Secondary Logo

Journal Logo

Original Research

Indirect Assessment of Lactate Minimum and Maximal Blood Lactate Steady-State Intensity for Physically Active Individuals

Sotero, Rafael da Costa; Pardono, Emerson; Campbell, Carmen Sílvia Grubert; Simões, Herbert Gustavo

Author Information
Journal of Strength and Conditioning Research: May 2009 - Volume 23 - Issue 3 - p 847-853
doi: 10.1519/JSC.0b013e318196b609
  • Free



The maximal blood lactate steady-state intensity (MLSS) is considered the gold standard for aerobic capacity evaluation (1-3,14,17,18), training prescription, and performance prediction (14,20,27,31). Among the several methods proposed to identify the running velocity corresponding to the MLSS, there is the lactate minimum protocol (LM) (1,14,31), which identifies an equilibrium point between blood lactate production and removal.

The LM is identified as the running velocity associated with the lowest blood lactate concentration ([lac]) during an incremental test performed after [lac] elevation (24,30,31). The MLSS is defined as the highest exercise intensity at which a [lac] increase does not suppress 0.05 mM·min−1 between the 10th and the 30th minutes of exercise at constant intensity (2,4).

Studies regarding LM, MLSS, and running performance have been conducted primarily on competitive athletes (14,17,24,25,31); there is a lack of research on physically active noncompetitive individuals. Because LM and MLSS are expected to occur at similar exercise intensities (1,17,30,31) and have been well correlated with middle-distance performance (14,24,25), it was hypothesized that LM-and, thus, MLSS intensity-could be estimated for physically active noncompetitive individuals in a 1600-m running performance.

The costs of laboratory equipment and technicians dealing with blood collection and analyses are elevated, so the LM and/or MLSS prediction for physically active, noncompetitive persons would be useful for trainers who do not have the resources for blood lactate analyses. The present study aimed to propose an equation to predict LM velocity through 1600-m running performance and to analyze its validity for estimating MLSS values for physically active young men.


Experimental Approach to the Problem

Twenty-two physically active young men volunteered to take part in this investigation. The research was initially conducted as study 1 (group 1 [G1]), with the purpose of elaborating a predictive equation for LM running velocity. Study 2 was then conducted with another group of participants (group 2 [G2]) to analyze the validity of the predictive equation (as obtained in study 1 for G1) to identify the exercise intensity corresponding to LM and MLSS velocities. All participants were instructed to avoid physical exercise, alcoholic drinks, and caffeine ingestion during the 24-hour period preceding the tests. All participants performed the following tests on a 400-m running track on different days and 48-72 hours apart. The overview of the procedures is presented in Figure 1.

Figure 1
Figure 1:
Overview of the experimental procedures.


A group of 12 participants took part in study 1 (G1), and another group of 10 individuals participated in study 2 (G2) after having signed an informed consent form stating both the risks and benefits of their participation. The local ethics committee for human research approved the methods involved in this study (no. 019/2004).

All volunteers were physically active men (engaged in a minimum of 30 minutes of physical exercise at least 3 times a week for noncompetitive purposes). These participants were physical education students who used to exercise on a recreational basis and for practical classes. Each participant's O2peak was predicted based on the time he would spend to complete a mile running test, as per the methods of Cureton et al. (11). The main characteristics of the participants of G1 (study 1) and G2 (study 2) are described in Table 1, and the general procedures of the studies 1 and 2 are detailed in Figure 1.

Table 1
Table 1:
Anthropometric characteristics of the participants in groups 1 (n = 12) and 2 (n = 10).

Study 1

The G1 volunteers performed the running tests described in the next 2 paragraphs.

The 1600mV test: In this test, the volunteers ran 1600 m as quickly as possible, to calculate their mean velocity (1600mV) and estimate their O2peak values (11). The 1600mV also was used to individualize the running velocities of the incremental stages of the LM test on the track, similar to the methods used in other studies (1,17,24,25);

The LM test: The LM test began with a maximal sprint of 150 m for [lac] elevation and, after a 10-minute recovery period, the participants underwent 6 progressive bouts of 800 m with 1-minute intervals for blood sampling (31). The 800-m bouts were performed at intensities of 78, 81, 84, 87, 90, and 93% of 1600mV. The running velocity corresponding to the lower [lac] during the incremental test was identified as the LM and was determined after applying the second-order polynomial function shown in Figure 2 (17,28).

