Maximal Lactate Steady State Research Articles

Predicting Maximal Lactate Steady State from Carminatti's Shuttle Run Test in Soccer Players.

The objective of the present study was to determine the validity of Carminatti's shuttle run incremental test-T-Car derived parameters in estimating the maximal lactate steady state determined in shuttle run format. Eighteen soccer players performed a T-Car test, and several trials to determine the maximal lactate steady state. From T-Car were derived the heart rate deflection point, peak speed, maximal heart rate and parameters resulting from percentage of peak measures. The validity was accessed by Bland-Altman plots, linear regressions, and two one-sided tests of equivalence analysis. The results showed the speed at 80.4% of T-Car peak speed, the heart rate deflection point and the 91.4% of maximal heart rate were equivalent to maximal lactate steady state (Mean difference; ±90% compatibility interval; -0.8; ±1.5%, -0.4; ±1.1%, and 0.0; ±2.7%, respectively). Additionally, peak speed during the T-Car test was a stronger predictor of maximal lactate steady state (MLSS [km/h]=2.57+0.65 × sPeak; r=0.82 [90% CI; 0.62-0.92], standard error of the estimate=3.6%; 90% CI ×/÷1.4). Therefore, soccer players can use the T-Car derived parameters as a noninvasive and practical alternative to estimate the specific maximal lactate steady state for soccer.

Exercise above the maximal lactate steady state does not elicit a V̇O2 slow component that leads to attainment of V̇O2max.

There is a pervasive belief that the severe-intensity domain is defined as work rates above the power associated with a maximal lactate steady state (MLSS) and by a oxygen uptake (V̇O2) response that demonstrates a rapid increase (primary phase) followed by a slower increase (slow component), which leads to maximal oxygen uptake (V̇O2max) if exercise is continued long enough. Fifteen university students performed 5 to 7 tests to calculate power at MLSS (154 ± 29W). The tests included 30min of exercise at each of 3 work rates: (i) below (-2 ± 1W) power at MLSS, (ii) above (+4 ± 1W) the power at MLSS, and (iii) well above (+19 ± 8W) power at MLSS. The V̇O2 response in each test was described using mathematical modeling. Contrary to expectation, the response at the supra-MLSS work rates had not 2, but 3, distinct phases: the primary phase and the slow component, plus a "delayed" third phase, which emerged after ∼15min. V̇O2max was not attained at supra-MLSS work rates. These results challenge commonly held beliefs about definitions and descriptions of exercise intensity domains. Novelty: The V̇O2 response at work rates that are too high to sustain a lactate steady state but not high enough to elicit V̇O2max features not 2, but 3, distinct phases. There is no consensus on whether intensity domains should be defined by their boundaries or by the responses they engender.

An Examination and Critique of Current Methods to Determine Exercise Intensity.

Prescribing the frequency, duration, or volume of training is simple as these factors can be altered by manipulating the number of exercise sessions per week, the duration of each session, or the total work performed in a given time frame (e.g., per week). However, prescribing exercise intensity is complex and controversy exists regarding the reliability and validity of the methods used to determine and prescribe intensity. This controversy arises from the absence of anagreed framework for assessing the construct validity of different methods used to determine exercise intensity. In this review, we have evaluated the construct validity of different methods for prescribing exercise intensity based on their ability to provoke homeostatic disturbances (e.g., changes in oxygen uptake kinetics and blood lactate) consistent with the moderate, heavy, and severe domains of exercise. Methods for prescribing exercise intensity include a percentage of anchor measurements, such as maximal oxygen uptake ([Formula: see text]), peak oxygen uptake ([Formula: see text]), maximum heart rate (HRmax), and maximum work rate (i.e., power or velocity-[Formula: see text] or [Formula: see text], respectively), derived from a graded exercise test (GXT). However, despite their common use, it is apparent that prescribing exercise intensity based on a fixed percentage of these maximal anchors has little merit for eliciting distinct or domain-specific homeostatic perturbations. Some have advocated using submaximal anchors, including the ventilatory threshold (VT), the gas exchange threshold (GET), the respiratory compensation point (RCP), the first and second lactate threshold (LT1 and LT2), the maximal lactate steady state (MLSS), critical power (CP), and critical speed (CS). There is some evidence to support the validity of LT1, GET, and VT to delineate the moderate and heavy domains of exercise. However, there is little evidence to support the validity of most commonly used methods, with exception of CP and CS, to delineate the heavy and severe domains of exercise. As acute responses to exercise are not always predictive of chronic adaptations, training studies are required to verify whether different methods to prescribe exercise will affect adaptations to training. Better ways to prescribe exercise intensity should help sport scientists, researchers, clinicians, and coaches to design more effective training programs to achieve greater improvements in health and athletic performance.

