Kinetics of Oxygen Uptake for Submaximal Exercise in Hyperoxia, Normoxia, and Hypoxia

1995 ◽  
Vol 20 (2) ◽  
pp. 198-210 ◽  
Author(s):  
Richard L. Hughson ◽  
John M. Kowalchuk
Keyword(s):  
Ventilatory Threshold ◽  
Binary Sequence ◽  
Submaximal Exercise ◽  
Work Rate ◽  
Oxygen Deficit ◽  
Gas Mixture ◽  
Pseudorandom Binary Sequence ◽  
Upper Limit ◽  
Kinetics Of ◽  
Hypoxic Gas Mixture

This study evaluated the dynamic response of [Formula: see text] in 6 healthy men at the onset and end of submaximal step changes in work rate during a pseudorandom binary sequence (PRBS) exercise test and during ramp incremental exercise to exhaustion while breathing three different gas mixtures. The fractional concentrations of inspired O2 were 0.14, 0.21, and 0.70 for the hypoxic, normoxic, and hyperoxic tests, respectively. Both maximal [Formula: see text] and work rate was significantly reduced in hypoxic tests compared to normoxic and hyperoxic tests. Maximal work rate was greater in hyperoxia than in normoxia. Work rate at ventilatory threshold was lower in hypoxia than in normoxia and hyperoxia but above the upper limit of exercise for the submaximal tests. Hypoxia significantly slowed the response of [Formula: see text] both at the onset and end of exercise compared to normoxia and hyperoxia. Hypoxia also modified the response to PRBS exercise, and again there was no difference between normoxia and hyperoxia. These data support the concept that [Formula: see text] kinetics can be slowed from the normoxic response by a hypoxic gas mixture. Key words: [Formula: see text]max, ventilatory threshold, oxygen deficit, pseudorandom binary sequence

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Robert Amelard ◽  
Eric T. Hedge ◽  
Richard L. Hughson
Keyword(s):  
Ventilatory Threshold ◽  
Wearable Sensors ◽  
Binary Sequence ◽  
Ground Truth ◽  
Cycle Ergometer ◽  
Convolutional Network ◽  
Metabolic System ◽  
Pseudorandom Binary Sequence ◽  
Temporal Prediction ◽  
Ergometer Exercise

AbstractOxygen consumption ($$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 ) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 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 $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 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 $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 . 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 $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 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.

Frequency domain analysis of ventilation and gas exchange kinetics in hypoxic exercise

1991 ◽  
Vol 71 (6) ◽  
pp. 2394-2401 ◽  
Author(s):  
H. C. Xing ◽  
J. E. Cochrane ◽  
Y. Yamamoto ◽  
R. L. Hughson
Keyword(s):  
Frequency Domain ◽  
Binary Sequence ◽  
Domain Analysis ◽  
Exponential Model ◽  
Frequency Domain Analysis ◽  
Pseudorandom Binary Sequence ◽  
Warm Up ◽  
Co2 Output ◽  
Gas Exchange Kinetics ◽  
Kinetics Of

The kinetics of O2 up-take (VO2), CO2 output (VCO2), ventilation (VE), and heart rate (HR) were studied during exercise in normoxia and hypoxia [inspired O2 fraction (FIO2) 0.14]. Eight male subjects each completed 6 on- and off-step transitions in work rate (WR) from low (25 W) to moderate (100–125 W) levels and a pseudorandom binary sequence (PRBS) exercise test in which WR was varied between the same WRs. Breath-by-breath data were linearly interpolated to yield 1-s values. After the first PRBS cycle had been omitted as a warm-up, five cycles were ensemble-averaged before frequency domain analysis by standard Fourier methods. The step data were fit by a two-component (three for HR) exponential model to estimate kinetic parameters. In the steady state of low and moderate WRs, each value of VO2, VCO2, VE, and HR was significantly greater during hypoxic than normoxic exercise (P less than 0.05) with the exception of VCO2 (low WR). Hypoxia slowed the kinetics of VO2 and HR in on- and off-step transitions and speeded up the kinetics of VCO2 and VE in the on-transition and of VE in the off-transition. Frequency domain analysis confined to the range of 0.003–0.019 Hz for the PRBS tests indicated reductions in amplitude and greater phase shifts in the hypoxic tests for VO2 and HR at specific frequencies, whereas amplitude tended to be greater with little change in phase shift for VCO2 and VE during hypoxic tests.(ABSTRACT TRUNCATED AT 250 WORDS)

Investigation of VO2 kinetics in humans with pseudorandom binary sequence work rate change

