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Changes in Cardiorespiratory Fitness Following Exercise Training Prescribed Relative to Traditional Intensity Anchors and Physiological Thresholds: A Systematic Review with Meta-analysis of Individual Participant Data

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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.
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Sports Medicine
https://doi.org/10.1007/s40279-024-02125-x
SYSTEMATIC REVIEW
Changes inCardiorespiratory Fitness Following Exercise Training
Prescribed Relative toTraditional Intensity Anchors andPhysiological
Thresholds: ASystematic Review withMeta‑analysis ofIndividual
Participant Data
SamuelJ.R.Meyler1· PaulA.Swinton2· LindsayBottoms1· LanceC.Dalleck3· BenHunter4· MarkA.Sarzynski5·
DavidWellsted1· CamillaJ.Williams6· DanielMuniz‑Pumares1
Accepted: 17 September 2024
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024
Abstract
Background It is unknown whether there are differences in maximal oxygen uptake (
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
O2max, (b) proportion of individuals increas-
ing
V
O2max beyond a minimum important difference (MID) and (c) response variability in
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
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
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
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
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
O2max and increased
the likelihood of increasing
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 compar-
ing 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
O2max trainability, and if meaningful, the
causative factors.
Extended author information available on the last page of the article
1 Introduction
Cardiorespiratory fitness, measured as maximum oxygen
uptake (
V
O2max), represents the upper limit of cardiopulmo-
nary-muscle oxidative function [1], quantifying the body’s
ability to transport and utilise oxygen [2]. As such,
V
O2max is
recognised as a key determinant of endurance performance,
with elite endurance athletes demonstrating some of the
highest
V
O2max values ever recorded [3, 4]. Additionally,
V
O2max is an important marker of cardiovascular health [57],
and low levels of
V
O2max are a strong risk factor for all-cause
and disease-specific mortality [5]. Despite being the only
major risk factor not routinely assessed in clinical practice,
growing epidemiological and clinical evidence suggests
that
V
O2max may be a stronger predictor of mortality than
traditionally assessed risk factors such as smoking, type 2
diabetes mellitus and obesity [7]. Increasing
V
O2max is thus
a commonly sought-after phenotypic change across different
S.J.R.Meyler et al.
Key Points
Prescribing exercise training relative to physiological
thresholds, rather than traditional intensity anchors,
elicited superior changes in cardiorespiratory fitness and
increased the proportion of individuals increasing cardi-
orespiratory fitness by at least one metabolic equivalent.
No difference in cardiorespiratory response variability
was observed between exercise groups. Comparisons
with controls, however, provided evidence of inter-
individual response variability that warrants further
investigation.
Considering the link between cardiorespiratory fitness
and both health and performance outcomes, future stud-
ies should aim to prescribe exercise relative to physi-
ological thresholds where possible.
the number of individuals attaining a change in
V
O2max
surpassing a predefined threshold, interventions commonly
adopt ‘additive’ approaches (for a review see [21]). For
example, augmenting the exercise stimulus (i.e. increasing
training volume, frequency, and/or intensity) often proves
effective in increasing response rate as a result of greater
group mean increases in
V
O2max [2225]. Aiming to elicit
superior changes in
V
O2max and reduce response variability
to the initial stimulus, an example of a ‘subtractive’ approach
[21], may be achieved through changing the method used
to prescribe exercise intensity [19]. However, the effects of
using different means of exercise prescription to do so are
unclear.
Exercise intensity is commonly prescribed relative to tra-
ditional (TRAD) intensity anchors (Table1) [15], whereby
recommended percentages of such values are used to pre-
scribe exercise in a given intensity domain. It is worth noting
that, whilst varying nomenclature is used to describe the dif-
ferent 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 exer-
cise tolerance [2733]. As changes in
V
O2max 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
O2max 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. training-induced changes in
V
O2max) regarding
their magnitude and variability. If the magnitude of training-
induced changes in
V
O2max can be increased among a larger
proportion of individuals, and the number of individuals expe-
riencing negligible changes in their
V
O2max is reduced, this
could have profound implications for improving health out-
comes and approaches to exercise prescription.
1.1 Objectives
Examining differences between physiological threshold (THR)
and TRAD exercise programmes [and using non-exercising
control groups (CON) where applicable], we sought to: (a)
compare the mean change scores in
V
O2max and the proportion
of individuals expected to attain increases in
V
O2max beyond
a MID of one MET (3.5 mL kg−1 min−1) between THR and
TRAD and (b) test the hypothesis that
V
O2max response vari-
ability is lower in the THR group compared with the TRAD
group.
populations. Evidence indicates that increasing
V
O2max by
one metabolic equivalent (MET; 3.5 mL kg−1 min−1) can
reduce mortality risk by ~ 10–30% [5, 8] and health care
costs by ~ 5% [9, 10]. In turn, a value of one MET can be
used as a minimum important difference (MID) when evalu-
ating changes in
V
O2max following a period of exercise train-
ing [7, 11].
Changes in
V
O2max can be explained by the Fick principle,
where
V
O2max is the product of maximum cardiac output and
arteriovenous oxygen difference [12, 13]. Adaptations caus-
ing changes in cardiac output (i.e. the product of heart rate
and stroke volume) represent ‘central’ adaptations whereby
phenotypic modifications alter convective oxygen delivery,
whereas adaptations causing changes in arteriovenous oxy-
gen difference reflect ‘peripheral’ adaptations, comprising
changes in oxygen extraction and utilisation [14]. The most
effective means of increasing
V
O2max is through endurance
training, typically in the form of constant load continuous
training and/or interval-based training, which are shown to
increase
V
O2max by ~ 5.5 and ~ 4.9 mL kg−1 min−1, respec-
tively [15]. However,
V
O2max is markedly reduced by periods
of inactivity (e.g. bed rest) [16]. Whilst both approaches of
exercise training have been demonstrated to be efficacious at
the group level [15], the individual effect of exercise training
on
V
O2max appears to exhibit a heterogenous distribution [17,
18], suggesting that some individuals do not attain some of
the benefits of exercise.
Several biological and methodological factors underpin
this apparent ‘response variability’ (for a review see [19]),
as well as measurement error and day-to-day biological vari-
ability [20]. To tackle response variability, and specifically
Cardiorespiratory Fitness and Exercise Intensity Prescription
2 Methods
2.1 Protocol andRegistration
This review was pre-registered on the International Pro-
spective Register of Systematic Reviews (PROSPERO;
id: CRD42021226644), and the present protocol has been
conducted in accordance with the Preferred Reporting
Items for Systematic Review and Meta-analysis of Individ-
ual Participant Data guidelines [37] (Supplementary file).
2.2 Eligibility Criteria
2.2.1 Type ofStudy
Randomised controlled and non-controlled training studies,
written in English, and published before October 2023, were
used.
2.2.2 Type ofParticipants
Participants were healthy males and females 18 years of
age with a body mass index (BMI) 30 kg m2, and not suf-
fering from any acute or chronic disease(s).
2.2.3 Type ofInterventions
Training interventions had to meet the following criteria:
(a) exercise training lasted 3 weeks; (b) consisted of either
continuous training, interval training or a combination of
both; (c) exercise was either walking, running or cycling;
(d)
V
O2max was directly measured pre- and post-intervention
via indirect calorimetry during an incremental test to task
failure; and (e) individuals were either allocated to tradition-
ally prescribed exercise training (TRAD), whereby exercise
intensity was prescribed relative to a physiological value,
as outlined in Table1, and/or to a physiological threshold
(THR) as outlined in Table2. The latter included studies
using the delta method (∆), whereby intensity is prescribed
using a physiological threshold and physiological value (i.e.
50% ∆ = gas exchange threshold + [0.5 × (critical power-gas
exchange threshold)]). Exercise groups involving additional
interventional manipulations, such as nutritional supplemen-
tation and/or environmental manipulation, were excluded.
