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1 Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1; and 2 Universitäts-Kinderklinik, 97080 Würzburg, Germany
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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We tested
the hypothesis that kinetics of O2 uptake
(
O2) measured in the
transition to exercise near or above peak
O2 (O2 peak) would be
slower than those for subventilatory threshold exercise. Eight healthy
young men exercised at ~57, ~96, and ~125% O2 peak. Data were
fit by a two- or three-component exponential model and with a
semilogarithmic transformation that tested the difference between
required O2 and measured
O2. With the
exponential model, phase 2 kinetics appeared to be faster at
125% O2 peak [time constant (
2) = 16.3 ± 8.8 (SE) s]
than at 57%
O2 peak (2 = 29.4 ± 4.0 s) but were not different from
that at 96%
O2 peak exercise (2 = 22.1 ± 2.1 s).
O2 at the completion of
phase 2 was 77 and 80%
O2 peak in tests
predicted to require 96 and 125%
O2 peak. When
O2 kinetics were calculated
with the semilogarithmic model, the estimated 2 at 96%
O2 peak (49.7 ± 5.1 s) and 125% O2 peak (40.2 ± 5.1 s) were slower than with the exponential model. These results are
consistent with our hypothesis and with a model in which the
cardiovascular system is compromised during very heavy exercise.
maximal oxygen uptake; ventilatory threshold; breath-by-breath; anaerobic metabolism; cardiovascular control; feedback; mathematical modeling
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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AT THE ONSET of light- to moderate-intensity submaximal
exercise (i.e., below the ventilatory threshold), O2 uptake
(O2) measured at the mouth
increases as a two-phase response. Phase 1, lasting ~15 s, is a
function of increased return of venous blood, most of which was pooled
in the periphery before exercise (11). Because phase 1 is mainly a
consequence of increased venous return, it is often called the
cardiodynamic phase. Phase 2, the primary phase, of the adaptive
process reflects the change in muscle oxidative metabolism as venous
return continues to increase and more O2 is extracted with
exercise. Phase 2 of the submaximal exercise response has been modeled
with an exponential function (8, 25, 40).
As the intensity of exercise increases above the ventilatory
threshold but remains below peak
O2
(O2 peak),
O2 adapts after a delay with
an additional phase (i.e., phase 3) during constant-load exercise (8,
9, 14). Phase 3 has been described as an additional phase in which the
O2 adds on top of the
metabolic requirement of that work rate (31). The results of some
experiments indicated no difference in phase 2 kinetics of
O2 at these heavier work
rates between ventilatory threshold and
O2 peak compared with
the responses during light-intensity exercise (7, 9, 31). In contrast,
other data, some obtained by the same research groups who found no
difference, were consistent with slower
O2 kinetics during
phase 2 for the heavier work rates below
O2 peak (10, 14, 15,
27, 28, 41). These different observations raise questions about the
mechanisms responsible for establishing the adaptation of oxidative
metabolism at the onset of heavy submaximal exercise.
For work rates near or above
O2 peak, there has been
little research of the kinetics of
O2 (9, 20, 29).
Margaria et al. (29) suggested that the rate of increase in
O2 should be
proportional to the difference between required
O2 and actual O2 for work rates above
O2 peak. They
concluded that there was no difference between the time constant ()
across a range of work rates that caused exhaustion in 30-120 s.
Furthermore, they suggested that this was not different from the measured during submaximal exercise. However, a technical limitation is apparent in their study, inasmuch as they show measured
O2 values during heavy
exercise that exceeded the reported values of
O2 peak in their
subjects. Hebestreit et al. (20) observed faster kinetics for phase 2 at 100-130%
O2 peak in boys and men
but noted that the curve-fitting procedure might have caused an
apparent speeding of O2
kinetics at the higher work rate. Therefore, new data are required to
resolve this question.
