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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. [ABSTRACT FROM AUTHOR]

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, r2 =.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. [ABSTRACT FROM AUTHOR]

To establish the criterion‐assessed energy and fluid requirements of female netball players, 13 adult players from a senior Netball Super League squad were assessed over 14 days in a cross‐sectional design, representing a two‐ and one‐match microcycle, respectively. Total energy expenditure (TEE) and water turnover (WT) were measured by doubly labeled water. Resting and activity energy expenditure were measured by indirect calorimetry and Actiheart, respectively. Mean 14‐day TEE was 13.46 ± 1.20 MJ day−1 (95% CI, 12.63–14.39 MJ day−1). Resting energy expenditure was 6.53 ± 0.60 MJ day−1 (95% CI, 6.17–6.89 MJ day−1). Physical activity level was 2.07 ± 0.19 arbitrary units (AU) (95% CI, 1.95–2.18 AU). Mean WT was 4.1 ± 0.9 L day−1 (95% CI, 3.6–4.7 L day−1). Match days led to significantly greater TEE than training (+2.85 ± 0.70 MJ day−1; 95% CI, +1.00– +4.70 MJ day−1; p = 0.002) and rest (+4.85 ± 0.70 MJ day−1; 95% CI, +3.13–+6.56 MJ day−1; p < 0.001) days. Matches led to significantly greater energy expenditure (+1.85 ± 1.27 MJ; 95% CI, +0.95–+2.76 MJ day−1; p = 0.001) than court‐based training sessions. There was no significant difference in TEE (+0.03 ± 0.35 MJ day−1; 95% CI, −0.74–+0.80 MJ day−1; p = 0.936) across weeks. Calibrated Actiheart 5 monitors underestimated TEE (−1.92 ± 1.21 MJ day−1). Energy and fluid turnover were greatest on match days, followed by training and rest days, with no difference across weeks. This study provides criterion‐assessed energy and fluid requirements to inform dietary guidance for female netball players. Highlights: The energy and fluid requirements of female netball players were greatest on match days, followed by training and rest days, with no difference across a one‐ or two‐match weekly microcycle. Therefore, players are encouraged to periodise their intake on a daily basis, aligning with the demands of their training and match schedule.Female netball players have in‐season energy requirements representative of a vigorously active lifestyle (physical activity level: >2.0 arbitrary units). Water turnover varied widely amongst participants (range: 62 mL fat‐free mass [FFM] day−1), while total energy requirements were more homogenous (range: 0.05 MJ FFM day−1).Calibrated Actiheart 5 monitors underestimated female netball player total energy expenditure in comparison to the doubly labelled water criterion (range: −0.38–3.84 MJ day−1). Further research is now required to investigate the validity of Actiheart for measuring team sport athlete energy expenditure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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: The aim of this review was 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) include 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 4125 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing 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 (PCs), 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 (ie, 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 visualize 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, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process. [ABSTRACT FROM AUTHOR]

Purpose: Feedback can enhance acute physical performance. However, its effects on physical adaptation have received little attention. Therefore, the purpose of this study was to determine the effect of feedback during a 4-wk training program on jump, sprint, and strength adaptations. Methods: A total of 28 semiprofessional male rugby union players were strength-matched into 2 groups (feedback and nonfeedback). During the 4-wk training program, the Feedback group received immediate, objective feedback on (1) mean concentric velocity during resistance training repetitions, (2) distance feedback for standing broad jumps, and (3) time for sprints. The Nonfeedback group was not provided additional information. Across the 4-wk mesocycle, subjects completed 3 strength and conditioning sessions per week. Countermovement jump, standing long jump, 10- and 20-m sprint, and 3-repetition-maximum barbell back squat and bench press were measured before and after the training intervention. Magnitude-based inferences assessed meaningful changes within and between groups. Results: The Feedback group showed small to moderate improvements in outcome measures, whereas the Nonfeedback group demonstrated trivial to small improvements. Improvements in countermovement-jump relative peak power (effect size ±90% confidence limits: 0.34 ± 0.42), 10-m (0.20 ± 0.35) and 20-m sprints (0.40 ± 0.21), and 3-repetition-maximum back squats (0.23 ± 0.17) were possibly to likely greater for the Feedback condition than for Nonfeedback. Conclusions: Providing augmented feedback during strength and conditioning routines can enhance training adaptations compared with athletes who do not receive feedback. Consequently, practitioners should consider providing kinematic outputs, displacement, or sprint time at the completion of each repetition as athletes train. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the frequencies and timings of rugby union match-play phases (ie, attacking, defending, ball in play [BIP], and ball out of play [BOP]) and then compare the physical characteristics of attacking, defending, and BOP between forwards and backs. Methods: Data were analyzed from 59 male rugby union academy players (259 observations). Each player wore a microtechnology device (OptimEye S5; Catapult, Melbourne, Australia) with video footage analyzed for phase timings and frequencies. Dependent variables were analyzed using a linear mixed-effects model and assessed with magnitude-based inferences and Cohen d effect sizes (ES). Results: Attack, defense, BIP, and BOP times were 12.7 (3.1), 14.7 (2.5), 27.4 (2.9), and 47.4 (4.1) min, respectively. Mean attack (26 [17] s), defense (26 [18] s), and BIP (33 [24] s) phases were shorter than BOP phases (59 [33] s). The relative distance in attacking phases was similar (112.2 [48.4] vs 114.6 [52.3] m·min−1, ES = 0.00 ± 0.23) between forwards and backs but greater in forwards (114.5 [52.7] vs 109.0 [54.8] m·min−1, ES = 0.32 ± 0.23) during defense and greater in backs during BOP (ES = −0.66 ± 0.23). Conclusions: Total time in attack, defense, and therefore BIP was less than BOP. Relative distance was greater in forwards during defense, whereas it was greater in backs during BOP and similar between positions during attack. Players should be exposed to training intensities from in-play phases (ie, attack and defense) rather than whole-match data and practice technical skills during these intensities. [ABSTRACT FROM AUTHOR]

