Identification of critical intensity from a single lactate measure during a 3-min, submaximal cycle-ergometer test

Agreement Between Maximal Lactate Steady State and Critical Power in Different Sports: A Systematic Review and Bayesian's Meta-Regression

ABSTRACT

Borszcz, FK, de Aguiar, RA, Costa, VP, Denadai, BS, and de Lucas, RD. Agreement between maximal lactate steady state and critical power in different sports: A systematic review and Bayesian's meta-regression. J Strength Cond Res 38(6): e320-e339, 2024-This study aimed to systematically review the literature and perform a meta-regression to determine the level of agreement between maximal lactate steady state (MLSS) and critical power (CP). Considered eligible to include were peer-reviewed and "gray literature" studies in English, Spanish, and Portuguese languages in cyclical exercises. The last search was made on March 24, 2022, on PubMed, ScienceDirect, SciELO, and Google Scholar. The study's quality was evaluated using 4 criteria adapted from the COSMIN tool. The level of agreement was examined by 2 separate meta-regressions modeled under Bayesian's methods, the first for the mean differences and the second for the SD of differences. The searches yielded 455 studies, of which 36 studies were included. Quality scale revealed detailed methods and small samples used and that some studies lacked inclusion/exclusion criteria reporting. For MLSS and CP comparison, likely (i.e., coefficients with high probabilities) covariates that change the mean difference were the MLSS time frame and delta criteria of blood lactate concentration, MLSS number and duration of pauses, CP longest predictive trial duration, CP type of predictive trials, CP model fitting parameters, and exercise modality. Covariates for SD of the differences were the subject's maximal oxygen uptake, CP's longest predictive trial duration, and exercise modality. Traditional MLSS protocol and CP from 2- to 15-minute trials do not reflect equivalent exercise intensity levels; the proximity between MLSS and CP measures can differ depending on test design, and both MLSS and CP have inherent limitations. Therefore, comparisons between them should always consider these aspects.

Easy Prediction of the Maximal Lactate Steady-State in Young and Older Men and Women

Maximal Lactate steady-state (MLSS) demarcates sustainable from unsustainable exercise and is used for evaluation/monitoring of exercise capacity. Still, its determination is physically challenging and time-consuming. This investigation aimed at validating a simple, submaximal approach based on blood lactate accumulation ([Δlactate]) at the third minute of cycling in a large cohort of men and women of different ages. 68 healthy adults (40♂, 28♀, 43 ± 17 years (range 19-78), VO_2max 45 ± 11 ml^-1·kg^-1·min^-1 (25-68)) performed 3-5 constant power output (PO) trials with a target duration of 30 minutes to determine the PO corresponding to MLSS. During each trial, [Δlactate] was calculated as the difference between the third minute and baseline. A multiple linear regression was computed to estimate MLSS based on [Δlactate], subjects` gender, age and the trial PO. The estimated MLSS was compared to the measured value by paired t-test, correlation, and Bland-Altman analysis. The group mean value of estimated MLSS was 180 ± 51 W, not significantly different from (p = 0.98) and highly correlated with (R² = 0.89) measured MLSS (180 ± 54 watts). The bias between values was 0.17 watts, and imprecision 18.2 watts. This simple, submaximal, time- and cost-efficient test accurately and precisely predicts MLSS across different samples of healthy individuals (adjusted R² = 0.88) and offers a practical and valid alternative to the traditional MLSS determination.

An Intensity-dependent Slow Component of HR Interferes with Accurate Exercise Implementation in Postmenopausal Women

Heart rate (HR) targets are commonly used to administer exercise intensity in sport and clinical practice. However, as exercise protracts, a time-dependent dissociation between HR and metabolism can lead to a misprescription of the intensity ingredient of the exercise dose.

Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise

This study tested the hypothesis that the respiratory compensation point (RCP) and breakpoint in deoxygenated [heme] [deoxy[heme]_BP, assessed via near-infrared spectroscopy (NIRS)] during ramp incremental exercise would occur at the same metabolic rate in the upright (U) and supine (S) body positions. Eleven healthy men completed ramp incremental exercise tests in U and S. Gas exchange was measured breath-by-breath and time-resolved-NIRS was used to measure deoxy[heme] in the vastus lateralis (VL) and rectus femoris (RF). RCP (S: 2.56 ± 0.39, U: 2.86 ± 0.40 L·min^-1, P = 0.02) differed from deoxy[heme]_BP in the VL in U (3.10 ± 0.44 L·min^-1, P = 0.002), but was not different in S in the VL (2.70 ± 0.50 L·min^-1, P = 0.15). RCP was not different from the deoxy[heme]_BP in the RF for either position (S: 2.34 ± 0.48 L·min^-1, U: 2.76 ± 0.53 L·min^-1, P > 0.05). However, the deoxy[heme]_BP differed between muscles in both positions (P < 0.05), and changes in deoxy[heme]_BP did not relate to ΔRCP between positions (VL: r = 0.55, P = 0.080, RF: r = 0.26, P = 0.44). The deoxy[heme]_BP was consistently preceded by a breakpoint in total[heme], and was, in turn, itself preceded by a breakpoint in muscle surface electromyography (EMG). RCP and the deoxy[heme]_BP can be dissociated across muscles and different body positions and, therefore, do not represent the same underlying physiological phenomenon. The deoxy[heme]_BP may, however, be mechanistically related to breakpoints in total[heme] and muscle activity.

Metabolic instability vs fibre recruitment contribution to the [Formula: see text] slow component in different exercise intensity domains

This study focused on the steady-state phase of exercise to evaluate the relative contribution of metabolic instability (measured with NIRS and haematochemical markers) and muscle activation (measured with EMG) to the oxygen consumption (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}$$\end{document}V˙O2) slow component (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}{_s}{_c}$$\end{document}V˙O2sc) in different intensity domains. We hypothesized that (i) after the transient phase, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}$$\end{document}V˙O2, metabolic instability and muscle activation tend to increase differently over time depending on the relative exercise intensity and (ii) the increase in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}{_s}{_c}$$\end{document}V˙O2sc is explained by a combination of metabolic instability and muscle activation. Eight active men performed a constant work rate trial of 9 min in the moderate, heavy and severe intensity domains. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}$$\end{document}V˙O2, root mean square by EMG (RMS), deoxyhaemoglobin by NIRS ([HHb]) and haematic markers of metabolic stability (i.e. [La⁻], pH, HCO₃⁻) were measured. The physiological responses in different intensity domains were compared by two-way RM-ANOVA. The relationships between the increases of [HHb] and RMS with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}$$\end{document}V˙O2 after the third min were compared by simple and multiple linear regressions. We found domain-dependent dynamics over time of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}$$\end{document}V˙O2, [HHb], RMS and the haematic markers of metabolic instability. After the transient phase, the rises in [HHb] and RMS showed medium–high correlations with the rise in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}$$\end{document}V˙O2 ([HHb] r = 0.68, p < 0.001; RMS r = 0.59, p = 0.002). Moreover, the multiple linear regression showed that both metabolic instability and muscle activation concurred to the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}{_s}{_c}$$\end{document}V˙O2sc (r = 0.75, [HHb] p = 0.005, RMS p = 0.042) with metabolic instability possibly having about threefold the relative weight compared to recruitment. Seventy-five percent of the dynamics of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{V}O_2}{_s}{_c}$$\end{document}V˙O2sc was explained by [HHb] and RMS.

Using ramp-incremental V̇O2 responses for constant-intensity exercise selection

Despite compelling evidence to the contrary, the view that oxygen uptake (V̇O₂) increases linearly with exercise intensity (e.g., power output, speed) until reaching its maximum persists within the exercise physiology literature. This viewpoint implies that the V̇O₂ response at any constant intensity is predictable from a ramp-incremental exercise test. However, the V̇O₂ versus task-specific exercise intensity relationship constructed from ramp-incremental versus constant-intensity exercise are not equivalent preventing the use of V̇O₂ responses from 1 domain to predict those of the other. Still, this "linear" translational framework continues to be adopted as the guiding principle for aerobic exercise prescription and there remains in the sport science literature a lack of understanding of how to interpret V̇O₂ responses to ramp-incremental exercise and how to use those data to assign task-specific constant-intensity exercise. The objectives of this paper are to (i) review the factors that disassociate the V̇O₂ versus exercise intensity relationship between ramp-incremental and constant-intensity exercise paradigms; (ii) identify when it is appropriate (or not) to use ramp V̇O₂ responses to accurately assign constant-intensity exercise; and (iii) illustrate the technical and theoretical challenges with prescribing constant-intensity exercise solely on information acquired from ramp-incremental tests. Actual V̇O₂ data collected during cycling exercise and V̇O₂ kinetics modelling are presented to exemplify these concepts. Possible solutions to overcome these challenges are also presented to inform on appropriate intensity selection for individual-specific aerobic exercise prescription in both research and practical settings.