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Prediction of triathon performance from ventilatory threshold measurements Langill, Robert H. 1993

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PREDICTION OF TRIATHLON PERFORMANCE FROMVENTILATORY THRESHOLD MEASUREMENTSbyROBERT HENRY LANGILLB.Sc., University of British Columbia, 1989.A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF PHYSICAL EDUCATIONinTHE FACULTY OF GRADUATE STUDIESSchool of Physical Education and RecreationWe accept this thesis as conformingto the required standardsTHE UNIVERSITY OF BRITISH COLUMBIAMarch, 1993.© Robert H. Langill, 1993.(Signature)In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.Department of Physical EducationThe University of British ColumbiaVancouver, CanadaDate DE-6 (2/88)iiABSTRACTThe purpose of this study was to predict Ironman Triathlon (2.4 mile swim,112 mile cycle, 26.2 mile run) performance times from ventilatory threshold(TVENT) measurements of swimming, cycling, and running. Ten trainedtriathletes (mean age = 29.7yrs., ht = 179.8cm, wt = 76.8kg, bodyfat = 11.4%)performed progressive intensity tests for treadmill running, cycle ergometry, andtethered swimming. The excess CO 2 elimination curve was used to determineTVENT in each component sport with the resulting estimated times of 64.2, 380.0,174.5, 672.8 minutes for swimming, cycling, running, and overall timerespectively. Individual estimates were then compared to actual segment andoverall times to produce the following linear regression equations for predictingactual from estimated time (in minutes):actual swim = 1.15 * estimated swim - 6.75actual cycle = 0.22 * estimated cycle + 262.6actual run = 3.03 * estimated run - 267.1actual overall = (-3.58 * est. swim) + (-0.10 * est. cycle) + (3.76 * est. run)+ 291.35Significant correlations of r = 0.83, 0.70, 0.76, and 0.89 were calculated betweenswim, cycle, run, and overall estimated versus actual times respectively. Thus,between 49 and 69% of the variance in actual time is explained by TVENT forthat component sport. Also, 78% of the total variability was accounted for by theTvENT estimation when the three sports were combined. These findings suggestthat while TvENT is able to account for a significant proportion of triathlonperformance time other factors such as fatigue, dehydration, terrain, heat, etc.are confounding the overall prediction.iiiTABLE OF CONTENTSABSTRACT^ iiTABLE OF CONTENTS^ iiiLIST OF TABLESACKNOWLEDGEMENT^ viCHAPTER 1: INTRODUCTION TO THE PROBLEM^ 11.1 Introduction to the Problem^  11.2 Statement of the Problem 41.2.1 Subproblems 41.3 Definitions ^ 41.4 Delimitations 51.5 Limitations 51.6 General Hypothesis^ 61.6.1 Secondary hypotheses^ 61.7 Rationale^ 71.8 Significance of the Study 8CHAPTER 2: LITERATURE REVIEW^ 102.1 Introduction^ 102.1.1 Single Endurance Sports 102.1.2 Triathlon 112.2 Single vs. Multiple Mode Sport^ 122.2.1 Specificity of training 122.2.2 Intrinsic and Extrinsic Factors^ 142.2.3 Cumulative Effects^ 162.3 Physiological Determinants of Endurance Performance^ 162.3.1 Maximal Aerobic Power 172.3.1a Swimming^ 182.3.1b Cycling 192.3.1c Running 192.3.1d Triathlon^ 192.3.1e Concerns 202.3.2 Economy of Motion 212.3.2a Swimming^ 212.3.2b Cycling 212.3.2c Running 222.3.2d Triathlon^ 222.3.2e Concerns 23iv2.3.3 Anaerobic Threshold^ 242.3.3a Swimming 242.3.3b Cycling 252.3.3c Running^ 252.3.3d Triathlon 262.3.3e Concerns 272.4 Conclusions^ 27CHAPTER 3: METHODS AND PROCEDURES^ 293.1 Subjects^ 293.2 Testing Procedures^ 293.3 Testing Protocols 303.4 Experimental Design and Data Analysis^ 34CHAPTER 4: RESULTS AND DISCUSSION 354.1 Results^ 354.2 Discussion 44CHAPTER 5: SUMMARY AND CONCLUSIONS^ 565.1 Summary^ 565.2 Conclusions 575.3 Recommendations^ 58BIBLIOGRAPHY^ 59APPENDIX A: Sample calculation for estimation of the swim component ^ 71APPENDIX B: Sample calculation for estimation of the cycle component ^ 73APPENDIX C: Sample calculation for estimation of the run component ^ 76APPENDIX D: Individual data for estimated and actual swim,cycle,and run ^ 77APPENDIX E: Mechanisms of lactate and ventilatory threshold^ 80vLIST OF TABLES1. Descriptive data for all subjects^ 352. Weekly training mileage and previous triathlon experience for allparticipants^ 363. Heart rate and lk;42 measured at TVENT and expressed as a percentageof their maximal value^ 384. Estimated and actual swimming times and paces for the IronmanTriathlon^ 405. Equations used in the determination of the cycling portion of thelronman Triathlon^ 406. Estimated and actual cycling times and paces for the Ironman Triathlon ^ 417. Estimated and actual run times and paces for the lronman Triathlon^ 428. Conversion factors from estimated to actual for each component andoverall time in the Ironman Triathlon^ 43viACKNOWLEDGEMENTFirstly, I would like to thank my committee members; Dr. Taunton, Dr.Coutts, and Dr. Vaughan for their contributions towards this project. Particularly,I would like to thank very much my committee chair Dr. Rhodes for his guidancein not only this thesis but my entire course of study towards my Master's Degree.My greatest admiration is extended to the subjects who participated in thisstudy. The time and energy they put into this project and training for a sport ofthis nature is a testimony to the type of people they are.Finally, I would also like to thank my parents, Gwen and Clarence, and mybrother John, for their support and their ability to listen to me complain evenwhen they probably did not understand exactly what I was complaining about.1CHAPTER 11.1 Introduction to the ProblemOver the years many attempts have been made to describe the factorswhich limit the ability to perform prolonged endurance exercise. Studies haveattempted to identify the physiological variables that are the most importantdeterminants of endurance performance. Among the key variables that havebeen shown to be significantly related to performance of endurance activitiesare: maximal oxygen uptake (VO2M ), economy of motion, fractional utilizationof VO2  (%VO2M ), anaerobic threshold, and fuel supply (Sjodin andSvedenhag, 1985).Early research (Owles, 1930; Harrison and Pilcher, 1930) supported theexistence of critical levels of work intensity above which there was accumulationof blood lactate with accompanying increases in CO 2 excretion and ventilation.This critical intensity was termed the "anaerobic threshold" and defined theworkrate at which the tissues oxygen supply first fell below demand and excessenergy needs were supported by anaerobic metabolism (Wasserman andMcllroy, 1964). In theory, below this point work may be performed for indefinitedurations since the energy requirements are being supplied predominantlythrough unlimited aerobic energy sources, while waste products are beingadequately removed (Anderson and Rhodes, 1989). In order to best define theanaerobic threshold point both noninvasive and invasive variables such as VE ,VE/V02 , VCO2 , excess CO2 , and blood lactate have all been monitored withvarying degrees of success. Particular emphasis has been placed on therefinement of noninvasive respiratory variables in order to minimize the need forinvasive procedures. This has allowed researchers to use progressive workload2increases to bring about nonlinear responses in respiratory exchange variablesto noninvasively define anaerobic threshold.While the nomenclature and mechanisms inherent in the anaerobicthreshold concept are still being challenged, there is widespread supportparticularly in the performance data. The threshold appears to represent a keyparameter defining the ability to maintain high intensity exercise (Whipp et al.,1981) and a critical intensity level above which endurance performance isseverely limited (Rhodes and McKenzie, 1984). It is suggested that exerciseintensity at anaerobic threshold represents a maximal steady state level that anathlete can sustain for an extended period without accumulating lactate.Anaerobic threshold related variables have been shown to have a closerelationship to endurance performance that is good or better than otherphysiological variables including 1%02 (Tanaka et al., 1981; Davis et al.,1979). Several performance studies have documented the value of anaerobicthreshold as a determinant of endurance activities. Studies of long distancerunning produced correlations of the order of 0.88 to 0.99 for distances rangingfrom 3.2 km to the marathon (Conconi et al., 1982; Farrell et al., 1979; Powers etal., 1983; Sjodin and Schele, 1982; Williams and Nute, 1983). Rhodes andMcKenzie (1984) extended the concept of anaerobic threshold by instead of justcorrelating it with performance used treadmill running velocity at the anaerobicthreshold to estimate actual marathon time with an r=0.94. While results havebeen strongest in running, other sports have shown some support. Miller et al.(1985) found anaerobic threshold to have a strong relationship (r=0.93) to 15 kmtime trial cycling performance. As well, Loat and Rhodes (1991) found theanaerobic threshold, as defined by excess CO2 , to be a workload that could besustained for one hour on a cycle ergometer. All of these represent situations3where anaerobic threshold has been correlated with some success toperformance in single sport endurance contests.The triathlon is an endurance contest in which participants competeconsecutively in three sports, usually swimming, cycling, and running. Theendurance triathlon has really only existed since 1978 when the Waikiki RoughWater Swim (3.9 km), the Around Oahu Bike Race (180.2 km), and the HonoluluMarathon (42.2 km) were combined into the Hawaii Ironman Triathlon (O'Tooleet al., 1989). The three sport performance dictates that the successful triathleteis one who has the ability to perform each sequential event at optimal pacewithout creating fatigue that will hinder performance in the next event. While thephysiological bases for success in the triathlon remain to be clarified it willobviously include the ability to maintain minimal alterations in homeostasis ofcardiovascular, haemodynamic, thermal and metabolic function for long periodsof time (O'Toole et al., 1989). The triathlete will seek to perform at a leveloptimizing potential for the duration of the event.Studies have demonstrated evidence that in single sport contests(particularly strong in running) anaerobic threshold can be used to represent thispoint of optimal pace for endurance sports. Since the triathlon is an endurancecontest by nature it seems reasonable that those factors which contribute tosuccess in single event endurance contests should also contribute to triathlonsuccess. Thus, the concept of anaerobic threshold should be able to be appliedhere in order to characterize performance just as in other endurance contests. Atthis point very few studies have looked at using ventilatory threshold as anindicator of success in triathloning.41.2 Statement of the ProblemThe purpose of this investigation was to determine if triathlon performance timescan be predicted from ventilatory threshold measurements of swimming, cycling,and running.1.2.1 Subproblems1) to determine if the velocity at ventilatory threshold during running, cycling,and swimming can estimate race pace (time) for that component of thetriathlon.2) to determine if each component can be used together to predict overalltriathlon time.1.3 Definitions1) Ventilatory threshold - (TvENT) the point where the aerobic energy responseis of insufficient magnitude to supply the tissues energy requirement andthere is an increased reliance on anaerobic processes with an accompanyingabrupt increase in excess CO2 .2) Maximal Steady State - the highest intensity work may be performed at fortheoretically indefinite durations.3) Excess CO 2 - nonmetabolic CO2 (EXCO 2) formed as a result of the hydrogenions of lactic acid being buffered by bicarbonate in the following reactions:HLa + NaHCO3 = NaLa + H2CO3 = CO2 + H2 O(Wasserman et al., 1973)5The calculation of excess CO2 will be based on the formula of Volkov et al.