There are ways to “predict performance” and evolution of our training
regimen, but we are afraid of looking at them.
The Germans mentioned that performance in triathlon is correlated with
the ability to run and not so much with the ability to swim. I took it from the presentation by the
Germans via twitter. They considered the
success of elite competitors.
18 févr. 2013
When I first heard this expression and thought about running and what
makes people run after finishing the bike and swim in triathlon. The wet
run is real; it is just for intelligent triathletes. As we mentioned in this
blog, there are benchmarks that are needed in order to perform in
triathlon. Swimming in 16:30 and biking a 5k on flat surface in 6:45 is
necessary to speak of “wet run.” This post comes after looking at the
numbers from Alistair Brownlee´s coach posted by Alfonso Andreu.
The Swiss use the ability to run after the bike to determine improvement
or how fast they can run the last 10k after the swim and bike. Sometimes it is difficult because the bike
has improved so much that the running prediction does not stand at the bike´s wattage
used in the protocol.
We know that triathlon is won by the best runner after the bike. Please see “wet run” in this blog.
But let´s look at a test and a protocol used to “predict performance.” This protocol has become standard now a day
but very few know when this started.
A few things we can say regarding this
testing:
1) Individual anaerobic threshold (IAT) was the best
predictor for performance. The term has
multiple edges or sides.
a)
As authors
mentioned, the most dedicated athletes were able to increase the anaerobic
threshold: “A more stable competition performance and thus lower deviation from
performance-diagnostic predictions can be assumed for ambitious competitors
than among the recreational runners.”
b)
These ambitious
runners can do reps on the track that hurt and they follow in a better way
their training regimen.
c)
To increase
anaerobic threshold we need to train anaerobically at race pace or above
it. The best way to do improve anaerobic
threshold is doing reps that last less than five minutes with the proper
recovery.
d)
I will leave
you with a good article to reflect on:
Predicting competition performance in long-distance
running by means of a treadmill test
[Special
Communications: Methods]
ROECKER, KAI;
SCHOTTE, OLIVER; NIESS, ANDREAS MICHAEL; HORSTMANN, THOMAS; DICKHUTH,
HANS-HERMANN
Universität
Tübingen, Medizinische Klinik und Poliklinik, Abteilung Sportmedizin, GERMANY
Submitted for
publication July 1997.
Accepted for
publication March 1998.
Address for
correspondence: Dr. Kai Roecker, Universität Tübingen, Abteilung Sportmedizin,
Hölderlinstr. 11, D-72074 Tübingen, Germany. E-mail:
kai.roecker@uni-tuebingen.de.
ABSTRACT
Predicting
competition performance in long-distance running by means of a treadmill test. Med. Sci. Sports Exerc., Vol. 30, No.
10, pp. 1552-1557, 1998.
Purpose: The
purpose of this study was to examine the power of 16 parameters beside the
individual anaerobic threshold (IAT) in predicting performance in various
competition distances.
Methods: This study examined 427 competitive runners to test
the prediction probability of the IAT and other parameters for various running
distances. All runners (339 men, 88 women; ages, 32.5 ± 10.14 yr; training, 7.1
± 5.53 yr; training distance, 77.9 ± 35.63 km·wk-1) performed an
increment test on the treadmill (starting speed, 6 or 8 km·h-1;
increments, 2 km·h-1; increment duration, 3 min to exhaustion). The
heart rate (HR) and the lactate concentrations in hemolyzed whole blood were
measured at rest and at the end of each exercise level. The IAT was defined as
the running speed at a net increase in lactate concentration 1.5 mmol·L-1
above the lactate concentration at LT.
Results: Significant correlations (r = 0.88-0.93) with the
mean competition speed were found for the competition distances and could be
increased using stepwise multiple regression (r = 0.953-0.968) with a set of
additional parameters from the training history, anthropometric data, or the
performance diagnostics.
Conclusions:
The running speed at a defined net lactate increase thus produces an increasing
prediction accuracy with increasing distance. A parallel curve of the identity
straight lines with the straight lines of regression indicates the independence
of at least a second independent performance determining factor.
The term
"anaerobic threshold" was introduced by Wasserman et al. (33) for the measurement of aerobic capacity in patients.
Above this "anaerobic threshold" according to Wasserman's definition,
performance is characterized by "a sustained metabolic acidemia."
