Athlete Performance Monitoring in Anti-Doping

摘要

The use of information technology within sport has significantly increased over recent years. These data and information have the potential to make a significant impact on sporting performance, and the nature of its related sciences too. For example, the retrospective analysis of sporting performance data affords the possibility to identify the impact of various technological advances and rule changes on world record performances in sports such as, javelin throwing (+95% over 76 years), pole vault (+86% in 94 years), and 1-h track cycling (+221% in 111 years; Haake, 2009 ). Similarly, such types of longitudinal data analysis may also be useful from an anti-doping perspective. In this regard, it has previously been shown that yearly world best performances increase with the emergence of new potent doping agents, such as anabolic steroids or EPO (Schumacher and Pottgiesser, 2009 ). Conversely, when new anti-doping tests are implemented, overall world best performances decrease as the effects of certain performance enhancing drugs become detectable, and are therefore avoided by athletes (Schumacher and Pottgiesser, 2009 ). These findings raise the possibility that performance monitoring can be useful for anti-doping efforts. As the aim of any doping regime is to improve sporting performance, it has been suggested performance data, in the form of an Athlete Performance Module (APM), may be useful in strengthening the sensitivity and applicability of the current Athlete Biological Passport (ABP) in the fight against doping in sports (Schumacher and Pottgiesser, 2009 ). However, there is a general view that performance biometrics alone are not sufficient evidence to establish doping, and as such, cannot demonstrate the use of a prohibited substance in accordance with the World Anti-Doping Code (Article 2.2). Even though sudden increases in performance can be caused by reasons other than doping (e.g., improved training or nutritional strategies), such observations may nevertheless provide worthwhile information in order to trigger targeted anti-doping tests of specific athletes (Iljukov et al., 2018 ). In addition, whilst not sufficient to convict an athlete for doping, an atypical individual performance profile may also be useful as corroborative evidence in, for example, an ABP case. However, to date, the use of performance data for anti-doping purposes by National Anti-Doping Organizations (NADOs) and International Federations (IFs) remains low. The need for robust performance data Even though access to performance data is growing, data quality, and accessibility remain important barriers to overcome, especially as differences exist across sports and levels of competition. For example, performance results and rankings are often available for top-level international events, but less so at more regional and national levels. This is important for the use of performance data in anti-doping given that mostly a change in performance, rather than the absolute level of performance may be an indicator of doping. Therefore, the availability of data for longitudinal tracking of athletes over the course of their careers is critical to identify an individual's performance progress. As such, a key component of this longitudinal monitoring is to be able to differentiate between “normal” increases in performance caused by maturation and training, from an “unnatural” improvement caused by doping. A lack of available data concerning performance development and variability therefore makes it difficult to interpret an individual athlete's performance changes, and whether they are “realistic” or not. From the limited studies that have investigated the within-athlete variability of performance, it is apparent that elite athlete individual variation of performance over a season in so-called Centimeter-Gram-Second (CGS) sports appears to be relatively small, with for example, the coefficient of variation ranging from 1.1 to 1.4% (90% CI: 1.0–1.6%) in track and field athletics (Malcata and Hopkins, 2014 ). Moreover, the between season variability in performance also appears relatively stable, for example coefficient of variations as low as 1% (90% CI: 0.9–1.1%) have been reported for elite rowing athletes (Smith and Hopkins, 2011 ). Thus, the priority for the development of an APM should be the collection of sport specific performance data, together with the identification of potential confounding factors affecting this data (e.g., pacing, tactics, environmental conditions etc.). Research is required in order to develop an evidence basis for “normal” seasonal variations and longitudinal changes in performance across sports. Moreover, there is a need to identify the typical rates of performances increases in sports as athletes' transition across junior categories to the elite rankings, as well as the inevitable decline in performance with aging (Berthelot et al., 2012 ). The actual performance metric(s) that should be used in an APM is