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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.
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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)
VO2max ↔ ↔
Lactate-threshold speed 3%
20-min run at 9% grade 3.8% 1.9%
Fiber type ↔ ↔
Enzyme activities
MCT 1 transporter ↔ ↓ 12%
MCT 4 transporter ↔ ↔
Citrate synthase ↔ ↔
Succinate dehydrogenase 6%
Na/K pump ?% ?%
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.
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... Therefore, the purpose of the present study was to investigate the effects of LBPPT uphill running on VO 2 , HR and LA. Specifically, the aim was to determine the influence of three different inclines (0%, 2% and 7%) and of two different body weight support settings (80% BW Set and 90% BW Set ) on the important physiological training parameters VO 2 , HR and LA [15][16][17]. In accordance with previous findings on conventional treadmills (CON) and LBPPT, respectively, it was hypothesized, that (1) an increase in incline leads to higher VO 2 , HR and LA values, and (2) increasing body weight support is associated with a decrease in VO 2 , HR and LA. ...
... Müller Gerätebau GmbH, Freital, Germany). Between the stages and after each test, rating of perceived exertion (RPE) was routinely analysed on the basis of the Borg RPE Scale (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) [20]. ...
... Post hoc comparisons showed a significant mean difference of 12 ± 2 bpm between 0% and 7% incline (p < 0.05, CI: [6][7][8][9][10][11][12][13][14][15][16][17][18][19], and of 10 ± 2 bpm between 2% and 7% incline (p < 0.05, CI: 3-16), respectively. No significant difference was found for 0% vs. 2% incline (p = 0.793). ...
Full-text available
Lower body positive pressure treadmills (LBPPTs) as a strategy to reduce musculoskeletal load are becoming more common as part of sports conditioning, although the requisite physiological parameters are unclear. To elucidate their role, ten well-trained runners (30.2 ± 3.4 years; VO2max: 60.3 ± 4.2 mL kg−1 min−1) ran at 70% of their individual velocity at VO2max (vVO2max) on a LBPPT at 80% body weight support (80% BWSet) and 90% body weight support (90% BWSet), at 0%, 2% and 7% incline. Oxygen consumption (VO2), heart rate (HR) and blood lactate accumulation (LA) were monitored. It was found that an increase in incline led to increased VO2 values of 6.8 ± 0.8 mL kg−1 min−1 (0% vs. 7%, p < 0.001) and 5.4 ± 0.8 mL kg−1 min−1 (2% vs. 7%, p < 0.001). Between 80% BWSet and 90% BWSet, there were VO2 differences of 3.3 ± 0.2 mL kg−1 min−1 (p < 0.001). HR increased with incline by 12 ± 2 bpm (0% vs. 7%, p < 0.05) and 10 ± 2 bpm (2% vs. 7%, p < 0.05). From 80% BWSet to 90% BWSet, HR increases of 6 ± 1 bpm (p < 0.001) were observed. Additionally, LA values showed differences of 0.10 ± 0.02 mmol l−1 between 80% BWSet and 90% BWSet. Those results suggest that on a LBPPT, a 2% incline (at 70% vVO2max) is not yet sufficient to produce significant physiological changes in VO2, HR and LA—as opposed to running on conventional treadmills, where significant changes are measured. However, a 7% incline increases VO2 and HR significantly. Bringing together physiological and biomechanical factors from previous studies into this practical context, it appears that a 7% incline (at 80% BWSet) may be used to keep VO2 and HR load unchanged as compared to unsupported running, while biomechanical stress is substantially reduced.
... Exercise intensity varies across training sessions and for convenience is often grouped into three categories, namely low intensity training (i.e., high volume, low intensity training), lactate threshold training (i.e., involves primarily continuous or intervals of moderate-intensity exercise) and high-intensity interval training (i.e., HIIT; mainly interval training, intermittent intervals, or short, high-intensity sprints) (Seiler, 2010;Stoggl & Sperlich, 2015). There is likely to be overlap in some physiological adaptations (e.g., maximal oxygen uptake [VO2max], capillary density, mitochondrial biogenesis, stroke volume, etc) to these different training stimuli, but the physiological and performance adaptations that occur with HIIT are often superior to those that occur with continuous endurance training (Helgerud et al., 2007;Ni Cheilleachair, Harrison, & Warrington, 2017). ...
