- 1Faculty of Health Sciences, Universidad Europea del Atlántico, Santander, Spain
- 2Faculty of Social Sciences and Humanities, Universidad Europea del Atlántico, Santander, Spain
Introduction: The study aims to explore whether NIRS derived data can be used to identify the second ventilatory threshold (VT2) during a maximal incremental treadmill test in non-professional runners and to determine if there is a correlation between SmO2 and other valid and reliable exercise performance assessment measures or parameters for maximal incremental test, such as lactate concentration (LT), RPE, HR, and running power (W).
Methods: 24 participants were recruited for the study (5 women and 19 men). The devices used consisted of the following: i) a muscle oxygen saturation analyzer placed on the vastus lateralis of the right leg, ii) the Stryd power meter for running, iii) the Polar H7 heart rate band; and iv) the lactate analyzer. In addition, a subjective perceived exertion scale (RPE 1-10) was used. All of the previously mentioned devices were used in a maximal incremental treadmill test, which began at a speed of 8 km/h with a 1% slope and a speed increase of 1.2 km/h every 3 min. This was followed by a 30-s break to collect the lactate data between each 3-min stage. Spearman correlation was carried out and the level of significance was set at p < 0.05.
Results: The VT2 was observed at 87,41 ± 6,47% of the maximal aerobic speed (MAS) of each participant. No relationship between lactate data and SmO2 values (p = 0.076; r = −0.156) at the VT2 were found. No significant correlations were found between the SmO2 variables and the other variables (p > 0.05), but a high level of significance and strong correlations were found between all the following variables: power data (W), heart rate (HR), lactate concentration (LT) and RPE (p < 0.05; r > 0.5).
Discussion: SmO2 data alone were not enough to determine the VT2, and there were no significant correlations between SmO2 and the other studied variables during the maximal incremental treadmill test. Only 8 subjects had a breakpoint at the VT2 determined by lactate data.
Conclusion: The NIRS tool, Humon Hex, does not seem to be useful in determining VT2 and it does not correlate with the other variables in a maximal incremental treadmill test.
1 Highlights
- Humon Hex is a small, portable and non-invasive device for measuring local SmO2 in a muscle and it provides instant feedback.
- Humon Hex is not a good device for identifying VT2 during a maximal incremental treadmill test.
- The Humon Hex monitor showed no correlation between SmO2 and the other variables such as lactate, heart rate, RPE, and W during a maximal incremental treadmill test.
2 Introduction
Monitoring and quantification in sports is necessary in order to improve sports performance, such as the muscle oxygen saturation variable, which is of great interest to both improve performance and to prevent the risk of injury in athletes (Farzam et al., 2018). In endurance sports, exercise intensity in the maximal lactate steady state is generally considered the “gold standard” to assess the athlete’s aerobic and endurance capacity, in addition to being able to prescribe training based on this variable (Beneke, 2003). It is also a precise variable in the sports performance of endurance athletes (Haverty et al., 1988).
Physiological thresholds such as lactate thresholds or ventilatory thresholds can have a direct affect on endurance performance and are used to prescribe exercise intensity for training, competition, and different tests (Bentley et al., 2007). Lactate thresholds can be determined using different methods, such as fixed blood lactate concentration (FBLC) or onset of blood lactate accumulation (OBLA), which determine the first lactate threshold at 2 mmol/L and the second lactate threshold at 4 mmol/L, respectively (Bentley et al., 2007; Kindermann et al., 1979). Individual thresholds are determined using different formulas (6). All of these methods have high correlations between velocity and power output at the lactate threshold (Faude et al., 2009). Maximal aerobic speed (MAS) is defined as the minimum running speed necessary to attain maximal oxygen uptake (V̇O2 max) during an incremental treadmill running test (Billat et al., 1994). Another tool, such as the running power meter, estimates mechanical work and the characteristics of each running step and stride. These functions make it very useful for optimizing performance and preventing injuries (Luedke et al., 2016). The subjective Rating of Perceived Exertion (RPE) measurement is a subjective effort chart that starts at 1 and goes up to 10, with 1 being the lightest and 10 being the maximum effort. This scale is very useful because it is a simple, non-invasive and free method to monitor training load (Haddad et al., 2017). One last tool is the measurement of heart rate (HR), which is a popular method for monitoring cardiorespiratory and metabolic demands during running, since HR increases in a linear fashion with oxygen consumption and energy expenditure during the performance of incremental tests in the laboratory (Born et al., 2017). HR accuracy during physical exercise is important for monitoring and quantifying exercise intensity (Boudreaux et al., 2018).
