Coupling of ventilation and gas exchange during transitions in work rate by humans

Coupling of [Formula: see text] and [Formula: see text] kinetics: insights from multiple exercise transitions below the estimated lactate threshold

During a step-change in exercise power output (PO), ventilation ([Formula: see text]) increases with a similar time course to the rate of carbon dioxide delivery to the lungs ([Formula: see text]). To test the strength of this coupling, we compared [Formula: see text] and [Formula: see text] kinetics from ten independent exercise transitions performed within the moderate-intensity domain. Thirteen males completed 3-5 repetitions of ∆40 W step transitions initiated from 20, 40, 60, 80, 100, and 120 W on a cycle ergometer. Preceding the ∆40 W step transitions from 60, 80, 100, and 120 W was a 6 min bout of 20 W cycling from which the transitions of variable ∆PO were examined. Gas exchange ([Formula: see text] and oxygen uptake, [Formula: see text]) and [Formula: see text] were measured by mass spectrometry and volume turbine. The kinetics of the responses were characterized by the time constant (τ) and amplitude (Δ[Formula: see text]/Δ[Formula: see text]). Overall, [Formula: see text] kinetics were consistently slower than [Formula: see text] kinetics (by ~ 45%) and τ[Formula: see text] rose progressively with increasing baseline PO and with heightened ∆PO from a common baseline. Compared to τ[Formula: see text], τ[Formula: see text] was on average slightly greater (by ~ 4 s). Repeated-measures analysis of variance revealed that there was no interaction between τ[Formula: see text] and τ[Formula: see text] in either the variable baseline (p = 0.49) and constant baseline (p = 0.56) conditions indicating that each changed in unison. Additionally, for Δ[Formula: see text]/Δ[Formula: see text], there was no effect of either variable baseline PO (p = 0.05) or increasing ΔPO (p = 0.16). These data provide further evidence that, within the moderate-intensity domain, both the temporal- and amplitude-based characteristics of V̇_E kinetics are inextricably linked to those of [Formula: see text].

Strategies to improve respiratory chemoreflex characterization by Duffin's rebreathing

NEW FINDINGS

What is the central question of this study? We assessed the test-retest variability of respiratory chemoreflex characterization by Duffin's modified rebreathing method and explored whether signal averaging of repeated trials improves confidence in parameter estimation. What is the main finding and its importance? Modified rebreathing is a reproducible method to characterize responses of central and peripheral respiratory chemoreflexes. Signal averaging of multiple repeated tests minimizes within- and between-test variability, improves the confidence of chemoreflex characterization and reduces the minimal change in parameters required to establish an effect. Future experiments that apply this method might benefit from signal averaging to improve its discriminatory effect.

