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Viewpoint: Using VO 2max as a marker of training status in athletes - can we do better?

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Tim Podlogar at University of Exeter
Tim Podlogar
Peter Leo
Peter Leo
James Spragg at University of Cape Town
James Spragg
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VIEWPOINT
Using V
_
O
2max
as a marker of training status in athletes—can we do better?
Tim Podlogar,
1,2,3
Peter Leo,
4
and James Spragg
5
1
School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom;
2
Faculty of
Health Sciences, University of Primorska, Izola, Slovenia;
3
Human Performance Centre, Ljubljana, Slovenia;
4
Division of
Performance Physiology & Prevention, Department of Sports Science, University of Innsbruck, Innsbruck, Austria; and
5
Health
through Physical Activity, Lifestyle and Sports (HPALS) Research Centre, Faculty of Health Sciences, University of Cape Town,
Cape Town, South Africa
INTRODUCTION
One of the fundamental premises of research is that find-
ings from a sample of a population can be extrapolated to the
population at large. Therefore, correct classification of the
sample population is of paramount importance, especially as
it has been shown that there is a nonuniform response to the
same intervention in athletes of differing training statuses (1,
2); for example, nitrate supplementation was thought to be
beneficial based on research in nonelite populations; however,
these findings have not been replicated in elite populations
(3). If a strategy described in a research study is to be applied
in real-world athletes, the expected outcomes should be simi-
lar. However, as demonstrated, this may not be the case if
the research study participants are not correctly classified.
In sports science studies, participants are most commonly
classified based on their maximal oxygen uptake (V
_
O
2max
)
and the maximal power at the end of a laboratory-based
graded exercise test (Wmax). The aim of this viewpoint is
to present the argument that the training status of partici-
pants could be better defined. To this end, we suggest that
the power/speed at the boundary of the heavy/severe exer-
cise intensity domain should be reported as the main
descriptor of training status in studies where the reader-
ship may be interested in the performance implications of
a given intervention (Fig. 1).
Limitations of Current Classification Practices
Measuring and reporting relative and absolute V
_
O
2max
val-
ues has a long history in the field of exercise sciences because
it not only offers a prognosis of health outcomes and mortal-
ity but also is more pertinent to the present article; it is
believed that normalization of V
_
O
2max
to body mass is a good
predictor of performance, and thus a broad descriptor of the
training status. As a result, V
_
O
2max
is used to describe study
participants. There is no doubt that V
_
O
2max
is a solid predictor
of endurance performance in a heterogeneous group of par-
ticipants (4). However, using V
_
O
2max
as the primary determi-
nant of participant classification has led to some common
issues within the literature (5).
First, there can be a mismatch between the actual perform-
ance level of athletes and their classification based on their
V
_
O
2max
values. For instance, participants have been classified
as elite despite not even competing at the lowest interna-
tional level (6,7). Second, V
_
O
2max
alone does not predict
differences in performance in a relatively homogenous
group (13). This is elegantly demonstrated by the nonsigni-
ficant differences in V
_
O
2max
in a group of U23 professional
cyclists (9) despite differences in their level of perform-
ance. Third, reported V
_
O
2max
in Olympians, professional
athletes, and world record holders would have them classi-
fied into inferior categories based purely on V
_
O
2max
(8,10,
11). Finally, there can be large discrepancies in V
_
O
2max
between athletes with similar performance capabilities,
for example, V
_
O
2max
in world-class marathon runners can
differ by up to 22 mL·kg
1
·min
1
(10).
Differences in actual performance between athletes may
therefore be related to additional factors (12). First, exercise
economy/efficiency; this parameter describes how well oxy-
gen is converted into locomotion at submaximal intensities
and has been shown to be significantly different between
groups with nonsignificant differences in V
_
O
2max
(13). It
has also been shown that V
_
O
2max
is inversely associated
with running economy (10), indicating that V
_
O
2max
per se
cannot independently predict performance. Second, inter-
individual differences in the maximal sustainable frac-
tional utilization of V
_
O
2max
(%V
_
O
2max
)(14); even though
alone this variable does not always account for differences
in performance (15).
Combined these findings show that V
_
O
2max
can only be
used as a descriptor and a predictor of performance when
other factors are also reported (16,41). This is well demon-
strated as athletes have been shown to improve their per-
formance irrespective of an increase in V
_
O
2max
(17,18).
Recently, a new framework for classification of study
participants has been published (2). This work highlights
similar drawbacks in current practice to those presented
here. It proposes a new classification system based primarily
on training norms and competition results. Although we
are supportive of the ideas presented in this article, the
advantage and disadvantage of this approach is that com-
petitive results are an aggregate of various factors (e.g.,
psychology, tactical skills) and not necessarily just physi-
ology. We believe that in addition to describing competitive
Correspondence: T. Podlogar (tim@tpodlogar.com).
Submitted 19 October 2021 / Revised 8 February 2022 / Accepted 15 February 2022
144 8750-7587/22 Copyright ©2022 the American Physiological Society. http://www.jap.org
J Appl Physiol 133: 144–147, 2022.
First published February 17, 2022; doi:10.1152/japplphysiol.00723.2021
Downloaded from journals.physiology.org/journal/jappl at Univ of Birmingham (104.028.086.085) on July 12, 2022.
status, classifying participants based on their performance
physiology provides an additional layer of information
that is useful from both an academic and applied
perspective.
A pertinent solution could be the application of an external
measure that predicts performance and is a product of the
aforementioned underlying physiological parameters (18).
The suitability of external measures to discriminate between
athletes can be demonstrated using two cycling case studies,
one of a multiple grand tour winner (20) and one of an athlete
with the highest ever recorded V
_O
2max
(21). Comparison
reveals that even having the highest V
_
O
2max
is no guarantee
of success. It also highlights that describing participants
according to an external measure, in this case, power at a
blood lactate concentration of 4 mmol·L
1
,ismorerevealing
than V
_
O
2max
. Namely, the multiple grand tour winner had a
lower V
_
O
2max
but displayed higher power at a lactate concen-
tration of 4 mmol·L
1
.
