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


Abstract and Figures

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.
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   Scand J Med Sci Sports. 2022;32:498–
Received: 28 May 2021 
  Revised: 8 November 2021 
  Accepted: 12 November 2021
DOI: 10.1111/sms.14101  
Effects of 16weeks of pyramidal and polarized training
intensity distributions in well- trained endurance runners
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 
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© 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
Luca Filipas, Department of Biomedical 
Sciences for Health, Università degli 
Studi di Milano, Via F.lli Cervi 93, 
20090 Segrate (Milano), Italy.
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  (
) and  5- km running  time  trial per-
formance. There were significant group×time interactions for relative 
(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 
, ~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 
, 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 
senting the only physiological correlate.
polarized training, pyramidal training, running performance, training intensity distribution, 
training periodization
FILIPAS et al.
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 ofboth polarized  and  pyramidal TID compared 
to other TID models  for  endurance sports.6,9– 11A 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.
Sixty well- trained male runners (38±7years, relative peak 
oxygen  consumption  (
):  67 ± 4 ml·kg−1·min−1) 
were  recruited  to  the  study  through  local  running 
clubs.  Inclusion  criteria  were  as  follows:  (1)  relative 
>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 
Experimental design
A four- armed parallel group randomized controlled trial 
was  used.  To  determine  the  sample  size  a  priori  (soft-
ware package, G*Power, the following input vari-
ables were selected as per an Ftest 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 
fof 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 
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  using  a 
1:1:1:1 ratio. The four groups were matched for age, rela-
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.
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.
Intervention period
  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
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 
20min Z1 + 50min Z2 20min Z1 + 55min 
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 
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 
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 
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 
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).
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,8confirming 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.
  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.18Individualized 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.
  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 of40– 60min  Z1 sessions. It in-
cluded  a  measurement  of  body  mass,  a  blood  lactate 
profile  test  and  a 
  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%.20The 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 
. 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 
  was  calculated  as  the  average  30- s 
 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 
 was ob-
tained. HR was measured using a Garmin HRM- Run chest 
strap (Garmin). 
 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% O2and 5.0% CO2). The 
smallest detectable change of K5 for 
 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 
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  (
)  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.
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, 
,  vBLa2,  vBLa4,  5- km  time  trial 
, 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, 
,  vBLa2,  vBLa4  and 
  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  (
),  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 dand 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).27Data 
analysis  was  conducted  using  the  Statistical  Package  for 
the Social Sciences, version 25 (SPSS Inc.).
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 
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
(L·min−1)4.5±0.3 4.5±0.3 4.5±0.4 4.4±0.3 0.7901
(ml·kg−1·min−1)68±4 69±3 68±5 68±4 0.9538
(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
, peak carbon dioxide production; 
, 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.
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, 
 and RPE.
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.
Body mass,
lactate profiles
For body mass, there was a significant main effect of time 
(F(1.6, 91.0)=6.4; p=0.0046; 
=0.10), while no interac-
tion group×time was found (F(6, 112)=0.9; p=0.4946; 
=0.05). For the absolute 
 (Figure2A) there was 
a  significant  main  effect  of  time  (F(1.9,  109) = 11.6; 
=0.26), while no interaction group×time 
was found (F(6, 112)=0.5; p=0.8128; 
=0.02). For the 
 (Figure2B), there was a significant main 
effect of time (F(1.4, 75.4)=35.8; p<0.0001; 
=0.40) and 
an interaction group × time (F(6, 112)=4.5; p= 0.0004; 
=0.19). For vBLa2 (Figure2C), there was a significant 
main  effect  of  time  (F(1.7,  93.2) = 62.6;  p < 0.0001; 
=0.53) and an interaction group×time (F(6, 112)=6.8; 
=0.27). For vBLa4 (Figure2D), there was a 
significant  main  effect  of  time  (F(1.6,  91.3) = 75.1; 
=0.57) and an interaction group×time (F(6, 
112)=5.7; p<0.0001; 
=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.
