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It is widely recognized that an athlete's 'pacing strategy', or how an athlete distributes work and energy throughout an exercise task, can have a significant impact on performance. By applying mathematical modelling (i.e. power/velocity and force/time relationships) to athletic performances, coaches and researchers have observed a variety of pacing strategies. These include the negative, all-out, positive, even, parabolic-shaped and variable pacing strategies. Research suggests that extremely short-duration events (< or =30 seconds) may benefit from an explosive 'all-out' strategy, whereas during prolonged events (>2 minutes), performance times may be improved if athletes distribute their pace more evenly. Knowledge pertaining to optimal pacing strategies during middle-distance (1.5-2 minutes) and ultra-endurance (>4 hours) events is currently lacking. However, evidence suggests that during these events well trained athletes tend to adopt a positive pacing strategy, whereby after peak speed is reached, the athlete progressively slows. The underlying mechanisms influencing the regulation of pace during exercise are currently unclear. It has been suggested, however, that self-selected exercise intensity is regulated within the brain based on a complex algorithm involving peripheral sensory feedback and the anticipated workload remaining. Furthermore, it seems that the rate and capacity limitations of anaerobic and aerobic energy supply/utilization are particularly influential in dictating the optimal pacing strategy during exercise. This article outlines the various pacing profiles that have previously been observed and discusses possible factors influencing the self-selection of such strategies.
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2008, Vol. 38, No. 3 (pp. 239-252)
ISSN: 0112-1642
Review Article
Pacing Strategies during Competition
Sports Med 2008; 38 (3): 239-252
R
EVIEW
A
RTICLE
0112-1642/08/0003-0239/$48.00/0
2008 Adis Data Information BV. All rights reserved.
Describing and Understanding
Pacing Strategies during
Athletic Competition
Chris R. Abbiss and Paul B. Laursen
School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western
Australia, Australia
Contents
Abstract .................................................................................... 239
1. Defining Pacing Strategies ................................................................ 240
1.1 Negative Pacing .................................................................... 241
1.2 ‘All-Out’ Pacing ..................................................................... 241
1.3 Positive Pacing ...................................................................... 242
1.4 Even Pacing ......................................................................... 244
1.5 Parabolic-Shaped Pacing ............................................................ 245
1.6 Variable Pacing ..................................................................... 246
2. Regulation of Pace ...................................................................... 247
3. Conclusion .............................................................................. 248
It is widely recognized that an athlete’s ‘pacing strategy’, or how an athlete
Abstract
distributes work and energy throughout an exercise task, can have a significant
impact on performance. By applying mathematical modelling (i.e. power/velocity
and force/time relationships) to athletic performances, coaches and researchers
have observed a variety of pacing strategies. These include the negative, all-out,
positive, even, parabolic-shaped and variable pacing strategies. Research suggests
that extremely short-duration events (30 seconds) may benefit from an explosive
‘all-out’ strategy, whereas during prolonged events (>2 minutes), performance
times may be improved if athletes distribute their pace more evenly. Knowledge
pertaining to optimal pacing strategies during middle-distance (1.5–2 minutes)
and ultra-endurance (>4 hours) events is currently lacking. However, evidence
suggests that during these events well trained athletes tend to adopt a positive
pacing strategy, whereby after peak speed is reached, the athlete progressively
slows. The underlying mechanisms influencing the regulation of pace during
exercise are currently unclear. It has been suggested, however, that self-selected
exercise intensity is regulated within the brain based on a complex algorithm
involving peripheral sensory feedback and the anticipated workload remaining.
Furthermore, it seems that the rate and capacity limitations of anaerobic and
aerobic energy supply/utilization are particularly influential in dictating the opti-
mal pacing strategy during exercise. This article outlines the various pacing
240 Abbiss & Laursen
profiles that have previously been observed and discusses possible factors influ-
encing the self-selection of such strategies.
In an attempt to enhance our understanding of cess in head-to-head competition, the performance
athletic performance, sport scientists have examined
time of the winning athlete need only be marginally
how work or energy expenditure is distributed
lower than that of other competitors in the same
during an exercise task.
[1-5]
This distribution of
race.
[8]
Thus, the actions of opponents or team mem-
work, or pattern of energy expenditure, has been
bers often influence race dynamics, making team,
termed ‘pacing’ or ‘pacing strategy’.
[2,6,7]
It is well
coach and individual tactics important to overall
documented that during athletic competitions, well
success.
[15]
Alternatively, events also exist whereby
trained athletes must regulate their rate of work
athletes may not race each other in direct head-to-
output in order to optimize overall performance.
[3,8,9]
head confrontation, but may instead be ‘racing the
However, scientific research examining the pacing
clock’.
[8,15]
In this race format, often known as a
strategies employed during competition is scarce.
[10]
time trial, results are determined by the time re-
Recently, St Clair Gibson et al.
[11]
have illustrated
quired to complete the given distance.
[16]
An advan-
how communication between the brain and peri-
tage of the individual time trial is that, in the absence
pheral physiological systems may regulate pace
of any direct head-to-head confrontation, labora-
during exercise. However, little is presently known
tory-based trials can somewhat replicate true com-
pertaining to the specific physiological, cognitive
petition, allowing performance to be accurately
and/or environmental factors that affect or control
modelled.
[7,17-19]
While the distribution of work (i.e.
the detailed distribution of work during exer-
pace) during a time trial has been shown to be
cise.
[11,12]
Furthermore, St Clair Gibson et al.
[11]
rec-
important to overall performance,
[3,9]
it is still un-
ognize that “further research is required to help
clear as to the optimal pacing strategies required to
clarify which of the different possible pacing strate-
ensure the best possible performance outcome under
gies are optimal for different sports and for different
the variable environmental conditions experienced
distances performed during athletic events.” There-
by athletes (e.g. climate, terrain, altitude, wind).
[11]
fore, the purpose of this article is to: (i) examine the
The overall speed of an athlete during a locomo-
literature pertaining to the various pacing strategies
tive task is dependent upon a number of factors,
that have been observed; (ii) provide recommenda-
including the mechanical power generated, momen-
tions for use of such pacing strategies under the
tum or kinetic energy, and the degree of resistive
varying situations encountered by athletes in the
forces that are experienced (i.e. aerodynamic/hydro-
field; and (iii) to determine possible underlying
dynamic resistance/drag, frictional resistance, gravi-
mechanisms responsible for the regulation of pace
ty).
[7,18-21]
Although recent technological advance-
during varying exercise tasks and conditions.
ments have allowed scientists to measure the
mechanical power produced by an athlete during
1. Defining Pacing Strategies
competition,
[22,23]
it is important to note that the term
‘pacing’ more accurately refers to performance
Most individual sporting events, such as running,
times or velocity and not the actual mechanical work
swimming, cycling, rowing, skiing and speed skat-
or power output produced. Despite this, the regula-
ing, are considered to be of a ‘closed-loop de-
tion of pace is largely dictated by the ability to resist
sign’.
[13]
That is, the athlete aims to finish a known
fatigue, making the mechanical power output gener-
distance in the shortest time possible.
[8,14]
Within
ated of extreme importance.
[7]
By modelling power/
these events, athletes are required to compete
velocity relationships and observing elite athletic
against others in either direct ‘head-to-head’ compe-
tition or individually against the clock.
[8]
For suc- performances, coaches and researchers have been
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
Pacing Strategies during Competition 241
able to gain some insight into understanding optimal
pacing strategies during varying competition scena-
rios.
[1,15,20]
In particular, it is believed that short-
duration sprint events (i.e. 30–60 seconds) may
benefit from an ‘all-out’ sprint strategy,
[1,8,24]
where-
as more extended (>2 minutes) endurance perform-
ance may be improved if athletes distribute energy
resources more evenly.
[7]
Indeed, a variety of pacing
profiles have been observed during different exer-
cise tasks and under differing exercise condi-
tions.
