Biomedical subjects

Thierry Busso

Publications and source records attributed to Thierry Busso.

6 recordsLinked to original sources

PubMed2003

Decrease in peak heart rate with acute hypoxia in relation to sea level VO(2max).

The aim of this study was to evaluate the influence of arterial oxygen saturation ( SaO(2)) on maximal heart rate during maximal exercise under conditions of acute hypoxia compared with normoxia. Forty-six males were divided into three groups depending on their sea level maximal oxygen consumption ( VO(2max)): high [GH, VO(2max)=64.2 (3.3) ml x min(-1) x kg(-1)], medium [GM, 50.8 (3.9) ml x min(-1) x kg(-1)] and low [GL, 41.0 (1.9) ml x min(-1) x kg(-1)]. All subjects performed a maximal exercise test in two conditions of inspired oxygen tension ( PIO(2), (149 mmHg and 70 mmHg). Among the GM group, seven subjects performed five supplementary incremental exercise tests at PIO(2) 136, 118, 104, 92, and 80 mmHg. Measurements of VO(2max) and SaO(2) using an ear-oxymeter were carried out at all levels of PIO(2). The decrease in SaO(2 )and peak heart rate (HR(peak)) with PIO(2) became significant from 104 and 92 mmHg. SaO(2) correlated with the decrease in HR(peak). For PIO(2)=70 mmHg, the decrease in VO(2max), SaO(2) and HR(peak) was, respectively, 44%, 62%, and 17.0 bpm for GH, 38%, 68%, and 14.7 bpm for GM, and 34%, 68%, and 11.8 bpm for GL. During maximal exercise in hypoxia, SaO(2) was lower for GH than GM and GL ( p<0.01). Among subjects in GH, five presented exercise-induced hypoxemia (EIH) when exercising in normoxia. The EIH group exhibited a greater decrement in HR(peak) than the non-EIH group at maximal hypoxic exercise (21.2 bpm vs. 15.0 bpm; p<0.05). When subjects are exposed to acute hypoxia, the lower SaO(2), due either to lower PIO(2) or to training status, is associated with lower HR(peak).

Acute Disease↗

PubMed2003

Variable dose-response relationship between exercise training and performance.

INTRODUCTION: The aim of this study was to propose a nonlinear model of the effects of training on performance. The new formulation introduced a variable to account for training-related changes in the magnitude and duration of exercise-induced fatigue. METHODS: Goodness-of-fit of the proposed model was compared with that of earlier models presented in the literature. Models were applied to six previously untrained subjects volunteers over a 15-wk endurance-training program composed of an 8-wk period with three sessions per week and a 4-wk period with five sessions, and the remaining weeks without training. Training sessions were composed of performance trial and intermittent exercise with 5-min work interspersed with 3-min recovery repeated four or five times. Performance was measured three times each week using average power during a 5-min all-out exercise. RESULTS: The training program resulted in 30 +/- 7% improvement in performance. The proposed model exhibited significantly improved fit with actual performance obtained in each subject. Standard error was 6.47 +/- 0.71 W for the proposed model and from 9.20 +/- 2.27 W to 10.31 +/- 1.56 W for earlier models. The model output using model parameters averaged over the six subjects was found to be similar to data published elsewhere obtained in athletes with more intense training. CONCLUSION: The data obtained allowed us to demonstrate an inverted-U-shape relationship between daily amounts of training and performance. The fit between experimental data and model-derived predictions in similar situations showed the usefulness of the proposed model to predict responses to training with varied regimens.

Adaptation, Physiological↗

PubMed2002

Modeling of end-tidal and arterial PCO2 gradient: comparison with experimental data.

PURPOSE: The aim of this study was to test whether a tidally ventilated homogeneous lung model can correctly describe arterial and end-tidal gas partial pressures and thus the difference in end-tidal and arterial gas partial pressures at rest and during exercise. METHODS: The implemented mathematical modeling described variations during the breathing cycle in CO2 and O2 fractions, alveolar volume, and pulmonary capillary gas exchange. Experimental data were obtained from measurements performed by 17 healthy subjects at rest and during 40, 50, 65, and 75% exercise .VO(2max) on a cycle ergometer. VO2, VCO2, and PET,CO2 were continuously measured using the MedGraphics CPX/D gas exchange system. Arterial gases were measured in brachial artery blood samples drawn simultaneously with gas exchange. Cardiac output was measured using the CO2 rebreathing method corrected by the blood sample data. The model was driven using experimental data for ventilation, VO2, VCO2, and cardiac output. RESULTS: The mean difference and the upper and lower limits of agreement between measured and simulated data were -0.004, +0.84, and -0.84 Torr for Pa,CO2; -0.06, +0.64, and -0.76 Torr for Pa,O2; -1.96, +2.84, and -6.76 Torr for PET,CO2; and +7.20, +25.80, and -11.40 Torr for PET,O2. Actual PET,CO2-Pa,CO2 difference increased significantly with workload (P < 0.0001) from 0.3 +/- 3 Torr at rest to 4.7 +/- 2.5 Torr at 75% .VO(2max). Model-simulated PET,CO2-Pa,CO2 difference also increased significantly with exercise (P < 0.0001) from 0.7 +/- 1.7 Torr at rest to 9.1 +/- 3.4 Torr at 75% .VO(2max). CONCLUSION: The lung model described actual arterial CO2 partial pressures better than variations in end-tidal CO2 partial pressures and thus better than the gradient in end-tidal arterial CO2 partial pressures.

