training-based collisions and competitive match-play. Players require support to consistently achieve desired manipulations of energy balance required for optimal physical development across pivotal youth development periods.

Illness in athletes can cause time-loss and performance restriction from training and competition, as well as negatively affect physiological functioning. Team sport athletes, and in particular rugby players, may be at increased risk of illness due to close-proximity to teammates and competitors, as well the high-intensity exercise and collisions involved in the sport itself. Despite a growing wealth of literature assessing illness epidemiology in rugby, little is known about the incidence of illness in male academy rugby players, and no studies have assessed the lived experience of illness through qualitative inquiry. Therefore, the aim of this thesis was to investigate the presence and experiences of illness in rugby using a mixed methods approach. This programme of research includes three study chapters which 1) investigate and summarise the incidence, prevalence, and count of illnesses across full-contact football-code team sports through a systematic review 2) assess the incidence, prevalence, and consequences of illness in male academy rugby league players and 3) explore the barriers and enablers to the uptake of illness prevention guidelines in rugby. Chapter 3 highlighted that full-contact football-code team sport athletes are most commonly affected by respiratory system illnesses; however consistent methods of reporting were lacking, and future research should utilise International Olympic Committee (IOC) recommended definitions and monitoring tools. Chapter 5 identified that consequences of illness, such as loss of body mass and sleep disruptions, may negatively impact a male academy rugby league player’s development. Chapter 7 highlighted that tackling inequalities in resources between men’s and women’s cohorts is critical to effectively implement illness prevention guidelines. Education of coaches and players is essential, and emphasis must be placed on continuing preventative behaviours adopted due to COVID-19. In summary, the findings from this thesis suggest that illnesses pose less epidemiological burden than injury, however the consequences of illness may be detrimental to athlete development and future progression. This thesis offers new behavioural science driven practical recommendations to effectively implement illness prevention guidelines in the rugby context.

OBJECTIVES: Describe the highest frequency and variability for tackle events in rugby league. Investigate seasonal differences in total tackle events per match over a seven-year period. DESIGN: Retrospective observational. METHODS: Tackle events (i.e., ball carrier events [attacker] and tackler involvements [defender]) from 864 male professional rugby league players competing in 1176 Super League matches from 2014 to 2020 were included. A series of linear mixed effect models were used to determine the frequency and variability during peak 1-, 3-, 5-, 10-, 20-, 40-min and whole-match tackle events per player per match at a positional group level. Differences between seasons for the total number of tackle events per match were compared using a one-way analysis of variance and with Tukey's honestly significant difference test. RESULTS: Tackle events were greatest for Props (51.5 [47.7-55.4] per match). Within-players, between-matches, and between-seasons variability was <10 % for tackle events. There were significantly less tackle events and tackler involvements per match in 2014 and a significantly more tackle events per match in season 2020b when compared with all other seasons. CONCLUSIONS: Large between-position variability in peak tackle events, ball carrier events, and tackler involvements would suggest that coaches should separate players into positional groups and prescribe training accordingly. Total number of tackle events, ball carrier events, and tackler involvements were significantly greater in season 2020b when compared to season 2014 to 2019 (inclusive) which may be a consequence of rule changes introduced to the sport.

The 2019 and 2020 Super League (SL) seasons included several competition rule changes. This study aimed to quantify the difference between the 2018, 2019 and 2020 SL seasons for duration, locomotor and event characteristics of matches. Microtechnology and match event data were analysed from 11 SL teams, comprising 124 players, from 416 competitive matches across a three-year data collection period. Due to an enforced suspension of league competition as a consequence of COVID-19 restrictions, and subsequent rule changes upon return to play, season 2020 was divided into season 2020a (i.e. Pre-COVID suspension) and season 2020b (i.e. Post-COVID suspension). Duration, locomotor variables, and match events were analysed per whole-match and ball-in-play (BIP) periods with differences between seasons determined using mixed-effects models. There were significant (ρ ≤ 0.05) reductions in whole-match and BIP durations for adjustables and backs in 2019 when compared to 2018; albeit the magnitude of reduction was less during BIP analyses. Despite reduced duration, adjustables reported an increased average speed suggesting reduced recovery time between bouts. Both forwards and adjustables also experienced an increase in missed tackles between 2018 and 2019 seasons. When comparing 2019 to 2020a, adjustables and backs increased their average speed and distance whilst all positional groups increased average acceleration both for whole-match and BIP analyses. When comparing 2020a to 2020b, all positional groups experienced reduced average speed and average acceleration for both whole-match and BIP analyses. Forwards experienced an increased number of tackles and carries, adjustables experienced an increased number of carries, and backs experienced an increased number of missed tackles when comparing these variables between season 2020a and 2020b. Rule changes have a greater effect on whole-match duration and locomotor characteristics than those reported during BIP periods which suggests the implemented rule changes have removed stagnant time from matches. Amendments to tackle related rules within matches (e.g., introduction of the 'six-again' rule) increases the number of collision related events such as carries and tackles.

In 2023, we described a collective approach to safety in rugby (including league, union and sevens), outlining the different stakeholders along the passive–active injury prevention intervention continuum.1 We highlighted the current ‘passive’ measures in place to reduce concussion risk including tackle law policies, and the importance of promoting ‘active’ measures such as good tackle technique training. To extend this collective approach, the purpose of this editorial is to (1) highlight the importance of collecting and reporting on ‘near misses’ to promote safety culture and (2) describe a systems thinking method to advocate for shared responsibility in injury prevention.

Objectives: Describe the injury and illness incidence, severity and burden during the men's, women's and wheelchair Rugby League World Cup (RLWC). Design: Retrospective cohort epidemiological study. Methods: Injury and illness diagnosis and estimated return-to-play duration following consensus definitions were reported for men's (n = 16 teams), women's (n = 8 teams) and wheelchair (n = 8 teams) players during the 2021 RLWC. Results: Match injury incidence per 1000 player-hours was 91.2 (95 % CI; 74.0 to 111.1, n = 98) injuries for men, 115.4 (95 % CI; 88.1 to 148.5, n = 60) injuries for women, and 80.0 (95 % CI; 45.7 to 129.9, n = 16) injuries for wheelchair, with the mean severity of 17, 10 and 8 days. Training injury incidence per 1000 player-hours was 2.4 (95 % CI; 1.3 to 3.9, n = 15) injuries for men, 1.7 (95 % CI; 0.5 to 4.3, n = 4) injuries for women, and 6.4 (95 % CI; 2.1 to 15.0, n = 5) injuries for wheelchair. Match concussion incidence per 1000 player-hours was 11.2 (95 % CI; 5.8 to 19.5, n = 12) concussions for men, 19.2 (95 % CI; 9.2 to 35.4, n = 10) concussions for women, and 10.0 (95 % CI; 1.2 to 36.1, n = 2) concussions for wheelchair. There were 13, 5 and 3 episodes of illness in men, women and wheelchair. Conclusions: Injury incidence was highest in women, whereas the highest severity was found in men. Concussion incidence was highest in women. The match injury incidence at the RLWC was similar to domestic elite rugby league. Different factors, such as variations in physical characteristics, rule differences, and travel could have contributed to injury and illness observations.

This study aimed to introduce a novel Bayesian Mixture Model approach to the development of an EPV model in rugby league, which could produce a smooth pitch surface and estimate individual possession outcome probabilities. 99,966 observations from the 2021 Super League season were used. A set of 33 centres (30 in the field of play, 3 in the opposition try area) were located across the pitch. Each centre held the probability of five possession outcomes occurring (converted/unconverted try, penalty, drop goal and no points). Probabilities at each centre were interpolated to all locations on the pitch and estimated using a Bayesian approach. An EPV measure was derived from the possession outcome probabilities and their points value. The model produced a smooth pitch surface, which was able to provide different possession outcome probabilities and EPVs for every location on the pitch. Differences between team attacking and defensive plots were visualised and an actual vs expected player rating system was developed. The model provides significantly more flexibility than previous zonal approaches, allowing much more insightful results to be obtained. It could easily be adapted to other sports with similar data structures.

The majority of head injuries in rugby union occur during tackles in which the head receives an impact. Head impacted tackles may be a result of poor tackle technique. Therefore, the purpose of this study was to analyse ball-carrier and tackler technique proficiency in head impacted tackles and compare the technique proficiency to successfully completed tackles in real-match situations. Design: Retrospective video analysis. Methods: Video footage of head impacts with the ‘head impacted player’ (n = 157) and the opposing player ‘impacting player’ (n = 156) were scored for contact technique using a list of technical criteria and compared to contact technique scores of role and tackle-type matched injury-free, successful tackles (n = 170). Results: Ball-carriers contacting their head during front-on head impacted tackles (mean 6.4, 95%CI 5.6–7.1 AU, out of a total score of 14) scored significantly less than the ‘impacting player’ (mean 8.1, 95%CI 7.1–9.1 AU, p < 0.01, ES = 0.5, small) and successful ball-carriers (successful ball-carrier mean 9.4, 95%CI 8.9–9.9 AU, p < 0.0001, ES = 1.1, moderate). Tackler contact proficiency scores during successful front-on tackles (mean 12.3, 95%CI 11.6–12.9 AU, out of a total score of 16) were significantly greater than tackler contact proficiency scores for the ‘head impacted player’ (mean 9.8, 95%CI 8.6–10.9, p < 0.001, ES = 0.8, moderate) and ‘impacting player’ (mean 9.3, 95%CI 8.4–10.1, p < 0.0001, ES = 1.0, moderate). Conclusions: Both the ball-carrier and tackler have a technical deficiency when there is a head impact in matches. The implication of this finding is that players and coaches need to acknowledge that both the ball-carrier and tackler are responsible for each other's safety during the tackle.

The cluster analysis of elite rugby league players identified groups of distinct playing positions that can be referred to as broad positional groups. However, the identified positional groups were based on traditional indicators (physical and technical–tactical) that provided no information about the exact match-based movement activities that led to such similarity grouping and the classification of elite rugby league players into these broad positional groups remains unexplored. Hence, this study finds the best model to classify elite rugby league players into positional groups, using data characterised by movement patterns to uncover the similar movement activities of distinct playing positions within a positional group. Key movement patterns for the positional group classification and differences between the groups were also investigated. A total of 18,173 unique movement patterns were derived from 422 players’ GPS data across the 2019 and 2020 seasons, where only 36 were identified as key patterns. The highest classification accuracy of 77.58% using all unique patterns and 74.5% accuracy using the key patterns was achieved, outperforming studies that used traditional indicators. Further analyses based on key patterns revealed differences between forwards and backs. These findings establish movement patterns as viable indicators to classify rugby league players into positional groups, enabling coaches and trainers to develop position-specific training programmes that cater to the unique physical demands of each position, leading to better player development and team performance. Movement patterns are therefore recommended as an alternative approach to quantifying players’ external loads and obtaining granular information.

PURPOSE: Injuries result from complex interactions between a variety of internal and external risk factors. Due to the complex-systems nature of injuries it is not possible to place responsibility for injury risk management solely within a single domain of professional practice. Instead, an interdisciplinary collaboration between technical/tactical coaches, strength and conditioning coaches, team doctors, physical therapists and sport scientists is more likely to have a meaningful impact on injury risk. The purpose of this study is to describe the development and application of a multidisciplinary model for reducing team injury risk in professional rugby union. METHODS: Epidemiological injury data was collected from a team of professional rugby union players over the course of 5 consecutive season. Following each season, a multidisciplinary audit was conducted to identify areas where risk mitigation strategies could be applied within the team environment. These strategies took the form of technical/tactical, strength and conditioning, player monitoring medical and therapeutic interventions that were all applied concurrently (Figure 1). The effectiveness of this program was assessed against the total team injury burden per season, as well as the burden from contact and non-contact injuries. 95% confidence intervals were calculated using standard equations, and values were considered significantly different if the 95% confidence intervals did not overlap. RESULTS: Overall team injury burden decreased significantly (9, 95%CI 5 to 13 %) from 2012 to 2016. Non-contact injuries were also significantly reduced (39, 95%CI 34 to 44 %), while contact injury burden was increased to a lesser degree (21, 95%CI 15 to 27 %) during the same period. Specific examples of multidisciplinary interventions are provided. CONCLUSIONS: The range of skills required to effectively manage injury risk in professional collision sport crosses disciplinary boundaries. The evidence presented here points to the effectiveness of a multidisciplinary approach to reducing injury risk. PRACTICAL APPLICATION: The multidisciplinary model of injury risk management will encourage increased collaboration across professional disciplines within sport. This model will likely be applicable across a range of team and individual sports.

Research has characterised the strength characteristics of elite youth male soccer players, although little is known about female players. This study investigated the influence of age and maturity status on strength characteristics in 157 female soccer players (U16; n=46, U14; n=43, U12; n=38, U10; n=30), recruited from three elite female soccer academies. Linear mixed models were used to determine the difference by age or maturation. Peak force (PF) was possibly and likely greater for older age groups, however relative PF was most likely trivial between consecutive age groups. Relative impulse at 100 and 300 ms was very likely greater at U12 than U10, likely and possibly less at U12 than U14, and most likely less and possibly greater at U16 than U14. Relative PF was likely less at Pre peak height velocity (PHV) than Circa and Circa than Post-PHV. Relative Impulse at 100 and 300 ms was most likely lower for Pre-PHV than Circa and Pre-PHV than Post-PHV, and possible greater at Circa than Post-PHV. Age and maturation impact upon PF and impulse, thus practitioners should account for individual maturation status when comparing players. These data provide reference strength data for elite youth female soccer players, which can be used when monitoring player development.

To minimize underperformance, injury, and illness, and to enhance readiness for training and match-play, post-match responses are commonly monitored within professional rugby. As no clear consensus exists regarding the magnitude and duration of post-match recovery, this review summarized the literature (17 studies yielded from literature searching/screening) reporting neuromuscular (countermovement jump [CMJ], peak power output [PP], and flight time [FT]), biochemical (creatine kinase [CK]) or endocrine (cortisol [C] and testosterone [T] concentrations), and subjective (wellness questionnaire and muscle soreness) indices after rugby match-play. For neuromuscular responses (11 studies), reductions in PP <31.5% occurred <30 minutes after match, returning to baseline within 48-72 hours. Post-match reductions in FT of <4% recovered after 48 hours. For biochemical and endocrine responses (14 studies), increases in CK, ranging from 120 to 451%, peaked between 12 and 24 hours, returning to baseline within 72 hours of match-play. Initial increases of <298% in C and reductions in T concentrations (<44%) returned to pre-match values within 48-72 hours. Mood disturbances (6 studies) required 48-72 hours to normalize after peak decrements of <65% at 24 hours. This review highlights that 72 hours were needed to restore perturbations in neuromuscular, biochemical and endocrine, and subjective/perceptual responses after competitive rugby match-play. Notably, only 4 studies reported responses in more ecologically valid scenarios (i.e., those in which regular training and recovery strategies were used) while also reporting detailed match demands. A lack of research focusing on youth players was also evident, as only 3 studies profiled post-match responses in younger athletes. Deeper insight regarding post-match responses in ecologically valid scenarios is therefore required.

In professional academy rugby league (RL) players, this two-part study examined; A) the within- and between-day reliability of isometric mid-thigh pulls (IMTP), countermovement jumps (CMJ), and a wellness questionnaire (n = 11), and B) profiled the responses with acceptable reliability (no between-trial differences and between-day coefficient of variation (CV) ≤10% and intraclass correlation coefficient (ICC) ≥0.8) for 120 h (baseline: -3, +24, +48, +72, +96, +120 h) following RL match-play (n = 10). In part A, force at 200, and 250 ms, and peak force (PF) demonstrated acceptable within- (CV%: 3.67-8.41%, ICC: 0.89-0.93) and between-day (CV%: 4.34-8.62%, ICC: 0.87-0.92) reliability for IMTP. Most CMJ variables demonstrated acceptable within-day reliability (CV%: 3.03-7.34%, ICC: 0.82-0.98), but only six (i.e., flight-time, PF, peak power (PP), relative PP, velocity at take-off (VTO), jump-height (JH)) showed acceptable between-day reliability (CV%: 2.56-6.79%, ICC: 0.83-0.91). Only total wellness demonstrated acceptable between-day reliability (CV%: 7.05%, ICC: 0.90) from the questionnaire. In part B, reductions of 4.75% and 9.23% (vs. baseline; 2.54 m∙s-1; 0.33 m) occurred at +24 h for CMJ VTO, and JH, respectively. Acceptable reliability was observed in some, but not all, variables and the magnitude and time-course of post-match responses were test and variable specific. Practitioners should therefore be mindful of the influence that the choice of recovery monitoring tool may have upon the practical interpretation of the data.

Objectives To measure the change in tackling technique of rugby union players following an acute bout of physically fatiguing exercise. Design Randomised cross-over study design with a physical fatigue condition and no-physical fatigue condition (control). Methods Nineteen male amateur club rugby union players ( n = 19) and a total of 887 tackles were analysed. During each condition, each player performed four sets of six tackles (three dominant and three non-dominant shoulder) on a contact simulator. Between each set of tackles in the physical fatigue condition, players performed the prolonged high-intensity intermittent running ability test. Using video, player's tackling proficiency for each tackle was measured by awarding either one point or zero points depending on whether a particular technique was performed or not. The sum of these points represents player’s tackling proficiency (score out of 9, measured in arbitrary units). Results In the non-dominant shoulder, a difference between fatigue and control was found at set two (Fatigue 7.3 [7.1–7.6] AU vs. Control 7.6 [7.4–7.9] AU, p = 0.06, ES = 0.3 small) and set three (Fatigue 7.3 [7.0–7.5] AU vs. Control 7.7 [7.5–7.9] AU, p = 0.006, ES = 0.5 small). During the control condition, tackling proficiency scores improved from baseline for non-dominant tackles (Baseline 7.4 [7.2–7.6] AU, vs Set two 7.6 [7.4–7.9] AU, p = 0.08 ES = 0.3 small; vs Set three 7.7 [7.5–7.9] AU, p = 0.05, ES = 0.4 small). Conclusions In conclusion, this study shows that physical fatigue can potentially affect rugby union players’ tackling technique. Therefore, players should develop technical capacity to resist the effects of physical fatigue during the tackle.

