Training Intensity Distribution

Full text

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V  : D 
Training Intensity Distribution
Stephen SEILER
Institute of Public Health, Sport, and Nutrition, University of Agder, Kristiansand, Norway
4
C

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S SEILER
Stephen Seiler earned his Ph.D. in Exercise physiology and exercise biochemistry from the University of Texas
at Austin. He is currently Professor in Sport Science and Dean of the Faculty of Health and Sport Sciences at the
University of Agder in Kristiansand, Norway. His research interests are focused on the endurance training process and
the optimization of training organization and training intensity distribution. He has published over 40 peer reviewed
publications, and over 100 popular science articles related to exercise physiology and the training process. Professor
Seiler has given nearly 100 scientic lectures in 15 countries. He is a fellow of the American College of Sports Medicine.
He has been physiological consultant for Dutch international speed skating teams, Team DPA and Team Telfort (2003-
2007). In addition to his duties as Dean, Stephen is currently senior research consultant to the Norwegian Olympic
Federation, sits on the editorial board for the International Journal of Sport Physiology and Performance, and coaches
his 7 year old son’s soccer team.
Stephen SEILER, Ph.D.

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Training Intensity Distribution
Introduction
Becoming really good at anything takes both a lot of time
and intense eort. For the aspiring endurance athlete, this
combination is manifested in the day-to-day integration
of work duration and intensity into each training session.
Whether an athlete rigidly follows a “periodization plan”
or decides what the training session will be as they put on
their training clothes each day, the fundamental questions
“How long? How hard?” have to be answered before every
training session. Debate about the relative impact of
dierent training intensity zones, high intensity interval
training, long slow distance, threshold training, etc.,
continues among athletes, coaches, and sport scientists.
is chapter explores research and practice on the topic
of training intensity distribution for long-term endurance
capacity development.
How Many Intensity Zones?
Quantifying training intensity can be a bit confusing.
ere are dierent approaches to measuring intensity,
both physiologically and perceptually. Most national sport
governing bodies employ a guiding intensity scale based on
ranges of heart rate relative to maximum and blood lactate
concentration. Typically, aerobic endurance training in
the intensity range of ~50% to 100% of maximal oxygen
uptake (VO2max) is divided into 5 intensity zones. Table 4.1
exemplies such a generalized scale. Using a standardized
intensity scale represents a tradeo. e approach fails
to account for individual variation in the relationship
between heart rate and blood lactate concentration,
or activity specic variation, such as the tendency for
maximal steady state concentrations of blood lactate to
C 4
Training Intensity Distribution
Stephen SEILER
Institute of Public Health, Sport, and Nutrition, University of Agder, Kristiansand, Norway
be higher in activities activating less muscle mass (Beneke
et al., 2001; Beneke & von Duvillard, 1996). However, for
the practitioner, these potential sources of error seem to
be outweighed by the improved communication that a
common scale facilitates between coach and athlete, as well
as across sports disciplines. Speaking the same training
intensity “language” may be particularly important in
improving the match between the intensity prescription
from a coach and an athlete’s interpretation of that
prescription (Foster et al., 2001).
Several recent studies examining training intensity
distribution (Esteve-Lanao et al., 2005; Seiler & Kjerland,
Intensity
Zone
Heart rate
(% max)
Lactate
(mmol.L-1)
Typical eective
work time within
zone
1 60-72 0.8-1.5 1-6 h
2 72-82 1.5-2.5 1-3 h
3 82-87 2.5-4.0 50-90 min
4 88-93 4.0-6.0 30-60 min
5 94-100 6.0-10.0 15-30 min
Table 4.1. A 5-zone intensity scale to prescribe and monitor
training of endurance athletes. is scale is typical of intensity-
zone scales used for endurance training prescription and
monitoring (From Norwegian Olympic Federation). Blood lactate
concentration references are based on hemolyzed blood (as
acquired from the Lactate Pro device). Eective work time within
zone for a 6 x 4 min interval session with 2 minute rest periods
would be 24 minutes

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S SEILER
2006; Zapico et al., 2007), or performance intensity
distribution in multi-day events (Lucia et al., 1999; Lucia
et al., 2003) have employed a simplied 3 intensity zone
approach based on individually determined rst and second
ventilatory turnpoints (Figure 4.1). Intensity distribution
studies based on ventilatory threshold derived zones are
not directly comparable with the 5-zone model, but what
is typically identied as “lactate threshold intensity”, or
2-4 mM blood lactate concentration, corresponds well
in practice with the intensity zone demarcated by the 1st
and 2nd ventilatory turnpoints. So, for practical purposes,
the 3-zone model and 5-zone model are superimposable,
but the lowest and highest intensity zones in the 3-zone
model are further divided into two zones. Which intensity
zone approach is best? For the young or inexperienced
endurance athlete, using 3 training intensity zones
(e.g. green, yellow, and red zones) has the advantage of
simplicity and makes communicating training intensity
goals more straightforward. For the experienced athlete,
the 5-zone model provides a more precise description of
training intensity distribution that may be important for
communicating subtle changes in training prescription.
