A Fast-Start Pacing Strategy Speeds Pulmonary Oxygen
Uptake Kinetics and Improves Supramaximal Running
Performance
Tiago Turnes, Amadeo Fe´lix Salvador, Felipe Domingos Lisboˆ a, Rafael Alves de Aguiar,
Roge´rio Santos de Oliveira Cruz, Fabrizio Caputo*
Human Performance Research Group, Center for Health and Sport Science, Santa Catarina State University, Floriano´polis, Brazil
Abstract
The focus of the present study was to investigate the effects of a fast-start pacing strategy on running performance and
pulmonary oxygen uptake (V˙O2) kinetics at the upper boundary of the severe-intensity domain. Eleven active male
participants (28610 years, 7065 kg, 17666 cm, 5764 mL/kg/min) visited the laboratory for a series of tests that were
performed until exhaustion: 1) an incremental test; 2) three laboratory test sessions performed at 95, 100 and 110% of the
maximal aerobic speed; 3) two to four constant speed tests for the determination of the highest constant speed (HS) that
still allowed achieving maximal oxygen uptake; and 4) an exercise based on the HS using a higher initial speed followed by a
subsequent decrease. To predict equalized performance values for the constant pace, the relationship between time and
distance/speed through log-log modelling was used. When a fast-start was utilized, subjects were able to cover a greater
distance in a performance of similar duration in comparison with a constant-pace performance (constant pace:
670 m622%; fast-start: 683 m622%; P = 0.029); subjects also demonstrated a higher exercise tolerance at a similar average
speed when compared with constant-pace performance (constant pace: 114 s630%; fast-start: 125 s626%; P = 0.037).
Moreover, the mean V˙O2 response time was reduced after a fast start (constant pace: 22.2 s628%; fast-start: 19.3 s629%;
P = 0.025). In conclusion, middle-distance running performances with a duration of 2–3 min are improved and V˙O2 response
time is faster when a fast-start is adopted.
Citation: Turnes T, Salvador AF, Lisboˆa FD, de Aguiar RA, Cruz RSdO, et al. (2014) A Fast-Start Pacing Strategy Speeds Pulmonary Oxygen Uptake Kinetics and
Improves Supramaximal Running Performance. PLoS ONE 9(10): e111621. doi:10.1371/journal.pone.0111621
Editor: Maria F. Piacentini, University of Rome, Italy
Received June 5, 2014; Accepted October 6, 2014; Published October 31, 2014
Copyright:  2014 Turnes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper.
Funding: This project was supported by National Council of Scientific and Technological Development (CNPq). Website: (www.cnpq.br). The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: fabrizio.caputo@udesc.br
Introduction
The pattern of speed (s) distribution chosen during an exercise
bout, i.e. pacing strategy, has been shown to have important
implications for both activation and proportional contribution of
oxidative metabolism to energy turnover [1]. The rationale behind
this phenomenon is that the rate of increase in oxygen uptake at
the exercise onset (V˙ O2 kinetics) is proportional to the rate of
phosphocreatine breakdown in active muscles per unit change in
time (i.e. the D[PCr]/Dt ratio) [2]. In this sense, adopting a higher
initial speed during a fast-start pacing strategy (FS) is thought to
increase D[PCr]/Dt ratios. This enhanced aerobic contribution
during the first few seconds of exercise spares an equivalent
amount of the anaerobic capacity that can then be used to
improve exercise performance [3]. Accordingly, the pacing
strategies employed to achieve times within two percent of the
world record time in the 800-m track event in international
athletics competitions demonstrate that a relatively fast-start over
the initial 200 m is the preferred strategy for running performance
[4].
Although non-running studies have indicated that a FS
improves high-intensity exercise performance by increasing the
speed of relatively slower V˙ O2 kinetics [mean response time
(MRT) approximately 40–50 s] [1,3,5], high-intensity running
exercises already possess a fast V˙ O2 response [6–8], which may
not increase to an extent that affects performance. Presently, the
only indirect evidence on this topic comes from Sandals et al. [4],
who demonstrated that middle-distance runners attained a lower
peak V˙ O2 during a constant speed 800-m pace time-to-exhaustion
on a treadmill in comparison with a race simulation involving
acceleration to a faster speed followed by a speed decline (i.e. FS).
Despite the higher V˙ O2 peak indicating a likely higher aerobic
contribution, V˙ O2 kinetics and total O2 consumed was not
measured by Sandals et al. [4]. Consequently, the actual effect of
FS on the overall V˙ O2 response during supramaximal running
performance is still unknown.
