Modelling of aerobic and anaerobic energy production during exhaustive exercise on a cycle ergometer

Chatagnon, Michel; Busso, Thierry

doi:10.1007/s00421-006-0236-3

Cited by 13 publications

(12 citation statements)

References 25 publications

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“…Previous applications of the segmented model postulated that the rise in _ VO 2 could be described by a mono-exponential function Chatagnon and Busso 2006). The analysis of the sensitivity to the value chosen for the time constant showed that it affected only the estimates of W 0 .…”

Section: Discussionmentioning

confidence: 99%

“…1 Schematic representation of how the segmented model describes the aerobic (P aer ) and anaerobic (P anaer ) contribution to total power output according to P c and P t , the critical power and the power eliciting maximal aerobic power, respectively. The curves were built with the average data from Chatagnon and Busso (2006), i.e. P c = 201 W, P t = 295 W and a = 0.446 giving P aermax = 253 W A target pedalling frequency of 75 rpm was imposed using a metronome.…”

Section: Experimental Datamentioning

confidence: 99%

“…When using power lower than maximal aerobic power to establish the power-time to exhaustion relationship, W 0 was almost twice the maximal oxygen deficit, although a significant correlation was observed between them (Chatagnon et al 2005). This led us to propose a segmented model with a break point (P t ) corresponding to the lowest power required to achieve the maximal aerobic power Chatagnon and Busso 2006), whereas P c remained the power asymptote for time tending towards infinity. This model assumed that the power produced at the expense of aerobic metabolism increases with total power output until a maximal level reached when the total power was equal to P t The model assumptions about contribution of aerobic and anaerobic metabolism are represented in Fig.…”

Section: Introductionmentioning

confidence: 95%

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A comparison of modelling procedures used to estimate the power–exhaustion time relationship

2009

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This study aimed to test the consistency of using the power required to elicit maximal oxygen uptake during incremental test (P (t)) to demarcate the range of power intensity in the modelling of the power-exhaustion time relationship. Different mathematical procedures were tested using data from ten subjects exercising on a cycle ergometer. After the determination of P (t) and the power at the ventilatory threshold, the subjects did six tests at constant power to exhaustion within 2-15 min. Estimates were obtained from a segmented model using two distinct equations of the anaerobic contribution to power below and above P (t), respectively. This model fit the overall data with a better adequacy than the simple hyperbolic model (standard error of 29.2 +/- 25.2 vs. 42.3 +/- 25.2 s). The power asymptotes were 225.7 +/- 27.3 W from the segmented model, 226.2 +/- 27.3 and 283.3 +/- 20.5 W from the simple model applied to data below and above P (t), respectively. The estimates from the segmented model were strongly correlated with their analogues from the simple model applied only to data below P (t) (R = 1.00 for power asymptote and curvature coefficient). They were not correlated with their analogues from the simple model applied only to data above P (t). These discrepancies between modelling procedures could arise from the method used to determine P (t) and the oversimplification of the oxygen uptake kinetics. These limitations could lead the segmented model to an overestimation of the anaerobic contribution which was around 15% of total energy expended at P (t).

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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A comparison of modelling procedures used to estimate the power–exhaustion time relationship

2009

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“…Furthermore, the simplifying assumption is also made that the anaerobic contribution to the overall energy requirement is nil up to _ VO 2max ; a fact that is not always necessarily true (e.g. see Busso and Chatagnon 2006;Chatagnon and Busso 2006).…”

Section: Discussionmentioning

confidence: 99%

The critical velocity in swimming

et al. 2007

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In supra-maximal exercise to exhaustion, the critical velocity (cv) is conventionally calculated from the slope of the distance (d) versus time (t) relationship: d = I + St. I is assumed to be the distance covered at the expense of the anaerobic capacity, S the speed maintained on the basis of the subject's maximal O(2) uptake (VO2max) This approach is based on two assumptions: (1) the energy cost of locomotion per unit distance (C) is constant and (2) VO2max is attained at the onset of exercise. Here we show that cv and the anaerobic distance (d (anaer)) can be calculated also in swimming, where C increases with the velocity, provided that VO2max its on-response, and the C versus v relationship are known. d (anaer) and cv were calculated from published data on maximal swims for the four strokes over 45.7, 91.4 and 182.9 m, on 20 elite male swimmers (18.9 +/- 0.9 years, 75.9 +/- 6.4 kg), whose VO2max and C versus speed relationship were determined, and compared to I and S obtained from the conventional approach. cv was lower than S (4, 16, 7 and 11% in butterfly, backstroke, breaststroke and front crawl) and I (=11.6 m on average in the four strokes) was lower than d (anaer). The latter increased with the distance: average, for all strokes: 38.1, 60.6 and 81.3 m over 45.7, 91.4 and 182.9 m. It is concluded that the d versus t relationship should be utilised with some caution when evaluating performance in swimmers.

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“…During this work/ ventilation mismatch, an individual's energy needs are met by anaerobic processes (McArdle et al, 2001). Complete utilization of the anaerobic work capacity has been used to model the point at which an individual is exhausted (Chatagnon and Busso, 2006). A measure of this anaerobic work is the magnitude of mismatch between energy expenditure and oxygen consumption.…”

Section: Methodsmentioning

confidence: 99%

Modeling energy expenditure and oxygen consumption in human exposure models: accounting for fatigue and EPOC

Isaacs

Glen

McCurdy

et al. 2007

J Expo Sci Environ Epidemiol

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Human exposure and dose models often require a quantification of oxygen consumption for a simulated individual. Oxygen consumption is dependent on the modeled individual's physical activity level as described in an activity diary. Activity level is quantified via standardized values of metabolic equivalents of work (METS) for the activity being performed and converted into activity-specific oxygen consumption estimates. However, oxygen consumption remains elevated after a moderate-or high-intensity activity is completed. This effect, which is termed excess post-exercise oxygen consumption (EPOC), requires upward adjustment of the METS estimates that follow high-energy expenditure events, to model subsequent increased ventilation and intake dose rates. In addition, since an individual's capacity for work decreases during extended activity, methods are also required to adjust downward those METS estimates that exceed physiologically realistic limits over time. A unified method for simultaneously performing these adjustments is developed. The method simulates a cumulative oxygen deficit for each individual and uses it to impose appropriate time-dependent reductions in the METS time series and additions for EPOC. The relationships between the oxygen deficit and METS limits are nonlinear and are derived from published data on work capacity and oxygen consumption. These modifications result in improved modeling of ventilation patterns, and should improve intake dose estimates associated with exposure to airborne environmental contaminants.

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Modelling of aerobic and anaerobic energy production during exhaustive exercise on a cycle ergometer

Cited by 13 publications

References 25 publications

A comparison of modelling procedures used to estimate the power–exhaustion time relationship

A comparison of modelling procedures used to estimate the power–exhaustion time relationship

The critical velocity in swimming

Modeling energy expenditure and oxygen consumption in human exposure models: accounting for fatigue and EPOC

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