... This may be due to an absence of a properly implemented training regime, meaning that, while more distance led to greater increases in maximal oxygen uptake, the absolute benefits were less than with a well-structured program. Increases in total training volume correlate well with improvements in physiological and performance variables (Seiler, 2010) and, although the data suggest low predictive ability here in our heterogenous group of cyclists, our results support the notion that athletes might look to increase their total training volume to improve these measured parameters. These data should be confirmed by further studies using objective training metrics obtained from GPS systems. ...
... 14 However, the acute physiological responses to exercise differ among elite athletes, and as such, standardized scales have been criticized as they fail to account for these individual variations. 2 These variations between athletes have not been fully described. Forcing all athletes into a "one-size fits all" by using fixed intensity zones may result in individual differences in physiological stimuli being neglected. ...
... 14 However, the acute physiological responses to exercise differ among elite athletes, and as such, standardized scales have been criticized as they fail to account for these individual variations. 2 These variations between athletes have not been fully described. Forcing all athletes into a "one-size fits all" by using fixed intensity zones may result in individual differences in physiological stimuli being neglected. ...
Purpose: Rating of perceived exertion (RPE) is a widely used tool to assess subjective perception of effort during exercise. The authors investigated between-subject variation and effect of exercise mode and sex on Borg RPE (6-20) in relation to heart rate (HR), oxygen uptake (VO2), and capillary blood lactate concentrations. Methods: A total of 160 elite endurance athletes performed a submaximal and maximal test protocol either during cycling (n = 84, 37 women) or running (n = 76, 32 women). The submaximal test consisted of 4 to 7 progressive 5-minute steps within ∼50% to 85% of maximal VO2. For each step, steady-state HR, VO2, and capillary blood lactate concentrations were assessed and RPE reported. An incremental protocol to exhaustion was used to determine maximal VO2 and peak HR to provide relative (%) HR and VO2 values at submaximal work rates. Results: A strong relationship was found between RPE and %HR, %VO2, and capillary blood lactate concentrations (r = .80-.82, all Ps < .05). The between-subject coefficient of variation (SD/mean) for %HR and %VO2 decreased linearly with increased RPE, from ∼10% to 15% at RPE 8 to ∼5% at RPE 17. Compared with cycling, running induced a systematically higher %HR and %VO2 (∼2% and 5%, respectively, P < .05) with these differences being greater at lower intensities (RPE < 13). At the same RPE, women showed a trivial, but significantly higher %HR and %VO2 than men (<1%, P < .05). Conclusions: Among elite endurance athletes, exercise mode influenced RPE at a given %HR and %VO2, with greater differences at lower exercise intensities. Athletes should manage different tools to evaluate training based on intensity and duration of workouts.
... There was no influence of baseline VO 2max on change in TT performance following HIIT or SIT when participants were categorized by training status. An important limitation of VO 2max is that it does not account for individual physiological differences in other correlates of endurance performance [70]. The highest VO 2 that can be maintained at submaximal physiological thresholds may be a better marker of endurance performance. ...