Two datasets were created from eligible studies, one con-
taining ‘controlled’ studies, where studies included volume-
matched THR and TRAD exercise group and a non-exer-
cising control group (CON), and ‘non-controlled’ studies,
Table 1 ‘Traditional’ anchors of exercise intensity
Intensity anchor Abbreviation Description
Maximum oxygen uptake
V
O2max Maximum oxygen uptake attained during maximal exercise
despite increases in external workload
Oxygen uptake reserve
V
O2RDifference between maximum and resting oxygen uptake
Maximum heart rate HRmax Maximum heart rate reached during maximal exercise despite
increases in external workload
Heart rate reserve HRR Difference between maximum and resting heart rate
Maximum work rate WRmax Maximum work rate achieved during an incremental exercise test
Table 2 Physiological thresholds delineating the boundaries of the moderate- and heavy-intensity domains and the heavy- and severe-intensity
domains
V
CO2 Carbon dioxide production,
V
E minute ventilation,
V
O2 oxygen uptake
Physiological threshold Description
Boundary between the moderate- and heavy-intensity domains
Lactate threshold (LT) Blood lactate concentration rises above baseline levels
Gas exchange threshold (GET) First breakpoint at which
V
CO2 increases disproportionately to
V
O2
First ventilatory threshold (VT1)An increase in
V
E/
V
O2 with no concurrent increase in
V
E/
V
CO2
Boundary between the heavy- and severe-intensity domains
Maximum lactate steady-state (MLSS) Highest constant workload that leads to a balance between lactate
production and elimination
Respiratory compensation point (RCP) Second breakpoint at which
V
E increases disproportionately to
V
O2
Second ventilatory threshold (VT2)A simultaneous increase in both
V
E/
V
O2 and
V
E/
V
CO2
Critical power (CP) Asymptote of the power–duration relationship
S.J.R.Meyler et al.
where data from any single THR and TRAD exercise group
were included.
2.3 Identifying Studies forSystematic Review—
Information Sources andSearch Strategy
The electronic databases PubMed and Scopus were searched
initially in 2021 and updated in 2023 such that papers pub-
lished before October 2023 were included. Databases were
searched using the following terms: ‘high-intensity inter-
val training’, ‘continuous training’, ‘endurance training’,
‘maximum oxygen uptake’, ‘peak oxygen uptake’, ‘
V
O2max’,
‘cardiorespiratory fitness’ and ‘healthy adults’. Additional
resources were sought via the scrutinisation of reference
lists, review articles and contact with research teams of rel-
evant papers. The literature search and study selection pro-
cess was carried out independently by two authors (S.M. and
B.H.) using a systematic review software (Covidence, Veri-
tas Health Innovation, Australia). A third reviewer (author
D.M.) resolved any disagreements regarding study eligibil-
ity. The title and abstracts were extracted from the database
searches and duplicates were removed automatically by the
Covidence software. Papers that were not relevant based on
the title were removed. Title and abstracts were screened to
identify studies that appeared to meet the predefined eligibil-
ity criteria. Full texts of studies passing the title and abstract
screening were then scrutinised to determine their eligibility
for inclusion in the review.
2.4 Data Collection Processes
Corresponding authors of eligible studies were contacted
via email or by other means of contact (e.g. ResearchGate
and social media). Authors were provided with a brief sum-
mary of the aims of the present study and invited to share
anonymised individual participant data (IPD). Anonymised
IPD included age, sex, height (cm), pre- and post-interven-
tion mass (kg),
V
O2max (mL kg−1 min−1 and L min−1) and
BMI for all individuals. Lead authors were contacted in the
same manner if corresponding authors were unreachable. A
follow-up email containing a deadline for response was sent
to authors in the absence of a reply.
2.5 IPD Integrity
Once IPD were received, data were checked for consist-
ency with the published report, at the individual level for
inconsistencies and missing data. Only individuals with a
complete data set were included in the review, and individ-
ual data not meeting the participant eligibility criteria were
excluded. Any discrepancies between IPD and published
reports were discussed with the study authors.
2.6 Risk ofBias
Risk of bias was assessed in each individual study by two
reviewers (S.M. and D.M.P.). For randomised trials, the
Cochrane risk-of-bias tool for randomised trials (RoB2)
was used [38]. The ROBINS-I tool was used for assessing
risk of bias in non-randomised and uncontrolled intervention
studies [39]. An inter-reviewer reliability analysis using the
kappa statistic (
k
) was performed to determine consistency
between reviewers (Supplementary file).
2.7 Specification ofOutcomes andEffect Measures
All analyses were conducted using
V
O2max
(mL kg−1 min−1) change scores, calculated for each indi-
vidual as the post-intervention value minus the baseline
value. Measures of effect were based on group differences
according to this absolute scale and percentage expected
to exceed the MID.
2.8 Synthesis Methods
Across all analyses, one-stage IPD meta-analysis mod-
els were developed. All models were conducted within
a Bayesian framework with random intercepts to account
for systematic variation across individual studies. Change
scores relative to baseline were calculated for each par-
ticipant on an absolute scale (mL kg−1 min−1), and distri-
butional models were used to estimate both the mean dif-
ference and standard deviation of the difference. A group
term (TRAD versus THR, or exercise versus control) was
added as a predictor for the mean and standard deviation,
with a log link used for the latter. Posterior distributions
were summarised by reporting the median and 95% credi-
ble intervals (CrI) for the mean and 75% CrIs for the stand-
ard deviation. Separate Bayes factors were estimated com-
paring models with and without the group predictor for
the mean and standard deviation. A Bayes factor greater
than 1.0 provided evidence supporting a group difference,
whereas values less than 1.0 provided evidence support-
ing no group difference. The overall strength of evidence
in favour of the different models was evaluated accord-
ing to a previously defined scale [40], with non-neutral
descriptions ranging from anecdotal to extreme evidential
strength (Table3).
To investigate the proportion of individuals exceeding
the MID, we used the posterior samples from the distribu-
tional model to generate posterior predictions (n = 10,000)
and calculated the proportion that exceeded the threshold
in each group. Default weakly informative priors were
used, including student-t and half-t priors with 3 degrees
of freedom. All analyses were performed using the R
Cardiorespiratory Fitness and Exercise Intensity Prescription
wrapper package brms interfaced with Stan to perform
sampling [41] and the R package bridgesampling to cal-
culate Bayes factors. Convergence of parameter estimates
was obtained for all models with Gelman–Rubin R-hat
values below 1.1 [42].
3 Results
3.1 Study Selection andIPD Obtained
See Fig.1 for the flow diagram pertaining to study identifi-
cation and screening, and the amount of IPD obtained.
3.2 IPD integrity andRisk ofBias Within Studies
There were no issues that needed to be raised following the
checking of IPD. Risk of bias assessments for individual
studies are included in the Supplementary file. There was no
need for any weighting adjustments prior to the subsequent
analyses. Risk was determined to be as follows: Domain 1:
low risk 87%, some concerns 3%, high risk 10%; Domain
2: low risk 100%; Domain 3: low risk 100%; Domain 4:
low risk 100%; Domain 5: low risk 10%, high risk 90%;
and Overall: low risk 10%, some concerns 80%, high risk
10% (Supplementary file). Of note, IPD from the HERIT-
AGE study [17] were included in the current analyses. Due
to the large amount of HERITAGE IPD analysed (n = 562)
compared with that of the other eligible studies combined
(n = 953), the analyses were run with and without its inclu-
sion. There was, however, no difference between the pri-
mary results in either case, and thus, the results are reported
including the HERITAGE IPD.
3.3 Study Characteristics
Individual study characteristics are presented in Table4,
and summary characteristics of THR and TRAD studies are
presented in Table5.
