Effects of 16 weeks of pyramidal and polarized training intensity distributions in well-trained endurance runners

Abstract

The aim of this study was to investigate the effects of four different training periodizations, based on two different training intensity distributions during a 16-week training block in well-trained endurance runners. Sixty well-trained male runners were divided into four groups. Each runner completed one of the following 16-week training interventions: a pyramidal periodization (PYR); a polarized periodization (POL); a pyramidal periodization followed by a polarized periodization (PYR→POL); and a polarized periodization followed by a pyramidal periodization (POL→PYR). The PYR and POL groups trained with a pyramidal or polarized distribution for 16 weeks. To allow for the change in periodization for the PYR→POL and POL→PYR groups, the 16-week intervention was split into two 8-week phases, starting with pyramidal or polarized distribution and then switching to the other. The periodization patterns were isolated manipulations of training intensity distribution, while training load was kept constant. Participants were tested pre-, mid- and post-intervention for body mass, velocity at 2 and 4 mmol·L-1 of blood lactate concentration (vBLa2, vBLa4), absolute and relative peak oxygen consumption (⩒O2peak) and 5-km running time trial performance. There were significant group x time interactions for relative ⩒O2peak (P < 0.0001), vBLa2 (P < 0.0001) and vBLa4 (P < 0.0001) and 5-km running time trial performance (P = 0.0001). Specifically, participants in the PYR→POL group showed the largest improvement in all these variables (~3.0% for relative ⩒O2peak, ~1.7% for vBLa2, ~1.5% for vBLa4, ~1.5% for 5-km running time trial performance). No significant interactions were observed for body mass, absolute ⩒O2peak, peak heart rate, lactate peak and rating of perceived exertion. Each intervention effectively improved endurance surrogates and performance in well-trained endurance runners. However, the change from pyramidal to polarized distribution maximized performance improvements, with relative ⩒O2peak representing the only physiological correlate.

Full text

498
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Scand J Med Sci Sports. 2022;32:498–511.wileyonlinelibrary.com/journal/sms
Received: 28 May 2021
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Revised: 8 November 2021
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Accepted: 12 November 2021
DOI: 10.1111/sms.14101
ORIGINAL ARTICLE
Effects of 16weeks of pyramidal and polarized training
intensity distributions in well- trained endurance runners
LucaFilipas1,2
|
MatteoBonato1,3
|
GabrieleGallo4,5
|
RobertoCodella1,2
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2021 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd.
1Department of Biomedical Sciences
for Health, Università degli Studi di
Milano, Milan, Italy
2Department of Endocrinology,
Nutrition and Metabolic Diseases,
IRCCS MultiMedica, Milan, Italy
3IRCCS Istituto Ortopedico Galeazzi,
Milan, Italy
4Department of Experimental
Medicine, Università degli Studi di
Genova, Genoa, Italy
5Centro Polifunzionale di Scienze
Motorie, Università degli Studi di
Genova, Genoa, Italy
Correspondence
Luca Filipas, Department of Biomedical
Sciences for Health, Università degli
Studi di Milano, Via F.lli Cervi 93,
20090 Segrate (Milano), Italy.
Email: luca.filipas@unimi.it
The aim of this study was to investigate the effects of four different training pe-
riodizations, based on two different training intensity distributions during a 16-
week training block in well- trained endurance runners. Sixty well- trained male
runners were divided into four groups. Each runner completed one of the follow-
ing 16- week training interventions: a pyramidal periodization (PYR); a polarized
periodization (POL); a pyramidal periodization followed by a polarized periodi-
zation (PYR→POL); and a polarized periodization followed by a pyramidal pe-
riodization (POL→PYR). The PYR and POL groups trained with a pyramidal
or polarized distribution for 16weeks. To allow for the change in periodization
for the PYR→POL and POL→PYR groups, the 16- week intervention was split
into two 8- week phases, starting with pyramidal or polarized distribution and
then switching to the other. The periodization patterns were isolated manipu-
lations of training intensity distribution, while training load was kept constant.
Participants were tested pre- , mid- and post- intervention for body mass, velocity
at 2 and 4mmol·L−1 of blood lactate concentration (vBLa2, vBLa4), absolute and
relative peak oxygen consumption (

VO2peak
) and 5- km running time trial per-
formance. There were significant group×time interactions for relative

VO2peak
(p<0.0001), vBLa2 (p<0.0001) and vBLa4 (p<0.0001) and 5- km running time
trial performance (p = 0.0001). Specifically, participants in the PYR → POL
group showed the largest improvement in all these variables (~3.0% for relative

VO2peak
, ~1.7% for vBLa2, ~1.5% for vBLa4, ~1.5% for 5- km running time trial
performance). No significant interactions were observed for body mass, absolute

VO2peak
, peak heart rate, lactate peak and rating of perceived exertion. Each in-
tervention effectively improved endurance surrogates and performance in well-
trained endurance runners. However, the change from pyramidal to polarized
distribution maximized performance improvements, with relative

VO2peak
repre-
senting the only physiological correlate.
KEYWORDS
polarized training, pyramidal training, running performance, training intensity distribution,
training periodization