(1975) where:ExCO2 = VCO2 - (RQrest * tiO2 )1.4 DelimitationsThis study was delimited by:1) a sample of triathletes from the Vancouver area between the ages of 18 and35 years with a minimum of two triathlons experience.2) a respiratory gas sampling rate set at 15 second intervals.3) the methodology applied to determine velocity at TvENT for swimming,cycling, and running.1.5 LimitationsThis study was limited by:1) the data collection capabilities of the Beckman Metabolic Measurement Cartand the Hewlett Packard Data Acquisition system interfaced with it.2) the individuals metabolic response to the exercise protocols.3) race day conditions (terrain, weather, equipment problems, etc.)61.6 General HypothesisPredicted time for the triathlon will correlate highly with actual triathlonperformance time. Specifically, a highly significant relationship will existbetween:1) treadmill run velocity at TVENT and the run section of the triathlon.2) tethered swim velocity at TVENT and the swim section of the triathlon.3) cycle ergometry velocity at TVENT and the cycle section of the triathlon.1.6.1 Secondary hypotheses1) a significant difference will exist between VO2mAx measured for swimming,cycling, and running such that:V02 MAX run > VO2 MAX cycle > V02 MA swim2) TVENT measured for swimming, cycling, and running will demonstrate thefollowing:a) TVENT absolute (expressed as a V02 ) will show a significant difference foreach component as follows:TVENT run > TVENT cycle TVENT- VE  swimb) TVENT relative (expressed as a %1102mAx ) will demonstrate anonsignificant difference between each component sport when expressedas a percentage of maximum.73) the relative heart rate at TvENT (expressed as a %HRmAx ) will also show anonsignificant difference between the three component sports of the triathlon.1.7 RationaleVelocity at TvENT has been used with success (r=0.94) in running byRhodes and McKenzie (1984). Farrell et al. (1979) observed that runners set arace pace which closely approximates the running velocity at which lactatebegins to accumulate in the plasma. These observations both support thehypothesis of a velocity which optimizes pace and minimizes fatigue (maximalsteady state). Since the physiological basis for success is similar in otherendurance sports it seems likely that this concept could be extended.The relationship between l%02mAx in swimming, cycling, and running isdocumented in the literature. Mean VO2mAx values have been reported asfollows:treadmill run^cycle ergometry^tethered swim60.5^ 57.9^52.5^Kohrt et al. (1987a)57.4 54.4 46.8^Kohrt et al. (1987b)Thus, cycling maximums represent about 4% less and swimming maximumsrepresent about 15% less than those achieved running on the treadmill. Fromthese results it is expected that the absolute TvENT in each component will alsoshow the same pattern. However, when TvENT is expressed in relative terms it isexpected that there will no longer be differences. This is supported by the workof Schneider et al. (1990) where a nonsignificant difference was found between8TvENT's expressed as a % kinfAx for cycle ergometry and treadmill running.Relative heart rate at TvENT is also expected to show this same nonsignificantdifference for each component sport. Roalstad et al. (1987) reported during theHawaii Ironman Triathlon that subjects maintained average heart rates ofapproximately 75% of their maximal heart rates during the bike and run portionsof the race. They also observed that the better finishing times were found inthose triathletes where heart rates fluctuated least over the course of the event.1.8 Significance of the StudyIf indeed ventilatory threshold defines a "critical intensity", then exerciseat workloads greater than this should result in a progressive metabolic acidosisthat becomes rate-limiting. Exercise time to exhaustion (duration) should havean inverse relationship to the amount exercise workrate (intensity) exceedsTVENT. It is important to determine if the protocols employed in this experimentactually do define a maximal steady state at which an athlete can perform. Mostendurance athletes regardless of endurance ability wish to perform for prolongedperiods of time without factors such as lactate accumulation limiting performance(maximal steady state). If it can be shown that TVENT does characterize thispoint of maximal performance it will have profound implications for theendurance athlete. It could be used in exercise prescription to select a trainingintensity which elicits maximal aerobic performance. There could also beapplication to evaluation of training over time based on TVENT changes.This study is also significant in that it takes the concepts of TVENT to aperformance level. Thresholds are often stated to be indicative of maximalendurance ability without actual validation. This study validates its statementsabout the TVENT by actually testing the theories in an endurance setting9(triathlon). The use of threshold concepts outside of the lab setting is the truesttest of these measures. An endurance triathlon will definitely test the ability ofTvENT to characterize maximal steady state during prolonged sport.10CHAPTER 22.0 Literature Review2.1 IntroductionEndurance sports have been a very popular area of research forphysiologists in the past few decades. This is undoubtedly due to the opportunityto observe a variety of factors under highly demanding conditions and therebygain a better understanding of human performance potential. Much of thisresearch in endurance capacity is designed so that a better idea of those factorscontributing to success in endurance contests will emerge. As such, this wouldhelp the physiologist to develop methods for predicting performance. The notionof predicting performance success is very attractive, but is a complicated andmultifaceted question encompassing areas of physiology, biomechanics,morphology, psychological profile, etc. (Toussaint and Beek, 1992). A newseries of questions emerge with the advent of the multiple sport contest oftriathlon, particularly with respect to the relationships between the componentsports. Research, to date, is conflicting but several lines of evidence suggest asimilar physiological basis that may allow prediction as has been attempted insingle contest endurance sports. This review will concentrate on thephysiological determinants of endurance performance in the emerging researcharea of triathlon by examining current findings in this sport and its componentsports of swimming, cycling, and running.2.1.1 Single Endurance SportsThere is little information concerning the aerobic capacity of competitiveendurance cyclists and swimmers, in contrast to the wealth of data on endurance11runners. While swimmers tend to train comparable distance to enduranceathletes (Costill et al., 1991) the majority of competitive swim events tend to besprint oriented contests lasting less than 3 minutes (Smith et al., 1984).Consequently, most of the research has focussed on maximal performance asopposed to sustained performance.By virtue of the man-machine interaction (Kyle and Mastropaolo, 1978), cyclingresearch has been confined more to biomechanical considerations of cadence,seat height, gear ratios, etc. It is unquestionably running where the majority ofperformance research has been done. This is likely the result of cycling andswimming having much greater biomechanical components while running ismore physiological (Sjodin and Svedenhag, 1985).Lack of research should not be taken to mean a lack of evidence for theability to determine endurance performance success in these sports. While oftenthere are few direct correlation studies, the sports exhibit characteristicssupporting the likelihood of these concepts.2.1.2 TriathlonWhile many sports have developed ultraendurance contests, triathlon is asport created specifically to test endurance ability (O'Toole and Douglas, 1989).As a contest the Ironman triathlon, with its 2.4 mile swim, 112 mile cycle and26.2 mile run, is synonymous with ultraendurance performance. There has beenan exponential rise in popularity from only 15 starters in the first Ironman (1978)to 1275 starters in 1988 (selected from over 20,000 applicants) (O'Toole andDouglas, 1989). This rise in popularity has promoted an increased interest withinthe scientific community, now focussed on examining the physiologicalconsequences of multisport training and racing. O'Toole et al. (1989) state thatsuccess in a triathlon depends upon the ability of the triathlete to perform each12of the sequential events at optimal pace without creating fatigue that will hinderperformance in the next event. A heterogeneous group of Ironman triathletesmaintained average heart rates of approximately 75% of maximum during thebike and run portions (Roalstad et al., 1987). Those with heart rates fluctuatingleast over the course of the event had the best finishing times suggesting thatthere is an optimal, sustainable pace that the triathlete will want to approximateto be successful. This suggests the key determinants of endurance performance,characterizing multisport endurance, may be similar to single sport endurance.2.2 Single vs. Multiple Mode Sport2.2.1 Specificity of trainingIt is the above statement of O'Toole et al. (1989) regarding "not creatingfatigue that will hinder the next sequential event" that is a key factordifferentiating multiple from single endurance sports. This suggests a new seriesof considerations that must be made to apply performance research to triathlon.The theory of training specificity suggests that adaptations to training arespecific to the mode of training (Kohrt et al., 1987a). An activity is required to taxthe metabolic pathways of choice (aerobic vs. anaerobic) and must also stress(or recruit) specific muscle groups and neural pathways used in the activity(Kohrt et al., 1989). Single sports necessitate adaptation to only one mode whilethe triathlete will need to reflect, to some degree, the specificity seen in eachcomponent sport. Clausen et al. (1973) postulated that there were both centraladaptations (resulting from nonspecific training) and peripheral adaptations(resulting from training specific to the muscle groups used). Muscles used inswimming are different from those used in cycling and running, and while thereis some overlap of muscle usage in cycling and running the range of motion,13recruitment patterns, length of muscles, type of contraction (concentric vs.eccentric) and speed of contraction are different (O'Toole et al., 1989). Thus,while all component sports are aerobic in nature they utilize different musclegroups (or the same muscles differently) to perform the necessary movementpatterns of swimming, cycling, and running. Cycle and swim training result inimprovements specific to that activity (Kohrt et al., 1987a) and produce more of alocalized muscle stress and peripheral adaptations in the muscles. Magel et al.(1978) states exercise using large muscle groups (eg: running) stresses theoxygen transport mechanisms, while exercise involving smaller muscle groups(eg: swimming) tends to stress oxygen utilization. Much of the specificity ofswimming is thought to relate to the reduction in active muscle mass coupledwith the reduction of antigravity work while in water, and the horizontal exerciseposition.Central to multisport training is the question of whether there is an abilityto transfer benefits from one activity to another with the concurrent("crosstraining") training done by triathletes. Both swim training (Magel et al.,1978) and cycle training (Pechar et al., 1974; Town and Sinning, 1982) havefailed to demonstrate improvements in running ability. As well, run training hasfailed to demonstrate improvements in swim ability (McArdle et al., 1978).Running to improve cycling is not as definitive with some studies (Pechar et al.,1974; Town and Sinning, 1982) showing an improvement indicative of ageneralized effect from run training, although this improvement tended to bequite small, suggesting only a small generalizable training effect. The work ofKohrt et al. (1987b) is the only study demonstrating a possible crosstrainingeffect by reducing training in cycling and swimming while maintaining run levels.At the end of a three month training phase, there was a reduction in both running14and cycling 1%02mAx , while swimming was maintained. It is therefore possible thatswim 1%02mAx was maintained by a generalized training effect from the other twocomponent sports.2.2.2 Intrinsic and Extrinsic FactorsWhile physiological variables govern performance to some degree, thereare undoubtedly other factors that must be considered to be reducing the abilityof the triathlete to maintain a constant pace. Many of the factors are present insingle endurance sports, but by virtue of the increased duration these factors aremore definitive "controllers of pace" in ultraendurance triathlons. To gain anunderstanding of the importance of these factors we only need to look at thereasons people require medical attention at the Hawaii Ironman. The primarilydiagnosed reasons are dehydration (52%), exhaustion (20%), trauma (13%),heat cramps (6%) and electrolyte imbalance (%5) (Laird, 1989). Undoubtedly,many participants will show combinations of these, not single problems. TheIronman distance triathlon, by virtue of its competition time, is directly related tomany of these medical problems experienced. Climate, season of the year, andtopography of the course provide extrinsic