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Optimizing Interval Training Through Power-Output Variation
Within the Work Intervals
Arthur H. Bossi, Cristian Mesquida, Louis Passfield, Bent R. Rønnestad, and James G. Hopker
Purpose:Maximal oxygen uptake (
˙
VO2max) is a key determinant of endurance performance. Therefore, devising high-intensity
interval training (HIIT) that maximizes stress of the oxygen-transport and -utilization systems may be important to stimulate
further adaptation in athletes. The authors compared physiological and perceptual responses elicited by work intervals matched
for duration and mean power output but differing in power-output distribution. Methods:Fourteen cyclists (
˙
VO2max
69.2 [6.6] mL·kg
−1
·min
−1
) completed 3 laboratory visits for a performance assessment and 2 HIIT sessions using either
varied-intensity or constant-intensity work intervals. Results:Cyclists spent more time at >90%˙
VO2max during HIIT with
varied-intensity work intervals (410 [207] vs 286 [162] s, P= .02), but there were no differences between sessions in heart-rate- or
perceptual-based training-load metrics (all P≥.1). When considering individual work intervals, minute ventilation (
˙
VE) was
higher in the varied-intensity mode (F= 8.42, P= .01), but not respiratory frequency, tidal volume, blood lactate concentration
[La], ratings of perceived exertion, or cadence (all F≤3.50, ≥.08). Absolute changes (Δ) between HIIT sessions were calculated
per work interval, and Δtotal oxygen uptake was moderately associated with Δ
˙
VE (r= .36, P= .002). Conclusions:In
comparison with an HIIT session with constant-intensity work intervals, well-trained cyclists sustain higher fractions of
˙
VO2max
when work intervals involved power-output variations. This effect is partially mediated by an increased oxygen cost of hyperpnea
and not associated with a higher [La], perceived exertion, or training-load metrics.
Keywords:intensity prescription, time at
˙
VO2max, elite cycling, maximal aerobic power, exercise hyperpnea
High-intensity interval training (HIIT) involves repeated bouts
of high-intensity exercise interspersed with recovery periods. This
method is typically employed to increase the training stimulus for
the cardiorespiratory system over prolonged continuous exercise.
Accordingly, much of the scientific work related to HIIT has
focused on maximal oxygen uptake (
˙
VO2max) improvements,
1–4
as the upper limit to the aerobic metabolism and a key determinant
of endurance performance.
5
It has been suggested that exercising at
high intensities is beneficial to improve
˙
VO2max,
4
particularly in
the case of well-trained athletes.
1–3
Therefore, accumulating time at
or close to
˙
VO2max (eg, >90% or >95%) during a HIIT session may
be important for training adaptation.
1–4,6–9
Previously, Billat et al
10
have demonstrated that the ability
to sustain exercise at >95%˙
VO2max can exceed 15 minutes if
power output is adjusted according to expired gas responses. In
comparison, constant work rate exercise or HIIT performed to
exhaustion produces time at >90% or >95%˙
VO2max of only a few
minutes.
1–3,6,7,10
Billat et al
10
used a protocol that commenced at
the lowest power output eliciting
˙
VO2max, and once attained,
power output was decreased progressively. Subsequently, power
output was regulated as per individual oxygen uptake (
˙
VO2)
responses, enabling >95%˙
VO2max to be sustained and time to
exhaustion prolonged.
10
Although this laboratory protocol is
appealing as a training session, it is not practical for the majority
of athletes. Alternatively, a HIIT session in which the work
intervals include power output variations might provide similar
means to increase time at >90%˙
VO2max.
Previous research suggests that power output distribution
affects physiological responses during standardized HIIT sessions,
6,9
with increased time at >90%˙
VO2max following decreasing-
intensity versus constant-intensity work intervals
6
and greater time
at >85%˙
VO2max following all-out versus constant-intensity work
intervals being reported.
9
Although the previously mentioned studies
did not investigate potential mechanisms, authors attributed the
results to a difference in
˙
VO2kinetics between HIIT modes,
6,9
as
faster
˙
VO2kinetics have been observed during decreasing-intensity
versus constant-intensity single bouts of exercise matched for mean
power output.
