International Journal of Sports Physiology and Performance, 2010, 5, 276-291
© Human Kinetics, Inc.
Stephen Seiler is with the Faculty of Health and Sport Sciences, University of Agder, Kristiansand,
What is Best Practice for Training
Intensity and Duration Distribution
in Endurance Athletes?
Successful endurance training involves the manipulation of training intensity,
duration, and frequency, with the implicit goals of maximizing performance,
minimizing risk of negative training outcomes, and timing peak tness and per-
formances to be achieved when they matter most. Numerous descriptive studies of
the training characteristics of nationally or internationally competitive endurance
athletes training 10 to 13 times per week seem to converge on a typical intensity
distribution in which about 80% of training sessions are performed at low intensity
(2 mM blood lactate), with about 20% dominated by periods of high-intensity
work, such as interval training at approx. 90% VO2max. Endurance athletes appear
to self-organize toward a high-volume training approach with careful application
of high-intensity training incorporated throughout the training cycle. Training
intensication studies performed on already well-trained athletes do not provide
any convincing evidence that a greater emphasis on high-intensity interval training
in this highly trained athlete population gives long-term performance gains. The
predominance of low-intensity, long-duration training, in combination with fewer,
highly intensive bouts may be complementary in terms of optimizing adaptive
signaling and technical mastery at an acceptable level of stress.
Keywords: elite athletes, training organization, VO2max, lactate threshold, interval
Endurance training involves manipulation of intensity, duration, and frequency
of training sessions over days, weeks, and months. Long slow distance, lactate
threshold training, and high-intensity interval training (HIT) are all familiar terms
for exercising within different regions on the intensity scale. The relative impact
of different combinations of intensity and duration of endurance training has been
studied and debated for decades among athletes, coaches, and scientists. Currently,
HIT has come into focus again based in part on recent ndings suggesting superior
central adaptations to short-term interval programs compared with continuous
exercise at lower intensity.1,2 However, the application of these ndings to the
long-term training of endurance athletes is unclear. The purpose of this brief review
Training Intensity Distribution 277
is to discuss the roles of training duration and training intensity in the long-term
physiological and performance development of endurance athletes.
Measuring Training Intensity
A review of training intensity and duration issues in endurance training should begin
with some discussion of how these variables are quantied. Measuring exercise
duration is straightforward. Training volume can be measured in terms of distance
(eg, yearly cycling or running kilometers) or time (annual training hours). The most
readily comparable unit across endurance sports is effective training hours. Quan-
tifying training intensity is more complicated. Describing and comparing training
intensity distribution requires a common intensity scale. Most national sport govern-
ing bodies employ a guiding intensity scale based on ranges of heart rate relative
to maximum and blood lactate concentration. Often, aerobic endurance training in
the intensity range of approximately 50% to 100% of VO2max is divided into ve
somewhat arbitrary intensity zones. Table 1 gives as an example a scale used by the
Norwegian Olympic Committee. Standardizing an intensity scale can be criticized
because the approach fails to account for individual variation in the relationship
between heart rate and blood lactate concentration, or activity-specic variation,
such as the tendency for maximal steady-state concentrations of blood lactate to
be higher in activities activating less muscle mass.3,4 In the practical performance
setting, these potential sources of error seem to be outweighed by the improved
communication that a common scale facilitates between coach and athlete and across
sports disciplines. A standardized 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. For example, Foster and col-
leagues quantied the tendency for midlevel athletes to train harder than planned
on easy days and at lower intensity than planned on hard days, relative to coach
prescriptions.5 It is important to point out that integrated approaches that multiply
training session time by a physiological or perceptual measure of intensity (yield-
ing TRIMPS6 or LOAD7,8) have also been developed and used to quantify training
Table 1 Example of a five-zone intensity scale to prescribe and
monitor training of endurance athletes
Typical accumulated duration
1 50–65 60–72 0.8–1.5 1–6 h
2 66–80 72–82 1.5–2.5 1–3 h
3 81–87 82–87 2.5–4 50–90 min
4 88–93 88–92 4.0–6.0 30–60 min
5 94–100 93–100 6.0–10.0 15–30 min
Note. This scale is typical of intensity zone scales used for endurance training prescription and moni-
toring. The scale above was developed by the Norwegian Olympic Federation as a general guideline
based on years of testing of cross-country skiers, rowers, and biathletes.
exposure. However, in this review, I will focus on training intensity distribution,
and these integrated approaches will not be presented in detail.