Adult↗

PubMed2002

Autonomic adaptations to intensive and overload training periods: a laboratory study.

PURPOSE: Looking for practical and reliable markers of fatigue is of particular interest in elite sports. One possible marker might be the autonomic nervous system activity, known to be well affected by physical exercise and that can be easily assessed by heart rate variability. METHODS: We designed a laboratory study to follow six sedentary subjects (32.7 +/- 5.0 yr) going successively through 2 months of intensive physical training and 1 month of overload training on cycloergometer followed by 2 wk of recovery. Maximal power output over 5 min (Plim5'), VO(2) and standard indices of heart rate variability were monitored all along the protocol. RESULTS: During the intensive training period, physical performance increased significantly VO(2peak) : +20.2%, < 0.01; Plim5': +26.4%, < 0.0001) as well as most of the indices of heart rate variability (mean RR, Ptot, HF, rMSSD, pNN50, SDNNIDX, SDNN, all < 0.05) with a significant shift in the autonomic nervous system toward a predominance of its parasympathetic arm (LF/HF, LFnu, HFnu, < 0.01). During the overload training period, there was a stagnation of the parasympathetic indices associated to a progressive increase in sympathetic activity (LF/HF, < 0.05). During the week of recovery, there was a sudden significant rebound of the parasympathetic activity (mean RR, HF, pNN50, rMSSD, all < 0.05). After 7 wk of recovery, all heart rate variability indices tended to return to the prestudy values. CONCLUSION: Autonomic nervous system status depends on cumulated physical fatigue due to increased training loads. Therefore, heart rate variability analysis appears to be an appropriate tool to monitor the effects of physical training loads on performance and fitness, and could eventually be used to prevent overtraining states.

Adaptation, Physiological↗

PubMed2002

Assessment of arterial gas pressures and cardiac output using a breathing lung model.

PURPOSE: The aim of this investigation was to evaluate a breathing lung model to estimate arterial gas partial pressures and cardiac output at rest and during exercise. METHODS: A mathematical model was used to describe variations in gas fractions, alveolar volume, and gas exchange in the pulmonary capillaries during the breathing cycle. Experimental data were obtained from 17 healthy subjects at rest and during exercise at 40, 50, 65, and 75% VO(2max) on a cycle ergometer. VO(2), VCO(2), and P(ET,CO2) were monitored continuously with a MedGraphics CPX/D gas exchange system. Arterial gases were measured in brachial artery blood samples drawn simultaneously with gas exchange. Cardiac output was measured using the CO(2) rebreathing method corrected by the arterial blood sample data. The model parameters including cardiac output, end-expiratory alveolar volume, and mixed-venous gas contents were estimated by fitting modelVCO(2) to experimental values over 50 breaths. RESULTS: The fit of model parameters gave arterial gas partial pressures not significantly different from measured data. Measured P(a,C02) and P(a,O2) were significantly correlated with model outputs (R(2) = 0.991 for P(a,CO2) and R(2) = 0.999 for P(a, CO2); P < 0.0001). The cardiac output values estimated using the breathing lung model were significantly correlated with the values obtained with the corrected CO rebreathing method (R(2) = 0.71; P < 0.0001). There was, however, a significant 2.3 L x min(-1) difference between these two methods. CONCLUSION: Results obtained with the proposed method were in good agreement with measured arterial gas partial pressures. Despite a certain degree of bias, the promising results for cardiac output demonstrate the reliability of this method that should be now evaluated using a gold standard method.

Adult↗

PubMed2002

Effects of training frequency on the dynamics of performance response to a single training bout.

The aim of this study was to analyze the effect of an increase in training frequency on exercise-induced fatigue by using a systems model with parameters free to vary over time. Six previously untrained subjects undertook a 15-wk training experiment composed of 1) an 8-wk training period with three sessions per week (low-frequency training), 2) 1 wk without training, 3) a 4-wk training period with five sessions per week [high frequency training (HFT)], and 4) 2 wk without training. The systems input ascribed to training loads was computed from interval exercises and expressed in arbitrary units. The systems output ascribed to performance was evaluated three times each week using maximal power sustained over 5 min. The time-varying parameters of the model were estimated by fitting modeled performances to the measured ones using a recursive least squares method. The variations over time in the model parameters showed an increase in magnitude and duration of fatigue induced by a single training bout. The time needed to recover performance after a training session increased from 0.9 +/- 2.1 days at the end of low-frequency training to 3.6 +/- 2.0 days at the end of HFT. The maximal gain in performance for a given training load decreased during HFT. This study showed that shortening recovery time between training sessions progressively yielded a more persistent fatigue induced by each training.

Adaptation, Physiological↗