ArticlePDF AvailableLiterature Review

What is Best Practice for Training Intensity and Duration Distribution in Endurance Athletes?


Abstract and Figures

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 fitness and performances 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 intensification 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.
Content may be subject to copyright.
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?
Stephen Seiler
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
intensication 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 quantied. 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-specic 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 quantied 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
(% max)
Heart rate
(% max)
Typical accumulated duration
within zone
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.
278 Seiler
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 identied 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 difcult 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
quantication 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 identication 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 prole 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 classied training intensity in
terms of several specic 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
280 Seiler
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 quantied 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 qualica-
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 intensication. 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 intensied training in highly trained athletes with stable threshold and
VO2max results.
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
282 Seiler
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. Specic 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 intensication
(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 intensication 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 intensied training program with a slight reduction in train-
ing hours. They observed that the nonresponders from year 1 showed a positive
response to the intensied 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)
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 ?% ?%
Note. A summary of results from refs. 32–34.
284 Seiler
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 modication 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 signicantly 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 prole during submaximal exercise, but this did not translate to a signicantly
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 specic time in the season. Further, peak athlete development
may take 10 y of specic 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 inuences 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 benets 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 amplication in already well-trained
(LIT+HIT) subjects appears limited at best.11,30 In contrast, peripheral adaptations
such as capillary densication 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 prole 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
286 Seiler
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 intensied 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 intensied 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
quantied 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–specic
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 prole.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 benet 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
288 Seiler
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 proles 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 sufcient 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, intensication 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 specic 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 qualiers 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;
discussion 305–296.
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.
290 Seiler
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-
tungsport. 1977;7:379–382.
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. Pugers Arch. 1998;436:391–397.
45. Guellich A, Seiler S. Lactate prole 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, Krysztoak 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.
... Conversely, the physical demand for track and field endurance athletes is well characterized, allowing practitioners to focus on specific intensity indicators. Actually, intensity criteria for endurance athlete have been selected based on %VȮ 2 max, % HR max, lactate concentrations, and self-report of training pace based on questionnaire and anchoring with different running paces (e.g., below-marathon pace, 10 K pace, and 3 K pace) coherently with physical demands (i.e., cyclic running activity involving aerobic metabolism prevalently) (79). ...
... For example, in running, speed of movement (m·s 21 ) has been considered as an absolute external exercise intensity indicator, whereas power (W), acceleration (m·s 22 ), and force (N) reflect absolute external exercise intensity in cycling or rowing (81). Regarding absolute internal exercise intensity, physiological responses such as oxygen consumption (VȮ 2 ) expressed in liters per minute (L·min 21 ) or HR (beats per minute [b·min 21 ]) have been evaluated (21,79). In addition, differently from absolute values, relative ones provide normalized score-both external (e.g., percentage of maximal speed or power) and internal (e.g., RPE and percentages of HRmax or VȮ 2 max) loads-in accordance with sport's demand (45,46,55,81). ...
... Previous studies have reported several conceptual issues considering both internal and external intensity indicators in soccer. For example, the literature found that S-RPE and HR derived measures were more associated with volume (i.e., total distance and low running intensity) than intensity (79,87). Moreover, it was also found that this correlation is lower when intensity thresholds increase (79,87). ...
Pillitteri, G, Clemente, FM, Petrucci, M, Rossi, A, Bellafiore, M, Bianco, A, Palma, A, and Battaglia, G. Toward a new conceptual approach to "intensity" in soccer player's monitoring: A narrative review. J Strength Cond Res 37(9): 1896-1911, 2023-In the last decade, monitoring physiological and match-related demands in soccer has become an increasingly common practice in sports sciences. One of the great challenges during monitoring process is the identification of key indicators that permit to generalize evidence and sustain decision-making process during training prescription. Actually, one of the major debates in the scientific community and among practitioners is the identification of the "intensity" concept. Defining a given training session or exercise based on "intensity" is difficult due to the fact that a huge amount of indicators are available (related both to the performed activities and to the athletes' psychophysiological responses). These indicators can lead to specific outcomes with different interpretations. The current narrative review aims to discuss the different measures approaches used in soccer to describe the intensity for both internal and external demands. In addition, a second purpose of this review is to propose general recommendations for combining intensity indicators with the aim of defining an overall intensity score of a training session or drill.
