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Edward A. Gannon, Keith A. Stokes and Grant Trewartha

Purpose:

To investigate strength and power development in elite rugby players during the different phases of a professional season.

Methods:

Sixteen professional rugby union athletes from an English premiership team were monitored for measures of lower-body peak force, force at 50 ms, force at 100 ms (all isometric squat), and power (explosive hack squat). Athletes were assessed at the start of preseason (T1), postpreseason (T2), midway through the competitive season (T3), and at the end of the competitive season (T4). Effect-size (ES) statistics with magnitude-based inferences were calculated to interpret differences in physical performance between the different stages of the season.

Results:

Very likely beneficial increases in force at 50 ms (+16%, ES = 0.75 ± 0.4) and 100 ms (+14%, ES = 0.63 ± 0.4) were observed between T1 and T2. A likely beneficial increase in power was observed between T2 and T3 (+4%, ES = 0.31 ± 0.2). Between T3 and T4, decreases in force at 50 ms (–6%, ES = –0.39 ± 0.3) and 100 ms (–9%, ES = –0.52 ± 0.4) occurred, while peak force and power were maintained. Over the full season (T1–T4) clear beneficial increases in all measures of strength and power were identified.

Conclusions:

Meaningful increases in strength and power can be achieved in professional English premiership rugby players over a full playing season. The greatest opportunity for strength and power development occurs during pre- to midseason phases, while these measures are maintained or decrease slightly during the latter stages of a season.

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Simon P. Roberts, Keith A. Stokes, Lee Weston and Grant Trewartha

Purpose:

This study presents an exercise protocol utilizing movement patterns specific to rugby union forward and assesses the reproducibility of scores from this test.

Methods:

After habituation, eight participants (mean ± SD: age = 21 ± 3 y, height = 180 ± 4 cm, body mass = 83.9 ± 3.9 kg) performed the Bath University Rugby Shuttle Test (BURST) on two occasions, 1 wk apart. The protocol comprised 16 × 315-s cycles (4 × 21-min blocks) of 20-m shuttles of walking and cruising with 10-m jogs, with simulated scrummaging, rucking, or mauling exercises and standing rests. In the last minute of every 315-s cycle, a timed Performance Test was carried out, involving carrying a tackle bag and an agility sprint with a ball, followed by a 25-s recovery and a 15-m sprint.

Results:

Participants traveled 7078 m, spending 79.8 and 20.2% of time in low- and high-intensity activity, respectively. The coefficients of variation (CV) between trials 1 and 2 for mean time on the Performance Test (17.78 ± 0.71 vs 17.58 ± 0.79 s) and 15-m sprint (2.69 ± 0.15 vs 2.69 ± 0.15 s) were 1.3 and 0.9%, respectively. There was a CV of 2.2% between trials 1 and 2 for mean heart rate (160 ± 5 vs 158 ± 5 beats⋅min−1) and 14.4% for blood lactate (4.41 ± 1.22 vs 4.68 ± 1.68 mmol⋅L−1).

Conclusion:

Results suggest that measures of rugby union-specifc high-intensity exercise performed during the BURST were reproducible over two trials in habituated participants.

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James A. Betts, Keith A. Stokes, Rebecca J. Toone and Clyde Williams

