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Submaximal markers of exercise intensity
A. P. Hills , N. M. Byrne & A. J. Ramage
Published online: 07 Feb 2011.
To cite this article: A. P. Hills , N. M. Byrne & A. J. Ramage (1998) Submaximal markers of exercise intensity,
Journal of Sports Sciences, 16:sup1, 71-76, DOI: 10.1080/026404198366696
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Journal of Sports Sciences, 1998, 16, S71± S76
0264 ± 0414 /98 Ó 1998 E & FN Spon
Submaximal markers of exercise intensity
A.P. HILLS,* N.M. BYRNE and A.J. RAMAGE
School of Human Movement Studies, Queensland University of Technology, Victor ia Park Road, Kelvin Grove,
Queensland 4059, Australia
Numerous approaches have been used to improve the science and art of exercise prescription, and particular
challenges exist in the prescription of exercise intensity. Traditionally, work in the area has been the province of
exercise physiologists interested in the improvement of training programmes for athletes, as opposed to the more
widespread recent interest in health-related ® tness and physical activity for all. The generalized approach to the
provision of guidelines for exercise prescription has meant that individuals have, at best, prediction equations
which provide a wide band of heart rate between which they can work to derive health bene® ts. This paper
explores some of the commonly employed submaximal markers of exercise intensity and proposes a number of
approaches for improvements beyond generalized equations.
Keywords: exercise intensity, heart rate, lactate threshold, perceived exertion.
Introduction
In recent years, there has been an increased interest
in the physiological and psychological bene® ts of
participation in regular physical activity (Blair et al.,
1992; Pate, 1995; US Department of Health and
Human Services, 1996). Although public health
agencies have helped to foster a groundswell of interest
in physical activity by outlining the health bene® ts of
participation, there are still numerous gaps in our
understanding of the speci® cs of exercise prescription.
Although considerable work has been undertaken
in areas such as cardiac rehabilitation, the gaps in
our knowledge are particularly pronounced for some
population groups. Recent work has also considered
the metabolic bases of potential bene® ts for sedentary
individuals who participate in regular activity, in-
dependent of the more traditional cardiovascular
(physiological) orientation. The advice in this area is
non-speci® c ± for example, to participate in long-
duration, low-intensity exercise to improve metabolic
® tness (Despres, 1994).
Traditional guidelines for exercise prescription have
come from the American College of Sports Medicine
(ACSM). Early work in the area was oriented to the
improvement of athletic performance through physical
® tness (Karvonen et al., 1957) in contrast to the current
* Author to whom all correspondence should be addressed.
public health focus on the enhancement of health
status. Furthermore, the need to use maximal tests of
physiological parameters was stressed in the earlier
prescription literature. The realization that maximal
testing is not feasible for many individuals and situations
has led to an interest in the relevance and application
of submaximal measurements. Therefore, exercise
intensity is expressed as a percentage of an individual’ s
measured or predicted maximal oxygen uptake (VÇ O 2
max), maximal heart rate (HRm ax) or heart rate reserve.
Numerous approaches have been used to assess the
relevance of, and relationship between, various sub-
maximal markers of exercise intensity. These have
included heart rate monitoring, measurement of blood
lactate and pH, metabolic equivalent (MET) values,
respiratory exchange ratio and rating of perceived
exertion. Of particular relevance to exercise prescription
for the wider population is the use of non-invasive
submaximal markers of exercise intensity. Given the
potential health risks associated with over-exertion by
sedentary individuals, monitoring heart rate is arguably
the best safeguard against cardiac complications and
other injuries.
After wider assessments have been undertaken,
heart rate monitoring can also assist individuals to
work at an intensity that enables progressive overload,
maximize the use of a chosen energy system, or enhance
body composition. A further advantage is the ability
to provide motivational support via instantaneous
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S72 Hills et al.
feedback throughout an activity session. The focus of
this paper is to consider the relative merits of current
guidelines for the prescription of exercise intensity,
speci® cally by considering the usefulness of generalized
prediction equations. A further aim is to assess the
potential use of selected submaximal markers of inten-
sity to improve exercise prescription.
