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Influence of accurate and inaccurate ‘split-time’ feedback upon 10-mile time trial cycling performance

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  Influence of accurate and inaccurate ‘split-time’ feedback upon 10-mile time trial cycling performance
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  ORIGINAL ARTICLE Influence of accurate and inaccurate ‘split-time’ feedbackupon 10-mile time trial cycling performance Mathew G. Wilson  • Andy M. Lane  • Chris J. Beedie  • Abdulaziz Farooq Received: 20 January 2011/Accepted: 18 April 2011   Springer-Verlag 2011 Abstract  The objective of the study is to examine theimpact of accurate and inaccurate ‘split-time’ feedback upon a 10-mile time trial (TT) performance and to quantifypower output into a practically meaningful unit of varia-tion. Seven well-trained cyclists completed four random-ised bouts of a 10-mile TT on a SRM TM cycle ergometer.TTs were performed with (1) accurate performance feed-back, (2) without performance feedback, (3) and (4) falsenegative and false positive ‘split-time’ feedback showingperformance 5% slower or 5% faster than actual perfor-mance. There were no significant differences in completiontime, average power output, heart rate or blood lactatebetween the four feedback conditions. There were signifi-cantly lower (  p \ 0.001) average  _ V  O 2  (ml min - 1 ) and _ V  E (l min - 1 ) scores in the false positive (3,485  ±  596;119  ±  33) and accurate (3,471  ±  513; 117  ±  22) feed-back conditions compared to the false negative (3,753  ± 410; 127  ±  27) and blind (3,772  ±  378; 124  ±  21) feed-back conditions. Cyclists spent a greater amount of time ina ‘20 watt zone’ 10 W either side of average power in thenegative feedback condition (fastest) than the accuratefeedback (slowest) condition (39.3 vs. 32.2%,  p \ 0.05).There were no significant differences in the 10-mile TTperformance time between accurate and inaccuratefeedback conditions, despite significantly lower average _ V  O 2  and  _ V  E scores in the false positive and accuratefeedback conditions. Additionally, cycling with a smallvariation in power output (10 W either side of averagepower) produced the fastest TT. Further psycho-physio-logical research should examine the mechanism(s) whylower  _ V  O 2  and  _ V  E scores are observed when cycling in afalse positive or accurate feedback condition compared to afalse negative or blind feedback condition. Keywords  Accurate and inaccurate feedback    Pacing strategies    Cycling performance Introduction As the margins of winning continue to diminish, exercisephysiologists have been exploring pacing strategies thatutilise sport-specific technology in order to minimise anathlete’s physiological limitations. Atkinson and Brunskill(2000) define pacing as ‘the within-race distribution of work rate’, and with specific reference to cycling; poweroutput. Several researchers have examined the effect of verbal and/or informative data feedback upon cyclingperformance and their respective pacing strategy (Albertuset al. 2005; Mauger et al. 2009, 2010; Tucker and Noakes 2009; Micklewright et al. 2010). There is a general assumption, however, that negative feedback (falsely tell-ing an athlete that he/she is performing at a slower pace orcompleting a smaller distance than expected) reducesperformance; however, experimental data to prove thisconjecture is limited. Micklewright et al. (2010) investi-gated the influence of accurate (accurate and full perfor-mance feedback), blind (no performance feedback) andfalse positive feedback (deceived feedback, whereby speed Communicated by Keith Phillip George.M. G. Wilson ( & )    A. FarooqASPETAR, Sports Medicine Department,Qatar Orthopaedic and Sports Medicine Hospital,PO Box 29222, Doha, Qatare-mail: [email protected] M. Lane    C. J. BeedieSchool of Sport, Performing Arts and Leisure,University of Wolverhampton, Walsall, UK   1 3 Eur J Appl PhysiolDOI 10.1007/s00421-011-1977-1  given was 5% greater than actual speed) upon 29 well-trained cyclists over 3 bouts of 20 km cycling TT. Mic-klewright et al. found that completion time, average powerand average speed did not change among the false positivefeedback group, but importantly, their pacing strategyaltered significantly. Cyclists in the false feedback groupperformed with a lower average cadence (  p \ 0.05) andhigher power during the first 5 km. Moreover, pacingchanged among the blind feedback condition, with a fastercompletion time (35.9  ±  3.1 vs. 36.8  ±  4.4 min,  p \ 0.05)and power increases during the final 5 km.Albertus et al. (2005) provided distance feedback to 15well-trained