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Received: 30 September 2022 Accepted: 22 December 2022
DOI: 10.1113/EP090878
RESEARCH ARTICLE
Variability in exercise tolerance and physiological responses to
exercise prescribed relative to physiological thresholds and to
maximum oxygen uptake
Samuel Meyler Lindsay Bottoms David Wellsted Daniel Muniz-Pumares
School of Life and Medical Sciences, University
of Hertfordshire, Hatfield, UK
Correspondence
Daniel Muniz-Pumares, School of Life and
Medical Sciences, University of Hertfordshire,
Hatfield, UK.
Email: d.muniz@herts.ac.uk
Handling Editor: Damian Bailey
Abstract
The objective of this study was to determine whether the variability in exercise
tolerance and physiological responses is lower when exercise is prescribed relative
to physiological thresholds (THR) compared to traditional intensity anchors (TRAD).
Ten individuals completed a series of maximal exercise tests and a series of moderate
(MOD), heavy (HVY) and severe intensity (HIIT) exercise bouts prescribed using THR
intensity anchors (critical power and gas exchange threshold) and TRAD intensity
anchors (maximum oxygen uptake;
VO2max). There were no differences in exercise
tolerance or acute response variability between MODTHR and MODTRAD. All individuals
completed HVYTHR but only 30% completed HVYTRAD. Compared to HVYTHR ,where
work rates were all below critical power, work rates in HVYTRAD exceeded critical
power in 70% of individuals. There was, however, no difference in acute response
variability between HVYTHR and HVYTRAD. All individuals completed HIITTHR but only
20% completed HIITTRAD. The variability in peak (F=0.274) and average (F=0.318)
blood lactate responses was lower in HIITTHR compared to HIITTRAD. The variability in
W′depletion (the finite work capacity above critical power) after the final interval bout
was lower in HIITTHR compared to HIITTRAD (F=0.305). Using physiological thresholds
to prescribe exercise intensity reduced the heterogeneity in exercise tolerance and
physiological responses to exercise spanning the boundary between the heavy and
severe intensity domains. To increase the precision of exercise intensity prescription,
it is recommended that, where possible, physiological thresholds are used in place of
VO2max.
KEYWORDS
critical power, exercise intensity, exercise prescription, interindividual differences
1INTRODUCTION
Cardiorespiratory fitness, measured as maximum oxygen uptake
(
VO2max), is an important marker of both endurance performance
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© 2023 The Authors. Experimental Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.
(Bassett & Howley, 2000) and cardiovascular health (Harber et al.,
2017). The most effective means of increasing
VO2max is via endurance
training (ET), encompassing high intensity interval training and/or
continuous exercise (Milanović et al., 2015). However, the effect of
Experimental Physiology. 2023;108:581–594. wileyonlinelibrary.com/journal/eph 581
582 MEYLER ET AL.
ET on
VO2max appears to be largely heterogeneous among individuals
(Bouchard et al., 1999; Williams et al., 2019).
Many factors may contribute to
VO2max response variability. Some
relate to unmodifiable factors, such as age and genetics, and some
to modifiable factors, such as training characteristics, whilst others
relate to measurement error and biological variability (Bonafiglia et al.,
2022; Meyler et al., 2021). A modifiable factor of interest is how
exercise intensity is prescribed, which, when manipulated, may reduce
response variability by creating a more homogeneous exercise and
training stimulus among individuals (Meyler et al., 2021). Improving
exerciseintensity prescription reflects a subtractive approach that may
be a means of reducing response variability without having to exhaust
additive approaches where additional stimuli are needed (Adams et al.,
2021), for example, by increasing training dose (Bonafiglia et al.,
2021).
Exercise intensity is prescribed along a continuum of intensity
domains partitioned into moderate, heavy (vigorous) and severe (near-
maximal to maximal), each of which is associated with domain-specific
metabolic and cardiopulmonary responses (Black et al., 2017; Carter
et al., 2002). Notably, these domains are delineated by physiological
thresholds, whereby the transition between the moderate and heavy
domain and the heavy and severe domain can be determined by the gas
exchange threshold (GET) and critical power (CP), respectively (Poole
et al., 2020; Wasserman et al., 1973). To target each intensity domain
and the associated exercisestimuli, intensity is commonly prescribed as
afixed%
VO2max (Milanović et al., 2015; Williams et al., 2019). However,
among individuals, this approach elicits marked variations in the acute
physiological responses to exercise and time to task failure despite
undertaking exercise at the ‘same’ relative intensity (Baldwin et al.,
2000; Iannetta et al., 2020; Lansley et al., 2011; Meyer et al., 1999;
Scharhag-Rosenberger et al., 2010).