Power/Speed at the Boundary of the Heavy and Severe
Exercise-Intensity Domains
An enticing option to classifying study participants (in en-
durance sports) would be to use the power/speed at the
boundary of the heavy and severe exercise-intensity domains.
This approach demonstrated a high practical utility in pre-
dicting endurance performance (22) and can differentiate
between performance in athletes with similar V
_
O
2max
(23).
The demarcating intensity between the two domains has
been described as critical power (CP), critical speed (CS), max-
imal lactate steady state (MLSS), or the second ventilatory
threshold (VT2) (24). Although all three represent physiologi-
cal landmarks occurring at a similar exercise intensity, the
current weight of evidence points toward the CP/CS model
offering the most comprehensive explanation of performance
over various exercise durations (25–29). It has also been sug-
gested that the CP/CS best represents the threshold between
steady and nonsteady exercise (24,30,31); however, the argu-
ments surrounding this topic are outside the scope of this
viewpoint (32,33).
We, therefore, propose that CP/CS rather than V
_
O
2max
should be used as the primary descriptor of participants’
training status.
CP/CS was first described as the asymptote of the curvilin-
ear relationship between power/speed and time to task fail-
ure (34). Subsequent developments in the understanding of
the mechanistic basis of the CP/CS means it is currently
understood to be the maximum power or speed at which
there is no metabolite-induced progressive derangement of
muscle cell homeostasis (35). By using the CP/CS concept,
one can also calculate the fixed work capacity above the CP/
CS (W’or D’). W’/D’represents a fixed work capacity above
the CP/CS that can be utilized within the severe exercise-in-
tensity domain (30). Using the CP/CS and W’/D’together it is
possible to predict performance in shorter events (28,36).
Thus, CP/CS, accompanied by the W’/D’, arguably gives an
insight into performance capacity across a wider range of
Figure 1. Classification of research study participants based on maximal oxygen uptake can lead to questionable translation of research results into practice
given that maximal oxygen uptake is not a good predictor of elite endurance performance.
USING V
_
O
2max
AS A MARKER OF TRAINING STATUS IN ATHLETES
J Appl Physiol doi:10.1152/japplphysiol.00723.2021 www.jap.org 145
Downloaded from journals.physiology.org/journal/jappl at Univ of Birmingham (104.028.086.085) on July 12, 2022.
durations and exercise modalities than either V
_
O
2max
or
Wmax (28), or indeed any other measure of the heavy/severe
exercise-intensity domain border (37). Indeed, the CP concept
has been applied to predict performance across exercise
durations from single repetition maximum (29) to marathon
performance (27).
Additional Benefits of Using Critical Power/Speed to
Determine Participant Status
Although there are methodical issues associated with
deriving CP/CS (38), the authors believe that if recognized
guidelines are applied, valid CP/CS estimates can be easily
obtained. CP/CS estimates can easily be derived in both for-
mal laboratory and field-based testing (9,39) without the use
of specialized equipment. Due to the ease of determination,
practitioners can easily derive CP/CS in their own athlete
populations and compare these values with those in a given
study to judge whether an intervention is warranted and
allow a better prediction of the magnitude of potential per-
formance improvements.
V
_
O
2max
and Wmax are also often used in studies to deter-
mine exercise intensity in subsequent interventions. However,
this approach is flawed, as there are interindividual differen-
ces in the percentage of V
_
O
2max
and Wmax at which bounda-
ries between different exercise intensity domains occur. Thus,
different physiological responses between participants can be
observed when anchoring exercise intensity to fractions of
V
_
O
2max
or Wmax (40). If the CP/CS is determined as part of the
classification process, these values can also be used to anchor
exercise intensity in any subsequent intervention.
CONCLUSIONS
Based on the arguments above, it is the authors’opinion
that researchers should be encouraged to describe study par-
ticipants based on the physiological parameters capable of
best predicting performance across a wide range of inten-
sities and to move away from reporting solely V
_
O
2max
.Itis
our belief that application of the CP/CS concept would pro-
vide the most appropriate way to do this.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by
the authors.
AUTHOR CONTRIBUTIONS
T.P., P.L., and J.S. drafted manuscript; T.P., P.L., and J.S. edited
and revised manuscript; T.P., P.L., and J.S. approved final version
of manuscript.
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Supplementary resource (1)

Graphical Abstract
Data
February 2022
Tim Podlogar · Peter Leo · James Spragg
... While the exact reason for this is unclear, it may be that, as POL training is generally characterized by high volumes of low intensity training, it provides sufficient stimulus for physiological adaptation (i.e., VO2max), but not sufficient high intensity training to maximize TT performance. This supposition is supported byPodlogar, et al. (2022), who reported that changes in VO2max may not necessarily correlate to improvements in TT performance in endurance athletes[65].Podlogar, et al. (2022) findings may be partially supported when examining the effect of training intensity distribution on VO2max in the present review, whereby POL caused a medium effect, and NP and NC a small effect, but this did not translate to larger improvements in TT for the POL group. However, it ...
... While the exact reason for this is unclear, it may be that, as POL training is generally characterized by high volumes of low intensity training, it provides sufficient stimulus for physiological adaptation (i.e., VO2max), but not sufficient high intensity training to maximize TT performance. This supposition is supported byPodlogar, et al. (2022), who reported that changes in VO2max may not necessarily correlate to improvements in TT performance in endurance athletes[65].Podlogar, et al. (2022) findings may be partially supported when examining the effect of training intensity distribution on VO2max in the present review, whereby POL caused a medium effect, and NP and NC a small effect, but this did not translate to larger improvements in TT for the POL group. However, it ...