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FILIPAS et al.
1
|
INTRODUCTION
Endurance coaches, athletes and scientists strive to find
the best combination of intensity, duration and frequency
of training sessions1 to achieve the desired physiological
adaptation for athletes and the best performance during
main competitions.2,3 Manipulating these variables dif-
ferently over time is traditionally referred to as training
periodization.4 To improve the understanding of train-
ing manipulation and monitoring, different training in-
tensity zones have been described, determined by either
physiological factors such as lactate threshold, ventilatory
thresholds, percentage of the maximum oxygen uptake,
percentage of the maximum heart rate, or subjective fac-
tors, such as the goal or rate of perceived exertion of a par-
ticular session.5
The concept of training intensity distribution (TID)
is defined as the amount of time that the athlete spends
in different zones of training intensity during exercise.6
Usually, TID is calculated by using three training zones:
zone 1 (Z1), below the first ventilatory threshold; zone 2
(Z2), between the first and the second ventilatory thresh-
old; zone 3 (Z3), above the second ventilatory threshold.7
The intensity distribution known as polarized training is
defined as having the highest percentage of time spent in
Z1, a smaller, but relatively high percentage in Z3, and only
a small portion of training in Z2 (ie, Z1>Z3>Z2). On the
other hand, pyramidal TID is characterized by accumulat-
ing a higher percentage of training time in Z2 and less in
Z3, but, as in the case of the polarized model, the highest
percentage of training is spent in Z1 (ie, Z1>Z2>Z3).5,8
Several experimental studies have shown the potential
benefits of both polarized and pyramidal TID compared
to other TID models for endurance sports.6,9– 11 A recent
review on this topic12 showed that these two models ap-
pear to be the most effective for boosting endurance per-
formance in middle- and long- distance runners. Previous
research has identified pyramidal training as the pri-
mary TID employed by well- trained and elite endurance
athletes, noting that certain world- class athletes adopt a
polarized training distribution in specific phases of the
season.7,13,14 There seems to be a pattern across the train-
ing season, from a focus on high- volume, low- intensity
training during the preparation period, to a pyramidal
TID during the pre- competition period, and ending with
a polarized TID during the competition phase15 in both
well- trained and elite runners.
To our knowledge, there are no studies that have
compared, under controlled circumstances, the effects
of changing the TID in well- trained endurance athletes’
periodization,12 although this is a common practice
among athletes. Therefore, the aim of this study was to
investigate the effects of modifying TID throughout a
16- week periodization in well- trained endurance run-
ners. Specifically, we sought to compare four different pe-
riodization patterns: 16- week pyramidal (PYR), 16- week
polarized (POL), 8- week pyramidal followed by 8- week
polarized (PYR→POL), and 8- week polarized followed by
8- week pyramidal (POL→PYR). Since it has been shown
that training load is crucial for adaptation to endurance
training,16,17 the periodization patterns employed isolated
manipulations of TID while keeping training load con-
stant, thus isolating the effect of TID from the manipula-
tions of training load. Our hypothesis was that, consistent
with the training cycles typically used by elite endurance
athletes,7 switching from pyramidal to polarized TID in
the final phase of a training period would result in higher
performance improvements compared to maintaining the
same distribution (pyramidal or polarized) or switching
from polarized to pyramidal TID.
2
|
MATERIALS AND METHODS
2.1
|
Participants
Sixty well- trained male runners (38±7years, relative peak
oxygen consumption (

VO2peak
): 67 ± 4 ml·kg−1·min−1)
were recruited to the study through local running
clubs. Inclusion criteria were as follows: (1) relative

VO2peak
>60ml·kg−1·min−1, (2) training frequency more
than five sessions per week, (3) running experience
>2 years, (4) regularly competing, and (5) absence of
known disease or exercise limitations. The study design
and procedures were approved by the local research ethics
committee (n° 52/20, attachment 4, 14May 2020) and fol-
lowed the ethical principles for medical research involving
human participants set by the World Medical Association
Declaration of Helsinki. Participants were provided with
written instructions outlining the procedures and risks
associated with the study and gave informed written
consent.
2.2
|
Experimental design
A four- armed parallel group randomized controlled trial
was used. To determine the sample size a priori (soft-
ware package, G*Power 3.1.9.2), the following input vari-
ables were selected as per an F test for ANOVA- repeated
measures- within factors analysis: a statistical power
(1−β) of 0.8, a probability α level of 0.05, an effect size
f of 0.35, four groups and a compliance >90%. These in-
puts were determined using the literature on training
intervention in high- level endurance athletes. As output
variables, an actual power of 0.81 and a critical F of 3.13

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FILIPAS et al.
were obtained. A sports physician and a certified endur-
ance coach screened the athletes for eligibility. After the
pre- intervention period and the pre- tests, participants
were randomly allocated to one of the four groups based
on balanced permutations generated by a web- based
computer program (www.rando mizat ion.com) using a
1:1:1:1 ratio. The four groups were matched for age, rela-
tive

VO2peak
and running performance in the 5- km time
trial. A weighting factor was used to properly match the
groups at the baseline, dividing athletes in three differ-
ent blocks based on performance in the pre- tests. The
four groups differed by periodization and/or TID: PYR,
POL, PYR→ POL and POL→PYR. The member of the
research team who conducted the randomization did not
take part in the remainder of the study. While participants
were aware of their allocation, they were blinded to the
true aims of the study. An overview of the experimental
protocol is shown in Figure1.
2.3
|
Pre- intervention period
Before the intervention, a 6- week pre- intervention pe-
riod was conducted to familiarize subjects with sessions
included in the intervention period and with testing pro-
tocols. During the pre- intervention period, participants
were instructed to perform only one session in Z2 and
one in Z3 each week, combined with a freely chosen vol-
ume between 250 and 350min. They were instructed to
complete 6sessions/week to have a similar training fre-
quency compared to the intervention period. All subjects’
training history during the previous year was monitored
using an online training diary (TrainingPeaks, Peaksware
LLC), years of running experience, previous peak perfor-
mance level, and previous/current injuries and diseases.
Participants had a mixture of polarized and pyramidal
training intensity distribution during the year before the
intervention. Pre- testing was performed at the end of the
pre- intervention (end- November), and subjects were then
randomized into one of four different training groups.
No strength and cross- training were prescribed and per-
formed during the pre- intervention period. The total
volume of training was completed in a running form.
All participants were instructed not to change their diet
throughout the training period.
2.4
|
Intervention period
2.4.1
|
Training organization
The training intervention was performed from early
December 2019 to the end of March 2020 (16 weeks),
which corresponded to the base period for these groups
of runners. It consisted of two 8- week mesocycles struc-
tured as 3+1micro- cycles. Participants were instructed
to follow a mesocycle week load structured as follows:
weeks 1– 3, 5– 7, 9– 11 and 13– 15had high training loads;
weeks 4 and 12had reduced training loads by 30% com-
pared with the previous three; and weeks 8 and 16had
reduced training loads by 40% compared with the previ-
ous three. The three zones model7 was used to calculate
the TID of the 8- week mesocycles: zone 1 (Z1), for inten-
sities below first ventilatory threshold; zone 2 (Z2), for
FIGURE  Schematic presentation
of the experimental design. Z1, zone 1 (ie,
volume below first ventilatory threshold);
Z2, zone 2 (ie, volume between first
and second ventilatory thresholds);
Z3, zone 3 (ie, volume above second
ventilatory threshold); PYR, pyramidal
training intensity distribution; POL,
polarized training intensity distribution;
PYR→POL, pyramidal→polarized
training intensity distribution;
POL→PYR, polarized→pyramidal
training intensity distribution

