Head Acceleration Events During Tackle, Ball-Carry, and Ruck Events in Professional Southern Hemisphere Men's Rugby Union Matches: A Study Using Instrumented Mouthguards.

OBJECTIVES: Describe head acceleration events (HAEs) experienced by professional male rugby union players during tackle, ball-carry, and ruck events using instrumented mouthguards (iMGs). DESIGN: Prospective observational cohort. METHODS: Players competing in the 2023 Currie Cup (141 players) and Super Rugby (66 players) seasons wore iMGs. The iMG-recorded peak linear acceleration (PLA) and peak angular acceleration (PAA) were used as in vivo HAE approximations and linked to contact-event data captured using video analysis. Using the maximum PLA and PAA per contact event (HAEmax), ordinal mixed-effects regression models estimated the probabilities of HAEmax magnitude ranges occurring, while accounting for the multilevel data structure. RESULTS: As HAEmax magnitude increased the probability of occurrence decreased. The probability of a HAEmax ≥15g was 0.461 (0.435-0.488) (approximately 1 in every 2) and ≥45g was 0.031 (0.025-0.037) (1 in every 32) during ball carries. The probability of a HAEmax >15g was 0.381 (0.360-0.404) (1 in every 3) and >45g 0.019 (0.015-0.023) (1 in every 53) during tackles. The probability of higher magnitude HAEmax occurring was greatest during ball carries, followed by tackles, defensive rucks and attacking rucks, with some ruck types having similar profiles to tackles and ball carries. No clear differences between positions were observed. CONCLUSION: Higher magnitude HAEmax were relatively infrequent in professional men's rugby union players. Contact events appear different, but no differences were found between positions. The occurrence of HAEmax was associated with roles players performed within contact events, not their actual playing position. Defending rucks may warrant greater consideration in injury prevention research.

Are rugby league players involved in more tackles than normal, prior to an injury sustained during a tackle event?

Rugby league has a relatively high injury risk, with the tackle having the greatest injury propensity. The number of tackles players engage in, prior to injurious tackles may influence injury risk, which has yet to be investigated. Therefore, this study investigated if rugby league players are involved in more tackles (as either tackler or ball carrier) (i) in the 10 minutes, or (ii) 1-min periods prior to an injurious tackle-event, (iii) differences for ball carriers vs. tacklers, and (iv) forwards vs. backs. Video analysis was utilised to quantify the number and rate of tackles in the 10-min periods prior to 61 tackle-related injuries. One thousand two hundred and eighty 10-min periods where players were not injured, were used as matched-controls. Generalized mixed linear models were used to analyse mean total and rate for tackles. Injured players were involved in significantly fewer tackles during the 10-min period, yet significantly more tackles during the final minute prior to the injurious tackle-event, compared to non-injured players. There were no differences between ball carriers vs. tacklers during the 10-min period. Both injured position groups were involved in significantly more tackles in the final minute. Additional match data sources are needed to further inform injury preventive strategies of tackle events.

Autonomic Nervous System Indices of Player Readiness During Elite-Level Rugby Union Game-Week Microcycles.

ABSTRACT: Grainger, A, Heffernan, S, Waldron, M, and Sawczuk, T. Autonomic nervous system indices of player readiness during elite-level rugby union game-week microcycles. J Strength Cond Res 36(11): 3173-3178, 2022-Elite-level rugby union (RU) is a high-intensity contact sport that involves large training and match volumes across a season, which can lead to postmatch fatigue. Autonomic nervous system (ANS) regulation and perceived fatigue have been suggested to relate to measures of training and match load in RU. However, there have been no studies to assess specific ANS variables in elite RU during in-season microcycles. Player readiness during game-week microcycles was measured via heart rate variability (HRV) indices, direct current potential and self-reported well-being among 13, elite, male RU players. To enable comparison, data collection days were categorized in relation to their proximity to match day, ranging from match day minus 3 (MD - 3), to match day plus 3 (MD + 3). Differences between match days were evaluated using general linear models and Cohen's d effect sizes. There were significant differences between MD and MD + 1 for ANS indices (RMSSD p = 0.04, d = -0.66, 95% CI 0.11-1.20; the standard deviation of NN intervals p = 0.04, d = -0.66, 95% CI 0.12-1.20; total power p = 0.05, d = -0.65, 95% CI 0.11-1.20) and wellness measures (readiness p = 0.18; d = -2.33, 95% CI, 1.54-3.13; energy p = 0.02; d = -2.24, 95% CI 1.44-3.03; soreness p = 0.00; d = -2.42, 95% CI 1.63-3.23). Match day plus 3 effects were significantly greater than MD + 1 in several ANS responses, with wellness recovering at a slower time-course than ANS responses. Measures of HRV are dysregulated postmatch, but based on their rapid recovery thereafter, using HRV to assess readiness of elite-level players in RU across a weekly microcycle could be limited and requires further investigation.

