Maximal Lactate Steady State Research Articles

The Method but Not the Protocol Affects Lactate-Threshold Determination in Competitive Swimmers.

The study validated variables corresponding to lactate threshold (LT) in swimming. Speed (sLT), blood lactate concentration (BLLT), oxygen uptake (VO2LT), and heart rate (HRLT) corresponding to LT were calculated by 2 different incremental protocols and validated in comparison with maximal lactate steady state (MLSS). Ten competitive swimmers performed a 7 × 200-m front-crawl incremental "step test" with 2 protocols: (1)with 30-second rests between repetitions (short-rest incremental protocols) and (2)on a 5-minute cycle (swim + rest time, long-rest incremental protocols). Five methods were used for the assessment of sLT and corresponding BLLT, VO2LT, and HRLT: intersection of 2 lines, Dmax, modified Dmax, visual inspection, and intersection of combined linear and exponential regression lines. Subsequently, swimmers performed two to three 30-minute continuous efforts to identify speed (sMLSS) and physiological parameters corresponding to MLSS. Both protocols resulted in similar sLT and corresponding physiological variables (P > .05). Bland-Altman plots showed agreement between protocols (sLT, bias: -0.017 [0.002]m·s-1; BLLT, bias: 0.0 [0.5]mmol·L-1; VO2LT, bias: -0.1 [2.2]mL·kg-1·min-1; HRLT. bias: -2 [8]beats·min-1). However, sLT calculated by modifiedDmax using short rest was higher compared with speed at MLSS (1.346 [0.076] vs 1.300 [0.101]m·s-1; P < .05). Calculated sLT, BLLT, VO2LT, and HRLT using all other methods in short-restand long-rest incremental protocols were no different compared with MLSS (P > .05). Both 7 × 200-m protocols are valid for determination of sLT and corresponding physiological parameters, but the modifiedDmax method may overestimate sLT.

Critical Power and Maximal Lactate Steady State in Cycling: "Watts" the Difference?

From a physiological perspective, the delineation between steady-state and non-steady-state exercise, also referred to as the maximal metabolic steady state, holds paramount importance for evaluating athletic performance and designing and monitoring training programs. The critical power and the maximal lactate steady state are two widely used indices to estimate this threshold, yet previous studies consistently reported significant discrepancies between their associated power outputs. These findings have fueled the debate regarding the interchangeability of critical power and themaximal lactate steady state in practice. This paper reviews the methodological intricacies intrinsic to the determination of these thresholds, and elucidates how inappropriate determination methods and methodological inconsistencies between studies have contributed to the documented differences in the literature. Through a critical examination of relevant literature and by integration of our laboratory data, we demonstrate that differences between critical power and themaximal lactate steady state may be reconciled to only a few Watts when applying appropriate and strict determination criteria, so that both indices may be used to estimate the maximal metabolic steady-state threshold in practice. To this end, we have defined a set of good practice guidelines to assist scientists and coaches in obtaining the most valid critical power and maximal lactate steady state estimates.

Resolving Differences between MLSS and CP by Considering Rates of Change of Blood Lactate during Endurance Exercise.

To develop a new method that more closely represents the heavy-to-severe exercise domain boundary by evaluating the rates of blood lactate accumulation during the constant power output exercise bouts that are employed in the assessment of the maximal lactate steady state (MLSS). Eight well-trained male cyclists completed five exercise tests of up to 30 min for determination of the traditional MLSS (MLSSTRAD) and a further four maximal tests for determination of critical power (CP). The rates of change of blood [lactate] between 10 min and the end of exercise in the MLSS tests were plotted against the corresponding power outputs and a two-segment linear regression model was used to identify individualised breakpoints in lactate accumulation vs. power output (MLSSMOD). MLSSMOD was significantly higher than MLSSTRAD (297 ± 41 vs. 278 ± 41 W; P < 0.001) but was not significantly different from CP (297 ± 41 W; P > 0.05); MLSSMOD and CP were closely aligned (r: 0.97; Bias: -0.52 W; SEE: 10 W; Limits of Agreement: -20 to 19 W). The rates of change of both blood [lactate] and V̇O2 were significantly greater, and exercise intolerance occurred before 30 min, at a power output slightly above MLSSMOD. A novel method for evaluating blood lactate kinetics during a traditional MLSS protocol produces a modified MLSS that is not different from CP and better represents the heavy-to-severe exercise domain boundary.

