Dr Heidi Thornton

We provide a novel analysis of the locomotor movements of athletes in the National Rugby League Women¿s (NRLW) competition by presenting the data of opposing teams expressed as a relative (%) difference and explore the association with match outcome. 117 rugby league athletes from the four NRLW clubs participated in this study. Mean speed (m·min-1), mean high-speed running (>12 km·h-1; m·min-1), and mean acceleration (m·s-2) were measured in 12 matches (370 individual match files) using the Global Navigation Satellite System (GNSS). Individual GNSS-derived data from each match-half were summed across each team and the association with total points and the points differential in each match-half was determined using linear mixed models. Greater high-speed running and lower mean acceleration were associated with more points being scored. A greater relative difference in mean high-speed running between competing teams was associated with a higher points differential. That is, if a team completed 10% more high-speed running than their opposition, they were likely to score an average of 3.2 points more during a given match-half. This unique analysis of GNSS-derived data may assist coaches and performance support staff to interpret the locomotor movements of female rugby league players with the appropriate considerations for the opposition team.

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.

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.