Left ventricular function and mechanics in backs and forwards elite rugby union players.

ABSTRACTPurpose: The aim of the present study was to assess left ventricular (LV) morphological and regional functional adaptations in backs and forwards elite rugby union (RU) players. METHODS: Thirty-nine elite male RU players and twenty sedentary controls have been examined using resting echocardiography. RU players were divided into two groups, forwards (n = 22) and backs (n = 17). Evaluations included tissue Doppler and 2D speckle-tracking analysis to assess LV strains and twisting mechanics. RESULTS: The elite RU players exhibited an LV remodelling characterized by an increase in LV mass indexed to body surface area (82.2 ± 13.2 vs. 99.9 ± 16.1 and 119.7 ± 13.4 g.m-2, in controls, backs and forwards; P < .001). Compared to backs, forwards exhibited lower global longitudinal strain (19.9 ± 2.5 vs. 18.0 ± 1.6%; P < .05), lower early diastolic velocity (16.5 ± 1.8 vs. 15.0 ± 2.3 cm.s-1; P < .05) and lower diastolic longitudinal strain rate (1.80 ± 0.34 vs. 1.54 ± 0.26 s-1; P < .01), especially at the apex. LV twist and untwisting velocities were similar in RU players compared to controls, but with lower apical (-46.2 ± 22.1 vs. -28.2 ± 21.7 deg.s-1; P < .01) and higher basal rotational velocities (33.9 ± 20.9 vs. 48.4 ± 20.7 deg.s-1; P < .05). CONCLUSION: RU players exhibited an increase in LV mass which was more pronounced in forwards. In forwards, LV global longitudinal strain was depressed, LV filling pressures were decreased, and LV relaxation depressed at the apex.Highlights Elite RU players exhibited LV hypertrophy, especially in forwards players.LV regional function suggested a drop in LV relaxation and an increase in LV filling pressures in RU players, with higher alterations in forwards.LV remodelling was associated with regional alterations in torsional mechanics: higher rotations and rotational diastolic velocities at the basal level of LV but lower rotation and rotational diastolic velocities at the apex were observed in RU players.

How does playing position affect fatigue-induced changes in high-intensity locomotor and micro-movements patterns during professional rugby union games?

AbstractWe questioned whether changes in high-intensity locomotor and micro-movements patterns between the first and second part of each half depend on playing position in the 2014-2015 European rugby union championship winning team. Thirty-three rugby players were grouped according to five playing positions. Players were equipped with micro-electromechanical system including a GPS sampling at 10 Hz and high temporal resolution micro-sensors during 17 Top14 and 7 European games. High-speed movements (HSM), high-intensity accelerations (HIA), repeated high-intensity efforts (RHIE), and high-intensity micro-movements (HIMM) were subsequently compared between four 20-min game periods. No significant group × time interactions were observed for any locomotor variables (p > 0.283). Irrespectively of playing position, the number of HSM (p = 0.019), decreased from 0-20 min to 60-80 min as well as from 40-60 to 60-80 min (p < 0.001) with HIA (p = 0.020) and RHIE (p < 0.001). Significant group × time interaction was found for HIMM (p = 0.03) with a significant decrease observed in back row forwards from 0-20 to 60-80 min periods (-17.5%; ES = 0.6; p = 0.031). In elite rugby union, fatigue-induced changes during the last 20 min are independent from playing positions in high-intensity locomotor patterns in contrary to HIMM. Training drills that include specific RHIE (high-speed and HIA efforts) may be useful to postpone match-related fatigue.

High-intensity Activity in European vs. National Rugby Union Games in the best 2014-2015 Team.

The purpose of this study is to evaluate the influence of competition level on running patterns for five playing position in the most successful 2014-2015 European rugby union team. Seventeen French rugby union championship and seven European rugby Champions Cup games were analysed. Global positioning system (sampling: 10 Hz) were used to determine high-speed movements, high-intensity accelerations, repeated high-intensity efforts and high-intensity micro-movements characteristics for five positional groups. During European Champions Cup games, front row forwards performed a higher number of repeated high-intensity efforts compared to National championship games (5.8±1.6 vs. 3.6±2.3; +61.1%), and back row forwards travelled greater distance both at high-speed movements (3.4±1.8 vs. 2.4±0.9 m·min-1; +41.7%) and after high-intensity accelerations (78.2±14.0 vs. 68.1 ±13.4 m; +14.8%). In backs, scrum halves carried out more high-intensity accelerations (24.7±3.1 vs. 14.8±5.0; +66.3%) whereas outside backs completed a higher number of high-speed movements (62.7±25.4 vs. 48.3±17.0; +29.8%) and repeated high-intensity efforts (13.5±4.6 vs. 9.7±4.9; +39.2%). These results highlighted that the competition level affected the high-intensity activity differently among the five playing positions. Consequently, training programs in elite rugby should be tailored taking into account both the level of competition and the high-intensity running pattern of each playing position.

Automatic detection of one-on-one tackles and ruck events using microtechnology in rugby union.

OBJECTIVES: To automate the detection of ruck and tackle events in rugby union using a specifically-designed algorithm based on microsensor data. DESIGN: Cross-sectional study. METHODS: Elite rugby union players wore microtechnology devices (Catapult, S5) during match-play. Ruck (n=125) and tackle (n=125) event data was synchronised with video footage compiled from international rugby union match-play ruck and tackle events. A specifically-designed algorithm to detect ruck and tackle events was developed using a random forest classification model. This algorithm was then validated using 8 additional international match-play datasets and video footage, with each ruck and tackle manually coded and verified if the event was correctly identified by the algorithm. RESULTS: The classification algorithm's results indicated that all rucks and tackles were correctly identified during match-play when 79.4±9.2% and 81.0±9.3% of the random forest decision trees agreed with the video-based determination of these events. Sub-group analyses of backs and forwards yielded similar optimal confidence percentages of 79.7% and 79.1% respectively for rucks. Sub-analysis revealed backs (85.3±7.2%) produced a higher algorithm cut-off for tackles than forwards (77.7±12.2%). CONCLUSIONS: The specifically-designed algorithm was able to detect rucks and tackles for all positions involved. For optimal results, it is recommended that practitioners use the recommended cut-off (80%) to limit false positives for match-play and training. Although this algorithm provides an improved insight into the number and type of collisions in which rugby players engage, this algorithm does not provide impact forces of these events.