3.4 Results ofSyntheses
3.4.1 Changes inMaximum Oxygen Uptake
Controlled studies ‘Extreme’ evidence (BF > 100) was
identified in support of a greater improvement in
V
O2max
for THR [4.1 (95% CrI: 3.1–5.0 mL kg−1 min−1)] com-
pared with TRAD [1.8 (95% CrI: 0.9–2.8 mL kg−1 min−1);
Fig.2]. Individuals were estimated to be approximately
four times more likely to experience an increase in
V
O2max greater than the MID with THR (64%) compared
with TRAD (16%). There was ‘anecdotal’ evidence
(BF = 0.55) in support of no difference in variation of
V
O2max change scores between THR [1.5 (75% CrI: 1.2–1.8
mL kg−1 min−1)] and TRAD [1.7 (75% CrI: 1.4–2.1
mL kg−1 min−1); Fig.2]. When THR and TRAD were
combined, ‘strong’ evidence (BF = 12.4) was identi-
fied in support of a greater variation in
V
O2max change
scores in the training groups [1.9 (75% CrI: 1.7–2.2)
mL kg−1 min−1)] compared with CON [1.3 (75% CrI:
1.1–1.6) mL kg−1 min−1); Fig.3].
Non-controlled studies In general, similar findings to
those obtained for controlled studies were observed in non-
controlled studies. ‘Very strong’ evidence (BF = 35.1) was
identified in support of a greater improvement in
V
O2max
for THR [4.4 (95% CrI: 3.7–5.2 mL kg−1 min−1)] com-
pared with TRAD [3.4 (95% CrI: 2.8–4.1 mL kg−1 min−1);
Fig.4]. Predictions using the fitted model estimated 60%
of individuals in the THR group should be expected to
increase
V
O2max beyond the MID, with 47% expected in
the TRAD group. ‘Anecdotal’ evidence (BF = 0.41) was
identified in support of no difference in variation of
V
O2max change scores between THR [3.0 (75% CrI: 2.7–3.3
mL kg−1 min−1)] and TRAD [3.2 (75% CrI: 2.9–3.5
mL kg−1 min−1); Fig.4].
4 Discussion
This is the first IPD meta-analysis to explore the magnitude
and variation in
V
O2max change scores elicited by training
programmes using THR and TRAD approaches. The main
findings were (1) prescribing exercise intensity using THR
approaches elicited superior mean changes in
V
O2max and
increased the likelihood of an individual increasing
V
O2max
beyond the MID, and (2) there appeared to be no difference
in response variability between THR and TRAD.
4.1 Mean Changes in
V
O2max
Superior increases in
V
O2max were observed in the THR
group compared with the TRAD group in both the controlled
Table 3 Category of evidence for Bayes factor interpretation
Bayes factor Strength of evidence
≥ 100 Extreme
30–100 Very strong
10–30 Strong
3–10 Moderate
1–3 Anecdotal
1 No evidence
S.J.R.Meyler et al.
and non-controlled analyses. In the controlled studies, it was
estimated that individuals were approximately four times as
likely to experience an increase in
V
O2max beyond the MID.
This estimate was based on the statistical model indicating
64% of participants undergoing THR would experience an
increase of ≥ 3.5 mL kg−1 min−1 compared with 16% of par-
ticipants undergoing TRAD. In the non-controlled studies,
greater variability was observed across the larger data set;
however, it was estimated that, on average, 60% and 47% of
individuals would experience changes beyond the MID in
the THR and TRAD groups, respectively.
Regarding the notion of increasing ‘response rates’,
herein defined as the proportion of individuals improving a
given parameter beyond a predefined threshold, the present
findings agree with previous literature whereby increased
V
O2max response rates are typically explained by greater mean
change scores as opposed to reductions in inter-individual
variability [25]. In turn, using THR approaches represents
a viable approach in increasing response rates, and thus,
increasing the likelihood of individuals attaining the health-
and performance-related benefits of exercise training. Impor-
tantly, instead of requiring increases in training dose follow-
ing TRAD (i.e. by increasing training intensity, frequency
and/or duration), using THR approaches appears to be an
effective method to increase the proportion of individuals
attaining increases in
V
O2max beyond a predefined threshold
in response to the initial exercise stimulus.
Fig. 1 Flow diagram of the study identification and screening process
Cardiorespiratory Fitness and Exercise Intensity Prescription
Table 4 Participant characteristics, sample size, training characteristics and
V
O2max change scores for studies included in the present individual participant data meta-analysis
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Arboleda
etal. [43]
2019 Sedentary
(29 ± 8)
M 18 TRAD INT Running (24) 15 × 30 s @
90–95%
HRmax
40.1 ± 6.2 43.7 ± 6.2 9.7 ± 9.8 3.6 ± 3.5
20 TRAD CT Running (24) 40 min @
65–75%
HRmax
43.5 ± 8.6 44.9 ± 8.7 4.3 ± 15.4 1.4 ± 6.7
Astorino
etal. [44]
2018 Active
(27 ± 8)
M/F 14 THR INT Cycling (9) 8–10 × 1 min
@ 130%
VT
38.8 ± 4.3 41 ± 4.6 5.7 ± 3.9 2.2 ± 1.6
Astorino
etal. [45]
2013 Sedentary
(24 ± 7)
F 4 TRAD INT Cycling (33) 6–10 × 1 min
@ 80–90%
Wmax
31.7 ± 4.4 36.4 ± 4.2 15.4 ± 9.1 4.7 ± 2.5
8 TRAD INT Cycling (33) 6–10 × 1 min
@ 60–80%
Wmax
30.6 ± 4.2 37.1 ± 3.4 22.1 ± 8.1 6.5 ± 1.6
Berger etal.
[46]
2006 Active
(23 ± 4)
M 8 TRAD CT Cycling
(18–24)
30 min @
60%
V
O2max
33.7 ± 3.8 39.8 ± 6 18 ± 7.7 6.1 ± 3
8 TRAD INT Cycling
(18–24)
20 × 1 min
@ 90%
V
O2max
34.6 ± 6.8 43 ± 8.3 24.3 ± 5.1 8.4 ± 2.1
Bonafiglia
etal. [47]
2016 Active
(20 ± 1)
M/F 21 TRAD CT Cycling (12) 30 min @
65%
V
O2max
42.2 ± 6.5 43.9 ± 6.5 4.4 ± 7.9 1.7 ± 3.2
Bouchard
etal. [17]
1999 Sedentary
(35 ± 14)
M/F 562 TRAD CT Cycling (60) 30–50 min @
55–75%
V
O2max
33.1 ± 8.6 38.7 ± 9.1 18 ± 9.9 5.6 ± 2.9
Branch etal.
[48, 49]
1997, 1999 Sedentary
(32 ± 5)
F 8 TRAD CT Running (40) 150–375
kcal/ses-
sion @ 80%
HRmax
36.2 ± 4.9 38.8 ± 7.5 6.8 ± 8.8 2.6 ± 3.8
10 TRAD CT Cycling (40) 150–375
kcal/ses-
sion @ 80%
HRmax
29.2 ± 7.9 35.5 ± 7 24.6 ± 13.7 6.3 ± 2.6
S.J.R.Meyler et al.
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
8 TRAD CT Cycling (40) 150–375
kcal/ses-
sion @ 40%
HRmax
29.8 ± 4.6 34.1 ± 6.6 16.5 ± 25.3 4.3 ± 7.9
Byrd etal.