A testing protocol was designed to investigate the effects of a FS
on aerobic metabolism and performance based on the highest
constant speed (HS) that still allows achieving maximal oxygen
uptake (V˙ O2max) during treadmill running. This is an important
aspect of this study, since Sandals et al. [4] used running speeds
that were not able to elicit V˙ O2max. Furthermore, HS is also a
physiological index representing the constant running speed at
which V˙ O2max is reached with the fastest V˙ O2 kinetics [9],
PLOS ONE | www.plosone.org
1
October 2014 | Volume 9 | Issue 10 | e111621
adding potential concerns about the effects of a FS on metabolic
control. Theoretically, the HS would be a suitable intensity in the
evaluation of the effects of a FS on middle-distance performance
and physiological responses because the physiological determi-
nants of the HS are probably similar to those responsible for
middle-distance running performance (i.e. an integrative contri-
bution of aerobic and anaerobic energy systems) [10–12]. To
predict equalized performance values for a constant pace that can
be readily compared to those derived from a FS performance, the
relationship between time (t) and distance (d) or t and s through
log-log modelling from a series of time-to-exhaustion tests was
used. For times to exhaustion in the 1–10-min range, the log-log
model has been shown to be appropriate and superior to the
critical-power model [13].
The focus of the present study was: 1) to compare performance
parameters (i.e. distance covered and time-to-exhaustion) using a
FS with those predicted from the log-log modelling for constant
pace performance; and 2) to assess the effect of a FS on the aerobic
contribution during running exercise crossing the upper boundary
of the severe-intensity domain [9,14]. It was hypothesized that a
FS would increase performance during supramaximal treadmill
running exercises by allowing VO2max to be achieved more
rapidly during the bout.
Methods
Subjects
Eleven active male subjects (28610 years, 7065 kg, 17666 cm,
5764 mL/kg/min) volunteered for this study. All participants
were apparently healthy, non-smokers, free from injury, not taking
any medication, and participating in physical activity at least three
times a week. Before commencing the study, all participants were
informed of the proceedings but remained naive to the study
rationale. Subjects were also instructed to avoid strenuous exercise
in the 24-h period preceding a test session and to arrive at the
laboratory in a rested and fully hydrated state. All volunteers gave
written informed consent to participate in this study, which had
been approved by the Santa Catarina State University Research
Ethics Committee. This work was performed in accordance with
the principals of the Declaration of Helsinki.
Experimental design
Subjects visited the laboratory for four phases of experimenta-
tion within a 3-week period, with at least 48-h separating each visit
(Figure 1). All tests were performed at the same time of day (62 h)
on a motorized treadmill (Inbramed Millenium Super ATL, Porto
Alegre, Brazil) set at a 1% gradient. The four phases of the study
comprised: 1) an incremental test in order to determine V˙ O2max,
maximal aerobic speed and the speed associated with lactate
threshold; 2) three laboratory test sessions for the determination of
the relationship between t6d, t6s, and additional values of
V˙ O2max; 3) the determination of the HS from two to four
constant speed tests; and 4) an exercise to exhaustion phase using a
higher initial speed followed by a subsequent decrease in speed
(i.e., FS protocol). During all tests, subjects were blinded to the
time elapsed during exercise and encouraged to continue for as
long as possible until volitional exhaustion.
For phases two, three and four, the tests were preceded by a
warm up consisting of 10 min of continuous running at the speed
of lactate threshold followed by a 5-min rest period. This warm-up
was employed because prior moderate-intensity exercise has been
demonstrated to have no influence on V˙ O2 kinetics during
subsequent severe-intensity running [15]. All the transitions from
rest to running were performed by the participants using the
support rails to suspend their body above the belt while they
developed cadence in their legs. Time-to-exhaustion measure-
ments started when the participant released the support rails and
started running on the treadmill belt.
Throughout each test, respiratory gas exchange was measured
breath-by-breath using an automated open-circuit gas analysis
system (Quark PFTergo, Cosmed Srl, Rome, Italy). Prior to each
test, gas analysers were calibrated using ambient air and gases
containing 16% oxygen and 5% carbon dioxide. The turbine flow
meter used for the determination of minute ventilation was
calibrated with a 3-L calibration syringe (Cosmed Srl, Rome,
Italy). For phases one and two, V˙ O2 was reduced to 15-s average
values and the highest 15-s V˙ O2 value of each test was used to
calculate subject’s V˙ O2max. For the remaining phases, the
achievement (or not) of V˙ O2max was calculated based on the
highest 15-s rolling average [9].
