Eur J Appl Physiol (1993) 67:150-158
European
Jouma, of
Applied
Physiology
and OccupationalPhysiolegy
© Springer-Verlag 1993
Aerobic and anaerobic indices contributing
to track endurance cycling performance
N. P. Craig 1, K. I. Norton 2, P. C. Bourdon 1, S. M. Woolford 1, T. Stanef 1, B. Squires 2, T. S. Olds z,
R. A. J. Conyers 3, and C. B. V. Walsh 4
1 South Australian Sports Institute, PO Box 219, Brooklyn Park SA 5032, Adelaide, South Australia, Australia
2 School of Sport and Leisure Studies, University of New South Wales, Oatley, Australia
3 Department of Biochemistry, Alfred Hospital, Melbourne, Victoria, Australia
4 Australian Institute of Sport, Kidman Park, Adelaide, South Australia, Australia
Accepted February 24, 1993
Summary. A group of 18 male high performance track
endurance and sprint cyclists were assessed to provide
a descriptive training season specific physiological profile, to examine the relationship between selected
physiological and anthropometric variables and cycling
performance in a 4000-m individual pursuit (IP4ooo)
and to propose a functional model for predicting success in the IP4ooo. Anthropometric characteristics, absolute and relative measurements of maximal oxygen
uptake (1202max), blood lactate transition thresholds
(Thla- and Than,i), 1202 kinetics, cycling economy and
maximal accumulated oxygen deficit (MAOD) were
assessed, with cyclists also performing a IP4ooo under
competition conditions. Peak post-competition blood
lactate concentrations and acid-base values were measured. Although all corresponding indices of Th~a- and
Than, i occurred at significantly different intensities
there were high intercorrelations between them (0.510.85). There was no significant difference in M A O D
when assessed using a 2 or 5 min protocol (61.4 vs 60.2
m l . k g - 1, respectively). The highest significant correlations were found among IP4ooo and the following:
VO2max (ml'kg-2/3" min-1; r = - 0.79), power output
at lactate threshold (Wth,~) (W; r = -0.86), half time of
1202 response whilst cycling at 115% 1202ma~ (s;
r=0.48) and M A O D when assessed using the 5 min
protocol (ml.kg -1; r = - 0.50). A stepwise multiple regression yielded the following equation, which had an r
of 0.86 and a standard error of estimate of 5.7 s: IP4000
(s) = 462.9 - 0.366 x (Wthla) -- 0.306 x ( M A O D ) 0.438 x (1202max)
where Wth, is in W, M A O D is in ml.kg -1 and 12Ozmax
is in ml. kga- 1. min - 1.
These results established that these male high performance track endurance cyclists had well-developed aerobic and anaerobic energy systems with VO2m~x, Thla
and M A O D being primary important factors in a
IP4ooo. Therefore, it is suggested that these variables
should be optimally trained and routinely monitored
Correspondence to" N. Craig
when preparing track endurance cyclists for competition.
Key words: Individual pursuit - Maximal oxygen uptake - Lactate threshold - Oxygen uptake kinetics Maximal accumulated oxygen deficit
Introduction
For master coaches to construct and implement specific physical training programmes and/or identify talent,
they must first have access to fundamental information
concerning the essential qualities for successful sporting performance. This may include the development of
a functional model to determine the relative contribution and kinetics of energy metabolism and other physiological factors that contribute to the performance
specialty. The knowledge and understanding of energy
costs and the involvement of various metabolic components will enable the coach and sports scientist to competently prescribe training programmes, develop specific assessment protocols and maximise training and
competition performance. Whilst this modelling concept is not new and has been applied to many sports, in
the sport of high performance cycling, most attention
has been focused on road cycling (Coyle et al. 1988,
1991; Krebs et al. 1986; Miller and Manfredi 1987). Relatively little research has been conducted on identifying the important physiological characteristics of track
cyclists in relation to their specific cycling events.
The principal track cycling events range from a 200m flying sprint lasting 10-11 s to the 50-km points score
lasting approximately 1 h. Unlike road cycling and the
longer track cycling events, where the majority of competition is completed at submaximal levels of metabolism, the shorter track cycling events require the cyclist
to tax maximally both the aerobic and anaerobic metabolic pathways (Neumann 1992). Of particular interest
to this study was the 4000-m individual pursuit (IP4ooo).
151
This e v e n t r e q u i r e s a n i n d i v i d u a l cyclist, f r o m a stat i o n a r y start, to p r o p e l as fast as p o s s i b l e a s p e c i a l l y
designed light-weight bicycle with a fixed gear over a
d i s t a n c e o f 4000 m. It is an e v e n t t h a t lasts a p p r o x i m a t e l y 270 s at i n t e r n a t i o n a l l e v e l u n d e r c o m p e t i t i o n
c o n d i t i o n s a n d p l a c e s a high d e m a n d o n b o t h t h e a e r o b i c a n d a n a e r o b i c e n e r g y p a t h w a y s ( B u r k e et al. 1981;
F a i n a et al. 1989; K e e n et al. 1985; N e u m a n n 1992;
P y k e et al. 1988). I t has b e e n s u g g e s t e d t h a t t h e r e l a tive c o n t r i b u t i o n s of a e r o b i c a n d a n a e r o b i c m e t a b o l i s m
to this s u p r a m a x i m a l e v e n t a r e a p p r o x i m a t e l y 80%
a n d 2 0 % , r e s p e c t i v e l y ( F a i n a et al. 1989; N e u m a n n
1992).