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FILIPAS et al.
TABLE  Plan of the 8- week mesocycle for PYR and POL. Participants in PYR repeated twice the 8- week pyramidal mesocycle, in POL repeated twice the 8- week polarized mesocycle, in
PYR→POL completed the 8- week pyramidal mesocycle and then the polarized one, in POL→PYR completed the 8- week polarized mesocycle and then the pyramidal one
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8
PYR Mon 70min Z1 70min Z1 70min Z1 50min Z1 70min Z1 70min Z1 70min Z1 50min Z1
Tue 20min Z1 + 55min
Z2
20min Z1 + 50min Z2 20min Z1 + 55min
Z2
40min Z1 20min Z1 +
55min Z2
20min Z1 + 50min Z2 20min Z1 + 55min Z2 40min Z1
Wed 70min Z1 70min Z1 70min Z1 20min Z1 + 40min Z2 70min Z1 70min Z1 70min Z1 Test 1
Thu 60min Z1 60min Z1 60min Z1 30min Z1 60min Z1 60min Z1 60min Z1 30min Z1
Fri 20min Z1 +
12×2min Z3 (r.
1min Z2)
20min Z1 + 4×7min
Z3 (r. 3min Z2)
20min Z1 +
3×12×40s Z3
(r. 20s Z2/3min
Z1)
20min Z1 + 2×10min Z3
(r. 5min Z2)
20min Z1 +
12×2min Z3
(r. 1min Z2)
20min Z1 + 4×7min Z3
(r. 3min Z2)
20min Z1 + 3×12×40s
Z3 (r. 20s Z2/3min
Z1)
Test 2
Sat Rest Rest Rest Rest Rest Rest Rest Rest
Sun 60min Z1 60min Z1 60min Z1 50min Z1 60min Z1 60min Z1 60min Z1 50min Z1
Z1 (min) 300 (77%) 300 (77%) 305 (77%) 210 (78%) 300 (77%) 300 (77%) 305 (77%)
Z2 (min) 67 (17%) 62 (16%) 67 (17%) 40 (15%) 67 (17%) 62 (16%) 67 (17%)
Z3 (min) 24 (6%) 28 (7%) 24 (6%) 20 (7%) 24 (6%) 28 (7%) 24 (6%)
Σ volume
(min)
391 390 396 270 391 390 396
TL 506 508 511 350 506 508 511
POL Mon 70min Z1 70min Z1 70min Z1 50min Z1 70min Z1 70min Z1 70min Z1 50min Z1
Tue 20min Z1 +
4×7min Z3 (r.
3min Z2)
20min Z1 + 8×4min
Z3 (r. 2min Z2)
20min Z1 +
4×7min Z3 (r.
3min Z2)
40min Z1 20min Z1 +
4×7min Z3
(r. 3min Z2)
20min Z1 + 8×4min Z3
(r. 2min Z2)
20min Z1 + 4×7min Z3
(r. 3min Z2)
40min Z1
Wed 70min Z1 70min Z1 70min Z1 20min Z1 + 2×10min Z3
(r. 5min Z2)
70min Z1 70min Z1 70min Z1 Test 1
Thu 60min Z1 60min Z1 60min Z1 30min Z1 60min Z1 60min Z1 60min Z1 30min Z1
Fri 20min Z1 +
12×2min Z3 (r.
1min Z2)
20min Z1 +
3×12×40s Z3 (r.
20s Z2/3min Z1)
20min Z1 +
12×2min Z3 (r.
1min Z2)
20min Z1 + 2×10min Z3
(r. 5min Z2)
20min Z1 +
12×2min Z3
(r. 1min Z2)
20min Z1 + 3×12×40s
Z3 (r. 20s Z2/3min Z1)
20min Z1 + 12×2min
Z3 (r. 1min Z2)
Test 2
Sat Rest Rest Rest Rest Rest Rest Rest Rest
Sun 60min Z1 60min Z1 60min Z1 50min Z1 60min Z1 60min Z1 60min Z1 50min Z1
Z1 (min) 300 (80%) 305 (78%) 300 (80%) 210 (81%) 300 (80%) 305 (78%) 300 (80%)
Z2 (min) 24 (6%) 28 (7%) 24 (6%) 10 (4%) 24 (6%) 28 (7%) 24 (6%)
Z3 (min) 52 (14%) 56 (15%) 52 (14%) 40 (15%) 52 (14%) 56 (15%) 52 (14%)
Σ volume
(min)
376 389 376 260 376 389 376
TL 504 529 504 350 504 529 504
Abbreviations: POL→PYR, polarized + pyramidal training intensity distribution; POL, polarized training intensity distribution; PYR→POL, pyramidal + polarized training intensity distribution; PYR, pyramidal
training intensity distribution; TL, training load; Z1, zone 1 (ie, volume below first ventilatory threshold); Z2, zone 2 (ie, volume between first and second ventilatory thresholds); Z3, zone 3 (ie, volume above second
ventilatory threshold).

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FILIPAS et al.
intensities between first and second ventilatory thresh-
olds; and zone 3 (Z3), for intensities above second venti-
latory threshold. Pyramidal and polarized TID consisted
of a higher percentage of training volume spent in Z1 and
less in Z2 and Z3, with the proportions of Z2 and Z3 as the
main distinguishing characteristic between these two TID
(ie, pyramidal: Z1>Z2>Z3; polarized: Z1>Z3>Z2).
The nature of the TID was also verified using the polari-
zation index,8 confirming that our distributions were ef-
fectively not- polarized and polarized. Table1shows the
two different 8- week training programs. PYR repeated
the 8- week pyramidal mesocycle twice; POL repeated
the 8- week polarized mesocycle twice; PYR→POL com-
pleted the 8- week pyramidal mesocycle and then the po-
larized one; POL→PYR completed the 8- week polarized
mesocycle and then the pyramidal one. Training impulse
(TRIMP) was calculated as volume×intensity according
to Lucia and colleagues.18 Pyramidal and polarized train-
ing distributions were matched using TRIMP for both
weekly and mesocycle training loads, to isolate the effect
of TID and different periodizations on physiological and
performance outcomes.
2.4.2
|
Training monitoring
All participants were provided with an online training
diary (TrainingPeaks, Peaksware LLC, Lafayette, CO,
United States) to record their training. The following vari-
ables were registered for each training session: (1) total
training duration, (2) total duration in each endurance
training zone (time in zone method19), and (3) training
load calculated using TRIMP.18 Individualized heart rate
(HR) zones were derived from the incremental ramp test,
linking HR zones to ventilatory thresholds. For this pur-
pose, two 5- min constant load tests were performed at the
velocity aligned to the two ventilatory thresholds after the
incremental ramp test, and the average HR of the last 30s
was considered as the threshold HR. There were no sig-
nificant differences among groups in training loads dur-
ing the intervention period compared with the previous
training year. Table1shows the arithmetic mean differ-
ences among groups for the training variables measured
as mean during the 16weeks.
2.4.3
|
Pre- , mid- and post- tests
All participants were asked to stay well- hydrated, to re-
frain from consuming alcohol and caffeine for at least 24- h
before testing, and to refrain from engaging in strenuous
exercise at least 36- h prior to testing. They were not al-
lowed to eat during the two hours preceding the tests. All
tests were performed under similar environmental condi-
tions (temperature: 6– 15 °C; wind:<8km·h−1) on a regu-
lar running track and at the same time of day (8:00– 10:00)
to avoid the influence of circadian rhythm.
The pre- , mid- and post- tests included the determina-
tion of body mass, two incremental tests and a 5- km time
trial. The testing sessions were performed at week 8 and
week 16, corresponding to tapering weeks according to
the mesocycle structure of the 16- week training interven-
tion (see Figure1), to limit the effect of cumulative train-
ing fatigue. Tests were performed on two different days,
separated by 48h.
The first testing session was carried out on the
Wednesday after 3days of 40– 60min Z1 sessions. It in-
cluded a measurement of body mass, a blood lactate
profile test and a