Leduc, C, Jones, B, Robineau, J, Piscione, J, and Lacome, M. Sleep quality and quantity of international rugby sevens players during pre-season. J Strength Cond Res 33(7): 1878-1886, 2019-The aim of this study was to investigate the influence of training load on objective and subjective sleep measures among elite rugby sevens players during pre-season. Nine international male rugby sevens players participated in this study. Actigraphic and subjective sleep assessment were performed on a daily basis to measure sleep parameters. Training load was measured during the entire pre-season period, and sleep data from the highest and lowest training load week were used in the analysis through magnitude-based inferences. During the highest training load, likely to possibly small, moderate decreases in time in bed (effect sizes; ±90% confidence limits: -0.42; ±0.44 for session rating of perceived exertion [sRPE], -0.69; ±0.71 for total distance covered [TDC]) and total sleep time (-0.20; ±0.37 for sRPE, -0.23; ±0.35 for TDC) were found. Possibly small (-0.21; ±0.35 for high-speed distance, -0.52; ±0.73 for acceleration/deceleration [A/D]) and likely moderate (-074; ±0.67 for TDC) decreases were observed in subjective sleep quality. Possibly small to very likely moderate changes in sleep schedule were observed. Sleep quantity and subjective quality seem to be deteriorated during higher loads of training. This study highlights the necessity to monitor and improve sleep among elite rugby sevens players, especially for the intense period of training.

The following errors are noted and corrected: 1. In Abstract, Results, sentence 5: ‘156.1 (121.2–191.0)’ should have been ‘171.2 (140.5–201.8)’. 2. In section Microtechnology, Rugby Union Training, final sentence: ‘contacts’ should have been ‘tackles’ and vice versa. 3. In section Video-Based Analysis, Rugby Union Match Play, sentence 3: ‘156.1 (121.2–191.0)’ should have been ‘171.2 (140.5–201.8)’. 4. In Table 4: The following additional data have been added to the Vaz et al. (2010) (89) row: Column 2: S12 competition: 95 matches; Column 5: 112.7 ± 33.1; Column 6: 99.4 ± 3.0. The original version of Table 4 has been replaced with the version shown below: 5. Fig. 5c: The two entries for Vaz et al. 2010 (89) have now been removed from this figure. The original version of Fig. 5 has been replaced with the version shown below.

The aims of this study were 3-fold: (1) to compare technical proficiency scores between training and matches for tackling, ball-carrying, and rucking outcomes; (2) to determine the relationship between technique in training and technique in matches for tackling, ball carrying, and rucking; and (3) to determine how contact technique (in training and matches) relates to match performance and injury outcomes. Twenty-four male players from an amateur rugby union club participated in the study. At the beginning of the season, players’ contact technique proficiency was assessed in a training drill. Contact technique in matches was assessed during 14 competitive matches. The technique proficiency was assessed using standardized criteria, and the outcomes of each tackle, ball carry, and ruck were recorded. In training and matches, positive performance outcomes were associated with higher contact technique proficiency scores. For instance, in both settings, tackle technique was significantly lower in missed tackles when compared to effective and ineffective tackles. Players’ contact technique scores in matches also had a positive effect on their tackle performance in matches. Ball-carry technique was associated with tackle breaks in matches (P < .05, r 2 = .31). In training and match environments, tackler, ball-carrier, and ruck technique scores were significantly associated with effective tackles, ball carries, and rucks. Despite the relationship between technical proficiency scores and performance, there were small to moderately higher scores in training compared with matches. The current study highlights the importance of contact skill training, in different environments and conditions, to ensure that skills developed in training are transferred to match performance.

Purpose: To test the effects of mental fatigue (MF) on tackling technique on the dominant and nondominant shoulders in rugby union. Methods: Twenty male amateur rugby union players and a total of 953 tackles were analyzed. A randomized crossover counterbalanced design was used across a non-MF (control) and an MF condition. During each condition, each player performed 24 tackles, divided into 4 sets of 6 tackles (3 tackles on each shoulder). In the MF condition, players performed the Stroop Task between each set of tackles. A video recording of each tackle was used to evaluate each player’s technical proficiency. A score of 1 point was awarded if a specific technique was performed correctly, and 0 point was given if not. The total score, measured in arbitrary units (AU) out of 11, represents the player’s overall tackling proficiency. Results: Overall, players displayed a significantly lower technical proficiency score in the MF condition compared to control (set 2: control 7.30 [7.04–7.57] AU vs MF 6.91 [6.70–7.12] AU, P = .009, effect size (ES) = 0.30 small and set 3: control 7.34 [7.11–7.57] AU vs MF 6.88 [6.66–7.11] AU, P = .002, ES = 0.37 small). For the nondominant shoulder, players had a significantly lower technical proficiency score during the MF condition at set 2 (control 7.05 [6.68–7.41] AU vs MF 6.69 [6.42–6.96] AU, P = .047, ES = 0.29 small) and set 3 (control 7.14 [6.83–7.45] AU vs MF 6.61 [6.35–6.87] AU, P = .007, ES = 0.49 small). Conclusions: MF can diminish a player’s overall tackling proficiency, especially when tackling on the nondominant shoulder. The physiological mechanism for this finding may be impaired executive function and suboptimal functioning of neural signals and pathways, which result in less skillful coordination of movement. To further understand and explain MF-induced physiological changes in tackling, the feasibility of monitoring brain activity (such as electroencephalogram) and neuromuscular function (such as electromyogram) needs to be investigated. The findings from this study may also contribute to the development of more effective tackle training programs for injury prevention and performance.

The coronavirus disease 2019 (COVID-19) pandemic has caused disruption to professional and recreational sports across the world. The SARS-CoV-2 virus can be transmitted by relatively large respiratory droplets that behave ballistically, and exhaled aerosol droplets, which potentially pose a greater risk. This review provides a summary of end-to-end SARS-CoV-2 transmission risk factors for sport and an overview of transmission mechanisms to be considered by all stakeholders. The risk of SARS-CoV-2 transmission is greatest indoors, and primarily influenced by the ventilation of the environment and the close proximity of individuals. The SARS-CoV-2 transmission risks outdoors, e.g. via water, and from fomites, appear less than initially thought. Mitigation strategies include good end-to-end scenario planning of activities to optimise physical distancing, face mask wearing and hygiene practice of individuals, the environment and equipment. The identification and removal of infectious individuals should be undertaken by means of the taking of temperature and COVID-19 symptom screening, and the use of diagnostic monitoring tests to identify asymptomatic individuals. Using adequate video footage, data from proximity technology and subject interviews, the identification and isolation of ‘close contacts’ should also be undertaken to limit SARS-CoV-2 transmission within sporting environments and into the wider community. Sports should aim to undertake activities outdoors where possible, given the lower SARS-CoV-2 transmission risk, in comparison to indoor environments.

ABSTRACT

The aims of this study were to test the change and retention of player's overall tackling technique and technical components following a player‐specific video‐based technical feedback and instruction intervention on both their dominant and non‐dominant shoulders. Twenty‐four (n = 24) rugby union players participated in a non‐randomized control‐intervention, which consisted of a video‐based technical feedback and instruction group (video‐based technical feedback) and a no video‐based technical feedback and instruction group (control). During 3 sessions (baseline, intervention, retention) separated by one week, participants in each group performed six tackles (3 tackles on each shoulder) on a tackle simulator. In total, 432 tackles (video‐based technical feedback = 216, control = 216) were analysed. Each tackle was analysed using a standardized list of technical criteria (arbitrary units, AU). For the dominant shoulder, tackling technique scores significantly improved from baseline to intervention for both groups. For the non‐dominant shoulder, only the video‐based technical feedback group improved their tackling technique from baseline to intervention (baseline 6.89 [6.33–7.45] AU vs. intervention 7.72 [7.35–8.10] AU p = .001, ES = 0.60 moderate). For the retention session, the video‐based technical feedback group scored significantly higher than the control group, for dominant (video‐based technical feedback 8.00 [7.60–8.40] AU vs. control 7.22 [6.83–7.62] AU p = .014, ES = 0.66 moderate) and non‐dominant (video‐based technical feedback 8.11 [7.81–8.41] AU vs. control 7.22 [6.90–7.55] p = .004, ES = 0.96 moderate) tackles. This study demonstrates the efficacy of video‐based technical feedback as a method to optimize tackle training for player safety and performance.

BACKGROUND: Collisions in rugby union and sevens have a high injury incidence and burden, and are also associated with player and team performance. Understanding the frequency and intensity of these collisions is therefore important for coaches and practitioners to adequately prepare players for competition. The aim of this review is to synthesise the current literature to provide a summary of the collision frequencies and intensities for rugby union and rugby sevens based on video-based analysis and microtechnology. METHODS: A systematic search using key words was done on four different databases from 1 January 1990 to 1 September 2021 (PubMed, Scopus, SPORTDiscus and Web of Science). RESULTS: Seventy-three studies were included in the final review, with fifty-eight studies focusing on rugby union, while fifteen studies explored rugby sevens. Of the included studies, four focused on training-three in rugby union and one in sevens, two focused on both training and match-play in rugby union and one in rugby sevens, while the remaining sixty-six studies explored collisions from match-play. The studies included, provincial, national, international, professional, experienced, novice and collegiate players. Most of the studies used video-based analysis (n = 37) to quantify collisions. In rugby union, on average a total of 22.0 (19.0-25.0) scrums, 116.2 (62.7-169.7) rucks, and 156.1 (121.2-191.0) tackles occur per match. In sevens, on average 1.8 (1.7-2.0) scrums, 4.8 (0-11.8) rucks and 14.1 (0-32.8) tackles occur per match. CONCLUSIONS: This review showed more studies quantified collisions in matches compared to training. To ensure athletes are adequately prepared for match collision loads, training should be prescribed to meet the match demands. Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more impacts and tackles than backs. Forwards also perform more very heavy impacts and severe impacts than backs in rugby union. To improve the relationship between matches and training, integrating both video-based analysis and microtechnology is recommended. The frequency and intensity of collisions in training and matches may lead to adaptations for a "collision-fit" player and lend itself to general training principles such as periodisation for optimum collision adaptation. Trial Registration PROSPERO registration number: CRD42020191112.

The efficacy of a multimodal recovery strategy implemented within 4 hours of rugby league (RL) training was investigated using repeated-measures, randomized, crossover methods in 10 professional academy RL players (age: 17 ± 1 years). Following standardized training (5,383 m covered, 350-m high-speed running, 28 repeated high-intensity efforts, 24 collisions), players completed a multimodal recovery (REC) strategy (i.e., ∼640 kcal meal + ∼1,285 kcal snacks or drinks, cold-water immersion, sleep hygiene recommendations) or control (i.e., ∼640 kcal meal: CONT) practices. Isometric mid-thigh pulls (IMTP), countermovement jumps (CMJ), and wellness questionnaires were completed before (−3 hours) and after (+24, +48 hours) training. The recovery strategy influenced IMTP peak force (p = 0.026), but between-trial differences were undetectable. No other between-trial effects (all p > 0.05) were seen for IMTP, CMJ, or wellness variables. Training-induced reductions in CMJ peak power (−4 ± 6% vs baseline: 4,878 ± 642 W) at +24 hours (p = 0.016) dissipated by +48 hours. Fatigue and lower-body soreness reduced by 16 ± 19% (p = 0.01) and 32 ± 44% (p = 0.024) at +48 hours versus +24 hours, respectively. Relative to CONT (i.e., posttraining nutrition), the effects of a single bout of recovery practices appeared limited when implemented after RL-specific training. Therefore, when training included limited collisions, balanced postexercise meals appeared equally effective relative to a multimodal recovery strategy. Transient changes in performance and wellness variables after training may have implications for practitioners. Consecutive training sessions, including a high frequency and intensity of eccentric muscle actions, should be carefully planned, especially near match-play.

This study aimed to assess the self-reported frequency and severity of gastrointestinal symptoms (GIS) at rest and around rugby training and match play in male and female rugby union players. An online questionnaire was sent to registered rugby union players (sevens or fifteens). Thirteen GIS were assessed alongside perceptions of appetite around rugby and rest using Likert and visual analog scales. Questions investigating a range of medical and dietary factors were included. Three hundred and twenty-five players (male n=271, female n=54) participated in the study. More frequent GIS (at least one GIS experienced weekly/more often) was reported by players at rest (n=203; 62%) compared to around rugby (n=154; 47%). The overall severity of GIS was low (mild discomfort), but a portion of players (33%) did report symptoms of moderate severity around rugby. Female players reported more frequent and severe symptoms compared to male counterparts (p<0.001). Self-reported appetite was significantly lower after matches compared to training. There were no dietary or medical factors associated with GIS severity scores. This study describes GIS characteristics in male and female rugby union players. Half of the players assessed experienced some form of GIS that may affect nutrition, training, or performance, and should thus be a consideration for practitioners supporting this cohort.

The original article can be found online at https://doi.org/10.1007/s40279-023-01953-7

OBJECTIVES: The aim of this study was to examine head acceleration event (HAE) propensity and incidence during elite-level men's and women's rugby union matches. METHODS: Instrumented mouthguards (iMGs) were fitted in 92 male and 72 female players from nine elite-level clubs and three international teams. Data were collected during 406 player matches (239 male, 167 female) using iMGs and video analysis. Incidence was calculated as the number of HAEs per player hour and propensity as the proportion of contact events resulting in an HAE at a range of linear and angular thresholds. RESULTS: HAE incidence above 10 g was 22.7 and 13.2 per hour in men's forwards and backs and 11.8 and 7.2 per hour in women's forwards and backs, respectively. Propensity varied by contact event, with 35.6% and 35.4% of men's tackles and carries and 23.1% and 19.6% of women's tackles and carries producing HAEs above 1.0 krad/s2. Tackles produced significantly more HAEs than carries, and incidence was greater in forwards compared with backs for both sexes and in men compared with women. Women's forwards were 1.6 times more likely to experience a medium-magnitude HAE from a carry than women's backs. Propensity was similar from tackles and carries, and between positional groups, while significantly higher in men than women. The initial collision stage of the tackle had a higher propensity than other stages. CONCLUSION: This study quantifies HAE exposures in elite rugby union players using iMGs. Most contact events in rugby union resulted in lower-magnitude HAEs, while higher-magnitude HAEs were comparatively rare. An HAE above 40 g occurred once every 60-100 min in men and 200-300 min in women. Future research on mechanisms for HAEs may inform strategies aimed at reducing HAEs.

Objectives Contact with the head should be avoided during a rugby league tackle, given the inherent risks of head injuries. This study aimed to characterise a sample of tackles, retrospectively identified as resulting in a potential head injury by the Rugby Football League (RFL) match review panel. Design Retrospective video analysis study. Methods 746 tackles, identified by the RFL match review panel from the men's 2018 and 2019 Super League seasons, were analysed. Video clips were coded using an adapted analysis framework, characterising tackle stage, head contact, affected player, offending player/surface, offending body part/surface and tackle sanctioning. Data were reported as frequencies and percentages. Results The majority of tackles resulting in a potential head injury occurred in the initial tackle contact stage (n = 590, 79.2%). The ball‐carrier was most frequently affected (n = 372, 49.9%) compared to initial tacklers (n = 213, 28.6%). The initial tackler was the most frequently impacting player (n = 268, 36.0%), with the majority of potential head injuries occurring from direct head contact by the arm (n = 230, 34.1%), shoulder (n = 170, 25.2%) and head/neck (n = 145, 21.5%) of the impacting player. Head contact was present in 90.6% (n = 675) of the tackles resulting in a potential head injury. Of the sample of tackles, 16.1% (n = 109) of direct head contact events received a sanction from on‐field match officials. Conclusion The initial tackle contact between the ball‐carrier and initial tackler remains the area of focus for research into potential head injuries in elite‐level men's rugby league, to improve awareness and understanding of the mechanisms of injury.

The purpose of the present study was to evaluate the anthropometry and fitness, and change in these characteristics over time, of youth rugby league players by using maturity status to determine annual categories instead of traditional chronological annual-age grouping. One hundred and twenty one male rugby league players were assessed using anthropometric (i.e., height, sitting height, body mass and sum of four skinfolds) and fitness (i.e., vertical jump, medicine ball chest throw, 10m and 20m sprint and multi stage fitness test; MSFT) measures over a 5 year period. Each player was classified into one of six maturity groups based on their maturity offset (Years from Peak Height Velocity; i.e., 1.5 YPHV). MANOVA analyses identified significant (p<0.001) main effects for maturity group for cross-sectional characteristics and longitudinal change in performance over time. Analyses demonstrated that more mature groups had greater anthropometric and fitness characteristics, except for endurance performance (MSFT -2.5 YPHV = 1872 ± 18 m vs 2.5 YPHV = 1675 ± 275m). For longitudinal changes in characteristics over time, a significant effect was only identified for height and sitting height (p<0.05). These findings provide comparative data for anthropometric and fitness characteristics and change in performance over time in accordance to maturity status within youth rugby league players. Classifying players into annual maturity groups may be an additional or alternative assessment method for evaluating anthropometry and fitness performance in adolescent populations. Further, tracking performance changes over time, especially in relation to maturation, may reduce the limitations associated with chronological annual-age grouping.

Understanding how players experience head‐acceleration events (HAE) whilst playing rugby is a priority area of research. In both rugby union and league, video analysis frameworks have been developed to comprehensively define key features of contact events. However, these frameworks were developed prior to recent advances in our understanding regarding the proportion of HAEs that occur due to head‐to‐ground mechanisms and do not consider important post‐contact variables. Therefore, there is a need to supplement the existing frameworks in order to capture how players fall and land post‐tackle. This study used the Delphi method with an interdisciplinary, international team of researchers, coaches and video analysts (working with a variety of playing levels in rugby union and league) to establish a consensus for defining falling and landing events. Subsequently, a draft framework was developed on which the research team provided feedback via online meetings, culminating in the falling/landing framework that each member of the research team rated agreement on, via a nine‐point Likert‐type scale, with consensus deemed to be reached when the median score was ≥ 7. The median scores were 8.0 (7.8–8.0), 8.0 (7.0–9.0) and 8.0 (8.0–9.0) for ‘Additional Contextual Characteristics for Carry and Tackle Events,’ ‘Falling Characteristics of Tackle and Carry Events,’ and ‘Landing Characteristics of Tackle and Carry Events,’ respectively. This novel framework defines more comprehensive falling and landing variables to capture post‐contact injury and performance markers in both rugby union and league, through a standardised approach.