... Over recent decades, endurance training optimisation has attracted considerable attention in the scientific literature, in an attempt to provide a more scientific basis to endurance performance through 'evidence-informed' coaching practice. In this sense, training strategies which seek to optimise physiological adaptations have been widely investigated, with a particular emphasis on training intensity distribution [e.g., [1][2][3], exercise modalities [e.g., [4][5][6][7][8][9] and the manipulation of training variables [e.g., [10][11][12]. Ensuring an integrated approach to periodization which covers all aspects of performance is considered important for continuously eliciting adaptations, managing fatigue/recovery, and avoiding stagnation during an athlete's competitive season [13][14][15][16]. ...
... Therefore, the purpose of the present review was to systematically investigate the effects of different HIIT/SIT interventions in comparison to low-intensity training (LIT) or MICT on physiological and performance adaptations in trained cyclists. To address the lack of reviews discriminating between HIIT and SIT, the secondary aims of this investigation were: (1) to examine the potential effects of HIIT differing in interval work-bout duration on performance outcomes; (2) to determine whether traditional HIIT modality is superior in inducing performance adaptations in comparison with SIT (or vice-versa); and (3) to investigate the moderating effects of intervention length in relation to overarching training adaptations. ...
... During the initial search, the following search limits were selected in order to optimise the search strategy: (1) Abstract available, (2) Journal articles, (3) Humans and (4) English language. ...
Full-text available
Background: In endurance cycling, both high-intensity interval training (HIIT) and sprint interval training (SIT) have become popular training modalities due to their ability to elicit improvements in performance. Studies have attempted to ascertain which form of interval training might be more beneficial for maximising cycling performance as well as a range of physiological parameters, but an amalgamation of results which explores the influence of different interval training programming variables in trained cyclists has not yet been conducted. Objective: The aims of this study were to: (1) systematically investigate training interventions to determine which training modality, HIIT, SIT or low-to moderate-intensity continuous training (LIT/MICT), leads to greater physiological and performance adaptations in trained cyclists; and (2) determine the moderating effects of interval work-bout duration and intervention length on the overall HIIT/SIT programme. Data Sources: Electronic database searches were conducted using SPORTDiscus and PubMed. Study Selection: Inclusion criteria were: (1) at least recreationally-trained cyclists aged 18-49 years (maximum/peak oxygen uptake [V O2max/V O2peak] ≥45 mL·kg-1 ·min-1); (2) training interventions that included a HIIT or SIT group and a control group (or two interval training groups for direct comparisons); (3) minimum intervention length of 2 weeks; (4) interventions that consisted of 2-3 weekly interval training sessions. Results: Interval training leads to small improvements in all outcome measures combined (overall main effects model, SMD: 0.33 [95%CI = 0.06 to 0.60]) when compared to LIT/MICT in trained cyclists. At the individual level, point estimates favouring HIIT/SIT were negligible (Wingate model: 0.01 [95%CI =-3.56 to 3.57]), trivial (relative V O2max/V O2peak: 0.10 [95%CI =-0.34 to 0.54]), small (absolute V O2max/V O2peak: 0.28 [95%CI = 0.15 to 0.40], absolute maximum aerobic power/peak power output: 0.38 [95%CI = 0.15 to 0.61], relative absolute maximum aerobic power/peak power output: 0.43 [95%CI =-0.09 to 0.95], physiological thresholds: 0.46 [95%CI =-0.24 to 1.17]), and large (time-trial/time-to-exhaustion: 0.96 [95%CI =-0.81 to 2.73]) improvements in physiological/performance variables compared to controls, with very imprecise interval estimates for most outcomes. In addition, intervention length did not contribute significantly to the improvements in outcome measures in this population, as the effect estimate was only trivial (βDuration: 0.04 [ 95%CI =-0.07 to 0.15]). Finally, the network meta-analysis did not reveal a clear superior effect of any HIIT/SIT types when directly comparing interval training differing in interval work-bout duration. Conclusion: The results of the meta-analysis indicate that both HIIT and SIT are effective training modalities to elicit physiological adaptations and performance improvements in trained cyclists. Our analyses highlight that the optimisation of interval training prescription in trained cyclists cannot be solely explained by interval type or interval work-bout duration and an individualised approach that takes into account the training/competitive needs of the athlete is warranted.