Exercise intensity: An overview
The speci® c goals for exercise prescription vary with
an individual’ s interests, needs, background and health
status. In all cases, prescription involves the designation
of a mode, frequency, duration and intensity of physical
activity (ACSM, 1995). Of these parameters, by far
the most diY cult to prescribe is intensity (Hills and
Byrne, 1998). The diY culty arises when one attempts
to determine an appropriate starting exercise intensity,
and the monitoring and adjustment of prescription
remains a challenge over time (Pollock et al., 1995). The
perennial question is: At what intensity should one work
to gain the desired bene® t, be this a speci® c component
of physiological or metabolic ® tness? Strategies have
included direct measurement of maximal values, a
prediction of maximal values from a submaximal test, or
the estimation of exercise intensity from generalized
prediction equations (Hills and Byrne, 1998). Although
a direct determination of a maximal value, for example
heart rate, may be the most accuratemethod, the use of
this approach is somewhat controversial. Similarly,
predictions from generalized equations may be grossly
inadequate. It is diY cult to ensure that particular
groups (for example, of a given age, sex, ® tness level
and disability), let alone individuals, receive the neces-
sary exercise advice in safe and scienti® c terms.
The ACSM, and more recently other health agencies,
have provided exercise prescription guidelines for
adults. Table 1, adapted from Pollock et al. (1995),
outlines the historical trends in prescription with refer-
ence to cardiorespiratory and musculoskeletal ® tness.
These general recommendations have widespread
acceptance but their lack of precision has been refer-
enced (Pollock et al., 1995; Hills and Byrne, 1998).
There are no speci® c exercise intensity guidelines for
the elderly, the overweight and obese, and other special
groups. The authors contend that the lack of speci® city
in relation to exercise intensity guidelines is a problem
that is not limited to particular populations, but holds
true for all individuals.
Beyond the generalized equations for exercise inten-
sity, there is a paucity of speci® c, scienti® cally based
guidelines. What options are available? What are the
advantages and disadvantages of the prescription
models that are in use? How can these generalized equa-
tions be improved to provide a more useful level or
threshold of intensity, rather than broad heart rate zones
to re¯ ect intensity?
Table 1 Standards, guidelines and position statements regarding physical activity for adults
Frequency Intensity Duration Mode Weight training
1978 ACSM position
statement
3± 5 days week - 1 60± 90% HRm ax ,
50± 85% VÇ O 2 max
or HR reserve
15± 60 min
continuous
Traditional aerobic
activities
Not stressed
1990 ACSM position
stand
3± 5 days week - 1 60± 90% HRm ax ,
50± 85% VÇ O 2 max
or HR reserve
20± 60 min
continuous
Aerobic activities 1 set 8± 12 reps,
major muscle
groups, 2 days
week - 1
1995 ACSM
guidelines
3± 5 days week - 1 50/60± 90% HRm ax ,
40/50± 85% VÇ O 2 max
or HR reserve
20± 60 min
continuous
Aerobic activities
(expanded)
1 set 8± 12 reps,
major muscle
groups, 2 days
week - 1
1995 AHA exercise
standardsa
Minimum
3 days week - 1
50± 60% VÇ O 2 max
or HR reserve
Minimum
30 min
Health promotion
activities
1 set 10± 15 reps,
8± 10 exercises,
2± 3 days week - 1
1995 CDC/ACSM
public health
statementb
Daily Moderate Accumulate
30 min day - 1
Health promotion
activities
Addressed, not
speci® c
Note: ACSM = American College of Sports Medicine; AHA = American Heart Association; CDC = Centres for Disease Control and Prevention;
VÇ O2 max = maximal oxygen consumption; HRm ax = maximum heart rate; reps = repetitions. 
a See Fletcher et al. (1995); 
b see Pate (1995) .