Several recent studies examining training intensity distribution9–11 or perfor-
mance intensity distribution in multiday events12,13 have employed individually
determined rst and second ventilatory turn points to demarcate three intensity
zones (Zone 1, Zone 2, and Zone 3; Figure 1). Intensity distribution studies based
on ventilatory threshold–derived zones are not directly comparable with the ve-
zone model, but what is typically identied as “lactate threshold intensity,” or the
approximately 2 to 4 mM blood lactate concentration range, corresponds well in
practice with the intensity zone demarcated by the rst and second ventilatory turn
points. Thus, for practical purposes, the three-zone model and ve-zone model
have common intensity anchor points around the lactate threshold. For well-trained
athletes, I will use the term low-intensity training (LIT) to refer to work eliciting
a stable lactate concentration of less than approximately 2 mM. 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 (ThT). For untrained / recreation-
ally trained subjects, we nd that a 2 mM lactate turn point is difcult to identify
because blood lactate often approaches this concentration already at very low
workloads (unpublished observations).
Published studies reporting the training characteristics of endurance athletes
have employed several methods of quantifying intensity distribution. Self-report of
training pace based on questionnaire and anchoring with different running paces (eg,
below-marathon pace, 10 K pace, 3 K pace) has been used alone14 and in conjunc-
tion with physiological testing.15 Intensity distribution based on standardized blood
lactate ranges and representative sampling during workouts has been reported for
elite swimmers16. “Time-in-zone” heart rate analysis has been employed based on
quantication of the training time spent within different heart rate ranges identi-
ed from preliminary threshold testing.9,10,17 The latter method gives total duration
and percentage of time with heart rate within each intensity zone. This method is
Figure 1 — A three-intensity-zone model based on identication of ventilatory thresholds.
Training Intensity Distribution 279
appealing since it is noninvasive, individualized, and straightforward analytically.
However, heart rate time-in-zone tends to underestimate the time spent working
at high intensity (due to heart rate lag time during intervals). More importantly, it
does not seem to correspond well with perceived effort for a given workout.10 For
example, applying heart rate time-in-zone analysis to an interval session such as 4
× 4 min at a workload eliciting 95% VO2max preceded by a 20 min warm-up and
followed by a 20 min cool-down will result in both average session heart rate and
time-in-zone distribution (dominated by time spent at low intensity) that misrepresent
the perceived effort and blood lactate prole of the session and probably also under-
represent the autonomic stress load.18 Nominally allocating each training session to
an intensity zone based on the intensity of the primary part of the workout, the “ses-
sion goal approach,” yields better matching between heart rate analysis and athlete
perception of session effort, or “session RPE,” in both cross-country skiers10 and
1st-division Norwegian soccer players (unpublished data). Typical software-based
heart rate analysis methods overestimate the amount of time spent training at low
intensity and underestimate the time spent at very high workloads, compared with
athlete perception of effort for a training bout. In training organization, the unit of
stress perceived and responded to by the athlete is the stress of entire training ses-
sions or perhaps training days, not minutes in any given heart rate zone.
How Do Elite Endurance Athletes Train?
Good empirical descriptions of the distribution of training intensity in well-trained
athletes constitute a fairly recent addition to the sport science literature. In 1991,
Robinson et al19 published “the rst attempt to quantify training intensity by use
of objective, longitudinal training data.” They studied training characteristics of 13
national-class male New Zealand runners with favorite distances ranging from 1500
m to the marathon. They 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 to
8 wk corresponding to the preparation phase, athletes reported that only 4% of all
training sessions were interval workouts or races. For the remaining training ses-
sions, average heart rate was 77% of their heart rate at 4 mM blood lactate (which
translates to approx. 60% of VO2max).
Billat et al performed physiological testing and training diary data collection
of elite French and Portuguese marathoners.15 They classied training intensity in
terms of several specic velocities: less than v-marathon, v-10,000m, and v-3,000m.
During the 12 wk 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 v-10K or v-3K (likely to
be >VT2). This distribution of training intensity was identical in both high-level
(< 2 h 16 min or < 2 h 38 min for males or females) and elite performers (< 2 h 11
min or < 2 h 32 min for males and females). But the elite athletes ran more total
kilometers and proportionally more kilometers at or above v-10K. Examination of
data from another descriptive study by Billat et al on elite male and female Kenyan
5 and 10 K runners demonstrated that approximately 85% of their weekly training
kilometers were run at below–lactate threshold velocity.20
Esteve-Lanao et al9 analyzed over 1000 heart rate records using the time-in-
zone approach to quantify the training of eight regional- and national-class Spanish
distance runners over a 6 mo period. Intensity zones were established with treadmill
testing. On average these athletes ran 70 km·wk–1 during the 6 mo period. Seventy-
one percent of running time was <VT1, 21% between VT1 and VT2, and 8% >VT2.
Mean training intensity was 64% VO2max. They also reported that performance
times in both long and short races were inversely correlated with total training
time in zone 1. They found no correlation between the volume of HIT performed
and race performance.
Rowers compete over a 2000 m distance requiring 6 to 7 min. Steinacker et al21
reported that extensive endurance training (60 to 120 min 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% to 10% of the
total rowed time. The data also suggested that German rowers preparing for the
world championships performed essentially no rowing at ThT intensity, but instead
trained either LIT or HIT in the 6 to 12 mM range.