factors that can magnify the medicalproblems. From the point of view of reduction in pace, there are few problemswith the swim unless it is in a body of water where waves, current, and coldwater temperature are prevalent. The most often cited problem in the bikeportion is cramping due to the time on the bike (Laird, 1989). Some athleteshave also cited mild dehydration at this point (Farber et al., 1991). It is duringthe run where problems start to become severe. The cumulative effects of time,distance, and heat begin to be manifested in dehydration, exhaustion, andelectrolyte imbalances (Laird, 1989).15In events over four hours electrolyte imbalance and dehydration aresuggested to be important factors in race performance (Hiller, 1989). vanRensenburg, et al. (1986) reported a decrease in body weight of 4.5% in atriathlon with mean finish time of 11.45 hours. Costill and Miller (1980) noted thatwork performance decreases and physiological indices of stress increase wheneven low levels of dehydration are combined with heat. This effect is magnified ifthere is a concomitant salt depletion. Heat cramps are often associated with thishyponatremia (Hiller, 1989). Costill and Miller (1980) estimated a 5-7% totalbody sodium chloride deficit in an endurance athlete sustaining a 5.8% bodyweight loss. Even with a modest sweat rate, combined with the heat of theHawaii Ironman, there could be a loss of 36g of salt from the total body sodiumstores of about 125g (Hiller, 1989). The solution considering that 70% ofhyponatremic athletes are also dehydrated is to ensure a recommendedsupplemental sodium intake combined with programmed hydration (Hiller, 1989).The exact protocols remain to be investigated.Available evidence on substrate utilization during prolonged exercisesupports an increase in fat metabolism (O'Toole et al., 1989). Free fatty acidincreases have been reported of between 2.9 and 4.6 times prerace values inultraendurance triathlons (Holly et al., 1986; Van Rensenburg et al., 1986). Theactual use and regulation of substrates appears to be similar to other enduranceactivities, although there is evidence ultraendurance athletes competing for 9-16hours may find standard rates of carbohydrate replacement to be insufficient tosustain this duration of exercise (Applegate, 1989). As well, the bike portion ofthe triathlon combines liquid and solid carbohydrate feedings in order to replaceenergy. Often carbohydrate replacement, particularly solid food, is stoppedabout one hour prior to the start of the run (O'Toole et al., 1989).162.2.3 Cumulative EffectsWhile these intrinsic factors will play a role in performance the cumulativeeffects of performing three endurance events in their own right sequentially willbring about changes in body physiology that must be dealt with. The study ofKreider et al. (1988) compared single sport performances to equivalentcomponent sport performances in a triathlon. One of the main differencesobserved in the cycling was the increase in core temperature during swimmingcausing thermoregulatory and cardiovascular responses to occur earlier intriathlon cycling. These responses were similar to changes found in the latterstages of the control cycling session. The subjects were unable to maintaincontrol cycling work rates as the triathlon cycling session progressed. Possiblythis decrease in performance was due to thermal stress and/or the early signs ofdehydration. The cumulative effects were even more evident in the run. The postcycling core temperature was 38.4°C versus an initial control run coretemperature of 37°C. The subjects perceived the work during the triathlon run toreflect increased physiological stress. A further 1.5% loss of body weightoccurred during the run and while core temperature and dehydration responseswere not excessive, the thermal stress would only be magnified during actualrace environmental conditions. Therefore, triathletes should frequently take influids to minimize elevation in core temperature, dehydration, and impairedperformance (Kreider et al., 1988).2.3 Physiological Determinants of Endurance PerformanceWhile many variables have been examined relative to enduranceperformance three key physiological ones have been the focus of research:1) economy of motion172) maximal aerobic power (t%02mAx )3) anaerobic threshold^(Pate and Branch, 1992).Each has been demonstrated to be a determinant of endurance performance,characterizing the ability of an athlete to sustain effort for prolonged periods.Economy refers to the rate of energy expenditure associated with a givenrate of power output or speed of movement (Pate and Branch, 1992). This will bedesirable to the endurance athlete given that being "economical" will result in alesser rate of energy expenditure, thus conserving energy to help sustainperformance. Maximal aerobic power (1%02mAx ) represents the upper rate limit ofone's ability to use oxygen in metabolism. High performance capacity has beenstrongly linked to this peak of aerobic metabolism (Sjodin and Svedenhag,1985). Finally, the anaerobic threshold defines a workload intensity at whichblood levels of lactic acid begin to rise significantly above normal resting values(MacDougall, 1977). In the simplest sense this is the point of imbalance betweenlactic acid production and its removal or uptake beyond which progressivelymore lactate accumulates in the blood. The association of lactate accumulationwith fatigue suggests anaerobic threshold represents the peak of the body'sability to perform prolonged exercise. Each of these measures hasdemonstrated characteristics necessary for endurance success in severalsports.2.3.1 Maximal Aerobic PowerSince the athlete wishes to develop, an ability to perform at the maximalsustainable oxygen consumption, early studies focused on 1%02 MAX as the sourceof relationships.1 82.3.1a SwimmingThe economical use of energy which would be a prerequisite for distanceswimming success is of less importance over short distances (Holmer, 1974). Asmost swim contests are short duration, the swimmer's selection of the optimalcombination of local muscle power and endurance will tend to favour power atthe expense of endurance (Craig and Pendergast, 1979). Thus, most of thepredictive research has focused on generating maximal energy output withoutmuch regard to the ability to sustain performance long term. Considering this it isnot surprising that the majority of correlations of swim performance have been to1%02^. Several researchers (Chatard et al., 1985; Costill et al., 1985; Montpetitet al., 1981) have found high correlations between 1%02mAx and performance.Chatard et al. (1990) found 368m swim performance mainly related to 1%0 24,,Ax(r=0.80). Costill et al. (1985) and Nomura (1983) also found high correlations ofthe order or r=0.80 and r=0.75 respectively between swim performance andtO2 mAx . The observation by LePere and Porter (1975) that 1%02^is greater inmore skilled swimmers suggest it could be used to differentiate performancesuccess. Often in endurance settings the measure of 1%02mAx is modified tobetter represent the actual level of performance and is called the fractionalutilization of 1%02mvc (%1%02mAx ). Magel et al. (1975) used a fractional utilizationof 70% of maximum determined in a swimming VO2 MAX test as the basis oftraining and was able to improve swim performance. Holmer (1974) found at asimilar workrate of 60-70% VO2 MAX blood lactate levels were not significantlyelevated, suggesting an ability for prolonged performance at this level.192.3.1b CyclingCycling has rarely been examined with VO2 M,ax as the criterion measure topredict performance. Krebs et al. (1986) found VO2 M,ax to be the most significantphysiological predictor related to 25 mile time trial cycling. Malhotra et al. (1984)studying performance time in an 84km cycling event found the highestcorrelation (r=-0.87) with 1%02mAx . In examining time to exhaustion, Aunola et al.(1990) found that 70% of the variance could be explained by the workrateperformed at maximum.2.3.1c Running1%02 hfAx is a sensitive indicator of level of marathon running ability (Sjodinand Svedenhag, 1985) and race pace (r>0.78) (Foster et al., 1977; Farrell et al.,1979; Maughan and Leiper, 1983). It is not only marathons that have producedhigh correlations with r=0.76 and r=0.97 for 10 km by Williams and Cavanagh(1987) and Morgan and Martin (1986) respectively. In addition, running researchhas also examined the velocity at 1%0 2 fax as a predictor variable and it wasfound to have a significant relationship (r=0.87) with 10 km race time (Sjodin andSvedenhag, 1985).2.3.1d TriathlonMaximal aerobic power has been found to range from no significantrelationship to Ironman performance in cycling (r=0.04) (O'Toole et al., 1987) toa significant relationship (r=-0.78)(Kohrt et al., 1987a). A similar result is seen inthe run with no relationship (r=-0.09) (O'Toole et al., 1987) to a moderaterelationship (r=-0.68)(Kohrt et al, 1987a). Peak 1%02 in tethered swimmingcorrelated (r=-0.50) with swim times to produce no significant relationship (Kohrt20et al., 1987a). van Rensburg et al. (1984) found correlations of r=-0.52 forcycling and r=-0.58 for running. As well, relationships between 1%0 2 how andperformance times were found for swimming (r=-0.49), cycling (r=-0.32) andrunning (r=-0.55) in the study of Dengel et al. (1989).2.3.1e ConcernsAlthough 1%02mAx has demonstrated success as a predictor ofperformance there are situations where its usefulness is severely reduced.1%02^has a very low relationship to performance in more homogeneoussamples of athlete. Despite heart rate and lactate data reflecting that swimmerswere improving endurance, this did not imply equivalent changes in 1%0 2mAx(Costill et al., 1991). Acevedo and Goldfarb (1989) found run times improvedwithout changes in 1%02 M,4x suggesting it may not be the best indicator ofendurance performance. When a subgroup of runners with more similarperformance capabilities were studied, there was a nonsignificant correlation(r=0.08) between marathon performance and 1%0 2mAx (Costill, 1972). Hespeculated that it was possible for two runners to have identical 1%02 MAX valuesbut differ drastically in their ability to utilize a large fraction of that capacity.Furthermore, relatively low VO2 nu x values have been found in some elitemarathoners (Costill et al., 1976; Costill et al., 1973). Thus, while having a highmaximal oxygen uptake is of great importance it does not equal success andother factors must be able to compensate for a low capacity.212.3.2 Economy of MotionSvedenhag and Sjodin (1985) found enhancement in runningperformance, occurring after l%02mAx had reached a plateau, to be associatedwith slow, steady improvement in economy. There is a paucity of economy ofmotion studies from a physiological point of view, most focus on morebiomechanical aspects.2.3.2a SwimmingOften physiological economy of motion has been successful because ofits ability to, in some way, represent technical ability factors. Such is the casewith swim economy which has been shown to be a good predictor of technicalability (Pendergast et al., 1977) and a prerequisite for success in performance(van Hardel, 1988). Often, it is these technical ability factors (stroke length,stroke rate, etc.) that are controlling performance (to some degree) and havebeen shown to have a moderate to high relationship with free swimming (Jensenand Tihanyi, 1978). Economy, expressed as high efficiency for swimmingstrokes, is a vital prerequisite to the maintenance of a high velocity over longdistances (Holmer, 1974). As well, diPrampero et al. (1974) and McArdle (1971)observed that even highly proficient swimmers have considerable variation in theenergy cost to swim at a given speed.2.3.2b CyclingAthletic performance velocity during cycling is determined by the higheststeady-state rate of oxygen consumption that can be tolerated and thebiomechanical economy of motion, defined as the velocity achieved for a givenoxygen consumption (Coyle et al., 1988). While efficiency is clearly important,22physiological economy of motion has not been measured in a performancesense.2.3.2c RunningThe variation in submaximal oxygen requirements of running at a specificspeed is indicative of differences in economy between subjects (Costill et al.,1973; Sjodin and Schele, 1982; Daniels, 1974). It is with this observation in mindthat economy of motion has received considerable attention for studyingrunning. Running economy, defined as the steady-state oxygen consumption fora given running speed, has been shown to account for a large and significantproportion of the variation in distance running performance among runnersroughly comparable in 1%02mAx (Morgan, 1989; Morgan