[9,11]
Such profiles include negative, all-out,
positive, even, parabolic-shaped and variable pacing
strategies. These pacing profiles are reviewed in the
context of determining situations in which each
strategy may be most favourable to athletic perform-
235
245
255
265
275
285
295
305
315
0 500 1000 1500 2000 2500 3000
Distance (m)
Power output (W)
35
35.5
36
36.5
37
37.5
38
38.5
39
39.5
Speed (km/h)
Power
Speed
Fig. 1. Speed and power output profiles during 3000-m track cy-
cling events. Note the dramatic increase in speed and power output
during the final 13% (400 m) of the event, resulting in the observa-
tion of a negative pacing strategy (reproduced from Foster et al.,
[2]
with permission from Georg Thieme Verlag).
ance.
power output commonly seen towards the end of
individual time-trial events may be the result of an
1.1 Negative Pacing
increase in motor unit recruitment
[28]
and the use of
An event is considered to have been performed
the anaerobic energy reserve.
[2]
with a negative-split, or through use of a negative
pacing strategy, when there is an increase in speed
1.2 ‘All-Out’ Pacing
observed over the duration of the event. Adoption of
such a pacing strategy is thought to improve pro-
During certain locomotive events, the cost asso-
longed exercise performance by reducing the rate of
ciated with acceleration can significantly influence
carbohydrate depletion,
[12]
lowering excessive oxy-
the pacing strategy required for optimal perform-
gen consumption (
˙
VO
2
)
[25]
and/or limiting accumu-
ance.
[1]
During the 100-m sprint, world-class run-
lation of fatigue-related metabolites (i.e. inorganic
ners spend ~50–60% of the race in the acceleration
phosphate, potassium and hydrogen ions) early on in
phase.
[34]
Consequently, 20–25% of the overall work
the exercise task.
[12,26,27]
Supporting this presump-
demand of a 100-m sprint event may be required
tion, Mattern et al.
[26]
showed that compared with
merely to alter the body’s kinetic energy from rest.
self-paced trials, a negative pacing strategy resulted
Furthermore, due to an increase in kinetic energy as
in a significantly lower blood lactate concentration
a result of increasing momentum, the energy re-
during the initial 9 minutes of a 20-km cycling time
quired to maintain a constant pace is lower than the
trial. Furthermore, the relatively low enforced start-
energy required to accelerate, especially when iner-
ing power output (15% lower than self-selected
tia is high (i.e. greater mass and velocity). Since the
power output) resulted in significant improvements
energy expenditure required to accelerate is inevita-
in overall performance.
[26]
ble, it is believed that this energy is best distributed
A negative pacing strategy is often observed, at the start of short events, as any submaximal
especially during middle-distance events, when movement speed ultimately results in slower per-
power output
[2,26,28-30]
and velocity
[2]
are increased formance times.
[1,7]
As this initial acceleration
towards the end of both simulated and actual time- period at submaximal speed is proportionately
trial events (figure 1). This final increase in exercise greater during short-duration sprint events, it is
intensity commonly occurs in events when athletes possible that optimal performance during these
are made aware of the remaining trial distance
[13,28]
events may be obtained when athletes start and
or duration.
[31-33]
It is believed that this increase in continue the event in an ‘all-out’ fashion
[1,35]
(figure
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
242 Abbiss & Laursen
forces experienced.
[7,20]
The longer the event, the
less important kinetic energy loss becomes relative
to the cost of aerodynamic/hydrodynamic resis-
tance.
[2,6,8,18,20,36]
With the use of various mathemati-
cal models and physiological constants calculated
from previous world record times, Keller
[24]
deter-
mined that optimal performance can be achieved
during running events of <291 m when athletes
adopt the all-out pacing strategy. A limitation of this
and other calculations,
[34,37]
however, is that models
are fundamentally based on a number of physiologi-
cal constants pertaining to the maximal acceleration,
velocity and the endurance or rate-of-fatigue in ath-
letes. Thus, the various physiological attributes of an
athlete will significantly influence the distance be-
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 10 20 30 40 50 60 70 80 90 100
Percentage of total time
Power output (W)
0
10
20
30
40
50
60
70
Velocity (km/h)
Power
Velocity
Fig. 2. Example of power output and velocity profiles during a
1000-m track cycling event. Note the high power output needed to
alter the kinetic energy from rest, and the gradual decline in velocity
beyond 40% (~25 sec) of the overall trial time.
yond which an all-out strategy is no longer optimal
to performance. Despite this, the predictions by Kel-
2). Despite adoption of this all-out strategy, the
ler
[24]
seem plausible as research suggests that anaer-
significant percentage of time spent in the accelera-
obic energy resources become significantly reduced
tion phase (at low relative speeds) during short
following all-out sprinting beyond this distance (i.e.
sprint events often results in ‘negative-split’ per-
~30–60 seconds duration).
[38]
formance times
[1,2,7,15]
(figure 2). Interestingly, the
greater percentage of the event time spent in the
1.3 Positive Pacing
acceleration phase during the current men’s 100-m
world record sprint time has actually resulted in a
A positive pacing strategy is one whereby an
slower average speed for the 100-m sprint when
athlete’s speed gradually declines throughout the
compared with the 200-m sprint (10.24 vs 10.35 m/
duration of the event. In both the 100-m and 200-m
sec, respectively).
swimming,
[39]
as well as the 2000-m rowing
After an athlete has reached peak velocity using
events,
[40]
national and elite calibre athletes have
an all-out pacing strategy, speed tends to gradually
been shown to adopt a positive pacing strategy. In
decrease
[15]
(figure 2), possibly resulting in subop-
addition, athletes who run within 2% of the world
timal performance times.
[1,7]
It should be noted,
record time in the 800-m running track event have
however, that during a time trial, any velocity/ener-
been shown to utilize a positive pacing strategy.
[25]
gy that exists when passing the finish line is essen-
Sandals et al.
[25]
reported that elite 800-m runners
tially wasted kinetic energy.
[1,7]
Thus, compared
typically run the first 200 m, the middle 400 m, and
with a constant pace, the all-out pacing strategy may
the final 200 m at 107.4%, 98.3% and 97.5% of the
result in considerably lower kinetic energy losses
average speed for the entire 800 m, respectively.
due to a slower finishing velocity.
[1,7]
Short-duration
Moreover, compared with an even pacing strategy
sprint performance may therefore benefit from the
(see section 1.4), positive pacing resulted in a signif-
all-out pacing strategy, despite a comparatively
icantly greater fractional peak
˙
VO
2
during the event
greater amount of energy lost to frictional resistance
(89.3 ± 2.4% vs 92.5 ± 3.1% maximum oxygen
as a result of the high peak velocity.
[1,7]
The choice
consumption [
˙
VO
2max
], respectively).
[25]
As pre-
between reducing wasted kinetic energy and/or pre-
viously stated in section 1.2, the relative time spent
serving energy lost to frictional resistance is depen-
accelerating may significantly influence pacing
dant upon the degree of kinetic energy lost at the
strategy, often resulting in the adoption of a negative
end of the race and the magnitude of the resistive pacing strategy during short-duration events. How-
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
Pacing Strategies during Competition 243
ever, in certain locomotive events such as swim- It is possible that a relatively fast starting strategy
ming and relay athletics, the relative time spent during competition may be the result of unrealistic
accelerating may be reduced. During swimming, the ambitious perceptions regarding personal athletic
dive start allows athletes to reach maximal velocity ability, whereby athletes begin their race at a pace
within a relatively short time. Similarly, the ‘flying designed to finish within the medallists or at a
start’ that occurs during the final three legs of relay personal best pace.
[2]
The adoption of this race tactic
athletic races reduces the influence of acceleration may be seen during a number of high-level competi-
on overall pacing strategy. Athletes may, therefore, tion events. Cyclists are often seen attempting to
adopt a positive pacing strategy during such events breakaway from the main group of riders during
due, at least in part, to the specific starting proce- numerous road cycling events (such as Le Tour de
dures. France, Giro d’Italia and La Vuelta a Espa
˜
na), pre-
sumably for the purpose of winning the race or days’
It has been shown that the adoption of a positive
stage. Such breakaways do not usually succeed un-
pacing strategy results in an increased
˙
VO
2
,
[25,41]
less either allowed to or misjudged by the peloton.
greater accumulation of fatigue-related metabo-
Despite few of these breakaways being successful, it
lites
[41,42]
as well as an increase in the rating of
is possible that this strategy may succeed often
perceived exertion
[41]
during the early stages of an
enough to be considered as a viable tactic to employ
exercise task. As a result, a reduction in the exercise
during high-level competitions. Indeed, in 2001,
intensity and the observed positive pacing profile
Ben Kimondiu, a pacesetter employed to set a high
likely evolves in response to these signals so that
pace at the start of the LaSalle Bank Chicago Mara-
catastrophic failure of any one physiological system
thon, continued beyond what was expected and ulti-
does not occur.
