Revisão informação sistema central

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

2006 Adis Data Information BV. All rights reserved.

The Role of Information ProcessingBetween the Brain and PeripheralPhysiological Systems in Pacing andPerception of EffortAlan St Clair Gibson,1,2 Estelle V. Lambert,1 Laurie H.G. Rauch,1 Ross Tucker,1Denise A. Baden,3 Carl Foster4 and Timothy D. Noakes1

1 Brain Sciences Research Group, MRC/UCT Research Unit of Exercise Science and SportsMedicine, University of Cape Town, Cape Town, South Africa

2 MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University ofCape Town, Cape Town, South Africa

3 Department of Psychology, University of Southampton, Southampton, UK4 Department of Exercise and Sport Science, University of Wisconsin, La Crosse,

Wisconsin, USA

ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7051. Pacing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7062. Regulation of Overall Pacing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7073. The Requirement of an Internal Clock for Pacing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7094. Feedback Regulation of Pacing Strategy during an Exercise Bout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7115. Information Processing between the Brain Pacing Algorithm and Peripheral Physiological

Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7126. The Sensation of Perceived Effort Associated with Particular Pacing Strategies . . . . . . . . . . . . . . . . . 7167. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719

This article examines how pacing strategies during exercise are controlled byAbstractinformation processing between the brain and peripheral physiological systems. Itis suggested that, although several different pacing strategies can be used byathletes for events of different distance or duration, the underlying principle ofhow these different overall pacing strategies are controlled is similar. Perhaps themost important factor allowing the establishment of a pacing strategy is knowl-edge of the endpoint of a particular event. The brain centre controlling paceincorporates knowledge of the endpoint into an algorithm, together with memoryof prior events of similar distance or duration, and knowledge of external(environmental) and internal (metabolic) conditions to set a particular optimalpacing 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 togenerate knowledge of the duration or distance still to be covered, so that poweroutput 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 ofan event in a feedforward manner, no event or internal physiological state will beidentical to what has occurred previously. Therefore, continuous adjustments tothe power output in the context of the overall pacing strategy occur throughout theexercise bout using feedback information from internal and external receptors.These continuous adjustments in power output require a specific length of time forafferent information to be assessed by the brain’s pace control algorithm, and forefferent neural commands to be generated, and we suggest that it is this time lagthat crates the fluctuations in power output that occur during an exercise bout.These non-monotonic changes in power output during exercise, associated withinformation processing between the brain and peripheral physiological systems,are crucial to maintain the overall pacing strategy chosen by the brain algorithm ofeach athlete at the start of the exercise bout.

Any athletic event has, of necessity, a beginning enabling an athlete to complete an exercise bout inand 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 couldfatigue before the endpoint, the athlete requires create the conscious awareness of perceived effortsome type of pacing strategy. Pacing strategies dif- associated with this pacing strategy.fer according to the length of the athletic event, theenvironment in which the event is performed, the 1. Pacing Strategiesmotivation of the athlete, the knowledge and experi-

While a large amount of research has focused onence of the athlete, and each athlete’s particularthe limits to human performance and fatigue duringphysiological capacity.exercise, only a few studies have examined theIn order to establish, maintain and alter a pacinginfluence of pacing on exercise performance.[1,2]

strategy for a particular event, the brain must pro-Indeed, Foster has suggested that research on pacingcess an enormous quantity of data from the externalstrategy during exercise is the ‘unexplored territoryenvironment and from the different physiologicalin sports performance’ (unpublished observation).systems of the body. These data are used to calculate

While there are an infinite number of possiblewhether the athlete’s power output and associatedpacing strategies that an athlete may adopt during ancurrent metabolic rate are appropriate for the dis-event, four broad categories of pacing strategiestance of the event still to be covered in the currenthave been described[1] (figure 1). These are:environmental conditions, given the athlete’s availa-• an all-out pacing strategy, in which the athleteble fuel reserves and current rate of heat production.

begins the event at the maximal possible pace andPacing, therefore, can be described as a strategyattempts to continue this maximal pace until theemployed to avoid catastrophic failure in any pe-event ends, although a decrement in pace mayripheral physiological system.occur towards the end of the event (figure 1a);Neither the control of pacing during an exercise

• a slow start strategy, in which the athlete startsbout by the brain, nor the relationship between pac-off at a submaximal pace and increases paceing and the sensation of perceived exertion associat-steadily though the event (figure 1b);ed with this strategy has been well described. The

• an even paced strategy, in which pace is main-aim of this article is, therefore, to examine mecha-tained at a constant submaximal rate throughoutnisms and to develop a hypothetical model of howthe event (figure 1c);the brain creates and maintains a pacing strategy

