Need a break? The locomotor and tackle pacing profile and loads of women's rugby league match-play following various between-match turnaround durations.

OBJECTIVES: The study investigated the locomotor and tackle pacing profile and loads of female rugby league players following various between-match turnaround durations. Specifically, the study examined the (1) pacing of locomotor and tackle loads across the time-course of a match and; (2) whole-match and peak locomotor and tackle loads of match-play. METHODS: Microtechnology data were collected from elite female rugby league players (n = 172) representing all National Rugby League Women's teams (n = 6 teams) across two seasons. Players were categorised into backs, adjustables, forwards or interchange players. Data was calculated for the whole-match (m), per minute (m.min-1) and peak (running: m.min-1; acceleration: m.s-2) locomotor and tackle loads (number and efficiency (%)) of match-play. The pacing as well as the locomotor and tackle loads of match-play were examined following short (≤6 days), normal (7 days) or long (≥8 days) turnarounds. RESULTS: The pacing profile of playing positions varied across short, normal and long match turnarounds. Trivial to moderate differences existed in the whole-match, per minute and peak locomotor loads across match turnaround durations (effect size ≤ 1.2). CONCLUSIONS: Following various between-match turnaround durations (i.e., short, normal and long match turnarounds), there were variations in the locomotor and tackle pacing profile and loads whereby, the pacing profile of positional groups was more affected than the load profile. The findings can be used in applied settings to guide the recovery strategies and training plans of female rugby league players to optimise performance and wellbeing across various match turnaround durations.

The propensity of non-concussive and concussive head contacts during elite-level women's rugby league matches: A prospective analysis of over 14,000 tackle events.

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

How fast is fast? Defining velocity zones in women's rugby league.

OBJECTIVES: The study aimed to: 1) apply a data-mining approach to identify velocity zone thresholds for female rugby league players and 2) apply these velocity zones to examine the locomotor demands of match-play. METHODS: Microtechnology data were collected from elite female rugby league players representing all National Rugby League Women's teams (n = 85 players; n = 224 files) over one season. Spectral clustering with a beta smoothing cut-off of 0.1 was applied to each player's instantaneous match-play velocity data for the identification of four zones. To account for outliers within repeated data-points, the velocity zones for each player were calculated as the median. The overarching velocity zones were determined through an incremental search to minimise the root mean square error. RESULTS: Through a data-mining approach, four velocity zones were determined. Rounded to the nearest 0.5 km.h-1 the velocity values across each zone were classified as low (<11.49 km.h-1), moderate (11.50 to 17.49 km.h-1), high (17.50 to 20.99 km.h-1) and very-high (>21.00 km.h-1). Practical application of the zones demonstrated positional group differences in the absolute (effect size (ES): 0.03 to 1.77) and relative (ES: 0.04 to 1.60) locomotor demands of match-play. The back positional group covered greater absolute and relative distances at a very-high velocity than all other positions. CONCLUSIONS: This work informs the velocity zones that could be applied consistently to women's rugby league data within practical (i.e., in the training and monitoring of players) and academic (i.e., as a model for future research to analyse locomotor demands) settings.

Changing gears: data-driven velocity zones to support monitoring and research in men's rugby league.

OBJECTIVES: The study aimed to (1) apply a data-mining approach to league-wide microtechnology data to identify absolute velocity zone thresholds and (2) apply the respective velocity zones to microtechnology data to examine the locomotor demands of elite match-play. METHODS: League-wide microtechnology data were collected from elite male rugby league players representing all National Rugby League (NRL) teams (n = 16 teams, one excluded due to a different microtechnology device; n = 4836 files) over one season. To identify four velocity zones, spectral clustering with a beta smoothing cut-off of 0.1 was applied to each players' instantaneous match-play velocity data. Velocity zones for each player were calculated as the median while the overarching velocity zones were determined through an incremental search to minimise root mean square error. RESULTS: The velocity zones identified through spectral clustering were 0-13.99 km · h-1 (i.e., low velocity), 14.00-20.99 km · h-1 (i.e., moderate velocity), 21.00-24.49 km · h-1 (i.e., high velocity) and >24.50 km · h-1 (i.e., very-high velocity). CONCLUSIONS: The application of spectral clustering (i.e., a data-mining method) to league-wide rugby league microtechnology data yielded insights into the distribution of velocity data, thereby informing the cut-off values which best place similar data points into the same velocity zones. As the identified zones are representative of the intensities of locomotion achieved by elite male rugby league players, it is suggested that when absolute zones are used, the consistent application of the identified zones would facilitate standardisation, longitudinal athlete monitoring as well as comparisons between teams, leagues and published literature.

