In swimming science, economy of movement is an interesting field of research. Several investigations have been conducted to understand the role of bioenergetical profile to performance. Most of those studies focused exclusively on the contribution of The individual correlations between E-tot and v ranged from R=0.95 aerobic system to produce energy for movement, even though all competitive swimming events also require significant (p=0.05) to R=0.90 (p&lt;0.01). For pooled data the relationship was contribution from anaerobic energetic system to cover total energy expenditure. R=0.70 (p&lt;0.01). The individual correlations between EC and d

Barbosa, Tiago M.

Colaço, P.

Fernandes, R.J.

Keskinen, K.L.

Lima, A.B.

Lima, F.

Novais, D.

Portela, A.

Vilas-Boas, J.P.

Revista Portuguesa de Ciências do Desporto

T Barbosa

F Lima

A Portela

D Novais

A Lima

P Colaço

P Gonçalves

R Fern

Kl Keskinen

CiteSeerX

Relationships between energy cost, swimming velocity and speed fluctuation in competitive swimming strokes

The purpose of this study was to analyse the relationships between the total energy expenditure (tot), the energy cost (EC), the intra-cycle variation of the horizontal velocity of displacement of centre of mass (dv) and the mean swimming velocity (v) in elite butterfliers

Lima, António B.

Colaço, Paulo

Gonçalves, Pedro

Machado, Leandro

Fernandes, Ricardo J.

Keskinen, Kari L.

Vilas-Boas, João Paulo

Biblioteca Digital do IPB

Rev Port Cien Desp 6(Supl.1) 71–132 73Significance was set at P < 0.05. RESULTSNormality of data was examined using the Kolmogorov-Smirnov test and subsequently confirmed. No correlation wasfound between CJ (CJ1: 21.4 ± 2.6 jumps; CJ2: 23.3 ± 2.6jumps) and the variables examined from the WAnT (FI: 48.3 ±7.1%; mP: 459.1 ± 45.3 W; pP: 667.8 ± 93.7 W). The two CJwere significantly correlated with each other (P < 0.05; ICC =0.99). The LoA for the CJ scores was -1.5 ± 2.35 jumps, pro-ducing a range of +0.85 to -2.85 jumps.    DISCUSSIONThe results indicated that the CJ is not a valid measure ofanaerobic leg power. This can be attributed to a) the lack of afixed resistance for the generated upward impulse, and b) thedifferent movement mechanics required (3) compared to theWAnT.  Additionally, although CJ is reliable, the LoA in whichresults from a re-test should lie in (1) are wide, making mean-ingful interpretation of results difficult. Therefore, coachesmust be cautious when utilising the CJ for evaluation andmonitoring purposes. Future studies should consider a) a larg-er sample, b) investigating other muscular performance param-eters, and c) conducting the CJ for 60 seconds.    