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Effect of High-Intensity Interval Training Versus Sprint Interval Training on Time-Trial Performance: A Systematic Review and Meta-Analysis


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Background: Two forms of interval training commonly discussed in the literature are high-intensity interval training (HIIT) and sprint interval training (SIT). HIIT consists of repeated bouts of exercise that occur at a power output or velocity between the second ventilatory threshold and maximal oxygen consumption (VO2max). SIT is performed at a power output or velocity above those associated with VO2max Objective: The primary objective of this study is to systematically review published randomized and pair- matched trials to determine which mode of interval training, HIIT versus SIT, leads to a greater improvement in TT performance in active and trained individuals. The second objective of this review is to perform a subgroup analysis to determine if there is a distinction between HIIT programs that differ in work-bout duration. Data Sources: SPORTDiscus (1800 - present) and Medline with Full Text (1946 - present) were used to conduct a systematic literature search Study Selection: Studies were selected for the review if they met the following criteria: 1. individuals (males and females) who were considered at least moderately-trained (~3-hours per week of activity) as specified by the authors of the included studies; 2. between the ages of 18 and 45 years; 3. randomized or pair-matched trials that included a HIIT and a SIT group; 4. provided detailed information about the interval training program; 5. were at least 2-weeks in duration; 6. included a TT test that required participants to complete a set distance. Results: A total of 6 articles met the inclusion criteria for the subjective and objective analysis. The pooled analysis was based on a random-effects model. There was no difference in the change in TT performance when comparing all HIIT versus SIT (0.9%; 90% CI: -0.1% to 1.9%, p = 0.18). However, subgroup analysis based on duration of work interval indicated a 2% greater improvement in TT performance following long-HIIT ( 4 min) when compared to SIT. There was no difference in change in VO2max/peak oxygen consumption (VO2peak) between groups. There was a moderate effect (ES = 0.70) in favour of HIIT over SIT in maximal aerobic power (MAP) or maximal aerobic velocity (MAV). Conclusion: The results of the meta-analysis indicate that long-HIIT may be the optimal form of interval training to augment TT performance. Additional research that directly compares HIIT exercise differing in work-bout duration would strengthen these results and provide further insight into the mechanisms behind the observed benefits of Long- HIIT.
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Sports Medicine (2020) 50:1145–1161
Eect ofHigh‑Intensity Interval Training Versus Sprint Interval Training
onTime‑Trial Performance: ASystematic Review andMeta‑analysis
MichaelA.Rosenblat1,3 · AndrewS.Perrotta2· ScottG.Thomas1,3
Published online: 7 February 2020
© Springer Nature Switzerland AG 2020
Background Two forms of interval training commonly discussed in the literature are high-intensity interval training (HIIT)
and sprint interval training (SIT). HIIT consists of repeated bouts of exercise that occur at a power output or velocity between
the second ventilatory threshold and maximal oxygen consumption (VO2max). SIT is performed at a power output or velocity
above those associated with VO2max.
Objective The primary objective of this study is to systematically review published randomized and pair-matched trials to
determine which mode of interval training, HIIT versus SIT, leads to a greater improvement in TT performance in active and
trained individuals. The second objective of this review is to perform a subgroup analysis to determine if there is a distinction
between HIIT programs that differ in work-bout duration.
Data Sources SPORTDiscus (1800–present) and Medline with Full Text (1946–present) were used to conduct a systematic
literature search.
Study Selection Studies were selected for the review if they met the following criteria: (1) individuals (males and females)
who were considered at least moderately trained (~ 3-h per week of activity) as specified by the authors of the included stud-
ies; (2) between the ages of 18 and 45years; (3) randomized or pair-matched trials that included a HIIT and a SIT group;
(4) provided detailed information about the interval training program; (5) were at least 2weeks in duration; (6) included a
TT test that required participants to complete a set distance.
Results A total of 6 articles met the inclusion criteria for the subjective and objective analysis. The pooled analysis was
based on a random-effects model. There was no difference in the change in TT performance when comparing all HIIT versus
SIT (0.9%; 90% CI −1.2–1.9%, p = 0.18). However, subgroup analysis based on duration of work interval indicated a 2%
greater improvement in TT performance following long-HIIT (4min) when compared to SIT. There was no difference in
change in VO2max/peak oxygen consumption (VO2peak) between groups. There was a moderate effect (ES = 0.70) in favor of
HIIT over SIT in maximal aerobic power (MAP) or maximal aerobic velocity (MAV).
Conclusion The results of the meta-analysis indicate that long-HIIT may be the optimal form of interval training to augment
TT performance. Additional research that directly compares HIIT exercise differing in work-bout duration would strengthen
these results and provide further insight into the mechanisms behind the observed benefits of long-HIIT.
1 Introduction
Endurance training programs should be optimized to
improve athletic performance while limiting the develop-
ment of fatigue or risk of injury. One of the most impor-
tant variables to consider when prescribing exercise is the
intensity at which an athlete trains as this metric strongly
influences physiological and performance adaptations [1].
The distribution of exercise intensity within a training pro-
gram has provoked great interest over the past decade [2].
The results of a recent meta-analysis of randomized con-
trolled trials examining intensity distribution suggests that
* Michael A. Rosenblat
1 Department ofExercise Science, Faculty ofKinesiology
andPhysical Education, University ofToronto, Toronto, ON,
2 Department ofKinesiology, Langara College, Vancouver,
BC, Canada
3 Training andPerformance Laboratory, Goldring Centre
forHigh Performance Sport, Department ofExercise
Science, Faculty ofKinesiology andPhysical Education,
University ofToronto, Toronto, ON, Canada
1146 M.A.Rosenblat et al.
a polarized training model, which includes approximately
20% of total training volume in the high-intensity exercise
domain and 80% in the low-intensity domain, may lead to a
greater improvement in endurance sport performance when
compared to other intensity distribution models [3].
However, there remains equivocal evidence regarding
the best method to program high-intensity training sessions
and, in particular, the variables defining interval training
stimuli. Interval training consists of repeated bouts of exer-
cise followed by rest or low-intensity exercise, each of which
can last from seconds to minutes in duration. Prescribing
interval training can be quite complex since performance
improvements may be influenced through the manipulation
of a number of programming variables including exercise
mode, duration, intensity, recovery, number of intervals, and
the frequency and distribution of interval training [4, 5]. In
addition to the variables required for programming a specific
exercise session, population characteristics such as age, sex,
training status and background can also impact performance
gains [4].
Two forms of interval training commonly discussed in
the literature are high-intensity interval training (HIIT) and
sprint interval training (SIT). HIIT consists of repeated bouts
of exercise that occur at a power output or velocity within
the severe-intensity domain [6], which occurs between the
second ventilatory threshold (VT2) and maximal oxygen
consumption (VO2max) [7]. In the case when an individual’s
VO2max cannot be determined through exercise testing, peak
oxygen consumption (VO2peak) is used to indicate the upper
border of the domain. SIT is performed at a power output or
velocity above those associated with VO2max [8]. As such, it
can be considered to be completed in the extreme exercise
Gaps in our understanding of the effects of interval train-
ing may remain in part due to the lack of standardization
for developing HIIT and SIT protocols. This concern was
addressed in the review by Viana etal., where the authors
explain that it may be difficult to generalize the results of
interval training programs due to inconsistent exercise pro-
tocols [9]. There are a number of interval training studies
that use HIIT programs that more closely represent SIT exer-
cise [1015]. Those programs include work-bouts that are
between 15s and twomin in duration. Due to the short work-
bout duration, a large portion of total energy production is
through anaerobic energy sources [16]. Previous investiga-
tions examining SIT protocols including short-interval rest
periods (e.g., 15s) have demonstrated similar responses
to that of HIIT, requiring a greater proportion of aerobic
metabolism [1720]. While these SIT protocols have been
shown to improve exercise performance, they may be less
effective for improving VO2max than HIIT protocols incor-
porating longer rest intervals [19]. In addition, confounding
evidence may arise as a consequence of SIT protocols that
incorporate a one-to-one work–rest ratio, whereby power
or velocity is decreased over multiple intervals, yet heart
rate remains elevated. As such, by definition, these intervals
digress to a typically HIIT protocol, with power or velocity
falling below VO2max.
Both HIIT and SIT produce adaptations that are beneficial
for endurance performance. A meta-analysis by Milanović
etal. shows that interval training can lead to improvements
in VO2max and can do so to a greater extent than moderate-
intensity continuous training (MICT) [21]. However, that
analysis did not differentiate between modes of interval train-
ing, including HIIT and SIT in the same analysis group. In
addition, most reviews that address aerobic performance use
VO2max as the primary outcome measure. Although VO2max
has been correlated with race performance [22], strong evi-
dence suggest other variables may positively influence per-
formance outcomes [23, 24]. An alternative measure, time-
trial (TT) performance, has demonstrated a high correlation
with endurance performance, and may directly simulate the
physiological responses required during competition [25,
26]. Time-to-exhaustion (TTE) tests have also been used as
substitute measures for VO2max. However, TTE tests have a
wider variability in results when compared to TT tests [27].
Previous reviews have compared interval training (HIIT,
SIT or combined) with either a non-exercising control or
MICT [21, 28]. There is sufficient evidence that interval
training can enhance performance to a greater extent than
other modes of endurance training. Currently, there remains
a paucity of reviews that compare the effects of HIIT ver-
sus SIT on markers of endurance sport performance. As
such, the primary objective of this study is to systematically
review published randomized and pair-matched trials to
determine which mode of interval training, HIIT versus SIT,
leads to a greater improvement in TT performance in active
Key Points
There was approximately a 2% greater improvement in
time-trial performance following long-duration high-
intensity interval training (HIIT) that consisted of work-
bouts that are 4minutes or greater when compared to
sprint interval training (SIT).
There was no difference in change in maximal or peak
oxygen consumption between HIIT and SIT.
There was a moderate effect (ES = 0.70) in favor of HIIT
over SIT in maximal aerobic power/velocity, with long-
duration HIIT producing the greatest increase in perfor-
mance, a 4% greater change when compared to SIT.
Effect of Interval Training on Time-Trial Performance
and trained individuals. Various studies have employed HIIT
work-bout durations ranging from 1min to 6min [10, 29].
In addition to the limited research comparing HIIT and SIT,
there are no reviews that compare the effects of HIIT exer-
cise protocols which differ in work-bout duration with SIT
protocols on endurance performance. Therefore, the second
objective of this review is to perform a subgroup analysis to
determine if there is a distinction between HIIT programs
that differ in work-bout duration.
2 Methods
2.1 Protocol andRegistration
The Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) was used as the protocol for the
design of the review [30]. The PRISMA guidelines include a
27-item checklist considered to improve reporting transpar-
ency, limiting the risk of publication and selection bias [30].