[4,42,43]
In support of this hypothesis,
mately won the race in 2:08:52. However, such a
well trained endurance cyclists (peak power output
tactic is seldom successful and instead often results
370W) have been shown to commence cycling
in a progressive reduction in exercise intensity due
time trials in the heat (35ºC) at relatively high power
to disturbances in physiological homeostasis (i.e.
outputs.
[5,28]
Following this initial high exercise in-
fatigue).
[2]
To illustrate this point, Thompson et
tensity, Tucker et al.
[5]
showed that power output
al.
[42]
asked swimmers to swim at 102% of their
declined at a significantly greater rate in hot (35°C)
maximal 200-m time-trial speed, and found that they
compared with cool (15ºC) conditions (2.35 ± 0.7 vs
slowed significantly during the latter half of the
1.61 ± 0.8 W/min). In this study, subjects were
event. Furthermore, this positive pacing strategy
required to cycle at a set perceived exertion (16 on
was associated with higher post-blood lactate con-
the Borg’s rating of perceived exertion scale) until
centrations, respiratory gas exchange ratios and per-
power output fell below 70% of initial power out-
ceived exertions compared with even split pacing
put.
[5]
As the rate of decline in power output was
trials.
[42]
correlated (r = 0.92) to preceding rates of heat
storage, the authors’ hypothesized that work rate is
Evidence suggests that self-selected exercise in-
continuously manipulated to limit the rate of rise in
tensity during an ultra-endurance event (>4 hours)
heat accrual and avoid the development of critically
also tends to progressively decrease (figure 3).
[46-52]
high core temperatures (~39.5–40.5ºC).
[5,44,45]
The
For example, it has been found that heart rate de-
reason athletes self-select such relatively high pow-
clines at an average rate of 1–2% per hour during
er outputs during an endurance exercise task in the
cycling and triathlon events lasting 6–24 hours, in
heat is unclear,
[5,28]
but may be related to the lack of
both recreational
[46,52]
and elite
[51]
athletes. Similar-
thermal stress at the commencement of the trial.
ly, power output and speed have also been found to
Further research is required to better understand the
significantly decline during the 180-km cycle phase
influence of environmental heat and thermoregula-
of an Ironman triathlon.
[47]
It is believed that this
tion on self-selected pacing strategies.
progressive reduction in exercise intensity may be
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
244 Abbiss & Laursen
distribution of pace has been shown in a study by
Padilla et al.,
[59]
who examined the speed of a cyclist
during a successful 1-hour track world record at-
tempt. From this study, it can be seen that the cyclist
was able to maintain a steady velocity during the
entire duration of the trial and that the speed of the
cyclist per lap deviated very little from the target
speed (53.0 km/hour) or the actual mean of 53.040
km/hour (figure 4).
[59]
The theoretical support for an
even pacing strategy is primarily based on critical
power models and mathematical laws of motion,
which indicate that velocity is dictated by the maxi-
mal constant force that an athlete can exert along
with the resistive forces experienced.
[17,60-62]
By us-
120
125
130
135
140
145
150
155
160
165
170
0 100 200 300 400 500 600 700
Time (min)
Heart rate (beats/min)
Heart rate
Swim
Cycle Run
Fig. 3. Example of a positive pacing strategy shown by a decline in
heart rate during the swim, cycle and run phases of the Ironman
triathlon; n = 27 (adapted from combined data of Laursen et
al.
[46,58]
).
ing mathematical modelling, Fukuba and Whipp
[62]
have shown that performance will be compromised
the result of increased glycogen depletion,
[33,53]
re-
if an athletes’ velocity or power drops below their
sulting in altered substrate utilization,
[50,51,54]
neuro-
physiological limits (i.e. ‘fatigue threshold’ or ‘criti-
muscular fatigue
[12,54-56]
and/or psychological fac-
cal power’) at any point during an endurance event,
tors associated with the perception of fatigue.
[12,51,57]
even if athletes attempt to make up for lost time with
Thus, the ability to resist fatigue may play a signif-
a final increase in speed towards the end of an event.
icant role in the regulation of pace during ultra-
Furthermore, as an athlete increases velocity, a
endurance events. The optimal pacing strategies to
greater percentage of the power generated is used to
employ during such prolonged exercise, however,
overcome fluid (i.e. air or water) resistance rather
are unknown and require further investigation.
than producing forward motion. Minimizing such
variations in pace may be especially important
1.4 Even Pacing
during events that incur a high degree of fluid resis-
tance. For example, the higher velocities reached
As previously mentioned in section 1.3, one’s
during cycling compared with running result in sig-
chosen starting strategy can significantly influence
the overall performance time, especially during
short-duration events. During more prolonged
events, however, starting strategy appears to have
less of an effect on overall performance times
[2,9]
because of the lower percentage of time spent in the
acceleration phase. Consequently, it has been sug-
gested that under stable external conditions (i.e.
environmental and geographic), a constant pace is
‘optimal’ for prolonged (>2 minutes) locomotive
events such as running, swimming, rowing, skiing,
speed skating and cycling.
[7,41,42]
Wilberg and
Pratt
[15]
showed that more successful Canadian na-
tional and international calibre pursuit (3000–4000-
m) and 1000-m track cyclists used more constant/
even pace race profiles, whereas less successful
riders did not. Further evidence supporting an even
36
38
40
42
44
46
48
50
52
54
56
58
60
0 20 40 60 80 100 200
180160140120 220
Lap number
Speed (km/h)
Actual speed
Average speed
53.040 km
Fig. 4. Average speed of a cyclist during progressive laps and the
entire 1-hour track cycling world record. Note that the speed of the
cyclist deviated very little from the trial mean resulting in the adop-
tion of an even pacing strategy (reproduced from Padilla et al.,
[59]
with permission).
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
Pacing Strategies during Competition 245
nificantly greater aerodynamic resistance. Further-
more, as a result of differences in fluid viscosity,
fluid resistance is greater during water-based sports
such as swimming and rowing compared with land-
based sports. As even minor fluctuations in speed
can result in a greater energy cost,
[63]
it is possible
that overall performance times during prolonged
events may be optimized when acceleration and
deceleration is minimized.
[18]
Further research is
need to understand the influence of improved skill
and technique on energy cost and pacing strategy,
especially during repetitive stroke/stride exercises
such as running, swimming and rowing.
96
98
100
102
104
106
0–500 500–1000 1000–1500 150–2000
Sector of race (m)
Normalized mean velocity (%)
Men 
Women
Fig. 6. Example of a reverse J-shaped pacing profile observed
during the 2000-m on-water rowing championships (reproduced
from Garland,
[40]
with permission).
1.5 Parabolic-Shaped Pacing
field competitions.
[36,40,47]
Using this technology, re-
Historically, research into the regulation and dis-
searchers have shown that athletes may progressive-
tribution of energy expenditure during an exercise
ly reduce speed during an endurance trial but tend to
task has examined the distribution of work over
increase speed during the latter portion of the
relatively long time periods or distances. In particu-
event.
[28,40]
This tactic ultimately results in U, J or
lar, studies focusing on pacing strategies have exam-
reverse J-shaped pacing strategies (figure 5). Evi-
ined differences in performance during the first and
dence for such a pacing profile was shown by Gar-
second halves of a race (i.e. split times).
[1,10]
How-
land
[40]
who examined the velocity of elite rowers
ever analysis of these split performance times is a
during the 2000 Olympic Games, 2001 and 2002
relatively simple or gross analysis of one’s overall
World Championships and the 2001 and 2002 Brit-
pacing strategy (i.e. positive, negative or even
ish Indoor Rowing Championships. In each of these
splits), and does not provide great insight into the
2000-m races, rowers completed the first 500 m in
distribution of work throughout the event. The re-
the fastest time (5.1 seconds faster than subsequent
cent development of more accurate and reliable
sections of the race), slowed during the middle 1000
power and time meters has allowed scientists to
m, but increased speed during the final 500 m of the
specifically examine performance profiles during
race; this resulted in the adoption of a reverse J-
shaped pacing strategy (figure 6).