5- km time trial
There  was  a  significant  main  effect  of  time  (F(1.7, 
93.7) = 71.4;  p < 0.0001; 
 = 0.56)  and  an  interaction 
group× time (F(6, 112)= 5.2; p=0.0001; 
= 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; 
=0.08), while there  was  no  interaction  group ×time 
(F(6,  112) = 0.3;  p = 0.9578; 
 = 0.02)  for 
(Figure3B). There were no  main  effects,  nor  interaction 
group× time (F(6, 112)= 0.5; p=0.8208; 
= 0.03) for 
HRpeak (Figure3C). There were no main effects, nor inter-
action group×time (F(6, 112)=0.7; p=0.6170; 
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.
Variables that predicted time trial
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
 (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
, 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.
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  · 
  –   86.9  ·  vBLa2  –   233.3  ·  vBLa4  + 
0.8  · 
  +  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-
 + 34.2 · vBLa2 –  74.4 · vBLa4 + 1.5 · 
+ 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-
 –  53.7 · vBLa2 + 13.7 · vBLa4 + 0.4 · 
+ 1846 (R2=0.859, p<0.0001).
6.  Time  trial  performance  enhancement  (in  seconds) 
from pre- to post- tests was predicted by the following 
7.  ∆ time trial performance=– 0.3 · ∆ body mass –  0.2 · ∆ 
 –  10.7 · ∆ vBLa2 –  45.3 · ∆ vBLa4 – 0.1 
· ∆ 
 –  0.2 (R2=0.939, p<0.0001).
8.  Body  mass  is  expressed  in  kg,  relative 
ml·kg−1·min−1, vBLa2 and vBLa4 in km·h−1, and 
in mmol·L−1.
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 
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).
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.
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
 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 
(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, 
, etc.). However, we 
must  recognize  that  vBLa2  and  vBLa4  include  running, 
  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.
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 
 (A), relative 
 (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
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)
(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 
vBLa2 0.6±0.7 
vBLa4 0.6±0.6 
Time trial 
HRpeak −0.2±1.1 
RPE 0.5±5.7 
, peak oxygen consumption; 
, 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.
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 VO2peakin 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 
  are  consistent  with  the  critical  discriminative 
role of 
. In fact, 
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 
  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 
compared to lower- level elite athletes.33
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  ofthese  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), 
 (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
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 
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, dehydra-
tion,  etc.),  HR  can  respond  unexpectedly  during  train-
ing sessions  for  a  similar  external  training  load.  In  fact, 
a  lower  systematic  response  during  training  could  be 
expected  when  compared  to  the  values  recorded  during 
incremental  tests.  Second,  even  though  the  polarization 
index  provides  an  objective  cut- off  to  distinguish  polar-
ized  from  non- polarized  distributions,  it  does  not  allow 
TABLE   Multiple regression models predicting time trial performance during pre- , mid-  and post- tests, and performance enhancement
coefficient B
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 
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
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  ̇
−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
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 
−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
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 
−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
−0.1 −0.02 0.3 0.4 0.6559
, peak oxygen consumption; 
, 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.
FILIPAS et al.
differentiation  between  sub- types  of  the  non- polarized 
TID  structures.  For  this  reason,  we  decided  to  use  this 
method just  as  a confirmation  of  the nature  of  the TID. 
Third, the 5- km run is mainly dependent upon the aerobic 
energy system, so other outputs may be expected in other 
competitive distances where other metabolic components 
are predominant.
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).
The authors thank all the athletes and the clubs involved 
in the study for their contribution.
Authors declare no conflict of interests.
The data that support the findings of this study are avail-
able  from  the  corresponding  author  upon  reasonable 
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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- 
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... Furthermore, the use of this approach was reported to be related to either high levels [67,69,70] or an improvement in RE [64,83]. Some research also found either improvements in [64,[83][84][85] or were related to high levels of vVO 2max [41,69,70]. A few studies, using high volumes in z1 and moderate volumes in z2 and z3, were associated with high levels of VO 2max [67,70,86]. ...
... A few studies, using high volumes in z1 and moderate volumes in z2 and z3, were associated with high levels of VO 2max [67,70,86]. Studies using this training pattern also found either improvements in [83,85] or were related with high levels of vLT2 [41,67,69,70]. In any case, there are comparatively few contemporary elite runners who have a total training volume <100 km/week, and most perform >160 km/week [53,55]. ...