Near-infrared spectroscopy (NIRS) is a validated tool for measuring venous oxygen saturation during exercise and rest (Mancini et al., 1985). It also has a high level of reliability during repeated incremental running and cycling tests (Austin et al., 2005). Despite the usefulness of this device in graded incremental tests, it is important to note that certain factors must be taken into account with this device, such as subcutaneous tissue, which includes muscle size, blood vessels, and adipose tissue. All these factors have an inference in the NIRS results, because the infrared rays measure is more difficult due to a thicker subcutaneous tissue. Additionally, there is currently a lack of standardization in the different using NIRS (Barstow, 1985). The lactate analyzer (Lactate Pro 2) was verified as reliable in research using a graded incremental rowing test, comparing it with a laboratory-based analyzer (Crotty et al., 2021). The same applied to the validity and reliability of the running power meter (Stryd) for variables of contact time and leg spring stiffness during a maximal incremental test (Imbach et al., 2020). Also, the study showed that it is a useful tool for quantifying running intensity at submaximal speeds (Imbach et al., 2020). The validity of the rating of perceived exertion (RPE) was verified in different sports. This verification study included a variety of sports disciplines, as cycling or running, and was found to be applicable in all scenarios, as graded or continuous intensity exercises (Haddad et al., 2017). In a separate study, the validity and reliability of the Polar H7 were evaluated using a graded cycling test and resistance exercises involving fifty subjects. The Polar H7 was compared to other HR monitoring devices, as Apple Watch Series, Fitbit or Garmin Vivosmart HR, and the results proved that it was not only the most valid and reliable device, but also the only device that was valid and reliable during all different levels of exercise intensity (Boudreaux et al., 2018).
Physiological thresholds such as lactate thresholds or ventilatory thresholds, are usually determined by incremental tests as a standard procedure (Bentley et al., 2007). But there is one main problem with maximal incremental tests to establish a lactate threshold, as there is a lack of consensus on the protocol to be used, i.e., in the variables of: total duration, duration of each stage, change in intensity of each stage, and total number of stages or incremental stages (Bentley et al., 2007; Jamnick et al., 2018). It was recommended that each stage last at least 3 min in order to be more reliable (Bentley et al., 2007). The number of stages during the test and the total duration of the test should depend on the initial intensity and power increments, although it is suggested that these increments be identical between each stage (Van Schuylenbergh et al., 2004).
In recent years, the measurement of muscle oxygen saturation (SmO2) has been increasing because it is based on NIRS technology, which is a portable and non-invasive tool that provides information on the change in oxygenation of the muscle tissue in which the tool is placed (Perrey and Ferrari, 2018). NIRS technology has been used to study diverse factors involved in sports, including biomechanics, the heterogeneity of the SmO2, muscle activation, muscle respiration, fatigue, and muscle damage caused by exercise. It has also been used to explore the effects of training and oxidative metabolism. This is in addition to being placed on different muscle groups such as the vastus lateralis, rectus femoris, gastrocnemius, deltoids, biceps, forearm, triceps, biceps femoris or vastus medialis. It was also used in different settings such as laboratory and field tests (Ferrari et al., 2011). SmO2 reflects the dynamic balance between oxygen supply and consumption (Ferrari et al., 2011). NIRS has been used in various training scenarios such as a long time trial, repeated sprints, a constant workload or in an incremental test (Perrey and Ferrari, 2018). During incremental exercises, the behavior of the SmO2 variable has four phases: i) an initial increase, ii) a linear or exponential decrease below the resting baseline values iii) leveling off when the participant reaches maximal volitional fatigue during this incremental test, being these results the lowest and below the resting baseline values and, and iv) an increase over baseline values during the first minutes of recovery (Bhambhani, 2004). There has been some controversy surrounding the use of NIRS devices during some incremental running or cycling tests, as some studies have shown correlations between ventilatory thresholds and SmO2 breakpoints (Austin et al., 2005; Feldmann et al., 2022), the first ventilatory threshold and SmO2 breakpoint (van der Zwaard et al., 2016), the second ventilatory threshold and SmO2 breakpoint (Fontana et al., 2015) or no clear correlations between ventilatory thresholds and SmO2 breakpoints (Racinais et al., 2014). For training monitoring purposes, NIRS is currently considered more useful than HR for monitoring running intensity in hilly terrain (Born et al., 2017).
The current researches aim to explore whether NIRS derived data can be used to identify the second ventilatory threshold (VT2) during a maximal incremental treadmill test in non-professional runners and to determine if there is a correlation between SmO2 and other valid and reliable exercise performance assessment measures or parameters for maximal incremental test, such as lactate concentration (LT), RPE, HR, and running power (W). The study will also investigate the relationship between SmO2 breakpoints and lactate thresholds. The maximal incremental treadmill test was chosen because it is useful in determining physiological thresholds for prescribing future exercise intensity for endurance sports. A 30-s break was included in the protocol to collect lactate data. This research seeks to address the lack of clarity in the scientific literature regarding the identification of the second ventilatory threshold during maximal incremental treadmill tests using a NIRS device and the correlation between NIRS-derived data and other monitoring tools.
3 Materials and methods
3.1 Study design
To answer the planned aim described above, a cross-sectional study design was carried out.
