There is a great discussion in the recent viewpoint from @timpodlogar, @peter__leo, @Spragg_Perform and commentators on the question of using V̇O2max as the principal (or exclusive) marker of athlete ‘training status’ when categorising athletes and subjects for sport science research.[1, 2, 3]
With the advantage of having read the commentaries and being able to exceed the word limit*, here is my perspective that:
- V̇O2max is not a good marker of ‘training status’, but,
- V̇O2max is a good measure of ‘fitness’, which is a related but separate construct in a 3-component model of athlete classification. However,
- V̇O2max cannot be the only parameter of fitness, much less the only parameter by which subjects are categorised in sport science
* A more concise, but still multi-tweet-thread summary is posted here
I like to think about classifying athletes along a 3-dimesional model that considers:
- Fitness: a measure of integrated physiological capacity and efficiency
- Performance: a marker for task- or sport-specific capability and competitive outcomes relative to peers
- Training status: an indication of sport-specific experience and existing adaptations that suggests how easy or difficult it may be to observe further enhancements
This model isn’t validated by any means. This is me trying to structure my thinking on the topic based on my own limited experience and education alongside far more experienced sport scientists.
The model is based heavily from existing literature on athlete classification, which tends to combine aspects of these three categories into a single model.[4, 5, 6, 7]
The first dimension quantifies physiological fitness. Traditionally, three interrelated physiological parameters can explain a large proportion of the variance in performance between heterogenous groups of athletes:[8, 9]
- V̇O2max, the maximal aerobic capacity representing the maximal sustainable flux or ‘throughput’ of energy (ATP) provision from O2.
- Exercise efficiency, which encompasses many different biological processes but broadly represents a common, if not inevitable trade-off between the capacity (max flux, eg. V̇O2max) of a given biological process and the ratio of input resources to output products over time. Including mechanical, thermodynamic, substrate, enzymatic, and other dimensions of efficiency.[10, 11, 12, 13]
- Fatigue threshold, which represents the fractional utilisation of V̇O2max that can be sustained at a metabolic equilibrium, eg. operationalised as CP, MLSS, FTP, RCP, LT2, Deoxy-BP, etc.
To me, with the weight of historical precedent (path dependency matters, as my business friends like to say) an incremental exercise test (IET) either ramp or step protocol is ideal to test physiological fitness in the lab setting.
An IET with pulmonary gas exchange can quantify all three measures mentioned above to explain performance outcomes, including a plurality of efficiency dimensions.
Furthermore, this can be done with no additional burden to subjects, and minimal financial burden to laboratories since all the data can come from a single test with a single (albeit historically quite expensive) metabolic cart.
In addition to V̇O2max, Wmax (peak power) or vV̇O2max (velocity at V̇O2max) and aerobic energy expenditure are measures of efficiency at maximal aerobic capacity.[14, 15] Ventilation and gas exchange can be used to demarcate intensity domains with application for prescribing training.
Substrate utilisation, although highly sensitive to transient factors (primarily diet and acute intramuscular & circulatory availability) can indicate metabolic flexibility with both health and performance outcomes, and has applications for prescriptive nutrition.[17, 18] V̇O2 kinetics, an important parameter of fitness as the ‘responsiveness’ of aerobic energy provision to exercise onset, can be detected with a ramp protocol.
Although, there are notable omissions unavailable with an IET protocol, including fatigue resistance, ‘durability’, repeatability, and ‘anaerobic’ capacity.[20, 21, 22, 23]
Whereas fitness relates primarily to physiological traits, performance relates to the athlete’s capability to integrate physiology with psychology, logistical preparation, pacing, strategy, tactics, luck, opportunity, and all the other intangible unquantifiable aspects that lead to success in competitive sport.[24, 25, 26, 27]
Performance categorisation should be sport-specific and may be classified by in-competition outcomes themselves, ie. being 100% sport-specific. Or otherwise, performances can be simulated in lab settings or collected from historical data.
Whereas I argue for V̇O2max as a marker of fitness rather than training status, I would similarly suggest that Podlogar et al’s recommendation for critical power / critical speed (CP / CS) may be more appropriate as a marker of performance, rather than training status. Without delving too far into the wonderful debate around CP,[28, 29] it seems to me that CP is a useful context-specific performance metric that has been maybe over-precisely defined as a physiological construct (MMSS) and whose operational methods have been wrangled to fit that definition.
Thus, as Valenzuela et al suggest in their commentary,[2; p. 148] a more parsimonious performance outcome measure might be a test of mean maximal power (MMP), or simply a time trial (TT) for set duration or distance.
