LAUSR

Regardless ofwhether or notwe are directly involved in elite sport, the vast majority of us can appreciate the extraordinary level of physical training necessary to compete at an Olympic or Paralympic Games. To provide a flavour, a recent report on the world's most successful female cross-country skier described a yearly training average of 937 ± 25 h during her most successful 5 year period. These hours were distributed across 543 ± 9 sessions, consisting of low-, moderateand high-intensity speed and strength training (Solli, Tønness, & Sandbak, 2017). This would be fairly typical for an elite endurance athlete; some will train more, some less, all within a sportand discipline-specific context. This volume of training undoubtedly places significant physiological and psychological stress upon the individual concerned. We know that availability of training is one of the major determining factors of success or failure for athletes in international competition (Drew, Ben, Raysmith, & Charlton, 2017). As such, the loss of training through either illness or injury is a critical threat to athletic performance. For the year before the Rio 2016 Olympics, UK Sport and the English Institute of Sport estimated that 108,845 days (∼298 years) and 17,173 days (∼47 years) of training were lost to injury and illness respectively, across 38 sports. This lost training time was caused by 4685 separate injury or illness incidents, experienced by 1144 athletes (UK Sport, unpublished data). Team GB finished in the top three of the medal table for both the Olympic and Paralympic Games at both the London 2012 and Rio 2016 games. That said, these injury and illness numbers reveal a significant opportunity. Can we reduce injury and illness? Can we use advances in technology to gain greater insight and turn this into performance gains? Capturing, quantifying and assessing the stress placed upon athletes during training and competition is routine practice in wellorganized elite sports. A variety of validatedmeasurements of external and internal load have been applied (Saw, Main, & Gastin, 2016). A popular example is the use of GPS technology, which, when combined with accelerometry, enables the accurate quantification of athletes’ movement. The acute:chronic workload concept has been used in multiple sports to provide insight into an athlete's preparedness and susceptibility to injury (Blanch & Gabbett, 2016). Technology aside, there are strong arguments that the most valid and accurate way of determining the load an athlete is experiencing is simply to ask them, with existing data supporting the efficacy of using subjective measures (Saw et al., 2016). Whether it is a movement analysis, an acute:chronic ratio or an athlete's subjective assessment, measures