The precise regulation of the cardiovascular system to match
O2 transport to O2 utilization is achieved by a
control system that integrates various feedforward and feedback signals
to achieve a coordinated distribution of blood flow between exercising
and nonexercising tissues. With a transition between two levels of energy expenditure, error signals are established in proportion to the
difference between the present and the required values of
O2 transport and muscle
O2 (21, 23, 41). The
physiological responses to these error signals differ between
individuals with varying levels of physical fitness (19) as well as
between exercise intensities, as considered above. It is desirable to
have a rapid adaptation to increased metabolic demand, inasmuch as this
will minimize accumulation of an O2 deficit and cause less
production of lactic acid. An understanding of the responses during
transition states can provide important information about the
mechanisms that limit the adaptive processes.
The ability of the cardiovascular system to achieve adequate
O2 transport throughout the early phase of very heavy
submaximal work rates has been questioned (15, 27, 28). This would lead
one to suspect that, during the high demand of work rates above that
required to achieve
O2 peak, O2
transport might also be limiting. Therefore, in contrast to the
conclusion of Margaria et al. (29), we hypothesized that
O2 kinetics would be slowed for work rates near or above
O2 peak compared with
subventilatory threshold work rates. The purpose of this study was to
examine the O2 during
exercise at ~96 and 125%
O2 peak and to
compare the results with those from exercise at 57%
O2 peak. To
achieve the best mathematical description of the kinetics response for O2, it was necessary to
incorporate some modifications of the exponential fitting based on
estimates of the predicted metabolic requirements
(O2 pred) of the exercise
load. Thus we present the justification for this approach and provide
an indication of potential error in estimation of kinetic parameters.
These results are considered with respect to a model of dynamic
cardiovascular control.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Eight healthy young men (20.8 ± 0.7 yr) participated in the study.
After receiving complete written and verbal details of the experimental
protocol, the subjects signed a consent form approved by the Office of
Human Research of the University of Waterloo. Ventilatory threshold,
measured as the point of increase in ventilatory equivalent for
O2
(E/O2)
with no change in ventilatory equivalent for CO2 output
(E/CO2)
(22), occurred at 2,480 ± 135 ml/min or ~62%
O2 peak.
Initial testing.
After the subjects were familiarized with the laboratory setting, each
completed two incremental exercise tests to exhaustion on an
electrically braked cycle ergometer (Lode Excalibur, Groningen, The
Netherlands). Subjects were allowed to select a comfortable pedal
frequency near 80 cycles/min. In the first, after a 5-min period at 30 W, the work rate increased at a rate of 30 W every 3 min (Fig.
1). The purpose of this test was to
establish the O2 at each
submaximal work rate. The second test also began with 5 min at
30 W. After this, the work rate increased by 30 W every 1 min. The
O2 peak was defined
as the highest O2 over a
15-s period during the first or second test. The rationale for two
separate tests was that the total duration of the first test was
sufficiently long that most subjects achieved lower
O2 peak in these
tests because of cumulative fatigue. To determine the O2-work rate
relationship, O2 values
below ventilatory threshold in the slow-incrementing test were examined
for a linear relationship to work rate. Additional values were
considered only if the correlation coefficient of the linear regression
did not decrease. This provided a conservative estimate of the lowest
value of the slope of O2 and work rate (Fig. 1). On the basis of this linear regression, the
work rate and the metabolic cost were predicted for exercise approximating 57, 96, and 125% of the individual subject's
O2 peak.
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Constant work rate tests.
In subsequent testing, each subject completed four repetitions of the
work rate at 57%
O2 peak and
two repetitions of each of the 96 and 125% work rates. On a given test
day, the subjects completed the 57% test first and then rested for 10 min before the 96 or 125% test. There is no evidence that the light
work rate tests might alter
O2 kinetics in the two
heavier-exercise tests (15). Exercise began with 4-5 min of
cycling at a baseline work rate of 30 W. The work rate then increased
as a step function to the required level for an additional 5 min for
the 57% tests and as long as the subjects could maintain it for the 96 and 125% tests. No warning was given at the time the work rate was
increased. Subjects were required to maintain a pedal frequency of
~80 cycles/min. Breath-by-breath data for
O2 and the mean heart rate
over each breath were collected continuously throughout the tests.
