The maximal metabolic steady state: redefining the ‘gold standard’

Abstract

Abstract The maximal lactate steady state (MLSS) and the critical power (CP) are two widely used indices of the highest oxidative metabolic rate that can be sustained during continuous exercise and are often considered to be synonymous. However, while perhaps having similarities in principle, methodological differences in the assessment of these parameters typically result in MLSS occurring at a somewhat lower power output or running speed and exercise at CP being sustainable for no more than approximately 20–30 min. This has led to the view that CP overestimates the ‘actual’ maximal metabolic steady state and that MLSS should be considered the ‘gold standard’ metric for the evaluation of endurance exercise capacity. In this article we will present evidence consistent with the contrary conclusion: i.e., that (1) as presently defined, MLSS naturally underestimates the actual maximal metabolic steady state; and (2) CP alone represents the boundary between discrete exercise intensity domains within which the dynamic cardiorespiratory and muscle metabolic responses to exercise differ profoundly. While both MLSS and CP may have relevance for athletic training and performance, we urge that the distinction between the two concepts/metrics be better appreciated and that comparisons between MLSS and CP, undertaken in the mistaken belief that they are theoretically synonymous, is discontinued. CP represents the genuine boundary separating exercise in which physiological homeostasis can be maintained from exercise in which it cannot, and should be considered the gold standard when the goal is to determine the maximal metabolic steady state.

Full text

INVITED REVIEW
The maximal metabolic steady state: redefining
the ‘gold standard’
Andrew M. Jones
1
, Mark Burnley
2
, Matthew I. Black
1
, David C. Poole
3
& Anni Vanhatalo
1
1 Sport and Health Sciences, University of Exeter, St. Luke’s Campus, Exeter, United Kingdom
2 School of Sport and Exercise Sciences, University of Kent, Medway, United Kingdom
3 Department of Kinesiology, Kansas State University, Manhattan, Kansas
Andrew M Jones PhD is Professor of Applied Physiology at the University of Exeter, UK. Jones
completed his undergraduate and PhD degrees in exercise physiology at the University of Brighton
before completing postdoctoral training in respiratory physiology at the University of California
Los Angeles. Jones’s research explores the limitations to human endurance with a focus on gas
exchange kinetics, exercise bioenergetics, causes of fatigue, exercise testing, and interventions
such as training and nutritional ergogenic aids that may enhance athletic performance.
Keywords
Fatigue, metabolism, performance.
Correspondence
Andrew M. Jones, Professor of Applied
Physiology, Sport and Health Sciences,
University of Exeter, EX12LU, United
Kingdom.
Tel: +44-01392-722886
E-mail: a.m.jones@exeter.ac.uk
Funding information
No funding information provided.
Received: 14 March 2019; Revised: 25 April
2019; Accepted: 27 April 2019
doi: 10.14814/phy2.14098
Physiol Rep, 7 (10), 2019, e14098,
https://doi.org/10.14814/phy2.14098
Abstract
The maximal lactate steady state (MLSS) and the critical power (CP) are two
widely used indices of the highest oxidative metabolic rate that can be sustained
during continuous exercise and are often considered to be synonymous. How-
ever, while perhaps having similarities in principle, methodological differences in
the assessment of these parameters typically result in MLSS occurring at a some-
what lower power output or running speed and exercise at CP being sustainable
for no more than approximately 20–30 min. This has led to the view that CP
overestimates the ‘actual’ maximal metabolic steady state and that MLSS should
be considered the ‘gold standard’ metric for the evaluation of endurance exercise
capacity. In this article we will present evidence consistent with the contrary con-
clusion: i.e., that (1) as presently defined, MLSS naturally underestimates the
actual maximal metabolic steady state; and (2) CP alone represents the boundary
between discrete exercise intensity domains within which the dynamic cardiores-
piratory and muscle metabolic responses to exercise differ profoundly. While
both MLSS and CP may have relevance for athletic training and performance, we
urge that the distinction between the two concepts/metrics be better appreciated
and that comparisons between MLSS and CP, undertaken in the mistaken belief
that they are theoretically synonymous, is discontinued. CP represents the gen-
uine boundary separating exercise in which physiological homeostasis can be
maintained from exercise in which it cannot, and should be considered the gold
standard when the goal is to determine the maximal metabolic steady state.
Introduction
Knowledge of the running speed or cycling power output
which generates the maximal sustainable oxidative meta-
bolic rate may be important in appraising athletic perfor-
mance potential and in guiding athletic training programs
(Jones and Carter 2000; Morton 2006; Jones et al. 2010;
Vanhatalo et al. 2011a). Performing training below, com-
pared to above, such a threshold will invoke acute
differences in oxidative and nonoxidative energy supply,
muscle and blood biochemistry, cardiorespiratory
responses, fatigue processes, and effort perception, which,
if repeated chronically, would be expected to promote dif-
ferent physiological adaptations (Holloszy and Coyle
1984; Jones and Carter 2000). While this notion is widely
accepted, a plethora of terms and techniques have
emerged which purport to describe or determine this
‘maximal metabolic steady state’. For example,
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phenomena derived from incremental exercise tests which
have been proposed to correspond to, or enable an accu-
rate estimation of, maximal metabolic steady state include
the lactate threshold (LT), gas exchange threshold (GET),
ventilatory threshold, lactate turn-point (LTP), anaerobic
threshold, the ‘onset of blood lactate accumulation’ corre-
sponding to an absolute blood lactate concentration ([lac-
tate]) of 4 mmol/L (OBLA), individual anaerobic
threshold, lactate minimum, and respiratory compensa-
tion threshold (Faude et al. 2009; Jones et al. 2018). Not
only do these terms reflect very different physiological
events and mechanisms, they may occur at contrasting
metabolic rates and, to a degree, may be a function of the
measurement technique or specific testing paradigm
employed (Jamnick et al. 2018). Accordingly this has led
to considerable confusion and misunderstanding in this
field (see Jones et al. 2018, for detailed critique).
As a first step towards greater clarity, it is essential to
appreciate the existence of discrete exercise intensity
domains within which the physiological responses to exer-
cise differ considerably (Whipp and Ward 1992; Poole
and Richardson 1997; Carter et al. 2002; Wilkerson et al.
2004; Black et al. 2017). The pulmonary O
2
uptake (
_
VO
2
)
and blood [lactate] responses to constant-power moder-
ate, heavy, and severe-intensity exercise are schematized
in Figure 1. These profiles indicate that the achievement
of steady-state values in these variables is markedly differ-
ent in these discrete exercise domains (i.e., rapid in the
moderate-intensity domain, delayed in the heavy-intensity
domain, and not possible in the severe-intensity domain).
These differences reflect variable energy system contribu-
tion and have clear implications for fatigue development
and exercise tolerance (Whipp and Ward 1992; Jones
et al. 2011). Some identifiable ‘thresholds’ during incre-
mental exercise demarcate the transition from moderate
to heavy-intensity exercise (i.e., LT and GET) whereas
others purport to demarcate the transition from heavy to
severe-intensity exercise (i.e., LTP and, arguably, OBLA).
The first threshold is relevant for ultra-endurance and
low-intensity endurance events and in occupational and
clinical physiology. However, it is the definition, evalua-
tion, and application of the second boundary, which typi-
cally occurs at 75–90%
_
VO
2
max and is therefore more
relevant to most types of high-level endurance exercise
performance (Jones and Poole 2008; Jones and Vanhatalo
2017), that is the focus of this review.
The appropriate approach for determination of the
maximal metabolic steady state (i.e., the threshold speed
or power output separating heavy- from severe-intensity
exercise) is controversial. The ‘gold standard’ is often
considered to be the so-called maximal lactate steady state
(MLSS; Beneke and von Duvillard 1996; Billat et al. 2003;
Faude et al. 2009). The MLSS is conventionally derived
from a series (typically 4–5) of 30 min continuous exer-
cise bouts, completed on separate days, at different but
constant running speeds or power outputs; blood [lactate]
is measured at rest and after every 5 min of exercise and
the MLSS is defined as the highest speed or power output
Figure 1. Schematic representation of the pulmonary oxygen
uptake (
_
VO
2
) (panel A) and blood [lactate] responses (panel B)
during moderate-intensity, heavy-intensity, and severe-intensity
exercise. During moderate-intensity exercise,
_
VO
2
and blood
[lactate] reach steady-state values rapidly. During heavy- and
severe-intensity exercise, there is an additional oxygen cost (termed
_
VO
2
slow component) above that expected from the extrapolation
of the moderate-intensity
_
VO
2
-power output relationship. During
heavy-intensity exercise, the attainment of (higher amplitude)
steady-state values for
_
VO
2
and blood [lactate] is delayed. The
magnitude of the
_
VO
2
slow component during heavy-intensity
exercise is illustrated by the dotted line provided in panel A. During
severe-intensity exercise,
_
VO
2
and blood [lactate] continue to rise
until
_
VO
2
max (panel A, dashed line) is attained with the limit of
tolerance occurring shortly thereafter.
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The Maximal Metabolic Steady State A. M. Jones et al.

