Assessing the Measurement Sensitivity and Diagnostic Characteristics of Athlete-Monitoring Tools in National Swimmers

• Published in: International journal of sports physiology and performance 2017-04, Vol.12 (Suppl 2), p.S295-S2-100
• Main Authors: Crowcroft, Stephen, McCleave, Erin, Slattery, Katie, Coutts, Aaron J
• Format: Article
• Language: English
• Subjects: Adult; Athletes; Athletic Performance - physiology; Fatigue; Fatigue - physiopathology; Female; Heart Rate; Humans; Longitudinal Studies; Male; Medical diagnosis; Reproducibility of Results; Self Report; Sensitivity and Specificity; Sleep; Swimming - physiology; Young Adult
• ISSN: 1555-0265; 1555-0273
• DOI: 10.1123/ijspp.2016-0406

To assess measurement sensitivity and diagnostic characteristics of athlete-monitoring tools to identify performance change. Fourteen nationally competitive swimmers (11 male, 3 female; age 21.2 ± 3.2 y) recorded daily monitoring over 15 mo. The self-report group (n = 7) reported general health, energy levels, motivation, stress, recovery, soreness, and wellness. The combined group (n = 7) recorded sleep quality, perceived fatigue, total quality recovery (TQR), and heart-rate variability. The week-to-week change in mean weekly values was presented as coefficient of variance (CV%). Reliability was assessed on 3 occasions and expressed as the typical error CV%. Week-to-week change was divided by the reliability of each measure to calculate the signal-to-noise ratio. The diagnostic characteristics for both groups were assessed with receiver-operating-curve analysis, where area under the curve (AUC), Youden index, sensitivity, and specificity of measures were reported. A minimum AUC of .70 and lower confidence interval (CI) >.50 classified a "good" diagnostic tool to assess performance change. Week-to-week variability was greater than reliability for soreness (3.1), general health (3.0), wellness% (2.0), motivation (1.6), sleep (2.6), TQR (1.8), fatigue (1.4), R-R interval (2.5), and LnRMSSD:RR (1.3). Only general health was a "good" diagnostic tool to assess decreased performance (AUC -.70, 95% CI, .61-.80). Many monitoring variables are sensitive to changes in fitness and fatigue. However, no single monitoring variable could discriminate performance change. As such the use of a multidimensional system that may be able to better account for variations in fitness and fatigue should be considered.