[40]
Little research is available describing these U, J
or reverse J-shaped pacing strategies,
[40,64]
but such
strategies may be the result of athletes adopting both
a positive and negative pacing strategy during an
event. For example, when Tucker et al.
[28]
had well
trained cyclists perform 20-km cycling time trials in
the heat, they showed that cyclists reduced power
output in an anticipatory fashion, likely in an at-
tempt to prevent the development of excessive exer-
cise-induced hyperthermia. This study also reported
that power output was increased during the final 5%
of the 20-km time trial despite core body tempera-
tures being at their highest (39.2 ± 0.6ºC).
[28]
It is
70
80
90
100
110
120
130
140
0 10 20 30 40 50 60 70 80 90 100
Percentage of completed distance
Percentage of average velocity
U-shaped
Reverse J-shaped
J-Shaped
Fig. 5. Example of U-shaped, reverse J-shaped and J-shaped pac-
ing profiles during exercise.
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
246 Abbiss & Laursen
believed that this increase in power output is the have been referred to as a variable pacing strategy
result of an increase in motor unit recruitment
[28]
within this article.
relating to an anaerobic energy reserve.
[2]
Conse-
Using the modelled motion of a cyclist,
[60]
quently, the choice of pacing strategy does not ap-
Swain
[18]
showed that despite identical mean 10-km
pear to be dictated by changes in any one physiolog-
time-trial power outputs, overall cycling perform-
ical system, but instead may be influenced via a
ance times were improved when cyclists increased
complex system of integrated feedback from a num-
power output on uphill sections, and reduced power
ber of sources, including prior experience and antic-
output on downhill sections of a race, compared to
ipated duration.
[12]
Furthermore, as cross-sectional
when the entire trial was conducted at a constant
studies cannot ever determine a cause-and-effect
power output (22.8 vs 24.3 minutes, respectively).
relationship, it is difficult to examine factors influ-
This model was based upon the notion that during a
encing pacing strategies with the use of such study
race, more of the overall time is spent cycling in the
designs. Future research should therefore take a
uphill/headwind sections compared with the down-
holistic and multidimensional approach in examin-
hill/tailwind sections.
[18]
By producing greater pow-
ing factors that may influence the regulation of work
er output on the uphill/headwind section of a race
rate during exercise.
and less during the downhill/tailwind section, ath-
letes are able to maintain a more constant speed,
1.6 Variable Pacing
resulting in improvements in overall performance
time.
[18,68]
Support for this model has been provided
Research into effective pacing strategies has been
by Atkinson and Brunskill
[36]
who examined the
complicated by a number of external factors, includ-
effects of a simulated headwind (first 8.05 km) and
ing race duration,
[2]
course geography
[18]
and envi-
tailwind (second 8.05 km) on self-selected and en-
ronmental conditions, such as wind
[7,59]
and environ-
forced (constant and variable [5% ± mean]) pacing
mental temperature.
[5,28]
As a result, the majority of
strategies during a 16.1-km laboratory-based time
research into pacing has been performed in control-
trial. It was found that when compared with a con-
led
[2,26,36,65]
or simulated
[36,66]
environmental condi-
stant or self-paced strategy, performance times were
tions. However, it is uncommon for athletes to expe-
improved when power output was increased into a
rience constant external conditions during actual
headwind (5% above average of self-paced trial) and
outdoor competition.
[36]
Under the varying external
reduced with a tailwind (5% below self-paced
conditions associated with field race conditions, it
trial).
[36]
Furthermore, during the women’s British
has been suggested that a variable pacing strategy
national time trial championships, cyclists who
may be optimal.
[36,66]
Variable pacing strategy is a
spent less time (compared with their overall race
term that has been used to define the fluctuations in
times) in the headwind section of the race produced
exercise intensity or work rate (i.e. power output)
the fastest overall times.
[10]
However, this race was
observed during exercise.
[36,66,67]
It should be noted
held on a 16-km out-and-back course, where the
that research investigating variable pacing strategies
cyclists experienced a tailwind for the initial 8 km
has examined changes in power output profiles rath-
followed by a headwind for the latter 8 km.
[10]
As a
er than changes in velocity or split performance
result, the fastest overall performance times may
times.
[36,66,67]
Indeed, as a variable pacing strategy is
have been due to the cyclists adopting a negative-
usually adopted in an attempt to counteract varia-
split pacing strategy rather than a variable pacing
tions in external conditions,
[18]
it seems likely that
strategy. It is also possible that the riders with faster
alterations in power output seen during exercise (i.e.
performance times may have had better aerodynam-
variable pacing strategy) are an attempt at maintain-
ic positioning resulting in a relatively faster time
ing a constant distribution of pace/velocity (i.e. even
into the headwind section of the race.
[36]
Despite this
pacing strategy). However, in order to coincide with
possibility, few researchers have examined the in-
previous research, such variations in power output
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
Pacing Strategies during Competition 247
fluence of technique and athletic skill on pacing in response to intrinsic (i.e. physiological, bi-
strategy.
[15,40]
Technique may be especially impor-
omechanical and cognitive) and extrinsic (i.e. envi-
tant to pacing strategy during events that experience
ronmental) sensory signals necessary to preserve
high resistive forces such as swimming, rowing and
physiological homeostasis.
[11,43,73]
Within this ‘cen-
cycling.
tral governor’ hypothesis,
[73]
it is also believed that
The physiological implications of a variable pac-
the end of an exercise task offers a reference point to
ing strategy are of interest to sports scientists, as an
which athletes adjust work rate in order to ensure
increase in exercise intensity can significantly in-
optimal performance.
[11,74,75]
This concept was origi-
crease the physiological demands of the exercise
nally proposed by Ulmer
[76]
and suggests that the
task.
[18,25,39,41]
Atkinson et al.
[67]
have recently
self-selection of exercise intensity may be control-
shown that two of seven subjects were unable to
led in a ‘teleoanticipatory’ manner, whereby athletes
maintain a variable pacing strategy when power
anticipate the work required to complete a given
output varied within ±5% of the mean trial power
exercise task. St Clair Gibson et al.
[11]
have since
output. Currently, studies examining the physiologi-
expanded upon this theory, suggesting that self-
cal mechanisms responsible for this inability to per-
selected exercise intensity may be regulated contin-
form a variable pacing strategy are inconclusive.
uously within the brain based on a complex al-
Atkinson et al.
[67]
and Liedl et al.
[66]
have collective-
gorithm involving peripheral sensory feedback and
ly shown that altering power output within ±5% of
the anticipated workload remaining. In accordance
the mean trial power output does not significantly
with the anticipatory regulation of pace hypothesis,
alter the mean heart rate,
˙
VO
2
, blood lactate, per-
Nikolopoulos et al.
[30]
showed that self-selected ex-
ceived exertion or pedal rate during either a 1-hour
ercise intensity (i.e. power and heart rate) was unaf-
or 800-kJ cycling time trial (~75%
˙
VO
2max
). De-
fected by the deception of correct distance feedback
spite this, Palmer et al.
[69]
showed that despite simi-
(between 34 and 46 km), suggesting that the regula-
lar heart rate,
˙
VO
2
, and perceived exertion, variable
tion of pace may be more influenced by the antici-
intensity exercise (~40–80%
˙
VO
2max
) resulted in
pated workload rather than actual distance per-
significantly greater plasma lactate concentration
formed.
[29,30,65]
and greater plasma glucose oxidation compared
In support of a central regulation of exercise
with a similar constant pace cycling trial (140 min-
intensity, numerous studies have shown that varia-
utes at ~65%
˙
VO
2max
). Further research is required
tions in power output, such as those often observed
to better understand the physiological implications
during prolonged self-paced competition,
[47]
are par-
of varying power output in order to determine the
alleled by changes in integrated surface electromy-
possible effects and limitations of a variable pacing
ography (iEMG).
[28,77]
In particular, St Clair Gibson
strategy.
et al.