Influence of Perceptions of Sleep on Well-Being in Youth Athletes.

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

The Frequency and Intensity of Representative and Nonrepresentative Late Adolescent Team-Sport Athletes' Training Schedules.

ABSTRACT: Scantlebury, S, Till, K, Sawczuk, T, Dalton-Barron, N, Phibbs, P, and Jones, B. The frequency and intensity of representative and nonrepresentative late adolescent team-sport athletes' training schedules. J Strength Cond Res 35(12): 3400-3406, 2021-This study aimed to identify and compare the training frequency and intensity (via session rating of perceived exertion load [sRPE load]) of representative and nonrepresentative late adolescent athletes. Thirty-six team sport athletes completed a web-based questionnaire daily over an 8-month period, reporting their training/match activities from the previous day. Athletes were categorized as representative (academy/county/international) or nonrepresentative (club/school) depending on the highest level of their sport they participated. Mean weekly frequencies and sRPE load of different training/match activities were quantified for each athlete across 5 school terms. Mann-Whitney U tests established the significance of differences and effect sizes between playing standards for mean weekly frequencies and mean sRPE load. Within-athlete weekly sRPE loads were highly variable for both playing standards; however, representative level athletes participated in significantly more activity outside of school compared with nonrepresentative athletes during November-December (effect size; 0.43-club technical training; 0.36-club matches), January-February (effect size; 0.78-club technical training; 0.75-club matches), and February-March (effect size; 0.63-club technical training; 0.44-club matches). Therefore, club and school coaches must ensure that all elements of representative athletes training schedules are coordinated and flexible to promote positive adaptions to training such as skill and physical development and prevent maladaptive responses such as overuse injury and nonfunctional overreaching. A cooperative and malleable training schedule between club/school coaches and the athlete will allow the athlete to perform on multiple fronts while also being able to meet the demands of additional stressors such as schoolwork.

A multi-nation examination of the fatigue and recovery time course during the inaugural Under-18 Six Nations rugby union competition.

The purpose of this study was to investigate the neuromuscular and perceptual fatigue responses of elite rugby players during the inaugural Under-18 (U18) Six Nations Festival. One hundred and thirty-three male players from five national squads (73 forwards, 60 backs) were examined during the competition. Each national squad was involved in three matches separated by 96 h each. Over the competition, players completed a daily questionnaire to monitor perceived well-being (WB) and performed daily countermovement jumps (CMJ) to assess neuromuscular function (NMF). Reductions in WB were substantial 24 h after the first and second match in forwards (d=0.77±0.21, p<0.0001; d=0.84±0.22, p< 0.001) and backs (d=0.89±0.22, p <0.0001; d=0.58±0.23, p<0.0001) but reached complete recovery in time for the subsequent match. Reductions in CMJ height were substantial 24 h after the first and second match for forwards (d=0.31±0.15, p=0.001; d=0.25±0.17, p=0.0205) and backs (d=0.40±0.17, p=0.0001; d=0.28±0.17, p=0.0062) and recovered at 48 h after match-play. Average WB and CMJ height attained complete recovery within matchday cycles in the investigated international competition. The findings of this study can be useful for practitioners and governing bodies involved with fixture scheduling and training prescription during competitive periods.

The peak duration-specific locomotor demands and concurrent collision frequencies of European Super League rugby.

Understanding the most demanding passages of European Super League competition can optimise training prescription. We established positional and match half differences in peak relative distances (m·min-1) across durations, and the number of collisions, high-speed- and very-high-speed-distance completed in the peak 10 min period. Moving-averages (10 s, 30 s, 1 min, 5 min, 10 min) of instantaneous speed (m·s-1) were calculated from 25 professional rugby league players during 25 matches via microtechnology. Maximal m·min-1 was taken for each duration for each half. Concurrently, collisions (n), high-speed- (5 to 7 m·s-1; m) and very-high-speed-distance (> 7 m·s-1; m) were coded during each peak 10 min. Mixed-effects models determined differences between positions and halves. Aside from peak 10 s, trivial differences were observed in peak m·min-1 between positions or halves across durations. During peak 10 min periods, adjustables, full- and outside-backs ran more at high-speed and very-high-speed whilst middle- and edge-forwards completed more collisions. Peak m·min-1 is similar between positional groups across a range of durations and are maintained between halves of the match. Practitioners should consider that whilst the overall peak locomotor "intensity" is similar, how they achieve this differs between positions with forwards also exposed to additional collision bouts.