Alternatively, using physiological thresholds to prescribe exercise
may improve intensity normalisation among individuals as they
consider the size and positioning of an individual’s intensity domains
relative to
VO2max. Compared to exercise prescribed relative to
VO2max,
more homogeneous physiological responses have been observed when
exercise is prescribed relative to physiological thresholds such as GET
(Lansley et al., 2011), lactate threshold (Baldwin et al., 2000)andthe
onset of blood lactate accumulation (McLellan & Jacobs, 1991). As it
has recently been argued that CP is the most accurate delineator of
the transition between the heavy and severe intensity domains (Jones
et al., 2019), using CP as an anchor of exercise intensity might improve
intensity normalisation among individuals when exercising at higher
intensities (Collins et al., 2022). However, exploring the variability in
exercise tolerance and acute physiological responses to exercise pre-
scribed relative to CP compared to traditional intensity anchors is yet
to be investigated. Nor has the magnitude of variability been explored
in relation to interval-based exercise. Additionally, it is of interest to
determine the variability in how exhaustive interval bouts are among
individuals. This can be achieved by modelling the depletion of an
individual’s finite work capacity (W′) that exists at intensities exceeding
critical power (Skiba & Clarke, 2021).
Highlights
∙What is the central question of this study?
∙Does prescribing exercise intensity using physio-
logical thresholds create a more homogeneous
exercise stimulus than using traditional intensity
anchors?
∙What is the main finding and its importance?
∙Prescribing exercise using physiological thresholds,
notably critical power, reduced the variability in
exercise tolerance and acute metabolic responses.
At higher intensities, approaching or exceeding the
transition from heavy to severe intensity exercise,
the imprecision of using fixed %
VO2max as an
intensity anchor becomes amplified.
The purpose of this study was, therefore, to compare the variability
in acute physiological responses to moderate intensity continuous
exercise, heavy intensity continuous exercise and high intensity inter-
val exercise prescribed relative to
VO2max (TRAD), and to GET and CP
(THR). We hypothesised that the magnitude of variability in the acute
physiological responses to exercise would be lower among individuals
when exercise is prescribed using THR compared to TRAD approaches.
2METHODS
2.1 Ethical approval
The study was approved by the University of Hertfordshire Health,
Science, Engineering and Technology Ethics Committee and Delegated
Authority (protocol: LMS/PGR/UH/04708) and was conducted in
accordance with the Declaration of Helsinki, except for registration in
database. All participants provided written informed consent.
2.2 Participants
Ten healthy, recreationally active individuals volunteered to
participate in the study (Table 1). Participants were 18+years
old, non-smokers, non-obese (BMI <30 kg m−2),andfreefromany
disease and musculoskeletal injuries.
2.3 Experimental design
This study implemented a randomised cross-over design. Participants
visited the laboratory six times (Figure 1) undergoing a block of
MEYLER ET AL.583
FIGURE 1 Experimental study protocol.
CWR, constant work rate tests; GXT, graded
maximal ramp exercise test; HIIT,
high-intensity interval training; HVY, heavy
intensity continuous exercise; MOD, moderate
intensity continuous exercise; THR,
threshold-based exercise; TRAD, traditionally
prescribed exercise.
TAB L E 1 Participant characteristics.
Sex
Males
(n=7)
Females
(n=3)
Tota l
(n=10)
Age (years) 22 ±426±923±6
Height (cm) 180 ±8168 ±5176 ±9
Mass (kg) 84 ±13 63 ±878±15
BMI (kg m−2)26 ±422 ±325 ±4
VO2max (ml kg−1min−1)37±540±338±4
VO2max (l min−1)3.11 ±0.35 2.52 ±0.12 2.93 ±0.41
Data are reported means ±SD. Abbreviations: BMI, body mass index;
VO2max, maximum oxygen uptake.
exercise testing (visits 1–3) followed by two batteries of exercise
bouts where the intensity was prescribed using both TRAD and THR
approaches (visits 4–6). Participants were randomly allocated into two
groups. Group 1 performed THR exercise first, followed by TRAD
exercise. Group 2 performed TRAD exercise first, followed by THR
exercise. Participants were blinded to the experimental conditions
being undertaken. Participants were asked to arrive at the laboratory
fully rested, and all sessions were performed at similar times of day and
separated by a minimum of 24 h. All exercise tests and exercise bouts
were performed on an electromagnetically braked cycle ergometer
(Excalibur Sport V2, Lode, Groningen, Netherlands).
2.4 Exercise testing
2.4.1 Maximalrampexercisetest
On visit one, participants performed a graded maximal ramp exercise
test (GXT) to determine GET,
VO2max and maximum heart rate (HRmax).
Participants completed a standardised warm-up consisting of 3 min
unloaded cycling at a self-selected cadence (70–90 rpm). Starting at
0 W, work rate increased by 30 W every minute until task failure.
Task failure was defined as a decrease in cadence >10 rpm below
self-selected test cadence for >5 s. Breath-by-breath pulmonary
gas exchange and heart rate (HR) data were collected continuously
throughout the test and averaged over 10 s periods.