male cyclists over four bouts of 20-km cyclingTT, during which cyclists received accurate, inaccurate andrandom distance feedback. In the inaccurate feedback TT,cyclists were deceived, being informed that they hadcompleted a kilometre when in fact the actual distance waseither shorter or longer by 250 m per km. Albertus et al.demonstrated no significant differences in completiontimes between feedback conditions, together with unalteredpacing strategies. More recently, Mauger et al. (2010)demonstrated that when cyclists receive accurate distancefeedback compared to inaccurate feedback, cyclists com-plete a 4-km time trial (TT) in a significantly faster time.Together with the investigation of informative feedback strategies upon performance, mobile cycle ergometers arenow routinely used within the laboratory and field settings,providing an additional source of information (power out-put, cadence, velocity, climatic conditions and heart rate)that assists the cyclist in selecting an optimal and sustain-able work rate in order to achieve a specific goal or out-come. However, the interpretation of such performancedata when competing maximally outdoors, such as in a10-mile TT, is problematic due to several environmentaland technological considerations. Accordingly, the major-ity of exercise physiologists working with such data willoften smooth the variable under investigation, e.g., averagepower output per unit time or distance. Indeed, the auto-matic retreat to smoothing of such data implies that fluc-tuations in data points are not meaningful, and that themean score captures the physiological ‘strain’ better. Assuch, variability in power output is often neglected as ameaningful phenomenon, and thus, is not treated as animportant source of information. Another simple, yeteffective method to analyse power data, is to ‘zone’ poweroutput per unit time (Tucker et al. 2006; Jobson et al.2009). However, these ‘zones’ have often been wideranging (around 200 W) (Ebert et al. 2006) leaving littleroom for interpretation of much smaller variances in powerdistribution (i.e. 10 W), but ones that nonetheless haveconsiderable practical meaning for the cyclist. SRM TM cranks (Ingenieurburo Schoberer, Julich, Germany) are amobile and/or laboratory-based cycle ergometer thatrecords power output, velocity and cadence every 0.5 s. Inthe present study it is proposed that the utilisation of SRM TM cranks, together with a small yet practicallymeaningful unit of variation (10 W power output zoneeither side of average power), may provide a simple andmore accurate reflection of the physiological ‘strain’ acyclist undertakes in a laboratory TT, than simplysmoothing data to a mean score.In conclusion, there has been little research that hasexamined the impact of false negative and false positive‘split-time’ feedback upon performance, thus utilisingconscious cues in well-trained cyclists who are especiallyused to dealing with such performance data. The presentstudy aimed to evaluate performance times and physio-logical variables in four different feedback conditionsduring a 10-mile cycling TT; accurate, blind, false positiveand false negative feedback. Accordingly, it was hypoth-esised that cyclists would perform a faster 10-mile TTunder accurate feedback conditions than blind, false posi-tive or false negative feedback conditions. Methods ParticipantsSeven healthy and well-trained cyclists (age; 34.1  ± 7.4 years) volunteered for the study. A full explanation of the data collection methods and risks were given to all par-ticipants and written informed consent was obtained. Thestudy received institutional ethics approval from the univer-sity at which the research was conducted. All participantsindicatedtheycycledtimetrialscompetitivelyandcompletedat least 8 h of cycle training per week. All participants hadextensiveexperienceofusingtheSRM TM cycleergometerinboth free and fixed wattage protocols (three cyclists ownedSRM TM cycle ergometers), and were well accustomed to theequipment and procedures of non-invasive and invasivephysiological assessment. Exclusion criteria included thecompletion of a cycling race or heavy training in the imme-diate48 hpriortothefirstTT.Importantly,participantsweredeceived uponrecruitment, being toldthat the purpose of theinvestigationwastostudythereliabilityofperformancetimesin10-mile TTcycling.However,oncompletionofthestudy,all participants were fully debriefed about the deception bythe senior investigator. Additionally, participants were pro-videdwithareportcontainingaccurateinformationregardingtheir performance.Time trialsParticipants completed four randomised bouts of a10-mile TT on the SRM TM ergometer with integrated