et al., 1989). In athleteswith a similar l%02mAx , Conley and Krahenbuhl (1980) and Morgan and Craib(1992) found running economy to significantly correlate with 10 km time (r=0.79to 0.83). Daniels (1974) recognized the ability of economy to account for nearlyidentical 2 mile run times among two champion male runners. Costill and Winrow(1970) state different performance ability in two runners with similar 1,%02mAxvalues could be attributed to individual differences in economy.2.3.2d TriathlonDespite its obvious importance very little triathlon research has focusedon economy. Roalstad (1989) found economy at a work rate of 160W tosignificantly relate (r=0.61) to bike finish time. Dengel et al. (1989) reportedsubmaximal V02 measured in each mode to be related to swim (r=0.72), cycle(r=0.60), and run (r=0.64) times. A correlation of r=0.78 between cycle ergometryand bike finish time in a half Ironman triathlon was also demonstrated (Dengel et23al., 1986). O'Toole et al. (1989) reported a relationship (r=0.61) between percentpeak V02 at 160W and bike finish time.2.3.2e ConcernsRunning economy has been found to exhibit the same problem as 1%02 MAXwhen a narrower subgroup of runners with similar performances in marathon arestudied (Davies and Thompson, 1979). Daniels et al. (1984) found individualstability in run economy varied by as much as 11 % within a particular testperiod. The relationship between run economy and level of training is equivocal,some studies showing worse economy in untrained and moderately trained(nonelite) runners and other studies demonstrating no difference betweentrained and untrained (Morgan and Craib, 1992). Relationships of runningeconomy with distance running are not always high, ranging from r=0.36 (Fosteret al., 1977) to r=0.83 (Conley and Krahenbuhl, 1980). Most studies involvingeconomy provide little rationale with respect to the chosen level for performancemeasurements. Considerable concern is raised by Pate et al. (1992) where itwas observed that running economy tended to be poorer in subjects with highermaximal aerobic power. This may suggest an artifact in the specific testingprotocols used in some studies with respect to the chosen speed formeasurement. If a speed is chosen to be submaximal for all athletes, those withthe highest l%02mAx will be at a very low relative percentage of 102 x , probablyappreciably lower than their typical training intensity (Pate et al., 1992). Thus,runners at lower 1%02^values may have trained more frequently at runningvelocities similar to those used in the investigation and were more mechanicallyefficient at those velocities (Bailey and Pate, 1991). The high tO 2mAx runnersmay have been uncomfortable trying to perform at the prescribed level and a24subsequent reduction in economy was found. Consequently, faster runners maybe uneconomical at speeds that are slower than their race pace (Williams andCavanagh, 1987).2.3.3 Anaerobic ThresholdConsidering endurance performance, the anaerobic threshold has beendescribed as a key parameter which defines the ability to sustain high-intensityexercise (Caiozzo et al., 1982).2.3.3a SwimmingAnaerobic threshold studies done in swimming have been geared tosetting training intensities for athletes in the pool. The observation that theenergy expenditure rate increases exponentially with velocity (Holmer, 1979)suggests an optimal point for endurance performance. Olbrecht et al. (1985)found that the speed swum for 30 and 60 minutes was nonsignificantly differentfrom lactate threshold (TLAc) predicted speed. Further, the lactic acidconcentration at the end of 30 minutes of swimming was similar (p > 0.05) tolactic acid concentration at Tukc. In swimmers with similar 1%02mAx valuesanaerobic threshold has demonstrated an ability to differentiate performancecapacity (Smith et al., 1984). Consistent with this is the observation thatsprinters (100, 200m swimmers), as compared with endurance performers (400,1500m swimmers), produce high lactate levels at speeds where the heart rate islow relative to its maximum (Treffene, 1979), indicative of a lower anaerobicthreshold. Mader et al. (1978), using TLAc, was successfully able to prescribeoptimal training pace for developing the speed of a group of East Germanswimmers.252.3.3b CyclingWhile anaerobic threshold is often measured in cycling ergometry tests,only recently has it been related to actual cycling performances. In the study ofAunola et al. (1990) it was found that endurance time demonstrated arelationship to oxygen consumption at the anaerobic threshold (r=0.66). Loatand Rhodes (1991), using TvENT as determined by excess CO 2, predicted alevel of work that could be performed for one hour without a significant elevationin blood lactate. The strongest support for anaerobic threshold in cycling are thestudies of Coyle et al. (1991, 1988). Coyle et al. (1988) states that it is moreaccurate to express an individual's metabolic capacity for endurance exercise byreporting 1%02 at Tukc than it is to report 1%02 . Further, Coyle et al. (1988)found "time to fatigue" to strongly relate to TLAc (r=0.90). Coyle et al. (1988)also demonstrates the sensitivity of TLAc with respect to "time to fatigue". Whensubjects performed at 88% of 1%02mAx , one group was only 8% above Tukc whilethe other was 34% above. The average times to fatigue were 60.8 and 29.1minutes, respectively. Coyle et al. (1991) found the average work ratemaintained during a one hour lab cycling performance best correlated to 1%0 2 atTukc (r=0.93). They then compared this sustainable Tukc determined work rateto a 40 km time trial performance with a strong relationship being evident(r=0.88). The oxygen consumption at TLA0 also seemed to be a sensitiveindicator of cycling proficiency, as the better endurance cyclists clearlydemonstrated higher thresholds.2.3.3c RunningIt would be impossible to mention all the anaerobic threshold studies thathave produced strong relationships to run performance. Exercise intensities26selected quite voluntarily appear to be near the point where other investigatorshave shown that lactic acid production begins to increase (Karlsson, 1970;Knuttgen and Saltin, 1972). Coen et al. (1991) states that it is possible toprovide training recommendations on the basis of anaerobic threshold,especially for endurance training. Variables such as TuNc account for a greaterproportion of the variance in running performance than running economy or1%02 (Farrell et al., 1979; Tanaka et al., 1986; Yoshida et al., 1987). TVENTduring incremental exercise seems to provide an indication of lactate maximalsteady-state while TVENT + 4.9% showed a marked increase in blood lactatebetween 15 and 30 minutes (Yamamoto et al., 1991). Farrell et al. (1979)observed runners to set a race pace which closely approximates the run velocityat which lactate begins to accumulate in the plasma. Studies (Farrell et al., 1979;LaFontaine et al., 1981) have used anaerobic threshold predictions for distancesfrom 3.2km to 42.2km with correlations between r=0.91 and r=0.98. Anotherstudy, supporting a performance relationship, was reported by Rhodes andMcKenzie (1984) where TVENT determined velocity was used to predict (r=0.94)actual marathon performance time. They suggested that run velocity at TVENTmay be critical in determining efficient running speed during marathons.2.3.3d TriathlonAnaerobic threshold determinations have produced similarly conflictingtriathlon results. TVENT demonstrated a weak relationship (r=0.58) while TLAcdemonstrated no relationship (r=-0.37) with performance times in the cyclingportion of the Ironman triathlon (Roalstad, 1989). Van Rensburg et al. (1984)found an r=-0.54 between V02 at the lactate turning point and cycle time.Despite these low correlations for anaerobic threshold Albrecht et al. (1986) was27able to set work loads based on TvENT that produced stable heart rate and 1%0 2from 60 minutes of cycling to the end of 45 minutes of running.2.3.3e ConcernsDespite the wealth of strong relationships to performances, and theabundance of research, anaerobic threshold remains a debatable andcontroversial area of research. Arguments question the existence of a threshold(Hughson et al., 1987) and methods of detection based on invasive ornoninvasive determination (Davis et al., 1976). The initial definition of anaerobicthreshold is constantly examined with respect to whether or not hypoxicconditions are present when lactic acid is being produced in the working tissues(Brooks, 1985). Powers et al. (1984), using gas exchange indices, were not ableto reproduce the high correlations found earlier and concluded that TuNc couldnot be accurately determined by these measures. As well, one of the most oftenused variables for estimating the point of TLA0 is based on a fixed (4 mmole)lactate concentration in the blood and has been criticized for over and under-estimating the true threshold (Stegmann and Kindermann, 1982; McLellan andJacobs, 1989).2.4 ConclusionsWhile the literature provides several lines of evidence to support aprediction concept in single sports there is a lack of research in multisportperformances. Even the research that has been done seems to be veryconflicting and demonstrates the wide range of variability within triathlonpopulations. The key determinants of success have all produced strong resultsin characterizing single sport performance ability although not without someconflicts representing methodological and mechanism concerns. The emergence28of other pace determining features with the passage of time and the effect of onesport on another make determination of triathlon success difficult. Despite thisdifficulty, there is a fundamental necessity for the successful triathlete to be onewith the ability to have a highly developed oxygen transport and utilizationsystem as well as the ability to efficiently produce a high energy output forprolonged periods without creating metabolic acidosis (O'Toole et al., 1989).This statement is indicative of an area of study that must have a basis in 1%0 211m ,economy of motion, and anaerobic threshold measurements that representperformance success. It is likely a lack of research and inappropriate researchthat has failed to produce better results in a triathlon setting.29CHAPTER 33.0 Methods and Procedures3.1 SubjectsEleven male subjects were selected from various running and triathlonclubs in the Vancouver area. Subjects were tested in the month prior to theirparticipation in the 1991 Canadian Ironman Triathlon; all were in a highly trainedstate. The subjects were asked to refrain from heavy exercise 24 hrs prior totesting and to be 3 hrs postabsorptive.3.2 Testing ProceduresAll testing was performed at the University of British Columbia with thecycling and running sessions in the J.M. Buchanan Exercise Science Lab andthe swimming sessions in the Aquatic Centre. During the first session, after theappropriate consent forms were signed, baseline measures of height, weight andbody composition (hydrostatic weighing) were determined. The formula forhydrostatic weighing was as follows:body density - W(W -UWW) RVCwhere^W = weight on land (kg)UWW = under water weighing (kg)RV = residual volumeC = temperature correctionBody density was then converted to percent body fat using the Sid equation30(%fat = ((4.95/density) - 4.5) *100). In addition, the first of three maximal oxygenconsumption tests (1%02mAx ) was performed either swimming, cycling or running.The remaining lab sessions consisted of the administration of the other two1%02^tests not administered on the first day. A minimum of 48 hrs separatedthe testing sessions to try and prevent any carry-over from the previous test.After the lab sessions were completed the last phase of testing consisted of timemeasurements at the actual triathlon in Penticton, B.C. All the timing was doneelectronically by Racemate, the official timers for the Ironman Canada Triathlon.This timing resulted in both transition times being included in the cyclingcomponent time. To eliminate this from the cycling time each subject wasinstructed to start their bike cyclometer (timer) as they left the transition area atthe start of the bike segment and stop it when they re-entered the transition atthe end of the bike segment. The difference between official time and thecyclometer time was a measure of the transition time and could then beeliminated from calculations.3.3 Testing ProtocolsDuring all lab testing sessions heart rate was monitored with a SporttesterPE3000 heart rate meter. Expired gases were collected and analyzed using aBeckman Metabolic Measurement Cart interfaced with a Hewlett Packard DataAcquisition System. All maximal oxygen consumption tests for determination ofTVENT were preceded