VO2max was
recorded as the highest mean
VO2during a 30 s period and GET as
the first disproportionate increase in carbon dioxide production (
VCO2)
from visual inspection of individual
VCO2versus
VO2plots (Keir et al.,
2022). GET was then confirmed by visual inspection of additional
breath-by-breath plots using an online exercise thresholds tool (Keir
et al., 2022), and agreement with another researcher (D.M.) was then
sought. To verify the attainment of
VO2max, a verification bout (VER),
intended to last between 3 and 6 min, was performed following 20 min
recovery post-GXT (Nolan et al., 2014). Work rate was set at 85% of
the maximum power output achieved in the GXT and was performed
to task failure (Poole & Jones, 2017). The attainment of
VO2max was
assumed if the difference between GXT and VER
VO2max was ≤5% and
the average value of the two tests was taken forward as
VO2max.
2.4.2 Constant work rate tests
On visits two and three, participants performed two constant work
rate tests (CWR) per day with an inter-trial recoverytime of 1 h in order
to estimate CP and W′(Hunter et al., 2021). Each CWR was intended to
elicit task failure between 2 and 15 min. Participants completed a 3-min
warm up, cycling at a low work rate of 25 W and self-selected cadence
(70–90 rpm). Work rate was then suddenly increased to the target
work rate and participants cycled to task failure at their self-selected
cadence. Attainment of
VO2max during CWR was again confirmed if
VO2max was ≤5% of determined
VO2max. To estimate CP and W′,three
models were used per participant (Muniz-Pumares et al., 2019)as
follows.
1. A non-linear power-time model:
Tlim =W′∕(P−CP)
where Tlim is time to task failure (s), Pis power output (W), CP is
the asymptote of the hyperbolic relationship, and W′is the curvature
constant.
2. A linear work-time model:
W=W′+CP ×Tlim
584 MEYLER ET AL.
TAB L E 2 Prescribed exercise bouts.
THR TRAD
MOD 30 min @ 90% GET 30 min @ 55%
VO2max
HVY 20 min @ 50% ∆
(GET +[0.5 ×(CP – GET)])
20 min@ 75%
VO2max
HIIT 5 ×3 min @ 110% CP 5 ×3 min @ 85%
VO2max
50% ∆is power at GET +50% difference between GET and CP.
Abbreviations: CP, critical power; GET, gas exchange threshold; HIIT, high-
intensity interval training; HVY, heavy intensity continuous exercise; MOD,
moderate intensity continuous exercise; THR, exercise prescribed relative
to physiological thresholds; TRAD, exercise prescribed relative to
VO2max;
VO2max, maximum oxygen uptake.
using linear regression analysis where Wis work (kJ), the y-intercept
represents W′, and the slope represents CP.
3. A linear 1/time model:
P=CP +W′×Tlim−1
where the y-intercept represents CP and the slope represents W′.
For each participant, the standard error of estimate (SEE) was
determined for CP and W′and the model producing the lowest
combined SEE for each individual was used to estimate CP and W′on
an individual basis (Black et al., 2017).
2.5 Exercise bouts
Intra-visit exercise bouts were all separated by a 1-h recovery period.
The intensity for exercise bouts was chosen to correspond to moderate
(MOD), heavy (HVY) and severe intensity exercise (which was in the
form of high intensity interval training; HIIT) (Table 2). MODTRAD and
HVYTRAD were prescribed as the midpoint between the ranges of
VO2max intended to elicit moderate (46–63%) and heavy (64–90%)
intensity exercise, respectively (American College of Sports Medicine,
2017). The HIIT protocols implemented a 1:1 work:rest ratio, with
active recovery at 20 W. HIIT exercise bouts were designed based
on the findings of Wen et al. (2019) whereby long intervals (≥2min)
and high volumes (≥15 min) at 80–90%
VO2max are recommended
to maximise training effects on
VO2max. The power output for both
HIITTHR and HIITTRAD was intended to correspond to severe intensity
exercise. When following the American College of Sports Medicine
(ACSM) guidelines on severe intensity exercise, intensities of ≥91%
VO2max are proposed. However, following pilot testing this was not
suitable when trying to complete ≥2 min intervals. Therefore, the
intensity for HIITTRAD was reduced to 85%
VO2max (‘heavy’ intensity
exercise according to the ACSMguidelines; American College of Sports
Medicine, 2017). The work rate in TRAD sessions was extrapolated
from the
VO2–intensity relationship derived from the GXT, with the
first minute of test
VO2data being removed from the calculation (Keir
et al., 2022).
