Oxygen Uptake and Near Infrared Spectroscopy Responses during Heavy Intensity Pseudorandom Binary Sequence Exercise

An individual typically experiences a wide range of metabolic demands during a normal day. Rarely does a person perform abrupt changes in physical activity nor does the demand last for a long period of time at that same intensity. However, much of the research into the control of oxygen uptake (VO2) kinetics has utilized square wave, constant load exercise to understand the underlying physiological responses to exercise. This study examined the integrated response of pulmonary VO2, microvascular oxygenation measured using near‐infrared spectroscopy (NIRS) and muscle activation using surface electromyography (sEMG) techniques during sinusoidal cycling where the exercise intensity continuously changes. Ten male subjects (age, 30 ± 9 yrs (±SD); height, 179 ± 8 cm; weight, 89.9 ± 18.7 kg) completed a maximal exercise test to determine the lactate threshold (LaT) and peak VO2 (VO2pk). Each subject completed 2 trials consisting of 4 min of cycling at 20 W followed by 4 bouts of sinusoidal cycling transitions. The work rate transitioned between 30 W and a work rate corresponding to 70% of the difference between the LaT and VO2pk (Δ70) as a sinewave function. Pulmonary VO2 was measured breath‐by‐breath using a metabolic cart, interpolated to 1 s and ensemble averaged to yield a single sinewave for analysis. Changes in hemoglobin oxygenation ([HHb]) and total hemoglobin concentrations [(THC)] were measured from VL using NIRS. Data were collected at 1 Hz and ensemble averaged to yield a single sinewave for analysis. Muscle activation (vastus medialis (VM), vastus lateralis (VL)) was assessed using sEMG and reported as the root mean square (RMS). The RMS of the VL and VM were divided into 5 s bins and compared at each crest (CR) and trough (TR) during the 4 sinewaves. The group mean transition in work rate from the TR to the crest CR of each sinewave was 208 ± 27 W. No difference in muscle activation was observed at the CR of each sinewave as shown by the similar RMS for VL and VM. Compared to baseline (1064 ± 136 ml/min), there was a significant (p<0.05) increase in VO2 (2567 ± 322 ml/min) that was 220 ml lower (p<0.05) than the predicted VO2 (assuming 10 ml/min/W) for the work rate (2788 ± 271 ml/min). VO2 did not return to baseline values during the TR (1806 ± 223 ml/min, p<0.05) resulting in an lower gain for VO2 than expected (4.7 ± 1.0 ml/min/W). A significant phase lag was observed for VO2 of 64.9 ± 14.0 deg. Mean [HHb] increased (p<0.05) from baseline (12.4 ± 2.9 μM) to the CR of the sinewave (17.7 ± 7.8 μM). Similar to VO2, there was a significant phase lag observed for [HHb] of 56.9 ± 16.0 deg. The difference in phase lag responses between VO2 and [HHb] was not different. These findings are consistent with previous studies reporting a lower gain and a significant phase lag for VO2 during sinewave exercise. Compared to studies utilizing square wave exercise which have shown [HHb] to adapt much faster than VO2, the similar phase lag for VO2 and [HHb] observed in the present study suggests that VO2 and [HHb] respond similarly to the increase in exercise intensity during sinewave exercise. Further investigations into the integration of VO2 and [HHb] during exercise that is continuously changing intensities are warranted.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

The purpose of this study was to compare the test-retest reliability of oxygen uptake (VO2) kinetics with carbon dioxide output (VCO2) kinetics using a pseudo random binary sequence (PRBS) exercise test. A reliable test of gas exchange kinetics would have the potential of being applied as a sports fitness test. Ten healthy male subjects agreed to participate in the study and all subjects completed two identical PRBS exercise tests (test 1 and test 2), separated by a 30 min period of inactivity. Three consecutive 300 s PRBS cycles were completed in each test with 20 s exercise intensity changes between 25 and 85 W using an electrically braked cycle ergometer. Fourier analysis was computed for frequencies 3.3, 6.7 and 10 mHz. Statistical analysis by two-way ANOVA with repeated measures did not reveal significant differences between test 1 and test 2 for either VO2 kinetics or VCO2 kinetics. Static gain of VO2 for test 1 [9.11 (SD 0.59) ml.min-1.W-1] and test 2 [9.23 (SD 0.64) ml.min-1.W-1] did not differ significantly between tests. The 95% limits of agreement for VCO2 kinetics displayed increased variability in comparison to VO2 kinetics at each frequency of amplitude ratio and phase shift. Systematic bias ranged between 0% and 4%, except at frequency 10 mHz of VCO2 kinetics phase shift which showed a 10% bias for slower VO2 kinetics in test 2. It is possible that the increased variability of VCO2 kinetics compared to VO2 kinetics might be attributable to a lower signal to noise ratio in VCO2 kinetics, variations in ventilation or the storage mechanisms of CO2. The lower variability of VO2 kinetics compared with VCO2 kinetics suggests that the PRBS test of VO2 kinetics has the greater potential for further development as an indicator of aerobic fitness.