VO2peak
test. The test started without
a warm- up, with 5min running at 14 km·h−1. Running
continued and velocity was increased by 0.5 km·h−1
every 5 min using an electronic pacesetter. Blood sam-
ples were obtained through the ear lobe at the end of
each 5- min bout and were analyzed for whole blood lac-
tate using a portable lactate analyzer (Lactate Pro, Arcray
Inc), reported to have good reliability and accuracy.20
The smallest detectable change of Lactate Pro for lactate
measurement is 1.1%.20 The test was terminated when a
lactate of 4mmol·L−1 or higher was measured. From this
continuous incremental running test, lactate thresholds
were calculated as the velocity that corresponded with 2
and 4 mmol·L−1 of blood lactate concentration (vBLa2,
vBLa4), as recently proposed in similar research.21 The
blood lactate profile was determined for each runner by
plotting lactate vs velocity values obtained during sub-
maximal continuous incremental running. Upon termina-
tion of the blood lactate profiling, participants had 20min
of recovery before completing another incremental run-
ning test to determine the

VO2peak
. The test was initiated
with 1min of running at 12km·h−1. Running velocity was
subsequently increased by 0.5km·h−1 every minute until
exhaustion.

VO2peak
was calculated as the average 30- s

VO2
measurements. HR>95% of known maximal HR, re-
spiratory exchange ratio >1.05, and lactate >8.0mmol·L−1
were used as criteria to evaluate whether

VO2peak
was ob-
tained. HR was measured using a Garmin HRM- Run chest
strap (Garmin).

VO2
was measured breath- by- breath by a
wearable metabolic system (K5, COSMED), reported to
have an accurate to acceptable reliability at all metabolic
rates.22,23 The turbine was calibrated with a 3- L syringe
(M9474, Medikro Oy). Gas analyzers were calibrated with
ambient air and gas mixture (16.0% O2 and 5.0% CO2). The
smallest detectable change of K5 for

VO2
measurement at
high intensities is 3.4%.22
The second testing session was carried out on Friday,
48h after the first session, and it was preceded by a 30- min

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FILIPAS et al.
Z1session. It consisted of a 5- km time trial on the track.
The smallest detectable change of a 5- km time trial in a
competitive environment is 3.2%.24 All the athletes were
familiar with this distance, as they performed it several
times during training and competitions. The test started
with their traditional warm- up routine, standardized
within individuals during pre- , mid- and post- tests.
Runners were instructed to perform the test to obtain their
best performance over the distance. Researchers provided
standardized encouragements at the end of each 400- m
lap. Peak HR (HRpeak) was calculated using the chest strap
as the mean HR during the last 30s of the time trial. Peak
blood lactate (
La−
peak
) was obtained through the ear lobe
within 1min of completion of the time trial using the por-
table lactate analyzer. Rating of perceived exertion (RPE)
was recorded using Borg's 6– 20scale25 for each athlete 15–
30min after the end of the time trial. All participants were
familiarized with the RPE scale prior to the commence-
ment of the study.
2.5
|
Statistical analysis
All data are presented as arithmetic mean±standard de-
viation. Normal distribution and sphericity of data were
checked with Shapiro- Wilk and Mauchly's tests, respec-
tively. Greenhouse- Geisser correction to the degrees of
freedom was applied when assumption of sphericity was
violated. To test for differences between groups at pre- tests
among all the physiological and performance variables and
in training load, one- way repeated measures analysis of
variance (ANOVA) with Tukey post- hoc tests was used.
Two- way repeated measures ANOVA (group and time as
factors) with Tukey post- hoc tests were performed to eval-
uate differences among groups at pre- , mid- and post- tests
for body mass,

VO2peak
, vBLa2, vBLa4, 5- km time trial
time,
La−
peak
, HRpeak and RPE. The variables that predicted
time trial performance at pre- , mid- and post- tests and per-
formance enhancement from pre- to post- tests were identi-
fied by multiple linear regression analysis. Body mass,
relative

VO2peak
, vBLa2, vBLa4 and
La−
peak
were used as
predictor variables. Significance was set at 0.05 (two- tailed)
for all analyses. Effect sizes for repeated measure ANOVA
are reported as partial eta squared (
𝜂2
p
), using the small
(<0.13), medium (0.13– 0.25) and large (>0.25) interpreta-
tion for effect size,26 while effect sizes for pairwise com-
parison were calculated using Cohen's d and are considered
to be either trivial (<0.20), small (0.21– 0.60), moderate
(0.61– 1.20), large (1.21– 2.00), or very large (>2.00).27 Data
analysis was conducted using the Statistical Package for
the Social Sciences, version 25 (SPSS Inc.).
3
|
RESULTS
3.1
|
Dropout from the intervention
Four participants (one for each group) were considered as
dropouts and were excluded from the final analysis due to
their absence from post- testing and/or<90% adherence to
prescribed training sessions. Sixty runners were included
in the analysis in total (37 ± 6 years, relative

VO2peak
:
68±4ml·kg−1·min−1).
3.2
|
Pre- test
Table2shows that there were no significant differences
between PYR, POL, PYR→POL and POL→PYR before
TABLE  Baseline variables derived from the incremental exercise test to exhaustion of the participants who completed the 16- week
training intervention, comparing four groups. Values were reported at exhaustion. Data are presented as mean±standard deviation
PYR (n=15) POL (n=15) PYR→POL (n=15) POL→PYR (n=15) p
VE (L·min−1) 144±8 146±6 145±9 145±8 0.9206