3.2 Participants
Initially, 24 participants were recruited for the study (5 women and 19 men). Three of the participants were excluded because the SmO2 measurements were erroneous. This was due to device movement, because in one of them it fell and the SmO2 measurements were not correct in the other two, possibly due to a limiting factor of this device. Finally, there were 21 participants (5 women and 16 men) who voluntarily participated in the study (mean ± standard deviation): age = 23.19 ± 4.18 years, weight = 68.93 ± 10.99 kg, and height = 1.75 ± 0.12 m. The inclusion criteria were: (I) performing a moderate exercise session at least 3 days a week for 30 min for the last 6 months, mainly involving movements such as running, cycling or swimming and (II) not having any injuries or disease which prevented the participant from performing the maximal incremental test. The exclusion criteria were: i) being in possession of an athletics sports license, which means that they are considered to be professional athletes. All participating subjects were informed of the protocol to be used and an informed consent form was signed before beginning participation in the study. The privacy of the data collected was respected following the principles of the Declaration of Helsinki. The study protocol was approved by the ethics committee of the European University of the Atlantic (CE-52).
3.3 Procedure
Firstly, the study procedure began with the recruitment of the convenience sample. All tests were performed in the University laboratory, at a temperature of ≈20°C. To start the test, all the measuring instruments were placed on the participants and they were checked to see that they worked correctly. The Humon Hex was placed on the vastus lateralis of the right leg, the Stryd was placed on the laces of the right shoe, the chest strap was adjusted with the Polar H7, and finally the RPE chart was explained. After that, a puncture was made in the lobe of the right ear in order to collect the lactate data later on. Before starting the maximal incremental test, different familiarization tests with the treadmill were explained and performed, so as to test the treadmill entry and exit protocol. The maximal incremental test began at a speed of 8 km/h, with a 1% slope and the speed was increased by 1.2 km/h every 3 min, and after each 3-min stage there was a 30-s stop to collect the blood sample (Campos et al., 2017). The test ended once 10 stages were completed or the participant reached their maximal volitional fatigue. The data was collected during the final phase of each step during the last 30 s. First of all, the running power was collected, followed by the HR, the % SmO2 and the RPE of the subject. The lactate data was collected during the 30-s stop phase and the test was always carried out under the same conditions, without receiving any feedback.
Once the participant finished the maximal test, recovery was performed for 3 min at a speed of 6 km/h, in which the HR and % SmO2 were recorded at the first minute and at the third minute. After recovery, the different devices were removed. As the participant left the laboratory, the data collected on the registration sheet was transferred to an Excel file to form the database. Finally, relative intensities were calculated by percentage of maximal aerobic speed (MAS), which was the last step completed.
3.4 Devices
A muscle oxygen saturation analyzer with NIRS technology (Humon Hex, Dynometrics Inc., Boston, MA, United States) was used to measure SmO2 in the vastus lateralis of the right leg. The lactate analyzer (Lactate Pro 2, Lactate Pro LT Arkay Inc., Kyoto, Japan) was used for lactate measurement with blood drops from the right ear lobe while the heart rate band (Polar H7, Polar Electro, Kemple, Finland) was used to measure the participant’s heart rate. The running power meter (Stryd Power meter, Stryd Inc., Boulder CO, United States) measured the running power of each step. In addition, the subjective perceived exertion scale (RPE 1-10) was used (Borg, 1998).
3.5 Statistical analysis
Descriptive data is presented as mean and standard deviation. The normality distribution of the data was verified through the Shapiro-Wilk test (p > 0.05), so the Spearman correlation test was carried out.
The criteria to interpret the magnitude of r for the previous correlation tests were the following: null correlation (0.00–0.09), smooth correlation (0.10–0.29), moderate correlation (0.30–0.49), good correlation (0.50–0.69), very good correlation (0.70–0.89), almost perfect correlation (0.90–0.99), and perfect correlation (1.00) (Hopkins et al., 2009).
Statistical significance was accepted at p < 0.05. Statistical analysis was performed with JASP (version 0.14.1.0, http://www.jasp-stats.org).
4 Results
The second ventilatory threshold (VT2) was observed (mean ± standard deviation): relative intensity = 87.41 ± 6.47% of MAS, speed = 12.62 ± 1.87 km/h, RPE = 6.58 ± 1.77 score, heart rate = 177.74 ± 12.63 beats per minute, watts = 252.79 ± 32.94 W and SmO2 = 60.47 ± 8.25.
Table 1 shows the maximal aerobic speed (MAS) values, the relative intensity of each step for each participant and their SmO2 values. The standard deviation of SmO2 goes from 4.04 to 11.12.
Table 2 shows the MAS values, the relative intensity of each step for each participant and their lactate concentration values (LT). Most of the participants have their second lactate threshold between stage 4 and 5, and these stages have a speed of 11.6 km/h and 12.8 km/h.