A Self-Powered Biosensor for Monitoring Maximal Lactate Steady State in Sport Training

A self-powered biosensor for monitoring the maximal lactate steady state (MLSS) during exercise has been developed for intelligently assisting training system. It has been presented to create poly (vinylidene fluoride) (PVDF)/Tetrapod-shaped ZnO (T-ZnO)/enzyme-modified nanocomposite film through an efficient and cost-effective fabrication process. This sensor can be readily attached to the skin surface of the tester. Due to the piezoelectric surface coupling effect, this biosensor can monitor/sense and analyze physical information in real-time under the non-invasive condition and work independently without any battery. By actively outputting piezoelectric signals, it can quickly and sensitively detect body movements (changes of joint angle, frequency relative humidity during exercise) and physiological information (changes of lactate concentration in sweat). A practical application has been demonstrated by an excellent professional speed skater (male). The purpose of this study is to increase the efficiency of MLSS evaluation, promote the development of piezoelectric surface coupling effect and motion monitoring application, develop an intelligently assisting training system, which has opened up a new direction for human motion monitoring.

Fatigue Etiology At Exhaustion When Cycling Above Vs At Or Below Maximal Lactate Steady-state Threshold.

Exercising above maximal lactate steady state (MLSS) has detrimental effects compared to exercising at MLSS. However, neuromuscular fatigue (NMF) to exercise above MLSS is unknown. PURPOSE: To evaluate NMF during exercise slightly below, at and above MLSS. METHODS: Nine men (24±3 yrs.; 76±7 kg; V̇O2max, 3.55±0.19 L·min-1) exercised to exhaustion at MLSS, 10 W below (-10W) and above (+10W) MLSS. NMF was evaluated before (B), at min 10 (10’), 30 (30’) and at exhaustion, by maximal voluntary contractions (MVC). Voluntary activation (VA) and contractile function of knee extensors [peak twitch torque (TwPt)] were tested using electrically-evoked contractions. Oxygen uptake (V̇O2) and blood lactate concentration ([Lac]) were measured. RESULTS: Time to exhaustion (TTE) for -10W (66±14 min) and +10W (44±8 min) were longer and shorter than MLSS (53±9 min) (p<0.05). V̇O2 was stable for -10W (10’, 2.9±0.19; 30’, 2.9±0.28; TTE, 2.9±0.25 L·min-1) and MLSS (10’, 3.0±0.21; 30’, 3.0±0.29; TTE, 3.1±0.23 L·min-1) (p>0.05), but increased at +10W (10’, 3.0±0.21; 30’, 3.2±0.32; TTE, 3.3±0.39 L·min-1; p<0.05). [Lac] was stable for -10W (10’, 4.1±1.1; 30’, 4.4±1.2; TTE, 4.0±1.0 mM; p>0.05). MLSS showed an increase at TTE (10’, 5.3±1.4; 30’, 5.8±1.4; TTE, 6.1±1.8 mM; p<0.05). +10W showed a constant increase (10’, 5.3±1.2; 30’, 8.0±1.6; TTE, 8.6±1.6 mM; p<0.05). MVC dropped from baseline for -10W (B, 561±178; 10’, 436±128; 30’, 420±131; TTE, 395±120 N; p<0.05), MLSS (B, 518±130; 10’, 405±96; 30’, 361±75; TTE, 344±73 N; p<0.05) and +10W (B, 517±172; 10’, 392±124; 30’, 384±146; TTE, 349±88 N; p<0.05), and was not different amongst conditions. VA dropped from baseline for -10W (B, 98±2; 10’, 90±6; 30’, 89±6; TTE, 89±6 %; p<0.05), MLSS (B, 97±2; 10’, 91±5; 30’, 91±5; TTE, 88±5 N; p<0.05) and +10W (B, 98±2; 10’, 94±5; 30’, 90±7; TTE, 87±7 %; p<0.05), and was not different amongst conditions. TwPt dropped from baseline for -10W (B, 173±30; 10’, 129±22; 30’, 119±17; TTE, 112±17 N; p<0.05), MLSS (B, 172±31; 10’, 129±28; 30’, 119±24; TTE, 111±22 N; p<0.05) and +10W (B, 167±33; 10’, 113±25; 30’, 103±27; TTE, 97±26 N; p<0.05). +10W showed greater decline throughout the bout compared to the other conditions. CONCLUSION: Despite different TTE amongst conditions, peripheral NMF was only different exercising slightly above MLSS compared to the other conditions.