VO2peak
(L·min−1)4.5±0.3 4.5±0.3 4.5±0.4 4.4±0.3 0.7901

VO2peak
(ml·kg−1·min−1)68±4 69±3 68±5 68±4 0.9538

VCO2peak
(L·min−1)5.1±0.4 5.2±0.3 5.2±0.4 5.2±0.3 0.8252
RER 1.16±0.03 1.18±0.04 1.16±0.04 1.18±0.02 0.1619
PETO2 (mm Hg) 121±5 123±4 123±5 123±4 0.5374
PETCO2 (mm Hg) 31±2 30±2 30±1 30±2 0.3355
HRpeak (bpm) 183±9 182±8 184±6 182±9 0.8891
Abbreviations:

VCO2peak
, peak carbon dioxide production;

VO2peak
, peak oxygen consumption; HRpeak, heart rate peak; PETCO2, end- tidal carbon dioxide
partial pressure; PETO2, end- tidal oxygen partial pressure; POL→PYR, polarized + pyramidal training intensity distribution; POL, polarized training intensity
distribution; PYR→POL, pyramidal + polarized training intensity distribution; PYR, pyramidal training intensity distribution; RER, respiratory exchange
ratio; VE, ventilation.

504
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FILIPAS et al.
the intervention period with respect to all the variables
derived from the incremental exercise test to exhaustion.
Table3 shows that there were no significant differ-
ences between PYR, POL, PYR→POL and POL→PYR
before the intervention period with respect to age, body
mass, vBLa2, vBLa4, 5- km time trial performance, HRpeak,
La−
peak
and RPE.
3.3
|
Training load
Effective TID and training load of participants in PYR,
POL, PYR → POL and POL → PYR are presented in
Table4. No significant differences were calculated be-
tween groups for training load.
3.4
|
Body mass,

V
O
2peak
and
lactate profiles
For body mass, there was a significant main effect of time
(F(1.6, 91.0)=6.4; p=0.0046;
𝜂2
p
=0.10), while no interac-
tion group×time was found (F(6, 112)=0.9; p=0.4946;
𝜂2
p
=0.05). For the absolute

VO2peak
(Figure2A) there was
a significant main effect of time (F(1.9, 109) = 11.6;
p<0.0001;
𝜂2
p
=0.26), while no interaction group×time
was found (F(6, 112)=0.5; p=0.8128;
𝜂2
p
=0.02). For the
relative

V
O
2peak
(Figure2B), there was a significant main
effect of time (F(1.4, 75.4)=35.8; p<0.0001;
𝜂2
p
=0.40) and
an interaction group × time (F(6, 112)=4.5; p= 0.0004;
𝜂2
p
=0.19). For vBLa2 (Figure2C), there was a significant
main effect of time (F(1.7, 93.2) = 62.6; p < 0.0001;
𝜂2
p
=0.53) and an interaction group×time (F(6, 112)=6.8;
p<0.0001;
𝜂2
p
=0.27). For vBLa4 (Figure2D), there was a
significant main effect of time (F(1.6, 91.3) = 75.1;
p<0.0001;
𝜂2
p
=0.57) and an interaction group×time (F(6,
112)=5.7; p<0.0001;
𝜂2
p
=0.23). Percentage changes and
effect sizes of pairwise comparisons pre- to mid- tests, mid-
to post- tests and pre- to post- tests in the four different
groups are presented in the Table5.
3.5
|
5- km time trial
There was a significant main effect of time (F(1.7,
93.7) = 71.4; p < 0.0001;
𝜂2
p
 = 0.56) and an interaction
group× time (F(6, 112)= 5.2; p=0.0001;
𝜂2
p
= 0.22) for
the time trial performance (Figure3A). There was a sig-
nificant main effect of time (F(1.8, 99.1)=4.5; p=0.0164;
𝜂2
p
=0.08), while there was no interaction group ×time
(F(6, 112) = 0.3; p = 0.9578;
𝜂2
p
 = 0.02) for
La−
peak
(Figure3B). There were no main effects, nor interaction
group× time (F(6, 112)= 0.5; p=0.8208;
𝜂2
p
= 0.03) for
HRpeak (Figure3C). There were no main effects, nor inter-
action group×time (F(6, 112)=0.7; p=0.6170;
𝜂2
p
=0.04)
for RPE (Figure3D). Percentage changes and effect sizes
of pairwise comparisons pre- to mid- tests, mid- to post-
tests and pre- to post- tests in the four different groups are
presented in the Table5.
3.6
|
Variables that predicted time trial
performance
Multiple regression analysis revealed that the variables
that predict time trial performance at different timepoints
(pre- , mid- and post- tests) are different compared to the
ones that predict time trial performance enhancement
from pre- to post- tests (Table6).
TABLE  Baseline characteristics of the participants who completed the 16- week training intervention, comparing four groups. Data are
presented as mean±standard deviation
PYR (n=15) POL (n=15) PYR→POL (n=15) POL→PYR (n=15) p
Age (years) 35±6 38±5 38±6 38±6 0.9321
Body mass (kg) 64±3 65±3 65±3 66±3 0.3037
vBLa2 (km·h−1) 16.3±1.1 16.4±0.8 16.2±1.2 16.4±1.1 0.9390
vBLa4 (km·h−1) 17.3±1.1 17.4±0.8 17.2±1.2 17.4±1.1 0.9572
Time trial time (s) 993±57 998±48 986±56 998±61 0.9433
La−
peak
(mmol·L−1) 9±2 10±2 10±3 10±2 0.6204
HRpeak (bpm) 179±12 177±12 177±12 177±10 0.9598
RPE (6– 20) 18±1 18±1 18±1 18±1 0.8951
Abbreviations:
La−
peak
, peak blood lactate; HRpeak, heart rate peak; POL→PYR, polarized + pyramidal training intensity distribution; POL, polarized training
intensity distribution; PYR→POL, pyramidal + polarized training intensity distribution; PYR, pyramidal training intensity distribution; vBLa2, velocity at
2mmol·L−1 of blood lactate concentration; vBLa4, velocity at 4mmol·L−1 of blood lactate concentration.

|
505
FILIPAS et al.
During pre- tests, time trial performance (in seconds)
was predicted by the following equation:
1. time trial performance = – 1.2 · body mass + 69 ·
relative