I will use the term “low intensity training” (LIT) to refer to
work eliciting a stable blood lactate concentration of < ~2
mM as measured using a Lactate Pro™ device (hemolyzed
blood). “High intensity training” (HIT) will refer to training
above maximum lactate steady state intensity (≥ 4 mM
blood lactate). Training in the region bounded by about 2
and 4 mM blood lactate will be referred to as “threshold
training” (T). It should be noted that this delineation
seems to work well for trained athletes. However, for
untrained/ recreationally trained subjects, we nd that a 2
Figure 4.1. A three intensity zone model based on identication
of ventilatory thresholds. ese zone-dividing breakpoints
correspond to about 2 mM and 4 mM blood lactate concentration,
but there is some individual and sport specic variation. VO2max:
maximal oxygen uptake; VT: ventilatory threshold; LT: lactate
threshold; MLSS: maximal lactate steady state.
mM lactate turnpoint can be dicult to identify because
blood lactate approaches this concentration already at very
low workloads (unpublished observations). Blood lactate
proles are more distinct and “textbook” when testing
highly trained athletes.
How Do Good Endurance Athletes Train?
Good empirical descriptions of the distribution of training
intensity in well-trained athletes are a fairly recent addition
to the sport science literature. In 1991, Robinson et al.
published “the rst attempt to quantify training intensity
by use of objective, longitudinal training data.” ey
studied training characteristics of 13 national class New
Zealand male runners with favorite distances ranging
from 1500 m to the marathon. ey used heart rate data
collected during training and related it to results from
standardized treadmill determinations of heart rate and
running velocity at 4 mM blood lactate concentration.
Over a data collection period of 6-8 weeks corresponding
to the preparation phase, athletes reported that only 4% of
all training sessions were interval workouts or races. For
the remaining training sessions, average heart rate was 77%
of their heart rate at 4 mM blood lactate (which translates
to roughly 65% of VO2max).
Billat et al. (2001) performed physiological testing and
training diary data collection of elite French and Portugese
marathoners. ey classied training intensity in terms
of several specic velocities: < vmarathon, v10000 m, and
v3000 m. During the 12 weeks preceding an Olympic trials
marathon, the athletes ran 78% of their training kilometers
at below marathon velocity, only 4% at marathon race
velocity (likely to be between VT1 and VT2), and 18% at
v10000 m or v3000 m (likely to be > VT2). is distribution
of training intensity was identical in both high level (< 2
h 16 min for males or < 2 h 38 min for females) and elite
performers (< 2 h 11 min or < 2 h 32 min for males and
females, respectively). But, the elite athletes ran more total
kilometers and proportionally more distance at or above
v10000 m. Examination of data from another descriptive
study by Billat et al. (2003) on elite male and female
Kenyan 5 and 10 km runners demonstrated that ~85% of
their weekly training distance were run at below lactate
threshold velocity.
Esteve-Lanao et al. (2005) analyzed over 1000 heart rate
records using the time-in-zone approach to quantify the
training of 8 regional and national class Spanish distance
runners over a 6-month period. Intensity zones were
established on the basis of treadmill testing. On average
these athletes ran 70 km.per week during the 6-month

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Training Intensity Distribution
period. Seventy-one percent of running time was <
VT1, 21% between VT1 and VT2, and 8% > VT2. Mean
training intensity was 64% VO2max. ey also found that
performance times were correlated with total LIT time, but
not volume of HIT.