REFERENCES1. Atkinson G, Nevill AM (1998). Statistical methods forassessing measurement error (reliability) in variables relevantto sports medicine. Sports Med 26: 217-2382. Rechichi C, Dawson B, Lawrence SR (2000). A multistageshuttle swimming test to assess aerobic fitness in competitivewater polo players. J Sci Med Sport 3: 55-643. Sanders R (1999). Analysis of the eggbeater kick used tomaintain height in water polo. J Appl Biomech 15: 284-291.RELATIONSHIPS BETWEEN ENERGY COST, SWIMMING VELOCITYAND SPEED FLUCTUATION IN ELITE BUTTERFLIERS.Barbosa T1, Lima AB2, Colaço P2, Gonçalves P2, MachadoL2, Fernandes R2, Keskinen KL3, Vilas-Boas JP21Department of Sports Sciences, Polytechnic Institute of Bragança,Bragança, Portugal2Faculty of Sport, University of Porto, Porto, Portugal3Finnish Society for Research in Sports and Physical Education,Helsinky, Finland.INTRODUCTIONThe purpose of this study was to analyse the relationshipsbetween the total energy expenditure ( tot), the energy cost(EC), the intra-cycle variation of the horizontal velocity of dis-placement of centre of mass (dv) and the mean swimmingvelocity (v) in elite butterfliers.METHODSFour elite butterfly swimmers were submitted to an incremen-tal set of nx200-m swims (n≤ 8). The velocity was increased by0.05 m.s-1 after each swim until exhaustion. Cardio-pulmonaryand gas exchange parameters were measured breath-by-breathfor each swim to analyse oxygen consumption (VO2) and otherenergetic parameters by portable metabolic cart (K4b2,Cosmed, Italy). A respiratory snorkel and valve system withlow hydrodynamic resistance was used to measure pulmonaryventilation and to collect breathing air samples. Blood samplesfrom the ear lobe were collected before and after each swim toanalyse blood lactate concentration (YSI 1500L, Yellow Springs,US). tot=VO2net+2.7[La-]net and EC= tot.v-1 were calculat-ed for each swim. The swims were videotaped in sagittal planewith a set of two cameras providing dual projection from bothunderwater and above the water surface as described elsewhere(Barbosa et al., 2005). APAS system (Ariel Dynamics Inc, USA)was used to analyse dv. Linear regressions between the totand v, between EC and dv, between EC and v and polynomialregressions between dv and v were computed. Partial correla-tions between EC and dv controlling v and between EC and vcontrolling dv were also calculated.RESULTS AND DISCUSSIONThe individual correlations between tot and v ranged fromr=0.95 (p=0.05) to r=0.90 (p<0.01). For pooled data therelationship was r=0.70 (p<0.01). The individual correla-tions between EC and dv controlling the effect of v rangedfrom r=0.99 (p=0.06) to r=-0.81 (p=0.09). For pooled data,the relationship between EC and dv was r=0.55 (p=0.01).The individual correlations between EC and v controlling theeffect of dv ranged from r=0.92 (p=0.02) to r=-0.84(p=0.36). When the pooled data was plotted it was observeda relationship of r=0.51 (p=0.02). The individual correlationsbetween dv and v ranged from r=0.99 (p=0.04) to r=-0.83(p=0.16). For pooled data, the relationship between dv and vwas r=0.47 (p=0.05). Therefore, when analysed on individ-ual bases, it is possible to observe different profiles betweenEC and dv, as well as, between EC and v. However, for pooledsample, it seems that increases of EC were related to increas-es of dv and v. REFERENCESBarbosa T, Keskinen KL, Fernandes R, Colaço C, Lima A, Vilas-Boas JP. (2005). Energy cost and intracyclic variation of thevelocity of the centre of mass in butterfly. Eur J Appl Physiol.93: 519-523.POSTER PRESENTATIONS