11,12
It is believed that
˙
VO2kinetics reflect changes in
oxidative metabolism within the muscle,
13,14
which, in turn, respond
to the energy state of the cells, in particular, the concentration of
adenosine diphosphate.
15
Higher work rates elevate adenosine
diphosphate concentrations and activate oxidative phosphorylation
more rapidly,
16
ultimately producing faster
˙
VO2kinetics at the onset
of decreasing-intensity compared with constant-intensity exer-
cise.
11,12
This mechanism leads to the possibility that multiple
changes in power output within the first half of a work interval
would maximize time at >90%˙
VO2max.
Despite the attractiveness of the
˙
VO2kinetics hypothesis,
ventilatory variables such as minute ventilation (
˙
VE) or respiratory
frequency (ƒ
R
) have been largely ignored as part of the physiolog-
ical responses to different patterns of power output distribu-
tion.
6,9,11,12
As the oxygen cost of hyperpnoea at high-intensity
exercise is substantial, reaching 15% of
˙
VO2max in some indivi-
duals,
17,18
exacerbated ventilatory responses caused by varied-
intensity work intervals may help to explain an increased time
at >90%˙
VO2max in this type of HIIT. Indeed, evidence suggests
work rate magnitude affects ventilatory response dynamics.
19
However, the strong association reported between ƒ
R
and ratings
Bossi, Passfield, and Hopker are with the School of Sport and Exercise Sciences,
University of Kent, Chatham, United Kingdom. Mesquida is with the Faculty of
Biology, University of Barcelona, Barcelona, Spain. Passfield is also with the
Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada. Bossi,
Mesquida, and Rønnestad are with the Dept of Sport Science, Inland Norway
University of Applied Science, Lillehammer, Norway. Hopker (J.G.Hopker@kent.
ac.uk) is corresponding author.
1
International Journal of Sports Physiology and Performance, (Ahead of Print)
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of perceived exertion (RPE)
20
suggests that the extra respiratory
drive may be associated with a higher perceptual strain and
premature fatigue,
21
potentially offsetting the benefits of being
able to spend a longer time at >90%˙
VO2max.
The purpose of this study was to compare the physiological
and perceptual responses elicited by work intervals matched for
duration and mean power output, but differing in power output
distribution. Specifically, constant-intensity work intervals were
prescribed in 1 HIIT session, whereas power output was repeatedly
varied within the work intervals of the other one. We tested the
following hypotheses: Higher fractions of
˙
VO2max would be
sustained in the varied-intensity mode, and ventilatory variables
would predict changes in
˙
VO2response.
Methods
Participants
A total of 14 well-trained male cyclists volunteered for this study
during their off-season. The Inland Norway University research
ethics committee at Lillehammer University College approved the
study in compliance with the Declaration of Helsinki.
Study Design
Participants visited the laboratory on 3 occasions, at the same time of
the day, separated by at least 48 hours. In the first visit, participants
completed a submaximal lactate threshold test and a maximal
incremental test to characterize their cycling ability and physiologi-
cal profile. They were also familiarized with the HIIT sessions used
during subsequent visits. In visits 2 and 3, participants performed in
randomized order 2 HIIT sessions with either varied-intensity or
constant-intensity work intervals, matched for duration and mean
power output. Acute physiological and perceptual responses were
compared between HIIT sessions at the same time points.
Participants were instructed to refrain from all types of intense
exercise 24 hours before each laboratory visit and to prepare as they
would for competition. They were instructed to consume identical
meals 1 hour before each laboratory visit and to refrain from
caffeine during the preceding 3 hours. All tests were performed
free from distractions, under similar environmental conditions
(16°C–17°C), with participants being cooled with a fan.
Ergometer Setup
All cyclists used the same bike (2017 Roubaix One. 3 size 56;
Fuji, Taichung, Taiwan) mounted on a cycle ergometer (KICKR;
Wahoo Fitness, Atlanta, GA) considered to be valid and reliable.