... While the extensive use of LGTIT (i.e., up to four sessions per week) represents a novelty in the training of elite distance runners, several studies have reported the combined use of LT2 and z4/z5 training during the training week. For example, runners may conduct two (or more) different interval training sessions per week covered at LT2 and VO 2max intensities, respectively (41,(68)(69)(70)85). On the one hand, the addition of a greater number (i.e., two or three) of 'high-intensity' sessions to those typically observed in highly trained and elite runners may represent an advantage in training adaptation, as assimilating this higher training load may provide greater performance improvements. ...
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The aim of the present study was to describe a novel training model based on lactate-guided threshold interval training (LGTIT) within a high-volume, low-intensity approach, which characterizes the training pattern in some world-class middle- and long-distance runners and to review the potential physiological mechanisms explaining its effectiveness. This training model consists of performing three to four LGTIT sessions and one VO2max intensity session weekly. In addition, low intensity running is performed up to an overall volume of 150–180 km/week. During LGTIT sessions, the training pace is dictated by a blood lactate concentration target (i.e., internal rather than external training load), typically ranging from 2 to 4.5 mmol·L−1, measured every one to three repetitions. That intensity may allow for a more rapid recovery through a lower central and peripheral fatigue between high-intensity sessions compared with that of greater intensities and, therefore, a greater weekly volume of these specific workouts. The interval character of LGTIT allows for the achievement of high absolute training speeds and, thus, maximizing the number of motor units recruited, despite a relatively low metabolic intensity (i.e., threshold zone). This model may increase the mitochondrial proliferation through the optimization of both calcium and adenosine monophosphate activated protein kinase (AMPK) signaling pathways.
... Zone two or moderate-intensity training, or threshold level training, is at the intensity between lactate and ventilatory thresholds one and two (VT1 and VT2). Zone three, or high-intensity training, is located at an intensity above lactate and ventilatory threshold two (>VT2) [5][6][7]. ...
... These TID models have been studied in different sports such as athletics [6,7,9,[14][15][16][17][18][19], triathlon [10,14,20,21], cross-country skiing [14], speed skating [22], and swimming [23]. These studies have been conducted in order to quantify the optimal internal and external loads, as well as methods to improve performance [24]. ...
... There is consensus in the literature for the determination of three intensity zones (low, moderate, and high) based on physiological parameters. The most popular tests for their determination are incremental tests with gas analysis evaluation and blood lactate concentration measurements [6,7,9,12,[14][15][16][17][18][19]. However, the difficult access and high costs of these tests in recreational athletes are highlighted. ...
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Problem: Intensity in endurance training is important for improving race time; its optimal handling in amateur runners has not been extensively studied. The polarized training intensity distribution (TID) model emerges as a possibility to reduce race time; however, effect of this model remains to be demonstrated compared to other TID models. Objective: The objective of this study is to explore the current state of the evidence its the gaps, according to the effect of the polarized TID model on race time in amateur runners compared to other TID models. Method: A scoping review without date restrictions was carried out in PubMed, EBSCO, SciELO, LILACS, and Google Scholar. Randomized controlled studies, quasi-experimental studies, and case studies, which comprise polarized TID model in amateur runners on race time, were include. Results: Five studies evaluated the effect on running time using the polarized TID model compared to other models in amateur runners; four of them did not show differences between groups in the race times in two, five, and ten km. Only one study showed a significant difference in the race time at 21 km. Conclusions: The model with polarized TID did not show significant differences in race time compared to other models, except for a case report in which the polarized TID was higher by 21 km compared to the threshold TID: 1 hour. 20 min. 22 seconds and 1 hour. 26 min. 34s, respectively. The scarce evidence found, the heterogeneity in the distances in the evaluated race time, the distribution of zones in the same TID, the duration of the interventions, and the monitoring of the loads, are the main limitations found in the studies. The polarized TID could contribute to adherence, lower perception of effort, and injury prevention. However, this must be tested in future studies.