... ntensity training, it provides sufficient stimulus for physiological adaptation (i.e., VO2max), but not sufficient high intensity training to maximize TT performance. This supposition is supported byPodlogar, et al. (2022), who reported that changes in VO2max may not necessarily correlate to improvements in TT performance in endurance athletes[65].Podlogar, et al. (2022) findings may be partially supported when examining the effect of training intensity distribution on VO2max in the present review, whereby POL caused a medium effect, and NP and NC a small effect, but this did not translate to larger improvements in TT for the POL group. However, it ...
View
... Such classification was developed for both men and women (Decroix et al., 2016). Recently, the approach proposed by De Pawn has been subjected to constructive criticism (Podlogar et al., 2022). Although VO 2max strongly correlates with athletic performance, it is not the only factor determining successful performances. ...
... This makes their classification based solely on VO 2max parameters challenging. Critical publication by Podlogar et al sparked discussion on the parameters that should be considered when creating classifications (Podlogar et al., 2022). In this paper, the idea that performance results should form the basis for classifying research participants is reiterated (Valenzuela et al., 2022). ...
View
... Furthermore, measurement of the CV and CP allows for calculation of the finite anaerobic work capacity that can be accomplished at work rates greater than the CV or CP, labeled D ′ for running and W ′ for cycling [20,21]. It is possible that the CV and D ′ measures can provide unique insight into metabolic efficiency during physical work in firefighter or other occupational populations, and they have previously been linked to aerobic capacity [10,22,23]. ...
Exploration of Heart Rate Recovery After Maximal Treadmill and Three-Minute All-Out Shuttle Tests in Firefighters
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The purpose of this study was to compare heart rate recovery (HRR) after a maximal treadmill (MAX-TM) and three-minute all-out (3MT) test between firefighters (FF) and a control (CON) group. Nine male CON and nine male FF participants completed height (m), weight (kg), body fat percent (BF%), normalized handgrip (GRIPNORM, kg/kg), and MAX-TM with direct gas analysis to capture aerobic capacity (VO2PEAK, mL/kg/min). A shuttle-sprint 3MT was used to measure critical velocity (CV, m/s) and D′ (m). Non-linear models determined HR decay (HRRτ), HR asymptote (HR∞), and HR amplitude (HRamp). Two-way GROUP (FF vs. CON) by TEST (MAX-TM vs. 3MT) repeated measures ANOVAs indicated a significant TEST (F = 7.004, p = 0.018) effect on HRamp. When divided by VO2PEAK classification (FITNESS), a significant TEST effect was observed (F = 7.661, p = 0.014) on HRamp. VO2PEAK was significantly related to CV (r = 0.583, p = 0.011), GRIPNORM (r = 0.668, p = 0.002), and BF% (r = −0.890, p < 0.001). Complete autonomic nervous system recovery may depend on the intensity of task demands and cardiorespiratory fitness.
View
... However, the most fatigue-resistant riders in the present study demonstrated a ~2% decline in the 5 km TT power output comparable to observations in top grand tour contenders [10]. Additionally, categorizing riders by functional power output metrics like peak sprint power, FTP, or critical power [33] rather than VO 2max measurements, the subjects in the present study would be ranked around the 50th percentile among professional cyclists [34], confirming their competitive elite status. ...
Performance and Fatigue Patterns in Elite Cyclists During 6 h of Simulated Road Racing
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  • Jul 2024
  • SCAND J MED SCI SPOR
Fatigue resistance is vital for success in elite road cycling, as repeated, intense efforts challenge the athletes' ability to sustain peak performance throughout prolonged races. The present study combined recurrent performance testing and physiological measures during 6 h simulated racing with laboratory testing to investigate factors influencing fatigue resistance. Twelve male national elite cyclists (25 ± 3 years; 76 ± 6 kg and VO2max of 5.2 ± 0.5 L/min) completed incremental power and maximal fat oxidation tests. Subsequently, they underwent field testing with physiological measures and fatigue responses evaluated through peak sprint power and 5 km time trial (TT) testing after 0, 2, 4, and 6 h of exercise. Peak power declined from 1362 ± 176 W in first sprint to 1271 ± 152 W after 2 h (p < 0.01) and then stabilized. In contrast, TT mean power gradually declined from 412 ± 38 W in the first TT to 384 ± 41 W in the final trial, with individual losses ranging from 2% to 14% and moderately correlated (r² = 0.45) to accumulated exercise time above lactate threshold. High carbohydrate intake (~90 g/h) maintained blood glucose levels, but post‐TT [lactate] decreased from 15.1 ± 2 mM to 7.1 ± 2.3 mM, while fat oxidation increased from 0.7 ± 0.3 g/min at 0 h to 1.1 ± 0.1 g/min after 6 h. The study identifies fatigue patterns in national elite cyclists. Peak sprint power stabilized after an initial impairment from 0 to 2 h, while TT power gradually declined over the 6 h simulated race, with increased differentiation in fatigue responses among athletes.
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... and active individuals (20.1-23.2%). This suggests that VȮ 2 max may not be the most reliable indicator of fitness level or training status (114). In addition, it is important to note that our analysis included different exercise modes, and therefore, categorical classifications (such as sedentary, trained, well-trained, etc.) should be avoided in regression analyses (2). ...