W h i l s t s e v e r a l s t u d i e s h a v e r e p o r t e d d a t a o n t h e ene r g e t i c s ( B u r k e 1986; F a i n a et al. 1989; M a r i o n a n d
L e g e r 1988; N e u m a n n 1992), p h y s i c a l a n d a n t h r o p o m e t r i c c h a r a c t e r i s t i c s ( M c L e a n a n d P a r k e r 1989; N e u m a n n 1992; P y k e et al. 1988) a n d t e s t p r o t o c o l s ( C r a i g
et al. 1989) f o r t r a c k cyclists, n o a t t e m p t h a s b e e n
m a d e to i d e n t i f y t h e k e y p h y s i o l o g i c a l v a r i a b l e s assoc i a t e d w i t h high p e r f o r m a n c e t r a c k cycling. H e n c e , t h e
p u r p o s e s of this s t u d y w e r e to:
1. E x a m i n e t h e r e l a t i o n s h i p b e t w e e n s e l e c t e d p h y s i o l ogical and anthropometric variables, including oxygen
uptake (VO2) kinetics and maximal accumulated oxyg e n d e f i c i t ( M A O D ) , a n d cycling p e r f o r m a n c e in a
IP4o00,
2. P r o p o s e a p h y s i o l o g i c a l m o d e l f o r p r e d i c t i n g success in t h e IP40o0, a n d
3. P r o v i d e a t r a i n i n g s e a s o n specific p h y s i o l o g i c a l p r o file o n h i g h p e r f o r m a n c e m a l e t r a c k cyclists.
Methods
Subjects. A group of 18 male high performance track endurance
(n = 12) and sprint cyclists (n =6) participated in the study. All
cyclists were scholarship holders at either the Australian or South
Australian Institute of Sport. Ability levels ranged from State
level competitors to World Champions with 6 of the 18 representing Australia at the 1992 Olympic Games. The cyclists were
fully acquainted with the laboratory testing procedures with possible risks being fully explained to the subjects before they gave
their written consent.
The cyclists were requested to participate in only light-recovery (less than 50 km at less than 75% maximal heart rate) training 24 h before any test. Laboratory testing was performed over a
10-day period while all track testing was performed on 1 day.
Testing was conducted at the end of a 4-week transition (recovery) phase of their yearly training programme.
Anthropometry. Stretch standing height was measured to the
nearest 0.1 cm with a wall stadiometer. Body mass was measured
to the nearest 50 g with calibrated Mettler TE electronic platform
scales, with the cyclist wearing cycling knicks. Eight skinfold sites
were measured (Telford et al. 1988) and subsequently used to
estimate relative body fat according to the guidelines outlined by
Withers et al. (1987). The body surface area for each subject was
computed from the equation of Dubois and Dubois (1916).
Measurement of 1202. All 1102 measurements were determined
using a breath-by-breath system (Ametek OCM-2, Ametek Pittsburgh, Pa., USA). For further analysis, and to avoid some of the
inherent random variations, data were averaged and plotted for
10-s intervals. The metabolic assessment system incorporated a
low-resistance valve (Hans Rudolf 2700, Kansas City, Mo., USA)
attached to an inertially-compensated unidirectional turbine volume transducer (Ametek). Expired gas samples were continually
monitored for 02 and CO2 concentrations by a S3A/I 02 analyser
(Ametek) and a CD-3A CO2 analyser (Ametek), respectively. A
time delay factor was used to align inspiratory gas volumes with
expiratory gas analysis. The gas analysers were calibrated before
and after each test using reference gases prepared by measuring
the mass of each gas component, while the volume transducer
was checked using a 1-1 syringe.
Cycle ergometer. All physiological tests were conducted on a
sport-specific, geared air-breaked cycle ergometer using a 23
tooth front fly-wheel sprocket. Further details of the ergometer
have been published previously (Craig et al. 1989). The ergometer was calibrated dynamically throughout the physiological range
of measurement using a torque meter. During each test, the ergometer was linked to a computer which continuously measured
and stored total work done and other associated work indices using specifically designed software. These indices were automatically corrected for changes in atmospheric temperature and pressure.
Heart rate. A Polar Sport Tester PE-4000 (Polar Electro OY, Hakamaantie, Kempele, Finland) heart rate monitor was used to
monitor and store heart rate every 5 s during the laboratory and
field tests. At the end of each test, the stored heart rate information was transferred to a computer using the Polar heart rate
analysis software for graphing and data analysis. A heart rate calibrator was used to verify the accuracy of the Polar Sport Tester
at rates of 50, 100, 150, 200, and 210 beats.min-1.
Blood biochem&try. At the completion of the laboratory and
field tests, arterialised capillary blood was taken from a hyperaemic fingertip. Hyperaemia was induced by liberally smearing
the fingertip with a cutaneous vasodilator (Finalgon: Boehringer
Ingelheim) 10-15 min before the capillary sample was required.