that does not result in a rise of blood [lactate] of greater
than 1 mmol/L between 10 and 30 min (Beneke 1995;
Jones and Doust 1998; see Figure 2). Alternatively, the
maximal metabolic steady state might be determined
using the critical power (CP; or critical speed for run-
ning
1
), which is derived from the hyperbolic relationship
between speed or power output and the duration for
which that speed or power output can be sustained (Hill
1925; Monod and Scherrer 1965; Hill and Smith 1999;
Hill et al. 2002; Jones et al. 2010; see Figure 3). The purpose of this article is to critique the MLSS and
CP concepts and to evaluate their validity in assessing the
maximal metabolic steady state. While the MLSS and CP
have some conceptual similarities, tend to approximate
one another and are often proposed to represent the same
phenomenon, methodological differences in their assess-
ment typically produce divergence (i.e., MLSS <CP) in
practice. It is important to emphasize, therefore, that
appreciating the possible advantages and disadvantages of,
and the potential differences and similarities between,
MLSS and CP is not merely a question of semantics.
Instead, this issue is rather fundamental because it has the
potential to influence performance prognosis and exer-
cise/training prescription. It is therefore relevant not only
to researchers in sport and exercise science but also to
Figure 2. Schematic representation of the blood [lactate] response
to a series of constant running speed tests performed on separate
days for the determination of MLSS. Trial 1 is representative of the
lowest running speed chosen and each trial is indicative of an
increment in speed until trial 5 (the highest running speed applied).
During trials 1, 2, 3, and 4, blood [lactate] does not increase by more
than 1 mmol/L between minutes 10 and 30. However, during trial 5,
blood [lactate] is 4.5 mmol/L at 10 min and 7.1 mmol/L at 30 min
(D2.6 mmol/L). Therefore, in spite of a gradual increase (D0.7 mM) in
blood [lactate] between minutes 10 and 30, trial 4 represents the
highest running speed at which blood [lactate] did not rise by more
than 1 mM - and it would therefore be defined as MLSS. Note
therefore that the actual MLSS, according to the accepted definition,
lies at a speed somewhere between trial 4 and trial 5, such that the
MLSS selected (trial 4) will necessarily be an underestimate. The
dashed line is indicative of the blood [lactate] attained at 10 min
during trial 4, and is projected to the end of the exercise trial.
Figure 3. Schematic representation of the power-duration
relationship with reference to the moderate-intensity (light gray
shaded area), and heavy-intensity (dark gray shaded area) exercise
intensity domains. The boundary between the moderate- and
heavy-intensity domains is given by the lactate or gas exchange
threshold (GET), and the boundary between the heavy- and severe-
intensity domains is given by the critical power (CP). The CP and
the work capacity available above CP (termed Wʹ) can be
determined using a series of constant power output trials
performed to the limit of tolerance within the severe-intensity
domain (i.e., >CP). The CP is defined as the power asymptote
(234 W in this example), and Wʹis characterized by the curvature
constant (25.6 kJ in this example), of this hyperbolic relationship
between power output and time. The Wʹis capacity-, but not rate-,
limited and therefore its contribution (in kJ) to severe-intensity
exercise is constant irrespective of exercise duration in the severe-
intensity domain. The greater the difference between the power
output being sustained and CP, the more rapidly W0will be
utilized, with the limit of tolerance coinciding with the exhaustion
of W0. The hyperbolic relationship between power and time can be
linearized by plotting work done against time, in which case the
slope of the line represents CP and the intercept represents W0,or
power against 1/time, in which case the slope of the line represents
W0and the intercept represents CP.
1
We note here that the term ‘intensity’ (e.g., ‘critical intensity’)
is inappropriate in this context. This is because at a given con-
stant speed or power output, the exercise intensity (i.e., the frac-
tion of the
_
VO
2max
required or the muscle metabolic
perturbation evoked) can change; this is especially true for exer-
cise in proximity to the critical speed/power where nonsteady-
state behavior is expected. It is also known that power output
(the rate of energy transfer from the skeletal muscle to perform
external work) and exercise intensity (the magnitude of the
metabolic fluctuation(s) evoked by the task) can be completely
dissociated depending on the work:recovery duration during
intermittent, compared to continuous, exercise (Davies et al.
2017). It is therefore preferable to use the term ‘critical’ along-
side the associated SI unit that is appropriate to the exercise
modality (power, speed or velocity, torque, etc.).
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A. M. Jones et al. The Maximal Metabolic Steady State

athletes, coaches, and exercise professionals. We contend
that: (1) MLSS and CP should no longer be considered
synonymous; and (2) CP has the more robust theoretical
underpinnings and evidence base and should henceforth
be considered the ‘gold standard’ for defining the maxi-
mal metabolic steady state, i.e. the boundary between the
heavy- and severe-intensity exercise domains.
Considerations Regarding the
Definition and Determination of
MLSS
The origin of the MLSS concept is somewhat obscure but
it may perhaps be attributed to the work of German
physiologists, Mader and Heck, in the 1980s (Heck et al.
1985; Mader and Heck 1986). Initially, MLSS was consid-
ered to occur at a fixed blood [lactate] of 2.2 mmol/L
(LaFontaine et al. 1981; Priest and Hagan 1987) or, more
often, 4 mmol/L (Sj€
odin et al. 1982; Stegmann and Kin-
dermann 1982; Heck et al. 1985; Mader and Heck 1986).
However, discoveries that the absolute blood [lactate] at
MLSS varied considerably both between individuals (Ben-
eke and von Duvillard 1996) and between exercise modal-
ities (Beneke et al. 2001) later led to MLSS being
reconsidered to represent the speed or power output at
MLSS, irrespective of the absolute blood [lactate]. Yama-
moto et al. (1991) defined MLSS as the highest power
output at which blood [lactate] did not increase from 15
to 30 min of continuous exercise and noted that “In spite
of the arbitrariness of the definition, the MLSS could be use-
ful for prescribing prolonged exercise because one can exer-
cise without continuous accumulation of blood [lactate] for
at least 30 min.” Later studies introduced a modified defi-
nition of MLSS, which remains widely used: the highest
power output at which the increase in blood [lactate] is
less than 1 mmol/L between 10 and 30 min of exercise
(Snyder et al. 1994; Beneke and von Duvillard 1996; Jones
and Doust 1998; Beneke et al. 2000). Beneke (2003)
reported a protocol-dependency of MLSS determination,
with 30 min exercise bouts producing lower power out-
put at MLSS than 20 min exercise bouts. However, the
rationale for the very specific, but apparently arbitrary,
definition of MLSS, including the 10–30 min timeframe
and the acceptable magnitude of change in blood [lac-
tate], is not clear.
There are several methodological concerns with the
assessment of MLSS that should be highlighted. Whether
a particular speed or power output is deemed to be above
or below the MLSS essentially depends upon just two
measurements of blood [lactate], typically made from a
fingertip or earlobe blood sample, one at 10 min and the
other at 30 min of exercise. If the increase in blood [lac-
tate] is <1 mmol/L then the speed or power output is
deemed to be below MLSS, whereas if the increase
is >1 mmol/L then the speed or power output is consid-
ered to be above MLSS. It should be appreciated, how-
ever, that blood [lactate] measurement using widely used
analyzers typically has an error of 0.2–0.4 mmol/L (Bon-
aventura et al. 2015) and that the reliability of blood [lac-
tate] measurement, which represents a combination of
both biological variation and analytical error, during sub-
maximal exercise testing is 11–52% (Saunders et al.
2004). With such potential inaccuracy, it is obvious that
the potential for false positives, i.e. that the speed or
power is deemed to be above MLSS when it is not, or
false negatives, i.e. that the speed or power is deemed to
be below MLSS when it is not, is rather high. It should
be noted also that MLSS is affected by brief interruptions
in exercise that are often necessary to facilitate blood
sampling (Beneke et al. 2003). Moreover, 30 min of
heavy- to severe-intensity exercise may result in hemo-
concentration as a consequence of fluid shifts and sweat-
ing-related dehydration which, if uncorrected, will further
impact the measured [lactate], at least if measured in
whole blood (Dill and Costill 1974). An added complica-
tion when exercise duration is extended for a given speed
or power output within this intensity domain is the well-
known shift in substrate utilization away from carbohy-
drate and towards fatty acid metabolism (Hermansen
et al. 1967), an adaptation which will tend to reduce
muscle lactate production. Importantly, it is not certain
that blood [lactate] at a given instant adequately reflects
the metabolic status of the working muscle (Jorfeldt et al.
1978; Tesch et al. 1982; Bergman et al. 1999). Dynamic
interaction between the rates of muscle lactate produc-
tion, lactate efflux from muscle to blood, and lactate
clearance/metabolism both within muscle and from the
blood by other organs (Stainsby and Brooks 1990), means
that a steady-state in blood [lactate] need not imply the
existence of a bioenergetic steady-state in contracting
skeletal muscle. There is also evidence that blood [lactate]
dynamics can be dissociated from whole-body oxidative
metabolic rate: there are examples of elevated and rising
blood [lactate] profiles in the face of a clearly steady-state
_
VO
2
(Scheen et al. 1981); and infusion of epinephrine
has been shown to alter blood [lactate] dynamics without
changing
_
VO
2
(Gaesser et al. 1994; Womack et al. 1995).
Collectively, these points suggest that blood [lactate], per
se, is neither an appropriate nor a sufficiently sensitive
metric to enable a confident assessment of whether a
specific speed or power output may be sustainable in a
metabolic steady-state.
Other aspects of the MLSS assessment protocol also
merit comment. During sustained heavy-intensity exer-
cise, blood [lactate] tends to rise curvilinearly with time,
with the rate of change of blood [lactate] tending to be
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The Maximal Metabolic Steady State A. M. Jones et al.