Effects of acute simulated altitude on the maximal lactate steady state in humans.

We sought to determine the effects of acute simulated altitude on the maximal lactate steady state (MLSS) and physiological responses to cycling at and 10 W above the MLSS-associated power output (PO) (MLSSp and MLSSp+10, respectively). Eleven (4 females) participants (means [SD]; 28 [4] yr; V̇o2max: 54.3 [6.9] mL·kg-1·min-1) acclimatized to ∼1,100 m performed 30-min constant PO trials in simulated altitudes of 0 m sea level (SL), 1,111 m mild altitude (MILD), and 2,222 m moderate altitude (MOD). MLSSp, defined as the highest PO with stable (<1 mM change) blood lactate concentration ([BLa]) between 10 and 30 min, was significantly lower in MOD (209 [54] W) compared with SL (230 [56] W; P < 0.001) and MILD (225 [58] W; P = 0.001), but MILD and SL were not different (P = 0.12). V̇o2 and V̇co2 decreased at higher simulated altitudes due to lower POs (P < 0.05), but other end-exercise physiological responses (e.g., [BLa], ventilation [V̇e], heart rate [HR]) were not different between conditions at MLSSp or MLSSp + 10 (P > 0.05). At the same absolute intensity (MLSSp for MILD), [BLa], HR, and V̇E and all perceptual variables were exacerbated in MOD compared with SL and MILD (P < 0.05). Maximum voluntary contraction, voluntary activation, and potentiated twitch forces were exacerbated at MLSSp + 10 relative to MLSSp within conditions (P < 0.05); however, condition did not affect performance fatiguability at the same relative or absolute intensity (P > 0.05). As MLSSp decreased in hypoxia, adjustments in PO are needed to ensure the same relative intensity across altitudes, but common indices of exercise intensity may facilitate exercise prescription and monitoring in hypoxia.NEW & NOTEWORTHY This study demonstrates the power output and metabolic rate associated with the maximal lactate steady-state (MLSS) decline in response to simulated altitude; however, common indices of exercise intensity remained unchanged when cycling was performed at the work rate associated with MLSS at each simulated altitude. These results support previous studies that investigated the effects of hypoxia on alternative measures of the critical intensity of exercise and will inform exercise prescription/monitoring across altitudes.

Cadence Paradox in Cycling-Part 2: Theory and Simulation of Maximal Lactate Steady State and Carbohydrate Utilization Dependent on Cycling Cadence.

To develop and evaluate a theory on the frequent observation that cyclists prefer cadences (RPMs) higher than those considered most economical at submaximal exercise intensities via modeling and simulation of its mathematical description. The theory combines the parabolic power-to-velocity (v) relationship, where v is defined by crank length, RPM-dependent ankle velocity, and gear ratio, RPM effects on the maximal lactate steady state (MLSS), and lactate-dependent carbohydrate oxidation (CHO). It was tested against recent experimental results of 12 healthy male recreational cyclists determining the v-dependent peak oxygen uptake (VO2PEAKv), MLSS (MLSSv), corresponding power output (PMLSSv), oxygen uptake at PMLSSv (VO2MLSSv), and CHOMLSSv-management at 100 versus 50per minute, respectively. Maximum RPM (RPMMAX) attained at minimized pedal torque was measured. RPM-specific maximum sprint power output (PMAXv) was estimated at RPMs of 100 and 50, respectively. Modeling identified that MLSSv and PMLSSv related to PMAXv (IPMLSSv) promote CHO and that VO2MLSSv related to VO2PEAKv inhibits CHO. It shows that cycling at higher RPM reduces IPMLSSv. It suggests that high cycling RPMs minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. The present theory-guided modeling approach is exclusively based on data routinely measured in high-performance testing. It implies a higher performance reserve above IPMLSSv at higher RPM. Cyclists may prefer high cycling RPMs because they appear to minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX.