Phase one - Incremental test.
The initial treadmill speed
was set at 8 km/h and was increased by 1 km/h every 3-min until
subject exhaustion. At the end of each stage, a 30-s rest period was
required in order to collect capillary blood samples (25 mL) from
the non-hyperaemic earlobe in order to measure blood lactate
concentration. The speed associated with the lactate threshold was
defined as the speed maintained during the stage prior to which
the first sudden and sustained increase in blood lactate above the
baseline level was observed. The maximal aerobic speed was
calculated according to the method of Kuipers et al. [16] as the
final speed achieved during the test. All subjects fulfilled at least
two of the following three criteria for achieving V˙ O2max during
the incremental test: 1) respiratory exchange ratio greater than 1.1;
2) a blood lactate concentration greater than 8 mmol/L; and 3) a
peak heart rate at least equal to 90% of the age-predicted
maximal.
Phase two - Predictive trials and V˙ O2max.
On separate
days and in a random order, each participant performed three
constant speed tests at 95, 100 and 110% of the maximal aerobic
speed. The time-to-exhaustion was measured to the nearest second
of the subject’s exhaustion. V˙ O2max was then calculated for each
subject by averaging the four V˙ O2max values obtained during the
incremental test and the three predictive trials. The total error in
the measurement of V˙ O2max was also calculated for each subject
from the same data as a coefficient of variation (%) [9].
Phase three - HS determination.
Subjects performed
between two and four constant speed tests to exhaustion in order
to determine the HS. To ensure whether subjects had (or had not)
attained V˙ O2max during these tests, the following criterion was
adopted: the maximal V˙ O2 value (calculated as the highest 15-s
rolling average) reached in each test should be within the total
error of measurement obtained for each subject during V˙ O2max
determination [9]. In the first test, speed was calculated to result in
exercise exhaustion within 120 s (as described below). If V˙ O2max
was attained, further subsequent tests at a 5% higher speed were
performed on separate days until V˙ O2max could not be reached.
Conversely, if during the first constant speed test V˙ O2max was not
reached, further tests were conducted with reduced speeds (5%)
until V˙ O2max had been elicited.
Phase four – Fast-start strategy protocol.
Finally, subjects
performed a FS protocol, in which the initial speed was set 10%
above the HS and then decreased progressively throughout the test
until reaching 90% of the HS at an exercise duration and distance
matched to those performed at the HS (Figure 1). The speed of the
treadmill was then maintained at 90% of the HS until voluntary
exhaustion of the subject.
Fast Start and Running Performance
PLOS ONE | www.plosone.org
2
October 2014 | Volume 9 | Issue 10 | e111621
Data analysis procedures
Log-log modeling.
Predicting the intensity that would be
expected to lead to exhaustion in 120 s was performed by fitting
the predictive trials (t and s) with a least-squares straight line to the
natural logarithms (log-log predictions) for each subject. The
performance parameters for constant-pace running were also
derived by log-log predictions (t vs. d or t vs. s), but using both the
predictive trials and the HS. Log-log modelling has demonstrated
good reliability in predicting time-trial performance over race-
specific distances and seems to be a better predictor in comparison
with the critical-power model [13,17]. Each runner’s times for the
standard competition distance of 800-m were also predicted using
both strategies. 800-m performance using a FS was predicted by
calculating the amount of the intercept used in the extra-time,
assuming that the FS does not change the slope of the relationship
between t and d. Measures of goodness of fit for each set of four
runs were the adjusted correlation coefficient (square root of the
R2 adjusted for degrees of freedom) and the standard error of the
estimate (SEE).
V˙ O2 responses.
To avoid being influenced by the amount of
data used in the comparison between the FS and HS, all of the
following calculations, except maximal accumulated O2 deficit
(MAOD), were analysed to individually fix the time window to the
shortest time to exhaustion recorded for each subject (i.e. iso-time).