Measurement of O2.
Breath-by-breath O2
was measured on a computerized system (First Breath, St. Agatha, ON,
Canada) that sampled inspired and expired gas volumes with an
ultrasonic flowmeter (Kou Consulting, Redmond, WA) and fractional
concentrations of O2, CO2, and N2 by mass spectrometry (model MGA-1100A, Marquette, Milwaukee, WI). The
flowmeter was placed in direct line with the mouthpiece, and air was
continuously sampled near the mouth. The total dead space of this
configuration was ~100 ml.
O2 was calculated with an algorithm that allowed for breath-by-breath changes in lung gas stores
and with computation of the effective lung volume (24). Calibration of
the flow/volume sensor was achieved immediately before each test by
manually pumping a 3-liter syringe through the flowmeter at a rate
similar to that achieved during the exercise test. The mass
spectrometer was calibrated with two precision gas tanks that spanned
the ranges of gas concentrations encountered during the tests.
Corrections were made for inspired and expired water vapor and
temperature to yield breath-by-breath values expressed in milliliters
per minute STPD. Heart rate was measured with an electrocardiograph (model 7803A, Hewlett-Packard) with a standard bipolar electrode placement.
Data analysis.
The individual test data were linearly interpolated between points to
give values at 1-s intervals (Fig. 2). The
multiple repetitions at each work rate for an individual subject were
averaged and then processed by two different means. The first was a
nonlinear curve-fitting procedure described previously for application
to submaximal exercise intensities (25). The second incorporated a
semilogarithmic transformation of the data and is similar to that used
previously (21, 29, 41). The outcome of this analysis would be almost
identical to that obtained by nonlinear curve fitting, with the
amplitude of the second term fixed at the predicted value.
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O2 response at
57% O2 peak was fit
adequately by the two-component exponential model. For the 96 and the
125% tests, a three-component model was required for all fitting. For
each model, we forced the first component to fit with a that
ensured that its contribution to fitting was complete before the second
component started. This is consistent with the modeling of Barstow et
al. (8) for heavy exercise. The second component is thought to reflect
muscle metabolism (6, 17). It was this second component that was the
most critical for the present study. As indicated, the time course of
this component was evaluated without any influence from the first
component. Furthermore, consideration of the second- component kinetics
ended when a noticeable third component started. Thus the model for the
fitting of the 57% work rates consisted of a baseline (G0) and two amplitude terms (G1 and G2), two (1 and 2), and two time delays
(TD1 and TD2). The model for fitting the 96 and
125% work rates differed by the addition of a third amplitude
(G3), time constant (3), and time delay
(TD3) (28)
![<A><AC>V</AC><AC>˙</AC></A><SC>o</SC><SUB>2</SUB>(<IT>t</IT>) = G<SUB>0</SUB> + G<SUB>1</SUB>[1 − <IT>e</IT><SUP>−(<IT>t</IT>−TD<SUB>1</SUB>)/&tgr;<SUB>1</SUB></SUP>] ⋅ <IT>u</IT><SUB>1</SUB> + G<SUB>2</SUB>[1 − <IT>e</IT><SUP>−(<IT>t</IT>−TD<SUB>2</SUB>)/&tgr;<SUB>2</SUB></SUP>] · <IT>u</IT><SUB>2</SUB>](/content/vol88/issue5/fulltext/1812/img001.gif)
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![<A><AC>V</AC><AC>˙</AC></A><SC>o</SC><SUB>2</SUB>(<IT>t</IT>) = G<SUB>0</SUB> + G<SUB>1</SUB>[1 − <IT>e</IT><SUP>−(<IT>t</IT>−TD<SUB>1</SUB>)/&tgr;<SUB>1</SUB></SUP>] ⋅ <IT>u</IT><SUB>1</SUB> + G<SUB>2</SUB>[1 − <IT>e</IT><SUP>−(<IT>t</IT>−TD<SUB>2</SUB>)/&tgr;<SUB>2</SUB></SUP>]](/content/vol88/issue5/fulltext/1812/img002.gif)
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![⋅ <IT>u</IT><SUB>2</SUB> + G<SUB>3</SUB>[1 − <IT>e</IT><SUP>−(<IT>t</IT>−TD<SUB>3</SUB>)/&tgr;<SUB>3</SUB></SUP>] ⋅ <IT>u</IT><SUB>3</SUB>](/content/vol88/issue5/fulltext/1812/img003.gif)
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TD1,
u2 = 0 for t < TD2 and
u2 = 1 for t TD2, and
u3 = 0 for t < TD3 and
u3 = 1 for t TD3.
