Rugby union is a dynamic and complex contact-sport in which power, speed, agility and endurance are required (Smart et al., 2013). Several studies to date have analyzed game demands in elite rugby union players (Austin et al., 2011; Cahill et al., 2013; Cuniffe et al., 2009; Lacome et al., 2014; Lindsay et al., 2015; Quarrie et al., 2013; Roberts et al., 2008). These studies have demonstrated that rugby union is characterized by frequent bouts of veryhigh intensity efforts: sprinting, tackling and wrestling to name just some. The motion analyses carried out in these studies also revealed that elite rugby union players covered 4500-7500 m including 300-800 m above high-speed running (HSR) (> 14.4 km x [h.sup.-1]) threshold with significant differences depending on playing positions. These studies also showed that the backs covered greater distance in HSR, and sprinting (> 25 km x [h.sup.-1]) zones, while the forwards performed more bouts of static exertion (scrummaging and rucking phases) and wrestling phases (Austin et al., 2011; Cahill et al., 2013; Roberts et al., 2008; Lacome et al., 2014). However, Gabbett (2015) and Reardon et al. (2015) demonstrated the limitations (underestimation and difficult interpretation) of using an absolute threshold value to analyze high-intensity running efforts. Furthermore, the sequence of wrestling and rucking phases generated in small side spaces require less continuous running activity but numerous accelerations and changes of velocity. Thus, it may be that the traditional approach is not representing true game demands, particularly the high-intensity running demands.
It has been stated that the analysis of the accelerations and the decelerations may represent an interesting assessment method to monitor running demands (Dalen et al., 2016). Linked to this, Lacome et al. (2014) examined accelerations in their analysis of demands during international rugby union games. Their findings revealed that the backs had a greater mean duration and maximal acceleration while the mean acceleration values were higher in the forwards. Furthermore, accelerations, decelerations, changes of direction and sprint running represent the predominant running activity in team sport games. Despite this, they too often get neglected (Dalen et al., 2016). Yet, these types of running actions contribute significantly to the increases in metabolic energy expenditure generated during a game (di Prampero et al., 2005; Osgnach et al., 2010). Indeed, several studies (Dalen et al., 2016; Lacome et al., 2014; Owen et al., 2015) were focused on the accelerations/decelerations during team sports games, because they showed that considering only the movement speed would induce an underestimation of high-intensity running efforts. Therefore, using accelerations/ decelerations should allow for a better estimate of positional demands in team sports such as rugby union.
The metabolic power approach, initially proposed by di Prampero et al. (2005) and substantiated by Osgnach et al. (2010) may represent an interesting complement to the traditional approach in order to evaluate running demands during team sport. Indeed, this approach takes into consideration the accelerations/decelerations and the speed at which they are performed. Furthermore, Kempton et al. (2015) highlighted that this approach contributed to a better understanding of rugby league game demands. However, the metabolic power approach is not without limitations. Buchheit et al. (2015) questioned the validity and the reliability of the metabolic power approach applied to soccer training drills. They showed that the metabolic power approach demonstrated poor reliability above 20 W x [kg.sup.-1]. They also highlighted that the metabolic power approach underestimated (~ 20%) the energy expenditure recorded through O2 consumption.
HR based methods also permit physiological demands during team sport games to be assessed (Esposito et al., 2004). Regarding rugby union, some studies (Deutsch et al., 1998; Sparks and Coetzee 2013) performed with young rugby union players highlighted important metabolic system demands (e.g. 50% of time is spent above 85% [HR.sub.max]). Moreover, Virr et al. (2014) highlighted significant difference in HR responses between the forwards and the backs during senior women's rugby union games: the forwards displayed higher mean HR and spent more time above 80% of [HR.sub.max] compared to the backs. Nevertheless, HR monitoring remains rarely used in studies analyzing physiological game demands in rugby union, particularly in professional players (Cunniffe et al. 2009). Therefore, using HR-based methods should allow coaches and players alike to better understand and estimate the metabolic demands during professional rugby union games
Assessing and understanding more precisely the running and metabolic demands of elite rugby union is fundamental for trainers, coaches and players alike to optimize the training process. It also allows for trainers to align the training demands with competition requirements and thus improve the specificity of training in these players. (Bradley et al., 2015; Vaz et al., 2015). Therefore, the aims of our study were (1) to analyze elite rugby union game demands using 3 different approaches: traditional, metabolic and heart rate-based methods (2) to explore the relationship between these methods and (3) to explore positional differences between the backs and forwards players.
Fourteen (7 forwards and 7 backs) professional rugby union players (24.1 [+ or -] 3.4 years; 101.4 [+ or -] 12.2 kg and 1.89 [+ or -] 0.07 m) playing in the first division in France (Top 14) volunteered to participate in this study. The positions represented for the forwards were: prop, 2nd row, wing flanker and number 8. The positions represented for the backs were: fly-half, center, winger and full-back. The different positions were represented to limit the influence of the specific demands of each position within the groups. Each position has been represented at least once or several times. All subjects gave informed consent to participate in the experiments in accordance with the Declaration of Helsinki. The study protocol was conducted in accordance with the ethical standards and the guide-lines of the Ethical Committee of the University of Rennes which approved this study protocol.
The physical activity and the HR were measured during 5 European Challenge cup (season 2014-2015) among the same team. The players' match activity was recorded by GPS technology (SPI-HPU, 15 Hz extrapolated from 5 Hz signal, GPSport, Canberra, Australia). The individual's running speed, accelerations and decelerations were assessed from GPS signal. HR was measured using HR monitors (Polar T34, Polar Electro, Kempele, Finland). The GPS units recorded and synchronized speed, accelerations/deceleration data from the GPS signal and HR data. Before each match, the GPS units and the HR monitors were positioned in purpose built vests to minimize unwanted movements during contact phases. At the end of the game, the data were downloaded using Team AMS software (GPSport system, Canberra, Australia). Individual [HR.sub.max] was established during the YoYo intermittent recovery test level 2 (YYIRT2) (Bangsbo et al., 2008) conducted 1 month before the first game. Each match (80 min) was analyzed across each 10 min segments (8 x 10 min segments per game) to allow for a range of statistical analyses comparing the two external approaches with the HR-based method.
Variables used in different approaches
Speed, metabolic and HR zones are presented in Table 1. In agreement with Coutts and Duffield (2010) and Kempton et al., (2015), the threshold for HSR distance and high metabolic power (HMP) distance were set to 14.4 km x [h.sup.-1] and 20 Wkg-1 respectively. The thresholds for accelerations and decelerations were set at [+ or -] 2.5 m x [s.sup.-2] (Cunniffe et al., 2009). The internal load was evaluated using training impulse method (TRIMP), calculated from the Stagno et al.'s (2007) method and the time spent above 85% of [HR.sub.max] was taken as the threshold for high HR exertion (HHRE).
The equations of energy-cost from di Prampero et al. (2005) and subsequently used by Osgnasch et al. (2010) provide the metabolic power variables such as metabolic load (absolute and relative), metabolic power average (MPA) and the distance covered in the different metabolic power...