Occasional errant breath values were removed from the data set
if they fell more than three standard deviations outside the local
mean (i.e. five-point rolling mean), and the integral area under the
V˙ O2 curve representing the total amount of O2 consumed during
exercise was calculated (OriginPro 8, OriginLab, Massachusetts,
USA). Thereafter, to characterize the V˙ O2 kinetics during the HS
and FS, we calculated the MRT for V˙ O2 by fitting a mono-
exponential curve to the raw data from the onset of exercise using
iterative nonlinear regression procedures:
VO2 tð Þ~VO2 p
ð ÞzA(1{e{(t=t))
where V˙ O2(t) is V˙ O2 at time t, V˙ O2(p) is the pre-test V˙ O2; A is the
asymptote of the increase in V˙ O2 above the pre-test value and t is
the time constant (equivalent to the MRT in this model). For the
measurement of V˙ O2(p), the participant remained standing on the
treadmill belt for 5 min prior to the test and the V˙ O2 of the last
two minutes were averaged. With only one transition performed in
each condition, more complex models were not considered
suitable [3]. In addition, because the two protocols resulted in
the rapid attainment of the V˙ O2max, a single exponential function
starting at the onset of exercise was considered the most
appropriate approach for characterizing the overall MRT [18].
The energy cost of running (i.e. the accumulated O2 demand)
was set in this study as 0.192 mL O2 per kg of body mass per
meter by using the average value reported by di Prampero et al.
[19] and correcting for the 1% treadmill gradient [20]. The
intercept representing the energy cost at rest (5.1 mL/kg/min)
comes from Medbo et al. [21]. The MAOD for each condition was
estimated by subtracting the total amount of O2 consumed from
the calculated O2 required.
Statistical Analysis
Calculations were performed with the aid of a spreadsheet for
straightforward crossover trial analysis [22]. When no comparisons
were involved, the means and between-subject standard deviations
were derived from the raw values of the measures; for all other
measures, they were derived by performing back-transformation of
the log-transformed values and the standard deviations were
presented as percentages. Data reliability was assessed by means of
the retest correlation (intraclass correlation coefficient; ICC) and
the measurement errors (typical error or SEE) along with 90%
confidence limits. The inflated typical errors were reported
because there were no identifiable individual responses to the
treatment. Uncertainties in the measurement errors are presented
as factors. To make inferences about true (population) values of the
effect (%) of a FS on performance and physiological responses, the
uncertainty in the effect was expressed as 90% confidence limits
and as likelihoods that the true value of the effect denotes real
positive (+ive) or negative (2ive) change; this was represented by
the probability (P) value derived from the t statistic followed by
qualitative interpretation [23]. To evaluate the relationship
between performance variables and to assess the association
between performance and a set of physiological variables, single
and multiple (stepwise) linear regressions analyses were used,
respectively.
Results
In the incremental test, subjects attained a maximal aerobic
speed of 16.161.8 km/h and the speed at lactate threshold was
9.862.7 km/h. The time-to-exhaustion for exercise at 95, 100 and
110% of the maximal aerobic speed was 5616143 s, 369682 s
and 214672 s, respectively. The calculated subject’s V˙ O2max was
39826429 mL/min. The individual error in the measurement of
V˙ O2max (i.e. the coefficient of variation of the four V˙ O2max
values) ranged between 0.7 and 7.8% (mean 6 SD of 3.062.3%).
During the third phase of the experiment, subjects attained a HS
at 20.162.0 km/h (time-to-exhaustion of 108634 s), representing
126613% of maximal aerobic speed.
Figure 1. Schematic representation of the protocol timing during the four phases. The superimposed data points are merely illustrative
data representing V˙O2 response during tests. V˙O2max, maximal oxygen uptake (dashed line); HS, highest speed (solid line); FS, fast-start pacing
strategy. See ‘‘Methods’’ for more details on phases one, two, three and four.
doi:10.1371/journal.pone.0111621.g001
Fast Start and Running Performance
PLOS ONE | www.plosone.org
3
October 2014 | Volume 9 | Issue 10 | e111621
Table 1. Comparison analysis of the FS performance variables with those predicted for constant pace from the log-log modelling.
Mean ± coefficient of variation (%)
Performance Measures
Constant Pace
FS
Correlation and 90%CL
SEE (%)a
% changes ±90%CL
P value
Qualitative Inference
Exercise tolerance at mean FS speed (s)
114630
125626
0.89 (0.71–0.96)
8
967
0.037
Benefit very likely
Time to cover FS distance (s)
128626
0.99 (0.98–1.00)
1.5
22.561.8
0.033
Distance covered at FS duration (m)
670622
683622
0.99 (0.98–1.00)
1.3
2.061.4
0.029
Predicted 800-mb (s)
155611
152610
0.97 (0.90–0.99)
1.5
22.061.6
0.046
Data are back-transformed means 6 coefficients of variation.
aUncertainties in these errors: 6/4 1.2. Multiply and divide the error by this number to obtain the 90% confidence for the true error.
bThe 800-m using a FS was predicted by calculating the amount of the intercept used in the extra-time assuming that the FS does not change the slope of the relationship between t and d.