TABLE 2. Values of maximal aerobical speed, lactate concentration and relative intensities for each participant.
No significant correlations were found between the variables of SmO2 and HR (r = −0.028; p = 0.752), SmO2 and W (r = 0.047; p = 0.593) and SmO2 and RPE (r = 0.035; p = 0.692), as these were results from null correlations, while the correlation between SmO2 and lactate (r = −0.118; p = 0.177) is slightly negative, but without significance. These correlations can be seen in Table 3.
TABLE 3. Correlations and significance of the used variables for the measurement in the maximal incremental test on treadmill.
In the comparison between the results of lactate and SmO2 in Figure 1 (p = 0.076; r = −0.156), we can see that there is no inflection point in the lactate thresholds with fixed concentrations (2 mmol/L and 4 mmol/L) and SmO2. The VT2 is at 85%–90% of MAS of each participant, being the mean value 4,3 mmol/L, but SmO2 does not show an inflection point at these relative intensities. Also, when verifying individual data, it can be seen that only 8 of them have some inflection point at SmO2 values at the same point as the VT2, 10 of them have no inflection point and 3 participants did not reach 4 mmol/L to determine the VT2.
FIGURE 1. Relationship between lactate concentration and muscle oxygen saturation at each MAS relative intensity of each participant. SmO2—The percentage of muscle oxygen saturation. LT—Lactate concentration in mmol/L. MAS: Maximal aerobic speed.
Nevertheless, in the results of the Spearman correlation (Table 3), we can observe a great significance between all the variables of W, HR, LT, and RPE, with good correlations, r = (0.5–0.69), or very good, r = (0.7–0.89).
Regarding the recovery of the participants, it is observed that there is a null and non-significant correlation between the variables of SmO2 and HR, from the last step completed and the first minute of recovery (r = 0.001; p = 0.997) (Figure 2), and from the first minute of recovery up to 3 min of recovery (r = 0.092; p = 0.690) (Figure 3).
FIGURE 2. Correlation between the variables of SmO2 and HR from the last completed step to the first minute of recovery and from the first minute of recovery to the 3 min of recovery.
FIGURE 3. Correlation between the variables of SmO2 and HR from the first minute of recovery to the 3 min of recovery. SmO2—The percentage of muscle oxygen saturation. HR—Heart Rate. DSMO2_LAST_1—The difference between the percentage of muscle oxygen saturation since the last completed step to the first recovery minute. DHR_LAST-1—The difference between the heart rate since the last completed step to the first recovery minute. DSMO2_LAST_3—The difference between the percentage of muscle oxygen saturation since the last completed step to the third minute of recovery. DHR_LAST_3—The difference between the heart rate since the last completed step to the 3 min of recovery.
5 Discussion
The main objective of the study was to identify the second ventilatory threshold during incremental tests using NIRS-derived data and to find out if these data correlated with other variables through a maximal incremental treadmill test in non-professional runners.
We found that the SmO2 data alone were not enough to determine the VT2, and there were no significant correlations between SmO2 and the other studied variables during the maximal incremental treadmill test. Only 8 subjects had a breakpoint at the VT2 determined by lactate data, so SmO2 is not sensitive enough to determine the VT2 by itself. This result is consistent with a previous study using a maximal cycle-ergometer ramp test with 25 cyclists (Racinais et al., 2014). However, other studies using incremental tests in cycling or running have observed correlations between one or both ventilatory thresholds and SmO2 (Austin et al., 2005; Fontana et al., 2015; van der Zwaard et al., 2016; Feldmann et al., 2022), which may be due to different NIRS devices used or certain differences in the protocol used, as graded incremental test with a lower speed increase (0.8 km/h each 3 min) than in our study (Austin et al., 2005) or a 30 s stages test with speed increase of 0.5 km/h until to exhaustion (Feldmann et al., 2022). In addition to this, it should be noted that in our study, a 30-s break was used between stages during the test. This factor may have exerted an impact on the outcomes, given that the 30-s break may have allowed for sufficient muscle recovery, thus affecting the determination of the ventilatory thresholds. We also found that SmO2 had significant variability between subjects, which could be a result of the limitations of the NIRS tool due to subcutaneous tissue (Barstow, 1985), but we cannot confirm it as this has not been measured. According to other studies (Thiel et al., 2011; Crum et al., 2017), it is shown that at higher intensity there is a greater standard deviation of SmO2, obtaining high values of standard deviation during the entire test on all the different relative intensities. This corroborates the great individual variability that this variable has, as already studied in another incremental test protocol. This protocol involved a race with 46 participants, of which 25 were runners and 21 were cyclists, all of whom were professional athletes who trained between 5 and 7 days a week (Austin et al., 2005). SmO2 seems to be a good indicator of effort for variable efforts, which are exercises with a changing intensity, such as on uphill and downhill terrain (Born et al., 2017), while it does not seem to be a great indicator of effort for incremental efforts (Paredes-Ruiz et al., 2020). However, there is some discrepancy since it does state that it is actually valid in an incremental cycle machine test (Crum et al., 2017), and in another incremental race test it is mentioned that it depends on the subject (Austin et al., 2005). Also, Humon Hex seems to be a good device to measure SmO2 in the vastus lateralis in endurance runners for submaximal 60 min run-to-exhaustion treadmill running test (Jaén-Carrillo et al., 2022). In this study, the SmO2 variable was not observed as a useful tool of effort and to determine the VT2 for a maximal incremental test in treadmill running, due to its great inter-subject variability and low intra-subject variability, without any clear inflection point for VT2. In addition to the above, in the designed incremental protocol, 3 participants were discarded due to erroneous measurements. These errors may haven been caused by various factors such as: i) the swinging motion generated by running, ii) changes in muscle contractions during the race, iii) the impact of running, and iv) sweat. All of these limiting factors have the potential to influence the accuracy of the NIRS device measurements and the correct placement of the sensor during running tests.
