Journal Logo

SPECIAL COMMUNICATIONS: Methodological Advances

Determination of Maximal Lactate Steady State in Healthy Adults: Can NIRS Help?


Author Information
Medicine & Science in Sports & Exercise: June 2013 - Volume 45 - Issue 6 - p 1208-1216
doi: 10.1249/MSS.0b013e3182828ab2
  • Free


The maximal lactate steady state (MLSS) is defined as the highest exercise intensity that can be maintained over time without a continual blood lactate accumulation. It reflects the equilibrium between lactate production and utilization at the whole body level, i.e., the highest exercise intensity still compatible with constant lactate concentration in the peripheral blood (6). MLSS is a well-known index of endurance and training status (7). It can be used in both athletes and sedentary subjects to stratify individuals for their level of fitness, to classify the functional level of patients, to set training objectives, to determine exercise volume, and to monitor the results of specific interventions (2).

The assessment of MLSS is somewhat invasive and “costly” because it requires an ad hoc time-consuming protocol. These characteristics make the test unsuitable for the application on a large scale, where simple, noninvasive, and relatively inexpensive as well as reliable methods are required. Several methods have been developed over the years to identify MLSS based on a simpler, more economical and less time-consuming approach. Yet, none of the currently available methods are free of faults. The direct methods, based on the determination of lactate concentration in blood (34), are also invasive and relatively time-consuming. The indirect methods, among which the gas exchange-based measures are the most commonly applied (3,38), are characterized by a lower accuracy compared to the direct ones, subjectivity in the determination, and they are prone to potentially large errors in subjects with irregular breathing pattern (38).

The near-infrared spectroscopy (NIRS) has been widely used in exercise physiology as a noninvasive technique for the functional evaluation of muscle oxidative metabolism (23). Muscle O2 extraction, as measured by NIRS, changes linearly during an incremental cycling exercise, showing one or more modifications in slope (deflection points) between 40% and 80% of V˙O2max. A possible overlap of these deflection points with functional indexes related to anaerobic metabolism has been suggested by previous studies conducted in different populations (healthy adults [5,17,24,32,37], chronic heart failure patients [24,36], and children [25]). All the studies come to the conclusion that a “threshold” intensity, correlated with the ventilatory threshold (5,25,37), the onset of blood lactate accumulation (17), or the respiratory compensation point (24), can be determined through noninvasive measures of muscle oxygen extraction during incremental exercise. Yet, the methodological differences among the studies and a suboptimal statistical approach (i.e., correlation analysis for the majority of studies) lead to incomparable and inconclusive results. Furthermore, the possible relationship between NIRS-derived “threshold” and the MLSS has never been evaluated.

In comparison to other technologies, the main strengths of NIRS-based measures are the noninvasive nature, the ability to evaluate small muscle masses, and the high sampling frequency that allows the characterization of the response to exercise in subjects with a limited exercise capacity. Furthermore, the re cently developed low-cost (29) and/or portable/wearable devices (19,23) allow the application of this technology on a large scale, in different types of effort and on the field.

By using a state-of-the-art quantitative NIRS device, deoxyHb as an index of muscle O2 extraction, in a relatively large, healthy, and heterogeneous male population, we tested the hypothesis that MLSS can be accurately determined in healthy subjects based on quantitative measures of deoxyHb.



Thirty-two healthy sedentary males (anthropometric characteristics are presented on Table 1) were recruited by local advertisement in the metropolitan area of Verona. Inclusion criteria were between age 18 and 75 yr and male sex. Exclusion criteria were smoking, metabolic or cardiovascular conditions or the use of medications that may interfere with the physiological response to the tests (diabetes, high blood pressure, chronic heart failure, etc.), and double skinfold thickness on the lateral aspect of the thigh above 20 mm. In conformity to the principles of the Declaration of Helsinki, the study was approved by the Ethical Committee of the Department, and subjects were informed of the aims, the procedures, and the possible risks involved in the study and they gave a written consent. A medical evaluation preceded the inclusion in the study.

Mean ± SD and range values of age, weight, stature, body mass index (BMI), maximum oxygen consumption (V˙O2max) and the gas exchange threshold (GET) for the whole group of participants.