2.2 Eligibility Criteria
2.2.1 Inclusion Criteria
Studies were selected for the review if they met the follow-
ing criteria: (1) Individuals (males and females) who were
considered at least moderately trained (~ 3h per week of
activity) as specified by the authors of the included studies;
(2) between the ages of 18 and 45years; (3) randomized or
pair-matched trials that included a HIIT and a SIT group;
(4) provided detailed information about the interval train-
ing program; (5) were at least 2weeks in duration; and (6)
included a TT test that required participants to complete a
set distance.
2.2.2 Exclusion Criteria
Studies were excluded if participants had pathology or if
interventions included the following: (1) nutritional inter-
ventions (supplements, hydration, fed state, etc.), (2) change
in environmental conditions (heat/cold, altitude, hypoxia/
hyperoxia, etc.), (3) inclusion of modalities (cryotherapy,
compression garments, etc.), and (4) pharmacological
2.3 Information Sources
An electronic search was conducted that included all publi-
cation years (up to and including December 2018). To mini-
mize selection bias and to perform a comprehensive search,
two databases were used to conduct a systematic literature
search and included SPORTDiscus (1800–present) and Med-
line with Full Text (1946–present).
2.4 Search
2.4.1 Search String
Key search terms that were produced from reviewing previ-
ous literature and using a number of synonyms of the dif-
ferent forms of interval training were grouped and searched
within the article title and abstract, and keywords using the
search conjunction ‘OR’. Combinations of the following
terms were used as search terms: ‘interval training’, ‘inter-
val exercise’ ‘anaerobic interval*’ ‘aerobic interval*’ ‘high
intensity interval*’ ‘sprint interval*’ ‘intermittent exercise’
‘intermittent training’ ‘repeated sprint*’.
2.4.2 Search Limits
To provide a more accurate search, the following limits were
selected: (1) English language, (2) humans, and (3) journal
article, all publications up to and including the year 2018.
2.5 Study Selection
The titles and abstracts of the search results were inde-
pendently assessed for suitability by two authors. Full-text
articles were retrieved if the titles or abstracts met the eli-
gibility criteria or if there was uncertainty. Disagreements
were resolved through a discussion between the two authors,
with a third to be consulted if the first two authors could not
reach agreement. The rationale for excluding articles was
2.6 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. Disagreements were discussed
between the two authors, with a third to be consulted if the
first two authors could not reach agreement.
2.7 Data Items
The following data were extracted from each of the articles
that were included in the review: study methodology (study
design and duration); the participant characteristics (sex,
age, height, mass, VO2max/VO2peak); intervention description
1148 M.A.Rosenblat et al.
(exercise mode, training program duration, interval sessions
performed each week, interval work-bout duration, interval
work-bout intensity (expressed as a percentage of the power
or velocity associated with VO2max/VO2peak); and outcomes
measures (VO2max/VO2peak, power at maximal oxygen uptake
(MAP), velocity at maximal oxygen uptake (MAV), and TT
performance). The correction factor used by Granata etal.
was used to standardized exercise intensity obtained from
testing protocols that exceeded 12min in duration [31].
2.8 Risk ofBias ofIndividual Studies
Two reviewers used the PEDro scale to assess the quality
of the studies included in the review. The PEDro scale is a
10-point ordinal scale used to determine the internal validity
of a study. The specific methodological components assessed
include: (1) randomization, (2) concealed allocation, (3)
baseline comparison, (4) blind participants, (5) blind thera-
pists, (6) blind assessors, (7) adequate follow-up, (8) inten-
tion-to-treat analysis, (9) between group comparisons, and
(10) point estimates and variability [32]. Participant eligibil-
ity is also a component of the PEDro scale; however, it is not
included in the final 10-point score.
2.9 Summary ofMeasures
The primary outcome assessed in this review is TT
performance. Secondary outcome measures include
VO2max/VO2peak and MAP/MAV.
2.10 Synthesis ofResults
Group data are reported as means and standard deviations
with pooled data reported as the standardized mean differ-
ence and its 90 percent confidence intervals. The standard-
ized mean difference, adjusted to account for small sample
size bias, was calculated to establish an effect size, (Hedges
adjusted g) [33]. Effect size values of 0.2, 0.6 and 1.2 were
interpreted as small, moderate and large effect sizes, respec-
tively [34].
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 standard error
of the mean (SEM) were converted to the standard deviation
(SD) using the following formula:
n . The SD
was estimated using the p value in instances, where the SEM
or SD was not available using the following formula:
. A p value expressed using an inequality
(e.g. ‘<’) was discussed as an equality (e.g., ‘=’), providing
a more conservative estimate of the SD. The mean value for
a training load characteristic in the respective subgroup was
used to provide an objective value where only a subjective
description was given. Where possible, between-group com-
parisons were made using the difference of means with the
standard error expressed as a 90 percent confidence
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 variabil-
ity and was used to accommodate for the differences in the
interventions in the individual studies [35]. The consistency
of the meta-analysis was assessed to determine the variabil-
ity in excess of that due to chance. A Chi-squared statistic
(Cochrane Q) was used to evaluate the level of heterogeneity.
The I2 statistic was used to determine the percentage of the
total variation in the estimated effect across studies.
To perform a sub-group analysis, studies were divided
into HIIT groups that differed in work-bout duration based
on oxygen uptake kinetics. Short-HIIT was defined as inter-
val bouts less than 2min in duration to coincide with the
approximate time to reach peak oxygen consumption [36].
Long-HIIT was considered bouts 4min or greater to ensure
that at least 50% of the total work-bout was completed at
VO2max. Medium-HIIT would fall between the subgroups
with work-bouts between 2min and less than 4min. Session
external work was defined as the product of interval inten-
sity, interval work-bout duration, and interval repetitions.
Total external work was defined as the product of session
external work, sessions per week and number of weeks. Both
measures of external work were described in arbitrary units
2.11 Risk ofBias Across all 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.
3 Results
3.1 Study Selection
The literature search was conducted on December 28, 2018.
The databases SPORTDiscus and Medline were used to
perform the search which yielded a total of 6994 results.
Following the removal of 1678 duplicates, 5316 titles and
abstracts were screened. A total of 28 full-text articles were
screened for eligibility. Six studies met the inclusion criteria
for the qualitative and quantitative analysis (Fig.1).
Effect of Interval Training on Time-Trial Performance
3.2 Study Characteristics
The studies included only male participants with a mean
range of 19–32years of age. Five of the studies included
participants who were endurance-trained individuals (V
O2max/VO2peak = 51.6–64.5 mL·kg−1·min−1) who partici-
pated in sports such as cycling, rowing, running and tri-
athlon [29, 3740] and one moderately trained individual
(VO2peak = 46.0mL·kg−1·min−1) [41]. The full details of
study characteristics can be found in Table1.
All six studies included a HIIT and a SIT group. The
HIIT group had interval bouts ranging from 1 to 6min in
duration and at intensities between 73% and 100% MAP. The
SIT groups consisted of 30-s all-out sprints and ranged from
114% to 175% MAP. Two of the studies included more than
one HIIT group [39, 40]. Overall, there was one short-HIIT
interval group [40], 5 that included medium-HIIT intervals
[3740], and 3 that comprised of long-HIIT bouts [29, 40,
41]. See Table2 for additional details.
3.3 Risk ofBias Within Studies
Two studies scored a 4 on the PEDro scale and four scored a
5, with a mean score of 4.7 out of 10 (Table3). There were
no studies that included subject blinding or assessor blind-
ing. In addition, only one study included concealed alloca-
tion. See Table3 for full details.
3.4 Results ofIndividual Studies
Three of the studies found a significantly greater improve-
ment in TT performance following HIIT when compared
Records identified through database
(n = 6994)
ScreeningIncluded Eligibility Identification
Additional records identified through
other sources
(n = 0)
Records after duplicates removed
(n = 5316)
Records screened
(n = 5316)
Records excluded
(n = 5288)
Full-text articles assessed
for eligibility
(n = 28)
Full-text articles excluded,
with reasons
(n = 22)
Precise details of
intervention unavailable: 2
Did not include time-
trial: 19
Time-trial not to set
distance: 1
Studies included in
qualitative synthesis
(n = 6)
Studies included in
quantitative synthesis
(n = 6)
Fig. 1 PRISMA diagram
1150 M.A.Rosenblat et al.
Table 1 Study characteristics
Study Study design Participant characteristics
(mean ± SD)
Intervention Outcome
Akca and Aras [37] Randomized controlled trial,
matched (4weeks)
Rowers (national level),
sex = male, age (years) = 21.8
± 2.4, body mass
(kg) = 79.3 ± 9.1, height
(cm) = 178.4 ± 6.0, VO2peak
(mL·kg−1·min−1) = 56.6 ± 5.7
Medium-HIIT (n = 10) 8 repetitions of 2.5min at 75%
MAP with 3min of active
recovery, 3 times per week
2-km rowing TT, VO2peak, MAP
SIT (n = 10) 10 repetitions of 30s at 125%
MAP with 4min of active
recovery, 3 times per week
Esfarjani and Laursen [38] Controlled trial, matched
Runners (moderately trained),
sex = male, age (years) = 19.0
± 2.0, body mass (kg) = 73
± 3, height (cm) = 172 ± 4,
VO2max (mL·kg−1·min−1) = 51.6
± 2.7
Medium-HIIT (n = 6) 8 repetitions of intervals for
60% of time at 87% MAV with
3.5min of active recovery, 2
times per week
3-km running TT, VO2max, MAV
SIT (n = 6) 12 repetitions of 30s at 114%
MAV with 4.5min of active
recovery, 2 times per week
Control (n = 5) 60min of continuous exercise, 4
times per week
Granata etal. [41] Randomized controlled trial,
matched (4weeks)
Active (moderately trained),
sex = male, age (years) = 20.9
± 2.0, body mass
(kg) = 82.1 ± 16.2, height
(cm) = 180.2 ± 9.1, VO2peak
(mL·kg−1·min−1) = 46.0 ± 7.3
Long-HIIT (n = 11) 4–7 repetitions of 4min at 73%
MAP with 2min of active
recovery, 3 times per week
20-km cycling TT, VO2peak, MAP
SIT (n = 10) 4–10 repetitions of 30s at 168%
MAP with 4min of passive
recovery, 3 times per week
Control (n = 10) 20–36min of continuous exer-
cise between 92 and 97% of
the second lactic threshold, 3
times per week
Inoue etal. [29] Randomized controlled trial
Cyclists (trained), sex = male,
age (years) = 32.1 ± 6.5, body
mass (kg) = 69.1 ± 5.3, height
(cm) = 175.6 ± 5.4, VO2max
(mL·kg−1·min−1) = 63.4 ± 4.5
Long-HIIT (n = 7) 7–10 repetitions of 4–6min at
maximal sustained effort with
4min of active recovery, 3
times per week
40-km cycling TT, VO2max, MAP
SIT (n = 9) 8–12 repetitions of 30-s all-out
sprints with 4min of active
recovery, 3 times per week
Effect of Interval Training on Time-Trial Performance
High-intensity interval training (HIIT), maximal aerobic power (MAP), maximal aerobic velocity (MAV), moderate-intensity interval training, not available (n/a), peak oxygen uptake (VO2peak),
sprint interval training (SIT), time-trial (TT)
Table 1 (continued)
Study Study design Participant characteristics
(mean ± SD)