Arterialised capillary blood has been shown to be an acceptable
alternative to arterial blood for measurement of acid-base status
(McEvoy and JoneS 1975). The blood sampling method was
standardised in the following manner:
1. Prior to puncturing with an autolet, the fingertip was cleaned
with an alcohol swab and wiped dry with a tissue;
2. The first drop of blood was discarded and then 25-125 ixl
whole blood was collected within 30 s in a heparinised glass capillary tube(s);
3. The blood specimen was immediately analysed for lactate concentration and acid-base status.
Blood lactate was analysed using the automatic 1500 Sport LLactate analyser (YSI Incorporated, Yellow Springs, USA) while
acid-base status was measured using an ABL30 Acid-Base analyser (Radiometer, Copenhagen, Denmark). Both analysers were
calibrated using precision standards, buffers and gases prior to
and during test sessions.
Maximal oxygen uptake. Maximal oxygen uptake (1202max) was
assessed using a continuous incremental cycling test lasting between 8 to 12 rain. Warm-up was not standardised, rather, subjects were instructed to prepare as they would in readiness for
competition. After completion of the warm-up, the cyclists were
required to pedal at a constant intensity for l-rain, commencing
at 200 W and increasing by 25 W each minute. The cyclists pedalled for complete minutes, even though during the last minute
they may have been unable to sustain the required intensity. The
fixed gear ratio employed enabled the cyclists to complete the
test in the cadence range of 120-130 rpm. Metabolic and heart
rate data were measured every 10 and 5 s respectively, while total
work done was recorded.every 60 s. The attainment of 1202~ax
was accepted when the VO2 for successive increments of 25 W
differed by less than 0.15 1.min-1 and was expressed in absolute
and relative terms according to the procedures of Nevill et al.
(1992).
152
Blood lactate transition thresholds. Determinations of lactate
threshold (Thl,-) and individual anaerobic threshold (Th,n,i)
were made during a 30-40 min continuous incremental cycling
test. After a warm-up, each cyclist was required to pedal at a constant power output for 5 min, beginning at 100 W and increasing
by 50 W. The test was complete when the cyclists could no longer
maintain the required power output. The 1202 was measured
during the last 2 min of each 5 min and these steady-state values
were used to determine for each subject, the linear relationship
between 1202 and power output, thus expressing the oxygen demand for all cycling intensities. Heart rate and work done were
measured and stored continuously. Arterialised blood samples
were taken at rest, during the last 30 s of each exercise period and
at 1, 2, 5, 7 and 10 rain post-test for the determination of lactate
concentration and acid-base status. The cyclists remained seated
during the post-test recovery period.
The Th~- was defined using the method proposed by Beaver
et al. (1985) while Th~,,i was determined using a modification of
the method described by Stegmann et al. (1981). Individual data
points for both the exercise and recovery blood lactate concentrations were plotted as a continuous function against time and
were fitted with a third order polynomial. Both blood lactate
transition thresholds were expressed in terms of corresponding
lactate concentration, power output, 1202, heart rate, and acidbase status.
Cycling economy. Two methods were used to quantify cycling
economy, both of which used the submaximal steady-state data
collected during the blood lactate transition threshold test. The
first approach required the calculation of the slope and intercept
for the regression line between 1202 (both absolute and relative)
and power output for each cyclist. The second method was that
proposed by Van Handel et al. (1988), which, after computing the
above regression line (relative VO2 vs power output), required
the calculation of an economy score for each cyclist.
1202 kinetics. To assess 1202 kinetics cyclists were required
1. To cycle to exhaustion at a constant intensity equivalent to
115% of their 1202max and
2. To cycle at a constant submaximal intensity of 250 W for
3 rain. The 1202 was measured for 2 min prior to exercise and
continuously throughout both tests.
To characterise the kinetic behaviour of 1202 during each
test, the data were fitted to the following equation using iterative
procedures of a nonlinear least squares regression:
1202 (t) = 1202int+ (VO2ss -- 1202int)"(1 - e -t"~
where 1202(0. is the 1202 at time (t) rain, 1202int is the initial or
pre-exercise VO2, 1202~s is the final (steady-state) 1202 and ris a
time constant (Linna.rsson 1974). The time constant describes the
rate of increase of VO> Half-times (s) to reach 1202ss were further computed by multiplying r by 41.58 (Hagberg et al. 1978).
using their road bikes and wearing racing attire. Environmental
conditions were relatively constant during the test period (temperature, 22.4-24.4 ° C; barometric pressure, 769.4-769.8 mmHg;
relative humidity, 36.0%-44.0%; wind speed, 0.4-2.43 m - s - l ) .
Heart rate was measured and stored continuously during the
IP4000 with blood sampling occurring at 1, 2, 5 and 7 rain post
IP40o0 for the determination of peak values of blood lactate and
acid-base status.
Statistical analys&. Descriptive statistics were performed on the
group data using standard statistical procedures. Least squares
linear regression analysis was used to calculate correlation coefficients among and between independent variables (laboratory
measures) and the dependent variable (IP4oo0 time). Stepwise
multiple regression analysis was performed to determine the best
possible combination of independent variables to predict the dependent variable. Paired Student's t-tests were used to determine
differences in anaerobic capacity when assessed with the 2- and
5-rain tests, between indices relating to the two blood lactate
transition thresholds and between post-test blood measures of
the IP4o0oand 5-rain test. A significance level of P<0.05 was used
in all analyses.
Results
The physical characteristics of the subjects used in this
study are presented in Table 1. None of these variables
was significantly correlated with IP4ooo.