On separate days, the subjects performed on the cycle ergometer: i) an incremental exercise to exhaustion and ii) three or four 30-min square wave exercises at increasing workload for MLSS detection, separated by a minimum interval of 48 h (4). All tests were performed at the same time of the day, after a standardized meal composed of 500 mL of water and 1–3 g·kg−1 of body weight of low-glycemic index CHO (the dose depending on the distance of the meal from the test) (i.e., 3 g·kg−1 for 3-h distance; 2 g·kg−1 for 2-h distance; 1 g·kg−1 for 1-h distance) (1) in comfortable and standardized ambient conditions (22°C–25°C and 55%–65% relative humidity).


All the exercise tests were performed on an electromechanically braked cycle ergometer (Excalibur Sport Device, Lode, the Netherlands), connected to and operated by a metabolic cart (Quark b2; Cosmed, Rome, Italy) that also allowed continuous, breath-by-breath measures of pulmonary ventilation and gas exchanges at the mouth. Before each test, the gas analyzers and the turbine flow meter of the system were calibrated following the manufacturer’s instructions and by using a gas mixture of known concentration (FO2: 0.16; FCO2: 0.05; N2 as balance) and a 3.0-L calibrated syringe. HR was measured by means of a short-distance telemetry cardiotachometer interfaced with the metabolic cart. The cycle ergometer seat was adjusted to obtain a complete extension of the legs during pedaling, and the initial settings were maintained throughout the study. The subjects’ preferred pedaling frequency was determined on the first test, and subjects were required to maintain it in all successive tests.

Incremental Exercise

It was preceded by a 3-min rest period. Thereafter, the workload was increased to 50 W for 3 min and then by 10–30 W·min−1. The increase in workload above the initial warm-up was chosen based on age and on an anticipation of the individual aerobic fitness with the aim to bring the subject to exhaustion within 8–12 min (2). The accepted criteria for maximal effort were RER > 1.1 and HR > 90% of the predicted maximum based on age (2). Should a test have not satisfied these criteria a posteriori, it was repeated.

Gas exchanges and HR were measured breath-by-breath throughout the exercise. During the incremental test, the muscle oxygen extraction was evaluated by means of a frequency-domain multidistance (FDMD) NIRS system (OxiplexTS™, ISS, Champaign, IL). After shaving, cleaning, and drying of the skin area, the NIRS lightweight plastic probe was longitudinally positioned on the belly of the vastus lateralis muscle, 15 cm above the patella and attached to the skin with a biadhesive tape. The position of the probe on the thigh was then pen-marked to allow repositioning on the following appointments and to detect a possible sliding of the probe during the test. Finally, the probe was secured with elastic bandages around the thigh. The apparatus was calibrated on each test day, after a warm-up of at least 30 min, following the manufacturer’s recommendations. The NIRS provides a continuous measurement (sampling frequency of 120 Hz) of absolute concentrations (µmol·L−1) of oxyhemoglobin (oxyHb), deoxyHb, total hemoglobin, and percent hemoglobin saturation.

Square Wave Exercises

On successive appointments, subjects performed three to four square wave tests, consisting of a 3-min rest, followed by a 30-min continuous exercise at constant workload. To reduce the number of exercise trials required for MLSS determination, the individual power output at the gas exchange threshold (GET; see Data analysis section) was used for the first square wave test. The intensity of the successive tests was defined based on the LA response to the first test, as described in Table 2.

Protocol of the square wave (SW) exercises used for detecting maximum lactate steady state (MLSS).

Gas exchange variables and HR were measured breath-by-breath at rest and in the first 10 min of exercise, with the same equipment used for the incremental test. The blood lactate concentration ([La]b, mmol) in arterialized capillary blood was assessed by means of an electroenzymatic method (Biosen C_line; EKF Diagnostic, Barleben, Germany) on 20-μL blood samples taken from an earlobe at rest and every 5 min throughout the exercise.

Data Analysis

Incremental exercise.

The GET was determined based on the breath-by-breath data obtained from the incremental exercise (3). Furthermore, maximal parameters (V˙O2max, HRmax, and RERmax) were calculated as the average of the highest 10 s before the exhaustion.

