Revisão informação sistema central

Sports Med 2006; 36 (8): 705-722
REVIEW ARTICLE 0112-1642/06/0008-0705/$39.95/0

 2006 Adis Data Information BV. All rights reserved.

The Role of Information Processing
Between the Brain and Peripheral
Physiological Systems in Pacing and
Perception of Effort
Alan St Clair Gibson,1,2 Estelle V. Lambert,1 Laurie H.G. Rauch,1 Ross Tucker,1
Denise A. Baden,3 Carl Foster4 and Timothy D. Noakes1
1 Brain Sciences Research Group, MRC/UCT Research Unit of Exercise Science and Sports
Medicine, University of Cape Town, Cape Town, South Africa
2 MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of
Cape Town, Cape Town, South Africa
3 Department of Psychology, University of Southampton, Southampton, UK
4 Department of Exercise and Sport Science, University of Wisconsin, La Crosse,
Wisconsin, USA

Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705
1. Pacing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706
2. Regulation of Overall Pacing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707
3. The Requirement of an Internal Clock for Pacing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709
4. Feedback Regulation of Pacing Strategy during an Exercise Bout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711
5. Information Processing between the Brain Pacing Algorithm and Peripheral Physiological
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712
6. The Sensation of Perceived Effort Associated with Particular Pacing Strategies . . . . . . . . . . . . . . . . . 716
7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719

Abstract 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 knowl-
edge 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

706 St Clair Gibson et al.

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.

Any athletic event has, of necessity, a beginning enabling an athlete to complete an exercise bout in
and an endpoint. In order to reach the endpoint of a the shortest possible time, while avoiding cata-
race in the fastest possible time, while maintaining strophic failure of any physiological system. A fur-
enough metabolic capacity to prevent premature ther aim is to examine how these mechanisms could
fatigue before the endpoint, the athlete requires create the conscious awareness of perceived effort
some type of pacing strategy. Pacing strategies dif- associated with this pacing strategy.
fer according to the length of the athletic event, the
environment in which the event is performed, the 1. Pacing Strategies
motivation of the athlete, the knowledge and experi-
ence of the athlete, and each athlete’s particular While a large amount of research has focused on
physiological capacity. the limits to human performance and fatigue during
In order to establish, maintain and alter a pacing exercise, only a few studies have examined the
strategy for a particular event, the brain must pro- influence of pacing on exercise performance.[1,2]
cess an enormous quantity of data from the external Indeed, Foster has suggested that research on pacing
environment and from the different physiological strategy during exercise is the ‘unexplored territory
systems of the body. These data are used to calculate in sports performance’ (unpublished observation).
whether the athlete’s power output and associated While there are an infinite number of possible
current metabolic rate are appropriate for the dis- pacing strategies that an athlete may adopt during an
tance of the event still to be covered in the current event, four broad categories of pacing strategies
environmental conditions, given the athlete’s availa- have been described[1] (figure 1). These are:
ble fuel reserves and current rate of heat production. • an all-out pacing strategy, in which the athlete
Pacing, therefore, can be described as a strategy begins the event at the maximal possible pace and
employed to avoid catastrophic failure in any pe- attempts to continue this maximal pace until the
ripheral physiological system. event ends, although a decrement in pace may
Neither the control of pacing during an exercise occur towards the end of the event (figure 1a);
bout by the brain, nor the relationship between pac- • a slow start strategy, in which the athlete starts
ing and the sensation of perceived exertion associat- off at a submaximal pace and increases pace
ed with this strategy has been well described. The steadily though the event (figure 1b);
aim of this article is, therefore, to examine mecha- • an even paced strategy, in which pace is main-
nisms and to develop a hypothetical model of how tained at a constant submaximal rate throughout
the brain creates and maintains a pacing strategy the event (figure 1c);

 2006 Adis Data Information BV. All rights reserved. Sports Med 2006; 36 (8)

Pacing Control Mechanisms 707

• a variable pace strategy, in which pace is maxi- record setting performances in the 10 000m running
mal in the first stage of an event, is moderated event was always the fastest.[7] Ansley et al.[8] simi-
during the middle of the event, and increased larly found that power output and integrated electro-
towards the end of the event (figure 1d). myographic (IEMG) activity of subjects performing
Studies performed to assess which of these dif- a 4km cycling trial was increased in the final 60
ferent strategies is optimal are inconclusive. Bishop seconds of each trial. However, Mattern et al.[9]
et al.[3] found that for a 2-minute kayaking laborato- found that for a 20km cycling time trial, starting
ry trial, the all-out pacing strategy produced superior 15% below average power output and increasing
results than an even pacing strategy. De Koning et power output at the end of the trial proved to be a
al.[4] found that for cycle racing on the track, an all- faster strategy than starting either 15% above aver-
out strategy was optimal for cycling a 1000m time age power output or maintaining average power
trial, whereas an all-out start followed by a constant output for the duration of the time trial. Therefore,
power output was optimal for a 4000m pursuit trial. there appears to be no clear optimal pacing strategy
Foster et al.[5] examined pacing strategies during identified by previous research, and it may be that
laboratory cycling trials of 500m, 1000m, 1500m each individual has a uniquely optimal pacing strate-
and 3000m duration and found that athletes chose an gy.[10] Further research is required to help clarify
initial power output that was high and subsequently which of the different possible pacing strategies are
decreased, with an increased power output in the optimal for different sports and for different dis-
final section of all these trials. Similarly, Kay et al.[6] tances performed during athletic events, or indeed
found that, during a 60-minute laboratory cycling whether there is no single optimal pacing strategy.
time trial interspersed with six sprints, power output
during each sprint decreased from the first to the 2. Regulation of Overall Pacing Strategy
fifth sprint, but increased during the sixth and final
sprint, which occurred in the last minute of the time Pacing strategies require continual regulation by
trial. Similarly, the last kilometre of three world the brain during an exercise bout. During an ‘all-out’
a b
100

80

60

40

20
Power output (W)

0

c d
100

80

60

40

20

0
0 1 2 3 4 5 6 0 1 2 3 4 5 6
Distance (km)
Fig. 1. Different pacing strategies used by athletes include: (a) an all-out pace strategy; (b) a slow start strategy; (c) an even pace strategy;
and (d) a variable pace strategy.



