Content uploaded by Angus M Hunter
Author content
All content in this area was uploaded by Angus M Hunter
Content may be subject to copyright.
This article was downloaded by: [University of Stirling Library]
On: 10 October 2011, At: 08:36
Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,
37-41 Mortimer Street, London W1T 3JH, UK
Journal of Sports Sciences
Publication details, including instructions for authors and subscription information:
http://www.tandfonline.com/loi/rjsp20
A 6-month analysis of training-intensity distribution
and physiological adaptation in Ironman triathletes
Craig M. Neal a , Angus M. Hunter a & Stuart D. R. Galloway a
a Health and Exercise Sciences Research Group, School of Sport, University of Stirling,
Stirling, UK
Available online: 04 Oct 2011
To cite this article: Craig M. Neal, Angus M. Hunter & Stuart D. R. Galloway (2011): A 6-month analysis of
training-intensity distribution and physiological adaptation in Ironman triathletes, Journal of Sports Sciences,
DOI:10.1080/02640414.2011.596217
To link to this article: http://dx.doi.org/10.1080/02640414.2011.596217
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions
This article may be used for research, teaching, and private study purposes. Any substantial or systematic
reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to
anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents
will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should
be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,
proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in
connection with or arising out of the use of this material.
A 6-month analysis of training-intensity distribution and physiological
adaptation in Ironman triathletes
CRAIG M. NEAL, ANGUS M. HUNTER, & STUART D. R. GALLOWAY
Health and Exercise Sciences Research Group, School of Sport, University of Stirling, Stirling, UK
(Accepted 8 June 2011)
Abstract
In the present study, we analysed the training-intensity distribution and physiological adaptations over a 6-month period
preceding an Ironman triathlon race. Ten athletes (mean +s: age 43 +3 years, mass 78.3 +10.3 kg, stature 1.79 +0.05 m)
participated in the study. The study consisted of three training periods (A, B, C), each of approximately 2 months’ duration,
and four testing weeks. Testing consisted of incremental tests to exhaustion for swimming, cycling and running, and
assessments for anthropometry plus cardiovascular and pulmonary measures. The lactate threshold and the lactate turnpoint
were used to demarcate three discipline-specific, exercise-intensity zones. The mean percentage of time spent in zones 1, 2,
and 3 was 69 +9%, 25 +8%, and 6 +2% for periods A–C combined. Only modest physiological adaptation occurred
throughout the 6-month period, with small to moderate effect sizes at best. Relationships between the training volume/
training load and the training-intensity distribution with the changes in key measures of adaptation were weak and probably
reflect differences in initial training status. Our results suggest that the effects of intensity distribution are small over short-
term training periods and future experimental research is needed to clarify the potential impact of intensity distribution on
physiological adaptation.
Keywords: Training zones, heart rate, endurance training, endurance performance, lactate threshold
Introduction
Zones of exercise intensity can be established from
blood lactate concentrations at increasing exercise
intensities (Kindermann, Simon, & Keul, 1979).
Intensities lower than the lactate threshold can be
categorized as zone 1, between the lactate threshold
and the lactate turnpoint as zone 2, and greater than
the lactate turnpoint as zone 3 (Skinner & McLellan,
1980). Blood lactate concentration remains at or
close to resting concentrations in zone 1, is raised but
production and removal rates re-establish equili-
brium in zone 2, and production exceeds maximum
clearance rates in zone 3 (Seiler & Kjerland, 2006).
Elite and sub-elite athletes have used the heart rates
associated with specific physiological thresholds to
evaluate the exercise-intensity distribution during
training sessions (Esteve-Lanao, San Juan, Earnest,
Foster, & Lucia, 2005; Schumacher & Mueller,
2002; Seiler & Kjerland, 2006). However, training
time in each of these three exercise-intensity zones
for optimal physiological adaptation and avoidance
of excessive stress leading to overtraining is yet to be
established.
Few studies of elite athletes have assessed the time
spent in each exercise-intensity zone in the build up
to competition (Lucia, Hoyos, Pardo, & Chicharro,
2000a; Lucia, Hoyos, Perez, & Chicharro, 2000b;
Schumacher & Mueller, 2002; Seiler & Kjerland,
2006). These studies used ventilatory thresholds to
demarcate the three intensity zones and used the
total time-in-zone approach (Seiler & Kjerland,
2006) to assess the time spent in each zone. It was
reported that training time spent in zone 1
was 480%, in zone 2 was 515%, and in zone 3
was 5% of the total training time. In contrast,
Esteve-Lanao and colleagues (2005) reported that in
a group of well-trained, sub-elite runners, less time
was spent in zone 1 (71%) and more time was spent
in zone 2 (21%) and zone 3 (8%). These authors
reported a negative correlation between time spent in
zone 1 and performance time during a cross-country
race (10.1 km). Thus, results to date suggest that
more time spent training in zone 1 is beneficial for
physiological adaptation and subsequent exercise
performance.
