Effect of Mathematical Modeling and Fitting Procedures on the Assessment of Critical Speed and Its Relationship With Aerobic Fitness Parameters.

Details

Ressource 1Download: fphys-12-613066.pdf (448.15 [Ko])
State: Public
Version: Final published version
License: Not specified
Serval ID
serval:BIB_BF88AC7E0221
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Effect of Mathematical Modeling and Fitting Procedures on the Assessment of Critical Speed and Its Relationship With Aerobic Fitness Parameters.
Journal
Frontiers in physiology
Author(s)
Patoz A., Pedrani N., Spicher R., Berchtold A., Borrani F., Malatesta D.
ISSN
1664-042X (Print)
ISSN-L
1664-042X
Publication state
Published
Issued date
2021
Peer-reviewed
Oui
Volume
12
Pages
613066
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
An accurate estimation of critical speed (CS) is important to accurately define the boundary between heavy and severe intensity domains when prescribing exercise. Hence, our aim was to compare CS estimates obtained by statistically appropriate fitting procedures, i.e., regression analyses that correctly consider the dependent variables of the underlying models. A second aim was to determine the correlations between estimated CS and aerobic fitness parameters, i.e., ventilatory threshold, respiratory compensation point, and maximal rate of oxygen uptake. Sixteen male runners performed a maximal incremental aerobic test and four exhaustive runs at 90, 100, 110, and 120% of the peak speed of the incremental test on a treadmill. Then, two mathematically equivalent formulations (time as function of running speed and distance as function of running speed) of three different mathematical models (two-parameter, three-parameter, and three-parameter exponential) were employed to estimate CS, the distance that can be run above CS (d'), and if applicable, the maximal instantaneous running speed (s <sub>max</sub> ). A significant effect of the mathematical model was observed when estimating CS, d', and s <sub>max</sub> (P < 0.001), but there was no effect of the fitting procedure (P > 0.77). The three-parameter model had the best fit quality (smallest Akaike information criterion) of the CS estimates but the highest 90% confidence intervals and combined standard error of estimates (%SEE). The 90% CI and %SEE were similar when comparing the two fitting procedures for a given model. High and very high correlations were obtained between CS and aerobic fitness parameters for the three different models (r ≥ 0.77) as well as reasonably small SEE (SEE ≤ 6.8%). However, our results showed no further support for selecting the best mathematical model to estimate critical speed. Nonetheless, we suggest coaches choosing a mathematical model beforehand to define intensity domains and maintaining it over the running seasons.
Keywords
Physiology (medical), Physiology, curve fitting, exercise prescription, exponential model, hyperbolic model, linear model, running
Pubmed
Web of science
Open Access
Yes
Create date
31/05/2021 8:19
Last modification date
30/06/2021 6:34
Usage data