Endocrine responses to repeated exercise have barely been investigated, and no data are available regarding the mediating influence of nutrition. On 3 occasions, participants ran for 90 min at 70% VO2max (R1) before a second exhaustive treadmill run at the same intensity (R2; 91.6 ± 17.9 min). During the intervening 4-hr recovery, participants ingested either 0.8 g sucrose · kg−1 · hr−1 with 0.3 g · kg−1 · hr−1 whey-protein isolate (CHO-PRO), 0.8 g sucrose · kg−1 · hr−1 (CHO), or 1.1 g sucrose · kg−1 · hr−1 (CHO-CHO). The latter 2 solutions therefore matched the former for carbohydrate or for available energy, respectively. Serum growth-hormone concentrations increased from 2 ± 1 μg/L to 17 ± 8 μg/L during R1 considered across all treatments (M ± SD; p ≤ .01). Concentrations were similar immediately after R2 irrespective of whether CHO or CHO-CHO was ingested (19 ± 4 μg/L and 19 ± 5 μg/L, respectively), whereas ingestion of CHO-PRO produced an augmented response (31 ± 4 μg/L; p ≤ .05). Growth-hormone-binding protein concentrations were unaffected by R1 but increased similarly across all treatments during R2 (from 414 ± 202 pmol/L to 577 ± 167 pmol/L; p ≤ .01), as was the case for plasma total testosterone (from 9.3 ± 3.3 nmol/L to 14.7 ± 4.6 nmol/L; p ≤ .01). There was an overall treatment effect for serum cortisol (p ≤ .05), with no specific differences at any given time point but lower concentrations immediately after R2 with CHO-PRO (608 ± 133 nmol/L) than with CHO (796 ± 278 nmol/L) or CHO-CHO (838 ± 134 nmol/L). Ingesting carbohydrate with added whey-protein isolate during short-term recovery from 90 min of treadmill running increases the growth-hormone response to a second exhaustive exercise bout of similar duration.

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Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale and Aki I.T. Salo

Purpose:

An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.

Methods:

Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.

Results:

PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R 2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R 2 = .77; 1.97% standard error of estimate).

Conclusions:

Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.

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Matthew J. Cross, Sean Williams, Grant Trewartha, Simon P.T. Kemp and Keith A. Stokes

Purpose:

To explore the association between in-season training-load (TL) measures and injury risk in professional rugby union players.

Methods:

This was a 1-season prospective cohort study of 173 professional rugby union players from 4 English Premiership teams. TL (duration × session-RPE) and time-loss injuries were recorded for all players for all pitch- and gym-based sessions. Generalized estimating equations were used to model the association between in-season TL measures and injury in the subsequent week.

Results:

Injury risk increased linearly with 1-wk loads and week-to-week changes in loads, with a 2-SD increase in these variables (1245 AU and 1069 AU, respectively) associated with odds ratios of 1.68 (95% CI 1.05–2.68) and 1.58 (95% CI 0.98–2.54). When compared with the reference group (<3684 AU), a significant nonlinear effect was evident for 4-wk cumulative loads, with a likely beneficial reduction in injury risk associated with intermediate loads of 5932–8651 AU (OR 0.55, 95% CI 0.22–1.38) (this range equates to around 4 wk of average in-season TL) and a likely harmful effect evident for higher loads of >8651 AU (OR 1.39, 95% CI 0.98–1.98).

Conclusions:

Players had an increased risk of injury if they had high 1-wk cumulative loads (1245 AU) or large week-to-week changes in TL (1069 AU). In addition, a U-shaped relationship was observed for 4-wk cumulative loads, with an apparent increase in risk associated with higher loads (>8651 AU). These measures should therefore be monitored to inform injury-risk-reduction strategies.

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Christopher M. Gaviglio, Blair T. Crewther, Liam P. Kilduff, Keith A. Stokes and Christian J. Cook

Purpose:

To assess the measures of salivary free testosterone and cortisol concentrations across selected rugby union matches according to game outcome.

Methods:

Twenty-two professional male rugby union players were studied across 6 games (3 wins and 3 losses). Hormone samples were taken 40 min before the game and 15 min after. The hormonal data were grouped and compared against competition outcomes. These competition outcomes included wins and losses and a game-ranked performance score (1–6).

Results:

Across the entire team, pregame testosterone concentrations were significantly higher during winning games than losses (P = 5.8 × 10−5). Analysis by playing position further revealed that, for the backs, pregame testosterone concentrations (P = 3.6 × 10−5) and the testosterone-to-cortisol ratio T:C (P = .038) were significantly greater before a win than a loss. Game-ranked performance score correlated to the team’s pregame testosterone concentrations (r = .81, P = .049). In backs, pregame testosterone (r = .91, P = .011) and T:C (r = .81, P = .05) also correlated to game-ranked performance. Analysis of the forwards’ hormone concentrations did not distinguish between game outcomes, nor did it correlate with game-ranked performance. Game venue (home vs away) only affected postgame concentrations of testosterone (P = .018) and cortisol (P = 2.58 × 10−4).

