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.
The authors are with the Sport & Exercise Discipline Group, University of Technology Sydney (UTS), Moore Park, NSW, Australia. Crowcroft and Slattery are also with the New South Wales Inst of Sport (NSWIS), Sydney, NSW, Australia.