Fiskerstrand and Seiler22 examined historical developments in training orga-
nization among elite rowers. Using questionnaire data, athlete training diaries, and
physiological testing records, they quantied training intensity distribution in 27
Norwegian athletes who had won world or Olympic medals in the 1970s, 1980s,
or 1990s. They 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 over-
speed sprint training had declined dramatically in favor of longer interval training
at 85% to 95% of VO2max, and (4) the number of altitude camps attended by the
athletes increased dramatically. Over this 30 y 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 al23 described the training of world-class junior rowers from Ger-
many during a 37-wk period culminating in national championships and qualica-
tion 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. Using the time-in-zone heart rate analysis method described above, fully
95% of all endurance training time was performed as LIT. This heavy dominance
of extensive endurance training persisted throughout the 9 mo period. However,
the relatively small volume of ThT and HIT shifted toward higher intensities from
the basic preparation phase to the competition phase. That is, the overall intensity
distribution became more polarized as athletes approached competition.
Professional road cyclists are known for performing very high training volumes,
up to 30 to 35,000 km·yr–1. Zapico and colleagues used the three-intensity zone
model to track training characteristics from November to June in a group of elite
Spanish U23 riders.11 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 2 compares the training intensity distribution
in the winter and spring mesocycles. Figure 3 shows physiological test results at
baseline, and at the end of each training mesocycle. Comparison of the training
Training Intensity Distribution 281
intensity distributions in the two periods shows that there was both an increase in
total training volume and a 4× 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, despite
a clear training intensication. Anecdotally, this is not an unusual nding. Time
at VO2max or time at VT2 power may be more sensitive variables to evaluate the
impact of intensied training in highly trained athletes with stable threshold and
Figure 2 — Cycling intensity and volume of elite Spanish U23 cyclists training in the
period November to June. Data redrawn from Zapico et al.11
Figure 3 — Response to periodization of training intensity and volume in elite Spanish U23
cyclists (see Figure 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.11
Cross-country skiing has adopted spectator-friendly 1000 to 1500 m sprint
races in the last decade (contested as a knockout tournament). Recently, Sandbakk
et al compared the training and physiology of eight international-class and eight
national-class (Norway) sprint cross-country skiers.24 The internationally elite
skiers distinguished themselves with higher VO2peak, vVO2peak, and exercise
time at VO2peak. Over a 6 mo registration period, the world-class skiers trained
about one-third greater volume (445 h vs 341), with almost all of this difference
in training time due to greater volumes of low-intensity training (86 more hours)
and speed training (9 more hours). The two groups performed identical volumes
of HIT over 6 mo (19 h in both groups, or about 45 min·wk–1).
Schumacher and Mueller25 demonstrated the validity of power balance model-
ing in predicting “gold medal standards” for physiological testing and power output
in the 4,000 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. These athletes trained to maintain an average com-
petition intensity of over 100% of power at VO2max with a program dominated
by LIT (29,000–35,000 km·y–1). In the 200 d preceding the Olympics, the pursuit
team performed “low-intensity, high-mileage” training at 50 to 60% of VO2max
on approximately 140 d. Stage races comprised approximately 40 d. Specic track
cycling at near competition intensities was performed on fewer than 20 d between
March and September. In the approximately 110 d preceding the Olympic nal,
high-intensity interval track training was performed on only 6 d.
The descriptive studies above highlight the paradoxical nding that even though
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. The duration of monitoring from published studies
varies from weeks to an entire season but seems to converge on a common intensity
distribution: about 80% of training sessions are LIT intensity and the remaining
20% are performed as ThT or HIT. For an athlete training 10 to 14 times per week,
this means that two to three of these sessions would be ThT or HIT training bouts.
This distribution ts well with ndings that adding two interval sessions per week
for 4 to 8 wk improves performance by 2% to 4% among well-trained endurance
athletes doing only basic endurance training.26–29 Additional increases in HIT
frequency do not induce further improvements and tend to induce symptoms of
Training Intensification Studies
Despite the consistency with which this general distribution is observed, one
can question whether the “80-20” training intensity distribution is a really a self-
organized optimum for high-performance athletes, or a product of tradition and/or
superstition. Several studies have examined the impact of training intensication
(with or without corresponding volume reduction) on physiology and/or perfor-
mance in well-trained athletes.
In 1997, Evertsen et al published the rst of three papers from a study involving
training intensication in 20 well-trained junior cross-country skiers competing
at the national or international level.32–34 In the 2 mo before study initiation, 84%
of training was carried out at 60% to 70% VO2max, with the remainder at 80% to
Training Intensity Distribution 283
90% of VO2max. They were then randomized to a moderate-intensity (MOD) or a
high-intensity training group (HIGH). The MOD group maintained essentially the
same training intensity distribution, but training volume was increased from 10 to
16 h·wk–1. The HIGH group reversed their baseline intensity distribution so that
83% of training time was performed at 80% to 90% of VO2max, with only 17%
performed as low-intensity endurance training. The HIGH group trained 12 h·wk–1.