Relationships between energy cost, swimming velocity and speed fluctuation in elite butterfliers

The purpose of the study was to analyse relationships between
total energy expenditure (Etot), energy cost (EC), intra-cycle
variation of the horizontal velocity of displacement of centre of
mass (dv) and mean swimming velocity (v). 17 Portuguese
elite swimmers (4 at Freestyle, 5 at Backstroke, 4 at
Breaststroke and 4 at Butterfly) were submitted to an incremental
set of nx200-m swims. Bioenergetical and biomechanical
parameters presented significant interrelationships. For
pooled data, the relationship between Etot and v was r=0.59
(p<0.01), between EC and dv was r=0.38 (p<0.01) and the
polynomial relationship, between dv and v was r=0.17
(p=0.28). Individual evaluation and identification of biomechanical
critical points may help the swimmers to become
more efficient at a certain swimming velocity

Lima, Francisco

Portela, Ana

Novais, Daniel

Rev Port Cien Desp 6(Supl.2) 185–197192Portuguese. J Human Movement Studies, 10: 69-752. Billat V, Koralstein J (1996). Significance of the velocity atVo2máx and time to exhaustion at this velocity. Sports Med, 22:90-1083. Billat V, Faina M, Sardella F, Marini C, Fanton F, Lupo S, deAngelis M, Koralstein J, dal Monte A (1996). A comparison oftime to exhaustion at Vo2máx in elite cyclists, Kayak paddlers,swimmers and runners. Ergonomics, 39 (2): 267-2774. Craig A, Pendergast D (1978). Relationships of stroke rate,distance per stroke and velocity in competitive. Med ScienceSports, 11 (3):278-2835. Demarie S, Sardella F, Billat V, Magini W, Faina M (2001).The VO2 slow component in swimming. Eur J Appl Physiol, 84: 95-996. Faina M, Billat V, Squadrone R, de Angelis M, Koralsztein J,dal Monte A (1997). Anaerobic contribution to the time toexhaustion at the minimal exercise intensity at wich maximaloxygen update occurs in elite cyclists, kayakists and swimmers.Eur J Appl Physiol, 76: 13-207. Fernandes R, Cardoso C, Soares S, Ascensão A, Colaço P,Vilas-Boas JP (2003). Time Limit and VO2 slow Component atintensities corresponding to VO2máx in swimmers. Int J SportsMed, 24: 576-5818. Fernandes RJ, Billat VL, Cruz AC, Colaço PJ, Cardoso CS,Vilas-Boas JP (2005). Has gender any effect on the relationshipbetween time limit at O2max velocity and swimming econo-my? J Hum Movement Stud, 49, 127-1489. Fernandes RJ, Billat VL; Cruz AC, Colaço PJ; Cardoso CS;Vilas-Boas JP (in press). Does net energy cost of swimmingaffect time to exhaustion at the individual’s maximal oxygenconsumption velocity? J Sports Med Phys Fitness10. Keskinen K, Rodríguez F, Keskinen O (2003). Respiratorysnorkel and valve system for breath-by-breath gas analysis inswimming. Scand J Med Sci Sports, 13 (5): 322-32911. Maglischo C, Maglischo E, Santos T (1987). The relation-ship between the forward velocity of the center of gravity andthe forward velocity of the hip in the four competitive strokes.J Swim Research, 3 (2): 11-1712. Marinho D, Vilas-Boas JP, Cardoso C, Barbosa T, Soares S,Fernandes R (2004). Stroke rate and stroke length in a typicalswimming time limit test at VO2max. In: E. van Praagh, J.Coudert, N. Fellmann, P. Duché (eds.), Book of Abstracts ofthe 9th Annual Congress of the European College of SportScience. Clermond-Ferrand, France, pp. 33813. Nigg B (1983). Selected methodology in biomechanics withrespect to swimming. In: A.P. Hollander, P.A. Huijing, G. deGroot (eds.), Biomechanics and Medicine in Swimming.Champaign, Illinois: Human Kinetics Publishers, pp 72-8014. Toussaint  HM, Meulemans A, de Groot G, Hollander AP,Schreurs A, Vervoorn K (1987). Respiratory valve for oxygenuptake measurements during swimming. Eur J Appl Physiol,56, 363-36615. Ungerechts B (1988). The relation of peak body accelera-tion and phases of movements in swimming. In: B.E.Ungerechts, K. Wilkie, K. Reischle (eds.), Swimming Science V.Champaign, Illinois: Human Kinetics Publishers, 61-6616. Vilas-Boas JP (1996). Speed fluctuations and energy cost ofdifferent breaststroke techniques. In: J.P. Troup, A.P. Hollander,D. Strasse, S.W. Trappe, J.M. and T.A. Trappe (Eds.).Biomechanics and Medicine in Swimming VII. London: E & FNSpon, 167-17117. Vilas-Boas JP, Cunha P, Figueiras T, Ferreira M, Duarte JV·(1997). Movement analysis in simultaneous technique. In: K.Daniel, U. Hoffmann, J. Klauck (Eds.). Cologne SwimmingSymposium. Bocknem: Sport Fahneman Verlag, 95-103.RELATIONSHIPS BETWEEN ENERGY COST, SWIMMING VELOCITYAND SPEED FLUCTUATION IN COMPETITIVE SWIMMING STROKESTiago M. Barbosa1, Francisco Lima2, Ana Portela2, DanielNovais2, Leandro Machado2, Paulo Colaço2, PedroGonçalves2, Ricardo Fernandes2, Kari L. Keskinen3, J.Paulo Vilas-Boas21Department of Sports Sciences, Polytechnic Institute of Bragança,Portugal2Faculty of Sport, University of Porto, Portugal3Finnish Society for Research in Sport and Physical Education,Finland.The purpose of the study was to analyse relationships betweentotal energy expenditure (Etot), energy cost (EC), intra-cyclevariation of the horizontal velocity of displacement of centre ofmass (dv) and mean swimming velocity (v). 