Studies of elite (Ingham, Carter, Whyte, & Doust,
2008) and sub-elite athletes (Esteve-Lanao et al.,
Correspondence: C. M. Neal, School of Sport, University of Stirling, Stirling FK9 4LA, UK. E-mail: craig.neal@stir.ac.uk
Journal of Sports Sciences, 2011; 1–9, iFirst article
ISSN 0264-0414 print/ISSN 1466-447X online Ó2011 Taylor & Francis
http://dx.doi.org/10.1080/02640414.2011.596217
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
2007) that have examined training-intensity distribu-
tion agree that a greater percentage of training time
spent in zone 1 is beneficial to performance and/or
physiological adaptation. These studies found that
with no difference in training load, a group focusing
on training in zone 1 (80%) realized a greater
improvement in performance (Esteve-Lanao et al.,
2007) and gained greater physiological adaptation
(Ingham et al., 2008) than a group who spent less
training time in that zone (*70%), suggesting that
more training in zones 2 and 3 (*30%) is not
necessarily beneficial.
Hence, there is evidence that, for both elite and
sub-elite athletes, the greatest performance and
physiological gains are achieved when training in
zone 1 accounts for 80% of time and training in
zones 2 and 3 combined accounts for less than 20%.
No observations of training-intensity distribution
have been made on multi-sport athletes in prepara-
tion for competition. Therefore, the aims of the
present study were to analyse training-intensity
distribution in a group of sub-elite triathletes in
three separate periods during 6 months of training
preceding an Ironman triathlon, and to quantify
effects on physiological adaptation in the three
disciplines using standard incremental tests.
Methods
Participants
Ten healthy participants (nine males, one female)
volunteered and provided written, informed consent
to take part in the study, which was approved by the
local ethics committee, in accordance with the
Declaration of Helsinki. All participants were mem-
bers of the same triathlon club. The mean (+s)
characteristics of the participants at the beginning of
the testing period were: age 43 +3 years, mass
78.3 +10.3 kg, and stature 1.79 +0.05 m. Seven of
the athletes had previously competed in an Ironman
triathlon event. All athletes had been involved in
endurance training for over 5 years.
Experimental approach to the problem
Incremental tests to volitional exhaustion in swim-
ming, cycling, and running were used to establish
three distinct, heart-rate-defined intensity zones
associated with two simple reproducible values
reflecting the lactate threshold and the lactate turn-
point: zone 1 (below the lactate threshold), zone 2
(above the lactate threshold but below the lactate
turnpoint), and zone 3 (above the lactate turnpoint).
The participants carried out their own training and
entered all training involving swimming, cycling, and
running, using the ‘‘total time-in-zone approach’’
(Seiler & Kjerland, 2006), into an online training log
(www.workoutlog.com). A modified approach to the
training impulse (TRIMP; Foster et al., 2001b) was
used to assess total training load. The study
consisted of three training periods: January–February
(A), March–April (B), and May–June (C), with the
mean duration of each period being 6.9 +0.8 weeks,
7.6 +0.8 weeks, and 6.7 +0.6 weeks, respectively.
There were four testing weeks (at baseline, 2 months,
4 months, and 6 months) in which swimming,
cycling, and running incremental tests were con-
ducted, at least 2 days apart and at the same time of
day (Figure 1). The lactate threshold and lactate
turnpoint were re-established in each testing week,
and the heart rates corresponding to the updated
lactate threshold and lactate turnpoint were used to
track time spent in each training zone for the
forthcoming training period.
Procedures
Habituation. Habituation trials were undertaken for
all test procedures approximately 2 months before
the study period. Training was recorded in the
training logs in the period between the habituation
trials and the study proper to enable participants to
understand the detail required in the online training
log.
Swimming test. The swimming test was a modified
version of the protocol used by Pyne and colleagues
(Pyne, Lee, & Swanwick, 2001) and was undertaken
in a 25-m pool, with participants wearing their own
wetsuit to replicate the Ironman triathlon swim. An
incremental test was used with seven 150-m stages,
each 5 s quicker than the previous, with a 3-min rest
period between stages. The fourth stage was per-
formed at the same pace as the athletes’ 400-m time-
trial time, which was assessed approximately 2 weeks
before the start of the study. The final stage was a
maximal ‘‘all-out’’ effort to assess the best 150-m
swim time. Strokes were counted for the third and
sixth 25 m of each stage and the difference between
stroke counts in the seven stages was considered the
‘‘deviation in stroke counts’’.
Cycling test. The protocol for the cycling test was
modified from a test previously described by Farina
and colleagues (Farina, Macaluso, Ferguson, & De
Figure 1. Outline of the study period.
