Dr Heidi Compton

Sprint capacity is an important attribute for team-sport athletes, yet themost appropriate method to analyze it is unclear. Purpose: To examine the relationship between sprint workloads using relative versus absolute thresholds and lower-body soft-tissue and bone-stress injury incidence in professional Australian rules football. Methods: Fifty-three professional Australian rules football athletes' noncontact soft-tissue and bone-stress lower-body injuries (N = 62) were recorded, and sprint workloads were quantified over ~18 months using the global positioning system. Sprint volume (m) and exposures (n) were determined using 2 methods: Absolute (>24.9 km h-1) and relative (=75%,=80%,=85%,=90%,=95% of maximal velocity). Relationships between threshold methods and injury incidence were assessed using logistic generalized additive models. Incidence rate ratios and model performances' area under the curve were reported. Results: Mean (SD) maximal velocity for the group was 31.5 (1.4), range 28.6 to 34.9 km h-1. In comparing relative and absolute thresholds, 75% maximal velocity equated to ~1.5 km h-1 below the absolute speed threshold, while 80% and 85% maximal velocity were 0.1 and 1.7 km h-1 above the absolute speed threshold, respectively. Model area under the curve ranged from 0.48 to 0.61. Very low and very high cumulative sprint loads=80% across a 4-week period, when measured relatively, resulted in higher incidence rate ratios (2.54-3.29), than absolute thresholds (1.18-1.58). Discussion: Monitoring sprinting volume relative to an athlete'smaximal velocity should be incorporated into athlete monitoring systems. Specifically, quantifying the distance covered at >80% maximal velocity will ensure greater accuracy in determining sprint workloads and associated injury risk.

Purpose: To compare relative and absolute speed and metabolic thresholds for quantifying match output in elite rugby league. Methods: Twenty-six professional players competing in the National Rugby League were monitored with global positioning systems (GPS) across a rugby-league season. Absolute speed (moderate-intensity running [MIRTh > 3.6 m/s] and high-intensity running [HIRTh > 5.2 m/s]) and metabolic (>20 W/kg) thresholds were compared with individualized ventilatory (first [VT1IFT] and second [VT2IFT]) thresholds estimated from the 30-15 Intermittent Fitness Test (30-15IFT), as well as the metabolic threshold associated with VT2IFT (HPmetVT2), to examine difference in match-play demands. Results: VT2IFT mean values represent 146%, 138%, 167%, and 144% increases in the HIR dose across adjustables, edge forwards, middle forwards, and outside backs, respectively. Distance covered above VT2IFT was almost certainly greater (ES range = 0.79-1.03) than absolute thresholds across all positions. Trivial to small differences were observed between VT1IFT and MIRTh, while small to moderate differences were reported between HPmetVT2 and HPmetTh. Conclusions: These results reveal that the speed at which players begin to run at higher intensities depends on individual capacities and attributes. As such, using absolute HIR speed thresholds underestimates the physical HIR load. Moreover, absolute MIR and high metabolic thresholds may over- or underestimate the work undertaken above these thresholds depending on the respective fitness of the individual. Therefore, using relative thresholds enables better prescription and monitoring of external training loads based on measured individual physical capacities.

Knowledge of the most intense periods of competitive soccer may assist in the development of specific training methodologies. Objectives: To quantify the peak running intensity of professional soccer and to establish the rate of decline in this intensity as a function of time. Methods: Activity profiles were obtained from 24 players across 40 professional matches. Peak values were calculated for moving averages 1¿10 minutes in duration for relative distance (m·min-1), high-speed relative distance (HS m·min-1), average acceleration/deceleration (m·s2) and metabolic power (Pmet). To quantify the decrease in running intensity for longer moving average durations, each measure was evaluated relative to the moving average duration, as a power law relationship. Results: Peak relative distance and Pmet were lowest for central defenders (effect size [ES] = 0.79¿1.84), whilst acceleration/deceleration intensity was highest for wide defenders (ES = 0.67¿1.42). Differences in the rate of decline in running intensity between positions were considered trivial to small, indicating a similar rate of decline in running intensity across positions. Conclusions: Using power law, the peak running intensities of professional soccer can now be predicted as a function of time, providing coaches with a useful tool for the prescription and monitoring of specific training drills.