[77]
found that reductions in iEMG paralleled a
decline in power output during repeated 1- and 4-km
2. Regulation of Pace
high-intensity bouts performed during a 100-km
cycling trial. However, contrary to these findings,
In 1965, Monod and Scherrer
[70]
first reported a
Hettinga et al.
[78]
recently showed that iEMG may
hyperbolic relationship between constant power out-
increase or remain unchanged despite a decline in
put and time to fatigue. This work was later expand-
power output towards the end of middle-distance
ed on
[71]
and developed into the whole-body critical
(4000 m) cycling time trials. In this study, iEMG of
power concept,
[72]
suggesting that fatigue and subse-
vastus lateralis and biceps femoris progressively
quent reductions in exercise intensity will occur if
increased throughout the trial irrespective of an
any contributing physiological system (i.e. anaerob-
evoked positive, negative or even pacing strategy.
[78]
ic energy supply) arises above its critical power.
Similarly, Hunter et al.
[79]
found that iEMG of rectus
Similarly, it has been suggested that muscle activa-
femoris remain constant during a 30-second Win-
tion and thus exercise intensity is centrally regulated
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
248 Abbiss & Laursen
gate anaerobic cycling test despite a 45% reduction power output (30%) during 5-km cycling time trials.
Evidence also suggests that pacing strategies may be
in power output. Collectively, these results suggest
influenced by the rate and capacity limitations of
that fatigue during exercise is not necessarily dictat-
different anaerobic and aerobic energy pathways to
ed by a centrally controlled downregulation of
supply the energy for sustained high-intensity
muscle activation. Instead, it appears that physiolog-
muscular contractions.
[2,17,78]
In support of this, Het-
ical changes within the muscle itself (i.e. peripheral
tinga et al.
[78]
recently showed during 4000-m cy-
fatigue) are also responsible for reductions in power
cling time trials that the power output originating
output and subsequent variations in pacing strate-
from anaerobic energy resources accurately traced
gies during short- and middle-distance events.
[78]
the pacing profile observed, whereas calculated aer-
However, the relative contributions and influence of
obic power output progressively increased through-
peripheral and central fatigue on the regulation of
out the trial. This finding occurred irrespective of
exercise intensity is poorly understood. Further re-
whether a positive, negative or even pacing strategy
search is needed to ascertain whether inconsisten-
was chosen. Correspondingly, mathematical models
cies with regard to the relationship between iEMG
aimed at determining optimal performance through-
and power output are associated with methodologi-
out a variety of exercise durations often incorporate
cal differences between studies. Indeed, iEMG has
mathematical constants pertaining to the contribu-
been found to parallel changes in power output
tion of anaerobic (i.e. adenosine triphosphate-phos-
during prolonged self-paced exercise,
[28,77]
but not
phocreatine, glycolysis) and aerobic (oxidation of
necessarily during short- and middle-distance
carbohydrates and lipids) energy supplies.
[61,83]
To
events.
[78,79]
date, however, few studies have examined the use of
As mentioned in section 1, the pacing strategy
such modelling along with individually measured
employed during an event plays an important role in
indicators of anaerobic and/or aerobic capacity so as
ensuring the best possible performance outcome.
to accurately predict performance and/or determine
During events of less than ~30 seconds in duration,
the optimal pacing strategy to employ during an
it seems that performance times will benefit from a
exercise task.
[59]
relatively fast starting strategy. During more pro-
longed events (>2 minutes), however, it appears that
3. Conclusion
athletes generally benefit from a more constant
pace.
[2,8,29,30,46,65,80]
During ultra-endurance events
It is understood that exercise performance can be
(>4 hours), athletes tend to adopt a positive pacing
significantly influenced by the distribution of work
strategy (see table I).
[46-52]
It is likely that differences
during an exercise task. However, the precise pacing
in pacing strategy observed under varying exercise
strategies that ensure the best possible performance
conditions and durations may be related to the rate
outcome under the variety of existing athletic com-
and capacity limits of various physiological sys-
petitions are not clear. It is possible that such uncer-
tems. Indeed, it has been suggested that exercise
tainty arises from the fact that an ‘optimal’ distribu-
intensity is controlled in a way that ensures that
tion of work will be influenced by numerous exter-
various physiological systems are maintained within
nal factors; including the specific activity being
certain critical limits.
[81,82]
Supporting this, Tucker
performed, the race duration, course geography and
et al.
[5]
showed that power output might be control-
environmental conditions. Research generally sug-
led in a way that regulates the rate of heat storage
gests that during extremely short-duration (30 se-
and prevents the development of hyperthermia
conds) events, athletes will benefit from an explo-
during prolonged cycling. Furthermore, Amann et
sive ‘all-out’ pacing strategy. During middle-dis-
al.
[82]
recently found that the manipulation of arterial
tance events (1.5–2 minutes) athletes tend to adopt a
oxygen levels (17.6–24.4 mL/O
2
/dL) resulted in
‘positive’ pacing strategy, whereby after peak speed
parallel increases to central neural drive (43%) and
is reached the athlete progressively slows. However,
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
Pacing Strategies during Competition 249
2008 Adis Data Information BV. All rights reserved. Sports Med 2008; 38 (3)
Table I. Summary of cross-sectional studies that have reported either self-selected or optimal pacing strategies during exercise of varying duration
Study Duration (min) Activity Distance (m) Athlete/model Pacing strategy Observed or
optimal
Tibshirani
[34]
0.16 Running 100 Elite runner Negative (all out) Observed
0.32 Running 200 Elite runner Negative (all out) Observed
van Ingen Schenau et al.
[84]
0.62 Speed skating 500 Mathematical model Negative (all out) Optimal
Foster et al.
[2]
0.67 Cycling 500 Trained cyclists Negative Observed
Keller
[24]
~0.75–0.83 Running <391 Mathematical model Negative (all out) Optimal
van Ingen Schenau et al.
[1]
0.97 Cycling 1 000 Mathematical model Negative (all out) Optimal
de Koning et al.
[7]
0.97 Cycling 1 000 Mathematical model Negative (all out) Optimal
van Ingen Schenau et al.
[84]
1.13 Speed skating 1 000 Mathematical model Negative (all out) Optimal
Foster et al.
[2]
1.45 Cycling 1 000 Trained cyclists Negative Observed
Sandals et al.
[25]
1.72 Running 800 Elite runners Positive Observed
Foster et al.
[2]
2.23 Cycling 1 500 Trained cyclists Positive Observed
Thompson et al.
[41]
2.64 Swimming 200 Trained – elite swimmers Positive Observed
Foster et al.
[8]
2.8 Cycling 2 000 Well trained cyclists Even Optimal
van Ingen Schenau et al.
[1]
4.27 Cycling 4 000 Mathematical model All out (<0.14 min) then even Optimal
Foster et al.
[2]
4.93 Cycling 3 000 Trained cyclists Even Observed
Garland
[40]
6.03–7.15 Rowing 2 000 Elite rowers Positive (reverse J-shaped) Observed
van Ingen Schenau et al.
[84]
7.04 Speed skating 5 000 Mathematical model All out (<0.13 min) then even Optimal
14.34 Speed skating 10 000 Mathematical model All out (<0.13 min) then even Optimal
Atkinson and Brunskill
[36]
27.68 Cycling 16 100 Trained – elite cyclists Even Optimal
Perrey et al.
[85]
30 Cycling Trained triathletes Even Observed
Padilla et al.
[59]
60 Cycling 53 000 Elite cyclist Even Observed
Laursen et al.
[46]
640 Swim/cycle/run 228 000 Well trained triathletes Positive Observed
Neumayr et al.
[51]
1 645 Cycling 525 000 Elite cyclist Positive Observed
250 Abbiss & Laursen
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during more prolonged events (>2 minutes) it seems
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Sports Med 1999; 27 (6): 359-79
Chris Abbiss is supported by an Australian Postgraduate
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Award (Department of Education, Science and Training,
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Australia) and an Edith Cowan University Excellence Award
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sity, Australia). There are no conflicts of interest that relate to
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the contents of this manuscript.