greater in the first 5–10 min than in the last 5–10 min of
a 30 min exercise bout (Fig. 4; Scheen et al. 1981; Jones
and Doust 1998). This has the potential to lead to a sce-
nario in which a speed or power output is deemed to be
above MLSS despite blood [lactate] being stable (or even
declining) over the last 10–15 min of the 30 min exercise
bout, when such a profile should instead be interpreted as
indicating the achievement of a delayed steady-state. The
assessment of MLSS also relies on subjects performing a
series of exercise trials on different days at discrete speeds
or power outputs which typically differ by 1 km/h or 10–
30 W, respectively (Jones and Doust 1998; Carter et al.
1999; Smith and Jones 2001; Pringle and Jones 2002; Ben-
eke 2003; Iannetta et al. 2018). By definition, the selection
of MLSS must be at one of these discrete speeds or power
outputs - with the inevitable outcome that the selected
MLSS must always be lower than the actual MLSS. For
example, if the behavior of blood [lactate] indicates that
the speed of 16 km/h is below MLSS and the speed of
17 km/h is above MLSS, then 16 km/h would be selected
as the MLSS. However, had it been applied, a speed of
16.5 km/h might also have produced a blood [lactate]
response consistent with exercise below MLSS such that
16.5 km/h would instead have been selected as MLSS. On
average, with differences of 1 km/h or 30 W between dis-
crete tests, the MLSS will be underestimated by 0.5 km/h
for running or 15 W for cycling, respectively. The limited
granularity inherent in the MLSS protocol therefore inevi-
tably results in underestimation of the ‘actual’ MLSS.
Indeed, it is crucial to appreciate that, as presently
defined and measured, MLSS must reside within the
heavy-intensity domain rather than at the boundary of the
heavy- and severe-intensity domains.
The precision of the MLSS estimate is naturally
enhanced by using smaller speed or power output differ-
ences from one trial to the next (e.g., 0.5 km/h for run-
ning, 15 W for cycling), but this approach is likely to
increase the number of trials needed for MLSS determina-
tion. Because blood [lactate] is sensitive to changes in the
hydration and nutritional status of the individual (Jacobs
1986), particularly in terms of muscle glycogen levels,
subjects must refrain from normal training and consume
a consistent diet over the testing period, which can extend
over five days. This limits the practicality of assessing
MLSS both for research purposes and in applied work
with athletes. Moreover, at least in less well-trained sub-
jects, the lengthy protocol required for MLSS assessment
might itself be sufficiently arduous that it stimulates
training adaptations which result in an increased MLSS.
Approaches which purport to enable accurate MLSS
assessment from fewer trials have been proposed (e.g.,
Billat et al. 1994; Kilding and Jones 2005) but these do
not obviate the other criticisms of MLSS assessment out-
lined above.
Consideration of Critical Power as
the Appropriate ‘Gold Standard’ for
Assessing Maximal Metabolic Steady
State
CP has strong historical, theoretical, physiological, and
mathematical foundations (Hill 1925; Wilkie 1960;
Monod and Scherrer 1965; Moritani et al. 1981; Poole
et al. 1988; Hill et al. 2002; Morton 2006; Jones et al.
2008; Vanhatalo et al. 2016; Mitchell et al. 2018). Indeed,
the hyperbolicity of the relationship between speed or
power output and the duration for which that speed or
power output can be sustained was first recognized by
AV Hill in 1925, following a review of world record per-
formances in various sports (Hill 1925; see Fig. 5 for pre-
sent-day data). This hyperbolic function, with its inherent
asymptote and curvature constant, is now recognized as a
fundamental bioenergetic property of living systems, hav-
ing been described in multiple other species (Full and
Herreid 1983; Full 1986; Lauderdale and Hinchcliff 1999;
Billat et al. 2005; Copp et al. 2010) and in both isolated
muscle and whole body exercise modalities (Monod and
Scherrer 1965; Hughson et al.1984; Poole et al. 1988;
Burnley 2009).
The CP is unique with regard to physiological
‘thresholds’ in that, although representing a critical
metabolic rate (Barker et al. 2006; Vanhatalo et al.
2016), its definition is based purely on the measurement
of mechanical work done and exercise tolerance. It is of
Figure 4. The blood [lactate] response to a constant power output
test indicative of MLSS (solid black line) versus a blood [lactate]
response which would be, according to the strict definition of MLSS
which considers only the absolute blood [lactate] values at 10 and
30 min, defined as being above MLSS (dotted line). Note, however,
that despite being supposedly above MLSS (dotted line), blood
[lactate] stabilized between 15 and 30 min. This highlights one of
the potential sources of error in defining MLSS from just two data
points and applying a rather arbitrary tolerance limit (D1.0 mmol/L)
for the increase in blood [lactate] between them.
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A. M. Jones et al. The Maximal Metabolic Steady State

significant interest, however, that CP separates two
domains of exercise that are characterized by distinct
physiological behavior. Perhaps most importantly, CP
represents a boundary above which exercise results in
the attainment of
_
VO
2
max, provided that exercise can
be sustained for sufficiently long (i.e. ≥approximately
2 min) for it to be reached (Poole et al. 1988; Hill and
Ferguson 1999; Hill and Smith 1999; Hill et al. 2002;
Vanhatalo et al. 2016). The size of the difference
between the power output being sustained and CP will
dictate the rate at which the finite work capacity
available above CP (W0) will be utilized but, for any
bout of exercise in the severe-intensity domain, the limit
of tolerance will coincide with the exhaustion of W0
and the simultaneous attainment of
_
VO
2
max (Murga-
troyd et al. 2011; Vanhatalo et al. 2011b). This means
that time to the limit of tolerance for any power output
in the severe-intensity domain can be accurately calcu-
lated with knowledge just of the power output to be
sustained and the individual’s CP and W0(Vanhatalo
et al. 2011a; Jones and Vanhatalo 2017). Moreover,
unlike MLSS, in which a change in blood [lactate] is
Figure 5. Panel A shows the hyperbolic running speed–time relationship plotted for the current (as of March 2019) world records from
1500 m to 5000 m (in blue, records held by different athletes) and the personal best times over the same distances run by an individual elite
distance runner (Eliud Kipchoge, EK, in red). Panel B shows that the hyperbolic curve constructed for the world records from 1500 m to
5000 m (in blue, same data as in Panel A) does not provide a good fit to world record performances over shorter (100 m to 800 m) or longer
(10,000 m to the marathon) distances. Thus, the hyperbolic relationship is valid for events which take between ~2 min and perhaps 15–20 min
to complete. The linear transformation of the hyperbolic relationship is shown in Panel C (distance–time plot where the slope of the linear
regression line gives critical speed, CS, and the intercept gives the curvature constant, D0) and Panel D (speed-1/time plot where the slope gives
D0and the intercept gives CS). The CS and D0estimates from the three equations, with the associated standard errors of the estimate, are
shown at the foot of the figure.
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The Physiological Society and the American Physiological Society.
The Maximal Metabolic Steady State A. M. Jones et al.

the sole index by which nonsteady-state physiological
behavior is classified, CP has been shown to separate
discrete exercise intensity domains which have distinct
muscle metabolic (Jones et al. 2008; Vanhatalo et al.
2016; Black et al. 2017), neuromuscular (Burnley et al.
2012; Black et al. 2017), respiratory gas exchange and
ventilation (Poole et al. 1988; Murgatroyd et al. 2014),
cardiovascular (Copp et al. 2010) and blood acid-base
(including [lactate]; Poole et al. 1988; Pringle and Jones
2002; Vanhatalo et al. 2016) profiles. These
comprehensive differences in physiological behavior
above and below CP are summarized in Figure 6.
Several studies have compared the independently deter-
mined MLSS and CP and reported that CP occurs at a
higher power output than MLSS (Jenkins and Quigley
1990; Smith and Jones 2001; Pringle and Jones 2002; Dek-
erle et al. 2003, 2005; Mattioni Maturana et al. 2016; cf.
Keir et al. 2015). On average, CP has been reported to be
~7% higher than MLSS (e.g., 4%, Smith and Jones 2001;
9%, Pringle and Jones 2002; 16%, Dekerle et al. 2003;
Figure 6. Mean SD muscle blood flow (panel A; Copp et al. 2010), muscle metabolic perturbation (pH, panel B; lactate, panel C; Black
et al. 2017), and the rates of change in muscle [PCr] (panel D; Black et al. 2017), neuromuscular excitability (panel E; Burnley et al. 2012), and
pulmonary
_
VO
2
(panel F; Black et al. 2017) following moderate-intensity (triangles), heavy-intensity (squares), and severe-intensity (circles)
exercise. The dotted vertical line indicates CP, and a line of best fit has been drawn for all trials performed below CP (i.e., moderate- and
heavy-intensity exercise; dashed line). Note the disproportionate changes in all variables during severe-intensity exercise (i.e., above CP) relative
to exercise performed below CP. These data delineate CP as a bioenergetic threshold above which fatigue development is expedited and
muscle and systemic homeostasis is precluded.
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2019 | Vol. 7 | Iss. 10 | e14098
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A. M. Jones et al. The Maximal Metabolic Steady State