The amplitude of O2 at the
end of the second component was determined from G0 + G1 + G2.
A semilogarithmic transformation of data was used by Henry (21) for
submaximal exercise, by Margaria et al. (29) for maximal and
supramaximal exercise, and by Whipp and Wasserman (41) across a range
of submaximal work rates. In the case of the simple exponential that
reaches a plateau equivalent to the required
O2, this model will yield
approximately the same time constant (2) as that
obtained by the nonlinear curve-fitting described above (Fig.
3). For this reason, we did not use this
approach to fit the 57% work rate tests. On the other hand, the
importance of the apparent plateau O2 (i.e., G0 + G1 + G2) compared with required
O2 becomes a major issue in
the fitting of the O2
response when the ability to attain the required level is restricted by
the upper limit of the O2 transport-utilization
system (i.e.,
O2 peak).
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O2 peak, we
had to assume the required value on the basis of the predicted work
rate. Thus we determined
![log&Dgr;<A><AC>V</AC><AC>˙</AC></A><SC>o</SC><SUB>2</SUB>(<IT>t</IT>) = log<SUB>10</SUB>[<A><AC>V</AC><AC>˙</AC></A><SC>o</SC><SUB>2pred</SUB> − <A><AC>V</AC><AC>˙</AC></A><SC>o</SC><SUB>2</SUB>(<IT>t</IT>)]](/content/vol88/issue5/fulltext/1812/img004.gif)
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O2 pred is the
O2 obtained from the linear
regression analysis of the incremental exercise test (Fig. 1) and
O2(t) is the
measured O2 at each time point.
The data obtained from this relationship were plotted to reveal the
semilogarithmic response (Fig. 3). Next, we incorporated information
from the nonlinear curve fitting to determine the range over which we
could apply a linear regression to determine log(2).
That is, the data were fit initially only between TD2 and
TD3 so that the same portion of the data set contributed to the analysis. This was refined as required for any obvious deviation from the linear response. From the slope of the linear regression applied to these data, was calculated as follows:
log(2) = log10(2)/(slope * 0.693).
Because of the nonlinear distribution of logarithmically transformed
data, there will be a slight bias to faster estimates of .
The semilogarithmic model is sensitive to error in the value of
O2 pred. To obtain an
estimate of the effect of error on the calculated log(2)
value, we allowed for a range of ±5% about o2 pred. Thus we
calculated log(2) as if the predicted metabolic requirement was 91 or 101%
O2 peak to see the
range around the 96% work rate. Likewise, around the 125%
tests, we calculated as if the requirement was 120 or 130%
O2 peak.
Statistics.
Because we used different methods to analyze the data for the 57% vs.
the 96 and 125% tests, we had to divide the statistical analysis.
First, we focused on the 2 value from the nonlinear curve fitting by completing a one-way repeated-measures ANOVA on the
main effect of work rate. Next, we compared the 2 value from nonlinear curve fitting with the log(2) value from
the semilogarithmic transformation for the 96 and 125% tests only by
two-way repeated-measures ANOVA with main effects of fitting procedure
and work rate. Significant differences were accepted for P < 0.05. Values are means ± SE.
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Incremental exercise tests.
The peak work rate attained in the slowly incrementing exercise tests
was 296 ± 14 W. At this work rate, the peak
O2 was 3,857 ± 154 ml/min
and the peak heart rate was 189 ± 4 beats/min. During the more
rapidly incrementing exercise tests, peak values were work rate 353 ± 14 W, O2 3,963 ± 167 ml/min, and heart rate 187 ± 4 beats/min.