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... Επιπλέον, οι ερευνητές στην προσπάθεια τους να κατανοήσουν την απόδοση των αθλητών στα ατομικά αθλήματα εξέτασαν τους τρόπους με τους οποίους οι αθλητές κατανέμουν την ταχύτητα κατά την διάρκεια ενός αγώνα (Garland, 2005;Hettinga, De Koning, Broersen, Van Geffen, & Foster, 2006;Hettinga, De Koning, Meijer, Teunissen, & Foster, 2007;Mauger, Jones, & Williams, 2009;Tucker & Noakes, 2009). Έτσι, οι Abbiss & Laursen (2008) εισήγαγαν την έννοια της στρατηγικής ρυθμού, η οποία χρησιμοποιείται από τους αθλητές για τον αποτελεσματικό χειρισμό ενός αγώνα. Συγκεκριμένα, οι ερευνητές αναφέρουν ότι για να νικήσουν οι αθλητές τους συναθλητές τους και ταυτόχρονα να φτάσουν νωρίς στον τερματισμό, προσπαθούν να ρυθμίσουν με τον καλύτερο δυνατό τρόπο την ταχύτητά τους. ...
... Συγκεκριμένα, οι ερευνητές αναφέρουν ότι για να νικήσουν οι αθλητές τους συναθλητές τους και ταυτόχρονα να φτάσουν νωρίς στον τερματισμό, προσπαθούν να ρυθμίσουν με τον καλύτερο δυνατό τρόπο την ταχύτητά τους. Στην προσπάθειά τους αυτή μπορεί να χρησιμοποιήσουν τις εξής στρατηγικές: (1) αρνητικός ρυθμός (negative pacing), (2) θετικός ρυθμός (positive pacing), (3) σταθερός ρυθμός (even pacing), (4) μέγιστος δυνατός ρυθμός (all-out pacing), (5) παραβολικός ρυθμός (parabolic-shaped pacing), και (6) μεταβλητός ρυθμός (variable pacing) (Abbiss & Laursen, 2008). ...
... Τέλος, υπό ποικίλες εξωτερικές συνθήκες (περιβαλλοντικές, αλλά και ως προς τη διάρκεια και τη γεωγραφία της διαδρομής), η μεταβλητή στρατηγική (ή μεταβλητός ρυθμός) μπορεί να είναι η πιο κατάλληλη. Η μεταβλητή στρατηγική είναι ένας όρος που χρησιμοποιείται για τον καθορισμό των διακυμάνσεων στην ένταση και στην ταχύτητα της άσκησης (Abbiss & Laursen, 2008). ...
Article
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The main aim of this study was to record and analyze the pacing strategies of fitness men during the 2015 National CrossFit Championship that took place in Thessaloniki, Greece. We were also interested to observe if the pacing strategies would be affected by the number and the type of the exercises performed by the athletes. More specifically, 363 fitness men participated in the competition, where their pacing strategies were recorded by using the expert software application SportScout. For the statistical analysis of the data, the non-parametric Crosstabs and Chi-square test of independence were conducted. According to our results, it was observed that (a) 50% of fitness men were using the positive pacing, (b) 21% were using the variable pacing, (c) 18% were using the even pacing, (d) 9% were using the parabolic-shaped pacing, and (e) 2% were using the negative pacing. In addition, 44% of the winners were using the even pacing. All the pacing strategies were dependent on the number of the exercises (p<.001) and the type of the exercises performed by athletes (p<.001). In conclusion, fitness men were using different pacing strategies in their attempt to achieve the best possible performance during the competition. The most widely used strategy was the positive pacing, while all the selected strategies were affected by the number and the type of the exercises performed by the athletes.
... The distribution of power output throughout a selfpaced endurance exercise has been defined as pacing strategy and has a great influence on overall performance (1,2). Although pacing strategy is mostly self-determined, athletes might benefit from a ''forced'', relatively high power start of a short-duration (e.g., 4-km) cycling time trial (2)(3)(4), which is performed in the severe-intensity domain (i.e., above critical power) (5). ...
... The distribution of power output throughout a selfpaced endurance exercise has been defined as pacing strategy and has a great influence on overall performance (1,2). Although pacing strategy is mostly self-determined, athletes might benefit from a ''forced'', relatively high power start of a short-duration (e.g., 4-km) cycling time trial (2)(3)(4), which is performed in the severe-intensity domain (i.e., above critical power) (5). In fact, the adopted pace during the first quarter of a short-duration cycling task lasting B5 min can meaningfully influence finishing time, suggesting that the first quarter of a short-duration time trial is a crucial part determining overall performance (6,7). ...
... Coefficient of variation was 0.57 ± 0.41% and typical error of measurement was 2.64 s (0.69%). There was no significant difference (F (2,22) =0.010; P=0.990; Z p 2 =0.001) between the three strategies for overall exercise performance (self-paced 379.8 ± 14.6 s, all-out+mean 380.0 ± 16.7 s, and all-out+5%mean 380.2±12.1 s, Figure 2). Similarly, there was no significant difference (F (2,22) =0.08, respectively, Table 1). ...
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In cycling, there is a body of evidence that supports that an all-out start strategy is superior to an even-pacing strategy, but it is unknown whether an all-out start strategy is superior to a self-paced strategy. In the present study, we investigated the effects of three different pacing strategies on 4-km cycling time trial performance. After preliminary trials (familiarization trials and a baseline 4-km cycling time trial), in a randomized and counterbalanced order, twelve male cyclists (32.3±7.2 years old, maximum rate of O2 uptake (V̇O2peak) 4.3±0.4 L/min) completed: 1) a self-paced 4-km cycling time trial; 2) an all-out start (∼10 s), followed by maintenance of the average baseline trial power for the first km and self-paced cycling for the remaining trial (all-out+mean); and 3) an all-out start (∼10 s), followed by a power 5% above the average baseline trial power for the first km and self-paced cycling for the remaining trial (all-out+5%mean). Although there was a significant interaction between power and distance (P=0.001) with different power distribution profiles throughout the trial, there was no significant difference (P=0.99) between the three strategies for overall exercise performance (self-paced 379.8±13.9 s, all-out+mean 380.0±16.0 s, and all-out+5%mean 380.2±11.5 s). Oxygen uptake, rating of perceived effort, and heart rate were also similar across the pacing strategies. Different all-out start strategies did not confer additional benefits to performance compared to a self-paced strategy.
... These elements shed light on the occurrence of fatigue that athletes face during a race, affecting their performance. Given the different distances competed in flatwater kayaking (i.e., from 200 m to 1000 m), athletes employ different pacing strategies to optimize power distribution throughout the sprint and to limit the occurrence of fatigue [15,16]. Therefore, pacing strategies represent a performance factor in flatwater sprint kayaking [17]. ...
... These elements showed that athletes implemented two different pacing strategies during exercise. Athletes implemented an all-out strategy during the 40 s sprint, characterized by a greater power being developed at the start and then maintained as long as possible, while an even-pace strategy, characterized by a parabolic-shape profile, was implemented during the 4 min exercise [16,17]. These two strategies, previously reported for well-trained kayakers [6,17] and rowers [25], may improve performance through optimization of the balance between power production and the occurrence of fatigue. ...
Article
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Paddling technique and stroke kinematics are important performance factors in flatwater sprint kayaking and entail significant energetic demands and a high strength from the muscles of the trunk and upper limbs. The various distances completed (from 200 m to 1000 m) require the athletes to optimize their pacing strategy, to maximize power output distribution throughout the race. This study aimed to characterize paddling technique and stroke kinematics during two maximal sprints of different duration. Nine nationally-trained participants (2 females, age: 18 ± 3 years; BMI: 22.2 ± 2.0 Kg m −1) performed 40 s and 4 min sprints at maximal intensity on a kayak ergometer. The main findings demonstrated a significantly greater mean stroke power (237 ± 80 W vs. 170 ± 48 W; p < 0.013) and rate (131 ± 8 spm vs. 109 ± 7 spm; p < 0.001) during the 40 s sprint compared to the 4 min sprint. Athletes used an all-out strategy for the 40 s exercise and a parabolic-shape strategy during the 4 min exercise. Despite the different strategies implemented and the higher muscular activation during the 40 s sprint, no change in paddling technique and body coordination occurred during the sprints. The findings of the present study suggest that the athletes constructed a well-defined profile that was not affected by fatigue, despite a decrease in power output during the all-out strategy. In addition, they regulated their paddling kinematics during the longer exercises, with no change in paddling technique and body coordination.