HR Eur J Appl Physiol  1 3  monitoring (Polar Electro, Finland). Participants werescheduled to complete all four conditions at the same timeof day separated by a minimum 48 h, to avoid diurnal andtraining effects, within similar laboratory environmentalconditions. The cycle ergometer frame was adjusted toreplicate the precise geometry of each of the participantsTT bicycles per trial. Prior to each TT, participants wererequired to abstain from eating and drinking, except forwater, for 3 h. Participants were also not permitted toconsume food or fluid during trials, as continuous breath-by-breath gas analysis data was collected (Oxycon Pro,Jaeger, Viasys Healthcare, Germany). At 1-mile intervalsblood lactate was recorded Lactate Pro TM (KDK Corpo-ration, Kyoto, Japan, Arkray Factory Inc., KDK Corpora-tion, Shiga, Japan). Following completion of each TT,retrospective analysis of power output and cadence recor-ded every 0.5 s and  _ V  O 2 ,  _ V  CO 2 ,  _ V  E and HR recordedevery 1 s by a metabolic cart was calculated globally aswell as per mile.  Accurate feedback  In the accurate feedback condition, participants were pro-vided with full performance and physiological data;namely elapsed time, power output, distance completed,current velocity, cadence,  _ V  O 2 ,  _ V  CO 2 ,  _ V  E and lactate.  Blind feedback  In the blind feedback condition, participants were not pro-vided with any performance and physiological data, includ-ing any verbal cues. Participants were simply instructed tostart cycling and stop cycling upon completion. False positive or false negative feedback  In the false positive feedback condition, participantsreceived inaccurate time feedback at 1-mile markersshowing their performance per mile to be 5% faster thantrue values. In the false negative feedback condition, par-ticipants received inaccurate time feedback at 1-milemarkers showing their performance per mile to be 5%slower than true values. Participants were not providedwith any further performance or physiological data. As thiswas a deceptive study using cyclists with extensive expe-rience of using performance data, 5% was set as a smallertime value may not have had the desired effect upon con-scious cues to alter rider performance, whilst larger timevalues would have been instantaneously known by theriders as deceptive feedback; invalidating the protocol asthe riders would have known they were being deceived.StatisticsStatistical analysis was performed using PASW statistics(18.0). A one-way within subject analysis of variance(ANOVA) with repeated measures was used to assess theeffects of feedback condition upon cycling performanceand physiological measures. Furthermore, power outputdata was ‘zoned’ in a 20 W group, 10 W either side of average power output per feedback condition, to examinethe small variances of power output time (%) distribution.A two-way ANOVA was used to assess difference in firstand second half TT performance. Mauchly’s test was usedto assess for sphericity and in case of violation Green-house–Geisser epsilon correction was used to adjustdegrees of freedom. All data was screened for assumptionsfor ANOVA namely normality test and homogeneityof variance test. Where a significant interaction wasfound, post hoc pairwise comparisons were performed withBonferroni test. Statistical significance was accepted at  p \ 0.05. Results There were no significant differences ( F  3,24  =  0.77,  p  =  0.97) in the completion time between the four feed-back conditions (Table 1). Furthermore, first ( F  3,24  =  0.45,  p  =  0.72) and second half ( F  3,24  =  0.03,  p  =  0.99) com-pletion times were not significantly different betweenconditions. A two-way ANOVA revealed there was nosignificant effect of feedback on the either halves of the TTper condition ( F  2,10  =  0.25,  p  =  0.75). However, therewere significant differences between first and second half performances ( F  1,6  =  26.1,  p  =  0.002) (Fig. 1); moreover,no interaction effect was observed ( F  3,18  =  1.11,  p  =  0.37). There were no significant differences in averagepower output ( F  3  =  1.91,  p [ 0.05), heart rate ( F  3  =  0.52,  p [ 0.05), or blood lactate ( F  3  =  1.05,  p [ 0.05) betweenthe four feedback conditions. There was, however, signif-icantly lower average  _ V  O 2  and  _ V  E scores (  p \ 0.001) inthe false positive and accurate feedback conditions com-pared to the false negative and blind feedback conditions(Table 1).When power output distribution was combined as a‘20 watt zone’ 10 W either side of average power(Table 2), cyclists spent a significantly greater amount of time in this ‘combined zone’ in the negative feedback condition