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 runningmal 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

a 4km cycling trial was increased in the final 60Studies performed to assess which of these dif-seconds of each trial. However, Mattern et al.[9]ferent strategies is optimal are inconclusive. Bishopfound that for a 20km cycling time trial, startinget al.[3] found that for a 2-minute kayaking laborato-15% below average power output and increasingry trial, the all-out pacing strategy produced superiorpower output at the end of the trial proved to be aresults than an even pacing strategy. De Koning etfaster strategy than starting either 15% above aver-al.[4] found that for cycle racing on the track, an all-age power output or maintaining average powerout strategy was optimal for cycling a 1000m timeoutput for the duration of the time trial. Therefore,trial, whereas an all-out start followed by a constantthere appears to be no clear optimal pacing strategypower output was optimal for a 4000m pursuit trial.identified by previous research, and it may be thatFoster et al.[5] examined pacing strategies duringeach individual has a uniquely optimal pacing strate-laboratory cycling trials of 500m, 1000m, 1500mgy.[10] Further research is required to help clarifyand 3000m duration and found that athletes chose anwhich of the different possible pacing strategies areinitial power output that was high and subsequentlyoptimal for different sports and for different dis-decreased, with an increased power output in thetances performed during athletic events, or indeedfinal section of all these trials. Similarly, Kay et al.[6]

whether there is no single optimal pacing strategy.found that, during a 60-minute laboratory cyclingtime trial interspersed with six sprints, power outputduring each sprint decreased from the first to the 2. Regulation of Overall Pacing Strategyfifth sprint, but increased during the sixth and finalsprint, which occurred in the last minute of the time Pacing strategies require continual regulation bytrial. Similarly, the last kilometre of three world the brain during an exercise bout. During an ‘all-out’

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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 intoas even during a short-duration maximal isometric account by the brain-pacing algorithm at the start ofcontraction, 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 processforce output appears to be reduced in a controlled would then send out efferent neural commands tomanner using different neural recruitment strate- generate appropriate power output, and metabolicgies.[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 inputpacing strategy involved, with changes in muscle supplying information from metaboreceptors, noci-recruitment occurring throughout the event in a ceptors, thermoreceptors, cardiovascular pressuremanner that would prevent catastrophic system fail- receptors and mechanoreceptors would inform theure.[19] Any of the other three pacing strategies de- teleoanticipation pacing centre in the brain aboutscribed in section 1 would require further regulation motion, force output, muscle metabolic rate and coreby the brain in addition to the regulation of the temperature changes associated with the chosenstarting power output, as modifications in power power output.[25-30] If the algorithm indicated a paceoutput must occur throughout the event in order to that was too fast to allow the athlete to reach thechange the pacing strategy during the event. endpoint of the race without premature fatigue

caused by a catastrophic failure occurring in anyFor these alterations in power output to occur in aphysiological system, further efferent neural com-deterministic way, the brain is required to monitormands would be modified to reduce the power out-whether the changes in power output are relevant input, and associated metabolic rate, to what the cen-the context of the ongoing pacing strategy. In ordertral algorithm perceived would be an appropriateto make these calculations, certain information mustlevel of activity. Conversely, if the algorithm indi-be available to the brain. It has been suggested thatcated the pace was too slow, further efferent neuralknowledge of the distance or time to be coveredcommands would be modified to increase the powerduring an event provides crucial input into a mathe-output, and metabolic rate would therefore also in-matical algorithm used by the brain to monitor andcrease.determine whether the current power output is ap-

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 powerparticular 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 physiologicalbout. For example, the algorithm used by the brain systems. As a result, control of metabolic activityin setting a particular pacing strategy will be very would be vested in the teleoanticipatory centre in thedifferent for a 5km compared with a 100km running brain and the chosen mathematical algorithm. Sinceor cycling event. the mathematical algorithm is selected for an appro-

priate endpoint and expected distance or duration ofIn the teleoanticipatory process described byexercise, knowledge of the endpoint must, therefore,Ulmer,[23] the brain algorithm for a particular eventbe one of the principle controllers of metabolicwith a known endpoint would initiate a particularactivity in peripheral physiological systems, as sug-pacing strategy at the start of the event, based ongested by Ulmer.[23]prior knowledge of previous similar events per-



indicated that the internal clock operates at a sub-conscious rather than a conscious level during ath-letic activity. The ability of athletes to reproducealmost identical pacing strategies, and hence overallperformances when completing sequential exercisetests of similar and known duration in the laborato-ry, even with minimal external information regard-ing distance covered or time elapsed,[33,34] providesfurther evidence of the robustness of this internalclock and the teleoanticipatory pacing mathematicalalgorithm.[22]

The robustness of the internal clock is not onlydemonstrated in athletes, but is also evident in otherspecies. For example, after a period of conditioning,the head entry of rats into a feeding cup occurs at thesame time prior to expected food delivery across arange of different conditions.[35] Kirkpatrick[35] sug-gested that the timing of head entry can be calculat-

