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Variability in exercise tolerance and physiological responses to exercise prescribed relative to physiological thresholds and to maximum oxygen uptake

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Experimental Physiology
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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; V̇O2maxV˙O2max{\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}). 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 V̇O2maxV˙O2max{\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}.
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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
Wdepletion (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
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 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 m2),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 m2)26 ±422 ±325 ±4
VO2max (ml kg1min1)37±540±338±4
VO2max (l min1)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(PCP)
where Tlim is time to task failure (s), Pis power output (W), CP is
the asymptote of the hyperbolic relationship, and Wis 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×Tlim1
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 Wand the model producing the lowest
combined SEE for each individual was used to estimate CP and Won
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 Wbalance model
The WBAL-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. WBAL-INT was calculated to the end of the final HIIT
bout or at task failure, whichever was sooner. WBAL-INT was calculated
as:
W
BALINT (t)=W
0
t
0[e(tu
𝜏W)]W
EXP (u)du
where WBAL-INT (t) is the amount of Wremaining at any given time t,
Wis the individual’s known W.WEXP represents the expended W,t
and urepresent time, and 𝜏Wis the time constant of the reconstitution
of the W.WEXP (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 l1at 20 l s1) 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
1min1(2.95 ±0.43 l min1)and
38 ±4mlkg
1min1(2.91 ±0.39 l min1), respectively, with a
difference of 1 ±3% (range: 2 to 5 ml kg1min1). 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
1min1(3.02 ±0.44 l min1)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 Wdepleted 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 Wdepleted 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 l1increase 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 l1from 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 ±60.234 100*<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 min1)F-ratio
VO2peak
(%
VO2max)F-ratio
HRpeak
(b min1)F-ratio
HRpeak
(%HRmax)F-ratio
BLapeak
(mmol l1)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 ±1.700.274
HIITTRAD 2.93 ±0.41 100 ±5184 ±12 98 ±510.91 ±3.23
Exercise bout
VO2avg
(l min1)F-ratio
VO2avg
(%
VO2max )F-ratio
HRavg
(b min1)F-ratio
HRavg
(%HRmax)F-ratio
BLaavg
(mmol l1)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 ±1.300.318
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 l1from 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 l1from 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 WBAL-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 Wdepletion
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, Wdepleted 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 WBAL-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 Wbalance 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
REFERENCES
Adams, G. S., Converse, B. A., Hales, A. H., & Klotz, L. E. (2021). People
systematically overlook subtractive changes. Nature,592(7853), 258–
261.
American College of Sports Medicine. (2017). ACSM’s guidelines for exercise
testing and prescription. 10th ed. Lippincott Williams & Wilkins.
Anselmi, F., Cavigli, L., Pagliaro, A., Valente, S., Valentini, F., Cameli, M.,
Focardi, M., Mochi, N., Dendale, P., Hansen, D., Bonifazi, M., Halle, M.,
& D’Ascenzi, F. (2021). The importance of ventilatory thresholds to
define aerobic exercise intensity in cardiac patients and healthysubjects.
Scandinavian Journal of Medicine & Science in Sports,31(9), 1796–1808.
MEYLER ET AL.593
Azevedo, L. F., Perlingeiro, P. S., Brum, P. C., Braga, A. M. W., Negrão, C. E.,
& de Matos, L. D. N. J. (2011). Exercise intensity optimization for men
with high cardiorespiratory fitness. Journal of Sports Sciences,29(6), 555–
561.
Baldwin, J., Snow, R. J., & Febbraio, M. A. (2000). Effect of training status and
relative exercise intensity on physiological responses in men. Medicine
and Science in Sports and Exercise,32(9), 1648–1654.
Bassett, D. R., & Howley, E. T. (2000). Limiting factors for maximum
oxygen uptake and determinants of endurance performance. Medicine
and Science in Sports and Exercise,32(1), 70–84.
Black, M. I., Jones, A. M., Blackwell, J. R., Bailey, S. J., Wylie, L. J., McDonagh,
S. T. J., Thompson, C., Kelly, J., Sumners, P., Mileva, K. N., Bowtell,
J. L., & Vanhatalo, A. (2017). Muscle metabolic and neuromuscular
determinants of fatigue during cycling in different exercise intensity
domains. Journal of Applied Physiology,122(3), 446–459.
Bonafiglia, J. T., Preobrazenski, N., Islam, H., Walsh, J. J., Ross, R., Johannsen,
N. M., Martin, C. K., Church, T. S., Slentz, C. A., Ross, L. M., Kraus, W. E.,
Kenny, G. P., Goldfield, G. S., Prud’homme, D., Sigal, R. J., Earnest, C. P.,
& Gurd, B. J. (2021). Exploring differences in cardiorespiratory fitness
response rates across varying doses of exercise training: A retrospective
analysis of eight randomized controlled trials. Sports Medicine,51(8),
1785–1797.
Bonafiglia, J. T., Swinton, P. A., Ross, R., Johannsen, N. M., Martin, C.
K., Church, T. S., Slentz, C. A., Ross, L. M., Kraus, W. E., Walsh, J. J.,
Kenny, G. P., Goldfield, G. S., Prud’homme, D., Sigal, R. J., Earnest, C.
P., & Gurd, B. J. (2022). Interindividual differences in trainability and
moderators of cardiorespiratory fitness, waist circumference, and body
mass responses: A large-scale individual participant data Meta-analysis.
Sports Medicine,52(12), 2837–2851.
Bouchard, C., An, P., Rice, T., Skinner,J. S., Wilmore, J. H., Gagnon, J., Pérusse,
L., Leon, A. S., & Rao, D. C. (1999). Familial aggregation of
VO2max response
to exercise training: Results from the Heritage family study. Journal of
Applied Physiology,87(3), 1003–1008.
Carter, H., Pringle, J. S. M., Jones, A. M., & Doust, J. H. (2002). Oxygen uptake
kinetics during treadmill running across exercise intensity domains.
European Journal of Applied Physiology,86(4), 347–354.
Chen, S., & Chen, H. (2010). Cohen’s f statistics. In N. Salkind (Ed.),
Encyclopedia of research design. SAGE Publications, Inc.
Collins, J., Leach, O., Dorff, A., Linde, J., Kofoed, J., Sherman, M., Proffit,
M., & Gifford, J. R. (2022). Critical power and work-prime account for
variability in endurance training adaptations not captured by
VO2max.
Journal of Applied Physiology,133(4), 986–1000.
D’Ascenzi, F., Cavigli, L., Pagliaro, A., Focardi, M., Valente, S., Cameli, M.,
Mandoli, G. E., Mueller, S., Dendale, P., Piepoli, M., Wilhelm, M., Halle,
M., Bonifazi, M., & Hansen, D. (2022). Clinician approach to cardio-
pulmonary exercise testing for exercise prescription in patients at risk of
and with cardiovascular disease. British Journal of Sports Medicine,56(20),
1180–1187.
