Mechanical Stiffness: A global parameter associated to elite sprinters performance

(1)

www.rbceonline.org.br

Revista

Brasileira

de

CIÊNCIAS

DO

ESPORTE

ORIGINAL

ARTICLE

Mechanical

stiffness:

a

global

parameter

associated

to

elite

sprinters

performance

Fernando

López

Mangini

a

_,

_Gabriel

_Fábrica

b,∗

a_Universidad_de_la_República_(UDeLaR),_Facultad_de_Medicina,_Unidad_de_{Investigación}_en_Biomecánica_de_la_Locomoción

Humana,Montevideo,Uruguay

b_Universidad_de_la_República_(UDeLaR),_Facultad_de_Medicina,_Departamento_de_Biofísica,_Montevideo,_Uruguay

Received24July2013;accepted12March2014 Availableonline12March2016

KEYWORDS

Stiffness; Sprint; Biomechanics; Performance

Abstract Thisstudyanalyzesvertical stiffnessasaglobalparameterthatcouldbedirectly associatedtosprinter’sperformance.Weevaluatedverticalstiffness,performance,heartrate andlactateconcentrationonfifteenmalesprintersthatranonatreadmillatgaittransition speedand13kmh−1_._Vertical_Stiffness_was_determined_by_the_ratio_of_the_vertical

accelera-tion peakandmaximum displacementofthecenterofmass.Physiologicalparameterswere measuredthroughouttheexperimentalprocedureandperformancewasestimatedbyathlete’s timerecordson100mtrackrace.Asexpected,verticalstiffnessandheartrateincreasedwith runningspeed.Wefoundahighcorrelationbetweenheartrateandverticalstiffnessatgait transitionspeed.However,at13kmh−1_,_lactate_peak_showed_a_higher_correlation_with_vertical

stiffness,suggestingagreaterparticipationoftheanaerobicsystem.Aninverserelationship betweenperformanceandverticalstiffnesswasfound,wherefasterathleteswerethestiffer ones.Performanceandlactatepeakpresentedthesameinverserelationship;fasterathletes hadhigherlactatepeaks.Asaresult,fasterathleteswerestifferandconsumemoreenergy.All inall,thesefindingssuggestthatmechanicalstiffnesscouldbeapotentialglobalparameterto evaluateperformanceinsprinters.

Rigidez mecânica: um parâmetro global associado com desempenho em velocistas deelite

Resumo Esteestudoanalisaarigidezverticalcomoumparâmetroglobalquepoderiaser dire-tamenteassociadoaodesempenhoemvelocistas.Avaliou-searigidezvertical,odesempenho, afrequênciacardíacaeaconcentrac¸ãodelactatoem15velocistasdosexomasculino,todos altamentetreinados,quecorreramemumaesteiraàvelocidadedetransic¸ãoea13km.h−1_. ∗_{Corresponding}_author.

E-mail:[email protected](G.Fábrica). http://dx.doi.org/10.1016/j.rbce.2016.02.004

(2)

Arigidezverticalfoideterminadapelarazãoentreopicodeacelerac¸ãoverticaleo desloca-mentomáximodocentrodemassa.Osparâmetrosfisiológicosforammesuradosnacoletade dadoseodesempenhofoiestimadoporregistrosdetempoem100metrosdecorrida.Como esperado,arigidezverticaleafrequênciacardíacaaumentaramcomavelocidade.Arigidez eafrequênciacardíacaobtiveramaltacorrelac¸ãonamenorvelocidade.Contudo,a13km.h−1

opicodelactatomostroualtacorrelaçãocomarigidez,oquesugereumamaiorparticipação dosistemaanaeróbico.Umarelaçãoinversafoiachadaentrerigidezeregistrosdetempo,nos quaisosatletasmaisrápidossãoosmaisrígidos.Alémdisso,osatletasmaisrápidosforamos queapresentaramosmaiorespicosdelactato.Assim,esteestudosugerequearigidezvertical poderiaserumparâmetroglobalparaavaliarodesempenhodosvelocistas.