This chapter aims to introduce the content of the Young Rugby Player: Science and Application book. This is achieved by summarising the codes of rugby (union and league) and introducing the concept of youth athletic development and its importance for all young rugby players. A key aspect here is providing participation, performance and development goals for all young rugby players regardless of their age, ability and experience. To support this, an applied example of the England Rugby Football Unions’ Age Grade Rugby system is presented. This applied example showcases how a national organisation has developed a system to support player participation, performance and development related to the needs of young rugby players. The chapter then concludes with a short and concise summary of each chapter, which are all key to supporting organisations and stakeholders in providing evidence-informed practices to young rugby players across the world.

© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ. Using an expert consensus-based approach, a rugby union Video Analysis Consensus (RUVAC) group was formed to develop a framework for video analysis research in rugby union. The aim of the framework is to improve the consistency of video analysis work in rugby union and help enhance the overall quality of future research in the sport. To reach consensus, a systematic review and Delphi method study design was used. After a systematic search of the literature, 17 articles were used to develop the final framework that described and defined key actions and events in rugby union (rugby). Thereafter, a group of researchers and practitioners with experience and expertise in rugby video analysis formed the RUVAC group. Each member of the group examined the framework of descriptors and definitions and rated their level of agreement on a 5-point agreement Likert scale (1: Strongly disagree; 2: Disagree; 3: Neitheragree or disagree; 4: Agree; 5: Strongly agree). The mean rating of agreement on the five-point scale (1: Strongly disagree; 5: Strongly agree) was 4.6 (4.3-4.9), 4.6 (4.4-4.9), 4.7 (4.5-4.9), 4.8 (4.6-5.0) and 4.8 (4.6-5.0) for the tackle, ruck, scrum, line-out and maul, respectively. The RUVAC group recommends using this consensus as the starting framework when conducting rugby video analysis research. Which variables to use (if not all) depends on the objectives of the study. Furthermore, the intention of this consensus is to help integrate video data with other data (eg, injury surveillance).

Objectives: To compare the game-play characteristics between the European Super League (ESL) and the National Rugby League (NRL) competitions. Methods: Eleven team performance indicators were extracted from each match played by every ESL and NRL team over their respective 2016 season. Data was averaged, classified according to competition (Two levels: ESL and NRL), and modelled using univariate and multivariate techniques. Specifically, effect size statistics enabled between group comparisons, while non-metric multidimensional scaling enabled multivariate insights into competition dissimilarity. Results: Seven of the 11 performance indicators showed ‘large’ to ‘very large’ effects. Notably, NRL game-play generated fewer ‘line breaks’, ‘errors’, ‘tackles’ and ‘dummy half runs’ relative to ESL game-play (d >1.2). Despite the NRL generating fewer ‘all runs’ (d = 1.27 [0.57-1.95]), game-play in this competition generated greater ‘all run distances’ relative to the ESL (d = 1.78 [1.02-2.51]). Non-metric multidimensional scaling revealed clear multivariate competition dissimilarity, with ESL and NRL teams orienting distinctive positions on the ordination surface. Further, there was a greater spread in the relative positioning of NRL teams compared to ESL teams, indicating greater team dissimilarity within the NRL. Conclusions: Our observations may be explained by differing competition rule interpretations, in addition to differing game strategies and player skill capabilities. Both coaches and talent recruitment managers associated with these competitions may consider our data to assist with the identification and recruitment of suitable players from these respective competitions.

In youth rugby union matches, tackle-related injuries account for 60% of all injuries, 62% of concussion injuries and almost 50% of spinal cord injuries (youth and amateur). Because of this high risk of injury, the inclusion of the tackle in youth rugby has been a topic of discussion in the public and a high priority research area for World Rugby. What a coach knows and his/her attitude toward player safety directly impacts the risk and performance profile of a player. The purpose of this study is to describe the tackle knowledge, attitudes and training behaviours of youth rugby coaches. The entire population of Western Province Rugby Union Premier A1 division (highest level of school rugby) under-19 rugby coaches (n = 8) completed a knowledge and attitude questionnaire and 96 field-training sessions were observed over four weeks. Coaches rated tackling (mean 3.9, 95% confidence interval 3.3–4.4), rucking (mean 3.8, 95% confidence interval 3.0–4.5) and ball-carrying (mean 3.6, 95% confidence interval 2.6–4.6) as high-risk of injury facets of play (H = 30.8, p < 0.001). Coaching proper technique was rated as very important for safety (mean 4.6, 95% confidence interval 4.2–5.0) and performance (mean 4.8, 95% confidence interval 4.4–5.0, U = 28, p > 0.05). Of the 96 observed training sessions, tackle training was recorded 16% of the time (vs. 84% no tackle training, p < 0.001). Coaches were aware of the risk of injury in the tackle and rated the coaching of proper technique of utmost importance. These positive knowledge and attitudes did not transfer into their tackle training. The discrepancy between coaches' tackle knowledge and attitudes, and their training of the tackle might be related to how competent they believe themselves to be in delivering tackle training.

Purpose: The purpose of this study was to examine the criterion and construct validity of an isometric mid-thigh pull dynamometer to assess whole body strength in professional rugby league players. Methods: Fifty-six male rugby league players, (33 senior and 23 youth professional players) performed four isometric mid-thigh pull efforts (i.e. two on the dynamometer and two on the force platform) in a randomised and counterbalanced order. Results: Isometric peak force was underestimated (P<0.05) using the dynamometer compared to the force platform (95% LoA: -213.5 ± 342.6 N). Linear regression showed that peak force derived from the dynamometer explained 85% (adjusted R2 = 0.85, SEE = 173 N) of the variance in the dependent variable, with the following prediction equation derived: predicted peak force = [1.046 * dynamometer peak force] + 117.594. Cross-validation revealed a non-significant bias (P>0.05) between the predicted and peak force from the force platform, and an adjusted R2 (79.6%), that represented shrinkage of 0.4% relative to the cross-validation model (80%). Peak force was greater for the senior compared to youth professionals using the dynamometer (2261.2 ± 222 cf. 1725.1 ± 298.0 N, respectively; P<0.05). Conclusion: The isometric mid-thigh pull assessed using a dynamometer underestimates criterion peak force but is capable of distinguishing muscle function characteristics between professional rugby league players of different standards.

ABSTRACT

Background: Despite its apparent popularity, participation in the sport of rugby union is accompanied by a significant risk of injury. Concerned parties have recently questioned whether this risk is acceptable within school populations. This is difficult to assess within the South African schools’ population as no recent longitudinal injury studies exist.Objectives: To determine the training habits, rugby-related exposure and injury risk within a population of South African high school first team rugby players.Methods: Training and match exposure in both school and provincial competition were examined and the resultant injuries were longitudinally observed for the duration of a South African high school rugby season.Results: Match (79, 95%CI 52-105 injuries/1 000 h) and training (7, 95%CI 3-11 injuries /1000h) injury incidences were demonstrated to be greater than previously reported incidences in similar populations in England and Ireland. Weeks where players were exposed to both school and provincial competition (34, 95%CI 19-49 injuries /1 000 h) had significantly (p<0.05) greater injury incidences than during school competition alone (19, 95%CI 12-26 injuries /1 000 h).Conclusion: The injury risk demonstrated was greater than expected and represents reasons for concern. Possible reasons for the high injury incidence recorded may be the frequency of games played within the season, and the overlap of school and provincial competitions. It should be noted that these results were taken from one school over one season and might not be representative of the incidence of school rugby injuries overall. However, this research demonstrates the need for a multischool longitudinal study within South African schools rugby to determine the overall risk.

Background In tackle-collision sports, the tackle has the highest incidence, severity, and burden of injury. Head injuries and concussions during the tackle are a major concern within tackle-collision sports. To reduce concussion and head impact risk, evaluating optimal tackle techniques to inform tackle-related prevention strategies has been recommended. The purpose of this study was to perform a systematic scoping review of player-level tackle training intervention studies in all tackle-collision sports. Methods The Arksey and O’Malley’s five-stage scoping review process and Levac et al.’s framework were used, along with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist. The main inclusion criteria were that the study included an intervention aimed at improving a player’s tackle abilities, and the intervention had to be delivered/implemented at the player-level in a training setting. Results Thirteen studies were included in this review, seven studies in American Football (54%), followed by a combined cohort of rugby union and rugby league players (three studies; 23%), rugby union (two studies; 15%), and one study reported on a rugby league cohort (8%). Studies focused primarily on the tackler, with the intervention incorporating a form of instruction or feedback, delivered through video or an expert coach. Other interventions included an 8-week strength and power training programme, designing practice sessions based on baseline data, and helmetless training in American Football. All interventions demonstrated a favourable change in the outcome measured—which included tackler and ball-carrier kinematics based on motion capture video, tackler proficiency scoring, tackling task analysis, head impact frequencies by xPatch head-impact sensor technology, head impact kinematics using head-impact sensors (helmet or skin patches) and football tackle kinematics with motion capture systems or video. Conclusion This review shows that a range of studies have been undertaken focusing on player-level training interventions. The quality of studies were rated as ‘good’, and all studies showed improvements in outcome measures. Coaches and policy makers should ensure tackle technique is profiled alongside other player characteristics, and an evidence-based approach to improving player tackling is adopted, improving both performance and reducing injury risk.

Over the past five decades, running has become increasingly popular across the world. Although running is accessible to many people and is associated with numerous health benefits, it also carries a risk of sustaining a running related injury (RRI) (Malisoux et al., Citation2020). Of these RRIs, understanding the biomechanical changes and issues runners’ face when transitioning to a new pair of shoes is of special interest and not well understood.

This text is essential reading for all scientists, students and applied researchers wanting to develop world-class, evidence-based programmes for their youth athletes.

Purpose: The purpose of this invited commentary is to discuss the use of principal component analysis (PCA) as a dimension reduction and visualisation tool to assist in decision making and communication when analysing complex multivariate data sets associated with the training of athletes. Conclusions: Using PCA it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PC), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (i.e. variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA it is possible to visualise complex data sets, containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, we present here an overview of PCA and associated R code to assist practitioners working within the field to integrate PCA into their athlete monitoring process.

The aim of this study was to identify between-position (forwards vs. backs) differences in movement variability in cumulative tackle events training during both attacking and defensive roles. Eleven elite adolescent male rugby league players volunteered to participate in this study (mean ± SD, age; 18.5 ± 0.5 years, height; 179.5 ± 5.0 cm, body mass; 88.3 ± 13.0 kg). Participants performed a drill encompassing four blocks of six tackling (i.e. tackling an opponent) and six tackled (i.e. being tackled by an opponent while carrying a ball) events (i.e. 48 total tackles) while wearing a micro-technological inertial measurement unit (WIMU, Realtrack Systems, Spain). The acceleration data were used to calculate sample entropy (SampEn) to analyse the movement variability during tackles performance. In tackling actions SampEn showed significant between-position differences in block 1 (p = 0.0001) and block 2 (p = 0.0003). Significant between-block differences were observed in backs (block 1 vs 3, p = 0,0021; and block 1 vs 4, p = 0,0001) but not in forwards. When being tackled, SampEn showed significant between-position differences in block 1 (p = 0.0007) and block 3 (p = 0.0118). Significant between-block differences were only observed for backs in block 1 vs 4 (p = 0,0025). Movement variability shows a progressive reduction with cumulative tackle events, especially in backs and when in the defensive role (tackling). Forwards present lower movement variability values in all blocks, particularly in the first block, both in the attacking and defensive role. Entropy measures can be used by practitioners as an alternative tool to analyse the temporal structure of variability of tackle actions and quantify the load of these actions according to playing position.

Introduction Small-sided games (SSGs) are used to train physical qualities while practicing sport specific skills. Live Global Positioning Systems (GPS) data can provide feedback during these games; however, the impact of feedback on subsequent locomotor performance is unknown. This study aimed to investigate if providing ‘live’ GPS feedback to players in between bouts of SSGs altered locomotor performance. Methods Using a reverse counterbalanced design, twenty male university rugby players received either feedback or no-feedback (control) during ‘off-side’ touch rugby SSGs. Eight 5v5, 6x4 minute SSGs were played over four days (2/day) with a 20-minute rest between SSGs and at least 72-hours rest between days. Teams were assigned to feedback (4-games) with verbal feedback provided during a 2-minute between bout rest interval, or no feedback (4-games) for the day. Locomotor performance was measured via a 10 Hz GPS and variables were analysed using a linear mixed model, reported using effect sizes (ES) and 90% confidence intervals and then interpreted via magnitude-based inferences. Results Over the full SSG (6x4 min bouts) there was a possibly trivial (ES = 0.15 [-0.03, 0.34]) difference between conditions in total distance (2200 (156) vs. 2177 (186) m). There was also possibly trivial (ES = 0.18 [0.00, 0.37]) and likely trivial (ES = -0.07 [-0.27, 0.13]) differences between conditions in low- and high-speed distance. Between bouts there was a possibly or likely trivial (ES = 0.08 to 0.14) difference in total distance for bouts 2, 4, 5 and 6, with unclear (ES = -0.01 [-0.24, 0.22]) differences in bout 3. There was a likely trivial (ES = 0.11 [-0.01, 0.22]) difference in total distance covered during the first minute of each bout. Discussion In this study, verbal feedback did not alter locomotor performance in rugby players during SSGs. These data suggest that technical and tactical aspects of SSGs might reduce any ergogenic effects of feedback, although it is unknown if the type of feedback provided nullified any potential effects. Furthermore, extrinsic motivating factors such as team success are likely to be perceived as more important than locomotor performance. Future research should endeavour to investigate if these findings are consistent across other forms of feedback, bout durations, football codes, playing levels or training modalities. Conclusions Verbal feedback of distance covered during bouts of SSGs does not alter subsequent locomotor performance.

Objective: This study aimed to: (a)identify the association between external-workloads and injury-risk in the subsequent week; and (b)understand the effectiveness of workload variables in establishing injury-risk. Design: Retrospective cohort study. Methods: Workload and injury data (soft-tissue)were collected from forty-eight professional male rugby league players. Load variables included duration (min), total distance (m), relative distance (m min

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Although sports on-field activities occur sequentially, traditional performance indicators quantify players’ activities without regard to their sequential nature. Nowadays, movement patterns are used to sequentially quantify players’ activities to understand match demands on players. However, the specific behavioural (i.e., signature) movement patterns of rugby league players per playing position remain unknown and the prediction of rugby league players into all nine playing positions based on their movement patterns is largely unexplored. Hence, this study identified the signature movement patterns of elite rugby league players per position and revealed the contribution of movement patterns towards the prediction of players into positions during the 2019 and 2020 seasons. Varying numbers of signature movement patterns were identified across playing positions with centres having the highest number of signature patterns (i.e. 1241). Random Forest best predicted elite rugby league players’ positions at 73.41% accuracy, 0.74 recall, and 0.73 f1-score and precision scores based on movement patterns relative frequency values and top contributing movement patterns were identified. Therefore, we recommend sports stakeholders recognize the signature and contributing movement pattern of players per playing position while making decisions regarding training programmes, talent identification and recruitment.

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.

BACKGROUND: Netball is the one of the most popular women's sports in the world. Since gaining professional status in 2008 there has been a rapid growth in research in the applied sports science and medicine of the sport. A scoping review of the area would provide practitioners and researchers with an overview of the current scientific literature to support on-court performance, player welfare and reduce injury. OBJECTIVE: The primary objective was to identify the current research on the applied sports science and medicine of netball. Additionally, the article provides a brief summary of the research in each topic of sports science and medicine in netball and identifies gaps in the current research. METHODS: Systematic searches of PubMed, SPORTDiscus, MEDLINE and CINAHL were undertaken from earliest record to Dec 2020 and reference lists were manually searched. The PRISMA-ScR protocol was followed. Studies were eligible for inclusion if they investigated netball as a sport or the applied sport science and medicine of netball athletes. RESULTS: 962 studies were identified in the initial search, 150 of which met the inclusion criteria. Injury was the most highly investigated sport science and medicine topic (n = 45), followed by physical qualities (n = 37), match characteristics (n = 24), biomechanics (n = 15), psychology (n = 13), fatigue and recovery (n = 9), training load (n = 4) and nutrition (n = 3). A range of cohorts were used from school to elite and international standards. All cohorts were female netballers, except for one study. A rapid growth in studies over recent years was demonstrated with 65% of studies published in the last decade. There still remains gaps in the literature, with a low evidence base for nutrition, training load and fatigue and recovery. CONCLUSION: This scoping review summarises the current evidence base and key findings that can be used in practice to enhance the applied sport science and medical support to netball athletes across a range of playing standards, and support the growth of the sport. It is evident that netball as a sport is still under-researched.

This study quantified the position-, duration-, and phase-of-play specific peak locomotor characteristics of senior professional rugby league match-play at a multi-club level. Match-play data were collected from 378 male professional rugby league players, from 11 clubs, across two competitive seasons. A total of 9643 match-observations were analysed; 10-Hz instantaneous velocity and acceleration from Catapult S5 microtechnology units were aligned with video footage to determine to the phase-of-play and duration-specific peak locomotor characteristics (average running speed, relative high-speed running [HSR;>5.5 m·s−1], average absolute acceleration). Linear mixed effect models were used to determine positional differences for each dependent variable and differences between phases-of-play. Positional differences for the duration-specific and phase-of-play peak locomotive characteristics were identified. Fullbacks had greater peak HSR during defensive sets (86 ± 70 m·min−1) vs. all other positions (effect size = 0.26 to 0.49, small). Wingers demonstrated the greatest between phase differences with greater peak locomotor characteristics (effect size = 1.23 to 1.65, large) during attacking-defensive set transition vs. defensive sets. The multi-club normative data, and the differences identified, provides practitioners with valuable information for the consideration of training practices; the incorporation of phases-of-play enables greater consideration of technical-tactical factors whilst preparing players for the peak periods of competition.

In collision sports, the tackle has the highest injury incidence, and is key to a successful performance. Although the contact load of players has been measured using microtechnology, this has not been related to tackle technique. The aim of this study was to explore how PlayerLoad™ changes between different levels of tackling technique during a simulated tackle. Nineteen rugby union players performed twelve tackles on a tackle contact simulator (n = 228 tackles). Each tackle was recorded with a video-camera and each player wore a Catapult OptimEyeS5. Tackles were analysed using tackler proficiency criteria and split into three categories: Low scoring(≤5 Arbitrary units (AU), medium scoring(6 and 7AU) and high scoring tackles(≥8AU). High scoring tackles recorded a higher PlayerLoad™ at tackle completion. The PlayerLoad™ trace was also less variable in the high scoring tackles. The variability in the PlayerLoad™ trace may be a consequence of players not shortening their steps before contact. This reduced their ability to control their movement during the contact and post-contact phase of the tackle and increased the variability. Using the PlayerLoad™ trace in conjunction with subjective technique assessments offers coaches and practitioners insight into the physical-technical relationship of each tackle to optimise tackle skill training and match preparation.