2C. M. Neal et al.
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
Vito, 2004). Participants used their own pedals and
shoes for the test. Handlebar position and saddle
height of the ergometer were individualized for each
participant and retained for subsequent tests. A 5-
min warm-up was completed at an external intensity
of 70 W at a self-selected cadence on a Lode cycle
ergometer (Excalibur Sport, Netherlands). This was
followed immediately by an incremental test to
volitional exhaustion, starting at an intensity of
70 W and increasing by 35 W every 3 min (25 W
for the female participant), with the cadence remain-
ing self-selected throughout. Immediately after this
test, the participants rested for 10 min before
completing a shorter high-intensity exercise test.
This test consisted of 1-min stages, starting at the
intensity of the penultimate stage of the previous test,
and increased by 35 W each stage (25 W for the
female athlete). The test continued until volitional
exhaustion. The end time was used to calculate peak
power output (PPO) (Kuipers, Verstappen, Keizer,
Geurten, & Van Kranenburg, 1985):
PPO ¼Wfinal þð½t=60PIÞ
where W
final
¼the power output of the final completed
stage, t¼the time achieved in the final non-completed
stage, and PI ¼the power increment.
Peak power output was also normalized to body
mass using allometric scaling (PPO/BM
b
), where
BM is body mass and bis a power exponent (Nevill,
Ramsbottom, & Williams, 1992). Allometric scaling
allows for meaningful comparisons of peak power
outputs, as the effect of body mass is properly
eliminated. The peak power output and body mass
from the baseline tests were plotted on a log-log scale
and the power exponent was derived from the slope
of the linear regression line (0.79).
Running test. All participants were accustomed to
treadmill running. Participants completed a standar-
dized warm-up for 5 min at 8 km h
71
on a
motorized treadmill (Powerjog, Cranlea and Co),
set at a 1% incline throughout the test to simulate the
energetic cost of outdoor running (Jones & Doust,
1996). This was followed by an incremental exercise
test to volitional exhaustion, starting at a speed of
9kmh
71
and increasing by 1 km h
71
every
3 min (Billat et al., 2003). Thirty seconds before
the end of each stage, participants supported their
weight with their hands and moved their feet to the
sides of the treadmill belt to allow blood sampling.
As in the cycling test, the participants rested for
10 min before completing a shorter high-intensity
exercise test. The test consisted of 30-s stages,
starting at the intensity of the penultimate stage of
the previous test, and increased by 0.5 km h
71
each stage. The test continued until volitional
exhaustion. The end time was used to calculate
maximal running speed (Speed
max
) (Kuipers et al.,
1985):
Speedmax ¼Speedfinal þðt=30Þ0:5
where Speed
final
¼the running speed of the final
completed stage and t¼the time achieved in the final
non-completed stage.
During each stage in the incremental tests for
the three disciplines, heart rate was recorded using
short-range radio telemetry (Polar Sports Tester,
Polar Electro, Kemplete, Finland). At the end of
each stage, a 5-mL capillary blood sample was
obtained – from the fingertip in the cycling and the
running tests and the earlobe in the swimming test.
The sample was analysed for lactate concentration
by micro-assay (LactatePro LT-1710, ArkRay Inc.,
Kyoto, Japan). The reliability and validity of this
device has been previously determined (Pyne et al.,
2000).
The lactate threshold was determined as the final
point before the blood lactate concentration in-
creased distinctly from its resting concentration
(Aunola & Rusko, 1984). The lactate turnpoint was
determined as the starting point of accelerated lactate
accumulation (around 3–6 mmol L
71
), depending
on the individual blood lactate profile (Aunola &
Rusko, 1984).
Reproducibility of the measures
The typical error of measurement for the measures
recorded in this study have been assessed in our
laboratory (C. Neal et al., unpublished data) and are
as follows: first, the typical error of measurement for
the swim speed corresponding to the lactate thresh-
old, lactate turnpoint, and best 150-m swim time was
2.9%, 2.5%, and 1.1%, respectively, second; the
typical error of measurement for the cycling power
output corresponding to the lactate threshold, lactate
turnpoint, and peak power output was 3.7%, 3.3%,
and 2.3%, respectively, and third, the typical error of
measurement for the run speed corresponding to the
lactate threshold, lactate turnpoint, and maximal
running speed was 3.1%, 2.6%, and 1.1%,
respectively.