Figure 2
Figure 2:
Determination of the lactate minimum (LM) (A) and maximal blood lactate steady state (MLSS) (B) for a single participant. The LM velocity (182.3 m·min−1) (A) elicited the MLSS (B), but 3% above (187.5 m·min−1) and 3% below it (176.5 m·min−1) did not. The highest intensity that elicited a blood lactate increase of no more than 0.05 mM·min−1 was the criterion for MLSS.

Generation of the Predictive Equation

From the results of the 1600mV and LM tests obtained for the participants of study 1 (G1), a predictive equation of LM was generated (Figure 3) and then applied to the participants of the study 2 (G2) to analyze the validity of the LMind test for estimating LM and MLSS velocities, as described below.

Figure 3
Figure 3:
Linear regression between mean velocity for a 1600-m run (1600mV) and lactate minimum (LM) for participants of group 1. LMind = indirect assessment corresponding to lactate minimum.

Study 2

The participants from study 2 (G2; n = 10) also performed the 1600-m test and the LM test, as already described for the G1/study 1. However, besides performing these tests, MLSS velocity also was determined, and LMind was calculated. For MLSS, the participants were submitted to 2-4 sessions of running for 30 minutes at constant intensity. The first 30-minute session was performed at each participant's LM velocity. Then, depending on whether [lac] stability was reached or not, the participants completed additional 30-minute running tests at velocities either below or above LM, with a 3% increase or decrease. The MLSS was considered as the highest velocity at which a [lac] variation was not higher than 0.05 mM·min−1 between the 10th and the 30th minutes of running, consistent with previous studies (2,4).

Blood Collection and Measurements

A 25-μl blood sample was obtained after each stage of the LM tests and at each 10-minute mark during the 30 minutes of constant-load tests. The samples were collected from the ear lobe in heparinized glass capillary tubes and put into Eppendorf tubes containing 50 μl of NaF 1%. Blood lactate concentrations were then determined by an electrochemical method (YSI-2700 SELECT, Yellow Springs Instruments, Yellow Springs, Ohio). Heart rate also was measured during all tests (Polar Sport Tester, Finland).

Statistical Analyses

A linear regression between LM and 1600-m velocity was applied to the G1 results (Figure 3) to yield a predictive equation for LM (LMind). The equation generated by G1-LMind (m·min−1) = (0.7507 × 1600mV) + 21.575-was applied to participants of study 2 to predict LMind for comparisons with the LM and MLSS results. The data were analyzed and expressed as mean ± SD. A 1-way repeated-measure analysis of variance (ANOVA) was applied using the software SPSS 11.5 for Windows to compare the intensities of LM, LMind, and MLSS using a post hoc Bonferroni test. The Pearson correlation coefficient was applied between the studied variables, and the level of significance adopted was p ≤ 0.05. The level of agreement between the studied variables was analyzed by the method of Bland and Altman (6).


The mean results of the 150-m, 1600-m, and LM running tests for both groups are presented in Table 2. A linear regression and Pearson moment correlation demonstrated the relationship between the velocities of LM and 1600mV (r2 = 0.96) determined for G1 (Figure 3), yielding the following predictive equation for LM: LMind = (0.7507 × 1600mV) + 21.575. The running velocities corresponding to the LM, LMind, and MLSS values for the 10 individuals from G2 (study 2) are expressed in Table 3. The ANOVA showed no differences between the running velocities corresponding to LM, LMind, and MLSS (p > 0.05), and a high correlation was observed for LM to LMind (r = 0.96), LM to MLSS (r = 0.87), and LMind to MLSS (r = 0.91) (p < 0.01).

Table 2
Table 2:
Mean results of performance, heart rate, and blood lactate for the 150-m run, 1600-m run, and lactate minimum (LM) tests for groups 1 (n = 12) and 2 (n = 10).
Table 3
Table 3:
Mean running velocities for lactate minimum (LM), indirect assessment corresponding to lactate minimum (LMind), and maximal blood lactate steady state (MLSS) for group 2.

The Bland-Altman technique (6,11) revealed an agreement between LMind and LM and between LMind and MLSS. The bias ±95% limits of agreement for comparisons between the LMind and LM results were 0.1 (8.9) m·min−1 and, between the LMind and MLSS results, −1.1 (12.1) m·min−1 (Figure 4).