[50]
2019 Sedentary
(32 ± 9)
M/F 11 THR INT + CT Cycling + Run-
ning (30)
CT: 30–50
min
@ < VT1
to > VT2;
INT:
8–12 × 60 s
@ 100%
V
O2max
33.6 ± 4 38.4 ± 4.4 14.3 ± 3.6 4.8 ± 1.1
11 TRAD INT + CT Cycling + Run-
ning (30)
CT: 30–50
min @
40–65%
HRR
30.4 ± 6.2 33 ± 7.2 8.1 ± 3.3 2.5 ± 1.2
Casaburi
etal. [51]
1987 Sedentary
(23 ± 1)
M/F 9 THR CT Cycling (40) 45 min @
50–75% ∆
34.5 ± 4.1 40.3 ± 4.1 17.3 ± 8 5.8 ± 2.5
Dalleck etal.
[52]
2008 Sedentary
(37 ± 6)
F 13 TRAD CT Walking
(30–40)
(~ 180 min)
250–1000
kcal/week
@ 50%
V
O2R
35.5 ± 5.9 37.9 ± 4.5 7.8 ± 8 2.5 ± 2.3
Dalleck etal.
[53]
2016 Sedentary
(68 ± 8)
M/F 10 THR CT Aerobic exer-
cise (21)
25–50 min
@ < VT1
to > VT2
25.9 ± 3.8 29.7 ± 4.9 14.7 ± 7.8 3.8 ± 2.2
9 TRAD CT Aerobic exer-
cise (21)
25–50 min
@ 40–65%
HRR
24.1 ± 12.3 26.3 ± 11.9 11.4 ± 9.5 2.2 ± 1.5
Daussin
etal. [54]
2008 Sedentary
(46 ± 8)
M/F 13 THR CT Cycling (24) 20–35 min
rep 5 min (4
min @ LT,
1 min @
90% Pmax)
29.1 ± 5.9 31.9 ± 6.4 9.8 ± 8.7 2.8 ± 2.5
13 THR INT Cycling (24) Work
matched
with INT
27.5 ± 5.3 32.3 ± 6.5 18.5 ± 16.7 4.8 ± 4.9
Cardiorespiratory Fitness and Exercise Intensity Prescription
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Fiorenza
etal. [55]
2019 Sedentary
(57 ± 8)
M 12 TRAD INT Cycling (18) 10–15 min of
‘10–20–30
training’,
3 min rec
between 5
min bouts
@ 30%,
50%, and
100% max
intensity
36.6 ± 8.4 39.4 ± 4.8 7.8 ± 7.8 2.8 ± 2.7
Ghiarone
etal. [56]
2019 Active
(26 ± 5)
M 8 THR INT + CT Cycling (18) Train twice
daily (3
days/
week) CT:
5 min @
LT1 + 100
min @ 50%
∆ (LT1 and
LT2), INT:
10 × 2 min
@ 20% ∆
(LT2 and
PPO)
36 ± 4.3 39.3 ± 5.4 9 ± 7 3.3 ± 2.5
7 THR INT + CT Cycling (18) Train once
daily
(6 day/
week) CT:
5 min @
LT1 + 100
min @ 50%
∆ (LT1 and
LT2), INT:
10 × 2 min
@ 20% ∆
(LT2 and
PPO)
37.9 ± 7.9 40.2 ± 6.6 8.6 ± 21.9 2.3 ± 5.8
S.J.R.Meyler et al.
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Gormley
etal. [57]
2008 Active
(22 ± 3)
M/F 14 TRAD CT Cycling (22) 30–40 min
@ 50–75%
HRR
34.5 ± 8.6 39.1 ± 9 13.9 ± 10.3 4.6 ± 3.3
12 TRAD INT Cycling (18) Weeks 1 and
2: 30–40
min @
50–75%
HRR,
Weeks 3–6:
5 × 5 min @
95% HRR
36.5 ± 5.8 43 ± 7.6 17.7 ± 10.2 6.5 ± 3.8
14 TRAD CT Cycling (23) 30–60 min @
50% HRR
35.5 ± 7.9 38.8 ± 9.1 10 ± 10.9 3.4 ± 4
Granata
etal. [58]
2016 Active
(21 ± 2)
M 10 THR INT Cycling (52) 4–7 × 4 min
@ 35–75%
∆ (LT and
WRpeak);
5–12 × 4
min @
30–80% ∆;
8–20 × 2
min @
50–80% ∆
45.1 ± 7.6 52.2 ± 7.8 16.2 ± 6.6 7.1 ± 2.8
Hov etal.
[59]
2022 Healthy
(23 ± 2)
M 10 TRAD INT Running (24) 4 × 4 min @
90–95%
HRmax
62.1 ± 4.8 66 ± 5 6.3 ± 2.4 3.9 ± 1.5
Jacques etal.
[60]
2021 Moderately
trained
(35 ± 10)
M 15 THR INT Cycling (36) 6–14 × 2 min
@ 40–70%
52.3 ± 9.8 56.5 ± 10 8.8 ± 12.6 4.2 ± 6.2
Landen etal.
[61]
2021 Moderately
trained
(35 ± 7)
F 18 THR INT Cycling (12) 6–14 × 2 min
@ 40–70%
44.5 ± 9 45.9 ± 8.1 3.9 ± 5.2 1.5 ± 2.1
Litleskare
etal. [62]
2020 Active
(25 ± 4)
M/F 12 TRAD CT Running (24) 30–60 min
@ 70–80%
HRpeak
47.9 ± 5.9 49.7 ± 6.2 3.9 ± 5.6 1.8 ± 2.6
Cardiorespiratory Fitness and Exercise Intensity Prescription
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Maturana
etal. [63]
2021 Sedentary
(27 ± 6)
M/F 21 THR CT Cycling (18) 60 min @
LTP1
30.4 ± 4.3 32.7 ± 4.2 7.9 ± 8.6 2.3 ± 2.6
21 TRAD INT Cycling (18) 4 × 4 min @
90% HRmax
31.9 ± 4.1 37.2 ± 4.1 17 ± 7.9 5.3 ± 2.1
Maunder
etal. [64]
2021 Active
(32 ± 7)
M 8 THR INT + CT Cycling (15) 4–6 × 8 min
@ VT2; 90
min @ 95%
VT1; 3 × 25
min @ 50%
∆ (VT1
and VT2);
6–10 × 3
min
52.5 ± 6.4 53.4 ± 6.9 1.7 ± 3.5 0.9 ± 1.8
McNicol
etal. [65]
2009 Active
(21 ± 5)
M/F 14 THR CT Running (18) 20 min @
0.8 km/h
less than
LTv + 0.1
km/h per
session
44 ± 5.5 47.6 ± 6.5 8.7 ± 12.2 3.6 ± 5.1
13 THR CT Running (18) 20 min @ 0.8
km/h less
than LTv
44 ± 6.9 45.3 ± 7 3 ± 2.4 1.3 ± 1
Mendes
etal. [66]
2013 Untrained
(23 ± 2)
M 13 THR CT Cycling (18) 24–39 min @
MLSS
44.9 ± 4.8 49.8 ± 4.5 11.2 ± 7.2 4.9 ± 3.1
Myrkos etal.
[67]
2023 Young adults
(21 ± 3)
M/F 13 TRAD INT Running (14) Running
bouts @
90% PTV
57.7 ± 8 61.4 ± 9.23 6.5 ± 5.7 3.8 ± 3.4
11 THR CT Running (14) − 2.5% of CV 58.2 ± 7.5 61.1 ± 6.4 5.6 ± 6.7 3 ± 3.7
Nicolini
etal. [68]
2019 Sedentary
(23 ± 4)
M 15 TRAD INT Cycling (18) 5 × 1 min @
105–135%
WRpeak
35.5 ± 4.8 40 ± 5.1 12.9 ± 7 4.5 ± 2.2
Nio etal.
