Oxygen uptake and heart rate kinetics during dynamic upper and lower body exercise: an investigation by time-series analysis

Cardiorespiratory kinetics in exercise physiology: estimates and predictions using randomized changes in work rate

Purpose

Kinetics of cardiorespiratory parameters (CRP) in response to work rate (WR) changes are evaluated by pseudo-random binary sequences (PRBS testing). In this study, two algorithms were applied to convert responses from PRBS testing into appropriate impulse responses to predict steady states values and responses to incremental increases in exercise intensity.

Methods

13 individuals (age: 41 ± 9 years, BMI: 23.8 ± 3.7 kg m⁻²), completing an exercise test protocol, comprising a section of randomized changes of 30 W and 80 W (PRBS), two phases of constant WR at 30 W and 80 W and incremental WR until subjective fatigue, were included in the analysis. Ventilation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}_{{\text{E}}}$$\end{document}V˙E), O₂ uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2), CO₂ output (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{CO}}_{2}$$\end{document}V˙CO2) and heart rate (HR) were monitored. Impulse responses were calculated in the time domain and in the frequency domain from the cross-correlations of WR and the respective CRP.

Results

The algorithm in the time domain allows better prediction for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{CO}}_{2}$$\end{document}V˙CO2, whereas for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}_{{\text{E}}}$$\end{document}V˙E and HR the results were similar for both algorithms. Best predictions were found for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2 and HR with higher (3–4%) 30 W steady states and lower (1–4%) values for 80 W. Tendencies were found in the residuals between predicted and measured data.

Conclusion

The CRP kinetics, resulting from PRBS testing, are qualified to assess steady states within the applied WR range. Below the ventilatory threshold, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2 and HR responses to incrementally increasing exercise intensities can be sufficiently predicted.

Confinement, partial sleep deprivation and defined physical activity-influence on cardiorespiratory regulation and capacity

INTRODUCTION

Adequate cardiorespiratory fitness is of utmost importance during spaceflight and should be assessable via moderate work rate intensities, e.g., using kinetics parameters. The combination of restricted sleep, and defined physical exercise during a 45-day simulated space mission is expected to slow heart rate (HR) kinetics without changes in oxygen uptake ([Formula: see text]) kinetics.

METHODS

Overall, 14 crew members (9 males, 5 females, 37 ± 7 yrs, 23.4 ± 3.5 kg m^-2) simulated a 45-d-mission to an asteroid. During the mission, the sleep schedule included 5 nights of 5 h and 2 nights of 8 h sleep. The crew members were tested on a cycle ergometer, using pseudo-random binary sequences, changing between 30 and 80 W on day 8 before (MD-8), day 22 (MD22) and 42 (MD42) after the beginning and day 4 (MD + 4) following the end of the mission. Kinetics information was assessed using the maxima of cross-correlation functions (CCF_max). Higher CCF_max indicates faster responses.

RESULTS

CCF_max(HR) was significantly (p = 0.008) slower at MD-8 (0.30 ± 0.06) compared with MD22 (0.36 ± 0.06), MD42 (0.38 ± 0.06) and MD + 4 (0.35 ± 0.06). Mean HR values during the different work rate steps were higher at MD-8 and MD + 4 compared to MD22 and MD42 (p < 0.001).

DISCUSSION

The physical training during the mission accelerated HR kinetics, but had no impact on mean HR values post mission. Thus, HR kinetics seem to be sensitive to changes in cardiorespiratory fitness and may be a valuable parameter to monitor fitness. Kinetics and capacities adapt independently in response to confinement in combination with defined physical activity and sleep.

Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians' decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients' cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected "breath by breath" by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.

The Fuzzy Kinetics Index: an indicator conflating cardiorespiratory kinetics during dynamic exercise

Purpose

The aim of the present study was to develop a novel index using fuzzy logic procedures conflating cardiorespiratory and pulmonary kinetics during dynamic exercise as a representative indicator for exercise tolerance.

Methods

Overall 69 data sets were re-analyzed: (age: 29 ± 1.2 y [mean ± SEM], height: 179 ± 1.0 cm; body mass: 78 ± 1.4 kg; peak oxygen uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙˙O_2peak): 48 ± 1.1 ml·min⁻¹·kg⁻¹), that comprised pseudo random binary sequence work rate (WR) changes between 30 and 80 W on a cycle ergometer, with additional voluntary exhaustion to estimate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2peak. Heart rate (HR), stroke volume (SV) and gas exchange (pulmonary oxygen uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2pulm)) were measured beat-to-beat and breath-by-breath, respectively. For estimation of muscle oxygen uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2musc) kinetics and for the analysis of kinetic responses of the parameters of interest (perfusion (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{Q}}$$\end{document}Q˙ = HR·SV), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2pulm, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2musc) the approach of Hoffmann et al. (2013) was applied. For calculation of the Fuzzy Kinetics Index \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{Q}}$$\end{document}Q˙, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2pulm, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2musc were used as input variables for the subsequent fuzzy- and defuzzyfication procedures.