The dynamics of ventilation (VE), oxygen uptake (VO2), carbon dioxide output (VCO2), and heart rate (fc) were studied in 12 healthy young men during upright and supine exercise. Responses to maximal and to two different types of submaximal exercise tests were contrasted. During incremental exercise to exhaustion, the maximal work rate, VO2max, VEmax, fc,max, and ventilatory threshold were all significantly reduced in supine compared to upright exercise (P less than 0.01-0.001). Following step increases or decreases in work rate between 25 W and 105 W, both VO2 and VCO2 responded more slowly in supine than upright exercise. Dynamics were also studied in two different pseudorandom binary-sequence (PRBS) exercise tests, with the work rate varying between 25 W and 105 W with either 5-s or 30-s durations of each PRBS unit. In both of these tests, there were no differences caused by body position in the amplitude or phase shifts obtained from Fourier analysis for any observed variable. These data show that the body position alters the dynamic response to the more traditional step increase in work rate, but not during PRBS exercise. It is speculated that the elevation of cardiac output observed with supine exercise in combination with the continuously varying work-rate pattern of the PRBS exercise allowed adequate, perhaps near steady-state, perfusion of the working muscles in these tests, whereas at the onset of a step increase in work rate, greater demands were placed on the mechanisms of blood flow redistribution.

BackgroundNon-invasive monitoring of cerebral tissue oxygen saturation (rcSO2) during transition is of growing interest. Different near-infrared spectroscopy (NIRS) techniques have been developed to measure rcSO2. We compared rcSO2 values during the immediate transition in preterm neonates measured with frequency-domain NIRS (FD-NIRS) with those measured with continuous-wave NIRS (CW-NIRS) devices in prospective observational studies.MethodsWe compared rcSO2 values measured with an FD-NIRS device during the first 15 min after birth in neonates with a gestational age ≥ 30 weeks but < 37 weeks born at the Erasmus MC- Sophia Children’s Hospital, Rotterdam, the Netherlands, with similar values measured with a CW-NIRS device in neonates born at the Medical University of Graz, Austria. Mixed models were used to adjust for repeated rcSO2 measurements, with fixed effects for time (non-linear), device, respiratory support and the interaction of device and respiratory support with time. Additionally, parameters such as total haemoglobin concentration and oxygenated and deoxygenated haemoglobin concentrations measured by FD-NIRS were analysed.ResultsThirty-eight FD-NIRS measurements were compared with 58 CW-NIRS measurements. The FD-NIRS rcSO2 values were consistently higher than the CW-NIRS rcSO2 values in the first 12 min, irrespective of respiratory support. After adjustment for respiratory support, the time-dependent trend in rcSO2 differed significantly between techniques (p < 0.01).ConclusionAs cerebral saturation measured with the FD-NIRS device differed significantly from that measured with the CW-NIRS device, differences in absolute values need to be interpreted with care. Although FD-NIRS devices have technical advantages over CW-NIRS devices, FD-NIRS devices may overestimate true cerebral oxygenation and their benefits might not outweigh the usability of the more clinically viable CW-NIRS devices.

Comparison of near infrared spectroscopy with functional MRI for detection of physiological changes in the brain independent of superficial tissue

PURPOSE This investigation compared anaerobic threshold (AT) measurements using near infrared spectroscopy (NIRS) and ventilation (V-slope, ventilatory equivalents, and excess CO2) detection methods. METHODS Twenty two males (age 24.2 ± 3.5 yr) and 22 females (age 21.1 ± 3.5 yr) participated as subjects. All subjects completed a single incremental cycle ergometer exercise test to exhaustion during which ventilatory gas exchange variables were obtained using open circuit spirometry. Vastus lateralis oxygenation values were obtained using NRIS. The cycle ergometer test consisted of increasing power output (50 W for males and 25 W for females) every 3 min. RESULTS Student t-tests indicated males had higher (p < 0.05) values for height, weight, age, VO2peak (L/min), and maximum power output (W). Females had higher (p < 0.05) percent body fat and total exercise time. Significant (p < 0.05) gender specific correlations for measurements of AT were noted between the NIRS and V-slope (r = 0.85, r = 0.78), ventilatory equivalents (r = 0.68, r = 0.50), and excess CO2 (r = 0.91, r = 0.83), for males and females, respectively. A 2-factor (method × sex) ANOVA indicated sex differences (p < 0.05) with respect to VO2(L/min), percent VO2peak, power output (W), and time at the AT. The percent VO2peak and power (W) at the AT were higher (p < 0.05) for the NIRS than ventilation methods. No other differences for measures of the AT were noted between methods. CONCLUSIONS These data suggest that the use of the non-invasive relatively inexpesive NIRS system is an acceptable method for the determination of AT. Further research is needed to determine the utility of NIRS in tracking training adaptations and assessing interindividual differences.