pacing strategy, a degree of pacing will still occur, formed by the athlete.[24] Other factors taken into
as even during a short-duration maximal isometric account by the brain-pacing algorithm at the start of
contraction, which produces substantially less force the event would be factors such as current environ-
output than is achieved during shortening contrac- mental conditions, current health status and meta-
tions, muscle is not completely recruited,[11-13] and bolic fuel reserves.[25] The algorithmic process
force output appears to be reduced in a controlled would then send out efferent neural commands to
manner using different neural recruitment strate- generate appropriate power output, and metabolic
gies.[12,14-17] Therefore, during an ‘all-out’ sprint rates in the different organs and physiological sys-
event of even a few seconds,[18] or a maximal iso- tems of the body.
metric voluntary contraction, there is likely to be a Once the athlete begins the event, afferent input
pacing strategy involved, with changes in muscle supplying information from metaboreceptors, noci-
recruitment occurring throughout the event in a ceptors, thermoreceptors, cardiovascular pressure
manner that would prevent catastrophic system fail- receptors and mechanoreceptors would inform the
ure.[19] Any of the other three pacing strategies de- teleoanticipation pacing centre in the brain about
scribed in section 1 would require further regulation motion, force output, muscle metabolic rate and core
by the brain in addition to the regulation of the temperature changes associated with the chosen
starting power output, as modifications in power power output.[25-30] If the algorithm indicated a pace
output must occur throughout the event in order to that was too fast to allow the athlete to reach the
change the pacing strategy during the event. endpoint of the race without premature fatigue
For these alterations in power output to occur in a caused by a catastrophic failure occurring in any
deterministic way, the brain is required to monitor physiological system, further efferent neural com-
whether the changes in power output are relevant in mands would be modified to reduce the power out-
the context of the ongoing pacing strategy. In order put, and associated metabolic rate, to what the cen-
to make these calculations, certain information must tral algorithm perceived would be an appropriate
be available to the brain. It has been suggested that level of activity. Conversely, if the algorithm indi-
knowledge of the distance or time to be covered cated the pace was too slow, further efferent neural
during an event provides crucial input into a mathe- commands would be modified to increase the power
matical algorithm used by the brain to monitor and output, and metabolic rate would therefore also in-
determine whether the current power output is ap- crease.
propriate in the context of the overall pacing strate- Therefore, muscle power output would be contin-
gy.[17,20-23] In a process described by Ulmer[23] as uously modified throughout the exercise bout using
‘teleoanticipation’, knowledge of the endpoint is this integrative teleoanticipatory control algo-
used by the brain as the anchor for creating the rithm[31] (figure 2). These modifications in power
particular algorithm for a particular exercise bout output would result in an associated change in meta-
and moderating power output during the exercise bolic rate of the different peripheral physiological
bout. For example, the algorithm used by the brain systems. As a result, control of metabolic activity
in setting a particular pacing strategy will be very would be vested in the teleoanticipatory centre in the
different for a 5km compared with a 100km running brain and the chosen mathematical algorithm. Since
or cycling event. the mathematical algorithm is selected for an appro-
In the teleoanticipatory process described by priate endpoint and expected distance or duration of
Ulmer,[23] the brain algorithm for a particular event exercise, knowledge of the endpoint must, therefore,
with a known endpoint would initiate a particular be one of the principle controllers of metabolic
pacing strategy at the start of the event, based on activity in peripheral physiological systems, as sug-
prior knowledge of previous similar events per- gested by Ulmer.[23]



Efferent indicated that the internal clock operates at a sub-
conscious rather than a conscious level during ath-
Afferent Pace
letic activity. The ability of athletes to reproduce
almost identical pacing strategies, and hence overall
HR 60 HR 180 HR 130 HR 160 performances when completing sequential exercise
RR 16 RR 50 RR 32 RR 45 tests of similar and known duration in the laborato-
ry, even with minimal external information regard-
BG 5
ing distance covered or time elapsed,[33,34] provides
BG 6 BG 5 BG 5.5
further evidence of the robustness of this internal
Start Early Later Endpoint clock and the teleoanticipatory pacing mathematical
exercise exercise
algorithm.[22]
Fig. 2. Changes in power output during an exercise bout are regu-
lated by a teleoanticipatory regulatory centre in the brain, which The robustness of the internal clock is not only
continuously alters power output by altering efferent neural com-
mand in order to maintain the overall pacing strategy while avoiding
demonstrated in athletes, but is also evident in other
catastrophic system failure. Afferent information from receptors re- species. For example, after a period of conditioning,
cording changes in peripheral physiological system variables such the head entry of rats into a feeding cup occurs at the
as heart rate (HR), respiratory rate (RR) and blood glucose concen-
trations (BG) is used by the teleoanticipatory centre to ensure the
same time prior to expected food delivery across a
adjustments in power output are appropriate for the duration of the range of different conditions.[35] Kirkpatrick[35] sug-
exercise bout that remains (reproduced from St Clair Gibson et gested that the timing of head entry can be calculat-
al.,[31] with permission from Elsevier).
ed as the mean expected time remaining until the
next food delivery as a function of mean time since
3. The Requirement of an Internal Clock prior food delivery. Birds migrating to the same
for Pacing Strategy destination leave at a similar time each year and do
not leave until they have sufficient fuel for their
A further crucial component of the brain’s pacing
journey in the form of increased body fat stores.
algorithm is the capacity to monitor the passage of
They alter their flight speed throughout their migra-
time. The brain’s algorithm cannot accurately calcu-
tory journey to accommodate the changing body-
late the changing metabolic requirements for the
weight as a result of altering fuel reserves, so as to
remainder of an exercise bout if it does not have
reach the end of their journey before completely
knowledge of the distance that has been covered and
expending all their fuel reserves.[36] Therefore, the
time that has passed during a particular event at a
internal clock and the associated pacing strategy that
particular pace. The brain’s internal time-keeping
mechanism during an exercise bout appears to be it enables, appears to be universal phenomena.
robust. Albertus et al.[32] altered the distance mark- The internal clock also appears to operate using a
ers during a 20km cycling time trial, making the scalar time scale, in that performance curves of
distances between each kilometre either the correct temporal tasks of similar duration or distance super-
distance, longer, shorter, or randomly longer or impose when measured on a relative compared with
shorter, while the subjects were informed that the an absolute time scale. This has been described as
distances covered were an exact kilometre. Despite scalar expectancy theory.[20,37,38] For example, par-
this deception, the time taken to complete each trial ticipants engaged in different types of tasks (vigi-
was similar. The authors concluded that these find- lance, rotary pursuit tracking and muscular activity)
ings indicated that the subject’s internal clock and showed an ‘endspurt’ effect whereby they increased
associated internal judgment of the distance covered their output/activity when the task was 90% com-
was robust, and was not affected by external verbal pleted.[39-41] This endspurt occurred at 90% of ex-
information supplied to each subject during the dif- pected task duration, irrespective of the length or
ferent trials. Interestingly, the subjects in this trial type of task, which suggests that relative rather than
did not appear to be aware of this deception, which absolute task duration was the controlling factor for



the internal clock used as part of the brain’s pacing being caused by decision-making processes associ-
algorithm.[20] ated with absolute timepoints during the trials.
During cycling time trials in which subjects were The internal clock appears to be affected by
deceived about the true distance of the trials, believ- nutrient intake. The head entry of rats into a feeding
ing them all to be 40km in length when they were cup was delayed to closer to the feeding time after
actually 34, 40 and 46km long, Nikolopoulos et being given a lecithin (phosphatidylcholine) or case-
al.[42] found that the subjects paced themselves simi- in (protein) snack.[48] In contrast, head entry was
larly in all trials. This indicated that their pacing premature after ingestion of a sucrose (carbohy-
strategy for each trial was based on perceived rather drate) snack. The authors suggested that the differ-
than actual distance covered. Furthermore, in three ent snacks induced changes in precursor levels of
of our own laboratory exercise trials of differing centrally acting neurotransmitters, which resulted in
duration and intensity, the ratings of perceived exer- changes in the neural pathways responsible for the
tion (RPE) were of a similar magnitude, (~18 out of function of the internal clock. This interpretation is
a possible 20 on the Borg RPE scale)[43,44] at the end supported by the presence of impaired timing of
of each of the exercise bouts. The three trials con- movement and perceptual timing deficits in patients
sisted of a 60-minute cycling time trial interspersed with Parkinson’s disease.[49] There is a dopamine
with six 1-minute sprints during the trial, including deficiency in the basal ganglia associated with this
one over the last kilometre,[6] a cycling trial with disorder. Therefore, while the internal clock respon-
increasing workloads until exhaustion lasting ~50 sible for pacing appears to be robust, it does seem to
minutes,[45] and a running maximal aerobic test per- be altered by ingestion of certain food types and by
formed on a treadmill lasting ~10 minutes.[46] The disease processes.
results of these studies suggest that both subcon- It must be noted that most of the discussion above
scious pacing strategies and conscious perception of is of exercise with a known endpoint, also described
effort utilise an internal clock based on scalar rather as ‘closed loop’ activity.[19,23] Exercise that is per-
than absolute time. formed with no known endpoint is known as ‘open
For the brain teleoanticipatory centre to utilise a loop’ activity. However, all athletes eventually stop
scalar internal clock, the internal clock’s scaling at some point in an ‘open-loop’ activity.[15,19] There-
mechanism must be based on memories of prior fore, during open loop activity, the subconscious
exercise bouts.[24] If scalar time is used, it also brain probably creates its own ‘closed loop’
suggests that power output and perceived exertion endpoint, and the athlete terminates exercise when
are both set at the beginning of an event.[17] As more this point is reached. The brain-controlling al-
memory representations of exercise bouts of differ- gorithm, therefore, also operates in scalar time fash-
ent durations are laid down from repeated training ion in ‘open-loop’ activity, and sets its own endpoint
bouts and athletic events, the accuracy of the scalar within the safety limits set by the brain algorithm in
internal clock is likely to improve.[24] Crystal et previous ‘closed loop’ activity. Therefore, perhaps
al.[47] have found that in rats, nonlinearity occurs in the concept of ‘open loop’ is a misnomer, and in real
the scalar timing of events. However, these non- life does not exist.
linearities are systematic and occur in a similar In summary, the important factors in setting an
fashion at the beginning and end of a particular overall pacing strategy for an exercise bout include
event, despite the finding that the start and end times knowledge of the endpoint and the associated dura-
of the response was proportional to the time inter- tion of the event, an internal clock using scalar
vals being tested. They suggested that the source of timing, and memory of pacing strategy from prior
these systematic nonlinearities was related to match- events. These factors would allow the athlete to set
ing the present timing function to the memory of an appropriate pacing strategy at the start of an event
previous similar timing representations, rather than that would allow them to achieve optimal perform-