Conclusions:

Monitoring game-day concentrations of salivary free testosterone may help identify competitive readiness in rugby union matches. The link between pregame T:C and rugby players in the back position suggests that monitoring weekly training loads and enhancing recovery modalities between games may also assist with favorable performance and outcome in rugby union matches.

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Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Danny Holdcroft and Aki I.T. Salo

Purpose: Athletes’ force–power characteristics influence sled velocity during the skeleton start, which is a crucial determinant of performance. This study characterized force–power profile changes across an 18-month period and investigated the associations between these changes and start performance. Methods: Seven elite- and 5 talent-squad skeleton athletes’ (representing 80% of registered athletes in the country) force–power profiles and dry-land push-track performances were assessed at multiple time points over two 6-month training periods and one 5-month competition season. Force–power profiles were evaluated using an incremental leg-press test (Keiser A420), and 15-m sled velocity was recorded using photocells. Results: Across the initial maximum strength development phases, increases in maximum force (F max) and decreases in maximum velocity (V max) were typically observed. These changes were greater for talent (23.6% and −12.5%, respectively) compared with elite (6.1% and −7.6%, respectively) athletes. Conversely, decreases in F max (elite −6.7% and talent −10.3%) and increases in V max (elite 8.1% and talent 7.7%) were observed across the winter period, regardless of whether athletes were competing (elite) or accumulating sliding experience (talent). When the training emphasis shifted toward higher-velocity, sprint-based exercises in the second training season, force–power profiles seemed to become more velocity oriented (higher V max and more negative force–velocity gradient), which was associated with greater improvements in sled velocity (r = .42 and −.45, respectively). Conclusions: These unique findings demonstrate the scope to influence force–power-generating capabilities in well-trained skeleton athletes across different training phases. To enhance start performance, it seems important to place particular emphasis on increasing maximum muscle-contraction velocity.

Open access

Sean Williams, Grant Trewartha, Matthew J. Cross, Simon P.T. Kemp and Keith A. Stokes

Purpose:

Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.

Methods:

Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.

Results:

Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.

Conclusions:

The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.

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James A. Betts, Milou Beelen, Keith A. Stokes, Wim H.M. Saris and Luc J.C. van Loon

Nocturnal endocrine responses to exercise performed in the evening and the potential role of nutrition are poorly understood. To gain novel insight, 10 healthy men ingested carbohydrate with (C+P) and without (C) protein in a randomized order and double-blind manner during 2 hr of interval cycling followed by resistancetype exercise and into early postexercise recovery. Blood samples were obtained hourly throughout 9 hr of postexercise overnight recovery for analysis of key hormones. Muscle samples were taken from the vastus lateralis before and after exercise and then again the next morning (7 a.m.) to calculate mixed-muscle protein fractional synthetic rate (FSR). Overnight plasma hormone concentrations were converted into overall responses (expressed as area under the concentration curve) and did not differ between treatments for either growth hormone (1,464 ± 257 vs. 1,432 ± 164 pg/ml · 540 min) or total testosterone (18.3 ± 1.2 vs. 17.9 ± 1.2 nmol/L · 540 min, C and C+P, respectively). In contrast, the overnight cortisol response was higher with C+P (102 ± 11 nmol/L · 540 min) than with C (81 ± 8 nmol/L · 540 min; p = .02). Mixed-muscle FSR did not differ between C and C+P during overnight recovery (0.062% ± 0.006% and 0.062% ± 0.009%/hr, respectively) and correlated significantly with the plasma total testosterone response (r = .7, p < .01). No correlations with FSR were apparent for the response of growth hormone (r = –.2, p = .4), cortisol (r = .1, p = .6), or the ratio of testosterone to cortisol (r = .2, p = .5). In conclusion, protein ingestion during and shortly after exercise does not modulate the endocrine response or muscle protein synthesis during overnight recovery.