57% O2 peak tests.
O2 increased rapidly at the
onset of exercise at 57%
O2 peak (Fig. 2, Table
1). The first component of the exponential model accounted for ~25% of the total response and lasted ~18 s.
The 2 value for the
O2 kinetics response was 29.4 ± 4.1 s.
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96 and 125% O2 peak
tests.
The amplitude of phase 1 of the kinetic response at the two higher
intensities of exercise was slightly greater than that observed at 57%
O2 peak exercise, but
it represented a smaller percentage of the total response (Table 1).
Phase 2 started ~12-14 s after the onset of exercise at these
two higher intensities. The 2 values for the
O2 kinetics responses were
22.1 ± 2.1 and 16.3 ± 3.1 s for the 96 and 125% tests,
respectively. Phase 3 started after ~40-73 s (Table 1), with the
shorter duration for the 125% tests. The 3 was
considerably slower than for the phase 2 response.
2) was more than twice as long as the corresponding
2 values from the exponential model, with no difference
between 96 and 125%.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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In this study, we examined the rate of change in
O2 during the transition from
baseline to light or very heavy exercise. In particular, we focused on
phase 2 of the adaptive response, because this phase has been taken to
reflect oxidative metabolism in the exercising muscles (6, 17) and to
provide indications of the rate-limiting steps (23, 37, 39). It should
be noted that phase 2 during the two higher intensities had an average duration of only 28 s for the 125% test to 59 s for the 96% test. The
magnitude of O2 increase
during phase 2 (see G2 in Table 1) was ~1,700 ml/min,
which amounted to 60% of the increase above baseline cycling. Thus our
analysis concentrated on a specific region of the adaptive response.
When we fit the data with the semilogarithmic transformation, we found
significantly slower kinetics of
O2 at the work rates that
corresponded to ~96 and 125%
O2 peak compared with
those measured at 57%
O2 peak.
These data supported our hypothesis that kinetics would be slower at
these high exercise intensities because of a limitation in
O2 transport. The results further showed the limitation of
nonlinear curve fitting to attempt to describe the kinetics of
O2 at high work rates. With
the curve-fitting approach the "required"
O2 determined from
the amplitude of the asymptote of the second component achieved only
~80% of O2 peak in
contrast to the 96-125%
O2 peak that was
actually required. Our findings contrast with those of Margaria et al.
(29), who reported no change in time course of
O2 as work rate
increased above
O2 peak. As noted in
the introduction, methodological problems might have limited their
study. Our results also provide an explanation for the apparent
acceleration of O2 kinetics when nonlinear curve fitting was applied to data from 100 to 130% O2 peak (20).
Investigation of O2 kinetics
at maximal and supramaximal exercise intensities requires a number of
assumptions, and there are inherent limitations. We had to assume that
we could predict the energy demand of high-intensity exercise on the
basis of a linear extrapolation from submaximal work rates.
Furthermore, it was necessary to assume that the energy demands of
these high-intensity work rates remained constant throughout the period
over which we evaluated the kinetics. Given the potential for error in
the linear extrapolation, we examined the influence of error on the calculated time constant.
Linear extrapolation to establish energy demand.
Several researchers have presented the assumptions and limitations
inherent in attempting to use the energy cost at submaximal exercise
intensities to predict those at higher intensities (3, 4, 30, 38).
Across a range of submaximal work rates up to approximately the
ventilatory threshold, O2
increases as a linear function of work rate (38). The
O2 at these intensities stays
relatively constant with continued, constant-load exercise (33). For
work rates above this level, the
O2 varies as a function of
time while the work rate remains constant so that O2 could equal the predicted
value but is more likely to be higher than predicted as the exercise
time is prolonged. For intensities slightly above ventilatory
threshold, O2 will normally
increase slightly and then remain at this higher
O2 (10, 33, 38, 41). However,
as a given constant-load exercise intensity approaches O2 peak, the
O2 will continue to increase
and often reach O2 peak
at these supposedly submaximal work rates (2, 10, 38). The mechanisms
responsible for the elevated
O2 during high-intensity
exercise will be considered in Constant energy demand of
high-intensity exercise.