Table 2 summarises the physiological measurements
of l?O2max, 1)'O2 kinetics during submaximal and supramaximal cycling, cycling economy and anaerobic
capacity determined by both a 2- and 5-min supramaximal protocol. All indices of l)Ozmax were significantly
correlated to IP4ooo, with correlation coefficients ranging r = - 0 . 6 1 for VO2m~x (1.rain-*) to r = - 0 . 7 9 for
relative l)O2m~x (ml.kg-2/3. min-1). In addition, maximal minute ventilation (body temperature and pressure, saturated with water vapour; l'min-*) and the
power output (W) at which VO2m~x was reached were
also significant predictors of IP4000 ( r = - 0 . 4 9 and
r = - 0 . 7 9 , respectively). Of particular interest to this
study was the confirmation of the significant positive
relationship between the rate of 1202 during the initial
stages of the supramaximal cycling test (as indicated by
Table 1. Anthropometric characteristics of high performance
track endurance and sprint cyclists (n = 18)
Anaerobic capacity. Anaerobic capacity was assessed as the
MAOD according to the procedures of Medbo et al. (1988) during 2 and 5 min of supramaximal cycling. Whilst a pacing strategy
was allowed, cyclists were instructed to complete as much work
as possible during both tests. During each test the work performed (kJ) was recorded every 10 s. The 02 requirement for the
mean power output of each 10-s period was then predicted by
linear extrapolation of the individual relationships between VO2
and submaximal power output described above. Subtraction of
the 1202 during each test for these 02 requirements yielded the
02 deficit. Heart rate was measured and stored continuously
throughout the tests with blood sampling occurring at 1, 2, 5 and
7 min post-test for the determination of peak values of blood lactate concentration and acid-base status.
Track testing. A timed, standing start IP4o0o was completed by
each cyclist 2 days before the laboratory testing. The IP40o0 was
performed on an outdoor 400-m concrete velodrome with cyclists
Parameter
Mean
SD
Range
Age (year)
Height (cm)
Mass (kg)
Body surface area (m 2)
Relative body fat (%)
Fat mass (kg)
Fat-free mass (kg)
Sum of 6 skinfolds (mm) a
Sum of 8 skinfolds (ram) b
20.1
179.3
75.30
1.94
9.6
7.30
68.00
54.9
68.1
1.7
3.5
6.00
0.07
1.6
1.70
4.60
10.8
14.8
17.1 - 23.7
173.0 -184.6
65.25- 85.35
1.80- 2.08
6.8 - 12.6
4.55- 10.40
59.90- 77.20
35.4 - 76:4
44.0 - 94.5
* Significant correlation with 4000-m individual pursuit
(P< 0.05); a skinfold sites, triceps, biceps, subscapular, suprailiac,
front thigh and medial calf; b skinfold sites, triceps, biceps, subscapular, supraspinale, abdominal, mid-axillary, front thigh and
medial calf
153
Table 2. Descriptive statistics for maximal oxygen uptake
(gOzmax), oxygen response kinetics, cycling economy and anaerobic capacity (n = 18)
Table 3. Lactate concentration ([la-]b), power output (W), oxy-
Parameter
(n=18)
Mean
SD
Range
Parameter
Maximal oxygen uptake
VO2max (1-min-1)
902max ( m l ' k g - i ' m i n -1)
V02max (ml.kg-2/3.min -1)
902max ( l ' m i n - l " m -a)
9E . . . . B~s (l'min -1)
f~. . . . (beats.min_l)
Power output at V02ma~
(W)
5.13"
68.5*
288,2*
2.65*
165.8"
197
0.36
6.4
22.7
0.20
24.1
9
4.58- 5.91
54.4 - 78.5
238.4 -324.2
2.31- 3.00
116.5 -207.6
179 -216
373*
31
314
-447
Oxygen uptake responses kinetics
Oxygen kinetics
(~-115% 902m~)
Oxygen kinetics half time
(VO2 tl/2 115% 90amax)
Oxygen kinetics (z250 W)
Oxygen kinetics half time
(V02 tm 250 W)
0.67*
0.12
0.48-- 0.87
27.8*
0.65
4.8
0.08
20.0 - 36.2
0.51- 0.82
26.8
3.3
21.1 - 34.2
0.0
11.6
-18.4 - 23.1
12.9/
317.8
0.6/
136.0
12.1 - 14.2/
124.5 -742.4
0.17/
4.2
0.01/
1.7
0.15- 0.19/
1.51- 8.85
4.65
61.4
4.44
60.2*
0.71
7.3
0.92
12.5
3.00- 5.52
44.3 - 69.4
2.87- 6.51
40.8 - 88.2
Anaerobic capacity
MAOD
MAOD
MAOD
MAOD
(1)b
(ml'kg-1) b
(i) °
(ml'kg-1) c
902 .... Maximal oxygen uptake; I?E.... maximal minute ventilation; BTPS, body temperature and pressure, saturated with water vapour; fc..... maximal heart rate; r 115% 1202 . . . . time constant (min) for oxygen response kinetics at 115% 902.m~,;