Galán-Rioja, M. Á., González-mohíno, F., Skiba, P. F., González-ravé, J. M.,
Galán-rioja, M. Á., & González-mohíno, F. (2022). Utility of the BAL
model in training programme design for masters cyclists. European
Journal of Sport Science, 1–10.
Hansen, D., Bonné, K., Alders, T., Hermans, A., Copermans, K., Swinnen,
H., Maris, V., Jansegers, T., Mathijs, W., Haenen, L., Vaes, J., Govaerts,
E., Reenaers, V., Frederix, I., & Dendale, P. (2019). Exercise training
intensity determination in cardiovascular rehabilitation: Should the
guidelines be reconsidered? European Journal of Preventive Cardiology,
26(18), 1921–1928.
Harber, M. P., Kaminsky, L. A., Arena, R., Blair, S. N., Franklin, B. A.,
Myers, J., & Ross, R. (2017). Impact of cardiorespiratory fitness on all-
cause and disease-specific mortality: Advances since 2009. Progress in
Cardiovascular Diseases,60(1), 11–20.
Hunter, B., Greenhalgh, A., Karsten, B., Burnley, M., & Muniz-Pumares, D.
(2021). A non-linear analysis of running in the heavy and severe intensity
domains. European Journal of Applied Physiology,121(5), 1297–1313.
Iannetta, D., Inglis, E. C., Mattu, A. T., Fontana, F. Y., Pogliaghi, S., Keir, D.
A., & Murias, J. M. (2020). A critical evaluation of current methods for
exercise prescription in women and men. Medicine and Science in Sports
and Exercise,52(2), 466–473.
Jones, A. M., Burnley, M., Black, M. I., Poole, D. C., & Vanhatalo, A. (2019).
The maximal metabolic steady state: Redefining the ‘gold standard’.
Physiological Reports,7(10), 1–16.
Keir, D. A., Iannetta, D., Mattioni Maturana,F., Kowalchuk, J. M., & Murias, J.
M. (2022). Identification of non-invasive exercise thresholds: Methods,
strategies, and an online app. Sports Medicine,52(2), 237–255.
Lansley, K. E., Dimenna, F. J., Bailey, S. J., & Jones, A. M. (2011). A new method
to normalise exercise intensity. International Journal of Sports Medicine,
32(07), 535–541.
Lehtonen, E., Gagnon, D., Eklund, D., Kaseva, K., & Peltonen, J. E. (2022).
Hierarchical framework to improve individualised exercise prescription
in adults: A critical review. BMJ Open Sport & Exercise Medicine,8(2),
e001339.
McLellan, T. M., & Jacobs, I. (1991). Muscle glycogen utilization and the
expression of relative exercise intensity. International Journal of Sports
Medicine,12(1), 21–26.
Meyer, T., Gabriel, H. H. W., & Kindermann, W. (1999). Is determination
of exercise intensities as percentages of
VO2max or HRmax adequate?
Medicine and Science in Sports and Exercise,31(9), 1342–1345.
Meyler, S., Bottoms, L., & Muniz-Pumares, D. (2021). Biological and
methodological factors affecting
VO2max response variability to end-
urance training and the influence of exercise intensity prescription. In
Experimental physiology (p. 1410). John Wiley & Sons, Ltd.
Mezzani, A., Hamm, L. F., Jones, A. M., McBride, P. E., Moholdt, T.,
Stone, J. A., Urhausen, A., & Williams, M. A. (2013). Aerobic exercise
intensity assessment and prescription in cardiac rehabilitation: A joint
position statement of the European association for cardiovascular pre-
vention and rehabilitation, the American association of cardiovascular
and pulmonary rehabilitation and the Canadian association of cardiac
rehabilitation. European Journal of Preventive Cardiology,20(3),442–467.
Milanović, Z., Sporiš, G., & Weston, M. (2015). Effectiveness of high-
intensity interval training (HIT) and continuous endurance training
for VO2max improvements: A systematic review and meta-analysis of
controlled trials. Sports Medicine,45(10), 1469–1481.
Muniz-Pumares, D., Karsten, B., Triska, C., & Glaister, M. (2019).
Methodological approaches and related challenges associated with
the determination of critical power and curvature constant. Journal of
Strength and Conditioning Research,33(2), 584–596.
Nolan, P. B., Beaven, M. L., & Dalleck, L. (2014). Comparison of intensities
and rest periods for VO2max verification testing procedures. Inter-
national Journal of Sports Medicine,35(12), 1024–1029.
Poole, D. C., Burnley, M., Vanhatalo, A., Rossiter, H. B., & Jones, A. M. (2016).
Critical power: An important fatigue threshold in exercise physiology.
Medicine and Science in Sports and Exercise,48(11), 2320–2334.
Poole, D. C., & Jones, A. M. (2017). Measurement of the maximum oxygen
uptake Vo2max: Vo2peak is no longer acceptable. Journal of Applied
Physiology,122(4), 997–1002.
Poole, D. C., Rossiter, H. B., Brooks, G. A., & Gladden, L. B. (2020). The
anaerobic threshold: 50+years of controversy. Journal of Physiology,
599(3), 737–767.
Preobrazenski, N., Bonafiglia, J. T., Nelms, M. W., Lu, S., Robins, L., LeBlanc,
C., & Gurd, B. J. (2019). Does blood lactate predict the chronic adaptive
response to training: A comparison of traditional and talk test pre-
scription methods. Applied Physiology, Nutrition, and Metabolism,44(2),
179–186.
Pymer, S., Nichols, S., Prosser, J., Birkett, S., Carroll, S., & Ingle, L. (2020).
Does exercise prescription based on estimated heart rate training zones
exceed the ventilatory anaerobic threshold in patients with coronary
heart disease undergoing usual-care cardiovascular rehabilitation? A
United Kingdom perspective. European Journal of Preventive Cardiology,
27(6), 579–589.
594 MEYLER ET AL.
Scharhag-Rosenberger, F., Meyer, T., Gäßler, N., Faude, O., & Kindermann,
W. (2010). Exercise at given percentages of VO2max: Heterogeneous
metabolic responses between individuals. Journal of Science and Medicine
in Sport,13(1), 74–79.
Skiba, P. F., & Clarke,D. C. (2021). The Wbalance model: Mathematical and
methodological considerations. International Journal of Sports Physiology
and Performance,16(11), 1561–1572.
Smyth, B., & Muniz-Pumares, D. (2020). Calculation of critical speed from
raw training data in recreational marathon runners. Medicine and Science
in Sports and Exercise,52(12), 2637–2645.