Relative Proximity of Critical Power and Metabolic/Ventilatory Thresholds: Systematic Review and Meta-Analysis.

Critical power (CP) has been redefined as the new 'gold standard' that represents the boundary between the heavy- and severe-exercise intensity domains and hence the maximal metabolic steady state (MMSS). However, several other "thresholds", for instance, the maximal lactate steady state [MLSS], ventilatory thresholds [VT1, VT2] and respiratory compensation point [RCP]) have been considered synonymous with CP. This study aimed to systematically review the scientific literature and perform a meta-analysis to determine the degree of correspondence/difference between CP and MLSS, VT1, VT2 and RCP. A literature search on 2 databases (Scopus and Web of Science) was conducted on October 2, 2019. After analyzing 356 resultant articles, studies were included if they met the following inclusion criteria: (a) studies were randomized controlled trials, (b) studies included interrelations between CP and VT1, VT2, MLSS, RCP. Articles were excluded if they constituted duplicate articles or did not meet the inclusion criteria. Nine studies met the inclusion criteria and were included in this meta-analysis. This resulted in 104 participants. A random effects weighted meta-analysis with correlation coefficients was used to pool the results. The pooled correlationcoefficientof CP andallthresholds analyzed wasr = 0.73 (p > 0.00001). The subgroup analysis for each threshold with CPdemonstrated significant correlation coefficients of r = 0.80 (95%CI [0.40; 1.21], Z = 3.90,p = 0.0001) for CP & RCP;r = 0.77 (CI 95% = [0.36;1.18],Z = 3.71,p = 0.0002) for CP &MLSS; r = 0.76(CI 95% = [0.31; 1.21],Z = 3.32,p = 0.0009) for CP & VT1. However, CP & VT2,r = 0.39 (CI 95% = [- 0.37; 1.15],Z = 1.01,p = 0.31) were not significantly correlated. Despite the significant correlations between CP and VT1, MLSS and RCP these variables and VT2 under- (VT1, 30%; MLSS, 11%) or over-estimated (RCP, 6%; VT2, 21%) CP. Regardless of the presence of significant correlations among CP and ventilatory or metabolic thresholds CP differs significantly from each. Thus, logically, if CP represents the best estimate of the heavy-severe exercise intensity transition none of the thresholds considered (i.e., VT1, VT2, MLSS, RCP), at least as determined in the studies analyzed herein, should be considered synonymous with such.

High-Intensity Interval Training Prescription for Combat-Sport Athletes.