5%, Dekerle et al. 2005; 9%, Mattioni Maturana et al.
2016; cf. 1%, Keir et al. 2015). Given that MLSS will nat-
urally underestimate the boundary between heavy- and
severe-intensity exercise, and taking into account the
magnitude of error associated with the determination of
both MLSS and CP, such a difference should not be con-
sidered surprising. However, these studies have inter-
preted the difference between MLSS and CP as evidence
that CP does not represent the highest sustainable oxida-
tive metabolic rate, with the inherent assumption that
MLSS is the gold standard. As discussed earlier, an alter-
native interpretation is that the lack of agreement between
MLSS and CP indicates that MLSS underestimates the
highest steady-state oxidative metabolic rate. Consistent
with this, it was reported that
_
VO
2
during continuous
exercise performed at 10 W above MLSS resulted in the
achievement of a steady-state
_
VO
2
equivalent to ~90%
_
VO
2peak
(Mattioni Maturana et al. 2016; Iannetta et al.
2018), behavior that is clearly indicative of heavy-intensity
exercise.
An argument frequently used against CP as represent-
ing the boundary between heavy- and severe-intensity
exercise is that exercise tolerance falls short of a ‘fatigue-
less task’ when subjects are asked to exercise continuously
at the predetermined CP (Poole et al. 1988; Jenkins and
Quigley 1990; McLellan and Cheung 1992; Bull et al.
2000; Brickley et al. 2002; McClave et al. 2011; Bergstrom
et al. 2013). This argument is based on a misinterpreta-
tion of the original definition of CP (Monod and Scherrer
1965) and is flawed, for two reasons. The first reason is
fundamental, in that the 2-parameter critical power
model is not applicable for the prediction of exercise tol-
erance precisely at CP (or below it). Indeed, the tolerable
limit (T
lim
) of exercise at CP would necessitate solving
the following equation:
Tlim ¼W0=ðPCPÞ;where P ¼CP;¼[Tlim ¼W0=0
Because Wʹ/0 is mathematically false, it is illogical to
judge the validity of the CP model on the basis of an
assumption of a ‘fatigueless task’ at CP. The second reason
is methodological, in that performing an exercise test pre-
cisely at CP does not account for the error associated with
the estimation of CP. While an advantage of the determi-
nation of the power-duration curve is that it enables esti-
mation of CP to a single watt, it is unreasonable to
consider that this value is absolute. The approaches used to
mathematically model CP will naturally be associated with
some error (which is quantifiable, e.g., as standard error or
95% confidence intervals) and T
lim
and CP will vary a little
in any individual from day to day (i.e., there is some inher-
ent biological variability; Poole et al. 1988). There is there-
fore a ‘bandwidth’ or ‘gray area’ surrounding the modeled
CP estimate, the size of which can be minimized to
approximately 3–5% with careful attention to protocol
(see below). For example, for a CP estimate of 300 W, and
a standard error of 2%, the ‘real’ CP will lie between 294
and 306 W. This means, however, that if this particular
subject is exercised at exactly 300 W, there is a 50% chance
that (s)he would be <CP and in the heavy-intensity
domain and a 50% chance that (s)he would be >CP and in
the severe-intensity domain. This would have important
implications for physiological responses, the nature and
dynamics of fatigue development, and exercise tolerance
(Black et al. 2017). For this reason, it is not surprising that
the time to the limit of tolerance when subjects are asked
to exercise at CP is highly variable (e.g., range of approxi-
mately 15 to 40 min or occasionally up to ~60 min;
McLellan and Cheung 1992; Bull et al. 2000; Brickley et al.
2002; McClave et al. 2011; Bergstrom et al. 2013), with the
group mean physiological responses being characteristic of
either heavy-intensity (Poole et al. 1988, 1990; Wakayoshi
et al. 1993) or severe-intensity (Jenkins and Quigley 1990;
McLellan and Cheung 1992; Brickley et al. 2002) exercise.
Evidently, the practice of requiring subjects to exercise at
CP is not an appropriate test of the validity of the concept.
Indeed, the very question of ‘how long’ CP can be sus-
tained is ill-conceived. The crux of the matter is that CP
separates an exercise domain within which physiological
homeostasis can be established (heavy-intensity domain)
from one in which it cannot and in which exercise toler-
ance is highly predictable (severe-intensity domain). It
should also be noted that the duration of exercise at MLSS
has never been ascertained, but this too will ultimately be
unsustainable (Black et al. 2017).
Appreciation of the relationship and differences
between MLSS and CP has been obfuscated by the persis-
tent but perplexing notion that the maximal metabolic
steady state should correspond to an exercise duration of
approximately 1 h. This is evident in the assumption that
MLSS corresponds to a so-called ‘functional threshold’
power that can be sustained for 60 minutes (Gavin et al.
2012; Morgan et al. 2018). This is a convenient but
entirely arbitrary definition that is devoid of physiological
meaning. There is nothing any more ‘special’ about
60 min of exercise compared to, for example, 65 min,
44 min, or 23 min. Indeed, maximal exercise of 60 min
duration is positioned squarely within the heavy-intensity
domain (Black et al. 2017) such that the physiological
responses to maximal exercise of 50–55 min or 65–
70 min duration, in terms of end-exercise values and
response dynamics, would likely be very similar. A more
justifiable scientific approach is to define the maximal
metabolic steady state as the speed or power output
which separates distinct physiological response behaviors,
irrespective of the corresponding exercise duration. Such
2019 | Vol. 7 | Iss. 10 | e14098
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ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
The Maximal Metabolic Steady State A. M. Jones et al.

an approach, which is enshrined in the CP concept,
would be expected to better predict performance capabil-
ity and be of greater utility in exercise/training prescrip-
tion (Jones et al. 2010; Vanhatalo et al. 2011a).
It is striking that, when the standard error surrounding
the estimation of CP is known and appropriately
accounted for, the physiological responses to exercise per-
formed slightly below and slightly above CP are pro-
foundly different (Burnley et al. 2006; Jones et al. 2008;
Murgatroyd et al. 2014; Vanhatalo et al. 2016). When CP
is measured carefully, the standard error associated with
the parameter estimate can be rather small (e.g., 4 W in
Vanhatalo et al. 2007) which provides confidence that the
authentic transition between an exercise domain wherein
homeostasis can be (eventually) achieved from one
wherein it cannot can be accurately assessed. As indicated
earlier, this is true not only for pulmonary gas exchange,
where a cardinal feature of severe-intensity exercise is the
development of a
_
VO
2
‘slow component’ that will result
in the attainment of
_
VO
2max
at or close to the point of
exercise intolerance (Poole et al. 1988; Hill et al. 2002;
Vanhatalo et al. 2007; Jones et al. 2010), but also for the
distribution of cardiac output (Copp et al. 2010), blood
acid-base balance (Poole et al. 1998; Vanhatalo et al.
2016), and indices of muscle metabolism (e.g., muscle
[PCr] and pH/lactate) whether assessed noninvasively
using
31
P-MRS (Jones et al. 2008) or invasively via biopsy
(Vanhatalo et al. 2016; Black et al. 2017). The CP there-
fore passes (literally) the ‘acid test’ of validity in separat-
ing the heavy- from the severe-intensity exercise domains.
It is important to emphasize that it is not just the abso-
lute values of key physiological variables (e.g.,
_
VO
2
, blood
[lactate], muscle [PCr]) at iso-time or end-exercise that
distinguishes severe- from heavy-intensity exercise, but
also the stark differences in the dynamic profiles of these
and other variables (i.e., delayed steady-state vs. non-
steady state behavior).
Exercise at different power outputs within the severe-
intensity exercise domain results in a similar muscle
metabolic status ([PCr], [Pi], pH, lactate) at the limit of
tolerance (Vanhatalo et al. 2010; Black et al. 2017), con-
sistent with the utilization of a uniform and finite W0and
the attainment of
_
VO
2
max (Murgatroyd et al. 2011; Van-
hatalo et al. 2011b) (Fig. 7), whereas these variables do
not show the same degree of perturbation in the heavy-
intensity domain (see Fig. 6). These results indicate that
CP differentiates exercise intensity domains within which
different mechanisms of fatigue development predominate
(Black et al. 2017). Indeed, the available evidence suggests
that exercise intolerance is associated with a relatively
greater contribution from ‘peripheral’ sites during exercise
>CP and a relatively greater contribution from ‘central’
factors along with muscle glycogen depletion during
exercise <CP (Burnley et al. 2012; Thomas et al. 2016;
Black et al. 2017). Differences in fatigue development
during exercise performed below and above CP are dis-
cussed in more detail elsewhere (Poole et al. 2016; Burn-
ley and Jones 2018). It is pertinent to reiterate here,
however, that it is possible for subjects to exercise above
MLSS and still produce physiological responses consistent
with heavy-intensity exercise (e.g., Mattioni Maturana
et al. 2016), suggesting that MLSS does not precisely sep-
arate exercise intensity domains wherein physiological
response profiles and mechanisms of fatigue development
are distinct.
Considerations for the Accurate
Assessment of the Power-Duration
Relationship
While we have reviewed evidence supporting CP as the
bona fide demarcator of the maximal metabolic steady
state, it is essential that great care is taken in its estimation
(Mattioni Maturana et al. 2018; Muniz-Pumares et al.
2019). There are two methods by which CP can be
assessed: the ‘conventional’ approach in which CP is mod-
eled from a series of severe-intensity ‘prediction trials’ per-
formed to the limit of tolerance at different speeds or
power outputs (Monod and Scherrer 1965; Poole et al.
1988); and the 3-min all-out test in which, as the name
implies, subjects exercise maximally for 3 min with the
end-test power representing the CP and the total work
done above CP representing the W0(Burnley et al. 2006;
Vanhatalo et al. 2007). If the CP is estimated using the
conventional approach, important considerations include
the number of trials and their duration (Hill 1993; Bishop
et al. 1998; Triska et al. 2018). It is essential that subjects
give their maximum effort in each trial and that cadence is
consistent across all trials. Ideally the shortest trial should
be 2–3 min and the longest should be more than 10 but
no longer than 15 min (Hill 1993; Vanhatalo et al. 2011a).
It has been recommended that there should be at least a
5 min difference between the shortest and longest trials
(Bishop et al. 1998) but the goodness of hyperbolic fit is
improved by making the range of times to exhaustion as
broad as possible (i.e., 8–12 min) within the severe-inten-
sity domain. The precise duration of the prediction trials
is of secondary importance to the attainment of
_
VO
2
max,
but it is unusual for
_
VO
2
max to be attained if exercise
duration is shorter than 1–2 min or longer than 15–
20 min (Hill et al. 2002; Vanhatalo et al. 2016).
_
VO
2
should be measured during each trial to verify attainment
of
_
VO
2
max, with this typically defined as the end-exercise
_
VO
2
exceeding 95% of the
_
VO
2
max measured during
ramp incremental exercise, to allow for biological and
methodological day-to-day variability (Katch et al. 1982).
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2019 | Vol. 7 | Iss. 10 | e14098
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A. M. Jones et al. The Maximal Metabolic Steady State

