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Thomas Sawczuk, Ben Jones, Sean Scantlebury, and Kevin Till

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.

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Emma Boocock, Sergio Lara-Bercial, Lea Dohme, Andrew Abraham, Dave Piggott, and Kevin Till

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Lea Dohme, Emma Boocock, Andrew Abraham, Dave Piggott, Kevin Till, and Sergio Lara-Bercial

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Thomas Sawczuk, Ben Jones, Mitchell Welch, Clive Beggs, Sean Scantlebury, and Kevin Till

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

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Kevin Till, Ben Jones, John O’Hara, Matthew Barlow, Amy Brightmore, Matthew Lees, and Karen Hind

Purpose:

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).

Methods:

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.

Results:

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.

Conclusions:

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.

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Amy Brightmore, John O’Hara, Kevin Till, Steve Cobley, Tate Hubka, Stacey Emmonds, and Carlton Cooke

Purpose:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

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A.J. Rankin-Wright, Jason Tee, Tom Mitchell, Ian Cowburn, Kevin Till, and Sergio Lara-Bercial

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Dave Piggott, Emma Boocock, Kevin Till, Lea Dohme, Andrew Abraham, and Sergio Lara-Bercial

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Sergio Lara-Bercial, Lea Dohme, Emma Boocock, Andrew Abraham, Dave Piggott, and Kevin Till

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Gregory Roe, Joshua Darrall-Jones, Christopher Black, William Shaw, Kevin Till, and Ben Jones

Purpose:

The purpose of this study was to investigate the validity of timing gates and 10-Hz global positioning systems (GPS) units (Catapult Optimeye S5) against a criterion measure (50-Hz radar gun) for assessing maximum sprint velocity (Vmax).

Methods:

Nine male professional rugby union players performed 3 maximal 40-m sprints with 3 min rest between efforts with Vmax assessed simultaneously via timing gates, 10-Hz GPSOpen (Openfield software), GPSSprint (Sprint software), and radar gun. Eight players wore 3 GPS units, while 1 wore a single unit during each sprint.

Results:

When compared with the radar gun, mean biases for GPSOpen, GPSSprint, and timing gates were trivial, small, and small, respectively. The typical error of the estimate (TEE) was small for timing gate and GPSOpen while moderate for GPSSprint. Correlations with radar gun were nearly perfect for all measures. Mean bias, TEE, and correlations between GPS units were trivial, small, and nearly perfect, respectively, while a small TEE existed when GPSOpenfield was compared with GPSSprint.

Conclusion:

Based on these findings, both 10-Hz GPS and timing gates provide valid measures of 40-m Vmax assessment compared with a radar gun. However, as error did exist between measures, the same testing protocol should be used when assessing 40-m Vmax over time. Furthermore, in light of the above results, it is recommended that when assessing changes in GPS-derived Vmax over time, practitioners should use the same unit for each player and perform the analysis with the same software, preferably Catapult Openfield.