... The term TID relates to the amount of time of the total training spent in different intensity zones, which have been shown to induce different physiological adaptations. 16 On the other hand, a recent study of Filipas and colleagues 17 showed that different TIDs during a 16-week training intervention did not induce different performance outputs in well-trained endurance runners under constant running training load. Hence, the rationale behind this intervention study was to understand how plyometric training could affect training programs with different TIDs, since they could potentially act on different physiological patterns. ...
... These improvements in performance may appear modest, but a similar percentage gain in elite sports would have meant winning the heat or being excluded from the final in all of middle-and long-distance events of athletics at the Tokyo 2020 Olympics. This is also a common magnitude of the effects of most intervention studies on high-level athletes in sports science, 17,34,35 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. ...
... As for the physiological parameters, only the POL + PLY seemed to slightly increase the absolute and relative ⩒O 2peak post-treatment (significant pairwise comparison PRE to POST, while no significant interaction group × time). Similar results were found in the study of Filipas and colleagues, 17 in which a 16-week polarized and pyramid training had only partially altered the ⩒O 2peak of well-trained athletes. ⩒O 2 is considered a discriminating factor for performance in heterogeneous groups of athletes, while it does not discriminate performance in homogeneous groups of athletes. ...
Full-text available
The aim of this 4‐armed parallel group randomized‐controlled trial was to evaluate if plyometric training could have different effects on running performance and physiological adaptations depending on the training intensity distribution (TID) in an 8‐week intervention in endurance athletes. Sixty well‐trained male runners (age: 34 ± 6 years, relative ⩒O2peak: 69 ± 3 mL·kg‐1·min‐1) were recruited and allocated to a pyramidal (PYR), pyramidal + plyometric training (PYR+PLY), polarized (POL), and polarized + plyometric training (POL+PLY) periodization. The periodization patterns were isolated manipulations of TID, while training load was kept constant. Participants were tested pre‐ and post‐intervention for body mass, velocity at 2 and 4 mmol·L‐1 of blood lactate concentration (vBLa2, vBLa4), absolute and relative ⩒O2peak and 5‐km running time trial performance, counter movement jump and squat jump. There were significant group x time interactions for vBla4 (P = 0.0235), CMJ (P = 0.0234), SJ (P = 0.0168), and 5‐km running time trial performance (P = 0.0035). Specifically, vBla4 and 5‐km running time trial performance showed the largest post‐intervention improvements in PYR+PLY (2.4% and 1.6%) and POL+PLY (2.1% and 1.8%), respectively. No significant interactions were observed for body mass, absolute and relative ⩒O2peak, peak heart rate, lactate peak and rating of perceived exertion. In conclusion, an 8‐week training periodization seems to be effective in improving performance of well‐trained endurance runners. Including plyometric training once a week appeared to be more efficacious in maximizing running performance improvements, independently from the TID adopted.
... Polarization index has the advantage of summarizing in a single variable the nature of the intensity distribution and has been recently used for this purpose. 18 If polarization index was >2.00, the training intensity distribution was defined as "polarized," while with polarization index ≤2.00, the training intensity distribution was defined as non-polarized. ...
... As a consequence, while cyclists B and C used block periodization throughout all the preparation, cyclists A first performed a high-volume pyramidal periodization phase in which he progressively increased volume (Weeks 49-7), than he reduced volume and increased medium and high-intensity and polarization progressively (week 8-12) until the first competition phase (Figure 1 and Figure 2) Basically, he followed a linear periodization, switching gradually from a pyramidal to a more polarized intensity distribution. Recently, Filipas et al. 18 reported that a pyramidal to polarized periodization could be superior than a pyramidal or polarized one in well-trained runners over a 16 week period. However, to the best of our knowledge, no studies have investigated the effect of combining sequentially linear and block periodization compared to only block or traditional one. ...