VO2peak
– 86.9 · vBLa2 – 233.3 · vBLa4 +
0.8 ·
La−
peak
+ 1821 (R2 = 0.881, p < 0.0001).
2. During mid- tests, time trial performance (in seconds)
was predicted by the following equation:
3. time trial performance=– 0.9 · body mass – 1.7 · rela-
tive

VO2peak
+ 34.2 · vBLa2 – 74.4 · vBLa4 + 1.5 ·
La−
peak
+ 1882 (R2=0.861, p<0.0001).
4. During post- tests, time trial performance (in seconds)
was predicted by the following equation:
5. time trial performance=– 1.1 · body mass – 2.2 · rela-
tive

VO2peak
– 53.7 · vBLa2 + 13.7 · vBLa4 + 0.4 ·
La−
peak
+ 1846 (R2=0.859, p<0.0001).
6. Time trial performance enhancement (in seconds)
from pre- to post- tests was predicted by the following
equation:
7. ∆ time trial performance=– 0.3 · ∆ body mass – 0.2 · ∆
relative

VO2peak
– 10.7 · ∆ vBLa2 – 45.3 · ∆ vBLa4 – 0.1
· ∆
La−
peak
– 0.2 (R2=0.939, p<0.0001).
8. Body mass is expressed in kg, relative

VO2peak
in
ml·kg−1·min−1, vBLa2 and vBLa4 in km·h−1, and
La−
peak
in mmol·L−1.
4
|
DISCUSSION
The main finding of this study was that a modification of
training intensity distribution throughout a 16- week pe-
riodization appeared to be slightly effective in improving
performance in well- trained runners. Changing the type of
periodization from pyramidal to polarized in the second half
of the periodization induced bigger improvements compared
to the simple pyramidal and polarized ones, or compared to
switching from polarized into pyramidal periodization.
The PYR → POL group's improvement was about 0.5%
higher than in the other groups, both in the 5- km time trial,
in vBLa2 and vBLa4. In general, gains in time trial perfor-
mance from pre- to post- tests were between 0.6 and 1.7%,
with the PYR→POL having at least a 5- s further improve-
ment compared to the other groups. Interestingly, this is sim-
ilar to the typical error of high- level athletes in completing
middle- and long- distance time trials.28 It follows that this
threshold can be considered worthwhile for high- level per-
forming athletes.
These improvements in performance may appear mod-
est, but a similar percentage gain in elite sports would have
meant winning the heat or being excluded from the final
in 75% of middle- and long- distance events of athletics at
the Tokyo 2020 Olympics. It must also be recognized that
most of the significant changes reported in this study are
TABLE  Average training intensity distribution and training load in PYR, POL, PYR→POL and POL→PYR for week 1– 8 and week 9– 16. Data are presented as mean±standard
deviation
Group
Week 1– 8 Week 9– 16
Z1 (min) Z2 (min) Z3 (min) Σ volume (min) TL Z1 (min) Z2 (min) Z3 (min) Σ volume (min) TL
PYR 279±40 55±20 25±3 358±63 463±77 279±39 54±19 24±5 358±63 462±78
POL 279±41 21±8 48±9 348±57 464±81 279±40 21±7 48±8 348±56 465±79
PYR→POL 280±37 54±19 24±3 358±63 462±78 279±40 21±9 47±8 347±56 463±77
POL→PYR 279±42 21±7 47±9 348±56 465±80 278±41 55±17 25±3 358±63 464±79
p0.824 0.992 0.812 0.992
Abbreviations: POL→PYR, polarized + pyramidal training intensity distribution; POL, polarized training intensity distribution; PYR→POL, pyramidal + polarized training intensity distribution; PYR, pyramidal
training intensity distribution; TL, training load; Z1, zone 1 (ie, volume below first ventilatory threshold); Z2, zone 2 (ie, volume between first and second ventilatory thresholds); Z3, zone 3 (ie, volume above second
ventilatory threshold).

506
|
FILIPAS et al.
below the smallest detectable change (SDC). Therefore,
these changes are within the magnitude of the technical
error and possibly do not represent true changes with
statistical certainty. This is common in most interven-
tion studies on high- level athletes in sports science,29,30
suggesting that relevant changes in the performance of
athletes who are already close to their physiological and
performance limits are very difficult to achieve through a
single intervention.
4.1
|
5- km time trial and lactate profiles
This study shows for the first time that the “pyrami-
dal→polarized” periodization pattern seems to be more
effective than the others in improving 5- km time trial per-
formance (even though below the SDC) in well- trained
runners. This is in line with the traditional approach of
elite endurance athletes, where TID changes from an
emphasis on a high- volume, low- intensity TID during
the base period, toward a pyramidal TID during the pre-
competition phase, and finally to a polarized TID during
the competitive phase.7
As

VO2peak
change did not have a notable increase con-
sidering both the SDC and the discrepancies between ab-
solute and relative values, the higher improvement in
performance in the PYR→POL group could be a conse-
quence of the higher improvement in vBLa2 and vBLa4,
compared to other groups. Therefore, it is likely that im-
provement in running performance is mainly attributable
to an enhanced running economy at these specific intensi-
ties. Indeed, as recently pointed out by Jones and col-
leagues,31 one of the limiting factors in endurance running
performance is running economy. This factor becomes in-
creasingly important as the athletes’ level rises, thus dis-
criminating between athletes with similar

V
O
2peak
(as in
the present study). All this, together with the results of the
multiple linear regression where we showed that perfor-
mance enhancements were mainly attributable to im-
provements in vBLa2 and vBLa4, further demonstrates
that submaximal variables (eg, vBLa2 and vBLa4) have a
significantly greater correlation with performance im-
provements (and, more generally, with performance) than
maximal variables (eg,

V
O
2peak
,
La−
peak
, etc.). However, we
must recognize that vBLa2 and vBLa4 include running,
whereas

VO2peak
,
La−
peak
where just physiological mea-
sures and do not integrate any performance measure.
Measuring velocity associated with these physiological
variables could have potentially led to different results.
4.2
|

V
O
2peak

VO2peak
results are in line with what was assumed about the
relative importance of the maximal variables in high- level
FIGURE  Changes between pre- , mid- and post- tests in the four different groups for absolute