Determination of the anaerobic threshold offered the great advantage that, unlike
for the measurement of the maximum parameter V˙O2max, the patient
did not have to exercise to total physical exhaustion to determine his aerobic
capacity.
Although there
is no direct agreement between the parameters of gas metabolism and the blood
lactate concentration from a physiological point of view, during the 1970s the
Wasserman threshold concept was adapted to the lactate increase (2,6,15,17). Measurement of the lactate concentration is
preferred over ergospirometric diagnostics in the care of athletes because of
the simpler method (8,17,25,32). The application of lactate diagnostics has also
achieved widespread acceptance in recreational sports and even for patients.
The lactate
concentration in blood can only be determined discontinuously. Therefore, the
use of graphic interpolating procedures is essential in evaluating the course
kinetics of the lactate concentration in exercise. Many of these usually
polynomial interpolation procedures (2,16,22) are, however, beset with qualitative weaknesses.
Since, however, the physiological definition of the basic model of lactate
formation under exercise is uncertain (6,16), a wide variety of procedures have arisen for
indirect determination of an anaerobic "lactate threshold." The
common concepts in use have only become established by practical experience in
their applications (11,15,28). There is, for example, still uncertainty concerning
the optimal step duration in the multistage testing protocols to predict the
performance capacity in long-term endurance.
Even in the
early 1980s studies were published on the predictive value of treadmill speed
at 4 mmol·L-1 for the mean marathon running speed (18,26). The number of subjects in these studies was,
however, too small in each case, and most of the studies had no means for
comparing different competition distances. Also, no test was made showing
whether inclusion of the lactate measurement really made better prediction of
the running performance possible compared with recording of only, for example,
the maximum treadmill speed achieved.
As a supplement
to earlier studies, this study is intended to examine the power of 16
parameters in predicting performance in various competition distances. The
basis of the test is data recorded in a simple routine treadmill test.
METHODS
Subjects. In this study 427 subjects (88 women, 339 men) were questioned about
corresponding competition performance in connection with exercise testing at a
sports medical outpatient clinic. All participants gave consent to participate
in the study. The individuals were metabolically healthy and participated
regularly in running competitions. A maximum time of 2 months elapsed between
the ergometer test and the competition date. Total body fat was determined by
the skinfold caliper technique of Brozek et al. (7) at three measuring sites. The anthropometric data are
shown in Table 1.
 |
TABLE 1.
Anthropometric data.
|
Exercise test. The subjects exercised in a multilevel increment test on a treadmill
(Fa. HP Cosmos, Traunstein, Germany) to subjective exhaustion. The initial
running speed depended on the known performance capacity of the athlete and was
6 or 8 km·h-1, with increments of 2 km·h-1 every 3 min.
The treadmill slope was 2%. This value provides the highest agreement between
treadmill speed and running speed on the running track or the street for the
type of ergometer used. The temperature in the exercise room was held constant
by an air conditioner at 20°C with a relative humidity of 50%.
The lactate
concentration in hemolyzed whole blood was determined by a semiautomatic
enzyme-chemical method (Eppendorff ESAT,D) at rest, after the end of each
exercise level, and 1, 3, and 5 min after the end of exercise. The HR was
evaluated at rest and at the end of each exercise level using a surface EKG.
The maximum
running speed (velmax), maximum lactate concentration (Lamax),
and maximum HR (HFmax) were used as parameters for subsequent
assessment.
Individual anaerobic "threshold". The IAT was determined by the method described by
Dickhuth et al. (11). Our own PC-routine (Borland C+ +), which connects
the curve segments between the individual lactate measured values by equalizing
SPLINE procedure (27), was used for investigator-independent calculation of
the IAT. The lactate threshold (LT) determined from this interpolated curve
over the minimum of the quotient lactate/performance was taken as the start of
increase in lactate concentration (Fig. 1). The IAT was defined as the running speed at a
lactate concentration of 1.5 mmol·L-1 over the lactate concentration
at LT.
 |
Figure
1-Determination of IAT: Performance at a lactate concentration of lactate at
LT + 1.5 mmol·L-1. The data ± SD of the group of subjects examined
are presented.
|
The running
speed at 4 mmol·L-1 blood lactate (v(4 mmol·L-1)) and the
blood lactate concentration at LT (La(LT)) were determined from the curve
smoothed as described. The HR curve smoothed in the same procedure was used to
determine the HR at IAT (HF(IAT)).