Lactate is the variable which is considered the “gold standard” for assessing aerobic capacity in endurance sports (Beneke, 2003), with HR being the most popular and simplest method to monitor and quantify on a daily basis (Born et al., 2017). Therefore, both variables are used in an essential way for incremental tests, and the gas analyzer is the other main tool in these tests. Therefore, Figure 1 shows the relationship of SmO2 and lactate during the test, which does not show any inflection point to determine lactate thresholds of SmO2 data. Although SmO2 has a little decrease at 85%–90% RI, it does not continue decreasing. So, it is not possible to determine a VT2 by SmO2 values. It should be noted that, according to the fixed lactate concentration model, ventilatory thresholds are typically located at lactate thresholds ranging from 2 to 4 mmol/L (Kindermann et al., 1979; Heck et al., 1985; Corrales-Gil et al., 2013). However, there is significant controversy regarding the different methods used to determine lactate thresholds, with many being deemed valid for this purpose. Given this, the present study chose to utilize the fixed lactate concentration model as it is a valid method for determining physiological thresholds (Faude et al., 2009). In addition, the correlation between lactate and SmO2 is mildly negative and without significance. Therefore, these results suggest that Humon Hex is not useful for determining lactate thresholds, as has already been observed in other studies that determined that there is no correlation between lactate and SmO2, nor can thresholds be estimated (Austin et al., 2005; Perrey and Ferrari, 2018). A possible cause of this could be the great variability between subjects (Anderson, 2017). This is due to the fact that no absolute value was found for the different lactate thresholds, in addition to the fact that no negative exponential difference was found for SmO2, which was in accordance with the exponential growth of lactate. Some studies had good results to determine ventilatory thresholds during graded exercise test with a bicycle ergometer; these results could differ with this study due to the different type of exercise and a different maximal incremental test, because in our study it has a 30 s stop between steps, which could change the results (Lin et al., 2020; Salas-Montoro et al., 2022). Also note that the NIRS device only measures a muscle locally, whereas lactate is collected directly from earlobe blood droplets.
However, SmO2 does not have a correlation with HR. This was observed in other studies in which runners were analyzed on changing terrain, with ascents and descents (Born et al., 2017), and although it does not have a correlation, it does prove to be a complementary tool to the HR for this type of situation. Furthermore, SmO2 could be a better monitoring variable for hilly terrain than HR, but further research is necessary on this topic. This is explained by the fact that both variables are affected by different external factors, such as muscle contractions or thermoregulation/dehydration (Perrey and Ferrari, 2018). In addition, SmO2 also did not present correlations with variables such as power (W) and RPE, as these variables were previously validated for maximal incremental efforts (Haddad et al., 2017; Imbach et al., 2020). Therefore, our findings seem to indicate that SmO2 is not a useful variable for a maximal incremental treadmill test with 30 s stop phase, as no significant correlation was found with all the other validated variables, in addition to several limitations such as running oscillation or the measurement of only a local muscle as a measure of internal load, but this can differ depending on the type of NIRS device. Nevertheless, regarding the correlated variables between lactate, HR, RPE and W, different positive correlations were observed, from good to very good and with great significance (r > 0.6, p < 0.05). These findings have already been found in many other studies (Montagu, 1962; Paton and Hopkins, 2001; Van Schuylenbergh et al., 2004; Corrales-Gil et al., 2013; Abe et al., 2015; Haddad et al., 2017; García-Pinillos et al., 2019).
Regarding the recovery relationship between SmO2 and HR, both in the first minute and in the first 3 min, the correlations observed were almost null (r < 0.1: p > 0.05). It was taken into account that HR is a good variable to determine recovery, in addition to having a good correlation with lactate to measure recovery. However, this was studied in research involving only 2 professional middle-distance runners, in which a decrease in lactate was accompanied by HR (Campos et al., 2017). With these results, it is not recommended to use the Humon Hex as a tool to measure recovery after a maximal incremental test on a treadmill, since the Humon data does not accompany the decrease in HR with an increase in muscle oxygen saturation in any significant way.
