Daniel A. Keir

Breath‐by‐breath pulmonary O2 uptake kinetics: effect of data processing on confidence in estimating model parameters

What is the central question of this study? In groups of young and older adults, we investigated whether techniques used as common practice for processing breath‐by‐breath pulmonary O2 uptake data from repeated step transitions in work rate into the moderate‐intensity exercise domain influence the model parameter estimations and confidence of describing the phase II pulmonary O2 uptake response. What is the main finding and its importance? Results demonstrate that regardless of age group, during transitions into the moderate‐intensity exercise domain, techniques for processing individual transitions did not affect parameter estimates describing the phase II pulmonary O2 uptake response; however, the confidence in the parameter estimation could be improved by the technique used to process individual trials.

Sex-related differences in muscle deoxygenation during ramp incremental exercise.

Sex-specific differences in the temporal profiles of fractional O2 extraction during incremental cycling were examined using changes in near-infrared spectroscopy (NIRS)-derived muscle deoxygenated hemoglobin concentration (Δ[HHb]) and breath-by-breath pulmonary O2 uptake ( .VO2p ) measurements. Subjects (men: n=10; women: n=10) Δ[HHb] data were normalized to 100% of the response, plotted as a function ( .VO2p, % .VO2p), power output (PO), and % PO, and fit with a piecewise double-linear regression model. The slope of the first segment of the double linear model was significantly greater in women compared to men when %Δ[HHb] was plotted as a function of .VO2p, % .VO2p and PO (p<0.05). Both sexes displayed a near-plateau in the %Δ[HHb] which occurred at an exercise intensity near the respiratory compensation point. Thus, young women display a poorer ability to deliver O2 to the exercising tissue compared to men and oxidative demands must be supplemented by a greater fractional O2 extraction.

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

What is the central question of this study? The finding that pulmonary oxygen uptake ( V̇O2p ) kinetics on transition to moderate exercise is invariant and exponential is consistent with a first‐order reaction controlling V̇O2p . However, slowed V̇O2 kinetics when initiating exercise from raised baseline intensities challenges this notion. What is the main finding and its importance? Here, we demonstrate how a first‐order system can respond with non‐first‐order response dynamics. Data suggest that progressive recruitment of muscle fibre populations having progressively lower mitochondrial density and slower microvascular blood flow kinetics can unify the seemingly contradictory evidence for the control of V̇O2p on transition to exercise.

The slow component of pulmonary O2 uptake accompanies peripheral muscle fatigue during high-intensity exercise

During constant-power output (PO) exercise above lactate threshold (LT), pulmonary O2 uptake (V̇o2 p) features a developing slow component (V̇o2 pSC). This progressive increase in O2 cost of exercise is suggested to be related to the effects of muscle fatigue development. We hypothesized that peripheral muscle fatigue as assessed by contractile impairment would be associated with the V̇o2 pSC Eleven healthy men were recruited to perform four constant-PO tests at an intensity corresponding to ∼Δ60 (very heavy, VH) where Δ is 60% of the difference between LT and peak V̇o2 p The VH exercise was completed for each of 3, 8, 13, and 18 min (i.e., VH3, VH8, VH13, VH18) with each preceded by 3 min of cycling at 20 W. Peripheral muscle fatigue was assessed via pre- vs. postexercise measurements of quadriceps torque in response to brief trains of electrical stimulation delivered at low (10 Hz) and high (50 Hz) frequencies. During exercise, breath-by-breath V̇o2 p was measured by mass spectrometry and volume turbine. The magnitude of V̇o2 pSC increased (P < 0.05) from 224 ± 81 ml/min at VH3 to 520 ± 119, 625 ± 134, and 678 ± 156 ml/min at VH8, VH13, and VH18, respectively. The ratio of the low-to-high frequency (10/50 Hz) response was reduced (P < 0.05) at VH3 (-12 ± 9%) and further reduced (P < 0.05) at VH8 (-25 ± 11%), VH13 (-42 ± 19%), and VH18 (-46 ± 16%), mirroring the temporal pattern of V̇o2 pSC development. The reduction in 10/50 Hz ratio was correlated (P < 0.001, r(2) = 0.69) with V̇o2 pSC amplitude. The temporal and quantitative association of decrements in muscle torque production and V̇o2 pSC suggest a common physiological mechanism between skeletal muscle fatigue and loss of muscle efficiency.