O2 peak
cannot be accurately predicted. Here, it is necessary to sum the total
of aerobic plus anaerobic energy contributions (3, 4, 29). A major
complication in the extrapolation of energy requirement during very
heavy exercise is the unknown contribution of slow-twitch compared with
fast-twitch muscle fibers. As work rate increases to levels above
O2 peak, the
fast-twitch fibers are recruited more (16, 18). Although animal
research might indicate a clear difference in metabolic cost to achieve
a given power output between fiber types (26), research with human
subjects suggests that any difference might be small (8). There is,
however, some evidence that the energy cost of cycling is higher in
those individuals with a higher percentage of fast-twitch fibers (12).
Thus, as more fast-twitch fibers are recruited at higher work rates,
the required O2 might
increase out of proportion to that seen at lower work rates. We
considered this potential for error in
O2 pred, as
presented below.
In this study, we have been careful to avoid an over-prediction of
the energy cost of the high-intensity exercise. The linear extrapolation used to define the higher work rates included only the
O2-work rate relationship
from 30 W to ~50%
O2 peak. We did not
include additional values at higher work rates if there was a reduction
in the correlation coefficient of the linear regression or if there was
an increase in the slope of the regression line (Fig. 1). See
Derivation of the model for consideration of the potential
error in the
O2 pred.
Constant energy demand of high-intensity exercise.
We assumed that energy demand would be unchanged from the start to the
end of exercise. This is probably true for the 57% O2 peak tests, inasmuch
as little change in
O2 is observed after 3 min of
exercise. It is unlikely that the total energy demand would remain
constant with time for the 96 and 125%
O2 peak tests, inasmuch
as O2 often increases with
time during heavy constant-load, but
sub-O2 peak, exercise
(2, 9, 10).
O2 at high
submaximal work rates. The source of the extra
O2 is primarily at the
working muscle (32). Elevated temperature, increased circulating
catecholamines, or metabolism of lactate has been postulated to cause
the extra O2 (7, 10). Also,
the free energy of ATP hydrolysis is reduced in heavy exercise (35).
Recently, it has been suggested that recruitment of fast-twitch fibers
adds to the energy cost (5, 8, 38). This hypothesis is based largely on
the observation from animal muscle that the O2 cost of
electrical stimulation is higher in fast- than in slow-twitch muscle
(26). An additional contribution that does not seem to have been
considered is the energy cost of activating fatigued muscle fibers that
produce little or no tension. Electromyography indicates that greater
motor unit recruitment is required at a given work rate as fibers
fatigue (13). The activated but fatigued fibers would still require
energy expenditure to maintain electrolyte homeostasis, even without
effective contractile activity contributing to an increase in metabolic cost.
The key issues for the present research are not whether the
O2 pred changed, but
whether it influenced the basic premise of our model. First, it is
unlikely that
O2 pred changed
enough during the period over which we analyzed
O2 kinetics to influence the
calculated . Second, we do believe that the data should be modeled
as a function of the metabolic demand. These issues are considered in
more detail in Derivation of the model.
Derivation of the model.
The exponential model that we used is similar to that employed in most
other research of O2
kinetics. The inclusion of one, two, or three exponential components is
based on goodness of fit derived from various nonlinear curve-fitting
functions (25, 27, 40). However, each of these models assumes that the
asymptote for each component represents the "true" end point. We
believe this to be the case for the 57%
O2 peak tests. The
difference between the measured
O2 at any time after the
onset of exercise and the required
O2 (i.e., steady-state
O2) during this 57% test
represented the error signal. The magnitude of the error signal was
reduced in an exponential manner, as typical of a linear first-order
system with a (21). By forcing the curve fitting of the phase 1 component to be complete before the start of the phase 2 component, we
avoided any interference with the estimate of the dynamic response of
phase 2. In this sense, the model was identical to that employed by
Barstow et al. (8). Furthermore, by allowing the phase 3 component to
begin at some time after the origin of the phase 2 component
(TD3 = 40-73 s; Table 1), we did not bias the estimate
of 2.