Objectives To establish the position and duration-specific running demands of Australian Football (AF) competition for the prescription and monitoring of specific training drills. Design An observational time-motion analysis was performed on 40 professional AF players during 30 games throughout the 2014¿15 competitive seasons. Methods Player movements were collected and peak values were calculated for moving averages of between 1¿10¿min in duration for relative distance (m¿min-1), high-speed relative distance (HS¿m¿min-1), average acceleration/deceleration (m¿s2) and metabolic power (Pmet). A mixed-model analysis was used to detect positional differences, and differences were described using a magnitude-based network. Results Relative distance was likely greater for midfielders (MID), and mobile forwards (MF) compared to tall backs (TB) across all moving average durations assessed, with MF peaking at 223¿±¿35¿m¿min-1 for a 1-min window. High-speed relative distance was at least likely to be greater for MF compared to all other positions, across all moving average durations (ES¿=¿0.27¿0.94). Acceleration/deceleration demands were similar across positions. Conclusions The present study demonstrated that the peak running intensities of AF are well above previously reported peak intensities when considering the distance-based running requirements of match-play. Whilst the acceleration-based metric was unable to detect large differences between positions, it is important to note their contribution to the overall competition demands. This study presents a useful framework for the prescription and monitoring of drills specific to AF competition requirements.

Purpose: To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes. Methods: TL and injury data were collected across 3 seasons (2013¿2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes' corresponding injury status was marked as "available" or "unavailable." Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted. Results: Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes. Conclusions: Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

Resistance exercise is difficult to quantify owing to its inherent complexity with numerous training variables contributing to the training dose (type of exercise, load lifted, training volume, inter-set rest periods, and repetition velocity). In addition, the intensity of resistance training is often inadequately determined as the relative load lifted (% 1-repetition maximum), which does not account for the effects of inter-set recovery periods, repetition velocity, or the number of repetitions performed in each set at a given load. Methods to calculate the volume load associated with resistance training, as well as the perceived intensity of individual sets and entire training sessions have been shown to provide useful information regarding the actual training stimulus. In addition, questionnaires to subjectively assess how athletes are coping with the stressors of training and portable technologies to quantify performance variables such as concentric velocity may also be valuable. However, while several methods have been proposed to quantify resistance training, there is not yet a consensus regarding how these methods can be best implemented and integrated to complement each other. Therefore, the purpose of this review is to provide practical information for strength coaches to highlight effective methods to assess resistance training, and how they can be integrated into a comprehensive monitoring program.

High levels of lean mass are important in collision-based sports for the development of strength and power, which may also assist during contact situations. While skinfold-based measures have been shown to be appropriate for cross-sectional assessments of body composition, their utility in tracking changes in lean mass is less clear. Purpose: To determine the most effective method of quantifying changes in lean mass in rugby league athletes. Methods: Body composition of 21 professional rugby league players was assessed on 2 or 3 occasions separated by = 6 wk, including bioelectrical impedance analysis (BIA), leanmass index (LMI), and a skinfold-based prediction equation (SkF). Dual-X-ray absorptiometry provided a criterion measure of fat-free mass (FFM). Correlation coefficients (r) and standard errors of the estimate (SEE) were used as measures of validity for the estimates. Results: All 3 practical estimates exhibited strong validity for cross-sectional assessments of FFM (r > .9, P < .001). The correlation between change scores was stronger for the LMI (r = .69, SEE 1.3 kg) and the SkF method (r = .66, SEE = 1.4 kg) than for BIA (r = .50, SEE = 1.6 kg). Conclusions: The LMI is probably as accurate in predicting changes in FFM as SkF and very likely to be more appropriate than BIA. The LMI offers an adequate, practical alternative for assessing in FFM among rugby league athletes.