Cadence Paradox in Cycling-Part 1: Maximal Lactate Steady State and Carbohydrate Utilization Dependent on Cycling Cadence.

To assess (1)whether and how a higher maximal lactate steady state (MLSS) at higher cycling cadence (RPM) comes along with higher absolute and/or fractional carbohydrate combustion (CHOMLSS), respectively, and (2)whether there is an interrelation between potential RPM-dependent MLSS effects and the maximally achievable RPM (RPMMAX). Twelve healthy males performed incremental load tests to determine peak power, peak oxygen uptake, and 30-minute MLSS tests at 50 and 100per minute, respectively, to assess RPM-dependent MLSS, corresponding power output, CHOMLSS responses, and 6-second sprints to measure RPMMAX. Peak power, peak carbon dioxide production, and power output at MLSS were lower (P = .000, ω2 = 0.922; P = .044, ω2 > 0.275; and P = .016, ω2 = 0.373) at 100per minute than at 50per minute. With 6.0 (1.5) versus 3.8 (1.2)mmol·L-1, MLSS was higher (P = .000, ω2 = 0.771) at 100per minute than at 50per minute. No corresponding RPM-dependent differences were found in oxygen uptake at MLSS, carbon dioxide production at MLSS, respiratory exchange ratio at MLSS, CHOMLSS, or fraction of oxygen uptake used for CHO at MLSS, respectively. There was no correlation between the RPM-dependent difference in MLSS and RPMMAX. The present study extends the previous finding of a consistently higher MLSS at higher RPM by indicating (1) that at fully established MLSS conditions, respiration and CHOMLSS management do not differ significantly between 100per minute and 50per minute, and (2)that linear correlation models did not identify linear interdependencies between RPM-dependent MLSS conditions and RPMMAX.

INSCYD physiological performance software is valid to determine the maximal lactate steady state in male and female cyclists.

The maximal lactate steady state (MLSS) is defined as the highest workload that can be maintained without blood lactate accumulation over time. The power output at MLSS (PMLSS) is regularly implemented to define training zones, quantify training progress, or predict race performance. The gold standard methodology for MLSS determination requires two to five trials of constant-load exercise, which limits the practical application in training. The INSCYD software can calculate the PMLSS (PMLSSINSCYD) based on physiological data that can be obtained during a ∼1 h laboratory visit. However, to the best of our knowledge, the validity of the most recent software version has not yet been investigated. This study aimed to assess the validity of the software's calculations on PMLSS in cycling. The data for this study were retrieved from two published scientific sources. Thirty-one cyclists (19 males, 12 females) performed a 15 s sprint to estimate the VLamax, a ramp test for the assessment, and two to five constant-load tests to determine the PMLSS. The INSCYD software was used to calculate the PMLSS based on the , VLamax, sex, body mass, and body composition. The PMLSSINSCYD was higher than the PMLSS in the entire sample (mean difference: 4.6 W, p < 0.05, 95% CI 0.8-8.3 W) and in men (mean difference: 6.6 W, p < 0.05, 95% CI 1.3-11.8 W), but not in women (mean difference: 0.8 W, n.s., 95% CI -3.7 to 5.3 W), which was within the typical error of the PMLSS estimations (∼3%). In 12 subjects (nine males, three females), the PMLSSINSCYD differed by 3.1-7.3% compared to the MLSS. The Pearson correlations between the measured PMLSS and the calculated PMLSS (PMLSSINSCYD) were very strong in men (r = 0.974, p < 0.001, 95% CI 0.933-0.99), women (r = 0.984, p < 0.001, 95% CI 0.931-0.996), and for the entire sample (r = 0.992, p < 0.001, 95% CI 0.982-0.996). In conclusion, the PMLSS can be accurately calculated using the INSCYD software, but it still requires advanced testing equipment to collect valid and VLamax data.