Statistics. Data recording and selection were made using a relational database
system on a PC. Statistical calculations were made using JMP (SAS Institute,
Cary, NC) and KaleidaGraph (Synergy Software, Reading, PA) on a personal
computer (Apple Macintosh, Cupertino, CA). All values are given as mean ± SD.
The procedure of linear regressions was applied to present the simple
predictive value. Percentile plots were created to present frequency
distributions. The influence of additional independent variables on competition
performance was tested using a forward multiple stepwise regression (1). A value of P
= 0.250 was selected as the probability to enter for the stepwise regression.
All parameters were tested for normal distribution by the Shapiro-Wilks test
for normality before further analysis.
The resting
lactate concentration in the total collective was 1.34 ± 0.48, which is not
significantly different from the lactate concentration at LT (1.27 ± 0.61
mmol·L-1). However, the occurrence of individual values of up to 3.5
mmol·L-1 lactate at rest as well as at LT is noteworthy. With a
maximum lactate concentration of 8.3 ± 2.9 mmol·L-1 and maximum HR
of 187.1 ± 15.2 beats·min-1, the subjects exercised in all
probability to exhaustion. At a maximum running speed of 18.01 ± 2.31 km·h-1,
the IAT in the total collective at performance of 14.77 ± 1.95 km·h-1
corresponded to 82.0 ± 19.8% of the maximum running speed attained.
The linear
regression between IAT, but also to velmax, and the competition
performance in each case showed statistically significant correlation in all
cases (P < 0.0001, Fig. 2, Table 2). It is noteworthy that the straight lines of
regression for all competition distances run parallel to the identity straight
lines in each case. There is a tendency for shorter distances to show poorer
correlation to the IAT and better correlation to velmax. The
correlation between LT and competition performance was not significant in any
case (Table 3).
 |
Figure 2-Left panels: Linear regression
between the results of the multiple regression (Table 4) and the average competition running time. The solid
lines are the straight lines of regression, the broken lines are the identity
lines □ = male, • = female. Right
panels: The relative deviations of the competion performance attained
vs the values predicted from the regression model in Table 4.
|
|
 |
TABLE 2. Mean
weekly running kilometers (km·wk-1), competition results (s), and
the regression equations between the competition results and the individual
anaerobic threshold (IAT) measured by the treadmill test.
|
|
 |
TABLE 3.
Independent correlations between mean competition velocity and parameters of
performance diagnostics.
|
 |
Table 4 shows the results of the forward stepwise regression
between the parameters measured and the various competition distances. These
calculations revealed that the IAT had the highest predictive value for race
distances of 10,000 m and longer. The longer the competition distance, the
fewer parameters had to be added to the IAT to predict the running performance.
DISCUSSION
The essential
result of this study is the conclusion that the IAT is the strongest predictor
of specific performance capacity in long-distance running compared with the
other parameters tested under practice- relevant conditions. As a supplement to
earlier studies by others (18,24,26,29,30), this study found that the IAT has the highest
predictive value both for a broad range of various running distances and when
taking various independent secondary conditions into account.
The essential
importance in determining IAT undoubtedly lies in the control and classification
of training in the desired metabolic range (8,21). However, performance prognosis is made in practice
based on IAT (22,31). Orientation to the reliability of performance
prediction and recommended competition speeds in treadmill tests is especially
important in marathon races. A "test race"-the most specific
performance control-cannot be practiced in marathons since even elite racers
can only participate in two or three marathon competitions per year.
Whether optimal
performance can be attained depends on many exogenic factors in competition
athletes. To determine the predictive value of the test parameters for best
possible competition performance, we allowed a maximum interval of 2 months
between the stress test and competition. If there were several competition
results within the period cited, we selected the best result for the
statistics. A training adaptive effect and changes in other performance
limiting parameters within the same time might have had negative effects on the
correlations calculated.
For the IAT as
defined here, it is in no way a parameter with a physiological basis, just as
the term "threshold" is confusing in this context. Contrary to the
recommendations, working in longitudinal assessment with fixed lactate
concentrations of 2, 3, or 4 mmol·L-1 does not appear appropriate
because of the demonstrably poorer predictive value of running speed at 4
mmol·L-1. Even at physical rest and in the range of the IAT,
individual values of 0.4-3.5 mmol·L-1 were attained by
endurance-trained athletes. This range of variation itself, which may be
elicited by nutrition (3), prior stress, muscle fiber distribution, or
distribution phenomena (6), makes a performance-diagnostic procedure based on
lactate kinetics, as opposed to absolute values, appear necessary (10).
