Assembling more O2 uptake responses: Is it possible to merely stack the repeated transitions?

Francescato, Maria Pia; Cettolo, Valentina; Bellio, Ruggero

doi:10.1016/j.resp.2014.06.004

“…Therefore, at least in the light exercise domain, mere stacking of multiple repetitions was proposed if the data were from the same O 2 on rest-to-exercise transient (Bringard et al 2014 ; Francescato et al 2014b , a ). As such, attempts at improving the time resolution beyond the single-breath duration could rely only on computational manipulations, such as superimposition of several trials and interpolation procedures (Francescato et al 2014a ; Francescato and Cettolo 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Training status affects between-protocols differences in the assessment of maximal aerobic velocity

Riboli

¹

,

Rampichini

²

,

Cè

³

et al. 2021

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Purpose Continuous incremental protocols (CP) may misestimate the maximum aerobic velocity (Vmax) due to increases in running speed faster than cardiorespiratory/metabolic adjustments. A higher aerobic capacity may mitigate this issue due to faster pulmonary oxygen uptake ($$\dot{V}$$ V ˙ O2) kinetics. Therefore, this study aimed to compare three different protocols to assess Vmax in athletes with higher or lower training status. Methods Sixteen well-trained runners were classified according to higher (HI) or lower (LO) $$\dot{V}$$ V ˙ O2max$$\dot{V}$$ V ˙ O2-kinetics was calculated across four 5-min running bouts at 10 km·h−1. Two CPs [1 km·h−1 per min (CP1) and 1 km·h−1 every 2-min (CP2)] were performed to determine Vmax$$\dot{V}$$ V ˙ O2max, lactate-threshold and submaximal $$\dot{V}$$ V ˙ O2/velocity relationship. Results were compared to the discontinuous incremental protocol (DP). Results Vmax, $$\dot{V}$$ V ˙ O2max, $$\dot{V}$$ V ˙ CO2 and VE were higher [(P < 0.05,(ES:0.22/2.59)] in HI than in LO. $$\dot{V}$$ V ˙ O2-kinetics was faster [P < 0.05,(ES:-2.74/ − 1.76)] in HI than in LO. $$\dot{V}$$ V ˙ O2/velocity slope was lower in HI than in LO [(P < 0.05,(ES:-1.63/ − 0.18)]. Vmax and $$\dot{V}$$ V ˙ O2/velocity slope were CP1 > CP2 = DP for HI and CP1 > CP2 > DP for LO. A lower [P < 0.05,(ES:0.53/0.75)] Vmax-difference for both CP1 and CP2 vs DP was found in HI than in LO. Vmax-differences in CP1 vs DP showed a large inverse correlation with Vmax, $$\dot{V}$$ V ˙ O2max and lactate-threshold and a very large correlation with $$\dot{V}$$ V ˙ O2-kinetics. Conclusions Higher aerobic training status witnessed by faster $$\dot{V}$$ V ˙ O2 kinetics led to lower between-protocol Vmax differences, particularly between CP2 vs DP. Faster kinetics may minimize the mismatch issues between metabolic and mechanical power that may occur in CP. This should be considered for exercise prescription at different percentages of Vmax.

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“…It can be easily demonstrated that a linear interpolation on a 0.01 s-by-0.01 s basis will result in a further narrowing, roughly by the factor √ 1s 0.01s = 10 , of the CI [1]; following this reasoning, the narrowest CI would be obtained by interpolating the data on an infinitesimal-by-infinitesimal basis.In the paper by Francescato et al[1] we demonstrate through Monte Carlo simulations that an asymptotic CI close to the correct one is obtainable by resampling the responses at a time interval slightly longer than the average breath duration.Narrower, but still correct, confidence intervals can be obtained only by increasing the original information used (i.e., increasing the number of repeated trials). The simplest method to assemble together the different trials is to append the responses one after the other ("stacking" of the repeated responses); this method allows obtaining accurate estimated parameters and congruent CI when the non-linear regression technique is applied [2].…”

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confidence: 99%

“…Narrower, but still correct, confidence intervals can be obtained only by increasing the original information used (i.e., increasing the number of repeated trials). The simplest method to assemble together the different trials is to append the responses one after the other ("stacking" of the repeated responses); this method allows obtaining accurate estimated parameters and congruent CI when the non-linear regression technique is applied [2].…”

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confidence: 99%

The 1-s interpolation of breath-by-breath O2 uptake data to determine kinetic parameters: the misleading procedure

Francescato

¹

,

Cettolo

²

2019

Self Cite

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Dear Editor-in-Chief, Zignoli et al. [3] have recently published a thorough paper concerning the state of the art concepts and future directions in modelling oxygen consumption in cycling exercise. Among the "Limitations of the hand-written models", the authors quote one of our papers [1], attributing to us a concept that had never been expressed in the cited paper and that is exactly the contrary of what is stated in the paper.As discussed in detail in our paper [1], the accuracy of the parameters estimated applying the non-linear regression procedure is not affected by whatever data treatment is applied (from nothing to the resampling and/or the averaging of more repetitions). Conversely, the confidence interval (CI) of the parameter estimates are sensible to the applied data treatment. In particular, by interpolating the oxygen uptake data at 1-s for kinetic analysis, no improvement of the parameter CI is obtained, but rather the CI necessarily becomes falsely smaller compared to the correct one. This phenomenon is due to the fact that the interpolation procedure increases the number of data points, but the already available information is only reiterated in the added data points, without introducing new information. It can be easily demonstrated that a linear interpolation on a 0.01 s-by-0.01 s basis will result in a further narrowing, roughly by the factor √ 1s 0.01s = 10 , of the CI [1]; following this reasoning, the narrowest CI would be obtained by interpolating the data on an infinitesimal-by-infinitesimal basis.In the paper by Francescato et al.[1] we demonstrate through Monte Carlo simulations that an asymptotic CI close to the correct one is obtainable by resampling the responses at a time interval slightly longer than the average breath duration.Narrower, but still correct, confidence intervals can be obtained only by increasing the original information used (i.e., increasing the number of repeated trials). The simplest method to assemble together the different trials is to append the responses one after the other ("stacking" of the repeated responses); this method allows obtaining accurate estimated parameters and congruent CI when the non-linear regression technique is applied [2].

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“…Conversely, the CIs reported for the NIRS and heart rate data were appropriate, since the native time resolution of these parameters is finest compared to the data acquisition one, and the data points were not "cloned".Notably, the statistical packages running the nonlinear regression are able to deal with data showing a variable time resolution, without the need of data reordered in time. As a matter of fact, it has been shown that, simply appending one after the other, the gas exchange data of the repeated transitions ("stacking"), without modifying the native time resolution, allow to obtain ASE values for the time parameters (time delay and time constant) that yield an appropriate quantification of uncertainty (Francescato et al 2014b). We believe that, to retain all the information contained in values collected with a time resolution that changes throughout the acquisition, the "stacking" procedure is the most correct one.…”

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confidence: 99%