Identification of critical intensity from a single lactate measure during a 3-min, submaximal cycle-ergometer test

ABSTRACT We tested the hypothesis that critical intensity in cycling can be determined from a single delta blood lactate in the third minute of a submaximal cycle ergometer trial. Fourteen healthy young men performed four to six constant-power-output trials on a cycle ergometer to the limit of tolerance. Critical intensity was calculated via a linear model and subsequently validated. Lactate was measured at baseline and at 3 min from exercise onset. Delta lactate was the difference between these measures. Based on individual trials, we obtained the delta lactate–% validated critical intensity relationship and thereafter an estimate of critical intensity was computed. Validated and estimated critical intensity were compared by effects sizes, paired-sample t-test and Bland–Altman analysis. Delta lactate was a linear function of the intensity of exercise, expressed as % validated critical intensity (R2 = 0.89). Estimated critical intensity was not different from (d = 0.03, P = 0.98) and highly correlated with (R2 = 0.88) validated critical intensity. The bias between measures was 0.03 W (≠0) with a precision of 7 W. The results suggest that critical intensity in cycling can be accurately and precisely determined from delta lactate during a sub-maximal trial and so provides a practical and valid alternative to direct determination.

The effects of short work vs. longer work periods within intermittent exercise on V̇o2p kinetics, muscle deoxygenation, and energy system contribution

We examined the effects of inserting 3-s recovery periods during high-intensity cycling exercise at 25-s and 10-s intervals on pulmonary oxygen uptake (V̇o2p), muscle deoxygenation [deoxyhemoglobin (HHb)], their associated kinetics (τ), and energy system contributions. Eleven men (24 ± 3 yr) completed two trials of three cycling protocols: an 8-min continuous protocol (CONT) and two 8-min intermittent exercise protocols with work-to-rest periods of 25 s to 3 s (25INT) and 10 s to 3 s (10INT). Each protocol began with a step-transition from a 20-W baseline to a power output (PO) of 60% between lactate threshold and maximal V̇o2p (Δ60). This PO was maintained for 8 min in CONT, whereas 3-s periods of 20-W cycling were inserted every 10 s and 25 s after the transition to Δ60 in 10INT and 25INT, respectively. Breath-by-breath gas exchange measured by mass spectrometry and turbine and vastus lateralis [HHb] measured by near-infrared spectroscopy were recorded throughout. Arterialized-capillary lactate concentration ([Lac-]) was obtained before and 2 min postexercise. The τV̇o2p was lowest (P < 0.05) for 10INT (24 ± 4 s) and 25INT (23 ± 5 s) compared with CONT (28 ± 4 s), whereas HHb kinetics did not differ (P > 0.05) between conditions. Postexercise [Lac-] was lowest (P < 0.05) for 10INT (7.0 ± 1.7 mM), was higher for 25INT (10.3 ± 1.9 mM), and was greatest in CONT (14.3 ± 3.1 mM). Inserting 3-s recovery periods during heavy-intensity exercise speeded V̇o2p kinetics and reduced overall V̇o2p, suggesting an increased reliance on PCr-derived phosphorylation during the work period of INT compared with an identical PO performed continuously.NEW & NOTEWORTHY We report novel observations on the effects of differing heavy-intensity work durations between 3-s recovery periods on pulmonary oxygen uptake (V̇o2p) kinetics, muscle deoxygenation, and energy system contributions. Relative to continuous exercise, V̇o2p kinetics are faster in intermittent exercise, and increased frequency of 3-s recovery periods improves microvascular O2 delivery and reduces V̇o2p and arterialized-capillary lactate concentration. The metabolic burden of identical intensity work is altered when performed intermittently vs. continuously.

