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Researched
and Composed by
Jacob Wilson, BSc. (Hons), MSc. CSCS and
Gabriel “Venom” Wilson, BSc. (Hons), CSCS
Abstract
Adaptation can be viewed as a constant flux of growth and decay and
further growth of the combination of two intervening factors on
performance. Banister et al. (1975) denotes these variables as fitness
and fatigue, while performance is seen as the difference between the
two. The fitness and fatigue theory of human performance is the current
dominant theory of how organisms adapt to various training stimuli. In
this context, a comprehensive examination of the theory, and its
application to performance will be reviewed. Special emphasis will be
placed on the taper. The taper is a concept in which an individual
reduces total training load in order to maximize performance.
Introduction
In the now infamous documentary on the 1975 Mr. Olympia contest, the
champion Arnold Schwarzenegger turned to Lou Ferrigno and expressed that
he did not get his timing right. Ferrigno was defeated by those words
both mentally, and finally physically only hours latter. Schwarzenegger
encapsulated the ‘temporal’ or timing element of bodybuilding. This
aspect is not only a part of this sport, but of all sports. The athlete
strives continually, day after day to achieve peak performance. When
viewed from an annual standpoint, Fitz-Clarke, Morton, and Banister
(1991) explain that the athlete can only peak once a year. Zatsiorsky
(1995) describes the performance of a team of athletes as the ‘Efficacy
coefficient.’ The Efficacy coefficient is determined by the following
equation.
Number of athletes who achieved their best performances during most
important competition of the season / Total number of athletes on the
team
In order to reach peak performance athletes typically go through
realization, regeneration, peak, or transformation cycles (Zatsiorsky,
1995, Pedmonte, 1982, Haff, 1994, Gambetta, 1992). A realization cycle
is a period of time in which training load is reduced to increase
performance (Banister, 1999, Gilbala, 1994, Houmard, 1994, Johns,
1992). This process is known as tapering. The taper itself has its
roots in Hull’s (1943) mathematical equation of human performance (See
Wilson, 2005 for a review). Wilson (2005) provided the following
overview of
Hull’s
contribution:
Hull
was the first to examine the effect of massed practice on performance.
Massed practice can be defined as practice in which work is longer than
rest periods (Schmidt, 1999). In weight training this would entail 1
minute sets, with only 30 seconds of rest between sets. Several reviews
on the subject ( Lee and Genovese, 1988, McGeoch and Irion, and Bilodeau
and Bilodeau, 1961) provide support for what is known as Hull’s 8th
postulate. Hergenhahn and Olson (2004) summarize the 8th
postulate as follows:
‘Responding Causes Fatigue, which operates against the elicitation of a
conditioned response.’ This is known as reactive inhibition. Reactive
inhibition entails the organism reacting to inhibit the action which
caused fatigue. Bourne and Archer (1956) had 5 groups perform a
tracking task with 0, 15, 30, 45, and 60 seconds of rest. It was found
that as rest decreased, performance decreased. Of particular interest
is that performance was severely depressed in the zero second condition;
however, after a day of rest, performance had risen drastically from the
end of the last trial.
The
effect of improving in the absence of practice is known as reminiscence
(Hergenhahn and Olson, 2004). This effect denoted by Hull provides the
current basis for tapering. According to Hull (1943) reactive
inhibition was masking the positive effects of practice, and a period of
rest was needed to dissipate this effect. Today, the taper is defined
as a period of rest, or lowered training load prior to competition meant
to enhance performance.
Building on the work of Hull (1943), Banister et al. (1975) provided a
two factor mathematical theory on human performance. This theory views
the human as a reactive system which integrates a single input termed
training impulse (TRIMP) and from this produces a single output known as
performance (Busso et al., 1997). The model proposed that the system
contained two controls or filters known as first order transfer
functions. These two filters were denoted fitness and fatigue (Banister
et al., 1975, Banister et al., 1985, Banister, 1991, Busso et al., 1991,
Morton et al., 1990). The fitness represented the positive benefits
induced by the training impulse, while the fatigue represented the
negative effects of training. Performance was suggested to be a second
order transfer function, and could be calculated by the difference
between Fitness and Fatigue:
Performance = Fitness – Fatigue
The fitness fatigue model is based on the principle of parsimony. The
principle of parsimony states that given two models with equal
predictive value, the more simplistic of the two should be utilized.