Vanhatalo, A., Doust, J. H., & Burnley, M. (2007). Determination of critical
power using a 3-min all-out cycling test. Medicine and Science in Sports and
Exercise,39(3), 548–555.
Wasserman, K., Whipp, B. J., Koyal, S. N., & Beaver, W. L. (1973). Anaerobic
threshold and respiratory gas exchange during exercise. Journal of
Applied Physiology,35(2), 236–243.
Wen, D., Utesch, T., Wu, J., Robertson, S., Liu, J., Hu, G., & Chen, H.
(2019). Effects of different protocols of high intensity interval training
for VO2max improvements in adults: A meta-analysis of randomised
controlled trials. Journal of Science and Medicine in Sport,22(8), 941–
947.
Williams, C. J., Gurd, B. J., Bonafiglia, J.T., Voisin, S., Li, Z., Harvey,N., Croci, I.,
Taylor, J. L., Gajanand, T., Ramos, J. S., Fassett, R. G., Little, J. P., Francois,
M. E., Hearon, C. M., Sarma, S., Janssen, S. L. J. E., van Craenenbroeck,
E. M., Beckers, P., Cornelissen, V. A., . .. Coombes, J. S. (2019). A multi-
center comparison of VO2peak trainability between interval training
and moderate intensity continuous training. Frontiers in Physiology,10,
19.
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
... Increases in blood lactate concentration ([BLa]) occur when the rate of muscle lactate production exceeds the rate of lactate clearance from blood and reflect the metabolic response induced by exercise (Broskey et al. 2020;Gorostiaga et al. 2014). Large interindividual variability exists in the observed [BLa] response induced by a range of exercise intensities (Bonafiglia et al. 2018;Bossi et al. 2023;Iannetta et al. 2020;McConell et al. 2020;Meyler et al. 2023;Scharhag-Rosenberger et al. 2010). However, the rigorous methodology required to isolate interindividual variability in exercise response from random error has yet to be implemented. ...
... Anchoring exercise intensity to V O 2 peak or WRpeak (traditional method of exercise prescription; TRAD) yields large variability in the [BLa] exercise response, possibly because of interindividual differences in LT%peak and CP%max. Threshold-based prescription (THR) anchors intensity to either LT and/ or CP and is speculated to standardize domain intensity, thereby reducing interindividual variability in exercise/ [BLa] response (Dalleck et al. 2016;Lansley et al. 2011;Meyler et al. 2023;Wolpern et al. 2015). However, previous observations of reduced interindividual variability following THR were also accompanied by reduced absolute exercise intensity compared to TRAD (Meyler et al. 2023). ...
... Threshold-based prescription (THR) anchors intensity to either LT and/ or CP and is speculated to standardize domain intensity, thereby reducing interindividual variability in exercise/ [BLa] response (Dalleck et al. 2016;Lansley et al. 2011;Meyler et al. 2023;Wolpern et al. 2015). However, previous observations of reduced interindividual variability following THR were also accompanied by reduced absolute exercise intensity compared to TRAD (Meyler et al. 2023). Thus, it is currently unclear if interindividual variability in [BLa] response is caused by interindividual differences in LT%peak and CP%peak. ...
Article
Full-text available
Purpose (1) To determine if the blood lactate concentration ([BLa]) response is a repeatable individual trait, and (2) To examine whether threshold-based prescription (THR) reduces interindividual variability in [BLa] response compared to traditional (maximally anchored) exercise prescription (TRAD). Method A crossover within-participant repeated measures design was used to assess [BLa] during the TRAD and THR exercise in 17 participants (9 M/8F). Participants initially undertook an incremental test to exhaustion to determine peak work rate (WRpeak), a lactate threshold (LT) test and a critical power (CP) test. All baseline tests were repeated twice. Participants then completed 6 15-min bouts of continuous cycling at 65%WRpeak (TRAD; 3 bouts) and 80% of the difference (Δ80) between LT and CP (THR; 3 bouts). [BLa] response was measured at 10 and 15 min of exercise. Results Across individuals, there was a wide range in [BLa] response, but within individual responses were repeatable. [BLa] ranges and mean individual 90% confidence interval width (CIw) were as follows: TRAD@10 min = 2.1–9.7 mmol, CIw = 0.5 mmol, THR@10 min = 3.4–9.3 mmol, CIw = 0.6 mmol, TRAD@15 min = 2.2–9.9 mmol, CIw = 0.6 mmol, THR@15 min = 3.6–12.3 mmol, CIw = 0.7 mmol. Levene’s tests revealed no significant differences in the variability of [BLa] response between TRAD and THR at 10 min (F = 0.523, p = 0.475) or 15 min (F = 0.351, p = 0.558) of exercise. Conclusion Our results demonstrate that true interindividual variability in the [BLa] response to exercise exists, but failed to confirm that variability in [BLa] response is reduced with the use of THR.
... However, using physiological thresholds, and particularly using CP to prescribe high-intensity exercise, has been shown to reduce response variability, which results in a more consistent exercise session among individuals (Meyler et al., 2023). Repeated over time, exercise sessions where the intensity is prescribed relative to physiological thresholds appear to have an impact on the effectiveness of endurance training at improving fitness outcomes, such asV O 2 max . ...
... 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. 2024;1-3. wileyonlinelibrary.com/journal/eph participants took part in a training programme where exercise intensity was anchored to a physiological threshold.Meyler et al. (2023) tested the consequences of this in the central paper of this connections article. They hypothesised that prescribing exercise relative to traditional anchors of exercise intensity, such as a percentage ofV O 2 max , would result in higher inter-individual variability in acute physiological responses to exercise, compared to when intensi ...
... It is worth noting that, whilst varying nomenclature is used to describe the different intensity domains in performance and health settings [26], the three-domain classification (moderate-, heavy-and severe-intensity exercise) will be referred to in the present study. Notably, TRAD approaches are evidenced to elicit marked variation in acute physiological responses and exercise tolerance [27][28][29][30][31][32][33]. As changes in V O 2max manifest in response to specific exercise-induced adaptive stimuli [34], when different stimuli are experienced by individuals over time, it is plausible that this may contribute to a portion of V O 2max response variability [19,31,35]. ...
... However, using physiological thresholds that demarcate the intensity domains as intensity anchors (Table 2) has been shown to elicit more homogeneous acute physiological responses to an exercise bout [29,32,33,36]. It is of interest to explore whether this has a positive impact on longer-term responses (i.e. ...