Rigidez mecánica: un parámetro global asociado con el rendimiento en velocistas deélite

Resumen Esteestudioanalizalarigidezverticalcomounparámetroglobalquepodríaestar directamenterelacionadoconelrendimientoenvelocistas. Seevaluó larigidezvertical,el rendimiento, la frecuencia cardíaca y la concentración delactato en 15velocistasde sexo masculino, muy entrenados, que corrieron en una cinta a la velocidad de transición y a 13km/h−1_._La_rigidez_vertical _se_calculó _por_el_cociente_entre_el_pico_de _aceleración

verti-calyeldesplazamientomáximodelcentrodemasa.Losparámetrosfisiológicossemidieron duranteelprocedimientoexperimentalyelrendimientoseestimóatravésdeltiempodecada atletaen100mllanos.Comoeradeesperar,larigidezverticalylafrecuenciacardíaca aumen-taronconla velocidad.Seencontróunaaltacorrelaciónentrefrecuenciacardíacayrigidez verticalalavelocidadmásbaja.Sinembargo,a13km/h−1_,_el_pico_de_lactato_mostró_una_alta

correlaciónconlarigidezvertical,loquesugeríamayorparticipacióndelsistemaanaeróbico. Seencontróunarelacióninversaentreelrendimientoylarigidezvertical,dondelosatletasmás rápidosfueronmásrígidos.Asimismo,elrendimientoyelpicodelactatopresentaronlamisma relacióninversa;losatletasmásrápidosmostraronlospicosmásaltosdelactato.Por consi-guiente,losatletasmásrápidosfueronmásrígidosyconsumieronmásenergía.Estosresultados sugierenquelarigidezmecánicapodríaserunparámetroglobalparaevaluarelrendimiento delosvelocistas.

Introduction

Arunningsprintercoordinatestheactionsofmanymuscles, tendons, and ligaments in its leg so that the overall leg behaves like a single mechanical spring during ground contact(Farleyetal.,1993).Infact,thesimplestmodelofa runningsprinterisaspring-masssystemconsistingofalinear springrepresentingthestancelimbandapointmass equiv-alenttobodymass (Blickhan,1989;Mcmahon and Cheng, 1990).Duringsupport,verticalforcecomponentisactingon thespringandthusmechanicalstiffnesscanbecalculated fromtheratioofthisforcetothechangeinspringlength.

The spring mass model has been used in optimization studies (Alexander, 2003) and totest hypothesis that are directly related tosport andtraining fields (Morin etal., 2011; DiMichele et al.,2012).The reasons for usingthis modelintheseareasaremainlyitssimplicityandglobality sinceevaluations generallyhave the tendency toanalyze oneorafewparametersonly.

Assumingthespring massmodel,previous studieshave used different methods and equipments for estimating mechanical stiffness when running (Brughelli and Cronin, 2008).Oneoftheapproachesmostcommonlyusedis verti-calstiffness(Kvert)(Mcmahon etal.,1987;Cavagnaetal.,

1988; Morin et al., 2005, 2011; Di Michele et al., 2012). This parameter relatestothepeakvertical forceand the vertical motion of the center of mass during the contact with the ground (Brughelli and Cronin, 2008). Both, the force peak and the displacement of the center of mass depend on muscular activation, fiber type and muscular volume (Herzog, 2000) and is thought to influence sev-eralathleticvariables,includingrateofforcedevelopment, storageofelasticenergy,stridefrequency,groundcontact time and sprint kinematics (Farley and Gonzalez, 1996; Mcmahon and Cheng, 1990). In this way, changes in Kvert

could influence sprinter’s performance when running and its quantification could provide a useful tool to evaluate sprinters.

(3)

It is widely known that strengthis amajor qualityfor athletestodevelop,thusitiscloselyassociatedtorunning speed. All elite sprinters have a common feature; huge leanmuscleswithhigh percentageofexplosivefibers that areintensivelyactivatedwhen racing(Kuboetal., 2011). Duringeach stepphase,athletes performmaximum force in averyshortperiodof time,recruitinghigh percentage ofmusclefibersandactivatingcontractileelementsto gen-erate intensive stretch-shorteningcycles in acoordinated manner (Nigg et al., 2000). Therefore, maximum power is perform during ground contact, increasing speed and improvingtimerecords.