... Aerobic training in endurance sports, such as rowing, is often divided into 5 intensity zones (T1-T5), encompassing the intensity range corresponding to 50-100% VȮ 2 peak (3,31,33). Seiler and Tønnessen (31) and Bourdon (3) both described 5-zone intensity models based on the relationship between blood lactate (BLa 21 ) concentration and exercise intensity, with lactate threshold 1 (LT1) and 2 (LT2) as physiological landmarks. However, there is no reported consensus among experts on how to demarcate intensity into 5 zones using only 2 physiological landmarks, and these models are often criticized because they do not account for individual variation in the BLa 21 response to exercise (31,33). ...
... Seiler and Tønnessen (31) and Bourdon (3) both described 5-zone intensity models based on the relationship between blood lactate (BLa 21 ) concentration and exercise intensity, with lactate threshold 1 (LT1) and 2 (LT2) as physiological landmarks. However, there is no reported consensus among experts on how to demarcate intensity into 5 zones using only 2 physiological landmarks, and these models are often criticized because they do not account for individual variation in the BLa 21 response to exercise (31,33). Consequently, the literature almost exclusively uses a broader 3-zone intensity model (zone 1 [Z1], lowintensity exercise below LT1; zone 2 [Z2], moderate-intensity exercise between LT1 and LT2; zone 3 [Z3], high-intensity exercise above LT2) to describe and compare training intensity distribution in rowing athletes. ...
... The demarcation of T4 (i.e., work performed at LT2) as a zone rather than a single physiological landmark has been described by Bourdon (3) to allow threshold training to be targeted, although this is different to other 5-zone models, which clearly split work performed above and below LT2 (31). Nonetheless, training zones in this investigation, as described by percentage heart rate (HR) max (Table 1), are similar to the 5-zone models proposed by Seiler and Tønnessen (31), Seiler (33), and Bourdon (3). In addition, the 5-zone model in this investigation accounts for individual variation in the BLa 21 response to exercises, which is reported as a potential limitation of the models by Seiler and Tønnessen (31) and Bourdon (3,33). ...
Watts, SP, Binnie, MJ, Goods, PSR, Hewlett, J, Fahey-Gilmour, J, and Peeling, P. Demarcation of intensity from 3 to 5 zones aids in understanding physiological performance progression in highly trained under-23 rowing athletes. J Strength Cond Res XX(X): 000-000, 2023-The purpose of this investigation was to compare 2 training intensity distribution models (3 and 5 zone) in 15 highly trained rowing athletes (n = 8 male; n = 7 female; 19.4 ± 1.1 years) to determine the impact on primary (2,000-m single-scull race) and secondary (2,000-m ergometer time trial, peak oxygen consumption [V̇O2peak], lactate threshold 2 [LT2 power]) performance variables. Performance was assessed before and after 4 months training, which was monitored through a smart watch (Garmin Ltd, Olathe, KS) and chest-strap heart rate (HR) monitor (Wahoo Fitness, Atlanta, GA). Two training intensity distribution models were quantified and compared: a 3-zone model (Z1: between 50% V̇O2peak and lactate threshold 1 (LT1); Z2: between LT1 and 95% LT2; Z3: >95% LT2) and a 5-zone model (T1-T5), where Z1 and Z3 were split into 2 additional zones. There was significant improvement in LT2 power for both male (4.08% ± 1.83, p < 0.01) and female (3.52% ± 3.38, p = 0.02) athletes, with male athletes also demonstrating significant improvement in 2,000-m ergometer time trial (2.3% ± 1.92, p = 0.01). Changes in V̇O2peak significantly correlated with high-quality aerobic training (percent time in T2 zone; r = 0.602, p = 0.02), whereas changes in LT2 power significantly correlated with "threshold" training (percent time in T4 zone; r = 0.529, p = 0.04). These correlations were not evident when examining intensity distribution through the 3-zone model. Accordingly, a 5-zone intensity model may aid in understanding the progression of secondary performance metrics in rowing athletes; however, primary (on-water) performance remains complex to quantify.