902 fin 115% VO2 . . . . time (s) to reach one-half of peak VO2
whilst cycling at 115% l~02max; r250 W, time constant (rain) for
oxygen response kinetics at 250 W; 9 0 2 hi2 250 W, time (s) to
reach one-half of steady-state 9 0 2 whilst cycling at 250 W;
MAOD, maximal accumulated oxygen deficit; a cycling economy
as determined by method of Van Handel et al. (1988); b determined by supramaximal 2-min protocol; c determined by supramaximal 5-min protocol (n =16); * significant correlation with
4000-m individual pursuit ( P < 0.05)
Mean
SD
Range
0.57
203*
2.70*
105.2"
2.92*
38.8*
1.51"
56.3*
144
72.8
7.414
25.5
0.21
28
0.49
16.2
0.29
5.2
0.16
4.3
11
5.4
0.020
0.9
0.27 - 1.20
150
-260
1.79 - 3.70
75.6 -139.4
2.41 - 3.50
30.6 - 48.7
1.20 - 1.84
48.0 - 64.8
118
-157
61.0 - 80.7
7.369- 7.443
23.6 - 27.4
0.96
30
0.61
18.5
0.34
6.1
0,19
5.4
9
2.5
0.032
2.0
1.35 - 5.03
242
-365
2.71 - 4.91
117.2 -185.4
3.54 - 4.85
43.0 - 64.2
1.77 - 2.46
67.1 - 86.2
154
-187
81.8 - 91.5
7.322- 7.422
18.3 - 26.7
Lactate threshold
[la-]b (mmol'1-1)
W (W)
W. (W-kg -1)
.W (W.m -2)
VOa (l'min -1)
9 0 2 ( m l ' k g - l - m i n -1)
9 0 2 (l-re. -2)
902 (% V02max)
Cycling economy
Cycling economy
(_+ml.kg-l.min-1) ~
Cycling economy
(ml.min-l.W-1;
slope/intercept)
Cycling economy
(ml.kg-l-min-l.W-a;
slope/intercept)
gen consumption (VO2), heart rate (fo), pH and bicarbonate concentration ([HC03]b) values at the lactate threshold and individual anaerobic threshold blood lactate transition thresholds
fc (beats'min -~)
fo (%fc .... )
pH
[HC03]b (mmol'1-1)
Individual anaerobic threshold
[la-]b (mmol.1-1)
W (W)
W (W.kg -1)
W (W.m -2)
VOz (l'min -1)
9 0 2 (ml'kg-~-min -1)
9Oz (l'm. -2)
9 0 2 (% V02max)
fc (beats'rain -1)
f¢ (%f~ . . . . )
pH
[HCO~-]b (mmol-1-1)
* Significant
(P<O.05)
2.78
293*
3.88*
151.5"
4.08*
54.1"
2.11"
78.6*
172
87.0
7.367
21.9
correlation
with
4000-m
individual
pursuit
Table 4. Descriptive statistics for variables related to the 4000-m
individual pursuit. Blood biochemistry was obtained post-test
and refers to peak values measured (n = 18)
Parameter
Mean
SD
Range
Time (s)
Speed (km-h -1)
Speed (m.s -1)
Peak fc (beats. min - 1)
Peak fc (%fc . . . . )
[la-]b (mmol'1-1)
pH
[HC03-]b (mmol'l -~)
339.7
42.5
11.8
194
97.8
10.04
7.141
10.6
14.1
1.8
0.5
9
1.9
1.81
0.072
2.3
314.4 -362.0
39.8 - 45.8
11.0 - 12.7
182
-212
94.3 -100.0
6.20 - 13.33
6.999- 7.286
6.4 - 15.8
For definitions see Table 3
b o t h ~" a n d h a l f t i m e v a l u e s ) a n d IP4ooo p e r f o r m a n c e
(r = 0.48). T h e o n l y o t h e r v a r i a b l e r e p o r t e d in T a b l e 2
to c o r r e l a t e s i g n i f i c a n t l y w i t h IP4ooo was t h e r e l a t i v e
MAOD
determined
during the 5-min protocol
(r=-0.50).
T h e r e was n o s i g n i f i c a n t d i f f e r e n c e in
M A O D w h e n a s s e s s e d b y e i t h e r a 2- o r 5 - m i n s u p r a maximal protocol.
T a b l e 3 lists t h e m e a s u r e d a n d d e r i v e d v a r i a b l e s rel a t i n g to b o t h t h e Thla- a n d Than,i b l o o d l a c t a t e t r a n s i tion thresholds. All corresponding indices of the two
thresholds were significantly different confirming that
t w o d i f f e r e n t t h r e s h o l d s w e r e assessed. V a r i a b l e s
w h i c h w e r e s i g n i f i c a n t l y r e l a t e d to IP400o w e r e t h e p o w -
e r o u t p u t a n d 1202 e x p r e s s e d in all f o r m s , for b o t h
thresholds (range r=-0.66
to r = - 0 . 8 6 ) ,
with the
h i g h e s t c o r r e l a t i o n b e l o n g i n g to t h e p o w e r o u t p u t ( W )
at Thla-.
M e a s u r e m e n t s r e l a t e d to IP4ooo a r e p r e s e n t e d in T a b l e 4. T r a c k p e r f o r m a n c e s in t h e 4 0 0 0 - m e v e n t v a r i e d
b y 47.6 s w i t h o v e r a l l t i m e s b e i n g i n d i c a t i v e o f high
p e r f o r m a n c e t r a c k cyclists using r o a d bicycles. A v e r a g e
t i m e s w e r e a p p r o x i m a t e l y 60 s o u t s i d e t h e w o r l d record pace.