Figure 4
Figure 4:
Limits of agreement between lactate minimum (LM)-indirect assessment corresponding to lactate minimum (LMind) and maximal blood lactate steady state (MLSS)-LMind for group 2.


The present study aimed to determine an equation for estimating the running velocity corresponding to LM and, thus, to the MLSS for physically active individuals on a running track. The description of the LM protocol was first published by Tegtbur et al. (31) in 1993. Since then, the LM protocol has been the focus of several investigations, most of them addressing performance (8,12,14,17), blood lactate kinetics (10,13,15,26), and several methodological variations of the LM test (9,20), both for athletes (13,14,20,24,25) and for nonathlete subjects (1,23,28). Many studies have used visual inspections of the [lac]-workload relationship for LM identification. However, studies regarding the application of mathematical models to determine more precisely the LM intensity (8-10,14,17,28,32) and to reduce the number of blood collections during the LM test have been conducted (19,29). However, no studies have suggested or investigated whether the results of running performance could be used to predict LM and MLSS velocity for physically active noncompetitive individuals.

To identify the LM, it is necessary to induce hyperlactatemia though maximal effort performed before the incremental test, in which several blood collections are needed. These procedures allow for the identification of an equilibrium point between blood lactate production and removal (25,31) that may be associated with MLSS. As with the LM, the MLSS determination also requires blood collection. Moreover, for the MLSS determination as an exercise intensity at which there is not a [lac] increase higher than 0.05 mM·min−1 (2,3,14), 2-5 tests on distinct days usually are required (3). Nevertheless, the formulation of a predictive equation is very interesting for practical reasons and for enabling a less expensive evaluation and a reduction of contamination risks associated with multiple blood collections.

The reproducibility of the LM test (2,5,17,22) and its validity for estimating anaerobic threshold and MLSS have been demonstrated (1,4,19,23,31,32). The LM prediction from 1600-m running velocity, as in the present study, is also interesting because performances over middle distances between 1000 and 5000 m have been shown to be reliable both for athletes and for nonathletes such as children and physically active adults (7,16).

The individualization of the intensities of the incremental stages for LM has been proposed on the basis of velocity in a previous performance test (8,13,14,20,24,25). In our study, such individualization was done on the basis of 1600-m running performance, which enabled the formulation of the predictive equation (Figure 2A). The 1600-m distance was selected because physically active individuals are supposed to cover it within 6-7 minutes, and thus the results could also be applied to estimate O2max according to the methods of Cureton et al. (11).

One limitation of the present study was that a maximal effort (i.e., 1600-m test) needed to be done to predict the LM and MLSS, which, in turn, might be uncomfortable, especially for nonathletes. So, despite the fact that all participants in the present study were able to perform the 1600-m time trial, other studies should attempt to predict MLSS velocity during a submaximal instead of a maximal testing protocol. Another limiting factor of the present investigation was the reduced number of participants. However, for the groups studied (G1 and G2), which were homogeneous, the predictive equation generated for G1 (LMind) accurately predicted MLSS and LM for G2. So, it is reasonable to expect that such a predictive equation, if applied to individuals with characteristics similar to those of the present study, may be adequate for estimating MLSS intensity for running.

The MLSS has been considered the gold standard among the protocols of functional assessment from the responses of blood lactate (5). In the present study, there were no significant differences between the running velocities corresponding to LM, LMind, and MLSS (p > 0.05) (Table 2). Additionally, high correlations were observed between the LM, LMind, and MLSS velocities, and the technique of Bland and Altman (6) demonstrated an agreement between LM-LMind and MLSS-LMind (Figure 4). So, whereas the costs of laboratory equipment and technicians dealing with blood collection and analyses for determining LM and/or MLSS are elevated, the LMind test is a practical and less expensive method that could enable the prediction of MLSS and LM. Such a predictive method was shown to be an accurate alternative for coaches or trainers to prescribe exercise intensities that would be sustained without blood lactate accumulation.

Finally, the findings of the present study indicate that both LM and LMind are valid estimates of running velocities associated with MLSS (Table 3) if determined in individuals with characteristics similar to the participants of the present study. So, it was concluded that the proposed equation-LMind = (0.7507 × 1600mV) + 21.575-may be applied to estimate the running velocity (m·min−1) corresponding to LM and MLSS from the 1600-m running performance in young, physically active individuals. Further studies should be done to formulate other predictive equations for different populations and from different running distances that would be submaximal instead of maximal.