[69]
2020 Untrained
(52 ± 4)
F 25 TRAD INT Cycling (36) 4 × 4 min @
90–95%
HRmax
29.4 ± 5.3 35.4 ± 5.4 21.6 ± 11.4 6.1 ± 2.9
O’Leary
etal. [70]
2017 Untrained
(26 ± 5)
M/F 10 THR CT Cycling (18) 90% LT
matched to
work (KJ)
done in INT
43.5 ± 5.9 47.4 ± 8 8.6 ± 8.4 3.9 ± 3.8
S.J.R.Meyler et al.
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
8 THR INT Cycling (18) 6–8 × 5 min
@ 50% ∆
44.8 ± 4.2 48.8 ± 5.4 8.8 ± 7.3 4 ± 3.1
Pothier etal.
[71]
2021 Untrained
(69 ± 5)
M/F 21 TRAD INT + CT Cycling (36) INT: 20 × 15
s @ 100–
110% MAP,
CT: 20 min
@ 65–75%
MAP
22.2 ± 6.2 24.3 ± 7 10.4 ± 16.6 2.1 ± 3.4
Preobrazen-
ski etal.
[72]
2019 Active
(21 ± 2)
M 14 TRAD CT Cycling (15) 30 min @
65% WRpeak
46 ± 6.7 49.7 ± 5.2 8.7 ± 7.3 3.7 ± 3.1
14 THR CT Cycling (15) 30 min @
‘NEG’
talk–test
stage
45.8 ± 5.9 51.2 ± 6.1 12.2 ± 7.1 5.4 ± 3
Reuter etal.
[73]
2023 Untrained
(46 ± 8)
M/F 16 TRAD CT Running/
walking (78)
50 min @
55% HRR
34.5 ± 3.8 35.3 ± 4 2.6 ± 9.1 0.8 ± 3.1
15 TRAD CT + INT Running/
walking (78)
CT: 50 min
@ 55%
HRR, INT:
4 × 4 min @
95% HRmax
33.9 ± 5.3 37.3 ± 4.9 10.7 ± 8 3.4 ± 2.7
Schaun etal.
[74]
2018 Healthy
(23 ± 4)
M 14 TRAD INT Running (48) 8 × 20 s @
130% v
V
O2max
46.8 ± 7.1 57.7 ± 6.7 24.9 ± 15.6 11 ± 6.2
14 THR CT Running (48) 30 min @
90–95% HR
at VT2
47.9 ± 7.5 56.6 ± 7.9 19.6 ± 15.6 8.8 ± 6
Schubert
etal. [75]
2017 Active
(30 ± 9)
M/F 11 TRAD INT Cycling (12) 6–8 × 90%
PPO
31.4 ± 9.7 33.1 ± 9.8 6.1 ± 5.2 1.8 ± 1.7
Scharhag-
Rosen-
berger
etal. [76]
2012 Untrained
(41 ± 6)
M/F 20 TRAD CT Running/
walking (156)
45 min @
60% HRR
37.8 ± 5.3 43.1 ± 7.1 14.2 ± 11.7 5.3 ± 4.2
Stensvold
etal. [77]
2015 (72 ± 2) M/F 77 TRAD CT Aerobic
exercise (156)
50 min @
70% HRpeak
31.1 ± 5.9 32.6 ± 6.1 5.5 ± 10.7 1.5 ± 3.4
Cardiorespiratory Fitness and Exercise Intensity Prescription
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
M/F 49 TRAD INT Aerobic
exercise (156)
Repetitions
of 4 min
intervals
with 3 min
recovery
periods
85–95%
HRpeak
31.8 ± 6.9 35.7 ± 6.7 13.7 ± 14.6 3.9 ± 4.3
Tarumi etal.
[78]
2022 Older adults
(70 ± 6)
M/F 28 TRAD CT + INT Running/
walking (156)
CT: 25–40
min @
75–85% and
85–90%
HRmax
22.5 ± 4 25.1 ± 4.1 15 ± 28.4 2.6 ± 5.5
Tjønna etal.
[79]
2013 Inactive
(42 ± 3)
M 10 TRAD INT Running/
walking (30)
1 × 4 min @
90% HRmax
39.2 ± 5.3 44.1 ± 5.6 12.9 ± 7.7 4.9 ± 2.7
12 TRAD INT Running/
walking (30)
4 × 4 min @
90% HRmax
44.8 ± 5.3 51 ± 4.6 14.4 ± 8.9 6.2 ± 3.6
Vanhatalo
etal. [80]
2008 Habitually
active
(29 ± 6)
M/F 9 THR INT Cycling (12) 2 × p/week 6 × 5
min at 105%
EP + 1 × p/
week 10 × 2
min @
50% WEP
expenditure
during the
first 2 min
period
50.7 ± 5.4 56 ± 6 10.5 ± 5.3 5.3 ± 2.7
Vollaard
etal. [81]
2009 Healthy
(24 ± 2)
M 23 TRAD CT Cycling (24) 45 min
@70%
V
O2max
49.2 ± 5.2 55.6 ± 7.1 13.1 ± 10.8 6.4 ± 4.9
Weatherwax
etal. [82]
2019 Sedentary
(46 ± 11)
M/F 16 THR CT Aerobic exer-
cise (33)
Energy
expenditure
of 5.6–15.4
kcal/kg/
week @
HR < VT1
to > VT2
30.5 ± 6.6 34 ± 7.7 11.4 ± 3.9 3.5 ± 1.5
S.J.R.Meyler et al.
Table 4 (continued)
Study Year Participants
(age)
Sex Number (n) Method Type Mode (ses-
sions)
Protocol
V
O2max (mL kg−1 min−1)Change Change
Pre Post Percentage (%) mL kg−1 min−1
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
13 TRAD CT Aerobic exer-
cise (33)
Energy
expenditure
of 5.6–15.4
kcal/kg/
week @
40–65%
HRR
25.2 ± 4.7 26.5 ± 4.2 6.1 ± 7.7 1.4 ± 1.9
Wolpern
etal. [83]
2015 Sedentary
(33 ± 10)
M/F 9 THR CT Running (31) 20–30 min @
HR < VT1
to > VT2
35.4 ± 8.8 39.5 ± 8.8 12.2 ± 5.2 4.1 ± 0.9
11 TRAD CT Running (31) 20–30 min
@ 40–65%
HRR
35.1 ± 5.5 36.4 ± 5.7 4 ± 5.4 1.4 ± 1.8
Yan etal.
[84]
2017 Moderately
trained
(32 ± 8)
M 66 THR INT Cycling (12) 6–14 × 2 min
@ 40–70%
49.2 ± 8 50.5 ± 7.7 3.2 ± 9.4 1.3 ± 4.3
THR exercise prescribed relative to physiological thresholds, TRAD exercise prescribed relative to a traditional intensity anchor, CT continuous exercise, INT interval exercise, HRmax maximum
heart rate, HRpeak peak heart rate, HRR heart rate reserve,
V
O2max maximum oxygen uptake,
V
O2R oxygen uptake reserve, VT ventilatory threshold, WEP work above end power during a 3 min
all out test, PPO peak power output, v
V
O2max velocity at
V
O2max, WRpeak peak work rate, MAP maximum aerobic power, LT lactate threshold, delta method, MLSS maximum lactate steady
state, LTv velocity at LT, CV critical velocity, PTV peak treadmill velocity, LTP lactate turn point, Pmax maximum power, M males, F females
Cardiorespiratory Fitness and Exercise Intensity Prescription
4.2 Variability in
V
O2maxChange Scores
An interesting finding of the current analysis was that greater
variability in
V
O2max change scores was observed in exercis-
ing groups compared with the non-exercising control group
(Fig.3). Whilst the magnitude of this evidence was small,
this provides evidence of inter-individual differences in
V
O2max trainability [18, 19, 8587]. This warrants further
investigation, as currently, differences in inter-individual
variability are often found to be attributable to measurement
error and biological variability as opposed to differences in
trainability [20, 88, 89].