The training intervention period lasted 5 mo. Intensity control was achieved using
heart rate monitoring and blood lactate sampling throughout the training period.
Despite 60% more training volume in MOD and approximately four times more
training at an intensity greater than or equal to lactate threshold in HIGH, physi-
ological and performance changes were quite modest in both groups of already
well-trained athletes (Table 2).
Gaskill et al reported the results of a 2 y project involving 14 cross-country
skiers.35 During the rst year, athletes trained similarly, averaging 660 training
hours with 16% HIT (nominal distribution of sessions). Physiological test results
and race performances during the rst year were used to identify seven athletes
who responded well to the training and seven who showed poor VO2max and lactate
threshold progression, and race results. In the second year, the positive respond-
ers continued using their established training program whereas the nonresponders
performed a markedly intensied training program with a slight reduction in train-
ing hours. They observed that the nonresponders from year 1 showed a positive
response to the intensied program in year 2 (VO2max, lactate threshold, race
result points). The positive responders from year 1 showed a similar development
in year 2 as year 1.
Esteve-Lanao et al randomized 12 subelite distance runners to one of two
training groups (Z1 and Z2) that were carefully monitored for 5 mo.36 They based
Table 2 Summary of responses to training intensification in well-
trained cross-country skiers32–34
(n = 10)
(n = 10)
VO2max ↔ ↔
Lactate-threshold speed ↑ 3% ↔
20-min run at 9% grade ↑ 3.8% ↑ 1.9%
Fiber type ↔ ↔
MCT 1 transporter ↔ ↓ 12%
MCT 4 transporter ↔ ↔
Citrate synthase ↔ ↔
Succinate dehydrogenase ↑ 6% ↔
Na/K pump ↑ ?% ↑ ?%
Note. A summary of results from refs. 32–34.
their training intensity distribution on the three-zone model described earlier. Based
on time-in-zone heart rate monitoring, Z1 performed 81, 12, and 8% of training in
zones LIT, ThT, and HIT respectively. The Z2 group performed more ThT, with
67, 25, and 8% of training performed in the three respective zones. Anecdotally,
the authors reported that in pilot efforts, they were unable to increase the total time
spent in intensity zone 3, as it was too hard for the athletes. Total training load was
matched between the groups using a modication of TRIMPS. Improvement in a
time trial performed before and after the 5 mo period revealed that the group that
had trained more zone 1 training showed signicantly greater race time improve-
ment (–157 ± 13 s vs –121.5 ± 7.1 s, P = .03).
Ingham et al37 randomized 18 experienced U.K. 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-d postseason “training-
free” period just before baseline testing, followed by a 12 wk period of rowing
ergometer training. One group performed 98% of all training between 60 and 75%
of peak oxygen consumption (LIT). The other group performed 70% training at
60% to 75% VO2max, as well as 30% of training at an intensity 50% of the way
between power at LT and power at VO2peak (MIX). In practice, the MIX group
performed HIT on 3 d·wk–1. The two groups performed virtually identical volumes
of training (approx. 1140 km on the ergometer), with ±10% individual variation.
Results of the study are summarized in Table 3. Sixteen of 18 subjects set new
personal bests for the 2000 m ergometer test at the end of the study. The authors
concluded that LIT and MIX training had similar positive effects on performance
and VO2max. The LIT regimen appeared to induce a greater right-shift in the blood
lactate prole during submaximal exercise, but this did not translate to a signicantly
greater gain in ergometer performance.
Table 3 Physiological and performance changes after two rowing
(n = 9)
(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 ↔ ↔
* P < .05 vs. LOW vs. MIXED.
Periodization of Training Variables
Elite endurance athletes train systematically >11 mo out of the year and may per-
form over 600 individual training sessions, all with the goal of achieving maximal
performance at a specic time in the season. Further, peak athlete development
may take 10 y of specic training,38 with highly successful athletes often using
Training Intensity Distribution 285
a 2- or 4-year cycle of preparation for world championships or Olympic events.
Training is planned in different periods or training cycles. Periodization language
often incorporates phase-duration terms such as micro-, meso-, and macrocyle, but
this taxonomy has evolved from coaching practice, not research. For the purposes
of this review I use the term short-term periodization to describe manipulation of
daily training variables over a few days up to a few weeks. Long-term periodization
of training refers to manipulation of training into cycles lasting weeks to several
months. Short-term manipulation of intensity and duration loads seems to be very
important for maintaining the athlete’s health and tolerance for training. Long-term
periodization is designed to facilitate the development of capacity over time, and
ensure that peak performance is timed appropriately.