Injury risk factors and barriers to their mitigation for women playing rugby league: a Delphi study.

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

The Validity of Automated Tackle Detection in Women's Rugby League.

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.

Isolated & Combined Wearable Technology Underestimate the Total Energy Expenditure of Professional Young Rugby League Players; A Doubly Labelled Water Validation Study.

ABSTRACT: Costello, N, Deighton, K, Cummins, C, Whitehead, S, Preston, T, and Jones, B. Isolated & combined wearable technology underestimate the total energy expenditure of professional young rugby league players; a doubly labelled water validation study. J Strength Cond Res 36(12): 3398-3403, 2022-Accurately determining total energy expenditure (TEE) enables the precise manipulation of energy balance within professional collision-based sports. Therefore, this study investigated the ability of isolated or combined wearable technology to determine the TEE of professional young rugby league players across a typical preseason and in-season period. Total energy expenditure was measured via doubly labelled water, the criterion method, across a fourteen-day preseason ( n = 6) and 7-day in-season ( n = 7) period. Practical measures of TEE included SenseWear Pro3 Armbands in isolation and combined with metabolic power derived from microtechnology units. SenseWear Pro3 Armbands significantly under-reported preseason (5.00 [2.52] MJ·d -1 ; p = 0.002) and in-season (2.86 [1.15] MJ·d -1 ; p < 0.001) TEE, demonstrating a large and extremely large standardized mean bias, and a very large and large typical error, respectively. Combining metabolic power with SenseWear Pro3 Armbands almost certainly improved preseason (0.95 [0.15] MJ·d -1 ; Effect size = 0.32 ± 0.04; p < 0.001) and in-season (1.01 [0.15] MJ·d -1 ; ES = 0.88 ± 1.05; p < 0.001) assessment. However, SenseWear Pro3 Armbands combined with metabolic power continued to significantly under-report preseason (4.04 [2.38] MJ·d -1 ; p = 0.004) and in-season (2.18 [0.96] MJ·d -1 ; p = 0.002) expenditure, demonstrating a large and very large standardized mean bias, and a very large and large typical error, respectively. These findings demonstrate the limitations of utilizing isolated or combined wearable technology to accurately determine the TEE of professional collision-based sport athletes across different stages of the season.

A new energetics model for the assessment of the power-duration relationship during over-ground running.

We evaluated the reliability of an over-ground running three-minute all-out test (3MT) and compared this to traditional multiple-visit testing to determine the critical speed (CS) and distance > CS (D´). Using a novel energetics model during the 3MT, critical power (CP) and work > CP (W´) were also evaluated for reliability and compared to the multiple-visit tests. Over-ground running speed was measured using Global Positioning Systems during fixed-speed trials on a 400 m track to exhaustion, at four intensities corresponding to: (i) maximal oxygen uptake (V˙O2max) (Vmax), (ii) 110% V˙O2max(110%Vmax), (iii) Δ70% (i.e. 70% of the difference between gas exchange threshold and Vmax) and (iv) Δ85%. The participants subsequently performed the 3MT across two days to determine its reliability. There were no differences between the multiple-visit testing and the 3MT for CS (P = 0.328) and D´ (P = 0.919); however, CP (P = 0.02) and W´ (P < 0.001) were higher in the 3MT. The reliability of the 3MT was stable (P > 0.05) between trials for all variables, with coefficient of variation ranging from 2.0-8.1%. The current over-ground energetics model can reliably estimate CP and W´ based on GPS speed data during the 3MT, which supports its use for most athletic training and monitoring purposes. The reliability of the over-ground running 3MT for power- and speed-related indices was sufficient to detect typical training adaptations; however, it may overestimate CP (∼ 25 W) and W´ (∼ 7 kJ) compared to multiple-visit tests.

Women's Rugby League: Positional Groups and Peak Locomotor Demands.