Despite the findings obtained, it is necessary to highlight certain limitations of this research. Firstly, the sample size was low compared to other investigations (n = 21). However, given the inclusion and exclusion criteria along with the need to use physiological variables, our findings are relevant to the scientific and sports community. In addition, 5 people in the sample were women, which is a good starting point for carrying out this same study solely with a female population in the future, given the different behavior of the physiological variables in submaximal efforts. Lastly, it was not possible to measure the subcutaneous tissue in order to take it into account in the evaluation of the results.
For future research studies, use of the Humon Hex device is highly recommended for variable intensity or changing terrain activities, where it has been shown to have greater usefulness and validity in its measurements (Born et al., 2017). It is also suggested to consider using this tool in sports that involve less oscillation, such as cycling, which is a more stationary sport and does not have the impact that could affect SmO2 measurements. Furthermore, it is essential to study how the stop phases during a maximal incremental test could affect the behavior of SmO2 in determining threshold breakpoints.
6 Conclusion
The study aimed to explore whether NIRS-derived data could be used to identify the second ventilatory threshold (VT2) during a maximal incremental treadmill test involving non-professional runners and to determine if there was a correlation between SmO2 and other exercise performance assessment measures or parameters, such as lactate concentration (LT), RPE, HR, and running power (W). The conclusion of the study is that the NIRS tool, Humon Hex, does not seem to be useful for determining VT2 in a maximal incremental treadmill test. Also, SmO2 values of this device do not correlate with other variables in these types of tests (p > 0.05). Furthermore, SmO2 does not seem a good indicator for measuring recovery versus using HR.
Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and approved by the study protocol adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of the European University of the Atlantic (CE-52). The patients/participants provided their written informed consent to participate in this study.
Author contributions
FO participated in the design of the study, data reduction/analysis and interpretation of results; MB-F participated in the design of the study and contributed to data collection; CL-F participated in the design of the study and contributed to data collection; JA-I participated in the design of the study and contributed to data collection. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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References
Abe, D., Yoshida, T., Ueoka, H., Sugiyama, K., and Fukuoka, Y. (2015). Relationship between perceived exertion and blood lactate concentrations during incremental running test in young females. BMC Sports Sci. Med. Rehabil. 7, 5. doi:10.1186/2052-1847-7-5
Anderson, P. (2017). Los límites del entrenamiento con control de frecuencia cardíaca y que información crucial proporciona el control de los niveles de oxígeno en los músculos. Humon. Available at: https://humon.es/wp-content/uploads/2018/08/Humon-5-de-mayo-2017.pdf (accessed at 06 16, 2022).
Austin, K. .G., Daigle, K. .A., Patterson, P., Cowman, J., Chelland, S., and Haymes, E. .M. (2005). Reliability of near-infrared spectroscopy for determining muscle oxygen saturation during exercise. Res. Q. Exerc Sport 76 (4), 440–449. doi:10.1080/02701367.2005.10599317
Barstow, T. .J. (1985). Understanding near infrared spectroscopy and its application to skeletal muscle research. J. Appl. Physiol. 126 (5), 1360–1376. doi:10.1152/japplphysiol.00166.2018
Beneke, R. (2003). Methodological aspects of maximal lactate steady state-implications for performance testing. Eur. J. Appl. Physiol. 89 (1), 95–99. doi:10.1007/s00421-002-0783-1
Bentley, D. .J., Newell, J., and Bishop, D. (2007). Incremental exercise test design and analysis: Implications for performance diagnostics in endurance athletes. Sports Med. 37 (7), 575–586. doi:10.2165/00007256-200737070-00002
Bhambhani, Y. .N. (2004). Muscle oxygenation trends during dynamic exercise measured by near infrared spectroscopy. Can. J. Appl. Physiol. 29 (4), 504–523. doi:10.1139/h04-033
Billat, V., Renoux, J. .C., Pinoteau, J., Petit, B., and Koralsztein, J. .P. (1994). Times to exhaustion at 100% of velocity at VO2max and modelling of the time-limit/velocity relationship in elite long-distance runners. Eur. J. Appl. Physiol. Occup. Physiol. 69 (3), 271–273. doi:10.1007/BF01094801
Borg, G. (1998). Borg’s perceived exertion and pain scales EEUU: Human Kinetics. Available at: https://www.researchgate.net/profile/Gunnar-Borg/publication/306039034_Borg%27s_Perceived_Exertion_And_Pain_Scales/links/57ac4a9b08ae7a6420c2b7ff/Borgs-Perceived-Exertion-And-Pain-Scales.pdf (accessed at 06 16, 2022).