Steinacker et al. (1998) reported that extensive endurance
training (60-120 minute sessions at < 2 mM blood lactate)
dominated the training volume of German, Danish, Dutch,
and Norwegian elite rowers. Rowing at higher intensities
was performed about 4-10% of the total rowed time. e
data also suggested that German rowers preparing for the
world championships performed essentially no rowing at
T intensity, but instead trained in a quite “polarized”
fashion, performing either LIT, or HIT in the 6-12 mM
range.
Fiskerstrand and Seiler (2004) examined historical
developments in training organization among elite rowers.
Using questionnaire data, athlete training diaries, and
physiological testing records, they quantied training
intensity distribution in 27 Norwegian athletes who had
won world or Olympic medals in the 1970s, 1980s, or
1990s. ey documented that over the three decades 1)
training volume had increased about 20% and LIT volume
increased relatively more; 2) the monthly hours of HIT had
actually been reduced by one-third; 3) very high intensity
“overspeed” sprint training had declined dramatically in
favor of longer interval training at 85-95% of VO2max; 4) the
number of altitude camps attended by the athletes increased
dramatically. Over this 30-year timeline, athletes had about
12% higher VO2max and a 10% improvement in rowing
ergometer performance with no change in average height
or body mass. However, most of this increase was seen
between the 1970s and 1980s, when major adjustments in
training intensity distribution were made.
Guellich et al. (2009) described the training of world
class junior rowers from Germany during a 37-week
period culminating in national championships and
qualication races for the world championships. Twenty-
seven of the 36 athletes studied won medals in the junior
world championships that followed the training period
analyzed. Based on time-in-zone heart rate analysis, fully
95% of all endurance training time was performed as LIT.
is heavy dominance of extensive endurance training
persisted throughout the 9-month period. However, the
relatively small volume of T and HIT shied towards
higher intensities from the basic preparation phase to
the competition phase. at is, the overall intensity
distribution became more polarized as athletes approached
competition.
Zapico and colleagues tracked training characteristics from
November to June in a group of elite Spanish U23 riders
(Zapico et al., 2007). In addition, physiological testing was
performed at season start and at the end of the Winter
and Spring mesocycles to compare training changes and
physiological test results. Figure 4.2 compares the training
intensity distribution in the winter and spring mesocycles.
Figure 4.3 shows physiological test results at baseline, and
at the end of each training mesocycle. Comparison of the
training intensity distributions in the two periods showed
that there was both an increase in total training volume
and a fourfold increase in HIT training during the Spring
mesocycle. However, physiological testing revealed no
further improvement in power at VT1, VT2 or at VO2max
between the end of the winter and spring mesocycles,
Figure 4.2. Cycling intensity and volume of elite Spanish U23
cyclists training in the period November to June. Data redrawn
from (Zapico et al., 2007). Note that the Spring mesocycle is
shorter by one month such that the daily training volume increase
from Winter to Spring is larger than it appears. VT: ventilatory
threshold.
Figure 4.3. Response to periodization of training intensity and
volume in elite Spanish U23 cyclists (see Figure 4.2). Results
from tests performed before starting the Winter mesocycle (Test
1), at the end of the Winter mesocycle (Test 2), and at the end of
the Spring mesocycle (Test 3). Data redrawn from (Zapico et al.,
2007). VT: ventilatory threshold; VO2max: maximal oxygen uptake.

34
S SEILER
despite a clear training intensication. Anecdotally, this is
not an unusual nding. Time sustained at VO2max or time
at VT2 power may be more sensitive variables to evaluate
the impact of intensied training in highly trained athletes
with stable threshold and VO2max results.
Cross-country skiing has adopted spectator friendly
1000-1500 m sprint races in the last decade (contested
as a knock-out tournament). Recently, the training and
physiology of 8 international class and 8 national class
(Norway) sprint XC skiers has been described (Sandbakk
et al., 2010). e internationally elite skiers distinguished
themselves with higher VO2peak, vVO2peak, and exercise time
at VO2peak. Over a 6-month registration period, the world
class skiers trained about one-third greater volume (445
vs 341 hours), with almost all of this dierence in training
time due to greater volumes of low intensity training (86
more hours) and speed training (9 more hours). e two
groups performed identical volumes of HIT over 6 months
(19 hours in both groups, or about 45 minutes per week).