VO2peak
(A), relative

VO2peak
(B), vBLa2
(C) vBLa4 (D). Significant difference between the tests (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001). No significant difference
between the tests (nsP>0.05). Data are presented individually for each participant and as overall mean

|
507
FILIPAS et al.
TABLE  Percentage changes and effect sizes of pairwise comparisons pre- to mid- tests, mid- to post- tests and pre- to post- tests in the four different groups. Data are presented as
mean±standard deviation (Cohen's d effect size)
PYR POL PYR→POL POL→PYR
∆%
(post- pre)
∆%
(mid- pre)
∆%
(post- mid)
∆%
(post- pre)
∆%
(mid- pre)
∆%
(post- mid)
∆%
(post- pre)
∆%
(mid- pre)
∆%
(post- mid)
∆%
(post- pre)
∆%
(mid- pre)
∆%
(post- mid)
Body mass −1.1±1.7
(0.19)
−0.3±1.9
(0.06)
−0.6±3.0
(0.12)
−1.0±1.6
(0.21)
−0.2±1.6
(0.04)
−0.8±2.1
(0.16)
−0.7±1.5
(0.14)
−0.9±1.8
(0.17)
0.2±1.9
(0.04)
−0.7±1.7
(0.16)
−1.2±1.7
(0.27)
0.6±2.3
(0.11)
Relative

VO2peak
1.3±2.2
(0.21)
0.4±2.1
(0.05)
0.9±0.7
(0.17)
2.1±3.0
(0.40)
2.1±2.6
(0.41)
0.0±1.3
(0.00)
3.0±2.8
(0.40)
0.3±2.0
(0.04)
2.7±1.6
(0.37)
3.0±2.7
(0.47)
2.9±2.0
(0.47)
0.0±1.2
(0.00)
vBLa2 0.6±0.7
(0.10)
0.4±0.7
(0.06)
0.2±0.5
(0.04)
1.3±1.3
(0.25)
1.0±0.9
(0.19)
0.3±0.8
(0.07)
1.7±0.7
(0.22)
0.4±0.4
(0.06)
1.3±0.7
(0.17)
0.9±0.9
(0.13)
1.0±0.8
(0.15)
−0.1±0.5
(0.02)
vBLa4 0.6±0.6
(0.10)
0.3±0.4
(0.05)
0.3±0.5
(0.05)
1.2±1.1
(0.25)
0.9±0.8
(0.19)
0.3±0.8
(0.07)
1.5±0.7
(0.22)
0.4±0.3
(0.06)
1.1±0.7
(0.16)
0.9±0.8
(0.15)
1.0±0.7
(0.16)
0.0±0.5
(0.01)
Time trial
time
−0.6±0.6
(0.11)
−0.3±0.4
(0.06)
−0.3±0.5
(0.05)
−1.1±1.1
(0.24)
−0.9±0.7
(0.19)
−0.3±0.7
(0.06)
−1.5±0.7
(0.28)
−0.4±0.3
(0.07)
−1.1±0.7
(0.21)
−0.9±0.8
(0.16)
−0.9±0.7
(0.16)
0.0±0.5
(0.01)
La−
peak
7.2±14.3
(0.24)
4.2±12.2
(0.14)
4.0±17.5
(0.11)
5.7±11.8
(0.27)
5.7±9.4
(0.27)
0.2±9.2
(0.00)
1.5±11.0
(0.08)
2.8±14.0
(0.11)
0.6±18.1
(0.03)
3.7±12.7
(0.18)
4.4±12.9
(0.21)
0.4±15.2
(0.03)
HRpeak −0.2±1.1
(0.02)
0.0±1.2
(0.01)
−0.1±1.6
(0.02)
−0.5±0.9
(0.07)
−0.4±1.3
(0.05)
−0.1±1.6
(0.02)
0.1±1.1.
(0.02)
0.4±1.0
(0.07)
−0.3±1.5
(0.05)
−0.1±1.1
(0.03)
0.1±1.3
(0.02)
−0.2±2.0
(0.05)
RPE 0.5±5.7
(0.08)
0.2±5.7
(0.00)
0.5±6.2
(0.09)
2.1±7.6
(0.37)
4.3±6.2
(0.82)
−2.0±6.8
(0.43)
1.4±8.0
(0.22)
0.3±7.9
(0.00)
1.4±7.6
(0.22)
1.3±6.9
(0.23)
2.8±7.0
(0.55)
−1.3±5.5
(0.29)
Abbreviations:

VO2peak
, peak oxygen consumption;
La−
peak
, peak blood lactate; HRpeak, heart rate peak; POL→PYR, polarized + pyramidal training intensity distribution; POL, polarized training intensity distribution;
PYR→POL, pyramidal + polarized training intensity distribution; PYR, pyramidal training intensity distribution; vBLa2, velocity at 2mmol·L−1 of blood lactate concentration; vBLa4, velocity at 4mmol·L−1 of blood
lactate concentration.

508
|
FILIPAS et al.
performance in homogeneous groups. Significant changes
of relative VO2peak (even below the SDC) were found in
all the three groups with different effect sizes, while no
effects were found on absolute VO2peak. The highlighted
trend of increase in relative VO2peak in the different groups
is mainly attributed to weight variations and not to real
changes in absolute VO2peak.
The different results between the 5- km time trial
performance, lactate profiles and the absolute/relative

VO2peak
are consistent with the critical discriminative
role of

VO2peak
. In fact,

VO2peak
helps to discern among
different categories of athletes and highlights general im-
provements in aerobic fitness. However, it is not the main
determinant factor in homogeneous groups of athletes.32
Indeed, a higher

VO2peak
is not always associated with
superior endurance running performances. Physiological
threshold and running economy have been demonstrated
to be better predictors.33 Recent studies have in fact shown
that high- level elite runners might have similar

VO2peak
compared to lower- level elite athletes.33
4.3
|
Mechanistic explanation
A clear mechanism that explains why the “pyrami-
dal→polarized” periodization pattern leads to superior
physiological and performance improvements remains
undefined. One of the most likely explanations could lie
in the role of training intensity in the peaking process.34
On one hand, it has been demonstrated that peak perfor-
mance is achieved by increasing relative intensity and de-
creasing volume of training during the tapering phase.35
On the other hand, certain guidelines of this phenomenon
have been proposed35 but are rarely followed by well-
trained or elite athletes in their yearly plans.7,11,36
Traditionally, training intensity is considered of para-
mount importance to maximize the physiological and per-
formance adaptations of well- trained athletes, showing
its greatest role in the consolidation of these benefits in
the 14days prior to the athletes’ main target race.11 Thus,
peaking cannot be explained by a single value; rather, it
is the result of a combination of muscular, cardiovascu-
lar, hormonal and psychological factors derived from high
intensity training37,38 which act in synergy to maximize
training adaptations. According to this logic, a gradual
increase of intensity throughout a training program can
facilitate the achievement of peak performance in cor-
respondence to the goal. We have shown how an early
increase in intensity in the first 8 weeks, as occurred in
the POL group, leads to a relevant improvement in per-
formance only in the mid- tests, while remaining almost
unchanged after the second 8weeks.
FIGURE  Changes between pre- , mid- and post- tests in the four different groups for time trial time (A),
La−
peak
(B), HRpeak (C) and RPE
(D). Significant difference between the tests (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001). No significant difference between the tests
(nsp>0.05). Data are presented individually for each participant and as overall mean