Purpose: To assess indirect markers of intestinal endothelial cell damage and permeability in academy rugby players in response to rugby training at the beginning and end of preseason. Methods: Blood and urinary measures (intestinal fatty acid binding protein and lactulose:rhamnose) as measures of gastrointestinal cell damage and permeability were taken at rest and after a standardised collision-based rugby training session in 19 elite male academy rugby players (age: 20 ± 1 years, backs: 89.3 ± 8.4 kg; forwards: 111.8 ± 7.6 kg) at the start of preseason. A subsample (n = 5) repeated the protocol after six weeks of preseason training. Gastrointestinal symptoms (GIS; range of thirteen standard symptoms), aerobic capacity (30–15 intermittent fitness test), and strength (1 repetition maximum) were also measured. Results: Following the rugby training session at the start of preseason, there was an increase (median; interquartile range) in intestinal fatty acid binding protein (2140; 1260–2730 to 3245; 1985–5143 pg/ml, p = 0.003) and lactulose:rhamnose (0.31; 0.26–0.34 to 0.97; 0.82–1.07, p < 0.001). After six weeks of preseason training players physical qualities improved, and the same trends in blood and urinary measures were observed within the subsample. Overall, the frequency and severity of GIS were low and not correlated to markers of endothelial damage. Conclusions: Rugby training resulted in increased intestinal endothelial cell damage and permeability compared to rest. A similar magnitude of effect was observed after six weeks of pre-season training. This was not related to the experience of GIS. [ABSTRACT FROM AUTHOR]

Purpose: To identify which combination metrics of external and internal training load (TL) capture similar or unique information for individual professional players during skills training in rugby union using principal-component (PC) analysis. Methods: TL data were collected from 21 male professional rugby union players across a competitive season. This included PlayerLoad™, total distance, and individualized high-speed distance (>61% maximal velocity; all external TL) obtained from a microtechnology device (OptimEye X4; Catapult Innovations, Melbourne, Australia) that was worn by each player and the session rating of perceived exertion (RPE) (internal TL). PC analysis was conducted on each individual to extract the underlying combinations of the 4 TL measures that best describe the total information (variance) provided by the measures. TL measures with PC loadings (PCL) above 0.7 were deemed to possess well-defined relationships with the extracted PC. Results: The findings show that from the 4 TL measures, the majority of an individual's TL information (first PC: 55–70%) during skills training can be explained by session RPE (PCL: 0.72–0.95), total distance (PCL: 0.86–0.98), or PlayerLoad (PCL: 0.71–0.98). High-speed distance was the only variable to relate to the second PC (PCL: 0.72–1.00), which captured additional TL information (+19–28%). Conclusions: Findings suggest that practitioners could quantify the TL of rugby union skills training with one of PlayerLoad, total distance, or session RPE plus high-speed distance while limiting omitted information of the TL imposed during professional rugby union skills training. [ABSTRACT FROM AUTHOR]

Aben, HGJ, Hills, SP, Cooke, CB, Davis, D, Jones, B, and Russell, M. Profiling the post-match recovery response in male rugby: A systematic review. J Strength Cond Res 36(7): 2050–2067, 2022—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. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate changes in performance of a 6-s cycle-ergometer test (CET) and countermovement jump (CMJ) during a 6-wk training block in professional rugby union players. Methods: Twelve young professional rugby union players performed 2 CETs and CMJs on the 1st and 4th mornings of every week before the commencement of daily training during a 6-wk training block. Standardized changes in the highest score of 2 CET and CMJ efforts were assessed using linear mixed modeling and magnitude-based inferences. Results: After increases in training load during wk 3 to 5, moderate decreases in CMJ peak and mean power and small decreases in flight time were observed during wk 5 and 6 that were very likely to almost certainly greater than the smallest worthwhile change (SWC), suggesting neuromuscular fatigue. However, only small decreases, possibly greater than the SWC, were observed in CET peak power. Changes in CMJ peak and mean power were moderately greater than in CET peak power during this period, while the difference between flight time and CET peak power was small. Conclusion: The greater weekly changes in CMJ metrics in comparison with CET may indicate differences in the capacities of these tests to measure training-induced lower-body neuromuscular fatigue in rugby union players. However, future research is needed to ascertain the specific modes of training that elicit changes in CMJ and CET to determine the efficacy of each test for monitoring neuromuscular function in rugby union players. [ABSTRACT FROM AUTHOR]