Frequency-domain near-infrared spectroscopy (FD-NIRS) is a noninvasive technique used to characterize in vivo tissue structure and molecular composition by leveraging the deep tissue penetration of red and near-infrared light. However, the size, complexity, expense, and lack of scalability of current FD-NIRS hardware have hindered its widespread clinical adoption and use outside the lab/clinic. The goal of this dissertation project is to address these limitations by developing a compact FD-NIRS device for wearable optical sensing and imaging. We hypothesize that the wearable design will enable quantitative and real-time measurement of tissue composition in an application such as measuring muscle oxygenation for athletes. In this work, we present the first wearable FD-NIRS systems, enabled by custom broad-bandwidth application-specific integrated circuits (ASICs). This is the first time a complete transceiver has been implemented on a chip for a multifrequency FD-NIRS system. Initially, the ASIC was characterized using a benchtop setup for its potential and performance in FD-NIRS measurements. It was then integrated into a single-channel, two-wavelength wearable system for the quantitative measurement of tissue oxygenation. This wearable FD-NIRS system accurately measured tissue optical properties during in vivo arteriovenous occlusion experiments at 14.7~Hz, quantifying oxy- and deoxyhemoglobin concentrations and confirming the functionality of the wearable FD-NIRS technology. In a high-speed configuration, this device was used to sample the cardiac pulsatile waveform at 250Hz. Encouraged by the success of the single-channel FD-NIRS device, a multichannel device intended for diffuse optical tomography (DOT) has been developed. The use of current multichannel FD-NIRS DOT systems are restricted to laboratory environments and are not comfortable for long-term monitoring. We designed our multichannel system for functional near-infrared spectroscopy (fNIRS) brain imaging in a wearable, modular, and fiber-free format to improve comfort and ease of use. This system consists of a high-density optode array with 16 sources and 16 detectors and FD-NIRS measurements based on a compact a vector network analyzer (VNA). A tile-based design approach was adopted, incorporating silicon photomultipliers (SiPMs), vertical cavity surface-emitting lasers (VCSELs), and radio frequency (RF)/direct current (DC) switching. A wearable forehead probe was designed to house these tiles, providing continuous, comfortable measurements and enabling real-time monitoring of brain activity and muscle oxygenation. Preliminary data on tile performance using tissue-simulating phantoms will be presented. We anticipate that our work will ultimately improve the tracking of neurological conditions, the efficacy of treatment, and brain function in both clinical and naturalistic (i.e., outside of the clinic / lab) settings.

Near-infrared spectroscopy is currently used to assess changes in the oxygen saturation of the muscle during exercise. The primary purpose of this study was to assess the reliability of near-infrared spectroscopy in determining muscle oxygen saturation (StO2) in the vastus lateralis during cycling and the gastrocnemius during running for exercise intensities at lactate threshold and maximal effort. Test-retest reliability was determined from an intraclass correlation coefficient obtained from a one-way analysis of variance. Reliability of muscle StO2 for the gastrocnemius at lactate threshold was R = .87, and R= .88 at maximal effort. Reliability of muscle StO2 for the vastus lateralis at lactate threshold was R= .94 and R = .99 at maximal effort.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work. Editor's evaluation The authors have established new formulas to predict maximum oxygen uptake for cyclists and runners based on submaximal exercise testing and anthropometric characteristics. This is an important study with a large and comprehensive dataset, which may be helpful for many exercise labs. The work is convincing, using appropriate and validated methodology in line with the current state-of-the-art, as shown by references to common exercise books. https://doi.org/10.7554/eLife.86291.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The oxygen uptake (VO2) is considered an important metric in assessing cardiorespiratory fitness, health status, or endurance performance potential (Guazzi et al., 2012). With the application of standardised procedures and interpretation protocols, during graded exercise tests (GXT), the (maximal oxygen uptake) VO2max can be established (Bentley et al., 2007). GXT is the most widely used assessment to examine the dynamic relationship between exercise and integrated physiological systems (Albouaini et al., 2007; Bentley et al., 2007). The information from GXT during cardiopulmonary exercise testing (CPET) can be applied across the spectrum of sport performance, occupational safety screening, research, and clinical diagnostics (Guazzi et al., 2017). VO2 max is often used as a boundary between severe and extreme intensity domains and by definition requires maximal effort from the tested subject (Gaesser and Poole, 1996). However, it is not always recommended or possible to undertake a test to exhaustion (Guazzi et al., 2012). For the athletes, the proximity of competition or injury history can allow submaximal testing, but not testing to exhaustion (Sassi et al., 2006). Testing that requires maximal effort may be disruptive to the training process or interfere with race performance (Coutts et al., 2007; Lamberts et al., 2011). Due to practical constraints, tests to exhaustion or peak-power-output tests are often performed only two or three times a year (Coutts et al., 2007). However, VO2 values are widely used in sport science and the decision-making process (Mann et al., 2013). VO2 is widely considered one of the major endurance