Relative pedaling forces are low during cycling

We quantified and compared the mechanical force demands relative to the maximum dynamic force (MDF) of 11 cyclists when pedaling at different intensities (ventilatory threshold, maximum lactate steady state, respiratory compensation point, and maximal aerobic power), cadences (free, 40, 60 and 80 rpm), and all-out resisted sprints. Relative force demands (expressed as %MDF) progressively increased with higher intensities (p < 0.001) and lower cadences (p < 0.001). Notwithstanding, relative force demands were low (<54 % MDF) for all conditions, even during the so-called ‘torque training’. These results might be useful when programming on-bike resistance training to improve torque production capacity.

Determination of speed and assessment of conditioning in horses submitted to a lactate minimum test-alternative approaches.

The maximal lactate steady state (MLSS) is a well-known gold standard method for determining the aerobic capacity of athletic horses. Owing to its high cost and complex execution, there is a search for standardized exercise tests that can predict this value in a single session. One of the methods described for this purpose is the lactate minimum test (LMT), which could be more accurate despite being adequate to predict MLSS. This study aimed to examine the impact of training on the speed corresponding to lactate minimum speed (LMS) and to apply new mathematical methods to evaluate the fitness level of horses based on the curve obtained by the LMT. Ten Arabian horses underwent a 6-week training program based on LMS calculated by second-degree polynomial regression (LMSP). In addition, the LMS was also determined by visual inspection (LMSV), bi-segmented linear regression (LMSBI) and spline regression (LMSS). From the curve obtained during the LMT, it was possible to calculate angles α, β and ω, as well as the total area under the curve (AUCTOTAL) before (AUCPRELMS) and after (AUCPOSLMS) the LMS. The methods for determining the LMS were evaluated by ANOVA, intraclass correlation coefficient (ICC) and effect size (ES) by Cohen's d test. The Pearson correlation coefficient (r) between the proposed LMS determination methods and other mathematical methods was also calculated. Despite showing a good correlation (ICC >0.7), the LMS determination methods differed from each other (p < 0.05), albeit without a significant difference resulting from conditioning. There were reductions in α:β ratio, angle α, and AUCPOSTLMS, with the latter indicating lower lactate accumulation in the incremental phase of LMT after conditioning, in addition to an improvement in the animals' aerobic capacity. Considering that the most common methods for determining the LMS are applicable yet with low sensitivity for conditioning assessment, the approaches proposed herein can aid in analyzing the aerobic capacity of horses subjected to LMT. The mathematical models presented in this paper have the potential to be applied in human lactate-guided training program trials with a comparable study basis.

Understanding optimal cadence dynamics: a systematic analysis of the power-velocity relationship in track cyclists with increasing exercise intensity.