Afferent

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Fig. 2. Changes in power output during an exercise bout are regu-lated by a teleoanticipatory regulatory centre in the brain, whichcontinuously alters power output by altering efferent neural com-mand in order to maintain the overall pacing strategy while avoidingcatastrophic system failure. Afferent information from receptors re-cording changes in peripheral physiological system variables suchas heart rate (HR), respiratory rate (RR) and blood glucose concen-trations (BG) is used by the teleoanticipatory centre to ensure theadjustments in power output are appropriate for the duration of theexercise bout that remains (reproduced from St Clair Gibson etal.,[31] with permission from Elsevier). ed as the mean expected time remaining until the

next food delivery as a function of mean time since3. The Requirement of an Internal Clock prior food delivery. Birds migrating to the samefor Pacing Strategy destination leave at a similar time each year and do

not leave until they have sufficient fuel for theirA 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 theweight as a result of altering fuel reserves, so as toremainder of an exercise bout if it does not havereach the end of their journey before completelyknowledge of the distance that has been covered andexpending all their fuel reserves.[36] Therefore, thetime that has passed during a particular event at ainternal clock and the associated pacing strategy thatparticular pace. The brain’s internal time-keepingit enables, appears to be universal phenomena.mechanism during an exercise bout appears to be

The internal clock also appears to operate using arobust. Albertus et al.[32] altered the distance mark-scalar time scale, in that performance curves ofers during a 20km cycling time trial, making thetemporal tasks of similar duration or distance super-distances between each kilometre either the correctimpose when measured on a relative compared withdistance, longer, shorter, or randomly longer oran absolute time scale. This has been described asshorter, while the subjects were informed that thescalar expectancy theory.[20,37,38] For example, par-distances covered were an exact kilometre. Despiteticipants engaged in different types of tasks (vigi-this deception, the time taken to complete each triallance, rotary pursuit tracking and muscular activity)was similar. The authors concluded that these find-showed an ‘endspurt’ effect whereby they increasedings indicated that the subject’s internal clock andtheir output/activity when the task was 90% com-associated internal judgment of the distance coveredpleted.[39-41] This endspurt occurred at 90% of ex-was robust, and was not affected by external verbalpected task duration, irrespective of the length orinformation supplied to each subject during the dif-type of task, which suggests that relative rather thanferent trials. Interestingly, the subjects in this trial

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-ated with absolute timepoints during the trials.algorithm.[20]

The internal clock appears to be affected byDuring cycling time trials in which subjects werenutrient intake. The head entry of rats into a feedingdeceived about the true distance of the trials, believ-cup was delayed to closer to the feeding time aftering them all to be 40km in length when they werebeing given a lecithin (phosphatidylcholine) or case-actually 34, 40 and 46km long, Nikolopoulos etin (protein) snack.[48] In contrast, head entry wasal.[42] found that the subjects paced themselves simi-premature after ingestion of a sucrose (carbohy-larly in all trials. This indicated that their pacingdrate) snack. The authors suggested that the differ-strategy for each trial was based on perceived ratherent snacks induced changes in precursor levels ofthan actual distance covered. Furthermore, in threecentrally acting neurotransmitters, which resulted inof our own laboratory exercise trials of differingchanges in the neural pathways responsible for theduration and intensity, the ratings of perceived exer-function of the internal clock. This interpretation istion (RPE) were of a similar magnitude, (~18 out ofsupported by the presence of impaired timing ofa possible 20 on the Borg RPE scale)[43,44] at the endmovement and perceptual timing deficits in patientsof each of the exercise bouts. The three trials con-with Parkinson’s disease.[49] There is a dopaminesisted of a 60-minute cycling time trial intersperseddeficiency in the basal ganglia associated with thiswith six 1-minute sprints during the trial, includingdisorder. Therefore, while the internal clock respon-one over the last kilometre,[6] a cycling trial withsible for pacing appears to be robust, it does seem toincreasing workloads until exhaustion lasting ~50be altered by ingestion of certain food types and byminutes,[45] and a running maximal aerobic test per-disease processes.formed on a treadmill lasting ~10 minutes.[46] The

results of these studies suggest that both subcon- It must be noted that most of the discussion abovescious pacing strategies and conscious perception of is of exercise with a known endpoint, also describedeffort 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

loop’ activity. However, all athletes eventually stopFor the brain teleoanticipatory centre to utilise aat some point in an ‘open-loop’ activity.[15,19] There-scalar internal clock, the internal clock’s scalingfore, during open loop activity, the subconsciousmechanism must be based on memories of priorbrain probably creates its own ‘closed loop’exercise bouts.[24] If scalar time is used, it alsoendpoint, and the athlete terminates exercise whensuggests that power output and perceived exertionthis point is reached. The brain-controlling al-are both set at the beginning of an event.[17] As moregorithm, therefore, also operates in scalar time fash-memory representations of exercise bouts of differ-ion in ‘open-loop’ activity, and sets its own endpointent durations are laid down from repeated trainingwithin the safety limits set by the brain algorithm inbouts and athletic events, the accuracy of the scalarprevious ‘closed loop’ activity. Therefore, perhapsinternal clock is likely to improve.[24] Crystal etthe concept of ‘open loop’ is a misnomer, and in realal.[47] have found that in rats, nonlinearity occurs inlife does not exist.the scalar timing of events. However, these non-