Schumacher and Mueller (2002) demonstrated the
validity of power balance modeling in predicting “gold
medal standards” for physiological testing and power
output in the 4000 m pursuit cycling race. However, less
obvious from the title was the detailed description of the
training program followed by the gold medal winning
team monitored in the study. ese athletes trained to
maintain an average competition intensity of over 100%
of power at VO2max with a program dominated by LIT
(29,000-35,000 km per year). In the 200 days preceding the
Olympics, the pursuit team performed “low-intensity, high
mileage” training at 50-60% of VO2max on ~140 days. Stage
races comprised ~40 days. Specic track cycling at near
competition intensities was performed on less than 20 days
between March and September. Remarkably, in the ~110
days preceding the Olympic nal, high intensity interval
track training was performed on only 6 days.
e descriptive studies above highlight the paradoxical
nding that while all Olympic endurance events are
performed at or above the lactate threshold (or ≥ 85%
VO2max), the large majority of the training performed is
completed below lactate threshold intensity.
I end this section with a unique set of case data that
exemplies the common training characteristics seen in the
descriptive studies above. Figure 4.4 summarizes an entire
“career” of training of a world and Olympic champion
female cross-country skier from Norway. e gure is based
on digitization of over 5,000 training sessions described in
daily training diaries completed during the athlete’s career,
using a standardized intensity scale as described in Figure
4.1 (Espen Tønnessen, unpublished data, with permission
from athlete Bente Skari). e athlete gradually increased
training volume from age 18 to 28, primarily by increasing
LIT volume. Maximal oxygen consumption ranged from 65
to 67 mL.kg-1.min-1 between age 18 and 21 but increased
to an average of 73 mL.kg-1.min-1 between age 25 and 27,
during a period when she increased her training volume
by about 50% from ~450 to 700 hours. Her rst World
Cup victory came at age 25. Skari increased the amount
of zone 5 training over the last 4 years of her career, but
physiological test results remained stable. From age 20 to
31, the average contribution of training at lactate threshold
intensity or higher to total training volume was 9.5%, or
50-75 hours per year.
e 80-20 Guideline
Endurance athletes need to perform T and HIT for
optimal adaptation and performance. Several studies have
shown that adding interval training to a program of only
basic endurance training (LIT) in trained subjects gives a
2-4% increase in performance (Kohrt et al., 1991; Lindsay
et al., 1996; Stepto et al., 1999; Weston et al., 1997). e
question is “how much” LIT and T/HIT should be
performed? e descriptive studies above from dierent
sports and race distances converge on a basic pattern: about
80% of training sessions are LIT and the remaining ~20%
are focused on T or HIT. More precisely, LIT training
seems to focus around ~65% VO2max (upper end of Zone 1
in Table 4.1) and about 90% VO2max (zone 4 in Table 4.1).
Figure 4.4. Summary of training volume and intensity distribution
over 14 years and over 5,000 training sessions from a world
and Olympic Champion female skier (data courtesy of Espen
Tønnessen, with permission from athlete Bente Skari). Training
intensity zones are as described in Table 4.1.

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Training Intensity Distribution
Intensifying Training in Well-Trained Athletes
e sport arena is quite Darwinian. We might therefore
assume that approaches that give a competitive advantage
tend to spread while those that do not die out. With this
process in mind, it is interesting that emphasis on high
training volume through lots of low intensity training is
common across sports. But, one can question whether the
“80-20” training intensity distribution is really optimal, or
just a product of tradition. Several studies have examined
the impact of training intensication (with or without
corresponding volume reduction) on physiology and/or
performance in XC skiers, rowers and runners.
In 1997, Evertsen et al. published the rst of three papers
from a study involving training intensication in 20 well-
trained junior cross-country skiers competing at the
national or international level (Evertsen et al., 2001; Evertsen
et al., 1999; Evertsen et al., 1997). In the two months before
study initiation, 84% of training was carried out at 60-70%
VO2max, with the remainder at 80-90% of VO2max. Athletes
were then randomized to a moderate intensity (MOD) or
a high intensity training group (HIGH). MOD maintained
essentially the same training intensity distribution, but
training volume was increased from 10 to 16 hours a week.