Anthropometric, cardiovascular, and pulmonary
measures
Anthropometry was undertaken before either the
cycling or the running test, and this remained
consistent for subsequent tests. On arrival at the
laboratories, participants’ body mass (Balance beam
scales, John White & Son, Fife, UK) and stature
(The Leicester Height Measure, Seca, UK) (baseline
Training and adaptation in Ironman triathletes 3
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
test only) were recorded. Participants were then
instructed to lie down to allow bio-impedance
analysis (Bodystat 1500, Bodystat Ltd., Isle of
Man, UK) to be performed. Participants remained
lying down and breathing was controlled with a
metronome (10 breaths per minute) for the assess-
ment of resting heart rate and heart rate variability
over a 10-min period (Polar Electro, Kemplete,
Finland), followed by a peak flow test (Mini-Wright
White (standard range), Clement Clarke Interna-
tional, Essex, UK). A morning 12-h fasted blood
sample was taken from an antecubital vein for the
assessment of haematocrit, using the micro-haema-
tocrit method and immune function markers (C.
Cosgrove et al., in review).
Testing was performed at the same time of day
(+2 h) to minimize the effect of diurnal biological
variation (Atkinson & Reilly, 1996). The laboratory
ambient temperature was controlled at 208C and the
relative humidity ranged between 30% and 50%.
Participants drank ad libitum throughout each test.
Each athlete completed a training and food diary in
the 2 days before the initial testing week and
replicated the training and diet prior to subsequent
test sessions. Participants were instructed to exercise
for no more than 60 min in zone 1 in each of the 2
days before the test sessions.
Statistical analyses
A fully repeated-measures analysis of variance
(ANOVA) was used to compare the training
volume/training load in each discipline/training zone
across training periods. Main effects of training
period (A, B, C), training discipline (swim, bike,
run), and training zone (1, 2, 3), and any interaction
between these with the training volume/load were
reported. One-way repeated-measures ANOVA was
used to compare physiological measures across
training periods. Post hoc analysis was undertaken
where significance was obtained with paired Stu-
dent’s t-tests using two-tailed values of P, with the
Bonferroni method of adjustment to prevent type I
error. Pearson product–moment correlation coeffi-
cients were calculated to determine associations
between training volume and time spent in each
training zone, and the percentage change in physio-
logical adaptations, both overall and for each
discipline. Statistical significance was set at
P50.05. All data are expressed as means +stan-
dard deviations (s). Effect sizes for the key responses
to the incremental tests were calculated from the
mean difference (baseline to 6 months) over the
standard deviation of the baseline measure. These
values were judged using the descriptors suggested
by Cohen (1988). Effect sizes were included to
highlight the size of the training adaptations, as the
P-value alone does not necessarily provide this
information.
Results
Training volume and training-intensity distribution
The mean volume of training for periods A, B, and C
combined was 203 +71 h, with a range of 92–266 h.
There were main effects of training period for the
mean training volume and the mean training load per
week (P50.05), with both being greater in period B
than in period A (P50.05) (Figures 2A and 3A).
There was also an interaction (P50.05) for both
mean training volume and mean training load per
week by discipline, with changes over training period
only for cycling. The mean cycling volume per week
and the mean cycling load per week were both
greater in period B than period A (P50.05)
(Figures 2B and 3B).
Figure 2. (A) Training volume per week (mean +s). (B) Training
volume for each discipline per week (mean +s).
a
Significantly
different from period A (P50.05).
4C. M. Neal et al.
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
There was a main effect of training zone for the
mean training volume per week (P50.05), with post
hoc analysis revealing that the mean time spent in
zone 1 was greater in period B than in period A
(P50.05). There was an interaction (P50.05) for
mean training volume per week by training zone,
with changes in training period observed only for
zone 1 (Figure 4). Besides the absolute training time
spent in each zone, it is also important to consider
the percentage of time spent in each zone. Percen-
tage time spent in zones 1, 2, and 3 across all training
periods was 69 +9%, 25 +8%, and 6 +2%,
respectively. The only difference in the percentage
of time spent training in each zone for the three
disciplines combined was time spent in zone 2,
which was greater in period A than period C
(P50.05) (Table I).
Incremental test responses
The mean responses to the swimming, cycling, and
running incremental test to exhaustion are shown in
Table II, III, and IV, respectively. There was a main
effect of training period for the cycling peak power
output normalized to body mass using allometric
scaling (P50.05). Post hoc analysis revealed that this
increase occurred only between the baseline and 6-
month time points (P50.05) (Table III). There
were main effects of training period for both running
speed at the lactate threshold and running speed at
the lactate turnpoint (P50.05). Running speed at
the lactate threshold improved from baseline to 2
months and 6 months, and running speed at the
lactate turnpoint improved from baseline to 6
months (P50.05) (Table IV). In both swimming
and cycling, there were main effects of training
period for maximum heart rate (P50.05). For both
disciplines, maximum heart rate was lower in the 4-
months test than the baseline and 2-months tests
(P50.05), but was higher in the 6-months test than
in the 4-months test (P50.05). The effect sizes for
the key changes in the swimming, cycling, and
running incremental tests to exhaustion from base-
line to 6 months were all trivial or small, except
running speed at the lactate threshold, which was
moderate (Table V).