linearities are systematic and occur in a similar In summary, the important factors in setting anfashion at the beginning and end of a particular overall pacing strategy for an exercise bout includeevent, 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 scalarvals being tested. They suggested that the source of timing, and memory of pacing strategy from priorthese systematic nonlinearities was related to match- events. These factors would allow the athlete to seting the present timing function to the memory of an appropriate pacing strategy at the start of an eventprevious 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,respiratory rate, blood pressure and cellular meta-4. Feedback Regulation of Pacingbolic rate. For example, as depicted in figure 2, heartStrategy during an Exercise Boutrate would increase to 180 beats/min, respiratory

The overall pacing strategy for athletic events rate would increase to 50 breaths/min and bloodcould 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 fromticipatory centre of the brain is absolutely correct, these receptors would travel back to be integratedthe power output during the event should not alter, into the teleoanticipatory algorithm. A continuouslyor should change corresponding to specific changes updated calculation would be performed by thein power demands produced by changes in terrain. brain, using the algorithm and comparing the currentHowever, 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 toheart rate changed continuously throughout the allow metabolic reserves to be maintained until theevent, and that these changes in heart rate were not end of the exercise bout. If the values were too highdirectly 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, theinitial pacing strategy. Lambert et al.[28] suggested values of the initial power output are too high, andthat while the initial pacing strategy is controlled by efferent commands are, therefore, generated by thea 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 metabolicmation 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 toenvironment. It has been suggested that feedforward

32 breaths/min and blood glucose concentrationcontrol must by nature have an element of uncertain-

would increase to 5.5 mmol/kg, which would bety to it, as the algorithm could not predict everymore acceptable values in the context of the distancesingle change in external or internal environmentsstill to be covered. Finally, in this example, near the(unpublished observations). Therefore, feedbackend of the event, and assuming that the subconsciouscontrol responsible for corrective responses areteleoanticipatory centre assesses that there is enoughbased on short-term homeostatic responses occur-metabolic reserve to complete the race, the athletering throughout the exercise bout utilising informa-would then be able to increase power output to allowtion received from the periphery.[28]

an endspurt. At the end of the event, heart rate wouldThe crucial component of this feedback control isincrease to 160 beats/min, respiratory rate to 45the information received from peripheral physiolog-breaths/min and blood glucose decrease to 5 mmol/ical systems. An example of how this feedbackkg, as a result of the increased metabolic demandscontrol might occur is depicted in figure 2. Theimposed by the endspurt.feedforward commands derived from the algorithm

In this example, feedback control creates contin-selected by the brain’s teleoanticipatory centre at theuous adjustments to the overall pacing strategy, andstart of the exercise bout would generate efferent

neural commands to increase muscle power output an athlete’s pace, power output and metabolic activ-



ity would change continuously during an exercisebout. In this model, the brain’s teleoanticipatorycentre algorithm would set an overall pacing strate-gy at the beginning of the event, while feedbackcontrol would fine tune and continuously update thepacing strategy to prevent catastrophic failure inperipheral physiological systems, which could occurif absolute substrate depletion resulted from a sus-tained metabolic rate that was inappropriatelyhigh.[28,51]

5. Information Processing between theBrain Pacing Algorithm and PeripheralPhysiological Systems

In the above model of the control of pacingstrategy, the assessment of the afferent feedbackinformation by the brain’s teleoanticipatory centredoes not occur only once during an exercise bout,but must occur repeatedly throughout the exercisebout, as argued in the example described in figure 2.We suggest that after a power output correction hasbeen made by the teleoanticipatory centre, by neces-sity a period of ‘uncertainty’ occurs immediatelyafter this change. This period of uncertainty extends

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)Fig. 3. Altering periods of ‘certainty’ (C) and ‘uncertainty’ (U) occurthroughout an exercise bout. During periods of certainty, poweroutput changes generated by the brain are initiated, based on as-sessment of peripheral afferent signals by a controlling brain al-gorithm in the context of the distance to be covered and the overallpacing strategy for the entire exercise bout. During periods of un-certainty, there is no knowledge of how these changes in poweroutput have affected the function of the peripheral physiologicalsystems because of a time lag between the initiation of the changesin power output and the associated changes produced in the pe-ripheral systems. A period of uncertainty changes to a period ofcertainty when afferent input informs the brain algorithm of theeffect of the previous changes. If they are not appropriate, the brainthen has the chance to make a further correction that is appropri-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 willgenerates 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 exerciseto assess the correctness of the previous efferent bout. During a period of certainty, even if the brainneural commands in the context of the overall pac-

algorithm has decided that no further alteration ining strategy.