HIGH reversed their baseline intensity distribution so that
83% of training time was performed at 80-90% of VO2max,
with only 17% performed as low intensity endurance
training. e HIGH group trained 12 hours per week. e
training intervention period lasted 5 months. Intensity
control was achieved using heart rate monitoring and
blood lactate sampling throughout the training period.
Despite reporting 60% more training volume in MOD and
~4 times more training above lactate threshold intensity in
HIGH, physiological and performance changes were quite
modest in both groups of already well-trained athletes
(Table 4.2).
Gaskill et al. (1999) reported the results of a 2-year
project involving 14 cross-country skiers. During the rst
year, athletes trained similarly, averaging 660 training
hours with 16% of training sessions identied as HIT.
Physiological test results and race performances during the
rst year were used to identify 7 athletes who responded
well to the training and 7 who showed little VO2max and
lactate threshold progression, and poor race results. In the
second year, the positive responders continued using their
established training program while the non-responders
performed a markedly intensied training program with a
slight reduction in training hours. ey observed that the
non-responders from year one showed a positive response
to the intensied program in year two (VO2max, lactate
threshold, race result points). e positive responders from
year one showed a similar development in year 2 as year 1.
Esteve-Lanao et al. (2007) randomized 12 sub-elite
distance runners to one of two training groups (Z1 and
Z2) that were carefully monitored for 5 months. ey
measured training intensity distribution using the 3-zone
model described earlier. Based on time in zone heart rate
monitoring, Z1 performed 81, 12, and 8% of training in
zones LIT, T, and HIT, respectively. Z2 performed twice
as much T, with 67, 25, and 8% of training performed in
the three respective zones. Total training load was matched
between the groups using a modication of TRIMP
(training impulse) units. A time trial performed before and
aer the 5-month period revealed that the group that had
trained more zone 1 training showed signicantly greater
race time improvement (-157 ± 13 s vs. -122 ± 7 s).
Ingham et al. (2008) randomized 18 experienced UK
national standard male rowers into two training groups
that were initially equivalent based on performance and
physiological testing. All the rowers had completed a
25-day post-season “training free” period just prior to
baseline testing followed by a 12-week period of rowing
ergometer training. One group performed 98% of all
training between 60 and 75% of VO2max (LIT). e other
group (MIX) performed 70% training at 60-75% VO2max,
as well as 30% of training at an intensity 50% of the way
between power at LT and power at VO2max. In practice,
MIX performed HIT on 3 days per week. e two groups
performed virtually identical volumes of training (~1140
km) on the ergometer. Results of the study are summarized
in Table 4.3. Sixteen of 18 subjects set new personal bests
Intensity
Increase
(n=10)
Volume
Increase
(n=10)
VO2max
Lactate-threshold speed 3%
20-min run at 9% grade 3.8% 1.9%
Fiber type
Enzyme activities
MCT 1 transporter 12%
MCT 4 transporter
Citrate synthase
Succinate dehydrogenase 6%
Na/K Pump ?% ?%
Table 4.2. Summary of responses to training intensication in well
trained cross-country skiers (Evertsen et al., 2001; Evertsen et al.,
1999; Evertsen et al., 1997)

36
S SEILER
for the 2000 m ergometer test at the end of the study. e
authors concluded that LIT and MIX training had similar
positive eects on performance and VO2max. LIT appeared
to induce a greater right-shi in the blood lactate prole
during sub-maximal exercise, but this did not translate to a
signicantly greater gain in ergometer performance.
90% or 100% of VO2max High Intensity Training?
Both threshold (T) and high intensity interval training
(HIT) are physiologically stressful and perceptually
demanding. Once intensity exceeds the lactate threshold,
small increases have non-linear eects on physiological
responses and accumulated work duration (Seiler, 2010;
Seiler et al., 2007). e work intensity and the accumulated
duration will combine to generate the adaptive signal for
a training session. Accepting that T and HIT sessions
typically make up about 20% of total training sessions,
another question is whether there is an optimal area of this
high intensity range for stimulating endurance adaptation.