... Polarized training is a training distribution of HIIT and low intensity training. It is evidence-based best practice for endurance athletes (Seiler, 2010), but the principles can be utilized by alpine ski racers. Two intensity zones can be set as: (1) below AT PO (≤ 2 mM blood lactate) and (2) 90% V O 2max PO or higher. ...
... Two intensity zones can be set as: (1) below AT PO (≤ 2 mM blood lactate) and (2) 90% V O 2max PO or higher. Research has shown that HIIT is efficient at increasing and maintaining performance in trained subjects but is typically used only for about 20% of endurance athletes' training volume, or one-to-two HIIT sessions per week (Seiler, 2010). Psilander (2014) suggests that low-intensity training balances the adaptive signaling and sympathetic stress of HIIT. ...
This chapter begins by providing an explanation of how each energy system within the human body operates and their relevance to alpine ski racing. Secondly, it debunks several myths about “traditional” endurance training for alpine ski racing and provides more effective tactics for meeting those demands while remaining complimentary to the vast array of training demands one requires at a high level of the sport. Several unique research studies are discussed at length in addition to numerous anecdotal experiences from a high-level coaching practitioner himself. Lastly, several factors that affect one's ability to reach a high level of alpine ski racing-specific endurance are discussed at length including but not limited to: genetics, altitude, gender, and training experience.
... The physiological and functional adaptations resulting from the use of this method are comparable to the use of medium and large volumes with low intensity. However, the authors claim that when using the LHT method, physiological changes occur faster, especially for athletes who have not previously used this method in the training process (Seiler, 2010). Also, a study was conducted in which the technology of sports training of young swimmers at the stage of initial specialization was substantiated, which provides for a phased flow of synchronization processes of physical and technical training with the implementation of intermediate control standards at the end of each stage of training (Ní Chéilleachair et al., 2017). ...
Full-text available
A goal is to investigate the influence of the method of low-volume high-intensity training on the indicators of speed and strength abilities in young swimmers of high skill level. The following research methods were used: testing the level of special speed-strength qualities in the water with the help of equipment that allows underwater video recording, the parameters of the level of special speed-strength qualities in the water were recorded. The obtained results of the study allowed us to note an increase in the level of overall speed and strength qualities, the upper shoulder girdle power increased by 16.80% and speed increased by 13.48% in the experimental group. The increase in the speed and strength qualities of the shoulder girdle muscles was: power by 23.40% and speed by 21.17%. Increase in the level of speed and strength qualities of the lower extremities: power by 10.99% and speed by 2.74%. Evaluation by independent experts showed that the application of the method of low-volume high-intensity training also affects the basic swimming skills of students (technical elements of the chosen method swimming, development of general physical qualities of a swimmer; rational structure of swimming technique in the chosen way, the strength of the stroke; stability and variability of swimming technique, development special speed-strength endurance of a swimmer). The indicators improved by an average of 0.6 points. Keywords: high-intensity training; methods of improving speed and strength qualities; swimming; young swimmers.
... The intensity is usually scaled based on the percentage of maximum heart rate (HR max ) [7] or as the relationship between the heart rate (HR) and lactate values [5,8,9]. The duration at each intensity can be allocated either as the time in zone, based on the recorded time in different intensity zones; the session goal, based on the main intensity goal of a single session; or as a combination of the time spent in each zone and the main goal for the session [10,11]. In general, as adolescent athletes age they are encouraged to train more systematically and with greater volume and specialization in order to prepare for more training and competition later in their career [12]. ...
