Search

Background: Repeated-sprint training (RST) is a common training method for enhancing physical fitness in athletes. To advance RST prescription, it is important to understand the effects of programming variables on physical fitness and physiological adaptation. Objectives: This study (1) quantifies the pooled effects of running RST on changes in 10 and 20 m sprint time, maximal oxygen consumption (VO2max), Yo-Yo Intermittent Recovery Test Level 1 (YYIR1) distance, repeated-sprint ability (RSA), countermovement jump (CMJ) height and change of direction (COD) ability in athletes, and (2) examines the moderating effects of program duration, training frequency, weekly volume, sprint modality, repetition distance, number of repetitions per set and number of sets per session on changes in these outcome measures. Methods: Pubmed, SPORTDiscus and Scopus databases were searched for original research articles up to 04 July 2023, investigating RST in healthy, able-bodied athletes, between 14 and 35 years of age, and a performance calibre of trained or above. RST interventions were limited to repeated, maximal running (land-based) sprints of ≤ 10 s duration, with ≤ 60 s recovery, performed for 2–12 weeks. A Downs and Black checklist was used to assess the methodological quality of the included studies. Eligible data were analysed using multi-level mixed-effects meta-analysis, with standardised mean changes determined for all outcomes. Standardised effects [Hedges G (G)] were evaluated based on coverage of their confidence (compatibility) intervals (CI) using a strength and conditioning specific reference value of G = 0.25 to declare an improvement (i.e. G > 0.25) or impairment (i.e. G < − 0.25) in outcome measures. Applying the same analysis, the effects of programming variables were then evaluated against a reference RST program, consisting of three sets of 6 × 30 m straight-line sprints performed twice per week for 6 weeks (1200 m weekly volume). Results: 40 publications were

Understanding the physical qualities of athletes can lead to improved training prescription, monitoring, and ranking. Consequently, testing and profiling athletes is an important aspect of strength and conditioning. However, results can often be difficult to interpret because of the wide range of available tests and outcome variables, the diverse forms of technology used, and the varying levels of standardization implemented. Furthermore, physical qualities can easily be misrepresented without careful consideration if fundamental scientific principles are not followed. This review discusses how to develop impactful testing batteries so that practitioners can maximize their understanding of athletic development while helping to monitor changes in performance to better individualize and support training. It also provides recommendations on the selection of tests and their outcome measures; considerations for the proper interpretation, setup, and standardization of testing protocols; methods to maximize testing information; and techniques to enhance visualization and interpretation.

Background: Maximal lower-body strength can be assessed both dynamically and isometrically; however, the relationship between the changes in these 2 forms of strength following resistance training is not well understood. Purpose: To systematically review and analyze the effects of resistance training on changes in maximal dynamic (1-repetition-maximum back squat, deadlift, and power clean) and position-matched isometric strength (isometric midthigh pull and the isometric squat). In addition, individual-level data were used to quantify the agreement and relationship between changes in dynamic and isometric strength. Methods: Databases were systematically searched to identify eligible articles, and meta-analysis procedures were performed on the extracted data. The raw results from 4 studies were acquired, enabling bias and absolute reliability measures to be calculated using Bland–Altman test of agreement. Results: Eleven studies met the inclusion criteria, which resulted in 29 isometric–dynamic change comparisons. The overall pooled effect was 0.13 in favor of dynamic testing; however, the prediction interval ranged from g = −0.49 to 0.75. There was no evidence of bias (P =.825) between isometric and dynamic tests; however, the reliability coefficient was estimated to be 16%, and the coefficient of variation (%) was 109.27. Conclusions: As a range of future effects can be expected when comparing isometric to dynamic strength changes following resistance training, and limited proportionality exists between changes in these 2 strength qualities, there is strong evidence that isometric and dynamic strength represent separate neuromuscular domains. These findings can be used to inform strength-assessment models in athlete populations.