Combat sports are composed of high-intensity actions (eg,attacks, defensive actions, and counterattacks in both grappling and striking situations depending on the specific sport) interspersed with low-intensity actions (eg,displacement without contact, stepping) or pauses (eg,referee stoppages), characterizing an intermittent activity. Therefore, high-intensity interval training (HIIT) is at the essence of combat-sport-specific training and is used as complementary training, as well. HIIT prescription can be improved by using intensity parameters derived from combat-sport-specific tests. Specifically, the assessment of physiological indexes (intensity associated with the maximal blood lactate steady state, maximal oxygen consumption, and maximal sprint) or of time-motion variables (high-intensity actions, low-intensity actions, and effort:pause ratio) is a key element for a better HIIT prescription because these parameters provide an individualization of the training loads imposed on these athletes. To present a proposal for HIIT prescription for combat-sport athletes, exemplifying with different HIIT protocols (HIIT short intervals, HIIT long intervals, repeated-sprint training, and sprint interval training) using combat-sport-specific actions and the parameters for the individualization of these protocols. The use of combat-sport-specific tests is likely to improve HIIT prescription, allowing coaches and strength and conditioning professionals to elaborate HIIT short intervals, HIIT long intervals, repeated-sprint training, and sprint interval training protocols using combat-sport actions, providing more specificity and individualization for the training sessions.

Intensity Thresholds and Maximal Lactate Steady State in Small Muscle Group Exercise.

The aim of our study is to determine the first (LTP1) and the second (LTP2) lactate turn points during an incremental bicep curl test and to verify these turn points by ventilatory turn points (VT1 and VT2) and constant-load exercise tests. Twelve subjects performed a one-arm incremental bicep curl exercise (IET) after a one repetition maximum (1RM) test to calculate the step rate for the incremental exercise (1RM/45). Workload was increased every min at a rate of 30 reps/min until maximum. To verify LTPs, VT1 and VT2 were determined from spirometric data, and 30 min constant-load tests (CL) were performed at 5% Pmax below and above turn points. Peak load in IET was 5.3 ± 0.9 kg (Lamax: 2.20 ± 0.40 mmol·L−1; HRmax: 135 ± 15 b·min−1; VO2max: 1.15 ± 0.30 L·min−1). LTP1 was detected at 1.9 ± 0.6 kg (La: 0.86 ± 0.36 mmol·L−1; HR 90 ± 13 b·min−1; VO2: 0.50 ± 0.05 L·min−1) and LTP2 at 3.8 ± 0.7 kg (La: 1.38 ± 0.37 mmol·L−1; 106 ± 10 b·min−1; VO2: 0.62 ± 0.11 L·min−1). Constant-load tests showed a lactate steady-state in all tests except above LTP2, with early termination after 16.5 ± 9.1 min. LTP1 and LTP2 could be determined in IET, which were not significantly different from VT1/VT2. Constant-load exercise validated the three-phase concept, and a steady-state was found at resting values below VT1 and in all other tests except above LTP2. It is suggested that the three-phase model is also applicable to small muscle group exercise.

VO2 Steady State at and Just Above Maximum Lactate Steady State Intensity.

Over recent decades the association between metabolic and gas exchange parameters during exercise has become evident. Different "thresholds" (such as lactate thresholds, critical power, EMG thresholds) and intensity domains appear to be linked to an upper limit of oxygen uptake steady state (V̇O2SS). The aim of this study was to investigate whether MLSS is associated with the upper limit for a V̇O2SS. Forty-five subjects underwent one incremental test and 4-6 30-minute MLSS tests on a cycle ergometer. A three-component model was used to describe V̇O2 response at PMLSS and just above PMLSS+1. To evaluate the results, breath-by-breath V̇O2 and lactate (LA) values were analyzed using the intraclass correlation coefficient (ICC), increasing (k-) values and the Wilcoxon test. According to the calculated k-values of LA and VO2 at PMLSS and PMLSS+1, no significant increase of VO2 occurred during both intensities (PMLSS and PMLSS+1) from minute 10 to minute 30, confirming the existence of a V̇O2SS. Additionally, the ICC of 0.94 confirmed high accordance of the VO2 kinetics at both intensities (PMLSS and PMLSS+1). This study shows that power output at MLSS workload does not represent an accurate cut for an upper limit of V̇O2SS.