performance determinants (Joyner and Coyle, 2008). Using VO2max to guide the selection process, prescribing training intensity, assessing training adaptations, or predicting race times is a common practice in high-performance sports (Bassett and Howley, 2000; Bentley et al., 2007; Hawley and Noakes, 1992; Noakes et al., 1990). VO2max is also one of the critical vital signs coordinating the function of the cardiovascular, respiratory, and muscular systems, it is an indicator of overall body health status (Kaminsky et al., 2017). Quantifying VO2max provides additional input regarding clinical decision-making, risk stratification, evaluation of therapy, and physical activity guidelines (Guazzi et al., 2012). For patients undertaking a test to exhaustion is rarely needed or possible due to health restraints or cardiac risk (Guazzi et al., 2016). For many years researchers have studied indirect methods of estimating VO2max(Sartor et al., 2013). Protocols such as the Astrand-Ryhming Test, Six-Minute Walk Test, or YMCA Step Test have been established and validated (Astrand and Ryhming, 1954; Beutner et al., 2015; Carey, 2022; Jalili et al., 2018). Moreover, estimation of the VO2 and heart rate (HR) values below the ventilatory threshold can be based on cardiorespiratory kinetics assessment using randomised changes in the work rate known as a pseudo-random binary sequences testing (Hoffmann et al., 2022). However, with the development of technology, the accessibility of laboratory testing and mobile testing improved (Montoye et al., 2020; Pritchard et al., 2021). Therefore, new opportunities to develop more precise yet simple and accessible methods and models to assess VO2max occur (Jurov et al., 2023). This appears to be especially important considering the low prediction accuracy of most of the VO2max formulae that were validated in our previous study (Wiecha et al., 2023). Recently, we have been observing the development of prediction methods with the usage of machine learning (ML) and artificial intelligence (AI) (Ashfaq et al., 2022). Both ML and AI are used in sport science as forecasting and decision-making support tools (Abut and Akay, 2015; Bobowik and Wiszomirska, 2022; Chmait and Westerbeek, 2021; Hammes et al., 2022; Rossi et al., 2021). There is growing evidence that VO2max prediction based on ML models, especially support vector ML and artificial neural network models, exhibits more robust and accurate results compared to MLR only (Abut and Akay, 2015; Ashfaq et al., 2022). Therefore, in this research, with the support of ML, we look for algorithms and prediction patterns that allow us to use values obtained during submaximal CPET and somatic measurements to estimate maximal VO2max values in male runners and cyclists. We stipulate that prediction models allow for accurate calculation of VO2max based on somatic or submaximal CPET variables. Materials and methods We have applied the development and validation of the prediction TRIPOD guidelines to conduct the study (see Supplementary Material 1TRIPOD Checklist for Prediction Model Development and Validation) (Collins et al., 2015). The study is based on retrospective data analysis from the CPET registry collected from 2013 to 2021 at the medical clinic (Sportslab, Warsaw, Poland). All CPET have been performed at the individual request of participants, as a part of regular training monitoring or performance assessment. Ethical approval Request a detailed protocol The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each study participant delivered written consent to undergo CPET and participate in the study. Derivation cohort Request a detailed protocol We selected the cohort with the use of rigorous exclusion/inclusion criteria. Due to the insufficient number of women in our database and the number of potential variables in the regression models for adequate power, we had to limit ourselves to conduct analysis in the male population only (Martens and Logan, 2021). Out of 6439 healthy, adult male cyclists and long-distance runners that undergone CPET, 4423 met the criteria as further: (1) age ≥18 years, (2) declared regular cycling or running training for ≥3 months, (3) had no extreme outliers ≤ or ≥±3 standard deviations (SD) from mean for all of the testing variables (beyond ≥±3 SD in VO2max), (4) lack of any injury, medical condition, or addiction in medical history that may affect exercise capacity, (5) not taking any medications with a modifying effect on exercise capacity, (6) maximum exertion achieved during CPET. We defined the maximum exertion in CPET as the fulfilment of the minimum six of the following criteria: (1) respiratory exchange ratio (RER) ≥1.10, (2) present VO2 plateau (growth <100 mL·min–1 in VO2 despite increased running speed or cycling power), (3) respiratory frequency (fR) ≥45 breaths·min–1, (4) declared subjective exertion intensity during CPET ≥18 in the Borg scale (Borg, 1970), (5) blood lactate concentration [La-]b ≥8 mmol·L–1, (6) growth in speed/power ≥10% of respiratory compensation point (RCP) values after exceeding the RCP, (7) peak heart rate (HRpeak) ≥15 beats·min–1 below predicted maximal heart rate (HRmax) (Lach et al., 2021). Participants’ selection procedure has been shown in Figure 1. Figure 1 Download asset Open asset Flowchart of the preliminary inclusion and exclusion process. Abbreviations: EA, endurance athlete; CPET, cardiopulmonary exercise testing; SD, standard deviation; TE, treadmill; RER, respiratory exchange ratio; VO2, oxygen uptake (mL·min−1·kg−1); [La−]b, lactate concentration (mmol·L−1); fR, breathing frequency (breaths·min−1); RCP, respiratory compensation point; HRpeak, peak heart rate (beats·min−1); HRmax, maximal heart rate (bpm). At both stages of the selection, some participants met several (>1) exclusion criteria. Somatic measurements and CPET protocols Request a detailed protocol Body mass was measured with a body composition (BC) analyser (Tanita, MC 718, Japan) with the multifrequency of 5 kHz/50 kHz/250 kHz via the bioimpedance analysis and normal testing mode. The participants’ skin was cleaned with alcohol before placing the electrodes on the skin. Prior to the test, the participants received instructions to refrain from exercising for 2 hr, consume a light meal rich in carbohydrates 2–3 hr beforehand, and maintain hydration by drinking isotonic beverages. Additionally, they were advised to abstain from medications, caffeine, and cigarettes on the day of the test. Running CPET (TE) was performed on a mechanical treadmill (h/p/Cosmos Quasar, Germany). Cycling CPET (CE) was performed on Cyclus-2 (RBM elektronik-automation GmbH, Leipzig, Germany). Hans Rudolph V2 mask (Hans Rudolph, Inc, Shawnee, KS, USA), breath-by-breath method with Cosmed Quark CPET gas exchange analysing device (Cosmed Srl, Rome, Italy), and Quark PFT Suite to Omnia 1.6 software were utilised. The gas analyser device was regularly calibrated with the reference gas (16% O2; 5% CO2) in accordance with the manufacturer’s instructions (Airgas USA, LLC, Plumsteadville, PA, USA). From 2013 to 2021, three Cosmed Quark CPET units were used. HR was measured with the Cosmed torso belt (Cosmed srl, Rome, Italy). [La-]b was measured via enzymatic-amperometric electrochemical technique with Super GL2 analyser (Müller Gerätebau GmbH, Freital, Germany). The [La-]b analyser was regularly calibrated before each measurement series. The 40 m2 indoor, air-conditioned laboratory with 20–22°C temperature and 40–60% humidity, and 100 m ASL provided the same conditions for all BC and CPET. Each CPET began with a 5 min personalised warm-up (walk or easy jog with ‘conversational’ intensity for running, easy pedalling with ‘conversational’ intensity for cycling). Then after the preparation (about 5 min), the continuous progressive step test was conducted. Due to the population diversity (training status), the running test speed started from 7 to 12 km·hr–1 with a 1% treadmill incline. The choice of initial starting speed was determined by the interview and sports results achieved. For example, those running less than 60 min at a distance of 10 km started the test at 7 km/hr, while those running 10 km for less than 35 min started the test at an initial speed of 12 km/hr. The pace increased by 1 km·hr–1 every 2 min with no change in incline. The cycling test began at 60–150 W, depending on the athletes training status. The power increased by 20–30 W every 2 min. It was recommended to maintain a constant cadence of 80–90 (repetition·min–1) during the test. The tests were terminated due to exhaustion: volitional inability to continue the activity or/and VO2 and HR plateau with increasing load or/and observed disturbance of coordination in running or/and inability to maintain the set cadence. Due to the graded protocol used, the cycling power and running speed values have been calculated as a function of time to better reflect the actual level for the test moment being determined (Kuipers et al., 1985). Before the test, after every step, and 3 min after the termination of the effort technician took a 20 µL blood sample from a fingertip. Samples were collected during the test without interrupting the effort. The samples were taken from the initial puncture. The first blood drop was collected into the swab and the second blood drop was drawn for further analysis into the capillary. VO2max was recorded as the highest value (15 s intervals) before the termination of the test. HRmax was recorded as the highest value obtained at the end of the test, without averaging. The anaerobic threshold (AT) was established with the following criteria: (1) common start of VE/VO2 and VE/VCO2 curves, (2) end-tidal partial pressure of oxygen raised constantly with the end-tidal partial pressure of carbon dioxide (Beaver et al., 1986). The was established with the following criteria: (1) PetCO2 must decrease after reaching maximal amount, (2) the presence of fast nonlinear growth in VE (second deflection), (3) the VE/VCO2 ratio achieved minimum and started to rise, and (4) a nonlinear increase in VCO2 versus VO2 (lack of linearity) (Beaver et al., 1986). The [La-]b was estimated for AT and RCP in relation to power or speed (Wiecha et al., 2022). Data analysis Request a detailed protocol Our comprehensive ML approach enables the evaluation of each formula by preliminary variables precision (at the stage of selection), then accuracy (during the model’s building) and recall (in internal validation). Individual CPET results were saved into the Excel file (Microsoft Corporation, Redmond, WA, USA) and a custom-made script was used to generate the database in Excel (Python programming). Further, mean, SD, and 95% confidence intervals (CI) were calculated. The normality of the distribution of the data was examined using the Shapiro-Wilk test and intergroup differences were calculated using the Student’s t-test for independent variables. Three-step variable selection procedures based on random forests were applied using the R package VSURF in RStudio software (R Core Team, Vienna, Austria; version 3.6.4) (Genuer et al., 2016). For each level of measurement (AT, RCP) and their combination (AT+RCP), significant variables were identified separately. The first step was dedicated to eliminate irrelevant variables from the dataset. Second step aimed to select all variables related to the response for interpretation purposes. The third step refined the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purposes (Genuer et al., 2017). Each time for variables selection, the anthropometric variables as in Tables 1–2 and the CPET parameters given in Tables 3–4 from a specific level of measurement (AT; RCP) and their combinations were visible. Table 1 Basic anthropometric characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDAge (years)36.235.6–36.98.4535.935.5–36.38.0535.534.9–36.28.14Height (cm)180.0179.6–180.56.04179.4179.1–179.76.13179.7179.2–180.26.61BM (kg)77.777.0–78.49.3577.777.3–78.19.2977.977.1–78.610.1BMI (kg·m–2)23.923.8–24.12.4324.124.0–24.22.4124.123.9–24.32.56BF (%)15.415.1–15.74.5515.515.3–15.74.5215.415.1–15.84.55FM (kg)12.211.9–12.64.6812.312.1–12.54.6512.311.9–12.74.92FFM (kg)65.565.0–66.06.4365.465.1–65.76.3165.665.1–66.16.86 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 2 Basic anthropometric characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDAge (years)37.336.6–38.09.1337.135.9–38.49.5037.636.5–38.88.46Height (cm)179.9179.4–180.46.27180.1179.2–181.06.96180.2179.4–181.06.13BM (kg)78.878.1–79.69.8079.177.7–80.510.479.878.4–81.310.9BMI (kg·m–2)24.324.1–24.62.6324.424.0–24.72.8024.624.2–25.02.96BF (%)16.415.7–17.14.9916.115.7–16.54.8116.215.5–16.84.87FM (kg)13.312.6–14.15.6613.012.6–13.45.2713.312.5–14.05.85FFM (kg)65.864.9–66.66.2565.865.4–66.36.0666.665.7–67.46.58 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 3 Cardiopulmonary exercise testing (CPET) characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)38.438.1–38.85.0138.538.3–38.74.8838.137.7–38.55.16RERAT0.870.86–0.870.040.870.86–0.870.040.870.86–0.870.04HRAT (beats·min–1)151.5150.8–152.310.3151.0150.6–151.510.8152.0151.2–152.810.8VEAT (L·min–1)79.178.1–80.012.278.377.8–78.912.077.276.3–78.212.0SPEEDAT (km·h–1)11.010.9–11.11.4511.011.0–11.11.3610.910.8–11.01.42LAAT (mmol·L–1)2.082.02–2.140.631.801.76–1.830.622.352.27–2.420.72rVO2RCP (mL·min–1·kg–1)47.547.0–48.05.8847.747.4–48.06.1547.346.8–47.86.16RERRCP1.001.00–1.000.041.001.00–1.000.041.001.00–1.000.03HRRCP (beats·min–1)173.4172.7–174.19.21173.2172.8–173.69.30174.3173.5–175.09.50VERCP (L·min–1)114.7113.5–116.015.9113.9113.1–114.616.7112.7111.4–114.016.2SPEEDRCP (km·h–1)14.013.9–14.11.7714.114.0–14.11.7013.913.8–14.11.75LARCP (mmol·L–1)4.724.63–4.821.044.404.34–4.451.044.814.69–4.931.14rVO2max (mL·min–1·kg–1)53.853.3–54.36.6754.354.0–54.66.9553.853.3–54.37.09 CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; SPEEDAT, velocity at anaerobic threshold; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; SPEEDRCP, velocity at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; rVO2max, maximal oxygen uptake relative to body mass. Table 4 Cardiopulmonary exercise testing (CPET) characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)33.032.5–33.45.8433.232.4–33.95.6833.732.9–34.55.89RERAT0.870.87–0.870.040.870.87–0.880.040.870.87–0.880.04HRAT CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; power at anaerobic threshold relative to body mass; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; power at respiratory compensation point relative to body mass; rVO2max, maximal oxygen uptake relative to body mass. selection variables were in the further only selected parameters were into multiple linear regression The data for MLR model building were randomly into that is testing, validation and of the a only significant were in the Derived are by the of mean and mean analysis was used to the model’s precision and accuracy during validation and tests to the fulfilment of MLR test the of in MLR test assessment between and test of Each model was examined the and any have not been 2 package in RStudio (R Core Team, Vienna, Austria; version version for and software version were used in was considered as the Results Somatic measurements and CPET results data of the runners models for testing, and validation are in Table while cyclists are in Table The runners of and for testing, and validation the cyclists and differences between of runners and cyclists were in BMI and between testing in all between validation only in CPET results for runners models are in Table 3 and for cyclists in Table Runners in the cohort achieved relative to body mass VO2max of in testing group and in validation group cyclists mean was and for testing, and validation to body mass oxygen uptake at anaerobic threshold in runners for ± ± and ± of in testing, and validation it was ± ± and ± of rVO2max, relative to body mass oxygen uptake at respiratory compensation point in runners for ± ± and ± of for testing, and validation while in cyclists for ± ± and ± of rVO2max, There were no significant differences in values between testing, and validation the runners and cyclists between runners and cyclists results were all significant Prediction models based on AT and RCP Full of MLR prediction models for cyclists are in Table for runners in Table The models prediction performance is as with and for cyclists from for somatic parameters to for RCP equations. For runners from for to for AT and equations. for cyclists models was the for RCP and the highest for For from for AT and to for equation. observed for cyclists was the for RCP in the validation group and the highest for while in runners the for AT and and the highest for The performance of prediction is in Figure Figure 2 Download asset Open asset of prediction for Abbreviations: maximal oxygen anaerobic threshold; RCP, respiratory compensation point; All values are in performance for running while the performance for cycling equations. performance of the prediction model for for for AT and for somatic-only equation. Table 5 VO2max prediction for cyclists. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass power at anaerobic threshold relative to body mass rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass VERCP, pulmonary ventilation at respiratory compensation point BF, body fat BM, body mass Table VO2max prediction for runners. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass SPEEDAT, velocity at anaerobic threshold FFM, fat mass VEAT, pulmonary ventilation at anaerobic threshold HRAT, heart rate at anaerobic threshold BF, body fat rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass SPEEDRCP, velocity at respiratory compensation point Models validation of each model for cyclists is in Table while for runners in Table the performance of our prediction was to that observed in the