|
509
FILIPAS et al.
One of the strengths of the present study is that
training load was constant between all four groups
to allow for isolated manipulations of TID. This ap-
proach can provide a unique insight into the effects
of periodization patterns on performance outcomes in
high- level athletes, where it is often difficult to control
for these types of interventions. In parallel, if the ex-
planation behind our results lies mainly in the peaking
effect of intensity, we could hypothesize that a reduc-
tion in training volume and load in the last 2– 3weeks
before post- tests would have further amplified the
results, as traditionally occurs with pre- competition
tapering.35
4.4
|
Limitations
The present study has some limitations that warrant a brief
discussion. First, we acknowledge the limitations of using
HR/TRIMPS for training quantification. It is a fact that,
for different reasons (eg, overreaching, dehydration, etc.),
HR can respond unexpectedly during training sessions
for a similar external training load. In fact, a lower sys-
tematic response during training could be expected when
compared to the values recorded during incremental tests.
Second, even though the polarization index provides an ob-
jective cut- off to distinguish polarized from non- polarized
distributions, it does not allow differentiation between
TABLE  Multiple regression models predicting time trial performance during pre- , mid- and post- tests, and performance enhancement
Variables
Unstandardized
coefficient B
Standardized
coefficient BSE t p
Pre- tests
Time trial performance (s) (Constant) 1821.0 0.00 92.9 19.6 <0.0001
F(5, 54)=166.8 Body mass −1.2 0.05 0.8 1.4 0.1663
p<0.0001 Relative

VO2peak
69.0 5.19 28.3 2.4 0.0182
R2=0.881 vBLa2 −86.9 −1.66 60.2 1.4 0.1545
vBLa4 −233.3 −4.46 90.1 2.6 0.0124
La−
peak
0.8 0.03 1.2 0.6 0.5410
Mid- tests
Time trial performance (s) (Constant) 1882.0 0.00 75.9 24.8 <0.0001
F(5, 54)=66.4 Body mass −0.9 −0.06 0.8 1.2 0.2295
p<0.0001 Relative 
V
O
2peak
−1.7 −0.13 1.7 1.0 0.3191
R2=0.861 vBLa2 34.2 0.66 54.7 0.6 0.5346
vBLa4 −74.4 −1.45 52.8 1.4 0.1646
La−
peak
1.5 0.07 1.1 1.4 0.1661
Post- tests
Time trial performance (s) (Constant) 1846.0 0.00 90.7 20.4 <0.0001
F(5, 54)=65.6 Body mass −1.1 −0.07 0.8 1.4 0.1607
p<0.0001 Relative

V
O
2peak
−2.2 −0.17 1.4 1.5 0.1395
R2=0.859 vBLa2 −53.7 −1.04 60.0 0.9 0.3754
vBLa4 13.7 0.27 60.0 0.2 0.8200
La−
peak
0.4 0.02 1.0 0.4 0.7175
Performance enhancement
∆ Time trial performance (s) (Constant) −0.2 0.00 0.6 0.3 0.7370
F(5, 54)=166.8 Body mass −0.3 −0.04 0.3 1.2 0.2520
p<0.0001 Relative

V
O
2peak
−0.2 −0.04 0.2 1.3 0.1967
R2=0.932 vBLa2 −10.7 −0.19 4.4 2.4 0.0183
vBLa4 −45.3 −0.79 4.6 9.9 <0.0001
La−
peak
−0.1 −0.02 0.3 0.4 0.6559
Abbreviations:

VO2peak
, peak oxygen consumption;
La−
peak
, peak blood lactate; vBLa2, velocity at 2mmol·L−1 of blood lactate concentration; vBLa4, velocity at
4mmol·L−1 of blood lactate concentration.

510
|
FILIPAS et al.
sub- types of the non- polarized TID structures. For this
reason, we decided to use this method just as a confirma-
tion of the nature of the TID. Third, the 5- km run is mainly
dependent upon the aerobic energy system, so other out-
puts may be expected in other competitive distances where
other metabolic components are predominant.
5
|
PERSPECTIVE
Endurance runners seem to benefit from a change in the
final phases of the periodization, from a pyramidal to a po-
larized model. This increase in relative intensity could favor
the pre- competition peaking phase and maximize perfor-
mance improvements. More generally, this study showed
how periodization based on high volumes in Z1 and reduced
volumes in Z2 and Z3 allows for significant improvements
even for well- trained runners, confirming that these types of
distributions are the most effective for endurance athletes.
Future studies are needed to confirm that the same find-
ings could be applied to runners of a lower or higher per-
formance level. It would also be interesting to check if the
results could be extended to other endurance disciplines (ie,
cycling, cross- country skiing, etc.), where mechanic loads
are less strenuous than running, allowing for more total
volume per week. Moreover, it would be relevant to verify
whether a training program with a higher percentage of ac-
cumulated training time at higher training intensities would
be even better, and if there is a threshold training intensity
in Z1 below which no physiological adaptation really oc-
curs. Finally, future research should aim to understand the
physiological foundations behind these findings.
In conclusion, a 16- week training periodization seems
to be effective in improving performance, albeit not
physiological ones, of well- trained endurance runners.
Switching from pyramidal into polarized after 8weeks of
periodization appeared to be more efficacious in maximiz-
ing performance improvements, compared to the other
forms of periodization (pyramidal, polarized and polar-
ized followed by pyramidal).
ACKNOWLEDGEMENTS
The authors thank all the athletes and the clubs involved
in the study for their contribution. Open Access Funding
provided by Universita degli Studi di Milano within the
CRUI-CARE Agreement. [Correction added on 31 May
2022, after first online publication: CRUI funding state-
ment has been added.]
CONFLICT OF INTEREST
Authors declare no conflict of interests.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are avail-
able from the corresponding author upon reasonable
request.
ORCID
Luca Filipas https://orcid.org/0000-0002-3828-9626
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How to cite this article: Filipas L, Bonato M, Gallo
G, Codella R. Effects of 16weeks of pyramidal and
polarized training intensity distributions in well-
trained endurance runners. Scand J Med Sci Sports.
2022;32:498– 511. doi:10.1111/sms.14101