Individual deoxyHb data from the incremental exercise were averaged at 1 s and plotted as a function of time. The NIRS-derived MLSS (NIRSMLSS) was identified by fitting the individual values of deoxyHb corresponding to the incremental portion of the exercise (i.e., excluding the initial warm-up phase) as a function of time. deoxyHb was preferred as an index of muscle oxygenation because it is less affected by changes in the volume of blood under the probe compared to other NIRS indexes (18,23). A double linear regression that minimized the squared sum of the residuals was then fitted by using a commercial software for data analysis (SigmaPlot 11.0; Systat Software, Inc., Chicago, IL) (Fig. 1):

where f is the double linear function, x is time, and y is deoxyHb; TD is the time coordinate corresponding to the interception of the two regression lines; i1 and i2 are the intercepts of the first and second linear functions, respectively; and s1 and s2 are the slopes.

Left, The concentration of deoxygenated hemoglobin (deoxyHb [mmol·L−1]) is plotted as a function of time (s) from the beginning of the incremental test up to exhaustion in three typical subjects (representative of the three age groups included in the study). The black line is the result of the double linear function fitting that was performed on the data of the incremental portion of the exercise (i.e., from the end of the warm-up phase up to exhaustion). The change in slope of the deoxyHb signal as a function of time corresponds to the NIRS-derived maximal lactate steady state (NIRSMLSS). Right, The concentration of lactate ([LA]) is plotted as a function of time (min) during three constant-load tests in the same three typical subjects. The highest workload still compatible with a constant [LA] (i.e., difference between [LA] at 10 and 30 min < 1 mmol·L−1 [4,6]) corresponds to the maximal lactate steady state.

On the basis of the cardiorespiratory data from the incremental exercise, the V˙O2 and HR at the time point corresponding to TD were calculated as the average of the last 10 s of the corresponding workload. These data identified the NIRS-derived MLSS (NIRSMLSS).

Square wave exercises.

MLSS was identified as the highest exercise intensity at which the difference between [LA] at 10 and 30 min was <1 mmol·L−1 (4,6). V˙O2 and HR at this workload were calculated as a 30-s average at the 10th minute of exercise.


Means ± SD were calculated for all parameters. A paired t-test was carried out to compare the average values of V˙O2 and HR at MLSS and NIRSMLSS. Linear regressions and the Pearson product–moment correlation was used to verify the relationship between individual values of V˙O2 and HR at MLSS and NIRSMLSS. The Bland–Altman analysis was applied to verify the agreement between measures (8).

Based on variances in V˙O2 measured in our laboratory (within-subject variation 2.5% ± 2.5%), using a power of 0.8 and an α level of 0.05, the sample size analysis for a paired t-test and correlation (Sigma Stat version 1; Jandel Scientific, S. Raphael, CA) indicated that the minimum number of subjects required to detect a significant difference (i.e., a 2.5% variation) was 10.


The anthropometric characteristics (age, weight, stature, BMI, V˙O2max, and GET) of the subjects included in the study are reported in Table 1.

All the subjects completed the incremental cycling exercise, reaching on exhaustion, an HRmax equal to 95% ± 6% of age-predicted value (35) and an RERmax of 1.17 ± 0.09, both suggestive of a maximal effort, even if with a degree of imprecision (30). In the whole group, the V˙O2max ranged from 22 to 60 mL·kg−1·min−1, the fitness level of the subgroups corresponding to the 80th, 50th, and 30th percentiles for young, middle age, and old adults, respectively (2). The GET (3), used to determine the workload for the first square wave exercise, was equal to 56% ± 10% V˙O2max (Table 1).

No sliding of the NIRS probe was documented in all the tests. The deoxyHb trend as a function of workload/time during an incremental or ramp exercise was detectable in all subjects: deoxyHb increased very little in the warm-up phase of the test. Thereafter, in the incremental portion of the exercise, deoxyHb increased linearly as a function of time and workload, up to a deflection point (i.e., a reduced slope or even a plateau) as a high-intensity exercise is approached (Fig. 1). The average value of deoxyHb corresponding to the inflection point was 40 ± 16 μmol·L−1.

All the subjects were able to complete the square wave exercises, and the MLSS was determined with no difficulty (Fig. 1). The mean value of lactate concentration at MLSS was 4.2 ± 1.1 mmol·L−1. The V˙O2 and HR (absolute and relative to V˙O2max and to HRmax) at MLSS were not different from those at NIRSMLSS (Table 3). Both were significantly higher than the values measured at GET.