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms’ movement patterns and machine learning classification modelling identified the best algorithm’s movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players.

The quantification of the technical, tactical and physical demands of match-play in youth rugby is important for the appropriate prescription of training practices. Differences in the demands of match-play have been identified between positions, playing standards, age grades and phases-of-play. This chapter presents the research that has explored the physical and technical-tactical match demands of youth rugby. The chapter then provides a practical overview of how practitioners can utilise match-demands data to assist in appropriate training prescription through the adaptation, manipulation and evaluation of training drills and practices. The chapter concludes with a range of recommendations and practical implications for the use of match-play data within youth rugby environments.

Using an expert consensus-based approach, a netball video analysis consensus (NVAC) group of researchers and practitioners was formed to develop a video analysis framework of descriptors and definitions of physical, technical and contextual aspects for netball research. The framework aims to improve the consistency of language used within netball investigations. It also aims to guide injury mechanism reporting and identification of injury risk factors. The development of the framework involved a systematic review of the literature and a Delphi process. In conjunction with commercially used descriptors and definitions, 19 studies were used to create the initial framework of key descriptors and definitions in netball. In a two round Delphi method consensus, each expert rated their level of agreement with each of the descriptors and associated definition on a 5-point Likert scale (1-strongly disagree; 2-somewhat disagree; 3-neither agree nor disagree; 4-somewhat agree; 5-strongly agree). The median (IQR) rating of agreement was 5.0 (0.0), 5.0 (0.0) and 5.0 (0.0) for physical, technical and contextual aspects, respectively. The NVAC group recommends usage of the framework when conducting video analysis research in netball. The use of descriptors and definitions will be determined by the nature of the work and can be combined to incorporate further movements and actions used in netball. The framework can be linked with additional data, such as injury surveillance and microtechnology data.

OBJECTIVES The current study retrospectively compared the physical qualities of elite academy rugby league players (aged 16-19 years) by career attainment level (i.e., academy or professional). DESIGN: Retrospective cross-sectional and longitudinal design. METHODS: Eighty-one academy rugby league players were assessed for physical qualities (height, body mass, skinfolds, speed, momentum, vertical jump, Yo-Yo Level 1 and 1-RM squat, bench press and prone row) at the Under 17-19 age categories between 2007 and 2012. Player's career attainment level was determined in 2014. Longitudinal changes in physical qualities between Under 17 and 19s were compared by career attainment level. RESULTS: Professional players demonstrated moderate significant advantages for height (d=0.98) and 1-RM squat (d=0.66) at the Under 17s, 1-RM bench press (d=0.76) at the Under 18s and 1-RM prone row (d=0.73) at the Under 19s age categories when compared to academy players. When assessed longitudinally (Under 17s-19s), professional players significantly outperformed academy players for 1-RM squat (η2=0.20). Professional players also demonstrated greater increases in body mass (8.2 vs. 2.9kg) and 10m momentum (47 vs. 17kgs-1) than academy players between the Under 17s and 19s. CONCLUSIONS: Advanced physical qualities, particularly height and absolute strength, within 16-19 year old players may contribute to attaining professional status in rugby league. Further, the development of body mass and momentum for players within an academy is an important consideration in the progress towards professional rugby league. Therefore, practitioners should aim to identify and develop the physical qualities, especially size and strength, within academy rugby league players.

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. 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.

BACKGROUND: With the increasing professionalisation of youth sports, training load monitoring is increasingly common in adolescent athletes. However, the research examining the relationship between training load and changes in physical qualities, injury, or illness in adolescent athletes is yet to be synthesised in a systematic review. OBJECTIVE: To systematically examine the research assessing internal and external methods of monitoring training load and physical qualities, injury, or illness in adolescent athletes. METHODS: Systematic searches of SPORTDiscus, Web of Science, CINAHL and SCOPUS were undertaken from the earliest possible records to March 2022. Search terms included synonyms relevant to adolescents, athletes, physical qualities, injury, or illness. To be eligible for inclusion, articles were required to: 1) be original research articles; 2) be published in a peer-reviewed journal; 3) participants aged between 10 and 19 years and participating in competitive sport; 4) report a statistical relationship between a measure of internal and/or external load and physical qualities, injury or illness. Articles were screened and assessed for methodological quality. A best-evidence synthesis was conducted to identify trends in the relationships reported. RESULTS: The electronic search yielded 4,125 articles. Following screening and a review of references, 59 articles were included. The most commonly reported load monitoring tools were session ratings of perceived exertion (n = 29) and training duration (n = 22). Results of the best-evidence synthesis identified moderate evidence of positive relationships between resistance training volume load and improvement in strength, and between throw count and injury. However, evidence for other relationships between training load and change in physical qualities, injury, or illness were limited or inconsistent. CONCLUSIONS: Practitioners should consider monitoring resistance training volume load for strength training. Additionally, where appropriate monitoring throw counts may be useful in identifying injury risk. However, given the lack of clear relationships between singular measures of training load with physical qualities, injury, or illness, researchers should consider multivariate methods of analysing training load, as well as factors that may mediate the load-response relationship, such as maturation.

Drawing from skill acquisition and development literature, we present a novel tackle skill training framework. The framework outlines the training purpose (technique proficiency, technique capacity, skill proficiency and skill capacity), skill workload measurements (available information, task difficulty, rating of perceived challenge, skill load), as well as the training conditions and coaching style for the tackle in rugby union. Using this framework and skill load measurements, we propose a pre-season tackle training plan. This tackle skill framework and skill load measurements serve as potential preventive measures for tackle injury risk while improving players’ tackle performance.

Rugby union is a late specialisation sport. As a consequence, youth players may still be engaged in other activities and sports throughout the year as they transition to rugby specialisation. Limited research exists quantifying rugby union training and matches as well as engagement in other activities and sports. Therefore, the aim of this study was to quantify and compare rugby union training, matches and other activities of elite youth U15 and U16 rugby union players at different stages of the season.

Practitioners prescribe numerous training modes to develop the varied physical qualities professional rugby league players must express during competition. The aim of the current study was to determine how the magnitude of external and internal training load per minute of time differs between modes in professional rugby league players. This data were collected from 17 players across 716 individual sessions (mean (SD) sessions: 42 (13) per player) which were categorised by mode (conditioning, small sided games, skills and sprint training). Derived from global positioning systems (5Hz with 15Hz interpolation), the distances covered within arbitrary speed- and metabolic-power-thresholds were determined to represent the external load. Session rating of perceived exertion (sRPE) and individualised training impulse (iTRIMP) represented the internal load. All data were made relative to session duration. The differences in time-relative load methods between each mode were assessed using magnitude based inferences. Small-sided-games and conditioning very likely to almost certainly produced the greatest relative internal and external loads. Sprint training provided players with the greatest sprinting and maximal-power distances without a concomitant increase in internal load. The metabolic-power method complements speed-based quantification of the external load, particularly during smallsided-games and skills training. In practice, establishing normative loads per minute of time for each mode can be useful to plan future training by multiplying this value by the planned session duration.

To investigate the effect of training mode (conditioning and skills) on multivariate training load relationships in professional rugby league via principal component analysis. Four measures of training load (internal: heart rate exertion index, session rating of perceived exertion; external: PlayerLoad™, individualised high-speed distance) were collected from 23 professional male rugby league players over the course of one 12-wk preseason period. Training was categorised by mode (skills or conditioning) and then subjected to a principal component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subject to a Varimax rotation. Skills extracted 1 principal component, explaining 57% of the variance. Conditioning extracted 2 principal components (1st: internal; 2nd: external), explaining 85% of the variance. The presence of multiple training load dimensions (principal components) during conditioning training provides further evidence of the influence of training mode on the ability of individual measures of external or internal training load to capture training variance. Consequently, a combination of internal- and external- training load measures is required during certain training modes.

The ability to accurately evaluate player and team performances in professional sport is particularly valuable. Doing so provides competitive advantages include extracting important information regarding the tactical strategies of future oppositions and producing player rating systems. A common method of evaluating player and team performances is via expected possession value (EPV) models. EPV models assign a value to every location and/or action on the pitch, which reflects the probability of points being scored within a given time period. EPV models have been produced in several sports, including football, basketball and ice hockey. However, there is limited research surrounding these models in rugby league. Rugby league has a unique set of rules, including a six tackle attacking set and five possible scoring options at the end of a possession. These two factors, alongside the poor data availability in the sport ensure that the majority of previous methods cannot be adapted for use in rugby league. Therefore the aim of this thesis was to develop new methodologies evaluating player and team performances in rugby league. In the first section of this thesis (studies 1 and 2), previous Markov models using zonal approaches were applied, adapted and extended in rugby league to provide insights into player and team performances. Six EPV models were produced with varying zone sizes using Markov Reward Processes. The Kullback-Leibler Divergence was used to evaluate the zone sizes which could reproduce future team attacking performances. The model was then extended to incorporate actions and context nodes using Markov Decision Processes. Novel methods of evaluating player and team performances were also produced. In the second section (studies 3 and 4), novel models producing smooth pitch surfaces were developed. The spatial trends of team attacking performances were evaluated using Kernel Density Estimation. Two novel Wasserstein distance metrics were used to provide valuable insights into team performances. A novel approach to the estimation of individual possession outcomes was also proposed using a Bayesian mixture model approach. The model used linear and bilinear interpolation techniques for its weights to produce a smooth pitch surface. Novel performance metrics evaluating player and team performances were also created. The research provides new methodologies for use within rugby league, providing zonal and smooth EPV models through which player and team performances can be evaluated. Professional experts were impressed with the results they provided and validated their use within the sport.

Despite the athlete monitoring cycle becoming increasingly popular within sport, very little evidence exists with regards to the relationships present between its measures or its relationship with illness incidence in youth athletes. The aim of this thesis was to evaluate the true predictive ability of an integrated athlete monitoring cycle model, incorporating measures of the training dose (training load), training recovery (sleep) and training response (wellness questionnaires (DWB and PRS), countermovement jumps and salivary IgA (s-IgA)), with regards to illness incidence in youth athletes. Study 1 outlined the reliability and usefulness of DWB (poor/marginal), PRS (poor/marginal) and CMJ (good/useful). Despite study 1’s findings, study 2 showed that CMJ was not suitable for use as a training response measure in youth athletes. Studies 3 and 4 supported the use of the sleep quality subscale as a training recovery measure rather than within the DWB training response measure (which was reduced to the four item DWBno-sleep). The overall DWBno-sleep score, fatigue, stress and mood were statistically related to the training recovery, whereas only muscle soreness was related to the training dose. Statistically, PRS was related to both the training dose and recovery. Despite the presence of these statistical relationships, only the effect of training load, including match exposure, on PRS was practically interpretable. Unfortunately, technical issues prevented the true predictive ability of an integrated athlete monitoring cycle model with regards to illness incidence being tested. However, study 5 showed that s-IgA measures could not accurately predict illness in youth athletes. Furthermore, analysis of the longitudinal trends of s-IgA, DWBno-sleep and PRS showed that the subjective fatigue/wellness measures were more responsive to qualitative events than objective measures of immune function. Overall, the results of this thesis provide support for the use of the integrated athlete monitoring cycle in youth athletes, particularly when subjective training response measures are included. However, future research needs to consider the true predictive ability of the proposed integrated athlete monitoring cycle model with regards to illness incidence.

Sports-related concussions (SRCs) typically occur when the brain is hyperthermic. Acute head-and-neck cooling should, therefore, reduce the brain’s metabolic demands, with the potential to improve recovery following an SRC. Elite ice hockey players who underwent head-and-neck cooling after sustaining a concussion (SRC) showed reduced return-to-play times, although further investigation is warranted. This paper aims to describe the methods proposed for investigating the clinical effects and feasibility of acute head-and-neck cooling in elite male rugby union players.

A quasi-experimental study will be conducted in two professional male rugby competitions (clusters): the United Rugby Championship ‘intervention group’ and the PREM Rugby ‘standard care group’. Both groups will follow World Rugby’s standardised, graduated return-to-play concussion management protocols. In addition to this, within 30 min of the SRC, the intervention group will be offered head-and-neck cooling for 45 min. The quantitative phase of the study will collect return-to-play times and clinical outcomes in both groups (sample size calculated: 100 concussions per cohort). The qualitative phase will explore the experiences of players and medical teams with the intervention. Intention-to-treat and per-protocol analyses, using appropriate regression modelling techniques, will adjust for possible confounders between the two groups, and thematic content analysis will be employed in the analysis of the respective phases.

The Clinical Observed Outcomes with Local HEad-neck cooling After Diagnosed concussions study will provide evidence regarding acute head-and-neck cooling as a potential adjunct treatment to current concussion management in elite male rugby union.

Concussions in contact sports are challenging for athletes, health professionals and sporting bodies to prevent, detect and manage. Design of interventions for primary prevention, early recognition of concussion and continuing to improve postconcussion management are essential for protecting athletes and promoting brain health. Over the last decade, there have been advancements in video technology for analysing head impact events and improvements in the clinical management of concussions. This study protocol describes how researchers, clinicians and staff from the Australasian National Rugby League (NRL) have brought these advancements together and developed a database of videos with head impact events and clinical outcomes. The intended outputs from this work will enhance the understanding of head impact events in NRL, from biomechanical and gameplay factors to concussion and return to play outcomes. Publishing this protocol increases the transparency of this large-scale effort to better identify head impacts and their relationship to concussions and player movement behaviour to contextualise these variables to generate new knowledge and support the reproducibility of these emerging findings. Between 2017 and 2023, over 5250 head contact cases were recorded in the database, from which >1700 head injury assessments were performed, and >600 concussions were diagnosed. Future studies using these data are planned to inform both primary and secondary injury prevention initiatives, such as risk analysis and prediction of game scenarios that result in concussion, as well as investigation of features and factors that help to inform the duration of recovery and return to play.

Background Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby. Methods This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts. Tackle events from professional rugby league matches were coded as either head-on-head or non-head-on-head impacts. These included non-televised standard-definition and televised high-definition video clips to train (n=341) and test (n=670) the framework. A computer vision framework consisting of two deep learning networks, an object detection algorithm and three-dimensional Convolutional Neural Networks, was employed and compared with the analyst-coded criterion. Sensitivity, specificity and positive predictive value were reported. Results The overall performance evaluation of the framework to classify head-on-head impacts against manual coding had a sensitivity, specificity and positive predictive value (95% CIs) of 68% (58% to 78%), 84% (78% to 88%) and 0.61 (0.54 to 0.69) in standard-definition clips, and 65% (55% to 75%), 84% (79% to 89%) and 0.61 (0.53 to 0.68) in high-definition clips. Conclusion The study introduces a novel computer vision framework for head-on-head impact detection. Governing bodies may also use the framework in real time, or for retrospective analysis of historical videos, to establish head-on-head rates and evaluate prevention strategies. Future work should explore the application of the framework to other head-contact mechanisms and also the utility in real time to identify potential events for clinical assessment.

Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework accounting for the movement sequences of athletes has previously been proposed using radio frequency data. This study developed a framework to identify sequential movement sequences using GPS-derived spatiotemporal data in team-sports and establish its stability. Thirteen rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies techniques to analyse i) geospatial data (i.e., decimal degree latitude and longitude), ii) determine players turning angles, iii) improve movement descriptor assignment, thus improving movement unit formation and iv) improve the classification and identification of players’ frequent SMP. The SMP framework allows for sub-sequences of movement units to be condensed, removing repeated elements, which offers a novel technique for the quantification of similarities or dis-similarities between players and playing positions. The SMP framework provides a robust and stable method that allows, for the first time the analysis of GPS-derived data and identifies the frequent SMP of field-based team-sport athletes. The application of the SMP framework in practice could optimise the outcomes of training of field-based team-sport athletes by improving training specificity.

This study aims to (a) quantify the movement patterns during rugby league match-play and (b) identify if differences exist by levels of competition within the movement patterns and units through the sequential movement pattern (SMP) algorithm. Global Positioning System data were analysed from three competition levels; four Super League regular (regular-SL), three Super League (semi-)Finals (final-SL) and four international rugby league (international) matches. The SMP framework extracted movement pattern data for each athlete within the dataset. Between competition levels, differences were analysed using linear discriminant analysis (LDA). Movement patterns were decomposed into their composite movement units; then Kruskal-Wallis rank-sum and Dunn post-hoc were used to show differences. The SMP algorithm found 121 movement patterns comprised mainly of "walk" and "jog" based movement units. The LDA had an accuracy score of 0.81, showing good separation between competition levels. Linear discriminant 1 and 2 explained 86% and 14% of the variance. The Kruskal-Wallis found differences between competition levels for 9 of 17 movement units. Differences were primarily present between regular-SL and international with other combinations showing less differences. Movement units which showed significant differences between competition levels were mainly composed of low velocities with mixed acceleration and turning angles. The SMP algorithm found 121 movement patterns across all levels of rugby league match-play, of which, 9 were found to show significant differences between competition levels. Of these nine, all showed significant differences present between international and domestic, whereas only four found differences present within the domestic levels. This study shows the SMP algorithm can be used to differentiate between levels of rugby league and that higher levels of competition may have greater velocity demands.

Rule changes within football-code team sports aim to improve performance, enhance player welfare, increase competitiveness, and provide player development opportunities. This manuscript aimed to review research investigating the effects of rule changes in football-code team sports. A systematic search of electronic databases (PubMed, ScienceDirect, CINAHL, MEDLINE, and SPORTDiscus) was performed to August 2023; keywords related to rule changes, football-code team sports, and activity type. Studies were excluded if they failed to investigate a football-code team sport, did not quantify the change of rule, or were review articles. Forty-six studies met the eligibility criteria. Four different football codes were reported: Australian rules football (n = 4), rugby league (n = 6), rugby union (n = 16), soccer (n = 20). The most common category was physical performance and match-play characteristics (n = 22). Evidence appears at a high risk of bias partly due to the quasi-experimental nature of included studies, which are inherently non-randomised, but also due to the lack of control for confounding factors within most studies included. Rule changes can result in unintended consequences to performance (e.g., longer breaks in play) and effect player behaviour (i.e., reduce tackler height in rugby) but might not achieve desired outcome (i.e., unchanged concussion incidence). Coaches and governing bodies should regularly and systematically investigate the effects of rule changes to understand their influence on performance and injury risk. It is imperative that future studies analysing rule changes within football codes account for confounding factors by implementing suitable study designs and statistical analysis techniques.