Born, D. .P., Stöggl, T., Swarén, M., and Björklund, G. (2017). Near-infrared spectroscopy: More accurate than heart rate for monitoring intensity in running in hilly terrain. Int. J. Sports Physiol. Perform. 12 (4), 440–447. doi:10.1123/ijspp.2016-0101
Boudreaux, B. .D., Hebert, E. .P., Hollander, D. .B., Williams, B. M., Cormier, C. L., Naquin, M. R., et al. (2018). Validity of wearable activity monitors during cycling and resistance exercise. Med. Sci. Sports Exerc 50 (3), 624–633. doi:10.1249/MSS.0000000000001471
Campos, Y., Guimaraes, M., Souza, H., Da Silva, G., Domingos, P., Resende, N., et al. (2017). Relación entre el umbral anaeróbico identificado a través del lactato sanguíneo y los ejercicios discontinuos y dinámicos resistidos en los corredores de larga distancia. PubliCe 2, 1–7. Available at: https://publice.info/articulo/relacion-entre-el-umbral-anaerobico-identificado-a-traves-del-lactato-sanguineo-y-los-ejercicios-discontinuos-y-dinamicos-resistidos-en-los-corredores-de-larga-distancia-2248-sa-Y58bdd021b7665 (accessed at 06 16, 2022).
Corrales-Gil, A., Hendrinkson-Gómez, A., and Coronel-Puchaeisela, F. (2013). Reyes-Janeiro I. Valoración de la frecuencia cardíaca y el lactato durante el test escalonado en corredores de medio fondo. Rev. IMD 8 (1). Available at: http://www.revmedep.sld.cu/index.php/medep/article/view/219 (accessed at 06 16, 2022).
Crotty, N. .M., Boland, M., Mahony, N., Donne, B., and Fleming, N. (2021). Reliability and validity of the lactate Pro 2 analyzer. Meas. Phys. Educ. Exerc Sci. 25 (3), 202–211. doi:10.1080/1091367X.2020.1865966
Crum, E. .M., O'Connor, W. .J., Van Loo, L., Valckx, M., and Stannard, S. .R. (2017). Validity and reliability of the Moxy oxygen monitor during incremental cycling exercise. Eur. J. Sport Sci. 17 (8), 1037–1043. doi:10.1080/17461391.2017.1330899
Farzam, P., Starkweather, Z., and Franceschini, M. .A. (2018). Validation of a novel wearable, wireless technology to estimate oxygen levels and lactate threshold power in the exercising muscle. Physiol. Rep. 6 (7), e13664. doi:10.14814/phy2.13664
Faude, O., Kindermann, W., and Meyer, T. (2009). Lactate threshold concepts: How valid are they? Sports Med. 39 (6), 469–490. doi:10.2165/00007256-200939060-00003
Feldmann, A., Ammann, L., Gächter, F., Zibung, M., and Erlacher, D. (2022). Muscle oxygen saturation breakpoints reflect ventilatory thresholds in both cycling and running. J. Hum. Kinet. 83, 87–97. doi:10.2478/hukin-2022-0054
Ferrari, M., Muthalib, M., and Quaresima, V. (2011). The use of near-infrared spectroscopy in understanding skeletal muscle physiology: Recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 369 (1955), 4577–4590. doi:10.1098/rsta.2011.0230
Fontana, F. .Y., Keir, D. .A., Bellotti, C., De Roia, G. .F., Murias, J. .M., and Pogliaghi, S. (2015). Determination of respiratory point compensation in healthy adults: Can non-invasive near-infrared spectroscopy help? J. Sci. Med. Sport 18 (5), 590–595. doi:10.1016/j.jsams.2014.07.016
García-Pinillos, F., Latorre-Román, P. .Á., Roche-Seruendo, L. .E., and García-Ramos, A. (2019). Prediction of power output at different running velocities through the two-point method with the Stryd™ power meter. Gait Posture 68, 238–243. doi:10.1016/j.gaitpost.2018.11.037
Haddad, M., Stylianides, G., Djaoui, L., Dellal, A., and Chamari, K. (2017). Session-RPE method for training load monitoring: Validity, ecological usefulness, and influencing factors. Front. Neurosci. 11, 612. doi:10.3389/fnins.2017.00612
Haverty, M., Kenney, W. .L., and Hodgson, J. .L. (1988). Lactate and gas exchange responses to incremental and steady state running. Br. J. Sports Med. 22 (2), 51–54. doi:10.1136/bjsm.22.2.51
Heck, H., Mader, A., Hess, G., Mücke, S., Müller, R., and Hollmann, W. (1985). Justification of the 4-mmol/l lactate threshold. Int. J. Sports Med. 6 (3), 117–130. doi:10.1055/s-2008-1025824
Hopkins, W. .G., Marshall, S. .W., Batterham, A. .M., and Hanin, J. (2009). Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc 41 (1), 3–13. doi:10.1249/MSS.0b013e31818cb278
