Abstract
INTRODUCTION:
Gas exchange data from maximum oxygen uptake (VO2max) testing typically requires post-processing. Different processing strategies can lead to varying VO2max values [1]. This affects their interpretation particularly in situations where small differences in VO2max matter (e.g. training monitoring or clinical classification) [2]. However, the exact processing strategies used in the literature have not been systematically investigated yet [3]. Previous research investigated differences across methods at the group level only [1].
METHODS:
Based on preregistered methods and code, we conducted a scoping review and an analysis of experimental data. Out of a random sample, we investigated 242 recently published articles which measured VO2max during ramp(-like) tests. Reported data processing methods and their rationale were extracted. We compared the most common processing strategies on a data set of 72 standardized running exercise tests in trained athletes.
RESULTS:
Almost all of the included studies (94.2%) failed to provide a rationale for the particular strategy chosen and 44.2% did not report their data processing strategy at all. In those which reported their strategy, most studies (79.5%) used binned time averages to determine VO2max, with a minority using moving time (6.8%), moving breath (5.7%) or other averaging methods (8.0%). Despite previous recommendations [3], no study reported the use of digital filters. The processing strategies found in the literature can lead to median differences in VO2max of more than 5% (range 0-7%) with considerable variation at the individual level.
CONCLUSION:
Data processing strategies have a meaningful impact on determining VO2max. Hence, we recommend to report the following seven relevant items: (1) metabolic cart model, (2) measurement mode, (3) analysis software, (4) preprocessing routine, (5) processing strategy type, (6) processing strategy parameters, (7) rationale for the processing. To improve reproducibility, we encourage authors to use available software solutions [5] and share their analysis code.
[1] Martin-Rincon et al. (2018) Scand J Med Sci Sports
[2] Johnson et al. (1998) Chest
[3] Robergs et al. (2010) Sports Med
[4] Nolte (2023) J Open Source Softw
Gas exchange data from maximum oxygen uptake (VO2max) testing typically requires post-processing. Different processing strategies can lead to varying VO2max values [1]. This affects their interpretation particularly in situations where small differences in VO2max matter (e.g. training monitoring or clinical classification) [2]. However, the exact processing strategies used in the literature have not been systematically investigated yet [3]. Previous research investigated differences across methods at the group level only [1].
METHODS:
Based on preregistered methods and code, we conducted a scoping review and an analysis of experimental data. Out of a random sample, we investigated 242 recently published articles which measured VO2max during ramp(-like) tests. Reported data processing methods and their rationale were extracted. We compared the most common processing strategies on a data set of 72 standardized running exercise tests in trained athletes.
RESULTS:
Almost all of the included studies (94.2%) failed to provide a rationale for the particular strategy chosen and 44.2% did not report their data processing strategy at all. In those which reported their strategy, most studies (79.5%) used binned time averages to determine VO2max, with a minority using moving time (6.8%), moving breath (5.7%) or other averaging methods (8.0%). Despite previous recommendations [3], no study reported the use of digital filters. The processing strategies found in the literature can lead to median differences in VO2max of more than 5% (range 0-7%) with considerable variation at the individual level.
CONCLUSION:
Data processing strategies have a meaningful impact on determining VO2max. Hence, we recommend to report the following seven relevant items: (1) metabolic cart model, (2) measurement mode, (3) analysis software, (4) preprocessing routine, (5) processing strategy type, (6) processing strategy parameters, (7) rationale for the processing. To improve reproducibility, we encourage authors to use available software solutions [5] and share their analysis code.
[1] Martin-Rincon et al. (2018) Scand J Med Sci Sports
[2] Johnson et al. (1998) Chest
[3] Robergs et al. (2010) Sports Med
[4] Nolte (2023) J Open Source Softw
| Originalsprache | Englisch |
|---|---|
| Titel | eProceedings of the European College of Sport Science (ECSS) : 28th Annual Congress of the European College of Sport Science, Explore Enlighten, Perform, 4-7 July 2023, France |
| Herausgeber*innen | G. Guilhem, G. Rabita, F. Brocherie, E. Tsolakidis, A. Ferrauti, J.W. Helge, M.F. Piacentini |
| Erscheinungsort | Paris |
| Herausgeber (Verlag) | ECSS |
| Erscheinungsdatum | 05.07.2023 |
| ISBN (Print) | 978-3-9818414-6-6 |
| Publikationsstatus | Veröffentlicht - 05.07.2023 |
| Veranstaltung | Annual Congress of the European College of Sport Science: Explore, Enlighten, Perform - Palais des Congrès de Paris, Paris, Frankreich Dauer: 04.07.2023 → 07.07.2023 Konferenznummer: 28 https://sport-science.org/index.php/congress/ecss-paris-2023 |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
-
SDG 3 – Gute Gesundheit und Wohlergehen
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