... However, contrary to our hypothesis, weak evidence was obtained in support of no difference in the variability of V O 2max change scores between THR and TRAD in both analyses. It has been shown that using THR approaches more effectively normalises exercise intensity among individuals compared with when using TRAD anchors, reducing the variability in exercise tolerance and eliciting more homogeneous acute physiological responses [29,32,33,36]. On the basis of the acute data presented in these studies, it was hypothesised that repeated performance of THR would manifest in a more consistent chronic stimulus across participants, resulting in reduced variation in change scores. ...
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Background It is unknown whether there are differences in maximal oxygen uptake (V{V}O2max) response when prescribing intensity relative to traditional (TRAD) anchors or to physiological thresholds (THR). Objectives The present meta-analysis sought to compare: (a) mean change in V{V}O2max, (b) proportion of individuals increasing V{V}O2max beyond a minimum important difference (MID) and (c) response variability in V{V}O2max between TRAD and THR. Methods Electronic databases were searched, yielding data for 1544 individuals from 42 studies. Two datasets were created, comprising studies with a control group (‘controlled’ studies), and without a control group (‘non-controlled’ studies). A Bayesian approach with multi-level distributional models was used to separately analyse V{V}O2max change scores from the two datasets and inferences were made using Bayes factors (BF). The MID was predefined as one metabolic equivalent (MET; 3.5 mL kg⁻¹ min⁻¹). Results In controlled studies, mean V{V}O2max change was greater in the THR group compared with TRAD (4.1 versus 1.8 mL kg⁻¹ min⁻¹, BF > 100), with 64% of individuals in the THR group experiencing an increase in V{V}O2max > MID, compared with 16% of individuals taking part in TRAD. Evidence indicated no difference in standard deviation of change between THR and TRAD (1.5 versus 1.7 mL kg⁻¹ min⁻¹, BF = 0.55), and greater variation in exercise groups relative to non-exercising controls (1.9 versus 1.3 mL kg⁻¹ min⁻¹, BF = 12.4). In non-controlled studies, mean V{V}O2max change was greater in the THR group versus the TRAD group (4.4 versus 3.4 mL kg⁻¹ min⁻¹, BF = 35.1), with no difference in standard deviation of change (3.0 versus 3.2 mL kg⁻¹ min⁻¹, BF = 0.41). Conclusion Prescribing exercise intensity using THR approaches elicited superior mean changes in V{V}O2max and increased the likelihood of increasing V{V}O2max beyond the MID compared with TRAD. Researchers designing future exercise training studies should thus consider the use of THR approaches to prescribe exercise intensity where possible. Analysis comparing interventions with controls suggested the existence of intervention response heterogeneity; however, evidence was not obtained for a difference in response variability between THR and TRAD. Future primary research should be conducted with adequate power to investigate the scope of inter-individual differences in V{V}O2max trainability, and if meaningful, the causative factors.
... Elevated BL levels and accompanying H + accumulation have long been associated with impaired musculoskeletal mitochondrial function and exercise tolerance [13,14], which are also directly related to the recovery process of the musculoskeletal system [15]. Tullberg et al. [16] reported that lower BL induced by PBMT could be caused by several factors, such as improved microcirculation and increased in blood flow. ...
... RPE was measured using the RPE scale (range, [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], which ranges from 6 to 20, a common tool for gauging the perceived exertion level during physical activity [22]. This scale evaluates the subjective experience of effort, strain, or fatigue that participants feel while exercising. ...
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The application of photobiomodulation therapy (PBMT) to delay skeletal muscle fatigue and shield against muscle damage represents a novel frontier in the field of exercise physiology. Further research is warranted to understand the physiological impact of PBMT on post-exercise recovery and muscle functionality. The objective of this research is to assess the impact of PBMT on the quadriceps following strenuous cycling, focusing on blood lactate (BL) levels, heart rate (HR), perceived exertion (RPE), and performance in the Wingate (WG) test. The study involved 12 male participants who were randomly allocated to either an active PBMT group or a control group, with treatments administered to the rectus femoris muscles bilaterally post-exhaustive cycling. The cycling exercise workload was 50 Watts (W); it increased by 50 W every 30 s at 60 rpm until the onset of exhaustion; and 30 s of active recovery was allowed between intervals. The BL, HR, and RPE were measured at several time points: pre-exercise, post-exercise, and at 10 min and 20 min post-exercise, as well as post-WG test. BL was significantly reduced in the PBMT group compared to the placebo group at the 10-min (p < 0.05) and 20-min (p < 0.01) marks post-exercise, and also post-WG test (p < 0.01). Additionally, HR was significantly lower in the PBMT group immediately following the WG test (p < 0.01). Both the mean (p < 0.05) and peak power outputs (p < 0.05) were found to be superior in the PBMT group. The application of PBMT to the quadriceps post-exhaustive exercise resulted in reduced BL and HR, along with improved WG test results, suggesting that PBMT may facilitate faster recovery following physical exertion.
... However, the identification of these anchors may be influenced by noteworthy variables such as initial load, load increment, stage duration, and cadence (in the case of cycling) [35]. A graded exercise test structured to a given population enables a highly individualized exercise prescription, reducing variability among individuals in performance responses, even for substantially heterogeneous groups such as cancer patients [35,36]. ...
... This highly individualized method holds significant potential in cancer studies due to the substantial heterogeneity among participants. Indeed, this approach has been shown to reduce variability in some responses, such as time to exhaustion and physiological markers as blood lactate [35,36]. ...
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Background High-intensity interval training (HIIT) performed before, during, and after cancer treatment can attenuate the adverse effects induced by anti-cancer drugs. A clear presentation and rationale of characteristics of HIIT variables is vital to produce the expected HIIT adaptations in cancer patients. However, there are concerns regarding the HIIT protocols used in the cancer literature. Objectives The aims were to (1) identify the characteristics of HIIT and the formats that have been prescribed, (2) analyze which anchors have been utilized to prescribe effort and pause intensity, (3) examine characteristics of the physical tests used for HIIT prescription, and (4) identify potential adverse events related to HIIT intervention. Methods This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, including PubMed, Scopus, and Web of Science databases. Results A total of 51 studies were retrieved, and the following results were found: (1) Only 25 studies reported all four essential variables for HIIT prescription [effort intensity (effort duration): pause intensity (pause duration)]. Of these studies, 23 used active pause and employed the following prescription (on average): [84% (116 s): 39% (118 s)] when percentage of maximal aerobic power (MAP) [maximal/peak oxygen uptake (V{{{V}}}O2max/peak)/MAP] was used; [124% (161 s): 55% (142 s)] when percentage of anaerobic threshold (AT) was used; [83% (230 s): 62% (165 s)] when maximal heart rate percentage (%HRmax) was used. From these 23 studies, 12 used V{{{V}}}O2max/peak/MAP (one of the most recommended variables for HIIT prescription). Seven studies adopted the HIIT-long format, and in the remaining five studies, the format was unclear. (2) Twenty-four studies used fractions of V{{{V}}}O2max/peak or mechanical variables like MAP as anchors for prescribing effort intensity, two studies used AT, 20 studies used fractions of HRmax/heart rate reserve, two studies used rate of perceived exertion (RPE), while one used RPE and %V{{{V}}}O2peak concomitantly, and two studies utilized RPE/%HRmax concomitantly. Two studies utilized passive resting, 12 studies used %V{{{V}}}O2peak/%MAP for prescribing pause intensity, four studies used AT, seven studies used %HRmax, one study used %HRmax/%V{{{V}}}O2peak, and two studies used absolute loads. (3) Ten studies did not report the characteristics of the physical tests employed, two studies used submaximal tests, and 39 studies utilized graded exercise tests. (4) Ten studies did not report if there were adverse events associated with the exercise program, while 34 studies did not report any adverse events. Conclusions Only 50% of the studies provided all the necessary variables for accurate HIIT prescription, raising concerns about the replicability, comprehension, and effective application of HIIT in cancer patients. Most of the studies that reported all variables appeared to have employed the HIIT-long format. Only a few studies used more individualized anchors (e.g., AT) to prescribe HIIT-long format for cancer patients, which is considered a very heterogeneous population.