Mean ± SD at maximum lactate steady state (MLSS) and NIRS-derived MLSS (NIRSMLSS) of oxygen consumption (V˙O2), percent maximal oxygen consumption (%V˙O2max), HR, and percent maximal HR (%HRmax).

The values of V˙O2 and HR at MLSS were highly correlated with the same values found at NIRSMLSS (Fig. 2). Furthermore, the results of the Bland–Altman analysis (Fig. 3) showed that the mean difference (bias) between measures of MLSS and NIRSMLSS was −0.015 L·min−1 and 3 bpm for V˙O2 and HR, respectively, and not significantly different from zero. Finally, the SD (precision) was 0.26 L·min−1 and 8 bpm, while the 95% limits of agreement ranged from + 0.5 to −0.5 L·min−1 and from + 19 to −13 bpm for V˙O2 and HR, respectively.

Correlation of V˙O2 (left graph) and HR (right graph) at maximum lactate steady state (MLSS) and NIRS-derived maximum lactate steady state (NIRSMLSS): Individual values of V˙O2 (left) and HR (right) measured at NIRSMLSS during the incremental cycling exercise are plotted as a function of the same measure at MLSS in the whole group. The identity (dashed) and the regression (solid) line are displayed along with the regression equation parameters.
Bland–Altman plots of the V˙O2 (left graph) and HR data (right graph) related to NIRSMLSS and MLSS. In the graphs, individual differences between the MLSS and NIRSMLSS values are plotted as a function of the average of the two measures. The solid line corresponds to the average difference between measures (i.e., bias), while the dashed lines correspond to the upper and lower limits of agreement (precision).


We tested the possible correspondence between the traditional measure of MLSS and an innovative, noninvasive measuring technique, based on the detection of deoxygenated hemoglobin (deoxyHb) deflection point (i.e., the NIRS-derived MLSS (NIRSMLSS)), during an incremental cycling exercise.

The main finding of this study is that, in a relatively large and heterogeneous group of healthy males, both the V˙O2 and HR at the NIRSMLSS are not significantly different from, and they are highly correlated with, the values of the same variables at MLSS. Therefore, MLSS can be easily, rapidly, safely, and accurately determined during a standard incremental exercise to exhaustion on the cycle ergometer, by measuring deoxyHb at the vastus lateralis, as a noninvasive index of muscle O2 extraction.

Previous studies had suggested a possible utilization of the NIRS technology for the determination of landmarks of exercise intensity (5,17,24,32,36,37). Some studies demonstrated a correlation between a variety of NIRS indexes measured during incremental exercise and a variety of “thresholds.” Yet, these studies differ in i) the site of measurement of NIRS signal (vastus lateralis [5,17,24,32,37] and respiratory muscles [25,36]), ii) the NIRS measuring devices (Runman NIM [5,25], NIRS HEO-100 [17], NIRO-200 Hamamatsu [37], Paratrend 7 Plus Diametrics Medical [32], OM-100A Shimadzu [24,36]), iii) the NIRS indexes of O2 extraction (oxygenation index [5,25], deoxyhemoglobin [deoxyHb] [37], oxyhemoglobin [oxyHb] [17,24,36], interstitial fluid pH [32]), iv) the marker of “change” in the NIRS signal (decrease of oxyHb below baseline value [5], first sharp decrease in oxygenation index [17], first and second inflection in oxyHb [24], early increase in deoxyHb slope [37], decreased interstitial fluid pH as a function of V˙O2 [32]), and v) the functional indexes (anaerobic threshold and respiratory compensation point by Wasserman method [24], ventilatory threshold by Beaver V-slope method [5,37], lactate threshold/onset of blood lactate accumulation by blood lactate determination [17] during a modified Bruce protocol [11]). In addition, the majority of the above studies have shown a correlation rather than a coincidence between NIRS-derived indexes and the respective functional indexes. While all the studies conclude that an NIRS “threshold” intensity can be detected that is correlated with a variety of functional indexes, the use of different populations, different NIRS devices (some of which are presently obsolete), as well as different NIRS parameters and “threshold” indexes makes the direct comparison among the studies impracticable. Furthermore, a possible coincidence of NIRS “threshold” with MLSS had never been investigated.