22,23
Saddle position was individually adjusted, and measures were noted
for replication. The bike was equipped with a crank-based power
meter (SRAM S975; SRM, Jülich, Germany), from which power
output and cadence were recorded. An indoor cycling training
software (TrainerRoad v1.0.0.49262; TrainerRoad LLC, Reno,
NV) was used to customize all testing sessions, which were per-
formed in ergometer mode. The laptop was connected to the KICKR
through Bluetooth and to the SRM through an ANT+ dongle. With
this setup, the resistance of the KICKR was controlled by the power
output and cadence readings of the SRM. Power output, cadence, and
heart rate (HR) were recorded by a cycle computer (PowerControl 8;
SRM) at a sampling rate of 1 Hz and subsequently analyzed using
open-source software (version 3.4; GoldenCheetah, London, UK;
http://www.goldencheetah.org). The KICKR and the SRM were
calibrated by the manufacturer prior to the study. Before each
use, a member of the research team warmed-up the KICKR by
riding for 10 minutes at 100 W and then performed the “spindown”
through the TrainerRoad software, which is a zero-offset calibration
of the strain gaugesbased on bearing and belt friction. The zero offset
procedure of the SRM was performed according to the manufac-
turer’s recommendations.
To examine the validity of the power outputs generated by
the KICKR through this setup, individual targets determined for
each HIIT session (see text below) were compared with the SRM
readings. A freely available spreadsheet
24
was used to assess data
at 77%, 84%, and 100% of maximal aerobic power (MAP) for
agreement, with a total of 288, 96 and 288 duplicates, respectively.
The comparison KICKR versus SRM revealed a typical error
of estimate of 7 W (90% confidence limits [CLs], 6 to 7 W);
correlation coefficient (r) of .98 (90% CL, 0.97 to 0.98); and mean
bias of −3 W (90% CL, −4to−3 W) at 77%MAP; a TEE of 2 W
(90% CL, 2 to 3 W), r= 1.00 (90% CL, 1.00 to 1.00) and mean bias
of 1 W (90% CL, 0 to 1 W) at 84%MAP; and a TEE of 8 W (90%
CL, 7 to 9 W), r= .97 (90% CL, 0.97 to 0.98) and mean bias of
11 W (90% CL, 10 to 12 W) at 100%MAP. Our ergometer setup
was therefore deemed valid.
Preliminary Testing
In the first visit, participant’s height and body mass were measured,
and they completed a cycling experience index questionnaire,
25
as
well as standalone questions about their training habits. In brief, by
adding up the scores from each question, individuals are assigned a
total score from 0 (representing a complete noncyclist) to 37
(representing a highly experienced and well-trained cyclist).
25
Participants subsequently completed a lactate threshold test, which
started at 125 W, increasing by 50 W every fifth minute (25 W if
blood lactate concentration [La] was ≥3 mmol·L
−1
), and terminated
when [La] reached ≥4 mmol·L
−1
. Blood samples were taken from a
fingertip at the last 30 seconds of each 5-minute bout and were
immediately analyzed (Biosen C-Line; EKF Diagnostics, Penarth,
United Kingdom). At the start of the test, cyclists chose their
cadence, which they subsequently held constant throughout the
remainder of the test. Power output at 4 mmol·L
−1
[La] was
calculated for each cyclist from the relationship between [La]
and power output in the last 2 stages, by using linear regression.
˙
VO2was measured during the last 3 minutes of each stage (15-s
sampling time) using a computerized metabolic system with mix-
ing chamber (Oxycon Pro; Erich Jaeger, Hoechberg, Germany).
Prior to every test, the gas analyzer was calibrated with certified
calibration gases of known concentrations, and the flow turbine
(Triple V; Erich Jaeger) was calibrated with a 3-L syringe (5530
series; Hans Rudolph Inc, Shawnee Mission, KS).
After the lactate threshold test, cyclists rode for 10 minutes at a
power outputbetween 50 and 100 W before performing the maximal
incremental test to determine
˙
VO2max, MAP and maximal work rate
(
˙
Wmax). The test started at 200 W with work rate being increased by
25 W every minute until voluntary exhaustion or an inability to
maintain cadence above 70 rev·min
−1
despite verbal encouragement.