... have affected ECG-waveform morphology due to shifts in the cardiac axis (Aström et al., 2003). External load prescription assumes that physiological responses are rather static (Jamnick et al., 2020;Maunder et al., 2021) and neglect the influence of internal and external factors leading to heterogeneity in exercise tolerance and physiological responses over time (e.g., personal or environmental factors, Gronwald, Törpel, et al., 2020;Meyler et al., 2023). ...
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Aim was to evaluate alterations of the non‐linear short‐term scaling exponent alpha1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability (HRV) as a sensitive marker for assessing global physiological demands during multiple running intervals. As a secondary analysis, agreement of ECG‐derived respiratory frequency (EDR) compared to respiratory frequency (RF) derived from the metabolic cart was evaluated with the same chest belt device. Fifteen trained female and male long‐distance runners completed four running bouts over 5 min on a treadmill at marathon pace. During the last 3 min of each bout gas exchange data and a single‐channel ECG for the determination of HR, DFAa1 of HRV, EDR and RF were analyzed. Additionally, blood lactate concentration (BLC) was determined and rating of perceived exertion (RPE) was requested. DFAa1, oxygen consumption, BLC, and RPE showed stable behaviors comparing the running intervals. Only HR (p < 0.001, d = 0.17) and RF (p = 0.012, d = 0.20) indicated slight increases with small effect sizes. In addition, results point towards inter‐individual differences in all internal load metrics. The comparison of EDR with RF during running revealed high correlations (r = 0.80, p < 0.001, ICC3,1 = 0.87) and low mean differences (1.8 ± 4.4 breaths/min), but rather large limits of agreement with 10.4 to −6.8 breaths/min. Results show the necessity of EDR methodology improvement before being used in a wide range of individuals and sports applications. Relationship of DFAa1 to other internal load metrics, including RF, in quasi‐steady‐state conditions bears the potential for further evaluation of exercise prescription and may enlighten decoupling mechanisms during prolonged exercise bouts.
... The 95% PI for the difference between POL and PYR was SMD of − 0.06, 95% PI − 0.40 to 0.28 for V O 2peak , and SMD − 0.05, 95% PI − 0.40 to 0.30 for TT performance. This is consistent with previous observations that when training is prescribed by individualized intensity zones, the variability in responsiveness to the intervention is reduced [3,67,68]. PIs show the real variability in responses among the population of endurance-trained athletes to different TID models. While there were no detectable differences between TID models at the group level in this review, individual athletes may respond better or worse to a particular intervention within a wide range around the group mean. ...
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Background Endurance athletes tend to accumulate large training volumes, the majority of which are performed at a low intensity and a smaller portion at moderate and high intensity. However, different training intensity distributions (TID) are employed to maximize physiological and performance adaptations. Objective The objective of this study was to conduct a systematic review and network meta-analysis of individual participant data to compare the effect of different TID models on maximal oxygen uptake (VO2max) and time-trial (TT) performance in endurance-trained athletes. Methods Studies were included if: (1) they were published in peer reviewed academic journals, (2) they were in English, (3) they were experimental or quasi-experimental studies, (4) they included trained endurance athletes, (5) they compared a polarized (POL) TID intervention to a comparator group that utilized a different TID model, (6) the duration in each intensity domain could be quantified, and (7) they reported VO2max or TT performance. Medline and SPORTDiscus were searched from inception until 11 February 2024. Results We included 13 studies with 348 (n = 296 male, n = 52 female) recreational (n = 150) and competitive (n = 198) endurance athletes. Mean age ranged from 17.6 to 41.5 years and VO2max ranged from 46.6 to 68.3 mL·kg⁻¹·min⁻¹, across studies respectively. Based on the time in heart rate zone approach, there was no difference in VO2max (SMD = − 0.06, p = 0.68) or TT performance (SMD = − 0.05, p = 0.34) between POL and pyramidal (PYR) interventions. There were no statistically significant differences between POL and any of the other TID interventions. Subgroup analysis showed a statistically significant difference in the response of VO2max between recreational and competitive athletes for POL and PYR (SMD = − 0.63, p < 0.05). Competitive athletes may have greater improvements to VO2max with POL, while recreational athletes may improve more with a PYR TID. Conclusions Our results indicate that the adaptations to VO2max following different TID interventions are dependent on performance level. Athletes at a more competitive level may benefit from a POL TID intervention and recreational athletes from a PYR TID intervention.
... Exercise has benefits for many clinically important outcomes in older adults, such as reducing fall risk, cardiovascular disease, and death. However, despite engaging in exercise at the same relative intensity, not only young but also older populations exhibit significant variations in acute physiological responses to exercise and the time to task failure [601]. Understanding the threshold and optimal levels of activity necessary for health promotion and disease management has become increasingly important in recent years [602]. ...