Background: This study aimed to investigate the changes in force-velocity (F/v) and power-velocity (P/v) relationships with increasing work rate up to maximal oxygen uptake and to assess the resulting alterations in optimal cadence, particularly at characteristic metabolic states. Methods: Fourteen professional track cyclists (9 sprinters, 5 endurance athletes) performed submaximal incremental tests, high-intensity cycling trials, and maximal sprints at varied cadences (60, 90, 120rpm) on an SRM bicycle ergometer. Linear and non-linear regression analyses were used to assess the relationship between heart rate, oxygen uptake (V.O2), blood lactate concentration and power output at each pedaling rate. Work rates linked to various cardiopulmonary and metabolic states, including lactate threshold (LT1), maximal fat combustion (FATmax), maximal lactate steady-state (MLSS) and maximal oxygen uptake (V.O2max), were determined using cadence-specific inverse functions. These data were used to calculate state-specific force-velocity (F/v) and power-velocity (P/v) profiles, from which state-specific optimal cadences were derived. Additionally, fatigue-free profiles were generated from sprint data to illustrate the entire F/v and P/v continuum. Results: HR, V.O2 demonstrated linear relationships, while BLC exhibited an exponential relationship with work rate, influenced by cadence (p < 0.05, η2 ≥ 0.655). Optimal cadence increased sigmoidally across all parameters, ranging from 66.18 ± 3.00rpm at LT1, 76.01 ± 3.36rpm at FATmax, 82.24 ± 2.59rpm at MLSS, culminating at 84.49 ± 2.66rpm at V.O2max (p < 0.01, η2 = 0.936). A fatigue-free optimal cadence of 135 ± 11rpm was identified. Sprinters and endurance athletes showed no differences in optimal cadences, except for the fatigue-free optimum (p < 0.001, d = 2.215). Conclusion: Optimal cadence increases sigmoidally with exercise intensity up to maximal aerobic power, irrespective of the athlete's physical condition or discipline. Threshold-specific changes in optimal cadence suggest a shift in muscle fiber type recruitment toward faster types beyond these thresholds. Moreover, the results indicate the need to integrate movement velocity into Henneman's hierarchical size principle and the critical power curve. Consequently, intensity zones should be presented as a function of movement velocity rather than in absolute terms.

Wearable device for continuous sweat lactate monitoring in sports: a narrative review.

In sports science, the use of wearable technology has facilitated the development of new approaches for tracking and assessing athletes' performance. This narrative review rigorously explores the evolution and contemporary state of wearable devices specifically engineered for continuously monitoring lactate levels in sweat, an essential biomarker for appraising endurance performance. Lactate threshold tests have traditionally been integral in tailoring training intensity for athletes, but these tests have relied on invasive blood tests that are impractical outside a laboratory setting. The transition to noninvasive, real-time monitoring through wearable technology introduces an innovative approach, facilitating continuous assessment without the constraints inherent in traditional methodologies. We selected 34 products from a pool of 246 articles found through a meticulous search of articles published up to January 2024 in renowned databases: PubMed, Web of Science, and ScienceDirect. We used keywords such as "sweat lactate monitoring," "continuous lactate monitoring," and "wearable devices." The findings underscore the capabilities of noninvasive sweat lactate monitoring technologies to conduct long-term assessments over a broad range of 0-100mM, providing a safer alternative with minimal infection risks. By enabling real-time evaluations of the lactate threshold (LT) and maximal lactate steady state (MLSS), these technologies offer athletes various device options tailored to their specific sports and preferences. This review explores the mechanisms of currently available lactate monitoring technologies, focusing on electrochemical sensors that have undergone extensive research and show promise for commercialization. These sensors employ amperometric reactions to quantify lactate levels and detect changes resulting from enzymatic activities. In contrast, colorimetric sensors offer a more straightforward and user-friendly approach by displaying lactate concentrations through color alterations. Despite significant advancements, the relationship between sweat lactate and blood lactate levels remains intricate owing to various factors such as environmental conditions and the lag between exercise initiation and sweating. Furthermore, there is a marked gap in research on sweat lactate compared to blood lactate across various sports disciplines. This review highlights the need for further research to address these shortcomings and substantiate the performance of lactate sweat monitoring technologies in a broader spectrum of sports environments. The tremendous potential of these technologies to supplant invasive blood lactate tests and pioneer new avenues for athlete management and performance optimization in real-world settings heralds a promising future for integrating sports science and wearable technology.

The origin of the maximal lactate steady state (MLSS).

The maximal lactate steady state, abbreviated as MLSS, is the maximal exercise intensity where the concentration of earlobe capillary or arterial blood lactate remains constant over time. In the late 1970s and early 1980s, we (i.e. Hermann Heck and co-workers) developed a direct test to determine the MLSS to investigate whether it occurred at a lactate concentration of 4 mmol.L- 1, as earlier predicted by Alois Mader and colleagues. The test consisted of each participant performing several constant-intensity running bouts of ≈ 30min at intensities close to the estimated MLSS. During each run, we measured lactate every 5min. Based on the results, we defined the MLSS as the "workload where the concentration of blood lactate does not increase more than 1 mmo.L- 1during the last 20 min of a constant load exercise". This MLSS protocol is impractical for performance testing as it requires too many exercise bouts, but it is a gold standard to determine the real MLSS. It is especially useful to validate indirect tests that seek to estimate the MLSS.

