A critical review of the history of low- to moderate-intensity steady-state VO2 kinetics

Assessment of Cardiorespiratory and Metabolic Contributions in an Extreme Intensity CrossFit® Benchmark Workout

Our purpose was to characterize the oxygen uptake kinetics (VO2), energy systems contributions and total energy expenditure during a CrossFit® benchmark workout performed in the extreme intensity domain. Fourteen highly trained male CrossFitters, aged 28.3 ± 5.4 years, with height 177.8 ± 9.4 cm, body mass 87.9 ± 10.5 kg and 5.6 ± 1.8 years of training experience, performed the Isabel workout at maximal exertion. Cardiorespiratory variables were measured at baseline, during exercise and the recovery period, with blood lactate and glucose concentrations, including the ratings of perceived exertion, measured pre- and post-workout. The Isabel workout was 117 ± 10 s in duration and the VO2 peak was 47.2 ± 4.7 mL·kg-1·min-1, the primary component amplitude was 42.0 ± 6.0 mL·kg-1·min-1, the time delay was 4.3 ± 2.2 s and the time constant was 14.2 ± 6.0 s. The accumulated VO2 (0.6 ± 0.1 vs. 4.8 ± 1.0 L·min-1) value post-workout increased substantially when compared to baseline. Oxidative phosphorylation (40%), glycolytic (45%) and phosphagen (15%) pathways contributed to the 245 ± 25 kJ total energy expenditure. Despite the short ~2 min duration of the Isabel workout, the oxygen-dependent and oxygen-independent metabolism energy contributions to the total metabolic energy release were similar. The CrossFit® Isabel requires maximal effort and the pattern of physiological demands identifies this as a highly intensive and effective workout for developing fitness and conditioning for sports.

Validation of Oura ring energy expenditure and steps in laboratory and free-living

BACKGROUND

Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living.

METHODS

Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count.

RESULTS

Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits.

CONCLUSION

This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

Training status affects between-protocols differences in the assessment of maximal aerobic velocity

Purpose

Continuous incremental protocols (CP) may misestimate the maximum aerobic velocity (V_max) due to increases in running speed faster than cardiorespiratory/metabolic adjustments. A higher aerobic capacity may mitigate this issue due to faster 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}$$\dot{V}$$\end{document}V˙O₂) kinetics. Therefore, this study aimed to compare three different protocols to assess V_max in athletes with higher or lower training status.

Methods

Sixteen well-trained runners were classified according to higher (HI) or lower (LO) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O_2max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂-kinetics was calculated across four 5-min running bouts at 10 km·h⁻¹. Two CPs [1 km·h⁻¹ per min (CP1) and 1 km·h⁻¹ every 2-min (CP2)] were performed to determine V_max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O_2max, lactate-threshold and submaximal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂/velocity relationship. Results were compared to the discontinuous incremental protocol (DP).

Results

V_max, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O_2max, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙CO₂ and VE were higher [(P < 0.05,(ES:0.22/2.59)] in HI than in LO. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂-kinetics was faster [P < 0.05,(ES:-2.74/ − 1.76)] in HI than in LO. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂/velocity slope was lower in HI than in LO [(P < 0.05,(ES:-1.63/ − 0.18)]. V_max and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂/velocity slope were CP1 > CP2 = DP for HI and CP1 > CP2 > DP for LO. A lower [P < 0.05,(ES:0.53/0.75)] V_max-difference for both CP1 and CP2 vs DP was found in HI than in LO. V_max-differences in CP1 vs DP showed a large inverse correlation with V_max, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O_2max and lactate-threshold and a very large correlation with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂-kinetics.

Conclusions

Higher aerobic training status witnessed by faster \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂ kinetics led to lower between-protocol V_max differences, particularly between CP2 vs DP. Faster kinetics may minimize the mismatch issues between metabolic and mechanical power that may occur in CP. This should be considered for exercise prescription at different percentages of V_max.