Results

For both absolute and relative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2peak a significant correlation has been observed with FKI, whereas the correlation coefficient is higher for relative (r = 0.430; p < 0.001; n = 69) compared to absolute \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2peak (r = 0.358; p < 0.01; n = 69). No significant correlations have been found between FKI and age, height or body mass (p > 0.05 each).

Conclusions

The significant correlations between FKI and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot {\text{V}}$$\end{document}V˙O_2peak represent a physiological connection between the regulatory and the capacitive system and its exercise performance. In turn, the application of FKI can serve as an indicator for healthy participants to assess exercise tolerance and sport performance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00421-021-04611-w.

The effect of test modality on dynamic exercise biomarkers in children, adolescents, and young adults

Cardiopulmonary exercise testing (CPET) modalities, treadmill (TM), and cycle ergometer (CE), influence maximal gas exchange and heart rate (HR) responses. Little is known regarding CPET modality effect on submaximal biomarkers during childhood and adolescence. Ninety‐four healthy participants (7–34 y.o., 53% female) performed TM and CE CPET to address two major gaps: (1) the effect of modality on submaximal CPET biomarkers, and (2) estimation of work rate in TM CPET. Breath‐by‐breath gas exchange enabled calculation of linear regression slopes such as V˙O₂/ΔHR and ΔV˙E/ΔV˙CO₂. Lean body mass (LBM) was measured with dual X‐ray absorptiometry. We tested a novel TM CPET estimate of work rate based on TM velocity², incline, and body mass (VIM). Like the linear relationship between V˙O₂ and work rate in CE CPET, V˙O₂ increased linearly with TM VIM. TM ΔV˙O₂/ΔHR was highly correlated with CE (r = 0.92), and each increased substantially with LBM (P < 0.0001 for TM and CE). ΔV˙O₂/ΔHR was to a small (~8.7%) but significant extent larger in TM (1.6 mL/min/beat, P = 0.04). In contrast, TM and CE ΔV˙E/ΔV˙CO₂ decreased significantly with LBM, supporting earlier observations from CE CPET. For both CE and TM, males had significantly higher ΔV˙O₂/ΔHR but lower ΔV˙E/ΔV˙CO₂ than females. Novel TM CPET biomarkers such as ΔVIM/ΔHR and ∆V˙O₂/ΔVIM paralleled effects of LBM observed in CE CPET. TM and CE CPET submaximal biomarkers are not interchangeable, but similarly reflect maturation during critical periods. CPET analysis that utilizes data actually measured (rather than estimated) may improve the clinical value of TM and CE CPET.

Simplified indices of exercise tolerance in patients with multiple sclerosis and healthy subjects: A case-control study

Among patients with multiple sclerosis (MS), the impairment of exercise tolerance is closely related to disability. Maximal oxygen uptake (VO_2max ) is the gold standard to assess exercise tolerance in healthy subjects (HS). Among patients with MS, the accuracy of VO_2max measurement is often impaired because the patients are unable to reach the maximal exercise intensity due to interdependent factors linked to the disease (such as pathological fatigue, pain, lack of exercise habit, and lack of mobility). This study assesses the accuracy of simplified indices for assessing exercise tolerance, which are more suitable in patients with MS. They are simple in the way they are either measurable during submaximal exercise (oxygen uptake efficiency slopes (OUES), physical working capacity at 75% of maximal heart rate (PWC_75% ), oxygen consumption at a respiratory exchange ratio of 1 (VO₂ @RER1)) or not based on gas exchange analysis (peak work rate (PWR)-based predictive equation and PWC_75% ). All indices were significantly lower in the MS group compared to the HS group (P < .001). OUES appeared highly correlated (r > .70, P < .001) with VO_2peak , in both groups, without difference between groups. PWR-based prediction of VO_2peak showed a standard error of the estimate of 315 mL min^-1 in HS and 176 mL min^-1 in MS. PWC_75% did not correlate to VO_2peak in neither group. These findings suggest an impairment of exercise tolerance functions in mildly disabled persons with MS, independently from other factors. Submaximal indices involving gas exchange analysis or peakWR-based estimation of VO_2peak are usable to accurately assess exercise tolerance.