Having a largermuscular volume,build up byfast and explosivefibers,withalargercrosssectionthanslowfibers andahighpercentageofactivatedfibers,coulddetermine anincreaseinforcepeakduringgroundcontactphase lead-ingtoanincreaseinKvert.

Nonetheless,an athletewithalargermuscular volume and ahigh percentage of activatedfibers would consume moreenergy.Kvert dependsdirectly ontheforce peakand

thisonmusclecharacteristicsthatwoulddetermindenergy consumption. Energy consumption during exercise can be estimated throughout different physiological parameters such as heart rate and blood lactate concentration. The analysisoftheseparameterswouldprovidefurtherinsight onKvertvaluesconsideringthemasarealfeedbackof

phys-iologicalprocesseshappeninginsidemuscletendonunitof thelimbs.

Theaimofthisstudyistoprovideabetter understand-ing ontheimportance ofmechanical stiffnessasa global parameter that could be directly associated to athlete’s performance.

Our hypothesis isthat athletes withhighermechanical stiffnesswillconsumemoreenergyandwillhavefastertime recordsin100m.

For that purpose, we analyzed Kvert of elite sprinters

througha3D filmingandreconstructionprocedureat con-trolledconditionsandlactateconcentrationandheartrate to assay their relationship withathlete’s performance. If the hypothesis is correct,the physiological and mechani-calparametersstudiedhereshouldfollow thesametrend ofchange andhave ahigh correlation withathlete’stime records.Asaresult,mechanicalstiffnesscouldbea poten-tialglobalparametertopredictperformanceinsprinters.

Methods

Experimentalapproachtotheproblem

Thestudy designiscomposedbytheanalysisofthree dif-ferentvariables.

By using a three dimensional filming method (cineme-try),athlete’smechanicalstiffness(Kvert)wasassayed.This

method,basedonthespring-massmodel,providesinsights on the motion of the center of mass when running. Sec-ondly, physiological parameters such as lactate peak and heartrateweremeasuredtoanalyzeenergyconsumption. These canbeconsidering asarealfeedbackof the physi-ologicalprocesseshappeninginsidemuscletendonunitsof thelimbs.

Finally,wemeasuredathlete’sperformancebytheirtime recordsin100mtrack race.Timerecordswere measured undercontrolledconditions(sameday,track,distance,wind speedandchronometer)forallsprinters.

With these information, we tested our hypotheses to provideabetter understanding onthe importanceofKvert

asaglobal parameterthatcouldbedirectly associatedto athlete’sperformance.

Subjects

Asampleoffifteenmaleathletes,allhighlytrainedsprinters (19.7±1.28years,69.50±2.68kg)voluntarilyparticipated inthisstudy.Thesamplewasconvenientlyselectedamong athleteswiththebesttimerecordsin100minthecountry.

The sample size (n) was estimated throughout a

comparisonmodel: n= 2(Za+Zb)

2_∗_S2

d2

where n=number of athletes, Za and Zb=risk values,

S2₌_variance _of _K

vert (taken from bibliography) and

d=minimumdifferencedetectedwhencomparingtwoKvert

valuesatdifferentspeeds.Also,weconsideredunilaerality andset˛=0.05andˇ=0.05.

Theexclusioncriteriaconsidered werethepresenceof anyinjurywithinthelast6monthorifthetrainingperiod wasdifferentfrompre-competition/competition.Oncethe sample was selected, none of the individuals had to be excludedfromthetrial.

This study complied with the requirements of the local Committee for Medical Research Ethics and cur-rentUruguayanlawandregulations.Theparticipantswere informed about the objectives and characteristics of the study,andtheirconsentwasobtained.

Procedures

Performance was measured by athlete’s time records on 100mtrackrace.Athleteswererequestedtoracea100m onan officialtrail in theNational AthleticStadium. Time recordswere measuredundercontrolledconditions(same day,track, distance,windspeed andchronometer)for all sprinters.