A protocol for the determination of the maximal lactate steady state in working dogs

This study aimed to describe a test protocol for the determination of the maximal lactate steady state (MLSS) in dogs. Ten animals were submitted to a variable number of sessions (3 to 5), with a duration of 30 minutes each. Every session started with a warm-up period of 10 minutes at 7 km/h, at a 0% inclination. The treadmill was then set at a 5% inclination and at a constant speed, maintained for 20 m. In subsequent sessions, speed was increased or decreased 0.5 km/h. Blood samples were collected, and blood lactate (BL), heart rate (HR), rectal temperature (RT) and glycaemia were determined at rest (T0), after the warm-up (T1) and every 5 minutes until the end of the test (T2-T5). Significant variations were observed in BL, HR and RT, when comparing values at rest and T1 with those at the MLSS level (P<0.01). Mean speed at MLSS was 14.6±1.47 km/h and BL was 2.7±0.45 mmol/L, a value below 4 mmol/L. A correlation between BL at MLSS and the visual lactate threshold was observed (0.87). A strong agreement between blood lactate levels at the MLSS level and LTv was also observed. The present results consist, to the authors' knowledge, in the first description of a protocol to determine the MLSS in dogs. It provides information for exercise monitoring and training planning.

Grey Zone: A Gap Between Heavy and Severe Exercise Domain.

Ozkaya, O, Balci, GA, As, H, Cabuk, R, and Norouzi, M. Grey zone: A gap between heavy and severe exercise domain. J Strength Cond Res 36(1): 113-120, 2022-The aim of this study was to determine a critical threshold (CT) interpreted as "the highest exercise intensity where V̇o2 can be stabilized before reaching 95% of V̇o2max (V̇o2peak)" and compare it with commonly used anaerobic threshold indices. Ten well-trained male cyclists volunteered for this study. Ventilatory threshold (VT) was determined from incremental tests. Multisession constant-load trials were performed to reveal V̇o2max. Mathematically modeled critical power (CP) was estimated through the best individual fit parameter method. Maximal lactate steady state (MLSS) was detected by 30-minute constant-load exercises. The individual CT load of each cyclist was tested by constant-load exercises to exhaustion with +15 W intervals until minimal power output to elicit V̇o2peak. The results showed that work rate corresponding to CT (329.5 ± 41.5 W) was significantly greater than that of the MLSS (269.5 ± 38.5 W; p = 0.000), VT (279.6 ± 33 W; p = 0.000), and CP (306.3 ± 39.4 W; p = 0.000), and CP overestimated both VT and MLSS (p = 0.000). There was no significant V̇o2 difference between the 10th and 30th minute of MLSS and MLSS + 15 W exercise (0.36-0.13 ml·min-1·kg-1; p = 0.621). Exercising V̇o2 response of MLSS + 15 W could not exceed the level of 95% V̇o2max (57.02 ± 3.87 ml·min-1·kg-1 and 87.2 ± 3.1% of V̇o2max; p = 0.000), whereas V̇o2 responses greater than 95% of V̇o2max were always attained during exercises performed at CT + 15 W (64.52 ± 4.37 ml·min-1·kg-1 and 98.6 ± 1% of V̇o2max; p > 0.05). In conclusion, this study indicates that there is a "grey zone" between heavy and severe exercise domain. This information may play a key role in enhancing athletic performance by improving the quality of training programs.

Time to exhaustion during cycling is not well predicted by critical power calculations.

Three to 5 cycling tests to exhaustion allow prediction of time to exhaustion (TTE) at power output based on calculation of critical power (CP). We aimed to determine the accuracy of CP predictions of TTE at power outputs habitually endured by cyclists. Fourteen endurance-trained male cyclists underwent 4 randomized cycle-ergometer TTE tests at power outputs eliciting (i) mean Wingate anaerobic test (WAnTmean), (ii) maximal oxygen consumption, (iii) respiratory compensation threshold (VT2), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of 5%-9%. Power outputs were 710 ± 63 W for WAnTmean, 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT2 and 247 ± 20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P < 0.001). The CP predictions overestimated TTE at WAnTmean (00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT2 (-04:18 ± 03:20 mm:ss; P < 0.05), and correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or lower power outputs (e.g., sprints and 40-km time trials). Novelty CP calculation enables to predict TTE at any cycling power output. We tested those predictions against measured TTE in a wide range of cycling power outputs. CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.