Temporal dissociation between muscle and pulmonary oxygen uptake kinetics: influences of perfusion dynamics and arteriovenous oxygen concentration differences in muscles and lungs

PURPOSE

The aim of the study was to test whether or not the arteriovenous oxygen concentration difference (avDO₂) kinetics at the pulmonary (avDO₂pulm) and muscle (avDO₂musc) levels is significantly different during dynamic exercise.

METHODS

A re-analysis involving six publications dealing with kinetic analysis was utilized with an overall sample size of 69 participants. All studies comprised an identical pseudorandom binary sequence work rate (WR) protocol-WR changes between 30 and 80 W-to analyze the kinetic responses of pulmonary ([Formula: see text]) and muscle ([Formula: see text]) oxygen uptake kinetics as well as those of avDO₂pulm and avDO₂musc.

RESULTS

A significant difference between [Formula: see text] (0.395 ± 0.079) and [Formula: see text] kinetics (0.330 ± 0.078) was observed (p < 0.001), where the variables showed a significant relationship (r_SP = 0.744, p < 0.001). There were no significant differences between avDO₂musc (0.446 ± 0.077) and avDO₂pulm kinetics (0.451 ± 0.075), which are highly correlated (r = 0.929, p < 0.001).

CONCLUSION

It is suggested that neither avDO₂pulm nor avDO₂musc kinetic responses seem to be responsible for the differences between estimated [Formula: see text] and measured [Formula: see text] kinetics. Obviously, the conflation of avDO₂ and perfusion ([Formula: see text] ) at different points in time and at different physiological levels drive potential differences in [Formula: see text] and [Formula: see text] kinetics. Therefore, [Formula: see text] should, in general, be considered whenever oxygen uptake kinetics are analyzed or discussed.

Oxygen Uptake Kinetics Is Slower in Swimming Than Arm Cranking and Cycling during Heavy Intensity

Oxygen uptake (V·O2) kinetics has been reported to be influenced by the activity mode. However, only few studies have compared V·O₂ kinetics between activities in the same subjects in which they were equally trained. Therefore, this study compared the V·O₂ kinetics response to swimming, arm cranking, and cycling within the same group of subjects within the heavy exercise intensity domain. Ten trained male triathletes (age 23.2 ± 4.5 years; height 180.8 ± 8.3 cm; weight 72.3 ± 6.6 kg) completed an incremental test to exhaustion and a 6-min heavy constant-load test in the three exercise modes in random order. Gas exchange was measured by a breath-by-breath analyzer and the on-transient V·O₂ kinetics was modeled using bi-exponential functions. V·O_2peak was higher in cycling (65.6 ± 4.0 ml·kg⁻¹·min⁻¹) than in arm cranking or swimming (48.7 ± 8.0 and 53.0 ± 6.7 ml·kg⁻¹·min⁻¹; P < 0.01), but the V·O₂ kinetics were slower in swimming (τ₁ = 31.7 ± 6.2 s) than in arm cranking (19.3 ± 4.2 s; P = 0.001) and cycling (12.4 ± 3.7 s; P = 0.001). The amplitude of the primary component was lower in both arm cranking and swimming (21.9 ± 4.7 and 28.4 ± 5.1 ml·kg⁻¹·min⁻¹) compared with cycling (39.4 ± 4.1 ml·kg⁻¹·min⁻¹; P = 0.001). Although the gain of the primary component was higher in arm cranking compared with cycling (15.3 ± 4.2 and 10.7 ± 1.3 ml·min⁻¹·W⁻¹; P = 0.02), the slow component amplitude, in both absolute and relative terms, did not differ between exercise modes. The slower V·O₂ kinetics during heavy-intensity swimming is exercise-mode dependent. Besides differences in muscle mass and greater type II muscle fibers recruitment, the horizontal position adopted and the involvement of trunk and lower-body stabilizing muscles could be additional mechanisms that explain the differences between exercise modalities.

Gas exchange kinetics following concentric-eccentric isokinetic arm and leg exercise

PURPOSE

To evaluate the effects of exercise velocity (60, 150, 240deg∙s^-1) and muscle mass (arm vs leg) on changes in gas exchange and arterio-venous oxygen content difference (avDO₂) following high-intensity concentric-eccentric isokinetic exercise.