2.6 Utilisation of the W′balance model
The W′BAL-INT model (Skiba & Clarke, 2021) was used to determine how
much of the work capacity above CP (W′) was depleted during the HIIT
exercise bouts. W′BAL-INT was calculated to the end of the final HIIT
bout or at task failure, whichever was sooner. W′BAL-INT was calculated
as:
W′
BAL−INT (t)=W′
0−
t
0[e(−t−u
𝜏W′)]W′
EXP (u)du
where W′BAL-INT (t) is the amount of W′remaining at any given time t,
W′is the individual’s known W′.W′EXP represents the expended W′,t
and urepresent time, and 𝜏Wis the time constant of the reconstitution
of the W′.W′EXP (u) is calculated as:
W′
EXP (u)={0,P
(u)CP
(P(u)−CP)du, P (u)>CP
and:
𝜏W′=546 ×e(−0.01DCP)+316
where DCP is the difference between CP and the power output (P)
during the recovery period.
2.7 Measurements
During all exercise tests and exercise bouts, gas exchange data were
measured continuously breath-by-breath using an online gas analyser
(MetaLyzer 3B, CortexBiophysik, Leipzig, Germany). Participants wore
a face mask with low dead space (125 ml) and breathed through a
low resistance (<0.1 kPa l−1at 20 l s−1) impeller turbine with O2
and CO2samples at 50 Hz. The gas analyser was calibrated prior
to each exercise session with gases of known concentration, and the
turbine volume transducer was calibrated using a 3-litre syringe (Hans
Rudolph, Inc., Kansas City, MO, USA). Rise time of the gas analyser
and transit delay for O2and CO2were <100 ms and 800–1200 ms,
respectively, allowing for breath-by-breathcalculation. Measurements
of
VO2and
VCO2were recorded breath-by-breath and exported as
10-s moving averages for subsequent analyses. Heart rate was
measured telemetrically throughout the exercise session and exported
as 10-s moving averages for subsequent analyses (Polar H10, Polar
Electro, Kempele, Switzerland). During the exercise bouts, capillary
blood samples (10 μl) were taken from the fingertip and analysed
(Biosen C-Line, EKF Diagnostics, Cardiff, UK) to determine blood
lactate concentration (BLa). For MOD and HVY, blood samples were
taken at rest, during the last 30 s of the warm-up, and then every 5 min
for the remainder of the exercise bout or at task failure. During HIIT,
blood samples were taken at rest and at the start of each recovery
period or until task failure.
MEYLER ET AL.585
2.8 Statistical analyses
To evaluate the magnitude of acute physiological response variability,
the standard deviation (SD) and mean responses were first calculated
for THR and TRAD during MOD, HVY and HIIT exercise bouts. The SD
values were then compared between THR and TRAD sessions using the
F-distribution. Where data for an individual were missing (i.e., at time
points after a premature cessation of exercise) a sensitivity analysis
was conducted to determine the effect of different assumptions
about the missing values on the mean to avoid missing data
biasing conclusions based on observed data. Taking into consideration
the sample size of the current study (n=10), interpretation of the
comparison between variances will consider both the P-value and the
magnitude of the F- ratio as an indicator of the magnitude of difference.
As the F-test is being used with n=10, the F-statistic will be treated
as an effect size estimator, and any ratio <0.33 will be considered of
sufficient magnitude to indicate a difference that could potentially be
significant with a larger sample (Chen & Chen, 2010). This approach
helps protect against accepting the null hypothesis when there is a lack
of power to truly evaluate the difference. The chi square test was used
to compare the proportion of individuals completing THR and TRAD
sessions. Differences in group means were compared using Student’s
t-test. Significance was accepted when P<0.05. Statistical analyses
were conducted using R (version 4.2.0; R Foundation for Statistical
Computing, Vienna, Austria) and JASP (version 0.16.2).
3RESULTS
3.1 Exercise tests
In the GXT and the verification test, the highest
VO2recorded over
a30speriodwas38±4mlkg
−1min−1(2.95 ±0.43 l min−1)and
38 ±4mlkg
−1min−1(2.91 ±0.39 l min−1), respectively, with a
difference of 1 ±3% (range: −2 to 5 ml kg−1min−1). Therefore,
VO2max was calculated as the average of values attained in the GXT and
verification test. Peak power output in GXT was 292 ±33 W. Power
output at GET was 113 ±17 W and occurred at 52 ±4%
VO2max.
Power output at CP was 172 ±27 W and occurred at 69 ±6%
VO2max. GET occurred at 67 ±12% CP. The highest
VO2attained in
all CWR trials was 39 ±5mlkg
−1min−1(3.02 ±0.44 l min−1)which
was not different from
VO2max (P=0.954). For individuals where linear
work-time CP model was used (n=9), fits were r2=0.99. The linear
1/Time model was used for the remaining individual (n=1) where the
fit was r2=0.99. Shortest time to exhaustion CWR trials were 196 ±36
s and longest were 796 ±167 s.
3.2 Exercise bouts
Summary data for each exercise bout are presented in Table 3.