with a warm-up and terminated upon volitional fatigue.The warm-up consisted of an explanation of the testing procedures followed by aphysiological warm-up with all the equipment in place against a light resistanceload. This provided the subjects with an opportunity to become familar with theequipment and the actual process for gathering data during the test. Volitional31fatigue was defined by the criteria for the particular test or by the reaching of1%02^as defined by the following criteria:1) The oxygen consumption ceases to increase linearly with rising workload andapproaches a plateau or drops slightly, the last two values agreeing within ±2ml•kg•min-1 .2) Heart rate should be close to the age predicted maximum.3) Respiratory exchange ratio is greater than 1.10.The three sport protocols were as follows:1) Swimming -All swimming sessions were confined to a roped-off section of the indoorpool to minimize interference from other swimmers and any wave action theymay cause. The water temperature in the pool at this time of year (July, August)tends to vary between 26 and 29 °C. The subjects were instructed to wear theirwetsuits (they would be worn in the race) and a rope tether was attached via twobelt loops on either side of a waist harness. The rope pulled at a slight anglefrom the bottom pulley of the frame to the waist harness. The protocol started atan initial load of 3.5 kg with 250 g increases each minute while the subject wasrequired to maintain swimming over a marked section on the bottom of the pool.The additional measure of stroke rate was determined for each minute of the testby counting the number of strokes over a 30 second interval. This procedurewas done from 15 seconds to 45 seconds of each minute to minimize the effectof workload changes on the stroke rate determination. Volitional fatigue wasdefined by either the subject's inability to maintain swimming over the markedposition in the pool against the load or their own termination. Subjects were32given a warning when they started to drop back to reassume the position overthe marker, if they were unable the test was terminated.2) Cycling -A mechanically braked Monarch bicycle ergometer was used with theprotocol starting at an initial workload of 88.2W and having progressiveincreases of 22.1 W each minute. Volitional fatigue was defined by either theinability to maintain the required test cadence of 90rpm or the subject's owntermination of the test. The cadence was clearly visible to the athlete at all timeson the Monarch's readout screen. If the subject cycled above or below the testcadence he was instructed to resume 90 rpm and maintain it for the duration ofthe test.3) Running -Procedure involved a continuous (zero grade) treadmill run protocolstarted at an initial velocity of 5 mph and increased 0.5 mph each minute.Volitional fatigue was defined by the athlete no longer being able to maintain thetreadmill velocity.Ventilatory thresholds were determined on the basis of a disproportionate(nonlinear) increase in the excess CO 2 (EXCO2) elimination curve over time.This determination was made by visual inspection of three observers not directlyinvolved in the study, who had experience in measuring threshold. All three werevolunteers with considerable background in testing endurance athletes andusing excess CO2 for determining TVENT in a research setting. Observationswere made separately and an agreement was reached on the location of the33threshold. In the event of a disagreement the VE/VOZ curve was used to clarifythe actual threshold point.The velocity that TvENT corresponded to was made for each componentsport as follows:1) Swimming -The stroke rate (strokes.minute -1 ) measured at TvENT was converted tovelocity by using a pool swim to measure the stroke length (metres•stroke -1 ).While swimming at the desired stroke rate the stroke length was measured overa marked (middle 15 m) section of the 25 m pool. This minimized the effect of theturn and the subjects were directed not to push-off hard from the ends of thepool. These two values were then multiplied together to give the velocity (seeAppendix A for a sample calculation).2) Cycling -TvENT was converted to velocity using a developed regression equationrelating workload at TvENT to velocity during a 40 km cycling performance (seeAppendix B for a precise explanation).3) Running -Simply the measured velocity of the treadmill at the determined thresholdpoint (see Appendix C for sample calculation).Each velocity was then converted to an estimated performance time based onthe distance of that component of the triathlon.343.4 Experimental Design and Data AnalysisAn analysis of variance was used to compare the dependent variablesI%02mAx, TVENT absolute, TVENT relative, and heart rate at TVENT (absolute andrelative) for each component sport. A statistical significance level of 0.01 wasused in the analyses. Simple linear regression correlation coefficients werecalculated to compare estimated and actual performance times (minus thetransition times) for each component sport. A multiple linear regression was thenperformed to predict overall triathlon time from the estimated component sporttimes.35CHAPTER 44.0 Results and Discussion4.1 ResultsEleven trained triathletes participated in this study prior to competition inthe 1991 Ironman Canada Triathlon. Note that one subject dropped out duringthe cycling portion of the triathlon, consequently his data were not used in thestudy. Descriptive data (age, height, weight, and body fat) for the 10 subjectsare presented in Table 1. Triathlon experience and training mileage issummarized in Table 2.Table 1: Descriptive data for all subjects.AGE(years)HEIGHT(cm)WEIGHT(kg)BODY FAT(%)5? 29.7 179.8 76.8 11.4SD ±3.1 ±5.6 ±7.9 ±2.236Table 2: Weekly training mileage and previous triathlon experience for allparticipants.WEEKLYSWIMMINGMILEAGECYCLING(km)RUNNINGNUMBER OFPREVIOUSTRIATHLONSX 16 10 296 48SD ±12 ±3.5 ±73 ±8The variables of heart rate and VO2 (ml.kg-1 .min-1 ) were measured as anabsolute value at TvENT and as a relative percentage of maximum (TvENT/MAX)for the three events of swimming, cycling, and running (Table 3). An ANOVAcomparing swimming, cycling, and running revealed significant differences(p<0.01) between cell means for the absolute and relative scores on heart rateand 1%02 . Tukey's HSD post hoc analysis demonstrated that the significantdifference in heart rate at TvENT (HSD = 17.27, p = 0.01) was betweenswimming and running (difference between pair of means (diff) = 20.5) andcycling and running (diff = 17.5) while differences between cycling and37swimming were nonsignificant (diff = 3). This pattern was also observed in therelative heart rate (HSD = 4.75, p = 0.01) where significant results wereswimming and running (diff = 5.2) and cycling and running (diff = 6.6) withnonsignificance between cycling and swimming (diff = 0.6). Similarly, VO2 atTvENT (HSD = 8.35, p = 0.01) revealed significant results for swimming andrunning (diff = 16.6) and cycling and running (diff = 11.6) with a nonsignificantresult between cycling and swimming (diff = 5). The final variable showing thispattern was the relative TvENT (HSD = 7.56, p = 0.01) where swimming andrunning (diff = 8.9) and cycling and running (diff = 14) were significant whilecycling and swimming (diff = 5.1) was nonsignificant. Investigation of the variableVO2 mAx (HSD = 8.83, p = 0.01) revealed swimming and running significant (diff =14.7), swimming and cycling significant (diff = 10.7) and cycling and runningnonsignificant (diff = 4).38Table 3: Heart rate and VO2 measured at TVENT and expressed as a percentageof their maximal value.XSWIMMINGSD )7CYCLINGSD 5eRUNNINGSDHEART RATEAT TVENT(bpm)147.6 ±13.2 150.6 ±13.5 168.1 ±10.3HEART RATERELATIVE(%)86.9 ±3.5 85.5 ±3.9 92.1 ±2.7V02AT TVENT(ml.kg-1 .min-1 )36.2 ±5.2 41.2 ±6.7 52.8 ±5.9V02RELATIVE(%)74.5 ±5.9 69.4 ±5.3 83.4 ±4.91102 mAx(ml.kg-1 -min-1)48.6 ±5.8 59.3 ±7.5 63.3 ±5.5* = significant difference (p < 0.01) between swimming and running**= mg' nificant difference (p < 0.01) between cycling and running= significant difference (p < 0.01) between cycling and swimming*It****Irk*irk*39Estimated times and race paces for each component event of the triathlon weredetermined utilizing physiological testing related to that sport. The variablesstroke rate, in strokes•min -1^= 32.5, SD = ±2.5), and stroke length, inmetres•stroke -1^= 1.88, SD = ±0.20), measured during the swim testingsessions were used to estimate time (min) and pace (min•mile -1 ) for thiscomponent of the triathlon. The resulting estimated and actual times (min) andpaces (min•mile -1 ) for swim testing are shown in Table 4 (for more detailregarding the actual determination of the swim estimation refer to Appendix A).Since mph could not be determined directly from testing for the cyclingcomponent, a linear regression was developed using excess CO 2 at TvENT•(Table 5). This equation related mph and this variable measured during thecycling component of the 1991 English Bay triathlon. Since the resultingprediction equation (r = 0.68) was based on a short course triathlon (40 km)cycling component it was necessary to extrapolate this to mph in Ironmandistance (112 mile) cycling. This was accomplished using 26 subjects whoparticipated in both the Ironman and the English Bay triathlon. The mph in bothcycling performances were correlated (r = 0.91) to estimate the necessarydistance correction factor (Table 5).40Table 4: Estimated and actual swimming times and paces for the IronmanTriathlon.TIME(min)ESTIMATEDPACE(min•mile-1)TIME(min)ACTUALPACE(min•mile-1))7 64.2 26.7 67.1 27.97SD ±5.6 ±2.3 ±7.8 ±3.2Table 5: Equations used in the determination of the cycling portion of theIronman Triathlon.EQUATION rPREDICTED MPH(based on 40 kmperformance)MPH = (EXCO2 * 0.845) + 12.07 0.68CONVERSIONFACTOR(40 km to 112 mile)MPH (112 mile) = (40 km MPH * 0.9988) - 3.35 0.9141These equations resulted in the estimated time (min) and pace (mph) shown withthe actual race values in Table 6. A more detailed explanation of this calculationfor the cycling component is presented in Appendix B.Table 6: Estimated and actual cycling times and paces for the lronman Triathlon.TIME(min)ESTIMATEDPACE(mph)TIME(min)ACTUALPACE(mph)5? 380.0(6:20)17.9 344.4(5:44)19.5SD ±45.2 ±2.1 ±13.0 ±0.75The run component of the triathlon utilized the velocity at TvENT on the treadmillto produce the estimated time (min) and pace (mph) shown with the actual timeand pace for the triathlon in Table 7. An explanation of the development of therun estimation is presented in Appendix C.42Table 7: Estimated and actual run times and paces for the Ironman Triathlon.TIME(min)ESTIMATEDPACE(mph)TIME(min)ACTUALPACE(mph)5? 174.5(2:55)9.1 258.6(4:19)6.4SD ±15.7 ±0.9 ±64.4 ±1.4From these results linear regression equations were developed to estimateactual time for each respective component of the triathlon, and to use that todevelop the overall predictor equation (Table 8).43Table 8: Conversion factors from estimated to actual for each component andoverall time in the Ironman Triathlon.EQUATION rSWIMMING ACTUAL SWIM = 1.15' ESTIMATED SWIM - 6.75 0.83CYCLING ACTUAL CYCLE = 0.22 * ESTIMATED CYCLE + 262.6 0.70RUNNING ACTUAL RUN = 3.03 * ESTIMATED RUN - 267.1 0.76OVERALL ACTUAL OVERALL = (-3.58 * EST. SWIM) + (-0.10 * EST.CYCLE) + (3.76 * EST. RUN)+ 291.350.89444.2 DiscussionDespite the controversy and debate with respect to nomenclature,mechanisms, detection, and interpretation (Brooks, 1985; Davis, 1985), there islittle dispute over the strong relationships that have been demonstrated betweenanaerobic threshold and performance. While much of this research has focusedon running studies (Farrell et al., 1979; Morgan et al., 1989; Maughan andLeiper, 1983), other investigations involving cycling, swimming, cross-countryskiing, etc. have produced similarly strong results (Mader et al., 1978; Coyle etal., 1991). Most of this performance research has been confined to single sportendurance events. Now, with the emergence of multiple sport endurancecontests (triathlon, duathlon), the ability of ventilatory threshold (T vENT) tocharacterize performance in this new setting is questionable. Not only mustmeasurements characterize the component sports but they must also take intoaccount the prior exercises which increase the physiological demands ofperforming subsequent events (Kreider et al., 1988). The purpose of this studywas to investigate if TvENT measured in swimming, cycling and running wascapable of predicting triathlon performance (time) success.The subject pool used in this study appears to be typical of other studiesin terms of the descriptive characteristics of the triathletes (Burke and Read,1987; Roalstad et al., 1987; Ireland