Individual Patterns in Blood-Borne Indicators of Fatigue - Trait or Chance

© 2016 National Strength and Conditioning Association. Julian, R, Meyer, T, Fullagar, HHK, Skorski, S, Pfeiffer, M, Kellmann, M, Ferrauti, A, and Hecksteden, A. Individual patterns in blood-borne indicators of fatigue - trait or chance. J Strength Cond Res 31(3): 608-619, 2017 - Blood-borne markers of fatigue such as creatine kinase (CK) and urea (U) are widely used to fine-tune training recommendations. However, predictive accuracy is low. A possible explanation for this dissatisfactory characteristic is the propensity of athletes to react to different patterns of fatigue indicators (e.g., predominantly muscular [CK] or metabolic [U]). The aim of the present trial was to explore this hypothesis by using repetitive fatigue-recovery cycles. A total of 22 elite junior swimmers and triathletes (18 ± 3 years) were monitored for 9 weeks throughout 2 training phases (low-intensity, high-volume [LIHV] and high-intensity, low-volume [HILV] phases). Blood samples were collected each Monday (recovered) and Friday (fatigued) morning. From measured values of CK, U, free-testosterone (FT), and cortisol (C) as determined in the rested and fatigued state, respectively, Monday-Friday differences (Δ) were calculated and classified by magnitude before calculation of ratios (ΔCK/ΔU and ΔFT/ΔC). Coefficient of variation (CV) was calculated as group-based estimates of reproducibility. Linear mixed modeling was used to differentiate inter- and intraindividual variability. Consistency of patterns was analyzed by comparing with threshold values (1.1 for all weeks). Reproducibility was very low for fatigue-induced changes (CV ≥ 100%) with interindividual variation accounting for 45-60% of overall variability. Case-wise analysis indicated consistent ΔCK/ΔU patterns for 7 individuals in LIHV and 7 in HILV; 5 responded consistently throughout. For ΔFT/ΔC the number of consistent patterns was 2 in LIHV and 3 in HILV. These findings highlight the potential value of an individualized and multivariate approach in the assessment of fatigue