Completion rates for MODTRAD and MODTHR were 100%. Completion
rates were lower for HVYTRAD compared to HVYTHR (30% vs. 100%,
P<0.001) and for HIITTRAD compared to HIITTHR (20% vs. 100%,
P<0.001). The percentage of the HVYTRAD and HIITTRAD completed
ranged between 32% and 100% (387–1200 s) and 17% and 100%
(310–1800 s), respectively. There was no difference in work rate
variance expressed as a percentage of CP between MODTHR and
MODTRAD (60 ±11 vs. 73 ±9; F=1.412); however, the variability was
lower in HVYTHR compared to HVYTRAD (83 ±6 vs. 113 ±13; F=0.234)
and in HIITTHR compared to HIITTRAD (110 ±0 vs. 134 ±15; F<0.001).
Expressed as a percentage of CP, intensities ranged between 45% and
79% and 57% and 85% in MODTHR and MODTRAD, respectively, 75%
and 94% and 96% and 132% in HVYTHR and HVYTRAD, respectively,
and 110 ±0% and 115% and 156% in HIITTHR and HIITTRAD,
respectively.
Physiological data from all exercise bouts are presented in Table 4.
There was no difference in the variability of peak or average
VO2,
HR or BLa between MODTHR and MODTRAD, or between HVYTHR
and HVYTRAD. There was no difference in the variability of peak or
average
VO2or HR between HIITTHR and HIITTRAD. The variability in
peak and average BLa was lower in HIITTHR compared to HIITTRAD.W′
depleted in the first 3-min interval during the HIIT exercise was greater
(P<0.001) in HIITTRAD (49 ±7%, 39–58%) compared to HIITTHR
(17 ±7%, 10–30%), and W′depleted at the end-point of exercise was
greater (P<0.001) in HIITTRAD (73 ±22%, 44–99%) compared to
HIITTHR (30 ±12%, 17–53%). The variability in W′depleted at the end
of HIIT was lower in HIITTHR compared to HIITTRAD (F=0.305).
4DISCUSSION
This study is the first to explore the variability in exercise tolerance
and acute physiological responses to moderate, heavy and severe
intensity exercise prescribed relative to GET and CP and to
VO2max.
When prescribing severe intensity exercise relative to
VO2max,the
magnitude of variability in exercise tolerance and metabolic responses
was greater than when exercise was prescribed relative to CP. This
study demonstrates that using CP to prescribe exercise intensity
creates a more homogeneous exercise stimulus among individuals.
All individuals completed MODTHR and MODTRAD to their entirety,
and the majority displayed physiological response profiles consistent
with moderate intensity exercise whereby early physiological steady-
state is attained (Figure 2). Accordingly,in MODTHR , only one individual
experienced a >1 mmol l−1increase in BLa from 600 s to 1800 s. This
supports the findings of McLellan and Jacobs (1991) and Baldwin et al.
(2000) who observed no differences in BLa response variability among
trained and untrained individuals when exercise was prescribed below
the onset of blood lactate accumulation and the lactate threshold,
respectively. When exercising at 55%
VO2max , only four individuals’
work rates were below GET, but the intensity was low enough such
that 30 min of exercise could be completed and only one individual
experienced an increase in BLa >1 mmol l−1from 600 s to 1800 s.
In the present study, work rates corresponding to 55%
VO2max and
586 MEYLER ET AL.
FIGURE 2 Individual (orange: MODTRAD; blue: MODTHR ) responses in oxygen uptake expressed relative to maximum oxygen uptake (a, b),
heart rate expressed relative to maximum heart rate (c, d),and blood lactate (e, f).
MEYLER ET AL.587
TAB L E 3 Summary of group data from exercise bouts.
Exercise
bout
Work rate
(W)
Work rate
(%CP) F-ratio
Individuals completing
exercise bout (%) P
Percentage of exercise
bout completed
MODTHR 102 ±15 60 ±11 1.412 100 — 100
MODTRAD 124 ±14 73 ±9100 100
HVYTHR 143 ±18 83 ±6†0.234 100*<0.001 100
HVYTRAD 193 ±19 113 ±13 30 32–100
HIITTHR 190 ±30 110 ±0†<0.001 100*<0.001 100
HIITTRAD 228 ±23 134 ±15 20 17–100
*Significant difference between THR and TRAD (P<0.05).
†Variance is significantly lower in THR group compared to TRAD (F<0.33). n=10. Abbreviations: HIIT, high intensity interval training; HVY, heavy
intensity exercise bout; MOD, moderate intensity exercise bout; THR, threshold-based exercise intensity prescription; TRAD, traditionally prescribed
exercise intensity.
TAB L E 4 Summary of group physiological data from exercise bouts.