The goodness of fit of prediction trial data to the
regression equation is dependent on the number of trials.
In practice, 3-4 (Smith and Jones 2001; Brickley et al.
2002; Pringle and Jones 2002; Dekerle et al. 2005; Black
et al. 2015) or 5-7 (Hughson et al. 1984; Gaesser and
Wilson 1988; Poole et al. 1990; Bull et al. 2000; Vanhat-
alo et al. 2007) trials are commonly used. The goodness
of fit, reported as r
2
-values, provides only a broad indica-
tion of accuracy. The reporting of standard errors (or
95% confidence intervals) associated with each parameter
is recommended, with accuracy deemed satisfactory when
standard error is less than 5% of the mean for CP and
less than 10% for the W0(Hill and Smith 1994, 1999).
Prediction trial data should be modeled iteratively and
additional trials are performed until these SE criteria
should be attained. Natural variability in human endur-
ance performance from one test to another means that
the parameter estimates derived from the three 2-para-
meter models (i.e., linear work-time model, the hyper-
bolic power-time model and the linear 1/time model; Hill
1993; Morton 2006) are rarely identical. Applying all
three models and finding the ‘best individual fit’ (i.e. the
model which produces the least combined error for CP
and W0) for each subject is a useful approach (Black et al.
2015, 2017). Variability in performance can also result in
small differences in the estimated CP when a small num-
ber of prediction trials are combined, even when all trials
are within the recommended range (e.g., 3, 7, and 12 min
Figure 7. Participants performing severe-intensity exercise attain the same “critical” muscle metabolic milieu ([PCr] panel A; pH panel B;
[lactate] panel C); have similar blood [lactate] values (panel D); experience equivalent decrements in neuromuscular excitability (panel E); and
achieve pulmonary
_
VO
2max
(panel F), at the limit of tolerance irrespective of task duration. These responses are observed following cycling
(black bars, Black et al. 2017) and knee-extension exercise performed in normoxia (white bars, Burnley et al. 2012; light gray bars, Vanhatalo
et al. 2010) and hyperoxia (70% O
2
, dark gray bars, Vanhatalo et al. 2010). Group mean SD values are displayed. Panel F, solid line
indicates
_
VO
2max
determined from ramp incremental test. S1 =severe-intensity exercise bout 1, et seq.
2019 | Vol. 7 | Iss. 10 | e14098
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ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
The Maximal Metabolic Steady State A. M. Jones et al.

vs. 2, 5, and 10 min; Triska et al. 2018). Minor differ-
ences are to be expected given that discrete prediction tri-
als of different durations can only provide an
approximation of the underpinning fundamental power-
time continuum; such differences do not undermine the
validity of CP but instead underline the importance of
employing appropriate strategies to minimize measure-
ment error.
Estimation of CP can be expedited by using the 3-min
all-out test, which is performed by cycling against a
fixed-resistance on a Lode Excalibur Sport cycle ergome-
ter or running on a track, and has been shown to pro-
vide valid and reliable estimates of CP (e.g., Burnley
et al. 2006; Vanhatalo et al. 2007, 2008a; Pettitt et al.
2012; Broxterman et al. 2013; Simpson et al. 2015). It is
important that subjects are highly motivated and fully
familiarized with the protocol in its entirety, and under-
stand that they must give a maximum effort throughout
the test. Great care should be taken in the normalization
of the fixed resistance for cycle ergometry. Variables mea-
sured in the 3-min all-out test are sensitive to manipula-
tion of cadence (Vanhatalo et al. 2008b; Wright et al.
2019), such that selection of a ‘preferred cadence’ that is
too high (≥90 rpm) tends to lead to underestimation of
Wʹand overestimation of CP. The experimenter must
not provide any time-based feedback during the test and
verbal encouragement must be kept consistent to ensure
that it is delivered with the same urgency and enthusiasm
throughout. A substantial body of evidence indicates that,
in recreationally active subjects, the peak
_
VO
2
in the 3-
minute all-out test typically reaches ~97–103% of ramp
test determined
_
VO
2
max (Burnley et al. 2006; Vanhatalo
et al. 2011b; Barker et al. 2012; Chidnok et al. 2013;
Black et al. 2015; Clark et al. 2018). Hence, for the crite-
rion test to be accepted as valid, there must be no indi-
cation of pacing in the speed or power output profile
(i.e. no incremental trend in speed or power output at
any point after the initial acceleration during the first 5–
10 sec), and the
_
VO
2
max must be attained and then sus-
tained for the remainder of the test (Jones et al. 2010;
Vanhatalo et al. 2016). If the
_
VO
2
attained during a 3-
minute all-out test is <95%
_
VO
2
max, the CP and Wʹ
estimates should not be considered accurate and the test
should be repeated.
Practical Application of the
Power-Duration Relationship
Quantifying the power-duration relationship using the
testing procedures outlined above provides not just the
CP (the asymptote of the relationship) but also the W0
(the curvature constant of the relationship). The CP is an
index of oxidative metabolic capacity that is sensitive to
endurance training (Gaesser and Wilson 1988; Poole et al.
1990; Jenkins and Quigley 1992; Vanhatalo et al. 2008a)
and the fraction of inspired O
2
(Vanhatalo et al. 2010;
Dekerle et al. 2012; Simpson et al. 2015; La Monica et al.
2018). The CP is highly correlated with endurance exer-
cise performance (Kolbe et al. 1995; Smith et al. 1999;
Black et al. 2014) and it has been estimated that 
elite
marathon runners sustain ~96% of their critical speed
during competition (Jones and Vanhatalo 2017). Impor-
tantly, the W0provides information on the finite amount
of work that can be completed during exercise >CP prior
to the attainment of the limit of tolerance (Fukuba et al.
2003; Chidnok et al. 2013) and is sensitive to interven-
tions that alter the
_
VO
2
slow component (Vanhatalo
et al. 2010; Murgatroyd et al. 2011). During severe-inten-
sity exercise, which incorporates endurance events in the
~2–25 min range, performance is a function of the inter-
action of CP with W0(Vanhatalo et al. 2011a; Jones and
Vanhatalo 2017). Therefore, while CP alone provides
information on the highest sustainable oxidative meta-
bolic rate during heavy-intensity exercise, knowing both
CP and W0enables highly accurate prediction of perfor-
mance during severe-intensity exercise (Vanhatalo et al.
2011a; Morgan et al. 2018) and is valuable in constructing
individually optimized interval training programmes
(Skiba et al. 2014; Jones and Vanhatalo 2017). It should
be appreciated, however, that both CP and W0are
dynamic quantities that can decline with time during
fatiguing exercise (Clark et al. 2018, 2019).
Conclusions
The maximal metabolic steady state concept is valuable
from multiple perspectives, such as enhancing our under-
standing of basic skeletal muscle energetics and fatigue
processes, for characterizing exercise intensity, and for
exploring and ameliorating limitations to human exercise
performance. Progress in these fields has been slowed,
however, by disagreement over definitions and proce-
dures, and by a fixation with the behavior of a single bio-
marker, blood [lactate]. In this article, we have outlined
concerns with the arbitrariness of the definition of, and
the procedures for evaluating, MLSS and we have pro-
vided a rationale for considering CP as the boundary
which separates steady-state (heavy-intensity) from non-
steady-state (severe-intensity) exercise. We recommend
that scientists and practitioners appreciate that MLSS and
CP are not, and should not be expected to be, either syn-
onymous or interchangeable. Quantitative and qualitative
differences between these entities is inevitable and are
caused by the conservative definition of MLSS leading to
an underestimation of the heavy/severe-intensity bound-
ary as represented by CP. Like all other measurements in
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2019 | Vol. 7 | Iss. 10 | e14098
Page 11
A. M. Jones et al. The Maximal Metabolic Steady State

