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Supplemental Digital Content is Available in the Text.Peek, RJ, Carey, DL, Middleton, KJ, Gastin, PB, and Clarke, AC. Association between position-specific impact and movement characteristics of professional rugby union players during game play. J Strength Cond Res37(1): 161–166, 2023—The aim of this study was to understand the association between impact and movement characteristics during whole game and peak 1- to 10-minute rolling windows in professional rugby union. Maximal impact (impacts·min−1) and corresponding running (m·min−1) characteristics as well as maximal running (m·min−1) and corresponding impact (impacts·min−1) characteristics were obtained for 160 athletes from 4 teams across the 2018 and 2019 Super Rugby seasons. A linear mixed-effects model reported a positive association between whole-game running and impacts, where greater impact characteristics corresponded with greater running characteristics. The average 1-minute peak running characteristics (150–160 m·min−1) typically occurred when no impacts occurred. The average 1-minute peak impact characteristics (4–6 impacts·min−1) corresponded with an average relative distance of 90–100 m·min−1. Worst case scenario observed impact characteristics as large as 15 impacts·min−1with a corresponding relative distance of 140 m·min−1. When training for peak period characteristics, running may be completed in isolation; however, peak impacts often occur in conjunction with moderate to high running movements. Given running and impact characteristics can appear concurrently within game play, this highlights the need to train them accordingly. As such, when prescribing training drills to replicate the peak characteristics in rugby union, consideration should be taken for both running and impact characteristics.

Tredrea, MSJ, Middleton, KJ, Bourne, MN, Carey, DL, Scanlan, AT, and Dascombe, BJ. Load centralization does not affect the kinetic and kinematic output of countermovement jumps. J Strength Cond Res36(4): 1084–1089, 2022—This study aimed to compare the kinetics, kinematics, and performance of countermovement jumps (CMJs) when completed with 2 different loading conditions (centralized or peripheral) across increasing loads. Seventeen subjects (12 men and 5 women) randomly completed 2 series of CMJs with increasing loads separated by a 30-minute rest period between conditions. Subjects were loaded with either a weighted vest (centralized) or straight barbell (peripheral). A randomized, counterbalanced crossover design was used with incremental loads of 10, 20, 30, 40, and 50% of body mass added to the vest or barbell. Measures of peak force, acceleration, velocity, and power were calculated across each subphase of the CMJs. No significant differences were observed in kinetic or kinematic variables between loading conditions. Within each condition there were significant reductions (p< 0.05) in peak concentric velocity and acceleration, as well as significant increases (p< 0.05) in peak force when the external load increased. Furthermore, braking and propulsive phase duration significantly increased (p< 0.05) and jump height significantly decreased (p< 0.05) as the external load increased. Countermovement jump performance was similar in both central and peripheral loading, whereas increasing load significantly affected jump height, force, velocity, and acceleration variables irrespective of load position. The training stimulus from an external load placed centrally or peripherally is similar regardless of where it is positioned; however, from a practical perspective, a weighted vest may provide a more mobile and safer alternative than a barbell.

Aims(1) To investigate whether a daily acute:chronic workload ratio informs injury risk in Australian football players; (2) to identify which combination of workload variable, acute and chronic time window best explains injury likelihood.MethodsWorkload and injury data were collected from 53 athletes over 2 seasons in a professional Australian football club. Acute:chronic workload ratios were calculated daily for each athlete, and modelled against non-contact injury likelihood using a quadratic relationship. 6 workload variables, 8 acute time windows (2–9 days) and 7 chronic time windows (14–35 days) were considered (336 combinations). Each parameter combination was compared for injury likelihood fit (using R2).ResultsThe ratio of moderate speed running workload (18–24 km/h) in the previous 3 days (acute time window) compared with the previous 21 days (chronic time window) best explained the injury likelihood in matches (R2=0.79) and in the immediate 2 or 5 days following matches (R2=0.76–0.82). The 3:21 acute:chronic workload ratio discriminated between high-risk and low-risk athletes (relative risk=1.98–2.43). Using the previous 6 days to calculate the acute workload time window yielded similar results. The choice of acute time window significantly influenced model performance and appeared to reflect the competition and training schedule.ConclusionsDaily workload ratios can inform injury risk in Australian football. Clinicians and conditioning coaches should consider the sport-specific schedule of competition and training when choosing acute and chronic time windows. For Australian football, the ratio of moderate speed running in a 3-day or 6-day acute time window and a 21-day chronic time window best explained injury risk.

Background: Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes.Hypothesis: Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality.Study Design: Cross-sectional study.Level of Evidence: Level 3.Methods: A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set.Results: Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality.Conclusion: The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits.Clinical Relevance: Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.