Anthropometry, cardiovascular, and pulmonary
measures
There was a main effect of training period for body
mass (P50.05), which was less at 6 months
(76.8 +10.0 kg) than at baseline (78.3 +10.3 kg)
and 2 months (77.9 +9.7 kg; P50.05). The
difference in percent body fat between baseline
(20.1 +2.5%) and 6 months (17.7 +3.5%) was
large, with an effect size of 1.0, despite the lack of a
main effect of training period. There was a main
effect of training period for peak flow, which was
higher at 6 months (621 +98 L min
71
) than at 2
months (612 +105 L min
71
) and 4 months
Figure 3. (A) Training load per week (mean +s). (B) Training
load for each discipline per week (mean +s).
a
Significantly
different from period A (P50.05).
Figure 4. Time spent in each zone per week (mean +s).
a
Significantly different from period A (P50.05).
Training and adaptation in Ironman triathletes 5
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
(598 +106 L min
71
;P50.05). There were no
main effects of training period for resting heart rate
(from 50 +6to50+7 beats min
71
), haematocrit
(from 44.5 +2.0 to 44.6 +2.3%) or heart rate
variability (stda: from 174 +66 to 164 +78 ms;
stdb: from 129 +53 to 96 +63 ms).
Training variables and relationships to physiological
adaptation
All of the significant relationships between training
variables and percentage change in physiological
adaptation were negative. The overall training
volume (periods A, B, and C combined) was
negatively correlated to the percentage change in
swimming lactate threshold and maximum running
Table I. Percentage of time spent in each zone during periods A,
B, and C (mean +s).
Training period Zone 1 Zone 2 Zone 3
Combined A 62+13 31 +12 7 +3
B71+724+85+3
C72+921+7
a
7+4
Swimming A 66 +24 26 +19 9 +11
B64+10 27 +99+6
C69+20 24 +16 8 +11
Cycling A 58 +15 34 +14 8 +5
B69+13 26 +12 5 +4
C71+15
a
22 +10
a
8+6
Running A 67 +22 28 +20 5 +4
B80+12 16 +94+5
C76+14 17 +11 6 +6
a
Significantly different from period A (P50.05)
Table II. Responses to the swimming incremental test to exhaustion (mean +s).
Baseline 2 months 4 months 6 months % Dbaseline to 6 months
150-m time at lactate threshold (s) 142 +16 141 +14 142 +14 141 +16 0.7
150-m time at lactate turnpoint (s) 130 +18 130 +15 130 +15 127 +14 2.3
Best 150-m time (s) 125 +18 122 +14 120 +14 121 +13 3.2
Maximum heart rate (beats min
71
) 167 +8 164 +6 160 +8*
a
164 +9
b
1.8
Deviation in stroke count (third 25 m) 4 +23+13+22+1 50.0
Deviation in stroke count (sixth 25 m) 4 +33+13+23+2 25.0
Note: See Figure 1 for details about the timing of the testing weeks for training periods A, B, and C.
*Significantly different from baseline (P50.05).
a
Significantly different from 2 months (P50.05).
b
Significantly different from 4 months
(P50.05).
Table III. Responses to the cycling incremental test to exhaustion (mean +s).
Baseline 2 months 4 months 6 months % Dbaseline to 6 months
Power at lactate threshold (W) 209 +33 213 +35 212 +29 216 +32 3.3
Power at lactate turnpoint (W) 263 +34 263 +35 267 +29 263 +33 0.0
Peak power output (W) 373 +54 369 +52 372 +43 376 +47 0.8
PPO/BM
0.79
3.43 +0.25 3.42 +0.24 3.46 +0.21 3.52 +0.23* 2.6
Maximum heart rate (beats min
71
) 175 +10 173 +10 169 +12* 174 +11
b
0.6
Note: See Figure 1 for details about the timing of the testing weeks for training periods A, B, and C.
*Significantly different from baseline (P50.05).
a
Significantly different from 2 months (P50.05).
b
Significantly different from 4 months
(P50.05).
Table IV. Responses to the running incremental test to exhaustion.
Baseline 2 months 4 months 6 months % Dbaseline to 6 months
Speed at lactate threshold (km h
71
) 12.9 +1.2 13.6 +1.6* 13.4 +1.5 13.9 +1.6* 7.8
Speed at lactate turnpoint (km h
71
) 15.3 +1.4 15.3 +1.5 15.6 +1.2 15.9 +1.3* 3.9
Maximum speed (km h
71
) 17.9 +1.7 18.0 +1.6 17.9 +1.5 18.2 +1.5 1.7
Maximum heart rate (beats min
71
) 179 +12 179 +8 177 +10 179 +9 0.0
Note: See Figure 1 for details about the timing of the testing weeks for training periods A, B, and C.