17 Portugueseelite swimmers (4 at Freestyle, 5 at Backstroke, 4 atBreaststroke and 4 at Butterfly) were submitted to an incre-mental set of nx200-m swims. Bioenergetical and biomechani-cal parameters presented significant interrelationships. Forpooled data, the relationship between Etot and v was r=0.59(p<0.01), between EC and dv was r=0.38 (p<0.01) and thepolynomial relationship, between dv and v was r=0.17(p=0.28). Individual evaluation and identification of biome-chanical critical points may help the swimmers to becomemore efficient at a certain swimming velocity.Key Words: competitive strokes, energy expenditure, energycost, speed fluctuation, velocity.INTRODUCTIONIn swimming science, economy of movement is an interestingfield of research. Several investigations have been conducted tounderstand the role of bioenergetics and its repercussions inperformance. Most of those studies focused exclusively on thecontribution of aerobic system to produce energy for move-ment even though all competitive swimming events alsorequire significant contribution from anaerobic energetic sys-tem to cover total energy expenditure. Particularly in swim-ming, environmental factors have hindered the measurement ofcardiorespiratory variables within the actual field setting.However, machinery to explore human aerobic energetics dur-ing field conditions has become available with the improve-ment of miniaturized metabolic measurement systems. Intra-cycle variation of horizontal velocity of centre of bodymass (dv) is a widely accepted criterion for biomechanicaldescription of swimming techniques. There is a positive rela-tionship between high dv and increased energy cost, especiallyin Breaststroke (12) and Butterfly stroke (2). In Backstroke andFreestyle the relationship was not so evident (1). In this per-spective, it is important to obtain a better understanding of therelationship between the energy cost and dv in the competitivestrokes.Some investigators suggested the possibility of high dv beingrelated with lower swimming velocities (e.g., 2, 12). It wasSWIMMING BIOENERGETICS··Rev Port Cien Desp 6(Supl.2) 185–197 193observed a significant and negative relationship between themean horizontal velocity and the speed fluctuation in Butterflystroke (10) and Breaststroke (9). Nevertheless, there is nostudy in the literature about the relationship between swim-ming velocity and dv, in Freestyle and Backstroke.The purpose of this study was to analyse the relationshipsbetween total energy expenditure, energy cost, intra-cycle vari-ation of horizontal velocity of displacement of centre of bodymass and mean velocity of swimming.METHODSSubjects17 elite swimmers (5 females and 12 males) of the Portuguesenational team, volunteered to serve as subjects. 4 swimmerswere evaluated performing Breaststroke (including 2 femaleswimmers), 4 swimmers performing Butterfly (including 1female swimmer), 5 swimmers performing Backstroke and 4swimmers performing Freestyle (including 2 female swim-mers).DesignThe subjects were submitted to an incremental set of nx200-mswims. The starting velocity was set at a speed, which repre-sented a low training pace, approximately 0.3 m.s-1 less than aswimmer’s best performance. The last trial should representthe swimmers all out pace. After each successive 200-m swim,the velocity was increased by 0.05 m.s-1 until exhaustionand/or until the swimmer could not swim at the predeter-mined pace. The resting period between swims was 30s to col-lect blood samples. Under-water pace-maker lights (GBK-Pacer,GBK Electronics, Aveiro, Portugal), on the bottom of the 25-mpool, were used to control the swimming velocity and to helpthe swimmers keep an even pace along each step. In addition,elapsed time for each swim was measured with a chronometerto control the swimmer’s velocity.Data CollectionThe swimmers breathed through a respiratory snorkel andvalve system (7, 11), connected to a telemetric portable gasanalyzer (K4 b2, Cosmed, Rome, Italy). The oxygen consump-tion (VO2) was measured for each swim breath-by-breath.Blood samples (25 µl) from