Augmented feedback is often used during resistance training to enhance acute physical performance and has shown promise as a method of improving chronic physical adaptation. However, there are inconsistencies in the scientific literature regarding the magnitude of the acute and chronic responses to feedback and the optimal method with which it is provided. Objective This systematic review and meta-analysis aimed to (1) establish the evidence for the effects of feedback on acute resistance training performance and chronic training adaptations; (2) quantify the effects of feedback on acute kinematic outcomes and changes in physical adaptations; and (3) assess the effects of moderating factors on the influence of feedback during resistance training. Methods Twenty studies were included in this systematic review and meta-analysis. This review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Four databases were searched, and studies were included if they were peer-reviewed investigations, written in English, and involved the provision of feedback during or following dynamic resistance exercise. Furthermore, studies must have evaluated either acute training performance or chronic physical adaptations. Risk of bias was assessed using a modified Downs and Black assessment tool. Multilevel meta-analyses were performed to quantify the effects of feedback on acute and chronic training outcomes. Results Feedback enhanced acute kinetic and kinematic outputs, muscular endurance, motivation, competitiveness, and perceived effort, while greater improvements in speed, strength, jump performance, and technical competency were reported when feedback was provided chronically. Furthermore, greater frequencies of feedback (e.g., following every repetition) were found to be most beneficial for enhancing acute performance. Results demonstrated that feedback improves acute barbell velocities by approximately 8.4% (g = 0.63, 95%

Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n = 2,381 observations) from nine Rugby Football Union Regional Academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d = 0.65 to 0.73) and maturation (d=-0.77 to -0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development.

Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n = 2,381 observations) from nine Rugby Football Union Regional Academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d = 0.65 to 0.73) and maturation (d=-0.77 to -0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development.

The purpose was to investigate the effects of travel on performance in the National Rugby League (NRL). A total of 4,704 observations from 2,352 NRL matches (2007-2019) were analysed. The effect of travel on match outcome (i.e., win/loss) was analysed using a generalized linear mixed model, and the points difference using a linear mixed model. For every 1,000 km travelled in the NRL, the estimated probability of winning a match was reduced by -2.7% [-5.7 to 0.3%] and the estimated points difference by -1.1 [-2.0 to -0.2] points. In relation to every 1,000 km travelled, the 2007-2009 seasons had the greatest reduction in the likelihood of winning a match (-2.7% [-4.7 to -0.6%]), with the 2018-2019 seasons having the greatest likelihood (1.1% [-1.2 to 3.3%]). Regarding inter-state travel, teams from the state of Queensland had the greatest reduction in the likelihood of winning a match while the team from the state of Victoria had the greatest likelihood, although there were no clear differences between states. These data suggest that travel has impacted performance in NRL matches although this effect has reduced over time. These findings are useful for practitioners that prepare athletes in sports where frequent short-haul travel is required.

The quantification of the technical, tactical and physical demands of match-play in youth rugby is important for the appropriate prescription of training practices. Differences in the demands of match-play have been identified between positions, playing standards, age grades and phases-of-play. This chapter presents the research that has explored the physical and technical-tactical match demands of youth rugby. The chapter then provides a practical overview of how practitioners can utilise match-demands data to assist in appropriate training prescription through the adaptation, manipulation and evaluation of training drills and practices. The chapter concludes with a range of recommendations and practical implications for the use of match-play data within youth rugby environments.

The use of unstable surfaces during resistance training has demonstrated a maintenance or reduction on force production. However, the use of unstable surface on force variability has not been assessed using non-linear methods that may be better suited to detect changes in movement variability throughout a given movement. Consequently, this study compared the use of stable vs unstable surfaces on force variability during bilateral squats performed with an inertial flywheel device (Eccoteck, Byomedic System SCP, Spain). Twenty healthy men (mean ± SD: age 22.9 ± 2.9 years, height 1.81 ± 0.7 m, body mass 76.4 ± 7.6 kg and 1RM back squat 110.9 ± 19.7 kg) with a minimum of four years in resistance training performed six sets of six repetitions of squats at maximal concentric effort with one minute rest between sets. Force output on the vertical axes was measured using a strain gauge and the results were processed using nonlinear sample entropy (SampEn). Results showed no differences for any of the dependent variables between stable and unstable conditions. SampEn showed no differences between conditions (chi-squared = 0.048 P = 0.827), while Forcemean and SampEn presented a small correlation (r = 0.184; p < 0.01). No changes in entropy were found over the course of the series. Together, these results suggest that the structure of force variability between stable and unstable surfaces are similar. This lack of difference between surfaces may be due to postural and anticipatory adjustments. Consequently, by introducing unstable surfaces to the flywheel bilateral squat exercise, practitioners may not observe changes in Forcemean and force variability when compared to stable surface training suggesting that increased training volumes or intensity may be required during unstable environments to cause a desired training stimulus., Peer Reviewed, Postprint (author's final draft)