In agreement with the literature, our data confirm the time course of deoxyHb that has been observed during either ramp or incremental cycling exercises: after a first part characterized by little changes, deoxyHb increases linearly up to approximately 70% V˙O2max; thereafter, either a reduced slope or an actual plateau is displayed (10,33). In our study, the V˙O2 measured at this deflection point (NIRSMLSS) was not significantly different from the V˙O2 at MLSS (P = 0.74), the measures were highly correlated (r2 = 0.81), and the bias between the measures was not significantly different from zero (−0.015 ± 0.26 L·min−1). Furthermore, the HR at NIRSMLSS was not significantly different from the HR at MLSS (P = 0.06), the measures were highly correlated (r2 = 0.76), and the bias between the measures was not significantly different from 0 (3 ± 8 bpm).

In summary, by using state-of-the-art quantitative NIRS technology, taking deoxyHb as an index of muscle oxygenation and MLSS as the measure of the landmark intensity at which lactate production exceeds lactate removal, our study supports the hypothesis that MLSS can be determined accurately and precisely by this method in a relatively large sample of healthy adult males characterized by a broad range of fitness level.

Notwithstanding the coincidence between NIRSMLSS and MLSS, the possible physiological mechanism underpinning the relationship between the highest exercise intensity still compatible with constant lactate concentration in the peripheral blood and the deflection of deoxyHb during an incremental trial remains to be elucidated.

Changes in the concentration of deoxyHb, as measured by NIRS, are considered a proxy for microvascular O2 extraction (14,18) and microvascular partial pressure of O2 (PO2mv) during exercise (20). Furthermore, while deoxyHb signal cannot be used as a substitute for arteriovenous O2 difference (a-vO2diff) (because the proportional contributions of arterial and venous blood to the overall signal are unknown), the two are considered to be related (22,33). For the above reasons, inferences into temporal changes in a-vO2diff can be reasonably made, and according to the Fick principle, it can be assumed that the pattern of deoxyHb during exercise can provide insight into the relationship between blood flow and oxygen uptake within the working muscles (9,33).

The time course of deoxyHb during incremental exercises has been interpreted to reflect the balance between bulk and microvascular blood flow and muscle O2 utilization that is influenced by the microvascular flow regulation and by muscle fibers recruitment pattern (9,12,16): the initial shallow slope of deoxyHb has been attributed to a good matching of blood flow to O2 extraction, thanks to a muscle pump effect and to the recruitment of predominantly Type I muscle fibers (characterized by a good matching of microvascular blood flow and muscle O2 utilization). As work rate continues to increase, the steeper slope of deoxyHb may be due to a slower adjustment in microvascular blood flow compared to muscle O2 utilization and to a shift from predominantly slow-twitch muscle fibers to include more fast-twitch muscle fibers (9,16). A progressive metabolic acidosis could, in principle, contribute, through the Bohr effect, to an increased O2 offloading from hemoglobin during this phase. Yet, a recent study by Boone et al. (10) suggested that systemic metabolic acidosis per se (induced by previous high-intensity priming exercise performed with upper limbs) does not modify the time course of oxygen extraction during an incremental exercise performed with the lower limbs. On the contrary, oxygen extraction for a given workload is increased when prior high-intensity priming is performed with the lower limbs. The authors conclude that i) the Bohr effect is probably not the mechanism behind the sigmoid increase of deoxyHb during a ramp exercise and ii) the increased oxygen extraction observed after lower limbs priming may be caused by the increased recruitment of fast-twitch fibers. In agreement with this view, Ferreira et al. (15) have shown that Type II fibers have a higher microvascular O2 extraction as a function of exercise intensity compared to Type I fibers.