ance during the event. Furthermore, this pacing to the appropriate level for the overall pacing strate-
strategy would allow them to reach the end of the gy. Together with the initial increase in power out-
event without catastrophic failure occurring in any put, the feedforward commands would also be re-
physiological system. sponsible for changes in other peripheral physiolog-
ical systems, such as increases in heart rate,
4. Feedback Regulation of Pacing respiratory rate, blood pressure and cellular meta-
Strategy during an Exercise Bout bolic rate. For example, as depicted in figure 2, heart
rate would increase to 180 beats/min, respiratory
The overall pacing strategy for athletic events rate would increase to 50 breaths/min and blood
could also be described as a feedforward control glucose concentration would drop to 5 mmol/kg.
mechanism. If this feedforward pacing strategy initi- Peripheral chemo- and mechanoreceptors would de-
ated at the start of the athletic event by the teleoan- tect these changes, and afferent information from
ticipatory centre of the brain is absolutely correct, these receptors would travel back to be integrated
the power output during the event should not alter, into the teleoanticipatory algorithm. A continuously
or should change corresponding to specific changes updated calculation would be performed by the
in power demands produced by changes in terrain. brain, using the algorithm and comparing the current
However, Palmer et al.[50] examined heart rate metabolic variables against those that would be re-
changes during a 104km cycling race, and found that quired for both the overall pacing strategy and to
heart rate changed continuously throughout the allow metabolic reserves to be maintained until the
event, and that these changes in heart rate were not end of the exercise bout. If the values were too high
directly related to changes in terrain. These heart or low, the pacing strategy would be adjusted ac-
rate changes may, therefore, not be related to the cordingly. In the example depicted in figure 2, the
initial pacing strategy. Lambert et al.[28] suggested values of the initial power output are too high, and
that while the initial pacing strategy is controlled by
efferent commands are, therefore, generated by the
a particular algorithm in a feedforward manner,
brain teleoanticipatory centre to reduce power out-
alterations in power output during the event are the
put, so that the power output decreases. This de-
result of feedback control mechanisms using infor-
crease in power output leads to reduced metabolic
mation from the peripheral physiological systems
activity, and, in the example, heart rate would de-
and receptors that detected changes in the external
crease to 130 beats/min, respiratory rate decreases to
environment. It has been suggested that feedforward
32 breaths/min and blood glucose concentration
control must by nature have an element of uncertain-
would increase to 5.5 mmol/kg, which would be
ty to it, as the algorithm could not predict every
more acceptable values in the context of the distance
single change in external or internal environments
still to be covered. Finally, in this example, near the
(unpublished observations). Therefore, feedback
control responsible for corrective responses are end of the event, and assuming that the subconscious
based on short-term homeostatic responses occur- teleoanticipatory centre assesses that there is enough
ring throughout the exercise bout utilising informa- metabolic reserve to complete the race, the athlete
tion received from the periphery.[28] would then be able to increase power output to allow
The crucial component of this feedback control is an endspurt. At the end of the event, heart rate would
the information received from peripheral physiolog- increase to 160 beats/min, respiratory rate to 45
ical systems. An example of how this feedback breaths/min and blood glucose decrease to 5 mmol/
control might occur is depicted in figure 2. The kg, as a result of the increased metabolic demands
feedforward commands derived from the algorithm imposed by the endspurt.
selected by the brain’s teleoanticipatory centre at the In this example, feedback control creates contin-
start of the exercise bout would generate efferent uous adjustments to the overall pacing strategy, and
neural commands to increase muscle power output an athlete’s pace, power output and metabolic activ-



ity would change continuously during an exercise
bout. In this model, the brain’s teleoanticipatory
centre algorithm would set an overall pacing strate-
gy at the beginning of the event, while feedback
control would fine tune and continuously update the 90
C U C U C U C U C U C U
pacing strategy to prevent catastrophic failure in

Power output (W)
80
peripheral physiological systems, which could occur
if absolute substrate depletion resulted from a sus- 70
tained metabolic rate that was inappropriately
high.[28,51] 60

5. Information Processing between the 50
0 1 2 3 4 5 6 7 8 9 10 11 12
Brain Pacing Algorithm and Peripheral Distance (km)
Physiological Systems Fig. 3. Altering periods of ‘certainty’ (C) and ‘uncertainty’ (U) occur
throughout an exercise bout. During periods of certainty, power
In the above model of the control of pacing output changes generated by the brain are initiated, based on as-
sessment of peripheral afferent signals by a controlling brain al-
strategy, the assessment of the afferent feedback
gorithm in the context of the distance to be covered and the overall
information by the brain’s teleoanticipatory centre pacing strategy for the entire exercise bout. During periods of un-
does not occur only once during an exercise bout, certainty, there is no knowledge of how these changes in power
output have affected the function of the peripheral physiological
but must occur repeatedly throughout the exercise
systems because of a time lag between the initiation of the changes
bout, as argued in the example described in figure 2. in power output and the associated changes produced in the pe-
We suggest that after a power output correction has ripheral systems. A period of uncertainty changes to a period of
certainty when afferent input informs the brain algorithm of the
been made by the teleoanticipatory centre, by neces-
effect of the previous changes. If they are not appropriate, the brain
sity a period of ‘uncertainty’ occurs immediately then has the chance to make a further correction that is appropri-
after this change. This period of uncertainty extends ate.
to the time when the resultant adjustments in meta-
bolic activity induced by the altered power output serve, amongst others, a period of uncertainty will
generates afferent signals from peripheral receptors. again exist. The periods of ‘certainty’ and ‘uncer-
These new afferent inputs are used by the algorithm tainty’ therefore alternate throughout the exercise
to assess the correctness of the previous efferent bout. During a period of certainty, even if the brain
neural commands in the context of the overall pac-
algorithm has decided that no further alteration in
ing strategy.
This period of uncertainty may also be described power output is immediately required, a re-assess-
as a lag phase. Once the brain algorithm has as- ment of power output after a further distance has
sessed whether the correction in power output it been completed by the athlete will necessitate also a
initiated is, or is not, suitable for the distance still to re-assessment of the afferent input. Therefore, even
be covered during the exercise bout, a period of when a period of certainty results in unchanged
‘certainty’ occurs. As a result of this new certainty, afferent neural command, this will still lead to a
the brain teleoanticipatory centre may induce a fur- period of uncertainty after a certain time period has
ther alteration in efferent neural command, which passed. The calculation performed by the algorithm
will again result in changes in power output that
will assess a shorter and shorter time period of the
produce associated changes in metabolic rate of
peripheral physiological systems (figure 3). Once exercise bout remaining for each cycle of uncertain-
again, until afferent information has passed to the ty and certainty as the exercise bout continues, and
brain teleoanticipatory centre regarding this further will end with a final ‘endspurt’ of certainty (figure
change in metabolic rate and its effect on fuel re- 3).