Slow V̇O 2 kinetics in acute hypoxia are not related to a hyperventilation-induced hypocapnia

We examined whether slower pulmonary O2 uptake (V˙O2p) kinetics in hypoxia is a consequence of: a) hypoxia alone (lowered arterial O2 pressure), b) hyperventilation-induced hypocapnia (lowered arterial CO2 pressure), or c) a combination of both. Eleven participants performed 3-5 repetitions of step-changes in cycle ergometer power output from 20W to 80% lactate threshold in the following conditions: i) normoxia (CON; room air); ii) hypoxia (HX, inspired O2 = 12%; lowered end-tidal O2 pressure [PETO2] and end-tidal CO2 pressure [PETCO2]); iii) hyperventilation (HV; increased PETO2 and lowered PETCO2); and iv) normocapnic hypoxia (NC-HX; lowered PETO2 and PETCO2 matched to CON). Ventilation was increased (relative to CON) and matched between HX, HV, and NC-HX conditions. During each condition VO2p˙ was measured and phase II V˙O2p kinetics were modeled with a mono-exponential function. The V˙O2p time constant was different (p < 0.05) amongst all conditions: CON, 26 ± 11s; HV, 36 ± 14s; HX, 46 ± 14s; and NC-HX, 52 ± 13s. Hypocapnia may prevent further slowing of V˙O2p kinetics in hypoxic exercise.

Reply: To PMID 25063837.