power output is immediately required, a re-assess-This period of uncertainty may also be describedment of power output after a further distance hasas a lag phase. Once the brain algorithm has as-been completed by the athlete will necessitate also asessed whether the correction in power output itre-assessment of the afferent input. Therefore, eveninitiated is, or is not, suitable for the distance still to

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 athe brain teleoanticipatory centre may induce a fur- period of uncertainty after a certain time period hasther alteration in efferent neural command, which passed. The calculation performed by the algorithmwill again result in changes in power output that

will assess a shorter and shorter time period of theproduce associated changes in metabolic rate of

exercise bout remaining for each cycle of uncertain-peripheral physiological systems (figure 3). Oncety and certainty as the exercise bout continues, andagain, until afferent information has passed to thewill end with a final ‘endspurt’ of certainty (figurebrain teleoanticipatory centre regarding this further

change in metabolic rate and its effect on fuel re- 3).



Periods of uncertainty and certainty will there-fore occur cyclically throughout the exercise bout.We suggest that this cyclical nature of feedforwardpower generation and afferent feedback informationresponses creates periods of discreet power outputduring each period of certainty that differs fromwhat occurred during the previous periods of cer-tainty. We further suggest that this period of discreetpower output represents a ‘quantal’ unit of informa-tion generated from the brain teleoanticipatory cen-tre. This quantal unit of information is sent as effer-ent neural command to the muscles generating thepower output perceived to be required by the calcu-lation performed by the algorithm in the context ofthe overall pacing strategy. There is, therefore, adiscreet quantal unit of power output associated witheach quantal unit of information generated by thebrain regulatory centre. If this model is correct, thenpower output generation would not be smooth, butwill be non-monotonic. Power output generation

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Fig. 4. Information about the level of power output required by thebrain’s teleoanticipatory centre at any point during an exercise boutis created by the pattern of neural firing in the motor region (M1) ofthe brain. It is sent to the skeletal muscles as a particular sequenceof action potentials (AP) in nerves innervating the active muscles.These action potentials therefore generate the correct quantity ofpower output (W) in the muscles. Therefore, the graph of the mea-surement of changing power output during an exercise bout is anindirect record of the changing information generated by the al-gorithm in the brain’s teleoanticipatory regulatory centre.would appear to be stochastic, but would actually be

deterministic, with each variation in power outputEvidence for this concept can be seen in theduring an exercise bout being a different quantal

findings of Terblanche et al.,[52] who compared pow-unit of power output created by changing efferenter output generated by cyclists during a 40-minuteneural command.cycling trial to power output generated in a simulat-

Examined in this manner, each ‘quantal’ unit of ed cycling time trial where simulation parametersefferent 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 wouldforce 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 outputcan 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 trialsby 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 duringdisplayed 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, andcapture 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 associatedously 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 tobility is controlled in a deterministic manner, albeit the end of the trial. Specifically, there was a largeby different neural regulatory mechanisms. low-frequency component in each subject’s power

output during the entire cycling bout. We speculateRecently, we analysed cycling data from a 20kmthat this resulted from the overall pacing strategy ofcycling time trial study. Figure 5 shows representa-the event, which is typified by the changes evidenttive traces from three of the subjects. One subjectin subject A. However, there were also a number of(subject A in figure 5) began at a high power output,higher frequency components during the trial, whichwhich was then reduced prior to an endspurt in thesuggest that different neural command processes orlast 10% of the trial. The other two subjects main-strategies occur throughout the trial.tained a relatively constant power output, with an

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 ofcaptured at a relatively high capture rate (every quantal unit of information being sent from the brain200m 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 throughoutthroughout the exercise bout. Fractal analysis of the the trial in the feedforward and feedback mannerdata showed that the subjects had a similar degree of already described in section 2. Each quantal unit offractality as described by others.[52,54] information, encapsulated in each frequency band,

would control different components of the cyclingThe data were further analysed by Fourier trans-bout. The overall pacing strategy would be repre-formation, which showed that rather than beingsented by the lower frequency bands. Specific activ-comprised of a single frequency, there were a num-ity, such as modulating the temporal function ofdifferent muscles in a limb associated with generat-ing power output, and perhaps even rotating individ-ual muscle fibres during the trial to enable poweroutput to be altered efficiently, would be represent-ed by higher frequency bands.

