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Training that is efficient and effective is of great importance to an athlete. One method of improving efficiency is by incorporating supersets into resistance training routines. However, the structuring of supersets is still unexplored. Therefore, the purpose of this study was to assess the effects of agonist-antagonist (A-A), alternate peripheral (A-P), and similar biomechanical (SB) superset configurations on rate of perceived exertion (RPE), kinetic and kinematic changes during the bench press. 10 subjects performed resistance training protocols in a randomized-crossover design, with magnitude-based inferences assessing changes/differences within and between protocols. Changes in RPE were very likely and almost certainly greater in the A-P and SB protocols when compared with the A-A, while all superset protocols had very likely to almost certain reductions in mean velocity and power from baseline. Reductions in mean velocity and power were almost certainly greater in the SB protocol, with differences between the A-A and A-P protocols being unclear. Decreases in peak force were likely and almost certain in the A-A and SB protocols respectively, with changes in A-P being unclear. Differences between these protocols showed likely greater decreases in SB peak forces when compared to A-A, with all other superset comparisons being unclear. This study demonstrates the importance of exercise selection when incorporating supersets into a training routine. It is suggested that the practitioner uses A-A supersets when aiming to improve training efficiency and minimize reductions in kinetic and kinematic output of the agonist musculature while completing the barbell bench press.

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

The influence of resistance training experience on the between-day reliability of commonly used strength measures in male youth athletes. J Strength Cond Res 31(7): 2005-2010, 2017-The purpose of this study was to determine the between-day reliability of commonly used strength measures in male youth athletes while considering resistance training experience. Data were collected on 25 male athletes over 2 testing sessions, with 72 hours rest between, for the 3 repetition maximum (3RM) front squat, chin-up, and bench press. Subjects were initially categorized by resistance training experience (inexperienced; 6-12 months, experienced; >2 years). The assessment of the between-day reliability (coefficient of variation [CV%]) showed that the front squat (experienced: 2.90%; inexperienced: 1.90%), chin-up (experienced: 1.70%; inexperienced: 1.90%), and bench press (experienced: 4.50%; inexperienced: 2.40%) were all reliable measures of strength in both groups. Comparison between groups for the error of measurement for each exercise showed trivial differences. When both groups were combined, the CV% for the front squat, bench press, and chin-up were 2.50, 1.80, and 3.70%, respectively. This study provides scientists and practitioners with the between-day reliability reference data to determine real and practical changes for strength in male youth athletes with different resistance training experience. Furthermore, this study demonstrates that 3RM front squat, chin-up, and bench press are reliable exercises to quantify strength in male youth athletes.

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

Training that is efficient and effective is of great importance to an athlete. One method of improving efficiency is by incorporating supersets into resistance training routines. However, the structuring of supersets is still unexplored. Therefore, the purpose of this study was to assess the effects of agonist-antagonist (A-A), alternate peripheral (A-P), and similar biomechanical (SB) superset configurations on rate of perceived exertion (RPE), kinetic and kinematic changes during the bench press. 10 subjects performed resistance training protocols in a randomized-crossover design, with magnitude-based inferences assessing changes/differences within and between protocols. Changes in RPE were very likely and almost certainly greater in the A-P and SB protocols when compared with the A-A, while all superset protocols had very likely to almost certain reductions in mean velocity and power from baseline. Reductions in mean velocity and power were almost certainly greater in the SB protocol, with differences between the A-A and A-P protocols being unclear. Decreases in peak force were likely and almost certain in the A-A and SB protocols respectively, with changes in A-P being unclear. Differences between these protocols showed likely greater decreases in SB peak forces when compared to A-A, with all other superset comparisons being unclear. This study demonstrates the importance of exercise selection when incorporating supersets into a training routine. It is suggested that the practitioner uses A-A supersets when aiming to improve training efficiency and minimize reductions in kinetic and kinematic output of the agonist musculature while completing the barbell bench press.

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

Background: In England, rugby union is a popular sport and is widely played within schools. Despite the large participation numbers, the movement and physical demands of the sport and how they progress by age have not been explored. Method: Ninety-six male rugby union players wore microtechnology devices during six rugby union matches within the education pathway to investigate the movement and physical demands of match-play. To quantify the positional differences and progression by age, data were obtained for participants at the under 16 (U16) (n=31 participants), under 18 (U18) (n=34 participants) and university (n=31 participants) levels. Players were further divided in forwards and backs. Data were analysed using magnitude-based inferences. Results: For the movement demands, U16 total distance and ‘striding’ was likely higher for forwards than backs, whereas at U18, unclear differences were observed and from university players the inverse was observed (very likely). In all age groups sprint distance was likely to very likely greater for backs than forwards. Forwards had greater physical demands than backs at all age groups. For consecutive age groups, U16 had a likely higher relative distance than U18, and U18 had a likely lower relative distance than university players. Physical demands were similar across age groups for forwards, and greater for backs at older age groups. Conclusion: The movement and physical demands of rugby union players participating in schools (U16 and U18), may not be as expected, however, the findings from university players show a similar pattern to the senior game.

Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.