Pedaling cadence was freely chosen, but participants were instructed
to avoid abrupt changes.
˙
VO2was continually measured, and
˙
VO2max was calculated as the highest 60-second mean. MAP
was calculated according to Daniels et al.
26
This method extrapolates
the relationship between submaximal power outputs and respective
measures of
˙
VO2to
˙
VO2max, by means of linear regression.
26
Power output data were recorded continuously throughout the test,
with
˙
Wmax calculated as the mean of the last 60 seconds of the
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incremental test. Straight after the maximal incremental test, a blood
sample was taken from a fingertip and immediately analyzed to
establish [La]. Cyclists reported their peak RPE using the Borg 6 to
20 scale immediately after terminating the test.
HIIT Sessions
Initially, participants performed a 15-minute warm-up based on
Borg 6 to 20 RPE scale. The warm-up consisted of 5 minutes
at an RPE of 11 (light), followed by three 1-minute intervals at
16 (between hard and very hard), interspersed with two 2-minute
blocks and a final 3 minutes, all at 9 (very light). Cyclists were
allowed to manipulate the work rate imposed by the cycle ergome-
ter to match the required RPE.
Both HIIT sessions started with 5 minutes at 50%MAP,
followed by six 5-minute work intervals at a mean intensity of
84%MAP, interspersed with 2.5-minute recovery at 30%MAP.
Varied-intensity work intervals consisted of three 30-second
surges at 100%MAP, interspersed with two 1-minute blocks,
and a final 1.5 minutes at 77%MAP. Constant-intensity work
intervals consisted of 5 minutes at 84%MAP. A detailed outline of
the warm-up and both work intervals can be seen in Figure 1.The
number of work intervals, their duration, and the duration of
recovery intervals were chosen based on athletes’perception
of what constitutes a valuable training session for aerobic capa-
city development. The mean intensity for the work intervals
was chosen based on pilot testing to warrant both HIIT sessions
would be completed with physiological responses typical of
exercise performed within the severe intensity domain. As for
the varied-intensity work intervals, the 30-second surges at 100%
MAP were chosen based on previous work of our lab with
cyclists
7
and cross-country skiers.
27
Given the superior time at
>90%˙
VO2max elicited by 30 seconds compared with longer work
intervals in the cycling study,
7
we reasoned that the 1.5 minutes at
100%MAP employed in the cross-country skiing study
27
could
be split into 3 surges to characterize the varied-intensity work
interval.
Heart rate was continuously measured during the entire HIIT
sessions.
˙
VO2was measured during the 5-minute work intervals
(5-s sampling time) using the same equipment and following the
calibration procedures adopted in the preliminary testing. Time at
>90%˙
VO2max was calculated by summing all raw
˙
VO2measures
over the established cutoff. At the end of each work interval,
fingertip blood samples were taken to assess [La], and RPE was
recorded. Participants self-selected their cadence, and water con-
sumption was not restricted. Twenty minutes after finishing the HIIT
Figure 1 —(A) Warm-up procedure based on RPE that was performed prior to both sessions of high-intensity interval training, (B) varied-intensity
work intervals, and (C) constant-intensity work intervals. The intensity of both sessions was prescribed as a percentage of the individual’s MAP, and
6 work intervals were completed. Both high-intensity interval-training sessions started with 5 minutes at 50%MAP, which is omitted from the figure for
clarity. %MAP indicates percentage of maximal aerobic power; RPE, rating of perceived exertion.
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Interval-Training Optimization 3
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sessions, session RPE (sRPE) was recorded. An individualized
training impulse (iTRIMP), which is a training-load metric based
on HR,
28
was also calculated to compare the training load between
HIIT sessions. Within the iTRIMP calculation, exercise intensity is
weighted according to participants’own HR–[La] exponential
relationship,
28
obtained during the preliminary testing. iTRIMP was
calculated for each HIIT session by summing the weighted scores
from every 5-second HR means.