Instrumented mouthguards (iMGs) have the potential to quantify head acceleration exposures in sport. The Rugby Football League is looking to deploy iMGs to quantify head acceleration exposures as part of the Tackle and Contact Kinematics, Loads and Exposure (TaCKLE) project. iMGs and associated software platforms are novel, thus limited validation studies exist. The aim of this paper is to describe the methods that will determine the validity (ie, laboratory validation of kinematic measures and on-field validity) and feasibility (ie, player comfort and wearability and practitioner considerations) of available iMGs for quantifying head acceleration events in rugby league. Phase 1 will determine the reliability and validity of iMG kinematic measures (peak linear acceleration, peak rotational velocity, peak rotational acceleration), based on laboratory criterion standards. Players will have three-dimensional dental scans and be provided with available iMGs for phase 2 and phase 3. Phase 2 will determine the on-field validity of iMGs (ie, identifying true positive head acceleration events during a match). Phase 3 will evaluate player perceptions of fit (too loose, too tight, bulky, small/thin, held mouth open, held teeth apart, pain in jaw muscles, uneven bite), comfort (on lips, gum, tongue, teeth) and function (speech, swallowing, dry mouth). Phase 4 will evaluate the practical feasibility of iMGs, as determined by practitioners using the system usability scale (preparing iMG system and managing iMG data). The outcome will provide a systematic and robust assessment of a range of iMGs, which will help inform the suitability of each iMG system for the TaCKLE project.

Objectives Quantify and identify factors associated with concussion underreporting in Super League rugby league players. Design Cross sectional survey. Methods During the 2022 season preseason, 422 Men's and Women's Super League players completed an online survey quantifying player demographics, rugby playing history, concussion history, prevalence of, and reasons for underreporting concussion, concussion knowledge and long-term implications and perceptions of concussion. Results Overall, 20% of respondents stated they did not report concussion-related symptoms to medical staff during the 2020 and 2021 seasons. The two most common reasons for underreporting concussion were ‘didn't want to be ruled out of a match’ (35%) and ‘didn't want to let down team’ (24%). 65% of players reported an appropriate level of knowledge about concussion and potential long-term implications at the start of their senior rugby career, versus 89% now. In relation to concussion knowledge, symptoms were correctly identified on 74% of occasions. 57% of players surveyed were concerned about the potential long-term implications from concussion, and 11% of players would encourage their/family members' children to not play rugby league. Conclusions The proportion of Super League players who did not report concussion symptoms was similar to rugby league players in Australia. The main reasons for not reporting concussion appeared to be due to perceptions of what is beneficial for the team, suggesting both performance and medical staff should collectively encourage players to report concussion. A player's attitude towards concussion is potentially an individual modifiable risk factor and should be considered within the concussion management of players.

Several microtechnology devices quantify the external load of team sports using Global Positioning Systems sampling at 5, 10, or 15 Hz. However, for short, explosive actions, such as collisions, these sample rates may be limiting. It is known that very high-frequency sampling is capable of capturing changes in actions over a short period of time. Therefore, the aim of this study was to compare the mean acceleration and entropy values obtained from 100 Hz and 1000 Hz tri-axial accelerometers in tackling actions performed by rugby players. A total of 11 elite adolescent male rugby league players (mean ± SD; age: 18.5 ± 0.5 years; height: 179.5 ± 5.0 cm; body mass: 88.3 ± 13.0 kg) participate in this study. Participants performed tackles (n = 200), which were recorded using two triaxial accelerometers sampling at 100 Hz and 1000 Hz, respectively. The devices were placed together inside the Lycra vests on the players’ backs. The mean acceleration, sample entropy (SampEn), and approximate entropy (ApEn) were analyzed. In mean acceleration, the 1000 Hz accelerometer obtained greater values (p < 0.05). However, SampEn and ApEn were greater with the 100 Hz accelerometer (p < 0.05). A large relationship was observed between the two devices in all the parameters analyzed (R2 > 0.5; p < 0.0001). Sampling frequency can affect the quality of the data collected, and a higher sampling frequency potentially allows for the collection of more accurate motion data. A frequency of 1000 Hz may be suitable for recording short and explosive actions.

To critically evaluate sport nutrition services available to male and female international rugby unions. Fifteen participants, representing 16 international rugby unions, including nine female and seven male teams (one participant worked with both a female and male union), responded to an online survey. Twelve of the unions recruited were ranked in the top 10 globally by World Rugby. Twelve unions employed accredited nutrition practitioners with significant experience (> 5 years: n = 5; > 10 years: n = 4) and advanced qualifications (master's degrees: n = 8; doctorates: n = 2). Three unions did not employ a qualified nutrition practitioner (female: n = 2; male: n = 1). Full-time employment was more common among nutrition practitioners serving male (n = 4/5) versus female (n = 3/6) unions. Practitioners served male unions for more hours per week (42 ± 28) than female unions (24 ± 20). Practitioners were involved in sport science meetings (n = 14/15), anti-doping education, menu design, strategy development (n = 13/15), body composition assessments, individual consultations (n = 12/15), focusing on fuelling, recovery and injury rehabilitation (n = 14/15). Participants were “moderately confident” (n = 8/15) in using behaviour change techniques. Most participants agreed on the lack of female-specific nutrition guidance (n = 14/15), relying on guidance for male players due to limited evidence (n = 7/9). This study provides the first critical reflection of sport nutrition service delivery within international rugby. The findings highlight gender disparities for female players, with reduced applied support and a lack of female-specific guidelines. Recommendations include enhancing practitioner training in behaviour change, hiring qualified nutritionists, deemphasising body composition assessment, and conducting more research to improve nutrition services, especially for women.

Ranking enables coaches, sporting authorities, and pundits to determine the relative performance of individual athletes and teams in comparison to their peers. While ranking is relatively straightforward in sports that employ traditional leagues, it is more difficult in sports where competition is fragmented (e.g. athletics, boxing, etc.), with not all competitors competing against each other. In such situations, complex points systems are often employed to rank athletes. However, these systems have the inherent weakness that they frequently rely on subjective assessments in order to gauge the calibre of the competitors involved. Here we show how two Internet derived algorithms, the PageRank (PR) and user preference (UP) algorithms, when utilised with a simple ‘who beat who’ matrix, can be used to accurately rank track athletes, avoiding the need for subjective assessment. We applied the PR and UP algorithms to the 2015 IAAF Diamond League men’s 100m competition and compared their performance with the Keener, Colley and Massey ranking algorithms. The top five places computed by the PR and UP algorithms, and the Diamond League ‘2016’ points system were all identical, with the Kendall’s tau distance between the PR standings and ‘2016’ points system standings being just 15, indicating that only 5.9% of pairs differed in their order between these two lists. By comparison, the UP and ‘2016’ standings displayed a less strong relationship, with a tau distance of 95, indicating that 37.6% of the pairs differed in their order. When compared with the standings produced using the Keener, Colley and Massey algorithms, the PR standings appeared to be closest to the Keener standings (tau distance = 67, 26.5% pair order disagreement), whereas the UP standings were more similar to the Colley and Massey standings, with the tau distances between these ranking lists being only 48 (19.0% pair order disagreement) and 59 (23.3% pair order disagreement) respectively. In particular, the UP algorithm ranked ‘one-off’ victors more highly than the PR algorithm, suggesting that the UP algorithm captures alternative characteristics to the PR algorithm, which may more suitable for predicting future performance in say knockout tournaments, rather than for use in competitions such as the Diamond League. As such, these Internet derived algorithms appear to have considerable potential for objectively assessing the relative performance of track athletes, without the need for complicated points equivalence tables. Importantly, because both algorithms utilise a ‘who beat who’ model, they automatically adjust for the strength of the competition, thus avoiding the need for subjective decision making.

To prevent the spread of infection during matches and training activities is a major challenge facing all sports returning from the enforced COVID-19 shutdown. During training and matches, rugby league players make contact with others which can result in SARS-CoV-2 virus transmission. While these interactions characterise the appeal of the game, a number of them can be avoided, including shaking hands and conversing after the match. This paper presents a framework underpinned by behavioural science (capability, opportunity, motivation and behaviour model, COM-B) to support stakeholders in helping players adopt new social distance norms and behaviours. This framework helps to ensure the players have the capability, opportunity, and motivation to adopt new COVID-19 risk minimising behaviours, which they will need to commit to 100%.

Throughout a rugby league (RL) season players are exposed to a high volume of competitive fixtures. Cumulative loads and inadequate nutrition may supress immune function, which would have negative consequences for health and performance. As no study has monitored immune function and dietary intake throughout a RL season, the aim of the study was to identify the pattern of dietary intake and critical time points where immune function and selected performance monitors are compromised. Following ethics approval, 20 male volunteer professional RL players (25.4±3.2 y, 98.2±8.4 kg) were monitored the day prior to a competitive fixture for 25 weeks of the season. Weekly changes in neuromuscular function (CMJ), wellbeing (5-point questionnaire), training loads (sRPE), salivary testosterone (sTest) and immunoglobulin A (sIgA) were recorded. Dietary intake (4-day food diary) was assessed at the start, middle and end of the season. Changes from the overall mean were inferred via Cohen’s d effect sizes using means and standard deviations which were calculated from a linear mixed model to account for missing data. Moderate increases in training load occurred in weeks 3, 5, 9, 10, 12 and 20 and a very large increase occurred in week 21. Moderate decreases in CMJ flight time occurred in week 14 and small decreases occurred in weeks 6, 7, 20, 22, 24, 25. For all wellbeing parameters small decreases occurred in weeks 7 and 17 with small increases in stress, soreness and fatigue also occurring in week 20. Mean sTest was 119.8±55.5 pg.ml-1 with small declines occurring in week 7, 10, 13, 20 and 22. Overall sIgA concentrations ranged from 1.03-1.18 µg.min-1, but compared to the overall mean (1.12 ± 0.17 µg.min-1) small decreases were observed in weeks 6, 9, 16, 21, 23, 24. Dietary intakes were consistent during the recording periods with mean energy intake ranging from 2811-3149 kcal.day-1. Carbohydrate, protein, and fat intakes ranged from 2.9-3.2 g.kg.BM-1, 2.0-2.1 g.kg.BM-1, and 1.1-1.4 g.kg.BM-1 respectively. The findings suggest that a consistent intake throughout a competitive season did not prevent reductions in immune function and selected performance monitors. Further research to develop appropriate periodised nutritional strategies to maintain immune function and support player health and performance is needed.

Purpose: To explore the effects of travel related to international rugby sevens competition on sleep patterns. Methods: Seventeen international male rugby sevens players participated in this study. Sleep assessments were performed daily during two separate Sevens World Series competition legs (Oceania and America). The duration of each competition leg was subdivided into key periods (pre-tour, pre-competition, tournament 1 and 2, relocation and post-tour) lasting 2 to 7 nights. Linear mixed models in combination with magnitude-based decision were used to assess 1) the difference between pre-season and key periods and 2) the effect of travel direction (eastward or westward). Results: Shorter total sleep time (hh:mm) was observed during tournament 2 (mean ± SD, 06:16 ± 01:08), relocation (06:09 ± 01:09) and pre-tour week (06:34 ± 01:24) compared with pre-season (06:52 ± 01:00). Worse sleep quality (AU) was observed during tournament 1 (6.1 ± 65 2.0) and 2 (5.7 ± 1.2) as well as during the relocation week (6.3 ± 1.5) than during pre-season (6.5 ± 1.8). When traveling eastward compared with westward, earlier fall asleep time was observed during tournament 1 (ES -0.57, 90%CI [-1.12 to -0.01]), relocation week (-0.70 [-1.11 to -0.28]), and post-tour (-0.57 [-0.95 to -0.18]). However, possibly trivial and unclear differences were observed during pre-competition week (0.15 [-0.15 to 0.45]) and tournament 2 (0.81 [-0.29 to 1.91]). Conclusion: Sleep patterns of elite rugby sevens players are robust to the effects of long-haul travel and jet lag. However, staff should consider promoting sleep during the tournament and 73 relocation week.

Background: Limited empirical evidence exists which has direct translation for nutrition practitioners working with team sport athletes. Furthermore, given the lack of rugby specific nutrition recommendations, it is important to provide a framework on the requirements of players in order to enhance the standard of nutritional practice in rugby. The primary aim of this research was to develop an expert consensus on optimum nutritional practices of rugby players. Methods: To obtain expert opinion, a Delphi Poll was implemented; this survey technique allows a consensus to be established where information is currently contradictory or insufficient (1). Following ethics approval, seven expert nutrition practitioners from across the United Kingdom (UK) and Ireland were recruited for the study. Practitioners were required to have at least two years continuous experience in professional rugby clubs. All recruited practitioners had at least three years elite experience and were working with international rugby teams at the time of data collection. During the initial stage of the research, three of the UK national nutrition leads were invited to participate in standardised open-ended interviews. A total of 359 statements, divided into 20 topic areas were generated from these interviews and were included in the first round of voting in the Delphi poll. Using a 5-point likert scale, all practitioners selected their level of agreement with the statements from strongly disagree to strongly agree. For a statement to be agreed upon as good practice, 75% of the practitioners were required to vote in agreement. Statements which were not agreed upon were recirculated for the second round of voting. During the first round of voting, all practitioners were invited to provide additional statements for inclusion in the final round. Results: Following the two rounds of voting a total of 201 statements were agreed upon, an indicative sample is provided below: Practitioners encouraged estimating energy needs from lean body mass measures but recommended that caution should be exerted when using predictive equations with rugby players. Recording dietary intakes should be done so with caution with additional supporting quality control measures adopted. High protein intakes (>2 g. kg. BM-1 ) are deemed acceptable by practitioners provided other nutrient consumption is not compromised. There appears to be varied practices in carbohydrate consumption across playing positions, and often players may sacrifice carbohydrate intake for body composition goals. Body composition is a primary driver for the rugby player, however influencing factors of vanity and body image play a large role which may compromise them as a rugby player. Social media has increased interest in food and can be used as an educational tool, however sometimes players receive inappropriate information. A trackable system of supplement use should be in place and any supplementation protocols implemented should have an evidence basis. Practitioners should always have an alternative food strategy to any supplements used. All supplement strategies should be assessed throughout the season for psychological and physiological variation and it is important to be aware of the large influence senior players have over young players regarding supplement use. Finally, a key message was that even in a team sport setting it is important to acknowledge that responses to nutrition and training are highly individual and variable. Despite obtaining much agreement in appropriate practices, a relatively high level of disparity in opinion occurred in the areas of fat intake, recovery and nutrition for supporting illness. Discussion: While current research in rugby nutrition is developing, this Delphi Poll study presents a unique approach to conducting research in the field of applied sport and exercise nutrition. These statements will facilitate expert practitioners in moving towards a consensus regarding optimum nutritional strategies for the rugby player. This research can be utilised by new or developing practitioners, while an empirical evidence base is being established. Conclusion: As well as providing a consensus, the present study highlights areas where practitioners should exercise caution regarding recommendations, as more research is required to help inform their practice. References: 1. Hasson F, Keeney, S, McKenna, H. Research guidelines for the Delphi survey technique, Journal of Advanced Nursing 2000; 32 1008-1015.

In recent years an understanding has developed that sports injuries are the emergent outcomes of complex, dynamic systems. Thus, the importance of local contextual factors on injury outcomes is increasingly being acknowledged. These realisations place injury prevention research at a crossroads. Currently, injury prevention researchers develop universally applicable injury prevention solutions, but the adoption of these solutions in practice is low. This occurs because implementation contexts are both unique and dynamic in nature, and as a result singular, static solutions are often incompatible. In contrast, practitioners address injury prevention through iterative cycles of trial and error, aiming to optimise the injury prevention process within their own unique contexts. The purpose of this critical review is to draw attention to the misalignment between research and practice-based approaches to injury prevention. In light of this, we propose alternative research approaches that acknowledge the process-driven nature of injury prevention in practice. We propose that a core focus of sport injury prevention research should be to provide practitioners with useful and relevant information to support their decision-making around their localised injury prevention practice. Through this approach injury prevention research ceases to be about what works, and begins to engage with understanding what works in what contexts and why?

In recent years there has been an exponential rise in the professionalism and success of female sports. Practitioners (e.g., sport science professionals) aim to apply evidence-informed approaches to optimise athlete performance and well-being. Evidence-informed practices should be derived from research literature. Given the lack of research on elite female athletes, this is challenging at present. This limits the ability to adopt an evidence-informed approach when working in female sports, and as such, we are likely failing to maximize the performance potential of female athletes. This article discusses the challenges of applying an evidence base derived from male athletes to female athletes. A conceptual framework is presented, which depicts the need to question the current (male) evidence base due to the differences of the "female athlete" and the "female sporting environment," which pose a number of challenges for practitioners working in the field. Until a comparable applied sport science research evidence base is established in female athletes, evidence-informed approaches will remain a challenge for those working in female sport.