... Dans ce cas, le temps total en endurance durant un test intermittent dépend toujours de quatre principaux paramètres : la puissance durant l'intervalle de travail, la puissance durant l'intervalle dit «de repos », la durée de l'intervalle de travail et la durée de l'intervalle de « repos » ( Figure 13, d'après Morton et Billat (129)). continus d'une durée maximale d'environ 2 à 3 minutes, la littérature indique que la performance optimale est généralement atteinte avec une stratégie all-out (un effort total, autrement dit les sujets doivent atteindre leur puissance/vitesse de pointe le plus rapidement possible dès le début du test et la maintenir aussi élevée que possible) ou un départ rapide (149)(150)(151)(152). Cependant, audelà de consignes de temps limite de 2 à 3 minutes, la stratégie de course optimale pour améliorer la performance reste à déterminer. ...
... Ainsi, le choix de la stratégie d'allure n'apparaît pas ordonné uniquement par la modification des afférences physiologiques engendrées par l'exercice, mais est aussi fonction de l'usage passé et de la durée d'effort appréciée (149). En effet, ce type de stratégie (départ rapide, diminution de l'allure puis augmentation finale), modélisé par des courbes en U, en J, ou en J inversé (149) Enfin, notre troisième étude sur le 3000m nous a permis de comprendre que même si l'on utilise des variables physiologiques (fréquence cardiaque, V O2) considérées comme limitantes de la performance, en tant que variable contrôle de la performance, celles-ci s'avéraient efficientes pour réaliser une performance. ...
Thesis
Cette thèse avait pour ambition de contribuer à la compréhension des effets des variables de contrôle sur la performance, que sont le temps, la vitesse, la perception de l'effort (article 1), la distance (article 2) ainsi que V̇O2 et la fréquence cardiaque (article 3). Nous avons pu réaliser ce travail en utilisant les nouvelles possibilités qu'offrent les nouvelles technologies affranchissant le physiologiste du tapis roulant tout en disposant de la possibilité de contrôler par Bluetooth® toutes les variables physiologiques. Nous avons mis en évidence que : 1) les athlètes étaient capables d'adapter et de reproduire des réponses physiologiques non seulement en intensité mais en durée (article 1), 2) lorsque la variable de contrôle est la distance avec une mise en situation de compétition, la contribution de l'énergie à V̇O2max était relativement identique en proportion de l'énergie aérobie et ce, du 100 au 10,000m (article 2). Il y aurait donc un continuum énergétique allant du sprint au 10 kilomètres qui pourrait être une information intégrée dans l'organisme de façon centrale (demi-fond et fond) ou métabolique (sprint), 3) Enfin, nous avons montré que même dans un effort assez long (12 minutes) et maximal, le coureur tirait bénéfice d'une aide de contrôle « physiologique » par la fréquence cardiaque ou V̇O2 pour parvenir à sa meilleure performance. En conclusion, ce travail de thèse propose une méthodologie dans laquelle le coureur devient autonome dans le choix de sa stratégie de vitesse en s'affranchissant des calculs de vitesse cible à partir des seuils physiologiques, V̇O2max et autres facteurs physiologiques rendus limitants en cela.
... The main characteristic of cyclic sports is that motor activities involve repetitive movements. Each movement is a complex kinetic unit in itself, which implies an alternating and harmonious connection into one appropriate coordinated rhythm 1 . The swimmers perform them with the same success during the cyclic motion of the swimming stroke 2 . ...
Article
This research aimed to determine differences in pace strategies between the two groups of elite swimmers at the 2019 World Championships in the 100m butterfly. The overall sample included in this study consisted of 16 male participants of the World Swimming Championships in South Korea, who were divided into two groups. The first group consisted of finalists (22.88 ± 3.83 years; n = 8), and the second group of semifinalists (24.63 ± 4.93 years; n = 8). The results are taken from the official results from the omega timing website. A T-test was used to determine the differences between groups. Finalist were faster than semifinalists for first lap (23.82 ± .45 vs. 24.26 ± .30 s, p= .035), second lap (27.34 ± .31 vs. 27.78 ± .34 s, p= .018) as well as final time (51.16 ± .74 vs. 52.04 ± .26 s, p= .007) but not in drop off (3.53 ± .22 vs. 3.51 ± .59 s, p= .946). Finalists of the 100-m butterfly event at international competitions swam a faster first and second lap compared to their competitors in the semifinal group. Both groups are elite swimmers and showed abilities to maintain swimming velocity in first and second laps, therefore minimizing the drop-off from the start to the end of the race. The pacing strategies of elite swimmers remain relatively stable. The obtained results can be a good starting point in the learning process and education of teachers who coach young swimmers.
... The duration of rowing races requires the athletes to manage their effort implementing their best pacing strategy. Further, the optimal pacing strategy may vary depending on race duration and format (Abbiss & Laursen, 2008;Dimakopoulou et al., 2018;Konings & Hettinga, 2018). Therefore, the study of pacing strategy in rowing plays an important role in understanding rowing performance and insights on optimal pacing strategy may increase the likelihood of competition success. ...
Article
This study aimed to describe and compare the pacing strategy and performance of world level under 19 (U19), under 23(U23) and senior single scullers during world championship races. Data are from 8 years of Rowing World Championships A and B finals of single scull races for U19, U23 and senior age scullers. Pacing was determined for velocity, stroke rate and, distance per stroke (SD). Scullers presented a fast start strategy for all races investigated. There was no significant difference in pacing between age groups. Velocity peaked at the 100m (U19) and 150m (U23 and senior) mark. The scullers in the older age groups showed superior performance scores of the studied variables. U19, Under 23 and senior single scullers present similar pacing regardless of their age group. Performance of single sculler improve from U19, to U23 to senior for both lightweight and heavyweight classes. The results of this study may serve as a reference for athletes, coaches, and sports scientists to be implemented in training.
... In mass-start competitions, all skiers start together, often on narrow tracks with limited possibilities to advance in the field. Accordingly, tactical choices are crucial but may consequently influence physiological and biomechanical demands (8, 9). For example, changing position in a narrow track across fluctuating terrain, which induces rapid changes in work rate, requires both tactical and technical flexibility (8). ...
Article
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Introduction Although five of six Olympic events in cross-country skiing involve mass-starts, those events are sparsely examined scientifically. Therefore, in this study, we investigated speed profiles, pacing strategies, group dynamics and their performance-determining impact in a cross-country skiing mass-start competition. Methods Continuous speed and position of 57 male skiers was measured in a six-lap, 21.8 km national mass-start competition in skating style and later followed up with an online questionnaire. Skiers ranked from 1 to 40 were split into four performance-groups: R1–10 for ranks 1 to 10, R11–20 for ranks 11 to 20, R21–30 for ranks 21 to 30, and R31–40 for ranks 31 to 40. Results All skiers moved together in one large pack for 2.3 km, after which lower-performing skiers gradually lost the leader pack and formed small, dynamic packs. A considerable accordion effect occurred during the first half of the competition that lead to additional decelerations and accelerations and a higher risk of incidents that disadvantaged skiers at the back of the pack. Overall, 31% of the skiers reported incidents, but none were in R1–10. The overall trend was that lap speed decreased after Lap 1 for all skiers and thereafter remained nearly unchanged for R1–10, while it gradually decreased for the lower-performing groups. Skiers in R31–40, R21–30, and R11–20 lost the leader pack during Lap 3, Lap 4, and Lap 5, respectively, and more than 60% of the time-loss relative to the leader pack occurred in the uphill terrain sections. Ultimately, skiers in R1–10 sprinted for the win during the last 1.2 km, in which 2.4 s separated the top five skiers, and a photo finish differentiated first from second place. Overall, a high correlation emerged between starting position and final rank. Conclusions Our results suggest that (a) an adequate starting position, (b) the ability to avoid incidents and disadvantages from the accordion effect, (c) tolerate fluctuations in intensity, and (d) maintain speed throughout the competition, particularly in uphill terrain, as well as (e) having well-developed final sprint abilities, are key factors determining performance during skating-style mass-start cross-country skiing competitions.