O2 at any time after the
onset of exercise was greater for the 125 or 96% than for the 57%
O2 peak tests (Fig. 1)
has been observed many times (2, 21, 29, 41). Nevertheless, this
pattern of absolute O2 must
not be confused with a description of the kinetics. The exponential must always be derived with respect to the plateau value. In the case
of the two heavy work rates, the apparent plateau value for phase 2 represented only ~80%
O2 peak, well below
O2 pred. Therefore, the
semilogarithmic transform examined kinetics relative to the predicted
requirement. Phase 3 kinetics continued to add to the
O2 response of phase 2 in a
pattern that has been described as the "slow component" (5). At
exhaustion, the O2 measured
in the 96 and 125% tests averaged 101.3 ± 7.4 and 96.1 ± 9.0% of
O2 peak respectively.
That is, the final O2 in
these high-intensity constant-load tests approached O2 peak, as anticipated
(2, 5, 38).
The kinetics of O2, when
assessed relative to the
O2 pred, were
significantly slower for the two heavier work rates than for the 57%
O2 peak work rate. In
an effort to better understand the role of the
o2 pred in modifying the kinetics of
O2 and to gain an
appreciation for potential error in our estimates, we determined the
kinetics for a range of ±5% about the predicted value. For the 96%
tests, an error of 5% in predicting the
O2 pred would cause an
error in log(2) of about ±6 s (Table 2). For the 125%
tests, this ±5% range was associated with an error of only about
±3 s. Within this confidence band, the log(2) is
clearly longer than the 2 associated with the
exponential curve fitting. Given the observations that the
O2-work rate relationship
increases at higher intensities of exercise (8), it is more likely that we have underestimated
O2 pred, and with it
log(2), than overestimated them. That is, calculated
log(2) might be even longer than our estimate. This
further emphasizes the importance of our approach to kinetic analysis
compared with the least-squares curve fitting most often used.
O2 and heart rate in heavy
exercise.
Previous research has not reached a firm conclusion about the effect of
work rate on the time course of
O2 across a range of work
rates. Several early studies suggested that
O2 kinetics were slower as
the work rate increased, with a clear slowing at work rates above
ventilatory threshold (10, 27, 31, 41). Next, there were studies that
suggested that O2 kinetics
were not different when 2 were compared for work rates
above and below ventilatory threshold (7, 9). More recently, several
investigations have indicated that kinetics of phase 2 are indeed
slower with heavy exercise (14, 15, 28). Part of the reason for the different outcomes of this research stems from the different
mathematical modeling approaches used. Early investigations that used a
model in which phases 1, 2, and 3 were mixed showed slowing of the
responses (10, 27, 31, 41). As models were developed that split the
phases and treated each separately, the outcome of research indicated
no difference (7, 9) or slower kinetics (14, 15, 28). Of the few
studies that have actually looked at kinetics during exercise at 100%
O2 peak,
2 was not different from that at lower work rates (9,
29). Indeed, when we applied a model similar to that of Barstow and
Molé (9), we found slightly faster 2 for the 96%
tests and significantly faster 2 at 125% O2 peak. This latter
observation was consistent with previous studies of very-high-intensity
exercise compared with exercise intensity below the ventilatory
threshold (20) and emphasizes the importance of the choice of model.