ABSTRACT Flywheels are a resistance training device that can increase lean body mass, strength, and power. However, due to their unique design and the inertia from the concentric portion directly relating to the force that is applied during the eccentric portion, monitoring the training stimulus can be difficult. Consequently, the aim of this study was to assess the validity of the kMeter app for quantifying force and power at a range of different isoinertial loads from a flywheel training device when compared against a criterion measure. Eleven subjects volunteered to take part in this study, with subjects completing between 5-35 repetitions of the harness squat with 0.05, 0.10, 0.15 kg·m2 isoinertial load. A synchronised dual force plate and tri-camera optoelectronic setup was used as the criterion measure to calculate force and power output, while the kMeter app was used as the practical measure. Very large to nearly perfect relationships were observed between the two measures, with trivial to moderate bias reported. Additionally, typical error of the estimate (TEE) was found to be <10% at all isoinertial loads. These findings suggest that the kMeter app, when used in conjunction with the kBox flywheel device, demonstrate acceptable levels of validity. However, due to the TEE, the kMeter app may not be able to accurately detect small differences and therefore be suitable for research purposes. These findings suggest that the kMeter app is an acceptable method of monitoring flywheel resistance training. Furthermore, it is advised that practitioners utilise mean power rather than mean force. Keywords: Flywheel; Validity; kBox; kMeter; Force; Power

Abstract

Darrall-Jones, J, Roe, G, Cremen, E, and Jones, B. Can team-sport athletes accurately run at submaximal sprinting speeds? Implications for rehabilitation and warm-up protocols. J Strength Cond Res 36(8): 2218–2222, 2022—The aim of this study is to examine the ability of team-sport athletes to accurately run at a range of submaximal sprint velocities (60–90% maximal velocity; Vmax) under verbal instruction without any objective feedback. Twelve professional male rugby union players (age 19.7 ± 0.9 years, body mass 98.3 ± 13.9 kg, height 184.0 ± 7.5 cm) were verbally instructed to complete three 40-m sprints at each of 60, 70, 80, and 90% of Vmax in a randomized order. Percentage Vmax achieved during each sprint was compared with criterion velocities calculated from Vmax testing undertaken a week prior. Players underestimated (ran faster) their sprint velocity when asked to run at 60% (very large to extremely large mean bias, 23%; range, 57–88% Vmax), 70% (large to very large, 11%; 67–93% Vmax), and 80% (small, 2%; 71–91% Vmax) of their Vmax, whereas overestimated (ran slower) their sprint velocity when asked to run at 90% Vmax (moderate, −4%; 77–95% Vmax). Team sport players may require objective feedback when performing submaximal sprinting to ensure that velocities achieved are similar to those prescribed. This may be particularly important where graded exposure to maximum velocities is required, for example during rehabilitation or warm-ups.

This study aimed to evaluate team attacking performances in rugby league via expected possession value (EPV) models. Location data from 59,233 plays in 180 Super League matches across the 2019 Super League season were used. Six EPV models were generated using arbitrary zone sizes (EPV-308 and EPV-77) or aggregated according to the total zone value generated during a match (EPV-37, EPV-19, EPV-13 and EPV-9). Attacking sets were considered as Markov Chains, allowing the value of each zone visited to be estimated based on the outcome of the possession. The Kullback-Leibler Divergence was used to evaluate the reproducibility of the value generated from each zone (the reward distribution) by teams between matches. Decreasing the number of zones improved the reproducibility of reward distributions between matches but reduced the variation in zone values. After six previous matches, the subsequent match's zones had been visited on 95% or more occasions for EPV-19 (95±4%), EPV-13 (100±0%) and EPV-9 (100±0%). The KL Divergence values were infinity (EPV-308), 0.52±0.05 (EPV-77), 0.37±0.03 (EPV-37), 0.20±0.02 (EPV-19), 0.13±0.02 (EPV-13) and 0.10±0.02 (EPV-9). This study supports the use of EPV-19 and EPV-13, but not EPV-9 (too little variation in zone values), to evaluate team attacking performance in rugby league.

Purpose: This study evaluated whether glycogen-associated water is a protected entity not subject to normal osmotic homeostasis. An investigation into practical and theoretical aspects of the functionality of this water as a determinant of osmolality, dehydration, and glycogen concentration was undertaken. Methods: In vitro experiments were conducted to determine the intrinsic osmolality of glycogen–potassium phosphate mixtures as would be found intra-cellularly at glycogen concentrations of 2% for muscle and 5 and 10% for liver. Protected water would not be available to ionic and osmotic considerations, whereas free water would obey normal osmotic constraints. In addition, the impact of 2 L of sweat loss in situations of muscle glycogen repletion and depletion was computed to establish whether water associated with glycogen is of practical benefit (e.g., to increase “available total body water”). Results: The osmolality of glycogen–potassium phosphate mixtures is predictable at 2% glycogen concentration (predicted 267, measured 265.0 ± 4.7 mOsmol kg

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Total energy expenditure (TEE) has been quantified in elite senior rugby league (RL) and rugby union (RU) players using multiple measures, with criterion measures lacking in RU and academy players. Robust measures of TEE are required as prediction equations used to estimate energy requirements are often unsuitable for athletes. This study quantified TEE of 27 elite male English academy (U16 and U20) and senior (U24) RL and RU players during a 14-day in-season period using doubly labelled water (DLW). Resting metabolic rate (RMR), using indirect calorimetry, and physical activity level (PAL) was also measured (TEE:RMR). Predicted TEE, determined by published equations, was compared to measured TEE by age group. Differences in TEE (RL, 4369 ± 979; RU, 4365 ± 1122; U16, 4010 ± 744; U20, 4414 ± 688; U24, 4761 ± 1523 Kcal.day-1) and PAL (overall mean 2.0 ± 0.4) were unclear. RMR was very likely greater for RL (2366 ± 296 Kcal.day-1) than RU players (2123 ± 269 Kcal.day-1). Relative RMR for U16, U20 and U24 (27 ± 4, 23 ± 3 and 26 ± 5 Kcal.Kg-1.day-1) was very likely greater for U20 than U24 players. Differences in TEE estimated by the Schofield, Cunningham and Harris-Benedict equations compared with DLW were unclear, likely and unclear for U16 (187 ± 614; -489 ± 564 and -90 ± 579 Kcal.day-1), likely, very likely and likely for U20 (-449 ± 698; -785 ± 650 and -452 ± 684 Kcal.day-1) and all unclear for U24 players (-428 ± 1292; -605 ± 1493 and -461 ± 1314 Kcal.day-1). Due to large variability between individuals, negligible differences in TEE were observed by code, and ~350-400 Kcal.day-1 differences between consecutive age groups were unclear. Differences in RMR may be due to training exposure and match play. The remaining components of TEE (i.e. thermic effect of feeding and activity thermogenesis) may reflect the differences in contact demands between codes, as RU players typically engage in more static exertions than RL players during match play. Prediction equations are currently insufficient to differentiate between individual variability in TEE. The importance of practitioners providing individual support for the elite rugby player is highlighted. Finally, the TEE measured in this study using the gold standard DLW method can be used as reference data for elite rugby players of different codes and ages, during an in-season training period.

Global anti-doping policy indicates that athlete support personnel (ASP, e.g., doctors, nutritionists) can play an important role in fostering supportive environments that protect against intentional and inadvertent doping. Yet, research into ASP anti-doping roles is limited and no study has examined how (if at all) different members of ASP work together. Therefore, this study investigated anti-doping roles of ASP in a single sports club environment via semi-structured interviews. Through inductive reflexive thematic analysis, three overarching themes were constructed: 1) Everyone has responsibility for anti-doping, but most of the work rests unevenly on a few shoulders, 2) Education is fundamental to doping prevention, and 3) (Preventing doping) It’s all about the way we work with players and each other. As the first study of its kind, the findings indicated that actions taken to prevent doping varied across ASP working together in the same environment. The nutritionist and medical staff were most active in anti-doping efforts and least active were strength and conditioning coaches. Factors underpinning anti-doping roles were individuals’ relevant expertise/training and overall job responsibilities (e.g., supplements, medications) related to risk of doping. Staff also connected their doping prevention efforts to the club’s person-centred philosophy, which prioritised ‘individualisation’ and supportive relationships. While the data indicates potential for anti-doping responsibilities to be shared amongst ASP who work well together and trust one another, it revealed that reliance on one or two ASP in any environment might allow other ASP to neglect their opportunity to have a positive influence on players’ doping-related decisions.

The objective of the study was to investigate the impact of static and dynamic stretching singularly or combined within a warm-up on a soccer-specific intermittent protocol (SSIP). Seven semi-professional players aged 21.9(±2.0) years old [height 181.5(±9.4) cm, body mass 74.8(±6.4) kg] following screening and ethical approval, completed a VO2max test and were familiarised with the SSIP. Within a 3-week period, participants undertook 3 different warm-ups, before completing the SSIP, consisting of three bouts of exercise with a 15-min recovery after the 2nd bout to simulate a soccer match. After the 3rd bout participants completed an intermittent time to exhaustion test (ITTE). Warm-ups consisted of 5-min activity, then 10-min of Static Stretches (SS), Dynamic Stretches (DS) and SS+DS (SS-DS) in a randomised-crossover order. Core temperature (Tc) and VO2 were recorded throughout. Testing took place in an environmental chamber, replicating UK conditions (9°C, 50% relative humidity). ITTE was 8.2(±2.3), 9.0(±4.6), and 10.7(±5.9) min for the SS, DS and SS-DS groups. Tc in the SS-DS (38.4±0.3°C) was higher (P<0.05) than the SS (37.9±0.4°C) during the 1st ITT bout. Tc was 38.3(±0.6)°C for the DS condition. Differences in Tc during the first two bouts disappeared in the 3rd. Participants in the DS (78±9.5%) and SS-DS (76.3±15.1%) exercised at a lower (P<0.05) %VO2max than the SS (83.0±15.4%). Combining SS and DS is more beneficial than SS alone during a warm-up, and may be more beneficial than DS alone. A second warm-up is necessary as benefits associated with the initial warm-up dissipate during the half-time break.

Sports invest in research to optimise performance and enhance athlete wellbeing. Involving stakeholders allows research priorities to be determined, maximising the adoption and relevance of research findings. A three‐round modified Delphi process was used to establish wellbeing and performance research priorities for Premiership Rugby (Professional men's rugby union competition in England). Up to 10 research priorities were provided during Round 1 (grouped into higher‐order categories and themes via content analysis). In Rounds 2 and 3, participants ranked higher‐order categories on a one to five Likert scale. Consensus was defined as ≥ 70% agreement. Sixty‐five participants responded in Round 1 (41 and 32 in Rounds 2 and 3). Staff and player experience of working or playing in the Premiership was 11.0 (4.5–16.5) and 7.0 (6.0–8.5) years. Following Round 1, 393 research priorities were provided and 53 higher‐order research priorities and 26 categories were identified, within three themes: performance, wellbeing and injury. Following Round 3, 21 research priorities reached consensus within performance (n = 7), wellbeing (n = 6) and injury (n = 8). Research priorities for a professional sports league, were established by the application of a pragmatic research lens, to ensure priorities were practically minded and also developed with minimal resource requirements, minimal burden for participants and in a short amount of time, which can be applied in other leagues. Research priorities deemed feasible and lacking a relevant evidence base can be addressed in future studies to maximise impact and compliment the ongoing research programmes already established by the professional league and governing body.

This study aimed to quantify the frequency of individual and team contact events during rugby union match play in top domestic and international men’s and women’s competitions. Analyst‐coded player individual and team contact event types (tackles, carries, attacking rucks and defensive rucks, lineouts, scrums and mauls) from the 2022/2023 rugby union season were analysed from top domestic and international competitions across the world using generalised linear mixed models. For both women’s and men’s rugby, competitions generally had similar numbers of contact events per playing position. Where differences were observed, most ranged between 0.5 and six per contact event per full game equivalent (FGE). Similar trends were observed when comparing women’s to men’s rugby. However, within‐game accumulation of these different contact events for certain positional groups may have a significant impact (e.g., a front five player called up from a Farah Palmer Cup team to play in WXV1 could be involved in as much as 6 more attacking rucks, 3 more tackles and 5 more mauls per game on average). Furthermore, the small differences between competitions per FGE may accrue across matches and thus result in far greater exposures across a season (e.g., a front five player in Premiership Rugby may make 48 more tackles over 20 matches than in Top 14 on average). Although a high proportion of contact events per FGE were similar between competitions and sexes per playing position, differences that were observed may have important implications for players transitioning between competitions and the long‐term exposure of players to higher‐risk contact events.

OBJECTIVE: Develop a questionnaire to monitor symptoms of player perceived shoulder function/dysfunction. DESIGN: 3-Stage Online Delphi Study. METHODS: Participants: surgeons, sports and exercise medics, academic researchers, strength and conditioning coaches, therapists and athletes split by level of expertise/experience. Stage-1: experts (n = 12) rated constructs/items from the steering group and made changes/proposed additional constructs/items. Stage-2: experts rated/amended new constructs/items from stage-1. Stage-3: experienced professionals (n = 25) rated/ranked constructs/items from stage 2. Consensus thresholds were defined per stage (≥50% agreement/4-5 rating on 1-5 Likert scale (stages 1-2), ≥68% agreement, and items ranked for perceived importance (stage-3)). RESULTS: Stage-1, all four constructs (a. Activities of daily living, b. Range of motion, c. Strength and conditioning, d. Sports specific training and competition) and 26/42 original items achieved consensus. Twelve items were combined into five items. Four new items were also proposed. Stage-2, the combined items and three of the four new items achieved consensus. Stage-3 the four constructs and 22 items all achieved consensus. CONCLUSIONS: Following a 3-stage online Delphi process, involving expert and experienced clinicians, practitioners and athletes, a new four construct, 22 item RSF questionnaire has been developed which can be used with rugby players, to monitor perceived shoulder performance and symptoms.

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.

This study aimed to investigate the incidence, severity, and burden of injury in English elite youth female soccer players. Qualified therapists at six English girls' academies prospectively recorded all injuries that required medical attention or caused time loss for matches and training in 375 elite youth female soccer players (under-10 , U12, U14 and U16) during the 2019/2020 season. One hundred- and eleven time-loss injuries (52 from training, 59 from matches) were sustained, resulting in 1,946 days absent (779 days from training injuries, 1,167 days from match injuries) from soccer activities. The injury incidence for matches (9.3/1000 hours, 95% CIs: 7.2-11.9) was significantly greater than training (1.1/1000 hours, 95% CIs: 0.9-1.5, p<0.001). Additionally, the injury burden for matches (183 days lost/1000 hours, 95% CIs: 142-237) was significantly greater than training (17 days lost/1000 hours, 95% CIs: 13-22, p<0.001). Injury incidence and burden were greatest in the U16 age group, and were found to increase with age. Whilst injury incidence and burden are greater in matches than training, a large proportion of preventable injuries, soft-tissue and non-contact in nature, were sustained in training. Findings provide comparative data for elite youth female soccer players.

Illness prevention is essential for athlete health management, but little is known about its uptake in sport. Prior to the pandemic, the International Olympic Committee (IOC) published a consensus statement recommending illness prevention guidelines are implemented in sports. Yet, little is known about guideline uptake. Therefore, this study aimed to explore the (1) illness experiences of rugby players and athlete support personnel and (2) barriers and enablers to illness prevention guideline uptake in rugby, using the lens of behaviour change theory. In a bid to inform and enhance athlete welfare, we sought to amplify the voices of participants through qualitative inquiry. Between August 2020 and May 2021, 16 semi-structured interviews were undertaken with players and athlete support personnel working across rugby. Analysis was conducted using Braun and Clarke’s reflexive thematic analysis. Prior to COVID-19, participants deemed illness to be of little concern, with experience of illnesses and the global pandemic critical enablers to guideline uptake. The rugby environment was a barrier to illness prevention, particularly in women’s and academy teams where resource deficiency was highlighted. ‘Rugby identity’ acted as both a barrier and enabler with participants’ passion for rugby driving both guideline adherence and non-adherence. Tackling resource inequalities between men’s and women’s cohorts is critical to effectively implement guidelines. Coach and player education is essential, and emphasis must be placed on continuing preventative behaviours adopted due to COVID-19. Our findings offer new insight into illness prevention, moving away from prevailing quantitative research, and instead voicing players’ experiences.

The purpose of the present study was to evaluate the anthropometric and physical characteristics of English regional academy rugby union academy players by age category (under 16, under 18 and under 21s). Data were collected on 67 academy players at the beginning of the pre-season period and comprised anthropometric (height, body mass and sum of 8 skinfolds) and physical (5 m, 10 m, 20 m & 40 m sprint, acceleration, velocity & momentum; agility 505; vertical jump; yo-yo intermittent recovery test level 1; 30-15 Intermittent Fitness Test; absolute and relative 3 repetition maximum (3RM) front squat, split squat, bench press, prone row and chin; and isometric mid-thigh pull). One way analysis of variance demonstrated significant increases across the three age categories (p < 0.05) for height (e.g., 16s = 178.8 ± 7.1; 18s = 183.5 ± 7.2; 21s = 186.7 ± 6.61 cm), body mass (e.g., 16s = 79.4 ± 12.8; 18s = 88.3 ± 11.9; 21s = 98.3 ± 10.4kg), countermovement jump height and peak power, sprint momentum, velocity and acceleration; absolute, relative and isometric (e.g., 16s = 2157.9 ± 309.9; 18s = 2561.3 ± 339.4; 21s = 3104.5 ± 354.0 N) strength. Momentum, maximal speed and the ability to maintain acceleration were all discriminating factors between age categories, suggesting that these variables may be more important to monitor rather than sprint times. These findings highlight that anthropometric and physical characteristics develop across age categories and provide comparative data for English academy Rugby Union players.

The physical match demands for professional rugby union are well established (Cahill et al., 2013, Journal of Sports Science, 31, 299–237). However, there is a lack of evidence for adolescent players, especially in the UK. Therefore, the purpose of this study was to quantify and compare the demands placed upon adolescent players representing county teams across three age groups (U16, U18 and U20) and two playing positions (forwards and backs). Two county representative games for each age group were assessed, with a total of 112 independent observations collected. Players were classified into age group categories and by position (forwards; U16 [n = 20], U18 [n = 21], U20 [n = 18] and backs; U16 [n = 15], U18 [n = 19], U20 [n = 19]). Match demands were analyzed via a microtechnology unit (OptimEye S5, Catapult Innovations, Melbourne, Australia) that contained a GPS system and triaxial accelerometer sampling at 10 and 100 Hz, respectively. The magnitudes of difference between age groups within positions for locomotive and accelerometer-based variables were investigated using Cohen’s d effect sizes (±90% CL). Institutional ethical approval was granted. For forwards, unclear differences between age groups were observed for total distance (TD), but relative distance (RD) showed very large (U16 vs. U20; d = −2.87 ± 0.53) and large (U18 vs. U20; d = −1.81 ± 0.52) differences between groups. Moderate effect sizes were found for both maximum sprint velocity (Vmax; d = −1.03 ±0.53) and total sprinting distance (d = −0.78 ± 0.53) between U16 and U20. When normalised for time, PlayerLoadSlowTM (PLslow · min-1) increased with age, showing moderate effects for U16 versus U18 (d = 0.68 ± 0.52) and U16 versus U20 (d = 0.80 ± 0.54). For backs, unclear differences between age groups were observed for TD, but RD showed moderate differences U16 versus U20 (d = −0.88 ± 0.58) and U18 versus U20 (d = −1.01 ± 0.54). Small effect sizes were observed for Vmax (d = −0.52 ± 0.54) and total sprinting distance (d = −0.46 ± 0.54) between U18 and U20, whereas U16 versus U20 showed a small difference for Vmax only (d = −0.46 ± 56). PLslow.min-1 increased with age, demonstrating a moderate difference between U16 and U18 (d = 0.86 ± 0.57) and a small difference between U16 and U20 (d = 0.56 ± 0.57). This study shows that the absolute locomotive demands are similar between age groups, although when expressed relative to time, differences were found. This is likely due to difference in playing time between age groups and the consequent fatigue and/or pacing strategies adopted by players. The increase in PLslowmin-1 with age suggests an increase in static exertions. Future research should look to explore the interaction between physical and technical performances at different ages of adolescent rugby.