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Submaximal markers of exercise intensity S73
The generalized nature of prescription of
exercise intensity
The widespread use of generalized prediction equations
has inherent dangers, particularly for certain sub-
populations, such as obese and hypertensive individuals,
as well as those with cardiovascular disease. The de-
lineation of a threshold of intensity in exercise prescrip-
tion could help to minimize cardiac, musculoskeletal
and psychological complications. More importantly,
there are also numerous limitations associated with the
routine use of an exercise test that requires an untrained
individual to exercise to exhaustion to ascertain a
questionable broad window of intensity at which one is
encouraged to exercise. The alternative option, the use
of equations to predict maximum performance capacity,
has consequently gained widespread acceptance, there-
by avoiding tests to maximum. Equations to determine
maximum heart rate, heart rate reserve or maximal
oxygen uptake have been devised, but variation may
exist in the heart rate response of individuals of diþ ering
levels of body fatness or cardiovascular function.
Research concerning the appropriateness of prediction
equations derived on normal-weight populations for
other populations has been limited. A selection of
equations used to predict one variable, maximum heart
rate, is presented in Table 2.
Maximal versus submaximal exercise
testing to determine exercise intensity
Maximal testing is required to measure maximal values,
but submaximal tests can be used to estimate these
values. There are two schools of thought regarding the
relative merits of maximal testing to determine exercise
capabilities. Some believe that all individuals should
be tested to maximum; however, others suggest that
the risks of testing outweigh the bene® ts. Further-
more, maximal testing may be contraindicated, as it
Table 2 A selection of equations used to predict HRm ax
(beats min - 1)
Population Equation
All
Males
Females
Male athletes
Female athletes
Sedentary males
Sedentary females
Obese
220 - age
220 - age
226 - age
205 - (0.5 ´ age)
211 - (0.5 ´ age)
214 - (0.8 ´ age)
209 - (0.7 ´ age)
200 - (0.5 ´ age)
is impossible for some sedentary individuals to com-
plete and unsafe for others. Brooks et al. (1996) have
suggested that maximal tests indicate peak as opposed
to maximal values.
Submaximal tests have been devised and modi® ed
over a number of years to cater for such concerns,
as well as to avoid the high cost of equipment and the
time-consuming nature of maximal testing (Porcari
et al., 1989). The primary aim of submaximal cardio-
respiratory tests is to determine the relationship
between an individual’ s heart rate response and oxygen
consumption during progressive exercise, then to use
the relationship to predict VÇ O 2 max. Heart rate
responses to submaximal exercise are based on the
incorrect assumption that maximum heart rate is rela-
tively constant among individuals of a particular age.
Therefore, the prediction of aerobic power calculated in
this manner may be subject to considerable error.
Submaximal physical ® tness tests are a low risk
when accompanied by appropriate pre-test screening,
such as the Physical Activity Readiness Questionnaire.
Tests can also be administered safely by quali ® ed
personnel in non-medical settings. The ACSM
(1995) stated that, `virtually all sedentary individuals
can begin a moderate exercise program safely . . .’ .
Although this statement may be true, it may have more
relevance if all individuals knew what was meant by
`moderate’ intensity, and if this was placed in the con-
text of an individualized exercise intensity or heart rate
threshold. Blair et al. (1992) have cited a number of
shortcomings in our knowledge of `how much physical
activity is good for health’ . These include the e þ ects of
activity on some individual health conditions and the
precise dose of activity required for speci® c bene® ts. Of
particular relevance to the discussion in the present
paper is another of the key issues raised by the same
group: the role, if any, of intensity of e þ ort.