Intervention Outcome
Laursen etal. [39] Controlled trial, matched
Endurance athletes (highly
trained), sex = male, age
(years) = 25 ± 6, body
mass (kg) = 75 ± 7, height
(cm) = 180 ± 5, VO2peak
(mL·kg−1·min−1) = 64.5 ± 5.2
Medium-HIIT-1 (n = 10) 8 repetitions at 60% of time at
MAP, with 1:2 work-active-
recovery ratio, 2 times per
40-km cycling TT, VO2peak, MAP
Medium-HIIT-2 (n = 10) 8 repetitions at 60% of time at
MAP, with active recovery
based on heart rate recovery, 2
times per week
SIT (n = 10) 12 repetitions of 30-s at 185%
MAP with 4.0min of active
recovery, 2 times per week
Control (n = 11) Regular, low-intensity base
training program
Stepto etal. [40] Randomized controlled trial
Cyclists (trained), sex = male,
age (years) = 26.3 ± 4.6, body
mass (kg) = 76.1 ± 11.1,
height (cm) = n/a, VO2peak
(mL·kg−1·min−1) = 63.1 ± 6.6
Short-HIIT (n = 4) 12 repetitions of 1.0min at
100% MAP with 4min of
active recovery, 2 times per
40-km cycling TT, VO2peak, MAP
Medium-HIIT (n = 4) 12 repetitions of 2min at 90%
MAP with 3min of active
recovery, 2 times per week
Long-HIIT (n = 4) 8 repetitions of 4min at 85%
MAP with 1.5min of active
recovery, 2 times per week
MIIT (n = 4) 4 repetitions of 8.0min at 80%
MAP with 1.0min of active
recovery, 2 times per week
SIT (n = 4) 12 repetitions of 30s at 175%
MAP with 4.5min of active
recovery, 2 times per week
1152 M.A.Rosenblat et al.
Table 2 Interval training program description
All values represent the mean value for each category; session external work is a product of interval repetitions, work duration and work intensity; total external work is a product of session
external work, program duration and session frequency
a The average training intensity for the respective subgroup was used due to missing data
Study Group Interval type Program
Recovery mode Recovery
external work
Total external
work (a.u.)
Akca and Aras [37] Medium-HIIT HIIT 4 2.0 8.0 2.5 75 Active 3.0 1500 12,000
SIT SIT 4 2.0 10.0 0.5 125 Active 4.0 625 5000
Esfarjani and Laursen [38] Medium-HIIT HIIT 10 2.0 8.0 3.3 87 Active 3.5 2296 45,936
SIT SIT 10 2.0 12.0 0.5 114 Active 4.5 684 13,680
Granata etal. [41] Long-HIIT HIIT 4 3.0 5.5 4.0 73 Active 2.0 1606 19,272
SIT SIT 4 3.0 6.6 0.5 168 Passive 4.0 554 6653
Inoue etal. [29] Long-HIIT HIIT 6 2.8 6.3 4.7 79aActive 4.0 2339 39,298
SIT SIT 6 2.8 7.5 0.5 153aActive 4.0 575 9664
Laursen etal. [39] Medium-HIIT-1 HIIT 4 2.0 8.0 2.4 100 Passive 4.8 1920 15,360
Medium-HIIT-2 HIIT 4 2.0 8.0 2.6 100 Passive 4.0 2640 16,640
SIT SIT 4 2.0 12.0 0.5 185 Passive 4.5 1110 8880
Stepto etal. [40] Short-HIIT HIIT 3 2.0 12.0 1.0 100 Active 4.0 2720 7200
Medium-HIIT HIIT 3 2.0 12.0 2.0 90 Active 3.0 2160 12,960
Long-HIIT HIIT 3 2.0 8.0 4.0 85 Active 1.5 1200 16,320
SIT SIT 3 2.0 12.0 0.5 175 Active 4.5 1050 6300
Effect of Interval Training on Time-Trial Performance
to SIT (Table4). There was no significant difference in
VO2max/VO2peak between the HIIT and SIT groups in any of
the studies (Table5). With respect to MAP/MAV, there was
a significantly greater improvement following HIIT when
compared to SIT in 4 of the subgroups (Table6).
3.5 Synthesis ofResults
3.5.1 Training Load
Three of the studies used incremental tests that were greater
than 12min to determine VO2max/VO2peak [37, 38, 41];
therefore, the correction factor was applied to standardize
exercise intensity. The average session external work was
significantly different between the HIIT and SIT groups
(p < 0.0001), with average values of 1980 ± 475 (a.u.) and
766 ± 248 (a.u.), respectively. However, there was no statis-
tically significant difference in average total external work
between HIIT and SIT, with 20,554 ± 13,070 (a.u.) and 8363
± 3122 (a.u.). The average intensity performed by the HIIT
groups was 88% ± 11% MAP/MAV with an average inter-
val work duration of 2.9 ± 1.2min per work-bout. The SIT
group’s average training intensity was 153% ± 28 MAP/
MAV with an average work-bout duration of 30s. The total
external work was 7200 ± 0 (a.u.), 20,579 ± 13,904 (a.u.),
and 24,963 ± 12,502 (a.u.), for the short-HIIT, medium-
HIIT and long-HIIT groups, respectively. There was no
difference in session external work or total external work
between the medium-HIIT and long-HIIT subgroups.
3.5.2 Time‑Trial
There was no difference in the change in TT performance
when comparing HIIT versus SIT (0.9%; 90% CI −0.2%
to 1.9%, p = 0.18) (Fig. 2). The subgroup analysis indi-
cates that there was approximately a 2% greater improve-
ment following long-HIIT when compared to SIT (2.0%;
90% CI: 0.7% to 3.3%, p = 0.01), producing a large effect
(ES = 0.88). There was a significant difference between
subgroups (p = 0.009), with longer HIIT bouts producing a
greater improvement in performance.
3.5.3 Maximal Oxygen Consumption/Peak Oxygen
There was no difference in change in VO2max/VO2peak
between any of the groups, including subgroups (Fig.3).
3.5.4 Maximal Aerobic Power/Maximal Aerobic Velocity
There was a moderate effect (ES = 0.70) in favor of HIIT
over SIT in MAP/MAV. This equates to a 2.4% greater
improvement following HIIT (2.4%; 90% CI 1.3–3.6%,
Table 3 Risk of bias of individual studies
Eligibility is not included in the final 10-point score
Study Eligibility Random
Blind subjects Blind therapists Blind assessors Adequate
group com-
Point esti-
Akca and Aras [37] 0 1 0 1 0 0 0 0 0 1 1 4
Esfarjani and Laursen [38] 0 0 0 1 0 0 0 1 1 1 1 5
Granata etal. [41] 0 1 0 1 0 0 0 1 0 1 1 5
Inoue etal. [29] 0 1 1 1 0 0 0 0 0 1 1 5
Laursen etal. [39] 0 0 0 1 0 0 0 1 0 1 1 4
Stepto etal. [40] 0 1 0 1 0 0 0 1 0 1 1 5
Mean score 4.7
1154 M.A.Rosenblat et al.
p = 0.0007) (Fig.4). There was a trend in HIIT subgroup
duration and change in MAP/MAV, similar to that observed
in TT performance, indicating a greater improvement in
MAP/MAV with longer-duration interval bouts (p = 0.0003).
Long-HIIT produced the greatest increase in MAP/MAV,
with a 4% (p < 0.00001) higher change when compared to
3.6 Risk ofBias Across Studies
A funnel plot of the standard difference in mean versus
standard error for TT results indicates that there is no evi-
dence of publication bias (p = 0.16) regarding the studies
included in the meta-analysis (Fig.5).
Table 4 Time-trial results
Negative between-group difference favours SIT, positive between-group difference favours HIIT
Study Measurement Group nPre (sec ± SD) Post (sec ± SD) Within-
group change
(% ± SD)
difference (%; 90%
Cohen’s d
Akca and Aras [37] 2-km rowing Medium-HIIT 10 411.6 ± 7.5 406.6 ± 7.0 1.2 ± 1.2 −0.2; −1.1 to 0.8 −0.13
SIT 10 412.0 ± 7.7 406.3 ± 7.1 1.4 ± 1.4
Esfarjani and
Laursen [38]
3-km running Medium-HIIT 6 679.0 ± 38.5 7.4 ± 2.6 4.0; 1.7 to 6.2 1.55
SIT 6 679.0 ± 32.0 3.4 ± 2.1
Granata etal. [41] 20-km cycling Long-HIIT 11 2247.7 ± 147.5 2138.1 ± 90.7 4.9 ± 3.2 3.5; 1.4 to 5.6 1.13
SIT 9 2162.3 ± 143.1 2131.9 ± 165.1 1.4 ± 2.5
Inoue etal. [29] 40-km cycling Long-HIIT 7 6091.1 ± 478.3 5785.4 ± 387.3 5.0 ± 2.6 2.1; 0.2 to 4.0 0.9
SIT 9 6143.1 ± 445.7 5960.7 ± 417.0 3.0 ± 1.7
Laursen etal. [39] 40-km cycling Medium-HIIT-1 8 3419.5 ± 188.0 3259.9 ± 211.2 4.8 ± 2.8 0.5; −1.8 to 2.9 0.17
Medium-HIIT-2 9 3491.0 ± 202.7 3299.8 ± 267.3 5.5 ± 2.2 −1.2; −0.8 to 3.3 0.44
SIT 10 3451.0 ± 228.6 3304.3 ± 162.5 4.3 ± 3.2
Stepto etal. [40] 40-km cycling Short-HIIT 3 3618.4 ± 301.7 3608.2 ± 283.0 0.3 ± 0.7 −2.1; −3.8 to
Medium-HIIT 4 3181.7 ± 39.3 3138.45 ± 106.0 1.4 ± 2.2 −0.9; −3.1 to 1.2 −0.44
Long-HIIT 4 3356.4 ± 156.5 3258.75 ± 123.9 2.9 ± 1.5 0.6; −1.4 to 2.6 0.29
SIT 4 3434.9 ± 209.7 3354.6 ± 165.0 2.3 ± 1.9
Table 5 Maximal oxygen consumption and peak oxygen consumption results
Negative between-group difference favours SIT, positive between-group difference favours HIIT
Study Group nPre (mL·min−1 ± SD) Post (mL·min−1 ± SD) Within-
group change
(% ± SD)
Between-group dif-
ference (%; 90% CI)
Cohen’s d
Akca and Aras [37] Medium-HIIT 10 4100.0 ± 650.0 4290.0 ± 630.0 4.6 ± 4.5 −0.8; −4.4 to 2.8 −0.15
SIT 10 4080.0 ± 660.0 4300.0 ± 610.0 5.4 ± 5.3
Granata etal. [41] Long-HIIT 11 3540.0 ± 298.0 3687.0 ± 348.0 4.2 ± 4.9 −2.2; −5.0 to 0.7 −0.51
SIT 9 3937.0 ± 718.0 4185.0 ± 707.0 6.3 ± 2.7
Laursen etal. [39] Medium-HIIT-1 8 4916.0 ± 485.0 5213.0 ± 470.0 6.0 ± 3.2 2.3; −0.7 to 5.2 0.55
Medium-HIIT-2 9 4982.0 ± 341.0 5242.0 ± 217.0 5.2 ± 2.8 1.4; −1.3 to 4.2 0.37
SIT 10 4776.0 ± 287.0 4956.0 ± 433.0 3.8 ± 4.4
Stepto etal. [40] Short-HIIT 3 4519.0 ± 1373.0 4430.0 ± 1228.0 −2.0 ± 4.3 −2.2; −8.0 to 3.6 −0.39
Medium-HIIT 4 5189.0 ± 501.0 5226.0 ± 356.0 0.7 ± 5.2 0.5; −5.5 to 6.5 0.08
Long-HIIT 4 4896.0 ± 248.0 5257.0 ± 491.0 7.4 ± 8.0 7.1; −0.6 to 14.9 0.93
SIT 4 4698.0 ± 381.0 4709.0 ± 563.0 0.2 ± 5.1
Effect of Interval Training on Time-Trial Performance
4 Discussion
4.1 Summary ofEvidence
This is the first systematic review to measure changes in
TT performance following an interval training program.