The reduced slope/plateau in deoxyHb that characterizes the high-intensity portion of the incremental exercise would imply either a reduced O2 utilization or an increased O2 delivery. At high-intensity exercise, microvascular blood flow could be increased because of the accumulation of metabolites (H+ ions, adenosine, and lactate) (21). Yet, the high level of force developed during each cycle may interfere with the muscle pump effect and dampen the increase in blood flow to the muscle at near-maximal exercise intensity (21). Therefore, we speculate that the reduced slope in deoxyHb is unlikely to be due to an increased slope of blood flow over V˙O2 but may, on the contrary, suggest that O2 extraction has an upper limit during dynamic exercise. In the severe-intensity domain, a plateau in the ability to use oxygen by slow, Type I fibers could be reached. Studies conducted in a rat model demonstrated that while the blood flow to the muscles continues to increase as a linear function of V˙O2, arteriovenous O2 difference, on the contrary, is a hyperbolic function of V˙O2 (15). The reaching of a plateau could therefore be an indirect index of a shift toward an anaerobic ATP production to sustain muscle contraction. Alternatively, the reduced slope in deoxyHb could be related to a larger and progressive recruitment of Type II fibers that have a low oxidative capacity but a high glycolytic capacity, compared to Type I fibers. As such, the increased contribution of glycolytic fibers to force production implies a progressively lower reliance on oxidative metabolism for ATP production. In agreement with this view, Ferreira et al. (15) have also shown that microvascular O2 extraction in Type II fibers reaches a plateau at a lower exercise intensity compared to Type I fibers.

In summary, while the muscle continues to produce power at an increasing rate, a shift toward an increased reliance on anaerobic sources for ATP production (caused by a saturation of the ability of Type I fibers to use O2 or by the larger contribution of Type II glycolytic fibers to force production or to a mix of both factors) could reduce the muscle’s O2 utilization rate. Under these conditions, although the total body V˙O2 may increase because of the contribution of other muscles (respiratory, trunk stabilizers, and other leg muscles), the ATP production within the working muscles may partially come from nonaerobic metabolic pathways (27). In agreement with the above speculation, Wilkerson et al. (39) documented a reduced gain in V˙O2 during square wave exercises in the severe-intensity domain.

The above explanations would be coherent with the finding of a coincidence between MLSS and NIRSMLSS.

Fitting strategy.

In literature, there are two main ways to fit deoxyHb response as a function of the increase in exercise intensity during a ramp test: the double linear regression model (28,33) and the sigmoid model (9,10,16). Any attempt to characterize a wide range of possible response profiles using mathematical modeling is somewhat associated to limitations. A recent study suggested that the sigmoid model, which has been more traditionally used in this context, may not always characterize properly the phenomenological response in all subjects, specially so for the end-exercise portion of the experiment (33). The sigmoid approach is aimed at characterizing the overall response and it is based on the assumption that the lower and upper nonlinear segments of it are symmetrical. This assumption, at least in some cases, may jeopardize the ability to accurately characterize single portions of the response. For the above reasons, we favored the utilization of a double linear function that considered primarily the steep increase in deoxyHb observed during the incremental exercise and the “plateau” phase that follows. A double linear model has been demonstrated superior in the description of the latter two components of the response of deoxyHb during incremental exercise (33).

Critical power (CP) is thought to provide a noninvasive estimate of the maximal work rate that can be sustained in a full steady-state aerobic condition or maximal V˙O2 steady state (26). Furthermore, CP may represent the landmark intensity below which exercise can be sustained for a very long time without fatigue. A possible coincidence of MLSS with CP has been suggested, yet not univocally demonstrated, by several studies (13,31). In our study, we did not measure CP. Furthermore, we did not aim at describing V˙O2 kinetics during square wave exercises performed below, at, and above MLSS. Therefore, based on our data, we can neither confirm nor exclude that the NIRSMLSS could actually coincide with CP and with a maximal V˙O2 steady state. The possibility that MLSS, the reduced slope/plateau in O2 extraction, and the maximal V˙O2 steady state occur together is very interesting and may allow some insights into the mechanisms that control/limit oxidative metabolism. Further research is needed to elucidate if CP actually coincides with MLSS and if it can be estimated based on the deoxyHb deflection point during an incremental/ramp exercise.

Our data confirm the hypothesis that the MLSS can be accurately determined with this approach, using a quantitative NIRS. Along with the noninvasiveness, compared to lactate-based techniques, NIRSMLSS offers the advantage of being objective and independent from irregularities of breathing pattern that can heavily affect ventilatory-based techniques. In comparison to other technologies, the main strengths of NIRS-based measures of MLSS are its noninvasive nature, its ability to evaluate even small muscle masses, and its high sampling frequency that allows the characterization of the response to exercise even in subjects with a limited exercise capacity and/or motivation. Furthermore, the recently developed low-cost (29) and/or portable/wearable devices (19,23) could allow the diffusion of this technology on a large scale, in different types of effort and on the field. The main limitation of a NIRS-based approach is related to its inability to evaluate the underlying muscle when a large fat layer (>30 mm) is present (23). Therefore, the methodological approach proposed in this study may be unsuitable for the evaluation of overweight/obese subjects and of a large portion of the female population.