After evaluating performance, athletes were taken to theLaboratoryofMovementintheHospital deClínicas to evaluateKvertandphysiologicalparameters.

Athletes were dressed up in black tight clothes and coveredwith18whitemarkersindifferentanatomical land-markstoidentify them for subsequent digitalization. The selectedpointswerethefifthmetatarsal,lateralmalleolus, femoralcondyle, greater trochanter, acromion, sphenoid, lateralepicondyleofthehumerus,radialstyloidprocessand headofthethirdmetacarpal.Priortoperformingthe exper-imental procedures, two anthropometric variables were measured: body mass and leg length (trochantericheight measuredfromthegreatertrochantertothefloor).

Eachsubjectranonatreadmillfor10minattwodifferent speeds. We used twodifferent running speedsto confirm thatKvert and physiological parameters varywithchanges

(4)

invelocity(ArampatzisandMetzle,1999).Duringthefirst 5min,athletesranattheirgaittransitionspeedestablished byFreud’sequation:

V2=0.5.g.l

where V is the running speed, 0.5 is Freud’s number for gait transition speed, g is acceleration due to gravity (9.8ms−2_on_earth)_and_l_is_the_leg_length_in_meters₍_Saibene

and Minetti, 2003). The remaining 5min athletes ran at 13kmh−1_,_speed_at _which_muscles _work_mainly _in

isomet-ric contraction (Fisher, 2010). We selected these running speedssincethemodelweareusingtocalculateKvert

per-fectlyadjustsandisvalidatlowrunningvelocities. Inthis way,althoughwearenotmeasuringKvert attheusualhigh

speedof a race, thisparameter calculated at low speeds reflectstheglobalmechanic characteristicsof theathlete (Cavagnaetal.,1988).

Images were recorded with 4 synchronized DCR-H28E digitalvideocameras,thesewereplacedon950ALS UNO-MATtripodsfollowinga90degreeanglelayoutwithineach other toensure that athlete’s sagital plane wascaptured bytwo cameras(Fig.1). In thisway, a three-dimensional reconstructionwasperformedtodeterminethepositionof athlete’scenterofmass(CM)andcalculatethemechanical stiffnessthroughoutKvert.

Oncethefilmingprocesswasfinalized,imageswere cap-turedandloadedinDvideowsoftware(DigitalforVideofor BiomechanicsWindows32bits).Thisprogramallows decen-tralizingthefieldsthatcomposetheimagesofeachframe ofthevideosothatthefrequencyofdataacquisitioninthis workwas50framespersecond(Barrosetal.,1999;Figueroa etal.,2003).

Upon finishing the above process, the position coor-dinates of the markers placed over specific landmarks were determined by digitalizing each marker referenced to a three dimensional coordinate system associated to a calibration volume. The system used for calibration, was chosen according to the International Society of

1 4

3 2

Figure1 Schematiclayoutofthecamerasplacedroundthe treadmill.Thearrowindicatesmovementdirection.

Biomechanics recommendations, in order to unify the kinematics datacommunication. Throughout a 3-D recon-struction,athree-columnmatrix wasobtained,describing thepositioncoordinatesofthemarkers.

The digitalized sequence was selected from 10 step cyclesof thelast minuteof each runningspeed toassure less stepvariability (Fisher, 2010). After establishing this sequenceinonecamera,wecalculatedtheframerangeto acquirethesamerangeinallcameras.Thustoperformthis, we synchronizedthe deviceswithasoundsignal thatwas recordedinthevideoandreadbythesoftware.TheCM coor-dinates weredetermined by 3D reconstructionanalysis in Matlab7.0® _(Mathworks,_Inc.),_considering_the_coordinates

ofthepartialcenterofmassofthefeet,legs,thighs,hands, forearms,arms,trunkandhead.Knowingthepositionofthe CMineachstep,weproceededtocalculatetheacceleration oftheCMbyderivingitspositiontwicewithrespecttotime. Fromthe accelerationcurve we determinedtheeffective contacttimeforeachstancephase(Cavagna,2006).Using thistimewecalculatethemaximumdisplacement(MD)of theCMfromthepositioncurve(Cavagna,2006).