Good nutrition is essential for the physical development of adolescent athletes, however data on dietary intakes of adolescent rugby players are lacking. This study quantified and evaluated dietary intake in 87 elite male English academy rugby league (RL) and rugby union (RU) players by age (under 16 (U16) and under 19 (U19) years old) and code (RL and RU). Relationships of intakes with body mass and composition (sum of 8 skinfolds) were also investigated. Using 4-day diet and physical activity diaries, dietary intake was compared with adolescent sports nutrition recommendations and the UK national food guide. Dietary intake did not differ by code, whereas U19s consumed greater energy (3366 ± 658 vs. 2995 ± 774 kcal·day-1), protein (207 ± 49 vs. 150 ± 53 g⋅day-1) and fluid (4221 ± 1323 vs. 3137 ± 1015 ml⋅day-1) than U16s. U19s consumed a better quality diet than U16s (greater intakes of fruit and vegetables; 4.4 ± 1.9 vs. 2.8 ± 1.5 servings·day-1; nondairy proteins; 3.9 ± 1.1 vs. 2.9 ± 1.1 servings⋅day-1) and less fats and sugars (2.0 ± 1. vs. 3.6 ± 2.1 servings⋅day-1). Protein intake vs. body mass was moderate (r = .46, p < .001), and other relationships were weak. The findings of this study suggest adolescent rugby players consume adequate dietary intakes in relation to current guidelines for energy, macronutrient and fluid intake. Players should improve the quality of their diet by replacing intakes from the fats and sugars food group with healthier choices, while maintaining current energy, and macronutrient intakes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study established the between-days reliability and sensitivity of a countermovement jump (CMJ), plyometric push-up, well-being questionnaire, and whole-blood creatine kinase concentration ([CK]) in elite male youth rugby union players. The study also established the between-days reliability of 1, 2, or 3 CMJs and plyometric-push-up attempts. Twenty-five players completed tests on 2 occasions separated by 5 d (of rest). Between-days typical error, coefficient of variation (CV), and smallest worthwhile change (SWC) were calculated for the well-being questionnaire, [CK], and CMJ and plyometric-push-up metrics (peak/mean power, peak/mean force, height, flight time, and flight-time to contraction-time ratio) for 1 maximal effort or taking the highest score from 2 or 3 maximal efforts. The results suggest that CMJ mean power (2 or 3 attempts), peak force, or mean force and plyometric-push-up mean force (from 2 or 3 attempts) should be used for assessing lower- and upper-body neuromuscular function, respectively, due to both their acceptable reliability (CV < 5%) and good sensitivity (CV < SWC). The well-being questionnaire and [CK] demonstrated between-days CVs >5% (7.1% and 26.1%, respectively) and poor sensitivity (CV > SWC). The findings from this study can be used when interpreting fatigue markers to make an objective decision about a player's readiness to train or compete. [ABSTRACT FROM AUTHOR]

Background: Short-sprint (≤ 20 m) performance is an important quality for success in the football codes. Therefore, developing an evidence base for understanding training methods to enhance short-sprint performance is key for practitioners. However, current systematic reviews are limited by (1) a lack of focus on football code athletes, (2) a lack of consideration of all training modalities and (3) a failure to account for the normal training practices undertaken by intervention groups within their analysis. Therefore, this review aimed to (1) conduct a systematic review of the scientific literature evaluating training interventions upon short-sprint performance within football code athletes, (2) undertake a meta-analysis to assess the magnitude of change of sport-sprint performance following training interventions and (3) identify how moderator variables affect the training response. Methods: A systematic search of electronic databases was conducted. A random-effects meta-analysis was performed to establish standardised mean difference with 95% confidence intervals. This identified the magnitude and direction of the individual training effects of intervention subgroups (primary, secondary, combined-specific, tertiary and combined training methods) on short-sprint performance while considering moderator variables (i.e., football code, sex, age, playing standard, phase of season). Results: 121 studies met the inclusion criteria, totalling 3419 athletes. Significant improvements (small-large) were found between pre- and post-training in short-sprint performance for the combined, secondary, tertiary and combined-specific training methods. No significant effect was found for primary or sport only training. No individual mode was found to be the most effective. Between-subgroup analysis identified that football code, age, playing standard and phase of season all moderated the overall magnitude of training effects. Conclusions: This review provides the largest systematic review and meta-analysis of short-sprint performance development methods and the only one to assess football code athletes exclusively. Practitioners can apply combined, secondary and tertiary training methods to improve short-sprint performance within football code athletes. The application of sport only and primary methods does not appear to improve short-sprint performance. Regardless of the population characteristics, short-sprint performance can be enhanced by increasing either or both the magnitude and the orientation of force an athlete can generate in the sprinting action. Trial Registration: OSF registration https://osf.io/kshqn/. [ABSTRACT FROM AUTHOR]