One prescription is not appropriate for everyone. The
ability to individualize prescription, and to account
for progressive overload based on dose ± response, is
of particular relevance. Current public health recom-
mendations for un® t and sedentary adults emphasize
the importance of accumulating 30 min of moderate
physical activity on most days of the week to gain signi® -
cant health bene® ts. If this prescription was followed by
such individuals, the volume and intensity of e þ ort may
not be enough to enable improvements. A more speci® c
approach may ensure commitment, adherence, safety
and satisfaction in the activity completed rather than
distaste of physical activity. A generic prescription
should not be seen as a panacea for physical activity
participation, but as an icebreaker beyond which a
submaximal markersuch as heart rate is routinely used
to guide adjustments in exercise intensity. Perhaps
30 min of physical activity per day should be a goal,
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S74 Hills et al.
but over and above the non-negotiable work and house-
hold duties of a normal day. The opportunity to con-
sider a `quick-® x’ in terms of physical activity is not
enough. To use an analogy, we already have fast food,
we certainly do not need fast exercise!
Submaximal markers
Exercise intensity is best quanti® ed using physiological
variables such as oxygen consumption, heart rate, rating
of perceived exertion or METs. Oxygen consumption
is the most commonly used parameter to measure
cardiorespiratory function, including one’ s response
to exercise, but it is not as practical as heart rate moni-
toring, because exercise prescription on the basis of
oxygen uptake requires knowledge of an individual’ s
VÇ O 2 max.
If the direct determination of, for example, maximal
oxygen consumption is considered unwarranted or
impractical for many individuals, and attempts to
Table 3 Commonly used markers of exercise intensity to
develop cardiorespiratory ® tness in adults (ACSM, 1995)
Physiological marker Training zone
Maximum heart rate
Heart rate reserve (HRR)
Maximal oxygen uptake
Rating of perceived exertion
Metabolic equivalent
60± 90% HRm ax
50± 85% HRR
50± 85% VÇ O2 max
12± 15 on RPE scale
(`somewhat hard’ to `hard’ )
60± 70% MET maximum
predict the maximum value and then infer exercise pre-
scription landmarks are similarly lacking, what options
are available? These issues are a challenge to the ® eld.
An important goal in exercise prescription should be to
explore submaximal markers of exercise intensity to
improve prescription beyond the generalized equations
for training heart rate zones currently in use. Table 3
provides a summary of markers commonly used to
establish training zones (to develop cardiorespiratory
® tness).
The landmark study of Karvonen et al. (1957)
® rst identi® ed a minimal level of exercise intensity or
threshold above which a training e þ ect was possible.
Karvonen et al. proposed a threshold of 60% of heart
rate reserve. Whereas this threshold may be appropriate
for young individuals of average ® tness, de Vries (1971)
found considerable diþ erences with respect to ® tness
and age. For example, de Vries suggested that the
threshold for men in their 60s and 70s was 40% of heart
rate reserve. The heart rate ranges for percentages of
HRm ax and heart rate reserve at various ages are
depicted in Fig. 1. Assuming the limits of these per-
centages are based on reasonable assumptions, the
range is such that it is only necessary to work within a
lower and upper margin. Where in the range does one
work? It would be extremely useful to be able to advise
individuals of a heart rate threshold rather than the
general advice of exercising higher in one’ s range as
® tness improves.
Heart rate
With modern technology, heart rate is the easiest of
the physiological variables to measure in the ® eld, and
VÇ O 2 and heart rate are closely related and linear over
Figure 1 Heart rate zones as de® ned by 50± 85% heart rate reserve (HRR) and 60± 90% HRm a x.
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Submaximal markers of exercise intensity S75
much of the range. There is general agreement
that changes in heart rate are more consistent during
exercise than at rest, and that there is greater linearity
in the relationship between heart rate and VÇ O 2 when
resting values are not included.
Prescription of exercise intensity based on prediction
equations of heart rate (%HRm ax and heart rate reserve)
are subject to a wide range of variability. For example,
the variation in the most commonly used prediction
equation (220 - age) has been calculated as ±10± 12
beats min - 1 in normal subjects (ACSM, 1995).
Of particular concern is the wide range provided
when one follows the current guidelines. For example,
the appropriate range of relative exercise intensity for
aerobic conditioning is identi® ed as between 60 and
90% of HRm ax (ACSM, 1995). Furthermore, it is sug-
gested that poorly conditioned subjects may respond
to a signi® cantly lower intensity of exercise than this.
