The Influence of Subjective Load on Injury and Illness in Elite Track Cycling
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Introduction: Training time loss due to injury or illness is a major predictor of performance outcome in Olympic athletes. Athlete monitoring is increasingly being utilised in elite sport to examine internal, external and psychological variables that may be related to injury and illness risk. Subjective load monitoring has been shown to be an effective method to monitor training load across a multitude of sports. The Cycling New Zealand (CNZ) high performance track cycling program currently collects external load in the form of power data from selected road and track sessions and subjective load is collected from gym training sessions. There is no load variable utilised across all disciplines and training modalities. Implementing a method to monitor subjective load across all aspects of training may help to reduce injury and illness risk along with optimizing performance in the CNZ high performance track cycling program. These methods may also help to inform athlete monitoring across the wider High Performance Sport New Zealand system. Aim: The primary aim of this research was to investigate the relationship between subjective load and injury or illness in elite CNZ track cycling athletes. Methods: A prospective longitudinal study of two cohorts of elite CNZ track cycling athletes was undertaken in the lead up to the Rio de Janeiro Olympic Games (n=6) and over the 2016/2017 international track cycling season (n=10). This study was approached using stages one and two of the Translating Research into Injury Prevention Practice (TRIPP) model which involved surveillance along with establishing the aetiology and mechanisms of injury and illness. Injury and illness surveillance was performed in line with the International Olympic Committee approach. To investigate the aetiology of injury and illness subjective load monitoring was conducted using the session rating of perceived exertion method with acute load, chronic load and acute to chronic workload ratios calculated. Data was analysed descriptively along with repeated measures logistic regression with exchangeable correlation matrix to determine any relationship between subjective load and injury or illness. A total of 270 weeks was analysed over the two data collections. Results: An increase in chronic load per 100 units increased the combined odds of injury or illness, and individual odds of injury by four percent (p=0.05). Increasing acute load per 100 units or acute to chronic workload ratio per one unit reduced the odds of illness by two percent and 63% respectively (p=0.05). Female athletes had a 20 times higher odds of illness than male athletes (p=0.05). Injury or illness did not always relate to time loss or modification of training and there was a high amount of individual athlete variation in response to load. Conclusions: Subjective load monitoring is an effective method to monitor injury and illness risk in CNZ high performance track cycling athletes. The ability to withstand high chronic loads may be a key factor in developing resilient CNZ athletes and further research is needed to understand the high rates of illness in female athletes. The methodology utilised in this research should be continued in High Performance Sport to collect ongoing data that may help improve performance outcomes for all athletes.