Purpose: Feedback can enhance acute physical performance. However, its effects on physical adaptation have received little attention. Therefore, the purpose of this study was to determine the effect of feedback during a 4-wk training program on jump, sprint, and strength adaptations. Methods: A total of 28 semiprofessional male rugby union players were strength-matched into 2 groups (feedback and nonfeedback). During the 4-wk training program, the Feedback group received immediate, objective feedback on (1) mean concentric velocity during resistance training repetitions, (2) distance feedback for standing broad jumps, and (3) time for sprints. The Nonfeedback group was not provided additional information. Across the 4-wk mesocycle, subjects completed 3 strength and conditioning sessions per week. Countermovement jump, standing long jump, 10- and 20-m sprint, and 3-repetition-maximum barbell back squat and bench press were measured before and after the training intervention. Magnitude-based inferences assessed meaningful changes within and between groups. Results: The Feedback group showed small to moderate improvements in outcome measures, whereas the Nonfeedback group demonstrated trivial to small improvements. Improvements in countermovement-jump relative peak power (effect size ± 90% confidence limits: 0.34 ± 0.42), 10-m (0.20 ± 0.35) and 20-m sprints (0.40 ± 0.21), and 3-repetition-maximum back squats (0.23 ± 0.17) were possibly to likely greater for the Feedback condition than for Nonfeedback. Conclusions: Providing augmented feedback during strength and conditioning routines can enhance training adaptations compared with athletes who do not receive feedback. Consequently, practitioners should consider providing kinematic outputs, displacement, or sprint time at the completion of each repetition as athletes train.

Edwards, T., Weakley, J., Banyard, H. G., Cripps, A., Piggott, B., Haff, G. G., & Joyce, C. (2021). Influence of age and maturation status on sprint acceleration characteristics in junior Australian football. Journal of Sports Sciences, 39(14), 1585-1593. https://doi.org/10.1080/02640414.2021.1886699

Banyard, H. G., Tufano, J. J., Weakley, J. J. S., Wu, S., Jukic, I., & Nosaka, K. (2020). Superior changes in jump, sprint, and change-of-direction performance but not maximal strength following 6 weeks of velocity-based training compared with 1-repetition-maximum percentage-based training. International Journal of Sports Physiology and Performance, 16(2), 232-242. https://doi.org/10.1123/IJSPP.2019-0999

Weakley, J., Wilson, K., Till, K., Banyard, H., Dyson, J., Phibbs, P., ... Jones, B. (2020). Show me, tell me, encourage me: The effect of different forms of feedback on resistance training performance. The Journal of Strength & Conditioning Research, 34(11), 3157-3163. https://doi.org/10.1519/jsc.0000000000002887

Pearson, J., Spathis, J. G., van den Hoek, D. J., Owen, P. J., Weakley, J., & Latella, C. (2020). Effect of competition frequency on strength performance of powerlifting athletes. The Journal of Strength & Conditioning Research, 34(5), 1213-1219. https://doi.org/10.1519/JSC.0000000000003563

The aim of this study was to assess the criterion validity, interunit reliability (accounting for technological and biological variance), and between-day reliability of a novel optic laser device (FLEX) for quantifying mean concentric velocity. To assess the validity against a three-dimensional motion capture system and interunit reliability with both technological and biological variation, 18 men and women completed repetitions at 20, 40, 60, 80, 90, and 100% of one repetition maximum in the free-weight barbell back squat and bench press. To assess interunit (technological only) reliability, a purpose-built, calibrated rig completed a set protocol with 2 devices. To assess between-day reliability of the technology, the same protocol was repeated 21 days later. Standardized bias, typical error of the estimate (TEE; %), and Pearson's correlation coefficient (r) were used to assess validity, whereas typical error and coefficient of variation (CV%) were calculated for reliability. Overall, TEE (±90 CL) between the FLEX and criterion measure was 0.03 (±0.004) and 0.04 (±0.005) m·s−1 in the back squat and bench press, respectively. For measures of reliability, overall interunit technological variance (CV% [± 90% confidence interval]) was 3.96% (3.83–4.12) but increased to 9.82% (9.31–10.41) and 9.83% (9.17–10.61) in the back squat and bench press, respectively, when biological variance was introduced. Finally, the overall between-day reliability was 3.77% (3.63–3.91). These findings demonstrate that the FLEX provides valid and reliable mean concentric velocity outputs across a range of velocities. Thus, practitioners can confidently implement this device for the monitoring and prescription of resistance training loads.