Previous meta-analyses that studied the effects of inter-
val training have focused solely on VO2max as the primary
outcome [21, 28, 4245]. Performance outcomes such as
VO2max may not account for individual physiological dif-
ferences at submaximal levels [46]. In fact, in a group of
athletes with the same VO2max, TT results are up to 10%
Table 6 Maximal aerobic power and maximal aerobic velocity results
Maximal aerobic power (MAP) is measured in watts, maximal aerobic velocity is measured in km·h−1, negative between-group difference
favours SIT, positive between-group difference favours HIIT
Study Measurement Group nPre
(mean ± SD)
Post (mean ± SD) Within-
group change
(% ± SD)
difference (%; 90%
Cohen’s d
Akca and Aras [37] MAP HIIT 10 336.0 ± 20.0 351.0 ± 21.0 4.5 ± 4.3 −0.9; −4.5 to 2.7 −0.18
SIT 10 335.0 ± 24.0 353.0 ± 26.0 5.4 ± 5.2
Esfarjani and Laursen
MAV Medium-HIIT 6 15.6 ± 0.7 16.6 ± 0.6 6.4 ± 6.1 −1.4; −7.8 to 5.1 −0.19
SIT 6 15.4 ± 0.5 16.6 ± 0.6 7.8 ± 7.4
Granata etal. [41] MAP Long-HIIT 11 264.1 ± 37.4 293.2 ± 34.3 11.0 ± 3.6 6.6; 2.7 to 10.4 1.27
SIT 9 280.8 ± 48.2 293.3 ± 51.5 4.5 ± 6.3
Inoue etal. [29] MAP Long-HIIT 7 299.8 ± 24.6 323.1 ± 24.0 7.8 ± 3.3 2.7; −1.3 to 6.6 0.49
SIT 9 294.8 ± 22.9 310.0 ± 22.7 5.1 ± 6.1
Laursen etal. [39] MAP Medium-HIIT-1 8 439.0 ± 28.9 459.6 ± 37.4 4.7 ± 3.1 1.7; −0.7 to 4.1 0.51
Medium-HIIT-2 9 429.3 ± 23.7 459.1 ± 27.2 6.9 ± 2.1 3.9; 1.9 to 6.0 1.37
SIT 10 425.5 ± 32.4 438.3 ± 36.1 3.0 ± 3.2
Stepto etal. [40] MAP Short-HIIT 3 349.7 ± 95.2 354.7 ± 91.6 1.4 ± 1.3 1.0; −0.3 to 2.3 0.94
Medium-HIIT 4 403.3 ± 21.0 411.0 ± 25.6 1.9 ± 1.5 1.5; 0.2 to 2.9 1.17
Long-HIIT 4 389.8 ± 24.3 407.5 ± 26.0 4.6 ± 0.7 4.2; 3.4 to 4.9 5.89
SIT 4 371.8 ± 28.6 373.3 ± 30.0 0.4 ± 0.5
Fig. 2 Forest plot of time-trial results
1156 M.A.Rosenblat et al.
faster in those athletes with higher relative VTs [47]. In a
TT test, athletes are expected to complete a set distance in
the shortest possible time. This approach may provide a
similar experience to endurance events where power output
can fluctuate much like a TT assessment, thereby increasing
the external validity of the measurement [48]. TT results
have been shown to be a reliable measurement (ICC = 0.99)
and are highly correlated with cycling (r = 0.99, p < 0.001)
and running (r = 0.95, p = 0.001) race performance [25, 26].
However, the physiological demands of TT tests may make
them impractical for coaches to perform regularly since
the potential for athlete fatigue may require alterations in
training programs. Nevertheless, in endurance sport science
research, these tests may be the best method of evaluating
Fig. 3 Forest plot of maximal oxygen consumption and peak oxygen consumption
Fig. 4 Forest plot of maximal aerobic power and maximal aerobic velocity
Effect of Interval Training on Time-Trial Performance
the effectiveness of training programs. These testing meth-
ods can then be translated into actual practice.
The results of this meta-analysis show that there is no
significant difference in TT performance change between
HIIT and SIT. There is some degree of commonality among
the various training programs in the studies making up the
meta-analysis. Specifically, all studies compared HIIT with
a SIT program that consisted of 30-s work-bouts followed by
a recovery period that would allow for full recovery between
bouts. However, the studies incorporated HIIT programs that
ranged in work-bout duration from 1 to 5min, making it dif-
ficult to generalize the results. Therefore, a subgroup analy-
sis was completed to analyze the results of HIIT programs of
similar work-bout duration. This subgroup analysis indicates
that longer-duration intervals may have a greater ability to
augment TT performance and MAP, when compared to SIT.
VO2max/VO2peak improved following both HIIT and SIT,
with no difference between interval subgroups. There were
conflicting results concerning the changes in VO2max and
TT performance between the studies by Stepto etal. and
Granata etal. [40, 41]. The distinction is likely due to the
design of the incremental test used to determine VO2max.
The Stepto study used 2.5-min stages, whereas the Gra-
nata study used 4-min stages. Tests that incorporate longer
stages may be more indicative of submaximal performance.
Time-to-exhaustion (TTE) at MAP from tests that use 2-min
stages is approximately 3.7min in highly trained cyclists
[49]; whereas, TTE at MAP following longer-duration tests
(3-min increments) can be as long as 6.8min in highly
trained cyclists [50]. This may be why there was a greater
improvement in TT and MAP with a lesser improvement in
VO2max in the study by Granata etal. when compared to the
study by Stepto etal.
4.2 Classication System forHigh‑Intensity Interval
While exercise intensity is the main component that can be
used to classify an interval training program as either HIIT
or SIT, the duration of the interval itself is a very important
consideration. Previous inquiries into interval training have
manipulated the work–rest ratio to optimize time spent near
or at VO2max [17, 19, 20, 51]. These types of protocols can
produce a significant acute increase in oxygen uptake during
exercise but to a lesser extent than longer HIIT bouts [17,
20, 52]. With respect to training adaptations, HIIT that con-
sists of very short work-bouts (15–30s) may not appear to
be as effective at improving exercise economy as programs
that utilize longer HIIT bouts [19, 51]. Therefore, it may be
beneficial to program HIIT intervals using longer-duration
work-bouts to optimize endurance performance.
There have been a few attempts to classify HIIT exercise
based on the duration of the interval work-bout [4, 28]. In a
previous review, short-HIIT was considered to be work-bouts
under 30s, medium-HIIT as 30s–2min, and long-HIIT as
2–4min in duration [28]. However, there remain inconclu-
sive physiological justifications for choosing these ranges. It
may be appropriate to identify subgroups of interval training
by considering the relative contributions of energy system
(anaerobic vs. aerobic) components. Providing an accurate
Fig. 5 Funnel plot of standard-
ized mean difference versus
standard error of time-trial
1158 M.A.Rosenblat et al.
classification for HIIT subgroups based on anaerobic con-
tributions can be challenging as previous literature suggests
that there is some difficulty in determining the degree of
anaerobic metabolism that takes place during HIIT [5].
Blood lactate levels as well as excess post-exercise oxygen
consumption (EPOC) are common methods that have been
used to determine anaerobic metabolism during interval
exercise although the reliability of such techniques remains
questionable [5].
It may be more productive to base categorization of inter-
vals on known oxygen uptake kinetics. Previous measure-
ments of oxygen uptake during high-intensity exercise indi-
cate that VO2max can be reached in as short a time as 117s
in trained individuals, while taking approximately 209s in
untrained individuals [53]. Since there is variability among
individuals of different training status, it would be appro-
priate to use work-bouts of 120s or less when defining an
anaerobic interval (short-HIIT) for trained individuals, as
the dominant source of total energy is supplied via anaero-
bic metabolism. Conversely, to perform an aerobic interval
(long-HIIT), more than 50% of the total work completed
should consist of exercise at VO2max. Therefore, long-HIIT
should comprise of bouts of exercise that are at least 4min
in duration. Finally, medium-HIIT would incorporate inter-
val exercise that is between 2 and 4min in duration. This
method of classification does not provide an individualized
approach to interval programming. Nevertheless, it does
specify the criteria for athletes and coaches to make an
informed decision concerning their training.
With respect to aerobic metabolism, the measurement
of oxygen uptake can become delayed in the heavy and
severe exercise domains due to the development of the VO2
slow-component which has been described as the continued
increase in oxygen consumption after 3min of constant-load
exercise [54]. Individually determined oxygen kinetics may
be a novel approach to classify HIIT but those data are not
available in the reviewed studies and as a consequence fixed
time points were employed. Future investigations are encour-
aged to examine this approach for evidence-based decision-
making that is specific to sedentary and athletic populations.
4.3 Limitations
There are elements regarding design of the individual studies
that may influence the results of the meta-analysis. Three of
the studies did not randomize participants into their respec-
tive groups [38, 39, 55] and only one included concealed
group allocation.These methodological issues increase the
possibility that the results will be swayed by confounding
variables. In addition, they may influence the decision as to
whether a study participant receives treatment [56]. With
respect to randomization, four of the studies did not match
participants by VO2max/VO2peak or TT results, potentially
producing non-homogeneity in the training groups [39]. The
lack of assessor and participant blinding in all the studies
might introduce significant bias. When subjects are blinded,
it is less likely that the results of treatment are due to a pla-
cebo effect [56]. Blinding assessors prevents their personal
bias from affecting the results [56].