A Critical Evaluation of Current Methods for Exercise Prescription in Women and Men.

Thus, the aim of this study was to compare contemporary gold-standard approaches for exercise prescription based on fixed percentages of maximum values to the well-established, but underutilized, "domain" schema of exercise intensity. One hundred individuals participated in the study (women, 46; men, 54). A cardiopulmonary ramp-incremental test was performed to assess V˙O2max, WRpeak, HRmax, and the lactate threshold (LT), and submaximal constant-work rate trials of 30-min duration to determine the maximal lactate steady-state (MLSS). The LT and MLSS were used to partition the intensity spectrum for each individual in three domains of intensity: moderate, heavy, and severe. V˙O2max in women and men was 3.06 ± 0.41 L·min and 4.10 ± 0.56 L·min, respectively. Lactate threshold and MLSS occurred at a greater %V˙O2max and %HRmax in women compared with men (P < 0.05). The large ranges in both sexes at which LT and MLSS occurred on the basis of %V˙O2max (LT, 45%-74%; MLSS, 69%-96%), %WRpeak (LT, 23%-57%; MLSS, 44%-71%), and %HRmax (LT, 60%-90%; MLSS, 75%-97%) elicited large variability in the number of individuals distributed in each domain at the fixed-percentages examined. Contemporary gold-standard methods for exercise prescription based on fixed-percentages of maximum values conform poorly to exercise intensity domains and thus do not adequately control the metabolic stimulus.

Menstrual and oral contraceptive cycle phases do not affect submaximal and maximal exercise responses.

To examine whether the menstrual or monophasic oral contraceptive cycle phases affect submaximal (oxygen uptake ( O2 ) kinetics, maximal lactate steady-state (MLSS)) and maximal ( O2max , time-to-exhaustion (TTE)) responses to exercise in healthy, active women. During the mid-follicular or inactive-pill phase and the mid-luteal or active-pill phase of the respectivemenstrual or oral contraceptive cycle, 15 non-oral contraceptive users (meanandstandard deviation(SD) (±): 27±6 years; 171±5cm; 65±7kg) and 15 monophasic oral contraceptive users (24±4 years; 169±10cm; 68±10kg) performed: one O2 kinetics test; one ramp-incremental test; two to three 30-minute constant-load cycling trials to determine the power output corresponding to MLSS (MLSSp ), followed by a TTE trial. The phase of the menstrual or oral contraceptive cycle did not affect the time constant of the O2 kinetics response (τ O2 ) (mid-follicular, 20±5seconds and mid-luteal, 18±3seconds; inactive-pill, 22±8seconds and active-pill, 23±6seconds), O2max (mid-follicular, 3.06±0.32Lmin-1 and mid-luteal, 3.00±0.33Lmin-1 ; inactive-pill, 2.87±0.39Lmin-1 and active-pill, 2.87±0.45Lmin-1 ), MLSSp (mid-follicular, 181±30W and mid-luteal, 182±29W; inactive-pill, 155±26W and active-pill, 155±27W), and TTE (mid-follicular, 147±42seconds and mid-luteal, 128±54seconds; inactive-pill, 146±70seconds and active-pill, 139±77seconds) (P>.05). The rate of perceived exertion (RPE) at minute 30 of the MLSSp trials was greater in the mid-follicular phase (6.2±1.5) compared with the mid-luteal phase (5.3±1.4) for non-oral contraceptive users (P=.022). The hormonal fluctuations between the menstrual and oral contraceptive cycle phases had no detectable effects on submaximal and maximal exercise performance, even when RPE differed.

Training-Induced Changes in the Respiratory Compensation Point, Deoxyhemoglobin Break Point, and Maximal Lactate Steady State: Evidence of Equivalence.