Practical Applications

The proposed predictive equation for lactate minimum intensity-LMind (m·min−1) = (0.7507 × 1600mV) + 21.57-may enable the prediction of the running velocity associated with maximal blood lactate steady state. Thus, coaches or trainers may benefit from this predictive equation because it is a practical, less expensive method, considering that the costs of directly determining LM/MLSS are elevated because of the necessary laboratory equipment and technicians dealing with blood lactate measurements.

The results of the present study can be applied in several ways. For example, after a 1600-m performance test is completed, the LM intensity could be estimated (LMind), and then some sessions of running training could be prescribed on an individual basis as follows:

  • - Moderate-intensity running: 30-50 minutes at a velocity below LMind (e.g., 90-95% LMind)
  • - Moderate- to high-intensity running: 20-30 minutes at a velocity slightly above but not higher than 3% above LMind
  • - High-intensity running: 10-20 minutes of exercise at a velocity approximately 5% above LMind
  • - Alternatively, a very-high-intensity running session, at a velocity 10-20% above LMind (which is around 95-100% of 1600mV), could also be done to improve performance. For this session, 2-4 bouts of 4-5 minutes of exercise with about 4-8 minutes of rest would be useful, with the purpose of reaching O2max during the exercise session.

In general, 3-5 aerobic sessions per week are recommended, with no more than 2 sessions being done at an intensity above LMind. Those high- or very-high-intensity exercise sessions should be alternated with moderate-intensity exercise sessions (e.g., below LMind).


The authors are grateful to National Counsel of Technological and Scientific Development (CNPq) for the financial support.