Measurement of Physical Activity by Shoe-Based Accelerometers-Calibration and Free-Living Validation

There is conflicting evidence regarding the health implications of high occupational physical activity (PA). Shoe-based accelerometers could provide a feasible solution for PA measurement in workplace settings. This study aimed to develop calibration models for estimation of energy expenditure (EE) from shoe-based accelerometers, validate the performance in a workplace setting and compare it to the most commonly used accelerometer positions. Models for EE estimation were calibrated in a laboratory setting for the shoe, hip, thigh and wrist worn accelerometers. These models were validated in a free-living workplace setting. Furthermore, additional models were developed from free-living data. All sensor positions performed well in the laboratory setting. When the calibration models derived from laboratory data were validated in free living, the shoe, hip and thigh sensors displayed higher correlation, but lower agreement, with measured EE compared to the wrist sensor. Using free-living data for calibration improved the agreement of the shoe, hip and thigh sensors. This study suggests that the performance of a shoe-based accelerometer is similar to the most commonly used sensor positions with regard to PA measurement. Furthermore, it highlights limitations in using the relationship between accelerometer output and EE from a laboratory setting to estimate EE in a free-living setting.

Reexamination of Accelerometer Calibration with Energy Expenditure as Criterion: VO2net Instead of MET for Age-Equivalent Physical Activity Intensity

Accelerometer calibration for physical activity (PA) intensity is commonly performed using Metabolic Equivalent of Task (MET) as criterion. However, MET is not an age-equivalent measure of PA intensity, which limits the use of MET-calibrated accelerometers for age-related PA investigations. We investigated calibration using VO_2net (VO_2gross - VO_2stand; mL⋅min^-1⋅kg^-1) as criterion compared to MET (VO_2gross/VO_2rest) and the effect on assessment of free-living PA in children, adolescents and adults. Oxygen consumption and hip/thigh accelerometer data were collected during rest, stand and treadmill walk and run. Equivalent speed (Speed_eq) was used as indicator of the absolute speed (Speed_abs) performed with the same effort in individuals of different body size/age. The results showed that VO_2net was higher in younger age-groups for Speed_abs, but was similar in the three age-groups for Speed_eq. MET was lower in younger age-groups for both Speed_abs and Speed_eq. The same VO_2net-values respective MET-values were applied to all age-groups to develop accelerometer PA intensity cut-points. Free-living moderate-and-vigorous PA was 216, 115, 74 and 71 min/d in children, adolescents, younger and older adults with VO_2net-calibration, but 140, 83, 74 and 41 min/d with MET-calibration, respectively. In conclusion, VO_2net calibration of accelerometers may provide age-equivalent measures of PA intensity/effort for more accurate age-related investigations of PA in epidemiological research.

Concordance between 13 C:12 C ratio technique respect to indirect calorimetry to estimate carbohydrate and Fat oxidation rates by means stoichiometric equations during exercise. A reliability and agreement study

Indirect calorimetry is a tool used routinely by sport/exercise physiologist to assess the metabolic response to training and to nutritional interventions. There are different stoichiometric equations to estimate fat (FatOxR ) and carbohydrates (CHOOxR ) oxidation rates, however there are not enough information in literature about what are the most accurate equations. The purpose of this study was to determine the concordance between indirect calorimetry and a method of reference for stoichiometric equations used to estimate FatOxR and CHOOxR . Concordance between indirect calorimetry and the method of reference (13 C to 12 C ratio (13 C:12 C ratio) technique) for key stoichiometric equations was assessed in well-trained triathletes. Subjects carried out a carbohydrate depletion-repletion protocol, labeling the glycogen stores with 13 C, and a laboratory test to assess the 13 C metabolic response during a wide range of aerobic intensities during exercise. All the equations showed a narrow agreement interval (Δ) (CHOOxR nPC (protein component negligible): -0.308, 0.308, CHOOxR PC (protein component): -0.268, 0.268, FatOxR nPC and PC: -0.032, 0.032 (g·min-1 )). FatOxR showed a similar concordance (28-32%) with CHOOxR nPC ranging from 55% to 75%, and for CHOOxR PC between 51% to 71%. None of the stoichiometric equations met a perfect agreement with the method of reference. The Jeukendrup and Wallis equation showed the best concordance for CHOOxR nPC whilst the Frayn and Ferrannini (Glu) equations had the best agreement for CHOOxR PC. All FatOxR equations showed similar concordances and they are able to be used indistinctly.