Exercise bout
VO2peak
(l min−1)F-ratio
VO2peak
(%
VO2max)F-ratio
HRpeak
(b min−1)F-ratio
HRpeak
(%HRmax)F-ratio
BLapeak
(mmol l−1)F-ratio
MODTHR 1.77 ±0.31 0.900 61 ±9 1.648 140 ±12 1.085 75 ±7 1.976 2.95 ±1.35 0.973
MODTRAD 2.02 ±0.32 69 ±7149 ±11 80 ±53.82 ±1.37
HVYTHR 2.27 ±0.37 0.947 78 ±7 1.777 160 ±11 0.701 85 ±5 0.979 4.68 ±1.48 0.361
HVYTRAD 2.80 ±0.38 96 ±6182 ±13 97 ±59.48 ±2.46
HIITTHR 2.73 ±0.37 0.825 93 ±5 1.116 176 ±11 1.190 94 ±6 1.395 7.45 ±1.70†0.274
HIITTRAD 2.93 ±0.41 100 ±5184 ±12 98 ±510.91 ±3.23
Exercise bout
VO2avg
(l min−1)F-ratio
VO2avg
(%
VO2max )F-ratio
HRavg
(b min−1)F-ratio
HRavg
(%HRmax)F-ratio
BLaavg
(mmol l−1)F-ratio
MODTHR 1.67 ±0.28 0.708 58 ±8 1.513 134 ±13 1.51 71 ±7 2.351 2.43 ±1.20 0.874
MODTRAD 1.91 ±0.33 65 ±6143 ±11 76 ±53.31 ±1.28
HVYTHR 2.20 ±0.34 0.828 75 ±6 1.049 154 ±10 0.657 82 ±5 1.136 4.12 ±1.30 0.403
HVYTRAD 2.71 ±0.37 93 ±6175 ±12 94 ±58.06 ±2.85
HIITTHR 2.61 ±0.32 0.703 89 ±5 0.889 171 ±12 1.031 91 ±6 1.925 6.50 ±1.30†0.318
HIITTRAD 2.85 ±0.38 97 ±5179 ±12 96 ±69.09 ±2.31
†Variance is significantly lower in THR group compared to TRAD (F<0.33). n=10. Abbreviations: BLaavg, average blood lactate; BLapeak, peak blood lactate;
HIIT,high intensity interval training; HRavg , average heart rate;HRmax , maximum heart rate; HRpeak, peak heart rate; HVY, heavy intensity exercise bout; MOD,
moderate intensity exercise bout; THR, threshold-based exercise intensity prescription; TRAD, traditionally prescribed exercise intensity;
VO2avg,average
oxygen uptake;
VO2max, maximum oxygen uptake;
VO2peak, peak oxygen uptake.
90% GET were both successful in prescribing continuous exercise that
could be tolerated for 30 min. If intensity control is a primary focus,
then using GET to prescribe moderate intensity exercise may be more
beneficial. Online tools are available to help determine an individual’s
thresholds from GXT values and should facilitate a switch from using
fixed %
VO2max to inform exercise prescription (Keir et al., 2022).
Completion rates for HVYTHR and HVYTRAD were 100% and
30%, respectively. In the three individuals who completed HVYTRAD,
the work rates associated with 75%
VO2max were below or at
CP (96–100% CP). For these individuals, the intensity elicited was
primarily consistent with heavy intensity exercise whereby exercise
can be continued for extended periods of time with physiological
perturbations reaching a delayed steady-state (Poole et al., 2016). In
the seven individuals who were not able to complete HVYTRAD,work
rates were all above CP (101–132% CP). Exercising above CP elicits
non steady-state exercise and continuation in this domain leads to the
eventual attainment of
VO2max and, ultimately, exhaustion (Poole et al.,
2016). Accordingly, in those who were not able to complete HVYTRAD
andwereexercising>CP, end
VO2and HR values reached ∼95%
VO2max
and ∼97% HRmax, respectively. In comparison, all individuals were able
to complete HVYTHR andwereallexercising<CP. Accordingly, end
VO2and HR values in HVYTHR were ∼76%
VO2max and ∼85% HRmax,
respectively.This highlights the disparity between the prescribed work
rates and the actual work rates elicited through TRAD compared to
THR prescription methods. Furthermore, compared to HVYTHR where
only one individual saw an increase of Bla >1 mmol l−1from 600 s
588 MEYLER ET AL.
FIGURE 3 Individual (orange: HVYTRAD;blue:HVY
THR) responses in oxygen uptake expressed relative to maximum oxygen uptake (a, b), heart
rate expressed relative to maximum heart rate (c,d), and blood lactate (e, f).
