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Rugby league is a contact team sport performed at an average intensity similar to that of other team sports (~70–80% VO_2max), made up of unsystematic movements of varying type, duration, and frequency. The high number of collisions, repeated eccentric muscle contractions associated with accelerating and decelerating, and prolonged aerobic nature of rugby league matches result in the development of fatigue in the days after exercise. Monitoring the presence and magnitude of this fatigue to maximize performance and training adaptation is an important consideration for applied sports scientists. Several methods have been proposed to measure the magnitude of fatigue in athletes. Perceptual measures (eg, questionnaires) are easy to employ and are sensitive to changes in performance. However, the subjective nature of such measures should be considered. Blood biochemical markers of fatigue may provide a more objective measure of homeostatic disturbances associated with fatigue; however, the cost, level of expertise required, and high degree of variability of many of these measures often preclude them from being used in the applied setting. Accordingly, simple measure of muscle function (eg, jump height) and simulated performance offer the most practical and appropriate method of determining the extent of fatigue experienced by rugby league players. A meaningful change in each measure of fatigue for the monitoring of players can be easily determined, provided that the reliability of the measure is known. Multiplying the coefficient of variation by 0.3, 0.9, and 1.6 can be used to determine a small, moderate, and large change, respectively.

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.

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.

Purpose: To assess the reliability and external validity of a rugby league movement-simulation protocol for interchange players (RLMSP-i) that was adapted to include physical contact between participants. Methods: A total of 18 rugby players performed 2 trials of a modified RLMSP-i, 7 d apart. The simulation was conducted outdoors on artificial turf with movement speeds controlled using an audio signal. Microtechnology was used to measure locomotive and accelerometer (ie, PlayerLoad™) metrics for both bouts (∼23 min each) alongside heart rate (HR) and rating of perceived exertion (RPE). Results: Reported for each bout, total distance (102 [3] m·min⁻¹ and 101 [3] m·min⁻¹), low-speed distance (77 [3] m·min⁻¹ and 79 [4] m·min⁻¹), high-speed distance (25 [3] m·min⁻¹ and 22 [4] m·min⁻¹), PlayerLoad (10 [1] AU·min⁻¹ and 10 [1] AU·min⁻¹), PlayerLoad slow (3.2 [0.6] AU·min⁻¹ and 3.2 [0.6] AU·min⁻¹), 2-dimensional PlayerLoad (6.0 [0.9] AU·min⁻¹ and 5.7 [0.8] AU·min⁻¹), and HR (86 [5]%HR_max and 84 [6]%HR_max) were similar to match play. The coefficient of variation (CV%) for locomotive metrics ranged from 1.3% to 14.4%, accelerometer CV% 4.4% to 10.0%, and internal load 4.8% to 13.7%. All variables presented a CV% less than the calculated moderate change during 1 or both bouts of the simulation except high-speed distance, percentage of the participant’s peak HR, and RPE. Conclusion: The modified RLMSP-i offers a reliable simulation to investigate influences of training and nutrition interventions on the movement and collision activities of rugby league interchange players.

Purpose: To examine the within- and between-sexes physical performance, well-being, and neuromuscular function responses across a 4-day international touch rugby (Touch) tournament. Methods: Twenty-one males and 20 females completed measures of well-being (fatigue, soreness, sleep, mood, and stress) and neuromuscular function (countermovement jump height, peak power output, and peak force) during a 4-day tournament with internal, external, and perceptual loads recorded for all matches. Results: Relative and absolute total, low-intensity (females), and high-intensity distance were lower on day 3 (males and females) (effect size [ES] = −0.37 to −0.71) compared with day 1. Mean heart rate was possibly to most likely lower during the tournament (except day 2 males; ES = −0.36 to −0.74), whereas rating of perceived exertion-training load was consistently higher in females (ES = 0.02 to 0.83). The change in mean fatigue, soreness, and overall well-being was unclear to most likely lower (ES = −0.33 to −1.90) across the tournament for both sexes, with greater perceived fatigue and soreness in females on days 3 to 4 (ES = 0.39 to 0.78). Jump height and peak power output were possibly to most likely lower across days 2 to 4 (ES = −0.30 to −0.84), with greater reductions in females (ES = 0.21 to 0.66). Well-being, countermovement jump height, and peak force were associated with changes in external, internal, and perceptual measures of load across the tournament (η ² = −.37 to .39). Conclusions: Elite Touch players experience reductions in well-being, neuromuscular function, and running performance across a 4-day tournament, with notable differences in fatigue and running between males and females, suggesting that sex-specific monitoring and intervention strategies are necessary.

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 (EE_GPS) were measured using microtechnology. In addition, energy expenditure was estimated from open-circuit spirometry (EE_VO2). 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 EE_GPS (7.2 ± 1.0 kcal/min) compared with EE_VO2 (13.2 ± 2.3 kcal/min). High-speed-running distance (r = .50, 95%CI –.66 to .84) was related to EE_VO2, 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.

Player loads and fatigue responses are reported in 15 professional rugby league players (24.3 ± 3.8 y) during a period of intensified fixtures. Repeated measures of internal and external loads, perceived well-being, and jump flight time were recorded across 22 d, comprising 9 training sessions and matches on days 5, 12, 15, and 21 (player exposure: 3.6 ± 0.6 matches). Mean training loads (session rating of perceived exertion × duration) between matches were 1177, 1083, 103, and 650 AU. Relative distance in match 1 (82 m/min) and match 4 (79 m/min) was very likely lower in match 2 (76 m/min) and likely higher in match 3 (86 m/min). High-intensity running (≥5.5 m/s) was likely to very likely lower than match 1 (5 m/min) in matches 2–4 (2, 4, and 3 m/min, respectively). Low-intensity activity was likely to very likely lower than match 1 (78 m/min) in match 2 (74 m/min) and match 4 (73 m/min) but likely higher in match 3 (81 m/min). Accumulated accelerometer loads for matches 1–4 were 384, 473, 373, and 391 AU, respectively. Perceived well-being returned to baseline values (~21 AU) before all matches but was very likely to most likely lower the day after each match (~17 AU). Prematch jump flight times were likely to most likely lower across the period, with mean values of 0.66, 0.65, 0.62, and 0.64 s before matches 1–4, respectively. Across a 22-d cycle with fixture congestion, professional rugby league players experience cumulative neuromuscular fatigue and impaired match running performance.