Power output
Periods of uncertainty and certainty will there- required
1W 2W 3W 2W
fore occur cyclically throughout the exercise bout.
Neuron firing
We suggest that this cyclical nature of feedforward in M1
power generation and afferent feedback information
Peripheral
responses creates periods of discreet power output nerve AP
during each period of certainty that differs from Power output
1W 2W 3W 2W
what occurred during the previous periods of cer- generated
tainty. We further suggest that this period of discreet

Graph generated of
power output represents a ‘quantal’ unit of informa- 4

power output
tion generated from the brain teleoanticipatory cen- 3

tre. This quantal unit of information is sent as effer- 2

ent neural command to the muscles generating the 1

power output perceived to be required by the calcu- Time
lation performed by the algorithm in the context of Fig. 4. Information about the level of power output required by the
the overall pacing strategy. There is, therefore, a brain’s teleoanticipatory centre at any point during an exercise bout
discreet quantal unit of power output associated with is created by the pattern of neural firing in the motor region (M1) of
the brain. It is sent to the skeletal muscles as a particular sequence
each quantal unit of information generated by the of action potentials (AP) in nerves innervating the active muscles.
brain regulatory centre. If this model is correct, then These action potentials therefore generate the correct quantity of
power output (W) in the muscles. Therefore, the graph of the mea-
power output generation would not be smooth, but surement of changing power output during an exercise bout is an
will be non-monotonic. Power output generation indirect record of the changing information generated by the al-
would appear to be stochastic, but would actually be gorithm in the brain’s teleoanticipatory regulatory centre.

deterministic, with each variation in power output
during an exercise bout being a different quantal Evidence for this concept can be seen in the
unit of power output created by changing efferent findings of Terblanche et al.,[52] who compared pow-
neural command. er output generated by cyclists during a 40-minute
cycling trial to power output generated in a simulat-
Examined in this manner, each ‘quantal’ unit of ed cycling time trial where simulation parameters
efferent command passing down the nerves to the such as variability of terrain, cadence and bi-
muscles, which generates the required changes in omechanical factors were matched to what would
force output, is a discreet unit of information gener- occur during racing conditions. The simulated pow-
ated by the brain algorithm (figure 4). The power er output changes were matched by the data ob-
output generated by this discreet unit of information tained from the field trial, in which power output
can be described as a record of this information. varied continuously throughout the trial. After per-
When a researcher describes this power output, they forming a non-linear analysis of the field trial data,
are describing a record of the information generated they found that the power output during both trials
by the brain, and when a graph of an entire exercise had a fractal dimension. This indicates that the non-
bout is plotted by the researcher, this may be thought monotonic variability in power output was not ran-
of as a record of each discreet unit of information dom but rather had a deterministic pattern ‘embed-
generated during the exercise bout (figure 4). There- ded in it’ as part of multiple systems dynamic con-
fore, each non-monotonic change in power output trol processes.[52] Hu et al.[53] also found that during
displayed on the graph is a discreet quantal unit of daily routines measured over 2 weeks, the ambulato-
information generated by the brain, as long as the ry activity of humans fluctuated continuously, and
capture rate of the data is shorter than the time that this fluctuation exhibited a fractal dimension.
required to generate one quantal unit of information Furthermore, Ivanov et al.[54] have shown that differ-
by the brain. ent physical and physiological measures, such as



heart rate and gait stride rate intervals also continu- ber of different dominant frequencies associated
ously fluctuate, each with different fractal dimen- with each person’s power output, and that the domi-
sions. They suggested that this physiological varia- nant frequency varied depending on the distance to
bility is controlled in a deterministic manner, albeit the end of the trial. Specifically, there was a large
by different neural regulatory mechanisms. low-frequency component in each subject’s power
Recently, we analysed cycling data from a 20km output during the entire cycling bout. We speculate
cycling time trial study. Figure 5 shows representa- that this resulted from the overall pacing strategy of
tive traces from three of the subjects. One subject the event, which is typified by the changes evident
(subject A in figure 5) began at a high power output, in subject A. However, there were also a number of
which was then reduced prior to an endspurt in the higher frequency components during the trial, which
last 10% of the trial. The other two subjects main- suggest that different neural command processes or
tained a relatively constant power output, with an strategies occur throughout the trial.
endspurt in the last 10% of the cycling bout (unpub- We suggest that these different frequency com-
lished observations). The data for each subject was ponents result from the different frequencies of
captured at a relatively high capture rate (every quantal unit of information being sent from the brain
200m of the cycling bout), and the power output of at a subconscious level and are responsible for con-
each subject can be seen to alter non-monotonically tinuously regulating the power output throughout
throughout the exercise bout. Fractal analysis of the the trial in the feedforward and feedback manner
data showed that the subjects had a similar degree of already described in section 2. Each quantal unit of
fractality as described by others.[52,54] information, encapsulated in each frequency band,
The data were further analysed by Fourier trans- would control different components of the cycling
formation, which showed that rather than being bout. The overall pacing strategy would be repre-
comprised of a single frequency, there were a num- sented by the lower frequency bands. Specific activ-
ity, such as modulating the temporal function of
440 different muscles in a limb associated with generat-
ing power output, and perhaps even rotating individ-
420
ual muscle fibres during the trial to enable power
400 output to be altered efficiently, would be represent-
Subject A
380
ed by higher frequency bands.
Power output (W)

In the examples described above in this section,
360
we have suggested that all the non-monotonic
340 changes in power output are created by alterations in
efferent neural command. However, some of the
320
fluctuations in power output may be ‘noise’ created
300 by activity in the peripheral physiological systems,
280
which does not alter afferent signals to the brain
algorithm and are, therefore, not associated with the
260 generation of centrally controlled changes in power
0 2 4 6 8 10 12 14 16 18 20
output. However, as suggested by Lambert et al.,[28]
Distance (km)
Fig. 5. Changes in power output for three subjects recorded during
these changes in power output may not be due to
a 20km cycling time trial. What is evident is that although all three ‘noise’ but rather may be caused by inherent control
pacing strategies are different, all have a similar non-monotonic, processes in the peripheral physiological systems
continuously altering power output throughout the exercise bout.
The overall pacing strategy of subject A is evident by the solid line
that occur as part of a complex system arrangement
overlying the original trace of his constantly changing pacing strate- of metabolic control. Therefore, it is possible that
gy. some of the non-monotonic changes and fractal na-



ture of the power output may result from peripheral during five 1km sprint bouts occurring every 20km
control factors, or by hysteresis, in the efferent neu- during a 100km cycling bout.[55] It is evident that the
ral command processes due to the inevitable time mean amplitude of the EMG activity for each cycle
delay in response to information from changes in the sprint decreases from the first to the fifth sprint.
peripheral physiological systems. Future research in However, what is even more obvious is that there is
this field will hopefully elucidate the contribution of a pedal stroke to pedal stroke variability in the
the peripheral control structures in determining the muscle recruitment activity in the rectus femorus
final power output. muscle activity. This continuous variability occurs
at each timepoint measured during the trial, and is
A further more obvious example of quantal unit
present whether mean recruitment activity is de-
control mechanisms can be observed in the muscle
creased or increased. We propose that each varying
recruitment patterns during both 60-minute[6] and
cycle stroke represents a different quantal unit of
100km cycling time trials[55] described previously in
efferent neural command sent from the brain
section 1. Figure 6 shows the muscle recruitment
teleoanticipatory centre to the rectus femorus mus-
pattern of the rectus femorus muscle as measured by
cle of the lower limb.
electromyographic (EMG) activity for 5 seconds
We further suggest that each of these different
a
1.5
10.5 min
levels of muscle activity associated with each differ-
1.0 ent cycle stroke may be part of a ‘planned strategy’
0.5 of muscle recruitment so that power output is gener-
0.0 ated in a quantity commensurate with the pacing
1.5
b strategy of the overall cycling bout. In this model,
32.5 min
1.0 each variation in cycle stoke is initiated in a deter-
0.5 ministic manner in order to maintain the overall
0.0
−0.5 pacing strategy. Therefore, perhaps the variability
Millivolts (EMG activity)