0441 Near-infrared spectroscopy (NIRS) is a noninvasive method of measuring muscle oxygenation. Several studies have examined relationship between the kinetics of local muscle oxygenation and systemic oxygen intake during incremental and constant work-rate exercise. However, none has reported about muscle oxygenation during prolonged exercise. PURPOSE: To examine the relationship between the kinetics of local muscle oxygenation and systemic oxygen intake during prolonged exercise below anaerobic threshold. METHODS: Ten healthy males completed an orientation trial, a maximal exercise test, and a 30-min submaximal exercise bout. During the exercise test, expiratory gas analysis was performed using an expiratory gas analyzer, and the kinetics of vastus lateralis muscle were determined by NIRS. RESULTS: Oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin decreased immediately after the onset of exercise and gradually increased from 5 to 30 min with an increase in VO2. The increase in deoxygenated hemoglobin may indicate that oxygen transported by arterial blood during exercise test is taken up by the vastus lateralis muscle for contraction, and oxygenated hemoglobin changes into deoxygenated hemoglobin. On the other hand, the increase in both oxygenated hemoglobin and total hemoglobin, an index of muscle blood volume, may represent a redistribution of blood to the exercising muscles. There was a positive correlation between oxygenated hemoglobin and VO2 (r = 0.38, p < 0.01), between deoxygenated hemoglobin and VO2 (r = 0.46, p < 0.01), and between total hemoglobin and VO2 (r = 0.52, p < 0.01) after the 5th min of prolonged exercise. CONCLUSIONS: These results indicate that the increase in VO2 during prolonged exercise is attributed to the increase in both O2 dissociation (deoxygenated hemoglobin) and blood flow (total hemoglobin, total hemoglobin) in the capillary bed of the muscles to meet aerobic energy demand.

Various near-infrared spectroscopy (NIRS) variables have been used to estimate muscle lactate threshold (LT), but no study has determined which common NIRS variable best reflects muscle estimated LT. Establishing the inflection point of 2 regression lines for deoxyhaemoglobin (DeltaHHb(i.p).), oxyhaemoglobin (DeltaO2Hb(i.p.)), and tissue oxygenation index (TOIi.p.), as well as for blood lactate concentration, we then investigated the relationships between NIRS variables and ventilatory threshold (VT), LT, or maximal tissue hemoglobin index (nTHImax) during incremental cycling exercise. DeltaHHb(i.p.) and TOI(i.p.) could be determined for all 15 subjects, but DeltaO2Hb(i.p.) was determined for only 11 subjects. The mean absolute values for the 2 measurable slopes of the 2 continuous linear regression lines exhibited increased changes in 3 NIRS variables. The workload and VO2 at DeltaO2Hb(i.p.) and nTHImax were greater than those at VT, LT, DeltaHHb(i.p.), and TOI(i.p.). For workload and VO2, DeltaHHb(i.p.) was correlated with VT and LT, whereas DeltaO2Hb(i.p.) was correlated with nTHImax, and TOI(i.p.) with VT and nTHImax. These findings indicate that DeltaO2Hb strongly corresponds with local perfusion, and TOI corresponds with both local perfusion and deoxygenation, but that DeltaHHb can exactly determine deoxygenation changes and reflect O2 metabolic dynamics. The finding of strongest correlations between DeltaHHb and VT or LT indicates that DeltaHHb is the best variable for muscle LT estimation.

The influence of subcutaneous adipose tissue (ATT) and oxygen (O2) delivery has been poorly defined in frequency domain (FD) near-infrared spectroscopy (NIRS). Therefore, the aim of this study was to investigate the possible influence of these variables on all FD NIRS responses using a reliable protocol. Moreover, these influences were also investigated when using relative oxy- and deoxyhemoglobin and -myoglobin (oxy[Hb + Mb] and deoxy[Hb + Mb]) values (in %). A regression analysis was carried out for ATT and maximal-minimum oxy[Hb + Mb], deoxy[Hb + Mb], oxygen saturation (SmO2), and total hemoglobin (totHb) amplitudes during an incremental cyclic contraction protocol (ICCP) in a group of 45 participants. Moreover, the same analysis was carried out between subcutaneous ATT and the relative oxy- and deoxy[Hb + Mb] values (in %). In the second part of this study, a regression analysis was performed for peak forearm blood flow (FBF) during ICCP and the absolute and relative NIRS values in a group of 37 participants. Significant exponential correlation coefficients were found between ATT and deoxy[Hb + Mb] (r = 0.53; P < 0.001), oxy[Hb + Mb] (r = 0.57; P < 0.001), and SmO2 amplitudes (r = 0.57; P < 0.001). No significant relations were found between ATT and relative oxy[Hb + Mb] (r = 0.37; P = 0.07) and deoxy[Hb + Mb] (r = 0.09; P = 0.82). Significant positive correlation coefficients were found between force at exhaustion and maximal FBF (r = 0.66; P < 0.001), maximal differences in deoxy[Hb + Mb] (r = 0.353; P = 0.032) and totHb (r = 0.512; P = 0.002) while no significant correlation coefficients were found between these maximal force values and maximal differences in oxy[Hb + Mb] (r = -0.267; P = 0.111) and SmO2 (r = -0.267; P = 0.111). Significant linear correlation coefficients were found between FBF and deoxy[Hb + Mb] (r = 0.51; P = 0.001), oxy[Hb + Mb] (r = -0.50; P = 0.001), SmO2 (r = -0.54; P = 0.001), and totHb amplitude (r = 0.61; P < 0.001). No significant correlations were found when using relative oxy[Hb + Mb] (r = -0.01; P = 0.957) and deoxy[Hb + Mb] (r = -0.02; P = 0.895). Based on these findings, caution is advised when using NIRS values, as subcutaneous ATT and O2 delivery significantly influence NIRS measurements. To eliminate these influences, use of relative deoxy[Hb + Mb] is advised, especially in clinical settings or in people with a higher subcutaneous ATT layer.