human (exercise) physiology, there is obligatory technical
error and biological variability inherent in estimating CP.
However, when these are minimized by sound experimen-
tal procedure, and properly quantified and accounted for,
it is evident that CP separates exercise intensity domains
with distinctive muscle metabolic and systemic cardiovas-
cular and respiratory response profiles. CP is therefore
the appropriate metric when the goal is to evaluate the
maximal metabolic steady state.
Conflict of Interest
None declared.
References
Barker, T., D. C. Poole, M. L. Noble, and T. J. Barstow. 2006.
Human critical power-oxygen uptake relationship at
different pedalling frequencies. Exp. Physiol. 91:621–632.
Barker, A. R., B. Bond, C. Toman, C. A. Williams, and N.
Armstrong. 2012. Critical power in adolescents:
physiological bases and assessment using all-out exercise.
Eur. J. Appl. Physiol. 112:1359–1370.
Beneke, R. 1995. Anaerobic threshold, individual anaerobic
threshold, and maximal lactate steady state in rowing. Med.
Sci. Sports Exerc. 27:863–867.
Beneke, R. 2003. Methodological aspects of maximal lactate
steady state-implications for performance testing. Eur. J.
Appl. Physiol. 89:95–99.
Beneke, R., and S. P. von Duvillard. 1996. Determination
of maximal lactate steady state response in selected sports
events. Med. Sci. Sports Exerc. 28:241–246.
Beneke, R., M. H€
utler, and R. M. Leith€
auser. 2000. Maximal
lactate-steady-state independent of performance. Med. Sci.
Sports Exerc. 32:1135–1139.
Beneke, R., R. M. Leith€
auser, and M. H€
utler. 2001.
Dependence of the maximal lactate steady state on the
motor pattern of exercise. Br. J. Sports Med. 35:192–196.
Beneke, R., M. Hutler, S. P. Von Duvillard, M. Sellens, and R.
M. Leithauser. 2003. Effect of test interruptions on blood
lactate during constant workload testing. Med. Sci. Sports
Exerc. 35:1626–1630.
Bergman, B. C., E. E. Wolfel, G. E. Butterfield, G. D.
Lopaschuk, G. A. Casazza, M. A. Horning, et al. 1999.
Active muscle and whole body lactate kinetics after
endurance training in men. J. Appl. Physiol. 87:1684–1896.
Bergstrom, H. C., T. J. Housh, J. M. Zuniga, D. A. Traylor, R.
W. Lewis, C. L. Camic, et al. 2013. Responses during
exhaustive exercise at critical power determined from the
3-minute all-out test. J. Sports Sci. 31:537–545.
Billat, V., F. Dalmay, M. T. Antonini, and A. P. Chassain.
1994. A method for determining
the maximal steady state of blood lactate concentration
from two levels of submaximal exercise. Eur. J. Appl.
Physiol. Occup. Physiol. 69:196–202.
Billat, V. L., P. Sirvent, G. Py, J. P. Koralsztein, and J. Mercier.
2003. The concept of maximal lactate steady state: a bridge
between biochemistry, physiology and sport science. Sports
Med. 33:407–426.
Billat, V. L., E. Mouisel, N. Roblot, and J. Melki. 2005. Inter-
and intrastrain variation in mouse critical running speed. J.
Appl. Physiol. 98:1258–1263.
Bishop, D., D. G. Jenkins, and A. Howard. 1998. The critical
power function is dependent on the duration of the
predictive exercise tests chosen. Int. J. Sports Med. 19:125–
129.
Black, M. I., J. Durant, A. M. Jones, and A. Vanhatalo. 2014.
Critical power derived from a 3-minute all-out test predicts
16.1-km road time-trial performance. Eur. J. Sport Sci.
14:217–223.
Black, M. I., A. M. Jones, S. J. Bailey, and A. Vanhatalo. 2015.
Self-pacing increases critical power and improves
performance during severe-intensity exercise. Appl. Physiol.
Nutr. Metab. 40:662–670.
Black, M. I., A. M. Jones, J. R. Blackwell, S. J. Bailey, L. J.
Wylie, S. T. McDonagh, et al. 2017. Muscle metabolic and
neuromuscular determinants of fatigue during cycling in
different exercise intensity domains. J. Appl. Physiol.
122:446–459.
Bonaventura, J. M., K. Sharpe, E. Knight, K. L. Fuller, R. K.
Tanner, and C. J. Gore. 2015. Reliability and accuracy of six
hand-held blood lactate analysers. J. Sports Sci. Med.
14:203–214.
Brickley, G., J. Doust, and C. A. Williams. 2002. Physiological
responses during exercise to exhaustion at critical power.
Eur. J. Appl. Physiol. 88:146–151.
Broxterman, R. M., C. J. Ade, D. C. Poole, C. A. Harms, and
T. J. Barstow. 2013. A single test for the determination of
parameters of the speed-time relationship for running.
Respir. Physiol. Neurobiol. 185:380–385.
Bull, A. J., T. J. Housh, G. O. Johnson, and S. R. Perry. 2000.
Effect of mathematical modeling on the estimation of
critical power. Med. Sci. Sports Exerc. 32:526–530.
Burnley, M. 2009. Estimation of critical torque using
intermittent isometric maximal voluntary contractions of
the quadriceps in humans. J. Appl. Physiol. 106:975–983.
Burnley, M., and A. M. Jones. 2018. Power-duration
relationship: Physiology, fatigue, and the limits of human
performance. Eur. J. Sport Sci. 18:1–12.
Burnley, M., J. H. Doust, and A. Vanhatalo. 2006. A 3-minute
all-out test to determine peak oxygen uptake and the
maximal steady state. Med. Sci. Sports Exerc. 38:1995–2003.
Burnley, M., A. Vanhatalo, and A. M. Jones. 2012. Distinct
profiles of neuromuscular fatigue during muscle
contractions below and above the critical torque in humans.
J. Appl. Physiol. 113:215–223.
2019 | Vol. 7 | Iss. 10 | e14098
Page 12
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
The Maximal Metabolic Steady State A. M. Jones et al.