However, contrary to our hypothesis, weak evidence
was obtained in support of no difference in the variability
of
V
O2max change scores between THR and TRAD in both
analyses. It has been shown that using THR approaches
more effectively normalises exercise intensity among indi-
viduals compared with when using TRAD anchors, reduc-
ing 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 par-
ticipants, resulting in reduced variation in change scores.
Previous studies have reported increased
V
O2max response
rates following exercise training prescribed using THR com-
pared with TRAD [50, 53, 82, 90]; however, in such studies
it was unclear whether the increased response rates were
the product of a reduction in response variability or simply
increased group mean changes in
V
O2max, or both. On the
basis of the present findings and a lack of evidence support-
ing a difference in the variability in
V
O2max change scores
following THR, it appears that increased response rates are
primarily explained by greater mean
V
O2max change scores.
Typically, THR studies implemented continuous training
[51, 53, 6567, 70, 72, 74, 82, 90, 91]. It is plausible that the
prescribed intensities were low enough that acute metabolic
responses to THR and TRAD exercise were not markedly
different, despite what may have been large differences in
external work rate among individuals [33]. Notably, when
intensities approach or exceed the boundary between the
heavy- and severe-intensity domains, marked differences in
exercise durations and responses can be observed despite
only minimal differences in external work rate [33, 9294].
Using physiological thresholds to prescribe and control exer-
cise around this transition may be where such approaches
hold their value. Furthermore, the activation of signalling
pathways associated with key physiological changes promot-
ing increases in
V
O2max (e.g. mitochondrial biogenesis) has
been shown to increase at intensities within the severe-inten-
sity domain compared with the moderate- and heavy-inten-
sity domains [95]. Thus, having the ability to prescribe exer-
cise accurately both above and below the boundary between
the heavy- and severe-intensity domains, as is shown when
using THR approaches, might have beneficial implications
for the manifestation of subsequent adaptation.
Various approaches are used to determine and apply
physiological thresholds for training purposes [96, 97].
Whilst they all aim to approximate the transition between
the moderate- and heavy-intensity domains or the heavy- and
severe-intensity domains, they are not identical [98]. Critical
power is often considered the most accurate representation
of the latter boundary [93] and as aforementioned is shown
to better control exercise intensity than when using TRAD
approaches. This is likely explained by the fact that using
TRAD approaches does not account for the relative position-
ing of an individual’s critical power relative to a maximum
physiological value, such as
V
O2max or HRmax [33]. Using
critical power as a tool for exercise prescription, however,
appears to be limited in exercise-related research, with only
one study in the present dataset [80] using the concept for
training purposes. An advantage of using critical power is
Table 5 Summary characteristics of controlled and non-controlled
THR and TRAD studies included in the present individual participant
data meta-analysis
Data are presented as means or mean ± standard deviation
V
O2max maximum oxygen uptake
Study group
Controlled studies THR TRAD CON
Studies (n) 4 4 4
Individuals (n) 46 44 49
Sex (M; F) 18; 28 16; 28 20; 29
Age (year) 43 ± 17 46 ± 17 44 ± 14
Body mass (kg) 72 ± 11 73 ± 11 75 ± 10
Baseline
V
O2max (mL kg−1 min−1)31 ± 7 29 ± 5 29 ± 6
Training duration (weeks) 13 ± 1 13 ± 1
Training sessions (n)29 ± 5 29 ± 5
Continuous exercise 3 3
Interval exercise 0 0
Combination 1 1
Non-controlled studies THR TRAD
Studies (n) 18 25
Individuals (n) 354 1190
Sex (M; F) 239; 115 565; 622
Age (year) 31 ± 12 38 ± 18
Body mass (kg) 75 ± 13 71 ± 13
Baseline
V
O2max (mL kg−1 min−1)42 ± 10 34 ± 9
Training duration (weeks) 7 ± 4 14 ± 14
Training sessions (n)23 ± 11 43 ± 39
Continuous exercise 13 19
Interval exercise 8 16
Combination 4 4
S.J.R.Meyler et al.
Fig. 2 Modelled changes in
V
O2max (mL kg−1 min−1) from controlled
studies comparing exercise prescription using traditional intensity anchors
or physiological thresholds. MID minimum important difference, THR
exercise training prescribed relative to physiological thresholds (n = 46),
TRAD exercise training prescribed relative to traditional intensity anchors
(n = 44), SD standard deviation; CrI credible interval
Fig. 3 Modelled changes in
V
O2max (mL kg−1 min−1) from con-
trolled studies comparing pooled data from all training groups and
control. ‘Train’ comprises data from groups where exercise training
is prescribed using either traditional intensity anchors or relative to
physiological thresholds (n = 90), and ‘Control’ comprises data from
non-exercising control groups (n = 49). MID minimum important dif-
ference, SD standard deviation, CrI credible interval
Cardiorespiratory Fitness and Exercise Intensity Prescription
that a given work rate can be used to define the exercise
session(s), negating the need to adjust exercise intensity to
match a given heart rate or
V
O2 response. In the HERITAGE
study, the fluctuation in the training work rate was the third
most impactful factor (6%) on
V
O2max response variability,
despite overall adherence being greater than 95% [99]. It
would thus be interesting to investigate whether a reduction
in response variability is observed to a higher degree were
critical power used as an intensity anchor, particularly when
comparing studies prescribing heavy- and/or severe-intensity
exercise using a traditional intensity anchor, as this is where
we may expect to see a more profound difference in response
variability.
It is worth noting that the relative intensity of exercise is
consistently shown to influence the magnitude of training-
induced adaptations [15, 54, 57, 65, 100102] and that simi-
lar adaptations can be observed following a small volume of
exercise performed at very high intensities and a large vol-
ume of exercise performed at lower intensities [103107]. In
a recent study by Inglis etal. [102], 84 healthy participants
Fig. 4 Forest plot of modelled mean (left) and standard deviation
(right) change in
V
O2max (mL kg−1 min−1) across non-controlled
studies comprising exercise prescription using either traditional
intensity anchors or physiological thresholds. Distributions repre-
sent ‘shrunken estimates’ based on all relevant effect sizes, the ran-
dom effects model fitted, and borrowing of information across studies
to reduce uncertainty. Circles and connected intervals represent the
median value and 95% credible intervals for the shrunken estimates.
Pooled estimates across conditions are presented in the centre of
the plot. The red line illustrates the minimum important difference
threshold. THR exercise training prescribed relative to physiological
thresholds, TRAD exercise training prescribed relative to traditional
intensity anchors
S.J.R.Meyler et al.
performed moderate-, heavy-, severe- or extreme- intensity
exercise training where exercise intensity was prescribed
using a ‘domain-based’ approach (i.e. THR) using the LT
and the maximum metabolic steady state (based on blood
lactate and
V
O2 responses) to determine the boundaries
between the moderate-to-heavy- and heavy-to-severe-inten-
sity domains, respectively. Interestingly, all exercise groups,
bar the moderate-intensity exercise group, increased
V
O2max,
the power output at the LT and the power output at the maxi-
mum metabolic steady state. Such results further support
the notion that exercise intensity is a key determinant of
training-induced changes in training-induced adaptations.
All in all, exercise intensity demonstrates a clear influence
on the magnitude of subsequent changes in markers such
as
V
O2max.