A Five-Week Periodized Carbohydrate Diet Does Not Improve Maximal Lactate Steady-State Exercise Capacity and Substrate Oxidation in Well-Trained Cyclists compared to a High-Carbohydrate Diet.

There is a growing interest in studies involving carbohydrate (CHO) manipulation and subsequent adaptations to endurance training. This study aimed to analyze whether a periodized carbohydrate feeding strategy based on a daily training session has any advantages compared to a high-carbohydrate diet in well-trained cyclists. Seventeen trained cyclists (VO2peak = 70.8 ± 6.5 mL·kg-1·min-1) were divided into two groups, a periodized (PCHO) group and a high-carbohydrate (HCHO) group. Both groups performed the same training sessions for five weeks. In the PCHO group, 13 training sessions were performed with low carbohydrate availability. In the HCHO group, all sessions were completed following previous carbohydrate intake to ensure high pre-exercise glycogen levels. In both groups, there was an increase in the maximal lactate steady state (MLSS) (PCHO: 244.1 ± 29.9 W to 253.2 ± 28.4 W; p = 0.008; HCHO: 235.8 ± 21.4 W to 246.9 ± 16.7 W; p = 0.012) but not in the time to exhaustion at MLSS intensity. Both groups increased the percentage of muscle mass (PCHO: p = 0.021; HCHO: p = 0.042) and decreased the percent body fat (PCHO: p = 0.021; HCHO: p = 0.012). We found no differences in carbohydrate or lipid oxidation, heart rate, and post-exercise lactate concentration. Periodizing the CHO intake in well-trained cyclists during a 5-week intervention did not elicit superior results to an energy intake-matched high-carbohydrate diet in any of the measured outcomes.

Comparison of NIRS exercise intensity thresholds with maximal lactate steady state, critical power and rowing performance.

This study aimed to compare the intensity of deoxygenated haemoglobin concentration ([HHb]) and tissue saturation index (TSI) breakpoints ([HHb]-BP and TSI-BP) with maximal lactate steady state (MLSS) and critical power (CP), and to describe their association with 2000-m rowing ergometer performance. Fourteen male rowers performed on a rowing ergometer: I) a discontinuous incremental test with 3-min stages (INC3); II) a continuous incremental test with 1-min stages (INC1); III) constant workload tests to determine MLSS; and IV) performance tests of 500 m, 1000 m, 2000 m and 6000 m to determine CP.CP (257 ± 39 W; 3.79 ± 4.1 L · min-1) was higher than [HHb]-BP3 (205 ± 26 W; 3.48 ± 2.9 L · min-1), [HHb]-BP1 (207 ± 27 W; 3.27 ± 3.2 L · min-1), and TSI-BP3 (218 ± 31 W; 3.51 ± 3.0 L · min-1), but not higher than TSI-BP1 (222 ± 34 W; 3.43 ± 3.2 L · min-1). MLSS (187 ± 26 W; 3.33 ± 3.2 L · min-1) was lower than TSI-BP3 and TSI-BP1 for power output, but not different in any comparison for ⩒O2. The limits of agreement for power output and ⩒O2 suggest poor agreement among these thresholds. The low level of agreement compromises the use of [HHb]-BP and TSI-BP for estimating MLSS and CP; therefore, these thresholds should not be considered interchangeable.

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach.

Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI.

Critical Velocity, Maximal Lactate Steady State, and Muscle MCT1 and MCT4 after Exhaustive Running in Mice.