A study was conducted to establish a method for quantitative evaluation of both the rate and degree of muscle oxygenation during ramp exercise using Near Infrared Spectroscopy (NIRS), and to determine the relationship of the indices to body composition and physical fitness. The subjects were 13 healthy men. After a warm-up period of 3 min at 20-W, the ramp exercise test was conducted. The exercise consisted of an increasing work rate at a slope of 20 W/min on a cycle ergometer performed until volitional fatigue. The NIRS probe used in the cycling exercise was placed on the vastus lateralis muscle. After 30 min of exercise, calibration was performed by cuff occlusion for 10 min with a pressure of 260 mmHg for quantitative determination of the NIRS curve. The oxygenation curve measured by NIRS during the exercise initially exhibited a linear decrease as the work rate increased. This rate of decrease in oxygenation was indicated by the NIRS slope (%/W) obtained from the calibration curve. In later stages of the exercise, the NIRS curve became flattened with increased work rate. The breaking point between the sloping phase and the flat phase was named the “NIRS Threshold 2, NT 2”. In addition, the rate of decrease in oxygenation at the end of exercise per maximal NIRS decrease obtained from the calibration curve was indicated as the %NIRS fall. The mean NIRS slope and %NIRS fall were 0.3±0.1%/W (range, 0.13 to 0.50%/W) and 29.9±11.8% (range, 12.0 to 50.0%), respectively. NT 2 was observed in 8 of the 13 subjects. The subjects were divided into two groups (NT 2 (+) and NT 2 (-) ) based on the appearance of NT 2. Both the NIRS slope and %NIRS fall in the NT 2 (+) group were significantly higher than those in the NT 2 (-) group. The NIRS slope was significantly correlated with VO2/wt at VT (r=0.73, p<0.05) and wattage at VT (r=0.86, p<0.0001) . The %NIRS fall was significantly correlated with VO2/wt at peak (r=0.80, P<0.001) . The NIRS slope and %NIRS fall were not significantly correlated with body mass index, %fat or thigh circumference.These findings suggest that the NIRS slope indicates the efficiency of oxygen exchange in muscles activated during incremental exercise, and that the %NIRS fall indicates the ability to utilize Oxy-Hb+Mb against maximal oxygenation capacity in muscles. The NIRS slope and %NIRS fall can therefore be used as indices of muscular limitation during exercise, and as indices of muscular adaptation during exercise.

Monitoring phase transition in adipose tissue and formation of lipid crystals is important in Cryo-procedures such as cryosurgery or Selective Cryolipolysis (SC). In this work, we exploited a Near-Infrared Spectroscopy (NIRS) method to monitor the onset of fat freezing/melting. Concurrent measurements using frequency domain NIRS and MR Spectroscopy during cooling/heating were performed on an in vitro porcine skin sample with a thick subcutaneous fat layer in a human MR scanner. The NIRS probe was placed on the skin measuring the average optical scattering of the fatty layer. Two fiber optic temperature probes were inserted in the area of the MRS and NIRS measurements. To further investigate the microscopic features of the phase-transition, an identical cooling/heating procedure was replicated on the same fat tissue while being imaged by Optical Coherence Tomography. The temperature relationships of optical scattering, MRS peak characteristics and OCT reflection intensity were analyzed to find signatures related to the onset of phase transition. The optical scattering in the fatty tissues decreases during the heating and increases by cooling. However, there is an inflexion in the rate of change of the scattering while the phase transition happens in the fatty layer. The methylene fat peaks on the MR Spectrum are also shown to be broadened during the cooling. OCT intensity displays a sharp increase at the transition temperature. The results from multiple samples show two transition points around 5-10 ˚C (cooling) and 15-20 ˚C (heating) through all three methods, demonstrating that adipose tissue phase change can be monitored non-invasively.

EMG breakpoints for detecting anaerobic threshold and respiratory compensation point in recovered COVID-19 patients