Banister (1991) explains: “ To be effective the conjecture must be
simple yet elegant. The criterion used in modeling behavior of even
complex systems in engineering is to invoke as few elements (black box
unknowns) as possible. These elements interact, enabling one to explain
the dynamic working behavior of the system. Parsimony is achieved in
the process of modeling and predicting the results by allowing only two
elements to contribute to performance, each element being precisely
specific and derived from a single quantitative measurement of training
contained in the training impulse score.”
Training
Impulse ( w(t) )
The
main goal of the two factor theory is to predict human performance in a
dose dependent fashion. While 30 years of modeling modifications have
been made ( Mujika, 2002 ) the gross equation for training impulse (TRIMP)
is as follows
TRIMP = Training Intensity * Duration
Banister et al. (1975) proposed that heart rate was the most stable
reflection of intensity, as a participant training at the same rate
along the same route has a remarkably consistent heart rate (Banister,
1991). The amplitude or intensity was then multiplied by the duration
in minutes that the activity occurred. The exact equation is as follows
TRIMP = Duration * (Heart Rate exercise – heart rate rest) / (Heart rate
maximum – heart rate rest)
Note
that the equation only includes beats above that which occur during
rest. Therefore, it takes into account only the extra beats needed to
perform exercise, not the beats needed to maintain a resting energy
output. Heart Rate exercise – Heart Rate Rest is known as ‘Span Heart
Rate’ while Heart rate maximum (found by subtracting the participants
age from 220 ) – Heart rate rest is known as Delta Heart Rate(Banister,
1991). In abbreviated terms the equation can be written as follows
w(t) = Tim(min) * Delta Heart Rate Ratio
Another factor to take into account is that the intensity of an exercise
bout does not produce linear changes in the human body. Linear simply
means that as intensity increases the body is stressed in a uniform
manner. That is, for every point that intensity increases, the stress
placed on the body increases by one point. This does not occur. To show
this, Mortin et al. (1990) used work done on the lactate response curve
by Green et al. (1983) to weight intensity. The work done by Green et
al.(1983) found that lactate rises with two break points or sharp rises
in which lactic acid accumulates in an exponential fashion to
intensity. These breakpoints are known as lactate threshold 1 and 2.
In this context, it would be misleading to treat intensity increases as
affecting the system in a uniform manner. Instead, a weighting factor
was calculated which increased according to the lactic acid curve. This
way, higher intensities would yield larger than linear increases in the
training dose. The same effect of intensity on lactic acid can be found
in numerous hormonal increases during exercise (King, 2003, Wilson,
2004, Wilson and Wilson, 2005, a, b, c, d)
Other training doses have also been calculated. For example Mujika et
al. (1996a) used lactic acid build up to denote intensity. They
formulated five different intensities based on lactic acid concentration
in the body. And each intensity was weighted higher than the previous
intensity.
The
training dose is calculated for each variation within a training
session, and then summed to yield the total dose for that session
(Banister, 1991). For example, if a participant is interval training on
the treadmill they may begin with a 10 minute warm up at 120 beats per
minute, followed by 10 minutes at 140 beats, followed by 10 at 160
beats. The TRIMP would be calculated for each interval and then added
together. Likewise consecutive training days could be added in this
fashion.
Utilizing
The Training Dose To Predict Performance
Once
the training impulse, denoted w(t) has been calculated, the fitness
impulse denoted p(t) and the fatigue impulse denoted f(t) can be
calculated. On the basis of numerous studies the model predicts that
the amplitude is approximately twice as high for the fatigue impulse
than the fitness impulse (Banister, 1991, Fitz-Clarke, 1991, Mujika,
1996, Zatsiorsky, 1995). Therefore calculations are as follows:
p(t) = K1w(t) and f(t) = K2w(t)
The
equation states that the effect of the fitness impulse p(t) is found by
multiplying the training dose w(t) by a weighting factor denoted K1,
where K1 is equal to 1. Therefore if the training dose is 1 unit then
p(t) would also equal 1. However, fatigue is multiplied or weighted by
K2, which is equal to 2, suggesting that the fatigue impulse is twice
the fitness impulse. This would explain why after a training session
that performance drops off to such a notable extent, as the fatigue
produced by the training dose is at a much higher amplitude then the
fitness produced.