The purpose of the present study was to evaluate the anthropometric, sprint and high-intensity running profiles of English academy rugby union players by playing positions, and to investigate the relationships between anthropometric, sprint and high intensity running characteristics. Data was collected from 67 academy players following the off-season period and consisted of anthropometric (height, body mass, sum of 8 skinfolds [∑SF]), 40 m linear sprint (5, 10, 20 30 & 40 m splits), the Yo-Yo intermittent recovery test level 1(Yo-Yo IRTL-1) and the 30-15 intermittent fitness test (30-15IFT). Forwards displayed greater stature, body mass and ∑SF; sprint times and sprint momentum, with lower high-intensity running ability and sprint velocities than backs. Comparisons between age categories demonstrated body mass and sprint momentum to have the largest differences at consecutive age categories for forwards and backs; whilst 20-40 m sprint velocity was discriminate for forwards between Under 16s, 18s and 21s. Relationships between anthropometric, sprint velocity, momentum and high-intensity running ability demonstrated body mass to negatively impact upon sprint velocity (10 m; r = -0.34 to -0.46); positively affect sprint momentum (e.g., 5 m; r = 0.85 to 0.93), with large to very large negative relationships with the Yo-Yo IRTL-1 (r= -0.65 to -0.74) and 30-15IFT (r= -0.59 to -0.79). These findings suggest that there are distinct anthropometric, sprint and high-intensity running ability differences between and within positions in junior rugby union players. The development of sprint and high-intensity running ability may be impacted by continued increases in body mass as there appears to be a trade-off between momentum, velocity and the ability to complete high-intensity running.

INTRODUCTION: Rugby League is an invasion sport characterized by frequent accelerations, decelerations, changes of direction and collisions between players. The game is played world wide at junior and senior age groups and across competitive levels ranging from amateur to elite. Rugby league academy competition is an important step along the pathway to professional player status. Despite the importance of this pathway, to date no research has examined injury risk at senior academy level in England. METHODS: Three professional rugby league academies were recruited to this observational prospective cohort investigation. Eighty-one players were included in the investigation. Physiotherapists from each academy were recruited and remunerated to act as injury surveillance officers throughout the season. Match injuries were recorded using a time-loss definition consistent with the consensus adopted within rugby union. Injury incidence , injury severity and injury burden were all calculated. RESULTS: Injury incidence of 85 <95%CI 67 to 103> injuries/1000 h was observed during the 59 matches played. This equates to 1.5 <95%CI 1.2 to 1.8> time-loss injuries per match. The mean severity of injury was 22 ± 19 days resulting in an overall injury burden of 1870 <95%CI 1785 to 1955 days/1000h>. The tackle event was the most common cause of injury <69% of injuries>, with the tackled player injured more frequently than the tackler . Forwards sustained a greater proportion of injuries than backs and ankle sprains <11, 95%CI 4 to 17 per 1000 h> were the most commonly diagnosed injuries, but the shoulder joint was the most commonly injured site <17, 95%CI 9 to 25 per 1000 h>. CONCLUSION: Overall, the incidence of injury for academy rugby league was similar to that reported in senior professional rugby league <78 injuries / 1000 h> , but the mean severity and overall burden of injury was lower . Injury patterns indicate that academy players are at a higher risk of concussion and shoulder joint injuries than senior professional players. This suggests that the specific focus for injury risk management in academy rugby league should be on players’ tackle technique and prevention strategies for concussion and shoulder injuries. REFERENCES: Fitzpatrick, A. C., Naylor, A. S., Myler, P., & Robertson, C. <2018>. A three-year epidemiological prospective cohort study of rugby league match injuries from the European Super League. Journal of science and medicine in sport, 21<2>, 160-165.

Introduction Age-grade (e.g., U18) rugby union players play in multiple playing levels across a season, including international and academy competition. One method for quantifying the physical characteristics of different playing levels is to calculate the maximum locomotor intensity using relative distance (m·min-1) and high-speed (>5.5 m·s-1) relative distance (HSm·min-1). The aims of the study were to quantify the maximum locomotor intensities from match-play and compare between U18 international and academy levels. Methods In total, 142 U18 male rugby union players provided 232 observations. During match- play each player wore a micro-technology device (S5 Optimeye, Catapult Sports) that contained a global positioning system. Using the raw instantaneous speed (m·s-1) downloaded at 10 Hz, variables were calculated through the use of a 0.1 s rolling mean for time durations (15, 30 s and 1, 2, 2.5, 3, 4, 5, 10 min) relevant to age-grade rugby union. Players were split into four positional groups of: front row, back five, scrum-halves, and inside and outside backs. A linear mixed model was used to account for the repeated measurements of players and then results were interpreted with effect sizes (ES) ±90% confidence intervals and classified as trivial (0.00-0.19); small (0.20-0.59); moderate (0.60-1.19) and large (1.20-1.99). Ethics approval was granted from Leeds Beckett University. Results Differences between levels in relative distance were trivial or small for all time durations and positions, with the relative distance ranging from 148 ± 16 to 189 ± 17 m·min-1 in the one-minute duration. High-speed relative distance for one-minute ranged from 26 ± 11 to 71 ± 24 HSm·min-1 and throughout all comparisons were greater in international players. The differences in high-speed relative distance were moderate to large (ES = 1.17 ±0.64 to 1.59 ±0.64) in front row players. The differences between the back five positional groups were small (ES = 0.31 ±0.52 to 0.45 ±0.57) for high-speed relative distance. There were small differences between the groups of scrum halves in the 15 s, 30 s and 1 min durations (ES = 0.56 ±0.79 to 0.59 ±0.78), with moderate to large differences in time durations ≥2 min (ES = 0.82 ±0.87 to 1.24 ±0.93). The differences in high-speed relative distance were trivial to small (ES = 0.02 ±0.51 to 0.39 ±0.58) in the inside and outside backs comparison. Conclusion Relative distance was similar between playing levels but appears to be comparable to data from senior international rugby union match-play in previous studies. There is a greater amount of high-speed relative distance per minute completed during U18 international matches compared to U18 academy matches. Coaches working with rugby players can use this information to appropriately overload the intensity of running, specific to time durations and positions.

PURPOSE: To compare the external loads and external:internal load ratios (EL:IL) during match-play of adolescent collision sport athletes playing at both elite (i.e., academy) and sub-elite (i.e., school) standards. METHODS: Following ethics approval, seventeen elite adolescent male rugby union players (mean ± SD age = 17 ± 1 years) were recruited for this study. Global positioning system (GPS) locomotor (i.e., relative distance [RD; m·min-1], low speed activity [LSA; relative distance <61% maximum velocity [Vmax]], high speed running [HSR; relative distance ≥61% Vmax]), and accelerometer (relative PlayerLoadTM [RPL; AU·min-1], PLSLOW [relative accelerations <2 m·s-1], PLFAST [relative accelerations ≥2 m·s-1]) external loads, and session rating of perceived exertion (sRPE) internal load measures were obtained from 22 matches resulting in 86 match files (39 sub-elite and 47 elite match files; 5 ± 2 match files per subject). Perceptual wellbeing measures (i.e., fatigue, sleep quality, upper-body and lower-body soreness, stress, and mood) were also recorded using a 5-point Likert scale on the mornings pre- and post-match. Data were analysed using Cohen’s d effect sizes (d) and magnitude-based inferences. RESULTS: Differences in external loads were unclear between playing standards for RD (d = -0.2) and RPL (d = 0.0). However, subjects’ EL:IL were very likely lower during elite compared to sub-elite matches for both RD:sRPE (d = -1.0) and RPL:sRPE (d = -0.8), due to the very likely greater sRPE during elite matches (d = -1.1). There were unclear differences between sub-elite and elite matches for distribution of GPS and accelerometer variables (i.e., LSA, HSR, PLSLOW, and PLFAST; Table 1). Changes in total perceptual wellbeing were possibly greater following elite compared to sub-elite matches, with unclear differences for changes in sleep quality, lower-body soreness, stress and mood. However, changes in perceptual fatigue and upper-body soreness were both likely greater following elite matches. CONCLUSIONS: Adolescent rugby union players had similar locomotor and accelerometer external loads (i.e., RD, LSA, HSR, RPL, PLSLOW and PLFAST) during both elite and sub-elite standard matches. However, EL:IL was substantially reduced in higher standard matches, indicating a higher perception of effort for a given external load. Greater changes in perceptual fatigue and upper-body soreness following elite match-play may be related to greater magnitude of collision-based activity at higher playing standards. This may be due to the increased body mass and running velocities of opponents at higher playing standards, which may not be fully accounted for using external load measures alone. PRACTICAL APPLICATION: As collision sports require more than just movement and acceleration demands, the inclusion of subjective load measures (i.e., sRPE) or EL:IL may provide a further insight to the true demands of training or match-play than GPS and accelerometer data alone.

Objectives: Due to the complex-systems nature of injuries, the responsibility for injury risk management cannot lie solely within a single domain of professional practice. Interdisciplinary collaboration between technical/tactical coaches, strength and conditioning coaches, team doctors, physical therapists and sport scientists is likely to have a meaningful impact on injury risk. This study describes the application and efficacy of a multidisciplinary approach to reducing team injury risk in professional rugby union. Design: Observational longitudinal cohort study. Methods: Epidemiological injury data was collected from a professional rugby union team for 5 consecutive seasons. Following each season, these data informed multidisciplinary intervention strategies to reduce injury risk. The effectiveness of these strategies was iteratively assessed to inform future interventions. Specific examples of intervention strategies are provided. Results: Overall team injury burden displayed a likely beneficial decrease (-8 %; injury rate ratio (IRR) 0.9, 95%CI 0.9 to 1.0) from 2012 to 2016. This was achieved through a most likely beneficial improvement in non-contact injury burden (-39 %; IRR 0.6, 95%CI 0.6 to 0.7). Contact injury burden was increased, but to a lesser extent (+18 %; IRR 1.2, 95%CI 1.1 to 1.3, most likely harmful) during the same period. Conclusions: The range of skills required to effectively manage complex injury phenomena in professional collision sport crosses disciplinary boundaries. The evidence presented here points to the effectiveness of a multidisciplinary approach to reducing injury risk. This model will likely be applicable across a range of team and individual sports.

The increased growth and professionalism of women’s football have led to an increased investment in talent identification and development of players from a young age. Governing bodies are now investing in talent identification and development environments such as academies, which are starting for girls as young as 10 years, and which are aligned with similar academy structures that are in place in the boys’ and men’s pathway. Talent identification in women’s football has traditionally been based on viewing players in a trial game or training session environment, whereby the players aim to impress coaches. This approach is not informed by scientific evidence, but rather coaches’ subjective preconceived notion of the ideal player, which, when used in isolation, may result in repetitive misjudgements and limited consistency. However, in recent years there has been an increased amount of research exploring talent identification and development in the women’s game. In this chapter, an overview of existing research is provided, as well as case study examples of talent-identification challenges and recommendations for talent identification and development practices.

This mixed methods study aimed to assess the agreement between coaches ranking of youth rugby league players compared against objective physical performance data and gather coaches' subjective descriptions of their players performance. Five hundred and eight male rugby league players (U16 n=255, U18 n=253) completed a fitness testing battery of anthropometric and physical performance measures. Subsequently, twenty-two rugby (n =11) and strength and conditioning (S&C) coaches (n =11) ranked each player's physical qualities using a 4-point Likert scale (1 - top 25%; 2 - 25-50%; 3 - 50-75%; and 4 - bottom 25%) and described their performance. U16 S&C coaches displayed fair agreement when assessing players body mass (39.3%, κ = 0.20). U18 rugby coaches demonstrated fair agreement for strength and size (42.5%, κ = 0.23) and body mass (48.7%, κ = 0.31) whilst both U18 rugby and S&C coaches showed fair agreement levels for endurance (39.8%, κ = 0.25, 44.3%, κ = 0.29), respectively. Three higher-order themes were identified from coaches' descriptions of players including physical, rugby and attitude characteristics when evaluating performance. Overall, coaches cannot accurately assess players physical performance against fitness testing data. Though, findings suggest coaches adopt a multidimensional approach when evaluating players performance. Practitioners within talent development systems should utilise both objective and subjective assessments when making decisions regarding players performance.

The purpose of this study was to (a) provide comparative isometric midthigh pull (IMTP) force-time characteristics for elite youth soccer players and (b) determine the effect of age and maturation on IMTP force-time characteristics. Elite male youth soccer players (U12 n = 51; U13 n = 54; U14 n = 56; U15 n = 45; U16 n = 39; and U18 n = 48) across 3 maturity offset groups (Pre n = 117; circa n = 84; and Post–peak height velocity n = 92) performed 2 maximal IMTP trials on a portable force platform (1,000 Hz). Absolute and relative values for peak force (PF) and impulse over 100 and 300 ms were analyzed. A full Bayesian regression model was used to provide probable differences similar to that of a frequentist p value. Advanced age and maturation resulted in superior IMTP force-time characteristics. Peak force demonstrated high probabilities of a difference between all consecutive age groups (p > 0.95). For absolute and relative impulse (100 and 300 ms), only 2 consecutive age groups (U14–15's and U16–18's) demonstrated high probabilities of a difference (p > 0.95) with large effects (d = 0.59–0.93). There were high probable differences between all maturity offset groups for PF and impulse with medium to large effects (d = 0.56–3.80). These were also reduced when expressed relative to body mass (relative PF and relative impulse). This study provides comparative IMTP force-time characteristics of elite male youth soccer players. Practitioners should consider individual maturation status when comparing players given the impact this has on force expression.

Fitness testing is common practice within youth athletes. However, the interpretation of fitness data often occurs within chronological annual-age categories, resulting in athletes being (dis)advantaged due to age or maturity discrepancies. Instead, evaluating fitness performance against rolling averages may be more appropriate. This article presents a novel method for analyzing fitness testing data in youth athletes using Z-scores according to rolling averages for both chronological age and maturity status. This analysis technique allows the dual ability to interpret youth fitness performance according to age and maturation, enhancing accuracy of data interpretation for talent identification, development and strength and conditioning programming.

Introduction Understanding the physical demands of rugby union can assist coaches in the preparation of players. Match demands in senior players for domestic competitions (Cahill et al., 2013) and international games (Quarrie et al., 2013) are well established. However, despite adolescent rugby union players playing concurrently at various standards, there is no study that has attempted to compare them. Therefore, the purpose of this study was to compare the physical demands of U18 school vs. academy rugby union match play. Methods A full season of games from the academy (6 games) were analysed and matched by six games from the school standard. Each player wore a microtechnology unit which contained a global positioning system and tri-axial accelerometer in addition to a heart rate monitor. The players were spilt into forwards and backs with only players who participated in the entire game included in the subsequent analysis (Forwards; school [n=25], academy [n=21] and Backs; school [n=25], academy [n=24]). All data were analysed using magnitude based inferences. Institutional ethical approval was granted. Results Forwards: Total distance was almost certainly greater in academy forwards (5461 ± 360 vs. 4881 ± 388 m). Distance walking was unclear between the two groups, but jogging, striding and sprinting was almost certainly, very likely and likely greater in academy forwards in comparison to school forwards. PlayerLoadTM slow was possibly greater for academy forwards whilst heart rate mean and maximum was likely lower for academy forwards. Backs: Total distance was very likely greater in academy backs (5597 ± 383 vs. 5260 ± 441 m). Distance walking and sprinting was unclear. Distance jogging was almost certainly greater in the academy backs and striding was possibly greater. PlayerLoadTM slow was possibly greater in academy backs whilst heart rate mean and maximum was unclear between the two groups. Discussion This study shows that academy rugby union provides forwards and backs with a greater physical demand than school players of the same position. The increase in PlayerLoadTM slow suggests an increase in static exertions for academy players. Future research should look to explore the interaction between physical and technical performances between different standards of adolescent rugby union.

This study aimed to investigate rugby league coaches’ perceptions of physical qualities for current and future performance, while also establishing the training practices of Under-16 and Under-19 players. Twenty-four practitioners (rugby coach, strength and conditioning coach) working within nine Super League clubs completed a questionnaire. The questionnaire required practitioners to rank eleven physical qualities (i.e., strength, power, acceleration, maximum speed, aerobic endurance, change of direction, agility, height, body mass, lean mass and fat mass) by importance for current performance, future performance and career longevity according to playing position (forwards, backs, hookers & halves). Practitioners were asked to provide detail on the frequency and duration of each type of training session completed during a typical week throughout each phase of the season; pre-season, in-season (early), in-season (mid), and in-season (late). Typically, practitioners ranked strength, power and acceleration qualities highest, and endurance and anthropometric qualities lowest. The importance of physical qualities varied according to each playing level and position. Training practices of U16 and U19 players differed during each phase of the season, with U19 players undertaking greater training volumes than U16s players. Overall, the physical qualities coaches perceived as most important were not reflected within their training practices. Rugby league practitioners can use this information as a reference source to design long term athletic development plans, prescribe training and during player development procedures. Moreover, these data can inform and improve training practices while influencing the design of pre-season preparatory phases and in-season periods.

Poster presentation

The purpose of this study was to determine the relationship between matchday wellness status and a technical-tactical performance construct during rugby match-play. One hundred and thirty-three male rugby union players (73 forwards and 60 backs) from five under-18 national squads who participated in the under-18 Six Nations competition completed a subjective wellness questionnaire on each matchday morning. Players subjectively rated each item (sleep quality, fatigue, muscle soreness, stress and mood) on a five-point Likert scale to calculate their daily wellness status (i.e., difference between matchday and baseline perceived wellness). Technical-tactical performance during match-play was quantified by coding individual key performance indicators (e.g., number of carries, number of tackles). Partial least squares correlation analysis (PLSCA) was employed to compute the latent variables of perceived wellness status (X matrix) and technical-tactical performance (Y matrix) for each player observation (n=271). The latent variables are a construct of each variable group, enabling higher dimensional data to be visualised more simply. Linear mixed-effect models were later conducted to assess the relationships between the latent variables. The effect of perceived wellness status on technical-tactical performance was statistically significant in forwards (p=0.042), not statistically significant in backs (p=0.120) and accounted for 4.9% and 1.9% variance in the technical-tactical performance construct, respectively. The findings of this study suggest that perceived wellness status can influence technical-tactical match performance, but the practical significance of these findings should be interpreted with caution given the amount of variance in technical-tactical performance accounted by the models.

