Introduction: Assessment of fatigue and recovery is essential for the daily fine-tuning of training load in competitive sports. However, large interindividual variability severely impedes on the diagnostic accuracy of fatigue indicators [1]. Individualization of reference ranges and joint consideration of selected parameters seem pertinent solutions to this dilemma. Therefore, we developed and evaluated an individualization algorithm based on a similar Bayesian rationale as the Athlete Biological Passport [2]. Beyond individualizing reference ranges for separate parameters, this procedure may also be generalized to the multivariate case. Method: Urea and creatine kinase (CK) were selected as exemplary fatigue indicators [1]. Prior distributions and repeated measures standard deviations were characterized based on data of 883 squad athletes from different sports. Equations for the individualization procedure were adapted from previously published material [3,4] to allow for discrimination of 2 physiological states and ensure gradual approximation of intraindividual variability. Evaluation was carried out using data from 5 consecutive fatigue-recovery cycles in 14 elite junior swimmers and triathletes. Group-based reference ranges were derived from that same dataset to ensure a best possible group-based reference for comparison. Results: Sensitivity was significantly higher with individualized reference ranges for CK (p=0,013) while a tendency towards significance could be demonstrated for urea (p=0,068). Numerical improvements in specificity failed to reach statistical significance in both cases. The success of individualization differed widely between athletes depending on the mean difference between fatigue states and intraindividual variability (`individual effect size`). Visualization of measured values together with the development of reference ranges over successive measurements offers an intuitive plausibility control for the individualization procedure and ensuing classifications. Discussion: Assessment of fatigue and recovery based on individualized reference ranges for CK and urea offers higher diagnostic accuracy as compared to even a sample specific group-based approach. Additional benefit from multivariate applications and transferability to other parameters remains to be established.
© Copyright 2016 21st Annual Congress of the European College of Sport Science (ECSS), Vienna, 6. -9. July 2016. Veröffentlicht von University of Vienna. Alle Rechte vorbehalten.