Lactate Equivalent for Maximal Lactate Steady State Determination in Soccer

Garcia-Tabar, I; Rampinini, E; Gorostiaga, EM

Lactate Equivalent for Maximal Lactate Steady State Determination in Soccer

Garcia-Tabar, I Rampinini, E Gorostiaga, EM

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Publication Type:: Journal Article
Citation:: Research Quarterly for Exercise and Sport, 2019, 90 (4), pp. 678 - 689
Issue Date:: 2019-10-02

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Field	Value	Language
dc.contributor.author	Garcia-Tabar, I	en_US
dc.contributor.author	Rampinini, E	en_US
dc.contributor.author	Gorostiaga, EM	en_US
dc.date.issued	2019-10-02	en_US
dc.identifier.citation	Research Quarterly for Exercise and Sport, 2019, 90 (4), pp. 678 - 689	en_US
dc.identifier.issn	0270-1367	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139181
dc.description.abstract	© 2019, © 2019 SHAPE America. Purpose: The association between an overlooked classical Lactate Threshold (LT), named “Minimum Lactate Equivalent” (LEmin), with Maximal Lactate Steady State (MLSS) has been recently described with good MLSS prediction results in endurance-trained runners. This study aimed to determine the applicability of LEmin to predict MLSS in lower aerobic-conditioned individuals compared to well-established blood lactate-related thresholds (BLTs). Method: Fifteen soccer players [velocity at MLSS (MLSSV) 13.2 ± 1.0 km·h−1; coefficient of variation (CV) 7.6%] conducted a submaximal discontinuous incremental running test to determine BLTs and 3–6 constant velocity running tests to determine MLSSV. Results: LEmin did not differ from conventional LTs (p > .05) and was 24% lower than MLSS (p < .001; ES: 3.26). Among LTs, LEmin best predicted MLSSV (r = 0.83; p < .001; SEE = 0.59 km·h−1). There was no statistical difference between MLSS and estimated MLSS using LEmin prediction formula (p = .99; ES: 0.001). Mean bias and limits of agreement were 0.00 ± 0.58 km·h−1 and ±1.13 km·h−1, respectively. LEmin best predicted MLSSV (r = 0.92; p < .001; SEE = 0.54 km·h−1) in the pooled data of soccer players and endurance-trained runners of the previous study (n = 28; MLSSV range 11.2–16.5 km·h−1; CV 9.8%). Conclusion: Results support LEmin to be one of the best single predictors of MLSS. This study is the sole study providing specific operational regression equations to estimate the impractical gold standard MLSSV in soccer players by means of a BLT measured during a submaximal single-session test.	en_US
dc.relation.ispartof	Research Quarterly for Exercise and Sport	en_US
dc.relation.isbasedon	10.1080/02701367.2019.1643446	en_US
dc.subject.classification	Sport Sciences	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Lactic Acid	en_US
dc.subject.mesh	Exercise Test	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Cross-Sectional Studies	en_US
dc.subject.mesh	Anaerobic Threshold	en_US
dc.subject.mesh	Running	en_US
dc.subject.mesh	Soccer	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Young Adult	en_US
dc.title	Lactate Equivalent for Maximal Lactate Steady State Determination in Soccer	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	90	en_US
utslib.for	1106 Human Movement and Sports Sciences	en_US
utslib.for	1302 Curriculum and Pedagogy	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
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	90	en_US

Abstract:

© 2019, © 2019 SHAPE America. Purpose: The association between an overlooked classical Lactate Threshold (LT), named “Minimum Lactate Equivalent” (LEmin), with Maximal Lactate Steady State (MLSS) has been recently described with good MLSS prediction results in endurance-trained runners. This study aimed to determine the applicability of LEmin to predict MLSS in lower aerobic-conditioned individuals compared to well-established blood lactate-related thresholds (BLTs). Method: Fifteen soccer players [velocity at MLSS (MLSSV) 13.2 ± 1.0 km·h−1; coefficient of variation (CV) 7.6%] conducted a submaximal discontinuous incremental running test to determine BLTs and 3–6 constant velocity running tests to determine MLSSV. Results: LEmin did not differ from conventional LTs (p > .05) and was 24% lower than MLSS (p < .001; ES: 3.26). Among LTs, LEmin best predicted MLSSV (r = 0.83; p < .001; SEE = 0.59 km·h−1). There was no statistical difference between MLSS and estimated MLSS using LEmin prediction formula (p = .99; ES: 0.001). Mean bias and limits of agreement were 0.00 ± 0.58 km·h−1 and ±1.13 km·h−1, respectively. LEmin best predicted MLSSV (r = 0.92; p < .001; SEE = 0.54 km·h−1) in the pooled data of soccer players and endurance-trained runners of the previous study (n = 28; MLSSV range 11.2–16.5 km·h−1; CV 9.8%). Conclusion: Results support LEmin to be one of the best single predictors of MLSS. This study is the sole study providing specific operational regression equations to estimate the impractical gold standard MLSSV in soccer players by means of a BLT measured during a submaximal single-session test.

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http://hdl.handle.net/10453/139181