The aims of this study were to (a) use a data-based approach to identify positional groups within National Rugby League Women's (NRLW) match-play and (b) quantify the peak locomotor demands of NRLW match-play by positional groups. Microtechnology (Global Navigational Satellite System [GNSS] and integrated inertial sensors; n = 142 files; n = 76 players) and match statistics (n = 238 files; n = 80 players) were collected from all NRLW teams across the 2019 season. Data-based clustering of match statistics was utilized to identify positional clusters through classifying individual playing positions into distinct positional groups. Moving averages (0.5, 1, 2, 3, 5, and 10 min) of peak running and average acceleration/deceleration demands were calculated via microtechnology data for each player per match. All analysis was undertaken in R (R Foundation for Statistical Computing) with positional differences determined via a linear mixed model and effect sizes (ES). Data-based clustering suggested that, when informed by match statistics, individual playing positions can be clustered into one of three positional groups. Based on the clustering of the individual positions, these groups could be broadly defined as backs (fullback, wing, and center), adjustables (halfback, five-eighth, and hooker), and forwards (prop, second-row, and lock). Backs and adjustables demonstrated greater running (backs: ES 0.51-1.00; p < 0.05; adjustables: ES 0.51-0.74, p < 0.05) and average acceleration/deceleration (backs: ES 0.48-0.87; p < 0.05; adjustables: ES 0.60-0.85, p < 0.05) demands than forwards across all durations. Smaller differences (small to trivial) were noted between backs and adjustables across peak running and average acceleration/deceleration demands. Such findings suggest an emerging need to delineate training programs in situations in which individual playing positions train in positional group based settings. Collectively, this work informs the positional groupings that could be applied when examining NRLW data and supports the development of a framework for specifically training female rugby league players for the demands of the NRLW competition.

Correction to: Quantifying the Collision Dose in Rugby League: A Systematic Review, Meta-analysis, and Critical Analysis.

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

The relative contribution of training intensity and duration to daily measures of training load in professional rugby league and union.

This study examined the relative contribution of exercise duration and intensity to team-sport athlete's training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load [sRPE-TL], individualised training impulse, total distance, BodyLoad™; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad™). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60-70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.

Time to embrace the complexity when analysing GPS data? A systematic review of contextual factors on match running in rugby league.

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

Quantifying the Collision Dose in Rugby League: A Systematic Review, Meta-analysis, and Critical Analysis.

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.

Exercise tolerance during flat over-ground intermittent running: modelling the expenditure and reconstitution kinetics of work done above critical power.

PURPOSE: We compared a new locomotor-specific model to track the expenditure and reconstitution of work done above critical power (W´) and balance of W´ (W´BAL) by modelling flat over-ground power during exhaustive intermittent running. METHOD: Nine male participants completed a ramp test, 3-min all-out test and the 30-15 intermittent fitness test (30-15 IFT), and performed a severe-intensity constant work-rate trial (SCWR) at the maximum oxygen uptake velocity (vV̇O2max). Four intermittent trials followed: 60-s at vV̇O2max + 50% Δ1 (Δ1 = vV̇O2max - critical velocity [VCrit]) interspersed by 30-s in light (SL; 40% vV̇O2max), moderate (SM; 90% gas-exchange threshold velocity [VGET]), heavy (SH; VGET + 50% Δ2 [Δ2 = VCrit - VGET]), or severe (SS; vV̇O2max - 50% Δ1) domains. Data from Global Positioning Systems were derived to model over-ground power. The difference between critical and recovery power (DCP), time constant for reconstitution of W´ ([Formula: see text]), time to limit of tolerance (TLIM), and W´BAL from the integral (W´BALint), differential (W´BALdiff), and locomotor-specific (OG-W´BAL) methods were compared. RESULTS: The relationship between [Formula: see text] and DCP was exponential (r2 = 0.52). The [Formula: see text] for SL, SM, and SH trials were 119 ± 32-s, 190 ± 45-s, and 336 ± 77-s, respectively. Actual TLIM in the 30-15 IFT (968 ± 117-s) compared closely to TLIM predicted by OG-W´BAL (929 ± 94-s, P > 0.100) and W´BALdiff (938 ± 84-s, P > 0.100) but not to W´BALint (848 ± 91-s, P = 0.001). CONCLUSION: The OG-W´BAL accurately tracked W´ kinetics during intermittent running to exhaustion on flat surfaces.

"How Am I Going, Coach?"-The Effect of Augmented Feedback During Small-Sided Games on Locomotor, Physiological, and Perceptual Responses.