Decades ago, Åstrand and Rodahl (1986) formulated the
question as: ‘It is an important but unsolved question
which type of training is most eective: to maintain a level
representing 90 percent of the maximal oxygen uptake for
40 min, or to tax 100 percent of the oxygen uptake capacity
for 16 min.’ is question remains open, but we have
recently compared the impact of 8 weeks of twice weekly
4 x 4 minutes, 4 x 8 min, and 4 x 16 min interval training
in masters cyclists training about 6 hours per week. Groups
were matched for initial training characteristics. Each
interval group trained with maximal session eort. A fourth
group trained only LIT during the intervention period. As
Figure 4.5 shows, we found that the group training 4 x 8
min at ~90% of HRmax showed signicantly greater overall
adaptive gains in power at LT, power at VO2max, and VO2max
than groups performing 4 x 16 min or 4 x 4 min interval
sessions with the same maximal eort (Seiler et al., in
press). is corresponds to intensity zone 4 in Table 4.1,
and seems to be an intensity zone that endurance athletes
focus much of their training at, choosing to accumulate
more minutes at ~90% VO2max instead of fewer minutes at
~95% VO2max intensity.
First Volume, then Intensity?
Since Matveyev (1964) introduced his now classic model
of periodization of volume and intensity in training more
than four decades ago, there has been considerable debate
regarding how best to organize long-term exposure to
training stimuli (i.e. volume, intensity, mode) for modern
endurance athletes. e traditional periodization model
was almost certainly inuenced by Soviet “production
plans” that may well have been inspired by the industrial
management philosophy of “scientic management”
founding father Frederick Winslow Taylor (John
Kiely, unpublished observations). Since then, several
dierent long-term periodization structures have been
conceptualized and described (Issurin, 2008; Issurin, 2010;
Tschiene, 1977; Tschiene, 1985) (see Chapter 2). However,
controlled studies comparing the physiological impact of
these dierent organizational structures on endurance
performance are scarce.
Few studies have actually documented the intensity and
volume distribution of endurance athletes over multiple
phases of their annual training cycle (Gaskill et al., 1999;
Guellich et al., 2009; Schumacher & Mueller, 2002; Zapico
LOW
(n=9)
MIXED
(n=9)
2000-m ergometer time 2% 1.4%
VO2max 11% 10%
Power at 2-mM lactate 10%* 2%
Power at 4-mM lactate 14%* 5%
VO2 kinetics
Table 4.3. Physiological and performance changes aer two
rowing programs consisting of either 98% of all training at 60-75%
of VO2max (LOW), or 70% at 60-75% of VO2max and 30% at 50% of
the dierence between the intensity at the lactate threshold and
the intensity at VO2max (MIXED) (Ingham et al., 2008). * P < 0.05
LOW vs. MIXED
Figure 4.5. Distribution of individual training responses to 3
dierent interval training prescriptions, all performed twice weekly
with maximal tolerable intensity. Averaged change in VO2max (l/
min), power at VO2max (W), and Power at 4 mM blood lactate
concentration (W) for each subject was categorized as negative
to trivial: < 4% improvement; moderate: 4–9% improvement; or
large: > 9% improvement. e distribution of individual responses
was signicantly dierent among the four groups (P < 0.05).
Figure from Seiler et al. (In press)

37
Training Intensity Distribution
et al., 2007). ese studies, unpublished case histories of
elite performers, and feedback from coaches all suggest
that while there is a clear increase in HIT moving from the
preparation to competition period, LIT continues to make
up the majority of total training volume. e classic “rst
volume, then intensity” periodization does not t well with
how athletes actually train. For example, Figure 4.6 shows
the intensity distribution and training volume performed
each month by 5 time world record setting distance runner
Ingrid Kristiansen during a season at the peak of her career
(unpublished data digitized from training diaries by Espen
Tønnessen, with permission from Ingrid Kristiansen).
While emphasis on HIT increased during the competitive
season, LIT continued to account for over 80% of training
volume. Very little is documented regarding the correlation
between responses to training in the preparation period and
capacity or performance months later in the competition
period. For example, we have recently observed that while
lactate prole responses to standardized testing before and
aer a 12-week period of basic preparation in national
class German track cyclists varied from strongly positive
to negative, these results were not correlated with end of
season success in championship events (Guellich & Seiler,
2010). Progress in understanding long-term periodization
will likely require systematic athlete monitoring by
governing bodies or Olympic centers in cooperation with
sport scientists. Boxer Mike Tyson remarked that “everyone
has a plan, until they get punched in the face”. For coaches,
a good training plan is an important starting framework,
but the ability of athlete and coach to “read the signals” and
make adjustments from week to week is probably critical
to successful training cycles. But it is a hard topic for sport
scientists to investigate.