and fractal dimension of these data and those studies
c
1.5 52.5 min described earlier in this section[52-54] are created and
1.0 maintained by the periods of certainty and uncer-
0.5 tainty associated with control processes attempting
0.0
to maintain an overall ‘pacing’ strategy, which oper-
1.5
d ates not only during exercise but also at rest.[31]
72.5 min
1.0 Further work is required to determine the veracity of
0.5
0.0
this hypothesis.
−0.5
In summary, we have proposed that whereas the
1.5
e overall pacing strategy for an event is determined at
99.5 min
1.0 the beginning of an event, this pacing strategy is
0.5 continually modified during the event in order to
0.0 maintain the overall pacing strategy in the presence
0 250 500 750 1000 of unexpected changes in the external environment
Time (min)
or internal physiological milieu, which differ from
Fig. 6. Normalised data of 5 seconds of electromyographic (EMG)
activity measured in the rectus femorus muscle of a cyclist during
those occurring in previous similar events from
five successive 1km sprints interspersed at 20km intervals during a which the original pacing strategy is generated. The
100km cycling trial. Apart from the visually obvious decrement in initial early power output generated by the brain
EMG activity from the first to the fifth sprint (graphs a to e, respec-
tively), what is also evident is that each pedal stroke has a con-
algorithm controlling the overall pacing strategy
stantly varying magnitude of EMG activity (reproduced from St Clair leads to a period of ‘uncertainty’ until afferent feed-
Gibson et al.,[55] with permission). back from peripheral internal and external receptors



creates a period of ‘certainty’ in which further feed- cling, the athlete is not aware of each individual foot
forward efferent commands occur and which correc- placement on the ground, or motor unit recruitment
tively alter the power output to maintain the slightly strategy that occurs for each revolution of the cycle
modified pacing strategy. These periods of ‘uncer- pedal. Neither is the athlete aware of the changes in
tainty’ and ‘certainty’ cycle continuously through- pace that occurs throughout the exercise bout in
out an exercise bout, creating ‘quantal’ units of non-monotonic fashion.
information generated during each burst of efferent In contrast to these continuous adjustments to
neural command. Power output generated during an power output, biomechanical and physiological ac-
exercise bout, therefore, alters with each different tivity that occur throughout an exercise bout, during
quantal unit of efferent neural command, which laboratory testing, it has been shown that RPE,
creates the non-monotonic changes in power output which is the conscious awareness of the sensation of
evident from data from exercise bouts in which the
fatigue, appears to increase linearly throughout the
rate of data capture is fast enough to capture each
exercise bout.[43] However, evidence suggests that
alteration in command.
this monotonic increase in perceived exertion de-
scribed in laboratory conditions may be due to the
6. The Sensation of Perceived Effort prescribed testing protocols utilised during laborato-
Associated with Particular ry testing, and differs to how effort is perceived
Pacing Strategies during a field event or routine activity where pacing
intensity is chosen by the athlete and additional
The relationship between the physical changes
external visual and other stimuli are present.[59,63-66]
associated with the generation and maintenance of a
particular pacing strategy, and the conscious knowl- The first evidence for this is that, as with power
edge of these changes and their causative control output, RPE appears to be set for a particular event
factors has still not been well explained. Previous using scalar rather than absolute parameters for each
theories have proposed that the perception of effort event of different distance or duration. If athletes are
and associated sensation of fatigue are directly and asked to perform an exercise bout at a particular
linearly correlated with changes in peripheral physi- RPE level on several different occasions, the exer-
ological variables such as heart rate, respiratory rate cise intensity is similar in each exercise bout,[33,65-69]
and blood lactic acid concentration.[43,56-58] More indicating that a particular level of perceived exer-
recently it has been suggested that the perception of tion is set in a feedforward fashion from the begin-
effort and fatigue is not tightly correlated with any ning of an exercise bout, and that this RPE is associ-
single peripheral variable, but rather is generated by ated with a particular level of physiological function
the same subconscious brain control processes that and power output. This ability to reproduce the
regulate pacing strategy during an event.[59,60] The exercise intensity associated with a particular RPE
absence of a relationship between the symptom of value has been shown to be improved by practice
fatigue and level of exercise intensity in patients and experience.[70-72] Furthermore, when athletes are
with chronic fatigue,[12,19] the relationship between asked to perform exercise at a particular RPE, the
RPE and the expected duration of the activity,[21] power output and associated physiological values
and the ability of hypnosis to alter perceived effort are not maintained at a similar level. Rather, after an
without an associated change in exercise intensi- initial short maintenance phase, power output de-
ty,[61] supports this central brain hypothesis for the creases either continuously throughout the rest of
generation of the sensation of fatigue.[62] the exercise bout during short events, or decreases
An athlete is not consciously aware of the majori- until a plateau in power output is reached during
ty of changes in power output that occur as part of longer distance events.[23,73] These changes were
the overall pacing strategy. As suggested previously also described as teleoanticipatory changes,[23,59]
by St Clair Gibson et al.,[62] when running or cy- and are a type of pacing strategy in which power



output is altered in order to maintain a set level of performed by athletes who were in either a fresh or
perceived exertion rather than reaching the endpoint fatigued state.
of an exercise bout in a certain time period. Howev- If this assumption is correct, and the same control
er, as the alterations in power output in each type of mechanism determines both power output and RPE,
activity are so similar, one must suggest that similar it is reasonable to suggest that RPE may also be
controlling strategies exist for each type of event, generated in a ‘quantal’ unit manner, similar to that
and the control of power output during an event and described earlier as occurring with the generation of
the perceived exertion during that event may be power output. Evidence for this can be found in a
controlled by the same regulatory processes in the recent study performed in our laboratory, where
brain. subjects received deceptive information about the
Further evidence for this hypothesis is evident in distance they had to run during a treadmill-based
data described by Noakes,[74] which were a re-inter- running trial.[21,78] Subjects were told that they were
pretation of data reported by Baldwin et al.[75] In this running either a 10- or 20-minute run on a treadmill
study, RPE increased linearly in a group of athletes at 75% of their peak treadmill running speed, as part
with either high or low muscle glycogen concentra- of the trial. However, after 9 minutes of the 10-min-
tions at the start of the trial. Similar to the RPE in the ute trial, the subjects were told they had to run for an
earlier study of Kay et al.,[6] RPE at the end of the of extra 10 minutes, so the time they ran was eventual-
the trial was submaximal as reported by the athletes ly also 20 minutes. In the group who had originally
using the Borg scale, reaching a maximum of ~18 been deceived and believed they were only running
out of a possible maximum score of 20 in both trials. 10 minutes, between minutes 10 and 11, RPE in-
The high-glycogen group lasted for a longer time creased significantly compared with the group that
period than the low-glycogen group, so that RPE had been told that they were to run for 20 minutes.
appeared to increase at different rates when RPE Furthermore, the RPE was significantly correlated
was plotted against time. However, Noakes[74] plot- with changes in affect and percentage of associative
ted the RPE of both groups as a percentage of time thoughts between minutes 10 and 11. Importantly,
completed during the event and found that RPE there was no change in speed or physiological pa-
increased almost identically in both groups. This is rameters such as heart rate or stride frequency in the
evidence that RPE during an event is generated deceived group, so the changes in RPE could not
using scalar time rather than absolute time, similar have been caused by anything other than psycholog-
to power output, as described previously in section ical factors.
3. These findings have been supported by a recent This trial indicates that RPE was increased by
study that showed that extrapolating oxygen uptake merely telling the athletes they had been deceived,
data collected at different submaximal RPE values without any physical changes in pace. Apart from
could accurately predict the maximal oxygen uptake indicating that RPE could not be linearly correlated
achieved, and therefore, the associated test endpoint with any measured physiological or physical factor,
during an incremental exercise test to exhaustion.[76] it is also further evidence for the quantal unit model
It may again be suggested that a similar mechanism, of RPE and power output generation. The same
or brain algorithm, is utilised to generate both power quantal unit of, in this case, running speed, must
output and RPE, utilising the same scalar time pa- have been generated between minutes 9 and 10 as
rameters set by knowledge of the distance to be between minutes 10 and 11, yet a significantly
covered and memory of prior similar exercise bouts. higher RPE score was described by the subjects
This brain mechanism does not appear to be affected between minutes 10 and 11. This indicates that, in
by prior fatiguing activity, as Eston et al.[77] have this example, an alteration in affect and percentage
recently shown that RPE had a similar scalar dimen- associative thoughts induced the selection of a dif-
sion during cycling trials to exhaustion that were ferent score, or ‘quantal’ unit of RPE, which was