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 whole match demands of rugby union are well established, however it is unclear how these vary during specific phases of play within a match. For example, the influence of phases of play (attacking or defending) on the movement and physical demands are yet to be quantified. Therefore, the aim of this study was to investigate the influence of attacking and defensive phases of play on the movement (e.g., running) and physical (e.g., accelerometer activity) demands for forwards and backs. With institutional ethics approved, 50 male academy rugby union players (age: 17.6 ± 0.6 years; stature: 183.0 ± 6.8 cm; body mass 89.4 ± 10.9 kg) from one regional rugby union academy were tracked during match-play using microsensor technology (Optimeye S5, Catapult Innovations, Melbourne, Australia). 260 observations were collected over 2 seasons (12 matches). Differences in maximum sprint velocity (Vmax), relative distance an§d PlayerLoadTM (PL.min-1) were assessed using magnitude based inferences. The mean length of matches were 74.8 ± 3.3 min, whilst the mean amount of time the ball was in play was 27.4 ± 2.9 min. The mean amount of time spent attacking per match was lower than defending (12.7 ± 3.1 vs. 14.7 ± 2.5 min). There were a lower number of attacking phases (27 ± 9) compared to defensive phases (31 ± 10) whilst the mean phase was similar in length (26 ± 17 vs. 26 ± 18 s). The demands were almost certainly greater when defending compared to attacking for forwards; Vmax (3.3 ± 1.8 vs. 4.1 ± 1.5 m.s-1), relative distance (97.9 ± 53.7 vs. 121.8 ± 48.8 m.min-1) and PL.min-1 (10.6 ± 5.3 vs. 12.7 ± 4.6 AU.min-1). When defending, relative distance was very likely greater (101.6 ± 66.4 vs. 121.4 ± 60.9 m.min-1), and Vmax (3.7 ± 2.1 vs. 4.2 ± 1.8 m.s-1) and PL.min-1 (10.7 ± 7.6 vs. 12.4 ± 7.4 AU.min-1) were both likely greater compared to attacking for backs. The movement and physical demands were consistency greater when defending for both positional groups, although a smaller disparity between phases was observed for backs than forwards. This indicates backs have greater movement demands during attacking phases, which was also reflected in the higher Vmax. The greater PL.min-1 for the forwards during defending suggests a greater involvement in tackles and rucks. These data provide practitioners with reference data when replicating match specific phases of play.

Adolescent rugby union players may participate with multiple teams concurrently, with elite players potentially participating with school, club, regional academy, county and national representative teams simultaneously. The accumulated workloads of these athletes may have a substantial impact on their athletic development, health and wellbeing, with excessive workloads related to both illness and injury (Gabbett et al., 2014, Sports Medicine, 7, 989-1003). Therefore, the aim of this study was to quantify the total accumulated weekly workloads of elite schoolboy rugby union players and examine the differences between playing positions. Following institutional ethics approval, twenty elite schoolboy rugby union players (age 17.4 ± 0.7 years) were recruited from an under-18 regional academy and categorised by playing position; forwards (n=10) and backs (n=10). Each participant was allocated a microtechnology unit (10 Hz global positioning system and accelerometer) to be worn during all rugby training to quantify external loads, whilst internal loads (session-rating of perceived exertion (s-RPE)) for rugby, strength, and conditioning/other training modalities were reported daily using an online training questionnaire. Data were collected during the in-season over a 10-week period, with a total of 97 complete weekly observations (5 ± 3 weeks per participant). Mean weekly data were calculated for each participant to control for multiple and uneven observations. Differences between-positions were analysed using Cohen’s d effect sizes (ES) and magnitude-based inferences. There were unclear differences between forwards and backs for total weekly training volumes (301 ± 107 vs. 301 ± 80 min) and s-RPE (1186 ± 380 vs. 1249 ± 365 AU). However, the total weekly external training loads were substantially different between positions (moderate-large) with backs recording likely-very likely greater total distance (13063 ± 3933 vs. 10195 ± 2242 m), low-speed activity (12142 ± 3672 vs. 9694 ± 2215 m), high-speed running (807 ± 387 vs. 482 ± 174 m), very high-speed running (34 ± 51 vs. 5 ± 8 m), peak velocity (8.0 ± 0.3 vs. 7.1 ± 0.4 m·s-1) and PlayerLoadTM (1246 ± 345 vs. 1002 ± 279 AU). Elite schoolboy rugby union players are exposed to high in-season workloads, which are greater than those previously reported in professional adult players during both in-season (Bradley et al., 2015, European Journal of Sport Science, 15, 469-479), and pre-season (Bradley et al., 2014, Journal of Strength and Conditioning Research, 29, 534-544) training periods. The appropriateness of these high workloads needs to be questioned for optimal athletic development and player welfare.

In late specialisation team sports, adolescent athletes may participate with multiple teams concurrently, as they are not contracted to one particular organisation. Therefore, the monitoring of accumulated training loads can be challenging. In a recent study, manual collection of rating of perceived exertion (RPE) was shown to be robust between 5 minutes and 24-hours post-exercise (Christen et al., 2016, International Journal of Sports Physiology and Performance, doi: 10.1123/ijspp.2015-0438). If a self-reported measure of global training load such as session-rating of perceived exertion (sRPE; intensity x time (Foster et al., 2001, Journal of Strength and Conditioning Research, 15, 109-115)) was proved valid, it could provide a simple solution for accurate and reliable training load monitoring. Therefore, the purpose of this study was to assess the level of agreement between the criterion session-rating of perceived exertion (sRPE30min) and practical measure of a remote web-based training load questionnaire 24-hours post-training (sRPE24h) in adolescent athletes. A secondary aim was to assess the level of agreement between weekly summated sRPE24h values (ƩsRPE24h) and a weekly web-based training diary (sRPEweekly) for all field-based training accumulated on a subsequent training week. Following institutional ethics approval, thirty-six male adolescent rugby union players (age 16.7 ± 0.5 years) were recruited from a regional academy. Criterion measures (sRPE30min) were recorded by the principle investigator 30-minutes post a field-based training session. Participants then completed the sRPE24h via a web-based training load questionnaire 24-hours post-training, reporting both session time and intensity. In addition, on a subsequent week, participants completed the sRPE24h daily and then completed the sRPEweekly at the end of the week to recall all training times and intensities over those 7-days. The agreement between sRPE30min and sRPE24h, as well as between ƩsRPE24h and sRPEweekly were assessed using mean percentage bias, typical error of the estimate (TEE), and Pearson correlation coefficients, all with 90% confidence limits. There were trivial biases between sRPE30min and sRPE24h for sRPE (0.3%; -0.9, 1.5), with small TEE (4.3%; 3.6, 5.4) and nearly-perfect correlations (0.99; 0.98, 0.99). There were trivial biases between ƩsRPE24h and sRPEweekly for sRPE (5.9%; -2.1, 14.2), with moderate TEE (28.5%; 23.3, 36.9) and very-large correlations (0.87; 0.78, 0.93). The results of this study show that the use of an online daily questionnaire is a valid and robust measure to remotely quantify training loads in adolescent athletes. However, the weekly training diary was found to have a substantial TEE, which would limit practical application.

Introduction The aim of the study was to assess the differences in external loads and movement demands between training and match-play in adolescent rugby union (RU) players. For mean training and match demands to be similar, athletes would have to be exposed to the equivalent loading of multiple matches within their training week, which may be impractical. An alternative analysis of comparing maximum demands instead of mean demands between conditions may offer a more appropriate comparison. Methods Sixty-one adolescent RU players (mean ± SD; age 17.0 ± 0.7 years) were recruited from four teams representing school and regional academy RU. Players were categorised into four independent groups based on playing standard and position; school forwards (SF; n=15), school backs (SB; n=15), academy forwards (AF; n=16) and academy backs (AB; n=15). Global positioning system and tri-axial accelerometer data were obtained from 61 match files during 6 matches, and 152 training files during 15 training sessions. Maximum data from individual training sessions that elicited the highest value for each variable were used in the comparison to match-play. This approach was used to account for variance in specific training objectives for respective sessions (i.e. collision or running focus), as mean data may dilute the magnitude of exposure of specific loads within the training week. Differences between training and match-play measures were analysed using paired t-tests, Cohen’s d effect sizes and 90% confidence interval. Results Within the SF group, training was significantly greater than matches for total and relative high speed running (HSR [≥61% maximum velocity (Vmax)] and HSR·min-1), total and relative very high speed running (VHSR [>90% Vmax] and VHSR·min-1), and relative peak velocity (%Vmax). Contrastingly, in the SB group, training were significantly less than match play for total distance (TD), %Vmax, VHSR and VHSR·min-1. Within the AF group, match-play was significantly greater than training for relative low speed activity (LSA·min-1 [<61% Vmax]). Finally, within the AB group, training was significantly greater than match-play for relative PlayerLoadTM (PL·min-1) and HSR·min-1. Discussion Players from a higher representative standard (i.e. academy) were exposed to external loads and movement demands during their training week comparative to the demands of match-play. However, within schools, due to positional differences during match-play, training sessions may under-prepare backs and over-prepare forwards. Coaches should ensure that training sessions reflect the standard- and position-specific demands of match-play to adequately prepare players for competition.

The purpose of the present study was to investigate the validity of an isometric mid-thigh pull dynamometer against a criterion measure (i.e., 1,000 Hz force platform) for assessing muscle strength in male youth athletes. Twenty-two male adolescent (age 15.3 ± 0.5 years) rugby league players performed four isometric mid-thigh pull efforts (i.e., two on the dynamometer and two on the force platform) separated by 5 minutes rest in a randomised and counterbalanced order. Mean bias, typical error of estimate (TEE) and Pearson correlation coefficient for peak force (PF) and peak force minus body weight (PFBW) from the force platform were validated against peak force from the dynamometer (DynoPF). When compared to PF and PFBW, mean bias (with 90% Confidence limits) for DynoPF was very large (-32.4 [-34.2 to -30.6] %) and moderate (-10.0 [-12.8 to -7.2] %), respectively. The TEE was moderate for both PF (8.1 [6.3 to 11.2] %) and PFBW (8.9 [7.0 to 12.4]). Correlations between DynoPF and PF (r 0.90 [0.79 to 0.95]) and PFBW (r 0.90 [0.80 to 0.95] were nearly perfect. The isometric mid-thigh pull assessed using a dynamometer underestimated PF and PFBW obtained using a criterion force platform. However, strong correlations between the dynamometer and force platform suggest that a dynamometer provides an appropriate alternative to assess isometric mid-thigh pull strength when a force platform is not available. Therefore, practitioners can use an isometric mid-thigh pull dynamometer to assess strength in the field with youth athletes but should be aware that it underestimates peak force.

Playing rugby union matches causes a number of fatigue responses, including reduced lower body neuromuscular function (NMF) (commonly measured by counter movement jump (CMJ))(1). The time course of this response following match play is well established in professional (2) and academy (3) level rugby union players, who take at least 60 hours for NMF to recover. No data exist for high school level rugby union players, but these players are often exposed to multiple game tournaments and festivals (2 games in 3 days, or 3 games in 5 days) within their competition structures. Aim. The aim of this case study is to document the NMF response to playing three rugby union matches within five days. This will provide useful information to practitioners who must manage fatigue and recovery of youth rugby union players who play multiple games within short time periods.

This study quantified the field-based external training loads of professional rugby league players using global positioning systems technology across a playing season. Eleven professional rugby league players were monitored during all field-based training activities during the 2014 Super League season. Training sessions undertaken in preseason (n = 211 observations), early (n = 194 observations), middle (n = 171 observations) and late (n = 206 observations) phases of the in-season were averaged for each player and used in the analyses. Large reductions in external training loads between preseason and in-season periods were observed. Within season, a decrease in intensity (relative distance, absolute and relative total-HSR) with a limited change in training duration was observed. These data provide a useful reference for coaches working with similar cohorts, while future research should quantify the adequacy of the training loads reported, considering impact on performance and injury.

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: Despite its apparent popularity, participation in the sport of rugby union is accompanied by a significant risk of injury. Concerned parties have recently questioned whether this risk is acceptable within school populations. This is difficult to assess within the South African schools’ population as no recent longitudinal injury studies exist. Objectives: To determine the training habits, rugby-related exposure and injury risk within a population of South African high school first team rugby players. Methods: Training and match exposure in both school and provincial competition examined and the resultant injuries were longitudinally observed for the duration of a South African high school rugby season. Results: Match (79, 95%CI 52-105 injuries/1 000 h) and training (7, 95%CI 3-11 injuries /1000h) injury incidences were demonstrated to be greater than previously reported incidences in similar populations in England and Ireland. Weeks where players were exposed to both school and provincial competition (34, 95%CI 19-49 injuries /1 000 h) had significantly (p<0.05) greater injury incidences than during school competition alone (19, 95%CI 12-26 injuries /1 000 h). Conclusions: The injury risk demonstrated was greater than expected and represents reasons for concern. Possible reasons for the high injury incidence recorded may be the frequency of games played within the season, and the overlap of school and provincial competitions. It should be noted that these results were taken from one school over one season and might not be representative of the incidence of school rugby injuries overall. However, this research demonstrates the need for a multischool longitudinal study within South African schools rugby to determine the overall risk.

Data from global positioning system (GPS) technology are typically presented as the distances covered in specific locomotor categories (e.g., walking, jogging, striding, sprinting). Differences are found when categorisations are made using either pre-defined absolute thresholds or thresholds relative to maximum speed. However, there are two distinct methods of using relative speed thresholds currently employed in the literature, although no study has attempted to compare them. Therefore, the purpose of this study was to compare the differences in data when analysing the same GPS files relative to speed, using either a maximum velocity sprint (Vmax) or a maximum velocity achieved during match-play (Vpeak). Following institutional ethics approval, 99 GPS files were analysed from rugby union match-play and split between forwards (n=59) and backs (n=40). The male participants involved were part of a Regional Academy and had the following characteristics (age: 17.5 ± 0.7 years; stature: 183.6 ± 6.6 cm; body mass: 90.6 ± 10.6 kg). Vmax was established by players performing a maximum 40 m sprint, whilst Vpeak was defined as the maximum velocity achieved during each match. The locomotor categories were defined as walking 0-20%, jogging 20-50%, striding 50-80% and sprinting 80-100% (Duthie et al., 2006) of either Vmax or Vpeak. Data were analysed using magnitude based inferences. The mean Vmax and Vpeak for all players were 8.7 ± 0.6 m.s-1 and 7.2 ± 0.9 m.s-1, respectively. There were almost certain differences in walking (2088 ± 298 vs. 1611 ± 435 m), striding (670 ± 244 vs. 1197 ± 375 m) and sprinting (28 ± 29 vs. 145 ± 73 m) between Vmax and Vpeak, for forwards. There was also a likely difference in jogging (2674 ± 313 vs. 2502 ± 301 m). Very likely differences were found for walking (2414 ± 288 vs. 2177 ± 347 m) and striding (708 ± 159 vs. 927 ± 347 m) for backs. There was also an almost certain difference in sprinting (66 ± 41 vs. 151 ± 49 m) whilst an unclear difference was found for striding (2409 ± 433 vs. 2338 ± 352 m) for backs. The use of relative thresholds using Vpeak seems to overestimate the distance covered in striding and sprinting whilst underestimating walking and jogging. Practitioners should look to use Vmax for relative speed thresholds as Vpeak from match-play is likely to change from match-to-match and consequently misrepresent the movement demands that players are exposed to.

Women's sport has seen substantial growth in recent years, with increased attention to athlete performance and welfare. To support the ongoing professionalisation of women's rugby, performance and wellbeing must be prioritised. This study used a three‐round Delphi‐process to establish performance and wellbeing research priorities for Premiership Women's Rugby (PWR) in England. In Round 1, players and staff provided research priorities, which were grouped into higher‐order categories and themes via content analysis. In Rounds 2 and 3, participants ranked higher‐order categories on a 1–5 Likert scale. Consensus was defined as ≥ 70% agreement. Seventy‐seven participants responded in Round 1 (47 and 43 in Rounds 2 and 3). Player and staff experience of playing or working in PWR was 5.0 (2.0–7.0) and 2.5 (2.0–4.0) years. Following Round 1321 research priorities were provided, 32 higher‐order research priorities and 14 categories were identified, within three themes: performance, wellbeing and injury. Following Round 3, nine research priorities reached consensus within performance ( n  = 1), wellbeing ( n  = 4) and injury ( n  = 4). The highest rated priority was ‘ Investigate the impact of being a dual‐career athlete on wellbeing, and any support mechanisms required ’ (79%). Future research should prioritise studies which are feasible and currently lack a comprehensive evidence‐base. This will enable researchers and governing bodies to address relevant knowledge gaps and inform ongoing performance and player safety initiatives. The research priorities identified in this study, by PWR players and staff, could be investigated to support the development of women's rugby domestically. These findings may also be applicable to other women's sports and leagues globally.

The ability of sporting organisations to identify and develop athletic talent into the sporting superstars of tomorrow has significantly grown and intensified in recent years. This chapter focuses on the talent identification step of this process. Talent identification is defined as ‘recognising participants with the potential at an earlier age to become elite performers in the future’ with multiple talent identification systems employed across the world. Talent identification processes can be informed by coach recommendations, training/competition observations and fitness assessments, although a collaborative approach is recommended. Although coaches believe they can identify talent, a multitude of research examining talent has emerged over the last two decades exploring the phenomenon of talent. This chapter reviews some of this research, with a focus on the physical qualities and methods that may be used as talent identification tools. The chapter then presents numerous problems related to talent identification within young athletes including the performance vs potential debate, early identification = early specialisation, annual age grouping and maturity variability, and unidimensional and cross-sectional approaches to talent identification. Numerous potential solutions to help practitioners optimise their talent identification programmes in young athletes are then discussed.