Finally,the effective vertical stiffness wasdetermined bytheratioof theverticalaccelerationpeak(avmax)and MD(BrughelliandCronin,2008)oftheCM.

Kvert=avmax.MD−1

Twophysiologicalparameterswereanalyzedinthisstudy (heartrateandlactateconcentration).Heartratewas mon-itored prior to performing the experimental procedures (basal heart rate) and during both running speeds by a Nike triax c8 heart rate monitor. The lactate concentra-tionwasdeterminedbyaRocheAccutrendpluslactimeter. This device worked throughout a colorimeter method to determine the amount of blood lactate concentration.To carryoutthemeasurementproceduresweused20specific reagentsforlactateand20disposablelancets(allpurchased fromRoche).Fourlactatemeasurementswereperformedto allathletes.Thefirstonewascompletedbeforethestartup oftheprotocol(basallactateconcentration)andtheother threewere accomplished2,5 and8min afterthe experi-mentalprocedureswerefinalized.

Statisticalanalysis

Kvert at both speeds was plotted against athlete’s time

records,andlactatepeakwasplottedagainstathlete’stime records.

Pairedsample t-test(p<0.01) wereperformed to ana-lyze howKvert and physiological parameters change when

increasingrunningspeed.

Pearsoncorrelationswereimplementedbetween: - Kvertandphysiologicalparametersatbothspeeds

- Kvertandathlete’stimerecordson100m

- Lactatepeakandathlete’stimerecordson100m. AllthestatisticanalysiswasperformedinSPSSStatistics 17.0.

(5)

Table1 Mean andstandarddeviation ofKvert andheart

ratebothrunningspeedsp≤0.01isthesignificanceofthe changeoftheseparametersfromgate transitionspeed to 13kmh−1_.

Variable Gaittransitionspeed 13kmh−1 _p

Kvert(s−1) 161.5±22.9 194.5±27.4 0.001

Heartrate 143.9±11.2bpm 177.2±5.7 0.001

Table2 Resultsofcorrelations,notethattheanalysisare notperformedforlactatepeakandKvertV1,sincethe

sam-plesweretakenafterthesecondrace(13kmh−1_)._The_level

ofsignificanceisshownbetweenbrackets.

Variable KvertV1 KvertV2

Lactatepeak --- 0.83(0.000)

Heartrate 0.74(0.001) 0.52(0.05)

Results

Allathletesanalyzedincreasedtheirheartrate,blood lac-tateconcentrationandKvert whenenhancingrunningspeed

asestablishedinpreviousinvestigations(Kerdoketal.,2002; BrughelliandCronin,2008;Fisher,2010).

Paired sample t-tests (p<0.01) showed significant changesinheartrateandKvertwhenraisingrunningvelocity

fromgaittransitionspeedto13kmh−1₍_Table₁_).

Also, blood lactate concentration exhibited an impor-tant peak (7.63±2.41mmoll−1₎ ₂_min _after _finalizing _the

experimental procedures which was significantly higher (p<0.01)thanthebasalconcentrationobtainedinthiswork (1.89±0.4mmoll−1_).

Pearson correlations between heart rate and Kvert for

gait transition speed were higher than the ones found at 13kmh−1_,_and_lactate_peak,_was_highly_associated_to_K

vert

values(r=0.83)whenrunningat13kmh−1₍_Table₂_).

HighcorrelationswherefoundwhenplottingKvertvalues

measuredatgatetransitionspeedandat13kmh−1_against

sprinter’s time records in 100m (r=0.907 and r=0.922 respectively), where the fastest sprinters (shortest time record)weretheoneswiththehighestKvert(Fig.2).