To evaluate whether the coherence in the oxygen uptake (V˙O2) associated with the respiratory compensation point (RCP), near-infrared spectroscopy-derived muscle deoxyhemoglobin ([HHb]) break point ([HHb]BP), and maximal lactate steady state (MLSS) would persist at the midpoint and endpoint of a 7-month training and racing season. Eight amateur male cyclists were tested in 3 separate phases over the course of a cycling season (PRE, MID, and POST). Testing at each phase included a ramp-incremental test to exhaustion to determine RCP and [HHb]BP. The PRE and POST phases also included constant power output rides to determine MLSS. Compared with PRE, V˙O2 at both RCP and [HHb]BP was greater at MID (delta: RCP 0.23 [0.14]L·min-1, [HHb]BP 0.33 [0.17]L·min-1) and POST (delta: RCP 0.21 [0.12], [HHb]BP 0.30 [0.14]L·min-1) (P < .05). V˙O2 at MLSS also increased from PRE to POST (delta: 0.17 [12]L·min-1) (P < .05). V˙O2 was not different at RCP, [HHb]BP, and MLSS at PRE (3.74 [0.34], 3.64 [0.40], 3.78 [0.23]L·min-1) or POST (3.96 [0.25], 3.95 [0.32], 3.94 [0.18]L·min-1) respectively, and RCP (3.98 [0.33]L·min-1) and [HHb]BP (3.97 [0.34]L·min-1) were not different at MID (P > .05). PRE-MID and PRE-POST changes in V˙O2 associated with RCP, [HHb]BP, and MLSS were strongly correlated (range: r = .85-.90) and demonstrated low mean bias (range = -.09 to .12L·min-1). At all measured time points, V˙O2 at RCP, [HHb]BP, and MLSS were not different. Irrespective of phase comparison, direction, or magnitude of V˙O2 changes, intraindividual changes between each index were strongly related, indicating that interindividual differences were reflected in the group mean response and that their interrelationships are beyond coincidental.

New On-Water Test for the Assessment of Blood Lactate Response to Exercise in Elite Kayakers.

Lactate thresholds are physiological parameters used to train athletes and monitor performance or training. Currently, the assessment of lactate thresholds in kayakers is performed in a laboratory setting utilizing specific ergometers; however, laboratory tests differ from on-water evaluation for several reasons. The aim of this study was to assess reliability and validity of a new on-water incremental test for the assessment of blood lactate response to exercise in flat-water kayakers. Maximal lactate steady state test (MLSS) was used as criterion measurement. Eleven junior (16.5 ± 1.9 yr) élite flat-water kayakers performed: i) an incremental cardiopulmonary test up to voluntary exhaustion on a stationary kayak ergometer to determine peak oxygen uptake; ii) an on-water 1000-m distance trial (T1000) to record best performance time and average speed (S1000); iii) two repetitions of on-water incremental kayaking test (WIK test); iv) several repetitions of on-water constant speed tests to determine MLSS. Speed, HR, and blood lactate concentrations were determined during on-water tests. The best performance time in T1000 was 262 ± 13 s, corresponding to an S1000 of 3.82 ± 0.19 m·s. Lactate threshold determined by modified Dmax method (LTDmod) during WIK test was 2.78 ± 1.02 mmol·L and the corresponding speed (SLT) was 3.34 ± 0.16 m·s. Test-retest reliability, calculated on SLT, was strong (ICC = 0.95 and r = 0.93). MLSS test corresponded to 3.06 ± 0.68 mmol·L and was reached at a speed (SMLSS) of 3.36 ± 0.14 m·s. Correlation coefficient between SLT and SMLSS was 0.90 (P = 0.0001). Interestingly, a significant correlation (r = 0.96, P < 0.0001) was observed between SLT and S1000. The WIK test showed good reliability and validity for the assessment of speed corresponding to LTDmod in flat-water kayakers and it could be a useful tool to monitor athletic performance. The speed value at LTDmod nicely predicted performance on 1000 m.

Is the Functional Threshold Power a Valid Metric to Estimate the Maximal Lactate Steady State in Cyclists?

