During the last nearly 50 years, the blood lactate curve and lactate thresholds (LTs) have become important in the diagnosis of endurance performance. An intense and ongoing debate emerged, which was mainly based on terminology and/or the physiological background of LT concepts. The present review aims at evaluating LTs with regard to their validity in assessing endurance capacity. Additionally, LT concepts shall be integrated within the ‘aerobic-anaerobic transition’ — a framework which has often been used for performance diagnosis and intensity prescriptions in endurance sports. Usually, graded incremental exercise tests, eliciting an exponential rise in blood lactate concentrations (bLa), are used to arrive at lactate curves. A shift of such lactate curves indicates changes in endurance capacity. This very global approach, however, is hindered by several factors that may influence overall lactate levels. In addition, the exclusive use of the entire curve leads to some uncertainty as to the magnitude of endurance gains, which cannot be precisely estimated. This deficiency might be eliminated by the use of LTs. The aerobic-anaerobic transition may serve as a basis for individually assessing endurance performance as well as for prescribing intensities in endurance training. Additionally, several LT approaches may be integrated in this framework. This model consists of two typical breakpoints that are passed during incremental exercise: the intensity at which bLa begin to rise above baseline levels and the highest intensity at which lactate production and elimination are in equilibrium (maximal lactate steady state [MLSS]). Within this review, LTs are considered valid performance indicators when there are strong linear correlations with (simulated) endurance performance. In addition, a close relationship between LT and MLSS indicates validity regarding the prescription of training intensities. A total of 25 different LT concepts were located. All concepts were divided into three categories. Several authors use fixed bLa during incremental exercise to assess endurance performance (category 1). Other LT concepts aim at detecting the first rise in bLa above baseline levels (category 2). The third category consists of threshold concepts that aim at detecting either the MLSS or a rapid/distinct change in the inclination of the blood lactate curve (category 3). Thirty-two studies evaluated the relationship of LTs with performance in (partly simulated) endurance events. The overwhelming majority of those studies reported strong linear correlations, particularly for running events, suggesting a high percentage of common variance between LT and endurance performance. In addition, there is evidence that some LTs can estimate the MLSS. However, from a practical and statistical point of view it would be of interest to know the variability of individual differences between the respective threshold and the MLSS, which is rarely reported. Although there has been frequent and controversial debate on the LT phenomenon during the last three decades, many scientific studies have dealt with LT concepts, their value in assessing endurance performance or in prescribing exercise intensities in endurance training. The presented framework may help to clarify some aspects of the controversy and may give a rationale for performance diagnosis and training prescription in future research as well as in sports practice.

Lactate threshold concepts: how valid are they?

Time over a distance, i.e. speed, is the reference for performance for all events whose rules are based on locomotion in different mechanical constraints. A certain power output has to be maintained during a distance or over time. The energy requirements and metabolic support for optimal performance are functions of the length of the race and the intensity at which it is completed. However, despite the complexity of the regulation of lactate metabolism, blood lactate measurements can be used by coaches for prediction of exercise performance. The anaerobic threshold, commonly defined as the exercise intensity, speed or fraction of maximal oxygen uptake (VO2max) at a fixed blood lactate level or at a maximal lactate steady-state (MLSS), has been accepted as a measure of the endurance. The blood lactate threshold, expressed as a fraction of the velocity associated with VO2max, depends on the relationship between velocity and oxygen uptake (VO2). The measurement of the post-competition blood lactate in short events (lasting 1 to 2 minutes) has been found to be related to the performance in events (400 to 800m in running). Blood lactate levels can be used to assist with determining training exercise intensity. However, to interpret the training effect on the blood lactate profile, the athlete’s nutritional state and exercise protocol have also to be controlled. Moreover, improvement of fractional utilisation of VO2max at the MLSS has to be considered among all discriminating factors of the performance, such as the velocity associated with VO2max.

Use of Blood Lactate Measurements for Prediction of Exercise Performance and for Control of Training Recommendations for Long-Distance Running

© 2019 The Authors. Published by Wiley. This is an open access article available under a Creative Commons licence. 
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.14814/phy2.14098

The maximal metabolic steady state: redefining the 'gold standard'.