PURPOSE: To investigate whether providing global positioning system feedback to players between bouts of small-sided games (SSGs) can alter locomotor, physiological, and perceptual responses. METHODS: Using a reverse counterbalanced design, 20 male university rugby players received either feedback or no feedback during "off-side" touch rugby SSGs. Eight 5v5, 6 × 4-minute SSGs were played over 4 d. Teams were assigned to a feedback or no-feedback condition (control) each day, with feedback provided during the 2-min between-bouts rest interval. Locomotor, heart rate, and differential rating of perceived exertion of breathlessness and leg-muscle exertion were measured and analyzed using a linear mixed model. Outcomes were reported using effect sizes (ES) and 90% confidence intervals (CI), and then interpreted via magnitude-based decisions. RESULTS: Very likely trivial to unclear differences at all time points were observed in heart rate and differential rating of perceived exertion measures. Possibly to very likely trivial effects were observed between conditions, including total distance (ES = 0.15; 90 CI, -0.03 to 0.34), high-speed distance (ES = -0.07; 90 CI, -0.27 to 0.13), and maximal sprint speed (ES = 0.11; 90% CI, -0.11 to 0.34). All within-bout comparisons showed very likely to unclear differences, apart from possible increases in low-speed distance in bout 2 (ES = 0.23; 90% CI, 0.01 to 0.46) and maximal sprint speed in bout 4 (ES = 0.21; 90% CI, -0.04 to 0.45). CONCLUSIONS: In this study, verbal feedback did not alter locomotor, physiological, or perceptual responses in rugby players during SSGs. This may be due to contextual factors (eg, opposition) or the type (ie, distance) or low frequency of feedback provided.

Physical Demands of Amateur Domestic and Representative Netball in One Season in New Zealand Assessed Using Heart Rate and Movement Analysis.

King, DA, Cummins, C, Hume, PA, and Clark, TN. Physical demands of amateur domestic and representative netball in one season in New Zealand assessed using heart rate and movement analysis. J Strength Cond Res 34(7): 2062-2070, 2020-The purpose of this descriptive cohort study was to describe physical demands of netball for positions and between playing levels using microtechnology. Data were collected from 34 female netball players across 3 teams at different levels (over 19 years representative [O19], under 19 years representative [U19], and open-age amateur club domestic) for 20 games using heart rate and microtechnology data. Total distance, maximal velocity, PlayerLoad ([PL] accumulated accelerometer-derived load), and individual PL vectors (PL forward [PLF], PL sideward [PLS] and PL vertical [PLV]) were examined. Analysis by playing level and netball position were conducted. The O19 players recorded a higher mean distance (3,365.7 ± 1,875.1 m) per match than U19 (p = 0.0095) players. The O19 players recorded a higher PL (p = 0.0003), PLF (p = 0.004), PLS (p = 0.0039), and PLV (p = 0.0352) than the domestic players. Domestic players recorded a higher maximal velocity than O19 players (p = 0.0003; d = 0.32) throughout the study. Domestic players recorded a higher average maximal heart rate (202.2 ± 28.2 b·min) than O19 (p < 0.0001) and U19 (p = 0.0002) players. Given the high physical demands of netball, individual player- and position-specific training programs are required to develop players for the specific demands of competition while also reducing the impact of excessive physical exertion to facilitate safer engagement within netball. The identification of the differing physical and physiological profiles of individual positional groups throughout match-play highlights the importance of integrating microtechnology into the routine monitoring of intermittent court-based sports, such as netball.

Match and Training Injuries in Women's Rugby Union: A Systematic Review of Published Studies.

BACKGROUND: There is a paucity of studies reporting on women's injuries in rugby union. OBJECTIVE: The aim of this systematic review was to describe the injury epidemiology for women's rugby-15s and rugby-7s match and training environments. METHODS: Systematic searches of PubMed, SPORTDiscus, Web of Science Core Collection, Scopus, CINAHL(EBSCO) and ScienceDirect databases using keywords. RESULTS: Ten articles addressing the incidence of injury in women's rugby union players were retrieved and included. The pooled incidence of injuries in women's rugby-15s was 19.6 (95% CI 17.7-21.7) per 1000 match-hours (h). Injuries in women's rugby-15s varied from 3.6 (95% CI 2.5-5.3) per 1000 playing-h (including training and games) to 37.5 (95% CI 26.5-48.5) per 1000 match-h. Women's rugby-7s had a pooled injury incidence of 62.5 (95% CI 54.7-70.4) per 1000 player-h and the injury incidence varied from 46.3 (95% CI 38.7-55.4) per 1000 match-h to 95.4 (95% CI 79.9-113.9) per 1000 match-h. The tackle was the most commonly reported injury cause with the ball carrier recording more injuries at the collegiate [5.5 (95% CI 4.5-6.8) vs. 3.5 (95% CI 2.7-4.6) per 1000 player-game-h; χ2(1) = 6.7; p = 0.0095], and Women's Rugby World Cup (WRWC) [2006: 14.5 (95% CI 8.9-23.7) vs. 10.9 (95% CI 6.2-19.2) per 1000 match-h; χ2(1) = 0.6; p = 0.4497; 2010: 11.8 (95% CI 6.9-20.4) vs. 1.8 (95% CI 0.5-7.3) per 1000 match-h; χ2(1) = 8.1; p = 0.0045] levels of participation. Concussions and sprains/strains were the most commonly reported injuries at the collegiate level of participation. DISCUSSION: Women's rugby-7s had a higher un-pooled injury incidence than women's rugby-15s players based on rugby-specific surveys and hospitalisation data. The incidence of injury in women's rugby-15s and rugby-7s was lower than men's professional rugby-15s and rugby-7s competitions but similar to male youth rugby-15s players. Differences in reporting methodologies limited comparison of results. CONCLUSION: Women's rugby-7s resulted in a higher injury incidence than women's rugby-15s. The head/face was the most commonly reported injury site. The tackle was the most common cause of injury in both rugby-7s and rugby-15s at all levels. Future studies are warranted on injuries in women's rugby-15s and rugby-7s. PROSPERO REGISTRATION NUMBER: CRD42018109054 (last updated on 17 January 2019).