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 dura¬tion (Sduration), sleep quality (Squality), and sleep index (SindeX; Sduration x 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 Station, 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. [ABSTRACT FROM AUTHOR]

Dalton-Barron, NE, McLaren, SJ, Black, CJ, Gray, M, Jones, B, and Roe, G. Identifying contextual influences on training load: an example in professional rugby union. J Strength Cond Res 35(2): 503-511, 2021-We aimed to investigate the contextual factors influencing training load (TL), as determined by session rating of perceived exertion (sRPE-TL), accumulated within a match-to-match microcycle in rugby union players. Session rating of perceived exertion-TL data were collected daily from 35 professional rugby union players from the same team in the English Championship over the course of an in-season period. Players were split by positional groups (backs and forwards) and sRPE-TL data were categorized as: field-based on-feet sRPE-TL (sRPEField-TL), gym-based sRPE-TL (sRPEGym-TL), and the total summation of both (sRPETotal-TL). Three 2-level linear mixed models were built for each dependent variable in each positional group, with magnitude-based inferences applied. Long between-match recovery cycles (≥7 days) resulted in very likely to almost certainly small to moderate increases in sRPE-TL for all modalities and positions (fixed effect [mean range] = 28.5%-42.0%), apart from sRPEField-TL for forwards. For backs, there was a very likely small decrease in sRPEField-TL as the season progressed (-16.7% per trimester). Losing the last league match was associated with very likely and almost certainly small decreases in sRPETotal-TL and sRPEGym-TL for backs (-20.7% and -36.4%, respectively). Losing the last match in any competition resulted in a very likely small increase in sRPEField-TL (21.2%) and a possibly small decrease sRPEGym-TL (-18.5%) for backs-with a likely smaller sRPEGym-TL for forwards (-33.4%). The strength of the upcoming opposition had no effect on sRPE-TL. Our findings highlight some of the multifactorial contextual factors that must be considered when planning and evaluating training microcycles. [ABSTRACT FROM AUTHOR]

Practitioners prescribe numerous training modes to develop the varied physical qualities that professional rugby league players must express during competition. The aim of this 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. These data were collected from 17 players across 716 individual sessions (mean [SD] sessions: 42 [13] per player) which were categorized by mode (conditioning [CON], small-sided games, skills, and sprint training). Derived from global positioning systems (5 Hz with 15 Hz interpolation), the distances covered within arbitrary speed and metabolic power thresholds were determined to represent the external load. Session rating of perceived exertion and individualized training impulse represented the internal load. All data were made relative to the session duration. The differences in time-relative load methods between each mode were assessed using magnitude-based inferences. Small-sided games and CON 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 the internal load. The metabolic power method complements speed-based quantification of the external load, particularly during small-sided 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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Understanding the locomotor characteristics of competition can help rugby league (RL) coaches optimise training prescription. To date, no research exists on the locomotor characteristics of women's RL. The aim was to compare whole match and peak locomotor characteristics of women's RL competition at international (RL World Cup [WRLWC]) and domestic level (Super League [WSL]). Microtechnology data were collected from 58 players from 3-WSL clubs and 1-WRLWC team. Participants were classified into forwards (n = 30) and backs (n = 28). Partial least squares correlation analysis established which variables were important to discriminate between the level of competition (international vs. domestic) and positional group (forwards vs. backs). Linear mixed-effects models estimated the differences between standards of competition and positional group for those variables. International forwards were most likely exposed to greater peak 1-min average acceleration (standardised mean difference = 1.23 [0.42 to 2.04]) and peak 3-min average acceleration (1.13 [0.41 to 1.85]) than domestic forwards. International backs likely completed greater peak 1-min average acceleration (0.83 [0.08 to 1.58]) than domestic backs and possibly greater high-speed-distances (0.45 [−0.17 to 1.07]). Findings highlight the need for positional specific training across levels to prepare representative players for the increased match characteristics of international competition. [ABSTRACT FROM AUTHOR]