Before continuing, recall Hull’s (1943) work on reactive inhibition. He
noted participants performance decreased during massed practice.
However, after a period of rest, in the absence of practice their
performance had increased (reminiscence). He postulated that the rest
allowed the participant to dissipate the reactive inhibition, while
maintaining positive gains. From this it can be deduced that fitness,
though at a lower amplitude then fatigue, has a more stable decay
constant. Banister (1991) and Fitz-Clarke et al. (1991) from numerous
studies, including fitness fatigue models on strength athletes (Busso et
al., 1990 ) have calculated the average decay constant for fitness to be
45 days, while the fatigue decay constant on average was 15 days. The
decay constant denotes the time it takes for a value to decrease by 37
%. Therefore, it is predicted that a period of rest (taper) takes
advantage of a rapidly decaying fatigue impulse to unmask the underlying
fitness component. In this context, adaptation is said to be viewed as
a constant flux of growth and decay and further growth of the
combination of two intervening factors on performance.
Such
a process also explains a phenomenon known as delayed transformation of
gains
(Hartmann, 1989, Plisk et al., 2003, Verkhoshansky, 1986, 1988,
Zatsiorsky, 1995). In this context, an athlete may reach a plateau and
increase work load, which leads to a further increase in gains. Once a
plateau is hit, workload is increased again, but gains do not follow. The athlete then decreases workload, to take advantage of the decay of
fatigue to reveal the gains that were masked (delayed transformation of
gains).
Models
Prediction on Duration of the Taper
The
taper is based on two further functions predicted by the model. The
first is defined as tn, which is the critical time before a competition
in which training can contribute positively to performance at a specific
date ( Mujika et al., 1996, Fitz-Clark et al., 1991). Given the decay
constants of 15 and 45 days for fatigue and fitness, Fitz-Clarke (1991)
calculated tn or the threshold for training benefits to be 16 +/-6
days. The second function is denoted tg and is defined as ‘the time
before competition necessary to reach a maximal benefit from training (Mujika
et al., 1996).’ Given the same constants, FItz-Clarke et al. (1991)
calculated tg to be 40 +/- 8 days. In this context, the taper should
occur between the time when training results in maximal performance (tg)
and the time when training contributes negatively to performance tn.
Fitz-Clark et al. found this to be between 16 +/- 6 and 40 +/-8 days.
The Models
Predictive Value and Optimal Duration for Tapering
Ultimately the numbers for decay constants, and amplitudes of first
order functions have been derived from numerous studies (Banister, 1991,
Fitz-Clark, 1991). However, authors of the models also explain that
their predictive value must take into account each athlete individually
(Banister, 1991, Mujika et al., 1996). For this to occur, each
athletes’ training impulse and predicted performance values are
typically plotted against actual performance (Banister, 1991, Mujika et
al., 1996). When done this way, decay constants can be adjusted to the
actual individual. Mujika et al. (1996) investigated a predictive model
on swim performance in elite swimmers based on a modified version of the
Banister (1975) model and found that predicted values could account for
up to 85 % of performance. When plotting actual values, they found that
the mean or average decay constant for fitness was 40 days, which is
very close to the predicted 45 day value, and that the fatigue decay
constant was 12 days on average, again close to the 15 day predicted
value in the Fitz-Clark (1991) and Banister (1991) studies. Further
these investigators found tn and tg, to be between 12 ± 6 and 32 ± 12 d
for the group of swimmers. Again this is close to the predicted values
in the Fitz-Clarke (1991) study, as well others (Johns et al., 1992,
Banister et al., 1985, Calvert et al., 1976, Morton et al., 1990 ) .