Agreement Between Maximal Lactate Steady State and Critical Power in Different Sports: A Systematic Review and Bayesian's Meta-Regression
Article
  • May 2024
  • J STRENGTH COND RES
Borszcz, FK, de Aguiar, RA, Costa, VP, Denadai, BS, and de Lucas, RD. Agreement between maximal lactate steady state and critical power in different sports: A systematic review and Bayesian's meta-regression. J Strength Cond Res 38(6): e320-e339, 2024-This study aimed to systematically review the literature and perform a meta-regression to determine the level of agreement between maximal lactate steady state (MLSS) and critical power (CP). Considered eligible to include were peer-reviewed and "gray literature" studies in English, Spanish, and Portuguese languages in cyclical exercises. The last search was made on March 24, 2022, on PubMed, ScienceDirect, SciELO, and Google Scholar. The study's quality was evaluated using 4 criteria adapted from the COSMIN tool. The level of agreement was examined by 2 separate meta-regressions modeled under Bayesian's methods, the first for the mean differences and the second for the SD of differences. The searches yielded 455 studies, of which 36 studies were included. Quality scale revealed detailed methods and small samples used and that some studies lacked inclusion/exclusion criteria reporting. For MLSS and CP comparison, likely (i.e., coefficients with high probabilities) covariates that change the mean difference were the MLSS time frame and delta criteria of blood lactate concentration, MLSS number and duration of pauses, CP longest predictive trial duration, CP type of predictive trials, CP model fitting parameters, and exercise modality. Covariates for SD of the differences were the subject's maximal oxygen uptake, CP's longest predictive trial duration, and exercise modality. Traditional MLSS protocol and CP from 2-to 15-minute trials do not reflect equivalent exercise intensity levels; the proximity between MLSS and CP measures can differ depending on test design, and both MLSS and CP have inherent limitations. Therefore, comparisons between them should always consider these aspects.
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... Measurements of physical work capacity using cycle ergometers and treadmills have been widely used to assess human endurance training status (Mazaheri et al., 2021;Wiecha et al., 2022). Commonly obtained parameters are achieved mechanic power output (MPO) to compare raw physical performance (Cavagna and Kaneko, 1977;Samozino et al., 2016) and maximal oxygen consumption (VO 2 max) to compare aerobic capacity (Levine, 2008;Wang et al., 2010) among patients (Mazaheri et al., 2021), recreational athletes (Shephard, 2009), and highly trained professionals (Podlogar et al., 1985). VO 2 and VO 2 max values are commonly reported per body mass, but less commonly per lean body mass, fat free mass, or skeletal muscle mass (SMM), in order to offset sex differences based on varying body composition between the sexes (Lewis et al., 1986;Price et al., 2022). ...
The relevance of body composition assessment for the rating of perceived exertion in trained and untrained women and men
Article
Full-text available
  • Aug 2023
Introduction: Mechanic power output (MPO) and oxygen consumption (VO2) reflect endurance capacity and are often stated relative to body mass (BM) but less often per skeletal muscle mass (SMM). Rating of perceived exertion (RPE) has previously shown conflicting results between sexes at submaximal intensities. Individual body composition, however, largely differs due to sex and training status. It was the aim of this study to evaluate RPE of untrained and trained individuals of both sexes considering body composition and to estimate whether RPE could be improved as a tool to determine endurance capacity. Methods: The study included 34 untrained adults (age 26.18 ± 6.34 years, 18 women) and 29 endurance trained (age 27.86 ± 5.19, 14 women) who were measured for body composition (InBody 770, InBody Europe B.V., Germany) and tested on a treadmill (Pulsar, H/P/Cosmos, Germany) for aerobic capacity (Metalyzer 3B, Cortex Biophysik GmbH, Germany) in an all-out exercise test applying the Bruce-protocol. VO2, MPO, heart rate (HR), and RPE were obtained at each exercise stage. VO2 and MPO were calculated per BM and SMM. RPE values were correlated with absolute VO2 and MPO, as well as relative to BM, and SMM. HR values and the parameters’ standardized values served for comparison to standard procedures. Results: VO2 and MPO were higher in men compared to women and in trained compared to untrained participants. No differences between groups and sexes exist when VO2 and MPO were calculated per BM. When calculated per SMM, VO2 and MPO indicate opposite results already at low intensity stages of exercise test. RPE values had highest correlation with MPO per SMM (R² = 0.8345) compared to absolute MPO (R² = 0.7609), or MPO per BM (R² = 0.8176). Agreement between RPE and MPO per SMM was greater than between RPE and HR (p = 0.008). Conclusion: Although RPE represents a subjective value at first glance, it was shown that RPE constitutes a valuable tool to estimate endurance capacity, which can be further enhanced if individual body composition is considered. Furthermore, MPO and VO2 should be considered relative to SMM. These findings might help to avoid over-exertion, especially among untrained people, by adjusting the training intensity for each subject according to the individual strain evaluated in an exercise test based on individual body composition.
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... In instances where the absolute but not the relative V O2max/V O2peak was provided, we used the mean body mass of participants at baseline to estimate relative V O2max/V O2peak and determine whether the article met the inclusion criteria. In an attempt to address recent and very pertinent concerns regarding the classification of participants' training status based on V O2max/V O2peak values [85] [83,84,86] and the absence of large-scale categorisation studies for cyclists with data on physiological thresholds/boundaries, it becomes challenging to accurately group participants in each study based on training status (i.e., recreationally-trained, trained, highly-trained, national level/professional cyclists). ...
