Individualized Monitoring of Muscle Recovery in Elite Badminton

TitelIndividualized Monitoring of Muscle Recovery in Elite Badminton
MedientypJournal Article
Year of Publication2019
AutorenBarth V, Käsbauer H, Ferrauti A, Kellmann M, Pfeiffer M, Hecksteden A, Meyer T
JournalFrontiers Physiol
Start Page778
Date Published06/2019

Purpose: Individualized reference ranges for serum creatine kinase (CK) and urea are a promising tool for the assessment of recovery status in high-level endurance athletes. In this study, we investigated the application of this approach in racket sports, specifically for the monitoring of elite badminton players during the preparation for their world championships. Methods: Seventeen elite badminton players were enrolled of which 15 could be included in the final analysis. Repeated measurements of CK and urea at recovered (R) and non-recovered (NR) time points were used for the stepwise individualization of group-based, prior reference ranges as well as for the evaluation of classificatory performance. Specifically, blood samples were collected in the morning following a day off (R) or following four consecutive training days (NR), respectively. Group based reference ranges were derived from the same data. Error rates were compared between the group-based and individualized approaches using the Fisher exact test. Results: Error rates were numerically lower for the individualized as compared to the group-based approach in all cases. Improvements reached statistical significance for urea (test-pass error rate: p = 0.007; test-fail error rate: p = 0.002) but not for CK (p vs. group-based: test-pass error rate: p = 0.275, test-fail error rate: p = 0.291). Regardless of the chosen approach, the use of CK was associated with lower error rates as compared to urea. Conclusion and Practical Applications: Individualized reference ranges seem to offer diagnostic benefits in the monitoring of muscle recovery in elite badminton. The lack of significant improvements in error rates for CK is likely due to the large difference between R and NR for this parameter with error rates that are already low for the group-based approach.

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