28
Data Analyses
Dependent variables were assessed for normality using Shapiro–
Wilk tests. Paired ttests were used to compare time at
>90%˙
VO2max, sRPE, and iTRIMP between HIIT sessions.
Two-way repeated-measures analyses of variance (work interval
mode ×work interval number) were performed to test for differ-
ences in mean
˙
VO2as a percentage of maximal (%˙
VO2max),
total
˙
VO2, mean
˙
VE, mean ventilatory equivalent for oxygen
(
˙
VE ·
˙
VO2
−1), mean ƒ
R
, mean tidal volume (V
T
), mean carbon
dioxide output (
˙
VCO2), mean HR, [La], RPE, and mean cadence.
Following the analysis of variance, Bonferroni pairwise compar-
isons were used to identify where significant differences existed
within the data. Cohen dor partial eta squared (η2
p) were computed
as effect size estimates. Absolute changes between HIIT sessions
were calculated for mean
˙
VE (Δ
˙
VE) and total
˙
VO2(Δ
˙
VO2) per
work interval. The association between Δ
˙
VE and Δ
˙
VO2was
modeled by multilevel analysis with participant as a random effect
(ie, random intercept). A correlation coefficient (r) was then
computed by adjusting for repeated observations within partici-
pants. Data were analyzed using SSPS (SSPS Statistics 25; IBM,
Armonk, NY), and significance level was set at P≤.05. Results are
presented as mean (SD) (90% CLs).
Results
Participants’characteristics are presented in Table 1.Therewasa
longer time at >90%˙
VO2max for HIIT with varied-intensity com-
pared with constant-intensity work intervals (410 [207] vs 286 [162] s
[90% CL, 312 to 508 vs 209 to 362 s]; t=2.63; P=.02; d=0.16;
Figure 2A), despite no difference in mean power output as measured
by the SRM crank (324 [30] vs 323 [30] W [90% CL, 310 to 338 vs
309to337W];t=1.35; P=.20; d= 0.01). There was also no
differences in sRPE (6.0 [1.8] vs 6.6 [1.7] [90% CL, 5.2 to 6.9 vs
5.8to7.5];t=−1.62; P=.13;d=−0.09; Figure 2B), or iTRIMP (178
[43] vs 181 [46] [90% CL, 157 to 198 vs 160 to 203]; t=−.43;
P=.68; d=−0.02; Figure 2C). The mean
˙
VO2responses to both
types of work intervals are presented in Figure 3.
Statistics and effect size estimations from the analysis
of variance are given in Table 2. No interactions between
work interval mode and work interval number were found for
%˙
VO2max (Figure 4A), total
˙
VO2(Figure 4B),
˙
VE (Figure 4C),
˙
VE ·
˙
VO2
−1,ƒ
R
(Figure 4D), V
T
(Figure 4E),
˙
VCO2(Figure 4F),
HR, [La] (Figure 4G), RPE (Figure 4H), or cadence (Figure 4I).
There was a main effect of work interval mode for %˙
VO2max, total
˙
VO2,
˙
VE,
˙
VE ·
˙
VO2
−1, and
˙
VCO2, but not for ƒ
R
,V
T
, HR, [La],
RPE, or cadence. A main effect of work interval number was found
for %˙
VO2max, total
˙
VO2,
˙
VE,
˙
VE ·
˙
VO2
−1,ƒ
R
,V
T
, HR, [La], and
RPE. Pairwise comparisons revealed differences between conse-
cutive work intervals for all variables (all Ps≤.05), except for
V
T
, in which work interval 1 was different from 3, 4, 5, and 6 (all
Ps≤.02). There was no main effect of work interval number for
˙
VCO2or cadence.
Multilevel analysis produced the following model
(y=mx+ b):
Δ
˙
VO2ðmLÞ=23.3 · Δ
˙
VE ðL · min−1Þþ239.6
ðmSE =4.4;P<.001;bSE =118.9;P=.06;ICC =.43Þ(1)
A moderate correlation was found between Δ
˙
VE and Δ
˙
VO2
(r= .36; r
2
= .13; P= .002).
