Purpose: To examine responses to a simulated rugby league protocol designed to include more stochastic commands, and therefore require greater vigilance, than traditional team-sport simulation protocols. Methods: Eleven male university rugby players completed 2 trials (randomized and control [CON]) of a rugby league movement simulation protocol, separated by 7 to 10 d. The CON trial consisted of 48 repeated ∼115-s cycles of activity. The stochastic simulation (STOCH) was matched for the number and types of activity performed every 5.45 min in CON but included no repeated cycles of activity. Movement using GPS, heart rate, rating of perceived exertion, and Stroop test performance was assessed throughout. Maximum voluntary contraction peak torque, voluntary activation (in percentage), and global task load were assessed after exercise. Results: The mean mental demand of STOCH was higher than CON (effect size [ES] = 0.56; ±0.69). Mean sprint speed was higher in STOCH (22.5 [1.4] vs 21.6 [1.6] km·h−1, ES = 0.50; ±0.55), which was accompanied by a higher rating of perceived exertion (14.3 [1.0] vs 13.0 [1.4], ES = 0.87; ±0.67) and a greater number of errors in the Stroop test (10.3 [2.5] vs 9.3 [1.4] errors; ES = 0.65; ±0.83). Maximum voluntary contraction peak torque (CON = −48.4 [31.6] N·m and STOCH = −39.6 [36.6] N·m) and voluntary activation (CON = −8.3% [4.8%] and STOCH = −6.0% [4.1%]) was similarly reduced in both trials. Conclusions: Providing more stochastic commands, which requires greater vigilance, might alter performance and associated physiological, perceptual, and cognitive responses to team-sport simulations.
Thomas Mullen, Craig Twist, and Jamie Highton
Jamie Highton, Thomas Mullen, and Craig Twist
To examine the influence of knowledge of exercise duration on pacing and performance during simulated rugby league match play.
Thirteen male university rugby players completed 3 simulated rugby league matches (RLMSP-i) on separate days in a random order. In a control trial, participants were informed that they would be performing 2 × 23-min bouts (separated by 20 min) of the RLMSP-i (CON). In a second trial, participants were informed that they would be performing 1 × 23-min bout of the protocol but were then asked to perform another 23-min bout (DEC). In a third trial, participants were not informed of the exercise duration and performed 2 × 23-min bouts (UN).
Distance covered and high-intensity running were higher in CON (4813 ± 167 m, 26 ± 4.1 m/min) than DEC (4764 ± 112 m, 25.2 ± 2.8 m/min) and UN (4744 ± 131 m, 24.4 m/min). Compared with CON, high-intensity running and peak speed were typically higher for DEC in bout 1 and lower in bout 2 of the RLMSP-i, while UN was generally lower throughout. Similarly, DEC resulted in an increased heart rate, blood lactate, and rating of perceived exertion than CON in bout 1, whereas these variables were lower throughout the protocol in UN.
Pacing and performance during simulated rugby league match play depend on an accurate understanding of the exercise endpoint. Applied practitioners should consider informing players of their likely exercise duration to maximize running.
Thomas Mullen, Jamie Highton, and Craig Twist
It is important to understand the extent to which physical contact changes the internal and external load during rugby simulations that aim to replicate the demands of match play. Accordingly, this study examined the role of physical contact on the physiological and perceptual demands during and immediately after a simulated rugby league match. Nineteen male rugby players completed a contact (CON) and a noncontact (NCON) version of the rugby league match-simulation protocol in a randomized crossover design with 1 wk between trials. Relative distance covered (ES = 1.27; ±0.29), low-intensity activity (ES = 1.13; ±0.31), high-intensity running (ES = 0.49; ±0.34), heart rate (ES = 0.52; ±0.35), blood lactate concentration (ES = 0.78; ±0.34), rating of perceived exertion (RPE) (ES = 0.72; ±0.38), and session RPE (ES = 1.45; ±0.51) were all higher in the CON than in the NCON trial. However, peak speeds were lower in the CON trial (ES = −0.99; ±0.40) despite unclear reductions in knee-extensor (ES = 0.19; ±0.40) and -flexor (ES = 0.07; ±0.43) torque. Muscle soreness was also greater after CON than in the NCON trial (ES = 0.97; ±0.55). The addition of physical contact to the movement demands of a simulated rugby league match increases many of the external and internal demands but also results in players’ slowing their peak running speed during sprints. These findings highlight the importance of including contacts in simulation protocols and training practices designed to replicate the demands of real match play.