Carter, H., A. M. Jones, and J. H. Doust. 1999. Effect of
6 weeks of endurance training on the lactate minimum
speed. J. Sports Sci. 17:957–967.
Carter, H., J. S. Pringle, A. M. Jones, and J. H. Doust. 2002.
Oxygen uptake kinetics during treadmill running across
exercise intensity domains. Eur. J. Appl. Physiol. 86:347–354.
Chidnok, W., F. J. Dimenna, S. J. Bailey, D. P. Wilkerson,
A. Vanhatalo, and A. M. Jones. 2013. Effects of pacing
strategy on work done above critical power during high-
intensity exercise. Med. Sci. Sports Exerc. 45:1377–1385.
Clark, I. E., A. Vanhatalo, S. J. Bailey, L. J. Wylie, B. S. Kirby,
B. W. Wilkins, et al. 2018. Effects of two hours of heavy-
intensity exercise on the power-duration relationship. Med.
Sci. Sports Exerc. 50:1658–1668.
Clark, I, A. Vanhatalo, C. Thompson, L. J. Wylie, S. J. Bailey,
B. Kirby, et al. 2019. Changes in the power-duration
relationship following prolonged exercise: estimation using
conventional and all-out protocols and relationship to
muscle glycogen. Am. J. Physiol. Regul. Integr. Comp.
Physiol.. https://doi.org/10.1152/ajpregu.00031.2019. [Epub
ahead of print]\
Copp, S. W., D. M. Hirai, T. I. Musch, and D. C. Poole. 2010.
Critical speed in the rat: implications for hindlimb muscle
blood flow distribution and fibre recruitment. J. Physiol.
588:5077–5087.
Davies, M. J., A. P. Benson, D. T. Cannon, S. Marwood, G. J.
Kemp, H. B. Rossiter, et al. 2017. Dissociating external
power from intramuscular exercise intensity during
intermittent bilateral knee-extension in humans. J. Physiol.
595:6673–6686.
Dekerle, J., B. Baron, L. Dupont, J. Vanvelcenaher, and P.
Pelayo. 2003. Maximal lactate steady state, respiratory
compensation threshold and critical power. Eur. J. Appl.
Physiol. 89:281–288.
Dekerle, J., P. Pelayo, B. Clipet, S. Depretz, T. Lefevre, and M.
Sidney. 2005. Critical swimming speed does not represent
the speed at maximal lactate steady state. Int. J. Sports Med.
26:524–530.
Dekerle, J., P. Mucci, and H. Carter. 2012. Influence of
moderate hypoxia on tolerance to high-intensity exercise.
Eur. J. Appl. Physiol. 112:327–335.
Dill, D. B., and D. L. Costill. 1974. Calculation of percentage
changes in volumes of blood, plasma, and red cells in
dehydration. J. Appl. Physiol. 37:247–248.
Faude, O., W. Kindermann, and T. Meyer. 2009.
Lactate threshold concepts: how valid are they? Sports Med.
39:469–490.
Fukuba, Y., A. Miura, M. Endo, A. Kan, K. Yanagawa, and B.
J. Whipp. 2003. The curvature constant parameter of the
power-duration curve for varied-power exercise. Med. Sci.
Sports Exerc. 35:1413–1418.
Full, R. J. 1986. Locomotion without lungs: energetics and
performance of a lungless salamander. Am. J. Physiol. 251:
R775–R780.
Full, R. J., and C. F. 2nd Herreid. 1983. Aerobic response to
exercise of the fastest land crab. Am. J. Physiol. 244:R530–R536.
Gaesser, G. A., and L. A. Wilson. 1988. Effects of continuous
and interval training on the parameters of the power-
endurance time relationship for high-intensity exercise. Int.
J. Sports Med. 9:417–421.
Gaesser, G. A., S. A. Ward, V. C. Baum, and B. J. Whipp.
1994. Effects of infused epinephrine on slow phase of O2
uptake kinetics during heavy exercise in humans. J. Appl.
Physiol. 77:2413–2419.
Gavin, T. P., J. B. Van Meter, P. M. Brophy, G. S. Dubis, K. N.
Potts, and R. C. Hickner. 2012. Comparison of a field-based
test to estimate functional threshold power and power output
at lactate threshold. J. Strength Cond. Res. 26:416–421.
Heck, H., A. Mader, G. Hess, S. M€
ucke, R. M€
uller, and W.
Hollmann. 1985. Justification of the 4-mmol/
l lactate threshold. Int. J. Sports Med. 6:117–130.
Hermansen, L., E. Hultman, and B. Saltin. 1967. Muscle
glycogen during prolonged severe exercise. Acta Physiol.
Scand. 71:129–139.
Hill, A. V. 1925. The physiological basis of athletic records.
Nature 116:544–548.
Hill, D. W. 1993. The critical power concept. A review. Sports
Med. 16:237–254.
Hill, D. W., and C. S. Ferguson. 1999. A physiological
description of critical velocity. Eur. J. Appl. Physiol. Occup.
Physiol. 79:290–293.
Hill, D. W., and J. C. Smith. 1994. A method to ensure
accuracy of estimates of anaerobic capacity derived using
the critical power concept. J. Sports Med. Physical Fitness.
34:23–37.
Hill, D. W., and J. C. Smith. 1999. Determination of critical
power by pulmonary gas exchange. Can. J. Appl. Physiol.
24:74–86.
Hill, D. W., D. C. Poole, and J. C. Smith. 2002. The
relationship between power and the time to achieve VO
(2max). Med. Sci. Sports Exerc. 34:709–714.
Holloszy, J. O., and E. F. Coyle. 1984. Adaptations of skeletal
muscle to endurance exercise and their metabolic
consequences. J. Appl. Physiol. Respir. Environ. Exerc.
Physiol. 56:831–838.
Hughson, R. L., C. J. Orok, and L. E. Staudt. 1984. A high
velocity treadmill running test to assess endurance running
potential. Int. J. Sports Med. 5:23–25.
Iannetta, D., E. C. Inglis, C. Fullerton, L. Passfield, and J. M.
Murias. 2018. Metabolic and performance-related
consequences of exercising at and slightly above MLSS.
Scand. J. Med. Sci. Sports 28:2481–2493.
Jacobs, I. 1986. Blood lactate. Implications for training and
sports performance. Sports Med. 3:10–25.
Jamnick, N. A., J. Botella, D. B. Pyne, and D. J. Bishop. 2018.
Manipulating graded exercise test variables affects the
validity of the lactate threshold and VO2 peak. PLoS ONE
13:e0199794.
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2019 | Vol. 7 | Iss. 10 | e14098
Page 13
A. M. Jones et al. The Maximal Metabolic Steady State

Jenkins, D. G., and B. M. Quigley. 1990. Blood lactate in
trained cyclists during cycle ergometry at critical power.
Eur. J. Appl. Physiol. Occup. Physiol. 61:278–283.
Jenkins, D. G., and B. M. Quigley. 1992. Endurance training
enhances critical power. Med. Sci. Sports Exerc. 24:1283–1289.
Jones, A. M., and H. Carter. 2000. The effect of endurance
training on parameters of aerobic fitness. Sports Med.
29:373–386.
Jones, A. M., and J. H. Doust. 1998. The validity of the lactate
minimum test for determination of
the maximal lactate steady state. Med. Sci. Sports Exerc.
30:1304–1313.
Jones, A. M., and D. C. Poole. 2008. Physiological demands of
endurance exercise. Pp. 43–55 in R. J. Maughan, (Ed).
Olympic Textbook of Science in Sport, IOC, Blackwell,
Oxford, UK.
Jones, A. M., and A. Vanhatalo. 2017. The ‘critical power’
concept: applications to sports performance with a focus on
intermittent high-intensity exercise. Sports Med. 47:65–78.
Jones, A. M., D. P. Wilkerson, F. DiMenna, J. Fulford, and D.
C. Poole. 2008. Muscle metabolic responses to exercise above
and below the “critical power” assessed using
31
P-MRS. Am.
J. Physiol. Regul. Integr. Comp. Physiol. 294:R585–R593.
Jones, A. M., A. Vanhatalo, M. Burnley, R. H. Morton, and D.
C. Poole. 2010. Critical power: implications for
determination of VO
2
max and exercise tolerance. Med. Sci.
Sports Exerc. 42:1876–1890.
Jones, A. M., B. Grassi, P. M. Christensen, P. Krustrup, J.
Bangsbo, and D. C. Poole. 2011. Slow component of VO
2
kinetics: mechanistic bases and practical applications. Med.
Sci. Sports Exerc. 43:2046–2062.
Jones, A. M., M. Burnley, and A. Vanhatalo. 2018. Aerobic
Exercise Performance. Pp. 319–352 In K. Norton, R. Eston,
eds. Kinanthropometry and Exercise Physiology. Routledge,
London. https://doi.org/10.4324/9781315385662
Jorfeldt, L., A. Juhlin-Dannfelt, and J. Karlsson. 1978.
Lactate release in relation to tissue lactate in human skeletal
muscle during exercise. J. Appl. Physiol. Respir. Environ.
Exerc. Physiol. 44:350–352.
Katch, V. L., S. S. Sady, and P. Freedson. 1982. Biological
variability in maximum aerobic power. Med. Sci. Sports
Exerc. 14:21–25.
Keir, D. A., F. Y. Fontana, T. C. Robertson, J. M. Murias, D.
H. Paterson, J. M. Kowalchuk, et al. 2015. Exercise intensity
thresholds: identifying the boundaries of sustainable
performance. Med. Sci. Sports Exerc. 47:1932–1340.
Kilding, A. E., and A. M. Jones. 2005. Validity of a single-visit
protocol to estimate the maximum lactate steady state. Med.
Sci. Sports Exerc. 37:1734–1740.
Kolbe, T., S. C. Dennis, E. Selley, T. D. Noakes, and M. I.
Lambert. 1995. The relationship between critical power and
running performance. J. Sports Sci. 13:265–269.
La Monica, M. B., D. H. Fukuda, T. M. Starling-Smith, R.
Wang, J. R. Hoffman, and J. R. Stout. 2018. Effects of
normobaric hypoxia on upper body critical power and
anaerobic working capacity. Respir. Physiol. Neurobiol.
249:1–6.
LaFontaine, T. P., B. R. Londeree, and W. K. Spath. 1981. The
maximal steady state versus selected running events. Med.
Sci. Sports Exerc. 13:190–193.
Lauderdale, M. A., and K. W. Hinchcliff. 1999. Hyperbolic
relationship between time-to-fatigue and workload. Equine
Vet. J. Suppl. 30:586–590.
Mader, A., and H. Heck. 1986. A theory of the metabolic
origin of anaerobic threshold. Int. J. Sports Med. 7:45–65.
Mattioni Maturana, F., D. A. Keir, K. M. McLay, and J. M.
Murias. 2016. Can measures of critical power precisely
estimate the maximal metabolic steady-state? Appl. Physiol.
Nutr. Metab. 41:1197–1203.
Mattioni Maturana, F., F. Y. Fontana, S. Pogliaghi, L.
Passfield, and J. M. Murias. 2018. Critical power: How
different protocols and models affect its determination. J.
Sci. Med. Sport. 21:742–747.
McClave, S. A., M. LeBlanc, and S. A. Hawkins. 2011. Sustainability
of critical power determined by a 3-minuteute all-out test in elite
cyclists. J. Strength Cond. Res. 25:3093–3098.
McLellan, T. M., and K. S. Cheung. 1992. A comparative
evaluation of the individual anaerobic threshold and the
critical power. Med. Sci. Sports Exerc. 24:543–550.
Mitchell, E. A., N. R. W. Martin, S. J. Bailey, and R. A.
Ferguson. 2018. Critical power is positively related to
skeletal muscle capillarity and type I muscle fibers in
endurance-trained individuals. J. Appl. Physiol. 125:737–745.
Monod, H., and J. Scherrer. 1965. The work capacity of a
synergic muscular group. Ergonomics 8:329–338.
Morgan, P. T., M. I. Black, S. J. Bailey, A. M. Jones, and A.
Vanhatalo. 2018. Road cycle TT performance: Relationship
to the power-duration model and association with FTP. J.
Sports Sci. 37:902–910. Nov 2:1-9. https://doi.org/10.1080/
02640414.2018.1535772.
Moritani, T., A. Nagata, H. A. deVries, and M. Muro. 1981.
Critical power as a measure of physical work capacity and
anaerobic threshold. Ergonomics 24:339–350.
Morton, R. H. 2006. The critical power and related whole-
body bioenergetic models. Eur. J. Appl. Physiol. 96:339–354.
Muniz-Pumares, D., B. Karsten, C. Triska, and M. Glaister.
2019. Methodological approaches and related challenges
associated with the determination of critical power and
curvature constant. J. Strength Cond. Res. 33:584–596.
Murgatroyd, S. R., C. Ferguson, S. A. Ward, B. J. Whipp, and
H. B. Rossiter. 2011. Pulmonary O
2
uptake kinetics as a
determinant of high-intensity exercise tolerance in humans.
J. Appl. Physiol. 110:1598–1606.
Murgatroyd, S. R., L. A. Wylde, D. T. Cannon, S. A. Ward,
and amd H. B. Rossiter. 2014. A ‘ramp-sprint’ protocol to
characterise indices of aerobic function and exercise
intensity domains in a single laboratory test. Eur. J. Appl.
Physiol. 114:1863–1874.
2019 | Vol. 7 | Iss. 10 | e14098
Page 14
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
The Maximal Metabolic Steady State A. M. Jones et al.