O2 (Fig. 2) and heart rate
(Fig. 4) response in the constant work rate
tests. An accurate assessment of O2 transport requires a
more complete understanding of total cardiac output, blood flow redistribution, and O2 extraction.
|
Cardiovascular control model. Feedback and feedforward components have been identified in regulation of the cardiovascular adaptation to exercise. The feedforward component is proportional to the activation of the motor cortex and appears to play a role, at least for static exercise, in the regulation of heart rate by vagal withdrawal (34). A feedback regulatory model requires that the controlled variable (muscle blood flow or O2 transport) be altered in proportion to an error signal. Two competing factors, the need for increased muscle blood flow and the necessity to maintain arterial blood pressure, interact to determine the appropriate signals to activate an increase in cardiac output and to evoke a coordinated cardiac and vasoconstrictor response. Neither the error signal nor the specific mechanism has been defined for the regulation of the cardiovascular response to support aerobic metabolism. Neural pathways have been identified that are responsive to mechanical or chemical signals (1). In very heavy exercise, a chemical error signal might be generated by the accumulation of metabolic by-products in proportion to the anaerobic contribution to energy supply in addition to factors, such as extracellular potassium, that are associated with muscle contraction itself. With rapid depletion of phosphocreatine and lactate accumulation, there will be increases in the concentrations of inorganic phosphate and hydrogen ion. These factors, as well as adenosine or other vasoactive metabolites, will act directly on the local vasculature to cause vasodilation, which could cause a drop in arterial blood pressure at the onset of exercise (36) and activate the arterial baroreflex mechanisms in support of increased blood flow.
The rapid responses of heart rate andO2 at the onset of the higher
work rates (96-125%
O2 peak) clearly
indicate that signals are being generated in proportion to the exercise
intensity. However, it is equally clear that the signals that initiate
the cardiovascular responses are not adequate in the first minutes of
the high-intensity exercise to allow
O2 to adapt as rapidly relative to required levels as in the lower-intensity (57%
O2 peak) exercise. This
is obvious from the fact that
O2 appears to be reaching a
plateau at the end of phase 2 that is only
~80% of O2 peak when the
metabolic demand ranged from 96 to 125%
O2 peak. This provides
strong evidence that the cardiovascular response to the onset of heavy
exercise is primarily under feedback regulation and that these signals
require time to develop. The more rapid increase in the absolute values
of heart rate and O2 as the metabolic demand exceeded maximal aerobic power was anticipated because
the cardiovascular system should not "know" a priori that it will
reach its upper bound before it reaches the required level of
O2 transport.
Conclusions.
The present results are consistent with the hypothesis that
O2 kinetics at the onset of
exercise near or above
O2 peak are slower than
observed for submaximal exercise. These data support the notion
that adaptation to exercise at high intensities is limited by
O2 transport (15, 28, 38). The observation that O2 at any time point after
the onset of exercise is higher for higher work rates should not be
confused with faster kinetics. Previous investigations that referenced
the O2 response to
the estimated plateau at the end of phase 2 gave an incorrect estimate of the kinetics of O2
because the asymptote was not determined by the metabolic error signal
but by the limitation of O2 transport. That is, it must be
seen that the cardiovascular and metabolic systems attempted to adapt
on the basis of the larger error signal that occurs with this very
heavy exercise. When O2 was
referenced with respect to the magnitude of
O2 pred, the slower
adaptation [log(2) in this study] indicates
that the muscle O2 cannot adapt at the same proportional rate for very heavy exercise as observed
at lower work rates because of functional limitations of O2
transport. Estimation of
O2 during very
heavy exercise required certain assumptions. As indicated previously,
our assumptions probably tended to underestimate
O2 pred. If the
predicted value had been greater because of changes in muscle
recruitment or some other factor, then the kinetics of
O2 would have been
even slower.
| |
ACKNOWLEDGEMENTS |
|---|
We are grateful to David Northey for excellent technical assistance.
| |
FOOTNOTES |
|---|
This research was supported by the Natural Sciences and Engineering Research Council of Canada. D. D. O'Leary is the recipient of a National Sciences and Engineering Research Council Graduate Scholarship.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Address for reprint requests and other correspondence: R. L. Hughson, Dept. of Kinesiology, University of Waterloo, Waterloo, ON, Canada N2L 3G1 (E-mail: hughson{at}healthy.uwaterloo.ca).
Received 28 April 1999; accepted in final form 10 January 2000.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
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