In 1992 Conconi et al. (20) presented an indirect and noninvasive method for the determination of anaerobic threshold (AnT) in an incremental field test for runners. This noninvasive method for the determination of anaerobic threshold is dependent on the occurrence of a deflection of the heart rate performance curve (HRPC). The aim of our study was to evaluate the degree and direction of the deflection of the HRPC and the relationship of the heart rate threshold (HRT) to the lactate turn point in a group of 227 healthy young subjects (age: 23 +/- 4 yr). The subjects were divided into three groups by means of second degree polynomial fitting (GI: regular deflection, kHR > 0.1; G II: no deflection, 0 < kHR < 0.1; G II: inverse deflection, k < -0.1). No significant differences between the groups were found in the anthropometric data or in the power output and the blood lactate concentration at both the first (LTP1) and second (LTP2) lactate turn points and at maximum performance (Pmax). Using the method of Conconi et al. (20), 85.9% of the subjects showed a "regular" deflection, 6.2% showed no deflection at all, and 7.9% showed even an inverted deflection of the HRPC. An HRT could be obtained in both G I and G III, and power output at HRT was not significantly different in comparison to that at the LTP2. No HRT could be assessed in G II. The heart rate at HRT and the LTP2 were significantly lower in G III compared with G I. The phenomenon of heart rate break point may be attractive in training regulation, but its application is limited because a heart rate deflection cannot be found even in young subjects in some cases.

Heart rate performance curve during incremental cycle ergometer exercise in healthy young male subjects.

During the last nearly 50 years, the blood lactate curve and lactate thresholds (LTs) have become important in the diagnosis of endurance performance. An intense and ongoing debate emerged, which was mainly based on terminology and/or the physiological background of LT concepts. The present review aims at evaluating LTs with regard to their validity in assessing endurance capacity. Additionally, LT concepts shall be integrated within the ‘aerobic-anaerobic transition’ – a framework which has often been used for performance diagnosis and intensity prescriptions in endurance sports. Usually, graded incremental exercise tests, eliciting an exponential rise in blood lactate concentrations (bLa), are used to arrive at lactate curves. A shift of such lactate curves indicates changes in endurance capacity. This very global approach, however, is hindered by several factors that may influence overall lactate levels. In addition, the exclusive use of the entire curve leads to some uncertainty as to the magnitude of endurance gains, which cannot be precisely estimated. This deficiency might be eliminated by the use of LTs. The aerobic-anaerobic transition may serve as a basis for individually assessing endurance performance as well as for prescribing intensities in

How Valid are They

The exercise intensity associated with an elevated but stable blood lactate (HLa) concentration during constant load work (the maximal steady state, MSS) has received attention as a candidate for the "optimal" exercise intensity for endurance training. Identification of MSS ordinarily demands direct measurement of HLa or respiratory metabolism. The purpose of this study was to test the ability of heart rate (HR) to identify MSS during steady state exercise, similar to that used in conventional exercise prescription. Trained runners (n = 9) and cyclists (n = 12) performed incremental and steady state exercise. MSS was defined as the highest intensity in which blood lactate concentration increased MSS. HR models related to the presence or absence of steady state conditions were developed from the upper 95% confidence interval of MSS and the lower 95% confidence interval of > MSS. Cross validation of the model to predict MSS was performed using 21 running and 45 cycling exercise bouts in a separate group. Using the MSS upper 95% confidence interval model 84% and 76% of workbouts were correctly predicted in cyclists and runners, respectively. Using the > MSS lower 95% confidence interval model, 76% and 81% of workbouts were correctly predicted in cyclists and runners, respectively. Prediction errors tended to incorrectly predict non-steady state conditions when steady state had occurred (16/26) (62%). We conclude that use of these simple HR models may predict MSS with sufficient accuracy to be useful when direct HLa measurement is not available.

A simplified approach to estimating the maximal lactate steady state.

The exercise intensity associated with an elevated but stable blood lactate (HLa) concentration during constant load work (the maximal steady state, MSS) has received attention as a candidate for the "optimal" exercise intensity for endurance training. Identification of MSS ordinarily demands direct measurement of HLa or respiratory metabolism. The purpose of this study was to test the ability of heart rate (HR) to identify MSS during steady state exercise, similar to that used in conventional exercise prescription. Trained runners (n = 9) and cyclists (n = 12) performed incremental and steady state exercise. MSS was defined as the highest intensity in which blood lactate concentration increased < 1.0 mM from minutes 10 to 30. The next higher intensity workbout completed was defined as > MSS. HR models related to the presence or absence of steady state conditions were developed from the upper 95% confidence interval of MSS and the lower 95% confidence interval of > MSS. Cross validation of the model to predict MSS was performed using 21 running and 45 cycling exercise bouts in a separate group. Using the MSS upper 95% confidence interval model 84% and 76% of workbouts were correctly predicted in cyclists and runners, respectively. Using the > MSS lower 95% confidence interval model, 76% and 81% of workbouts were correctly predicted in cyclists and runners, respectively. Prediction errors tended to incorrectly predict non-steady state conditions when steady state had occurred (16/26) (62%). We conclude that use of these simple HR models may predict MSS with sufficient accuracy to be useful when direct HLa measurement is not available.

473 a simplified approach to estimating the maximal lactate steady state

395 serum testosterone levels following heavy training

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

T. Woulfe

Author Tools

Chat about Author

Papers

A simplified approach to estimating the maximal lactate steady state.

473 a simplified approach to estimating the maximal lactate steady state

395 serum testosterone levels following heavy training