Int J Sports Med 2016; 37(07): 539-546
DOI: 10.1055/s-0042-102653
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Estimation of the Maximal Lactate Steady State in Endurance Runners

I. Llodio
1   Studies, Research and Sport Medicine Center, Government of Navarra, Pamplona, Spain
2   Faculty of Physical Activity and Sports Science University of the Basque Country Vitoria-Gastelz, Spain
,
E. M. Gorostiaga
1   Studies, Research and Sport Medicine Center, Government of Navarra, Pamplona, Spain
,
I. Garcia-Tabar
1   Studies, Research and Sport Medicine Center, Government of Navarra, Pamplona, Spain
,
C. Granados
2   Faculty of Physical Activity and Sports Science University of the Basque Country Vitoria-Gastelz, Spain
,
L. Sánchez-Medina
1   Studies, Research and Sport Medicine Center, Government of Navarra, Pamplona, Spain
› Author Affiliations
Further Information

Publication History



accepted after revision 25 January 2016

Publication Date:
26 April 2016 (online)

Abstract

This study aimed to predict the velocity corresponding to the maximal lactate steady state (MLSSV) from non-invasive variables obtained during a maximal multistage running field test (modified University of Montreal Track Test, UMTT), and to determine whether a single constant velocity test (CVT), performed several days after the UMTT, could estimate the MLSSV. Within 4–5 weeks, 20 male runners performed: 1) a modified UMTT, and 2) several 30 min CVTs to determine MLSSV to a precision of 0.25 km·h−1. Maximal aerobic velocity (MAV) was the best predictor of MLSSV. A regression equation was obtained: MLSSV=1.425+(0.756·MAV); R2=0.63. Running velocity during the CVT (VCVT) and blood lactate at 6 (La6) and 30 (La30) min further improved the MLSSV prediction: MLSSV=VCVT+0.503 – (0.266·ΔLa30–6); R2=0.66. MLSSV can be estimated from MAV during a single maximal multistage running field test among a homogeneous group of trained runners. This estimation can be further improved by performing an additional CVT. In terms of accuracy, simplicity and cost-effectiveness, the reported regression equations can be used for the assessment and training prescription of endurance runners.

 
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