The stepwise multiple regression equations for pred i c t i n g t h e c r i t e r i o n IP4ooo f r o m t h e b e s t w e i g h t e d s u m
154
Table 5. Multiple regression equations for predicting the 4000-m
individual pursuit (IP4ooo) from invasive and non-invasive variables
Variables
Non-invasive
me, ~t/02maxa
Invasive
W~,a, MAOD,
Multipleregression equation
r
SEE (s)
IP4ooo= 441..2 + 4.316 x (mr) 25.940 x (VO2max)
0.63 9.1
462.9- 0.366 X (LTPO)
-.0.306 x (MAOD) -0.438 x
IP40o0 =
VO2max b
(VQ~ax)
complete the IP4ooo by approximately 0.6 s due to added rolling resistance. However, the model of Olds et
al. (in press) also predicts that an increase of 2.7 kg in
body mass will increase projected IP4000 time by 2.1 s,
due to this added mass affecting frontal surface area.
In support of the above, it is interesting to note that fat
mass (kg) was one of the independent variables selected in the prediction of IP4ooo (Table 5).
Maximal oxygen uptake
0.86 5.7
mr, fat mass (kg); VO2maxa, maximal oxygen uptake (1.min-~);
VOzmaxb, maximal oxygen uptake (ml-kg-l.min-~); MAOD,
maximal accumulated oxygen deficit determined by supramaximal 5-rain protocol (ml.kg-1); V(Th,~,power output at lactate
threshold (W); SEE, standard error of the estimate
of physiological variables for the cyclists are presented
in Table 5. The best overall equation, which included
invasive and non-invasive measurements, yielded an r
of 0.86 which would indicate that of the total IP4ooovariance of the 18 subjects, 74.0% was accounted for by a
linear combination of predictors. The corresponding
standard error of estimate was 5.7 s.
Discussion
Anthropometry
The anthropometric profile of this group of high performance track cyclists showed many similarities to results by others using comparable athletes (Craig et al.
1989; McLean and Parker 1989; Neumann 1992; Pyke
et al. 1988; Telford et al. 1988, 1990). As is common
with almost all high performance athletes, these cyclists have low body fat contents. However, when assembling a profile, it is important to remember that
many physiological and anthropometric indices can
vary depending upon the particular training phase of
the yearly programme. White et al. (1982), in monitoring seasonal changes in body fat of Olympic track cyclists, have reported that one track cyclist decreased his
body fat index (sum of four skinfolds) from 28.8 to
22.0 mm even though his body mass increased from
68.9 to 70.2 kg. Longitudinal data collected in our laboratory would indicate that high performance track endurance cyclists consistently achieve body fat contents
(sum of six skinfolds, Table 1) below 40 mm the week
prior to a World championship or Olympic competition.
With respect to track cycling, increased nonfunctional mass has a triple effect in decreasing performance; it increases the energy cost of acceleration, rolling resistance and the projected frontal area of the cyclist (and hence air resistance). In estimating the effect
of added mass on an IP4ooo, both Kyle (1991) and Olds
et al. (in press) have predicted that a 2.7-kg increment
in bicycle and/or cyclist mass will increase the time to
With the use of aerodynamic equipment and specialised training techniques, average speeds of 50 k m - h - 1
or greater are now being achieved in the IP4000. According to the results of Di Prampero et al. (1979) and
Whitt and Wilson (1983)., cycling at such speeds would
require a steady-state VOa ranging from 90 to 100
m.l'kg-l'min -1. Assuming the track cyclist has a
VO2max of 76 m l . k g - l - m i n -1, this would mean that
he would be operating at approximately 120%-130%
1202max with a concomitantly large contribution from
the anaerobic metabolic ' pathways. Thus, a high
1202m~x, together with the ability to achieve it quickly
and maintain it, would enable a large, rapid and sustained aerobic energy release and reduce premature
reliance upon a large proportion of the finite 02 deficit. Considering the above, and the fact that in the simulated pursuit part of this study the calculated relative contributions of the aerobic and anaerobic metabolic pathways were 84% and 16% respectively, it is
not surprising that the indices of ~¢~O2rnax have a high
value and are significantly correlated with IP400o
( - 0 . 6 1 - - 0 . 7 9 ) . Moreover, the relationship between
power output at 1202max and IP400o would suggest that
the maximal exercise intensity that the athlete can
achieve during a 1202max test is an important variable
predicting athletic potential in this event (Tables 2,
5).
The 1202 .... values reported in Table 2 are very
similar to previous reports on track cyclists (Burke et
al. 1977; Sjogaard et al. 1985; Telford et al. 1990) although they are approximately 10% lower than those
reported by Neumann (1992) and Pyke et al. (1988).
However, it should be emphasised that our subjects
comprised both sprint and track endurance cyclists,
with m e a n 1202max values equal to 62.4 and 71.5
m l . k g - l . m i n -1, respectively. Added to this, our subjects were assessed at the end of the transition phase of
the yearly training programme, a phase in which the
cyclists would be expected to exhibit their lowest
VO2max score. Like body fat, 1202max can show significant variation throughout the training year as a result
of alterations in the amount of training and its intensity. For example, Sjogaard et al. (1985) have reported
changes of up to 22% in the relative VO2m~× of a Danish track cyclist over a 12-month period. Olds et al. (in
press) have predicted that a 15% improvement in
VO2m~x (5.14--5.91 l'min -1) would enable the track
cyclist to complete an IP4ooo approximately 15.5 s faster.