Full-text available
The purpose of this study was to retrospectively describe the longitudinal changes of training variables in adolescent biathletes based on performance level. Thirty biathletes (15 men and 15 women) were included in the study and categorized as either national level biathletes (NLB, n = 21) or national team biathletes (NTB, n = 9). Retrospective training data was collected from training diary covering the biathletes' four years (Y1-Y4) as student-athletes at upper secondary school. Training data was divided into physical and shooting training variables. A linear mixed-effect model was used for comparing the difference of the performance group and year of upper secondary school on training characteristics. The NTB group achieved a greater annual training volume than the NLB group, especially during Y4 (594±71 h·y-1 vs 461±127 h·y-1, p < 0.001), through an increase in duration of each session and by completing more weekly training volume during the general phase (13.7±4.6 vs 10.0±4.9 h·w-1, p = 0.004). No difference was observed in relative training intensity distribution between the groups. The total number of shots fired was also greater for the NTB (9971±4716 vs 7355±2812 shots·y-1, p = 0.003). There was an equal frequency in illness and injury for both the NLB and NTB. Accordingly, the results of the present study describe longitudinal changes of biathlon training in adolescent biathletes that also may affect performance development.
Exercise is recognized for its potential role in reducing the risk of certain cancers. However, the molecular mechanisms behind this risk reduction are not fully understood. Here, we hypothesized that aerobic physical exercise induces cancer attenuating effects through the modulation of oxidative stress and inflammation. To test this hypothesis, twenty male Sprague Dawley rats with chemically induced prostate tumors were divided into two groups: Prostate cancer (PC) in the absence and presence of exercise (PC+Ex). Rats in the PC+Ex group performed exercises on a treadmill for 8 weeks, 5 sessions per week, at an intensity of 60% of maximum capacity. Weight and feed efficiency, Ki-67, apoptosis, prostatic inflammation, and markers of oxidative stress were analysed. We found that aerobic physical exercise significantly decreased prostate cell proliferation (p<0.05) across modulation, tumor size, and prostate weight. The PC+Ex group also significantly reduced anti-apoptosis protein expression (p<0.05) and increased pro-apoptotic protein expression. Furthermore, physical exercise increased enzymatic antioxidant defenses in the prostate, plasma, and whole blood. Moreover, PC+Ex reduced lipid peroxidation and protein carbonyl levels (p<0.05). In the prostate, there was an increase in antiinflammatory cytokines (IL-10), and a reduction in pro-inflammatory cytokines (IL-6, TNF-α, and NF-κB) after 8 weeks of physical exercise. In conclusion, we found that aerobic physical exercise is a functional, beneficial, and applicable approach to control PC progression, because it modifies the systemic environment, including the regulation of glucose and circulating lipids. This modification of the cancer cells’ environment has anti-inflammatory and antioxidant effects that attenuate tumor growth.
Literature diverges about the performance improvement after dry-land training. Thus, the objective of the present study was to compare the effect of two models of dry-land training. Twenty-nine swimmers were divided into three groups, combined strength and power training (PTG), only strength training (STG), and a control group (CG). Measurements were taken for six weeks, before dry-land exposure (M1), after four weeks of specific training with exposure to dry-land training by two groups (M2), and after two weeks of taper without exposure to dry-land training (M3). Strength in specific exercises, jumping tests, and 50, 100, and 200m freestyle performance were evaluated on M1 and M3, while hematological and strength parameters in tethered swimming were measured in M1, M2, and M3. PTG showed time-effect improvement for 200, 100, and 50m performance (p<0.014), CG for 200 and 100m (p<0.047), and STG only for 100m (p:0.01). No differences were found in Δ performance between groups. PTG showed improvement in the peak force of tethered swimming on M2 (p:0.019), followed by a decrease on M3 (p:0.003). PTG and STG also showed an increase in creatinine, lactate dehydrogenase (LDH), and creatine kinase (CK) after M2 (p<0.038). Finally, it was concluded that both dry-land training sessions could change hematological parameters and improve physical attributes on dry-land and tethered swimming tests without improving performance.