1990 ◽  
Vol 68 (2) ◽  
pp. 796-801 ◽  
Author(s):  
R. L. Hughson ◽  
D. A. Winter ◽  
A. E. Patla ◽  
G. D. Swanson ◽  
L. A. Cuervo
Keyword(s):  
Fourier Analysis ◽  
Mean Square Error ◽  
Binary Sequence ◽  
Frequency Component ◽  
Cycle Ergometer ◽  
Work Rate ◽  
High Frequency Component ◽  
Mean Square ◽  
Pseudorandom Binary Sequence ◽  
The Mean

The dynamic response of oxygen uptake (VO2) was investigated with two different cycle ergometer tests in which the work rate changed as a pseudorandom binary sequence (PRBS). One sequence had 15 units, each of 30-s duration for a total of 450 s (PRBS1). The second had 63 units, each of 5-s duration for a total of 315 s (PRBS2). The useful range of frequencies available for investigation of the dynamic characteristics of the VO2 response as described by their bandwidth were 0.002-0.013 Hz for PRBS1 and 0.003-0.089 Hz for PRBS2. Eight subjects each completed both PRBS tests. Data from four or five consecutive sequences were ensemble averaged to reduce the biological noise. A Fourier analysis was then conducted, with the range of frequencies investigated spanning those of the bandwidth for PRBS2. This was up to the 28th harmonic. For PRBS1, the VO2 response could be adequately reconstructed by including Fourier coefficients only up to the 5th harmonic. In contrast, for PRBS2, there was still a clear pattern in the residuals at the 5th harmonic. The data were not adequately reconstructed until higher-frequency components up to the 28th harmonic were included. Evidence for this came from analysis of the mean square error. The mean square error at the 28th harmonic was reduced to 83 +/- 8% of the mean square error at the 5th harmonic for PRBS1 and to 31 +/- 3% for PRBS2 (P less than 0.0001). These data obtained by Fourier analysis and reconstructed for comparison with the original VO2 response indicate the presence of a high-frequency component that was not apparent when a test with a smaller bandwidth was used as the work rate forcing.

scholarly journals Reactive quenching of electronically excited NO<sub>2</sub><sup>∗</sup> and NO<sub>3</sub><sup>∗</sup> by H<sub>2</sub>O as potential sources of atmospheric HO<sub><i>x</i></sub> radicals

2018 ◽  
Vol 18 (19) ◽  
pp. 14005-14015 ◽  
Author(s):  
Terry J. Dillon ◽  
John N. Crowley
Keyword(s):  
Gas Phase ◽  
Laser Excitation ◽  
Pulsed Laser ◽  
Work Rate ◽  
Rate Coefficients ◽  
Phase Reaction ◽  
Gas Phase Reaction ◽  
Upper Limit ◽  
Upper Limits ◽  
Electronically Excited

Abstract. Pulsed laser excitation of NO2 (532–647 nm) or NO3 (623–662 nm) in the presence of H2O was used to initiate the gas-phase reaction NO2∗+H2O → products (Reaction R5) and NO3∗+H2O → products (Reaction R12). No evidence for OH production in Reactions (R5) or (R12) was observed and upper limits for OH production of k5b/k5<1×10-5 and k12b/k12<0.03 were assigned. The upper limit for k5b∕k5 renders this reaction insignificant as a source of OH in the atmosphere and extends the studies (Crowley and Carl, 1997; Carr et al., 2009; Amedro et al., 2011) which demonstrate that the previously reported large OH yield by Li et al. (2008) was erroneous. The upper limit obtained for k12b∕k12 indicates that non-reactive energy transfer is the dominant mechanism for Reaction (R12), though generation of small but significant amounts of atmospheric HOx and HONO cannot be ruled out. In the course of this work, rate coefficients for overall removal of NO3∗ by N2 (Reaction R10) and by H2O (Reaction R12) were determined: k10=(2.1±0.1)×10-11 cm3 molecule−1 s−1 and k12=(1.6±0.3)×10-10 cm3 molecule−1 s−1. Our value of k12 is more than a factor of 4 smaller than the single previously reported value.

scholarly journals Dietary Nitrate Influence on Oxygen Uptake Response to Pseudorandom Binary Sequence Cycling Protocols

2021 ◽  
Vol 35 (S1) ◽  
Author(s):  
Barry Scheuermann ◽  
Tyler Falor ◽  
Andrew Misko ◽  
Jordan Monnier ◽  
Britton Scheuermann
Keyword(s):  
Oxygen Uptake ◽  
Binary Sequence ◽  
Dietary Nitrate ◽  
Pseudorandom Binary Sequence ◽  
Oxygen Uptake Response
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