a b
80 20
75 18
70 16
14
65
12
60 10
Power output (W)

55 8
50 6

RPE
c
80 20 d
75 18
70 16
14
65
12
60 10
55 8
50 6
0 5 10 15 20 25 0 5 10 15 20 25
Distance covered (km)
Fig. 7. Hypothetical model of changes in power output during a field athletic event. Graphs (a) and (c) are power output changes that are
identical until 19km of a 25km event. In graph (a), the athlete breaks away from a bunch of runners and increases his power output until the
end of the event. In graph (c), the athlete is dropped by the bunch of runners and his pace does not increase towards the end of the event.
Graphs (b) and (d) are the possible ratings of perceived exertion (RPE) associated with these two different possible race scenarios. In
graph (b), RPE decreases due to the increased positive effect associated with knowledge that the athlete is likely to win the exercise bout.
In graph (d), the athlete’s RPE increases due to the increased negative effect associated with knowledge that the athlete is unlikely to win.

associated with the same power output. This sug- cise bout, while the trend would likely be an in-
gests that the RPE score for a particular timepoint crease in RPE as the bout continues, RPE changes
selected by the brain algorithm is based not only on are contingent on other factors and can change non-
afferent information during the current exercise bout monotonically throughout the event (figure 7). Run-
and prior experience of similar exercise bouts, but ning at the same pace, an RPE ‘quantal unit’ used at
also of the psychological state of the athlete. This a point in the race may be very different depending
shows that the RPE quantal unit chosen by the brain on the unique situation of the event and on external
at any stage of the exercise bout will be altered by factors occurring at that moment.
different input from any one of the different factors Finally, as suggested in the study of Baden et
variables used by the brain algorithm to select both al.,[21] affect and percentage associative thoughts are
RPE and power output. associated with changes in RPE. This finding may
Further anecdotal evidence for this theory is de- explain why RPE, and indeed our perception of life,
rived from athletes competing in athletic events. As appears to be continuous rather than ‘quantal’ inter-
opposed to findings in the laboratory, RPE does not spersed with gaps without any perception. This may
increase linearly during a race, but changes non- occur because we do not focus on one specific
monotonically throughout the event. For example, if thought, activity or sensation for a long period of
a leading athlete moves ahead of a group of athletes time, and even when concentrating on a particular
and is likely to win an event, RPE is dramatically thought or sensation, a change in affect or mood will
reduced (unpublished observation). However, when alter our perception of that sensation. For example,
an athlete can no longer keep up with a group of during an exercise bout, the athlete has a number of
fellow athletes, and is left behind, RPE can be dissociative thoughts and does not think only about
dramatically increased. Furthermore, with intermit- their level of fatigue and effort.[20,80,81] They may
tent crowd support, RPE is reduced during the peri- also think of their tactics in relation to other athletes
od of crowd support.[79] Therefore, during an exer- and the reasons why they should carry on perform-



ing the event. These could be described as associa- ronmental conditions and internal metabolic func-
tive thoughts related to the exercise bout, but are not tion and fuel reserves. This calculation establishes a
specifically related to the perception of effort and power output that will allow the athlete to reach the
the ‘feeling’ of fatigue. While we may believe that end of the exercise bout at the fastest speed possible
we are thinking in a continuous manner, we do not without inducing catastrophic failure in any physio-
think in a continuous manner on any one thought logical system, which would have occurred if the
without other thoughts intruding on our conscious chosen speed was excessive at any point during the
state. Therefore, conscious perception of perceived event.
exertion does occur in a quantal fashion, with ‘gaps’ Although the initial pace is set at the beginning of
in between each conscious thought of this particular the event in a feedforward manner, no event or
state, but these gaps do not appear to occur, as our
internal physiological state will be identical to what
conscious perception is continuously filled with oth-
has occurred previously. Therefore, continuous ad-
er associative or dissociative thoughts, giving the
justments to the power output in the context of the
perception that our awareness of life is a continuous
overall pacing strategy occur throughout the exer-
sensation.
cise bout using feedback information from internal
It has been suggested that only when change in a and external receptors monitoring the external envi-
particular perceptual state occurs, do we become ronment and internal metabolic activity. We propose
aware of it,[62,82,83] and only when a quanta of RPE is that an internal clock, which appears to use scalar
different to the previous RPE level, do we actually rather than absolute time scales, is used by the brain
‘feel’ that a change of effort perception has oc- to generate knowledge of the distance or duration of
curred.[59] As it is easier to be aware of an increase in
the activity still to be covered, so that power output
a sensation than a decrease in a sensation, it is more
and metabolic rate can be altered appropriately.
likely athletes would perceive the increases in RPE
during an event to a greater degree than the reduc- We have further suggested that periods of ‘cer-
tions in RPE. Hence RPE changes during an event tainty’ and ‘uncertainty’ must occur throughout the
may appear to increase linearly rather than being exercise bout. Periods of certainty occur after affer-
non-monotonic, unless the reductions in RPE are ent information from the periphery had been re-
profound, such as occurs when breaking away from ceived, when the brain has knowledge of what pow-
a group of fellow athletes, or receiving sudden unex- er output is required to complete the event within the
pected crowd support. Further work is required to context of the overall pacing strategy. Periods of
asses the veracity of this suggestion. uncertainty occur after the efferent neural com-
mands have been generated to affect the changes in
7. Conclusions power output, which have been ascertained to be
necessary by the brain algorithm, but before the
In this article, we have examined how pacing physiological and biochemical effects of this novel
strategies are controlled by the brain during exer- power output command can be sensed. Because of
cise. We have suggested that although there are these continuously altering periods of certainty and
several different pacing strategies used by athletes uncertainty, we have further suggested that efferent
for different duration or distances of exercise, the and afferent information occurs as discreet ‘quantal’
underlying principles of how these different overall units of information. We further suggest that a re-
pacing strategies are controlled are similar. cord of these quantal units of efferent neural infor-
Possibly the most important factor establishing mation can be found in the non-monotonic changes
the pacing strategy is knowledge of the endpoint. in power output that occur throughout an exercise
The brain teleoanticipatory centre incorporates this bout, using non-linear methods of analysis. Similar-
knowledge into an algorithm, together with memory ly, we have proposed that perception of effort is also
of previous events and knowledge of external envi- not a linear phenomenon, but alters non-monotoni-