Full-text available
Background Interval training has become an essential component of endurance training programs because it can facilitate a substantial improvement in endurance sport performance. Two forms of interval training that are commonly used to improve endurance sport performance are high-intensity interval training (HIIT) and sprint interval training (SIT). Despite extensive research, there is no consensus concerning the optimal method to manipulate the interval training programming variables to maximize endurance performance for differing individuals.Objective The objective of this manuscript was to perform a systematic review and meta-analysis of interval training studies to determine the influence that individual characteristics and training variables have on time-trial (TT) performance.Data SourcesSPORTDiscus and Medline with Full Text were explored to conduct a systematic literature search.Study SelectionThe following criteria were used to select studies appropriate for the review: 1. the studies were prospective in nature; 2. included individuals between the ages of 18 and 65 years; 3. included an interval training (HIIT or SIT) program at least 2 weeks in duration; 4. included a TT test that required participants to complete a set distance; 5. and programmed HIIT by power or velocity.ResultsTwenty-nine studies met the inclusion criteria for the quantitative analysis with a total of 67 separate groups. The participants included males (n = 400) and females (n = 91) with a mean group age of 25 (range 19–45) years and mean \(V{\text{O}}_{{2{\text{max}}}}\) of 52 (range 32–70) mL·kg−1·min−1. The training status of the participants comprised of inactive (n = 75), active (n = 146) and trained (n = 258) individuals. Training status played a significant role in improvements in TT performance with trained individuals only seeing improvements of approximately 2% whereas individuals of lower training status demonstrated improvements as high as 6%. The change in TT performance with HIIT depended on the duration but not the intensity of the interval work-bout. There was a dose–response relationship with the number of HIIT sessions, training weeks and total work with changes in TT performance. However, the dose–response was not present with SIT.Conclusion Optimization of interval training programs to produce TT performance improvements should be done according to training status. Our analysis suggests that increasing interval training dose beyond minimal requirements may not augment the training response. In addition, optimal dosing differs between high intensity and sprint interval programs.
... Fitness can also affect mental and emotional development.One of the benefits of fitness is the formation of strong and well-toned physique. 2 Physical fitness as defined by The World Health Organization (WHO) is the capability of a person to perform muscular work under specified conditions and criteria, while exercise is defined as any bodily movement produced by skeletal muscles that result in energy expenditure. Exercises can be divided into two which are aerobic exercise and anaerobic exercise. ...
Full-text available
VO2 max or oxygen consumption maximum value is a gold standard indicator towards cardiovascular and aerobic endurance because it refers to the maximum amount of oxygen used by an individual in one minute during maximum physical activity. The study's purpose was to see the correlation between duration spent in the fitness center and VO2 max value among adults. This study used the analytical study method, cross-sectional type. We chose the participant from members of Gold’s Gym fitness center at Cihampelas, aged 18-45 years old, with 3 months minimum of activity, and exercise frequency around three times per week. Theparticipants who had cardiovascular and pulmonary disease, serious physical injury, had already exercised during the time of observation, or professionally trained athletes were excluded. We used the Queen College step test for the instrument. There were 47 participants in this study. Spearman’s correlation coefficient was calculated to measure the correlation between duration spent and VO2 max, and the result was 0.77(95% CI 0.64; 0.85), p<0.001 which is categorized as a strong correlation. From this study, we found that with the increase of duration spent in the fitness center, the VO2 max level is also increasing, and vice versa. This result is supported by a study from the United States which finds a correlation between VO2max and performance times of recreational triathletes. In conclusion, there is a positive correlation between duration spent in the fitness center and VO2 max level among adults.
... Intensities were individualised by adjusting training paces based on the percent maximal HR (% HRmax). 27 They were instructed not to participate in other exercise training and continue with their nutrition habits throughout the experiment. ...
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Background Previous studies were conducted only on elite athletes, and they investigate acute training responses of cardiac troponin I (CTnI). However, cardiac troponin was found to be elevated in young and inexperienced athletes than adults, and immature myocardium is more susceptible to injury, which needs further consideration. Aim Therefore, we aimed to observe the association between CTnI and cardiovascular parameters in response to chronic endurance training adaptation in young athletes. Methods Fifteen participants aged (19.5±1.3) years were selected and placed in endurance running at 70%–80% HRmax intensity for 35 min per training for the first week and additional 2 min each week from the second to the last week for 12 weeks. Serum cardiac troponin and cardiovascular parameters were assessed at pre-training and after 12 weeks of training. Result We find a significant CTnI level (p<0.05) and it is positively correlated with systolic blood pressure (BP) (r=0.425). Moreover, CTnI was statistically significant (p<0.01) and positively associated with mean arterial pressure (r=0.516) with a moderate correlation. Besides, CTnI showed a significant (p<0.001) and positive relationship with resting heart rate (r=0.605) and a moderate correlation. We did not find a significant relationship between CTnI and diastolic BP in response to endurance training adaptation. Conclusion In conclusion, serum CTnI was significantly and positively associated with cardiovascular parameters in young amateur athletes in response to 12-week endurance training adaptation.