Full-text available
Athletes experience minor fatigue and acute reductions in performance as a consequence of the normal training process. When the balance between training stress and recovery is disproportionate, it is thought that overreaching and possibly overtraining may develop. However, the majority of research that has been conducted in this area has investigated overreached and not overtrained athletes. Overreaching occurs as a result of intensified training and is often considered a normal outcome for elite athletes due to the relatively short time needed for recovery (approximately 2 weeks) and the possibility of a supercompensatory effect. As the time needed to recover from the overtraining syndrome is considered to be much longer (months to years), it may not be appropriate to compare the two states. It is presently not possible to discern acute fatigue and decreased performance experienced from isolated training sessions, from the states of overreaching and overtraining. This is partially the result of a lack of diagnostic tools, variability of results of research studies, a lack of well controlled studies and individual responses to training. The general lack of research in the area in combination with very few well controlled investigations means that it is very difficult to gain insight into the incidence, markers and possible causes of overtraining. There is currently no evidence aside from anecdotal information to suggest that overreaching precedes overtraining and that symptoms of overtraining are more severe than overreaching. It is indeed possible that the two states show different defining characteristics and the overtraining continuum may be an oversimplification. Critical analysis of relevant research suggests that overreaching and overtraining investigations should be interpreted with caution before recommendations for markers of overreaching and overtraining can be proposed. Systematically controlled and monitored studies are needed to determine if overtraining is distinguishable from overreaching, what the best indicators of these states are and the underlying mechanisms that cause fatigue and performance decrements. The available scientific and anecdotal evidence supports the existence of the overtraining syndrome; however, more research is required to state with certainty that the syndrome exists.
Full-text available
Purpose: Between inefficient training and overtraining, an appropriate training stimulus (in terms of intensity and duration) has to be determined in accordance with individual capacities. Interval training at the minimal velocity associated with VO 2max (vVO 2max ) allows an athlete to run for as long as possible at VO 2max . Nevertheless, we don't know the influence of a defined increase in training volume at vVO 2max on aerobic performance, noradrenaline, and heart rate. Methods: Eight subjects performed 4 wk of normal training (NT) with one session per week at vVO 2max , i.e., five repetitions run at 50% of the time limit at vVO 2max , with recovery of the same duration at 60% vVO 2max , They then performed 4 wk of overload training (OT) with three interval training sessions at vVO 2max . Results: Normal training significantly improved their velocity associated with VO 2max (20.5 ± 0.7 vs 21.1 ± 0.8 km.h - 1 , P = 0.02). As a result of improved running economy (50.6 ± 3.5 vs 47.5 ± 2.4 mL.min -1 .kg -1 . P = 0.02), VO 2max was not significantly different (71.6 ± 4.8 vs 72.7 ± 4.8 mL.min -1 .kg -1 ). Time to exhaustion at vVO 2max ). was not significantly different (301 ± 56 vs 283 ± 41 s) as was performance (i.e., distance limit run at vVO 2max : 2052.2 ± 331 vs 1986.2 ± 252.9 m). Heart rate at 14 km.h - decreased significantly after NT (162 ± 16 vs 155 ± 18 bpm. P < 0.01). Lactate threshold remained the same after normal training (84.1 ± 4.8% vVO 2max ). Overload training changed neither the performance nor the factors concerning performance. However, the submaximal heart rate measured at 14 kmh -1 decreased after overload training (155 ± 18 vs 150 ± 15 bpm). The maximal heart rate was not significantly different after NT and OT (199 ± 9.5, 198 ± 11, 194 ± 10.4, P = 0.1 Resting plasma norepinephrine (veinous blood sample measured by high pressure liquid chromatography), was unchanged (2.6 vs 2.4 nm.L - 1 , P = 0.8). However, plasma norepinephrine measured at the end of the vVO 2max test increased significantly (11. 1 vs 26.0 nm.L - 1 , P = 0.002). Conclusion: Performance and aerobic factors associated with the performance were not altered by the 4 wk of intensive training at vVO 2max despite the increase of plasma noradrenaline.