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The aim of this study was to describe individual training strategies in preparation to Giro d’Italia of three world class road cyclists who achieved a top 5 in the general classification. Day‐to‐day power meter training and racing data of three road cyclists (age: 26, 27, 25 years; relative maximum oxygen consumption: 81, 82, 80 mL·min‐1·kg‐1; relative 20‐min record power output: 6.6, 6.6, 6.4 W·kg‐1) of the 22 weeks (December‐May) leading up to the top 5 in Giro d’Italia general classification were retrospectively analyzed. Weekly volume and intensity distribution were considered. Cyclists completed 17, 22, 29 races, trained averagely for 19.7 (7.9), 16.2 (7.0), 14.7 (6.2) hours per week, with a training intensity distribution of 91.3‐6.5‐2.2, 83.6‐10.6‐5.8, 86.7‐8.9‐4.4 in zone 1‐zone 2‐zone 3 before the Giro d’Italia. Two cyclists spent 55 and 39 days at altitude, one did not attend any altitude camp. Cyclists adopted an overall pyramidal intensity distribution with a relevant increase in high‐intensity volume and polarization index in races weeks. Tapering phases seem to be dictated by race schedule instead of literature prescription, with no strength training performed by the three cyclists throughout the entire periodization.
... Altering the TID in an appropriate manner during the training season has been shown to be superior to adhering rigidly to a single pattern [114]. However, even though such adjustments are common in practice, little is presently known about them. ...
... [1][2][3] These features are quite well understood, and experimental studies have later tested the training models derived by the best practitioners. 4,5 However, when successful athletes are asked to explain the reasons behind their success, they often highlight the quality of their training. This aspect has so far received limited attention in sport science, and several fundamental questions related to this feature need to be addressed. ...
Full-text available
When successful athletes are asked to explain the reasons behind their success, they often highlight the quality of their training. This aspect has so far received limited attention in sport science, and several fundamental questions related to this feature need to be addressed. What is training quality? What factors affect training quality? Who makes the call whether the training process is of good quality or not? How can training quality be assessed and improved? In this editorial, we briefly address these questions, provide a point of departure for further discussions, and encourage future studies to explore this topic more thoroughly.
... In addition to the wining paper from the Health, Disease and Physical Activity section, the following papers from the other sections of the journal were highly commended. In Physiology & Biochemistry, the paper by Filipas et al. 2 investigated the effects of polarized or pyramidal periodizations during a 16-week training period in 60 already well-trained endurance athletes. Polarized training occurs where individuals spend more time training at both a low intensity (below the first ventilatory threshold) and a high training intensity (above the second ventilatory threshold) relative to the intermediate zone (between these two thresholds). ...
... There has been a recent debate on whether or not polarized training is optimal for endurance performance (Burnley et al., 2022;Foster et al., 2022). Recent research demonstrated that the training process often combines pyramidal and polarized intensity distribution approaches to maximize performance outcomes (Filipas et al., 2021). In addition, there was no separation between training and racing regarding intensity distribution, thus racing might have added more time in HR zone 2 as shown by previous research Padilla et al., 2001). ...
This study investigated the physiological, performance and training characteristics of U23 cyclists and assessed the requirements of stepping up to the elite/international ranks. Twenty highly trained U23 cyclists (age, 22.1 ± 0.8 years; body mass, 69.1 ± 6.8 kg; VO2max, 76.1 ± 3.9 ml·kg⁻¹·min⁻¹) participated in this study. The cyclists were a posteriori divided into two groups based on whether or not they stepped up to elite/international level cycling (U23ELITE vs. U23NON-ELITE). Physiological, performance and training and racing characteristics were determined and compared between groups. U23ELITE demonstrated higher absolute peak power output (p = .016), 2 min (p = .026) 5 min (p = .042) and 12 min (p ≤ .001) power output as well as higher absolute critical power (p = .002). Further, U23ELITE recorded more accumulated hours (p ≤ .001), covered distance (p ≤ .001), climbing metres (p ≤ .001), total sessions (p ≤ .001), total work (p ≤ .001) and scored more UCI points (p ≤ .001). These findings indicate that U23ELITE substantially differed from U23NON-ELITE regarding physiological, performance and training and racing characteristics derived from laboratory and field. These variables should be considered by practitioners supporting young cyclists throughout their development towards the elite/international ranks.