The same inverse relationship and similar correlation (r=0.95)wasfoundwhenplottingbloodlactatepeakagainst

300

Kv

er

t (s

–2)

Sprinter’s time records in 100 meters (s)

Kvert values at gait transition speed Kvert at 13 Km.h–1

250 200 150 100 50 0 10.7 10.8 10.9 11 11.1 11.2 11.3 11.4 11.5

Figure 2 Kvert measured at gait transition speed and at

13kmh−1_vs_time_records_in₁₀₀_m. 14 12 10 8 6 4 2 0 10.7 10.8 10.9 11 11.1 11.2 11.3 11.4 11.5

Lactate peak (mmol.L

–1)

Sprinter’s time records in 100 meters (s)

Figure3 Lactatepeakvstimerecordsin100m.

athlete’stimerecords,wherethefastestathleteshadthe highestlactatepeaks(Fig.3).

Discussion

Inthis study, we focuson howKvert is associated toelite

athlete’s performance. Therefore, to achieve this aim, we calculated Kvert and analyzed heart rate and blood

lactate concentration to estimate energy consumption, considering these variables asa realfeedback of physio-logical processes happening inside muscle tendon unit of thelimbs. To estimate Kvert, we useda method based on

imagingreconstruction. Although thiscouldbeconsidered asalimitationofthestudymethodssinceforceplatesare moreprecise,we understandit isfundamental toanalyze and evaluate the study parameters at controlled speed. Thus,weconsideredcinemetryasthebestoption.

We used two different running speeds (gait transition speed and 13kmh−1₎ _to _confirm _that _K

vert varies when

increasingrunningvelocity(ArampatzisandMetzle,1999). Weselectedtheserunningspeedssincethemodelwe are usingto calculate Kvert better adjusts and is valid at low

runningvelocities. Athighervelocities,asymmetriesoccur intheverticaldisplacementoftheCMduringtheeffective ground contactphase(Cavagnaet al.,1988). In thisway, althoughwearenotmeasuringKvertattheusualhighspeed

ofarace,thisparametercalculatedatlowspeedsreflects theglobalmechaniccharacteristicsoftheathlete.

TheKvert average valuesfound forgaittransitionspeed

(161.5±22.9s−2₎_and_for₁₃_km_h−1_(194.5_±_27.4_s−2_),_were

slightly below the values usually established in previous investigations.Forinstance,Ferrisetal.(1999)found val-ues of 18kNm−1 ₍₂₂₅_s−2₎ _when _running _at _10.8_km_h−1

(Ferrisetal.,1999);DuttoandSmith(2002)founddataof 23.5kNm−1₍₂₉₃_s−2₎_for_a_running_speed_of₁₄_km_h−1₍_Dutto

andSmith,2002).

The difference in Kvert valuesfound in thisstudy from

the ones reported in literature is probably associated to themethodusedtocalculatethisparameter,sinceinthis study athletes ran on a treadmill while in most back-ground investigationsKvert is determined by using a force

platform (Brughelli and Cronin, 2008). In this way, our resultsincludedthestiffnessofthetreadmill(Kerdoketal., 2002) that promoted the whole body-treadmill system to become more compliant than running in a non-compliant surface.However, we found a significant increase in Kvert

(6)

values(p=0.001) when enhancing running speed which is coherent with previous studies findings (Arampatzis and Metzle,1999; Brughelli andCronin, 2008). The significant changes in Kvert when raising running speed have been

fully explained in former studies by enhancing the force peak and decreasing the displacement of the CM during thestance phase (Brughelli andCronin, 2008). These dif-ferences in force and displacement are determined by changes in muscular contractile capacity. This combined withmetabolicchangescouldmaketheneuromuscular sys-temregulatethemuscle-tendonstiffnessbyincreasingthe level of pre-activation and thus affecting global stiffness (Fisher, 2010). It has been demonstrated that enhancing running speed, increases Kvert and as a result, a smaller

amount of elastic energy is used (Brughelli and Cronin, 2008). The supporting explanation for this statement is that Kvert depends onboth, the vertical acceleration and

the effective deformation of the lower limb, while the elasticenergydependsonthesquareddeformation. There-fore,thesmaller effective deformationof the lowerlimb due to the increase on running speed becomes deter-minant in the use of elastic properties (Fisher, 2010). Thus,this should reflect an increase onenergy consump-tion, as analyzed in previous studies where Kvert was

assayed in different running speeds in fatigue condition (Borranietal.,2003;Candauetal.,1998;Slawinskietal., 2008).