The
investigators also examined the effects of three tapers utilized by the
swimmers during the season. The first lasted for 14 days, the second
for 21 days, and the third for 41 days. The athletes increased
performance by 3 percent in both the 14 and 21 day tapers, but did not
get significant increases in the 41 day taper. In another study, Martin
et al. (1994) had participants perform 6 weeks of high intensity aerobic
training, followed by a 14 day taper. It was found that cycling
performance increased by 8% and QUAD strength increased by 8-9%. In
another study, Zarkadas et al. (1995) investigated the effect of a 10
day and 13 day taper interspersed between 3 months of intense training
in triathletes. In the 10 day taper, a 4% improvement was found in
their 5 km criterion run and a 6 % increase In the 13 day taper. The
greater results in the 13 day taper may reflect a longer time period to
dissipate the fatiguing impulse. Mujika et al. (1996b) investigated the
effect of a 28 day taper following 12 weeks of intense training in elite
swimmers. It was found that performance decreased slightly during the
most intense aspect of the 12 weeks by .5 %, but increased by nearly 2.5
% during the taper. Trappe et al. (2000) and Trappe et al. (2001) found
that 21 day tapers in elite swimmers elicited an increase in power, swim
performance, and muscular size. In another study, participants were
placed on an elbow flexor strength training program for three weeks
followed by a 10 day taper. It was found that maximum voluntary
contraction increased significantly in all participants (Gibala, 1994).
From
the above data, it can be seen that significant increases in performance
ranged from 10 – 28 day tapers. However, the range has extended even
further with increases seen from 10-35 days in swimmers (Mujika et al.,
2003) and as low as six to seven in runners ( Mujika et al., 2000,
MUjaka et al., 2002, Shepley et al., 1992). These data roughly fall
within the ranges found in the Mujika (1996) study in which the optimal
taper ranged from 12-32 days, as well as the estimates by Fitz-Clark
(1991) who found the optimal tapers to be between 16 +/- 6 and 40 +/- 8
days.
Current evidence suggests that the optimal taper duration be decided
based on past training length and intensity (Kubukeli
et al. 2002, Zatsiorsky, 1995). While the exact duration of taper which
elicits a detraining effect is not precisely known ( Mujaki, 2000),
Kubukely et al.
(2002 ) suggests that both intensity and duration of previous training
effect the time needed to dissipate fatigue. These investigators
suggest a minimum of a 2 week taper for extremely hard and long previous
training, with lower periods ( i.e. 6-10 days) for lower volume, and
duration training phases. Similarly, Zatsiorsky (1995) suggests that
tapers should last for 4 +/- 2 weeks, with training phases containing
numerous shock cycles on the upper end, and lower volume moderate
training phases on the lower end.
A Clearer
Look at Fitness and Fatigue
In
the Mujika (1996) study on elite swimmers, both the 14 and 21 day tapers
produced a 3 % increase in performance. Fitness in this study was
denoted PI and summated all factors contributing to increased
performance, where as fatigue denoted NI was a summation of all factors
contributing to decreased performance. Statistical analysis among first
order functions found a significant decrease in NI during the first two
tapers, with no significant increase or decrease in PI. This again
supports the Hull (1943) reactive inhibition, and Banister (1975) two
factor theory.
Fitness and fatigue in modeled studies are examined on various
parameters, such as hormonal concentrations ( Busso et al., 1990, Busso
et al., 1992). One of the major determinants used is the testosterone
to cortisol ratio ( Adlercreutz et al., 1986, Stone et al., 1991 ). A
decrease in the testosterone to cortisol ratio is associated with
fatigue, while an increase is associated with fitness (Fry et al.
2000). Therefore, an increase in performance can occur with an increase
in testosterone (fitness) or a decrease in cortisol (dissipation of
fatigue) levels (Busso
et al., 1992, Mujika et al., 1996, Mujika et al. 1996b). Briefly,
testosterone is associated with anabolism, while cortisol is associated
with catabolism ( King 2002, Wilson and Wilson 2005). In this
context, Busso et al. (1992) modeled the performance of elite weight
lifters during a four week intense training phase, followed by a two
week taper. During the four week intense training program testosterone
levels decreased, and this was significantly correlated with fatigue.