Physiological and Performance Adaptations to Interval Training in Endurance-Trained Cyclists: An Exploratory Systematic Review and Meta- Analysis
Preprint
Full-text available
  • Jul 2023
Background: In endurance cycling, both high-intensity interval training (HIIT) and sprint interval training (SIT) have become popular training modalities due to their ability to elicit improvements in performance. Studies have attempted to ascertain which form of interval training might be more beneficial for maximising cycling performance as well as a range of physiological parameters, but an amalgamation of results which explores the influence of different interval training programming variables in trained cyclists has not yet been conducted. Objective: The aims of this study were to: (1) systematically investigate training interventions to determine which training modality, HIIT, SIT or low-to moderate-intensity continuous training (LIT/MICT), leads to greater physiological and performance adaptations in trained cyclists; and (2) determine the moderating effects of interval work-bout duration and intervention length on the overall HIIT/SIT programme. Data Sources: Electronic database searches were conducted using SPORTDiscus and PubMed. Study Selection: Inclusion criteria were: (1) at least recreationally-trained cyclists aged 18-49 years (maximum/peak oxygen uptake [V O2max/V O2peak] ≥45 mL·kg-1 ·min-1); (2) training interventions that included a HIIT or SIT group and a control group (or two interval training groups for direct comparisons); (3) minimum intervention length of 2 weeks; (4) interventions that consisted of 2-3 weekly interval training sessions. Results: Interval training leads to small improvements in all outcome measures combined (overall main effects model, SMD: 0.33 [95%CI = 0.06 to 0.60]) when compared to LIT/MICT in trained cyclists. At the individual level, point estimates favouring HIIT/SIT were negligible (Wingate model: 0.01 [95%CI =-3.56 to 3.57]), trivial (relative V O2max/V O2peak: 0.10 [95%CI =-0.34 to 0.54]), small (absolute V O2max/V O2peak: 0.28 [95%CI = 0.15 to 0.40], absolute maximum aerobic power/peak power output: 0.38 [95%CI = 0.15 to 0.61], relative absolute maximum aerobic power/peak power output: 0.43 [95%CI =-0.09 to 0.95], physiological thresholds: 0.46 [95%CI =-0.24 to 1.17]), and large (time-trial/time-to-exhaustion: 0.96 [95%CI =-0.81 to 2.73]) improvements in physiological/performance variables compared to controls, with very imprecise interval estimates for most outcomes. In addition, intervention length did not contribute significantly to the improvements in outcome measures in this population, as the effect estimate was only trivial (βDuration: 0.04 [ 95%CI =-0.07 to 0.15]). Finally, the network meta-analysis did not reveal a clear superior effect of any HIIT/SIT types when directly comparing interval training differing in interval work-bout duration. Conclusion: The results of the meta-analysis indicate that both HIIT and SIT are effective training modalities to elicit physiological adaptations and performance improvements in trained cyclists. Our analyses highlight that the optimisation of interval training prescription in trained cyclists cannot be solely explained by interval type or interval work-bout duration and an individualised approach that takes into account the training/competitive needs of the athlete is warranted.
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... By combining data from male and female participants with a variety of training statuses, our results showed that participants with a higher VO 2 max had an attenuated increase in both IL-6 and hepcidin compared with individuals with a lower VO 2 max, albeit to a small degree. The use of VO 2 max as a proxy for training status is, however, imperfect and is not a sole predictor of performance across intensities [60]. Furthermore, differences in measurement methods [61] and modality used [62] limit the accuracy of a specific individual's training status classification or characterization of their underlying physiology. ...
Factors Influencing the Hepcidin Response to Exercise: An Individual Participant Data Meta-analysis
Article
Full-text available
  • Jun 2023
  • SPORTS MED
Background Hepcidin, the master iron regulatory hormone, has been shown to peak 3–6 h postexercise, and is likely a major contributor to the prevalence of iron deficiency in athletes. Although multiple studies have investigated the hepcidin response to exercise, small sample sizes preclude the generalizability of current research findings. Objective The aim of this individual participant data meta-analysis was to identify key factors influencing the hepcidin–exercise response. Methods Following a systematic review of the literature, a one-stage meta-analysis with mixed-effects linear regression, using a stepwise approach to select the best-fit model, was employed. Results We show that exercise is associated with a 1.5–2.5-fold increase in hepcidin concentrations, with pre-exercise hepcidin concentration accounting for ~ 44% of the variance in 3 h postexercise hepcidin concentration. Although collectively accounting for only a further ~ 3% of the variance, absolute 3 h postexercise hepcidin concentrations appear higher in males with lower cardiorespiratory fitness and higher pre-exercise ferritin levels. On the other hand, a greater magnitude of change between the pre- and 3 h postexercise hepcidin concentration was largely attributable to exercise duration (~ 44% variance) with a much smaller contribution from VO2max, pre-exercise ferritin, sex, and postexercise interleukin-6 (~ 6% combined). Although females tended to have a lower absolute 3 h postexercise hepcidin concentration [1.4 nmol·L⁻¹, (95% CI [− 2.6, − 0.3]), p = 0.02] and 30% less change (95% CI [–54.4, – 5.1]), p = 0.02) than males, with different explanatory variables being significant between sexes, sample size discrepancies and individual study design biases preclude definitive conclusions. Conclusion Our analysis reveals the complex interplay of characteristics of both athlete and exercise session in the hepcidin response to exercise and highlights the need for further investigation into unaccounted-for mediating factors.
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Fractional utilization of the 10-minute treadmill test velocity in running performance
Article
  • Sep 2024
This study aimed to evaluate the applicability of the 10-minute submaximal treadmill test (T10 test), a self-paced test, in determining critical speed (CS) and predicting running performance. Specifically, we sought to identify the percentage of T10 velocity (vT10) that runners performed in official distance races, and to compare physiological and performance indicators between sexes. 60 recreational runners (n = 34 males and n = 26 females) underwent a maximum incremental test, the novel T10 test, and ran 1200-m and 2400-m on the track. Runners self-reported their best performance times. Generalized Linear Model was used to compare running performances between sexes. For both males and females, the %vT10 in 5km, 10km, and half-marathon races occurred at 107.5% and 106.5%, 99.9% and 100.8%, and 92.6% and 97.1%, respectively. There was no interaction effect (p = .520) and no main effect of sex (p = .443). There was a main effect of distance (p < .001), indicating that %vT10 in the 5km race differed from that found in the 10km race (p = .012), as well as in the half-marathon (p < .001). Our findings suggest that %vT10 values can be used to determine pace in recreational endurance runners for race distances regardless of sex.