and Micheli, 1987). The triathletes tend to beolder than comparable single sport endurance performers (Kreider et al., 1988;Kohrt et al., 1987a; O'Toole et al., 1987). This is probably a factor of the"newness" of the sport and as it grows the population of triathletes will beyounger. As well, most athletes come to triathloning after competing in one ofthe single sports, so it would be expected that they are older. In terms of theother descriptive characteristics of height, weight, and body fat, they are45undistinguished from other single sport athletes (O'Toole et al., 1987; Roalstad,1989).The weekly mileage for these subjects is similar to other triathletes withrespect to swimming (typical = 10.5 km) and cycling (typical = 300 km) while therunning is low (typical = 72 km) (O'Toole et al., 1989). This low weekly runmileage will be discussed in the context of how it may relate to the results foundin this study. As with other studies, there is a wide interindividual variability withrespect to training mileage in the three component sports (O'Toole, 1989).The values for heart rate and oxygen consumption are consistent withrespect to other research (Dengel et al., 1989). The hierarchy of 1%02mAx valuesis typical (run>cycle>swim) but the absolute scores are less than comparablesingle sport athletes (Astrand and Rodahl, 1977). This may be reflective of thereduced training volume in any one area and/or specific adaptations thattriathletes undergo in training related to their sport (Kohrt et al., 1987b). Theabsolute scores demonstrate the expected pattern between running andswimming, and running and cycling with the exception of run-cycle I%02mAx •While the differences between run-cycle were nonsignificant on this variable thedrop from running to cycling is consistent with previous triathlon research. Thedifference of 6.7% compares well to the work of Kohrt et al. (1987a) and O'Tooleet al. (1987) where the maximums are 3 to 6% less in cycling. This drop of 3 to6% for I%02 „,„u( from running to cycling is somewhat less than highly trainedrunners would show (9 to 11%) (Roalstad, 1989). The hierarchy tends todemonstrate the highest^values for treadmill running, with cyclingapproximately 90% (Faulkner et al., 1971; Miyamura et al., 1978) and swimmingapproximately 80% (Holmer et al., 1974; Magel et al., 1975) of the runningvalue. This relationship is altered in athletes who are trained cyclists or46swimmers. Highly trained cyclists may equal or exceed their treadmill runningVO2MAx on cycle ergometry testing (Kohrt et al., 1989) likely by virtue of a moresport specific testing procedure. Several studies (Roalstad, 1989; Hagberg etal., 1978; Withers et al., 1981) state that triathletes most resemble cyclists.While the triathletes generally do not equal or exceed their treadmill run VO2 ,like elite cyclists there is a training specificity, resulting in less of a gap betweenthe run and cycle VO2 mAx . This relationship is not unique to this study (O'Tooleet al., 1987; Kohrt et al., 1987b) and may reflect a specific adaptation to triathlontype training versus single sport training.For the variable V02 „„Ax only swimming and cycling illustrated thehypothesized result, with the cycling 1102mAx significantly greater than theswimming V02MAX. This study demonstrated a swim V02 AfAx value at 77% of therunning VO2 hfAx which is typical of trained runners tested in both modes. Sincehighly trained swimmers may reduce the difference between swimming andrunning, it was thought triathletes might demonstrate this result, although to alesser degree. Studies by Dengel et al. (1986) and Kohrt et al. (1987b) found areduction in the difference, while Kohrt et al. (1989) did not. Thus, the literaturetends to be equivocal on this point. The similar finding to Kohrt et al. (1989) mayindicate that the relatively low weekly volume of training (elite swimmers' dailytraining distance is about equal to triathletes' weekly swim training distance) wasinsufficient to develop training specificity as seen in cycling. The variables HRand VO2 at TvENT were similar (p > 0.05) for swimming and cycling. This mightbe accounted for by a slightly low estimated threshold in cycling (Kohrt et al.,1989). The mean VO2mAx value is typical of other studies which have rangedfrom 54.4 ml.kg-1 .min-1 (Kohrt et al., 1987b) to 66.7 ml-kg -1 .min-1 (O'Toole et al.,1987) for men on cycle ergometry testing. The low threshold value might be47accounted for by the type of training most subjects performed. Most participantsfollowed a high volume (about 296 km•week -1 ) format with very little "quality"training. To improve TvENT most studies indicate a high intensity (near or slightlyabove threshold) type protocol (Pate and Branch, 1992). O'Toole (1989)documented a low amount of interval training ( 20%) on the bike by a largesurvey of triathletes. Thus, since most subjects have trained based on volumefor cycling as opposed to higher intensity, the ability of TvENT to separatedifferences in levels of performance is lessened. Subjects are working at a lowerpercentage of maximum in cycling than the other two component sports asdemonstrated in these results.On the relative measures of HR and VO2 it was hypothesized that therewould be nonsignificant differences between swimming, cycling and running.This was only true for swimming and cycling with running being significantlygreater than either of the other component sports. The high percentage mayreflect the previous training of these athletes. Approximately 75% of triathleteshave a running background (Ireland and Micheli, 1987) and the higher TvENTmay reflect a relationship between experience and performance. Certainly,studies have shown years of experience to be highly correlated withperformance (Magel et al., 1975; O'Toole, 1989). Kohrt et al. (1989) suggest thatthis lower percentage found in cycling may indicate that triathletes have agreater potential to improve in cycling than in running. This lower TvENT incycling may reflect the need to devote a greater proportion of training or at leastan increase in the intensity of the training in order to achieve the similar trainingstatus as found in running. In this study, the one subject from a cyclingbackground had the highest cycling TvENT estimated percentage. This may alsosimply reflect large interindividual variability, coupled with the small sample size48of the present study. This condition may result in extreme scores influencingresults more than if a large sample study were employed.The results of swim testing produced an estimate 2.9 minutes faster thanactual swim time for that component of the Ironman triathlon. This result (r=0.83)indicates that 69% of the actual time variability was accounted for by theestimated time value. This result seems consistent with the TvENT concept.TvENT assumes participants will set a maximum pace that could be sustainedindefinitely, without inducing metabolic acidosis (Coyle et al., 1988; Williams andCavanagh, 1987; Kumagai, 1982). Since this is the first event, sport intrinsicfatiguing factors (fluid, fuel, etc.) are not likely to severely limit pace (O'Toole etal., 1989). As well, the low number of confounding factors would suggest that thecorrelation between actual and estimated swim performance should be thehighest of the three component sports, and indeed this was the case. Thus, thisphysiological variable, explaining 69% of the variability, stresses the importanceof other factors, particularly mechanical efficiency which may also affectperformance (Kohrt et al., 1989). The study of Toussaint (1990), comparing eliteswimmers to triathletes, found that they both had similar ability to do work butthe differences in technique separated the triathletes from the more skilledswimmers. The inefficient swimmer may expend more than twice the caloriesduring submaximal swimming compared to the efficient swimmer. This wouldlead to a generalized and localized fatigue (Holly et al., 1986). This isparamount in a triathlon when considering the majority of the competitivedistance is still to come and it is necessary to conserve energy. From theperspective of competitive experience, the subjects were well prepared (averageof 16 triathlons completed) and less likely to push pace this early in the event.Only two subjects were faster (both 4 minutes) than their estimated time, onebeing the strongest swimmer in the study.49Cycling results were much more confusing, with the estimated time being36 minutes slower than actual time. The correlation between estimated andactual time resulted in 49% (r=0.70) of the variance being explained by theestimator. It was expected that all estimated results would be faster than actualtimes. Given the increasing influences of intrinsic factors (fuel, fluid,thermoregulatory mechanisms, etc.) and extrinsic factors (heat, humidity, terrain,etc.) which would reduce the ability to maintain speed and the inability of TvENTto account for these, it seemed a reasonable assumption. There are severalpossible explanations for this observation. Firstly, it may reflect an inadequacy inthe chosen methodology. The inability to measure speed directly in the testingenvironment necessitated the indirect determination using a short coursetriathlon performance. Since many of the factors that adversely. effectperformance will also be present in a short course triathlon (however to a lesserdegree) the cycling equation developed will in some way account for thisvariability and reduce the ideal speed determined by a solely lab developedestimation. As well, the extrinsic factors of terrain, weather, etc. will also bepresent, confounding the determination of cycling pace. Another possibleexplanation is that the methodology employed underestimates the truethreshold. The few studies that have looked at triathletes TvENT relative toperformance have been unsuccessful with correlations between TvENT and bikefinish times in an Ironman triathlon (r =-0.26)(0 1Toole et al., 1989). While someof the reduced relationship from swim to cycle is methodological, considerablevariability is due to factors not present while swimming (terrain, heat, etc.). Alsothe cumulative effect of time magnifies the factors that were evident in the swim.Certainly, one of the conclusions of the low correlation studies has been that theupper limit of pace for cycling is being set by other factors as opposed tophysiological considerations represented by TvENT (O'Toole et al., 1989). The50emergence of other factors was evident in the bike portion of the triathlon, asone subject dropped out due to dehydration. By this time in the triathlon, thecumulative effects of time, distance, and heat are beginning to take their toll interms of dehydration, electrolytic abnormalities, and exhaustion (Laird, 1989).Although the swim is a relatively small component of the total event, it may havepronounced effects on subsequent performance. Kreider et al. (1988) found thatprior swimming would increase core temperature at the onset of cycling. Thusly,thermoregulatory and cardiovascular adjustments occur sooner than they wouldwith no prior swimming. In this study the lower correlation may also be reflectiveof a very homogeneous sample (344.4 ±13 minutes), with respect to actualcycling time. In addition, the idea of maintenance of efficiency as the ultimatedeterminant of triathlon success (Kreider et al., 1988) may be less physiological(TVENT) and more biomechanical (technique), accounting for a considerableportion of the variability.The run results produced the greatest disparity between estimated andactual times with an average of one hour and 24 minutes difference. As would beexpected the estimated time was faster than the actual time. There wasconsiderable variability (258.6 ±64.4 minutes) in run performances with theestimated time able to explain 58% of the variability in actual time. The effectsof prior activity in swimming and cycling (subjects have been active betweenabout 6 and 7 hours) are likely to be even more pronounced. The largedifference between estimated and actual times provides support for other factorssetting pace limits. While these intrinsic and extrinsic factors are present,another observation may explain some of the decrease in run race pace. Thedifference in combined swim and cycle time from fastest to slowest subject isone hour and one minute while the combined swim, cycle, and run differencefrom fastest to slowest is 3 hours and 48 minutes. This is a considerable amount51of variability to be added during the run segment. This difference is largelyaccounted for by the inability of slower subjects to maintain pace. The well-trained runners are better able to maintain race paces that approximate theirtraining pace, while the less well-trained