Although the critical velocity (CV) protocol has been used to determine the aerobic capacity in rodents, there is a lack of studies that compare CV with maximal lactate steady state intensity (iMLSS) in mice. As a consequence, their physiological and molecular responses after exercise until exhaustion at CV intensity remain unclear. Thus, we aimed to compare and correlate CV with iMLSS in running mice, following different mathematical models for CV estimation. We also evaluated their physiological responses and muscle MCT1 and MCT4 after running until exhaustion at CV. Thirty C57BL/6J mice were divided into two groups (exercised-E and control-C). Group E was submitted to a CV protocol (4 days), using linear (lin1 and lin2) and hyperbolic (hyp) mathematical models to determine the distance, velocity, and time to exhaustion (tlim) of each predictive CV trial, followed by an MLSS protocol. After a running effort until exhaustion at CV intensity, the mice were immediately euthanized, while group C was euthanized at rest. No differences were observed between iMLSS (21.1 ± 1.1 m.min-1) and CV estimated by lin1 (21.0 ± 0.9 m.min-1, p = 0.415), lin2 (21.3 ± 0.9 m.min-1, p = 0.209), and hyp (20.6 ± 0.9 m.min-1, p = 0.914). According to the results, CV was significantly correlated with iMLSS. After running until exhaustion at CV (tlim = 28.4 ± 8,29 min), group E showed lower concentrations of hepatic and gluteal glycogen than group C, but no difference in the content of MCT1 (p = 0.933) and MCT4 (p = 0.123) in soleus muscle. Significant correlations were not found between MCT1 and MCT4 and tlim at CV intensity. Our results reinforce that CV is a valid and non-invasive protocol to estimate the maximal aerobic capacity in mice and that the content of MCT1 and MCT4 was not decisive in determining the tlim at CV, at least when measured immediately after the running effort.

Is Running Power a Useful Metric? Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State.

We sought to determine the utility of Stryd, a commercially available inertial measurement unit, to quantify running intensity and aerobic fitness. Fifteen (eight male, seven female) runners (age = 30.2 [4.3] years; V·O2max = 54.5 [6.5] ml·kg-1·min-1) performed moderate- and heavy-intensity step transitions, an incremental exercise test, and constant-speed running trials to establish the maximal lactate steady state (MLSS). Stryd running power stability, sensitivity, and reliability were evaluated near the MLSS. Stryd running power was also compared to running speed, V·O2, and metabolic power measures to estimate running mechanical efficiency (EFF) and to determine the efficacy of using Stryd to delineate exercise intensities, quantify aerobic fitness, and estimate running economy (RE). Stryd running power was strongly associated with V·O2 (R2 = 0.84; p < 0.001) and running speed at the MLSS (R2 = 0.91; p < 0.001). Stryd running power measures were strongly correlated with RE at the MLSS when combined with metabolic data (R2 = 0.79; p < 0.001) but not in isolation from the metabolic data (R2 = 0.08; p = 0.313). Measures of running EFF near the MLSS were not different across intensities (~21%; p > 0.05). In conclusion, although Stryd could not quantify RE in isolation, it provided a stable, sensitive, and reliable metric that can estimate aerobic fitness, delineate exercise intensities, and approximate the metabolic requirements of running near the MLSS.