Lillo-Beviá, JR, Courel-Ibáñez, J, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, and Pallarés, JG. Is the functional threshold power a valid metric to estimate the maximal lactate steady state in cyclists? J Strength Cond Res 36(1): 167-173, 2022-The aims of this study were to determine (a) the repeatability of a 20-minute time-trial (TT20), (b) the location of the TT20 in relation to the main physiological events of the aerobic-anaerobic transition, and (c) the predictive power of a list of correction factors and linear/multiple regression analysis applied to the TT20 result to estimate the individual maximal lactate steady state (MLSS). Under laboratory conditions, 11 trained male cyclists and triathletes (V̇o2max 59.7 ± 3.0 ml·kg-1·min-1) completed a maximal graded exercise test to record the power output associated with the first and second ventilatory thresholds and V̇o2max measured by indirect calorimetry, several 30 minutes constant tests to determine the MLSS, and 2 TT20 tests with a short warm-up. Very high repeatability of TT20 tests was confirmed (standard error of measurement of ±3 W and smallest detectable change of ±9 W). Validity results revealed that MLSS differed substantially from TT20 (bias = 26 ± 7 W). The maximal lactate steady state was then estimated from the traditional 95% factor (bias = 12 ± 7 W) and a novel individual correction factor (ICF% = MLSS/TT20), resulting in 91% (bias = 1 ± 6 W). Complementary linear (MLSS = 0.7488 × TT20 + 43.24; bias = 0 ± 5 W) and multiple regression analysis (bias = 0 ± 4 W) substantially improved the individual MLSS workload estimation. These findings suggest reconsidering the TT20 procedures and calculations to increase the effectiveness of the MLSS prediction.

Maximal Lactate Steady State Versus the 20-Minute Functional Threshold Power Test in Well-Trained Individuals: "Watts" the Big Deal?

To (1)compare the power output (PO) for both the 20-minute functional threshold power (FTP20) field test and the calculated 95% (FTP95%) with PO at maximal lactate steady state (MLSS) and (2)evaluate the sensitivity of FTP95% and MLSS to training-induced changes. Eighteen participants (12 males: 37 [6]y and 6 females: 28 [6]y) performed a ramp-incremental cycling test to exhaustion, 2 to 3 constant-load MLSS trials, and an FTP20 test. A total of 10 participants returned to repeat the test series after 7 months of training. The PO at FTP20 and FTP95% was greater than that at MLSS (P = .00), with the PO at MLSS representing 88.5% (4.8%) and 93.1% (5.1%) of FTP and FTP95%, respectively. MLSS was greater at POST compared with PRE training (12 [8]W) (P = .002). No increase was observed in mean PO at FTP20 and FTP95% (P = .75). The results indicate that the PO at FTP95% is different to MLSS, and that changes in the PO at MLSS after training were not reflected by FTP95%. Even when using an adjusted percentage (ie,88% rather than 95% of FTP20), the large variability in the data is such that it would not be advisable to use this as a representation of MLSS.

Lactate Equivalent for Maximal Lactate Steady State Determination in Soccer

ABSTRACTPurpose: 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.

Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?

Functional threshold power (FTP), determined as 95% of the average power during a 20-min time trial, is suggested as a practical test for the determination of the maximal lactate steady state (MLSS) in cycling. Therefore, the objective of the present study was to determine the validity of FTP in predicting MLSS. A total of 15 cyclists, 7 classified as trained and 8 as well trained (mean [SD] maximal oxygen uptake 62.3 [6.4]mL·kg-1·min-1, maximal aerobic power 329 [30]W), performed an incremental test to exhaustion, an FTP test, and several constant-load tests to determine the MLSS. The bias ± 95% limits of agreement (LoA), typical error of the estimate (TEE), and Pearson coefficient of correlation (r) were calculated to assess validity. For the power-output measures, FTP presented a bias ± 95% LoA of 1.4% ± 9.2%, a moderate TEE (4.7%), and nearly perfect correlation (r = .91) with MLSS in all cyclists together. When divided by training level, the bias ± 95% LoA and TEE were higher in the trained group (1.4% ± 11.8% and 6.4%, respectively) than in the well-trained group (1.3% ± 7.4% and 3.0%, respectively). For the heart-rate measurement, FTP presented a bias ± 95% LoA of -1.4% ± 8.2%, TEE of 4.0%, and very large correlation (r = .80) with MLSS. Therefore, trained and well-trained cyclists can use FTP as a noninvasive and practical alternative to estimate MLSS.