Understanding differences in locomotor and collision characteristics between phases of play can help rugby league coaches develop training prescription. There are no data currently available describing these differences at the elite international level. The aim of our study was to determine the differences in average speed (m∙min−1), high-speed running (>5.5 m∙s−1) per minute and collision frequencies per minute (n∙min−1) between attack and defence during the 2017 Rugby League World Cup (RLWC). Methods: Microtechnology data were collected from 24 male professional rugby league players from the same international squad across six matches of the RLWC. Data were then subject to exclusion criteria and stratified into forwards (n = 9) and backs (n = 7) before being analysed with linear mixed-effects models. Results: When comparing attack with defence, forwards and backs had substantially slower average speeds (effect size [ES]; ±90% confidence limits: −2.31; ±0.31 and −1.17; ±0.25) and substantially greater high-speed distance per minute (1.61; ±0.59 and 4.41; ±1.19). Forwards completed substantially more collisions per minute when defending (2.75; ±0.32) whilst backs completed substantially more when attacking (0.63; ±0.70). There was greater within- and between-player variability for collision frequency (coefficient of variation [CV] range; 25–28%) and high-speed distance (18–33%) per minute when compared to average speed (6–12%). Conclusions: There are distinct differences in locomotor and collision characteristics when attacking and defending during international rugby league match-play, yet the variability of high-speed running and collisions per minute is large. These data may be useful to plan or evaluate training practices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Rugby union (RU) is a skill-collision team sport played at junior and senior levels worldwide. Within England, age-grade rugby governs the participation and talent development of youth players. The RU player development pathway has recently been questioned, regarding player performance and well-being, which sport science research can address. The purpose of this review was to summarise and critically appraise the literature in relation to the applied sport science of male age-grade RU players in England focussing upon (1) match-play characteristics, (2) training exposures, (3) physical qualities, (4) fatigue and recovery, (5) nutrition, (6) psychological challenges and development, and (7) injury. Current research evidence suggests that age, playing level and position influence the match-play characteristics of age-grade RU. Training exposures of players are described as 'organised chaos' due to the multiple environments and stakeholders involved in coordinating training schedules. Fatigue is apparent up to 72 h post match-play. Well-developed physical qualities are important for player development and injury risk reduction. The nutritional requirements are high due to the energetic costs of collisions. Concerns around the psychological characteristics have also been identified (e.g. perfectionism). Injury risk is an important consideration with prevention strategies available. This review highlights the important multi-disciplinary aspects of sport science for developing age-grade RU players for continued participation and player development. The review describes where some current practices may not be optimal, provides a framework to assist practitioners to effectively prepare age-grade players for the holistic demands of youth RU and considers areas for future research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to the unique energetic demands of professional young collision sport athletes, accurate assessment of energy balance is required. Consequently, this is the first study to simultaneously investigate the energy intake, expenditure and balance of professional young rugby league players across a pre-season period. The total energy expenditure of six professional young male rugby league players was measured via doubly labelled water over a fourteen-day assessment period. Resting metabolic rate was measured and physical activity level calculated. Dietary intake was reported via Snap-N-Send over a non-consecutive ten-day assessment period, alongside changes in fasted body mass and hydration status. Accordingly, energy balance was inferred. The mean (standard deviation) difference between total energy intake (16.73 (1.32) MJ.day−1) and total energy expenditure (18.36 (3.05) MJ.day−1) measured over the non-consecutive ten-day period was unclear (−1.63 (1.73) MJ.day−1; ES = 0.91 ± 1.28; p = 0.221). This corresponded in a most likely trivial decrease in body mass (−0.65 (0.78) kg; ES = 0.04 ± 0.03; p = 0.097). Resting metabolic rate and physical activity level across the fourteen-day pre-season period was 11.20 (2.16) MJ.day−1 and 1.7 (0.2), respectively. For the first time, this study utilises gold standard assessment techniques to elucidate the distinctly large energy expenditures of professional young rugby league players across a pre-season period, emphasising a requirement for equally large energy intakes to achieve targeted body mass and composition adaptations. Accordingly, it is imperative that practitioners regularly assess the energy balance of professional young collision-sport athletes to ensure their unique energetic requirements are achieved. [ABSTRACT FROM AUTHOR]

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 predetermined 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. [ABSTRACT FROM AUTHOR]

Background: Quantifying the peak match demands within the football codes is useful for the appropriate prescription of external training load. Wearable microtechnology devices can be used to identify the peak match demands, although various methodologies exist at present.Objectives: This systematic review aimed to identify the methodologies and microtechnology-derived variables used to determine the peak match demands, and to summarise current data on the peak match demands in the football codes.Methods: A systematic search of electronic databases was performed from earliest record to May 2018; keywords relating to microtechnology, peak match demands and football codes were used.Results: Twenty-seven studies met the eligibility criteria. Six football codes were reported: rugby league (n = 7), rugby union (n = 5), rugby sevens (n = 4), soccer (n = 6), Australian Football (n = 2) and Gaelic Football (n = 3). Three methodologies were identified: moving averages, segmental and ‘ball in play’. The moving averages is the most commonly used (63%) and superior method, identifying higher peak demands than other methods. The most commonly used variables were relative distance covered (63%) and external load in specified speed zones (57%).Conclusion: This systematic review has identified moving averages to be the most appropriate method for identifying the peak match demands in the football codes. Practitioners and researchers should choose the most relevant duration-specific period and microtechnology-derived variable for their specific needs. The code specific peak match demands revealed can be used for the prescription of conditioning drills and training intensity. [ABSTRACT FROM AUTHOR]