There are a limited number of studies that compare the
effects of HIIT and SIT on TT performance in a healthy,
active population. Six studies met the inclusion criteria for
the qualitative and quantitative analysis (Fig.1). The sub-
group analysis was performed to provide further insight into
the effects of HIIT programs differing in duration. There
were only data from one study for a comparison of the short-
HIIT group, limiting the ability to provide a strong con-
clusion for this analysis. There were 5 comparisons using
medium-HIIT and 3 using long-HIIT (Fig.2). Previous
reviews have included as few as 4 studies with only 2 groups
for subgroup analysis [57]. However, while there are reviews
that include a small sample size, there is a greater risk of
error due to the heterogeneity of the individual studies. In
addition, the small sample size of the individual studies, spe-
cifically the study by Stepto etal. [40], may skew the results
by increasing the variability. Therefore, future investigations
should include larger sample sizes when feasible.
There remains some debate around participant character-
istics that can influence the response to a training interven-
tion. Participant age and fitness level are two variables that,
in theory, can affect the outcome of an intervention. The
studies in the current review included participants between
the ages of 19 and 32years of age. Due to the small range,
it is unlikely that age differences could have affected the
results. A study by Støren etal. directly compared the effects
of age on changes in VO2max following an interval training
program [58]. They included males (n = 72) and females
(n = 22) between the ages of 20 and 70 + years, and found
no difference in relative improvement in VO2max [58].
Training status as determined by baseline VO2max may
affect the magnitude of a training response as there are
physiological limits of oxygen consumption [59]. There
was a broad range of baseline VO2max/VO2peak values in the
studies included in this review, with values ranging from
46.0 to 64.5mL·kg−1·min−1. This is meaningful because
the study by Støren etal. found a significant difference in
individual training response, with those participants who
were inactive demonstrating the greatest improvement in
performance. In contrast, a recent study of well-trained
cyclists (VO2max = 57.9 ± 6.8mL·kg−1·min−1) displayed a
15% improvement in VO2max following an interval training
program, indicating that interval training can elicit a sub-
stantial response in highly trained individuals [60]. However,
changes in VO2max may not appropriately indicate improve-
ments in submaximal endurance performance. The results
of study by Granata etal. show the greatest in improvement
Effect of Interval Training on Time-Trial Performance
in MAP and TT performance following long-HIIT when
compared to the other studies. Baseline measurements of
VO2max for the participants in that study was significantly
lower than in the other studies, indicating that initial train-
ing status can influence the magnitude of improvements in
TT performance. However, there was no significant differ-
ence in the results when the data were removed from the
The studies included in this review incorporated three
different modes of exercise including cycling [29, 3941],
rowing [37] and running [38]. Exercise mode may influence
the acute physiological responses that occur during interval
exercise, producing differences in long-term adaptations.
Cycling and running have been shown to elicit different
acute responses during exercise such as variations in oxygen
uptake kinetics, peak oxygen consumption, skeletal muscle
oxidative capacity and neuromuscular responses [61]. There
is also literature that demonstrates similar physiological dif-
ferences between cycling and rowing [62]. To the best of our
knowledge, there are no studies that compare the effects of
an interval training program using different modes of exer-
cise on measurements of endurance performance. Due to the
differences in the acute responses, it is likely that there is a
difference in the magnitude of the response between exercise
modes, potentially limiting the generalizability of the results.
However, further investigations are required to determine if
variations in training adaptations occur.
There was a significant difference in training load between
the HIIT and SIT groups. However, the variance in training
load may not influence performance. A recent meta-analysis
showed an inverse relationship between training load and
performance following SIT, with a decline in improvement
for every two SIT bouts above 4 repetitions [45]. Therefore,
it is unlikely that the difference in performance was as a
result of variations in external work. There was no differ-
ence in total external work between the medium-HIIT and
long-HIIT groups. We believe that the greater performance
improvement following long-HIIT is likely due to the higher
aerobic contribution of a longer interval bout. There was
no difference in changes in VO2max/VO2peak between any of
the groups. However, there was a greater increase in MAP/
MAV following long-HIIT. Since MAP/MAV is an indica-
tor of exercise economy [63], the greater degree of aerobic
metabolism that occurs during long-HIIT likely would pro-
duce improvements that contribute to submaximal exercise.
The differences in the distance for the TT measures used
to assess performance may influence the results of the meta-
analysis. Previous inquiry suggests that there is a greater
anaerobic contribution to the TTs of shorter duration [64].
The majority of the TTs included in this review incorpo-
rated longer-duration tests (between 20 and 40km) [3941],
which require a greater aerobic contribution [64]. Therefore,
it could be postulated that the results of the review would
favor long-HIIT due to the aerobic nature of the interven-
tion. Two studies in the meta-analysis included TTs that
were between 6 and 12min in duration [37, 38]. However,
there was no significant change in the results when they are
removed from the analysis.
4.4 Practical Application
The results of the subgroup analysis show long-HIIT to be
the most beneficial form of interval training to augment
performance. While exercise intensity is one of the most
important variables to consider when programming interval
training [1], work-bout duration may be essential to opti-
mize changes in performance. Interval training that includes
work-bouts between 4 and 6min in duration at an intensity
between VT2 and VO2max with 2–4min of recovery would
be best to improve TT performance.
4.5 Future Direction
This is the first analysis to directly compare HIIT and SIT
with markers of endurance sport performance. The results of
the individual studies indicate that SIT may provide benefits
for shorter-duration TTs and HIIT for longer TTs. It may
be beneficial to investigate this hypothesis through a direct
comparison of HIIT and SIT on short- and long-duration
TTs. The subgroup analysis shows that different adaptations
may occur as a result of manipulating the interval work-
bout duration. The study by Stepto etal. directly compared
short-, medium- and long-HIIT groups on TT performance;
however, due to small sample size,a statistically significant
change in the performance variables was not detected [40].
Future studies with larger sample sizes that directly com-
pare medium-HIIT and long-HIIT could provide additional
insight into the benefit of these programs.
5 Conclusion
The subgroup analysis indicates that there was a large effect
in TT performance favoring long-HIIT over SIT. There-
fore, the results based on the currently available literature
suggest that longer-duration HIIT bouts may provide opti-
mal performance adaptation and should be incorporated in
an endurance training program. Additional research that
directly compares HIIT exercise differing in work-bout
duration would strengthen these results and provide further
insight into the mechanisms behind the observed benefits
of long-HIIT.
1160 M.A.Rosenblat et al.
Compliance with Ethical Standards
Funding No sources of funding were used to assist in the preparation
of this article.
Conflicts of interest Michael Rosenblat, Andrew Perrotta and Scott
Thomas declare that they have no conflicts of interest relevant to the
content of this review.
Data availability statement All data supporting the results in this man-
uscript are available within the results sections or in the cited articles.
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... 20 sets: 15 sec work, 45 sec rest; 2. 25 sets: 40 sec work, 20 sec rest). It was an original program inspired by studies with HIIT and SIT (Rosenblat et al., 2020). There was a requirement to maintain high intensity during all work sets-significant intensity drop was to be avoided. ...
... The same modality in the intervention program and the testing program is a common practice (Rosenblat et al., 2020). The 2 km row test was a nonspecific endurance test-the participants were not introduced to the proper form before or during the research. ...
... To develop cardiorespiratory fitness, differently designed HIIT and SIT sessions with modalities, such as running, cycling or rowing, were found effective (Batacan et al., 2016;Sultana et al., 2019;Rosenblat et al., 2020). Based on significant VO2peak (by 10.62%) and V'Emax (by 7.66%) improvement, air bike can also be labelled as an effective tool. ...
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Physical fitness is an important part of overall health. High-intensity interval training (HIIT) is a popular form of exercise that has been repeatedly proven as a functional way of developing cardiorespiratory fitness. Air bike is a widespread cardio machine suitable for HIIT. The aim of this research was to verify the effect of HIIT using air bike on the development of selected physical fitness parameters and compare it to moderate-intensity continuous training (MICT). Twenty active young adults (age 22.1±2.5) were the subject of the research in the research. The participants underwent a complex strength and endurance test, a spiroergometric examination, and a body composition analysis. The experimental group (EG) did HIIT twice a week with work intervals (15–45 seconds), while the control group did MICT in a comparable time period. The results have shown significant improvement in back squat (8.25%), pulling strength (7.07%), aerobic endurance (18.74%), and VO2peak (10.62%). Comparison of the groups has shown a significant difference in bench press (ES=1.01), back squat (ES=0.68), anaerobic endurance (ES=0.97), aerobic endurance (ES=1.456), and VO2peak (ES=0.92). According to the results, we can conclude that HIIT using air bike is an effective way of developing multiple aspects of physical fitness and is thus suitable for training programs that aim to develop health and sports performance
... Two of the most frequently investigated forms of interval training include high-intensity interval training (HIIT) and sprint interval training (SIT). High-intensity interval training has been defined as repeated short-to-long bouts of exercise performed at a power output or velocity within the severeintensity domain between the second ventilatory threshold and maximal oxygen consumption (VȮ 2 max) (86). Therefore, HIIT requires "near maximal" efforts that elicit an intensity of $80% of maximal heart rate (HRmax) or VȮ 2 max (108). ...
... Also, it is frequently performed using a range of exercise modes, including running (31), cycling (99), rowing (1), and swimming (97); resulting in wide applicability to trained and untrained populations. In contrast, SIT is defined by exercise performed at a power output or velocity above VȮ 2 max (i.e., "all-out" efforts in the extreme-intensity domain) necessitating short bouts of exercise (86). Within research studies, SIT is most often performed on a cycle ergometer, allowing a controlled application of training intensity through the application of substantive resistance over 6to 30-second intervals (2,62,111). ...
... Previous systematic reviews and meta-analyses have attempted to synthesize an increasing evidence base focusing on SIT performed on a cycle ergometer and its effects on aerobic capacity (44,86,94,109,110) and sprint power (110). These evidence synthesis studies have generally included data from healthy individuals between 18 and 45 years, who were either sedentary or engaged in moderate frequency recreational activities (44,86,94,109,110). ...
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Hall, AJ, Aspe, RR, Craig, TP, Kavaliauskas, M, Babraj, J, and Swinton, PA. The effects of sprint interval training on physical performance: a systematic review and meta-analysis. J Strength Cond Res XX(X): 000–000, 2022—The present study aimed to synthesize findings from published research and through meta-analysis quantify the effect of sprint interval training (SIT) and potential moderators on physical performance outcomes (categorized as aerobic, anaerobic, mixed aerobic-anaerobic, or muscular force) with healthy adults, in addition to assessing the methodological quality of included studies and the existence of small study effects. Fifty-five studies were included (50% moderate methodological quality, 42% low methodological quality), with 58% comprising an intervention duration of ≤4 weeks and an array of different training protocols. Bayesian’s meta-analysis of standardized mean differences (SMD) identified a medium effect of improved physical performance with SIT (ES0.5 = 0.52; 95% credible intervals [CrI]: 0.42–0.62). Moderator analyses identified overlap between outcome types with the largest effects estimated for anaerobic outcomes (ES0.5 = 0.61; 95% CrI: 0.48–0.75). Moderator effects were identified for intervention duration, sprint length, and number of sprints performed per session, with larger effects obtained for greater values of each moderator. A substantive number of very large effect sizes (41 SMDs > 2) were identified with additional evidence of extensive small study effects. This meta-analysis demonstrates that short-term SIT interventions are effective for developing moderate improvements in physical performance outcomes. However, extensive small study effects, likely influenced by researchers analyzing many outcomes, suggest potential overestimation of reported effects. Future research should analyze fewer a priori selected outcomes and investigate models to progress SIT interventions for longer-term performance improvements.