METHODS

Fourteen subjects (26.9±3.1years) performed a 3×20-repetition isokinetic exercise protocol. Recovery beat-to-beat cardiac output (CO) and breath-by-breath gas exchange were recorded to determine post-exercise half-time (t_1/2) for oxygen uptake (V˙O₂pulm), carbon dioxide output (V˙CO₂pulm), and ventilation (V˙_E).

RESULTS

Significant differences of the t_1/2 values were identified between 60 and 150deg∙s^-1. Significant differences in the t_1/2 values were observed between V˙O₂pulm and V˙CO₂pulm and between V˙CO₂pulm and V˙_E. The time to attain the first avDO₂-peak showed significant differences between arm and leg exercise.

CONCLUSIONS

The present study illustrates, that V˙O₂pulm kinetics are distorted due to non-linear CO dynamics. Therefore, it has to be taken into account, that V˙O₂pulm may not be a valuable surrogate for muscular oxygen uptake kinetics in the recovery phases.

Faster heart rate and muscular oxygen uptake kinetics in type 2 diabetes patients following endurance training

Cardiorespiratory kinetics were analyzed in type 2 diabetes patients before and after a 12-week endurance exercise-training intervention. It was hypothesized that muscular oxygen uptake and heart rate (HR) kinetics would be faster after the training intervention and that this would be detectable using a standardized work rate protocol with pseudo-random binary sequences. The cardiorespiratory kinetics of 13 male sedentary, middle-aged, overweight type 2 diabetes patients (age, 60 ± 8 years; body mass index, 33 ± 4 kg·m^-2) were tested before and after the 12-week exercise intervention. Subjects performed endurance training 3 times a week on nonconsecutive days. Pseudo-random binary sequences exercise protocols in combination with time series analysis were used to estimate kinetics. Greater maxima in cross-correlation functions (CCF_max) represent faster kinetics of the respective parameter. CCF_max of muscular oxygen uptake (pre-training: 0.31 ± 0.03; post-training: 0.37 ± 0.1, P = 0.024) and CCF_max of HR (pre-training: 0.25 ± 0.04; post-training: 0.29 ± 0.06, P = 0.007) as well as peak oxygen uptake (pre-training: 24.4 ± 4.7 mL·kg^-1·min^-1; post-training: 29.3 ± 6.5 mL·kg^-1·min^-1, P = 0.004) increased significantly over the course of the exercise intervention. In conclusion, kinetic responses to changing work rates in the moderate-intensity range are similar to metabolic demands occurring in everyday habitual activities. Moderate endurance training accelerated the kinetic responses of HR and muscular oxygen uptake. Furthermore, the applicability of the used method to detect these accelerations was demonstrated.

V̇O2 and HR kinetics before and after International Space Station missions

PURPOSE

Heart rate (HR), pulmonary and muscle oxygen uptake ([Formula: see text]O2pulm, [Formula: see text]O2musc) kinetics after changes of work rate (WR) indicate regulatory characteristics related to aerobic metabolism. We analysed whether the kinetics of HR, [Formula: see text]O2pulm and [Formula: see text]O2musc are slowed after missions to the International Space Station (ISS). The changes of the kinetics were correlated with [Formula: see text]O2peak data.

METHODS

10 astronauts [4 females, 6 males, age: 48.0 ± 3.8 years, height: 176 ± 7 cm, mass: 74.5 ± 15.9 kg (mean ± SD)] performed an incremental test to determine [Formula: see text]O2peak (before missions on L-110 days, after return on R+1/+10/+36 days), and a cardio-respiratory kinetics test (CRKT) with randomized 30-80 W WR changes to determine HR, [Formula: see text]O2pulm and [Formula: see text]O2musc kinetics by time-series analysis (L-236/-73, R+6/+21). Kinetics were summarized by maximum and related lag of cross-correlation function (CCFmax, CCFlag) of WR with the analysed parameter.

RESULTS

Statistically, significant changes were also found for CCFmax([Formula: see text]O2musc) between L-236 and R+6 (P = 0.010), L-236 and R+21 (P = 0.030), L-72 and R+6 (P = 0.043). Between pre-to-post mission change in [Formula: see text]O2peak and CCFmax(HR), a correlation was shown (r SP = 0.67, P = 0.017).

CONCLUSION

The [Formula: see text]O2musc kinetics changes indicate aerobic detraining effects which are present up to 21 days following space flight. The correlations between changes in [Formula: see text]O2peak and HR kinetics illustrate the key role of cardiovascular regulation in [Formula: see text]O2peak. The addition of CRKT to ISS flight is recommended to obtain information regarding the potential muscular and cardiovascular deconditioning. This allows a reduction in the frequency of higher intensity testing during flight.