The benefit of a TT is that it integrates physiological fitness with pacing and experience; the same performance might be achieved by athletes following different pacing strategies relating to their different physiological phenotypes. eg. higher V̇O2max and lower exercise economy, or the reverse, which reflects real-world heterogeneity among ‘equal’ competitors.[30, 31]
Of course, TT performance outcomes could be taken from competition itself if the interest is primarily on measuring an athlete relative to their peers. Performance categories can be based on world-record performances or official rankings, but questions may arise about for how long a historical personal best remains relevant to current performance levels. Age gating could be used not just to compare similar cohorts of athletes across age, but to provide a time range in which historical performances can be considered.
Another option could be to constrain categorisation to performances demonstrated in the lab setting in which the investigation is being performed. This out of competition, ‘on-demand’ performance may better represent the athlete’s average capabilities, rather than categorise them from their absolute peak performance, which likely represents an outlier to their own typical abilities.
Of course, longer events will not be able to be reproduced in the lab, and it is often not desirable for athletes to be asked to perform ‘all-out’ outside of competition, which can disrupt training and impose risk of injury.
To me, training status is differentiated from fitness or performance as it represents how long an athlete has worked at their sport; how much of the low-hanging fruit of, for example, physiological adaptations and skill acquisition, have they already realised, and therefore how difficult may it be to elicit further enhancements to sport-specific fitness and performance with a sport science intervention.
One can imagine a gifted novice with no training history in a particular sport but high baseline fitness who has similar performance and a greater capacity to improve with training, compared to an experienced but lower fitness veteran. And of course, one can consider the difficulty in observing improvements to elite athletes’ physiological measurements and performances with training interventions.[32, 33, 34]
Despite this ostensibly being the premise of the viewpoint, I unfortunately have the least informed opinion about how best to operationalise training status. Simple self-report of training years and weekly or annual volume may be the most parsimonious and would at least be consistent with existing literature.
More detailed parameterisations of training load are common in most contemporary sports, but validity and consistency or ‘comparability’ between sports may be questioned.[35, 36, 37]
Recommendations for Authors
Mentioned at the top of this article, current athlete classification systems tend to combine aspects of these three dimensions into a single, comprehensive framework.[4, 5, 6, 7] This may still be appropriate and necessary, especially for quantitative meta-comparison between athlete groups across studies.
However, appreciating the significant overlap between these three dimensions, I think it is useful to consider athletes within each criteria intentionally, if not independently, and consider how aspects of each may contribute to the research question, study design, observed results, and interpretations of a sport science investigation.
Instead of a single cohesive classification, my opinion is that authors should provide operational definitions for how they grouped their subjects, according to which parameters are most relevant in their context.
Of course, this is already happening, and the purpose of the published viewpoint and my own article is to get authors to think a little bit more rigorously about how they report athlete classification.
I won’t offer a list of recommended jargon here, but for example, we will aim to report our subject groups as something like ‘well-trained amateur female and male cycle-sport athletes of heterogenous fitness’. Which is ambiguous on it’s own, but would be further defined in the text:
- Well-trained: Training Status; defined as 2+ yrs of sport-specific training.
- Amateur: Performance; competitive at an amateur, sub-national level.
- Female and male: sex is another dimension that must be considered.
- Cycle-sport athletes: defining a sport or modality in which specific training is expected; eg. cyclists & triathletes.
- Heterogenous fitness: Fitness; further defined by a combination of both V̇O2max and Wmax at minimum, likely with an additional measure of fractional utilisation at a relevant fatigue threshold, eg. Deoxy-BP for NIRS, or RCP for pulmonary gas exchange.
- Podlogar T, Leo P, Spragg J. (2022) Viewpoint: Using VO2max as a marker of training status in athletes – can we do better? J Appl Physiol. https://doi.org/10.1152/japplphysiol.00723.2021
- Valenzuela PL, Mateo-March M, Muriel X, et al. (2022) Commentaries on Viewpoint: Using VO2max as a marker of training status in athletes – can we do better? J Appl Physiol. https://doi.org/10.1152/japplphysiol.00224.2022
- Podlogar T, Leo P, Spragg J. (2022) Last Word on Viewpoint: Using VO2max as a marker of training status in athletes–can we do better? J Appl Physiol. https://doi.org/10.1152/japplphysiol.00238.2022
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- Decroix L, De Pauw K, Foster C, et al. (2016) Guidelines to Classify Female Subject Groups in Sport-Science Research. Int J Sports Physiol Perform. https://doi.org/10.1123/ijspp.2015-0153
- McKay AKA, Stellingwerff T, Smith ES, et al. (2021) Defining Training and Performance Caliber: A Participant Classification Framework. Int J Sports Physiol Perform. https://dx.doi.org/10.1123/ijspp.2021-0451
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