Such findings incite the argument that the method used
to prescribe exercise intensity is in fact irrelevant and
that, irrespective of whether a THR or TRAD approach is
used, whichever approach elicits the highest relative exer-
cise intensity will likely induce the largest increases in
V
O2max thereafter. On this point, it is important to consider
the findings of Collins etal. [101], who conducted a train-
ing intervention comprising continuous exercise (CT) pre-
scribed at 44% of the maximum power output achieved in a
gradedexercise testing (PGXT) and interval exercise (INT)
prescribed at 80% PGXT. Of note, there were instances where
exercise intensity, when expressed relative to critical power,
was higher in the CT group compared with the INT group,
and as a result, these individuals experienced superior
changes in critical power post-training. At first glance, this
contradicts the argument that high-intensity INT is superior
to lower-intensity CT [15, 54, 57, 65, 100102]; however, it
instead supports the notion that, when prescribing exercise
intensity, whether that be for CT or INT, anchoring intensity
relative to a maximum physiological value such as
V
O2max
is not appropriate [31, 33, 35, 108]. Overall, the authors
concluded that the higher the training intensity is when
expressed relative to critical power, instead of relative to
V
O2max, the greater the training-induced changes thereafter
[101]. Therefore, whilst the argument may be presented that
the method used to prescribe exercise intensity is inconse-
quential so long as high-intensity exercise is prescribed, a
THR-based approach should be used to ensure that exercise
intensity is in fact ‘high’ for a given individual based on their
unique intensity domains and not just intended to be ‘high’
based on a generalised approach to exercise intensity pre-
scription. Additionally, in the instance that an individual has
not achieved a change in
V
O2max, for example, above a given
‘response’ threshold, the possibility exists that the intensity
elicited when using a TRAD-based approach was simply too
low when expressed relative to critical power [19, 33, 101,
109]. Using critical power, or an alternative threshold, would
negate this issue of heterogeneity in the prescribed exercise
intensity; however, further studies are warranted to confirm
this notion [101].
4.3 Limitations
A limitation of the present study was the limited amount
of IPD obtained from those meeting the inclusion criteria.
Whilst 236 studies met the predefined criteria (Fig.1), the
response rate of IPD obtained was 18% (N = 42). Addition-
ally, THR IPD (n = 354) were limited compared with those
of TRAD IPD (n = 1190). Exercise training programmes
are still routinely prescribed at intensities anchored rela-
tive to traditional parameters (i.e.
V
O2max and HRmax) [15,
18, 85, 110, 111]. Understandably, using HR-based param-
eters can negate the need for laboratory or field testing,
making this an attractive approach, albeit not the most
accurate. However, the continued use of
V
O2max as an
intensity anchor is surprising given its known inaccuracy
in controlling exercise intensity among individuals [29,
31, 33, 36, 112, 113]. Moreover, if measuring
V
O2max, data
used to determine given physiological thresholds (e.g. gas
exchange threshold) are readily available. Alternatively,
self-assessed threshold tools such as rating of perceived
exertion (RPE) can be used as surrogates for physiologi-
cal thresholds and provide an easily accessible means
of prescribing and controlling exercise intensity based
on a threshold-based approach [114116]. For example,
using the 6–20 Borg scale, moderate-, heavy-, and severe-
intensity exercise can be prescribed at approximately ≤ 13,
14–16, and 17, respectively [115]. Lehtonen etal. [115]
also note that pairing RPE with external work (e.g. run-
ning pace or power output) and internal physiological
response (e.g. heart rate) would add a further element of
sophistication to the prescription and monitoring of exer-
cise training. There is also evidence demonstrating that
critical power and the running equivalent critical speed
can be derived from habitual training data and/or a series
of time trials [117119]. This allows for a more accessible
means of critical speed and/or critical power determina-
tion, negating the need for laboratory-based testing [119].
The results presented herein will hopefully encourage
greater consideration of using THR approaches, where
possible, when designing future exercise research studies.
Another limitation of the present study is that, compared
with the controlled dataset, the non-controlled dataset con-
tained IPD from studies with marked differences in study
characteristics. For example, studies adopted various train-
ing doses, populations, modes of exercise and training types
(Table3). Effects of these differences were easily observed
when comparing the range in mean
V
O2max improvements
estimated across the different studies (Fig.4). Despite dif-
ferences in study characteristics, overall findings from both
analyses were consistent. As such, the two datasets and
Cardiorespiratory Fitness and Exercise Intensity Prescription
analyses complement each other and help account for their
individual limitations; for example, the controlled studies
comprise similar study characteristics but are small in sam-
ple size, whereas the non-controlled studies express a much
larger sample size but marked differences in study charac-
teristics. If enough data were available, stricter eligibility
criteria could have been used such that a more homogeneous
non-controlled dataset could have been analysed. This would
have allowed a more robust comparison between THR and
TRAD studies regarding the magnitude and variability in
V
O2max change scores, as was done in the controlled study
analysis. However, more THR studies are needed for this
to occur. Additionally, information regarding adherence to
training at the individual level in each independent study
was not sought. Thus, conclusions concerning the impact
of training adherence on subsequent
V
O2max change scores
cannot be elucidated.
5 Conclusion
The current IPD meta-analysis found no difference in
V
O2max response variability between training programmes
utilising THR and TRAD approaches. However, using THR
approaches appears to be a more effective means of increas-
ing the likelihood of an individual attaining meaningful
increases in
V
O2max, and thus, increased response rates may
be more commonly observed using such approaches. The
current analysis also provides some evidence supporting the
existence of inter-individual differences in
V
O2max trainabil-
ity on the basis of greater variation in change scores between
exercise and control groups. Future primary research should
be conducted with adequate power to investigate the scope
of inter-individual differences in
V
O2max trainability, and if
meaningful, the causative factors.
Acknowledgements No additional acknowledgements.
Declarations
Funding No funding and financial assistance was used for this review.
Conflicts of Interest Authors Samuel J. R. Meyler, Paul A. Swinton,
Lindsay Bottoms, Lance C. Dalleck, Ben Hunter, Mark A. Sarzynski,
David Wellsted, Camilla J. Williams and Daniel Muniz-Pumares de-
clare that they have no conflicts of interest relevant to the content of
this review.
Availability of Data and Material Datasets analysed in the current
review are available upon reasonable request but are subject to per-
mission from original authors.
Ethics Approval Each study received ethical approval from their
respective institutions, conformed to the guidelines of the Declaration
of Helsinki and obtained written informed consent from each partici-
pant prior to commencing data collection.
Author Contributions All authors: (1) made substantial contributions
to the conception or design of the work or to the acquisition, analysis,
or interpretation of data and (2) drafted the work or revised it critically
for important intellectual content. All authors read and approved the
final version of the manuscript.
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Authors and Aliations
SamuelJ.R.Meyler1· PaulA.Swinton2· LindsayBottoms1· LanceC.Dalleck3· BenHunter4· MarkA.Sarzynski5·
DavidWellsted1· CamillaJ.Williams6· DanielMuniz‑Pumares1
* Daniel Muniz-Pumares
d.muniz@herts.ac.uk
1 School ofLife andMedical Sciences, University
ofHertfordshire, Hatfield, England, UK
2 School ofHealth Sciences, Robert Gordon University,
Aberdeen, Scotland,UK
3 Recreation, Exercise andSport Science Department, Western
Colorado University, Gunnison, CO, USA
4 School ofHuman Sciences, London Metropolitan University,
London, UK
5 Department ofExercise Science, University ofSouth
Carolina, Columbia, SC, USA
6 School ofHuman Movement andNutrition Sciences, The
University ofQueensland, Brisbane, QLD, Australia
... Indeed, in the data compiled by Williams et al. (2019), the majority of studies prescribed exercise intensity this way and only ∼13% of the Specifically, 64% of participants increasedV O 2 max beyond a clinically relevant threshold of 1 MET when intensity was prescribed relative to physiological thresholds, compared to 16% when intensity was prescribed using traditional anchors (Meyler et al., 2024). This recent meta-analysis suggests further work is needed to establish how physiological thresholds, and in particular CP, can be applied to design effective training interventions in different groups, and particularly in clinical populations. ...