... ej. evitando una fatiga prematura que "pase factura", debido a una salida excesivamente rápida al comienzo de la prueba), el diseño de una adecuada estrategia y la elección previa a la carrera de un RC óptimo que permita al atleta desarrollar un PB que evite o minimice fluctuaciones en el ritmo de competición, será clave en el éxito en esta distancia (Abbiss & Laursen, 2008;Angus, 2014). En este sentido, los diferentes PB durante la competición de maratón, y su relación con el rendimiento final, han sido objeto de estudio de diversos trabajos, proponiendo diferentes perfiles o formas en las que los corredores compiten (Hanley, 2016;Renfree & Casado, 2018;Santos-Lozano et al., 2014). ...
Chapter
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El ritmo de competición (RC) es sumamente importante para establecer un óptimo pacing behaviour (PB) y así alcanzar un mayor rendimiento en la carrera a pie. Por lo tanto, el objetivo del presente estudio fue determinar posibles diferencias en el PB desarrollado en base al grupo etario y sexo durante la Maratón de Berlín. 43.970 atletas formaron parte del estudio, divididos en grupos de edad en base a lo establecido por la IAAF. Para determinar el PB, diez segmentos de distancia fueron tenidos en cuenta (0–5 km, 5–10 km, 10–15 km, 15–20 km, 20–21,1 km, 21,1–25 km, 25–30 km, 30–35 km, 35–40 km, 40–42,2 km). En los hombres se dió un grado de asociación significativo entre PB y los diferentes grupos de edad (X2 (26) = 380; p <0,01; CC=0,111). El negative pacing (NP) fue el perfil dominante en los grupos de edad masculinos. Las mujeres mostraron asociaciones significativas entre el grupo de edad y el PB desarrollado (X2 (24) = 178 p <0,01; CC=0,115), siendo el NP el más utilizado en casi todos los grupos etarios. Finalmente, los atletas más jóvenes tienen una mayor tendencia al even pacing (EP), mientras que los grupos de edad más avanzada tienen preferencia por el NP. No se observaron diferencias entre los perfiles más utilizados por sexo o grupo etario. Sin embargo, se pudo constatar que los corredores más rápidos desarrollaron un EP, para casi todos los grupos de edad y sexo.
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Apresentação no 19º CONGRESSO MUNDIAL QUADRENUAL DA ASSOCIAÇÃO INTERNACIONAL DE EDUCAÇÃO FÍSICA E ESPORTE PARA MENINAS E MULHERES (IAPESGW) O resumo da apresentação está na pagina 146.
Conference Paper
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The subject of the study is a descriptive analysis of the prevalence of kyphosis and lordosis in the population, regardless of age, gender or sport. The aim of this paper is to determine the presence of kyphosis and lordosis. The following databases were searched for collecting previous studies of kyphosis and lordosis: PubMed/Medline, PEDro, SCIndeks, DOAJ. The research titles found, abstracts and whole texts were then read and analyzed. In order for the research to be accepted for final analysis, we had to satisfy one of two criteria: that in the papers were analyzed the representation of kyphosis and lordosis. Based on the results, a reduced Napoleon Volansky method, inspection, somatometry, and somatoscopy, as well as a 'Spinal mouse' for the assessment of deformities of lordosis and kyphosis. Based on the results obtained, it can be concluded that both deformities occur independently of age or gender, as well as that sports practitioners are also susceptible to these spinal deformities. The volume of the reviewed papers indicates a small amount of information derived from scientific research. Despite the small number of papers analyzed, it can be concluded that the degree of spinal deformity in youth and children is very high. On the basis of such a conclusion, it is inevitable that further research should focus more on this problem, as well as measures to prevent the occurrence of these deformities. Also, the inclusion of corrective exercise should be considered to reduce the proportion of deformity.
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This review presents information that is useful to athletes, coaches and exercise scientists in the adoption of exercise protocols, prescription of training regimens and creation of research designs. Part 2 focuses on the factors that affect cycling performance. Among those factors, aerodynamic resistance is the major resistance force the racing cyclist must overcome. This challenge can be dealt with through equipment technological modifications and body position configuration adjustments. To successfully achieve efficient transfer of power from the body to the drive train of the bicycle the major concern is bicycle configuration and cycling body position. Peak power output appears to be highly correlated with cycling success. Likewise, gear ratio and pedalling cadence directly influence cycling economy/efficiency. Knowledge of muscle recruitment throughout the crank cycle has important implications for training and body position adjustments while climbing. A review of pacing models suggests that while there appears to be some evidence in favour of one technique over another, there remains the need for further field research to validate the findings. Nevertheless, performance modelling has important implications for the establishment of performance standards and consequent recommendations for training.
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This article traces the study of interrelationships between power output, work done, velocity maintained or distance covered and the endurance time taken to achieve that objective. During the first half of the twentieth century, scientists examined world running records for distances from <100m to >1000km. Such examinations were empirical in nature, involving mainly graphical and crude curve-fitting techniques. These and later studies developed the use of distance/time or power/time models and attempted to use the parameters of these models to characterise the endurance capabilities of athletes. More recently, physiologists have proposed theoretical models based on the bioenergetic characteristics of humans (i.e. maximal power, maximal aerobic and anaerobic capacity and the control dynamics of the system). These models have become increasingly complex but they do not provide sound physiological and mathematical descriptions of the human bioenergetic system and its observed performance ability. Finally, we are able to propose new parameters that can be integrated into the modelling of the power/time relationship to explain the variability in endurance time limit at the same relative exercise power (e.g. 100% maximal oxygen uptake).
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The purpose of this study was to determine if runners who completed a 100 km ultramarathon race in the fastest times changed their running speeds differently compared to those runners who ran an overall slower race. Times were taken from the race results of the 1995 100 km IAU World Challenge in Winschoten, Netherlands. Race times and 10 km split times were analyzed. Runners (n = 67) were divided into groups of ten with the last group consisting of seven runners. The mean running speed for each 10 km segment was calculated using each runner's 10 km split times. Mean running speed was calculated using each runner's race time. The first 10 km split time was normalized to 100, with all subsequent times adjusted accordingly. The mean running speed for each group at each 10 km split was then calculated. The faster runners started at a faster running speed, finished the race within 15 % of their starting speed, and maintained their starting speed for longer (approximately 50 km) before slowing. The slower runners showed a greater percentage decrease in their mean running speed, and were unable to maintain their initial pace for as long. It is concluded that the faster runners: 1) ran with fewer changes in speed, 2) started the race at a faster running speed than the slower runners, and 3) were able to maintain their initial speed for a longer distance before slowing. Key PointsFaster runners in the 100 km race;ran with fewer changes in running speed compared to the slower runners;started the race at a faster running speed than the slower runners;were able to maintain their initial running speed for longer distances than slower runners.
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This article examines how pacing strategies during exercise are controlled by information processing between the brain and peripheral physiological systems. It is suggested that, although several different pacing strategies can be used by athletes for events of different distance or duration, the underlying principle of how these different overall pacing strategies are controlled is similar. Perhaps the most important factor allowing the establishment of a pacing strategy is knowledge of the endpoint of a particular event. The brain centre controlling pace incorporates knowledge of the endpoint into an algorithm, together with memory of prior events of similar distance or duration, and knowledge of external (environmental) and internal (metabolic) conditions to set a particular optimal pacing strategy for a particular exercise bout. It is proposed that an internal clock, which appears to use scalar rather than absolute time scales, is used by the brain to generate knowledge of the duration or distance still to be covered, so that power output and metabolic rate can be altered appropriately throughout an event of a particular duration or distance. Although the initial pace is set at the beginning of an event in a feedforward manner, no event or internal physiological state will be identical to what has occurred previously. Therefore, continuous adjustments to the power output in the context of the overall pacing strategy occur throughout the exercise bout using feedback information from internal and external receptors. These continuous adjustments in power output require a specific length of time for afferent information to be assessed by the brain's pace control algorithm, and for efferent neural commands to be generated, and we suggest that it is this time lag that crates the fluctuations in power output that occur during an exercise bout. These non-monotonic changes in power output during exercise, associated with information processing between the brain and peripheral physiological systems, are crucial to maintain the overall pacing strategy chosen by the brain algorithm of each athlete at the start of the exercise bout.
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