cally throughout an exercise bout. We have also 7. Noakes TD, St Clair Gibson A. Logical limitations to the
‘catastrophe’ models of fatigue during exercise in humans. Br J
suggested that perception of effort is made available Sports Med 2004; 38: 648-9
to our conscious processes in ‘quantal’ units rather 8. Ansley L, Schabort E, St Clair Gibson A, et al. Regulation of
pacing strategies during successive 4-km time trials. Med Sci
than as a continuous feeling or awareness of effort Sports Exerc 2004; 36: 1819-25
and fatigue. 9. Mattern CO, Kenekick RW, Kertzer R, et al. Impact of starting
We have described a model in which exercise strategy on cycling performance. Int J Sports Med 2001; 22:
350-5
activity is controlled by the brain using pacing strat- 10. Foster C, Green MA, Snyder AC, et al. Physiological responses
egies that induce continuous changes in power out- during simulated competition. Med Sci Sports Exerc 1993; 25:
877-82
put and perceived effort throughout the event, and
11. Adams GR, Harris RT, Woodard D, et al. Mapping of electrical
that these changes occur as discreet ‘quantal’ units activity using MRI. J Appl Physiol 2000; 74: 532-7
throughout the event due to the length of time re- 12. Enoka RM, Stuart DG. Neurobiology of muscle fatigue. J Appl
Physiol 1992; 72: 1631-48
quired to generate power output in response to affer-
13. Yue GH, Ranganathan VK, Siemionow V, et al. Evidence of
ent feedback from peripheral internal and external inability to fully activate human limb muscle. Muscle Nerve
receptors. In this model of the control of exercise, 2000; 23: 376-84
14. Gandevia SC. Spinal and supraspinal factors in human muscle
information flow around the body is the important fatigue. Physiol Rev 2001; 81: 1725-89
underlying principle allowing exercise to be per- 15. Kayser B. Exercise starts and ends in the brain. Eur J Appl
formed according to an overall pacing strategy, Physiol 2003; 90: 405-10
16. Marsden CD, Meadows JC, Merton PA. ‘Muscular wisdom’
based on knowledge of the endpoint and knowledge that minimizes fatigue during prolonged effort in man: peak
of previous events of similar duration and intensity. rates of motor unit discharge and slowing of discharge during
Further work is needed to explore how this informa- fatigue. In: Desmedt, JE, editor. Motor control mechanism in
health and disease. New York: Raven, 1983: 169-211
tion is processed between different physiological 17. St Clair Gibson A, Lambert EV, Lambert MI, et al. Exercise and
systems and different types of control structures. fatigue control mechanisms. Int Sport Med J 2001; 2 (3): 14
18. Ansley L, Robson PJ, St Clair Gibson A, et al. Evidence for
anticipatory strategies during supra-maximal exercise lasting
Acknowledgements longer than 30s. Med Sci Sports Exerc 2004; 36: 309-14
19. St Clair Gibson A, Lambert MI, Noakes TD. Neural control of
Funding for the work described in this review was provid- force output during maximal and submaximal exercise. Sports
ed by Medical Research Council of South Africa, the Univer- Med 2001; 31: 637-50
sity of Cape Town Harry Crossley and Nellie Atkinson Staff 20. Baden DA. Goals and expectancies: psychological and physio-
Research Funds, Discovery Health, and the National Re- logical effects of anticipating the end [dissertation]. Southamp-
ton: University of Southampton, 2002
search Foundation of South Africa through the THRIP initia- 21. Baden DA, Warwick-Evans LA, Lakomy J. Am I nearly there?
tive. To the knowledge of the authors, there are no conflicts of The effect of anticipated running distance on perceived exer-
interest that are directly or indirectly related to the contents of tion and attentional focus. J Sports Exerc Psychol 2004; 27:
this manuscript. 215-31
22. St Clair Gibson A, Noakes TD. Evidence for complex system
integration and dynamic neural regulation of skeletal muscle
References recruitment during exercise in humans. Br J Sports Med 2004;
1. Foster C, Schrager M, Snyder AC, et al. Pacing strategy and 38: 797-806
athletic performance. Sports Med 1994; 17: 77-85 23. Ulmer H-V. Concept of an extracellular regulation of muscular
2. Lucia A, Hoyos S, Santalla AJ, et al. Tour de France versus metabolic rate during heavy exercise in humans by
Vuelta a Espana: which is harder? Med Sci Sports Exerc 2003; psychophysiological feedback. Experentia 1996; 52: 416-20
35: 872-8 24. Paterson S, Marino FE. Effect of deception of distance on
3. Bishop D, Bonetti D, Dawson B. The influence of pacing prolonged cycling performance. Percept Mot Skills 2004; 98:
˙
strategy on VO2 and supramaximal kayak performance. Med 1017-26
Sci Sports Exerc 2002; 6: 1041-7 25. Rauch HGL, St Clair Gibson A, Lambert EV. A signalling role
4. De Koning JJ, Bobbert MF, Foster C. Determination of optimal for muscle glycogen in the regulation of pace during prolonged
pacing strategy in track cycling with an energy flow model. J exercise. Br J Sports Med 2005; 39: 34-8
Sci Med Sport 1999; 2: 266-77 26. Arbogast S, Vassilakopoulos T, Darques JL, et al. Influence of
5. Foster C, deKoning JJ, Hettinga F, et al. Effect of competitive oxygen supply on activation of group IV muscle afferents after
distance on energy expenditure during simulated competition. low frequency muscle stimulation. Muscle Nerve 2000; 23:
Int J Sports Med 2004; 25: 198-204 1187-93
6. Kay D, Cannon J, Marino FE, et al. Evidence for neuromuscular 27. Haouzi P, Hill JM, Lewis BK, et al. Responses of group III and
fatigue during cycling in warm humid conditions. Eur J Appl IV muscle afferents to distension of the peripheral vascular
Physiol 2001; 84: 115-21 bed. J Appl Physiol 1999; 87: 545-53