Scantlebury, S, Till, K, Sawczuk, T, Phibbs, P, and Jones, B. Validity of retrospective session rating of perceived exertion to quantify training load in youth athletes. J Strength Cond Res 32(7): 1975–1980, 2018—Youth athletes frequently participate in multiple sports or for multiple teams within the same sport. To optimize player development and minimize undesirable training outcomes (e.g., overuse injuries), practitioners must be cognizant of an athlete's training load within and outside their practice. This study aimed to establish the validity of a 24-hour (s-RPE24) and 72-hour (s-RPE72) recall of session rating of perceived exertion (s-RPE) against the criterion measure of s-RPE collected 30 minutes' post training (s-RPE30). Thirty-eight adolescent athletes provided a s-RPE30 following the first field based training session of the week. Approximately 24 hours later subjects were asked to recall the intensity and duration of the previous days training. The following week subjects once again provided an s-RPE30 measure after training before recalling the intensity and duration of the session approximately 72 hours later. A nearly perfect correlation (0.98 [0.97–0.99]) was found between s-RPE30 and s-RPE24, with a small typical error of estimate (TEE; 8.3% [6.9–10.5]) and trivial mean bias (−1.1% [−2.8 to 0.6]). Despite a large correlation between s-RPE30 and s-RPE72 (0.73 [0.59–0.82]) and a trivial mean bias (−0.2% [−6.8 to 6.8]), there was a large TEE (35.3% [29.6–43.9]). s-RPE24 provides a valid measure of retrospectively quantifying s-RPE; however, the large error associated with s-RPE72 suggests that it is not a suitable method for monitoring training load in youth athletes. [ABSTRACT FROM AUTHOR]

The aim of this study was to quantify the mean weekly training load (TL) of elite adolescent rugby union players participating in multiple teams and examine the differences between playing positions. Twenty elite male adolescent rugby union players (17.4 ± 0.7 years) were recruited from a regional academy and categorized by playing position: forwards (n = 10) and backs (n = 10). Global positioning system and accelerometer microtechnology was used to quantify external TL, and session rating of perceived exertion (sRPE) was used to quantify internal TL during all sessions throughout a 10-week in-season period. A total of 97 complete observations (5 ± 3 weeks per participant) were analyzed, and differences between positions were assessed using Cohen's d effect sizes (ES) and magnitude-based inferences. Mean weekly sRPE was 1,217 6 364 arbitrary units (AU) (between-subject coefficient of variation [CV] = 30%), with a total distance (TD) of 11,629 6 3,445 m (CV = 30%), and PlayerLoad (PL) of 1,124 ± 330 AU (CV = 29%). Within-subject CV ranged between 5 and 78% for sRPE, 24 and 82% for TD, and 19 and 84% for PL. Mean TD (13,063 ± 3,933 vs. 10,195 ± 2,242 m) and PL (1,246 ± 345 vs. 1,002 ± 279 AU) were both likely greater for backs compared with forwards (moderate ES); however, differences in sRPE were unclear (small ES). Although mean internal TLs and volumes were low, external TLs were higher than previously reported during preseason and in-season periods in senior professional players. Additionally, the large between-subject and within-subject variation in weekly TL suggests that players participate in a chaotic training system. [ABSTRACT FROM AUTHOR]

The aims of this study were to determine the variability of weekly match and training loads in adolescent rugby union players across a competitive season, and to investigate the effect of match frequency on load distribution across different activities. Internal match and training load data (i.e. session-rating of perceived exertion (sRPE)) were collected daily from 20 players from a regional academy across a 14-week season. Data were analysed using a mixed-effects linear model, and variability was reported as a coefficient of variation (CV). Differences between 0-, 1-, 2-, and 3-match weeks were assessed using Cohen's d effect sizes and magnitude-based inferences. Mean weekly total match and training sRPE load was 1425 ± 545 arbitrary units (AU), with a between-player CV of 10 ± 6% and within-player CV of 37 ± 3%. Mean week-to-week change in total sRPE load was 497 ± 423 AU (35%), and 40% of weekly observations were outside the suggested acute: chronic workload ratio 'safe zone'. Total weekly sRPE loads increased substantially with match frequency (1210 ± 571, 1511 ± 489, and 1692 ± 517 AU, for 0-, 1-, and 2-match weeks, respectively), except for 3-match weeks (1520 ± 442 AU). Weekly match and training loads were highly variable for adolescent rugby players during the competitive season, and match frequency has a substantial effect on the distribution of loads. Therefore, match and training loads should be coordinated, monitored, and managed on an individual basis to protect players from negative training consequences, and to promote long-term athlete development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the physical and movement demands between training and matchplay in schoolboy and academy adolescent rugby union (RU) players. Sixty-one adolescent male RU players (mean ± SD; age 17.0 ± 0.7 years) were recruited from four teams representing school and regional academy standards. Players were categorised into four groups based on playing standard and position: schoolboy forwards (n = 15), schoolboy backs (n = 15), academy forwards (n = 16) and academy backs (n = 15). Global positioning system and accelerometry measures were obtained from training and matchplay to assess within-group differences between conditions. Maximum data were analysed from 79 match files across 8 matches (1.3 ± 0.5 matches per participant) and 152 training files across 15 training sessions (2.5 ± 0.5 training sessions per participant). Schoolboy forwards were underprepared for low-intensity activities experienced during match-play, with schoolboy backs underprepared for all movement demands. Academy forwards were exposed to similar physical demands in training to matches, with academy backs similar to or exceeding values for all measured variables. Schoolboy players were underprepared for many key, position-specific aspects of match-play, which could place them at greater risk of injury and hinder performance, unlike academy players who were better prepared. [ABSTRACT FROM AUTHOR]