ABSTRACT

We assessed the test-retest variability of central and peripheral respiratory chemoreflex characterization by Duffin's modified rebreathing method and explored whether signal averaging of repeated trials improves confidence in parameter estimation. Over four laboratory visits, 13 participants (mean ± SD age, 25 ± 5 years) performed six repetitions of modified rebreathing in isoxic-hypoxic conditions [end-tidal P O 2 ${P_{{{\rm{O}}_{\rm{2}}}}}$ ( P ET , O 2 ${P_{{\rm{ET,}}{{\rm{O}}_{\rm{2}}}}}$ )  = 50 mmHg] and isoxic-hyperoxic conditions ( P ET , O 2 ${P_{{\rm{ET,}}{{\rm{O}}_{\rm{2}}}}}$   = 150 mmHg). End-tidal P C O 2 ${P_{{\rm{C}}{{\rm{O}}_{\rm{2}}}}}$ ( P ET , C O 2 ${P_{{\rm{ET,C}}{{\rm{O}}_{\rm{2}}}}}$ ), P ET , O 2 ${P_{{\rm{ET,}}{{\rm{O}}_{\rm{2}}}}}$ and minute ventilation ( V ̇ $\dot {\rm V}$ _E ) were measured breath-by-breath, by gas analyser and pneumotachograph. The V ̇ $\dot {\rm V}$ _E versus P ET , C O 2 ${P_{{\rm{ET,C}}{{\rm{O}}_{\rm{2}}}}}$ relationships were fitted with a piecewise model to estimate the ventilatory recruitment threshold (VRT) and the slope above the VRT ( V ̇ $\dot {\rm V}$ _E S). Breath-by-breath data from the three within- and between-day trials were averaged using two approaches [simple average (fit then average) and ensemble average (average then fit)] and compared with a single-trial fit. Variability was assessed by intraclass correlation (ICC) and coefficient of variance (CV), and the minimal detectable change was computed for each approach using two independent sets of three trials. Within days, the VRT and V ̇ $\dot {\rm V}$ _E S exhibited excellent test-retest variability in both hyperoxic conditions (VRT: ICC = 0.965, CV = 2.3%; V ̇ $\dot {\rm V}$ _E S: ICC = 0.932, CV = 15.5%) and hypoxic conditions (VRT: ICC = 0.970, CV = 2.9%; V ̇ $\dot {\rm V}$ _E S: ICC = 0.891, CV = 17.2%). Between-day reproducibility was also excellent (hyperoxia, VRT: ICC = 0.930, CV = 2.2%; V ̇ $\dot {\rm V}$ _E S: ICC = 0.918, CV = 14.2%; and hypoxia, VRT: ICC = 0.940, CV = 3.0%; V ̇ $\dot {\rm V}$ _E S: ICC = 0.880, CV = 18.1%). Compared with a single-trial fit, there were no differences in VRT or V ̇ $\dot {\rm V}$ _E S using the simple average or ensemble average approaches; however, ensemble averaging reduced the minimal detectable change for V ̇ $\dot {\rm V}$ _E S from 2.95 to 1.39 L min^-1  mmHg^-1 (hyperoxia) and from 3.64 to 1.82 L min^-1  mmHg^-1 (hypoxia). Single trials of modified rebreathing are reproducible; however, signal averaging of repeated trials improves confidence in parameter estimation.

Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities

Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of [Formula: see text] dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of [Formula: see text]. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml min^-1, [-262, 218]), spanning transitions from low-moderate (-23 ml min^-1, [-250, 204]), low-high (14 ml min^-1, [-252, 280]), ventilatory threshold-high (-49 ml min^-1, [-274, 176]), and maximal (-32 ml min^-1, [-261, 197]) exercise. Second-by-second classification of physical activity across 16,090 s of predicted [Formula: see text] was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.

Differential kinetics of the cardiac, ventilatory, and gas exchange variables during walking under moderate hypoxia

We investigated the effects of moderate hypoxia (FiO₂ = 15%) on different kinetics between pulmonary ventilation (V˙E) and heart rate (HR) during treadmill walking. Breath-by-breathV˙E, oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), and HR were measured in 13 healthy young adults. The treadmill speed was sinusoidally changed from 3 to 6 km·h^-1 with four oscillation periods of 1, 2, 5, and 10 min. The amplitude (Amp), phase shift (PS) and mean values of these kinetics were obtained by harmonic analysis. The mean values of all of these responses during walking at a sinusoidally changing speed became greater under hypoxia compared to normoxia (FiO₂ = 21%), indicating that moderate hypoxia could achieve an increased energy expenditure (increased V˙O2 and V˙CO2) and hyperventilation. The Amp values of the V˙E, V˙O2, and V˙CO2 kinetics were not significantly different between normoxia and hypoxia at most periods, although a significantly smaller Amp of the HR was observed at faster oscillation periods (1 or 2 min).The PS of the HR was significantly greater under hypoxia than normoxia at the 2, 5, and 10 min periods, whereas the PS of the V˙E, V˙O2, and V˙CO2 responses was not significantly different between normoxia and hypoxia at any period. These findings suggest that the lesser changes in Amp and PS in ventilatory and gas exchange kinetics during walking at a sinusoidally changing speed were remarkably different from a deceleration in HR kinetics under moderate hypoxia.