155
Blood lactate transition thresholds
There is a scarcity of information in the literature on
blood lactate transition thresholds in high performance
track cyclists. Telford et al. (1990) have reported the
Than,i (power output corresponding to the break point
in the blood lactate curve) in Australian male track cyclists to have occurred at 325 W which was slightly
higher than the 293 W reported in this study. However,
when the track endurance cyclists were considered
alone, this value was 303 (SD 30) W and would probably be higher during the specific preparation and competition phase of the training programme.
The present study would indicate that whilst the absolute and relative VO2 at Thla- and Than, i were significantly different, they were also strongly related, with
the correlation coefficients among the descriptors being at least r=0.51. Added to this were the significant
correlations of absolute and relative 1702 at Thla- and
Th~n,i with relative 1702m~× (r=0.46 to 0.82). These results are consistent with those reported by Yoshida et
al. (1987).
Previous studies have reported high correlations between various blood lactate transition thresholds and
endurance performance (Jacobs 1986). However, to
our knowledge, the significant correlations of Thl,and Than,i indices (Table 3) with IP4ooo are the first to
be reported for a short-term, supramaximal aerobic
event. Furthermore, the correlation between relative
1702 at Th~- and IP4ooo ( r = - 0 . 8 3 ) was higher than
the correlation between relative VOzm,x and IP4ooo
( r = -0.76). This is a similar trend to that reported by
Yoshida et al. (1987) and Tanaka and Matsura (1984).
Studies by Ivy et al. (1980) and Aunola et al. (1988)
have demonstrated that blood lactate transition threshold indices would seem to reflect the muscle metabolic
status or peripheral component of the oxygen transport system, VO2max however being closely related to
and limited by central mechanisms (Saltin 1985). Thus,
considering that 80%-85% of the required energy for a
IP4000 was supplied by aerobic metabolism it would
seem appropriate that both the central and peripheral
components of the aerobic energy system be related to
IP4ooo. Finally, the fact that both the central and peripheral components were correlated to IP4ooo has implications with regard to coaching in that the optimal
way to train these two components may not necessarily
be the same (Saltin 1985).
Cycling economy
The rationale for assessing cycling economy in these
cyclists was that if we assume that the race pace of the
cyclists is one that maximally utilises physiological capacities without inducing premature fatigue, then
changes (e.g. equipment, training adaptations) that allow the cyclist to use less energy at a given cycling
speed should prove advantageous. This would allow a
faster cycling speed with the same relative effect on
physiological capacities. This rationale is supported by
Olds et al. (in press) who have predicted that a 10%
improvement in cycling economy would decrease
IP4ooo time by approximately 7 s. However, the nonsignificant correlations between cycling economy and
performance ( r = - 0 . 1 5 - - 0 . 3 9 ) is in agreement with
other studies (Bulbulian et al. 1986; Deason et al.
1991). Despite this, these findings should not be interpreted as meaning than an economical style of cycling
is of little importance to IP4o00 performance. The lack
of significant correlation was probably due, in part, to
the homogeneity of the cycling economy among the cyclists studied (Table 2). This is not surprising since an
amount of training in excess of 35000 k m . y e a r - 1 is
common for these cyclists.
1702 kinetics
At the onset of exercise there is a latency in the attainment of a steady-state 1)'O2 with the time course reported to be influenced by exercise intensity (Hagberg
et al. 1978; Whipp and Wasserman 1972), previous
priming exercise (Di Prampero et al. 1989), state of
training (Hickson et al. 1978; Powers et al. 1985; Zhang
et al. 1991), mode of exercise (Cerretelli et al. 1979)
and substrate availability (Maassen et al. 1988). Whilst
1?O2 kinetics during sub- and supramaximal exercise
have been studied extensively (Hagberg et al. 1978;
Hickson et al. 1978; Powers et al. 1985; Whipp and
Wasserman 1972), this study was the first to examine
the relationship between VO2 kinetics and performance. The half times of 26.8 and 27.8 s for VO2tl/2
250 W and 1?O2~1~2 115% 1?O2. . . .
respectively, are
consistent with the results of others when performing
similar exercise intensities (Hickson et al. 1978; Powers
et al. 1985; Yoshida et al. 1992). The nonsignificant difference between the two half times is somewhat surprising as a number of studies have demonstrated that
the time required to attain a steady-state 1?O2 is longer
at higher exercise intensities load (Hagberg et al. 1978;
Hickson et al. 1978; Whipp and Wasserman 1972).
However, our results are consistent with Zhang et al.
(1991), who have reported no difference in the time to
reach 75% of the VO2 response when increasing exercise capacity from 75% to 100%. Furthermore, at least
one study has found 1?O2 kinetics were faster as exercise increased above 1?O2m,x (Camus et al. 1985). Other findings of this study were the significant correlations of relative .902 . . . . 1702 at Thla- and Than,i with
1702 tl/a 115% VO2 . . . . These results are consistent
with those reported by others (Hagberg et al. 1978;
Hickson et al. 1978; Powers et al. 1985; Yoshida et al.
1992; Zhang. et al. 1991), and would support the hypothesis that VO2 kinetics and its improvement may be
influenced by both central and peripheral adaptations
(Berry and Moritani 1985; Yoshida et al. 1992).