However, lutinzing hormone concentrations also increased and were
significantly correlated with increases in fitness ( r = .9, meaning
that 81 % of the increase in lutinzing hormone was in common with
predicted increases in fitness). During the two week taper,
testosterone levels increased along with increased performance, and this
was highly correlated (r = .97) with the increased lutinzing hormone
concentration seen in the four week period. Again, this lends support
to the two factor theory. King (2003) explains that lutinzing hormone
stimulates an increase in testosterone levels. In another investigation
Hakkinen et al. (1987) found significant increases in cortisol during a
4 week intensive training cycle with a concomitant decrease in the
testosterone/cortisol ratio. However, after a two week taper cortisol
levels decreased (fatigue was dissipated) which increased the
testosterone / cortisol ratio. Similarly Mujika et al. (1996b)
investigated the testosterone / cortisol ratio during 12 weeks of
intense training and 4 weeks of tapering in elite swimmers. Performance
slightly decreased during the 12 weeks and this was significantly
correlated to a decrease in the testosterone / cortisol ratio, while
performance increased during the taper, and was correlated with an
increase in the testosterone / cortisol ratio.
Wilson (2003) in his investigation into precompetition carbohydrate
depleting and replenishing strategies also provided support for the
fitness and fatigue model. To summarize his research, 6 days before a
contest bodybuilders deplete glycogen stores through high volume
training and low carbohydrate diets. During this phase glycogen is
depleted and this represents fatigue. However, simultaneously an enzyme
known as glycogen synthase which is responsible for glycogen formation
increases in activity. Following the 3 days of depletion, training is
lowered and carbohydrates are increased. During this time glycogen is
replenished rapidly to the point where it reached pre depletion stages.
This represents a dissipation of fatigue; however, the fitness gain
remains longer ( its decay constant is slower) as glycogen synthase
activity is still high, leading to 3-5 times the original levels of
muscle glycogen saturation.
Summary
Banister et al. (1975) suggested that an organism should be viewed as a
system which receives input in the form of training, and produces output
in the form of performance. The model suggests that the duration and
intensity of training, termed training impulse, effects the system by
causing a fatiguing and fitness effect. In this context, performance is
calculated by subtracting the negative fatiguing impulse from the
positive fitness impulse.
Generally fatigue is thought to have a two fold higher initial amplitude
or effect on the organism than fitness. However, the positive fitness
adaptations obtained from the training impulse last three times longer
than the fatigue. Tapering protocols are implemented to take advantage
of the differences in these decay constants. Because fatigue dissipates
faster than fitness, a relatively short period of lowered volume in
training can remove the fatigue, while maintaining positive
adaptations.
Jacob Wilson
President Abcbodybuilding / The Journal of HYPERplasia Research
jwilson@abcbodybuilding.com
Gabriel “Venom” Wilson
Executive of Bioenergetic Research
Venom@abcbodybuilding.com
References
and Sources Cited
Adlercreutz, H., M. Härkönen, K. Kuoppasalmi,
H. Nveri,
I.
Huhtaniemi, H. Tikkanen, K. Remes, A. Dessypris, and J. Karvonen. Effect
of training on plasma anabolic and catabolic steroid hormones and their
response during physical exercise. Int. J. Sports Med. 7(Suppl):27–28.
1986.
Banister, E. W., Calvert, T. W., Savage, M. V. (1975) A systems model of
training for
athletic performance. J. Sports. Med. 7, p.57-61.
Busso, Thierry, Christian Denis, Re´gis Bonnefoy, Andre´ Geyssant, and
Jean-Rene´ Lacour. Modeling of adaptations to physical training by using
a recursive least
squares algorithm. J. Appl. Physiol. 82(5): 1685–1693, 1997
Banister, E. W. and C. L. Hamilton. Variations in iron status with
fatigue modelled from training in female distance runners.Eur. J. Appl.
Physiol. 54:16-23, 1985
Busso, T., R. Candau, and J. R. Lacour. Fatigue and fitness modelled
from the effects of training on performance. Eur. J. Appl. Physiol.
69:50-54, 1994.
Busso, T., C. Carasso, and J. R. Lacour. Adequacy of a systems structure
in the modeling of training effects on performance.J. Appl. Physiol.