No funding was received for this study, and the authors declare no conflict of interest.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.


1. American College of Sports Medicine, American Dietetic Association, and Dietitians of Canada. Nutrition and athletic performance. Med Sci Sports Exerc. 2009; 41 (3): 709–31.
2. American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and Prescription. 8th ed. Bethesda (MD): Lippincott Williams & Wilkins; 2010. 84–6, 112, 153.
3. Beaver WL, Wasserman K, Whipp BJ. A new method for detecting anaerobic threshold by gas exchange. J Appl Physiol. 1986; 60 (6): 2020–7.
4. Beneke R. Methodological aspects of maximal lactate steady state—implications for performance testing. Eur J Appl Physiol. 2003; 89 (1): 95–9.
5. Bhambhani YM, Buckley SM, Susaki T. Detection of ventilatory threshold using near infrared spectroscopy in men and women. Med Sci Sports Exerc. 1997; 29 (3): 402–9.
6. Billat VL, Sirvent P, Py G, Koralsztein JP, Mercier J. The concept of maximal lactate steady state. Sports Med. 2003; 33 (6): 407–26.
7. Billat VL. Use of blood lactate measurements for prediction of exercise performance and for control of training. Sports Med. 1996; 22 (3): 157–75.
8. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1 (8476): 307–10.
9. Boone J, Koppo K, Barstow TJ, Bouckaert J. Effect of exercise protocol on deoxy[Hb+Mb]: incremental step versus ramp exercise. Med Sci Sports Exerc. 2010; 42 (5): 935–42.
10. Boone J, Bouckaert J, Barstow TJ, Burgois J. Influence of priming exercise on muscle deoxy[Hb+Mb] during ramp cycle exercise. Eur J Appl Physiol. 2012; 112 (3): 1143–52.
11. Bruce RA. Methods of exercise testing. Step test, bicycle, treadmill, isometrics. Am J Cardiol. 1974; 33 (6): 715–20.
12. Chin LM, Kowalchuk JM, Barstow TJ, et al. The relationship between muscle deoxygenation and activation in different muscles of the quadriceps during cycle ramp exercise. J Appl Physiol. 2011; 111 (5): 1259–65.
13. Dekerle J, Baron B, Dupont L, Vanvelcenaher J, Pelayo P. Maximal lactate steady state, respiratory compensation threshold and critical power. Eur J Appl Physiol. 2003; 89 (3–4): 281–8.
14. Delorey DS, Kowalchuk JM, Paterson DH. Relationship between pulmonary O2 uptake kinetics and muscle deoxygenation during moderate-intensity exercise. J Appl Physiol. 2003; 95 (1): 113–20.
15. Ferreira LF, McDonoughc P, Behnke BJ, Muscha TI, Poole DC. Blood flow and O2 extraction as a function of O2 uptake in muscles composed of different fiber types. Respir Physiol Neurobiol. 2006; 153 (3): 237–49.
16. Ferreira LF, Koga S, Barstow TJ. Dynamics of noninvasively estimated microvascular O2 extraction during ramp exercise. J Appl Physiol. 2007; 103 (6): 1999–2004.
17. Grassi B, Quaresima V, Marconi C, Ferrari M, Cerretelli P. Blood lactate accumulation and muscle deoxygenation during incremental exercise. J Appl Physiol. 1999; 87 (1): 348–55.
18. Grassi B, Pogliaghi S, Rampichini S, et al. Muscle oxygenation and pulmonary gas exchange kinetics during cycling exercise on-transitions in humans. J Appl Physiol. 2003; 95 (1): 149–58.
19. Hamaoka T, McCully KK, Niwayama M, Chance B. The use of muscle near-infrared spectroscopy in sport, health and medical sciences: recent developments. Phil Trans R Soc A. 2011; 369 (1955): 4591–604.
20. Koga S, Kano Y, Barstow TJ, et al. Kinetics of muscle deoxygenation and microvascular PO2 during contractions in rat: comparison of optical spectroscopy and phosphorescence—quenching techniques. J Appl Physiol. 2012; 112 (1): 26–32.