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Background HD-tDCS is capable to increase the focality of neuromodulation and has been recently applied to improve endurance performance in healthy subjects. Objective/hypothesis Whether these putative advantages could be exploited in active subjects with type 1 diabetes mellitus (T1D) remains questionable. Methods In a double-blind, randomized crossover order, 11 high-level cyclists (27 ± 4.3 years; weight: 65.5 ± 8.6 kg; height: 180 ± 8 cm; VO2peak: 67.5 ± 2.9 mL·min⁻¹·kg⁻¹) with T1D underwent either HD-tDCS (F3, F4) or control (SHAM) and completed a constant-load trial (CLT) at 75% of the 2nd ventilatory threshold plus a 15-km cycling time-trial (TT). Results After HD-tDCS, the total time to cover the TT was 3.8% faster (P < 0.01), associated with a higher mean power output (P < 0.01), and a higher rate of power/perception of effort (P < 0.01) and power/heart rate at iso-time (P < 0.05) than the SHAM condition. Physiological parameters during CLT and TT did not differ in both conditions. Conclusions These findings suggest that upregulation of the prefrontal cortex could enhance endurance performance in high-level cyclists with T1D, without altering physiological and perceptual responses at moderate intensity. Present data open to future applications of HD-tDCS to a wider population of active T1D-subjects.
In this paper, the fuzzy neural network model is studied, the real-time regulation model of physical training intensity is analyzed and a real-time regulation system based on a fuzzy neural network is designed. The real-time, accurate and effective regulation of the physiological load intensity in the body of the exerciser is consistent with the predetermined goals of the training program. In this paper, we propose an RBF neural network, combined with the plan and demand of physical training operation situation sensing, and considering that most of the biological training operation data is fuzzy, this paper connects a fuzzy logic inference system and a neural network and proposes a network operation situation sensing model based on an RBF neural network structure. The RBF neural network and the traditional fuzzy neural network are compared. The experiments prove that this paper’s fuzzy neural network model has a faster training speed. In this paper, we use time-realistic control equipment to monitor the physical training process of athletes so that we can grasp the training situation of athletes in real-time and ensure that athletes can achieve better training results by changing training methods and changing training loads in time for those athletes who cannot reach their sports goals. In the process of physical fitness training monitoring, an effective monitoring of training, time-accurate regulation monitoring has the advantage of timely feedback on the training situation. This model has a better convergence effect during exercise and a higher accuracy of posture prediction during testing.
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The requirements of running a 2 hour marathon have been extensively debated but the actual physiological demands of running at ~21.1 km/h have never been reported. We therefore conducted laboratory-based physiological evaluations and measured running economy (O2 cost) while running outdoors at ~21.1 km/h, in world-class distance runners as part of Nike's 'Breaking 2' marathon project. On separate days, 16 male distance runners (age, 29 ± 4 years; height, 1.72 ± 0.04 m; mass, 58.9 ± 3.3 kg) completed an incremental treadmill test for the assessment of V̇O2peak, O2 cost of submaximal running, lactate threshold and lactate turn-point, and a track test during which they ran continuously at 21.1 km/h. The laboratory-determined V̇O2peak was 71.0 ± 5.7 ml/kg/min with lactate threshold and lactate turn-point occurring at 18.9 ± 0.4 and 20.2 ± 0.6 km/h, corresponding to 83 ± 5 % and 92 ± 3 % V̇O2peak, respectively. Seven athletes were able to attain a steady-state V̇O2 when running outdoors at 21.1 km/h. The mean O2 cost for these athletes was 191 ± 19 ml/kg/km such that running at 21.1 km/h required an absolute V̇O2 of ~4.0 L/min and represented 94 ± 3 % V̇O2peak. We report novel data on the O2 cost of running outdoors at 21.1 km/h, which enables better modelling of possible marathon performances by elite athletes. Using the value for O2 cost measured in this study, a sub-2 hour marathon would require a 59 kg runner to sustain a V̇O2 of approximately 4.0 L/min or 67 ml/kg/min.