The most frequently occurring contact events in rugby union are the tackle and ruck. The ability repeatedly to engage and win the tackle and ruck has been associated with team success. To win the tackle and ruck, players have to perform specific techniques. These techniques have not been studied at the highest level of rugby union. Therefore, the purpose of this study was to identify technical determinants of tackle and ruck performance at the highest level of rugby union. A total of 4479 tackle and 2914 ruck events were coded for the Six Nations and Championship competitions. Relative risk ratio (RR), the ratio of the probability of an outcome occurring when a characteristic was observed (versus the non-observed characteristic), was determined using multinomial logistic regression. Executing front-on tackles reduced the likelihood of offloads and tackle breaks in both competitions (Six Nations RR 3.0 Behind tackle, 95% confidence interval [95% CI]: 1.9–4.6, effect size [ES] = large,P < 0.001); Championship RR 2.9 Jersey tackle, 95% CI: 1.3–6.4, ES = moderate,P = 0.01). Fending during contact increased the chances of offloading and breaking the tackle in both competitions (Six Nations RR 4.5 Strong, 95% CI: 2.2–9.2, ES = large,P = P < 0.001; Championship RR 5.1 Moderate, 95% CI: 3.5–7.4, ES = large,P < 0.001). For the ruck, actively placing the ball increased the probability of maintaining possession (Six Nations RR 2.2, 95% CI: 1.1–4.3, ES = moderate,P = 0.03); Championship RR 4.0, 95% CI: 1.3–11.8, ES = large,P = 0.01). The techniques identified in this study should be incorporated and emphasised during training to prepare players for competition. Furthermore, these techniques need to be added to coaching manuals for the tackle and ruck. [ABSTRACT FROM PUBLISHER]

The purpose of this study was to investigate the validity of global positioning system (GPS) and micro-electrical- mechanical-system (MEMS) data generated in real time through a dedicated receiver. Postsession data acted as the criterion as it is used to plan the volume and intensity of future training and is downloaded directly from the device. Twenty-five professional rugby league players completed 2 training sessions wearing an MEMS device (Catapult S5, firmware version: 5.27). During sessions, real-time data were collected through the manufacturer receiver and dedicated software (Openfield v1.14), which was positioned outdoors at the same location for every session. The GPS variables included total-, low- (0-3 m⋅ s-1), moderate- (3.1-5 m⋅ s-1), high- (5.1 -7 m⋅ s-1), and very high-speed (>7.1 m⋅ s-1) distances. Micro-electrical-mechanical-system data included total session PlayerLoad. When compared to postsession data, mean bias for total-, low-, moderate-, high-, and very high-speed distances were all trivial, with the typical error of the estimate (TEE) small, small, trivial, trivial and small, respectively. Pearson correlation coefficients for total-, low-, moderate-, high- and very-high-speed distances were nearly perfect, nearly perfect, perfect, perfect, and nearly perfect, respectively. For PlayerLoad, mean bias was trivial, whereas TEE was moderate and correlation nearly perfect. Practitioners should be confident that when interpreting real-time speed-derived metrics, the data generated in real-time are comparable with those downloaded directly from the device postsession. However, practitioners should refrain from interpreting accelerometer-derived data (i.e., PlayerLoad) or acknowledge the moderate error associated with this real-time measure. [ABSTRACT FROM AUTHOR]

This study aims 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: Player-Load™, 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. [ABSTRACT FROM AUTHOR]

Previous research in academy rugby league players has evaluated the development of physical qualities according to chronological age. However, no study has considered the training age, defined as the number of formalized years of strength and conditioning training, of these players. Therefore, the purpose of this study was to present and compare the annual changes in physical qualities of academy rugby league players according to training age. Sixty-one academy players undertook a fitness testing assessment, including anthropometric (height, body mass, sum of 4 skinfolds) and physical (10 and 20 m sprint, 10m momentum, vertical jump, Yo-Yo intermittent recovery test level 1 [Yo-Yo IRTL1], one-repetition maximum [1RM] squat, bench press and prone row) measures at the start of preseason on 2 consecutive annual occasions. Players were categorized into one of 3 training age groups (i.e., 0, 1, or 2 years) and were analyzed using magnitude-based inferences. Almost certain, very likely or likely annual improvements were identified for body mass, 10m momentum, Yo-Yo IRTL1, vertical jump, and all strength measures for the 3 training age groups. When training age groups were compared, 1 year showed possibly or likely lower strength increases than 0 years training age. However, the 2 years training age group demonstrated possibly or likely increased strength changes compared with 1 year. These findings suggest that training age is an important consideration for strength and conditioning practitioners but it is likely to be a combination of chronological age, biological maturity, and training experience alongside dynamic interplayer variability that influences the physical development of academy rugby league players. [ABSTRACT FROM AUTHOR]