*Significantly different from baseline (P50.05).
a
Significantly different from 2 months (P50.05).
b
Significantly different from 4 months
(P50.05).
6C. M. Neal et al.
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
speed (r¼70.63 and r¼70.69, respectively;
P50.05). Similarly, overall training volume in zone
3 (periods A, B, and C combined) was negatively
correlated to the percentage change in maximum
running speed (r¼70.88, P50.05). The time
spent swimming in zone 2 was negatively correlated
to the percentage change in swimming lactate
threshold (r¼70.66, P50.05), and the time spent
cycling in zone 3 was negatively correlated to the
percentage change in cycling lactate threshold
(r¼70.79, P50.05). The percentage change in
maximum running speed was negatively correlated
to both the overall running volume (periods A, B,
and C combined) (r¼70.69, P50.05) and the
running time in zone 1 (r¼70.79, P50.05).
Finally, the running time in zone 3 was negatively
correlated to the percentage change in running
lactate turnpoint (r¼70.80, P50.05).
Discussion
Over three 2-month periods preceding an Ironman
triathlon, with a mean training volume in periods A,
B, and C of 8.1, 11.0, and 9.9 h per week, the
percentage of training time spent in zones 1, 2, and 3
was 62%, 31%, and 7% respectively in period A,
71%, 24%, and 5% in period B, and 72%, 21%, and
7% in period C. However, only modest physiological
adaptations occurred during the entire training
period, as shown by mostly trivial and small effect
sizes. The statistically significant relationships be-
tween the training variables and the changes in the
physiological adaptations were negative. This is
probably because the participants completing the
highest training volumes were already more well
trained, and thus had less room for improvement in
the key markers for adaptation (Wenger & Bell,
1986).
Previous research has shown that it is beneficial
both for elite and sub-elite endurance athletes to
spend 80% of training time in zone 1. This has
been shown in descriptive studies of athletes in the
lead up to competition (Lucia et al., 2000a, 2000b;
Schumacher & Mueller, 2002; Seiler & Kjerland,
2006), and in studies that have assessed differences
arising from an emphasis of training in zone 1 rather
than in zones 2 and 3 (Esteve-Lanao et al., 2007;
Ingham et al., 2008). For all of the disciplines
combined in the present study, mean percentage
time in zone 1 was never 80%, with 62%, 71%, and
72% of training in zone 1 in periods A, B, and C,
respectively. Similarly, for the individual disciplines,
only once for the three periods was the mean
percentage of time spent in zone 1 80% (80%,
running in training period B). It has been suggested
that less experienced athletes train too hard during
low-intensity sessions and not hard enough during
high-intensity sessions (Foster, Heimann, Esten,
Brice, & Porcari, 2001a), thus leading to a high
percentage of training time in zone 2. Therefore, the
lack of time in zone 1, and thus relatively more time
in zone 2, could explain the modest adaptations in
the incremental test responses.
Seiler and colleagues (Seiler, Haugen, & Kuffel,
2007) demonstrated a delayed recovery of the
autonomic nervous system immediately after training
in zones 2 and 3, compared with training in zone 1.
The authors suggested that the first ventilatory
threshold demarcates a clear threshold for autonomic
recovery. The authors further proposed that training
in zone 1 probably induces a targeted increase or
maintained stimulus for adaptation without inducing
a meaningful systemic stress response. Training in
zone 2 leads to delays in recovery, yet provides less
stimulus for adaptation than training in zone 3
(Seiler et al., 2007). Other authors agree that zone 2
provides a suboptimal stimulus for eliciting further
gains in endurance (Londeree, 1997). Esteve-Lanao
and colleagues (2007) suggested that when training
time in zone 2 exceeds a certain threshold (420% of
training time) at the expense of zone 1 training time,
endurance is impaired via a down-regulation of the
sympathetic nervous system. In the present study,
percentage time spent in zone 2 for all of the
disciplines combined was always above 20% (i.e.
31%, 24%, and 21% in training periods A, B, and C,
respectively). Training time in zone 2 is comparable
with a group in the study by Esteve-Lanao et al.
(2007), whose performance improvement was less
than that of a group who spent less training time in
zone 2 (12%).
The largest adaptations observed during the study
period, determined by effect sizes, were for the
Table V. Effect sizes for the key responses to the swimming,
cycling, and running incremental tests to exhaustion from baseline
to 6 months.