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Purpose To evaluate the intra-unit (RELINTRA) and inter-unit reliability (RELINTER) of two structurally identical units of the metabolic analyser K5 (COSMED, Rome, Italy) that allows to utilize either breath-by-breath (BBB) or dynamic mixing chamber (DMC) technology. Methods Identical flow- and gas-signals were transmitted to both K5s that always operated simultaneously either in BBB- or DMC-mode. To assess RELINTRA and RELINTER, a metabolic simulator was applied to simulate four graded levels of respiration. RELINTRA and RELINTER were expressed as typical error (TE%) and Intraclass Correlation Coefficient (ICC). To assess also inter-unit differences via natural respiratory signals, 12 male athletes performed one incremental bike step test each in BBB- and DMC-mode. Inter-unit differences within biological testing were expressed as percentages. Results In BBB, TE% of RELINTRA ranged 0.30–0.67 vs. RELINTER 0.16–1.39 and ICC ranged 0.57–1.00 vs. 0.09–1.00. In DMC, TE% of RELINTRA ranged 0.38–0.90 vs. RELINTER 0.03–0.86 and ICC ranged 0.22–1.00 vs. 0.52–1.00. Mean inter-unit differences ranged -2.30–2.20% (Cohen’s ds (ds) 0.13–1.52) for BBB- and -0.55–0.61% (ds 0.00–0.65) for DMC-mode, respectively. Inter-unit differences for and RER were significant (p < 0.05) at each step. Conclusion Two structurally identical K5-units demonstrated accurate RELINTRA with TE < 2.0% and similar RELINTER during metabolic simulation. During biological testing, inter-unit differences for and RER in BBB-mode were higher than 2% with partially large ES in BBB. Hence, the K5 should be allocated personally wherever possible. Otherwise, e.g. in multicenter studies, a decrease in total reliability needs to be considered especially when the BBB-mode is applied.
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The training intensity distribution (TID) of endurance athletes has retrieved substantial scientific interest since it reflects a vital component of training prescription: (i) the intensity of exercise and its distribution over time are essential components for adaptation to endurance training and (ii) the training volume (at least for most endurance disciplines) is already near or at maximum, so optimization of training procedures including TID have become paramount for success. This paper aims to elaborate the polarization-index (PI) which is calculated as log10(Zone 1/Zone 2∗Zone 3∗100), where Zones 1–3 refer to aggregated volume (time or distance) spent with low, mid, or high intensity training. PI allows to distinguish between non-polarized and polarized TID using a cut-off > 2.00 a.U. and to quantify the level of a polarized TID. Within this hypothesis paper, examples from the literature illustrating the usefulness of PI-calculation are discussed as well as its limitations. Further it is elucidated how the PI may contribute to a more precise definition of TID descriptors.
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This review aimed to examine the current evidence for three primary training intensity distribution types; 1) Pyramidal Training, 2) Polarised Training and 3) Threshold Training. Where possible, we calculated training intensity zones relative to the goal race pace, rather than physiological or subjective variables. We searched 3 electronic databases in May 2017 (PubMed, Scopus, and Web of Science) for original research articles. After analysing 493 resultant original articles, studies were included if they met the following criteria: a) participants were middle- or long-distance runners; b) studies analysed training intensity distribution in the form of observational reports, case studies or interventions; c) studies were published in peer-reviewed journals and d) studies analysed training programs with a duration of 4 weeks or longer. Sixteen studies met the inclusion criteria, which included 6 observational reports, 3 case studies, 6 interventions and 1 review. According to the results of this analysis, pyramidal and polarised training are more effective than threshold training, although the latest is used by some of the best marathon runners in the world. Despite this apparent contradictory findings, this review presents evidence for the organisation of training into zones based on a percentage of goal race pace which allow for different periodisation types to be compatible. This approach requires further development to assess whether specific percentages above and below race pace are key to inducing optimal changes.
High-intensity interval training (HIIT) is highly beneficial for health and fitness and is well tolerated. Treadmill-based HIIT normally includes running interspersed with walking. The purpose of this study was to compare ungraded running and graded walking HIIT on perceived exertion, affective valence, and enjoyment. Thirty-four active, healthy adults completed maximal testing and two 20-min HIIT trials alternating between 85% of VO 2 peak and a comfortable walking speed. Affective valence, enjoyment, and perceived exertion, both overall (ratings of perceived exertion [RPE]-O) and legs only (RPE-L), were measured. RPE-O and affective valence were similar between HIIT trials ( p > .05), RPE-L was higher for walk HIIT ( p < .05), and enjoyment was higher for run HIIT ( p < .05). Findings indicate that both walk and run HIIT produce exertion, affective, and enjoyment responses that are positive and possibly supportive of exercise behavior. Walk HIIT may be desirable for individuals who are unable or do not want to run.