athletes demonstrate an inability tomaintain training pace (O'Toole, 1989). This may be the result of these subjectshaving difficulty maintaining a constant TvENT estimated pace for some portionof the triathlon prior to running. The variable terrain found at the IronmanCanada Triathlon would also be an interfering factor. The considerable elevationchanges during the bike portion produce extreme speed variability making itdifficult to maintain a steady work output. Roalstad et al. (1987) found triathleteswith the least fluctuations in heart rates tended to have better finishing timesthan those with wide variability. The terrain creates a condition that mustultimately place some constraints on the ability of TvENT to estimate during thebike portion of the triathlon. This result is certainly consistent with the concept ofTvENT and the expected result if a subject were to perform for any length of timeabove the defined limit of aerobic ability (Farrell et al., 1979). The homogeneouscycling performances may also support this result, if the slower cyclists' actualperformances were faster than expected. The greatest difference between actualand estimated time was in fact between the slowest cyclists with allperformances much faster than estimated (by an average of 74 minutes).Therefore, there is some evidence for cycling above TvENT with a subsequentadverse effect on running. This would explain some of the wide variability seenin the slower subjects. Another factor that may account for variability in runningis the low mileage done for this component in training. O'Toole (1989) found thatof the subjects who did not finish the two main reasons were; (1). inappropriaterace strategy and (2). too little training (particularly long distance bike rides orlong runs). Some coaches suggest that training in swimming and cycling52provides a "crosstraining" benefit that would offset the low mileage training inrunning. Previous research suggests that this is not true. Swim training (Gergleyet al., 1984; Magel et al., 1975) and cycle training (Town and Sinning, 1982;Pechar et al., 1974) have both failed to demonstrate improvement in run training.Thus, a lack of sufficient run mileage may also represent some of the variability.When all three component sports are taken into account in a multiplelinear regression a strong correlation to the actual overall time (r=0.89) resulted.Thus, the combined variability represented by each of the component sports isable to predict overall time better, accounting for 78% of variability, than itsindividual component sport. This also suggests that each sport's variabilityrepresents an important part of the variability seen in a combined sport liketriathlon.These results support the importance of an understanding of these otherfactors influencing performance. Successful triathlon performance appears to beimpacted upon by the ability to maintain thermal and mechanical efficiencythroughout the event (Kreider et al., 1988). Most of the metabolic abnormalitiesthat develop are time dependent and cumulative as the triathlon progresses. Anexample of this time effect is found in the research of Laird (1989) where ingoing from a short course triathlon to Ironman distance, the average percentdehydration increased from 1.7 to 3.7%. Certainly, absolute fluid loss increasesover longer distances. While the most marked differences are seen in the run,they are more the effect of the total duration as opposed to the marathon itself(Farber et al., 1991). Relationships reflect this emergence of pace determiningfactors if we consider that in the study of Rhodes and McKenzie (1984) anr=0.94 was found between estimated and actual time. While this study, using theexact same equipment and methodology found r=0.76 for the marathon runcomponent of the triathlon. In comparison to other endurance activities53indications of dehydration were much greater in Ironman triathletes (vanRensburg et al., 1986). The most common reason for requiring medical attentionat the Hawaii Ironman is dehydration (Hiller et al., 1987; Laird, 1987). Sawka etal. (1985) recognized that dehydration accompanied with thermal stress maysignificantly decrease work output. In terms of electrolytes, incidence ofhyponatraemia are directly related to race distance (O'Toole et al., 1989). Salt-depletion heat exhaustion is characterized by fatigue, nausea, muscle cramps,etc. (Laird, 1989) This combined with dehydration and increased sweat rate willhave profound results on fatigue. Certainly, nutritional replacement has beenwell documented with respect to its role in endurance performance (Costill,1988). Emerging research suggests there are likely extra considerations thatneed to be made in events of the duration of ultraendurance triathlons. Typicalcarbohydrate replacement protocol, like that done in marathoning, may not be asufficient rate of replacement for these increased duration events (Applegate,1989). As well, the use of solid food is well accepted in ultraendurance triathlons(Applegate, 1989). Further research is needed with respect to the appropriateprotocols for nutritional replacement during Ironman distance triathlons tominimize the adverse effects of running out of energy.Despite the other factors interfering with the actual estimation ofperformance, the underlying mechanisms support the concept of TvENT•Ultimately, the triathlon is a contest requiring a series of endurance events to bedone sequentially at optimal pace without creating fatigue that will hinderperformance in the next event (O'Toole et al., 1989). TvENT is designed toidentify the point where work may be performed for indefinite durations asenergy requirements are being supplied predominantly through unlimited aerobicenergy sources, while waste products are being adequately removed (Andersonand Rhodes, 1989). While there are many different methods for determining54TVENT, excess carbon dioxide (CO 2) combines a strong mechanism base with astrong relationship to performance research. Carbon dioxide production willincrease with exercise due to the metabolism of fats and carbohydrates until theTVENT breakaway point at which time there will be a secondary (nonmetabolic)increase in CO2 due to the buffering of lactic acid by bicarbonate (Anderson andRhodes, 1989). This provides for a direct tie between the generation of excessCO2 and the increased production of lactic acid which is strongly related tofatigue (Sjodin and Svedenhag, 1985). Studies suggest that 90-94% of the lacticacid produced is immediately buffered by the bicarbonate buffering system(Wasserman et al., 1986). Thus, excess CO2 is representing the magnitude oflactic acid production through the glycolytic pathways and the organism'sbuffering capacity (Anderson and Rhodes, 1991) and will continue to increase aslong as the rate of lactic acid production is increasing (Loat and Rhodes, 1991).While excess CO2 changes do not represent the actual occurrence of bloodlactate accumulation they do seem to track the changes taking place (Andersonand Rhodes, 1989). This "tracking effect" demonstrates the strong relationship(r=0.92) between excess CO2 and blood lactate changes (Langill and Rhodes,1992). It is suspected that this difference is due to the excess CO2 being freelydiffusable across the cell membrane while the lactate molecule experiences a"translocation hindrance" delaying increases in the blood (Stainsby, 1986). It issuggested that this hindrance represents a delayed lactate transport caused bylow muscle membrane lactate permeability and a change in capacitive effects inmuscle concentration of lactic acid (Stainsby, 1986). This resulting delay inblood lactate accumulation may in fact suggest that excess CO 2 more directlyapproximates changes in intramuscular lactate production and accumulation(Volkov et al., 1975; Issekutz and Rodahl, 1961). Combined with this support forexcess CO2's ability to define a critical intensity of lactate accumulation, the55performance research supports similarly strong ties. Hearst and Rhodes (1982)found exercising at TVENT to be an appropriate intensity to maintain asignificantly low blood lactate concentration while exercising at an intensity 1km•hr-1 above TVENT elevated the lactate. The study of Loat and Rhodes (1991)found excess CO2 defined a TVENT that could be sustained for one hour ofcycling without significant elevations in blood lactate.Endurance, as it relates to competition, can be thought of as the capacityto achieve and maintain a high average speed over the distance of the race. It iswith this in mind, that the concept of TVENT has emerged to try to identify thispoint of maximum sustainable pace. While other factors such as substratedepletion, temperature regulation, fluid and electrolyte balance will contribute tofatigue and make it difficult to represent a sustainable work rate (Carnevale andGaesser, 1991) there is still underlying physiological mechanisms that TVENTappears to represent. Between 49 and 69% of the individual component sportvariability and 78% of the total triathlon variability is accounted for by thesemeasurements. Despite adverse conditions having reduced the ability of TVENTto determine pace there still remains a connection between performance andthreshold measurements that extends from single to multiple endurance sportcontests. Triathlon is a comparatively new sport and considerably more researchis needed into the determinants of success. Particular emphasis should focus onunderstanding the unique physiological adaptations that take place in triathletesrelative to single sport athletes and examining the role the various factors play indecreasing performance. However, research should not dismiss the use ofTVENT in this sport setting.56CHAPTER 55.0 Summary and Conclusions5.1 SummaryIn theory, there is considerable evidence to suggest that it is possible toestimate athletic performance in endurance sports. These sports are contestedby athletes who will, regardless of their competitive ability, try to sustain a racepace that maximizes their physiological capacities. The use of lab data toestimate such performance is an extremely attractive concept since it could offera more scientific basis for training and selection of athletes.Several physiological factors (^Aux , economy of motion, and anaerobicthreshold) have been investigated with varying degrees of success. Amongthese, the anaerobic threshold seems to have the most potential forcharacterizing endurance sport performance. The term "anaerobic threshold"has undergone considerable change in the years since its initial development byWasserman and Mcllroy (1964). Its measurement based on the ventilatorymeasure excess CO2 has produced strong relationships to endurance sportperformance in running and cycling (Rhodes and McKenzie, 1984; Loat andRhodes, 1991; Hearst and Rhodes, 1982). It has also been shown to have astrong relationship to the lactate threshold (Anderson and Rhodes, 1991; Langilland Rhodes, 1992) and possibly the intracellular production and accumulation oflactate (Issekutz and Rodahl, 1961).Anaerobic thresholds have been used in sports like swimming, cycling,and particularly running to develop performance based relationships with somesuccess. The comparatively new sport of triathloning has presented a new set of57questions with regard to estimating performance. Due to the multiple modes ofcompetition within the same contest it is not known whether the same featuresgoverning single sports also apply to triathlon. Problems of performingsequential events and extreme duration pose restrictions that must beconsidered. It was the purpose of this investigation to determine if ventilatorythreshold measurements in swimming, cycling, and running could be extended totriathlon in an effort to characterize performance.This study found the estimated single component sport times to correlateto actual triathlon times in swimming, cycling, and running with r=0.83, 0.70, 0.76respectively. Overall triathlon performance found r=0.89 between the combinedthree sport estimated times and the actual total time. Linear regressionequations were produced such that actual time could be calculated fromestimated times (in minutes) with the following results:actual swim = 1.15 * estimated swim - 6.75actual cycle = 0.22 * estimated cycle + 262.6actual run = 3.03 * estimated run - 267.1actual overall = (-3.58 * est. swim) + (-0.10 * est. cycle) + (3.76 * est. run)+ 291.35These findings demonstrated an underlying physiological base for triathlonperformance that was represented by ventilatory threshold. Unfortunately, otherfactors resulting from the cumulative effect of time, heat, and terrain diminish theability to use ventilatory threshold as a predictor.5.2 Conclusions1) Ventilatory threshold accounted for 78% of the variability in pace in anultraendurance sport setting.2) This ability is compromised by other factors that confound the ability toestimate pace.585.3 Recommendations1) It would be beneficial to make some measurements of pace at the beginning,middle, and end of each component sport during the race to see how wellsubjects actually do maintain constant pace.2) The development of a correction factor that could be carried-over from onecomponent sport to the next to account for cumulative effects is needed.3) A more direct methodology for estimating the cycling component time wouldlikely improve this weakest of the correlations.4) Instead of comparing the TvENT determined speeds to an actual triathlon,compare then to a simulated laboratory triathlon to eliminate some of theextraneous variables.5) At this stage most of the triathlon research is equivocal, so continuedresearch in this area is necessary to gain a better idea of those factorscontributing to triathlon success.6) Still more research is • needed into anaerobic threshold, particularly theunderlying intracellular mechanisms.59BIBLIOGRAPHYAcevedo EO, Goldfarb AH. 