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

This study investigated the changes in measures of neuromuscular fatigue and physical performance in young professional rugby union players during a preseason training period. Fourteen young (age: 19.1 ± 1.2 years) professional rugby union players participated in the study. Changes in measures of lower body neuromuscular fatigue (countermovement jump (CMJ) mean power, mean force, flight-time) and physical performance (lower body strength, 40 m sprint velocity) were assessed during an 11-week preseason period using magnitude-based inferences. CMJ mean power was likely to very likely decreased during week 2 (-8.1 ± 5.5% to -12.5 ± 6.8%), and likely to almost certainly decreased from weeks 5 to 11 (-10 ± 4.3% to -14.7 ± 6.9%), while CMJ flight-time demonstrated likely to very likely decreases during weeks 2, and weeks 4-6 (-2.41 ± 1% to -3.3 ± 1.3%), and weeks 9-10 (-1.9 ± 0.9% to -2.2 ± 1.5%). Despite this, possible improvements in lower body strength (5.8 ± 2.7%) and very likely improvements in 40 m velocity (5.5 ± 3.6%) were made. Relationships between changes in CMJ metrics and lower body strength or 40 m sprint velocity were trivial or small (<0.22). Increases in lower body strength and 40 m velocity occurred over the course of an 11-week preseason despite the presence of neuromuscular fatigue (as measured by CMJ). The findings of this study question the usefulness of CMJ for monitoring fatigue in the context of strength and sprint velocity development. Future research is needed to ascertain the consequences of negative changes in CMJ in the context of rugby-specific activities to determine the usefulness of this test as a measure of fatigue in this population.

Fluid and sodium balance is important for performance and health; however, limited data in rugby union players exist. The purpose of the study was to evaluate body mass (BM) change (dehydration) and blood[Na] change during exercise. Data were collected from 10 premiership rugby union players, over a 4-week period. Observations included match play (23 subject observations), field (45 subject observations), and gym (33 subject observations) training sessions. Arrival urine samples were analyzed for osmolality, and samples during exercise were analyzed for [Na]. Body mass and blood[Na] were determined pre- and postexercise. Sweat[Na] was analyzed from sweat patches worn during exercise, and fluid intake was measured during exercise. Calculations of fluid and Na loss were made. Mean arrival urine osmolality was 423 ± 157 mOsm·kg, suggesting players were adequately hydrated. After match play, field, and gym training, BM loss was 1.0 ± 0.7, 0.3 ± 0.6, and 0.1 ± 0.6%, respectively. Fluid loss was significantly greater during match play (1.404 ± 0.977 kg) than field (1.008 ± 0.447 kg, p = 0.021) and gym training (0.639 ± 0.536 kg, p < 0.001). Fluid intake was 0.955 ± 0.562, 1.224 ± 0.601, and 0.987 ± 0.503 kg during match play, field, and gym training, respectively. On 43% of observations, players were hyponatremic when BM increased, 57% when BM was maintained, and 35% when there was a BM loss of 0.1-0.9%. Blood[Na] was the representative of normonatremia when BM loss was >1.0%. The findings demonstrate that rugby union players are adequately hydrated on arrival, fluid intake is excessive compared with fluid loss, and some players are at risk of developing hyponatremia.

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

Objective: To determine the association between local environmental factors with child weight status in a longitudinal study, using a semi-parametric, group-based method, while also considering social and early life factors. Methods: Standardized, directly measured BMI from 4-10 y of age, and group-based trajectory modeling (PROC TRAJ) were used to estimate developmental trajectories of weight change in a Québec birth cohort (n = 1,566). Associations between the weight trajectories and living location, social cohesion, disorder, and material and social deprivation were estimated after controlling for social and early life factors. Results: Four weight trajectory groups were estimated: low-increasing (9.7%); low-medium, accelerating (36.2%); medium-high, increasing (43.0%); and high-stable (11.1%). In the low-increasing and medium-high trajectory groups, living in a semi-urban area was inversely related to weight, while living in a rural area was positively related to weight in the high-stable group. Disorder was inversely related to weight in the low-increasing group only. Other important risk factors for high-stable weight included obesity status of the mother, smoking during pregnancy, and overeating behaviors. Conclusions: In this study, associations between local environment factors and weight differed by trajectory group. Early life factors appear to play a more consistent role in weight status. Further work is needed to determine the influence of place on child weight. © 2012 Carter et al.

Background: Myocardial extravascular extracellular volume fraction (Ve) measures quantify diffuse fibrosis not readily detectable by conventional late gadolinium (Gd) enhancement (LGE). Ve measurement requires steady state equilibrium between plasma and interstitial Gd contrast. While a constant infusion produces steady state, it is unclear whether a simple bolus can do the same. Given the relatively slow clearance of Gd, we hypothesized that a bolus technique accurately measures Ve, thus facilitating integration of myocardial fibrosis quantification into cardiovascular magnetic resonance (CMR) workflow routines. Assuming equivalence between techniques, we further hypothesized that Ve measures would be reproducible across scans. Methods. In 10 volunteers (ages 20-81, median 33 yr, 3 females), we compared serial Ve measures from a single short axis slice from two scans: first, during a constant infusion, and second, 12-50 min after a bolus (0.2 mmol/kg gadoteridol) on another day. Steady state during infusion was defined when serial blood and myocardial T1 data varied <5%. We measured T1 on a 1.5 T Siemens scanner using a single-shot modified Look Locker inversion recovery sequence (MOLLI) with balanced SSFP. To shorten breath hold times, T1 values were measured with a shorter sampling scheme that was validated with spin echo relaxometry (TR = 15 sec) in CuSO4-Agar phantoms. Serial infusion vs. bolus Ve measures (n = 205) from the 10 subjects were compared with generalized estimating equations (GEE) with exchangeable correlation matrices. LGE images were also acquired 12-30 minutes after the bolus. Results: No subject exhibited LGE near the short axis slices where Ve was measured. The Ve range was 19.3-29.2% and 18.4-29.1% by constant infusion and bolus, respectively. In GEE models, serial Ve measures by constant infusion and bolus did not differ significantly (difference = 0.1%, p = 0.38). For both techniques, Ve was strongly related to age (p < 0.01 for both) in GE