FS: fast-start pacing strategy.
doi:10.1371/journal.pone.0111621.t001
Table 2. Observed changes in physiological responses after a FS in comparison with constant speed exercise.
Mean ± coefficient of variation (%)
Physiological Measures
HS
FS
Correlation and 90%CL
Inflated Errora (%)
% changes ±90%CL
P value
Qualitative Inferenceb
Pretest V˙O2 (mL/min)
555614
511618
0.51 (0.02–0.80)
11
2868
0.095
Very likely –ive
V˙O2 Mean Response Time (s)
22.2628
19.3629
0.80 (0.50–0.93)
13
21368
0.025
Very likely –ive
Amplitude (mL/min)
3396612
341968
0.74 (0.39–0.90)
5.5
0.764.1
0.769
Unclear
V˙O2max (mL/min)
3871610
387468
0.93 (0.80–0.98)
2.6
0.162.0
0.941
Unclear
O2 consumed at iso-time (mL)
5373645
5503646
1.00 (0.99–1.00)
2.6
2.462.0
0.051
Very likely +ive
MAOD (mL)
2385634
2425630
0.89 (0.70–0.96)
11
268
0.713
Unclear
Data are back-transformed means 6 coefficients of variation.
aUncertainties in these errors: 6/41.5. Multiply and divide the error by this number to obtain the 90% confidence for the true error.
bThe effect was deemed unclear if the chances that the true effect has the same sign than that of the observed effect were lower than 75%.
FS: fast-start pacing strategy; HS: highest constant speed; MAOD: maximal accumulated O2 deficit.
doi:10.1371/journal.pone.0111621.t002
Fast Start and Running Performance
PLOS ONE | www.plosone.org
4
October 2014 | Volume 9 | Issue 10 | e111621
The adjusted correlation coefficients for the log-log modelling of
the sets of four runs were all at least 0.999 for the relationships
between t6d and averaged 0.993 (SD of 0.008) for the relationship
between t6s. The application of the models revealed a very large
correlation between the HS and predicted constant-pace 800-m
performance [r = 20.80 (–0.93 to 20.43)]. Stepwise multiple
regression analyses further demonstrated that the major predictors
of both the HS and predicted 800-m performance were, in order
of importance, relative MAOD, relative V˙ O2max and MRT. The
increase in multiple correlation coefficients with the addition of
each predictor was 0.59–0.81–0.89 for the HS and 0.59–0.88–0.89
for the predicted 800-m performance. The upper and lower 90%
confidence limits for the full models were identical: 0.68–0.96.
Table 1 shows the comparison of the various performance
parameters obtained during the FS with the approximations
derived by the models for constant pace exercise. The benefit of
the FS was very likely for all performance variables. In addition,
the performance improvement was also very likely beneficial when
comparing the predicted 800-m performance using both strategies.
The V˙ O2 responses observed during the HS and FS performances
were compared at iso-time and iso-distance (Figure 2 and
Table 2). The FS very likely reduced the MRT and increased
the amount of O2 consumed. There was no clear difference in
MAOD between the experimental conditions. Moreover, the
accumulated
O2
deficit
spared
at
iso-time
with
the
FS
(1456179 mL) was quite similar to that used to maintain the
exercise during the FS after the iso-time (1346185 mL).
Discussion
Non-running
studies
of
similar
duration,
most
of
them
conducted in cycling, reported that improvements in time trials
or time-to-exhaustion performances with a FS are usually
accompanied by faster V˙ O2 kinetics and higher O2 consumption
for a given time [1,3,5]. The results of this study are in
concordance with these previous reports demonstrating the
benefits of a FS in comparison with more conservative pacing
strategies during treadmill running. In spite of the lower
magnitude of MRT reductions, which were not correlated with
change in performance in the present study, the faster achieve-
ment of V˙ O2max induced by the FS increased the aerobic
contribution even in an already fast V˙ O2 response. This seems to
have resulted in a spared quantity of the anaerobic capacity,
measured in the present study by the oxygen deficit. This quantity
was equivalent to those used to prolong the exercise tolerance at a
running intensity correlated with that of 800-m performance.