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European Journal of AppliedPhysiology 56: 7-11, 1987.71APPENDIX ASample calculation for the estimation of time in the swimming component of theIronman Triathlon.1) The ventilatory threshold (TvENT) point was determined by a graph of excessCO2 vs. time during the tethered swim performance.2) Since stroke rate was measured each minute, the TvENT stroke rate wassimply the value at its time of occurrence.eg: TVENT = 4 minutes^stroke rate at TvENT= 30 strokes•min-13) To determine the stroke length that corresponded to this stroke rate severalswim repeats were performed. The subject swam the measured stroke rateover a marked section of the pool while the number of strokes to cover thedistance was counted.72eg: Swimming at TvENT stroke rate = 30 strokes•rnin -1 ,determined stroke length = 1.96 metres•stroke -14) These 2 values were then multiplied together to give the velocity at TvENTeg: 30 strokes•min -1 * 1.96 metres•stroke-1 = 58.8 metres•min -15) This was then converted to minutes using the known distance of the swimcomponent (2.4 miles)eg: 1 minute•58.8 metres -1 * 1600 metres•mile-1 *2.4 miles= 65.3 minutes73APPENDIX BSample calculation for the estimation of time in the cycling component of theIronman triathlon.The above graph indicates the time of occurrence of TvENT. Unfortunately,velocity cannot be readily determined therefore a series of stages wereincorporated to estimate this value.Stage 1:A relationship was developed between Excess CO 2 at TvENT and mph in a shortcourse triathlon (1991 English Bay Triathlon). This variable was chosen basedon several studies that have shown its strong relationship to performance inendurance sports (Langill and Rhodes, 1992; Loat and Rhodes, 1991; Rhodesand McKenzie, 1984). A separate subject pool was used and the following linearregression was developed:mph = ( EXCO2 * 0.845) + 12.07^r = 0.6874Stage 2:Since this equation was being developed in one subject pool and used inanother it was desired that the measured variable show similar values for bothpopulations. An ANOVA comparing Ironman and English Bay triathletes found anonsignificant difference between excess CO2 used in the prediction equationbut a significant difference in speed (in mph).TVENT t p valueVARIABLEEXCO2 1.93 > 0.05This necessitated some form ofcorrection for the differences in speedbetween cycling 40 km and cycling112 miles in a triathlon.Stage 3:A sample of 26 subjects made up of males in the same age category as thisstudy who took part in both events was determined. A linear regression wasperformed to relate mph in the Ironman cycling to mph in the English Baycycling. This equation was then used as a speed correction factor:mph (112 mile) = (mph (40 km) * 0.9988) - 3.35r = 0.91Stage 4:These equations were then used to determine mph at TVENT for the Ironmansubjects as follows:eg: EXCO2 at TVENT = 11.5475mph at TvENT = (11.54 * 0.845) + 12.07= 21.82 mph (uncorrected for speed difference)mph (112 mile) at TvENT = 21.82 * 0.9988 - 3.35= 18.4 (corrected for speed difference)Stage 5:Mph were then converted to time using the distance for this component of thetriathlon:112 miles•18.4 mph-1 *60 min•hr-1 = 364.4 min (6:04)76APPENDIX CSample calculation for the estimation of time in the running component of theIronman triathlon.1) In the graph of excess CO 2 vs. time each minute corresponded to a differenttreadmill velocityeg: minute 8 = 8.5 mph2) Mph at the threshold was then converted to minutes since the distance of therun component was known (26.2 miles)26.2 miles•8.5 mph-1 *60 min•hrl = 185 min (3:05)77APPENDIX DIndividual data comparing estimated and actual times for swimming, cycling, andrunning.1) Swim times (minutes) for individual subjects.Ell estimatedI=1 actualSUBJECT 1 2 3 4 5 6 7 8 9 10ESTIMATED 64 66 76 64 62 58 58 65 61 72ACTUAL 71 70 78 65 58 54 60 76 67 75782) Cycle times (minutes and hours:minutes) for individual subjects.SUBJECT 1 2 3 4 5 6 7 8 9 10ESTIMATED 6:04 6:24 5:14 7:47 6:33 5:33 6:54 5:59 6:58 5:54ACTUAL 5:39 5:40 5:38 6:08 5:44 5:18 5:48 5:56 6:02 5:55793) Run times (minutes and hours:minutes) for individual subjects.SUBJECT 1 2 3 4 5 6 7 8 9 10ESTIMATED 2:30 2:55 2:37 2:55 2:55 2:30 3:05 3:05 3:17 3:05ACTUAL 3:40 3:36 3:14 3:41 4:35 3:26 5:56 4:49 6:18 3:5180APPENDIX EMechanisms of Lactate and Ventilatory ThresholdThe recognition by Owles (1930) that there was a critical exerciseintensity above which there was accumulation of blood lactate, combined withincreased CO2 excretion and ventilation is the beginning of what would becomethe anaerobic threshold concept. It is hypothesized that above this "criticalintensity" there is a failing of the cardiovascular and/or the respiratory responseto supply the energy demanded through the aerobic pathways resulting in workcapacity being limited (Knuttgen, 1962). Theoretically, at the anaerobic thresholdpoint work may be performed for indefinite durations since aerobic energysources are supplying the required energy and waste products are beingadequately removed. Thus, the mechanisms responsible for threshold are thosewhich limit aerobic performance and, in some way, control the relationshipbetween endurance capacity and induced fatigue. The determination of thispoint by invasive and noninvasive means and their underlying mechanisms arethe source of considerable debate.One of the predominant categories of threshold determination involvesthe invasive measurement of lactate. Lactate is produced to supplement theaerobic energy supply and its presence in the bloodstream reflects increasedreliance on the glycolytic pathways of energy production (Jones and Ehrsam,1982). If there is an imbalance between pyruvate formation and its oxidation inthe Kreb's Cycle a subsequent conversion to lactic acid will occur to allow for thecontinuation of glycolysis (Stainsby, 1986; Jones, 1980). The lactate formed inthe dissociation of lactic acid provides a source of fuel (Anderson and Rhodes,1989) to the working muscle and other tissues of the body.81In progressive intensity exercise there is an initial rise in blood lactate(Brooks, 1986) which is maintained (or slightly increased) until a point wherethere is a disproportional or abrupt increase in concentration. Whether tissuehypoxia is accompanying this increase in lactic acid production is stillquestioned (Brook, 1985; Gollnick et al., 1986; Wasserman et al., 1986). It isspeculated that this point represents an imbalance in lactate production andremoval resulting in an increased reliance on anaerobic metabolism and therelease of lactate into the blood (Davis et al., 1983). If the biproducts of lacticacid are allowed to accumulate within the working tissues there will be a rapidonset of fatigue (Anderson and Rhodes, 1989). Increased lactate productionresults in an increased hydrogen ion release, decreasing muscle and blood pH(Wenger and Reed, 1976) which would limit energy production by anaerobicglycolysis (Hultman and Sahlin, 1980). The fatigue accompanying decreasedcellular pH may be the result of an alteration in the membrane permeability orinterference with calcium ion binding at the actomyosin binding sites (Wengerand Reed, 1976).The actual lactate threshold (Tukc), determination based on thesemechanisms, has involved several methodologies. TLAc has been characterizedby an absolute blood lactate concentration of 2mmole (Hughson et al., 1982) or4mmole (Sjodin and Jacobs, 1981), an increase above resting levels(Wasserman et al., 1973) and that point where there is an abrupt increase inlactate accumulation (Aunola and Rusko, 1984). Most of the methods are notwithout controversy, particularly the absolute blood lactate concentrations whichhave been questioned by several researchers (McLellan and Jacobs, 1989;Stegmann and Kindermann, 1982) on the basis of individual lactate kinetics.The other predominant category for threshold determination is based onnoninvasive ventilatory parameters. The prime stimulators of ventilation82accompanying increasing levels of exercise are increased CO 2 production, anassociated fall in blood bicarbonate due to buffering of metabolic acids, and arise in arterial pH (Anderson and Rhodes, 1989). Changes in breathing rate anddepth are made to provide a matching of alveolar ventilation to blood perfusion(Sutton and Jones, 1979). Ventilation is strongly linked to CO 2 output(Wasserman and Whipp, 1975) with the relationship demonstrating ventilationincreases in proportion to CO 2 production (Swanson, 1979). Prior to thethreshold, CO2 production will rise in response to the aerobic metabolism of fatsand carbohydrates. Above the threshold, in this case ventilatory threshold(TVENT), there will be an increased CO 2 load resulting from the production andbuffering of lactic acid by bicarbonate as follows:HLa + NaHCO3 = NaLa + H2 CO3 = CO2 + H20Thus, above TVENT increases in ventilation above those responsible for arterialCO2 compensation do not allow for the complete compensation for the pursuinglactic acidosis (Wasserman and Whipp, 1975). An independent ventilatorystimulus is created to increase ventilation and lower the hydrogen ion content ofthe blood. This will result in a constraining of the buffering capacity of the bloodand pH will drop (Whipp et al., 1980) with the accompanying 'fatigue" resultsalready discussed.The ability to use ventilatory measures to represent invasive changes hasreceived considerable attention. While Powers et al. (1984) were not able toproduce high correlations and concluded that TLAc could not be accuratelydetermined by gas exchange variables several other researchers haveemployed TVENT with considerable success (Wasserman et al., 1973; Clode etal., 1969; Anderson and Rhodes, 1991). Again, as with TLAc, considerablevariability exists with respect to the different methods of detection. Caiozzo et al.(1982) found R to be the least sensitive, with VE/VO2 , 1 ./CO2 to all provide83excellent predictions of Tugs. One other measure used in detection hasproduced very strong links to performance (Rhodes and McKenzie, 1984; Hearstand Rhodes, 1982; Loat and Rhodes, 1991). It has been found that thenonmetabolic (excess) CO 2, resulting from the buffering of lactate, will begenerated as long as the rate of lactic acid production is increasing as this willmean additional hydrogen ions to buffer (Wasserman et al., 1986). While thehydrogen ion and CO 2 within the muscle readily diffuse into the bloodstream thelactate molecule has a translocation hindrance to its movement out of the muscle(Stainsby, 1986). This may result in increases in excess CO 2 being detectedbefore a significant rise in blood lactate, allowing excess CO 2 to more accuratelyreflect cellular lactate production and accumulation (Issekutz and Rodahl, 1961).Volkov et al. (1975) found the excess CO 2 "index" to directly relate to themagnitude of lactate production through the glycolytic pathway and theorganism's buffering processes. Further investigation by Langill and Rhodes(1992) found that a strong relationship exists between excess CO 2 and bloodlactate (r=0.92) and that their respective curves over time with increasingexercise parallel one another.Despite the controversy surrounding Tukc and T vENT there isconsiderable support for the concept (Wasserman et al., 1986; Davis, 1985).While many of the exact mechanisms are still being questioned, the inconclusiveresults are more likely a result of the need for further research, particularly theintracellular events that these thresholds are based on.


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