MEYLER ET AL.589
FIGURE 4 Intensity domain distribution from two representative individuals from the present study. For Individual (a), critical power (CP)
occurs at a higher percentage of maximum oxygen uptake (
VO2max) compared to person (b). When prescribed exercise at 75%
VO2max, for person
(a) this elicited heavy intensity exercise but severe intensity exercise for person (b). If exercise is prescribed relative to CP, this considers the
positioning of CP relative to the individual’s
VO2max.
to 1200 s, four individuals saw an increase >1 mmol l−1from 600 s
to 1200 s in HVYTRAD (Figure 3). Exercising at 50% ∆, thus, better
normalised exercise intensity among individuals, controlling exercise
intensity in the heavy intensity domain. This approach also elicited 46%
less variability in work rates (F=0.234). Overall, these findings are
consistent with those of Lansley et al. (2011), whereby four individuals
(44%) could not complete 20 min of exerciseat 70%
VO2max, all reaching
VO2max and volitional exhaustion before 20 min had elapsed. Similarly,
Scharhag-Rosenberger et al. (2010) noted two (10%) and 17 (81%)
individuals were not able to complete 60 min continuous exercise
at 60% and 75%
VO2max, respectively. It is thus clear that using a
fixed %
VO2max does not control exercise intensity effectively among
individuals.
Notably, the physiological thresholds which delineate the intensity
domains occur at different percentages of
VO2max among individuals
(Azevedo et al., 2011; Hansen et al., 2019; Pymer et al., 2020). Thus,
by using physiological thresholds to inform intensity prescription, the
size and positioning of an individual’s intensity domains are considered
(Figure 4). In the present study, when exercising at 75%
VO2max,which
is commonly but erroneously assumed to elicit heavy intensity exercise
at the individual level, this resulted in exerciseundertaken above CP for
70% of individuals, and elicited severe intensity responses to exercise.
This corroborates the work of Collins et al. (2022)wherebyexercise
prescribed at 40% and 80% of GXT maximum power output elicited
work rates of 60–72% and 109–148% CP, respectively. Combined
with the present findings, this further advocates the use of CP as a
primary anchor of exercise intensity. Due to the variability in work
rates expressed relative to CP when intensity is prescribed using a
fixed %
VO2max, future work should determine whether the greater
heterogeneity in the exercise stimulus contributes to the commonly
observed
VO2max response variability following a period of traditionally
prescribed training.
Unlike Lansley et al. (2011), who observed lower inter-individual
variability in the acute cardiopulmonary responses to exercise at
40% ∆(where ∆was determined as GET +[0.4 ×(
VO2max −GET)])
compared to 70%
VO2max, no such differences were observed in the
present study between HVYTHR and HVYTRAD sessions (Figure 3).
Based on the marked differences in exercise tolerance in HVYTRAD and
HVYTHR, it is surprising that no additional differences in metabolic or
cardiopulmonary response variability were observed.
Completion rates for HIITTHR and HIITTRAD were 100% and 20%,
respectively. In HIITTRAD, two subjects completed all five intervals,
four completed four intervals, three completed three intervals, and
one individual completed one interval (Figure 5). This demonstrates
the large variability in the exercise stimulus elicited when exercising
at a work rate corresponding with 85%
VO2max compared to that of
110% CP. Compared to all individuals exercising at 110% CP in HIITTHR,
work rates ranged between 115% and 156% CP in HIITTRAD, explaining
the variability in time to task failure demonstrated in Figure 5.This
is noteworthy given recent findings by Collins et al. (2022)whereby
changes in endurance performance were influenced strongly by the
intensity of the exercise programme when expressed relative to CP.
The variability in peak and average BLa responses to HIITTHR were
53% (F=0.274) and 56% (F=0.318) lower than those in HIITTRAD,
respectively (Table 4,Figure6). Observing no differences in HR and
VO2response variability between HIIT sessions may be explained by
a ceiling effect whereby the physiological parameters approach their
maximum values and thus room for variance begins to diminish. The
observation of reductions in individuals’
VO2from the last completed
bout to that eliciting task failure (Figure 5) is likely explained by the
shorter exercisetime and thus a shortened amount of time in which
VO2
can rise.
In the present study, the W′BAL-INT model was used retrospectively
(Figure 7). However, this model can be used to design and prescribe
HIIT sessions (Galán-Rioja et al., 2022), for example, designing and
prescribing sessions for each individual that target a given W′depletion
at the end of bout 1 or at the end of the final bout. Despite not doing so
in the present study, 5 ×3 min at 110% CP was effective in creating
a more homogeneous exercise stimulus than that of HIITTRAD.For
examp le, W′depleted at the end of HIITTHR was 30 ±12% compared to
590 MEYLER ET AL.
FIGURE 5 Individual (orange: HIITTRAD; blue: HIITTHR) responses in oxygen uptake expressed relative to maximum oxygen uptake (a, b), heart
rate expressed relative to maximum heart rate (c,d), and blood lactate (e, f). Int: severe intensity interval bout.
MEYLER ET AL.591
FIGURE 6 Individual (white circles) and mean (diamonds, orange:
TRAD; blue: THR) values for average blood lactate during MOD (a) and
peak blood lactate values during HVY (B) and HIIT (c). †Lower
variability in THR versus TRAD exercise (F<0.33). n=10.