Consequently, these results are in accordance with the established
models of mitochondrial respiratory control, in which changes in
muscle [PCr], [ADP] and [Pi] per unit change in time are
responsible for mitochondrial respiratory control through the rate
of oxidative phosphorylation in the active muscles during exercise
[24,25].
The approximations derived from the log-log modelling for
changes in time trial performance in the present study (2.5%) are
within the effects usually reported for human performance
experiments where the end-point is known. Although our subjects
were not competitive runners, the significance of this effect in
terms of magnitude should be discussed from a practical
Figure 2. Group mean pulmonary V˙ O2 response during the highest speed and fast-start pacing strategy. For graphical presentation,
data were matched at the shortest time to exhaustion recorded and interpolated to show second-by-second values. The vertical solid line represents
the onset of exercise and the horizontal dashed line is the mean V˙O2max. The mean 6 SD of pre-test V˙O2 in each condition are also shown.
doi:10.1371/journal.pone.0111621.g002
Fast Start and Running Performance
PLOS ONE | www.plosone.org
5
October 2014 | Volume 9 | Issue 10 | e111621
perspective. Hopkins et al. [26] have demonstrated through
simulations, that the increase in the chances of winning an event
varies uniformly when a particular subject benefits with an
enhancement corresponding to multiples of the within-subject
random variation within a group of identical subjects (i.e. between-
subject variation of zero, equal to a repeated measures design).
Even though a more careful analysis of the reliability of subject’s
performance has not been conducted, the typical error of
measurement usually lies around 2–3% for groups with similar
characteristics [17]. Therefore, we can be confident that the
observed enhancement is meaningful for this group of subjects
because the ratio between the observed effect and the typical error
should increase the chances of winning by approximately 30% in
cases where the subject runs against himself in a hypothetical
simulated event, which can be considered as a moderate effect
[30]. For the log-log predictions of 800-m performance the
benefits of a FS decreased slightly, yet were still meaningful,
probably as a consequence of the amount of anaerobic energy
spared with the higher aerobic contribution becoming relatively
lower in comparison with the total energy cost as the time/
distance increases.
Although the present study has demonstrated that a FS
enhanced performance by speeding an already fast aerobic
response to exercise in non-athletes, extrapolating these results
to middle-distance runners is an interesting issue. Athletes present
even faster V˙ O2 kinetics for a given running speed and obviously
have higher absolute speeds during performances of similar
distance [27,28], which could prevent meaningful accelerations
in V˙ O2 kinetics. Indeed, Thomas et al. [29] observed that elite
runners reached V˙ O2max in a very fast time during an 800-m race
(around 45 s). Conversely, it was demonstrated that the time to
reach V˙ O2max in runners is liable to be reduced as a function of
the initial speed at very high intensities [30]. In addition, athletes
have higher values of V˙ O2max relative to body mass and,
consequently, they may still spare an important amount of energy,
although with a lesser acceleration in V˙ O2 kinetics. In other
words, since the athletes present higher values of V˙ O2 along the
transition from rest to exercise, a lower effect of the FS in the speed
of V˙ O2 kinetics may not be a problem because the absolute
amount of energy spared would be similar between athletes and
non-athletes [27]. Therefore, although it is recognized that athletes
generally present lower performance improvements in terms of
magnitude than non-athletes for a given intervention, it is
hypothesized that they would also benefit from a FS, since
enhancements as low as 0.5% are considered important to elite
runners [31,32].
The linear regression analysis, irrespective of being single or
multiple, demonstrated that both the HS and 800-m speeds are
linked with each other and are highly influenced by the same
physiological parameters. There is no novelty in the fact that
success
with
middle-distance
running
is
dependent
on
an
integrative contribution from the aerobic and anaerobic variables
that allow a runner to maintain a rapid velocity during a race [10–
12], and the results of this study are consistent with the notion that
having fast V˙ O2 kinetics is also important. Similarly, it is intuitive
that a large anaerobic capacity allows for greater endurance at any
given intensity and thus to continue reaching V˙ O2max at higher
relative intensities [33]. Therefore, large anaerobic energy stores
combined with a high aerobic power should yield a higher HS,
especially when allied to a fast V˙ O2 kinetics. Therefore, it is
hypothesized that the HS may group together several intervening
factors for middle-distance performance into a single physiological
index, which has proven sensitive to high-intensity training in an
ongoing study (unpublished observations).