Since Matveyev introduced his now-classic model of periodization of volume
and intensity in training four decades ago,39 there has been considerable debate
regarding how best to organize long-term exposure to training stimuli (ie, volume,
intensity, mode) for modern endurance athletes. A number of long-term periodiza-
tion structures have been conceptualized and described.39–43 However, controlled
studies comparing the impact of these different organizational structures on endur-
ance performance are lacking. One underlying assumption that inuences long-term
training organization principles in endurance training seems to be that adaptation
of peripheral and central components of the respiratory chain are differentially
impacted by training intensity and duration, with differing time courses and adap-
tive scope. Myocardial function may be somewhat more responsive to the greater
ventricular lling and preload associated with near-maximal exercise intensity.1,2
The physiological and performance impact of adding HIT to endurance-trained
athletes who have not been performing HIT is rapid.26–28 However, other rapidly
derived benets of HIT, such as increased buffer capacity,28 and relevant pacing
experience are likely to be integrated into this performance impact as well. The
cardiovascular impact of further intensity amplication in already well-trained
(LIT+HIT) subjects appears limited at best.11,30 In contrast, peripheral adaptations
such as capillary densication and mitochondrial volume expansion (measured
directly or indirectly as improvements in fractional utilization capacity) appear to
(1) continue to respond to training over many months44 and (2) appear responsive
to large volumes of LIT.11,37,45 At the same time, there is some evidence suggest-
ing that the blood lactate–power relationship may actually be neutral to, or even
negatively impacted by, large volumes of HIT in well-trained athletes.37,45 However,
mechanistic explanations for these observations are lacking.
Few studies have actually documented the intensity and volume distribution
of endurance athletes over multiple phases of their annual training cycle.11,23,25,35
These studies—unpublished case histories of elite performers, and feedback from
coaches—all suggest that although there is a clear increase in HIT moving from
the preparation to competition period, the emphasis on substantial volumes of
low-intensity training remains quite strong. 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.46 For example, we have
recently observed that whereas lactate prole responses to standardized testing
before and after a 12 wk 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.45 Progress in understanding
long-term periodization will likely require systematic athlete monitoring by govern-
ing bodies or Olympic centers in cooperation with sport scientists.
Short-term periodization of training, 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 (ie, physiological
adaptations) and fatigue (ie, stress responses and associated negative health out-
comes). This practical dichotomization was introduced by Banister and colleagues
in their modeling studies of the training process.6,47,48 The predictive value and
stability of their mathematical approach to the relationship between training input
and tness outcome has been challenged.49 Conceptually, the model remains useful
in that it predicts that 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).
Over a period of days, athletes normally perform LIT and ThT/HIT sessions.
Horses are trained similarly, with alternating “easy days” of continuous running
and “hard days” of interval training. Bruin and colleagues50 performed a long-term
training study of horses in which they manipulated the hard-easy rhythm of the
horses’ training in two ways. After 187 d of daily training in hard-easy fashion, hard
training days were intensied by performing more total high-intensity running, with
easy days left unchanged. The horses exhibited improved running performance over
the next 75 d. After 261 d, the easy days were intensied by having the horses run
faster for the same duration. Within 5 d, the horses were no longer able to complete
the HIT and showed clear signs of decompensation and overtraining symptoms.
Foster extended this nding to human athletes and conceptualized training monotony
as increasing the risk of negative adaptations to training.51 High training stress was
quantied as a product of large training volumes, high perceived intensity, and low
day-to-day variation in training load. Elite athletes often train twice or even three
times per day, making the rest interval between training sessions typically between
4 and 12 h. Achieving this training frequency without excessive stress appears to
require careful management of training intensity.
Connecting Training Characteristics to Cellular
Signaling and Stress Responses
The studies outlined above combine to suggest that over the long term, (1) success-
ful endurance athletes achieve excellent results when accumulating a high train-
ing volume by emphasizing frequent exposure to 60 to 180 min bouts performed
at approximately 60 to 75% of VO2max (ie, LIT) in combination with a modest
proportion of training performed at intensities between 85 and 100% of VO2max
(about 20% of training sessions), and (2) when HIT is heavily emphasized by adding
interval workouts and decreasing the volume of LIT, the effects are equivocal at
best. While these conclusions are based on a growing body of published studies,
they are unrevealing and unsatisfying from a mechanistic viewpoint.
Ultimately, endurance training is a stimulus for cell signaling, gene expres-
sion, and resulting increased rates of protein synthesis. Changes in physiological
capacity over time are hypothesized to be the net result of transient increases in
gene expression during recovery from repeated bouts of exercise.52 It is therefore
Training Intensity Distribution 287
appealing to try to link training behavior to cellular events associated with training
adaptation. Unfortunately, details regarding how intensity and duration of exercise
combine to modulate cell signaling are only beginning to emerge in the literature.