Pettitt, R. W., N. Jamnick, and I. E. Clark. 2012. 3-minute all-
out exercise test for running. Int. J. Sports Med. 33:426–431.
Poole, D. C., and R. S. Richardson. 1997. Determinants of
oxygen uptake. Implications for exercise testing. Sports
Med. 24:308–320.
Poole, D. C., S. A. Ward, G. W. Gardner, and B. J.
Whipp. 1988. Metabolic and respiratory profile of the
upper limit for prolonged exercise in man. Ergonomics
31:1265–1279.
Poole, D. C., S. A. Ward, and B. J. Whipp. 1990. The effects of
training on the metabolic and respiratory profile of high-
intensity cycle ergometer exercise. Eur. J. Appl. Physiol.
Occup. Physiol. 59:421–429.
Poole, D. C., M. Burnley, A. Vanhatalo, H. B. Rossiter, and A.
M. Jones. 2016. Critical power: an
important fatigue threshold in exercise physiology. Med. Sci.
Sports Exerc. 48:2320–2334.
Priest, J. W., and R. D. Hagan. 1987. The effects of maximum
steady state pace training on running performance. Br. J.
Sports Med. 21:18–21.
Pringle, J. S., and A. M. Jones. 2002.
Maximal lactate steady state, critical power and EMG
during cycling. Eur. J. Appl. Physiol. 88:214–226.
Saunders, P. U., D. B. Pyne, R. D. Telford, and J. A. Hawley.
2004. Reliability and variability of running economy in elite
distance runners. Med. Sci. Sports Exerc. 36:1972–1976.
Scheen, A., J. Juchmes, and A. Cession-Fossion. 1981. Critical
analysis of the “anaerobic threshold” during exercise at
constant workloads. Eur. J. Appl. Physiol. Occup. Physiol.
46:367–377.
Simpson, L. P., A. M. Jones, P. F. Skiba, A. Vanhatalo, and D.
Wilkerson. 2015. Influence of hypoxia on the power-
duration relationship during high-intensity exercise. Int. J.
Sports Med. 36:113–119.
Sj€
odin, B., I. Jacobs, and J. Svedenhag. 1982. Changes in onset
of blood lactate accumulation (OBLA) and muscle enzymes
after training at OBLA. Eur. J. Appl. Physiol. Occup.
Physiol. 49:45–57.
Skiba, P. F., D. Clarke, A. Vanhatalo, and A. M. Jones. 2014.
Validation of a novel intermittent W’ model for cycling
using field data. Int. J. Sports Physiol. Perform. 9:900–904.
Smith, C. G., and A. M. Jones. 2001. The relationship between
critical velocity, maximal lactate steady-state velocity
and lactate turnpoint velocity in runners. Eur. J. Appl.
Physiol. 85:19–26.
Smith, J. C., B. S. Dangelmaier, and D. W. Hill. 1999. Critical
power is related to cycling time trial performance. Int. J.
Sports Med. 20:374–378.
Snyder, A. C., T. Woulfe, R. Welsh, and C. Foster. 1994. A
simplified approach to estimating the maximal lactate steady
state. Int. J. Sports Med. 15:27–31.
Stainsby, W. N., and G. A. Brooks. 1990. Control of lactic acid
metabolism in contracting muscles and during exercise.
Exerc. Sport Sci. Rev. 18:29–63.
Stegmann, H., and W. Kindermann. 1982. Comparison of
prolonged exercise tests at the individual anaerobic
threshold and the fixed anaerobic threshold of 4 mmol.l(-1)
lactate. Int. J. Sports Med. 3:105–110.
Tesch, P. A., W. L. Daniels, and D. S. Sharp. 1982. Lactate
accumulation in muscle and blood during submaximal
exercise. Acta Physiol. Scand. 114:441–446.
Thomas, K., M. Elmeua, G. Howatson, and S. Goodall. 2016.
Intensity-dependent contribution of neuromuscular fatigue
after constant-load cycling. Med. Sci. Sports Exerc. 48:1751–
1760.
Triska, C., B. Karsten, C. Beedie, B. Koller-Zeisler, A.
Nimmerichter, and H. Tschan. 2018. Different durations
within the method of best practice affect the parameters of
the speed-duration relationship. Eur. J. Sport Sci. 18:332–340.
Vanhatalo, A., J. H. Doust, and M. Burnley. 2007.
Determination of critical power using a 3-minute all-out
cycling test. Med. Sci. Sports Exerc. 39:548–555.
Vanhatalo, A., J. H. Doust, and M. Burnley. 2008a. A 3-
minute all-out cycling test is sensitive to a change in critical
power. Med. Sci. Sports Exerc. 40:1693–1699.
Vanhatalo, A., J. H. Doust, and M. Burnley. 2008b. Robustness
of a 3 min all-out cycling test to manipulations of power
profile and cadence in humans. Exp. Physiol. 93:383–390.
Vanhatalo, A., J. Fulford, F. J. DiMenna, and A. M. Jones.
2010. Influence of hyperoxia on muscle metabolic responses
and the power-duration relationship during severe-intensity
exercise in humans: a 31P magnetic resonance spectroscopy
study. Exp. Physiol. 95:528–540.
Vanhatalo, A., A. M. Jones, and M. Burnley. 2011a.
Application of critical power in sport. Int. J. Sports Physiol.
Perform. 6:128–136.
Vanhatalo, A., D. C. Poole, F. J. DiMenna, S. J. Bailey, and A.
M. Jones. 2011b. Muscle fiber recruitment and the slow
component of O2 uptake: constant work rate vs. all-out
sprint exercise. Am. J. Physiol. Regul. Integr. Comp. Physiol.
300:R700–R707.
Vanhatalo, A., M. I. Black, F. J. DiMenna, J. R. Blackwell, J. F.
Schmidt, C. Thompson, et al. 2016. The mechanistic bases of
the power-time relationship: muscle metabolic responses and
relationships to muscle fibre type. J. Physiol. 594:4407–4423.
Wakayoshi, K., T. Yoshida, M. Udo, T. Harada, T. Moritani,
Y. Mutoh, et al. 1993. Does critical swimming velocity
represent exercise intensity at maximal lactate steady state?
Eur. J. Appl. Physiol. Occup. Physiol. 66:90–95.
Whipp, B. J., and S. A. Ward. 1992. Pulmonary gas exchange
dynamics and the tolerance to muscular exercise: effects of
fitness and training. Ann. Physiol. Anthropol. 11:207–214.
Wilkerson, D. P., K. Koppo, T. J. Barstow, and A. M. Jones.
2004. Effect of work rate on the functional ‘gain’ of Phase II
pulmonary O
2
uptake response to exercise. Respir. Physiol.
Neurobiol. 142:211–223.
Wilkie, D. R. 1960. Man as a source of mechanical power.
Ergonomics 3:1–8.
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2019 | Vol. 7 | Iss. 10 | e14098
Page 15
A. M. Jones et al. The Maximal Metabolic Steady State

Womack, C. J., S. E. Davis, J. L. Blumer, E. Barrett, A. L.
Weltman, and G. A. Gaesser. 1995. Slow component of O2
uptake during heavy exercise: adaptation to endurance
training. J. Appl. Physiol. 79:838–845.
Wright, J., S. Bruce-Low, and S. A. Jobson. 2019. The 3-
minuteute all-out cycling test is sensitive to changes in
cadence using the Lode Excalibur Sport ergometer. J. Sports
Sci. 37:156–162.
Yamamoto, Y., M. Miyashita, R. L. Hughson, S. Tamura, M.
Shinohara, and Y. Mutoh. 1991. The ventilatory threshold
gives maximal lactate steady state. Eur. J. Appl. Physiol.
Occup. Physiol. 63:55–59.
2019 | Vol. 7 | Iss. 10 | e14098
Page 16
ª2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
The Maximal Metabolic Steady State A. M. Jones et al.