A Perspective on High-Intensity Interval Training for Performance and Health

Interval training is a simple concept that refers to repeated bouts of relatively hard work interspersed with recovery periods of easier work or rest. The method has been used by high-level athletes for over a century to improve performance in endurance-type sports and events such as middle- and long-distance running. The concept of interval training to improve health, including in a rehabilitative context or when practiced by individuals who are relatively inactive or deconditioned, has also been advanced for decades. An important issue that affects the interpretation and application of interval training is the lack of standardized terminology. This particularly relates to the classification of intensity. There is no common definition of the term “high-intensity interval training” (HIIT) despite its widespread use. We contend that in a performance context, HIIT can be characterized as intermittent exercise bouts performed above the heavy-intensity domain. This categorization of HIIT is primarily encompassed by the severe-intensity domain. It is demarcated by indicators that principally include the critical power or critical speed, or other indices, including the second lactate threshold, maximal lactate steady state, or lactate turnpoint. In a health context, we contend that HIIT can be characterized as intermittent exercise bouts performed above moderate intensity. This categorization of HIIT is primarily encompassed by the classification of vigorous intensity. It is demarcated by various indicators related to perceived exertion, oxygen uptake, or heart rate as defined in authoritative public health and exercise prescription guidelines. A particularly intense variant of HIIT commonly termed “sprint interval training” can be distinguished as repeated bouts performed with near-maximal to “all out” effort. This characterization coincides with the highest intensity classification identified in training zone models or exercise prescription guidelines, including the extreme-intensity domain, anaerobic speed reserve, or near-maximal to maximal intensity classification. HIIT is considered an essential training component for the enhancement of athletic performance, but the optimal intensity distribution and specific HIIT prescription for endurance athletes is unclear. HIIT is also a viable method to improve cardiorespiratory fitness and other health-related indices in people who are insufficiently active, including those with cardiometabolic diseases. Research is needed to clarify responses to different HIIT strategies using robust study designs that employ best practices. We offer a perspective on the topic of HIIT for performance and health, including a conceptual framework that builds on the work of others and outlines how the method can be defined and operationalized within each context.

Estimation of maximal lactate steady state using the sweat lactate sensor

A simple, non-invasive algorithm for maximal lactate steady state (MLSS) assessment has not been developed. We examined whether MLSS can be estimated from the sweat lactate threshold (sLT) using a novel sweat lactate sensor for healthy adults, with consideration of their exercise habits. Fifteen adults representing diverse fitness levels were recruited. Participants with/without exercise habits were defined as trained/untrained, respectively. Constant-load testing for 30 min at 110%, 115%, 120%, and 125% of sLT intensity was performed to determine MLSS. The tissue oxygenation index (TOI) of the thigh was also monitored. MLSS was not fully estimated from sLT, with 110%, 115%, 120%, and 125% of sLT in one, four, three, and seven participants, respectively. The MLSS based on sLT was higher in the trained group as compared to the untrained group. A total of 80% of trained participants had an MLSS of 120% or higher, while 75% of untrained participants had an MLSS of 115% or lower based on sLT. Furthermore, compared to untrained participants, trained participants continued constant-load exercise even if their TOI decreased below the resting baseline (P < 0.01). MLSS was successfully estimated using sLT, with 120% or more in trained participants and 115% or less in untrained participants. This suggests that trained individuals can continue exercising despite decreases in oxygen saturation in lower extremity skeletal muscles.

A Physiological Anchor for the Perception of Effort.

This is a two-part study to determine one or more reliable physiological anchors for perception of effort. The purpose of Study 1 was to compare ratings of perceived exertion (RPE) at the ventilatory threshold (VT) in running, cycling, and upper body exercise with the premise that if RPE at VT did not differ across exercise modes, VT might provide a unique set of physiological inputs for perception of effort. For 27 participants, values for VT and for RPE at VT (Borg 6 to 20 scale) averaged 9.4km⋅h-1 (SD = 0.7) and 11.9km⋅h-1 (SD = 1.4) respectively in running, 135W (SD = 24) and 12.1W (SD = 1.6) in cycling, and 46W (SD = 5) and 12.0W (SD = 1.7) in upper body exercise. RPE did not differ, suggesting that VT may anchor effort perception. In Study 2, 10 participants performed cycle ergometer exercise for 30 minutes at their VT (M = 101W, SD = 21), at their maximal lactate steady state (M = 143W, SD = 22), and at their critical power (CP; M = 167W, SD = 23). Mean end-exercise RPE were 12.1 (SD = 2.1), 15.0 (SD = 1.9), and 19.0 (SD = 0.5), respectively. The very close clustering of RPE during exercise at CP hints that the confluence of physiological responses at CP may (also) serve as a determinant in perception of effort.