What is known is that multiple signaling pathways exist;53 redundancies among
mechanical, metabolic, neuronal, and hormonal signaling factors are likely;52
intensity and duration effects on signaling may interact in ber type–specic
ways;54 and the potency of the gene expression response to a given exercise signal
(intensity × duration) changes rapidly with repeated exercise.55,56 At present, any
attempt to reconcile training behavior in elite performers with the molecular biol-
ogy of cellular signaling is doomed to some measure of both incompleteness and
overinterpretation. Accepting that, one simple reconciliation of signaling studies
with athlete practice might be that (1) exercise duration and exercise intensity can
drive gene expression for mitochondrial protein proliferation through different
pathways and (2) ceiling effects for signal amplitude are seen rapidly with repeated
high-intensity interval exercise, whereas increased exercise frequency at reduced
intensity may provide greater scope for expansion of the total signal (amplitude ×
frequency) for gene expression.
Training induces stress responses as well. Increased training intensity is asso-
ciated with a nonlinear increase in sympathetic stress that appears to track well
with relative intensity increases and the lactate prole.57 In highly trained athletes,
training more frequently and/or for longer durations at relatively low exercise
intensities may induce a lower overall stress load and facilitate more rapid recovery
compared with highly intensive training sessions above the lactate threshold.18 An
intensity distribution strategy that allows frequent training (twice daily) may give
an important long-term adaptive advantage via what can be conceptually described
as optimization of the ratio between adaptive signal and stress response. Recent
studies comparing twice daily training with training the same total volume every
other day suggest that training twice daily induced greater peripheral adaptations.58,59
One mechanism for this benet may be the signal-amplifying effect of reduced
muscle glycogen (in the second daily workout). We have also found that autonomic
nervous system recovery (measured via heart rate variability) is very rapid after
training bouts at 60% VO2max for up to 120 min, but becomes markedly delayed
in highly trained subjects when exercise intensity increases to an intensity eliciting
>3 mM blood lactate. We also observed that highly trained subjects (often training
twice daily) recovered parasympathetic control after a standardized HIT session
dramatically faster than a group of subjects training about once a day.18 Similarly,
elite female rowers can train for 2 h at 60% VO2max with only minor hormonal
or immune system disturbance.60 Unfortunately, longitudinal data are needed to
reveal whether progression in training volume and frequency gradually induces,
or is naturally facilitated by, more rapid recovery of the autonomic nervous system
and hormonal balance after training. Thus, the question could be posed as, is rate
of recovery from training a trainable characteristic of the endurance athlete?
There is reasonably strong evidence for concluding that an approximate 80-to-
20 ratio of LIT to ThT/HIT intensity training gives excellent long-term results
among endurance athletes. Frequent, low-intensity (≤2 mM blood lactate), longer
duration training is effective in stimulating physiological adaptations. The idea of
a dichotomous physiological impact of HIT and LIT is probably exaggerated, as
both methods seem to generate overlapping physiological adaptation proles and
are likely complementary. Over a broad range, increases in total training volume
correlate well with improvements in physiological variables and performance. HIT
is a critical component in the training of all successful endurance athletes. However,
about two HIT training sessions per week seems to be sufcient for inducing physi-
ological adaptations and performance gains without inducing excessive stress over
the long term. When already well-trained athletes markedly intensify training over
weeks to months, the impact is equivocal, with reported effects varying widely. In
athletes with an established endurance base and tolerance for relatively high training
loads, intensication of training may yield small performance gains at acceptable
risk of negative outcomes. An established 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. Periodization of
training by elite athletes is achieved with modest reductions in total volume and a
careful increase in the volume of training performed above the lactate threshold as
athletes transition from preparation to competition training phases. Greater polar-
ization of training intensity characterizes this transition, both in terms of the net
training distribution as well as within micro- and macrocycles of training. However,
compared with classic training periodization models, with large swings in volume
and intensity, the basic intensity distribution remains quite similar throughout the
year. Almost no research is available investigating the impact of different models
of long-term training periodization for endurance athletes.
1. Daussin FN, Ponsot E, Dufour SP, et al. Improvement of VO2max by cardiac output
and oxygen extraction adaptation during intermittent versus continuous endurance
training. Eur J Appl Physiol. 2007;101:377–383.
2. Helgerud J, Hoydal K, Wang E, et al. Aerobic high-intensity intervals improve VO2max
more than moderate training. Med Sci Sports Exerc. 2007;39:665–671.
3. Beneke R, Leithauser RM, Hutler M. Dependence of the maximal lactate steady state
on the motor pattern of exercise. Br J Sports Med. 2001;35:192–196.
4. Beneke R, von Duvillard SP. Determination of maximal lactate steady state response
in elected sports events. Med Sci Sports Exerc. 1996;28:241–246.
5. Foster C, Heiman KM, Esten PL, et al. Differences in perceptions of training by coaches
and athletes. South African Journal of Sports Medicine. 2001;8:3–7.