the hyperemisized ear lobe werecollected to analyze blood lactate concentration (YSI 1500 L,Yellow Springs, Ohio, USA) before and after each swim, as wellas, 1, 3, 5 and 7 minutes after the last swim. Total energyexpenditure (Etot) was calculated using the VO2 net and theblood lactate net (difference between the highest value meas-ured in the end of the stage and the rest value), transformedinto VO2 equivalents using a 2.7 mlO2.kg-1.l-1 constant (5). Theenergy cost (EC) was calculated dividing the Etot by the swim-ming velocity (v).The swims were videotaped (50 Hz) in sagital plane with a pairof cameras (JVC GR-SX1 SVHS and JVC GR-SXM 25 SVHS,Yokoama, Japan), providing a dual-media images from bothunderwater and above the water perspectives as described else-where (2). The images of the two cameras were real time syn-chronized and edited on a mixing table (Panasonic DigitalMixer WJ-AVE55 VHS, Japan) to create one single image. ArielPerformance Analysis System (Ariel Dynamics Inc, California,USA) and a VCR (Panasonic AG 7355, Japan) at a frequency of50 Hz were used to perform a kinematical analysis of thestroke cycles, including the dv of the centre of mass.Zatsiorsky’s model with an adaptation by de Leva (3) was usedwith the division of the trunk in 3 articulated parts. A filterwith a cut-off frequency of 5Hz was used for the analysis of thehorizontal velocity curve of the centre of mass.Statistical proceduresMeans and standard deviations of all variables were calculated.Coefficients of variation for the horizontal velocity of the cen-tre of mass along with the stroke cycle were calculated. Linearregressions between the Etot and v, between EC and dv andpolynomial regressions of 2nd order between dv and v werecomputed. Partial correlations between EC and dv controlling vand between EC and v controlling dv were also calculated. Thelevel of statistical significance was set at p≤ 0.05.RESULTS AND DISCUSSIONFigure 1 presents the relationships between the bioenergeticaland biomechanical variables studied. The relationship betweenEtot and v was r=0.70 (p<0.01) at Butterfly stroke, r=0.88(p<0.01) at Breaststroke, r=0.67 (p<0.01) at Backstroke andr=0.85 (p<0.01) at Freestyle. The relationship between ECand dv was r=0.55 (p=0.01) at Butterfly stroke, r=-0.20(p=0.43) at Breaststroke, r=0.38 (p=0.05) at Backstroke andr=0.79 (p<0.01) at Freestyle.  Polynomial model presented abetter adjustment than the linear approach, for the relationshipbetween dv and v. The polynomial relationship between dv andv was r=0.47 (p=0.05) at Butterfly stroke, r=0.65 (p=0.02) atBreaststroke, r=0.45 (p=0.06) at Backstroke and r=0.65(p<0.01) at Freestyle. For pooled data the relationshipbetween Etot and v was r=0.59 (p<0.01), between EC and dvwas r=0.38 (p<0.01), and the polynomial relationshipbetween dv and v was r=0.17 (p=0.28).Figure 1. Relationships analysed between the bioenergetical and biome-chanical variables, for each competitive stroke and for pooled sample.SWIMMING BIOENERGETICSRev Port Cien Desp 6(Supl.2) 185–197194In all situations, increases of Etot were significantly related toincreases in swimming velocity. The increase of Etot is due to thenecessity to overcome drag force, which is related to v. The high-er adjustment of the linear relationship compared to the cubicone is due to the decrease of internal mechanical work to com-pensate the hydrostatic torque at higher velocities (4). Increasesof the dv promoted significant increases of the EC, except forBreaststroke. Speed fluctuation while swimming as compared toswimming with constant velocity leads to an increase in theamount of total energy expenditure done by the swimmer (2).This increase is related to the need of overcoming the inertia andthe drag force. Polynomial relationship between dv and v pre-sented a better adjustment than the linear one. This phenome-non is described on regular bases for terrestrial locomotion (8).The parabolic function is explained by the curve between forceand velocity for neuromuscular activity (6, 8). So, the data sug-gests that the neuromuscular activation of several muscles in amulti-segment and multi-joint movement follows the force-velocity relationship pattern for a single joint system (6).Table 1 presents the partial correlations between EC and dvcontrolling the effect of v and the partial correlation betweenEC and v controlling the effect of dv. It seems that the increas-es of EC are strongly related to dv. Moreover, increases of ECare also strongly related to v, when controlling the effect of dvin the four competitive strokes. However, when a large