The significant correlations between ~- 115%
V.'O2max and IP4000 (r=0.48) and 1702h/2 115%
VO2max and IP400o (r = 0.48) would support the suggestion that the time to achieve a peak VO2 is important
in sporting events requiring the highest rate of energy
156
release over a period of 4-5 min (Thoden 1991). Rapid
adjustment of VO2 kinetics during the initial stages of
an IP4ooo should be advantageous to performance in
two ways;
1. By reducing the reliance upon a proportion of the
02 deficit which could then be spread over a greater
time such as that required in a IP4ooo and
2. By reducing early production of lactic acid in the
muscles which would otherwise affect the rate of crossbridge cycling and other cellular mechanisms (Hultman et al. 1990).
The significant correlations of the supramaximal
1202 kinetic indices with IP4ooo and the proposed associated benefits of having "fast" $702 kinetics, both suggest that IP4oooperformance could be enhanced if VO2
kinetics were trainable. Olds et al. (in press) have
mathematically modelled the effect of training or detraining VO2 kinetics on the performance of a IP4ooo. It
would.appear that a nominal change of say 7 s for halftime VO2, while all other variables are kept constant,
could theoretically affect IP4ooo by about 0.7%. Whilst
studies by several groups (Berry and Moritani 1985;
Cerretelli et al. 1979; Hickson et al. 1978; Yoshida et al.
1992) have demonstrated the trainability of 1202 kinetics, optimal training regimes and their associated effect on performance have yet to be empirically determined.
Anaerobic capacity
The existence of a significant anaerobic energy contribution during a sporting event is often indicated by a
relatively high postcompetition blood lactate concentration (Saltin 1990). In the IP4o0o under competition
conditions, blood lactate concentrations of 11.6 to
22.0 retool-l-1 have been reported (Burke et al. 1981;
Neumann 1992; Pyke et al. 1988) suggesting that anaerobic metabolism plays an important role in the required energy production. In support of this, the large
M A O D and its significant correlation with IP4ooo
( r = -0.50), when assessed during the 5-min protocol,
would seem to imply that anaerobic capacity is an important physiological component for the IP4ooo cyclist.
The nonsignificant difference in M A O D when assessed by a 2- and 5-rain supramaximal protocol is consistent with other results (Medbo et al. 1988). However, when expressed relative to body mass, only the 5min M A O D significantly correlated with IP4ooo- Furthermore, when the M A O D results were analysed for
the separate groups (sprint and track endurance) the
sprint cyclists achieved a significantly higher M A O D in
the 2-rain protocol, whilst there was no significant difference in the 2- and 5-min protocol values for the
track endurance cyclists. The question of test duration
specificity when assessing the M A O D of cyclists specialising in different track events is an important one
requiring additional research.
The M A O D indices reported in Table 2 are slightly
lower than those reported on runners (Medbo and
Burgess 1990; Scott et al. 1991), but greater than those
reported on elite kayakers (Terrados et al. 1991) and
club level road cyclists (Withers et al. 1991). These differences may be in accordance with varying amounts of
exercising muscle mass and the stage of the training
season. As previously stated, the cyclists in this study
were not in peak physical condition and higher values
could be expected with specific training (Medbo and
Burgess 1990). This lack of peak physical condition,
along with equipment and track conditions, would also
explain the relatively slow IP4ooo times and low postcompetition blood lactate concentration reported in
Table 4. Whilst our highest M A O D value was 88.2
ml.kg-1, Saltin (1990) has suggested that a value of at
least 100 ml.kg -1 is a likely estimate for a highly
trained IP4o00 cyclist. Olds et al. (in press) have hypothesised the practical benefits of specific anaerobic capacity training by suggesting a 10% increase in M A O D
would decrease IP4ooo time by approximately 1 s
(12 m).
Multiple regression equations
The multiple regression equations reported in Table 5
have standard error of estimates of 5.7 to 9.1 s, indicating they may be adequate for the physiological and
performance monitoring of an IP4ooo cyclist. Equation
2 in particular, whilst involving an invasive measurement (power output at lactate threshold), would be
valuable to the coach and sports scientist as it includes
both aerobic and anaerobic metabolic components.
4000-m Individual pursuit
The times and speeds presented in Table 4, whilst not
world or even national class, are indicative of those by
high performance track cyclists when one considers the
range of subjects (track endurance and sprint cyclists state to international level), phase of training (end of
transition) and conditions (road bikes; 400-m concrete
outdoor velodrome). Any question of a lack of subject
motivation is eliminated by the fact that there were no
significant differences between the peak heart rate
(194 vs 193 beats" min-1), blood lactate concentration
(10.04 vs 11.21 mmol-l-~), blood pH (7.141 vs 7.121)
and blood bicarbonate concentration (10.6 vs 10.1
retool-l-I) of the IP4ooo and laboratory 5-rain simulated individual pursuit, respectively. The lactate concentration and acid-base results reported in Table 4
would also support the contention that a heavy reliance was placed upon anaerobic energy sources during both tests.
Finally, the lactate concentrations reported in Table
4 are lower than those reported by Burke et al. (1981)
and Pyke et al. (1988). However, this is not surprising
as the data of those researchers was collected at a national championship competition.
In conclusion, the results of this study would indicate that highly developed aerobic and anaerobic energy systems are necessary to compete successfully in
157
track e n d u r a n c e cycling. I n particular, IP4000 was closely related to VO2max, Thla- and M A O D . This o f
course, has c o a c h i n g implications with respect to designing and i m p l e m e n t i n g specialised and o p t i m a l
training p r o g r a m m e s .
Acknowledgements. This study was supported by a grant from the
Australian Sports Commission.
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