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Aging, a universal and inevitable process, is characterized by a progressive accumulation of physiological alterations and functional decline over time, leading to increased vulnerability to diseases and ultimately mortality as age advances. Lifestyle factors, notably physical activity (PA) and exercise, significantly modulate aging phenotypes. Physical activity and exercise can prevent or ameliorate lifestyle-related diseases, extend health span, enhance physical function, and reduce the burden of non-communicable chronic diseases including cardiometabolic disease, cancer, musculoskeletal and neurological conditions, and chronic respiratory diseases as well as premature mortality. Physical activity influences the cellular and molecular drivers of biological aging, slowing aging rates—a foundational aspect of geroscience. Thus, PA serves both as preventive medicine and therapeutic agent in pathological states. Sub-optimal PA levels correlate with increased disease prevalence in aging populations. Structured exercise prescriptions should therefore be customized and monitored like any other medical treatment, considering the dose-response relationships and specific adaptations necessary for intended outcomes. Current guidelines recommend a multifaceted exercise regimen that includes aerobic, resistance, balance, and flexibility training through structured and incidental (integrated lifestyle) activities. Tailored exercise programs have proven effective in helping older adults maintain their functional capacities, extending their health span, and enhancing their quality of life. Particularly important are anabolic exercises, such as Progressive resistance training (PRT), which are indispensable for maintaining or improving functional capacity in older adults, particularly those with frailty, sarcopenia or osteoporosis, or those hospitalized or in residential aged care. Multicomponent exercise interventions that include cognitive tasks significantly enhance the hallmarks of frailty (low body mass, strength, mobility, PA level, and energy) and cognitive function, thus preventing falls and optimizing functional capacity during aging. Importantly, PA/exercise displays dose-response characteristics and varies between individuals, necessitating personalized modalities tailored to specific medical conditions. Precision in exercise prescriptions remains a significant area of further research, given the global impact of aging and broad effects of PA. Economic analyses underscore the cost benefits of exercise programs, justifying broader integration into health care for older adults. However, despite these benefits, exercise is far from fully integrated into medical practice for older people. Many healthcare professionals, including geriatricians, need more training to incorporate exercise directly into patient care, whether in settings including hospitals, outpatient clinics, or residential care. Education about the use of exercise as isolated or adjunctive treatment for geriatric syndromes and chronic diseases would do much to ease the problems of polypharmacy and widespread prescription of potentially inappropriate medications. This intersection of prescriptive practices and PA/exercise offers a promising approach to enhance the well-being of older adults. An integrated strategy that combines exercise prescriptions with pharmacotherapy would optimize the vitality and functional independence of older people whilst minimizing adverse drug reactions. This consensus provides the rationale for the integration of PA into health promotion, disease prevention, and management strategies for older adults. Guidelines are included for specific modalities and dosages of exercise with proven efficacy in randomized controlled trials. Descriptions of the beneficial physiological changes, attenuation of aging phenotypes, and role of exercise in chronic disease and disability management in older adults are provided. The use of exercise in cardiometabolic disease, cancer, musculoskeletal conditions, frailty, sarcopenia, and neuropsychological health is emphasized. Recommendations to bridge existing knowledge and implementation gaps and fully integrate PA into the mainstream of geriatric care are provided. Particular attention is paid to the need for personalized medicine as it applies to exercise and geroscience, given the inter-individual variability in adaptation to exercise demonstrated in older adult cohorts. Overall, this consensus provides a foundation for applying and extending the current knowledge base of exercise as medicine for an aging population to optimize health span and quality of life.
... Similarly, two short-term continuous exercise training programs of different intensities (i.e., 50% and 70% of V O 2max ) elicited similar speeding of the V O 2 kinetics response (Murias et al. 2016). However, the intensity of the continuous training was prescribed as a percentage of V O 2max , a method that has been demonstrated to lack accuracy in ensuring uniform metabolic disturbance across individuals (Scharhag-Rosenberger et al. 2009;Iannetta et al. 2020;Meyler et al. 2023). Furthermore, the aforementioned exercise interventions were work-matched in only one of the studies (Berger et al. 2006), which does not allow for the isolation of the role of intensity. ...
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Purpose This study examined the effect of 3 and 6 weeks of intensity domain-based exercise training on V˙O2{\dot{\text{V}}\text{O}}_{{2}} kinetics changes and their relationship with indices of performance. Methods Eighty-four young healthy participants (42 M, 42 F) were randomly assigned to six groups (14 participants each, age and sex-matched) consisting of: continuous cycling in the (1) moderate (MOD)-, (2) lower heavy (HVY1)-, and (3) upper heavy-intensity (HVY2)- domain; interval cycling in the (4) severe-intensity domain (i.e., high-intensity interval training (HIIT), or (5) extreme-intensity domain (i.e., sprint-interval training (SIT)); or (6) control (CON). Training participants completed two three-week phases of three supervised sessions per week, with physiological evaluations performed at PRE, MID and POST intervention. All training protocols, except SIT, were work-matched. Results There was a significant time effect for the time constant (τV˙O2\tau {\dot{\text{V}}\text{O}}_{{2}}) between PRE (31.6 ± 10.4 s) and MID (22.6 ± 6.9 s) (p < 0.05) and PRE and POST (21.8 ± 6.3 s) (p < 0.05), but no difference between MID and POST (p > 0.05) and no group or interaction effects (p > 0.05). There were no PRE to POST differences for CON (p < 0.05) in any variables. Despite significant increases in maximal V˙O2{\dot{\text{V}}\text{O}}_{{2}} (V˙O2max{\dot{\text{V}}\text{O}}_{{{\text{2max}}}}), estimated lactate threshold (θLT), maximal metabolic steady state (MMSS), and peak power output (PPO) for the intervention groups (p < 0.05), there were no significant correlations from PRE to MID or MID to POST between ΔτV˙O2\Delta \tau {\dot{\text{V}}\text{O}}_{{2}} and ΔV˙O2max\Delta {\dot{\text{V}}\text{O}}_{{{\text{2max}}}} (r = – 0.221, r = 0.119), ΔPPO (r = – 0.112, r = – 0.017), ΔθLT (r = 0.083, r = 0.142) and ΔMMSS (r = – 0.213, r = 0.049)(p > 0.05). Conclusion This study demonstrated that (i) the rapid speeding of V˙O2{\dot{\text{V}}\text{O}}_{{2}} kinetics was not intensity-dependent; and (ii) changes in indices of performance were not significantly correlated with ΔτV˙O2\Delta \tau {\dot{\text{V}}\text{O}}_{{2}}.
... The three-zone TID model was initially proposed by Skinner and McLellan [5] on the basis of changes in gas exchange and blood lactate. More recently, three-zone TID models have been aligned with the moderate, heavy and severe exercise intensity domains, whereby each exercise domain elicits distinct and well-defined physiological responses to exercise [6][7][8]. Using a three-zone TID framework, zone 1 (Z1) comprises intensities up to the lactate threshold or gas exchange threshold, zone 2 (Z2) consists of intensities above lactate threshold, but below the maximal metabolic steady state (normally determined as critical speed (CS), see Jones et al. [9]), and zone 3 (Z3) comprises high-intensity exercise, where the intensity of exercise exceeds CS [10]. ...