numberof observations from several competitive strokes are pooledtogether, the dependence of EC from v it is not so evident.Table 1. Partial correlations between energy cost (EC), speed fluctua-tion (dv) and swimming velocity (v).Correlation between EC Correlation between EC and dv controlling v and v controlling dvFreestyle r= 0.62 (p<0.01) r=0.43 (p=0.05)Backstroke r= 0.55 (p<0.01) r=0.56 (p<0.01)Breaststroke r= 0.60 (p=0.01) r=0.86 (p<0.01)Butterfly stroke r= 0.55 (p=0.01) r=0.51 (p=0.02)Pooled sample r= 0.39 (p<0.01) r=0.16 (p=0.14)CONCLUSIONThe bioenergetical and biomechanical parameters analyzed pre-sented significant relationships in each of the competitivestrokes, so that changes in dv enhanced EC and Etot consider-ably. Biomechanical evaluation of swimming technique, andidentification of execution critical points, may, consequently, becritical for performance enhancement in a biologically restrict-ed supply of energy. ACKNOWLEDGMENTSTo the Portuguese Swimming Federation and the swimmers ofthe National Team.REFERENCES1. Alves F, Gomes-Pereira J, Pereira F. (1996). Determinants ofenergy cost of front crawl and backstroke swimming and com-petitive performance. In: Troup JP, Hollander AP, Strasse D,Trappe SW, Cappaert JM, Trappe TA (eds). Biomechanics andMedicine in Swimming VII. London: E & FN Spon, 185-192.2. Barbosa TM, Keskinen KL, Fernandes R, Colaço P, Lima AB,Vilas-Boas JP. (2005). Energy cost and intracyclic variation ofthe velocity of the centre of mass in butterfly. Eur J ApplPhysiol 93: 519-5233. De Leva P (1996). Adjustments to Zatsiorsky-Seluyanov’ssegment inertia parameters. J Biomechanics 29: 1223-12304. Di Prampero P, Pendergast D, Wilson D, Rennie D (1974).Energetics of swimming in man. J Appl Physiol 37: 1-55. Di Prampero P, Pendergast D, Wilson D, Rennie D (1978).Blood lactic acid concentrations in high velocity swimming. In:Eriksson B and Furberg B (eds). Swimming Medicine IV.Baltimore: University Park Press, 249-2616. Enoka R (1988). Neuromechanical Basis of Kinesiology.Illinois: Human Kinetics7. Keskinen KL, Rodríguez FA, Keskinen OP (2003).Respiratory snorkel and valve system for breath-by-breath gasanalysis in swimming. Scand J Med Sci Sports 13: 322 – 3298. Minetti A (2000). The three modes of terrestrial locomotion.In: Nigg B, MacIntosh B, Mester J (eds.). Biomechanics andBiology of movement. Illinois: Human Kinetics, 67-789. Takagi H, Sugimoto S, Nishijima N, Wilson B (2004).Differences in stroke phases, arm-leg coordination and velocityfluctuation due to event, gender and performance level inbreaststroke. Sports Biomechanics 3: 15-2710. Togashi T, Nomura T (1992). A biomechanical analysis ofthe novice swimmer using the butterfly stroke. In: MacLarenD, Reilly T, Lees A (eds). Biomechanics and Medicine inSwimming VI. London: E & FN Spon, 87-9011. Toussaint H, Meulemans A, de Groot G, Hollander AP, SchreursA, Vervoon K (1987). Respiratory valve for oxygen uptake meas-urement during swimming. Eur J Appl Physiol 56: 363-36612. Vilas-Boas, J.P. (1996). Speed fluctuations and energy costof different breaststroke techniques. In: Troup JP, Hollander AP,Strasse D, Trappe SW, Cappaert JM, Trappe TA (eds).Biomechanics and Medicine in Swimming VII. London: E & FNSpon, 167-17113. Williams K (2000). The dynamic of running. In: Zatsiorsky V(ed.). Biomechanics in sport. Oxford: Blackwell Science, 161-183.METABOLIC AND MECHANICAL CHARACTERISTICS OF OLYMPICFEMALE GOLD MEDALISTFutoshi Ogita1, Takao Tanaka1, Hiroyuki Tamaki1, AkiraWagatsuma1, Takafumi Hamaoka1, Huub M. Toussaint21National Institute of Fitness and Sports, Kanoya, Japan2University of Professional Education Amsterdam, Amsterdam, TheNetherlands.To clarify metabolic and mechanical characteristics of a superi-or swimmer, an Olympic gold medalist was compared to 16elite Japanese college swimmers who belonged to an inter-col-lege champion team. Maximal oxygen uptake (VO2max), maxi-mal blood lactate concentration (LAmax), a drag coefficient, adrag exponent, drag-swimming speed relationship and maximalpropulsive power (MPP) were determined. In the comparisonof VO2max, LAmax, and MPP, no marked differences wereobserved between the gold medalist and the other swimmers.On the other hand, the drag-swimming speed relationshiprevealed lower drag for the gold medalist, especially at higherswimming speed (near race pace). Taken all, it is suggestedthat mechanical (technical) factors, such as propelling efficien-cy and the stroke technique to reduce drag, should be consid-ered as more significant determinants of superior swimmingperformance.SWIMMING BIOENERGETICS