Traumatic brain injuries in New Zealand: National Insurance (Accident Compensation Corporation) claims from 2012 to 2016.

AIM: To provide epidemiological data and related costs to the national health insurance scheme for traumatic brain injury (TBI) in New Zealand. METHOD: A retrospective analytical review utilising detailed descriptive minor and moderate-to-severe epidemiological TBI data obtained from the Accident Compensation Corporation (ACC) for 2012-2016. Injuries were analysed by three levels of increasing severity: moderate, moderate-to-serious (MSC) and severe claims categories. RESULTS: Over the January 2012 to December 2016 period there were 97,955 claims for TBI costing ACC $1,450,643,667 [equivalent to £$743,417,120]. Falls accounted for nearly half (41.7%, 8262), and over a quarter (39.9%; $67,626,000 [£34,662,176]) of the moderate claims for TBI. Motor vehicle accidents recorded the highest percentage (36.5%), total costs ($610,978,229 [£313,170,000]) and highest mean cost per-moderate claim per-year ($47,372 ±â€¯$2401 [£24,282 ±â€¯£1231]) for MSC TBI claims. This was similar for severe claims where motor vehicles accidents accounted for 56% of the total serious claims, 65.1% of the costs with a mean cost per-serious claim of $64,913 ±â€¯4331 [£32,759 ±â€¯£2186] per-year. CONCLUSION: There were 97,955 TBI injury claims lodged over the duration of the study with 36% (n = 35,304) classified as MSC. The incidence of total TBI in New Zealand was 432 per 100,000 population, and 155 per 100,000 for MSC TBI claims. Despite the growing number of studies reporting on the effects of sports-related TBI, there is a paucity of studies reporting on the longitudinal effects of TBI in falls, assaults and motor vehicle accidents. Further research is warranted into the assessment and management of intimate partner violence and child abuse victims for TBI's.

Modelling the relationships between volume, intensity and injury-risk in professional rugby league players.

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 (mmin-1), high speed distance ([m]>20kmh-1), very-high speed distance ([m]>25kmh-1), acceleration and deceleration efforts (count) and PlayerLoad (Arbitrary Unit: AU). Cumulative two-, three- and four-weekly loads; Acute:Chronic Workload Ratio (ACWR); Mean-Standard Deviation Workload Ratio (MSWR) and strain values were calculated and divided into three equally-sized bins (low, moderate and high). Generalised Estimating Equations analysed relationships between workload variables and injury probability in the subsequent week. RESULTS: Injury-risk increased alongside increases in the ACWR for duration, total distance and PlayerLoad. Conversely, injury-risk decreased (Area Under Curve: 0.569-0.585) with increases in the four-weekly duration, total distance, accelerations, decelerations and PlayerLoad. For relative distance, high four-weekly workloads (high: >60mmin-1) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 mmin-1) were negatively associated. CONCLUSIONS: A range of external workload metrics and summary statistics demonstrate either positive or negative associations with injury-risk status. Such findings provide the framework for the development of decision-support systems in which external workload metrics (e.g. total or high speed distance) can be uniquely and routinely monitored across a range of summary statistics (i.e. cumulative weekly loads and ACWR) in order to optimise player performance and welfare.