Discussion
Consistent with our first hypothesis, well-trained cyclists sustained
higher fractions of
˙
VO2max when they performed the varied-
intensity compared with constant-intensity work intervals during
a HIIT session. Time at >90%˙
VO2max, %˙
VO2max sustained, and
total
˙
VO2, all suggest an increased aerobic cost elicited by the
varied-intensity work intervals. Importantly, this increased demand
was not accompanied by a higher ƒ
R
, HR, [La], RPE, or cadence.
Furthermore, we found no differences between conditions in sRPE
or iTRIMP, which may suggest that varied-intensity work intervals
produce a higher training stimulus per dose of exercise. Consistent
with our second hypothesis,
˙
VE was also higher during the varied-
intensity compared with constant-intensity work intervals. In addi-
tion, Δ
˙
VE was moderately associated with Δ
˙
VO2, suggesting
differences in the oxygen cost of hyperpnoea partially explain
the magnitude of
˙
VO2differences between HIIT sessions.
Varying power output between 100% and 77%MAP within
the work intervals of a HIIT session increased the mean time at
>90%˙
VO2max by 43%, from 286 seconds (4 min 46 s) produced by
Table 1 Participant Characteristics and Preliminary
Testing Results, Mean (SD)
Age, y 24 (6)
Height, cm 184 (5)
Body mass, kg 75.0 (5.2)
˙
VO2max, mL·kg
−1
·min
−1
69.2 (6.6)
˙
VO2max, L·min
−1
5.16 (0.35)
˙
Wmax, W·kg
−1
5.77 (0.66)
˙
Wmax, W 430 (35)
Maximal aerobic power, W·kg
−1
5.18 (0.56)
Maximal aerobic power, W 387 (33)
Maximal heart rate, beats·min
−1
191 (9)
[La]
peak
, mmol·L
−1
13.3 (1.3)
˙
VEpeak, L·min
−1
211 (17)
ƒ
Rpeak
, cycles·min
−1
63 (10)
V
Tpeak
, L 3.4 (0.5)
RER
peak
1.19 (0.04)
RPE
peak
19.4 (0.6)
4 mmol·L
LT
−1
, W·kg
−1
3.77 (0.53)
4 mmol·L
LT
−1
, W 282 (34)
Cycling Experience Index 26 (6)
Races in the previous season 13 (11)
Training in the previous season, h 523 (218)
Current training, h·wk
−1
11 (5)
Abbreviations: [La]
peak
, peak blood lactate concentration; ƒ
Rpeak
, peak breathing
frequency; LT, lactate threshold; RER
peak
, peak respiratory exchange ratio;
RPE
peak
, peak rating of perceived exertion;
˙
VEpeak, peak minute ventilation;
˙
VO2max, maximal oxygen uptake; V
Tpeak
, peak tidal volume;
˙
Wmax, maximal
work rate during the incremental test.
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the constant-intensity work intervals (84%MAP) to 410 seconds
(6 min 50 s). This result stands out as we did not manipulate the
mean intensity and length of the work and recovery intervals, or
total HIIT duration, which often is the case in studies assessing time
at or close to
˙
VO2max.
1–3,7,8
Previously, Billat et al
10
demonstrated
that effort could be minimized, and exercise sustained for more
than 15 minutes at >95%˙
VO2max, when power output was manip-
ulated according to expired gas responses. Despite HIIT with
varied-intensity work intervals produced a shorter duration at
>90%˙
VO2max compared with that of Billat et al,
10
our results
provide evidence for a more practical approach to programming
this type of training.
Unique to our study was that varied-intensity work intervals
increased
˙
VO2without affecting most variables reflecting the
physiological and perceptual strain of exercise. In contrast, Zadow
et al
9
reported times at >85%˙
VO2max of 2 minutes 31 seconds
and 2 minutes 4 seconds, for respectively all-out and constant-
intensity work intervals, but with greater HR, RPE, and sRPE.