Relationships between energy cost, swimming velocity and speed fluctuation in elite butterfliersT Barbosa1, F Lima2, A Portela2, D Novais2, A Lima2, P Colaço2, P Gonçalves2, R Fernandes2, KL Keskinen3,4, JP Vilas-Boas21 Department of Sport Sciences, Polytechnic Institute of Bragança, Bragança, Portugal2 Faculty of Sport, University of Porto, Porto, Portugal3 Finnish Society for Research in Sports and Physical Education, Helsinky, Finland4 Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä , Finland3. RESULTS1. INTRODUCTIONIn swimming science, economy of movement is an interesting field of research. Several investigations have been conducted tounderstand the role of bioenergetical profile to performance. Most of those studies focused exclusively on the contribution of The individual correlations between E-tot and v ranged from R=0.95aerobic system to produce energy for movement, even though all competitive swimming events also require significant (p=0.05) to R=0.90 (p<0.01). For pooled data the relationship wascontribution from anaerobic energetic system to cover total energy expenditure. R=0.70 (p<0.01). The individual correlations between EC and dvcontrolling the effect of v ranged from R=0.99 (p=0.06) to R=-0.81Intra-cycle variation of horizontal velocity of centre of body mass (dv) is a widely accepted criterion for biomechanical evaluationof swimming techniques. There are some evidences of positive relationships between high dv and increased energy cost in(p=0.09). For pooled data, the relationship between EC and dv wasButterfly stroke (Barbosa et al. (2005). In this perspective, it is important to obtain a better understanding of this relationship. R=0.55 (p=0.01). The individual correlations between EC and vcontrolling the effect of dv ranged from R=0.92 (p=0.02) to R=-0.84Some investigators suggested that high dv is related with lower swimming velocities (e.g., Barbosa et al., 2005). It was (p=0.36). When pooled data was plotted, it was observed aobserved a significant and negative relationship between mean horizontal velocity and dv in Butterfly stroke (Togashi and association of R=0.51 (p=0.02). The individual correlations betweenNomura, 1992). Nevertheless, there are no sufficient studies in the literature about the relationship between swimming velocityand dv, in this particular swimming technique.dv and v ranged from R=0.99 (p=0.04) to R=-0.83 (p=0.16). Forpooled data, the relationship between dv and v was R=0.47 (p=0.05).The purpose of this study was to analyse the relationships between total energy expenditure (E-tot), energy cost (EC), intra- Therefore, when analysed on individual bases, it is possible tocyclic variation of horizontal velocity (dv) and mean swimming velocity (v) in elite butterfliers. observe different profiles between EC and dv, as well as, between EC Figure 1. Relationship between total energy expenditure and v. However, for pooled sample, it seems that increases of EC (E-tot) and swimming velocity (v).2. METHODS were related to increases of dv and v.Subjects. 4 elite butterfliers, including 3 males and 1 female were studied (19.5±3.3 years old; 66.5 ±10.7kg of body mass;175.7 ±8.3-cm of height; 9.3 ±3.8% fat mass; 123.53 ±16.78 for 200-m butterfly best time in 25-m swimming pool).Design. The subjects were submitted to an incremental set of nx200-m swims. The velocities and increments were chosen inagreement with swimmers. The starting velocity was set at a speed, wich represented a low training pace, approximately 0.3m.s-1 less than a swimmer’s best performance. The last trial should represent the swimmers all out pace. After each 200-mswim, the velocity was increased by 0.05 m.s-1 until exhaustion and/or until the swimmer could not swim at the predeterminedpace. The resting period between swims was 30-s to collect blood samples.Under-water pace-maker lights (GBK-Pacer, GBK Electronics, Aveiro, Portugal) on the bottom of the 25-m pool, were used tocontrol the swimming velocity and to help swimmers to keep an even pace along each step. In addition, elapsed time for eachswim was measured with a chronometer to control the swimmer’s velocity.Data collection. The swimmers breathed through a respiratory snorkel and valve system (Toussaint et al., 1987; Keskinen et al., 2003), connected to a telemetric portable gas analyzer (K4 b2, Cosmed, Rome, Italy). The oxygen consumption (VO2) was Figure 3. Relationship between energy cost (Ec) and Figure 2. Relationship between speed fluctuation (dv) measured for each swim breath-by-breath. Blood samples (25 µl) from the hyperemisized ear lobe were collected to analyze speed fluctution (dv).and swimming velocity (v).blood lactate concentration (YSI 1500 L, Yellow Springs, Ohio, USA) before and after each swim, as well as, 1, 3, 5 and 7 minutes after the last swim. Total energy expenditure (E-tot) was calculated using the VO2 net and the blood lactate net (difference between the highest value measured in the end of the stage and the rest value), transformed into VO2 equivalents 4. DISCUSSION AND CONCLUSIONSusing a 2.7 mlO2.kg-1.l-1 constant (di Prampero et al., 1978). The energy cost (EC) was calculated dividing the E-tot by the swimming velocity (v). The purpose of this study was to analyse the relationships between E-tot, EC, dv and v in elite butterfliers. The main conclusionwas that there are significant relationships between bioenergetical and biomechanical variables in butterfly stroke.The swims were videotaped (50 Hz) in sagital plane with a pair of cameras (JVC GR-SX1 SVHS and JVC GR-SXM 25 SVHS,Yokoama, Japan), providing a dual-media images from both underwater and above the water perspectives as described The individual correlations between E-tot and v ranged from R=0.95 (p=0.05) to R=0.90 (p<0.01). For pooled data theelsewhere (Barbosa et al., 2005). The images of the two cameras were real time synchronized and edited on a mixing table relationship was R=0.70 (p<0.01). E-tot was significantly related to increases in swimming velocity. The increase of E-tot is due(Panasonic Digital Mixer WJ-AVE55 VHS, Japan) to create one single image. Ariel Performance Analysis System (Ariel to the necessity to overcome drag force, which is related to v (D = k.v2).Dynamics Inc, California, USA) and a VCR (Panasonic AG 7355, Japan) at a frequency of 50 Hz were used to perform a The individual correlations between EC and dv, controlling the effect of v, ranged from R=0.99 (p=0.06) to R=-0.81 (p=0.09). Forkinematical analysis of the stroke cycles, including the dv of the centre of mass. Zatsiorsky’s model with an adaptation by de pooled data, the relationship between EC and dv was R=0.55 (p=0.01).Leva (1996) was used with the division of the trunk in 3 articulated parts. A filter with a cut-off frequency of 5Hz was used forthe analysis of the horizontal velocity curve of the centre of mass (Winter, 1990). The individual correlations between EC and v controlling the effect of dv ranged from R=0.92 (p=0.02) to R=-0.84 (p=0.36).When pooled data was plotted it was observed a relationship of R=0.51 (p=0.02).Statistical procedures. Means and standard deviations of all variables were calculated. Coefficients of variation for the Increases of the dv promoted significant increases of the EC. Speed fluctuation while swimming, as compared to swimming withhorizontal velocity of the centre of mass along with the stroke cycle were calculated. Linear regressions between the E-tot and constant velocity, leads to an increase in the amount of total energy expenditure done by the swimmer (Barbosa et al., 2005).v, between EC and dv and polynomial regressions of 2nd order between dv and v were computed. Partial correlations between Therefore, when analysed on individual bases, it is possible to observe different profiles between EC and dv, as well as,EC and dv controlling v and between EC and v controlling dv were also calculated. The level of statistical significance was set between EC and v. However, for overall sample, it seems that increases of EC was related to increases of dv and v.at P≤0.05.The individual correlations between dv and v ranged from R=0.99 (p=0.04) to R = -0.83 (p= 0.16). For pooled data, therelationship between dv and v was R=0.47 (p=0.05).Polynomial relationship between dv and v presented a better adjustment than the linear one. This phenomenon is described onregular bases for terrestrial locomotion (Minetti, 2000). The parabolic function is explained by the curve between force andvelocity for neuromuscular activity (Enoka, 1988; Minetti, 2000). So, data suggests that the neuromuscular activation of severalmuscles in a multi-segment and multi-joint movement follows the force-velocity relationship pattern for a single joint system(Enoka, 1988).5. REFERENCES1. Barbosa TM, Keskinen KL, Fernandes R, Colaço P, Lima AB, Vilas-Boas JP. (2005). Energy cost and intracyclic variation of the velocity of the centre ofmass in butterfly. Eur J Appl Physiol 93: 519-523.2.De Leva P (1996) Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J Biomechanics 29: 1223-12303.Di Prampero P, Pendergast D, Wilson D, Rennie D (1978). Blood lactic acid concentrations in high velocity swimming. In: Eriksson B and Furberg B(eds). Swimming Medicine IV. Baltimore: University Park Press, 249-261.4. Enoka R (1988). Neuromechanical Basis of Kinesiology. Illinois: Human Kinetics.5. Keskinen KL, Rodríguez FA, Keskinen OP (2003). Respiratory snorkel and valve system for breath-by-breath gas analysis in swimming. Scand J MedSci Sports, 13: 322-329.6.Minetti A (2000). The three modes of terrestrial locomotion. In: Nigg B, MacIntosh B, Mester J (eds.). Biomechanics and Biology of Movement. Illinois:Human Kinetics, 67-787.Togashi T, Nomura T (1992). A biomechanical analysis of the novice swimmer using the butterfly stroke. In: MacLaren D, Reilly T, Lees A (eds).Biomechanics and Medicine in Swimming VI. London: E & FN Spon, 87-90.8.Toussaint H, Meulemans A, de Groot G, Hollander AP, Schreurs A,Vervoon K (1987) Respiratory valve for oxygen uptake measurement duringswimming. Eur J Appl Physiol 56: 363-3669.Winter D (1990) Biomechanics and Motor Control of Human Movement. Chicheste: John Wiley and sons.

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Relationships between energy cost, swimming velocity and speed fluctuation in elite butterfliers

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