Discipline Measure
Effect
size Descriptor*
Swimming 150-m time at lactate
threshold (s)
0.06 Trivial
150-m time at lactate
turnpoint (s)
0.21 Small
Best 150-m time (s) 0.24 Small
Cycling Power at lactate threshold (W) 0.22 Trivial
Power at lactate turnpoint (W) 0.02 Trivial
Peak power output (W) 0.06 Trivial
PPO/BM
0.79
0.34 Small
Running Speed at lactate threshold
(km h
71
)
0.76 Moderate
Speed at lactate
turnpoint (km h
71
)
0.42 Small
Maximum speed (km h
71
) 0.13 Trivial
*Cohen (1988).
Training and adaptation in Ironman triathletes 7
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
running lactate threshold and the running lactate
turnpoint. A positive effect of zone 1 training has
been reported (Esteve-Lanao et al., 2007; Ingham
et al., 2008), and therefore these adaptations could
have occurred because of the greater percentage of
training time spent in zone 1 for running (74%) than
for swimming (66%) and cycling (66%). Negative
effects of excess training in zone 2 have been
demonstrated (Esteve-Lanao et al., 2007; Londeree,
1997). Adaptations in the running lactate threshold
and lactate turnpoint could be attributable to a lower
percentage of training time spent in zone 2 for
running (20%) than swimming (25%) and cycling
(27%), despite these relationships not being signifi-
cant. The lack of adaptation in swimming and
cycling was possibly because of a small percentage
of training time in zone 1 and a large percentage of
training time in zone 2. Although the relationships
between the training variables and the change in the
running lactate threshold and lactate turnpoint were
not significant in this small group of athletes, we
believe this is still an important finding.
After the increase in training volume and training
load from period A to period B, the maximum heart
rate in swimming and cycling was lower in the 4-
months test than the baseline and 2-month tests but
it increased in the 6-month test. The decreased rate
in the 4-months tests could be attributable to a
down-regulation of the sympathetic nervous system,
as previously suggested by Esteve-Lanao et al.
(2007). Over-reaching can cause a decrease in
maximum heart rate without a change in heart rate
variability (Hedelin, Kentta, Wiklund, Bjerle, &
Henriksson-Larsen, 2000), as in the present study.
However, formal identification of over-reaching also
requires a decrease in performance (Halson &
Jeukendrup, 2004), which was not the assessed in
the present study. However, it is a possibility that
some of the athletes were fatigued/over-reaching
during period B, before recovering during period
C. This is normal for endurance athletes, with
performance being intentionally depressed during
over-reaching phases to allow for a supercompensa-
tion effect before key competitions (Fiskerstrand &
Seiler, 2004).
It is clear from the magnitude of the effect sizes
that adaptations in athletes preparing for an Ironman
triathlon are small. It is possible that the incremental
tests conducted were not sensitive enough to detect
adaptations that had occurred. This has important
implications for monitoring training adaptation in
athletes using these methods. Although speculative,
while the lactate threshold/lactate turnpoint did not
improve markedly, the percentage of the lactate
threshold/lactate turnpoint at which the participants
were able to race in the Ironman triathlon could have
been increasing throughout the study. Therefore, it is
possible that performance measures such as longer
time-trials for each of the disciplines would have
detected greater improvements.
Conclusions
To the best of the authors’ knowledge, this is the first
study to have assessed training-intensity distribution
in a group of multi-sport athletes training for an
Ironman triathlon. This follows on from studies of
cyclists, runners, rowers, and cross-country skiers.
Given the number of variables associated with
assessing the training-intensity distribution in mul-
ti-sport athletes, it is not easy to draw conclusions as
to the effectiveness of the training in the different
disciplines on the key measures of adaptation in the
different disciplines. The present study highlights the
need for future research to focus on experimental
manipulation of training-intensity distribution and
thus improve our understanding of its impact on the
training-induced adaptations in endurance athletes.
Acknowledgements
The authors would like to thank the participants for
their commitment and cooperation throughout the
study.
References
Atkinson, G., & Reilly, T. (1996). Circadian variation in sports
performance. Sports Medicine,21, 292–312.
Aunola, S., & Rusko, H. (1984). Reproducibility of aerobic and
anaerobic thresholds in 20–50 year old men. European Journal of
Applied Physiology,53, 260–266.
Billat, V., Lepretre, P. M., Heugas, A. M., Laurence, M. H.,
Salim, D., & Koralsztein, J. P. (2003). Training and
bioenergetic characteristics in elite male and female Kenyan
runners. Medicine and Science in Sports and Exercise,35, 297–
304.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences
(2nd edn.). Hillsdale, NJ: Lawrence Erlbaum.
Esteve-Lanao, J., Foster, C., Seiler, S., & Lucia, A. (2007). Impact
of training intensity distribution on performance in endurance
athletes. Journal of Strength and Conditioning Research,21, 943–
949.
Esteve-Lanao, J., San Juan, A. F., Earnest, C. P., Foster, C., &
Lucia, A. (2005). How do endurance runners actually train?