Moderate-Intensity Oxygen Uptake Kinetics: Is a Mono-Exponential Function Always Appropriate to Model the Response?

PURPOSE

This study investigated the existence of the oxygen uptake ([Formula: see text]) overshoot and the effects of exercise intensity and fitness status on the [Formula: see text] response during moderate-intensity exercise.

METHODS

Twelve "high-fitness" (M_age = 26 ± 5 years; M_height = 184.1 ± 5.4 cm; M_{body mass} = 76.6 ± 8.9 kg; mean peak oxygen uptake ([Formula: see text]peak) = 59.0 ± 3.3 mL·kg^-1·min·^-1) and 11 "moderate-fitness" (M_age = 29 ± 5 years; M_height = 178.7 ± 7.5 cm; M_{body mass} = 81.7 ± 10.9 kg; M_V̇O2peak = 45.2 ± 3.1 mL·kg^-1·min·^-1) participants performed square-wave transitions from unloaded cycling to 3 different intensities (70%, 82.5%, and 95% of the gas exchange threshold). The data were modeled using both a mono-exponential function (Model 1) and a function that included a switch-on component (Model 2). The overshoot was computed by subtracting the steady state from the peak of the modeled response and by calculating the area of the curve that was above steady state.

RESULTS

The goodness of fit was affected by model type (p = .002) and exercise intensity (p < .001). High-fitness participants displayed a smaller τ (p < .05) and a larger amplitude (p < .05) and were more likely to overshoot the steady state (p = .035). However, while exercise intensity did affect the amplitude (p < .001), it did not affect τ (p ≥ .05) or the likelihood of an overshoot occurring (p = .389).

CONCLUSION

While exercise intensity did not alter the [Formula: see text] response, fitness status affected τ and the likelihood of an overshoot occurring. The overshoot questions the traditional approach to modeling moderate-intensity [Formula: see text] data.

Impact of 60 days of 6° head down tilt bed rest on muscular oxygen uptake and heart rate kinetics: efficacy of a reactive sledge jump countermeasure

PURPOSE

The effects of 60 days of head down tilt bed rest (HDBR) with and without the application of a reactive jump countermeasure were investigated, using a method which enables to discriminate between pulmonary ([Formula: see text]O₂pulm) and muscular ([Formula: see text]O₂musc) oxygen uptake kinetics to control for hemodynamic influences.

METHODS

22 subjects were randomly allocated to either a group performing a reactive jumps countermeasure (JUMP; n = 11, male, 29 ± 7 years, 23.9 ± 1.3 kg m^- 2) or a control group (CTRL; n = 11, male, 29 ± 6 years, 23.3 ± 2.0 kg m^- 2). Heart rate (HR) and [Formula: see text]O₂pulm were measured in response to repeated changes in work rate between 30 and 80 W before (BDC-9) and two times after HDBR (R+ 2, R+ 13). Kinetic responses of HR, [Formula: see text]O₂pulm, and [Formula: see text]O₂musc were assessed applying time series analysis. Higher maxima in cross-correlation functions (CCF_max(x)) between work rate and the respective parameter indicate faster kinetics responses. Statistical analysis was performed applying multifactorial analysis of variance.

RESULTS

CCF_max([Formula: see text]O₂musc) and CCF_max([Formula: see text]O₂pulm) were not significantly different before and after HDBR (P > 0.05). CCF_max(HR) decreased following bed rest (JUMP: BDC-9: 0.30 ± 0.09 vs. R+ 2: 0.28 ± 0.06 vs. R+13: 0.28 ± 0.07; CTRL: 0.35 ± 0.09 vs. 0.27 ± 0.06 vs. 0.33 ± 0.07 P = 0.025). No significant differences between the groups were observed (P > 0.05). Significant alterations were found for CCF_max of mean arterial blood pressure (mBP) after HDBR (JUMP: BDC-9: 0.21 ± 0.07 vs. R+ 2: 0.30 ± 0.13 vs. R+ 13: 0.28 ± 0.08; CTRL: 0.25 ± 0.07 vs. 0.38 ± 0.13 vs. 0.28 ± 0.08; P = 0.008).