Jamie Highton, Thomas Mullen, Jonathan Norris, Chelsea Oxendale, and Craig Twist
This aim of this study was to examine the validity of energy expenditure derived from microtechnology when measured during a repeated-effort rugby protocol. Sixteen male rugby players completed a repeated-effort protocol comprising 3 sets of 6 collisions during which movement activity and energy expenditure (EEGPS) were measured using microtechnology. In addition, energy expenditure was estimated from open-circuit spirometry (EEVO2). While related (r = .63, 90%CI .08–.89), there was a systematic underestimation of energy expenditure during the protocol (–5.94 ± 0.67 kcal/min) for EEGPS (7.2 ± 1.0 kcal/min) compared with EEVO2 (13.2 ± 2.3 kcal/min). High-speed-running distance (r = .50, 95%CI –.66 to .84) was related to EEVO2, while PlayerLoad was not (r = .37, 95%CI –.81 to .68). While metabolic power might provide a different measure of external load than other typically used microtechnology metrics (eg, high-speed running, PlayerLoad), it underestimates energy expenditure during intermittent team sports that involve collisions.
Thomas Mullen, Craig Twist, Matthew Daniels, Nicholas Dobbin, and Jamie Highton
Purpose: To identify the association between several contextual match factors, technical performance, and external movement demands on the subjective task load of elite rugby league players. Methods: Individual subjective task load, quantified using the National Aeronautics and Space Administration Task Load Index (NASA-TLX), was collected from 29 professional rugby league players from one club competing in the European Super League throughout the 2017 season. The sample consisted of 26 matches (441 individual data points). Linear mixed modeling revealed that various combinations of contextual factors, technical performance, and movement demands were associated with subjective task load. Results: Greater number of tackles (effect size correlation ± 90% confidence intervals; η 2 = .18 ± .11), errors (η 2 = .15 ± .08), decelerations (η 2 = .12 ± .08), increased sprint distance (η 2 = .13 ± .08), losing matches (η 2 = .36 ± .08), and increased perception of effort (η 2 = .27 ± .08) led to most likely–very likely increases in subjective total task load. The independent variables included in the final model for subjective mental demand (match outcome, time played, and number of accelerations) were unclear, excluding a likely small correlation with technical errors (η 2 = .10 ± .08). Conclusions: These data provide a greater understanding of the subjective task load and their association with several contextual factors, technical performance, and external movement demands during rugby league competition. Practitioners could use this detailed quantification of internal loads to inform recovery sessions and current training practices.
Emily L. Mailey, Neha P. Gothe, Thomas R. Wójcicki, Amanda N. Szabo, Erin A. Olson, Sean P. Mullen, Jason T. Fanning, Robert W. Motl, and Edward McAuley
The criteria one uses to reduce accelerometer data can profoundly influence the interpretation of research outcomes. The purpose of this study was to examine the influence of 3 different interruption periods (i.e., 20, 30, and 60 min) on the amount of data retained for analyses and estimates of sedentary time among older adults. Older adults (N = 311, M age = 71.1) wore an accelerometer for 7 d and reported wear time on an accelerometer log. Accelerometer data were downloaded and scored using 20-, 30-, and 60-min interruption periods. Estimates of wear time, derived using each interruption period, were compared with self-reported wear time, and descriptive statistics were used to compare estimates of sedentary time. Results showed a longer interruption period (i.e., 60 min) yields the largest sample size and the closest approximation of self-reported wear time. A short interruption period (i.e., 20 min) is likely to underestimate sedentary time among older adults.