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Polarized training has greater impact on key endurance variables than threshold, high intensity, or high volume training

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Endurance athletes integrate four conditioning concepts in their training programs: high-volume training (HVT), “threshold-training” (THR), high-intensity interval training (HIIT) and a combination of these aforementioned concepts known as polarized training (POL). The purpose of this study was to explore which of these four training concepts provides the greatest response on key components of endurance performance in well-trained endurance athletes. Methods: Forty eight runners, cyclists, triathletes, and cross-country skiers (peak oxygen uptake: (VO2peak): 62.6 ± 7.1 mL·min⁻¹·kg⁻¹) were randomly assigned to one of four groups performing over 9 weeks. An incremental test, work economy and a VO2peak tests were performed. Training intensity was heart rate controlled. Results: POL demonstrated the greatest increase in VO2peak (+6.8 ml·min·kg⁻¹ or 11.7%, P < 0.001), time to exhaustion during the ramp protocol (+17.4%, P < 0.001) and peak velocity/power (+5.1%, P < 0.01). Velocity/power at 4 mmol·L⁻¹ increased after POL (+8.1%, P < 0.01) and HIIT (+5.6%, P < 0.05). No differences in pre- to post-changes of work economy were found between the groups. Body mass was reduced by 3.7% (P < 0.001) following HIIT, with no changes in the other groups. With the exception of slight improvements in work economy in THR, both HVT and THR had no further effects on measured variables of endurance performance (P > 0.05). Conclusion: POL resulted in the greatest improvements in most key variables of endurance performance in well-trained endurance athletes. THR or HVT did not lead to further improvements in performance related variables.
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ORIGINAL RESEARCH ARTICLE
published: 04 February 2014
doi: 10.3389/fphys.2014.00033
Polarized training has greater impact on key endurance
variables than threshold, high intensity, or high volume
training
Thomas Stöggl1,2*and Billy Sperlich3
1Department of Sport Science and Kinesiology, University of Salzburg, Salzburg, Austria
2Department of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, Sweden
3Institute of Sport Science, University of Würzburg, Würzburg, Germany
Edited by:
Niels H. Secher, University of
Copenhagen, Denmark
Reviewed by:
Niels H. Secher, University of
Copenhagen, Denmark
Stefanos Volianitis, Aalborg
University, Denmark
*Correspondence:
Thomas Stöggl, Department of
Sport Science and Kinesiology,
University of Salzburg, Schlossallee
49, 5400 Hallein/Rif, Salzburg,
Austria
e-mail: thomas.stoeggl@sbg.ac.at
Endurance athletes integrate four conditioning concepts in their training programs:
high-volume training (HVT), “threshold-training” (THR), high-intensity interval training
(HIIT) and a combination of these aforementioned concepts known as polarized training
(POL). The purpose of this study was to explore which of these four training concepts
provides the greatest response on key components of endurance performance in
well-trained endurance athletes.
Methods: Forty eight runners, cyclists, triathletes, and cross-country skiers (peak oxygen
uptake: (VO2peak): 62.6±7.1mL·min1·kg1) were randomly assigned to one of four
groups performing over 9 weeks. An incremental test, work economy and a VO2peak tests
were performed. Training intensity was heart rate controlled.
Results: POL demonstrated the greatest increase in VO2peak (+6.8 ml·min·kg1or 11.7%,
P<0.001), time to exhaustion during the ramp protocol (+17.4%, P<0.001) and peak
velocity/power (+5.1%, P<0.01). Velocity/power at 4 mmol·L1increased after POL
(+8.1%, P<0.01) a n d H IIT (+5.6%, P<0.05). No differences in pre- to post-changes
of work economy were found between the groups. Body mass was reduced by 3.7%
(P<0.001) following HIIT, with no changes in the other groups. With the exception of
slight improvements in work economy in THR, both HVT and THR had no further effects
on measured variables of endurance performance (P>0.05).
Conclusion: POL resulted in the greatest improvements in most key variables of
endurance performance in well-trained endurance athletes. THR or HVT did not lead to
further improvements in performance related variables.
Keywords: lactate threshold, peak power, peak oxygen uptake, time to exhaustion, work economy
INTRODUCTION
Athletes participating in endurance sports such as running,
cycling, and cross-country skiing integrate four conditioning
concepts into their training program to maximize athletic perfor-
mance. The first conditioning concept is prolonged high-volume
low-intensity exercise (HVT). The second is training at or near the
lactate threshold (THR); third is low-volume high-intensity inter-
val training (HIIT) and the fourth concept is a combination of the
aforementioned concepts known as “polarized” training (POL).
There is a debate as to which of these training concepts may be
superior in maximizing adaptations and performance.
HVT executed with low (LOW) intensity [approximately
65–75% of peak oxygen uptake (VO2peak)<80% of peak heart
rate (HRpeak)or<2 mmol·L1blood lactate (Laursen and
Jenkins, 2002; Seiler and Kjerland, 2006)] and prolonged dura-
tion is thought to be a fundamental training concept in preparing
for endurance events. This type of exercise improves VO2peak
by increasing stroke and plasma volume and induces molecular
adaptations for capillary and mitochondrial biogenesis, thereby
improving the efficiency of metabolic key components for energy
fueling (Romijn et al., 1993; Midgley et al., 2006).
HIIT has revealed great improvements in athletic performance
and related key variables of endurance (e.g., time to exhaustion,
time trial performance, VO2peak, maximal and submaximal run-
ning speed, running economy) in both trained and untrained
individuals (Laursen and Jenkins, 2002). These improvements
were largely due to increases in O2availability, extraction and
utilization and the increases in VO2peak (Daussin et al., 2007;
Helgerud et al., 2007). A condensed 2 week block of 10–13 ses-
sions of HIIT led to a 7% increase in VO2peak (Stølen et al.,
2005).
Training at or close to the lactate threshold (LT) (Faude et al.,
2009), referred to as “threshold training, improves endurance
performance, particularly in untrained participants (Denis et al.,
1984; Londeree, 1997). However, Norwegian world-class sprint
cross-country skiers demonstrated greater training volume close
to the LT when compared to national-level skiers (Sandbakk
et al., 2011). Furthermore, in elite cross-country skiers greater
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Stöggl and Sperlich Endurance training concepts
improvements in running speed at lactate threshold and perfor-
mance in a 20-min run when exercising at an intensity elicit-
ing 3–4 mmol·L1lactate compared with low intensity training
(<3–4 mmol·L1)werefound(Evertsen et al., 2001). In contrast,
experimental and correlational data from well-trained athletes
suggest that training time close to LT may be ineffective, or even
counterproductive (Esteve-Lanao et al., 2007; Guellich and Seiler,
2010).
Retrospective analysis of the intensity, duration and fre-
quency of the training load of international-level cross-country
skiers (Seiler and Kjerland, 2006), rowers (Steinacker et al.,
1998), cyclists (Schumacher and Mueller, 2002), and runners
(Billat et al., 2001; Esteve-Lanao et al., 2005) revealed that elite
endurance athletes completed most of their yearly training ses-
sions at either intensities below (75% of total training volume)
or well above (15–20% of total training volume) their LT. Six
weeks of cycling using POL resulted in greater systemic adap-
tation in already well-trained athletes when compared to THR
(Neal et al., 2013). However, no study has investigated the POL
concept in well-trained endurance athletes to determine whether
this concept may be superior to the aforementioned training
strategies.
Inmanyendurancesports,fivekeyvariableshavebeenused
as a benchmark to compare athletic performance in and between
endurance athletes: (i) VO2peak (Bassett and Howley, 2000); (ii)
velocity/power output at the lactate threshold (V/PLT )(Bassett
and Howley, 2000; Midgley et al., 2007; Faude et al., 2009); (iii)
work economy (Di Prampero et al., 1986; Helgerud et al., 2001);
(iv) peak running velocity or power output (V/Ppeak)(Midgley
et al., 2007); and (v) time to exhaustion (TTE) (Laursen and
Jenkins, 2002). The aim of this study was to compare the effects
of four training concepts (HVT vs. THR vs. HIIT vs. POL) on the
aforementioned key variables of endurance performance in well-
trained athletes. We hypothesized that the POL and HIIT group
would lead to superior improvements compared with HVT and
THR.
MATERIALS AND METHODS
PARTICIPANTS
Forty eight healthy competitive endurance athletes who partici-
pated in either cross-country skiing, cycling, triathlon, middle—
or long-distance running volunteered to take part in this study
(mean ±SD:age:31±6 years, body mass: 73.8 ±9kg,
height: 180 ±8 cm). All participants were well-trained [62.6
±7.1 mL·min1·kg1(range: 52–75 mL·min1·kg1)] athletes,
accustomed to a workload of more than five sessions per week
(10–20 h·wk1)andhadfrequentlybeeninvolvedinendurance
competitions for at least 8–20 years. Participants were members
of the Austrian cross-country skiing national team (n=8), run-
ning (n=21), triathlon (n=4) or cycling (n=15) teams during
or since the year before the current study. Retrospective analysis
of training protocols over 6 months prior to the study revealed
that none of the participants had regularly executed HIIT. All had
followed a HVT training protocol with a maximum of two THR
training sessions per week.
Based on the participants’ baseline VO2peak and training mode
(running or cycling), all athletes were randomized into HIIT,
HVT, THR or POL. At baseline, the four groups were not sta-
tistically different with regard to age, height, body mass or
VO2peak . During an initial visit, study details and participation
requirements were explained, and written informed consent was
obtained. The study received approval from the University of
Salzburg Austria Ethics Committee and was conducted in accor-
dance with the Declaration of Helsinki.
DESIGN
The intervention lasted 9 weeks plus 2 days of pre- and post-
testing. All athletes (n=15 cyclists; n=3 triathletes) engaging
in cycling training trained with their own bike and completed
all tests on a bicycle ergometer (Ergoline, Ergoselect 100P; Bitz,
Germany) using their own cycling shoes and pedal system.
Other athletes (n=16 runners, n=6 triathletes, n=8 cross-
country skiers) ran during the study and completed their pre-
and post-testing on a motorized treadmill (HP Cosmos, Saturn,
Traunstein, Germany). All participants were instructed not to
change their diet throughout the training period and to main-
tain strength training, if it was part of their training program.
Participants’ nutritional intake was not standardized or con-
trolled during the study, but for the 3h prior to all testing in which
food intake was not permitted. The training intensity was con-
trolled by HR based on the baseline incremental test: (i) LOW
(HR at blood lactate value <2 mmol·L1); (ii) LT (HR corre-
sponding to a blood lactate of 3–5 mmol·L1); (iii) HIGH (>90%
HRpeak)]. The HR was measured during each training session and
athletes documented training mode, exercise duration, and inten-
sity in a diary. As a control and for detailed analysis, HR for all
training sessions was stored digitally and analyzed retrospectively.
HVT INTERVENTION
The HVT included three blocks each lasting 3 weeks: 2 weeks of
high-volume training followed by 1 week of recovery (Figure 1A).
The two high volume weeks each included six training sessions
with three 90 min LOW sessions, two 150–240 min LOW sessions
(according to the training mode: running, cycling, or roller ski-
ing) and one 60 min LT session using different types of interval
training (e.g., 5 ×7 min with 2 min recovery, 3 ×15 min with
3 min recovery). The recovery week included three training ses-
sions with two 90 min LOW sessions and one 150–180 min LOW
session.
THR INTERVENTION
The THR included three blocks, each lasting 3 weeks: 2 weeks
of high volume and intensity training followed by 1 week of
recovery (Figure 1B). The two high volume and intensity weeks
each included six training sessions with two 60 min interval ses-
sions at the LT (5 ×6 min and 2min recovery in the first block,
6×7mininthesecondblockand6×8 min in the last block),
one 90 min LT session with longer intervals (3 ×15 min with
3 min active recovery in the first block and 3 ×20 min for the
remaining two blocks), one 75 min session with varying changes
in intensity (“fartlek”) (intensities resulting in a blood lactate
of 1.5–5 mmol·L1) and two 90 min LOW sessions. The recov-
ery week included one 60 min LOW session and two 60min LT
interval sessions (5 ×6 min with 2 min of active recovery).
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Stöggl and Sperlich Endurance training concepts
FIGURE 1 | Training program for 3-weeks of (A) high volume (HVT), (B)
threshold (THR), (C) polarized (POL) training, and (D) the training
program for the first block of high intensity interval training (HIIT),
excluding the recovery week. LOW, low training intensity (<2 mmol·L1);
LT, training intensity around the lactate threshold (3–5 mmol·L1); FL, fartlek;
HIIT, high intensity interval training (>90% HRpeak); R, recovery day.
HIIT INTERVENTION
The HIIT included two interval blocks of 16 days with one adap-
tation week prior to and one recovery week after each block.
The adaptation week included two 60 min HIIT sessions, three
90 min LOW sessions, one 120min LOW session and 1 day of
recovery. The condensed 16 day interval block included 12 HIIT
sessions within 15 days, integrating four blocks of three HIIT
sessions for three consecutive days followed by 1 day of recov-
ery. The recovery week contained four LOW sessions of 90 min
and 3 days without any training (not presented in Figure 1D).
All of the HIIT sessions included a 20 min warm-up at 75%
of HRpeak,4×4 min at 90–95% of HRpeak with 3 min active
recovery and a 15 min cool-down at 75% HRpeak based on the
protocol proposed earlier (Helgerud et al., 2007). The LOW
sessions lasted 90–150 min depending on the training mode
(running vs. cycling) at an intensity resulting blood lactate of
<2 mmol·L1.
POL INTERVENTION
The POL included three blocks, each lasting 3 weeks: 2 weeks of
high volume and intensity training followed by 1 week of recov-
ery (Figure 1C). The high volume and intensity week included six
sessions with two 60 min HIIT sessions, two 150–240 min long
duration LOW sessions (duration according to training mode:
cycling, running or roller skiing), which included six to eight
maximal sprints of 5 s separated by at least 20 min, and two
90 min LOW sessions. The recovery week included one 60 min
HIIT session, one 120–180 min LOW session and one 90min
LOW session.
PRE AND POST-TESTING
All participants were asked to report well-hydrated and to refrain
from consuming alcohol and caffeine for at least 24-h, as well as
from engaging in strenuous exercise at least 48-h prior to testing.
The pre- and post-tests included the determination of body mass,
an incremental test protocol, a work economy and VO2peak ramp
protocol.
On the first day participants performed an incremental test on
a treadmill (7.2 km·h1; increment: 1.8 km·h1every 5 min, with
30 s recovery between stages, inclination 1%) or cycle ergome-
ter (80 W; increment: 40 W every 5 min, cadence >80 rpm) until
volitionalexhaustionwasachievedtoassessthepeakveloc-
ity/power output (V/Ppeak), HR, blood lactate, as well as the
velocity, power output and HR at 2 and 4 mmol·L1blood lac-
tate (V/P2,V/P
4and HR2,HR
4). The participants’ HR was
recorded by telemetry (Suunto t6, Helsinki, Finland) at 2-s inter-
vals. The mean HR over the last 30 s of each increment was used
for statistical analysis. A 20 µl blood sample from the right ear-
lobe was collected immediately after each increment, as well as
3 and 5 min after the completion of the test into a capillary
tube (Eppendorf AG, Hamburg, Germany). All samples were ana-
lyzed amperometric-enzymatically (Biosen 5140, EKF-diagnostic
GmbH, Magdeburg, Germany) in duplicate, and the mean of the
two measures was used for statistical analysis. The lactate sensor
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Stöggl and Sperlich Endurance training concepts
was calibrated before each test using a lactate standard sample
of 12 mmol·L1. Results within a range of ±0.1 mmol·L1were
accepted.
One day after the incremental tests, all athletes completed a
combined work economy and VO2peak ramp protocol to deter-
mine their submaximal and peak VO2(VO2submax and VO2peak)
and HR (HRsubmax and HRpeak), as well as time to exhaustion
(TTE). First, the intensity for running was set at 8 km·h1(incli-
nation: 5%) on the treadmill, and for cycling at 200 W with
a cadence of >80 rpm for 10 min to determine VO2submax and
HRsubmax for this intensity. The mean VO2andHRduringthe
last 5 min of these tests were used for statistical purposes. The
intensity was then increased every 30 s by 0.5 km·h1(inclina-
tion: 10%) on the treadmill or 15 W on the cycle ergometer until
exhaustion. The overall time for the ramp test was defined as time
to exhaustion (TTE). VO2was measured with an open circuit
breath-by-breath spirograph (nSpire, Zan 600 USB, Oberthulba,
Germany), which was calibrated prior to each test using high pre-
cision gas (15.8% O2,5%O
2in N; Praxair, Düsseldorf, Germany)
and a 1L syringe (nSpire, Oberthulba, Germany). All respiratory
data were averaged every 30 s. VO2peak was achieved if three of
the four following criteria were met: (1) plateau in VO2, i.e., an
increase <1.0 mL·min1·kg1despite an increase in velocity or
power output; (2) respiratory exchange ratio >1.1; (3) HR ±5%
of age predicted HRpeak; and (4) peak blood lactate (LApeak)>
6 mmol·L1after exercise. Reliability analysis of The VO2peak test
(n=18) revealed ICC values of 0.96 for VO2peak and 0.98 for
TTE.
STATISTICAL ANALYSES
All data exhibited a Gaussian distribution verified by the Shapiro-
Wilk’s test and, accordingly, the values are presented as means ±
SD.Two-Way2×4 repeated-measures ANOVA (2 times: pre–
post, 4 groups) to test for global differences between pre- and
post-intervention, the four training groups and the interaction
effect between both factors was applied. When a significant
main effect over time was observed, paired t-tests within each
group were conducted. Based on the different units of peak
power/velocity and power/velocity at 2 and 4mmol·L1blood
lactate in the incremental and VO2peak test, percent changes
between pre- to post-values were calculated, and a One-Way
ANOVA between groups was performed using Tukey’s post-hoc
analysis. Furthermore, within group changes for these variables
were calculated using Wilcoxon tests. An alpha value of <0.05
was considered significant. The Statistical Package for the Social
Sciences (Version 20.0; SPSS Inc., Chicago, IL, USA) and Office
Excel 2010 (Microsoft Corporation, Redmond, WA, USA) were
used for statistical analysis.
RESULTS
Forty-one participants completed the 9 week training protocol,
fulfilling more than 95% of the training program and staying
within the given HR zones. Seven subjects (2 in HIIT, 1 in HVT
and 4 in THR) withdrew from the study due to illness (n=2), or
were excluded due to changes in competition schedule (n=3) or
for not fulfilling the training protocol (n=2). The total training
hours, number of training sessions and their percent distribu-
tion within LOW, LTP, and HIIT are presented in Tab l e 1 .POL
and HVT had higher training volume compared with THR and
HIIT (P<0.05–0.001), while having a similar number of train-
ing sessions (P>0.05). HVT demonstrated the greatest amount
of LOW, THR of LT, and HIIT in HIGH training sessions (all,
P<0.05).
Body mass after HIIT was reduced by 3.7±3.0% (baseline:
73.5±6.8 kg, post: 70.7±6.5kg, P<0.01) with no significant
change in the HVT, THR or POL groups. The reduction in
body mass after HIIT was greater compared to other training
interventions (P<0.001).
Percent changes in variables from pre- to post-training
and between the training concepts during the VO2peak-
ramp, work economy, and incremental tests are presented in
Tab l e 2 . POL demonstrated the greatest increase in VO2peak
Table 1 | The distribution of volume and training intensity within the 9 weeks training intervention (excluding strength training).
POL HIIT THR HVT F-Value P-Value
Total hours 104 ±20$66 ±1*84 ±7*102 ±11$a
F(3,37)=20 <0.001
Number of sessions 54 ±347±149±358±3aF(3,37)=1.6 n.s.
Amount of training at low
intensity (%)
37 ±9(68±12%)*20 ±1(43±1%)§23 ±6(46±7%)§49 ±7(83±6%)*aF(3,37)=41 <0.001
Amount of training at lactate
threshold (%)
3±4(6±8%)*0 (0%)*26 ±2(54±7%)*9±3(16±6%)*aF(3,37)=197 <0.001
Amount of training at high
intensity (%)
14 ±3(26±7%)*27 ±1(57±1%)*0 (0%)†‡ 1±1(1±1%)†‡ aF(3,37)=769 <0.001
The values presented are means ±SD. F- and P-values were obtained by One-Way ANOVA (4 training groups). POL, polarized training group; HIIT, High intensity
interval training group; THR, threshold training group; HVT, high volume training group.
*Different from all other groups.
Different from training group “POL.
Different from training group “HIIT.
$Different from training group “THR.
§Different from training group “HVT.
aMain effect between groups.n.s., not significant.
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Stöggl and Sperlich Endurance training concepts
Table 2 | Changes in physiological variables from pre- to post-training.
POL HIIT THR HVT F-Value P-Value
Pre Post Pre Post Pre Post Pre Post
VO2peak -test
VO2peak
[L·min1·kg1]
60.6±8.367.4±7.7*** 63.7±7.166.6±5.8*63.2±4.660.8±7.160.5±9.462.1±9.8F(1,37)=13.6a<0.001
11.7±8.4% 4.8±5.6% 4.1±6.7%†††‡ 2.6±4.5%F(3,37)=0.5bNS
F(3,37)=11.9c<0.001
VO2peak
[L·min1]
4.4±1.04.9±1.1*** 4.6±0.54.7±4.94.4±0.84.3±9.24.8±0.74.9±0.7F(1,37)=6.4a<0.05
10.4±7.9% 1.1±7.6%3.7±7.0%††† 2.9±4.5%F(3,37)=0.6bNS
F(3,37)=8.0c<0.001
HRpeak [bpm] 187 ±7186±7185±9182±11 18 0 ±10 179 ±9183±4183±4F(1,37)=1.9aNS
0.6±1.9% 1.3±2.3% 0.2±1.9% 0.3±1.9% F(3,37)=1.4bNS
F(3,37)=1.0cNS
LApeak
[mmol·L1]
10.2±1.710.7±1.79.6±1.710.2±1.79.5±1.69.9±2.210.1±1.79.9±2.0F(1,37)=1.7aNS
7.5±20.4% 6.4±8.3% 5.3±19.1% 1.0±11 .8% F(3,37)=0.4bNS
F(3,37)=0.4cNS
Work economy
VO2submax
[mL·min·1kg1]
38.2±5.539.7±5.034.8±6.135.9±6.234.7±5.133.7±4.435.2±4.435.3±4.7F(1,37)=0.5aNS
4.6±10.5% 3.8±12.0% 2.1±7.80.6±9.5% F(3,37)=2.0bNS
F(3,37)=1.0cNS
VO2submax
[%VO2peak]
62.4±12.359.0±9.8*54.8±5.053.2±5.354.9±7.956.0±8.158.9±8.657.7±9.6F(1,37)=2.3aNS
4.8±7.6% 2.5±10.5% 2.5±11 .32.1±7.0% F(3,37)=1.3bNS
F(3,37)=1.4cNS
HRsubmax [bpm] 136 ±10 135 ±13 141 ±13 131 ±6** 143 ±3139±3*136 ±7134±5F(1,37)=11.7a<0.01
3.3±6.4% 6.7±4.4% 2.7±1.01.1±3.4% F(3,37)=0.4bNS
F(3,37)=1.7cNS
HRsubmax
[%HRpeak]
74 .3±5.472.5±6.376.1±4.771.9±2.2*77.8±3.675.8±3.9** 74 .2±2.173.0±1.6F(1,37)=9.3a<0.01
2.4±6.3% 5.4±4.5% 2.6±0.91.6±2.0% F(3,37)=0.7bNS
F(3,37)=1.0cNS
(Continued)
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Stöggl and Sperlich Endurance training concepts
Table 2 | Continued
POL HIIT THR HVT F-Value P-Value
Pre Post Pre Post Pre Post Pre Post
Incremental test
HR2[bpm] 139 ±9136±13 138 ±10 141 ±9152±12 151 ±9138±19 138 ±18 F(1,37)=0.1aNS
2.3±6.1% 2.2±5.9% 0.1±9.50.3±6.5% F(3,37)=3.7b<0.05
F(3,37)=0.5cNS
HR4[bpm] 157 ±14 157 ±13 163 ±12 162 ±11 1 7 1 ±9169±9160±12 162 ±12 F(1,37)=0.2aNS
0.1±3.9% 0.3±3.8% 1.2±3.81.1±4.8% F(3,37)=2.6bNS
F(3,37)=0.5cNS
HRpeak [bpm] 186 ±6184±5*191 ±10 19 1 ±10 187 ±9184±9187±7184±4F(1,37)=5.5a<0.05
0.9±1.3% 0.02 ±2.3% 1.2±2.71.4±2.9% F(3,37)=1.2bNS
F(3,37)=0.6cNS
LApeak
[mmol·L1]
10.6±1.511.1±1.611.1±1.811.9±2.39.9±1.810.2±2.510.8±1.49.8±1.3F(1,37)=0.2aNS
4.7±13.4% 7.7±21.1% 4.0±20.0% 7.2±21.0% F(3,37)=1.6bNS
F(3,37)=1.1cNS
The values presented are means ±SD. F and P values were obtained by Two-Way ANOVA (2 ×4: time ×training group) with repeated measures. POL, polarized training group; HIIT, High intensity interval training
group; THR, threshold training group; HVT, high volume training group; VO2, oxygen uptake; HR, heart rate; LApeak, peak blood lactate concentration; *p<0.05; ** p<0.01; ***p<0.001 significant difference
within groups from pre- to post-training. p<0.05; †††p<0.001 significant different from POL training group.p<0.05 significant different from HIIT training group. aMain effect between pre- and post-test.
bMain effect between training groups. ctime ×training group interactive effect.
with an 11.7±8.4%, (60.6±8.3–67.4±7.7ml·min1·kg1;
P<0.001), followed by HIIT with a 4.8±5.6% increase (P<
0.05). The change in VO2peak in POL was higher compared to
THR and HVT (P<0.001 and P<0.05). Absolute VO2peak
increased in POL by 10.4±7.9% (P<0.001), which was greater
compared with the other training concepts (HIIT and HVT P<
0.05, THR P<0.001). No changes from pre to post and no dif-
ferences between training groups with respect to HRpeak,LA
peak,
and HR2&4 were detected (P>0.05). Work economy increased
following HIIT (6.7±4.4% decrease in HR, P<0.01) and
THR (2.7±1.0% decrease in HR, P<0.05) with no signif-
icant differences between the other concepts. Work economy
expressed as percent of VO2peak was only improved after POL
(4.8±7.6%, P<0.05) with no significant differences between
the other training groups.
The changes in TTE, V/Ppeak and V/P2&4 from pre- to post-
training and between the single training groups are presented in
Tab l e 3 . The largest percentage increase in TTE, assessed using the
VO2peak ramp test, was observed in response to POL (+17.4%,
P<0.001) followed by HIIT (+8.8%, P<0.01); however, no
statistical differences were found between the four training con-
cepts. V/Ppeak in the incremental test increased in response to
POL and HIIT (5.1±3.0% and 4.4±2.8%, both P<0.01) with
both groups demonstrating greater changes than HVT (P<0.01
and P<0.05). V/P4increased after POL (8.1±4.6%, P<0.01)
and HIIT (5.6±4.8%, P<0.01) demonstrating greater changes
after POL compared to THR and HVT (both P<0.05).
DISCUSSION
The purpose was to determine whether HIIT, HVT, THR, or POL
provides the greatest impact on key variables of endurance per-
formance in well-trained athletes. The main findings were that
(1) POL led to the greatest improvement in VO2peak,TTEand
V/Ppeak;(2)V/P
4increased after POL and HIIT; (3) no significant
differences in work economy were observed pre to post between
any of the groups; and finally (4) body mass decreased by 3.7% in
response to HIIT.
There are several challenges associated with conducting an
exercise training intervention such as the one presented here.
Firstly, the compliance of all athletes is paramount to the success-
ful completion of the study and for the subsequent examination
of the intervention. The athletes attended more than 95% of all
training sessions and all completed their predetermined train-
ing load (intensity based on HR zones, duration, and frequency),
which was confirmed by logging the daily training dose in a diary
and retrospective analysis of HR data. Secondly, an experimen-
tal study is difficult to conduct in elite athletes because typically
neither the athletes nor their coaches like to have the athletes’
training intensity, duration or frequency altered. However, we
successfully managed to conduct the current study in well-trained
male and female athletes (VO2peak: 52–75 mL·min1·kg1)overa
9weekperiod.
Of the four training concepts, POL resulted in the greatest
increase in VO2peak,TTE,V/P
peak and, together with HIIT, in
V/P4.Asmentioned,POLwasconrmedbyretro-perspective
analysis of the intensity, duration and frequency distribution of
the training load in highly trained athletes (Steinacker et al.,
Frontiers in Physiology | Exercise Physiology February 2014 | Volume 5 | Article 33 |6
Stöggl and Sperlich Endurance training concepts
Table 3 | Per cent changes in velocity (V) and power (P) and at various lactate thresholds as well as peak velocity and power.
POL HIIT THR HVT F-Value P-Value
TTE 17.4±16.1*** 8.8±8.6** 6.2±9.08.0±10.3aF(3,37)=2.0NS
V/P29.3±12.412.1±8.8** 2.0±13.80.8±13.3aF(3,37)=1.9NS
V/P48.1±4.6** 5.6±4.8*1.4±4.31.2±6.6aF(3,37)=4.5<0.01
V/Ppeak 5.1±3.0** 4.4±2.8** 1.8±4.81.5±4.9††‡ aF(3,37)=4.6<0.01
The values presented are means ±SD. F and P values were obtained by One-Way ANOVA (4 training groups) calculated over the per cent differences between pre-
to post-training. POL, polarized training group; HIIT, High intensity interval training group; THR, threshold training group; HVT, high volume training group; TTE, time
to exhaustion during the ramp test; V/P2, velocity or power at 2mmol·L1; V/P4, velocity or power at 4 mmol·L1; V/Ppeak, peak velocity or power in the incremental
test; *p<0.05; **p<0.01; ***p<0.001 significant difference within groups from pre- to post-training.
p<0.05; ††p<0.01 significant different from POL training group.
p<0.05significant different from HIIT training group.
aMain effect between groups.
1998; Billat et al., 2001; Schumacher and Mueller, 2002; Seiler
and Kjerland, 2006; Esteve-Lanao et al., 2007). In these studies,
it was demonstrated that endurance athletes perform approxi-
mately 75% of their yearly training program either below or well
above (15–20%) the LT, but little at the LT. In the current study,
POL mimicked this distribution (LOW =68%, LTP =6%, HIGH
=26%). Only the study of Neal et al. (2013) demonstrated that
6 weeks of POL resulted in greater systemic adaptation in trained
cyclists when compared to THR, hence supporting our findings.
In moderately trained persons, HVT improves metabolic and
hemodynamic adaptations over 3 days (Green et al., 1987, 1990;
Coyle, 1999). However, a greater volume of training (3–5 weeks
with 3–5 sessions·wk1) is needed to improve VO2peak (Laursen
and Jenkins, 2002). One reason due to why athletes may choose a
high amount of HVT may be due to that HVT leads to improved
fat and glucose utilization (Romijn et al., 1993), which is ben-
eficial for long lasting endurance events. Therefore, it might be
reasonable to implement HVT in the training programs of elite
endurance athletes for improving oxidative ux, which is impor-
tant for converting energy aerobically and recovery after and
during HIIT sessions with large anaerobic portions. When HVT
becomes the major component of a training program and HIIT
sessions are neglected, no further improvement in VO2peak and
performance in already well-trained athletes occur (Costill et al.,
1988; Laursen and Jenkins, 2002); in line with the findings of
the present study. Further improvements of well-trained ath-
letes require adding high intensity training sessions to HVT, as
demonstrated in POL. However, due to that the participants of
this study mainly used HVT prior to this experiment, the HVT
model might not have provided an adequate stimulus for further
adaptations.
The advantage of HIIT compared to HVT lies in a shorter
period of training time for similar muscular adaptations (Gibala
et al., 2006; Burgomaster et al., 2008). In response to HIIT, sev-
eral central and peripheral adaptations including increased stroke
(Helgerud et al., 2007) and blood volume (Shepley et al., 1992),
O2extraction (Daussin et al., 2007), and improvements in aero-
bic and anaerobic metabolism (Macdougall et al., 1998), such as
increased mitochondrial biogenesis and oxidative capacity, have
been reported (Gibala et al., 2006; Daussin et al., 2007, 2008;
Burgomaster et al., 2008). The aforementioned adaptations in
response to HIIT explain the often documented increases in TTE,
time trial performance (Lindsay et al., 1996), lactate and ventila-
tory threshold (Acevedo and Goldfarb, 1989; Edge et al., 2005)
and VO2peak (Laursen and Jenkins, 2002; Gibala et al., 2006;
Midgley et al., 2006; Daussin et al., 2007, 2008; Burgomaster et al.,
2008).
The present study, as well as that of Helgerud et al. (2007),
demonstrated that training at or near VO2peak maybemoreeffec-
tive in enhancing VO2peak when compared to HVT or THR.
However, POL, a combination of HVT and HIIT, may be supe-
rior for enhancing VO2peak and performance. Numerous studies
using “blocked” or “condensed” HIIT (i.e., several HIIT session
in 1 or 2 weeks) aim to increase VO2peak (Stølen et al., 2005).
Furthermore, in HIIT intervention studies (2–3 HIIT sessions per
week), VO2peak increased approximately 9% over a 10 week train-
ing intervention (McMillan et al., 2005) and 11% over a 6 week
training intervention (Helgerud et al., 2001) in youth and junior
soccer players, suggesting a 0.5% increase in VO2peak per HIIT
training session. In the current study, the increase in VO2peak
following 9 weeks of HIIT (27 HIIT sessions) was 4.8% (0.18%
increase per training session), while POL resulted in an 11.7%
increase in VO2peak with fewer HIIT sessions (14 HIIT) (0.84%
increase per training session). This result may be explained by:
(1) peak adaptation might have been reached following the first
HIIT block, and therefore, repeated HIIT bouts did not pro-
duce any further improvements in VO2peak or performance, or
(2) the combination of HVT and HIIT, much like in POL, leads
to greater long-term adaptations in endurance performance than
with exclusively HIIT or HVT.
THR improves VO2peak, lactate or ventilatory thresholds and
endurance performance in untrained persons (Denis et al., 1984;
Londeree, 1997; Gaskill et al., 2001). These findings contrast those
of the current study, as we did not observe improvements in
VO2peak ,V/P
4,TTEorV/P
peak in our elite athletes in response to
THR. Additionally, it is possible that in well-trained endurance
athletes, repeated training bouts at LT might generate unwar-
ranted sympathetic stress (Chwalbinska-Moneta et al., 1998),
while offering no further stimulus for performance enhance-
ment (Londeree, 1997). In this context, especially within the THR
group, significant variability in the individual changes in VO2peak
from pre- to post-intervention were observed (range: 20 to
www.frontiersin.org February 2014 | Volume 5 | Article 33 |7
Stöggl and Sperlich Endurance training concepts
+4%). However, some THR training might be beneficial to
well-trained athletes since world-class sprint cross-country skiers
demonstrated greater training volume in the low and moderate
intensity zones compared with national-level skiers (Sandbakk
et al., 2011).
The body mass of the well-trained athletes decreased by 3.7%
(approximately 3 kg) after HIIT, but not in response to the other
training concepts. HIIT favors lipid oxidation and promotes adi-
pose tissue loss (Perry et al., 2008; Boutcher, 2011). Depending
on the athlete’s baseline value, reduction in body mass may neg-
atively impact immune function and overall health, as well as
induce a catabolic state. Training blocks with increased volume
and/or exercise intensity might induce symptoms of overreach-
ing, reduced physical capacity, burnout symptoms including
tiredness, and lack of energy (Angeli et al., 2004). However,
despite the large differences in the individual responses in some
of the training groups, none of the athletes demonstrated reduced
TTE or V/Ppeak after the study, nor did they report any of the
aforementioned symptoms during and after the 9 week interven-
tion. Based on the observed changes in body mass and smaller
increases in VO2peak in the HIIT group compared with previ-
ously published data (McMillan et al., 2005; Stølen et al., 2005;
Helgerud et al., 2007), longer blocks of training periods with high
intensities could provoke these symptoms.
Except for significant decreases in %VO2peak in the POL group
and HRsubmax/%HRpeak in the HIIT and THR groups (with no
group differences), no improvements in work economy were
found in the current study. Helgerud et al. (2007) reported a 5%
improvement in running economy after THR, HIIT, and HVT
with no differences between groups. These improvements were
mainly attributed to an increased amount of running training.
Therefore, the applied training regimes were largely responsi-
ble for the changes in V/Ppeakand VO2peak, while work economy
remained fairly constant. V/P4was only improved in POL (8.1%)
and HIIT (5.6%). This is consistent with findings demonstrating
that running velocity at lactate threshold follows the improve-
ments in VO2peak (Helgerud et al., 2001; McMillan et al., 2005).
LIMITATIONS AND PERSPECTIVES
Standardized methodology of performance diagnostics (incre-
mental test and VO2peak ramp protocol) was utilized to evaluate
the effects of the four endurance training interventions on key
variables of endurance performance. However, a direct transfer to
specific competition situation (e.g., time trial) need to be estab-
lished in future research. Furthermore, the increase of about 11%
in VO2peak with POL within 9 weeks is large for well-trained
endurance and has to be put in perspective within the annual
training periodization. Long-term training studies are warranted
to evaluate these aspects.
CONCLUSION
In this study of elite athletes performing HIIT, HVT, THR or
POL training, POL results in the greatest improvements in key
variables of endurance performance (VO2peak,TTE,V/P
peak,and
V/P4). HIIT led to a decrease in body mass and less pronounced
increases in VO2peak compared with previous findings using short
term (1–2 weeks) HIIT, suggesting that a 9 week HIIT should be
applied with care. Exclusive training with THR or HVT did not
lead to further improvements in endurance performance related
variables in well-trained athletes.
DISCLOSURE OF FUNDING
No funding was received for this work from the National
Institutes of Health, the Welcome Trust, the Howard Hughes
Medical Institute, or other funding agencies to PubMed Central.
None of the authors had any professional relationships with com-
panies or manufacturers who will benefit from the results of the
present study. The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
Conception and design of the experiments: Thomas Stöggl,
Billy Sperlich. Performance of the experiments: Thomas Stöggl.
Data analysis: Thomas Stöggl, Billy Sperlich. Preparation of the
manuscript: Thomas Stöggl and Billy Sperlich. Both authors read
and approved the final manuscript.
ACKNOWLEDGMENTS
The authors would like to thank the athletes, coaches, and
research assistants involved in this study for their participation,
enthusiasm, and cooperation. The authors would like to express
appreciation for the support of Donna Kennedy. Special thanks
to Julia Stöggl, Andreas Hochwimmer and Thomas Damisch for
their great assistance in recruitment, care and control of the ath-
letes during the training intervention and pre- and post-testing.
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 02 October 2013; accepted: 16 January 2014; published online: 04 February
2014.
Citation: Stöggl T and Sperlich B (2014) Polarized training has greater impact on key
endurance variables than threshold, high intensity, or high volume training. Front.
Physiol. 5:33. doi: 10.3389/fphys.2014.00033
This article was submitted to Exercise Physiology, a section of the journal Frontiers in
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... POL consists of approximately 75-80% of training sessions performed in Z1, < 10% in Z2, and 15% to 20% in Z3 [2]. Alternatively, the term "threshold" (THR) TID is used to describe training programs which incorporated a greater portion of training sessions in Z2 (e.g., 40-50-10%) [7][8][9][10]. This TID may be more common in untrained and/or recreational athletes [11]. ...
... Several early experimental studies were conducted to determine the effectiveness of POL compared with THR on endurance performance [7][8][9][10]. These studies found mixed results, partially explained by small sample sizes (6-15 participants per group). ...
... The synthesized results for the MD and SMD for V O 2peak for the intention-to-treat analysis and for the per-protocol analysis can be found in Fig. 3. There were nine studies (n = 264) that were included in the intention-to-treat analysis for V O 2peak [10,25,45,[47][48][49][50][51]53]. The NMA consisted of POL (n = 119), PYR (n = 61), THR (n = 27), HIGH (n = 22), and LOW (n = 35) TID models. ...
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Background Endurance athletes tend to accumulate large training volumes, the majority of which are performed at a low intensity and a smaller portion at moderate and high intensity. However, different training intensity distributions (TID) are employed to maximize physiological and performance adaptations. Objective The objective of this study was to conduct a systematic review and network meta-analysis of individual participant data to compare the effect of different TID models on maximal oxygen uptake (VO2max) and time-trial (TT) performance in endurance-trained athletes. Methods Studies were included if: (1) they were published in peer reviewed academic journals, (2) they were in English, (3) they were experimental or quasi-experimental studies, (4) they included trained endurance athletes, (5) they compared a polarized (POL) TID intervention to a comparator group that utilized a different TID model, (6) the duration in each intensity domain could be quantified, and (7) they reported VO2max or TT performance. Medline and SPORTDiscus were searched from inception until 11 February 2024. Results We included 13 studies with 348 (n = 296 male, n = 52 female) recreational (n = 150) and competitive (n = 198) endurance athletes. Mean age ranged from 17.6 to 41.5 years and VO2max ranged from 46.6 to 68.3 mL·kg⁻¹·min⁻¹, across studies respectively. Based on the time in heart rate zone approach, there was no difference in VO2max (SMD = − 0.06, p = 0.68) or TT performance (SMD = − 0.05, p = 0.34) between POL and pyramidal (PYR) interventions. There were no statistically significant differences between POL and any of the other TID interventions. Subgroup analysis showed a statistically significant difference in the response of VO2max between recreational and competitive athletes for POL and PYR (SMD = − 0.63, p < 0.05). Competitive athletes may have greater improvements to VO2max with POL, while recreational athletes may improve more with a PYR TID. Conclusions Our results indicate that the adaptations to VO2max following different TID interventions are dependent on performance level. Athletes at a more competitive level may benefit from a POL TID intervention and recreational athletes from a PYR TID intervention.
... Previous research has consistently demonstrated that TID models produce different effects in the short-and long-term on key endurance performance-related variables. These variables are: _ VO 2 max; the energy cost of the sport-specific movement pattern, which is a complex influenced by different underpinning factors (i.e., cardiorespiratory, biomechanical, neuromuscular); and the ability to maintain a submaximal exercise intensity (i.e., high % of _ VO 2 max) related to the critical power/speed, that is near to the anaerobic threshold (1,3,11,25,36,37,46,53,73). In fact, the interaction of these variables determines athletes' endurance sport-specific performance (i.e., time-trial [TT] and competition performance) (29,36,38). ...
... The interventions in the included studies focused on running (19)(20)(21)46), cycling (47,57,63), swimming (2,55), triathlon (58,68), rowing (76), speed skating (84), and a mix of endurance sports (72,73). In this regard, 7 of the included studies were performed with highly trained/national level athletes (i.e., tier 3), Please note that ES is only reported for intra-subject significant differences. ...
... There were 8 studies describing changes in _ VO 2 max or _ VO 2 peak. Stöggl and Sperlich (73) showed that POL leads to a greater improvement than THR and LIT after 9 weeks of intervention in a group of well-trained endurance athletes of different modalities (i.e., tier 3). In addition, 2 studies (2,68) reported positive changes in _ VO 2 max but with no significant differences compared with the PYR model in a group of highly trained women swimmers (i.e., tier 3) and recreational triathletes (i.e., tier 2). ...
Article
This scoping review aimed to analyze the long-term effects of polarized training (POL) on key endurance physiological-and performance-related variables and to systematically compare them with other training intensity distribution (TID) models in endurance athletes of different performance levels. Four TID models were analyzed: POL, pyramidal (PYR), threshold (THR), and block (BT) training models. The literature search was performed using PubMed, SportDiscus, Scopus, and Web of Science databases. Studies were selected if they met the following criteria: compared POL with any other TID model, included healthy endurance athletes, men, and/or women; reported enough information regarding the volume distribution in the different training intensity zones (i.e., zone 1, zone 2, and zone 3), assessed physiological (i.e., maximum/peak oxygen uptake, speed or power at aerobic and anaerobic thresholds, economy of movement), and performance in competition or time-trial variables. Of the 620 studies identified, 15 met the eligibility criteria and were included in this review. According to scientific evidence, POL and PYR models reported greater maximum oxygen uptake enhancements. Both POL and PYR models improved the speed or power associated with the aerobic threshold. By contrast, all TID models effectively improved the speed or power associated with the anaerobic threshold. Further research is needed to establish the effects of TID models on the economy of movement. All TID models were effective in enhancing competitive endurance performance, but testing protocols were quite heterogeneous. The POL and PYR models seem to be more effective in elite and world-class athletes, whereas there were no differences between TID models in lower-level athletes.
... athletes [runners (Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018), cyclists (Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Clark et al., 2014;Hanstock et al., 2020;Skovereng et al., 2018;Stenqvist et al., 2020;Sylta et al., 2016;Rønnestad and Vikmoen, 2019;Stepto et al., 1999), duathletes or triathletes (Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Hanstock et al., 2020;, cross-country skiers (Sandbakk et al., 2013;Stöggl and Sperlich, 2014;Johansen et al., 2021;Sandbakk et al., 2011), and rowers (Stevens et al., 2015)], with mean baselinė VO 2max values ≥60 mL kg -1 ·min -1 for men (Sandbakk et al., 2013;Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Clark et al., 2014;Hanstock et al., 2020;Skovereng et al., 2018;Stenqvist et al., 2020;Sylta et al., 2016;Rønnestad and Vikmoen, 2019;Stepto et al., 1999;Johansen et al., 2021;Sandbakk et al., 2011;Stevens et al., 2015) and ≥55 mL kg -1 ·min -1 for women (Sandbakk et al., 2013;Stöggl and Sperlich, 2014;Menz et al., 2015;Sandbakk et al., 2011), or in healthy other elite athletes [first league, national team, or international level in ball (Wells et al., 2014;Helgerud et al., 2001;Akdoğan et al., 2021;Iaia et al., 2015;Purkhus et al., 2016;Soares-Caldeira et al., 2014;Thomassen et al., 2010;Venturelli et al., 2008;Chtara et al., 2017;Hermassi et al., 2018;Selmi et al., 2018;Delextrat et al., 2018;Dupont et al., 2004), racket (Liu et al., 2021), canoe sports (Sheykhlouvand et al., 2018a;Sheykhlouvand et al., 2018b;Sheykhlouvand et al., 2016;Yang et al., 2017), and alpine skiers (Breil et al., 2010)]. We included one study on the upperbody exercise of male endurance athletes withVO 2max values of ∼55 mL kg -1 ·min -1 as their runningVO 2max values met our inclusion criteria (Johansen et al., 2021). ...
... athletes [runners (Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018), cyclists (Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Clark et al., 2014;Hanstock et al., 2020;Skovereng et al., 2018;Stenqvist et al., 2020;Sylta et al., 2016;Rønnestad and Vikmoen, 2019;Stepto et al., 1999), duathletes or triathletes (Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Hanstock et al., 2020;, cross-country skiers (Sandbakk et al., 2013;Stöggl and Sperlich, 2014;Johansen et al., 2021;Sandbakk et al., 2011), and rowers (Stevens et al., 2015)], with mean baselinė VO 2max values ≥60 mL kg -1 ·min -1 for men (Sandbakk et al., 2013;Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Clark et al., 2014;Hanstock et al., 2020;Skovereng et al., 2018;Stenqvist et al., 2020;Sylta et al., 2016;Rønnestad and Vikmoen, 2019;Stepto et al., 1999;Johansen et al., 2021;Sandbakk et al., 2011;Stevens et al., 2015) and ≥55 mL kg -1 ·min -1 for women (Sandbakk et al., 2013;Stöggl and Sperlich, 2014;Menz et al., 2015;Sandbakk et al., 2011), or in healthy other elite athletes [first league, national team, or international level in ball (Wells et al., 2014;Helgerud et al., 2001;Akdoğan et al., 2021;Iaia et al., 2015;Purkhus et al., 2016;Soares-Caldeira et al., 2014;Thomassen et al., 2010;Venturelli et al., 2008;Chtara et al., 2017;Hermassi et al., 2018;Selmi et al., 2018;Delextrat et al., 2018;Dupont et al., 2004), racket (Liu et al., 2021), canoe sports (Sheykhlouvand et al., 2018a;Sheykhlouvand et al., 2018b;Sheykhlouvand et al., 2016;Yang et al., 2017), and alpine skiers (Breil et al., 2010)]. We included one study on the upperbody exercise of male endurance athletes withVO 2max values of ∼55 mL kg -1 ·min -1 as their runningVO 2max values met our inclusion criteria (Johansen et al., 2021). ...
... athletes [runners (Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018), cyclists (Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Clark et al., 2014;Hanstock et al., 2020;Skovereng et al., 2018;Stenqvist et al., 2020;Sylta et al., 2016;Rønnestad and Vikmoen, 2019;Stepto et al., 1999), duathletes or triathletes (Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Hanstock et al., 2020;, cross-country skiers (Sandbakk et al., 2013;Stöggl and Sperlich, 2014;Johansen et al., 2021;Sandbakk et al., 2011), and rowers (Stevens et al., 2015)], with mean baselinė VO 2max values ≥60 mL kg -1 ·min -1 for men (Sandbakk et al., 2013;Smith et al., 2003;Stöggl and Sperlich, 2014;Menz et al., 2015;Salazar-Martinez et al., 2018;Clark et al., 2014;Hanstock et al., 2020;Skovereng et al., 2018;Stenqvist et al., 2020;Sylta et al., 2016;Rønnestad and Vikmoen, 2019;Stepto et al., 1999;Johansen et al., 2021;Sandbakk et al., 2011;Stevens et al., 2015) and ≥55 mL kg -1 ·min -1 for women (Sandbakk et al., 2013;Stöggl and Sperlich, 2014;Menz et al., 2015;Sandbakk et al., 2011), or in healthy other elite athletes [first league, national team, or international level in ball (Wells et al., 2014;Helgerud et al., 2001;Akdoğan et al., 2021;Iaia et al., 2015;Purkhus et al., 2016;Soares-Caldeira et al., 2014;Thomassen et al., 2010;Venturelli et al., 2008;Chtara et al., 2017;Hermassi et al., 2018;Selmi et al., 2018;Delextrat et al., 2018;Dupont et al., 2004), racket (Liu et al., 2021), canoe sports (Sheykhlouvand et al., 2018a;Sheykhlouvand et al., 2018b;Sheykhlouvand et al., 2016;Yang et al., 2017), and alpine skiers (Breil et al., 2010)]. We included one study on the upperbody exercise of male endurance athletes withVO 2max values of ∼55 mL kg -1 ·min -1 as their runningVO 2max values met our inclusion criteria (Johansen et al., 2021). ...
Article
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Introduction Meta-analysts have found that high-intensity interval training (HIIT) improves physical performance, but limited evidence exists regarding its effects on highly trained athletes, measures beyond maximum oxygen uptake ( V ˙ O2max), and the moderating effects of different types of HIIT. In this study, we present meta-analyses of the effects of HIIT focusing on these deficits. Methods The effects of 6 types of HIIT and other moderators were derived from 34 studies involving highly trained endurance and elite athletes in percent units via log-transformation from separate meta-regression mixed models for sprint, time–trial, aerobic/anaerobic threshold, peak speed/power, repeated-sprint ability, V ˙ O2max, and exercise economy. The level of evidence for effect magnitudes was evaluated based on the effect uncertainty and the smallest important change of 1%. Results Compared with control training, HIIT showed good to excellent evidence for the substantial enhancement of most measures for some athlete subgroups in practically important study settings defined by effect moderators (maximum of 12.6%, for endurance female athletes after 6 weeks of aerobic traditional long intervals). The assessment of the moderators indicated good evidence of greater effects as follows: with more aerobic types of HIIT for V ˙ O2max (+2.6%); with HIIT added to conventional training for most measures (+1.1–2.3%); during the competition phase for V ˙ O2max (+4.3%); and with tests of longer duration for sprint (+5.5%) and time trial (+4.9%). The effects of sex and type of athlete were unclear moderators. The heterogeneity of HIIT effects within a given type of setting varied from small to moderate (standard deviations of 1.1%–2.3%) and reduced the evidence of benefit in some settings. Conclusion Although athletes in some settings can be confident of the beneficial effects of HIIT on some measures related to competition performance, further research is needed. There is uncertainty regarding the mean effects on exercise economy and the modifying effects of sex, duration of intervention, phase of training, and type of HIIT for most measures. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=236384.
... To meta-analyze the relationships between mean changes in different types of HIIT and control training on performance-related measures, we drew upon 23 studies in the companion paper containing at least two measures or predictors of performance: here, 12 studies were conducted on highly trained classical endurance athletes [runners (Smith et al., 2003;Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018), cyclists (Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018;Clark et al., 2014;Skovereng et al., 2018;Rønnestad and Vikmoen, 2019;Sylta et al., 2016;Laursen et al., 2002;Stepto et al., 1999), duathletes or triathletes (Smith et al., 2003;Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018;Laursen et al., 2002), cross-country skiers (Stöggl and Sperlich, 2014;Sandbakk et al., 2013;Sandbakk et al., 2011), and rowers (Stevens et al., 2015)]; the remaining 11 studies included elite other athletes [first league, national team, or international level in ball sports (Wells et al., 2014;Helgerud et al., 2001;Akdoğan et al., 2021;Iaia et al., 2015;Soares-Caldeira et al., 2014;Thomassen et al., 2010;Hermassi et al., 2018;Selmi et al., 2018), canoe sports (Sheykhlouvand et al., 2018;Yang et al., 2017), and alpine skiers (Breil et al., 2010)]. The PRISMA flow diagram and the inclusion criteria for these studies were comprehensively outlined in Part I . ...
... To meta-analyze the relationships between mean changes in different types of HIIT and control training on performance-related measures, we drew upon 23 studies in the companion paper containing at least two measures or predictors of performance: here, 12 studies were conducted on highly trained classical endurance athletes [runners (Smith et al., 2003;Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018), cyclists (Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018;Clark et al., 2014;Skovereng et al., 2018;Rønnestad and Vikmoen, 2019;Sylta et al., 2016;Laursen et al., 2002;Stepto et al., 1999), duathletes or triathletes (Smith et al., 2003;Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018;Laursen et al., 2002), cross-country skiers (Stöggl and Sperlich, 2014;Sandbakk et al., 2013;Sandbakk et al., 2011), and rowers (Stevens et al., 2015)]; the remaining 11 studies included elite other athletes [first league, national team, or international level in ball sports (Wells et al., 2014;Helgerud et al., 2001;Akdoğan et al., 2021;Iaia et al., 2015;Soares-Caldeira et al., 2014;Thomassen et al., 2010;Hermassi et al., 2018;Selmi et al., 2018), canoe sports (Sheykhlouvand et al., 2018;Yang et al., 2017), and alpine skiers (Breil et al., 2010)]. The PRISMA flow diagram and the inclusion criteria for these studies were comprehensively outlined in Part I . ...
... To meta-analyze the relationships between mean changes in different types of HIIT and control training on performance-related measures, we drew upon 23 studies in the companion paper containing at least two measures or predictors of performance: here, 12 studies were conducted on highly trained classical endurance athletes [runners (Smith et al., 2003;Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018), cyclists (Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018;Clark et al., 2014;Skovereng et al., 2018;Rønnestad and Vikmoen, 2019;Sylta et al., 2016;Laursen et al., 2002;Stepto et al., 1999), duathletes or triathletes (Smith et al., 2003;Stöggl and Sperlich, 2014;Salazar-Martinez et al., 2018;Laursen et al., 2002), cross-country skiers (Stöggl and Sperlich, 2014;Sandbakk et al., 2013;Sandbakk et al., 2011), and rowers (Stevens et al., 2015)]; the remaining 11 studies included elite other athletes [first league, national team, or international level in ball sports (Wells et al., 2014;Helgerud et al., 2001;Akdoğan et al., 2021;Iaia et al., 2015;Soares-Caldeira et al., 2014;Thomassen et al., 2010;Hermassi et al., 2018;Selmi et al., 2018), canoe sports (Sheykhlouvand et al., 2018;Yang et al., 2017), and alpine skiers (Breil et al., 2010)]. The PRISMA flow diagram and the inclusion criteria for these studies were comprehensively outlined in Part I . ...
Article
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Introduction Our recent meta-analyses have demonstrated that high-intensity interval training (HIIT) causes a range of mean changes in various measures and predictors of endurance and sprint performance in athletes. Here, we extend the analyses to relationships between mean changes of these measures and consider implications for understanding and improving HIIT that were not apparent in the previous analyses. Methods The data were mean changes from HIIT with highly trained endurance and elite other (mainly team sport) athletes in studies where two or more measures or predictors of performance were available. Relationships between changes in pairs of measures were visualized in scatterplots with points identified by aerobic and anaerobic types of HIIT; simple linear relationships were quantified via log-transformation of factor changes with a meta-regression mixed model. Results In endurance athletes, there were positive linear relationships between mean changes in time-trial speed/power (reflecting competition endurance performance) and mean changes in endurance performance predictors [peak speed/power, maximal oxygen uptake (V̇O2max), and aerobic/anaerobic threshold]. There were substantial differences in time-trial speed/power between studies not explained by each predictor. Exercise economy had an unclear relationship with time-trial speed/power but a decisively negative relationship with V̇O2max. In other athletes, repeated-sprint ability had a weak positive relationship with sprint speed/power. The scatter of points in some plots was associated with the type of HIIT. Discussion Differences in time-trial performance between studies for a given change in peak speed/power, V̇O2max, or threshold speed/power imply that time trials should be included when assessing effects of HIIT on endurance performance. Relationships between V̇O2max, time-trial speed/power, and exercise economy suggest that combining aerobic and anaerobic types of HIIT could be more effective for endurance performance. Sprints and repeated-sprint ability are important performance measures for team-sport athletes; their poor relationship implies that both should be measured when assessing HIIT. Clinical Trial Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=236384
... Previous research has consistently demonstrated that TID models produce different effects in the short-and long-term on key endurance performance-related variables. These variables are: _ VO 2 max; the energy cost of the sport-specific movement pattern, which is a complex influenced by different underpinning factors (i.e., cardiorespiratory, biomechanical, neuromuscular); and the ability to maintain a submaximal exercise intensity (i.e., high % of _ VO 2 max) related to the critical power/speed, that is near to the anaerobic threshold (1,3,11,25,36,37,46,53,73). In fact, the interaction of these variables determines athletes' endurance sport-specific performance (i.e., time-trial [TT] and competition performance) (29,36,38). ...
... The interventions in the included studies focused on running (19)(20)(21)46), cycling (47,57,63), swimming (2,55), triathlon (58,68), rowing (76), speed skating (84), and a mix of endurance sports (72,73). In this regard, 7 of the included studies were performed with highly trained/national level athletes (i.e., tier 3), Please note that ES is only reported for intra-subject significant differences. ...
... There were 8 studies describing changes in _ VO 2 max or _ VO 2 peak. Stöggl and Sperlich (73) showed that POL leads to a greater improvement than THR and LIT after 9 weeks of intervention in a group of well-trained endurance athletes of different modalities (i.e., tier 3). In addition, 2 studies (2,68) reported positive changes in _ VO 2 max but with no significant differences compared with the PYR model in a group of highly trained women swimmers (i.e., tier 3) and recreational triathletes (i.e., tier 2). ...
Article
Rivera-Köfler, T, Varela-Sanz, A, Padrón-Cabo, A, Giráldez-García, MA, and Muñoz-Pérez, I. Effects of polarized training vs. other training intensity distribution models on physiological variables and endurance performance in different-level endurance athletes: a scoping review. J Strength Cond Res XX(X): 000-000, 2024-This scoping review aimed to analyze the long-term effects of polarized training (POL) on key endurance physiological- and performance-related variables and to systematically compare them with other training intensity distribution (TID) models in endurance athletes of different performance levels. Four TID models were analyzed: POL, pyramidal (PYR), threshold (THR), and block (BT) training models. The literature search was performed using PubMed, SportDiscus, Scopus, and Web of Science databases. Studies were selected if they met the following criteria: compared POL with any other TID model, included healthy endurance athletes, men, and/or women; reported enough information regarding the volume distribution in the different training intensity zones (i.e., zone 1, zone 2, and zone 3), assessed physiological (i.e., maximum/peak oxygen uptake, speed or power at aerobic and anaerobic thresholds, economy of movement), and performance in competition or time-trial variables. Of the 620 studies identified, 15 met the eligibility criteria and were included in this review. According to scientific evidence, POL and PYR models reported greater maximum oxygen uptake enhancements. Both POL and PYR models improved the speed or power associated with the aerobic threshold. By contrast, all TID models effectively improved the speed or power associated with the anaerobic threshold. Further research is needed to establish the effects of TID models on the economy of movement. All TID models were effective in enhancing competitive endurance performance, but testing protocols were quite heterogeneous. The POL and PYR models seem to be more effective in elite and world-class athletes, whereas there were no differences between TID models in lower-level athletes.
... Three studies also included more than one endurance sport in their study design. Two of these studies included running, cycling, triathlon, and cross-country skiing [29,35], while another study performed polarized training intervention on both cross-country skiers and biathletes [30]. ...
... The average training intensity distribution across the eleven included studies was 81.3 ± 8.0% LIT, 3.4 ± 3.2% MIT, and 15.4 ± 6.3% HIT. Treff, et al. [39] and Röhrken, et al. [37] showed the largest volume of LIT (respectively, 92 and 91%), while the lowest percentage of LIT (68%) was observed in two studies [29,35]. Additionally, four studies reported 0% as their total training volume of MIT [28,34,37,40], with the largest volume of MIT (11%) found in Carnes and Mahoney [31]. ...
... Additionally, four studies reported 0% as their total training volume of MIT [28,34,37,40], with the largest volume of MIT (11%) found in Carnes and Mahoney [31]. Lastly, Stöggl and Sperlich [29] and Stöggl and Björklund [35] reported the highest training volume of HIT (26%) among the included studies, while Kim, et al. [30] and Treff, et al. [39] had the smallest HIT volume (7% and 6%, respectively) in their polarized training interventions. ...
Article
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High-intensity training (HIT) has commonly been the most effective training method for improvement in maximal oxygen uptake (VO2max) and work economy, alongside a substantial volume of low-intensity training (LIT). The polarized training model combines both low-and high-intensity training into a specific training intensity distribution and has gained attention as a comprehensive approach. The objective of this review was to systematically search the literature in order to identify the effects of polarized training intensity distribution on VO2max, peak oxygen uptake (VO2peak), and work economy among endurance athletes. A literature search was performed using PubMed and SPORTDiscus. A total of 1836 articles were identified, and, after the selection process, 14 relevant studies were included in this review. The findings indicate that a polarized training approach seems to be effective for enhancing VO2max, VO2peak , and work economy over a short-term period for endurance athletes. Specifically, a training intensity distribution involving a moderate to high volume of HIT (15-20%) combined with a substantial volume of LIT (75-80%) appears to be the most beneficial for these improvements. It was concluded that polarized training is a beneficial approach for enhancing VO2max, VO2peak , and work economy in endurance athletes. However, the limited number of studies restricts the generalizability of these findings.
... Given the duration and intensity required to maintain a high level of performance throughout these events, we can infer that CrossFit ® competition primarily engages both aerobic and anaerobic energy systems, placing it within the realm of endurance-based activities. In sports with similar energy system demands and load times (in competition), athletes often utilize TID heavily focused on LiT, with at least 80% of LiT (Seiler and Kjerland, 2006;Seiler, 2010;Sperlich et al., 2023;Stöggl and Sperlich, 2014). Consequently, reviews (Hydren and Cohen, 2015;Seiler and Kjerland, 2006;Seiler, 2010;Stöggl and Sperlich, 2014) have suggested that a polarized (POL) TID may elicit superior training adaptations than high-intensity-focused approaches, particularly in endurance sports. ...
... In sports with similar energy system demands and load times (in competition), athletes often utilize TID heavily focused on LiT, with at least 80% of LiT (Seiler and Kjerland, 2006;Seiler, 2010;Sperlich et al., 2023;Stöggl and Sperlich, 2014). Consequently, reviews (Hydren and Cohen, 2015;Seiler and Kjerland, 2006;Seiler, 2010;Stöggl and Sperlich, 2014) have suggested that a polarized (POL) TID may elicit superior training adaptations than high-intensity-focused approaches, particularly in endurance sports. Against this background, we examined the effect of polarized vs. traditional HIFT training on relevant CrossFit ® performance surrogate parameters. ...
... Based on previous endurance sportsrelated reviews and meta-analyses (Hydren and Cohen, 2015; Frontiers in Physiology 02 frontiersin.org Rosenblat et al., 2019;Seiler and Kjerland, 2006;Seiler, 2010;Stöggl and Sperlich, 2014), we hypothesized that these findings could be transferred to a HIFT training setting and may impact the programming in HIFT. They may necessitate a re-evaluation of current training paradigms, potentially leading to a shift in how HIFT training is structured and implemented. ...
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Purpose High-intensity functional interval training (HIFT) is predominantly composed of high exercise training intensities (HiT) and loads. Both have been linked to a higher risk of overtraining and injuries in inexperienced populations. A polarized training approach is characterized by high amounts of low-intensity training (LiT) and only approximately 5%–20% HiT. Compared to HIT-based training, this approach can result in temporary training load and intensity reductions without diminishing training gains. Thus, we aimed to examine the effects of traditional (TRAD) HIFT vs. polarized (POL) HIFT on relevant performance parameters. Methods Thirty athletes (15 females, age: 26.6 ± 5.0 years, height: 1.76 ± 0.13 m, body mass: 79.6 ± 12.4 kg, prior experience: 2.3 ± 2.0 years, training volume: 6.1 ± 2.4 h/wk) were randomly assigned to 6 weeks of either POL (78% LiT, 22% threshold intensity training (ThT) to HiT) or TRAD (26% LiT, 74% ThT to HiT). HIFT performance testing focused on maximal strength (squat: SQ1RM, deadlift: DL1RM, overhead press: OHP1RM, high pull: HP1RM), endurance (peak oxygen uptake: V̇O2peak, lactate threshold: LT, peak power output (PPO), and benchmark HIFT workout (Jackie: 1000 m rowing, 50 thrusters, and 30 pull-ups for time). Results POL (785 ± 71 au) completed significantly (p ≤ 0.001; SMD = 4.55) lower training load (eTRIMP) than TRAD (1,273 ± 126 au). rANCOVA revealed no statistical relevant group×time interaction effects (0.094 ≤ p ≤ 0.986; 0.00 ≤ ηp ² ≤ 0.09) for SQ1RM, DL1RM, OHP1RM, high pull, V̇O2peak, LT, PPO, and Jackie performance. Both groups revealed trivial to moderate but significant (rANCOVA time effects: p ≤ 0.02; 0.01 ≤ ηp ² ≤ 0.11; 0.00 ≤ SMD ≤ 0.65) performance gains regarding DL1RM, OHP1RM, HP1RM, and Jackie. Conclusion Despite a notably lower total training load, conditioning gains were not affected by a polarized functional interval training regimen.
... Polarized training program (POL) is effective in improving cyclists' aerobic capacity [1][2][3]. POL is described as a training cycle characterized by polarization of training intensity and incorporates low-intensity training as well as high-intensity training [2,4]. The volume of lowintensity training sessions is approximately 80% of the total training volume, while the highintensity training is approximately 20% of the total training volume [2,4,5]. ...
... Polarized training program (POL) is effective in improving cyclists' aerobic capacity [1][2][3]. POL is described as a training cycle characterized by polarization of training intensity and incorporates low-intensity training as well as high-intensity training [2,4]. The volume of lowintensity training sessions is approximately 80% of the total training volume, while the highintensity training is approximately 20% of the total training volume [2,4,5]. ...
... POL is described as a training cycle characterized by polarization of training intensity and incorporates low-intensity training as well as high-intensity training [2,4]. The volume of lowintensity training sessions is approximately 80% of the total training volume, while the highintensity training is approximately 20% of the total training volume [2,4,5]. In polarized training programs, moderate-intensity training at the level of the lactate threshold or the second ventilatory threshold (VT2) is not used, or these training sessions account for a small part of the training program (approximately 5% of the total training volume) [6,7]. ...
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... Compliance was ensured by having participants complete a weekly record that was returned to the researchers after the program. The running program was a polarized design, consisting of two training intensities (i.e., high and low) [16]. Training intensities during the program were set using the heart rate derived at the aerobic threshold (AeT) and estimated ventilatory threshold (VT). ...
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... 2019). The low-volume, highintensity (HIGH) TID model allocates the highest percentage of training time (50-70%) to zone three (Hydren & Bruce, 2015;Stöggl & Sperlich, 2015;Treff, et al., 2019). Finally, the high-volume, low-intensity (HVT) TID model assigns almost all training time (~100%) to zone one (Hydren & Bruce, 2015;Stöggl & Sperlich, 2015;T. Stöggl & Sperlich, 2014). In scientific evidence, the POL and PYR TID models are identified as the most effective for long-distance performance in highly trained and elite athletes and are the most used at these levels (Bourgois, et al., 2019;Campos, et al., 2021;Casado, et al., 2022;Haugen, et al., 2022;Muñoz, et al., 2014;K. S. Seiler & Kjerland, 2006;S. Seil ...
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During the last nearly 50 years, the blood lactate curve and lactate thresholds (LTs) have become important in the diagnosis of endurance performance. An intense and ongoing debate emerged, which was mainly based on terminology and/or the physiological background of LT concepts. The present review aims at evaluating LTs with regard to their validity in assessing endurance capacity. Additionally, LT concepts shall be integrated within the ‘aerobic-anaerobic transition’ — a framework which has often been used for performance diagnosis and intensity prescriptions in endurance sports. Usually, graded incremental exercise tests, eliciting an exponential rise in blood lactate concentrations (bLa), are used to arrive at lactate curves. A shift of such lactate curves indicates changes in endurance capacity. This very global approach, however, is hindered by several factors that may influence overall lactate levels. In addition, the exclusive use of the entire curve leads to some uncertainty as to the magnitude of endurance gains, which cannot be precisely estimated. This deficiency might be eliminated by the use of LTs. The aerobic-anaerobic transition may serve as a basis for individually assessing endurance performance as well as for prescribing intensities in endurance training. Additionally, several LT approaches may be integrated in this framework. This model consists of two typical breakpoints that are passed during incremental exercise: the intensity at which bLa begin to rise above baseline levels and the highest intensity at which lactate production and elimination are in equilibrium (maximal lactate steady state [MLSS]). Within this review, LTs are considered valid performance indicators when there are strong linear correlations with (simulated) endurance performance. In addition, a close relationship between LT and MLSS indicates validity regarding the prescription of training intensities. A total of 25 different LT concepts were located. All concepts were divided into three categories. Several authors use fixed bLa during incremental exercise to assess endurance performance (category 1). Other LT concepts aim at detecting the first rise in bLa above baseline levels (category 2). The third category consists of threshold concepts that aim at detecting either the MLSS or a rapid/distinct change in the inclination of the blood lactate curve (category 3). Thirty-two studies evaluated the relationship of LTs with performance in (partly simulated) endurance events. The overwhelming majority of those studies reported strong linear correlations, particularly for running events, suggesting a high percentage of common variance between LT and endurance performance. In addition, there is evidence that some LTs can estimate the MLSS. However, from a practical and statistical point of view it would be of interest to know the variability of individual differences between the respective threshold and the MLSS, which is rarely reported. Although there has been frequent and controversial debate on the LT phenomenon during the last three decades, many scientific studies have dealt with LT concepts, their value in assessing endurance performance or in prescribing exercise intensities in endurance training. The presented framework may help to clarify some aspects of the controversy and may give a rationale for performance diagnosis and training prescription in future research as well as in sports practice.