Full-text available
To compare the intensity distribution during cycling training among elite track cyclists who improved or decreased in ergometer power at 4 mM blood lactate during a 15 wk training period. 51 young male German cyclists (17.4 ± 0.5 y; 30 international, 21 national junior finalists) performed cycle ergometer testing at the onset and at the end of a 15 wk basic preparation period, and reported their daily volumes of defined exercise types and intensity categories. Training organization was compared between two subgroups who improved (Responders, n = 17; DeltaP(La4) x kg(-1) = +11 ± 4%) or who decreased in ergometer performance (Non-Responders, n = 17; DeltaP(La4) x kg(-1) = -7 ± 6%). Responders and Non-Responders did not differ significantly in the time invested in noncycling specific training or in the total cycling distance performed. They did differ in their cycling intensity distribution. Responders accumulated significantly more distance at low intensity (<2 mM blood lactate) while Non-Responders performed more training at near threshold intensity (3-6 mM). Cycling intensity distribution accounted for approx. 60% of the variance of changes in ergometer performance over time. Performance at t1 combined with workout intensity distribution explained over 70% of performance variance at t2. Variation in lactate profile development is explained to a substantial degree by variation in training intensity distribution in elite cyclists. Training at <2 mM blood lactate appears to play an important role in improving the power output to blood lactate relationship. Excessive training near threshold intensity (3-6 mM blood lactate) may negatively impact lactate threshold development. Further research is required to explain the underlying adaptation mechanisms.
Full-text available
The present study investigated the physiological characteristics of eight world-class (WC) and eight national-class (NC) Norwegian sprint cross country skiers. To measure the physiological response and treadmill performance, the skiers performed a submaximal test, a peak aerobic capacity (VO2peak) test, and a peak treadmill speed (V(peak)) test in the skating G3 technique. Moreover, the skiers were tested for G3 acceleration outdoors on asphalt and maximal strength in the lab. The standard of sprint skating performance level on snow was determined by International Ski Federation points, and the training distribution was quantified. WC skiers showed 8% higher VO2peak and twice as long a VO(2) plateau time at the VO2peak test, and a higher gross efficiency at the submaximal test (all P<0.05). Furthermore, WC skiers showed 8% higher V(peak) (P<0.05), but did not differ from NC skiers in acceleration and maximal strength. WC skiers performed more low- and moderate-intensity endurance training and speed training (both P<0.05). The current results show that aerobic capacity, efficiency, and high speed capacity differentiate WC and NC sprint skiers and it is suggested that these variables determine sprint skiing performance.
An abstract is unavailable. This article is available as HTML full text and PDF.
Low muscle glycogen content has been demonstrated to enhance transcription of a number of genes involved in training adaptation. These results made us speculate that training at a low muscle glycogen content would enhance training adaptation. We therefore performed a study in which seven healthy untrained males performed one-knee legged exercise training at a low glycogen (Low) protocol, whereas the other leg was trained at a high glycogen (High) protocol. Both legs were trained equally regarding workload and training amount. Day one: Both legs (Low+High) were trained for 1 h followed by 2 h of rest at a fasting state, where after one leg (Low) was trained for one more hour. Day 2: Only one leg (High) trained for 1 h. Days 1 and 2 were repeated for 10 weeks. As an effect of training, the increase in maximal workload was identical for the two legs. However, time till exhaustion at 90% was markedly more increased in the Low leg compared with the High leg. Resting muscle glycogen and the activity of the mitochondrial enzyme hydroxyacyl CoA dehydrogenase (HAD) increased with training, but only significantly so in LOW, whereas citrate synthase (CS) activity increased in both low and high. There was a more pronounced increase in CS activity when Low was compared with High. In conclusion, the present study suggests that training twice every second day may be superior to daily training.
Maximal oxygen uptake (V̇O2max) was measured in 51 females and males classified as either world-class, medium-class or less successful elite skiers. The V̇O2max in the male world-class skiers was significantly higher (mean 85.6 ml·kg−1·min−1 or 355 ml·min−1 kg−23) than in the other elite skiers. World-class and medium-class female skiers had identical mean V̇O2max expressed in ml·kg−1·min−1 (70.7 and 70.6, respectively), but the values differed significantly when the unit ml·min−1kg−2/3 was used (274 and 264, respectively). V̇O2max expressed as ml·min−1·kg−2/3 reflects differences in performance capability among elite skiers better than the unit ml·kg−1·min−1.