than the accurate feedback condition (39.3 vs.32.2%,  p \ 0.05). Furthermore, the negative feedback condition demonstrated the smallest fluctuation in poweroutput distribution. Eur J Appl Physiol  1 3  Discussion The aim of the present study was to evaluate the influenceof accurate and inaccurate ‘split-time’ feedback upon10-mile TT cycling performance in well-trained malecyclists. It was hypothesised that cyclists would perform afaster 10-mile TT under accurate feedback conditions thanwhen compared to blind, false positive or false negativefeedback conditions. Accordingly, the hypothesis is rejected,as there were no significant differences in completion timebetween the four feedback conditions.Findings from the present study are not consistent withMauger et al.’s (2010) observation that the provision of accurate feedback enhances TT performance. However, thepresent study cannot conclude on the usefulness of positiveor negative feedback upon performance. Whilst completiontime, average power output, cadence, HR and Bla weresimilar between these two conditions,  _ V  O 2  and  _ V  E weresignificantly reduced during the positive feedback condi-tion. However, the difference in completion time betweenthe two conditions was only 1.4 s (0.13%), and whilst thefalse positive feedback condition was associated with asignificant reduction in oxygen consumption throughoutthe TT, it was the false negative condition that demon-strated a reduced power output fluctuation 10 W either sideof average power. Thus, the individual contribution of either a reduced power output fluctuation or reduced oxy-gen consumption during 10-mile TT performance isunclear at this stage, with both seemingly offering benefitstowards performance.There is limited physiological understanding for themechanisms behind the reduced metabolic ‘strain’ underfalse positive and accurate feedback conditions. Interdis-ciplinary sport scientists have observed that false positivebeliefs associated with the administration of placebosimproves performance in the majority of experimentalparticipants (Beedie and Foad 2009); however, the exactmechanism for this performance improvement outside of psychological parameters (emotional intelligence, motiva-tion and arousal, pleasant and unpleasant emotion, self efficacy, and self belief) are still limited. Exercise psy-chologists have consistently demonstrated substantialdecrements in blood glucose and increases in metabolic Table 1  Performance and physiological data per feedback conditionFalse negative False positive Blind AccurateTT completion time (s) 1,533  ±  62(1,425–1,600)1,535  ±  61(1,430–1,620)1,542  ±  56(1,466–1,620)1,547  ±  73(1,464–1,679)Completion time differencefrom fastest TT in s (%)– 1.4 (0.13) 8.7 (0.57) 14.3 (0.92)HR (bpm) 167  ±  15 (134–191) 168  ±  14 (138–191) 168  ±  15 (135––191) 170  ±  15 (132–201) _ V  O 2  (ml min - 1 ) 3,753  ±  410(3,034–4,668)3,485  ±  596*(2,177–4,665)3,772  ±  378(3,062–4,796)3,471  ±  513*(2,537–4,449) _ V  E (l min - 1 ) 127  ±  27 (95–194) 119  ±  33* (71–181) 124  ±  21 (91–182) 117  ±  22* (78–172)Bla (mmol) 9.7  ±  2.4 (0.8–15.7) 8.9  ±  2.7 (1.6–13.9) 9.3  ±  2.8 (3.2–16.2) 9  ±  2.9 (3.2–13.9)Watts (W) 243  ±  27 (199–293) 244  ±  23 (210–298) 243  ±  24 (204–287) 252  ±  22 (216–288)RPM 99.9  ±  4.4 (0–117) 100.4  ±  5.8 (0–127) 100  ±  5.4 (0–132) 100.2  ±  5.7 (0–119)Values are expressed as mean  ±  SD and (range), or time difference and (%) TT   time trial,  HR  heart rate,  _ V  O 2  volume of oxygen,  _ V  E volume expired,  Bla  blood lactate,  RPM   revolutions per minute* Significantly different from false negative and blind feedback conditions (  p \ 0.001) Fig. 1  First and second half performances per feedback condition Table 2  Zoned time percentage power distributionCondition(fastest toslowest) \ - 10 Wbelow averagepower (%) [ - 10 or \ ? 10either side of average power (%) [ ? 10 Wabove averagepower (%)False negative 36.8 39.3* 23.8False positive 41.9 33.7 24.5Blind 38.2 37.2 24.6Accurate 40.6 32.2 27.2* Significantly different than accurate feedback condition (  p \ 0.05)Eur J Appl Physiol  1 3  strain (  _ V  O 2  and HR) (Gailliot et al. 2007) in individualswho are attempting to regulate conscious and unconsciousemotion(s) (Baumeister et al. 2011). Since negative emo-tions are likely associated with negative beliefs aboutcurrent performance status (such as in the false negativeand blind feedback TT conditions), it is theoretically pos-sible that in order to maintain TT performance togetherwith the effortful process of emotion regulation induced bythese two feedback conditions (Muraven and Baumeister2000), an increase in metabolic strain is likely to