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The purpose of this study was to quantify the total energy expenditure (TEE) of international female rugby union players. Fifteen players were assessed over 14 days throughout an international multi-game tournament, which represented two consecutive one-match microcycles. Resting metabolic rate (RMR) and TEE were assessed by indirect calorimetry and doubly labelled water, respectively. Physical activity level (PAL) was estimated (TEE:RMR). Mean RMR, TEE, and PAL were 6.60 ± 0.93 MJ·day−1 (1578 ± 223 kcal·day−1), 13.51 ± 2.28 MJ·day−1 (3229 ± 545 kcal·day−1), and 2.0 ± 0.3 AU, respectively. There was no difference in TEE (13.74 ± 2.31 (3284 ± 554 kcal·day−1) vs. 13.92 ± 2.10 MJ·day−1 (3327 ± 502 kcal·day−1); p = 0.754), or PAL (2.06 ± 0.26 AU vs. 2.09 ± 0.23 AU; p = 0.735) across microcycles, despite substantial decreases in training load (total distance: −8088 m, collisions: −20 n, training duration: −252 min). After correcting for body composition, there was no difference in TEE (13.80 ± 1.74 (3298 ± 416 adj. kcal·day−1) vs. 13.16 ± 1.97 (3145 ± 471 adj. kcal·day−1) adj. MJ·day−1, p = 0.190), RMR (6.49 ± 0.81 (1551 ± 194 adj. kcal·day−1) vs. 6.73 ± 0.83 (1609 ± 198 adj. kcal·day−1) adj. MJ·day−1, p = 0.633) or PAL (2.15 ± 0.14 vs. 1.87 ± 0.26 AU, p = 0.090) between forwards and backs. For an injured participant (n = 1), TEE reduced by 1.7 MJ·day−1 (−401 kcal·day−1) from pre-injury. For participants with illness (n = 3), TEE was similar to pre-illness (+0.49 MJ·day−1 (+117 kcal·day−1)). The energy requirements of international female rugby players were consistent across one-match microcycles. Forwards and backs had similar adjusted energy requirements. These findings are critical to inform the dietary guidance provided to female rugby players. [ABSTRACT FROM AUTHOR]

This study aims to establish the validity and reliability of the prone Yo-YoIRL1 in elite female rugby league players (part one) and determine the anthropometric and physical characteristics contributing to 15m prone Yo-YoIRL1 performance (part two). Part one, 21 subjects completed one Yo-YoIRL1, one 20m and two 15m prone Yo-YoIRL1 tests over four sessions, with 7–14 days in-between. Part two, ten subjects completed a testing battery, including body mass, height, dual-energy x-ray absorptiometry, isometric mid-thigh pull, isometric bench-press, 10m and 20m sprints and an incremental treadmill test (V˙O2max). The 15m prone YoYoIRL1 demonstrated poor reliability with a typical error of 68m (21%) and a smallest worthwhile change of 54m (9%). Validity analysis found the prone versions of the YoYoIRL1 were not sensitive measures of intermittent running performance. Both prone YoYoIRL1 test distances demonstrated large mean bias (76% and -37% respectively) and typical error of the estimate (19% and 21%, respectively) in comparison to the YoYoIRL1. Body mass (r = -0.89), lean mass (r = -0.64), body fat % (r = -0.68), V˙O2max (l∙min-1) (r = -0.64), IMTP (r = -0.69), IBP (r = -0.15), 10m (r = -0.77) and 20m (r = -0.72) momentum displayed large negative relationships with 15m prone Yo-YoIRL1 performance. Due to the poor validity of the 20m prone YoYoIRL1, the poor validity and reliability of the 15m prone YoYoIRL1, and the anthropometric and physical characteristics which negatively impact performance, practitioners should reconsider the use of the prone YoYoIRL1 test to monitor high intensity intermittent running performance. [ABSTRACT FROM AUTHOR]