Exercise prescription guidelines for fat loss, rather than
aerobic conditioning, are even more general. `Low-
intensity, long-duration’ exercise is recommended, and
emphasis is placed on the expenditure of 1260± 2100 kJ
per session. Total energy expenditure (regardless of
intensity) is of primary importance for fat loss, at least
in the short term (Ballor et al., 1990). But the intensity
required to maximize both energy expenditure and
appropriate metabolic adaptations, which is still able to
be tolerated by the relatively untrained individual,
has not been determined.
Although the relative intensity of exercise can be
addressed through prescription based on a percentage
of maximal values, these values may not be the most
appropriate measure of endurance capacity, as they are
not measures of a level of intensity able to be maintained
for prolonged periods of time. It may be more appro-
priate to measure a submaximal marker of intensity
that is related to the level of exercise and may re¯ ect
more closely the ability to sustain a given work rate,
as well as any changes in exercise capacity.
Lactate threshold
The lactate threshold is the work rate at which blood
lactate ® rst begins to increase above resting levels.
This is in contrast to the anaerobic threshold, which
represents the point beyond which there is a rapid rise
in blood lactate above about 4 mmol l - 1. The lactate
threshold has also been referred to as the `aerobic
threshold’ and is used to determine appropriate training
intensities (Belman and Gaesser, 1990; Aellen et al.,
1993; Casaburi et al., 1995). The lactate threshold is
highly correlated with endurance performance, and is
independent of VÇ O 2 max and gender (Tanaka et al.,
1990; Evans et al., 1995) and is a trainable physiological
variable in both untrained and highly trained individuals
(Belman and Gaesser, 1990; Casaburi et al., 1995;
Weltman, 1995). Therefore, the lactate threshold is able
to provide a submaximal marker of exercise intensity
that is relative to the training status of the individual.
The lactate threshold may also represent a level of
maximal sustainable energy expenditure at a tolerable
workload.
An equation to predict the heart rate that corresponds
to the lactate threshold may be a practical format to
increase the accuracy of submaximal exercise prescrip-
tion. The eþ ects of various conditions on the heart
rate± lactate threshold relationship are not well under-
stood. Therefore, it is unclear whether a single equation
would be possible, thereby avoiding the need for
population-speci® c equations.
Rating of perceived exertion
Perceived exertion may be described as a psycho-
physical measure of an individual’ s perception of
exercise intensity, commonly expressed in scales such
as the Borg 6± 20 (Noble et al., 1983; Borg, 1985). The
rating of perceived exertion (RPE) is a useful sub-
maximal indicator of intensity because of its relative
simplicity. The measure does not require sophisticated
equipment, as is the case for physiological variables.
Additionally, the RPE response to graded exercise
correlates highly with cardiorespiratory and metabolic
variables such as VÇ O 2, heart rate, ventilation and blood
lactate concentration (ACSM, 1995). As perceived
exertion and heart rate increase as exercise intensity
rises, the measurement of one parameter enables the
prediction of the other. Some of the shortcomings of
the procedure include the subjectivity of the measureand the need for a true relationship between the RPE
and heart rate to be known for an individual before
RPE can be used accurately. However, once this
relationship is understood, individuals may then rely
less on heart rate and more on RPE (ACSM, 1995).
In this respect, Williams and Eston (1989) have
described RPE as a more accurate indicator of intensity
than any other single psychological or physiological
factor.
In summary, it is reasonable to suggest that the
development of predictive equations to de® ne an
individual’ s threshold for exercise intensity based on
age and heart rate, plus ratings of perceived exertion
and blood lactate at given work rates, has considerable
merit. The derivation of such an equation(s) would
take the guesswork out of exercise prescription and
enable accurate, safe and eY cient levels of intensity at
which one could exercise. This would eliminate the
need for extensive testing protocols that are unrealistic
for most individuals and potentially dangerous for
some.
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S76 Hills et al.
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