Imbach, F., Candau, R., Chailan, R., and Perrey, S. (2020). Validity of the Stryd power meter in measuring running parameters at submaximal speeds. Sports (Basel) 8 (7), 103. doi:10.3390/sports8070103
Jaén-Carrillo, D., Roche-Seruendo, L. .E., Cartón-Llorente, A., and García-Pinillos, F. (2022). Agreement between muscle oxygen saturation from two commercially available systems in endurance running: Moxy Monitor versus Humon Hex. Proc. Inst. Mech. Eng. P J. Sport Eng. Technol. 236 (3), 231–237. doi:10.1177/17543371211015764
Jamnick, N. .A., Botella, J., Pyne, D. .B., and Bishop, D. .J. (2018). Manipulating graded exercise test variables affects the validity of the lactate threshold and [formula: See text]. PLoS One 13 (7), e0199794. doi:10.1371/journal.pone.0199794
Kindermann, W., Simon, G., and Keul, J. (1979). The significance of the aerobic-anaerobic transition for the determination of work load intensities during endurance training. Eur. J. Appl. Physiol. Occup. Physiol. 42 (1), 25–34. doi:10.1007/BF00421101
Lin, C. .W., Huang, C. .F., Wang, J. .S., Fu, L. .L., and Mao, T. .Y. (2020). Detection of ventilatory thresholds using near-infrared spectroscopy with a polynomial regression model. Saudi J. Biol. Sci. 27 (6), 1637–1642. doi:10.1016/j.sjbs.2020.03.005
Luedke, L. .E., Heiderscheit, B. .C., Williams, D. .S., and Rauh, M. .J. (2016). Influence of step rate on shin injury and anterior knee pain in high school runners. Med. Sci. Sports Exerc 48 (7), 1244–1250. doi:10.1249/MSS.0000000000000890
Mancini, D. .M., Bolinger, L., Li, H., Kendrick, K., Chance, B., and Wilson, J. R., Validation of near-infrared spectroscopy in humans.J. Appl. Physiol. (1985);77(6):2740–2747. doi:10.1152/jappl.1994.77.6.2740
Montagu, A. (1962). Adaptive value of labor pains. JAMAAP 179 (11), 887. doi:10.1001/jama.1962.03050110055011
Paredes-Ruiz, M. .J., Jódar-Reverte, M., Ferrer-Lopez, V., and Martínez-González-Moro, I. (2020). Quadriceps muscle oxygenation during a maximum stress test in middle-aged athletes. Monten J. Sports Sci. Med. 9 (2), 43–49. doi:10.26773/mjssm.200908
Paton, C. .D., and Hopkins, W. .G. (2001). Tests of cycling performance. Sports Med. 31 (7), 489–496. doi:10.2165/00007256-200131070-00004
Perrey, S., and Ferrari, M. (2018). Muscle oximetry in sports science: A systematic review. Sports Med. 48 (3), 597–616. doi:10.1007/s40279-017-0820-1
Racinais, S., Buchheit, M., and Girard, O. (2014). Breakpoints in ventilation, cerebral and muscle oxygenation, and muscle activity during an incremental cycling exercise. Front. Physiol. 5, 142. doi:10.3389/fphys.2014.00142
Salas-Montoro, J. .A., Mateo-March, M., Sánchez-Muñoz, C., and Zabala, M. (2022). Determination of second lactate threshold using near-infrared spectroscopy in elite cyclists. Int. J. Sports Med. 43 (8), 721–728. doi:10.1055/a-1738-0252
Thiel, C., Vogt, L., Himmelreich, H., Hübscher, M., and Banzer, W. (2011). Reproducibility of muscle oxygen saturation. Int. J. Sports Med. 32 (4), 277–280. doi:10.1055/s-0030-1269922
van der Zwaard, S., Jaspers, R. .T., Blokland, I. .J., Achterberg, C., Visser, J. M., den Uil, A. R., et al. (2016). Oxygenation threshold derived from near-infrared spectroscopy: Reliability and its relationship with the first ventilatory threshold. PLoS One 11 (9), e0162914. doi:10.1371/journal.pone.0162914
Keywords: heart rate, lactate, NIRS, power meter, RPE
Citation: Osmani F, Lago-Fuentes C, Alemany-Iturriaga J and Barcala-Furelos M (2023) The relationship of muscle oxygen saturation analyzer with other monitoring and quantification tools in a maximal incremental treadmill test. Front. Physiol. 14:1155037. doi: 10.3389/fphys.2023.1155037
Received: 31 January 2023; Accepted: 09 May 2023;
Published: 18 May 2023.
Edited by:
Ale Quartiroli, University of Wisconsin–La Crosse, United StatesReviewed by:
Scott Nolan Drum, Northern Arizona University, United StatesDavid Varillas-Delgado, Universidad Francisco de Vitoria, Spain
Copyright © 2023 Osmani, Lago-Fuentes, Alemany-Iturriaga and Barcala-Furelos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Florent Osmani, florent.osmani@uneatlantico.es