71:2044-2049, 1991.
Calvert, T. W., E. W. Banister, M. V. Savage, and T. Bach. A systems
model of the effects of training on physical performance.IEEE Trans.
Syst. Man. Cybernet. 6:94-102, 1976.
Gibala, M. J., J. D. Macdougall, and D. G. Sale. The effects of tapering
on strength performance in trained athletes. Int. J. Sports. Med. 15:
492-497, 1994.
Stone, M.H., R.E. Keith, J.T. Kearney, S.J. Fleck, G.D. Wilson, and N.T.
Triplett. Overtraining: a review of the signs, symptoms, and possible
causes. J. Appl. Sports Sci. Res. 5:35–50. 1991.
MUJIKA, IŃIGO; BUSSO, THIERRY; LACOSTE, LUCIEN; BARALE, FRÉDÉRIC;
GEYSSANT, ANDRÉ; CHATARD, JEAN-CLAUDE Modeled responses to training and
taper in competitive swimmers Medicine & Science in Sports & Exercise:
Volume 28(2) February 1996 pp 251-258
Fitz-Clarke,
J. R., R. H. Morton, and E. W. Banister. Optimizing athletic performance
by influence curves. J. Appl. Physiol. 71:1151-1158, 1991.
Steven S. Plisk, Strength and Conditioning Journal: Vol. 25, No. 6, pp.
19–37.
Hartmann, J., and H. Tünnemann. Fitness and Strength Training. Berlin:
Sportverlag. 1989.
Mujika, I., A. Goya, S. Padilla, A. Grijalba, E. Gorostiaga, and J.
Ibańez. Physiological responses to a 6-day taper in middle-distance
runners: influence of training intensity and volume. Med. Sci. Sports
Exerc. 32: 511-517, 2000.
Mujika, I., A. Goya, E. Ruiz, A. Grijalba, J. Santisteban, and S.
Padilla. Physiological and performance responses to a 6-day taper in
middle-distance runners: influence of training frequency. Int. J. Sports
Med. 23: 367-373, 2002.
Kubukeli, Z. N., T. D. Noakes, and S. C. Dennis. Training techniques to
improve endurance exercise performances. Sports Med. 32: 489-509, 2002.
Banister, E. W. Modeling elite athletic performance. In:Physiological
Testing of Elite Athletes, H. J. Green, J. D. McDougal, and H. Wenger
(Eds.). Champaign, IL: Human Kinetics Publishers, 1991, pp. 403-424.
Busso, T., K. Hakkinen, A. Pakarinen, ET AL. A systems model of training
responses and its relationships to hormonal responses in elite
weight-lifters. Eur. J. Appl. Physiol. 61:48-54, 1990.
Busso, T., K. Hakkinen, A. Pakarinen, H. Kauhanen, P. V. Komi, and J. R.
Lacour. Hormonal adaptations and modelled responses in elite
weightlifters during 6 weeks of training. Eur. J. Appl. Physiol.
64:381-386, 1992.
Hakkinen K, Pakarinen A, Alen M, Kauhanen H, Komi PV. Relationships
between training volume, physical performance capacity, and serum
hormone concentrations during prolonged training in elite weight
lifters. Int J Sports Med. 1987 Mar;8 Suppl 1:61-5.
Fry
et al. (2000) relationship between serum testosterone, cortisol, and
weightlifting, Journal of Strength and Conditioning
Pedemonte, J. 1982: Updated Acquisitions About Training Periodization:
Part One. National Strength Coaches Association Journal: Vol. 4, No. 5,
pp. 56–60.
Haff, G. PhD, CSCS. 2004: Roundtable Discussion: Periodization of
Training—Part 1. Strength and Conditioning Journal: Vol. 26, No. 1, pp.
50–69.
Gambetta, V. 1991: PRINCIPLES OF PROGRAM DESIGN: Some thoughts on new
trends in training theory. National Strength & Conditioning Association
Journal: Vol. 13, No. 1, pp. 24–26.
Banister, E. W., J. B. Carter, and P. C. Zarkadas. Training theory and
taper: validation in triathlon athletes. Eur. J. Appl. Physiol. 79:
182-191, 1999.