1. Bacon, L and Kern, M. Evaluating a test protocol for predicting maximum lactate steady state. J Sports Med Phys Fitness 39: 300-308, 1999.
2. Beneke, R. Maximal lactate steady state concentration (MLSS): experimental and modelling approaches. Eur J Appl Physiol 88: 361-369, 2003.
3. Beneke, R. Methodological aspects of maximal lactate steady state-implications for performance testing. Eur J Appl Physiol 89: 95-99, 2003.
4. Beneke, R, Hutler, M, and Leithauser, RM. Dependence of the maximal lactate steady state on the motor pattern of exercise. Br J Sports Med 35: 192-196, 2001.
5. Beneke, R, Hutler, M, Von Duvillard, SP, Sellens, M, and Leithauser, RM. Effect of test interruptions on blood lactate during constant workload testing. Med Sci Sports Exerc 35: 1626-1630, 2003.
6. Bland, JM and Altman, DG. Measuring agreement in method comparison studies. Stat Methods Med Res 8: 135-160, 1999.
7. Campbell, CSG, Simões, HG, and Denadai, BS. Reprodutibilidade do limiar anaeróbio individual e lactato mínimo determinados em teste de pista. Rev Bras Atividade Fís Saúde 3: 24-31, 1999.
8. Carter, HJ, Jones, AM, and Doust, JH. Effect of 6 weeks of endurance training on the lactate minimum speed. J Sports Sci 17: 957-967, 1999.
9. Carter, H, Jones, AM, and Doust, JH. Effect of incremental test protocol on the lactate minimum speed. Med Sci Sports Exerc 31: 837-845, 1999.
10. Carter, H, Jones, AM, and Doust, JH. Changes in blood lactate and pyruvate concentrations and the lactate-to-pyruvate ratio during the lactate minimum speed test. J Sports Sci 18: 213-225, 2000.
11. Cureton, KJ, Sloniger, MA, O'Bannon, JP, Black, DM, and McCormack, WP. A generalized equation for prediction of VO2peak from 1-mile run/walk performance. Med Sci Sports Exerc 27: 445-451, 1995.
12. Dantas De Luca, R, Rocha, R, Burini, RC, Coelho Greco, C, and Denadai, BS. The lactate minimum test protocol provides valid measures of cycle ergometer VO(2)peak. J Sports Med Phys Fitness 43: 279-284, 2003.
13. Denadai, BS and Higino, WP. Effect of the passive recovery period on the lactate minimum speed in sprinters and endurance runners. J Sci Med Sports 7: 488-496, 2004.
14. Jones, AM and Doust, JH. The validity of the lactate minimum test for determination of the maximal lactate steady state. Med Sci Sports Exerc 30: 1304-1313, 1998.
15. Junior, PB, Neiva, CM, and Denadai, BS. Effect of an acute β-adrenergic blockade on the blood glucose response during lactate minimum test. J Sci Med Sports 4: 257-265, 2001.
16. Laursen, PB, Francis, GT, Abbiss, CR, Newoton, MJ, and Nosaka, K. Reliability of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports Exerc 39: 1374-1379, 2007.
17. Macintosh, BR, Esau, S, and Svedahl, K. The lactate minimum test for cycling: estimation of the maximal lactate steady state. Can J Appl Physiol 27: 232-249, 2002.
18. Mattern, CO, Gutilla, MJ, Bright, DL, Kirby, TE, Hinchcliff, KW, and Devor, ST. Maximal lactate steady state declines during the aging process. J Appl Physiol 95: 2576-2582, 2003.
19. Pardono, E, Sotero Rda, C, Hiyane, W, Mota, MR, Campbell, CS, Nakamura, FY, and Simões, HG. Maximal lactate steady-state prediction through quadratic modeling of selected stages of the lactate minimum test. J Strength Cond Res 22: 1073-1080, 2008.
20. Ribeiro, L, Balikian, P, Malachias, P, and Baldissera, V. Stage length, spline function and lactate minimum swimming speed. J Sports Med Phys Fitness 43: 312-318, 2003.
21. Rikli, RE, Petray, C, and Baumgartner, TA. The reliability of distance run tests for children in grades K-4. Res Q Exerc Sport 63: 270-276, 1992.
    22. Simões, HG and Baldissera, V. Comparison between protocols of anaerobic threshold determination in track for runners. Master's thesis, Universidade Federal de São Carlos, São Carlos, Brazil, 1997.
    23. Simões, HG, Campbell, CS, Kushnick, MR, Nakamura, A, Katsanos, CS, Baldissera, V, and Moffatt, RJ. Blood glucose threshold and the metabolic responses to incremental exercise tests with and without prior lactic acidosis induction. Eur J Appl Physiol 89: 603-611, 2003.
    24. Simões, HG, Grubert Campbell, CS, Kokubun, E, Denadai BS, and Baldissera, V. Blood glucose responses in humans mirror lactate responses for individual anaerobic threshold and for lactate minimum in track tests. Eur J Appl Physiol Occup Physiol 80: 34-40, 1999.
    25. Simões, HG, Denadai, BS, Badissera, V, Campbell, CS, and Hill, DW. Relationships and significance of lactate minimum, critical velocity, heart rate deflection and 3000m track-tests for running. J Sports Med Phys Fitness 45: 441-451, 2005.
    26. Smith, MF, Balmer, J, Coleman, DA, Bird, SR, and Davison, RC. Method of lactate elevation does not affect the determination of the lactate minimum. Med Sci Sports Exerc 34: 1744-1779, 2002.
    27. Snyder, AC, Woulfe, T, Welsh, R, and Foster, C. A simplified approach to estimating the maximal lactate steady state. Ind J Sports Med 15: 27-31, 1994.
    28. Sotero, RC, Campbell, CSG, Pardono, E, Puga, GM, and Simoes, HG. Polynomial adjustment as a new technique for determination of lactate minimum velocity with reduced blood sampling. Braz J Kinanthropometry Hum Perform 4: 321-326, 2007.
    29. Sotero, RC, Pardono E, Athayde, T, Campbell, CSG, and Simões, HG. Polynomial function identifies the lactate minimum by a variety of incremental stages. Med Sci Sports Exerc 38: 504, 2006.
    30. Svedahl, K and Macintosh, BR. Anaerobic threshold: the concept and methods of measurement. Can J Appl Physiol 28: 299-323, 2003.
    31. Tegtbur, U, Busse, MW, and Braunmann, KM. Estimation of an individual equilibrium between lactate production and catabolism during exercise. Med Sci Sports Exerc 25: 620-627, 1993.
    32. Voltarelli, FA, Gobatto, CA, and Mello, MAR. Determination of anaerobic threshold in rats using the lactate minimum test. Braz J Med Biol Res 35: 1389-1394, 2002.

    lactate minimum; maximal lactate steady state; predictive equation

    © 2009 National Strength and Conditioning Association