We determined whether mitogen-activated protein kinase (MAPK) and 5'-AMP-activated protein kinase (AMPK) signalling cascades are activated in response to intense exercise in skeletal muscle from six highly trained cyclists (peak O(2) uptake (.V(O2,peak)) 5.14 +/- 0.1 l min(-1)) and four control subjects (Vdot;(O(2))(,peak) 3.8 +/- 0.1 l min(-1)) matched for age and body mass. Trained subjects completed eight 5 min bouts of cycling at approximately 85% of .V(O2,peak) with 60 s recovery between work bouts. Control subjects performed four 5 min work bouts commencing at the same relative, but a lower absolute intensity, with a comparable rest interval. Vastus lateralis muscle biopsies were taken at rest and immediately after exercise. Extracellular regulated kinase (ERK1/2), p38 MAPK, histone H3, AMPK and acetyl CoA-carboxylase (ACC) phosphorylation was determined by immunoblot analysis using phosphospecific antibodies. Activity of mitogen and stress-activated kinase 1 (MSK1; a substrate of ERK1/2 and p38 MAPK) and alpha(1) and alpha(2) subunits of AMPK were determined by immune complex assay. ERK1/2 and p38 MAPK phosphorylation and MSK1 activity increased (P < 0.05) after exercise 2.6-, 2.1- and 2.0-fold, respectively, in control subjects and 1.5-, 1.6- and 1.4-fold, respectively, in trained subjects. Phosphorylation of histone H3, a substrate of MSK1, increased (P < 0.05) approximately 1.8-fold in both control and trained subject. AMPKalpha(2) activity increased (P < 0.05) after exercise 4.2- and 2.3-fold in control and trained subjects, respectively, whereas AMPKalpha(1) activity was not altered. Exercise increased ACC phosphorylation (P < 0.05) 1.9- and 2.8-fold in control and trained subjects. In conclusion, intense cycling exercise in subjects with a prolonged history of endurance training increases MAPK signalling to the downstream targets MSK1 and histone H3 and isoform-specific AMPK signalling to ACC. Importantly, exercise-induced signalling responses were greater in untrained men, even at the same relative exercise intensity, suggesting muscle from previously well-trained individuals requires a greater stimulus to activate signal transduction via these pathways.
This study examines the effect of training intensity on the activity of enzymes in m. vastus lateralis. Elite junior cross-country skiers of both sexes trained 12-15 h weeks-1 for 5 months at either moderate (60-70% of VO2max, MIG) or high training intensity (80-90% of the VO2max, close to the lactate threshold; HIG). Muscle biopsies for enzyme analyses and fibre typing were taken before and after the training period. Histochemical analyses on single fibres were done for three enzymes (succinate dehydrogenase [SDH], hydroxybutyrate dehydrogenase [HBDH], glycerol-3-phosphate dehydrogenase [GPDH]), while the activity of citrate synthase [CS] and phosphofructokinase [PFK] was measured on whole biopsies. The activity of GPDH was low in ST fibres and high in FT fibres. The activity of SDH and HBDH was high in both ST and FTa fibres but low in the FTb fibres. The HIG increased their performance more than the MIG did during the training period as judged from scores on a 20-min run test. The SDH activity rose by 6% for the HIG (P < 0.02). No effects of training were found in the activities of CS, HBDH or GPDH, neither in the two training groups nor for the two genders (P > or = 0.16). The PFK activity fell by 10% for the HIG (P=0.02), while no change was found for the MIG. For GPDH, CS and SDH the women's activity was approximately 20% less than the value for the men (P < 0.03). For PFK and HBDH there was no sex difference (P > or = 0.27). There were positive correlations between the activity of three of the enzymes (CS, SDH and GPDH) and the performance parameters (VO2max, cross-country skiing and running performance; r > or = 0.6, P < 0.01). No correlations were found between the PFK or HBDH activities and the performance parameters (r < or = 0.16, P > 0.05). This study suggests that intensities near the lactate threshold affect biochemical and physiological parameters examined in this study as well as the performance of elite skiers, and that the rate-limiting enzymes may be more sensitive to training than non-rate-limiting enzymes.