... However, questions remain with respect to optimal training regimes. Rosenblat, Perrotta and Thomas (Rosenblat et al., 2020) found that TT performance was favoured by long intermittent training (L-INT) (≥4 min) over sprint interval training (SIT). Other studies have found improvements in TT performance and maximal aerobic power (MAP) with the use of efforts between 1-5 min compared to short efforts (30 s) (Sylta et al., 2017). ...
... Although we did not evaluate these physiological variables in our study, the improvements obtained in CP and W' in a certain way may be due to the improvement in exercise tolerance during the intervention period using the W´B AL-INT model. Previous studies have shown that L-INT at different intensities improves TT performance (Rosenblat et al., 2020). In addition, Sylta et al. (2017) found an increase in peak aerobic power output (PPO), power at intensity of 4 mmol of blood lactate and V̇O 2peak in the first 4 weeks of a L-INT (4 × 16 min) training intervention, without changes in the subsequent 4 and 8 weeks. ...
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The present study aims to determine the utility of integrating balance model (W´BAL-INT) in designing interval training programs as assessed by improvements in power output, critical power (CP), and W prime (W´) defined as the finite work capacity above CP. Fourteen male cyclists (age = 42 ± 7 yr, body mass = 69.6 ± 6.5 kg, height = 175 ± 5 cm, CP = 302 ± 32 W, relative CP = 4.35 ± 0.66 W·kg-1) were randomized into two training groups: Short-Medium-Long intervals (SML-INT; n = 7) or Long intervals (L-INT, n = 7) [training sessions separated by 72 h], along with 3-4 sessions of moderate intensity training per week, for 4 weeks. All sessions were designed to result in the complete depletion of the W´ as gauged by the W´BAL-INT. CP and W´ were assessed using the specified efforts (i.e., 12, 7 and 3 min) and calculated with the 2-parameter CP linear model. Training loads between the groups were compared using different metrics. CP improved in both the SML-INT and L-INT groups by 5 ± 4% and 6 ± 5% (p < 0.001) respectively, without significant changes in W´. Mean maximal power over 3, 7 and 12 min increased significantly in the SML-INT group by 5%, 4% and 9%, (p < 0.05) without significant changes in the L-INT group. There were no differences between groups in training zone distribution or training load using BikeScore and relative intensity, but there was significantly (p < 0.05) higher TRIMPS for the Long-INT group. Therefore, W´BAL model may prove to be a useful tool for coaches to construct SML-INT training programs.
... Interestingly, there is evidence showing that many studies and training interventions use brief interval times ( < 1min), which lead to a dissociation be tween the speed/work prescribed and the V O 2 demanded, poten tially affecting training outcomes [49][50][51][52]. Moreover, the use of V O 2 kinetics to classify highintensity aerobic interval training (HIIT) has been recently suggested as a novel approach for evidencebased pre scription of highintensity exercise; however, to date no conclusive data are available [53]. Therefore, in the following sections, we will explore the V O 2 dynamics during highintensity aerobic exercise and suggest that at least 1 to 2min intervals are necessary for a proper and specific training adaptation to further increase endurance per formance and cardiovascular health, because a close association be tween speed/work and V O 2 is achieved. ...
... Although other studies have revised interval training prescriptions [53,76,77,120], these have a more generalized approach. Recent ly an article published by Goulding et al. [121] also referred to the implications of V O 2 kinetics for exercise prescription; however, un like our approach, they linked V O 2 kinetics with exercise tolerance and fatigue processes. ...
Endurance training results in diverse adaptations that lead to increased performance and health benefits. A commonly measured training response is the analysis of oxygen uptake kinetics, representing the demand of a determined load (speed/work) on the cardiovascular, respiratory, and metabolic systems, providing useful information for the prescription of constant load or interval-type aerobic exercise. There is evidence that during high-intensity aerobic exercise some interventions prescribe brief interval times (<1-min), which may lead to a dissociation between the load prescribed and the oxygen uptake demanded, potentially affecting training outcomes. Therefore, this review explored the time to achieve a close association between the speed/work prescribed and the oxygen uptake demanded after the onset of high-intensity aerobic exercise. The evidence assessed revealed that at least 80% of the oxygen uptake amplitude is reached when phase II of oxygen uptake kinetics is completed (1-2 minutes after the onset of exercise, depending on the training status). We propose that the minimum work-time during high-intensity aerobic interval training sessions should be at least 1 minute for athletes and 2 minutes for non-athletes. This suggestion could be used by coaches, physical trainers, clinicians, and sports or health scientists for the prescription of high-intensity aerobic interval training
... High-intensity interval training (HIIT) is an exercise protocol consisting of high-intensity work intervals (≥ 80% of maximum or peak heart rate), followed by low-intensity recovery intervals (Weston et al., 2014). Sprint interval training (SIT) is an intermittent exercise protocol involving workloads that exceed the normal requirement to reach peak oxygen consumption (i.e., maximal all-out effort) (Gibala et al., 2012;Rosenblat et al., 2020). ...
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Background High-intensity interval training (HIIT) has been shown to confer cognitive benefits in healthy adults, via a mechanism purportedly driven by the exercise metabolite lactate. However, our understanding of the exercise parameters (e.g., work interval durations, session volumes, work-to-rest ratio) that evoke a peak blood lactate response in healthy adults is limited. Moreover, evidence relating HIIT-induced blood lactate and cognitive performance has yet to be reviewed and analyzed. The primary objective of this systematic review is to use network meta-analyses to compare the relative impact of different HIIT work interval durations, session volumes, and work-to-rest ratios on post-exercise blood lactate response in healthy adults. The secondary objective is to determine the relationship between HIIT induced blood lactate and acute post-HIIT cognitive performance. Methods A systematic review is being conducted to identify studies measuring blood lactate response following one session of HIIT in healthy adults. The search was carried out in (1) MEDLINE, (2) EMBASE, (3) Cochrane Central Register of Controlled Trials, (4) Sport Discus, and (5) Cumulative Index to Nursing and Allied Health Literature Plus with Full Text (CINAHL+). After abstract and full-text screening, two reviewers will independently extract data on key outcomes variables and complete risk of bias assessment using the Cochrane risk of Bias Tool and the Risk of Bias in Non-Randomized Studies of Interventions tool. Network meta-analyses will be used to generate estimates of the comparative effectiveness of blood lactate on cognitive outcomes using corresponding rankings for each work interval, session volume, and work-to-rest category. Where applicable, meta-regressions analyses will be performed to test the relationship between changes in blood lactate and changes in cognitive performance. Analyses will be conducted using MetaInsight Software Discussion This study will provide evidence on how to structure a HIIT protocol to elicit peak blood lactate response in healthy adults, and will increase our understanding of the relationship between HIIT induced blood lactate response and associated cognitive benefits. Systematic review registration: This review has been registered in the PROSPERO database (CRD42020204400).
... Dalam isitilah lain HIIT shock microcyle ini disebut sebagai latihan system block. Membandingkan latihan interval intensitas tinggi dengan sprint intensitas tinggi terhadap waktu time trial (TT) ternyata antara HIIT dan SI hasilnya tidak jauh berbeda, tetapi secar lebih mendalam terdapat HIIT lebih besar terjadi peningkatan sampai 2% (Rosenblat et al., 2020). Bahkan dilakukan penelitian terhadap orang yang mengalami gangguan mental HIIT dengan memperhatikan standar keamanan ternyata latihan HIIT ini lebih meningkatkan depresi dibandingkan dengan latihan moderat, tetapi secara fisiologi HITT dan moderat sama sama pengaruhnya terhadap kebugaran kardiorespirasi (Korman et al., 2020). ...
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Pengabdian ini merupakan kegiatan untuk memfasilitasi persiapan pelatihan dalam melakukan pemusatan latihan. Pemahaman terhadap program latihan menjadi hal yang sangat penting. Mitra dalam kegiatan ini adalah KONI sebagai penanggungjawab kegiatan diikuti oleh pelatih-pelatih yang melaksanakan pemusatan latihan untuk persiapan PON Papua 2021. Metode yang dipergunakan adalah diskusi dialog yang dilakasanakan selama lebih kurang 2 minggu. Secara kuantitatif tidak terdapat skor yang dapat dilaporkan. Secara kualitatif, pelatih-pelatih seluruh kecabangan olahraga mendapatkan pemahaman, pengetahuan dan keterampilan baru dalam penyusunan serta pelaksanaan program latihan.
... Sprint interval training (SIT) is defined as "all-out" sprints (>90% of VO 2 max) interspersed with recovery periods. Regardless of the SIT protocol used, several studies have associated this type of exercise with a range of health benefits including large improvements in cardiorespiratory fitness, metabolic function, and body composition outcomes (Keating et al., 2017;Rosenblat et al., 2020). The effects of SIT on postprandial hyperinsulinemia have recently been meta-analyzed by Khalafi et al., with the authors reporting mixed results (Khalafi et al., 2022). ...
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There is evidence supporting that acute sprint interval training (SIT) might improve metabolic responses to postprandial glucose, but results are inconclusive. The aim of the present study was to explore the effects of acute SIT on metabolic response and substrate utilization in individuals with overweight/obesity after an oral 75-gram glucose challenge. Thirty- three participants with overweight/ obesity (32.7 ± 8.3 years, 24 male, 9 female) participated in the study and a crossover design was followed. After the 75-gram glucose load, participants were randomly allocated to two groups: no exercise (resting) or SIT protocol. Metabolic data including respiratory quotient (RQ) and substrate utilization rates (fats and carbohydrates) were collected using the COSMED Q-NRG + ® calorimeter. The RQ was significantly lower in the acute SIT group (0.76 [0.01]; P<0.0001) than in the resting group (0.80 [0.01]; P=0.036) at the 120-min postprandial time point, and the RQ area under the curve (AUC) was also lower in the SIT group (mean difference of −6.62, 95% CI −12.00 to −1.24; P=0.0161). The contribution of fat to energy expenditure increased after SIT during the postprandial period whereas the contribution of carbohydrates decreased. The AUC for fat contribution was significantly higher (mean difference 2311.9, 95% confidence interval [CI] 578.8 to 4043.3; P=0.0098) and the AUC for carbohydrate contribution was significantly lower (mean difference −2283.1, 95% CI −4040.2 to −527.1; P=0.0117) in the SIT group than in the resting group. In conclusions, acute SIT might have a positive effect on metabolic responses to postprandial glucose and, accordingly, should be recommended for improving metabolism in people with overweight/obesity.