28. Lambert EV, St Clair Gibson A, Noakes TD. Complex system 49. Malapani C, Rakitin B, Levy R, et al. Coupled temporal memo-
model of fatigue: integrative homeostatic control of peripheral ries in Parkinson’s disease: a dopamine-related dysfunction. J
physiological systems during exercise in humans. Br J Sports Cogn Neurosci 1998; 10: 316-31
Med 2005; 39: 52-62 50. Palmer GS, Hawley JA, Dennis SC, et al. Heart rate responses
29. Rotto DM, Kaufman MP. Effect of metabolic products of mus- during a 4-d cycle race. Med Sci Sports Exerc 1994; 26:
cular contraction on discharge of group III and IV afferents. J 1278-83
Appl Physiol 1988; 64: 2306-13 51. Cafarelli E. Peripheral contributions to the perception of effort.
30. Tucker R, Rauch LGH, Harley YX, et al. Impaired exercise Med Sci Sports Exerc 1982; 14: 382-9
performance in heat is associated with an anticipatory reduc- 52. Terblanche E, Wessels JA, Stewart RI, et al. A computer simu-
tion in skeletal muscle recruitment. Eur J Physiol 2004; 48: lation of free-range exercise in the laboratory. J Appl Physiol
422-30 1999; 87: 1386-91
31. St Clair Gibson A, Goedecke JH, Harley YX, et al. Metabolic 53. Hu K, Ivanov PC, Chen Z, et al. Non-random fluctuations and
setpoint control mechanisms in different physiological systems multi-scale dynamics regulation of human activity. Physica A
at rest and during exercise. J Theor Biol 2005 Sep 7; 236 (1): 2004; 337: 307-18
60-72 54. Ivanov PC, Hausdorff JM, Havlin S, et al. Levels of complexity
32. Albertus Y, Tucker R, St Clair Gibson A, et al. Effect of in scale-invariant neural signals [online]. Available from URL:
distance feedback on pacing strategies, perceived exertion and http://arxiv.org/abs/cond-mat/0409545 [Accessed 2006 Jul 17]
heart rate during 20 km cycle time trials. Med Sci Sports Exerc 55. St Clair Gibson A, Schabort EJ, Noakes TD. Reduced neuro-
2005; 37: 461-8 muscular activity and force generation during prolonged cy-
33. Dunbar CC, Robertson RJ, Braun R, et al. The validity of cling. Am J Physiol Regul Integr Comp Physiol 2001; 281:
regulating exercise intensity by ratings of perceived exertion. R187-96
Med Sci Sports Exerc 1992; 24: 94-9 56. Bar-Or O, Skinner JS, Buskirk ER, et al. Physiological and
34. Schabort EJ, Hopkins WG, Hawley JA. Reproducibility of self- perceptual indicators of physical stress in 41-60 year old men
paced treadmill performance of trained treadmill runners. Int J who vary in conditioning level and body fatness. Med Sci
Sports Med 1997; 18: 1-4 Sports 1972; 4: 96-100
35. Kirkpatrick K. Packet theory of conditioning or timing. Behav 57. Gamberale F. Perceived exertion, heart rate, oxygen uptake and
Processes 2002; 57: 89-106 blood lactate in different work operations. Ergonomics 1972;
36. Kvist A, Lindstrom A, Green M, et al. Carrying large fuel loads 15: 545-54
during sustained flight is cheaper than expected. Nature 2001; 58. Noble BJ, Metz KF, Pandolf KB, et al. Perceptual responses to
411: 752-3 exercise: a multiple regression study. Med Sci Sports 1973; 5:
37. Gibbon J, Church RM. Time left: linear versus logarithmic 104-9
subjective time. J Exp Psychol Anim Behav Process 1981; 7: 59. Hampson DB, St Clair Gibson A, Lambert MI, et al. The
87-107 influence of sensory cues on the performance of effort during
exercise and central regulation of exercise performance. Sports
38. Church RM, Meck WH, Gibbon J. Application of scalar timing
Med 2001; 31: 935-52
theory to individual trials. J Exp Psychol Anim Behav Process
1994; 20: 135-55 60. Mihevic PM. Sensory cues for perceived exertion: a review.
Med Sci Sports Exerc 1981; 13: 150-63
39. Catalano JF. Effect of perceived proximity to end of task upon
end-spurt. Percept Mot Skills 1973; 36: 363-77 61. Williamson JW, McColl R, Mathews D, et al. Hypnotic manipu-
lation of effort sense during dynamic exercise: cardiovascular
40. Catalano JF. End-spurt in addition of numbers. Percept Mot
responses and brain activation. J Appl Physiol 2001; 90:
Skills 1974; 39: 121-2
1392-9
41. Catalano JF. End-spurt following simple repetitive muscular 62. St Clair Gibson A, Baden DA, Lambert MI, et al. The conscious
movement. Percept Mot Skills 1974; 39: 763-6 perception of the sensation of fatigue. Sports Med 2003; 33:
42. Nikolopoulos V, Arkinstall MJ, Hawley JA. Pacing strategy in 167-76
simulated cycle time-trials is based on perceived rather than 63. Ceci R, Hassmen P. Self-monitored exercise at three different
actual distance. J Sci Med Sport 2001; 4: 212-9 RPE intensities in treadmill vs field running. Med Sci Sports
43. Borg GA. Perceived exertion: a note on ‘history’ and methods. Exerc 1991; 23: 732-8
Med Sci Sports 1973; 5: 90-3 64. Dishman RK, Farquhar RP, Cureton KJ. Responses to preferred
44. Borg GA. Borg’s perceived exertion and pain scales. Cham- intensities of exertion in men differing in activity levels. Med
paign (IL): Human Kinetics, 1998 Sci Sports Exerc 1994; 26: 783-90
45. Hunter AM, St Clair Gibson A, Mbambo Z, et al. The effects of 65. Eston RG, Davies BL, Williams JG. Use of perceived effort
heat stress on neuromuscular activity during endurance exer- ratings to control exercise intensity in young healthy adults.
cise. Pflugers Arch 2002; 444: 738-43 Eur J Appl Physiol Occup Physiol 1987; 56: 222-4
46. St Clair Gibson A, Lambert MI, Hawley JA, et al. Measurement 66. Parfitt G, Rose E, Markland D. The effect of prescribed and
of maximal oxygen uptake from two different laboratory pro- preferred intensity exercise on psychological affect and the
tocols in runners and squash players. Med Sci Sports Exerc influence of baseline measures on affect. J Health Psychol
1999; 31: 1226-9 2000; 5: 231-40
47. Crystal JD, Church RM, Broadbent HA. Systematic non-lineari- 67. Smutok MA, Skrinar GS, Pandolf KB. Exercise intensity: sub-
ties in the memory representation of time. J Exp Psychol Anim jective regulation by perceived exertion. Arch Phys Med
Behav Process 1997; 23: 267-82 Rehabil 1980; 61: 569-74
48. Meck WH, Church RM. Nutrients that modify the speed of 68. Ward DS, Bar-Or O, Longmuir P, et al. Use of RPE to control
internal clock and memory storage processes. Behav Neurosci exercise intensity in wheelchair bound children and adults.
1987; 101: 465-75 Pediatr Exerc Sci 1995; 7: 94-102



69. Williams JG, Eston RG, Stretch C. Use of ratings of perceived 78. Baden DA, McLean TL, Tucker R, et al. The effect of anticipa-
exertion to control exercise intensity in children. Pediatr Exerc tion during unknown or unexpected exercise duration on rating
Sci 1991; 3: 21-7
of perceived exertion, affect and physiological function. Br J
70. Buckley J, Eston RG, Sims J. Reliability and validity of a Braille
ratings of perceived exertion scale. Br J Sports Med 2000; 34: Sports Med 2005; 39 (10): 742-6
297-302 79. Noakes TD. Lore of running. Cape Town: Oxford University
71. Eston RG, Williams JG. Reliability of ratings of perceived effort Press, 1992
for regulation of exercise intensity. Br J Sports Med 1988; 22:
153-5 80. Morgan WP, Pollock ML. Psychologic characterization of the
72. Eston RG, Parfitt G, Campbell L, et al. Reliability of effort elite distance runner. Ann N Y Acad Sci 1977; 301: 382-403
perception for regulating exercise intensity in children using a 81. Schomer H. Mental strategies and the perception of effort of
Cart and Load Effort Rating (CALER) Scale. Pediatr Exerc Sci
2000; 12: 388-97 marathon runners. Int J Sport Psychol 1986; 17: 41-59
73. Cain WS, Stevens JC. Constant-effort contractions related to the 82. Damasio A. The feeling of what happens: body, emotion and the
electromyogram. Med Sci Sports 1973; 5: 121-7 making of consciousness. London: Vintage Press, 2000
74. Noakes TD. Linear relationship between the perception of effort
and the duration of constant load exercise that remains. J Appl 83. Parvizi J, Damasio A. Consciousness and the brainstem. Cogni-
Physiol 2004; 96: 1571-2 tion 2001; 79: 135-59
75. Baldwin J, Snow RJ, Gibala MJ, et al. Glycogen availability
does not affect the TCA cycle or TAN pools during prolonged,
fatiguing exercise. J Appl Physiol 2003; 94: 2181-7 Correspondence and offprints: Dr Alan St Clair Gibson,
76. Eston RG, Lamb KL, Parfitt G, et al. The validity of predicting
maximal oxygen uptake from a perceptually-regulated graded MRC/UCT Research Unit for Exercise Science and Sports
exercise test. Eur J Appl Physiol 2005; 94: 221-7 Medicine, Sport Science Institute of South Africa, PO Box
77. Eston R, Parfitt G, Maccombie M, et al. Scalar time representa-
115, Newlands, 7725, South Africa.
tion of perceived effort during exercise is not altered by an
antecedent fatiguing exercise bout. J Appl Physiol. In press E-mail: agibson@sports.uct.ac.za


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