One possible limitation of the present investigation is the lack of
randomization between the HS and FS. While the nature of the
present experiment rendered it impossible to control any possible
order effects, the subjects were mostly accustomed with exercising
to exhaustion. Furthermore, it is likely that three predictive trials
plus two-to-four tests for the determination of the HS, the latter of
which were at or very close to the HS and FS intensities, provided
enough familiarization to prevent learning effects in the last two
non-randomized trials. Conversely, if the high number of tests
performed until exhaustion had caused an accumulated fatigue in
the subjects, the observed result should be the opposite to what was
found in the present study if there was no systematic effect of the
FS on performance. Therefore, it is unlikely that any order effects
influenced these findings.
In conclusion, the results generated suggest that running
performance over 2–3 min is improved when a FS is adopted.
The higher aerobic contribution resulting from faster V˙ O2 kinetics
in the early phase of exercise spares an important amount of the
finite anaerobic capacity, which can be used as an additional
energy source to improve middle-distance performance. It is
recommend that future studies investigate how these effects would
behave/interact in the presence of other strategies that are
commonly used to speed V˙ O2 kinetics such as prior exercise.
Author Contributions
Conceived and designed the experiments: FC. Performed the experiments:
TT AFS FDL RAA RSOC FC. Analyzed the data: TT AFS FDL RAA
RSOC FC. Contributed reagents/materials/analysis tools: FC. Contrib-
uted to the writing of the manuscript: TT AFS FDL RAA RSOC FC.
References
1. Bailey SJ, Vanhatalo A, DiMenna FJ, Wilkerson DP, Jones AM (2011) Fast-start
strategy improves VO2 kinetics and high-intensity exercise performance. Med
Sci Sports Exerc 43: 457–467.
2. Poole DC, Jones AM (2012) Oxygen uptake kinetics. Comprehensive Physiology
2: 933–996.
3. Jones AM, Wilkerson DP, Vanhatalo A, Burnley M (2008) Influence of pacing
strategy on O2 uptake and exercise tolerance. Scand J Med Sci Sports 18: 615–
626.
4. Sandals LE, Wood DM, Draper SB, James DV (2006) Influence of pacing
strategy on oxygen uptake during treadmill middle-distance running. Int
J Sports Med 27: 37–42.
5. Bishop D, Bonetti D, Dawson B (2002) The influence of pacing strategy on V˙ O2
and supramaximal kayak performance. Med Sci Sports Exerc 34: 1041–1047.
6. Jones AM, McConnell AM (1999) Effect of exercise modality on oxygen uptake
kinetics during heavy exercise. Eur J Appl Physiol Occup Physiol 80: 213–219.
7. Carter H, Jones AM, Barstow TJ, Burnley M, Williams CA, et al. (2000) Oxygen
uptake kinetics in treadmill running and cycle ergometry: a comparison. J Appl
Physiol (1985) 89: 899–907.
8. Caputo F, Denadai BS (2006) Exercise mode affects the time to achieve
V˙ O2max without influencing maximal exercise time at the intensity associated
with V˙ O2max in triathletes. Int J Sports Med 27: 798–803.
9. Caputo F, Denadai BS (2008) The highest intensity and the shortest duration
permitting attainment of maximal oxygen uptake during cycling: effects of
different methods and aerobic fitness level. Eur J Appl Physiol 103: 47–57.
10. di Prampero PE, Capelli C, Pagliaro P, Antonutto G, Girardis M, et al. (1993)
Energetics of best performances in middle-distance running. J Appl Physiol
(1985) 74: 2318–2324.
11. Brandon LJ (1995) Physiological factors associated with middle distance running
performance. Sports Med 19: 268–277.
12. Billat VL, Lepretre PM, Heugas AM, Koralsztein JP (2004) Energetics of
middle-distance running performances in male and female junior using track
measurements. Jpn J Physiol 54: 125–135.
13. Hinckson EA, Hopkins WG (2005) Reliability of time to exhaustion analyzed
with critical-power and log-log modeling. Med Sci Sports Exerc 37: 696–701.
14. Hill DW, Poole DC, Smith JC (2002) The relationship between power and the
time to achieve.VO(2max). Med Sci Sports Exerc 34: 709–714.