In the examples described above in this section,we have suggested that all the non-monotonicchanges in power output are created by alterations inefferent neural command. However, some of thefluctuations in power output may be ‘noise’ createdby activity in the peripheral physiological systems,which does not alter afferent signals to the brainalgorithm and are, therefore, not associated with thegeneration of centrally controlled changes in poweroutput. However, as suggested by Lambert et al.,[28]

these changes in power output may not be due to‘noise’ but rather may be caused by inherent controlprocesses in the peripheral physiological systemsthat occur as part of a complex system arrangementof metabolic control. Therefore, it is possible thatsome of the non-monotonic changes and fractal na-

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Fig. 5. Changes in power output for three subjects recorded duringa 20km cycling time trial. What is evident is that although all threepacing strategies are different, all have a similar non-monotonic,continuously altering power output throughout the exercise bout.The overall pacing strategy of subject A is evident by the solid lineoverlying the original trace of his constantly changing pacing strate-gy.



ture of the power output may result from peripheral during five 1km sprint bouts occurring every 20kmduring a 100km cycling bout.[55] It is evident that thecontrol factors, or by hysteresis, in the efferent neu-mean amplitude of the EMG activity for each cycleral command processes due to the inevitable timesprint decreases from the first to the fifth sprint.delay in response to information from changes in theHowever, what is even more obvious is that there isperipheral physiological systems. Future research ina pedal stroke to pedal stroke variability in thethis field will hopefully elucidate the contribution ofmuscle recruitment activity in the rectus femorusthe peripheral control structures in determining themuscle activity. This continuous variability occursfinal power output.at each timepoint measured during the trial, and is

A further more obvious example of quantal unitpresent whether mean recruitment activity is de-

control mechanisms can be observed in the musclecreased or increased. We propose that each varying

recruitment patterns during both 60-minute[6] andcycle stroke represents a different quantal unit of

100km cycling time trials[55] described previously inefferent neural command sent from the brain

section 1. Figure 6 shows the muscle recruitmentteleoanticipatory centre to the rectus femorus mus-

pattern of the rectus femorus muscle as measured bycle of the lower limb.

electromyographic (EMG) activity for 5 secondsWe further suggest that each of these different

levels of muscle activity associated with each differ-ent cycle stroke may be part of a ‘planned strategy’of muscle recruitment so that power output is gener-ated in a quantity commensurate with the pacingstrategy of the overall cycling bout. In this model,each variation in cycle stoke is initiated in a deter-ministic manner in order to maintain the overallpacing strategy. Therefore, perhaps the variabilityand fractal dimension of these data and those studiesdescribed earlier in this section[52-54] are created andmaintained by the periods of certainty and uncer-tainty associated with control processes attemptingto maintain an overall ‘pacing’ strategy, which oper-ates not only during exercise but also at rest.[31]

Further work is required to determine the veracity ofthis hypothesis.

In summary, we have proposed that whereas theoverall pacing strategy for an event is determined atthe beginning of an event, this pacing strategy iscontinually modified during the event in order tomaintain the overall pacing strategy in the presenceof unexpected changes in the external environmentor internal physiological milieu, which differ fromthose occurring in previous similar events fromwhich the original pacing strategy is generated. Theinitial early power output generated by the brainalgorithm controlling the overall pacing strategyleads to a period of ‘uncertainty’ until afferent feed-back from peripheral internal and external receptors

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Fig. 6. Normalised data of 5 seconds of electromyographic (EMG)activity measured in the rectus femorus muscle of a cyclist duringfive successive 1km sprints interspersed at 20km intervals during a100km cycling trial. Apart from the visually obvious decrement inEMG 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-stantly varying magnitude of EMG activity (reproduced from St ClairGibson et al.,[55] with permission).



creates a period of ‘certainty’ in which further feed- cling, the athlete is not aware of each individual footforward efferent commands occur and which correc- placement on the ground, or motor unit recruitmenttively alter the power output to maintain the slightly strategy that occurs for each revolution of the cyclemodified pacing strategy. These periods of ‘uncer- pedal. Neither is the athlete aware of the changes intainty’ and ‘certainty’ cycle continuously through- pace that occurs throughout the exercise bout inout an exercise bout, creating ‘quantal’ units of non-monotonic fashion.information generated during each burst of efferent In contrast to these continuous adjustments toneural 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, duringquantal 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 ofevident from data from exercise bouts in which the fatigue, appears to increase linearly throughout therate of data capture is fast enough to capture each exercise bout.[43] However, evidence suggests thatalteration in command. this monotonic increase in perceived exertion de-

scribed in laboratory conditions may be due to the6. The Sensation of Perceived Effort prescribed testing protocols utilised during laborato-Associated with Particular ry testing, and differs to how effort is perceivedPacing Strategies during a field event or routine activity where pacing

intensity is chosen by the athlete and additionalThe relationship between the physical changes

external visual and other stimuli are present.[59,63-66]associated with the generation and maintenance of a