6. Banister EW, Good P, Holman G, et al. Modeling the training response in athletes. In:
Landers DM, ed. Sport and elite performers. Champaign: Human Kinetics; 1986:7–23.
7. Foster C, Daines E, Hector L, et al. Athletic performance in relation to training load.
Wis Med J. 1996;95:370–374.
8. Foster C, Hector LL, Welsh R, et al. Effects of specic versus cross-training on running
performance. Eur J Appl Physiol Occup Physiol. 1995;70:367–372.
9. Esteve-Lanao J, San Juan AF, Earnest CP, et al. How do endurance runners actually train?
Relationship with competition performance. Med Sci Sports Exerc. 2005;37:496–504.
10. Seiler KS, Kjerland GO. Quantifying training intensity distribution in elite endurance
athletes: is there evidence for an “optimal” distribution? Scand J Med Sci Sports.
Training Intensity Distribution 289
11. Zapico AG, Calderon FJ, Benito PJ, et al. Evolution of physiological and haematological
parameters with training load in elite male road cyclists: a longitudinal study. J Sports
Med Phys Fitness. 2007;47:191–196.
12. Lucia A, Hoyos J, Carvajal A, et al. Heart rate response to professional road cycling:
the Tour de France. Int J Sports Med. 1999;20:167–172.
13. Lucia A, Hoyos J, Santalla A, et al. Tour de France versus Vuelta a Espana: which is
harder? Med Sci Sports Exerc. 2003;35:872–878.
14. Karp JR. Training characteristics of qualiers for the U.S. Olympic Marathon Trials.
Int J Sports Physiol Perform. 2007;2:72–92.
15. Billat VL, Demarle A, Slawinski J, et al. Physical and training characteristics of top-
class marathon runners. Med Sci Sports Exerc. 2001;33:2089–2097.
16. Mujika I, Chatard JC, Busso T, et al. Effects of training on performance in competitive
swimming. Can J Appl Physiol. 1995;20:395–406.
17. Esteve-Lanao J, Lucia A, deKoning JJ, et al. How do humans control physiological
strain during strenuous endurance exercise? PLoS ONE. 2008;3:e2943.
18. Seiler S, Haugen O, Kuffel E. Autonomic recovery after exercise in trained athletes:
intensity and duration effects. Med Sci Sports Exerc. 2007;39:1366–1373.
19. Robinson DM, Robinson SM, Hume PA, et al. Training intensity of elite male distance
runners. Med Sci Sports Exerc. 1991;23:1078–1082.
20. Billat V, Lepretre PM, Heugas AM, et al. Training and bioenergetic characteristics
in elite male and female Kenyan runners. Med Sci Sports Exerc. 2003;35:297–304;
21. Steinacker JM, Lormes W, Lehmann M, et al. Training of rowers before world cham-
pionships. Med Sci Sports Exerc. 1998;30:1158–1163.
22. Fiskerstrand A, Seiler KS. Training and performance characteristics among Norwegian
international rowers 1970-2001. Scand J Med Sci Sports. 2004;14:303–310.
23. Guellich A, Seiler S, Emrich E. Training Methods and Intensity Distribution of Young
World-Class Rowers. Int J Sports Physiol Perform. 2009;4:448–460.
24. Sandbakk Ø, Holmberg HC, Leirdal S, et al. The Physiology of World Class Sprint
Skiers. Scand J Med Sci Sports., 2010. doi: 10.1111/j.1600-0838.2010.01117.x.
25. Schumacher YO, Mueller P. The 4000-m team pursuit cycling world record: theoretical
and practical aspects. Med Sci Sports Exerc. 2002;34:1029–1036.
26. Lindsay FH, Hawley JA, Myburgh KH, et al. Improved athletic performance in highly
trained cyclists after interval training. Med Sci Sports Exerc. 1996;28:1427–1434.
27. Stepto NK, Hawley JA, Dennis SC, et al. Effects of different interval-training programs
on cycling time-trial performance. Med Sci Sports Exerc. 1999;31:736–741.
28. Weston AR, Myburgh KH, Lindsay FH, et al. Skeletal muscle buffering capacity and
endurance performance after high-intensity interval training by well-trained cyclists.
Eur J Appl Physiol Occup Physiol. 1997;75:7–13.
29. Driller MW, Fell JW, Gregory JR, et al. The effects of high-intensity interval training
in well-trained rowers. Int J Sports Physiol Perform. 2009;4:110–121.
30. Billat VL, Flechet B, Petit B, et al. Interval training at VO2max: effects on aerobic
performance and overtraining markers. Med Sci Sports Exerc. 1999;31:156–163.
31. Halson SL, Jeukendrup AE. Does overtraining exist? An analysis of overreaching and
overtraining research. Sports Med. 2004;34:967–981.
32. Evertsen F, Medbo JI, Bonen A. Effect of training intensity on muscle lactate transporters
and lactate threshold of cross-country skiers. Acta Physiol Scand. 2001;173:195–205.