Fast Start and Running Performance
PLOS ONE | www.plosone.org
6
October 2014 | Volume 9 | Issue 10 | e111621
15. Draper SB, Wood DM, Corbett J, James DV, Potter CR (2006) The effect of
prior moderate- and heavy-intensity running on the V˙ O2 response to exhaustive
severe-intensity running. Int J Sports Physiol Perform 1: 361–374.
16. Kuipers H, Verstappen FT, Keizer HA, Geurten P, van Kranenburg G (1985)
Variability of aerobic performance in the laboratory and its physiologic
correlates. Int J Sports Med 6: 197–201.
17. Laursen PB, Francis GT, Abbiss CR, Newton MJ, Nosaka K (2007) Reliability
of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports
Exerc 39: 1374–1379.
18. Wilkerson DP, Koppo K, Barstow TJ, Jones AM (2004) Effect of work rate on
the functional ‘gain’ of Phase II pulmonary O2 uptake response to exercise.
Respir Physiol Neurobiol 142: 211–223.
19. di Prampero PE, Atchou G, Bruckner JC, Moia C (1986) The energetics of
endurance running. Eur J Appl Physiol Occup Physiol 55: 259–266.
20. di Prampero PE, Salvadego D, Fusi S, Grassi B (2009) A simple method for
assessing the energy cost of running during incremental tests. J Appl Physiol
(1985) 107: 1068–1075.
21. Medbo JI, Mohn AC, Tabata I, Bahr R, Vaage O, et al. (1988) Anaerobic
capacity determined by maximal accumulated O2 deficit. J Appl Physiol 64: 50–
60.
22. Hopkins WG (2003) A spreadsheet for analysis of straightforward controlled
trials. Sportscience 7. Available: sportsci.org/jour/03/wghtrials.htm.
23. Hopkins WG (2007) A spreadsheet for deriving a confidence interval,
mechanistic inference and clinical inference from a p value. Sportscience 11:
16–20. Available: sportsci.org/2007/wghinf.htm.
24. Bose S, French S, Evans FJ, Joubert F, Balaban RS (2003) Metabolic network
control of oxidative phosphorylation: multiple roles of inorganic phosphate.
J Biol Chem 278: 39155–39165.
25. Chance B, Williams GR, Holmes WF, Higgins J (1955) Respiratory enzymes in
oxidative phosphorylation. V. A mechanism for oxidative phosphorylation.
J Biol Chem 217: 439–451.
26. Hopkins WG, Hawley JA, Burke LM (1999) Design and analysis of research on
sport performance enhancement. Med Sci Sports Exerc 31: 472–485.
27. Caputo F, Mello MT, Denadai BS (2003) Oxygen uptake kinetics and time to
exhaustion in cycling and running: a comparison between trained and untrained
subjects. Arch Physiol Biochem 111: 461–466.
28. Caputo F, Denadai BS (2004) Effects of aerobic endurance training status and
specificity on oxygen uptake kinetics during maximal exercise. Eur J Appl
Physiol 93: 87–95.
29. Thomas C, Hanon C, Perrey S, Le Chevalier JM, Couturier A, et al. (2005)
Oxygen uptake response to an 800-m running race. Int J Sports Med 26: 268–
273.
30. Hanon C, Thomas C (2011) Effects of optimal pacing strategies for 400-, 800-,
and 1500-m races on the V˙ O2 response. J Sports Sci 29: 905–912.
31. Carr AJ, Hopkins WG, Gore CJ (2011) Effects of acute alkalosis and acidosis on
performance: a meta-analysis. Sports Med 41: 801–814.
32. Van Montfoort MC, Van Dieren L, Hopkins WG, Shearman JP (2004) Effects
of ingestion of bicarbonate, citrate, lactate, and chloride on sprint running. Med
Sci Sports Exerc 36: 1239–1243.
33. Billat LV, Koralsztein JP (1996) Significance of the velocity at V˙ O2max and time
to exhaustion at this velocity. Sports Med 22: 90–108.
Fast Start and Running Performance
PLOS ONE | www.plosone.org
7
October 2014 | Volume 9 | Issue 10 | e111621

A fast-start pacing strategy speeds pulmonary oxygen uptake kinetics and improves supramaximal running performance.
Turnes, Tiago,Salvador, Amadeo Félix,Lisbôa, Felipe Domingos,de Aguiar, Rafael Alves,Cruz, Rogério Santos de Oliveira,Caputo, Fabrizio