Match and Training Load Exposure and Time-Loss Incidence in Elite Rugby Union Players

• Main Authors: Ben E. W. Cousins, John G. Morris, Caroline Sunderland, Anthony M. Bennett, Golnaz Shahtahmassebi, Simon B. Cooper
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-11-01
• Series: Frontiers in Physiology
• Subjects: RPE; GPS; exponentially weighted moving average; acute:chronic workload ratio; monitoring; mixed effect models
• Online Access: https://www.frontiersin.org/article/10.3389/fphys.2019.01413/full

ObjectiveTo investigate the impact of match and training load on time-loss incidence in elite, professional Rugby Union players.Materials and MethodsEighty-nine Rugby Union players were monitored over two seasons of training and competition. Load was measured for all training sessions and matches using subjective [session ratings of perceived exertion (sRPE) load; RPE × session duration] and objective [global positioning systems (GPS); distance and high-speed running distance] methods and quantified using multiple approaches; absolute match and training load, acute:chronic workload ratio (ACWR), exponentially weighted moving average (EWMA) and cumulative 7, 14, 21, and 28 d sums. Mixed effect models were used to assess the effect of each variable on time-loss incidence.ResultsOf the 474 time-loss incidences that occurred across the two seasons, 50.0% were contact injuries (86.5% occurred during matches and 13.5% during training), 34.8% were non-contact injuries (31.5% occurred during matches and 68.5% during training) and 15.2% were cases of illness. The absolute match and training load variables provided the best explanation of the variance in time-loss incidence occurrence [sRPE load: p < 0.001, Akaike information criterion (AIC) = 2936; distance: p < 0.001, AIC = 3004; high-speed running distance: p < 0.001, AIC = 3025]. The EWMA approach (EWMA sRPE load: p < 0.001, AIC = 2980; EWMA distance: p < 0.001, AIC = 2980; EWMA high-speed running distance: p = 0.002, AIC = 2987) also explained more of the variance in time-loss incidence occurrence than the ACWR approach (ACWR sRPE load: p = 0.091, AIC = 2993; ACWR distance: p = 0.008, AIC = 2990; ACWR high-speed running distance: p = 0.153, AIC = 2994).ConclusionOverall, the absolute sRPE load variable best explained the variance in time-loss incidence, followed by absolute distance and absolute high-speed running distance. Whilst the model fit using the EWMA approach was not as good as the absolute load variables, it was better than when the same variables were calculated using the ACWR method. Overall, these findings suggest that the absolute match and training load variables provide the best predictors of time-loss incidence rates, with sRPE load likely to be the optimal variant of those examined here.