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Simon Roberts, Grant Trewartha, and Keith Stokes

Purpose:

To assess the validity of a digitizing time–motion-analysis method for field-based sports and compare this with a notational-analysis method using rugby-union match play.

Method:

Five calibrated video cameras were located around a rugby pitch, and 1 subject completed prescribed movements within each camera’s view. Running speeds were measured using photocell timing gates. Two experienced operators digitized video data (operator 1 on 2 occasions) to allow 2-dimensional reconstruction of the prescribed movements.

Results:

Accuracy for total distance calculated was within 2.1% of the measured distance. For intraoperator and interoperator reliability, calculated distances were within 0.5% and 0.9%, respectively. Calculated speed was within 8.0% of measured photocell speed with intraoperator and interoperator reliability of 3.4% and 6.0%, respectively. For the method comparison, two 20-minute periods of rugby match play were analyzed for 5 players using the digitizing method and a notational time–motion method. For the 20-minute periods, overall mean absolute differences between methods for percentage time spent and distances covered performing different activities were 3.5% and 198.1 ± 138.1 m, respectively. Total number of changes in activity per 20 minutes were 184 ± 24 versus 458 ± 48, and work-to-rest ratios, 10.0%:90.0% and 7.3%:92.7% for notational and digitizing methods, respectively.

Conclusion:

The digitizing method is accurate and reliable for gaining detailed information on work profiles of field-sport participants and provides applied researchers richer data output than the conventional notational method.

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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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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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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.

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.