CONCLUSIONS

Despite hemodynamic changes, [Formula: see text]O₂ kinetics seem to be preserved for a longer period of HDBR, even without the application of a countermeasure.

New Methods for Processing and Quantifying VO2 Kinetics to Steady State: VO2 Onset Kinetics

Current methods of oxygen uptake (VO₂) kinetics data handling may be too simplistic for the complex physiology involved in the underlying physiological processes. Therefore, the aim of this study was to quantify the VO₂ kinetics to steady state across the full range of sub-ventilatory threshold work rates, with a particular focus on the VO₂ onset kinetics. Ten healthy, moderately trained males participated in five bouts of cycling. Each bout involved 10 min at a percentage of the subject's ventilation threshold (30, 45, 60, 75, 90%) from unloaded cycling. The VO₂ kinetics was quantified using the conventional mono-exponential time constant (tau, τ), as well as the new methods for VO₂ onset kinetics. Compared to linear modeling, non-linear modeling caused a deterioration of goodness of fit (main effect, p < 0.001) across all exercise intensities. Remainder kinetics were also improved using a modified application of the mono-exponential model (main effect, p < 0.001). Interestingly, the slope from the linear regression of the onset kinetics data is similar across all subjects and absolute exercise intensities, and thereby independent of subject fitness and τ. This could indicate that there are no functional limitations between subjects during this onset phase, with limitations occurring for the latter transition to steady state. Finally, the continuing use of mono-exponential modeling could mask important underlying physiology of more instantaneous VO₂ responses to steady state. Consequently, further research should be conducted on this new approach to VO₂ onset kinetics.

Lessons from Popper for science, paradigm shifts, scientific revolutions and exercise physiology

A connection has been made to the possible role of the central governor model (CGM) to be a paradigm shift within the exercise sciences. Unfortunately, very little evidence was presented to support this notion, and a narrow view of scientific philosophy was used to reflect on the role of the CGM in understanding exercise physiology and the pursuit of a more ideal scientific method. When contrasting the scientific philosophies of Kuhn to Popper, and applying the tenant of falsification to the research and commentary on the CGM, it is probable that the scholarship pertaining to the CGM adheres more to pseudoscience than science. To improve the scientific contributions of research on the CGM, fellow scientists need to adopt a more critical platform where questions are raised and research designs are employed in efforts to refute the theory. The inability to falsify a theory is the most meaningful way to prove that it is likely to be correct. To support this development, the CGM needs to be more carefully worded to form a theory that clearly reveals key features that can be researched and potentially falsified. In addition, the wording of the CGM needs to allow scientists to make predictions that can then be tested in controlled experimental research studies. Until this happens for the CGM and all other pertinent paradigms within exercise physiology, the discipline will never rise out of the abyss of normal science to extraordinary science involving paradigm shifts and scientific revolutions.

Data collection, handling, and fitting strategies to optimize accuracy and precision of oxygen uptake kinetics estimation from breath-by-breath measurements

Phase 2 pulmonary oxygen uptake kinetics (ϕ2 τV̇o_2P) reflect muscle oxygen consumption dynamics and are sensitive to changes in state of training or health. This study identified an unbiased method for data collection, handling, and fitting to optimize V̇o_2P kinetics estimation. A validated computational model of V̇o_2P kinetics and a Monte Carlo approach simulated 2 × 10⁵ moderate-intensity transitions using a distribution of metabolic and circulatory parameters spanning normal health. Effects of averaging (interpolation, binning, stacking, or separate fitting of up to 10 transitions) and fitting procedures (biexponential fitting, or ϕ2 isolation by time removal, statistical, or derivative methods followed by monoexponential fitting) on accuracy and precision of V̇o_2P kinetics estimation were assessed. The optimal strategy to maximize accuracy and precision of τV̇o_2P estimation was 1-s interpolation of 4 bouts, ensemble averaged, with the first 20 s of exercise data removed. Contradictory to previous advice, we found optimal fitting procedures removed no more than 20 s of ϕ1 data. Averaging method was less critical: interpolation, binning, and stacking gave similar results, each with greater accuracy compared with analyzing repeated bouts separately. The optimal procedure resulted in ϕ2 τV̇o_2P estimates for transitions from an unloaded or loaded baseline that averaged 1.97 ± 2.08 and 1.04 ± 2.30 s from true, but were within 2 s of true in only 47-62% of simulations. Optimized 95% confidence intervals for τV̇o_2P ranged from 4.08 to 4.51 s, suggesting a minimally important difference of ~5 s to determine significant changes in τV̇o_2P during interventional and comparative studies.NEW & NOTEWORTHY We identified an unbiased method to maximize accuracy and precision of oxygen uptake kinetics (τV̇o_2P) estimation. The optimum number of bouts to average was four; interpolation, bin, and stacking averaging methods gave similar results. Contradictory to previous advice, we found that optimal fitting procedures removed no more than 20 s of phase 1 data. Our data suggest a minimally important difference of ~5 s to determine significant changes in τV̇o_2P during interventional and comparative studies.

The influence of metabolic and circulatory heterogeneity on the expression of pulmonary oxygen uptake kinetics in humans

We examined the relationship amongst baseline work rate (WR), phase II pulmonary oxygen uptake (V̇(O2p)) time constant (τV̇(O2p)) and functional gain (G(P)=ΔV̇(O2p)/ΔWR) during moderate-intensity exercise. Transitions were initiated from a constant or variable baseline WR. A validated circulatory model was used to examine the role of heterogeneity in muscle metabolism (V̇(O2m)) and blood flow (Q̇(m)) in determining V̇(O2p) kinetics. We hypothesized that τV̇(O2p) and G(P) would be invariant in the constant baseline condition but would increase linearly with increased baseline WR. Fourteen men completed three to five repetitions of ∆40 W step transitions initiated from 20, 40, 60, 80, 100 and 120 W on a cycle ergometer. The ∆40 W step transitions from 60, 80, 100 and 120 W were preceded by 6 min of 20 W cycling, from which the progressive ΔWR transitions (constant baseline condition) were examined. The V̇(O2p) was measured breath by breath using mass spectrometry and a volume turbine. For a given ΔWR, both τV̇(O2p) (22-35 s) and G(P) (8.7-10.5 ml min(-1) W(-1)) increased (P < 0.05) linearly as a function of baseline WR (20-120 W). The τV̇(O2p) was invariant (P < 0.05) in transitions initiated from 20 W, but G(P) increased with ΔWR (P < 0.05). Modelling the summed influence of multiple muscle compartments revealed that τV̇(O2p) could appear fast (24 s), and similar to in vivo measurements (22 ± 6 s), despite being derived from τV̇(O2p) values with a range of 15-40 s and τQ̇(m) with a range of 20-45 s, suggesting that within the moderate-intensity domain phase II V̇(O2p) kinetics are slowed dependent on the pretransition WR and are strongly influenced by muscle metabolic and circulatory heterogeneity.

The effects of intensity on V̇O2 kinetics during incremental free swimming

Swimming and training are carried out with wide variability in distances and intensities. However, oxygen uptake kinetics for the intensities seen in swimming has not been reported. The purpose of this study was to assess and compare the oxygen uptake kinetics throughout low-moderate to severe intensities during incremental swimming exercise. We hypothesized that the oxygen uptake kinetic parameters would be affected by swimming intensity. Twenty male trained swimmers completed an incremental protocol of seven 200-m crawl swims to exhaustion (0.05 m·s(-1) increments and 30-s intervals). Oxygen uptake was continuously measured by a portable gas analyzer connected to a respiratory snorkel and valve system. Oxygen uptake kinetics was assessed using a double exponential regression model that yielded both fast and slow components of the response of oxygen uptake to exercise. From low-moderate to severe swimming intensities changes occurred for the first and second oxygen uptake amplitudes (P ≤ 0.04), time constants (P = 0.01), and time delays (P ≤ 0.02). At the heavy and severe intensities, a notable oxygen uptake slow component (>255 mL·min(-1)) occurred in all swimmers. Oxygen uptake kinetics whilst swimming at different intensities offers relevant information regarding cardiorespiratory and metabolic stress that might be useful for appropriate performance diagnosis and training prescription.