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Cannabis Containing THC Impairs 20-Minute Cycling Time Trial Performance Irrespective of the Method of Inhalation
Article
Full-text available
  • Feb 2024
  • J APPL PHYSIOL
Herein we examine the human exercise response following cannabis inhalation, with consideration of varied cannabinoid concentrations and different inhalation methods. A semi-randomized crossover study design was used, with measures of physiological and psychological responses to submaximal and maximal exercise. Participants completed exercise after 1) smoking THC-predominant cannabis (S-THC), 2) inhaling aerosol (vaping) from THC-predominant cannabis (V-THC), 3) inhaling aerosol from CBD-predominant cannabis (V-CBD), or 4) under control conditions. All exercise was performed on a cycle ergometer, with submaximal testing performed at 100W, and exercise performance evaluated using an all-out 20-min time trial. Metabolism was characterised via analysis of expired gases while subjective ratings of perceived exertion (RPE) were reported. During submaximal cycling, heart rate was higher during S-THC and V-THC compared to both control and V-CBD (all p<0.02). During maximal exercise, V̇ E was lower in V-THC compared to control, S-THC, and V-CBD (all p<0.03), as was S-THC compared to control (p<0.05). Both V̇O 2 and RPE were similar between conditions during maximal exercise (both p>0.1). Mean power output during the 20-min time trial was significantly lower in the S-THC and V-THC conditions compared to both control, and V-CBD (all p<0.04). Cannabis containing THC alters the physiological response to maximal and submaximal exercise, largely independent of the inhalation method. THC-containing cannabis negatively impacts vigorous exercise performance during a sustained 20-min effort, likely due to physiological and psychotropic effects. Inhalation of cannabis devoid of THC and primarily containing CBD has little physiological effect on the exercise response or performance.
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Defining Training and Performance Caliber: A Participant Classification Framework
Article
Full-text available
  • Jan 2021
Throughout the sport-science and sports-medicine literature, the term “elite” subjects might be one of the most overused and ill-defined terms. Currently, there is no common perspective or terminology to characterize the caliber and training status of an individual or cohort. This paper presents a 6-tiered Participant Classification Framework whereby all individuals across a spectrum of exercise backgrounds and athletic abilities can be classified. The Participant Classification Framework uses training volume and performance metrics to classify a participant to one of the following: Tier 0: Sedentary; Tier 1: Recreationally Active; Tier 2: Trained/Developmental; Tier 3: Highly Trained/National Level; Tier 4: Elite/International Level; or Tier 5: World Class. We suggest the Participant Classification Framework can be used to classify participants both prospectively (as part of study participant recruitment) and retrospectively (during systematic reviews and/or meta-analyses). Discussion around how the Participant Classification Framework can be tailored toward different sports, athletes, and/or events has occurred, and sport-specific examples provided. Additional nuances such as depth of sport participation, nationality differences, and gender parity within a sport are all discussed. Finally, chronological age with reference to the junior and masters athlete, as well as the Paralympic athlete, and their inclusion within the Participant Classification Framework has also been considered. It is our intention that this framework be widely implemented to systematically classify participants in research featuring exercise, sport, performance, health, and/or fitness outcomes going forward, providing the much-needed uniformity to classification practices.
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Power profiling and the power-duration relationship in cycling: a narrative review
Article
Full-text available
  • Feb 2022
  • EUR J APPL PHYSIOL
Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P ( t ) = W ′/ t + CP ( W ′—work capacity above CP; t —time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.
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Interaction of exercise bioenergetics with pacing behavior predicts track distance running performance
Article
Full-text available
  • Oct 2021
The best possible finishing time for a runner competing in distance track events can be estimated from their critical speed (CS) and the finite amount of energy that can be expended above CS (D'). During tactical races with variable pacing, the runner with the 'best' combination of CS and D' and, therefore, the fastest estimated finishing time prior to the race, does not always win. We hypothesized that final race finishing positions depend on the relationships between the pacing strategy employed, the athletes' initial CS, and their instantaneous D' (i.e., D' balance) as the race unfolds. Using publicly available data from the 2017 IAAF World Championships men's 5,000 m and 10,000 m races, race speed, CS, and D' balance were calculated. The correlation between D' balance and actual finishing positions was non-significant utilizing start-line values but improved to R ² > 0.90 as both races progressed. The D' balance with 400 m remaining was strongly associated with both final 400 m split time and proximity to the winner. Athletes who exhausted their D' were unable to hold pace with the leaders, whereas a high D´ remaining enabled a fast final 400 m and a high finishing position. The D' balance model was able to accurately predict finishing positions in both a 'slow' 5,000 m and a 'fast' 10,000 m race. These results indicate that while CS and D' can characterize an athlete's performance capabilities prior to the start, the pacing strategy that optimizes D' utilization significantly impacts final race outcome.
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Training Characteristics and Power Profile of Professional U23 Cyclists throughout a Competitive Season
Article
Full-text available
  • Mar 2021
Background: The purpose of this study was to investigate differences in the power profile derived from training and racing, the training characteristics across a competitive season and the relationships between training and power profile in U23 professional cyclists. Methods: Thirty male U23 professional cyclists (age, 20.0 ± 1.0 years; weight, 68.9 ± 6.9 kg; V˙O2max, 73.7 ± 2.5 mL·kg-1·min-1) participated in this study. The cycling season was split into pre-, early-, mid- and late-season periods. Power data 2, 5, 12 min mean maximum power (MMP), critical power (CP) and training characteristics (Hours, Total Work, eTRIMP, Work·h-1, eTRIMP·h-1, Time<VT1, TimeVT1-2 and Time>VT2) were recorded for each period. Power profiles derived exclusively from either training or racing data and training characteristics were compared between periods. The relationships between the changes in training characteristics and changes in the power profile were also investigated. Results: The absolute and relative power profiles were higher during racing than training at all periods (p ≤ 0.001-0.020). Training characteristics were significantly different between periods, with the lowest values in pre-season followed by late-season (p ≤ 0.001-0.040). Changes in the power profile between early- and mid-season significantly correlated with the changes in training characteristics (p < 0.05, r = -0.59 to 0.45). Conclusion: These findings reveal that a higher power profile was recorded during racing than training. In addition, training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season.
