Training load monitoring in rugby union

Abstract

The aim of this study was to examine the different measures of training load monitoring used in rugby union and their correlation with each other. The present study also aimed to determine whether individual or arbitrary definitions of high speed running metres using GPS technology correlate to physiological measures of training load. This study is the first study to use biomarkers of training load to determine the definitions of high speed running metres. This study was conducted by using GPS technology, musculoskeletal reviews, perception of wellness monitoring and session rate of perceived exertion data from elite U20 Rugby Union players during different phases of a season. Study 1 examined the reliability and validity of GPS units to measure rugby union specific game demands. This was achieved by comparing interunit measures of distance, velocity and acceleration in conjunction with known measures of given metrics. This study observed that distance (m), max velocity (ms-1) and distance covered at a velocity of >2ms-1 had a strong ICC between units (>0.9), however distance covered showed a significant difference to the predicted distance covered (p<0.05). Max velocity showed no significant difference (p>0.05) as did number of tackles. However, number of tackles had a lower but still large ICC (0.665). The number of accelerations in different bands showed a very large ICC (>0.8), however, the CV was poor (29-68%). The CV was good for distance and velocity. This study concluded that GPS units sampling at 10 Hz are reliable and valid for reporting distance and velocity related metrics, however, may not be as reliable and valid for acceleration and tackle related metrics. Study 2 examined the correlation between internal measures of training load with individual and arbitrary definitions of high speed running metres. This was achieved by comparing distances covered above given velocity thresholds with session rate of perceived exertion and biomarkers of training load. This study observed that high speed running metres as defined by 60% of VMax and MAS had a large, positive correlation with sRPE derived training load (r = 0.518 and 0.574, p < 0.01) compared to the arbitrary measure (r = 0.431, p < 0.01) which had a moderate positive correlation. All other correlations were moderate or trivial. This study concluded that individual definitions of high speed running metres having a stronger relationship with internal training load response as observed by its correlation with training load. Study 3 examined the correlation between measures of training load monitoring during an 11-week period of international competition. This was achieved by comparing the different session rate of perceived exertion for the full week and groin squeeze, countermovement jump, knee to wall, testosterone, cortisol, their ratio, sleep quality, mood, fatigue, stress, and soreness. This study observed that there were moderate, positive correlations between stress and body weight, stress and soreness, groin squeeze and countermovement jump and knee to wall difference and countermovement jump. There were moderate, negative correlations between countermovement jump and mood and sleep. There are strong, positive correlations between sleep and mood, stress and fatigue and fatigue and soreness. All other correlations were trivial. This study concluded that perception of wellness measures are sensitive to physiological, neuromuscular or musculoskeletal responses to training load. This correlation may be indicative of capacity to perform, likelihood to get injured or recovery from a training or competition bout. More cost-effective measures such as groin squeeze may be able to indicate capacity for neuromuscular performance.