... In endurance sports, including mountain bike cycling, training is increasingly intensified through the use of a polarized training strategy, which consists of sustained lowintensity training as well as high-intensity interval training [25][26][27]. The volume of lowintensity training sessions is approximately 80% of the total training volume, while highintensity training is approximately 20% of the total training volume [28]. ...
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This study evaluated the acute effects of sprint interval training and chronic effects of polarized training on choice reaction time in cyclists. Twenty-six mountain bike cyclists participated in the study and were divided into experimental (E) and control (C) groups. The cyclists trained for 9-weeks and performed five training sessions each week. Types of training sessions: (1) sprint interval training (SIT) which consisted of 8–16, 30 s repetitions at maximal intensity, (2) high-intensity interval training (HIIT) included 5 to 7, 5-min efforts at an intensity of 85–95% maximal aerobic power (Pmax), and (3) endurance training (ET) performed at an intensity of 55–60% Pmax, lasting 120–-180 min. In each week the cyclists performed: in group E a polarized training program, which included 2 × SIT, 1 × HIIT and 2 × ET, while in group C 2 × HIIT and 3 × ET. Before (acute effects) and after the 9-week training period (chronic effects) participants performed laboratory sprint interval testing protocol (SITP), which consisted of 12 maximal repetitions lasting 30 s. During SITP maximal and mean anaerobic power, as well as lactate ion concentration and blood pH were measured. Choice reaction time (RT) was measured 4-times: before and immediately after the SITP test—before and after the 9-week training period. Evaluated the average choice RT, minimal choice RT (shortest reaction), maximal choice RT (longest reaction), and the number of incorrect reactions. Before the training period as acute effects of SITP, it was observed: a shorter average choice RT (F = 13.61; p = 0.001; η2 = 0.362) and maximal choice RT (F = 4.71; p = 0.040; η2 = 0.164), and a decrease the number of incorrect reactions (F = 53.72; p = 0.000; η2 = 0.691), for E and C groups. After the 9-week training period, chronic effects showed that choice RT did not change in any of the cyclists' groups. Only in the E group after the polarized training period, the number of incorrect reactions decreased (F = 49.03; p = 0.000; η2 = 0.671), average anaerobic power increased (F = 8.70; p = 0.007; η2 = 0.274) and blood pH decreased (F = 27.20; p = 0.000; η2 = 0.531), compared to the value before the training period. In conclusion, a shorter choice RT and a decrease in the number of incorrect reactions as acute effects of SITP, and a decrease in the number of incorrect reactions and higher average power as chronic effects of the polarized training program are beneficial for mountain bike cyclists.
... The investigations which reported findings on acute responses of BFR + HIIT were only limited to maximal sprint-based protocols (SIT and RST). As these sprintbased HIIT modalities are of an all-out, maximal intensity and highly anaerobic nature, the acute responses (metabolic, neuromuscular, perceptual, etc.) observed may be very different from that of submaximal exercise [63][64][65][66]. Hence, there is a need to compare acute responses of other BFR + HIIT protocols, e.g. ...
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Background The implementation of blood flow restriction (BFR) during exercise is becoming an increasingly useful adjunct method in both athletic and rehabilitative settings. Advantages in pairing BFR with training can be observed in two scenarios: (1) training at lower absolute intensities (e.g. walking) elicits adaptations akin to high-intensity sessions (e.g. running intervals); (2) when performing exercise at moderate to high intensities, higher physiological stimulus may be attained, leading to larger improvements in aerobic, anaerobic, and muscular parameters. The former has been well documented in recent systematic reviews, but consensus on BFR (concomitant or post-exercise) combined with high-intensity interval training (HIIT) protocols is not well established. Therefore, this systematic review evaluates the acute and chronic effects of BFR + HIIT. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant studies. A systematic search on 1 February 2022, was conducted on four key databases: ScienceDirect, PubMed, Scopus and SPORTDiscus. Quality of each individual study was assessed using the Physiotherapy Evidence Database (PEDro) scale. Extraction of data from included studies was conducted using an adapted version of the 'Population, Intervention, Comparison, Outcome' (PICO) framework. Results A total of 208 articles were identified, 18 of which met inclusion criteria. Of the 18 BFR + HIIT studies (244 subjects), 1 reported both acute and chronic effects, 5 examined acute responses and 12 investigated chronic effects. Acutely, BFR challenges the metabolic processes (vascular and oxygenation responses) during high-intensity repeated sprint exercise—which accelerates central and peripheral neuromuscular fatigue mechanisms resulting in performance impairments. Analysis of the literature exploring the chronic effects of BFR + HIIT suggests that BFR does provide an additive physiological training stimulus to HIIT protocols, especially for measured aerobic, muscular, and, to some extent, anaerobic parameters. Conclusion Presently, it appears that the addition of BFR into HIIT enhances physiological improvements in aerobic, muscular, and, to some extent, anaerobic performance. However due to large variability in permutations of BFR + HIIT methodologies, it is necessary for future research to explore and recommend standardised BFR guidelines for each HIIT exercise type.
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The power-time relationship is an important concept in exercise physiology because it provides a systematic framework for understanding the mechanical bases of fatigue and exhaustion during exercise, as well as a tool for diagnosing physical fitness and monitoring training. This relationship is well known during high intensity exercise, sustainable with stable critical power (CP) values, reaching maximum work rates over time until exercise intolerance is reached. This amount of work above the CP (W ́) is constant at different speeds depending on the proximity of the power output to CP. The appeal of this concept of CP in recent years has been broadened through its application to high intensity intermittent exercise, however, there are no intermittent training programs (IWC) based on CP/W ́ that can evaluate and optimize the exercise. athletic performance in road cycling. Therefore, the objective of this Doctoral Thesis is to analyze the power-time relationship (CP) and the metabolic thresholds for the evaluation and optimization of sports performance during intermittent exercise through a reconstitutive model (W'BAL) in road cyclists. For this, a systematic review and Meta-Analysis has been carried out that determines the degree of correspondence between CP and VT1, MLSS, VT2 and RCP (Article I), in addition a study has been carried out that determines the influence between the load based on the weight body and muscle mass during the WAnT performance test above CP in the severe intensity domain (Article II), in order to develop a reconstitutive intermittent training program (Article III), which can estimate, evaluate and monitor the CP during training (Article IV). The main results of this Doctoral Thesis suggest that a) VT1 and MLSS underestimate CP while RCP and VT2 overestimate it, and MLSS and MMSS do not mean the same thing. b) In the WAnT test, the protocols can be used interchangeably in the conditions of body weight (BW) and muscle mass or fat- XIII free (LBM) that evaluate anaerobic capacity above CP. c) IWC significantly improves CP and MMP in field tests TT12min, TT7min and TT3min after 4 weeks of training. d) Estimated CP3p and CP2p of the MMP values during the sessions of the IWC training program, differ from the CP extracted from the formal field test. e) IWC can estimate CP from 7min MMP values, track and monitor its changes during 4 weeks of training. This Doctoral Thesis shows that the development of a CP training program applied to intermittent reconstitutive exercise (WBAL) has important applications in the optimization of sports performance in road cyclists.
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Background In athletes, hip and groin pain is considered to be associated with hip intra-articular pathologies and hip osteoarthritis (OA). A greater understanding of the relationship between hip and groin pain and imaging findings is required. Objective Our objective was to undertake a systematic review and meta-analysis to determine the prevalence of hip intra-articular pathologies and hip OA in athletes with and without hip and groin pain. Methods Seven electronic databases were searched on 29 January 2018 for studies investigating the prevalence of hip intra-articular pathologies and hip OA using X-ray, magnetic resonance imaging, magnetic resonance arthrography or computed tomography. The search, study selection, quality appraisal and data extraction were performed by two independent reviewers. When studies were considered homogenous, meta-analysis was undertaken. A strength of evidence was given to pooled results. Results Twenty studies reporting on the prevalence of hip intra-articular pathologies and hip OA in athletes were identified. Included studies were considered moderate to high risk of bias, with only three studies adjudged as low risk of bias. In asymptomatic athletes, limited evidence identified a labral tear prevalence of 54% per person and moderate evidence of 33% per hip. In symptomatic athletes, moderate evidence of a labral tear prevalence of 20% per hip was found. Moderate evidence of a cartilage defect prevalence of 10% per person was reported in asymptomatic athletes. In symptomatic athletes, cartilage defect prevalence was 7–40%. In asymptomatic athletes, the prevalence of hip OA was 0–17%, compared with 2% in symptomatic athletes. Conclusion The prevalence of hip intra-articular pathologies and hip OA in symptomatic and asymptomatic athletes is variable. Labral tears and cartilage defects appear to be seen often in athletes with and without pain. Hip OA is rarely seen in athletes either with or without hip and groin pain. Study Registration PROSPERO registration CRD42017082457.
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Cycle training is an important training modality of elite rowers. Cycling is the preferred alternative to on-water and ergometer rowing as it provides a reduction in compressive forces on the thoracic cage and upper extremities while still creating a local and central acclimation to endurance training. It is hypothesised, however, that there will be differences in physiological characteristics between Concept II (CII) rowing and WattBike (WB) cycling due to the principle regarding the specificity of training that elite rowers undertake. Understanding these differences will ensure more accurate training prescription when cycling. Twenty international level male rowers, [ V ˙ O2PEAK 5.85 ± 0.58 L.min-1 (CI ± 0.26 L.min-1)] participated in two identical discontinuous incremental exercise tests on a CII rowing and WB cycle ergometer. Ergometer modalities were randomised and counterbalanced among the group and tests occurred 7 days apart. V ˙ O2, V ˙ CO2, V ˙ E(STPD) and HR were significantly higher for every submaximal power output on the CII compared with the WB. Maximal power output on the WB was higher than on the CII [42 ± 33 W (CI ± 14 W) p < 0.000] but V ˙ O2PEAK was similar between modalities. Minute ventilation at maximal exercise was 11 L.min-1 lower on CII than on WB. When data were expressed relative to modality specific V ˙ O2PEAK, power output was consistently lower on the CII as was submaximal V ˙ CO2, RER, RPE, mechanical efficiency and BLa concentration at 75% V ˙ O2PEAK. Across all power outputs and exercise modalities, 77% of the variance in RPE could be explained by the variance in BLa. These results demonstrate that elite rowers can attain similar V ˙ O2PEAK scores regardless of modality. Substantial physiological and metabolic differences are evident between CII rowing and WB cycling when power output is the independent variable with the latter being over 40 W higher. The difference in displayed power output between the ergometer modalities is attributed to differences in mechanical efficiency and a degree of power output not being accounted for on the CII ergometer. Given the lack of consistency between CII and WB power output, other physiological measures, such as HR, are better suited to prescribe WB ergometer sessions.