Houmard, J. A., and R. A. Johns. Effects of taper on swim performance:
practical implications. Sports Med. 17: 224-232, 1994.
Johns, R. A., J. A. Houmard, R. W. Kobe, et al. Effects of taper on swim
power, stroke distance and performance. Med. Sci. Sports Exerc. 24:
1141-1146, 1992
GREEN, H. J., R. L. HUGHSON, G. W. ORR, AND D. A. RANNEY. Anaerobic
threshold, blood lactate and muscle metabolites in progressive exercise.
J. Appl. Physiol. 54: 1032-1038, 1983.
King, J. Endocrine insanity. Journal of Hyperplasia Research. June,
2003
Kubukeli, Z. N., T. D. Noakes, and S. C. Dennis. Training techniques to
improve endurance exercise performances. Sports Med. 32: 489-509, 2002.
Mujika, I., J. C. Chatard, S. Padilla, C. Y. Guezennec, and A. Geyssant.
Hormonal responses to training and its tapering off in competitive
swimmers: relationships with performance. Eur. J. Appl. Physiol. 74:
361-366, 1996. b
Mujika, I., J. C. Chatard, and A. Geyssant. Effects of training and
taper on blood leucocyte populations in competitive swimmers:
relationships with cortisol and performance. Int. J. Sports Med. 17:
213-217, 1996. c
Morton, R. H., J. R. Fitz-Clarke, and E. W. Banister. Modeling human
performance in running. J. Appl. Physiol. 69: 1171-1177, 1990
Mujika, I., A. Goya, S. Padilla, A. Grijalba, E. Gorostiaga, and J.
Ibańez. Physiological responses to a 6-day taper in middle-distance
runners: influence of training intensity and volume. Med. Sci. Sports
Exerc. 32: 511-517, 2000.
Neufer, P. D. The effect of detraining and reduced training on the
physiological adaptations to aerobic exercise training. Sports Med. 8:
302-321, 1989.
Shepley, B., J. D. Macdougall, N. Cipriano, J. R. Sutton, M. A.
Tarnopolsky, and G. Coates. Physiological effects of tapering in highly
trained athletes. J. Appl. Physiol. 72: 706-711, 1992.
Trappe, S., D. Costill, and R. Thomas. Effect of swim taper on whole
muscle and single fiber contractile properties. Med. Sci. Sports Exerc.
32: 48-56, 2001.
Trappe S, Costill D, Thomas R. Effect of swim taper on whole muscle and
single muscle fiber contractile properties.Med Sci Sports Exerc. 2000
Dec;32(12):48-56.
Verkhoshansky, Y.V. Fundamentals of Special Strength-Training in Sport.
Livonia, MI: Sportivny Press. 1986.
Verkhoshansky, Y.V. Programming and Organization of Training. Livonia,
MI: Sportivny Press. 1988.
Wilson, J., and Wilson, G., Fast Acting Hormones and their Role in Fuel
use during Exercise. Journal of Hyperplasia Research. January, 2005. (a)
Wilson, J., and Wilson, G., Slow Acting Hormones and their Role in Fuel
use during Exercise. Journal of Hyperplasia Research. January, 2005. (b)
Wilson, J., and Wilson, G., Analysis of Nutrient use during Low,
Moderate, and High Intensity Exercise. Journal of Hyperplasia Research.
January, 2005. (c)
Wilson, J., and Wilson, G., Direct Comparisons of Fuel use during Low,
Moderate, and High Intensity Exercises. Journal of Hyperplasia Research.
January, 2005. (d)
Wilson, J. Exercise Endocrinology Principles and Catecholamines. Journal
of Hyperplasia Research. January, 2004
Zarkadas, P. C., J. B. Carter, and E. W. Banister. Modelling the effect
of taper on performance, maximal oxygen uptake, and the anaerobic
threshold in endurance triathletes. Adv. Exp. Med. Biol. 393: 179-186,
1995.
Zatsiorsky, V.M. Science and Practice of Strength Training. Champaign,
IL: Human Kinetics,
1995.
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