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Background The training characteristics and training intensity distribution (TID) of elite athletes have been extensively studied, but a comprehensive analysis of the TID across runners from different performance levels is lacking. Methods Training sessions from the 16 weeks preceding 151,813 marathons completed by 119,452 runners were analysed. The TID was quantified using a three-zone approach (Z1, Z2 and Z3), where critical speed defined the boundary between Z2 and Z3, and the transition between Z1 and Z2 was assumed to occur at 82.3% of critical speed. Training characteristics and TID were reported based on marathon finish time. Results Training volume across all runners was 45.1 ± 26.4 km·week⁻¹, but the fastest runners within the dataset (marathon time 120–150 min) accumulated > three times more volume than slower runners. The amount of training time completed in Z2 and Z3 running remained relatively stable across performance levels, but the proportion of Z1 was higher in progressively faster groups. The most common TID approach was pyramidal, adopted by > 80% of runners with the fastest marathon times. There were strong, negative correlations (p < 0.01, R² ≥ 0.90) between marathon time and markers of training volume, and the proportion of training volume completed in Z1. However, the proportions of training completed in Z2 and Z3 were correlated (p < 0.01, R² ≥ 0.85) with slower marathon times. Conclusion The fastest runners in this dataset featured large training volumes, achieved primarily by increasing training volume in Z1. Marathon runners adopted a pyramidal TID approach, and the prevalence of pyramidal TID increased in the fastest runners.
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The present study aims to determine the utility of integrating balance model (W´BAL-INT) in designing interval training programs as assessed by improvements in power output, critical power (CP), and W prime (W´) defined as the finite work capacity above CP. Fourteen male cyclists (age = 42 ± 7 yr, body mass = 69.6 ± 6.5 kg, height = 175 ± 5 cm, CP = 302 ± 32 W, relative CP = 4.35 ± 0.66 W·kg-1) were randomized into two training groups: Short-Medium-Long intervals (SML-INT; n = 7) or Long intervals (L-INT, n = 7) [training sessions separated by 72 h], along with 3-4 sessions of moderate intensity training per week, for 4 weeks. All sessions were designed to result in the complete depletion of the W´ as gauged by the W´BAL-INT. CP and W´ were assessed using the specified efforts (i.e., 12, 7 and 3 min) and calculated with the 2-parameter CP linear model. Training loads between the groups were compared using different metrics. CP improved in both the SML-INT and L-INT groups by 5 ± 4% and 6 ± 5% (p < 0.001) respectively, without significant changes in W´. Mean maximal power over 3, 7 and 12 min increased significantly in the SML-INT group by 5%, 4% and 9%, (p < 0.05) without significant changes in the L-INT group. There were no differences between groups in training zone distribution or training load using BikeScore and relative intensity, but there was significantly (p < 0.05) higher TRIMPS for the Long-INT group. Therefore, W´BAL model may prove to be a useful tool for coaches to construct SML-INT training programs.
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Although many studies have assumed variability reflects variance caused by exercise training, few studies have examined whether interindividual differences in trainability are present following exercise training. The present individual participant data (IPD) meta-analysis sought to: (1) investigate the presence of interindividual differences in trainability for cardiorespiratory fitness (CRF), waist circumference, and body mass; and (2) examine the influence of exercise training and potential moderators on the probability that an individual will experience clinically important differences. The IPD meta-analysis combined data from 1879 participants from eight previously published randomized controlled trials. We implemented a Bayesian framework to: (1) test the hypothesis of interindividual differences in trainability by comparing variability in change scores between exercise and control using Bayes factors; and (2) compare posterior predictions of control and exercise across a range of moderators (baseline body mass index (BMI) and exercise duration, intensity, amount, mode, and adherence) to estimate the proportions of participants expected to exceed minimum clinically important differences (MCIDs) for all three outcomes. Bayes factors demonstrated a lack of evidence supporting a high degree of variance attributable to interindividual differences in trainability across all three outcomes. These findings indicate that interindividual variability in observed changes are likely due to measurement error and external behavioural factors, not interindividual differences in trainability. Additionally, we found that a larger proportion of exercise participants were expected to exceed MCIDs compared with controls for all three outcomes. Moderator analyses identified that larger proportions were associated with a range of factors consistent with standard exercise theory and were driven by mean changes. Practitioners should prescribe exercise interventions known to elicit large mean changes to increase the probability that individuals will experience beneficial changes in CRF, waist circumference and body mass.
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Physical activity (PA) guidelines for the general population are designed to mitigate the rise of chronic and debilitating diseases brought by inactivity and sedentariness. Although essential, they are insufficient as rates of cardiovascular, pulmonary, renal, metabolic and other devastating and lifelong diseases remain on the rise. This systemic failure supports the need for an improved exercise prescription approach that targets the individual. Significant interindividual variability of cardiorespiratory fitness (CRF) responses to exercise are partly explained by biological and methodological factors, and the modulation of exercise volume and intensity seem to be key in improving prescription guidelines. The use of physiological thresholds, such as lactate, ventilation, as well as critical power, have demonstrated excellent results to improve CRF in those struggling to respond to the current homogenous prescription of exercise. However, assessing physiological thresholds requires laboratory resources and expertise and is incompatible for a general population approach. A case must be made that balances the effectiveness of an exercise programme to improve CRF and accessibility of resources. A population-wide approach of exercise prescription guidelines should include free and accessible self-assessed threshold tools, such as rate of perceived exertion, where the homeostatic perturbation induced by exercise reflects physiological thresholds. The present critical review outlines factors for individuals exercise prescription and proposes a new theoretical hierarchal framework to help shape PA guidelines based on accessibility and effectiveness as part of a personalised exercise prescription that targets the individual.
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During incremental exercise, two thresholds may be identified from standard gas exchange and ventilatory measurements. The first signifies the onset of blood lactate accumulation (the lactate threshold, LT) and the second the onset of metabolic acidosis (the respiratory compensation point, RCP). The ability to explain why these thresholds occur and how they are identified, non-invasively, from pulmonary gas exchange and ventilatory variables is fundamental to the field of exercise physiology and requisite to the understanding of core concepts including exercise intensity, assessment, prescription, and performance. This review is intended as a unique and comprehensive theoretical and practical resource for instructors, clinicians, researchers, lab technicians, and students at both undergraduate and graduate levels to facilitate the teaching, comprehension, and proper non-invasive identification of exercise thresholds. Specific objectives are to: (1) explain the underlying physiology that produces the LT and RCP; (2) introduce the classic non-invasive measurements by which these thresholds are identified by connecting variable profiles to underlying physiological behaviour; (3) discuss common issues that can obscure threshold detection and strategies to identify and mitigate these challenges; and (4) introduce an online resource to facilitate learning and standard practices. Specific examples of exercise gas exchange and ventilatory data are provided throughout to illustrate these concepts and a novel online application tool designed specifically to identify the estimated LT (θLT) and RCP is introduced. This application is a unique platform for learners to practice skills on real exercise data and for anyone to analyze incremental exercise data for the purpose of identifying θLT and RCP.