In their Letter to the Editor entitled ‘Interpreting the confidence intervals of model parameters of breath-by-breath pulmonary O2 uptake’, Francescato and colleagues correctly point out that in some instances, the use of the 95% confidence interval (CI95) may not truly reflect the precision of parameter estimation when fitting phase II pulmonary O2 uptake (V̇O2p ) data using the non-linear regression analysis technique. Specifically, they mention that artificially narrower CI95 values (associated with the parameter estimates of the non-linear model) can be generated by increasing the number of data points used in the non-linear regression procedure (e.g. by linearly interpolating on a second-by-second basis). The authors are correct that we did not make this point in our paper. However, it should be emphasized that the main focus of our study was to determine whether different data-processing techniques affected parameter estimation and confidence of phase II V̇O2p kinetics. Using real breath-by-breath V̇O2p data, our analyses showed that: (i) phase II V̇O2p kinetic parameter estimates were not different when modelling V̇O2p data using like-trials that were combined without processing of any kind, after interpolation (using two interpolation techniques) and/or after bin averaging; (ii) the ‘noise’ statistics for V̇O2p were highly variable both between subjects and within subject trial repeats; (iii) though variable, the amplitude of breath-by-breath V̇O2p ‘noise’ is not different between low or moderate exercise intensities or between young and older individuals; and (iv) the statistics of breath-by-breath V̇O2p ‘noise’ were normally distributed independent of age group or steady-state exercise intensity (Keir et al. 2014). In their study, Francescato and colleagues (2014) indicate that resampling V̇O2p responses (individual trials) to a time interval slightly longer than the average breath duration provides the best method by which to obtain an asymptotic CI95 that contains the ‘true’ parameter. A shortcoming of this recommendation is that the ‘true’ parameter values normally are not known and thus the confidence interval, as defined (‘the range of values that over a set of notional repeated samples, would contain the true parameter of interest’) cannot truly be established. Pulmonary O2 uptake kinetic analyses provide a means for quantifying the rate at which pulmonary (and muscle) O2 uptake adjusts in response to a change in metabolic demand, and the parameter used to describe the rate of this adjustment is the time constant (τ). When fitting real breath-by-breath data, the value of τ can be influenced by a number of factors, including the inclusion of data from phase I (and phase III) and the level of noise within the non-steady-state (transient) phase of the V̇O2p response. To circumvent these issues, it is common practice to perform and combine repeat trials (Lamarra et al. 1987) and to constrain the fitting window (at least within the moderate-intensity domain) to data that appear beyond an identified phase I–phase II transition or that appear 20 s after the onset of exercise (Murias et al. 2011). While these strategies do help to improve ‘confidence’ during the fitting procedure, there are always situations where the signal-to-noise ratio of the V̇O2p data is less than desirable (particularly in situations where the change in V̇O2p is small and/or the individual has a large noise amplitude). We appreciate the information presented in the study by Francescato et al. (2014) and acknowledge that this type of modelling exercise can be useful to help understand the technical aspects of characterizing dynamic physiological responses. However, when dealing with real breath-by-breath data there is considerable interbreath variability in the V̇O2p signal (with respect to ‘noise’ and temporal pattern), both between subjects and within subject repeats, and the ‘true’ values of the parameters are not known. It is therefore difficult to state with appropriate ‘confidence’ that the ‘true’ parameter estimates lie within a predicted range. In practice, higher ‘confidence’ in the fit can be obtained only by collecting quality data and carefully modelling the phase II V̇O2p response, with attention paid to the parameters and statistical outcomes (CI95, χ) and the goodness of fit of the model as determined by visual inspection of the model best-fit relationship with the real data and the resulting residuals, especially within the non-steady-state region of interest.In their Letter to the Editor entitled ‘Interpreting the confidence intervals of model parameters of breath-by-breath pulmonary O2 uptake’, Francescato and colleagues correctly point out that in some instances, the use of the 95% confidence interval (CI95) may not truly reflect the precision of parameter estimation when fitting phase II pulmonary O2 uptake (V̇O2p ) data using the non-linear regression analysis technique. Specifically, they mention that artificially narrower CI95 values (associated with the parameter estimates of the non-linear model) can be generated by increasing the number of data points used in the non-linear regression procedure (e.g. by linearly interpolating on a second-by-second basis). The authors are correct that we did not make this point in our paper. However, it should be emphasized that the main focus of our study was to determine whether different data-processing techniques affected parameter estimation and confidence of phase II V̇O2p kinetics. Using real breath-by-breath V̇O2p data, our analyses showed that: (i) phase II V̇O2p kinetic parameter estimates were not different when modelling V̇O2p data using like-trials that were combined without processing of any kind, after interpolation (using two interpolation techniques) and/or after bin averaging; (ii) the ‘noise’ statistics for V̇O2p were highly variable both between subjects and within subject trial repeats; (iii) though variable, the amplitude of breath-by-breath V̇O2p ‘noise’ is not different between low or moderate exercise intensities or between young and older individuals; and (iv) the statistics of breath-by-breath V̇O2p ‘noise’ were normally distributed independent of age group or steady-state exercise intensity (Keir et al. 2014). In their study, Francescato and colleagues (2014) indicate that resampling V̇O2p responses (individual trials) to a time interval slightly longer than the average breath duration provides the best method by which to obtain an asymptotic CI95 that contains the ‘true’ parameter. A shortcoming of this recommendation is that the ‘true’ parameter values normally are not known and thus the confidence interval, as defined (‘the range of values that over a set of notional repeated samples, would contain the true parameter of interest’) cannot truly be established. Pulmonary O2 uptake kinetic analyses provide a means for quantifying the rate at which pulmonary (and muscle) O2 uptake adjusts in response to a change in metabolic demand, and the parameter used to describe the rate of this adjustment is the time constant (τ). When fitting real breath-by-breath data, the value of τ can be influenced by a number of factors, including the inclusion of data from phase I (and phase III) and the level of noise within the non-steady-state (transient) phase of the V̇O2p response. To circumvent these issues, it is common practice to perform and combine repeat trials (Lamarra et al. 1987) and to constrain the fitting window (at least within the moderate-intensity domain) to data that appear beyond an identified phase I–phase II transition or that appear 20 s after the onset of exercise (Murias et al. 2011). While these strategies do help to improve ‘confidence’ during the fitting procedure, there are always situations where the signal-to-noise ratio of the V̇O2p data is less than desirable (particularly in situations where the change in V̇O2p is small and/or the individual has a large noise amplitude). We appreciate the information presented in the study by Francescato et al. (2014) and acknowledge that this type of modelling exercise can be useful to help understand the technical aspects of characterizing dynamic physiological responses. However, when dealing with real breath-by-breath data there is considerable interbreath variability in the V̇O2p signal (with respect to ‘noise’ and temporal pattern), both between subjects and within subject repeats, and the ‘true’ values of the parameters are not known. It is therefore difficult to state with appropriate ‘confidence’ that the ‘true’ parameter estimates lie within a predicted range. In practice, higher ‘confidence’ in the fit can be obtained only by collecting quality data and carefully modelling the phase II V̇O2p response, with attention paid to the parameters and statistical outcomes (CI95, χ) and the goodness of fit of the model as determined by visual inspection of the model best-fit relationship with the real data and the resulting residuals, especially within the non-steady-state region of interest.