... Long-lasting low-intensity activities recruit mainly slow twitch muscle fibers, while higher intensity activities will recruit also fast twitch muscle fibers for relatively short durations [35]. Thus, VO2max development requires low intensity (50-65% of VO2max) and long-duration activities in combination with short, high-intensity (about 90% of VO2max) bouts [36]. ...
Full-text available
Depending on their cardiorespiratory fitness (CRF), people may perceive the exertion of incident physical activity (PA) differently. Therefore, the use of relative intensity thresholds based on individual fitness have been proposed to evaluate the accumulation of PA at different intensity levels. A subsample of the FinFit2017-study, 1952 adults (803 men and 1149 women) aged 20–69 years, participated in this study. Their maximal oxygen uptake (VO2max) was predicted with a 6 min walk test, and they were instructed to wear a triaxial hip-worn accelerometer for one week. The participants were divided into CRF tertiles by five age groups and sex. Raw acceleration data were analyzed with the mean amplitude deviation method in 6 s epochs. Additionally, the data were smoothed with 1 min and 6 min exponential moving averages. The absolute intensity threshold for moderate activity was 3.0 metabolic equivalent (MET) and for vigorous 6.0 MET. Correspondingly, the relative thresholds were 40% and 60% of the oxygen uptake reserve. Participants in the lowest CRF tertile were the most active with relative thresholds, and participants in the highest CRF tertile were the most active with absolute thresholds. High-fit people easily reached the absolute thresholds, while people in the lowest CRF tertile had to utilize most of their aerobic capacity on a daily basis simply to keep up with their daily chores or peers.
... The observed changes were mainly in line with previous studies using a similar type of training approach [16,24]. Regarding the training intensity distribution, typically 80% LIT and 20% MOD/HIT are stated to be a recommendable basis for endurance athletes [25]. In the present study, both group's average value was quite close to that. ...
Full-text available
The purpose of the study was to examine the effects of progressively increased training intensity or volume on the nocturnal heart rate (HR) and heart rate variability (HRV), countermovement jump, perceived recovery, and heart rate-running speed index (HR-RS index). Another aim was to analyze how observed patterns during the training period in these monitoring variables were associated with the changes in endurance performance. Thirty recreationally trained participants performed a 10-week control period of regular training and a 10-week training period of either increased training intensity (INT, n = 13) or volume (VOL, n = 17). Changes in endurance performance were assessed by an incremental treadmill test. Both groups improved their maximal speed on the treadmill (INT 3.4 ± 3.2%, p < 0.001; VOL 2.1 ± 1.8%, p = 0.006). In the monitoring variables, only between-group difference (p = 0.013) was found in nocturnal HR, which decreased in INT (p = 0.016). In addition, perceived recovery decreased in VOL (p = 0.021) and tended to decrease in INT (p = 0.056). When all participants were divided into low-responders and responders in maximal running performance, the increase in the HR-RS index at the end of the training period was greater in responders (p = 0.005). In conclusion, current training periods of increased intensity or volume improved endurance performance to a similar extent. Countermovement jump and HRV remained unaffected, despite a slight decrease in perceived recovery. Long-term monitoring of the HR-RS index may help to predict positive adaptations, while interpretation of other recovery-related markers may need a more individualized approach.
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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.
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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.
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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.
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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.
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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.