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The objective of this review was to systematically search the literature to identify and analyze data from randomized controlled trials that compare the effects of a polarized training model (POL) versus 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 VO2max/peak > 50 mLkgmin-1, a POL group, a THR group, assessed either internal (e.g. VO2max) or external (e.g. time trial) measurements of endurance sport performance. The databases SPORTDiscus, Medline and CINAHL yielded a combined 329 results. Four studies met the inclusion criteria for the qualitative analysis, and three for the meta-analysis. Two of the four studies included in the review scored a 4/10 on the PEDro Scale and two scored a 5/10. With respect to outcome measurements, three studies included time trial performance, three included VO2max or VO2peak, two 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 = -0.66; 95% CI: -1.17 to -0.15) 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.
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Physical inactivity represents the fourth leading risk factor for mortality, and it has been linked with a series of chronic disorders, the treatment of which absorbs ~ 85% of healthcare costs in developed countries. Conversely, physical activity promotes many health benefits; endurance exercise in particular represents a powerful stimulus to induce mitochondrial biogenesis, and it is routinely used to prevent and treat chronic metabolic disorders linked with sub-optimal mitochondrial characteristics. Given the importance of maintaining a healthy mitochondrial pool, it is vital to better characterize how manipulating the endurance exercise dose affects cellular mechanisms of exercise-induced mitochondrial biogenesis. Herein, we propose a definition of mitochondrial biogenesis and the techniques available to assess it, and we emphasize the importance of standardizing biopsy timing and the determination of relative exercise intensity when comparing different studies. We report an intensity-dependent regulation of exercise-induced increases in nuclear peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) protein content, nuclear phosphorylation of p53 (serine 15), and PGC-1α messenger RNA (mRNA), as well as training-induced increases in PGC-1α and p53 protein content. Despite evidence that PGC-1α protein content plateaus within a few exercise sessions, we demonstrate that greater training volumes induce further increases in PGC-1α (and p53) protein content, and that short-term reductions in training volume decrease the content of both proteins, suggesting training volume is still a factor affecting training-induced mitochondrial biogenesis. Finally, training-induced changes in mitochondrial transcription factor A (TFAM) protein content are regulated in a training volume-dependent manner and have been linked with training-induced changes in mitochondrial content.
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Interval training (IT) has been used for many decades with the purpose to increase performance and promote health benefits while demanding a relatively small amount of time. IT can be defined as intermittent periods of intense exercise separated by periods of recovery and has been divided into high-intensity interval training (HIIT), sprint interval training (SIT) and repeated sprint training (RST). IT use resulted in the publication of many studies and many of them with conflicting results and positions. The aim of this article was to move forward and understand studies’ protocol in order to draw accurate conclusions, as well as to avoid previous mistakes and effectively reproduce previous protocols. When analyzing the literature, we found many inconsistencies, such as, the controversial concept of ‘supramaximal’ effort, a misunderstanding regarding the term ‘high intensity’ and the use of different strategies to control intensity. The adequate definition and interpretation of training intensity seems to be vital, since the results of IT are largely dependent on it. These observations are only a few examples of the complexity involved with IT prescription, discussed to illustrate some problems with the current literature regarding IT. Therefore, it is our opinion that it is not possible to draw general conclusions about IT without considering all variables used in IT prescription, such as, exercise modality, intensity, effort andrest times and participants’ characteristics. In order to help guide researchers and health professionals in their practices it is important that experimental studies report their methods in as much detail as possible and future reviews and meta-analyses should critically discuss the articles included in light of their methods to avoid inadequate generalizations.
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Purpose: Several studies have demonstrated that physiological variables predict cycling endurance performance. However, it is still unclear whether the predictors will change over different performance durations. The aim of this study was to assess the correlations between physiological variables and cycling time trials with different durations. Methods: Twenty trained male cyclists (maximal oxygen uptake [VO2max] = 60.5 ± 5.6 mL/kg/min) performed 4 separate experimental trials during a 2-week period. Cyclists initially completed an incremental exercise test until volitional exhaustion followed by 3 maximal cycling time trials on separate days. Each time trial consisted of 3 different durations: 5 min, 20 min, and 60 min performed in a randomized order. Results: The main results showed that the physiological measures strongly correlated with long cycling performances rather than short and medium time trials. The time-trial mean power output was moderately high to highly correlated with peak power output and VO2max (r = .61-.87, r = .72-.89, respectively), and was moderately to highly correlated with the lactate threshold Dmax method and second ventilatory threshold (r = .52-.75, r = .55-.82, respectively). Conclusions: Therefore, trained cyclists should develop maximal aerobic power irrespective of the duration of time trial, as well as enhancements in metabolic thresholds for long-duration time trials.
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Purpose: To investigate single-day time-to-exhaustion (TTE) and time trial (TT) based laboratory tests values of critical power (CP), Wprime (W') and respective oxygen kinetics responses. Methods: Twelve cyclists performed a maximal ramp test followed by three TTE and three TT efforts interspersed by a 60-min recovery between efforts. Oxygen uptake was measured during all trials. The mean response time (MRT) was calculated as a description of the overall V̇O2 kinetic response from the onset to 2 min of exercise. Results: TTE determined CP was 279 ± 52W and TT determined CP was 276 ± 50W (P = 0.237). Values of W' were 14.3 ± 3.4 kJ (TTE W') and 16.5± 4.2 kJ (TT W') (P = 0.028). Whilst a high level of agreement (-12 to 17 W) and a low prediction error of 2.7% was established for CP, for W' limits of agreements were markedly lower (-8 to 3.7 kJ) with a prediction error of 18.8%. The mean standard error for TTE CP values was significantly higher than that for TT CP values (2.4 ± 1.9% vs. 1.2 ± 0.7% W). The standard error for TTE W' and TT W' were 11.2 ± 8.1% and 5.6 ± 3.6%, respectively. The V̇O2 response was significantly faster during TT (~22 s) than TTE (~28 s). Conclusions: The time-trial protocol with a 60-min recovery period offers a valid, time-saving and less error containing alternative to conventional and more recent testing methods. Results however cannot be transferred to W'.
Abstract Objectives To examine the effects of different protocols of high-intensity interval training (HIIT) on VO2max improvements in healthy, overweight/obese and athletic adults, based on the classifications of work intervals, session volumes and training periods. Design Systematic review and meta-analysis. Methods PubMed, Scopus, Medline, and Web of Science databases were searched up to April 2018. Inclusion criteria were randomised controlled trials; healthy, overweight/obese or athletic adults; examined pre- and post-training VO2max/peak; HIIT in comparison to control or moderate intensity continuous training (MICT) groups. Results Fifty-three studies met the eligibility criteria. Overall, the degree of change in VO2max induced by HIIT varied by populations (SMD = 0.41–1.81, p < 0.05). When compared to control groups, even short-intervals (≤30 s), low-volume (≤5 min) and short-term HIIT (≤4 weeks) elicited clear beneficial effects (SMD = 0.79–1.65, p < 0.05) on VO2max/peak. However, long-interval (≥2 min), high-volume (≥15 min) and moderate to long-term (≥4–12 weeks) HIIT displayed significantly larger effects on VO2max (SMD = 0.50–2.48, p < 0.05). When compared to MICT, only long-interval (≥2 min), high-volume (≥15 min) and moderate to long-term (≥4–12 weeks) HIIT showed beneficial effects (SMD = 0.65–1.07, p < 0.05). Conclusions Short-intervals (≤30 s), low-volume (≤5 min) and short-term (≤4 weeks) HIIT represent effective and time-efficient strategies for developing VO2max, especially for the general population. To maximize the training effects on VO2max, long-interval (≥2 min), high-volume (≥15 min) and moderate to long-term (≥4–12 weeks) HIIT are recommended. Keywords Cardiorespiratory fitnessExerciseHigh-intensity intermittent exerciseMeta-analysis
High Intensity Interval Training (HIIT) can be performed with different effort to rest time-configurations, and this can largely influence training responses. The purpose of the study was to compare the acute physiological responses of two HIIT and one moderate intensity continuous training (MICT) protocol in young men. A randomised cross-over study with 10 men [age, 28.3 ± 5.5years; weight, 77.3 ± 9.3 kg; height, 1.8 ± 0.1 m; peak oxygen consumption (VO2peak), 44 ± 11⁻¹.min⁻¹]. Participants performed a cardiorespiratory test on a treadmill to assess VO2peak, velocity associated with VO2peak (vVO2peak), peak heart rate (HRpeak) and perceived exertion (RPE). Then participants performed three protocols equated by distance: Short HIIT (29 bouts of 30s at vVO2peak, interspersed by 30s of passive recovery, 29 min in total), Long HIIT (3 bouts of 4 min at 90% of vVO2peak, interspersed by 3 min of recovery at 60% of vVO2peak, 21 min in total) and MICT (21 min at 70% of vVO2peak). The protocols were performed in a randomised order with ≥48 h between them. VO2, HRpeak and RPE were compared. VO2peak in Long HIIT was significantly higher than Short HIIT and MICT (43 ± 11 vs 32 ± 8 and 37 ± 8⁻¹.min⁻¹, respectively, P < 0.05), as well as peak HR (181 ± 10 vs 168 ± 8 and 167 ± 11, respectively, P < 0.05), and RPE (17 ± 4 vs 14 ± 4 and 15 ± 4, respectively, P < 0.05), with no difference between Short HIIT and MICT. In conclusion, Long HIIT promoted higher acute increases in VO2, HR and RPE than Short HIIT and MICT, suggesting a higher demand on the cardiorespiratory system. Short HIIT and MICT presented similar physiologic and perceptual responses, despite Short HIIT being performed at higher velocities.
High intensity interval training (HIIT) elicits similar physiological adaptations as moderate intensity continuous training (MICT) despite less time commitment. However, there is debate whether HIIT is more aversive than MICT. This study compared physiological and perceptual responses between MICT and three regimes of HIIT. Nineteen active adults (age = 24.0 ± 3.3 yr) unfamiliar with HIIT initially performed ramp exercise to exhaustion to measure maximal oxygen uptake (VO2 max) and determine workload for subsequent sessions, whose order was randomized. Sprint interval training (SIT) consisted of six 20 s bouts of "all-out" cycling at 140% of maximum watts (Wmax). Low volume (HIITLV) and high volume HIIT (HIITHV) consisted of eight 60 s bouts at 85% Wmax and six 2 min bouts at 70% Wmax, respectively. MICT consisted of 25 min at 40% Wmax. Across regimes, work was not matched. Heart rate, VO2, blood lactate concentration (BLa), affect, and rating of perceived exertion (RPE) were assessed during exercise. Ten minutes post-exercise, Physical Activity Enjoyment (PACES) was measured via a survey. Results revealed significantly higher (p<0.05) VO2, heart rate, BLa, and RPE in SIT, HIITLV, and HIITHV versus MICT. Despite a decline in affect during exercise (p<0.01) and significantly lower affect (p<0.05) during all HIIT regimes versus MICT at 50, 75, and 100 % of session duration, PACES was similar across regimes (p=0.65) although it was higher in women (p=0.03). Findings from healthy adults unaccustomed to interval training demonstrate that HIIT and SIT are perceived as enjoyable as MICT despite being more aversive.