Collision activity during training increases total energy expenditure measured via doubly labelled water.

PURPOSE: Collision sports are characterised by frequent high-intensity collisions that induce substantial muscle damage, potentially increasing the energetic cost of recovery. Therefore, this study investigated the energetic cost of collision-based activity for the first time across any sport. METHODS: Using a randomised crossover design, six professional young male rugby league players completed two different 5-day pre-season training microcycles. Players completed either a collision (COLL; 20 competitive one-on-one collisions) or non-collision (nCOLL; matched for kinematic demands, excluding collisions) training session on the first day of each microcycle, exactly 7 days apart. All remaining training sessions were matched and did not involve any collision-based activity. Total energy expenditure was measured using doubly labelled water, the literature gold standard. RESULTS: Collisions resulted in a very likely higher (4.96 ± 0.97 MJ; ES = 0.30 ± 0.07; p = 0.0021) total energy expenditure across the 5-day COLL training microcycle (95.07 ± 16.66 MJ) compared with the nCOLL training microcycle (90.34 ± 16.97 MJ). The COLL training session also resulted in a very likely higher (200 ± 102 AU; ES = 1.43 ± 0.74; p = 0.007) session rating of perceived exertion and a very likely greater (- 14.6 ± 3.3%; ES = - 1.60 ± 0.51; p = 0.002) decrease in wellbeing 24 h later. CONCLUSIONS: A single collision training session considerably increased total energy expenditure. This may explain the large energy expenditures of collision-sport athletes, which appear to exceed kinematic training and match demands. These findings suggest fuelling professional collision-sport athletes appropriately for the "muscle damage caused" alongside the kinematic "work required".

Relationships Between Training Load, Sleep Duration, and Daily Well-Being and Recovery Measures in Youth Athletes.

PURPOSE: To assess the relationships between training load, sleep duration, and 3 daily well-being, recovery, and fatigue measures in youth athletes. METHODS: Fifty-two youth athletes completed 3 maximal countermovement jumps (CMJs), a daily well-being questionnaire (DWB), the perceived recovery status scale (PRS), and provided details on their previous day's training loads (training) and self-reported sleep duration (sleep) on 4 weekdays over a 7-week period. Partial correlations, linear mixed models, and magnitude-based inferences were used to assess the relationships between the predictor variables (training and sleep) and the dependent variables (CMJ, DWB, and PRS). RESULTS: There was no relationship between CMJ and training (r = -.09; ±.06) or sleep (r = .01; ±.06). The DWB was correlated with sleep (r = .28; ±.05, small), but not training (r = -.05; ±.06). The PRS was correlated with training (r = -.23; ±.05, small), but not sleep (r = .12; ±.06). The DWB was sensitive to low sleep (d = -0.33; ±0.11) relative to moderate; PRS was sensitive to high (d = -0.36; ±0.11) and low (d = 0.29; ±0.17) training relative to moderate. CONCLUSIONS: The PRS is a simple tool to monitor the training response, but DWB may provide a greater understanding of the athlete's overall well-being. The CMJ was not associated with the training or sleep response in this population.

The influence of training load, exposure to match play and sleep duration on daily wellbeing measures in youth athletes.

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

A multidimensional approach to identifying the physical qualities of male English regional academy rugby union players; considerations of position, chronological age, relative age and maturation.

Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n = 2,381 observations) from nine Rugby Football Union regional academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d = 0.65-0.73) and maturation (d = -0.77 to -0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development.Highlights Practitioners should record and incorporate position, chronological age, relative age and maturation into the physical evaluation of elite academy rugby union players.The regression equations provided within this study offer highly generalisable comparative values that are specific to a players chronological and biological development.Through the use of enhanced player evaluation practitioners will be able to make more informed decisions surrounding talent identification and athlete development.

The anthropometric and physical qualities of women's rugby league Super League and international players; identifying differences in playing position and level.

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

Development of an expected possession value model to analyse team attacking performances in rugby league.

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.

Prediction of Upper Respiratory Illness Using Salivary Immunoglobulin A in Youth Athletes.

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