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Adam Jones, Richard Page, Chris Brogden, Ben Langley, and Matt Greig

Context: The influence of playing surface on injury risk in soccer is contentious, and contemporary technologies permit an in vivo assessment of mechanical loading on the player. Objective: To quantify the influence of playing surface on the PlayerLoad elicited during soccer-specific activity. Design: Repeated measures, field-based design. Setting: Regulation soccer pitches. Participants: Fifteen amateur soccer players (22.1 [2.4] y), injury free with ≥6 years competitive experience. Interventions: Each player completed randomized order trials of a soccer-specific field test on natural turf, astroturf, and third-generation artificial turf. GPS units were located at C7 and the mid-tibia of each leg to measure triaxial acceleration (100 Hz). Main Outcome Measures: Total accumulated PlayerLoad in each movement plane was calculated for each trial. Ratings of perceived exertion and visual analog scales assessing lower-limb muscle soreness were measured as markers of fatigue. Results: Analysis of variance revealed no significant main effect for playing surface on total PlayerLoad (P = .55), distance covered (P = .75), or postexercise measures of ratings of perceived exertion (P = .98) and visual analog scales (P = .61). There was a significant main effect for GPS location (P < .001), with lower total loading elicited at C7 than mid-tibia (P < .001), but with no difference between limbs (P = .70). There was no unit placement × surface interaction (P = .98). There was also a significant main effect for GPS location on the relative planar contributions to loading (P < .001). Relative planar contributions to loading in the anterioposterior:mediolateral:vertical planes was 25:27:48 at C7 and 34:32:34 at mid-tibia. Conclusions: PlayerLoad metrics suggest that playing surface does not influence mechanical loading during soccer-specific activity (not including tackling). Clinical reasoning should consider that PlayerLoad magnitude and axial contributions were sensitive to unit placement, highlighting opportunities in the objective monitoring of load during rehabilitation.

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Adam Jones, Chris Brogden, Richard Page, Ben Langley, and Matt Greig

Context: Contemporary synthetic playing surfaces have been associated with an increased risk of ankle injury in the various types of football. Triaxial accelerometers facilitate in vivo assessment of planar mechanical loading on the player. Objective: To quantify the influence of playing surface on the PlayerLoad elicited during footwork and plyometric drills focused on the mechanism of ankle injury. Design: Repeated-measures, field-based design. Setting: Regulation soccer pitches. Participants: A total of 15 amateur soccer players (22.1 [2.4] y), injury free with ≥6 years competitive experience. Interventions: Each player completed a test battery comprising 3 footwork drills (anterior, lateral, and diagonal) and 4 plyometric drills (anterior hop, inversion hop, eversion hop, and diagonal hop) on natural turf (NT), third-generation artificial turf (3G), and AstroTurf. Global positioning system sensors were located at C7 and the mid-tibia of each leg to measure triaxial acceleration (100 Hz). Main Outcome Measures: PlayerLoad in each axial plane was calculated for each drill on each surface and at each global positioning system location. Results: Analysis of variance revealed a significant main effect for sensor location in all drills, with PlayerLoad higher at mid-tibia than at C7 in all movement planes. AstroTurf elicited significantly higher PlayerLoad in the mediolateral and anteroposterior planes, with typically no difference between NT and 3G. In isolated inversion and eversion hopping trials, the 3G surface also elicited lower PlayerLoad than NT. Conclusions: PlayerLoad magnitude was sensitive to unit placement, advocating measurement with greater anatomical relevance when using microelectromechanical systems technology to monitor training or rehabilitation load. AstroTurf elicited higher PlayerLoad across all planes and drills and should be avoided for rehabilitative purposes, whereas 3G elicited a similar mechanical response to NT.

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Nessan Costello, Jim McKenna, Louise Sutton, Kevin Deighton, and Ben Jones

Designing and implementing successful dietary intervention is integral to the role of sport nutrition professionals as they attempt to positively change the dietary behavior of athletes. High-performance sport is a time-pressured environment where immediate results can often supersede pursuit of the most effective evidence-based practice. However, efficacious dietary intervention necessitates comprehensive, systematic, and theoretical behavioral design and implementation, if the habitual dietary behaviors of athletes are to be positively changed. Therefore, this case study demonstrates how the Behaviour Change Wheel was used to design and implement an effective nutritional intervention within a professional rugby league. The eight-step intervention targeted athlete consumption of a high-quality dietary intake of 25.1 MJ each day to achieve an overall body mass increase of 5 kg across a 12-week intervention period. The capability, opportunity, motivation, and behavior model and affordability, practicability, effectiveness/cost-effectiveness, acceptability, safety, and equity criteria were used to identify population-specific intervention functions, policy categories, behavior change techniques, and modes of intervention delivery. The resulting intervention was successful, increasing the average daily energy intake of the athlete to 24.5 MJ, which corresponded in a 6.2 kg body mass gain. Despite consuming 0.6 MJ less per day than targeted, secondary outcome measures of diet quality, strength, body composition, and immune function all substantially improved, supporting sufficient energy intake and the overall efficacy of a behavioral approach. Ultimately, the Behaviour Change Wheel provides sport nutrition professionals with an effective and practical stepwise method to design and implement effective nutritional interventions for use within high-performance sport.

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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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Gregory Roe, Joshua Darrall-Jones, Kevin Till, Padraic Phibbs, Dale Read, Jonathon Weakley, and Ben Jones

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.

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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.

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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.

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Joshua Darrall-Jones, Gregory Roe, Shane Carney, Ryan Clayton, Padraic Phibbs, Dale Read, Jonathon Weakley, Kevin Till, and Ben Jones

Purpose:

To evaluate the difference in performance of the 30-15 Intermittent Fitness Test (30–15IFT) across 4 squads in a professional rugby union club in the UK and consider body mass in the interpretation of the end velocity of the 30-15IFT (VIFT).

Methods:

One hundred fourteen rugby union players completed the 30-15IFT midseason.

Results:

VIFT demonstrated small and possibly lower (ES = –0.33; 4/29/67) values in the under 16s compared with the under 21s, with further comparisons unclear. With body mass included as a covariate, all differences were moderate to large and very likely to almost certainly lower in the squads with lower body mass, with the exception of comparisons between senior and under-21 squads.

Conclusions:

The data demonstrate that there appears to be a ceiling to the VIFT attained in rugby union players that does not increase from under-16 to senior level. However, the associated increases in body mass with increased playing level suggest that the ability to perform high-intensity running increases with age, although not translating into greater VIFT due to the detrimental effect of body mass on change of direction. Practitioners should be aware that VIFT is unlikely to improve, but it needs to be monitored during periods where increases in body mass are evident.

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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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Dan Weaving, Clive Beggs, Nicholas Dalton-Barron, Ben Jones, and Grant Abt

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.