Purpose: The portable metabolic analyzer COSMED K5 (Rome, Italy) allows to switch between breath-by-breath (BBB) and dynamic mixing chamber (DMC) mode. This study aimed to evaluate the reliability and validity of the K5 in BBB and DMC at low, moderate, and high metabolic rates. Methods: Two K5 simultaneously operated in BBB or DMC, while (i) a metabolic simulator (MS) produced four different metabolic rates (repeated eight times) and (ii) 12 endurance trained participants performed four bike exercise stages at 30, 40, 50, and 85% of their individual power output at V[Combining Dot Above]O2max (repeated three times). K5-data were compared to predicted simulated values and consecutive Douglas bag (DB) measurements. Results: Reliability did not differ significantly between BBB and DMC, while the typical error (TE%) and intraclass correlation coefficients (ICC) for oxygen uptake (V[Combining Dot Above]O2), carbon dioxide output (V[Combining Dot Above]CO2) and minute ventilation (V[Combining Dot Above]E) ranged from 0.27 to 6.18% and 0.32 to 1.00 within four metabolic rates, respectively. Validity indicated by mean differences ranged between 0.61 to -2.05% for V[Combining Dot Above]O2, 2.99 to -11.04% for V[Combining Dot Above]CO2 and 0.93 to -6.76% for VE compared to MS and DB at low to moderate metabolic rates and was generally similar for MS and bike exercise. At high rates, mean differences for V[Combining Dot Above]O2 amounted to -4.63 to -7.27% in BBB and -0.38 to -3.81% in DMC, indicating a significantly larger difference of BBB at the highest metabolic rate. Conclusion: The K5 demonstrated accurate to acceptable reliability in BBB and DMC at all metabolic rates. Validity was accurate at low and moderate metabolic rates. At high metabolic rates, BBB underestimated V[Combining Dot Above]O2 while DMC showed superior validity. To test endurance athletes at high workloads, the DMC-mode is recommended.
The aim of this study was to analyze the pacing profiles of Olympic and International Association of Athletics Federations (IAAF) World Championship long-distance finalists, including the relationship with their recent best times. The times for each 1,000-m split were obtained for 394 men and women in 5,000-and 10,000-m finals at 5 championships. Athletes' best times from the previous 32 months were also obtained. Similar pacing profiles were used by athletes grouped by finishing position in 5,000-m races. Women adopted a more even pacing behavior, highlighting a possible sex-based difference over this distance. Pacing behavior over 10,000 m was more similar between men and women compared with over 5,000 m. The main difference between men and women was that in the men's 10,000 m, as in the men's 5,000 m, more athletes were able to follow the leading group until the final stages. There were large or very large correlations between athletes' best times from the previous 32 months and their result; the fastest finishers also ran closer to their previous 32 months' best times. Despite differences in pacing behavior between events, long-distance runners should nonetheless stay close to the front from the beginning to win a medal.
Purpose: To analyse the pacing profiles of the world top 800-m annual performances between 2010 and 2016, comparing male and female strategies. Methods: One hundred and forty two performances were characterised for overall race times and 0-200 m, 200-400 m, 400-600 m, 600-800 m split times using available footage from YouTube. Only the best annual performance for each athlete was considered. Overall race and splits speed were calculated so that each lap speed could be expressed as a percentage of the mean race speed. Results: The mean speed of the men's 800-m was 7.73 ± 0.06 m.s-1, with the 0-200 m split faster than the others. After the first split, the speed decreased significantly during the three subsequent splits (p <0.001). The mean speed of the women's 800-m was 6.77 ± 0.05 m.s-1, with a significative variation in speed during the race (p <0.001). The first split was faster than the others (p <0.001). During the rest of the race, speed is almost constant and no difference observed between the other splits. Comparison between men and women revealed that there was an interaction between split and gender (p <0.001), showing a different pacing behaviour in 800-m competitions. Conclusion: The world best 800-m performances revealed an important difference in pacing profile between men and women. Tactics could play a greater role in this difference, but physiological and behavioural characteristics are likely also important.