Inthis paper,wefound thatheart rateincreaseswhen enhancing running speed and that blood lactate concen-tration shows a significant peak (7.63±2.41mmoll−1₎

compared to the basal values (1.89±0.4mmoll−1_),

con-firming that higher energy is consumed. In addition, increasingrunningspeedimpliesamajoreffortandagreater number of muscle fibers activated, thus raising muscu-lar activation andthe force performed during the ground contactphase(Herzog,2000).Asaresult,moreenergy(ATP) andoxygen is consumedby the musclefibers that is sup-portedbyanintensifybloodsupplydrivenfromtheheart, increasingitsrateandvolumeofventricularejection(Nigg etal.,2000).Pearsoncorrelationsbetweenheartrateand Kvert forgaittransitionspeed(r=0.74)washigherthanthe

one found at 13kmh−1 _(r₌_0.52), _probably _because_when

runningatalowervelocity,themainsystemrestoringATP is the aerobic system. On the other hand, running at a fasterspeed derives in agreater muscleactivity implying an increase on the amount of lactate concentration. The presence of this molecule in the muscle fibers, promotes theacidificationofthecellsenvironmentwhichspeedsup oxygenrelease tothe tissues, profiting cellular breathing (Niggetal.,2000).Wefoundthatlactateconcentration,was highlyassociatedtoKvert values(r=0.83)whenrunning at

13kmh−1_while_heart_rate_correlation_decreased_(r₌_0.52).

Thisshowsanimportantparticipationoftheanaerobic sys-teminrestoringATP.

Wefound thatfasterathleteswerestiffer andalsothe onesthatreachedthehighestlactatepeak,thusconsuming moreenergy.Therefore,changesinphysiologicalvariables highlighttheimportanceofKvertasaglobalparameter.

Hav-ingalargermuscularvolumebuildupbyfastandexplosive fiberswithalargercrosssectionthanslowfibersandahigh percentageofactivatedfiberscoulddetermineanincrease in force peak during ground contact phase leading to an

increase in Kvert.This increase in muscular activation due

toalargervolumewillconsumemoreenergy.Asaresult, stiffersprinterswouldconsumemoreenergy.

ThefindingsofthisstudysuggestthatKvertisanimportant

determinant and a potential global parameter to evalu-atesprinter’sperformance.Consideringthatitismeasured whenracingandsimpletoevaluate,thisparameterhasan importantpracticalapplicationinthecoachingfield.Inthis sense, for future studies we will focus on a longitudinal approachtoanalyzehowdifferentcapacitiessuchaspower, explosive force andcoordination canaffect Kvert and

ath-lete’sperformance.Uptodate,therehavebeennostudies thatanalyzetheeffectsoftraininginotherwaysof estimat-ingmechanicalstiffness,suchaslegstiffness,andrunning speed.

Conclusions

Whenincreasingrunningspeed,heartrateandKvertfollow

thesametrendofchange.

At gaittransition speed, Kvert is highly correlatedwith

heartrate. At13kmh−1_K

vertishighlycorrelatedwithlactatepeak.

Our results suggest that the fastest athletes have a greatermechanicalrigidityandconsumemoreenergy.

Conflicts

of

interest

Theauthorsdeclarenoconflictsofinterest.

References

AlexanderRMcN.Modellingapproachesinbiomechanics.PhilTrans RSocLondB2003;358:1429---35.

ArampatzisBG,MetzleV.Theeffectofspeedonlegstiffnessand jointkineticsinhumanrunning.JBiomech1999;32:1349---53. BarrosRML,BrezinkoferR,LeiteNJ,FigueroaPL.Desenvolvimento

eavaliac¸ãodeunsistemaparaanálisetridimensionalde movi-mentoshumanos.RevBrasEngBioméd1999;15:234---9. Blickhan R. The spring-mass model for running and hopping. J

Biomech1989;22:1217---27.