Exercise hyperpnea and hypercapnic ventilatory responses in women

We studied the relationship between exercise hyperpnea (i.e., ventilatory dynamics) at the onset of exercise and hypercapnic ventilatory response (HCVR), and their differences between the follicular (FP) and luteal (LP) phases of the menstrual cycle in six healthy females. HCVR was tested under three O(2) conditions: hyperoxia (FiO(2)=1.0), normoxia (0.21), and hypoxia (0.12). HCVR was defined as the relationship between the end-tidal P(CO2) and minute ventilation (V(E)) using the regression line of the CO(2) slope and a mimetically apneic threshold of CO(2). HCVR provocation and measurements were conducted using an inspired CO(2) concentration of up to approximately 8 mmHg higher than the end-tidal P(CO2) level of basal isocapnic the end-tidal P(CO2) at each menstrual both the slope and threshold in HCVR showed no statistically significant difference between LP and FP under any inspired FiO(2) conditions. In the case of exercise hyperpnea during the onset of submaximal exercise, the mean response time (MRT) in V(E) dynamics showed no significant difference between LP and FP. Consequently, MRT in V(E) response was not related to the slope in HCVR. During steady-state exercise, even though the V(E)/V(CO2) showed no significance between LP and FP, V(E)/V(CO2) was significantly related to the slope in HCVR (r=0.59, P<0.05). Exercise ventilation (i.e., V(E)/V(CO2)) would partly be adjusted by the enhancement of the chemoreflex drive to CO(2) only during the steady-state exercise.

Chemoreflex drive and the dynamics of ventilation and gas exchange during exercise at hypoxia

We tested the hypothesis that the promotion of hypoxic ventilatory responsiveness (HVR) and/or hypercapnic ventilatory responsiveness (HCVR) mostly acting on the carotid body with a changing work rate can be attributed to faster hypoxic ventilatory dynamics at the onset of exercise. Eleven subjects performed a cycling exercise with two repetitions of 6 minutes while breathing at FIO(2) = 12%. The tests began with unloaded pedaling, followed by three constant work rates of 40%, 60%, and 80% of the subject's ventilatory threshold at hypoxia. Reference data were obtained at the 80% ventilatory threshold work rate during normoxia. Using three inhaled 100% O(2) breath tests, a comparison of hypoxia and normoxia revealed an augmentation of HVR in hypoxia, which then significantly increased proportionally with the increase in work rate. In contrast, HCVR using three inhaled 10% CO(2) breath tests was unaffected by the difference in work rate at hypoxia but did exceed its level at normoxia. The decrease in the half-time of hypoxic ventilation became significant with an increase in work rates and was significantly lower than at normoxia. Using a multiregression equation, HVR was found to account for 63% of the variance of hypoxic ventilatory dynamics at the onset of exercise and HCVR for 9%. O(2) uptake on-kinetics and off-kinetics under hypoxic conditions were significantly slower than under normoxic conditions, whereas they were not altered by the changing work rates at hypoxia. These results suggest that the faster hypoxic ventilatory dynamics at the onset of exercise can be mostly attributed to the augmentation of HVR with an increase in work rates rather than to HCVR. Otherwise, O(2) uptake dynamics are affected by the lower O(2), not by the changing work rates under hypoxic conditions.

VCO2 and VE kinetics during moderate- and heavy-intensity exercise after acetazolamide administration

The effect of carbonic anhydrase inhibition with acetazolamide (Acz) on CO2 output (VCO2) and ventilation (VE) kinetics was examined during moderate- and heavy-intensity exercise. Seven men [24 +/- 1 (SE) yr] performed cycling exercise during control (Con) and Acz (10 mg/kg body wt iv) sessions. Each subject performed step transitions (6 min) in work rate from 0 to 100 W [below ventilatory threshold (<VET)] and to an O2 uptake corresponding to approximately 50% of the difference between the work rate at VET and peak O2 uptake [above ventilatory threshold (>VET)]. VE and gas exchange were measured breath by breath. The time constant (tau) was determined for exercise <VET by using a single-exponential model (fit between 20 s and end-exercise); the mean response time (MRT) was determined for exercise >VET by using a three-component model (fit from the start of exercise). VCO2 kinetics were slower in Acz (<VET, tau = 45 +/- 6 s; >VET, MRT = 75 +/- 10 s) than Con (<VET, tau = 34 +/- 6 s; >VET, MRT = 54 +/- 7 s). During <VET exercise, VE kinetics were slower in Acz (tau = 48 +/- 6 s) than Con (tau = 34 +/- 6 s), but >VET kinetics were faster in Acz (MRT = 85 +/- 17 s) than Con (MRT = 106 +/- 16 s). Carbonic anhydrase inhibition slowed VCO2 kinetics during both moderate- and heavy-intensity exercise, demonstrating impaired CO2 elimination in the nonsteady state of exercise. The slowed VE kinetics in Acz during exercise <VET is consistent with a mechanism coupling VE kinetics with the flow of CO2 to the lungs.