Assessing the measurement sensitivity and diagnostic characteristics of athlete-monitoring tools in national swimmers

Crowcroft, S; McCleave, E; Slattery, K; Coutts, AJ

Assessing the measurement sensitivity and diagnostic characteristics of athlete-monitoring tools in national swimmers

Crowcroft, S McCleave, E Slattery, K Coutts, AJ

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Publication Type:: Journal Article
Citation:: International Journal of Sports Physiology and Performance, 2017, 12 pp. 95 - 100
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Crowcroft, S	en_US
dc.contributor.author	McCleave, E	en_US
dc.contributor.author	Slattery, K	en_US
dc.contributor.author	Coutts, AJ https://orcid.org/0000-0002-1782-7691	en_US
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	International Journal of Sports Physiology and Performance, 2017, 12 pp. 95 - 100	en_US
dc.identifier.issn	1555-0265	en_US
dc.identifier.uri	http://hdl.handle.net/10453/100230
dc.description.abstract	© 2017 Human Kinetics, Inc. 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 (AUC-.70, 95% CI, .61-.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.	en_US
dc.relation.ispartof	International Journal of Sports Physiology and Performance	en_US
dc.relation.isbasedon	10.1123/ijspp.2016-0406	en_US
dc.subject.classification	Sport Sciences	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Fatigue	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Longitudinal Studies	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Sleep	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Swimming	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Athletic Performance	en_US
dc.subject.mesh	Young Adult	en_US
dc.subject.mesh	Athletes	en_US
dc.subject.mesh	Self Report	en_US
dc.title	Assessing the measurement sensitivity and diagnostic characteristics of athlete-monitoring tools in national swimmers	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.for	1106 Human Movement and Sports Sciences	en_US
utslib.for	1116 Medical Physiology	en_US
utslib.for	1701 Psychology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Sports and Exercise Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHSP - Health Services and Practice
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

© 2017 Human Kinetics, Inc. 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 (AUC-.70, 95% CI, .61-.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.

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http://hdl.handle.net/10453/100230