73 ±22% in HIITTRAD, a lower variability of 55% (F=0.305). This helps
explain the greater variability observed in exercise tolerance following
HIITTRAD and further highlights the disadvantages of using fixed
%
VO2max to prescribe exercise. It is of interest to determine whether
using the W′BAL-INT model to design and prescribe HIITTHR further
amplifies the reduction in response variability to HIIT sessions and
enables the prescription of more challenging but achievable interval
sessions.
Whilst the addition of CP determination can be time costly and
requires the means of determining power output, the marked benefit it
has on exercise intensity control is arguably justified. Alternatively, the
3-min all-out test has been established as a time-efficient alternative to
the traditional means of determining CP; however, this requires large
amounts of motivation, and a familiarisation session is recommended
in order to obtain reliable data thereafter (Vanhatalo et al., 2007).
Alternatively, determining critical speed, the running analogue of CP,
is somewhat easier as this can be determined from training data (i.e.,
performance or training bests for a given distance) which does not
require laboratory equipment beyond a stopwatch and a measure
of distance (Smyth & Muniz-Pumares, 2020). Recent studies are
exploring the use of self-assessed threshold tools such as rate of
perceived exertion and the ‘Talk Test’ to estimate individuals’ physio-
logical thresholds (Lehtonen et al., 2022; Preobrazenski et al., 2019).
This is an interesting avenue aiming to encourage the rollout of
individualised, population-wide approaches of exercise prescription
that do not require access to laboratory facilities (Lehtonen et al.,
2022). Additionally, the benefit of using such approaches is also being
realised for use in various clinical populations (Anselmi et al., 2021;
D’Ascenzi et al., 2022; Mezzani et al., 2013; Pymer et al., 2020).
Finally, whilst it is recommended that practitioners prescribe
exercise interventions known to elicit the largest mean changes in
VO2max in order to maximise the number of individuals experiencing
clinically important cardiorespiratory changes (Bonafiglia, 2022), using
physiological thresholds to anchor exerciseintensity may have a similar
effect, without having to exhaust training volume whereby a more
appropriate exercise stimulus is created from the beginning.
4.1 Conclusions
Overall, prescribing exercise relative to
VO2max consistently over-
estimated the boundary between the heavy and severe intensity
domains in the present study, in turn causing greater heterogeneity in
exercise tolerance and metabolic responses to exercise. More routine
testing of individuals’ CP is thus encouraged such that CP can be used
to inform and prescribe exercise more appropriately. Future research
exploring the feasibility and manipulation of CP determination across
different populations is recommended.
592 MEYLER ET AL.
FIGURE 7 W′balance during HIITTRAD (orange) and HIITTHR (blue) for an individual who completed both HIITTRAD and HIITTHR (a, b) and for
an individual who completed HIITTHR but not HIITTRAD (c, d).
4.2 Perspective
Due to the widespread usage of traditional intensity anchors (e.g.,
%
VO2max) in training programmes and exercise research studies, it is
plausible that this contributes to a heterogeneous training stimulus
and thus, at least in part, the variability in physiological outcomes. This
may have large implications on longer term training adaptations and
the variability of these adaptations among individuals. Future research
determining whether this is the case is encouraged. If improving
exercise intensity control by use of physiological thresholds does
reduce the variability in subsequent exercise-induced adaptations
among individuals, this could have marked benefits on improving
exercise interventions and increasing the number of individuals
attaining the desired exercise-induced adaptations targeting both
health- and performance-related outcomes.
AUTHOR CONTRIBUTIONS
Samuel Meyler drafted the manuscript and Daniel Muniz-Pumares,
Lindsay Bottoms, David Wellsted were all involved in the editing of the
manuscript. All authors have read and approved the final version of this
manuscript and agree to be accountable for all aspects of the work in
ensuring that questions related to the accuracy or integrity of any part
of the work are appropriately investigated and resolved. All persons
designated as authors qualify for authorship, and all those who qualify
for authorship are listed.
CONFLICT OF INTEREST
Samuel Meyler, Lindsay Bottoms, David Wellsted and Daniel Muniz-
Pumares declare that they have no conflicting interests. The results
of the present study are presented clearly, honestly, and without
fabrication, falsification, or inappropriate data manipulation.
FUNDING INFORMATION
There was no funding used for the current research study.
DATA AVAILABILITY STATEMENT
Data is available upon reasonable request.
ORCID
Samuel Meyler https://orcid.org/0000-0003-0976-697X
Lindsay Bottoms https://orcid.org/0000-0003-4632-3764
Daniel Muniz-Pumares https://orcid.org/0000-0002-6748-9870
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
How to cite this article: Meyler, S., Bottoms, L., Wellsted, D., &
Muniz-Pumares, D. (2023). Variability in exercise tolerance and
physiological responses to exercise prescribed relative to
physiological thresholds and to maximum oxygen uptake.
Experimental Physiology,108, 581–594.
https://doi.org/10.1113/EP090878
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