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Background Although structured exercise training is strongly recommended in cardiac patients, uncertainties exist about the methods for determining exercise intensity (EI) and their correspondence with effective EI obtained by ventilatory thresholds. We aimed to determine the first (VT1) and second ventilatory threshold (VT2) in cardiac patients, sedentary subjects and athletes comparing VT1 and VT2 with EI defined by recommendations. Methods We prospectively enrolled 350 subjects (mean age: 50.7±12.9 years; 167 cardiac patients, 150 healthy sedentary subjects, 33 competitive endurance athletes). Each subject underwent ECG, echocardiography, and cardiopulmonary exercise testing. The percentages of peak VO2, peak heart rate (HR), and HR reserve were obtained at VT1 and VT2 and compared with the EI definition proposed by the recommendations. Results VO2 at VT1 corresponded to high rather than moderate EI in 67.1% and 79.6% of cardiac patients, applying the definition of moderate exercise by the previous recommendations and the 2020 guidelines, respectively. Most cardiac patients had VO2 values at VT2 corresponding to very-high rather than high EI (59.9% and 50.3%, by previous recommendations and 2020 guidelines, respectively). A better correspondence between ventilatory thresholds and recommended EI domains was observed in healthy subjects and athletes (90% and 93.9%, respectively). Conclusions EI definition based on percentages of peak HR and peak VO2 may misclassify the effective EI and the discrepancy between the individually determined and the recommended EI is particularly relevant in cardiac patients. A ventilatory threshold-based rather than a range-based approach is advisable to define an appropriate level of EI.
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Improving objects, ideas or situations—whether a designer seeks to advance technology, a writer seeks to strengthen an argument or a manager seeks to encourage desired behaviour—requires a mental search for possible changes1–3. We investigated whether people are as likely to consider changes that subtract components from an object, idea or situation as they are to consider changes that add new components. People typically consider a limited number of promising ideas in order to manage the cognitive burden of searching through all possible ideas, but this can lead them to accept adequate solutions without considering potentially superior alternatives4–10. Here we show that people systematically default to searching for additive transformations, and consequently overlook subtractive transformations. Across eight experiments, participants were less likely to identify advantageous subtractive changes when the task did not (versus did) cue them to consider subtraction, when they had only one opportunity (versus several) to recognize the shortcomings of an additive search strategy or when they were under a higher (versus lower) cognitive load. Defaulting to searches for additive changes may be one reason that people struggle to mitigate overburdened schedules11, institutional red tape12 and damaging effects on the planet13,14. Observational and experimental studies of people seeking to improve objects, ideas or situations demonstrate that people default to searching for solutions that add new components rather than for solutions that remove existing components.
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Objective This study tested the hypothesis that greater mean changes in cardiorespiratory fitness (CRF), in either the absence or presence of reduced interindividual variability, explain larger CRF response rates following higher doses of exercise training.Methods We retrospectively analyzed CRF data from eight randomized controlled trials (RCT; n = 1590 participants) that compared at least two doses of exercise training. CRF response rates were calculated as the proportion of participants with individual confidence intervals (CIs) placed around their observed response that lay above 0.5 metabolic equivalents (MET). CIs were calculated using no-exercise control group-derived typical errors and were placed around each individual’s observed CRF response (post minus pre-training CRF). CRF response rates, mean changes, and interindividual variability were compared across exercise groups within each RCT.ResultsCompared with lower doses, higher doses of exercise training yielded larger CRF response rates in eight comparisons. For most of these comparisons (7/8), the higher dose of exercise training had a larger mean change in CRF but similar interindividual variability. Exercise groups with similar CRF response rates also had similar mean changes.Conclusion Our findings demonstrate that larger CRF response rates following higher doses of exercise training are attributable to larger mean changes rather than reduced interindividual variability. Following a given dose of exercise training, the proportion of individuals expected to improve their CRF beyond 0.5 METs is unrelated to the heterogeneity of individual responses.
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Responses to exercise at a given percentage of one's maximum rate of oxygen consumption (V̇O2MAX), or percentage of the power associated with V̇O2MAX during a graded exercise test (i.e., PGXT), vary. Purpose: Determine if differences in Critical Power (PCRIT, maximum metabolic steady state) and Work-prime (W', the amount of work tolerated above steady state) are related to training-induced changes in endurance. Methods: PCRIT, W', V̇O2MAX and other variables were determined before and after 22 adults completed 8 weeks of either moderate-intensity continuous training (MICT) or high-intensity interval training (HIIT) performed at fixed percentages of PGXT. Results: On average, PCRIT increased to a greater extent following HIIT (MICT: 15.7 ± 3.1% vs. HIIT: 27.5 ± 4.3%; P=0.03), but the magnitude of change varied widely within each group (MICT: 4-36%, HIIT: 4-61%). The intensity of the prescribed exercise relative to pre-training PCRIT, not PGXT, accounted for most of the variance in changes to PCRIT in response to a given protocol (R2=0.61-0.64; P<0.01). While PCRIT and V̇O2MAX were related before training (R2=0.92, P<0.01), the training-induced change in PCRIT was not significantly related to the change in V̇O2MAX (R2=0.06, P=0.26). Before training, time-to-failure at PGXT was related to W' (R2=0.52; P<0.01), but not V̇O2MAX (R2=0.13; P=0.10). Training-induced changes in time-to-failure at the initial PGXT were better captured by the combined changes in W' and PCRIT (R2=0.77, P<0.01), than by the change in V̇O2MAX (R2=0.24; P=0.02). Conclusion: Differences in PCRIT and W' account for some of the variability in responses to endurance exercise.
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Exercise training is highly recommended in current guidelines on primary and secondary prevention of cardiovascular disease (CVD). This is based on the cardiovascular benefits of physical activity and structured exercise, ranging from improving the quality of life to reducing CVD and overall mortality. Therefore, exercise should be treated as a powerful medicine and critical component of the management plan for patients at risk for or diagnosed with CVD. A tailored approach based on the patient’s personal and clinical characteristics represents a cornerstone for the benefits of exercise prescription. In this regard, the use of cardiopulmonary exercise testing is well-established for risk stratification, quantification of cardiorespiratory fitness and ventilatory thresholds for a tailored, personalised exercise prescription. The aim of this paper is to provide a practical guidance to clinicians on how to use data from cardiopulmonary exercise testing towards personalised exercise prescriptions for patients at risk of or with CVD.
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Since its publication in 2012, the W' balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model's intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W' balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W' during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W' balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise.