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BRIEF REVIEW
POLARIZED VS.THRESHOLD TRAINING INTENSITY
DISTRIBUTION ON ENDURANCE SPORT PERFORMANCE:
AS
YSTEMATIC REVIEW AND META-ANALYSIS OF
RANDOMIZED CONTROLLED TRIALS
MICHAEL A. ROSENBLAT,
1
ANDREW S. PERROTTA,
2
AND BILL VICENZINO
3
1
Department of Exercise Science, Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, Ontario,
Canada;
2
Department of Experimental Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British
Columbia, Canada; and
3
School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, University
of Queensland, Brisbane, Queensland, Australia
ABSTRACT
Rosenblat MA, Perrotta AS, and Vicenzino B. Polarized vs.
threshold training intensity distribution on endurance sport perfor-
mance: A systematic review and meta-analysis of randomized
controlled trials. J Strength Cond Res 33(12): 3491–3500,
2019—The objective of this review was to systematically search
the literature to identify and analyze data from randomized con-
trolled trials that compare the effects of a polarized training model
(POL) vs. a threshold training model (THR) on measurements of
sport performance in endurance athletes. This systematic
review and meta-analysis is registered with PROSPERO
(CRD42016050942). The literature search was performed on
November 6, 2016 and included SPORTDiscus (1800–
present), CINAHL Complete (1981–present), and Medline with
Full Text (1946–present). Studies were selected if they included:
random allocation, endurance-trained athletes with greater than 2
years of training experience and V
_
O
2
max/peak .50 ml$kg$min
21
,
a POL group, a THR group, assessed either internal (e.g., V
_
O
2
max)
or external (e.g., time trial) measurements of endurance sport per-
formance. The databases SPORTDiscus, Medline and CINAHL
yielded a combined 329 results. Four studies met the inclusion
criteria for the qualitative analysis, and 3 for the meta-analysis. Two
of the 4 studies included in the review scored a 4/10 on the PEDro
Scale and 2 scored a 5/10. With respect to outcome measure-
ments, 3 studies included time-trial performance, 3 included
V
_
O
2
max or V
_
O
2
peak, 2 studies measured time-to-exhaustion, and
one study included exercise economy. There was sufficient data to
conduct a meta-analysis on time-trial performance. The pooled
results demonstrate a moderate effect (ES = 20.66; 95% CI:
21.17 to 20.15) favoring the POL group over the THR group.
These results suggest that POL may lead to a greater improvement
in endurance sport performance than THR.
KEY WORDS polarized training, aerobic performance, time trial,
time- to-exhaustion
INTRODUCTION
There are a number of variables to consider when
designing an exercise training program aiming to
improve endurance sport performance. Some of
these variables include training frequency, training
duration, and training intensity (5). Previous investigations
have identified training intensity to be an essential variable
that can be manipulated to either positively or negatively alter
markers of performance (27). Training intensity can be quan-
tified by using different measurements including heart rate (1),
blood lactate concentration (3), velocity at maximal oxygen
uptake (V
_
O
2
max) (4), and rating of perceived exertion (8).
Previous literature has suggested that it is common for
athletes to use standardized scales that group these measure-
ments into a range of values to provide a description of dif-
ferent training zones (36). However, these methods may not
accurately account for athlete-specific physiological differen-
ces, including those regarding the power or speed that can be
maintained at specific thresholds (36).
In regard to the observed differences in physiological
response at a given fraction of V
_
O
2
max, practitioners have
divided training intensity into 3 or more zones separated by
physiological thresholds such as the lactate threshold, ven-
tilatory thresholds (VTs), respiratory compensation thresh-
old, and critical power (38). This approach improves the
specificity for programming as each athlete’s physiological
thresholds can occur at a different percentage of their
V
_
O
2
max (36). One common approach is to divide intensity
into 3 zones: a low-intensity zone below the first ventila-
tory threshold; a moderate-intensity zone occurring
Address correspondence to Michael A. Rosenblat, m.rosenblat@mail.
utoronto.ca.
33(12)/3491–3500
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Ó2018 National Strength and Conditioning Association
VOLUME 33 | NUMBER 12 | DECEMBER 2019 | 3491
Copyright © 2018 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
between the first and second ventilatory threshold, and
a high-intensity zone residing above the second ventilatory
threshold (25).
High-intensity training has been shown to lead to greater
improvements in markers of endurance sport performance
including V
_
O
2
max, time-trial performance, exercise econ-
omy, and time-to-exhaustion in endurance-trained athletes
(15,37). However, a high volume of high-intensity training
can lead to inadequate recovery causing undesirable effects
including a decrease in running performance and exercising
heart rate, disturbed sleep, elevated perceived fatigue, and an
increase in the incidence of respiratory tract infections
(13,24). To balance the positive and negative effects of
high-intensity training, it might be necessary to consider
the distribution and frequency of high-intensity training to
design an appropriate endurance training program.
A number of prospective cohort studies have identified
how endurance athletes typically distribute the different
training zones in their training program. These athletes
(cross-country skiers, rowers, track runners, cross-country
runners, marathoners, and ironman athletes) typically fol-
lowed a program in which approximately 75–85% of total
training volume was performed in the low-intensity zone, 5–
10% in the moderate-intensity zone, and 15–20% in the high-
intensity zone (9,29,31,38,41,43). The structure of training has
been described as a polarized training intensity distribution
(POL) model as proposed by Stephen Seiler (38). A threshold
training intensity distribution (THR) model or more tradi-
tional training model, differs from a POL model in that a sig-
nificant percentage of training (35–55%) is completed in the
moderate-intensity zone with a smaller percentage of training
(45–55%) completed in the low-intensity zone (38).
Figure 1. PRISMA flow diagram.
Polarized vs. Threshold Intensity Distribution
3492
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TABLE 1. Study characteristics.*
Study Study design
Participants Intervention
OutcomeSport
Characteristics
(mean 6SD) Group
TID % (Z1,
Z2, Z3)
Workload
(TRIMP/
wk 6SD)
Esteve-
Lanao
et al.
(10)
Randomized
controlled
trial (5-mo)
Competitive, sub-
elite male
runners (n= 20)
Age = 27 62 y, mass = 64 61.1 kg,
height = 174.6 61.9 cm, V
_
O
2
max
(ml$kg
21
$min
21
) = 69.5 66.0,
experience .5y
POL (n= 10) 80, 10, 10 452 623 10.4-km running time (s),
V
_
O
2
max (ml$kg
21
$min
21
)THR (n= 10) 65, 25, 10 460 626
Mun
˜oz
et al.
(30)
Randomized
controlled
trial (10-wk)
Recreational
runners (n= 32)
Age = 34 628 y, mass = 69.2 69.7 kg,
height = 175 66 cm, V
_
O
2
max
(ml$kg
21
$min
21
)=6367.9,
experience .5.5 y
POL (n= 16) 75, 5, 20 330 667 10-km run time (min), V
_
O
2
max
(ml$kg
21
$min
21
)THR (n= 16) 45, 35, 20 370 698
Neal
et al.
(32)
Randomized,
crossover,
within
subject (6-
wk)
Well-trained,
competitive male
cyclists (n= 12)
Age = 37 66 y, mass = 76.8 66.6 kg,
height = 178 66 cm, V
_
O
2
max
(ml$kg
21
$min
21
) = NA, experience .4y
POL (n= 6) 80, 0, 20 517 690 40-km cycling time (s), 95%
PPO exercise capacity (s)THR (n= 6) 57, 43, 0 633 6
119
Sto
¨ggl
et al.
(39)
Randomized
controlled
trial (9-wk)
Competitive
endurance
athletes (48)
Age = 31 66 y, mass = 73.8 69 kg,
height = 180 68 cm, V
_
O
2
peak
(ml$kg
21
$min
21
) = 62.6 67.1,
experience .8y
POL (n= 12) 68, 6, 26 NA V
_
O
2
peak (L$min
21
),
VO
2
submax (%V
_
O
2
peak),
VO
2
submax
(ml$kg
21
$min
21
), ramp test
THR (n= 12) 46, 54, 0 NA
*TID = training intensity distribution; Z1 = training zone 1; Z2 = training zone 2; Z3 = training zone 3; TRIMP = training impulse; V
_
O
2
max = maximal oxygen uptake; POL = polarized
training; THR = threshold training; PPO = peak power output; V
_
O
2
peak = peak oxygen uptake; TTE = time-to-exhaustion; NA = not available.
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Current reviews (20,36,40) focusing on the capability of
a POL and THR training model to influence endurance
sport performance have proposed a POL model that may
elicit superior training adaptations. However, there remains
a lack of quantitative analyses examining the magnitude of
variance in endurance performance measures when using
each training model. As such, the objectives of this review
were to (a) provide a systematic review of randomized con-
trol trials examining POL and THR training models and (b)
to quantitatively examine the effect of using a THR or POL
training model for improving endurance performance meas-
ures in trained endurance athletes using a meta-analysis.
METHODS
Experimental Approach to the Problem
This systematic review is registered with PROSPERO
(CRD42016050942) and follows the Preferred Reporting
Items for Systematic Reviews and Meta-Analysis (PRSIMA)
guidelines protocol (28).
Subjects
Studies were selected if they included: random allocation,
endurance-trained athletes with greater than 2 years of
training experience and V
_
O
2
max/peak .50 ml$kg
21
$min
21
,
a POL group, a THR group, assessed either internal (e.g.,
V
_
O
2
max) or external (e.g., time trial) measurements of endur-
ance sport performance. Studies were excluded if partici-
pants were untrained or had pathology.
Procedures
An electronic search was conducted that included all
publication years (up to November 6, 2016). To minimize
selection bias and to perform a comprehensive search, 3
databases were used to conduct the literature search and
included SPORTDiscus (1800–present), CINAHL Complete
(1981–present), and Medline with Full Text (1946–present).
Search Strategy. The following search string (including all
fields) was used: training intensity distribution OR polarized
training OR polarized training OR threshold training.
Study Records: Data Manage-
ment. Records were imported
to Papers 3 (Labtiva, Inc.),
a PDF management applica-
tion, where they were re-
viewed for selection.
Selection Process. The titles and
abstracts of the search results
were independently assessed
for suitability by 2 authors.
Full-text articles were retrieved
if the titles or abstracts met the
eligibility criteria or if there was
uncertainty. Disagreements were
resolved through a discussion between the 2 authors, with
a third to be consulted if the first 2 authors could not reach
agreement. The rationale for excluding articles was
documented.
Data Collection Process. A data collection form was created
using the Cochrane Data Extraction and Assessment Form
template. One author was responsible for collecting the data
and the second author checked the extracted data. Disagree-
ments were discussed between the 2 authors, with a third to be
consulted if the first 2 authors could not reach agreement.
Data Items. The following data were extracted from each study
included in the review: study methodology (study design,
duration); participant characteristics (age, sex, height, mass,
absolute and relative V
_
O
2
max/peak, experience, sport); interven-
tion and comparator description (exercise type, training-intensity
distribution, periodization, intensity zone, workload); and out-
come measures.
Outcomes and Prioritizations. The primary outcome assessed
in this review is time-trial performance. Secondary outcomes
include time-to-exhaustion, exercise economy, V
_
O
2
max
(L$min
21
), and V
_
O
2
peak (L$min
21
).
Risk of Bias in Individual Studies. Two reviewe rs used the
PEDro scale to assess the internal validity of the studies
included in the review. The PEDro scale is a 10-point
ordinal scale used to determine specific methodological
components including: randomization, concealed alloca-
tion, baseline comparison, blind participants, blind thera-
pists, blind assessors, adequate follow-up, intention-to-treat
analysis, between-group comparisons, point estimates, and
variability (21). Participant eligibility is also a component of
the PEDro scale; however, it is not included in the final 10-
point score.
Data Synthesis. Group data are reported as means and SDs
with pooled data reported as the standardized mean differ-
ence and its 95% CI. The standardized mean difference,
adjusted to account for small sample size bias, was calculated
TABLE 2. Risk of bias in individual studies.*
Study 1 2 3 4 5 6 7 8 9 10 11 PEDro score
Esteve-Lanao et al. (10) 1 1 0 1 0 0 0 1 0 1 1 5
Mun
˜oz et al. (30) 1 1 0 1 0 0 0 0 0 1 1 4
Neal et al. (32) 1 1 0 0 0 0 0 1 0 1 1 4
Sto
¨ggl et al. (39) 1 1 0 1 0 0 0 1 0 1 1 5
*(1) Eligibility; (2) randomization; (3) concealed allocation; (4) baseline comparison; (5)
blind subjects; (6) blind therapists; (7) blind assessors; (8) adequate follow-up; (9) intention-
to-treat analysis; (10) between group comparisons; (11) point estimates and variability.
Eligibility is not included in the final 10-point score.
Polarized vs. Threshold Intensity Distribution
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TABLE 3. Results of individual studies.*
Outcome Study Measurement Group Pre Post
Within-group
change 6SD
Between-group
difference (95% CI)
TT Esteve-Lanao
et al. (10)
10.4-km run (min 6SD) POL (n= 6) 37.5 62.1 34.9 6NA 22.6 60.53 20.60 (20.74 to 20.46)
THR (n= 6) 37.9 62.1 35.9 6NA -2.0 60.29
Mun
˜oz et al.
(30)
10-km run (min 6SD) POL (n= 15) 39.3 64.9 37.3 64.7 22.0 61.5 20.60 (20.78 to 20.42)
THR (n= 15) 39.4 63.9 38.0 64.4 21.4 61.2
Neal et al. (32) 40-km cycle (min 6SD) POL (n= 11) NA NA 22.3 62.2 21.90 (22.4 to 21.4)
THR (n= 11) NA NA 20.40 62.9
V
_
O
2
max/
peak
Esteve-Lanao
et al. (10)
V
_
O
2
max (ml$kg
21
$min
21
6SD) POL (n= 6) 68.6 65.9 NA NA NA
THR (n= 6) 70.3 69.7 NA NA
Mun
˜oz et al.
(30)
V
_
O
2
max (ml$kg
21
$min
21
6SD) POL (n= 15) 61.0 68.4 NA NA NA
THR (n= 15) 64.1 67.3 NA NA
Sto
¨ggl et al.
(39)
V
_
O
2
peak (L$min
21
6SD) POL (n= 12) 4.4 61.0 4.9 61.1 0.50 60.40 0.60 (0.19 to 1.0)
THR (n= 8) 4.4 60.80 4.3 69.2 20.10 63.30
TTE Neal et al. (32) 95% PPO (% 6SD) POL (n= 11) NA NA 85.0 643.0% 48.0% (40.2 to 55.8)
THR (n= 11) NA NA 37.0 645.0%
Sto
¨ggl et al.
(39)
Ramp test (% 6SD) POL (n= 12) NA NA 17.4 616.1% 8.6% (5.9 to 11.3)
THR (n= 8) NA NA 8.8 68.6%
EE Sto
¨ggl et al.
(39)
V
_
O
2
submax (ml$kg
21
$min
21
6SD) POL (n= 12) 38.2 65.5 39.7 65.0 1.5 62.2 2.5 (2.1 to 2.9)
THR (n= 8) 34.7 65.1 33.7 64.4 21.0 62.4
*SD = standard deviation; TT = time trial; POL = polarized training; THR = threshold training; NA = not available; V
_
O
2
max = maximal oxygen uptake; V
_
O
2
peak = peak oxygen
uptake; TTE = time-to-exhaustion; PPO = peak power output; EE = Exercise economy.
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to establish an effect size (Hedges’ adjusted g) (14). Effect
size values of 0.2, 0.6 and 1.2 were interpreted as small,
moderate and large effect sizes, respectively (17).
The authors of the included studies were contacted for data
that were not presented in their publications (e.g., pre- and
post-test data). Data expressed using the SE of the mean were
converted to the standard deviation. Where possible,
between-group comparisons were made by using the differ-
ence of means with the standard error expressed as a 95% CI.
Individual study results were combined using Review
Manager 5.3 with a random-effect meta-analysis model. This
method considers both within- and between-study variability
and was used to accommodate for the differences in the
interventions in the individual studies (22). An effect favoring
the POL group is displayed as a positive value and an effect
favoring the THR group is displayed as a negative value.
The consistency of the meta-analysis was assessed to
determine the variability in excess of that because of chance.
A chi-squared statistic (Cochrane Q) was used to evaluate
the level of heterogeneity. The I
2
statistic was used to deter-
mine the percentage of the total variation in the estimated
effect across studies.
Risk of Bias Across Studies. The relationship between the
effect size and the sample size was determined visually using
a funnel plot. Egger’s test was used to quantitatively assess
for small sample size bias.
Statistical Analyses
No additional analysis was completed.
RESULTS
Study Selection
A literature search was conducted on November 6, 2016.
The databases SPORTDiscus, Medline, and CINAHL
yielded a combined 329 results. After the removal of 48
duplicates, 281 titles and abstracts were screened. A total of 6
full-text articles were screened for eligibility. Four studies
met the inclusion criteria for the qualitative analysis and 3
studies were used in a meta-analysis (Figure 1).
Figure 2. Forest plot of standardized mean difference in time-trial performance.
Figure 3. Funnel plot of the standardized mean difference vs. SE of time-trial performance.
Polarized vs. Threshold Intensity Distribution
3496
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Study Characteristics
All 4 studies included in the review were randomized controlled
trials that ranged from 6-weeks to 5-months in duration
(10,30,32,39). Participants were allocated to a POL intervention
group or a comparison group. All studies included a THR
group (10,30,32,39). One study also included a high-intensity
interval training group and a high-volume low-intensity training
group (10). Two of the studies included runners (10,30), one
study included cyclists (32), and one incorporated cyclists,
cross-country skiers, middle- or long-distance runners, and
triathletes (39). All studies included internal and external meas-
urements of performance. The external measurements include
10-km running time trial, 40-km cycling time trial, time-to-
exhaustion, and exercise economy. Internal measurements
include absolute and relative V
_
O
2
max and V
_
O
2
peak (Table 1).
Risk of Bias Within the Studies
Two of the 4 studies included in the review scored a 4/10 on
the PEDro scale, and 2 scored a 5/10 (Table 2).
Results of Individual Studies
The studies included a total of 112 participants. All partic-
ipants were randomly allocated to their respective groups
before baseline data collection (10,30,32,39). The authors only
included baseline and follow-up data for participants who
completed the intervention programs (98 participants)
(10,30,32,39). All authors of the studies were sent emails re-
questing individual and group data that were not published in
their respective publications. Three of the 4 authors re-
sponded to the email (10,30,39), 2 of which provided addi-
tional data (30,39). Only data from one of the authors were
incorporated into the results table (Table 3) (30).
Three studies included time-trial performance as an out-
come measure (10,30,32), all of which showed a significant
difference between the POL group and THR group in time-
trial performance, favoring the POL group (Table 3). Three
studies included V
_
O
2
max/V
_
O
2
peak (10,30,39), 2 of which did
not include postintervention results (10,30). The one study
that included follow-up results found a significant difference
between the POL and THR groups, favoring the POL group
(Table 3). Neal et al. and Sto
¨ggl et al. both compared POL
and THR on time-to-exhaustion (32,39). Both studies found
a greater improvement in time-to-exhaustion in the POL
group (32). Only the study by Sto
¨ggl et al. (39) included
exercise economy and found a POL model to be more ben-
eficial than a THR model.
Data Synthesis
There was only sufficient data to complete a quantitative
analysis on time-trial performance. A qualitative analysis of
performance markers, including V
_
O
2
max/peak, time-to-
exhaustion, exercise economy and time trial, is examined
in the discussion section. Three randomized clinical trials
were included in the meta-analysis (10,30,32). There was
a moderate effect favoring the POL group over the THR
group (ES = 20.66; 95% CI: 21.17 to 20.15) (Figure 2).
Risk of Bias Across Studies
A funnel plot of the standard difference in mean vs. standard
error indicates that there is no evidence of publication bias
(p= 0.52) regarding the studies included in the meta-analysis
(Figure 3).
DISCUSSION
The pooled results demonstrate a significantly greater
improvement in time-trial performance for the POL group
when compared to the THR group (Figure 2). In a time-trial
performance test, an athlete is required to complete a set
amount of work or distance in the least amount of time
possible (18). Time-trial test results have demonstrated to
be significantly correlated to cycling (R= 0.98, p,0.05)
and running (R= 0.95, p,0.05) race performance (33,34).
There was a sufficient amount of data to complete a meta-
analysis on time-trial performance.
The main difference between a POL model and a THR
model is the percentage of time spent in the 3 training zones.
Most notably, a POL model includes approximately 75–85%
of total training in the low-intensity zone, whereas a THR
model only includes about 35–55% of training in the low-
intensity zone. A prospective cohort study by Esteve-Lanao
et al. (9) found a positive relationship with training time in
the low-intensity zone during a 6-month macrocycle and
long-distance cross-country race performance in elite run-
ners. Mun
˜oz et al. (29) discovered a similar relationship with
training time spent in the low-intensity zone and ironman
race performance. However, Mun
˜oz et al. (29) also found
that ironman athletes spent approximately 58% of total race
time in the moderate-intensity zone. These results appear to
conflict with the principle of training specificity; therefore,
a further understanding of the mechanisms behind a POL
model is required.
One study in particular attempted to link specific periph-
eral adaptations with time-trial performance. Neal et al. (32)
compared POL and THR on changes in lactate transporters
(MCT1 and MCT4) to determine if intensity distribution
affected muscle fiber type. MCT1 is found in type I oxidative
(slow-twitch) muscles fibers, whereas MCT4 is only found in
type II fast-twitch fibers (32). One could hypothesize that
the large volume of low-intensity training would lead to an
increase in MCT1 because of the specificity of the training
model. However, the results of the study did not indicate
a change in type I-specific transporters (32). The absence of
oxidative fiber type changes may be due to the short dura-
tion of the intervention (6-weeks). Previous investigations
have shown that it can take up to 5-months to increase type
I muscle fiber density (12); therefore, studies of longer dura-
tion may be necessary to observe histological changes.
Current consensus has described V
_
O
2
max as the maximal
rate of oxygen that can be consumed, transported, and used
by an individual (2). It is defined by either a plateau in oxy-
gen utilization (V
_
O
2
changes #150 ml$min
21
) or a respira-
tory exchange ratio of greater than 1.15 (42). It has been
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suggested that if these physiological values are not reached
between the last 2 stages of work, the test results would
represent a V
_
O
2
peak (19). V
_
O
2
max/peak is a measurement
that is commonly used to assess aerobic power (6) and it is
highly correlated with 10-km running (R=20.95, p,0.05)
and marathon (R=20.96, p,0.05) performance (11).
Three of the studies measured the effects of POL and
THR on V
_
O
2
max/peak (10,30,39). A meta-analysis could not
be completed as a result of postintervention results only
being provided in the study by Sto
¨ggl et al. (39). The results
of their study indicate a significant difference in V
_
O
2
peak
favoring POL over THR (MD = 0.60 L$min
21
; 95% CI:
0.19–1.0) (39).
As previously discussed, workload measurements such as
V
_
O
2
max/peak do not account for individual physiological
differences (36). Lucia et al. (26) suggested that the percent-
age of V
_
O
2
max at which the first and second VTs occur may
be a better predictor of race performance over V
_
O
2
max as
a standalone measurement. A study by Coyle et al. (7) com-
pared 40-km cycling time-trial performance in trained cy-
clists with the same V
_
O
2
max (;69 ml$kg
21
$min
21
) but
different VTs. The results of the study demonstrate that
time-trial performance in cyclists with a higher relative VT
were 10% faster than cyclists with lower relative VTs (7).
Although V
_
O
2
max/peak may be related to endurance sport
performance (6,11), the proximity of VT
2
to V
_
O
2
max appears
to be a better measurement of endurance sport performance.
There are training adaptations that have yet to be
investigated regarding metabolism and changes in physio-
logical thresholds through POL training model. Hetlelid
et al. (16) showed that well-trained runners have VTs (VT
1
and VT
2
) that occur at a greater percentage of their V
_
O
2
max
when compared to recreationally trained runners. The study
also indicates that well-trained athletes have the ability to
metabolize approximately 3 times the amount of fatty acids
during a session of high-intensity interval compared with
recreationally trained runners (16). Because highly trained
endurance athletes tend to follow a POL training model
(9,29,31,38), there may be a link between a POL model with
the ability of highly trained endurance athletes to metabolize
fatty acids at a high rate (16). Additional studies investigating
the effects of a POL training model on adaptations in fat
metabolism and VT may provide insight into the mecha-
nisms regarding improved race-pace performance at a mod-
erate intensity.
Time-to-exhaustion is considered an open-looped test
that may have less external validity than close looped tests
(e.g., 40-km time trial) as such it may fail to provide a realistic
indicator of athletic performance (18). Hopkins et al. (18)
emphasizes that athletes may terminate the test as a result
of feelings of boredom and lack of motivation rather than
because of exercise-related fatigue.
Two studies examined the effects of a POL and THR
training model on time-to-exhaustion (32,39). Neal et al. (32)
examined time-to-exhaustion through having participants
cycle at 95% of their predetermined PPO and Sto
¨ggl et al.
(39) used the total time achieved on an incremental running
or cycling ramp test. The results of both studies indicate that
a POL model leads to a significantly greater improvement in
time-to-exhaustion than a THR model (Table 3). Because of
the methodological differences used to assess time-to-
exhaustion, a meta-analysis was not conducted.
Exercise economy can be described as the energy demand
for a given velocity or power output (35) and has been
shown to be related to endurance sport performance (44).
Only the study by Sto
¨ggl et al. examined the effects of POL
and THR on exercise economy. They found a significant
difference between POL and THR groups regarding the
VO
2
submax (%V
_
O
2
peak) required to maintain a power out-
put of 200 W during a submaximal cycling test (39). There
was also a significant difference between the POL and THR
groups in VO
2
submax (ml$kg
21
$min
21
) favoring the THR
group (MD = 2.50 ml$kg
21
$min
21
; 95% CI: 2.1–2.9)
(Table 3).
To differentiate between the influence of anthropometric
(e.g., changes in body mass) vs. physiological changes,
exercise economy may be better demonstrated when
measured using an absolute (L$min
21
)V
_
O
2
value as opposed
to relative (ml$kg
21
$min
21
) measurement. Because these
values were not provided as absolute measurements, it
may be difficult to conclude that a THR model is more
effective than a POL model for improving exercise economy
through physiological adaptations.
There are a number of limitations that may affect the
quality of evidence included in this review. Only 4 random-
ized trials met the inclusion criteria, and only 3 could be used
in the meta-analysis. In addition, the pooled results only
included a sample size of 64 participants. The limited
number of studies combined with a small sample size makes
it difficult to definitively state that a POL model will lead to
greater improvements in time-trial performance than a THR
model.
Methodological design issues are evident as 2 of
the studies scored a 4/10 on the PEDro scale and 2 scored
a 5/10 on the PEDro scale. More specifically, issues such as
the absence of participant blinding, assessor blinding, and
concealed allocation are present in all studies included in the
review. An intention-to-treat analysis was not included in
any of the studies, possibly affecting the ability to control for
confounding variables. The limitations in methodology may
affect the internal validity of the included studies and
increase the risk a bias.
Some of the studies included outcomes that were mea-
sured at baseline; however, postintervention results were not
provided. The performance variables included in this review
focused only on measures examined preintervention and
postintervention. Furthermore, there was limited standard-
ization of the training loads between the POL and THR
groups. The lack of consistency in the training protocols
may affect the strength of the results of the meta-analysis.
Polarized vs. Threshold Intensity Distribution
3498
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Copyright © 2018 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
Although the design issues are important to consider
when addressing the validity of the results, it is also
necessary to consider the population from which the
sample was taken. There are limited randomized trials
that include highly trained endurance athletes, as such
studies could alter their training program and negatively
affect performance. Therefore, although the described
limitations can influence the interpretation of the results,
the scarcity of trials with this population should add
significant value.
Polarized training model training has been discussed in
great detail in the literature over the past decade. Further
investigations involving a greater methodological approach
are necessary to confidently determine the effects of a POL
training model on endurance performance. As athletes
prepare for competition, they tend to increase their total
workload by manipulating both training duration and
intensity (23). As such, future inquiries should address how
training-intensity distribution before the taper period can
influence event performance during a racing season. Because
of the disconnect between a POL training model and the
principle of specificity, additional studies should investigate
the link between the physiological and metabolic adapta-
tions that occur following a POL training model and race-
pace performance.
PRACTICAL APPLICATIONS
High-intensity aerobic training is a critical component in an
exercise program to improve endurance sport performance
(15,37). However, a high frequency of high-intensity training
may lead to significant declines in sport performance (24).
The findings of this meta-analysis indicate that a POL train-
ing model may lead to a significantly greater improvement in
endurance performance than a THR training model. The
methodological limitations of the included studies may affect
their external validity; however, they are currently the high-
est level of evidence available on the topic. Endurance sport
coaches should acknowledge that the distribution of training
intensity may affect endurance sport performance and
should consider a POL training model when structuring
a training program.
A total of 4 randomized controlled trials have been
published on the effects of a POL training model on
endurance sport performance. The pooled results of all
studies show a moderate effect that indicates that a POL
model can lead to a greater improvement in time-trial
performance time than a THR model.
ACKNOWLEDGMENTS
There are no financial, professional, or personal relationships
that would be considered a conflict of interest. The
manuscript has been read and approved by all authors. All
authorship requirements have been met, and each author
believes that the manuscript represents true and honest work
and reports no conflict of interest. The results of the study
are presented clearly, honestly, and without fabrication,
falsification, or inappropriate data manipulation. The results
of the present study do not constitute endorsement by the
authors or the NSCA.
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