occur.The present study gave ‘split-time’ performance feed-back, and differing pacing strategies in the blind andinaccurate feedback conditions were observed compared tothe accurate feedback condition, in which second half performances were significantly slower than first half (Fig. 1). However, the reduction in second half perfor-mance did not impact upon overall completion timebetween the deceived conditions, with all three producingnon-significant 10-mile TT completion times. Conse-quently, the present study does not support the notion thataccurate feedback enhances work rate thus producing afaster performance, or that false negative feedback reduceswork rate and thus produces a slower performance.The reduced power variation observed in the negativefeedback condition supports the notion that variation inpower output during a 10-mile TT should be minimal.Atkinson et al’s. (2007) review of optimal pacing strategiesfor road cycling, concluded that when variable conditionsof road gradient and wind velocity do not vary (as in alaboratory), power output should not vary. However, thepresent study also found conflicting data for the beneficialuse of cycling power ergometers. When power outputdistribution was combined as a ‘20 watt zone’ 10 W eitherside of average power, cyclists spent a significantly greateramount of time in this ‘combined zone’ in the negativefeedback condition than the accurate feedback condition(39.3 vs. 32.2%,  p \ 0.05), thus demonstrating a smootherpower curve than the accurate feedback condition. Con-tradictorily, however, only the accurate feedback conditionproduced matching split times for first and second half TTperformances, demonstrating that despite a increasedcompletion time than all other feedback conditions, mobilecycling ergometers may allow a cyclist to develop asmoother power output (similar to that observed in thenegative feedback condition) through training, enhancingoverall TT performance. Limitations The number of cyclists recruited was relatively small andwhilst each participant cycled for a minimum of 8 h perweek, the cyclists were not of true-elite TT standard. Theprotocol did not complete a test–retest per feedback con-dition. There are clear ethical concerns when deceivingpeople for a long time and having multiple measures oneach condition would do this. It is recognised that forscientific accuracy, all feedback conditions should berepeated several times to ensure accuracy of data. How-ever, getting competitive cyclists to return to the laboratoryfor a further four occasions was not possible; hence thestudy looked for brevity. It is interesting to note that thestudy demonstrated no learning affect by the non-signifi-cant difference in performance time between feedback conditions. Future interdisciplinary work should look toquantify other mechanistic causes for the non-improve-ments in performance with either accurate or deceptivefeedback. These mechanisms may include physiologicalafferents (glucose, insulin, direct muscle temperature,ATP), psychological (emotional intelligence, arousal,motivation, exercise disassociation), and neurophysiologi-cal (neural firing patterns and frequencies). Furthermore,despite cost limitations, anticipatory and pacing researchshould look to utilise other scientific techniques such asmulti-muscle electromyography, positron emission tomogra-phy and functional magnetic resonance imaging. Conclusion In conclusion, there were no significant differences in10-mile TT performance time between accurate and inac-curate feedback conditions. However, the provision of falsepositive feedback produced a comparable completion timeto false negative feedback, but was associated with a sig-nificantly reduced metabolic ‘strain’. Further psycho-physiological research should look to examine the mech-anism(s) why lower  _ V  O 2  and  _ V  E scores are observed whencycling in a false positive or accurate feedback conditioncompared to a false negative or blind feedback condition.Finally, a smooth power curve 10 W on either side of average power produced the fastest TT. Cyclists shouldutilise mobile cycle ergometers to train and develop theirability to cycle is a smooth fashion, optimising 10-mile TTperformance. Acknowledgments  The support of the Economic and SocialResearch Council (ESRC) UK is gratefully acknowledged (RES-060-25-0044: ‘‘Emotion regulation of others and self’’ [EROS]). References Albertus Y, Tucker R, St Clair Gibson A, Lambert EV, Hampson DB,Noakes TD (2005) Effect of distance feedback on pacingstrategy and perceived exertion during cycling. Med Sci SportsExerc 37(3):461–468Eur J Appl Physiol  1 3
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