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

Abstract

Purpose: To assess measurement sensitivity and diagnostic characteristics of athlete-monitoring tools to identify performance change. Methods: 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. Results: 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 95% CI,(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76)(77)(78)(79)(80). Conclusion: 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.Keywords: subjective questionnaires, heart-rate variability, training monitoring, swimmingThe primary goal of high-performance coaches and sport science staff is to deliver well-controlled training programs for achieving peak performance. Traditional approaches to training prescription include increases in both training volume and intensity before a taper. 1 However, during such heavy training periods athletes may be at a higher risk of negative training outcomes such as injury, illness, and/or overreaching. 2 Indeed, these negative outcomes in training have been associated with a lower chance of achieving success in competition. 3 Therefore, to better understand if athletes are tolerating life stressors and training demands, athletes are monitored through a number of physiological and subjective measures. While observational studies have shown monitoring tools fluctuate with changes to training load, illness, and overreaching, no studies are yet to examine the sensitivity, specificity, or diagnostic characteristics of these monitoring tools for identifying performance change. 2,4,5 In medicine, a common approach used to assess this characteristic is testing a binary outcome, which yields 2 discrete functions to infer an unkno...

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