The first evidence for this is that, as with powerparticular pacing strategy, and the conscious knowl-output, RPE appears to be set for a particular eventedge of these changes and their causative controlusing scalar rather than absolute parameters for eachfactors has still not been well explained. Previousevent of different distance or duration. If athletes aretheories have proposed that the perception of effortasked to perform an exercise bout at a particularand associated sensation of fatigue are directly andRPE level on several different occasions, the exer-linearly correlated with changes in peripheral physi-cise intensity is similar in each exercise bout,[33,65-69]ological variables such as heart rate, respiratory rateindicating that a particular level of perceived exer-and blood lactic acid concentration.[43,56-58] Moretion is set in a feedforward fashion from the begin-recently it has been suggested that the perception ofning of an exercise bout, and that this RPE is associ-effort and fatigue is not tightly correlated with anyated with a particular level of physiological functionsingle peripheral variable, but rather is generated byand power output. This ability to reproduce thethe same subconscious brain control processes thatexercise intensity associated with a particular RPEregulate pacing strategy during an event.[59,60] Thevalue has been shown to be improved by practiceabsence of a relationship between the symptom ofand experience.[70-72] Furthermore, when athletes arefatigue and level of exercise intensity in patientsasked to perform exercise at a particular RPE, thewith chronic fatigue,[12,19] the relationship betweenpower output and associated physiological valuesRPE and the expected duration of the activity,[21]

are not maintained at a similar level. Rather, after anand the ability of hypnosis to alter perceived effortinitial short maintenance phase, power output de-without an associated change in exercise intensi-creases either continuously throughout the rest ofty,[61] supports this central brain hypothesis for the

generation of the sensation of fatigue.[62] the exercise bout during short events, or decreasesuntil a plateau in power output is reached duringAn athlete is not consciously aware of the majori-longer distance events.[23,73] These changes werety of changes in power output that occur as part ofalso described as teleoanticipatory changes,[23,59]the overall pacing strategy. As suggested previouslyand are a type of pacing strategy in which powerby St Clair Gibson et al.,[62] when running or cy-



output is altered in order to maintain a set level of performed by athletes who were in either a fresh orfatigued state.perceived exertion rather than reaching the endpoint

of an exercise bout in a certain time period. Howev- If this assumption is correct, and the same controler, 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 becontrolling strategies exist for each type of event, generated in a ‘quantal’ unit manner, similar to thatand the control of power output during an event and described earlier as occurring with the generation ofthe perceived exertion during that event may be power output. Evidence for this can be found in acontrolled by the same regulatory processes in the recent study performed in our laboratory, wherebrain. subjects received deceptive information about the

distance they had to run during a treadmill-basedFurther evidence for this hypothesis is evident inrunning trial.[21,78] Subjects were told that they weredata described by Noakes,[74] which were a re-inter-running either a 10- or 20-minute run on a treadmillpretation of data reported by Baldwin et al.[75] In thisat 75% of their peak treadmill running speed, as partstudy, RPE increased linearly in a group of athletesof the trial. However, after 9 minutes of the 10-min-with either high or low muscle glycogen concentra-ute trial, the subjects were told they had to run for antions at the start of the trial. Similar to the RPE in theextra 10 minutes, so the time they ran was eventual-earlier study of Kay et al.,[6] RPE at the end of the ofly also 20 minutes. In the group who had originallythe trial was submaximal as reported by the athletesbeen deceived and believed they were only runningusing the Borg scale, reaching a maximum of ~1810 minutes, between minutes 10 and 11, RPE in-out of a possible maximum score of 20 in both trials.creased significantly compared with the group thatThe high-glycogen group lasted for a longer timehad been told that they were to run for 20 minutes.period than the low-glycogen group, so that RPEFurthermore, the RPE was significantly correlatedappeared to increase at different rates when RPEwith changes in affect and percentage of associativewas plotted against time. However, Noakes[74] plot-thoughts between minutes 10 and 11. Importantly,ted the RPE of both groups as a percentage of timethere was no change in speed or physiological pa-completed during the event and found that RPErameters such as heart rate or stride frequency in theincreased almost identically in both groups. This isdeceived group, so the changes in RPE could notevidence that RPE during an event is generatedhave been caused by anything other than psycholog-using scalar time rather than absolute time, similarical factors.to power output, as described previously in section

3. These findings have been supported by a recent This trial indicates that RPE was increased bystudy 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 fromcould accurately predict the maximal oxygen uptake indicating that RPE could not be linearly correlatedachieved, 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 modelIt may again be suggested that a similar mechanism, of RPE and power output generation. The sameor brain algorithm, is utilised to generate both power quantal unit of, in this case, running speed, mustoutput and RPE, utilising the same scalar time pa- have been generated between minutes 9 and 10 asrameters set by knowledge of the distance to be between minutes 10 and 11, yet a significantlycovered and memory of prior similar exercise bouts. higher RPE score was described by the subjectsThis brain mechanism does not appear to be affected between minutes 10 and 11. This indicates that, inby prior fatiguing activity, as Eston et al.[77] have this example, an alteration in affect and percentagerecently 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