Relationship with competition performance. Medicine and
Science in Sports and Exercise,37, 496–504.
Farina, D., Macaluso, A., Ferguson, R. A., & De Vito, G. (2004).
Effect of power, pedal rate, and force on average muscle fiber
conduction velocity during cycling. Journal of Applied Physiol-
ogy,97, 2035–2041.
Fiskerstrand, A., & Seiler, K. S. (2004). Training and perfor-
mance characteristics among Norwegian international rowers
1970–2001. Scandinavian Journal of Medicine and Science in
Sports,14, 303–310.
Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin,
L. A., Parker, S. et al. (2001a). A new approach to monitoring
exercise training. Journal of Strength and Conditioning Research,
15, 109–115.
8C. M. Neal et al.
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
Foster, C., Heimann, K. M., Esten, P. L., Brice, G., & Porcari, J.
P. (2001b). Differences in perceptions of training by coaches
and athletes. South African Journal of Medicine,8, 3–7.
Halson, S. L., & Jeukendrup, A. E. (2004). Does overtraining
exist? An analysis of overreaching and overtraining research.
Sports Medicine,34, 967–981.
Hedelin, R., Kentta, G., Wiklund, U., Bjerle, P., & Henriksson-
Larsen, K. (2000). Short-term overtraining: Effects on perfor-
mance, circulatory responses, and heart rate variability.
Medicine and Science in Sports and Exercise,32, 1480–1484.
Ingham, S. A., Carter, H., Whyte, G. P., & Doust, J. H. (2008).
Physiological and performance effects of low- versus mixed-
intensity rowing training. Medicine and Science in Sports and
Exercise,40, 579–584.
Jones, A. M., & Doust, J. H. (1996). A 1% treadmill grade most
accurately reflects the energetic cost of outdoor running.
Journal of Sports Sciences,14, 321–327.
Kindermann, W., Simon, G., & Keul, J. (1979). The significance
of the aerobic–anaerobic transition for the determination of
work load intensities during endurance training. European
Journal of Applied Physiology and Occupational Physiology,42,
25–34.
Kuipers, H., Verstappen, F. T., Keizer, H. A., Geurten, P., & van
Kranenburg, G. (1985). Variability of aerobic performance in
the laboratory and its physiologic correlates. International
Journal of Sports Medicine,6, 197–201.
Londeree, B. R. (1997). Effect of training on lactate/ventilatory
thresholds: A meta-analysis. Medicine and Science in Sports and
Exercise,29, 837–843.
Lucia, A., Hoyos, J., Pardo, J., & Chicharro, J. L. (2000a).
Metabolic and neuromuscular adaptations to endurance train-
ing in professional cyclists: A longitudinal study. Japanese
Journal of Physiology,50, 381–388.
Lucia, A., Hoyos, J., Perez, M., & Chicharro, J. L. (2000b). Heart
rate and performance parameters in elite cyclists: A longitudinal
study. Medicine and Science in Sports and Exercise,32, 1777–
1782.
Nevill, A. M., Ramsbottom, R., & Williams, C. (1992). Scaling
physiological measurements for individuals of different body
size. European Journal of Applied Physiology and Occupational
Physiology,65, 110–117.
Pyne, D. B., Boston, T., Martin, D. T., & Logan, A. (2000).
Evaluation of the Lactate Pro blood lactate analyser. European
Journal of Applied Physiology,82, 112–116.
Pyne, D. B., Lee, H., & Swanwick, K. M. (2001). Monitoring the
lactate threshold in world ranked swimmers. Medicine and
Science in Sports and Exercise,33, 291–297.
Schumacher, Y. O., & Mueller, P. (2002). The 4000-m team
pursuit cycling world record: Theoretical and practical aspects.
Medicine and Science in Sports and Exercise,34, 1029–1036.
Seiler, S., Haugen, O., & Kuffel, E. (2007). Autonomic recovery
after exercise in trained athletes: Intensity and duration effects.
Medicine and Science in Sports and Exercise,39, 1366–1373.
Seiler, K. S., & Kjerland, G. O. (2006). Quantifying training
intensity distribution in elite endurance athletes: Is there
evidence for an ‘‘optimal’’ distribution? Scandinavian Journal
of Medicine and Science in Sports,16, 49–56.
Skinner, J. S., & McLellan, T.M. (1980). The transition from
aerobic to anaerobic metabolism. Research Quarterly for Exercise
and Sport,51, 234–248.
Wenger, H. A., & Bell, G. J. (1986). The interactions of intensity,
frequency and duration of exercise training in altering
cardiorespiratory fitness. Sports Medicine,3, 346–356.
Training and adaptation in Ironman triathletes 9
Downloaded by [University of Stirling Library] at 08:36 10 October 2011