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The anaerobic threshold: 50+ years of controversy
Article
Full-text available
The anaerobic threshold (AT) remains a widely recognized, and contentious, concept in exercise physiology and medicine. As conceived by Karlman Wasserman, the AT coalesced the increase of blood lactate concentration ([La⁻]), during a progressive exercise test, with an excess pulmonary carbon dioxide output (V̇CO2). Its principal tenets were: limiting oxygen (O2) delivery to exercising muscle→increased glycolysis, La⁻ and H⁺ production→decreased muscle and blood pH→with increased H⁺ buffered by blood [HCO3⁻]→increased CO2 release from blood→increased V̇CO2 and pulmonary ventilation. This schema stimulated scientific scrutiny which challenged the fundamental premise that muscle anoxia was requisite for increased muscle and blood [La⁻]. It is now recognized that insufficient O2 is not the primary basis for lactataemia. Increased production and utilization of La⁻ represent the response to increased glycolytic flux elicited by increasing work rate, and determine the oxygen uptake (V̇O2) at which La⁻ accumulates in the arterial blood (the lactate threshold; LT). However, the threshold for a sustained non‐oxidative contribution to exercise energetics is the critical power, which occurs at a metabolic rate often far above the LT and separates heavy from very heavy/severe‐intensity exercise. Lactate is now appreciated as a crucial energy source, major gluconeogenic precursor and signalling molecule but there is no ipso facto evidence for muscle dysoxia or anoxia. Non‐invasive estimation of LT using the gas exchange threshold (non‐linear increase of V̇CO2 versus V̇O2) remains important in exercise training and in the clinic, but its conceptual basis should now be understood in light of lactate shuttle biology. image
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Physiological demands of running at 2-hour marathon race pace
Article
Full-text available
  • Nov 2020
The requirements of running a 2 hour marathon have been extensively debated but the actual physiological demands of running at ~21.1 km/h have never been reported. We therefore conducted laboratory-based physiological evaluations and measured running economy (O2 cost) while running outdoors at ~21.1 km/h, in world-class distance runners as part of Nike's 'Breaking 2' marathon project. On separate days, 16 male distance runners (age, 29 ± 4 years; height, 1.72 ± 0.04 m; mass, 58.9 ± 3.3 kg) completed an incremental treadmill test for the assessment of V̇O2peak, O2 cost of submaximal running, lactate threshold and lactate turn-point, and a track test during which they ran continuously at 21.1 km/h. The laboratory-determined V̇O2peak was 71.0 ± 5.7 ml/kg/min with lactate threshold and lactate turn-point occurring at 18.9 ± 0.4 and 20.2 ± 0.6 km/h, corresponding to 83 ± 5 % and 92 ± 3 % V̇O2peak, respectively. Seven athletes were able to attain a steady-state V̇O2 when running outdoors at 21.1 km/h. The mean O2 cost for these athletes was 191 ± 19 ml/kg/km such that running at 21.1 km/h required an absolute V̇O2 of ~4.0 L/min and represented 94 ± 3 % V̇O2peak. We report novel data on the O2 cost of running outdoors at 21.1 km/h, which enables better modelling of possible marathon performances by elite athletes. Using the value for O2 cost measured in this study, a sub-2 hour marathon would require a 59 kg runner to sustain a V̇O2 of approximately 4.0 L/min or 67 ml/kg/min.
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An Examination and Critique of Current Methods to Determine Exercise Intensity
Article
Full-text available
  • Oct 2020
  • SPORTS MED
Prescribing the frequency, duration, or volume of training is simple as these factors can be altered by manipulating the number of exercise sessions per week, the duration of each session, or the total work performed in a given time frame (e.g., per week). However, prescribing exercise intensity is complex and controversy exists regarding the reliability and validity of the methods used to determine and prescribe intensity. This controversy arises from the absence of an agreed framework for assessing the construct validity of different methods used to determine exercise intensity. In this review, we have evaluated the construct validity of different methods for prescribing exercise intensity based on their ability to provoke homeostatic disturbances (e.g., changes in oxygen uptake kinetics and blood lactate) consistent with the moderate, heavy, and severe domains of exercise. Methods for prescribing exercise intensity include a percentage of anchor measurements, such as maximal oxygen uptake (V˙O2max{\dot{\text{V}}\text{O}}_{{{\text{2max}}}}), peak oxygen uptake (V˙O2peak{\dot{\text{V}}\text{O}}_{{{\text{2peak}}}}), maximum heart rate (HRmax), and maximum work rate (i.e., power or velocity—W˙max⁡{\dot{\text{W}}}_{{\max}} or V˙max⁡{\dot{\text{V}}}_{{\max}}, respectively), derived from a graded exercise test (GXT). However, despite their common use, it is apparent that prescribing exercise intensity based on a fixed percentage of these maximal anchors has little merit for eliciting distinct or domain-specific homeostatic perturbations. Some have advocated using submaximal anchors, including the ventilatory threshold (VT), the gas exchange threshold (GET), the respiratory compensation point (RCP), the first and second lactate threshold (LT1 and LT2), the maximal lactate steady state (MLSS), critical power (CP), and critical speed (CS). There is some evidence to support the validity of LT1, GET, and VT to delineate the moderate and heavy domains of exercise. However, there is little evidence to support the validity of most commonly used methods, with exception of CP and CS, to delineate the heavy and severe domains of exercise. As acute responses to exercise are not always predictive of chronic adaptations, training studies are required to verify whether different methods to prescribe exercise will affect adaptations to training. Better ways to prescribe exercise intensity should help sport scientists, researchers, clinicians, and coaches to design more effective training programs to achieve greater improvements in health and athletic performance.