Repeated physical contact in rugby union is thought to contribute to post-match fatigue; however, no evidence exists on the effect of contact activity during field-based training on fatigue responses. Therefore, the purpose of this study was to examine the effect of contact during training on fatigue markers in rugby union players. Twenty academy rugby union players participated in the cross-over study. The magnitude of change in upper- and lower-body neuromuscular function (NMF), whole blood creatine kinase concentration [CK] and perception of well-being was assessed pre-training (baseline), immediately and 24 h post-training following contact and non-contact, field-based training. Training load was measured using mean heart rate, session rating of perceived exertion (sRPE) and microtechnology (Catapult Optimeye S5). The inclusion of contact during field-based trainingalmost certainlyincreased mean heart rate (9.7; ±3.9%) and sRPE (42; ±29.2%) and resulted inlikelyandvery likelygreater decreases in upper-body NMF (−7.3; ±4.7% versus 2.7; ±5.9%) and perception of well-being (−8.0; ±4.8% versus  −3.4; ±2.2%) 24 h post-training, respectively, andalmost certainlygreater elevations in [CK] (88.2; ±40.7% versus 3.7; ±8%). The exclusion of contact from field-based trainingalmost certainlyincreased running intensity (19.8; ±5%) and distance (27.5; ±5.3%), resulting inpossiblygreater decreases in lower-body NMF (−5.6; ±5.2% versus 2.3; ±2.4%). Practitioners should be aware of the different demands and fatigue responses of contact and non-contact, field-based training and can use this information to appropriately schedule such training in the weekly microcycle. [ABSTRACT FROM PUBLISHER]

The primary aim of the study was to assess the level of agreement between the criterion session rating of perceived exertion (sRPE30min) and a practical measure of a self-reported Web-based training load questionnaire 24 hours after training (sRPE24h) in adolescent athletes. The secondary aim was to assess the agreement between weekly summated sRPE24h values (ΣsRPE24h) and a weekly Web-based training diary (sRPEweekly) for all field-based training accumulated on a subsequent training week. Thirty-six male adolescent rugby players (age, 16.7 ± 0.5 years) were recruited from a regional academy. Measures of sRPE30min were recorded 30 minutes after a typical field-based training session. Participants then completed the sRPE24h via a Web-based training load questionnaire 24 hours after training, reporting both session duration and intensity. In addition, on a subsequent week, participants completed the sRPE24h daily and then completed the sRPEweekly at the end of the week, using the same Web-based platform, to recall all field-based training session durations and intensities over those 7 days. Biases were trivial between sRPE30min and sRPE24h for sRPE (0.3% [-0.9 to 1.5]), with nearly perfect correlations (0.99 [0.98-0.99]) and small typical error of the estimate (TEE; 4.3% [3.6-5.4]). Biases were trivial between ΣsRPE24h and sRPEweekly for sRPE (5.9% [ - 2.1 to 14.2]), with very large correlations (0.87 [0.78-0.93]) and moderate TEE of 28.5% [23.3-36.9]. The results of this study show that sRPE24h is a valid and robust method to quantify training loads in adolescent athletes. However, sRPEweekly was found to have a substantial TEE (28.5%), limiting practical application [ABSTRACT FROM AUTHOR]

This study evaluated the anthropometric, speed and endurance characteristics of English academy soccer players, comparing players who obtained a 'professional' contract at 18 years old with those that did not ('academy'); 443 male academy soccer players from an English professional club undertook anthropometric (height and body mass), speed (10 and 20m sprint) and endurance (Yo-Yo intermittent endurance test level 2 [Yo-Yo]) assessments between 2005 and 2012. Significant improvements with age were found for speed and endurance at each annual age group up until U18 age category. Significant differences were only observed between 'professional' and 'academy' players for 10m (p = 0.003, η2 = 0.01) and 20m (p = 0.001, η2 = 0.01) speed at U16 and U18 and Yo-Yo performance (p = 0.001, η2 = 0.12) at U18 age category. Practitioners should use speed and endurance assessments for monitoring physical development of players rather than for talent identification purposes. [ABSTRACT FROM AUTHOR]

The article discusses the adoption of multivariate approaches in measuring and monitoring the training load (TL) of professional sports team players. Topics discussed include the use of a ratio in ascertaining a single measure across two moving-average periods, the limitation of developing the validity of a TL measure, and the factors affecting the outcomes of training. The adoption of the partial least squares correlation analysis (PLSCA) is emphasized.