33. Evertsen F, Medbo JI, Jebens E, et al. Effect of training on the activity of ve muscle
enzymes studied on elite cross-country skiers. Acta Physiol Scand. 1999;167:247–257.
34. Evertsen F, Medbo JI, Jebens E, et al. Hard training for 5 mo increases Na(+)-
K+ pump concentration in skeletal muscle of cross-country skiers. Am J Physiol.
35. Gaskill SE, Serfass RC, Bacharach DW, et al. Responses to training in cross-country
skiers. Med Sci Sports Exerc. 1999;31:1211–1217.
36. Esteve-Lanao J, Foster C, Seiler S, et al. Impact of training intensity distribution on
performance in endurance athletes. J Strength Cond Res. 2007;21:943–949.
37. Ingham SA, Carter H, Whyte GP, et al. Physiological and performance effects of low-
versus mixed-intensity rowing training. Med Sci Sports Exerc. 2008;40:579–584.
38. Balyi I. Long-term athletic development: the B.C. approach. Sports Aider. 2002;18:1–4.
39. Matwejew LP. Periodisering des sportlichen Trainings. Berlin: Bartels & Wernitz;
40. Issurin V. Block periodizarion versus traidtional training theory: a review. J Sports Med
Phys Fitness. 2008;48:65–75.
41. Issurin V. A modern apporach to high.performance training: the block composition. In:
Blumenstein B, Lidor R, Tenenbaum G, eds. Psychology of Sport Training. Oxford:
Meyer & Meyer Sport; 2007:216–234.
42. Tschiene P. Einige neue Aspekte zur Periodiserung des Hochleistungstrainings. Leis-
43. Tschiene P. Veranderungen in der Struktur des Jahrestrainingszyklus. Leichtathletik.
44. Tyler CM, Golland LC, Evans DL, et al. Skeletal muscle adaptations to prolonged
training, overtraining and detraining in horses. Pugers Arch. 1998;436:391–397.
45. Guellich A, Seiler S. Lactate prole changes in relation to training characteristics in
junior elite cyclists. Int J Sports Physiol Perform. 2010;5:316–327.
46. Ingjer F. Maximal oxygen uptake as a predictor of performance ability in women and
men elite cross country skiers. Scand J Med Sci Sports. 1991;1:25–30.
47. Banister EW, Calvert TW. Planning for future performance: implications for long term
training. Can J Appl Physiol. 1980;5:170–176.
48. Morton RH, Fitz-Clarke JR, Banister EW. Modeling human performance in running.
J Appl Physiol. 1990;69:1171–1177.
49. Hellard P, Avalos M, Lacoste L, et al. Assessing the limitations of the Banister model
in monitoring training. J Sports Sci. 2006;24:509–520.
50. Bruin G, Kuipers H, Keizer HA, et al. Adaptation and overtraining in horses subjected
to increasing training loads. J Appl Physiol. 1994;76:1908–1913.
51. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med
Sci Sports Exerc. 1998;30:1164–1168.
52. Fluck M, Hoppeler H. Molecular basis of skeletal muscle plasticity–from gene to form
and function. Rev Physiol Biochem Pharmacol. 2003;146:159–216.
53. Coffey VG, Hawley JA. The molecular bases of training adaptation. Sports Med.
54. Hildebrandt AL, Pilegaard H, Neufer PD. Differential transcriptional activation of
select metabolic genes in response to variations in exercise intensity and duration. Am
J Physiol Endocrinol Metab. 2003;285:E1021–E1027.
55. McConell GK, Lee-Young RS, Chen ZP, et al. Short-term exercise training in humans
reduces AMPK signalling during prolonged exercise independent of muscle glycogen.
J Physiol. 2005;568:665–676.
56. Yu M, Stepto NK, Chibalin AV, et al. Metabolic and mitogenic signal transduction in
human skeletal muscle after intense cycling exercise. J Physiol. 2003;546:327–335.
57. Chwalbinska-Moneta J, Kaciuba-Uscilko H, Krysztoak H, et al. Relationship between
EMG blood lactate, and plasma catecholamine thresholds during graded exercise in
men. J Physiol Pharmacol. 1998;49:433–441.
58. Hansen AK, Fischer CP, Plomgaard P, et al. Skeletal muscle adaptation: training twice
every second day vs. training once daily. J Appl Physiol. 2005;98:93–99.
Training Intensity Distribution 291
59. Yeo WK, Paton CD, Garnham AP, et al. Skeletal muscle adaptation and performance
responses to once a day versus twice every second day endurance training regimens.
J Appl Physiol. 2008;105:1462–1470.
60. Nieman DC, Nehlsen-Cannarella SL, Fagoaga OR, et al. Immune response to two hours
of rowing in elite female rowers. Int J Sports Med. 1999;20:476–481.