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Skeletal muscle AMPK is not activated during 2 h of moderate intensity exercise at ∼65% V̇O2peak in endurance trained men
Article
Full-text available
Key points AMP‐activated protein kinase (AMPK) is considered a major regulator of skeletal muscle metabolism during exercise. However, we previously showed that, although AMPK activity increases by 8–10‐fold during ∼120 min of exercise at ∼65% V̇O2peak in untrained individuals, there is no increase in these individuals after only 10 days of exercise training (longitudinal study). In a cross‐sectional study, we show that there is also a lack of activation of skeletal muscle AMPK during 120 min of cycling exercise at 65% V̇O2peak in endurance‐trained individuals. These findings indicate that AMPK is not an important regulator of exercise metabolism during 120 min of exercise at 65% V̇O2peak in endurance trained men. It is important that more energy is directed towards examining other potential regulators of exercise metabolism. Abstract AMP‐activated protein kinase (AMPK) is considered a major regulator of skeletal muscle metabolism during exercise. Indeed, AMPK is activated during exercise and activation of AMPK by 5‐aminoimidazole‐4‐carboxyamide‐ribonucleoside (AICAR) increases skeletal muscle glucose uptake and fat oxidation. However, we have previously shown that, although AMPK activity increases by 8–10‐fold during ∼120 min of exercise at ∼65% V̇O2peak in untrained individuals, there is no increase in these individuals after only 10 days of exercise training (longitudinal study). In a cross‐sectional study, we examined whether there is also a lack of activation of skeletal muscle AMPK during 120 min of cycling exercise at 65% V̇O2peak in endurance‐trained individuals. Eleven untrained (UT; V̇O2peak = 37.9 ± 5.6 ml.kg⁻¹ min⁻¹) and seven endurance trained (ET; V̇O2peak = 61.8 ± 2.2 ml.kg⁻¹ min⁻¹) males completed 120 min of cycling exercise at 66 ± 4% V̇O2peak (UT: 100 ± 21 W; ET: 190 ± 15 W). Muscle biopsies were obtained at rest and following 30 and 120 min of exercise. Muscle glycogen was significantly (P < 0.05) higher before exercise in ET and decreased similarly during exercise in the ET and UT individuals. Exercise significantly increased calculated skeletal muscle free AMP content and more so in the UT individuals. Exercise significantly (P < 0.05) increased skeletal muscle AMPK α2 activity (4‐fold), AMPK αThr¹⁷² phosphorylation (2‐fold) and ACCβ Ser²²² phosphorylation (2‐fold) in the UT individuals but not in the ET individuals. These findings indicate that AMPK is not an important regulator of exercise metabolism during 120 min of exercise at 65% V̇O2peak in endurance trained men.
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Field-Derived Power–Duration Variables to Predict Cycling Time-Trial Performance
Article
Full-text available
  • Jan 2019
Purpose: To evaluate the predictive validity of critical power (CP) and the work above CP (W') on cycling performance (mean power during a 20-min time trial; TT20). Methods: On 3 separate days, 10 male cyclists completed a TT20 and 3 CP and W' prediction trials of 1, 4, and 10 min and 2, 7, and 12 min in field conditions. CP and W' were modeled across combinations of these prediction trials with the hyperbolic, linear work/time, and linear power inverse-time (INV) models. The agreement and the uncertainty between the predicted and actual TT20 were assessed with 95% limits of agreement and a probabilistic approach, respectively. Results: Differences between the predicted and actual TT20 were "trivial" for most of the models if the 1-min trial was not included. Including the 1-min trial in the INV and linear work/time models "possibly" to "very likely" overestimated TT20. The INV model provided the smallest total error (ie, best individual fit; 6%) for all cyclists (305 [33] W; 19.6 [3.6] kJ). TT20 predicted from the best individual fit-derived CP, and W' was strongly correlated with actual TT20 (317 [33] W; r = .975; P < .001). The bias and 95% limits of agreement were 4 (7) W (-11 to 19 W). Conclusions: Field-derived CP and W' accurately predicted cycling performance in the field. The INV model was most accurate to predict TT20 (1.3% [2.4%]). Adding a 1-min-prediction trial resulted in large total errors, so it should not be included in the models.
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Case studies in Physiology: temporal changes in determinants of aerobic performance in individual going from alpine skier to world champion time trial cyclist
Article
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  • Jun 2019
This paper reports temporal changes in physiological measurements of exercise performance in a young man transitioning from alpine skiing until he became a world junior champion time trial cyclist after only 3 yr of bike-specific training. At the time he became World Champion he also achieved among the highest reported maximal oxygen uptake (V̇o 2max ) value, 96.7 ml·min ⁻¹ ·kg ⁻¹ , or 7,397 ml/min in absolute terms at 76.5 kg, which had increased by 29.6% from 74.6 ml·min ⁻¹ ·kg ⁻¹ pre-bike-specific training. After 15 mo with almost no structured exercise training, V̇o 2max returned to 77.0 mL·min ⁻¹ ·kg ⁻¹ and was similar to the value reported before specific bike training, albeit with absolute term (6,205 ml/min) still being 11.3% higher. Part of the explanation for his athletic achievements is likely also related to the up to 20.9% improvement in Power@4 mmol/l (W). Although genetic profiles of endurance athletes have not generated data suggesting a shared genetic signature associated with elite endurance performance, this case study highlights the importance of intrinsic biological factors in elite endurance performance. NEW & NOTEWORTHY This study shows that very high V̇o 2max values (>70 ml·min ⁻¹ ·kg ⁻¹ ) can be found in individuals not previously specializing in aerobic training and that values of >90 ml·min ⁻¹ ·kg ⁻¹ , as well as a cycling world junior champion title, can be achieved in such individuals with just 3 yr of dedicated exercise training.
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