The procedure
applied in our study takes the first moment of the first increase in lactate
(LT) into account. The addition of a lactate constant corresponds to a net
increase in lactate concentration. The dimension of this net increase is
deduced from experience, which is based on a definition in agreement with
marathon running speeds (11). A modification of the exercise protocol or the type
of exercise would require the adjustment of the constants according to this
principle. The term "threshold" is, however, not meaningful in this
context and was only used in this study for reasons of comparison.
Our studies
show a significant correlation between IAT and the performance in the tested
running distances. This confirms that the so-called "aerobic oxidative
work capacity," which is considered an essential parameter for performance
in endurance sports, is most likely covered by the IAT (10). It also shows that the straight lines of regression
for all tested running distances are nearly parallel to the corresponding
straight lines of identity. This observation confirms that there is at least
one further dominant IAT-independent factor besides the aerobic-oxidative
energy supply (31,34). In shorter running distances, this second factor is
most likely identical with the so-called "anaerobiclactacid work
capacity" (14). After a certain length of exercise, the substrate
availability becomes the additional limitation to performance capacity.
In agreement
with earlier studies that measured the relationship between training scope and
extent of performance (4,12,13), our data also showed a weak relationship between the
extent of IAT and the weekly training kilometers (r = 0.67, P < 0.001), but no correlation to
the years of training (r = 0.04). This is most likely evidence of the marked
genetic component in performance capacity (5,23).
In the stepwise
multiple regression performed with the various independent variables is a
decreasing influence of the parameters velmax and Lamax
on competition performance with increasing competition distances. Both
parameters are determined partly by the "anaerobic work capacity."
The longer the competition distance, the more the substrate availability and
glycogen storage quantity are considered to be performance-determining factors (9). The mean exercise scope of an athlete is considered
the determining parameter besides genetic prerequisites. Sjödin et al. (26) demonstrated the scope of training as an essential parameter
in addition to the IAT in a multiregression analysis. The "weekly
mileage" is more influential for the long distances of half-marathon and
marathon than for the 1500-m and 5000-m distances in our regression models.
Longer
competition distances tend to be more predictable from the performance
diagnostic data than the shorter distances, and fewer parameters are needed to
describe the predictive model. This finding contradicts the opinion that
corresponding to longer competition duration, testing protocols with longer
individual stages are necessary to effectively determine specific performance
capacity. Since marathon performance is largely defined by the factor
"aerobicoxidative capacity" (19), it is understandable that competition performance is
largely determined by the IAT. However, the described independent proportion of
the "anaerobic-lactacid work capacity" has a negative influence on
the correlation between IAT and competition performance for the shorter
competitions.
However, it may
be concluded from the observations that a test to determine specific
performance capacity in long-term endurance athletes need not apply long
increments. The basic advantage of using shorter levels is a higher resolution
of measured data, a lower interpolation error, and thus higher reliability (20).
An identical or
even greater amount of training of the 1500-m and 5000-m runners compared with
marathon runners indicates that the track runners were on the average more
likely to be ambitious competitors, whereas more of the marathon runners were
recreational runners. A more stable competition performance and thus lower
deviation from performance-diagnostic predictions can be assumed for ambitious
competitors than among the recreational runners. This opinion is supported by
the fact that the correlation of marathon performance shows a greater
scattering versus the IAT in the lower performance range than in the higher
range. However, there is a quite high correlation between the IAT and
competition performance, despite the probable high influence of the anaerobic
capacity on the shorter distances.
The quality of
the prediction of competition performance from treadmill diagnostic data is
surprisingly good and its precision can be estimated. However, the importance
of performance prognosis-especially on the shorter distances-should not be
overestimated. The performance of shorter test competitions is more easily
accomplished here. The use of a test for tempo definition and support of
training plans only becomes meaningful for the very long, stressful distances.
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Keywords:
LACTATE
THRESHOLD; PERFORMANCE DIAGNOSTICS; EXERCISE; INDIVIDUAL ANAEROBIC THRESHOLD;
STEPWISE REGRESSION ANALYSIS
© Williams
& Wilkins 1998. All Rights Reserved.
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