Purpose: To assess the relationships between training load, sleep duration, and 3 daily well-being, recovery, and fatigue measures in youth athletes. Methods: Fifty-two youth athletes completed 3 maximal countermovement jumps (CMJs), a daily well-being questionnaire (DWB), the perceived recovery status scale (PRS), and provided details on their previous day’s training loads (training) and self-reported sleep duration (sleep) on 4 weekdays over a 7-week period. Partial correlations, linear mixed models, and magnitude-based inferences were used to assess the relationships between the predictor variables (training and sleep) and the dependent variables (CMJ, DWB, and PRS). Results: There was no relationship between CMJ and training (r = −.09; ±.06) or sleep (r = .01; ±.06). The DWB was correlated with sleep (r = .28; ±.05, small), but not training (r = −.05; ±.06). The PRS was correlated with training (r = −.23; ±.05, small), but not sleep (r = .12; ±.06). The DWB was sensitive to low sleep (d = −0.33; ±0.11) relative to moderate; PRS was sensitive to high (d = −0.36; ±0.11) and low (d = 0.29; ±0.17) training relative to moderate. Conclusions: The PRS is a simple tool to monitor the training response, but DWB may provide a greater understanding of the athlete’s overall well-being. The CMJ was not associated with the training or sleep response in this population.
Thomas Sawczuk, Ben Jones, Sean Scantlebury and Kevin Till
Lea Dohme, Emma Boocock, Andrew Abraham, Dave Piggott, Kevin Till and Sergio Lara-Bercial
Sergio Lara-Bercial, Lea Dohme, Emma Boocock, Andrew Abraham, Dave Piggott and Kevin Till
Emma Boocock, Sergio Lara-Bercial, Lea Dohme, Andrew Abraham, Dave Piggott and Kevin Till
Dave Piggott, Emma Boocock, Kevin Till, Lea Dohme, Andrew Abraham and Sergio Lara-Bercial
A.J. Rankin-Wright, Jason Tee, Tom Mitchell, Ian Cowburn, Kevin Till and Sergio Lara-Bercial
Amy Brightmore, John O’Hara, Kevin Till, Steve Cobley, Tate Hubka, Stacey Emmonds and Carlton Cooke
To evaluate the movement and physiological demands of Australasian National Rugby League (NRL) referees, officiating with a 2-referee (ie, lead and pocket) system, and to compare the demands of the lead and pocket referees.
Global positioning system devices (10 Hz) were used to obtain 86 data sets (lead, n = 41; pocket, n = 45) on 19 NRL referees. Total distance, relative distance covered, and heart rate per half and across match play were examined within and between referees using t tests. Distance, time, and number of movement “efforts” were examined in 6 velocity classifications (ie, standing <0.5, walking 0.51–2.0, jogging 2.01–4.0, running 4.01–5.5, high-speed running 5.51–7.0, and sprinting >7.0 m/s) using analysis of variance. Cohen d effect sizes are reported.
There were no significant differences between the lead and pocket referees for any movement or physiological variable. There was an overall significant (large, very large) effect for distance (% distance) and time (% time) (P < .001) between velocity classifications for both the lead and pocket referees. Both roles covered the largest distance and number of efforts at velocities of 0.51–2.0 m/s and 2.01–4.0 m/s, which were interspersed with efforts >5.51 m/s.
Findings highlight the intermittent nature of rugby league refereeing but show that there were no differences in the movement and physiological demands of the 2 refereeing roles. Findings are valuable for those responsible for the preparation, training, and conditioning of NRL referees and to ensure that training prepares for and simulates match demands.
Gregory Roe, Joshua Darrall-Jones, Kevin Till, Padraic Phibbs, Dale Read, Jonathon Weakley and Ben Jones
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.
Kevin Till, Ben Jones, John O’Hara, Matthew Barlow, Amy Brightmore, Matthew Lees and Karen Hind
To compare the body size and 3-compartment body composition between academy and senior professional rugby league players using dual-energy X-ray absorptiometry (DXA).
Academy (age 18.1 ± 1.1 y, n = 34) and senior (age 26.2 ± 4.6 y, n = 63) rugby league players received 1 total-body DXA scan. Height, body mass, and body-fat percentage alongside total and regional fat mass, lean mass, and bone mineral content (BMC) were compared. Independent t tests with Cohen d effect sizes and multivariate analysis of covariance (MANCOVA), controlling for height and body mass, with partial eta-squared (η2) effect sizes, were used to compare total and regional body composition.
Senior players were taller (183.2 ± 5.8 vs 179.2 ± 5.7 cm, P = .001, d = 0.70) and heavier (96.5 ± 9.3 vs 86.5 ± 9.0 kg, P < .001, d = 1.09) with lower body-fat percentage (16.3 ± 3.7 vs 18.0 ± 3.7%, P = .032, d = 0.46) than academy players. MANCOVA identified significant overall main effects for total and regional body composition between academy and senior players. Senior players had lower total fat mass (P < .001, η 2 = 0.15), greater total lean mass (P < .001, η 2 = 0.14), and greater total BMC (P = .001, η 2 = 0.12) than academy players. For regional sites, academy players had significantly greater fat mass at the legs (P < .001, η 2 = 0.29) than senior players.
The lower age, height, body mass, and BMC of academy players suggest that these players are still developing musculoskeletal characteristics. Gradual increases in lean mass and BMC while controlling fat mass is an important consideration for practitioners working with academy rugby league players, especially in the lower body.
Nick Dobbin, Richard Hunwicks, Ben Jones, Kevin Till, Jamie Highton and Craig Twist
Purpose: To examine the criterion and construct validity of an isometric midthigh-pull dynamometer to assess whole-body strength in professional rugby league players. Methods: Fifty-six male rugby league players (33 senior and 23 youth players) performed 4 isometric midthigh-pull efforts (ie, 2 on the dynamometer and 2 on the force platform) in a randomized and counterbalanced order. Results: Isometric peak force was underestimated (P < .05) using the dynamometer compared with the force platform (95% LoA: −213.5 ± 342.6 N). Linear regression showed that peak force derived from the dynamometer explained 85% (adjusted R 2 = .85, SEE = 173 N) of the variance in the dependent variable, with the following prediction equation derived: predicted peak force = [1.046 × dynamometer peak force] + 117.594. Cross-validation revealed a nonsignificant bias (P > .05) between the predicted and peak force from the force platform and an adjusted R 2 (79.6%) that represented shrinkage of 0.4% relative to the cross-validation model (80%). Peak force was greater for the senior than the youth professionals using the dynamometer (2261.2 ± 222 cf 1725.1 ± 298.0 N, respectively; P < .05). Conclusion: The isometric midthigh pull assessed using a dynamometer underestimates criterion peak force but is capable of distinguishing muscle-function characteristics between professional rugby league players of different standards.