Purpose: Sleep is recognized as an important recovery strategy, yet little is known regarding its impact on postmatch fatigue. The aims of this study were to (1) describe sleep and postmatch fatigue, (2) understand how sleep is affected by contextual and match factors, and (3) assess how changes in sleep can affect postmatch fatigue. Methods: Twenty-three male rugby union players were monitored across 1 season (N = 71 player–match observations). Actigraphy was used during preseason to establish baseline sleep quality and quantity. Sleep was then measured 1 and 2 days after each match day (MD + 1 and MD + 2). Global positioning systems, notational analysis, and rating of perceived exertion represented external and internal load from matches. Subjective wellness and a standardized run were used to characterize postmatch fatigue 2 days prior (baseline) and at MD + 1 and MD + 2. Linear mixed models established the magnitude of change (effect size [ES]) between baseline, MD + 1, and MD + 2 for sleep and postmatch fatigue. Stepwise forward selection analysis ascertained the effect of match load on sleep and the effect of sleep on postmatch fatigue. Each analysis was combined with magnitude-based decisions. Results: Sleep characteristics and neuromuscular and perceptual postmatch fatigue were negatively affected at MD + 1 and MD + 2 (ES = small to very large). Kickoff and travel time had the greatest effect on sleep (ES = small). Wellness and soreness were influenced by sleep (fall-asleep time and fragmentation index) and collisions, respectively (ES = small). Conclusion: Sleep quality and quantity were affected independently of the match load (ie, running activity) sustained, and changes in sleep marginally affected postmatch fatigue. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the reliability and validity of an affordable wireless force sensor in measuring mean and peak forces during resistance training.A Suiff Pro wireless force sensor (Suiff, Spain) and a MuscleLab force platform (Ergotest, Norway) were used concurrently to assess tensile load and the ground reaction force resulting from an upright row exercise. Thirteen participants (28.2 ± 5.7 years, 76.2 ± 9.6 kg, 178.2 ± 9.2 cm) performed the exercise under three velocity conditions and isometrically. Each condition involved three sets of exercise. Mean (Fmean) and peak (Fpeak) force values from both sensors were collected and compared.Suiff Pro exhibited excellent reliability for Fmean and Fpeak (ICCs = 0.99). When compared to the criterion measures, Suiff Pro showed trivial standardized bias for Fmean (Mean = 0.00 [CI 95% = 0.00 to 0.01]) and Fpeak (-0.02 [-0.04 to 0.00]). The standardized typical error was also trivial for Fmean (0.03 [0.02 to 0.03]) and Fpeak (0.07 [0.05 to 0.09]). Correlations with the MuscleLab force platform were nearly perfect: Fmean (0.97 [0.94 to 0.98]; p<0.001); Fpeak (0.96 [0.92 to 0.97]; p<0.001).The findings demonstrate that the Suiff Pro sensor is reliable and valid device for measuring force during isometric and dynamic resistance training exercises. Therefore, practitioners can confidently use this device to monitor kinematic variables of resistance training exercises and to obtain real-time augmented feedback during a training session. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether providing global positioning system feedback to players between bouts of small-sided games (SSGs) can alter locomotor, physiological, and perceptual responses. Methods: Using a reverse counterbalanced design, 20 male university rugby players received either feedback or no feedback during "off-side" touch rugby SSGs. Eight 5v5, 6 × 4-minute SSGs were played over 4 d. Teams were assigned to a feedback or no-feedback condition (control) each day, with feedback provided during the 2-min between-bouts rest interval. Locomotor, heart rate, and differential rating of perceived exertion of breathlessness and leg-muscle exertion were measured and analyzed using a linear mixed model. Outcomes were reported using effect sizes (ES) and 90% confidence intervals (CI), and then interpreted via magnitude-based decisions. Results: Very likely trivial to unclear differences at all time points were observed in heart rate and differential rating of perceived exertion measures. Possibly to very likely trivial effects were observed between conditions, including total distance (ES = 0.15; 90 CI, −0.03 to 0.34), high-speed distance (ES = −0.07; 90 CI, −0.27 to 0.13), and maximal sprint speed (ES = 0.11; 90% CI, −0.11 to 0.34). All within-bout comparisons showed very likely to unclear differences, apart from possible increases in low-speed distance in bout 2 (ES = 0.23; 90% CI, 0.01 to 0.46) and maximal sprint speed in bout 4 (ES = 0.21; 90% CI, −0.04 to 0.45). Conclusions: In this study, verbal feedback did not alter locomotor, physiological, or perceptual responses in rugby players during SSGs. This may be due to contextual factors (eg, opposition) or the type (ie, distance) or low frequency of feedback provided. [ABSTRACT FROM AUTHOR]

Purpose: Prescribing resistance training using velocity loss thresholds can enhance exercise quality by mitigating neuromuscular fatigue. As little is known regarding performance during these protocols, we aimed to assess the effects of 10%, 20%, and 30% velocity loss thresholds on kinetic, kinematic, and repetition characteristics in the free-weight back squat. Methods: Using a randomized crossover design, 16 resistance-trained men were recruited to complete 5 sets of the barbell back squat. Lifting load corresponded to a mean concentric velocity (MV) of ∼0.70 m·s−1 (115 [22] kg). Repetitions were performed until a 10%, 20%, or 30% MV loss was attained. Results: Set MV and power output were substantially higher in the 10% protocol (0.66 m·s−1 and 1341 W, respectively), followed by the 20% (0.62 m·s−1 and 1246 W) and 30% protocols (0.59 m·s−1 and 1179 W). There were no substantial changes in MV (−0.01 to −0.02 m·s−1) or power output (−14 to −55 W) across the 5 sets for all protocols, and individual differences in these changes were typically trivial to small. Mean set repetitions were substantially higher in the 30% protocol (7.8), followed by the 20% (6.4) and 10% protocols (4.2). There were small to moderate reductions in repetitions across the 5 sets during all protocols (−39%, −31%, −19%, respectively), and individual differences in these changes were small to very large. Conclusions: Velocity training prescription maintains kinetic and kinematic output across multiple sets of the back squat, with repetition ranges being highly variable. Our findings, therefore, challenge traditional resistance training paradigms (repetition based) and add support to a velocity-based approach. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the differences and long-term reliability in perceptual, metabolic, and neuromuscular responses to velocity loss resistance training protocols. Using a repeated, counterbalanced, crossover design, twelve team-sport athletes completed 5-sets of barbell back-squats at a load corresponding to a mean concentric velocity of ~0.70 m·s−1. On different days, repetitions were performed until a 10%, 20% or 30% velocity loss was attained, with outcome measures collected after each set. Sessions were repeated after four-weeks. There were substantial between-protocol differences in post-set differential ratings of perceived exertion (dRPE, i.e., breathlessness and leg muscles, AU) and blood lactate concentration (B[La], mmol·L−1), such that 30%>20%>10% by small to large magnitudes. Differences in post-set countermovement jump (CMJ) variables were small for most variables, such that 30%<20%<10%. Standard deviations representing four-week variability of post-set responses to each protocol were: dRPE, 8–11; B[La], 0.8–1.0; CMJ height, 1.6–2.0; CMJ PPO, 1.0–1.8; CMJ PCV, 0.04–0.06; CMJ 100ms-Impulse, 5.7–11.9. Velocity loss thresholds control the magnitude of perceptual, metabolic, and neuromuscular responses to resistance training. For practitioners wanting to reliably prescribe training that can induce a given perceptual, metabolic, or neuromuscular response, it is strongly advised that velocity-based thresholds are implemented. [ABSTRACT FROM AUTHOR]