Variation Versus Monotony
Short term training organization, involving day-to-day
manipulation of intensity and duration over a few weeks,
has been investigated more extensively. Endurance athletes
train, rest, and repeat. Training (intensity, duration) and
recovery (rest interval, nutrition) variables interact to
induce both tness (i.e. physiological adaptations) and
fatigue (i.e. stress responses and associated negative
health outcomes). is practical tness versus fatigue
dichotomization was introduced by Banister and colleagues
in their modeling studies of the training process (Banister
et al., 1975; Banister & Calvert, 1980; Morton et al., 1990).
e predictive value of their mathematical approach has
not held up very well (Hellard et al., 2006), but conceptually,
the model remains inuential. Day-to-day organization of
training, recovery, and nutritional strategies should tend
to maximize the gain in tness for a given long-term cost
(fatigue, stress, and risk of negative health outcomes).
During any given week, athletes normally perform some
mixture of LIT and T/HIT sessions. Horses are trained
similarly, with alternating “easy days” of continuous
running and “hard days” of interval training. Bruin et al.
(1994) performed a long-term training study of horses
where they manipulated the hard-easy rhythm of the
horses’ training in two ways. Aer 187 days of daily training
in hard-easy fashion, hard training days were intensied
by performing more total high intensity running, with
easy days le unchanged. e horses exhibited improved
running performance over the next 75 days. Aer 261
days, the easy days were intensied by having the horses
run faster for the same duration. Within 5 days the horses
were no longer able to complete the HIT and showed clear
signs of decompensation and overtraining symptoms.
Foster (1998) applied this nding to human athletes and
conceptualized training monotony as increasing the risk
of negative adaptations to training (Foster, 1998). High
training stress was quantied as a product of large training
volumes, high perceived intensity, and low day-to-day
variation in training load. Elite athletes oen train twice
or even 3 times per day, making the rest interval between
training sessions typically between 4 and 12 hours.
Achieving this training frequency without excessive stress
appears to require careful “training intensity discipline.
Keeping “easy sessions easy and hard sessions hard” seems
to be a characteristic that successful athletes share. is is
probably good advice for all endurance athletes.
Figure 4.6. Training volume and intensity distribution of world
champion distance runner Ingrid Kristiansen during an annual
training cycle at the age of 29-30 years, a time when she set world
records for 5,000 and 10,000 m (data courtesy of Espen Tønnessen
with permission from Ingrid Kristiansen). Training intensity
zones are as described in Table 4.1.

38
S SEILER
Summary: Getting the Intensity Balance Right
• Athletes respond individually to training. However,
available evidence points to some general guidelines
for successful integration of volume and intensity in the
endurance training process.
• An~80-20ratioofLOWtoT/HITintensitytraining
is common and apparently gives excellent long-term
results among endurance athletes.
• Frequentlow intensity (≤ 2 mM blood lactate), longer
duration training is eective in stimulating physiological
adaptations, particularly peripheral adaptations.
• eideaofadichotomousphysiologicalimpactofHIT
and LIT is probably exaggerated, as both methods seems
to generate overlapping physiological adaptation proles
and are likely complementary.
• At a high performance level, youc annot shortcut the
need for a high training volume with large increases in
intensity.
• HIT is a critical component of the training of all
successful endurance athletes. About 2 HIT training
sessions per week seems to strike a good balance between
positive eects and stress load.
• WithintheHITrange,accumulatingtrainingminutesat
90% of VO2max appears to be as or even more eective and
somewhat less stressful than training shorter sessions at
95-100% VO2max.
• An established basic endurance base built from high
volumes of training may be an important precondition
for tolerating and responding well to a substantial
increase in training intensity over the short term.
• etransitionfromthepreparationtotransitionphase
of training is marked by modest reductions in total
training volume, and a careful increase in the amount of
training performed above the lactate threshold.
• Greater polarization of training intensity characterizes
this transition; HIT training is increased and intensied
but LIT gets easier.

39
Training Intensity Distribution
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