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Fig. 7. Hypothetical model of changes in power output during a field athletic event. Graphs (a) and (c) are power output changes that areidentical until 19km of a 25km event. In graph (a), the athlete breaks away from a bunch of runners and increases his power output until theend 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. Ingraph (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 changesselected 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 atalso of the psychological state of the athlete. This a point in the race may be very different dependingshows that the RPE quantal unit chosen by the brain on the unique situation of the event and on externalat 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 etvariables used by the brain algorithm to select both al.,[21] affect and percentage associative thoughts areRPE 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 mayincrease linearly during a race, but changes non- occur because we do not focus on one specificmonotonically throughout the event. For example, if thought, activity or sensation for a long period ofa leading athlete moves ahead of a group of athletes time, and even when concentrating on a particularand is likely to win an event, RPE is dramatically thought or sensation, a change in affect or mood willreduced (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 offellow athletes, and is left behind, RPE can be dissociative thoughts and does not think only aboutdramatically increased. Furthermore, with intermit- their level of fatigue and effort.[20,80,81] They maytent crowd support, RPE is reduced during the peri- also think of their tactics in relation to other athletesod 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 aspecifically related to the perception of effort and power output that will allow the athlete to reach thethe ‘feeling’ of fatigue. While we may believe that end of the exercise bout at the fastest speed possiblewe 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 thewithout other thoughts intruding on our conscious chosen speed was excessive at any point during thestate. Therefore, conscious perception of perceived event.exertion does occur in a quantal fashion, with ‘gaps’ Although the initial pace is set at the beginning ofin between each conscious thought of this particular the event in a feedforward manner, no event orstate, but these gaps do not appear to occur, as our internal physiological state will be identical to whatconscious 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 theperception 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 proposeaware of it,[62,82,83] and only when a quanta of RPE is that an internal clock, which appears to use scalardifferent 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 ofcurred.[59] As it is easier to be aware of an increase in the activity still to be covered, so that power outputa 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

We have further suggested that periods of ‘cer-during an event to a greater degree than the reduc-tainty’ and ‘uncertainty’ must occur throughout thetions in RPE. Hence RPE changes during an eventexercise bout. Periods of certainty occur after affer-may appear to increase linearly rather than beingent information from the periphery had been re-non-monotonic, unless the reductions in RPE areceived, when the brain has knowledge of what pow-profound, such as occurs when breaking away fromer output is required to complete the event within thea group of fellow athletes, or receiving sudden unex-context of the overall pacing strategy. Periods ofpected crowd support. Further work is required touncertainty occur after the efferent neural com-asses the veracity of this suggestion.mands have been generated to affect the changes inpower output, which have been ascertained to be7. Conclusionsnecessary by the brain algorithm, but before thephysiological and biochemical effects of this novelIn this article, we have examined how pacing

strategies are controlled by the brain during exer- power output command can be sensed. Because ofcise. We have suggested that although there are these continuously altering periods of certainty andseveral different pacing strategies used by athletes uncertainty, we have further suggested that efferentfor 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-

mation can be found in the non-monotonic changesPossibly the most important factor establishingin power output that occur throughout an exercisethe pacing strategy is knowledge of the endpoint.bout, using non-linear methods of analysis. Similar-The brain teleoanticipatory centre incorporates thisly, we have proposed that perception of effort is alsoknowledge into an algorithm, together with memory

of previous events and knowledge of external envi- not a linear phenomenon, but alters non-monotoni-



7. Noakes TD, St Clair Gibson A. Logical limitations to thecally throughout an exercise bout. We have also‘catastrophe’ models of fatigue during exercise in humans. Br J

suggested that perception of effort is made available Sports Med 2004; 38: 648-98. Ansley L, Schabort E, St Clair Gibson A, et al. Regulation ofto our conscious processes in ‘quantal’ units rather

pacing strategies during successive 4-km time trials. Med Scithan as a continuous feeling or awareness of effort Sports Exerc 2004; 36: 1819-25and fatigue. 9. Mattern CO, Kenekick RW, Kertzer R, et al. Impact of starting

strategy on cycling performance. Int J Sports Med 2001; 22:We have described a model in which exercise350-5

activity is controlled by the brain using pacing strat- 10. Foster C, Green MA, Snyder AC, et al. Physiological responsesduring simulated competition. Med Sci Sports Exerc 1993; 25:egies that induce continuous changes in power out-877-82put and perceived effort throughout the event, and

11. Adams GR, Harris RT, Woodard D, et al. Mapping of electricalthat these changes occur as discreet ‘quantal’ units activity using MRI. J Appl Physiol 2000; 74: 532-7

12. Enoka RM, Stuart DG. Neurobiology of muscle fatigue. J Applthroughout the event due to the length of time re-Physiol 1992; 72: 1631-48quired to generate power output in response to affer-

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2000; 23: 376-84receptors. In this model of the control of exercise,14. Gandevia SC. Spinal and supraspinal factors in human muscleinformation flow around the body is the important fatigue. Physiol Rev 2001; 81: 1725-89

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