21. Laughlin MH, Korthuis RJ, Dunker DJ, Bache RJ. Control of blood flow to cardiac and skeletal muscle during exercise. In: Rowell LB, Shepherd JT, editors. Handbook of Physiology. New York (NY): Oxford University Press; 1996. pp. 705–69.
22. Mancini DM, Bolinger L, Li H, Kendrick K, Chance B, Wilson JR. Validation of near-infrared spectroscopy in humans. J Appl Physiol. 1994; 77 (6): 2740–7.
23. McCully KK, Hamaoka T. Near-infrared spectroscopy: what can it tell us about oxygen saturation in skeletal muscle? Exerc Sport Sci Rev. 2000; 28 (3): 123–7.
24. Miura T, Takeuchi T, Sato H, et al. Skeletal muscle deoxygenation during exercise assessed by near-infrared spectroscopy and its relation to expired gas analysis parameters. Jpn Circ J. 1998; 62 (9): 649–57.
25. Moalla W, Dupont G, Berthoin S, Ahmaidi S. Respiratory muscle deoxygenation and ventilatory threshold assessments using near infrared spectroscopy in children. Int J Sports Med. 2005; 26 (7): 576–82.
26. Moritani T, Nagata A, deVries HA, Muro M. Critical power as a measure of physical work capacity and anaerobic threshold. Ergonomics. 1981; 24 (5): 339–50.
27. Mortensen SP, Dawson EA, Yoshiga CC, et al. Limitations to systemic and locomotor limb muscle oxygen delivery and uptake during maximal exercise in humans. J Physiol. 2005; 566 (1): 273–85.
28. Pogliaghi S, Bellotti C, De Roia G, Schena F. Anaerobic threshold determination in young males: can NIRS help? Med Sci Sports Exerc. 2010; 42 (5): S528.
29. Pogliaghi S, Casiello L, Bandera A. Validation of a continuous-wave, single-distance NIRS oxymeter for the determination of muscle oxygenation during cycling. Med Sci Sports Exerc. 2009; 41 (5): S282.
30. Poole DC, Wilkerson DP, Jones AM. Validity of criteria for establishing maximal O2 uptake during ramp exercise tests. Eur J Appl Physiol. 2008; 102 (4): 403–10.
31. Pringle JS, Jones AM. Maximal lactate steady state, critical power and EMG during cycling. Eur J Appl Physiol. 2002; 88 (3): 214–26.
32. Soller BR, Yang Y, Stuart MCL, Wilson C, Hagan RD. Noninvasive determination of exercise-induced hydrogen ion threshold through direct optical measurement. J Appl Physiol. 2008; 104 (3): 837–44.
33. Spencer MD, Murias JM, Paterson DH. Characterizing the profile of muscle deoxygenation during ramp incremental exercise in young men. Eur J Appl Physiol. 2012; 112 (9): 3349–60.
34. Svedahl K, MacIntosh BR. Anaerobic threshold: the concept and methods of measurement. Can J Appl Physiol. 2003; 28 (2): 299–323.
35. Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 2001; 37 (1): 153–6.
36. Terakado S, Takeuchi T, Miura T, et al. Early occurrence of respiratory muscle deoxygenation assessed by near-infrared spectroscopy during leg exercise in patients with chronic heart failure. Jpn Circ J. 1999; 63 (2): 97–103.
37. Wang L, Yoshikawa T, Hara T, Nakao H, Suzuki T, Fujimoto S. Which common NIRS variable reflects muscle estimated lactate threshold most closely? J Appl Physiol Nutr Metab. 2006; 31 (5): 612–20.
38. Wasserman K, Whipp BJ, Koyl SN, Beaver WL. Anaerobic threshold and respiratory gas exchange during exercise. J Appl Physiol. 1973; 35 (2): 236–43.
39. Wilkerson DP, Koppo K, Barstow TJ, Jones AM. Effect of work rate on the functional gain of phase II pulmonary O2 uptake response to exercise. Respir Physiol Neurobiol. 2004; 142 (2–3): 211–23.


©2013The American College of Sports Medicine