... It is plausible that such a reduction in the variability of acute physiological responses to exercise might result in an analogous reduction in variability and/or a greater magnitude ofV O 2 max changes following endurance training. This was the question of a recent metaanalysis byMeyler et al. (2024), which explored the effect of using physiological thresholds to prescribe exercise training on both the magnitude and variability of changes inV O 2 max . The authors collected individual participants' data from four exercise-matched studies (139 participants), in which two groups of participants were prescribed exercise relative to a traditional maximal anchor or a physiological threshold in otherwise identical training programmes. ...
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Purpose: This study aimed to compare estimations of critical speed (CS) and work completed above CS (D'), and their analogies for running power (critical power [CP] and W'), derived from raw data obtained from habitual training (HAB) and intentional maximal efforts in the form of time trials (TTs) and 3-minute all-out tests (3MTs) in recreational runners. The test-retest reliability of the 3MT was further analyzed. Methods: Twenty-three recreational runners (4 female) used a foot pod to record speed, altitude, and power output for 8 consecutive weeks. CS and D', and CP and W', were calculated from the best 3-, 7-, and 12-minute segments recorded in the first 6 weeks of their HAB and in random order in weeks 7 and 8 from 3 TTs (3, 7, and 12 min) and three 3MTs (to assess test-retest reliability). Results: There was no difference between estimations of CS or CP derived from HAB, TT, and 3MT (3.44 [0.63], 3.42 [0.53], and 3.76 [0.57] m · s-1 and 281 [41], 290 [45], and 305 [54] W, respectively), and strong agreement between HAB and TT for CS (r = .669) and CP (r = .916). Limited agreement existed between estimates of D'/W'. Moderate reliability of D'/W' was demonstrated between the first and second 3MTs, whereas excellent reliability was demonstrated for CS/CP. Conclusion: These data suggest that estimations of CS/CP can be derived remotely, from either HAB, TT, or 3MT, although the lower agreement between D'/W' warrants caution when using these measures interchangeably.
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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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Maximal oxygen uptake (V̇O2max ) may be the single most important factor for long-distance running performance. Interval training, enabling high intensity, is forwarded as the format that yields the largest increase in V̇O2max . However, it is uncertain if an optimal outcome on V̇O2max , anaerobic capacity, and running performance is provided by training with a high aerobic intensity or high overall intensity. Thus, we randomized 48 aerobically well-trained men (23±3years) to three commonly applied interval protocols, one with high aerobic intensity (HIIT) and two with high absolute intensity (sprint interval training; SIT), 3x week for 8 weeks: 1) HIIT: 4x4 minutes at ~95% maximal aerobic speed (MAS) with 3 minutes active breaks. 2) SIT: 8x20 seconds at ~150% MAS with 10 seconds passive breaks. 3) SIT: 10x30 seconds at ~175% MAS with 3.5 minutes active breaks. V̇O2max increased more (P<0.001) following HIIT,4x4min (6.5±2.4%, P<0.001) than SIT,8x20sec (3.3±2.4%, P<0.001) and SIT,10x30sec (n.s.). This was accompanied by a larger (P<0.05) increase in stroke volume (O2 -pulse) following HIIT,4x4min (8.1±4.1%, P<0.001) compared to SIT,8x20sec (3.8±4.2%, P<0.01) and SIT,10x30 (n.s.). Anaerobic capacity (maximal accumulated oxygen deficit) increased following SIT,8x20sec (P<0.05), but not after HIIT,4x4min, nor SIT,10x30sec. Long-distance (3000-meter) endurance performance increased (P<0.05-P<0.001) in all groups (HIIT,4x4min: 5.9±3.2%; SIT,8x20sec: 4.1±3.7%; SIT,10x30sec: 2.2±2.2%), with HIIT increasing more than SIT,10x30sec (P<0.05). Sprint (300-meter) performance exhibited within-group increases in SIT,8x20sec (4.4±2.0%) and SIT,10x30sec (3.3±2.8%). In conclusion, HIIT improves V̇O2max more than SIT. Given the importance of V̇O2max for most endurance performance scenarios, HIIT should typically be the chosen interval format.
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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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Introduction This study assessed the effect of individualized, domain-based exercise intensity prescription on changes in maximal oxygen uptake (V̇O 2max ) and submaximal thresholds. Methods Eighty-four young healthy participants (42 Females, 42 Males) were randomly assigned to six age, sex, and V̇O 2max -matched groups (14 participants each). Groups performed continuous cycling in the 1) moderate (MOD)-, 2) lower heavy (HVY1)-, and 3) upper heavy-intensity (HVY2)- domain; interval cycling, in the form of 4) high-intensity interval training (HIIT) in the severe-intensity domain, or 5) sprint-interval training (SIT) in the extreme-intensity domain; or no exercise for, 6) control (CON). All training groups except SIT, were work-matched. Training participants completed three sessions per week for six weeks with physiological evaluations performed at PRE, MID and POST intervention. Results Compared to the change in V̇O 2max (∆V̇O 2max ) in CON (0.1 ± 1.2 mL·kg ⁻¹ ·min ⁻¹ ), all training groups except MOD (1.8 ± 2.7 mL·kg ⁻¹ ·min ⁻¹ ), demonstrated a significant increase (p < 0.05). HIIT produced the highest increase (6.2 ± 2.8 mL·kg ⁻¹ ·min ⁻¹ ) followed by HVY2 (5.4 ± 2.3 mL·kg ⁻¹ ·min ⁻¹ ), SIT (4.7 ± 2.3 mL·kg ⁻¹ ·min ⁻¹ ), and HVY1 (3.3 ± 2.4 mL·kg ⁻¹ ·min ⁻¹ ), respectively. The Δ PO at the estimated lactate threshold (θ LT ) was similar across HVY1, HVY2, HIIT and SIT which were all greater than CON (p < 0.05). The Δ V̇O 2 and Δ PO at θ LT for MOD was not different from CON (p > 0.05). HIIT produced the highest Δ PO at maximal metabolic steady state, which was greater than CON, MOD, and SIT (p < 0.05). Conclusions This study demonstrated that i) exercise intensity is a key component determining changes in V̇O 2max and submaximal thresholds and ii) exercise intensity domain-based prescription allows for a homogenous metabolic stimulus across individuals.
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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.
Article
Background: Current evidence is inconsistent on the benefits of aerobic exercise training for preventing or attenuating age-related cognitive decline in older adults. Objective: To investigate the effects of a 1-year progressive, moderate-to-high intensity aerobic exercise intervention on cognitive function, brain volume, and cortical thickness in sedentary but otherwise healthy older adults. Methods: We randomized 73 older adults to a 1-year aerobic exercise or stretching-and-toning (active control) program. The primary outcome was a cognitive composite score calculated from eight neuropsychological tests encompassing inductive reasoning, long-term and working memory, executive function, and processing speed. Secondary outcomes were brain volume and cortical thickness assessed by MRI, and cardiorespiratory fitness measured by peak oxygen uptake (VO2 ). Results: One-year aerobic exercise increased peak VO2 by ∼10% (p < 0.001) while it did not change with stretching (p = 0.241). Cognitive composite scores increased in both the aerobic and stretching groups (p < 0.001 for time effect), although no group difference was observed. Total brain volume (p < 0.001) and mean cortical thickness (p = 0.001) decreased in both groups over time, while the reduction in hippocampal volume was smaller in the stretching group compared with the aerobic group (p = 0.040 for interaction). Across all participants, improvement in peak VO2 was positively correlated with increases in cognitive composite score (r = 0.282, p = 0.042) and regional cortical thickness at the inferior parietal lobe (p = 0.016). Conclusions: One-year aerobic exercise and stretching interventions improved cognitive performance but did not prevent age-related brain volume loss in sedentary healthy older adults. Cardiorespiratory fitness gain was positively correlated with cognitive performance and regional cortical thickness.