2Complex training research has indicated that 3-4 minutes may be an optimum 3 intracomplex rest interval. The purpose of this study was to determine if a heavy 4 resistive exercise causes performance enhancement of a slow stretch-shortening cycle 5 exercise and if there is an optimal rest interval. Eighteen subjects performed 6 countermovement jumps before and after a 5RM back squat lifting protocol. This 7 procedure was repeated 4 times over 2 days using rest intervals of 30 seconds, 2, 4 8 and 6 minutes. Flight time and peak ground reaction force were the dependent 9 variables. All jumps were performed on a specially constructed sledge and force 10 platform apparatus. Repeated measures ANOVA found a significant reduction in 11 flight time at the 30 second and 6 minute interval (p < 0.05). No significant difference 12 was found between men and women. Only the men showed an enhancement in jump 13 performance after the 4 minute interval. The improvement window was different for 14 each subject and an analysis of the greatest increase and decrease in flight time and 15 peak ground reaction force was conducted, showing a significant decrease for men 16 and women and a significant increase in flight time for men and peak ground reaction 17 force for women. The results suggest that complex training can benefit and/ or inhibit 18 countermovement jump performance depending on the rest interval. The individual 19 determination of the intracomplex rest interval may be necessary in the practical 20 setting. 21 22

BackgroundThe loads to which professional rugby players are subjected has been identified as a concern by coaches, players and administrators. In November 2014, World Rugby commissioned an expert group to identify the physical demands and non-physical load issues associated with participation in professional rugby.ObjectiveTo describe the current state of knowledge about the loads encountered by professional rugby players and the implications for their physical and mental health.FindingsThe group defined ‘load’ as it relates to professional rugby players as the total stressors and demands applied to the players. In the 2013–2014 seasons, 40% of professional players appeared in 20 matches or more, and 5% of players appeared in 30 matches or more. Matches account for ∼5–11% of exposure to rugby-related activities (matches, team and individual training sessions) during professional competitions. The match injury rate is about 27 times higher than that in training. The working group surmised that players entering a new level of play, players with unresolved previous injuries, players who are relatively older and players who are subjected to rapid increases in load are probably at increased risk of injury. A mix of ‘objective’ and ‘subjective’ measures in conjunction with effective communication among team staff and between staff and players was held to be the best approach to monitoring and managing player loads. While comprehensive monitoring holds promise for individually addressing player loads, it brings with it ethical and legal responsibilities that rugby organisations need to address to ensure that players’ personal information is adequately protected.ConclusionsAdministrators, broadcasters, team owners, team staff and the players themselves have important roles in balancing the desire to have the ‘best players’ on the field with the ongoing health of players. In contrast, the coaching, fitness and medical staff exert significant control over the activities, duration and intensity of training sessions. If load is a major risk factor for injury, then managing training loads should be an important element in enabling players to perform in a fit state as often as possible.

1 2Alternating a resistance exercise with a plyometric exercise is referred to as 3 complex training. This study examined the effect of various resistive loads on 4 the biomechanics of performance of a fast stretch shortening cycle activity 5 and determined if an optimal resistive load exists for complex training. Twelve 6 elite level rugby players performed three drop jumps before and after three 7 back squat resistive loads of 65%, 80% and 93% of 1RM. All drop jumps were 8 performed on a specially constructed sledge and force platform apparatus. 9Flight time, ground contact time, peak ground reaction force, reactive strength 10 index and leg stiffness were the dependent variables. Repeated measures 11 ANOVA found that all resistive loads significantly reduced (p < 0.01) flight 12 time, but lifting at the 93% load caused a significant improvement (p < 0.05) in 13 ground contact time and leg stiffness. From a training perspective, the results 14 indicate that the heavy lifting will encourage the fast stretch shortening cycle 15 activity to be performed with a stiffer leg spring action, which in turn may 16 benefit performance. However, it is unknown if these acute changes will 17 produce any long-term adaptations to muscle function. 18 19 20

This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running.INDEX TERMS Accelerometry, artificial neural networks, human biomechanics, motion analysis, kinematics, sports performance.

This study assessed the relationship of long and short stretch-shortening cycle test scores to sprint performances in trained female athletes. Seventeen trained, female, high school, competitive sprinters completed the following tests: countermovement jump for vertical distance (CMJ), bounce drop jump for height with minimum ground contact time (BDJ index), and ground contact time (GCT) during the BDJ and a 5-step bound (5B) test. Group mean and SD values were as follows: height, 167.7 +/- 3.7 cm; body mass, 59.9 +/- 7.2 kg; and percentage of body fat (PF), 20.3 +/- 1.8%. Sprint performances at 30-, 100-, and 300-m distances were assessed. Stretch-shortening cycle performance and sprint results (mean +/- SD) were as follows: CMJ, 33.8 +/- 3.8 cm; BDJ index, 166.7 +/- 24.7 cm/s; 5B test, 10.98 +/- 0.76 m; 30-m sprint, 4.58 +/- 0.17 seconds; 100-m sprint, 12.9 +/- 0.61 seconds; and 300-m sprint, 45.03 +/- 2.94 seconds. Correlations indicated that no relationship existed between PF and the dependent sprint variables. Significant correlations (p < 0.05) existed between CMJ and 30-m (r = -0.60), 100-m (r = -0.64), and 300-m (r = -0.55) sprint times; BDJ index and 30-m (r = -0.79) and 100-m (r = -0.75) sprint times; and 5B test and 300-m sprint time (r = -0.54). Multiple regression analysis found significant T values for BDJ index with 30- and 100-m sprints and CMJ and PF with 300 m. Results indicated that the BDJ index and CMJ tests were significantly related to sprint performances in female athletes.