BorraniF, Candau R,Perrey S, Millet GY, Millet GP,RouillonJD. DoesthemechanicalworkinrunningchangeduringtheVO2slow

component?MedSciSportsExerc2003;35:50---7.

BrughelliM,CroninJ.Influenceofrunningvelocityonvertical,leg andjoint stiffness:modelingandrecommendationsforfuture research.SportsMed2008;38:647---57.

Candau R, BelliA, Millet GY,Georges D,BarbierB, RouillonJD. Energy costand runningmechanics duringa treadmillrun to voluntaryexhaustioninhumans.EurJApplPhysiolOccupPhysiol 1998;77:479---85.

CavagnaGA,FranzettiP,HenglundN,WillemsP.Thedeterminants ofthestepfrequencyinrunning,trottingandhoppinginman andothervertebrates.JPhysiol1988;399:81---92.

CavagnaGA.Thelanding-take-offasymmetryinhumanrunning.J ExpBiol2006;209:4051---60.

Di Michele R, Del Curto L, Merni F. Mechanical and metabolic responsesduringahigh-intensitycircuittrainingworkoutin com-petitiverunners.JSportsMedPhysFitness2012;52:33---9. DuttoDJ, Smith GA.Changes inspring-mass characteristics

dur-ing treadmill running to exhaustion. Med Sci Sports Exer 2002;34:1324---31.

(7)

FarleyC,GlasheenJ,McMahonT.Runningsprings:speedandanimal size.JExpBiol1993;185:71---86.

FarleyCT,GonzalezO.Legstiffnessandstridefrequencyinhuman running.JBiomech1996;29:181---6.

FerrisDP,LiangK,FarleyCT.Runnersadjustlegstiffnessfortheir firststeponanewrunningsurface.JBiomech1999;32:787---94. FigueroaPJ,LeiteNJ,BarrosRML.Aflexiblesoftwarefortracking ofmarkersusedinhumanmotionanalysis.ComputMethodsProg Biomed2003;72:155---65.

FisherG. AnálisedaMecânicadaCorridacomesemo efeitoda Fadiga[Dissertac¸ãoMestrado].PortoAlegre:Ciênciasdo Movi-mento Humano, Universidade Federal do Rio Grandedo Sul, UFRGS;2010.

HerzogW.Cellularandmolecularmusclemechanics.In:HerzogW, editor.Skeletalmuscle mechanics:from mechanismsto func-tion.NewYork:JohnWiley&Sons,Ltd;2000.p.33---52. KerdokAE,BiewenerAA,McMahonTA,WeyandPG,HerrHM.

Ener-geticsandmechanicsofhumanrunningonsurfacesofdifferent stiffness.JApplPhysiol2002;92:469---78.

McmahonTA, ValiantG, FrederickEC.Grouncho running.JAppl Physiol1987;62:2326---37.

Kubo K, Ikebukuro T, Yata H, Tomita M, Okada M. Mor-phological and mechanicalproperties of muscle and ten-don in highly trained sprinters. J Appl Biomech 2011;27: 336---44.

McmahonTA,ChengGC.Themechanicsofrunning:howdoes stiff-nesscouplewithspeed?JBiomech1990;23:65---78.

Morin J, Delleau G, Kyrolainen H, Jeannin T, Belli A. A simple methodformeasuringstiffnessduringrunning.JApplBiomech 2005;21:167---80.

Morin JB, Tomazin K, Edouard P, Millet GY. Changes in running mechanics and spring-mass behavior induced by a mountain ultra-marathonrace.JBiomech2011;44:1104---7.

NiggB,MacIntoshB,MesterJ.Biomechanicsandbiologyof move-ment.Champaign,IL,USA:HumanKinetics;2000.

Saibene F, Minetti AE. Biomechanical and physiological aspects of legged locomotion in humans. Eur J Appl Physiol 2003;88:297---316.

SlawinskiJ, Heubert R, Quievre J, Billat V, Hannon C.Changes in spring-mass model parameters and energy cost during track running to exhaustion. J Strength Cond Res 2008;22: 930---6.