9
Collectively, these results suggest there may be a tolerance limit for
the magnitude of power output variation that allows cyclists to
optimize time at >90%˙
VO2max without compromising exercise
capacity. Another strength of our work is that HIIT sessions
were matched for all prescription elements affecting the exercise
dose, except power output distribution. For instance, Lisbôa et al
6
reported longer time at >90%˙
VO2max (4 min 19 s vs 2 min 03 s)
following decreasing-intensity versus constant-intensity work
intervals, but conditions were matched by participant’s capacity
to perform work above critical power.
6
Work and recovery interval
durations were not controlled, potentially affecting time at a high
fraction of
˙
VO2max more than the power output distribution
itself.
1–3,7,8
Thus, the higher time at >90%˙
VO2max was likely
achieved by a change in exercise dose.
High-intensity interval training can be prescribed with differ-
ent formats according to the aim of the training session. To produce
the longest times at or close to
˙
VO2max, short work intervals
(<1 min) have been recommended.
1–3,7,8
In agreement with this
proposition, adding repeated power output variations within longer
5-minute work intervals increased time at >90%˙
VO2max. Never-
theless, there is contrasting evidence from training studies, with
evidence that both short
8,29
and long work intervals
4,30
may trigger
a potent stimulus for increasing
˙
VO2max. This suggests time at
>90%˙
VO2max is unlikely to be the only training variable driving
˙
VO2max enhancements. Its relatively poor reliability must also be
considered.
31
Despite these considerations, we speculate that our
novel HIIT session, if repeated over time, may combine the benefits
of both short and longer work intervals. Further work is necessary
to confirm this hypothesis.
Ventilatory responses to work intervals of different power
output distributions have been previously neglected.
6,9
Figure 2 —(A) Time spent over 90% of
˙
VO2max, (B) sRPE, and
(C) individualized training impulse (iTRIMP). Open circles represent
each participant, and black squares represent the mean values for high-
intensity interval-training sessions with varied-intensity and constant-
intensity WI. sRPE indicates session rating of perceived exertion;
˙
VO2max,
maximal oxygen uptake; WI, work interval. *Different from constant
WI (P=.02).
Figure 3 —Mean
˙
VO2responses (5-s sampling time) to varied-
intensity (dotted line) and constant-intensity (solid line) work intervals.
The horizontal dashed line represents 90% of maximal V
:
O2(mean of all
participants). SD is omitted from the figure for clarity. As individual
participants reached 90% of maximal V
:
O2at different time points, dotted
and solid lines do not reflect the mean time spent over 90% of maximal
V
:
O2.V
:
O2indicates oxygen uptake.
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Table 2 Statistics and Effect-Size Estimations From the Analysis of Variance for Each Variable Analyzed
Interaction Main effect of interval mode
Main effect of interval
number
FPη2
pFPη2
pFPη2
p
%˙
VO2max 1.03 .39 .07 11.26 .005* .46 43.71 <.001* .77
Total
˙
VO21.00 .40 .07 10.78 .006* .45 42.85 <.001* .77
˙
VE 0.32 .76 .02 8.42 .01* .39 32.01 <.001* .71
˙
VE ·
˙
VO2
−10.61 .56 .05 5.65 .03* .30 35.60 <.001* .73
ƒ
R
0.55 .59 .04 3.50 .08 .21 41.79 <.001* .76
V
T
0.36 .75 .03 2.02 .18 .13 12.32 <.001* .49
˙
VCO21.06 .39 .07 18.69 .001* .59 2.38 .09 .15
Heart rate 0.16 .87 .01 <0.01 .93 <.01 68.57 <.001* .85
Blood lactate concentration 0.50 .58 .04 1.54 .24 .11 35.75 <.001* .73
Rating of perceived exertion 0.58 .66 .04 1.72 .21 .12 3.99 <.001* .70
Cadence 0.87 .46 .06 0.06 .82 <.01 1.09 .36 .08
Abbreviations: %˙
VO2max, percentage of maximal oxygen uptake; ƒ
R
, breathing frequency;
˙
VO2, oxygen uptake;
˙
VCO2, carbon dioxide output;
˙
VE, minute ventilation;
˙
VE ·
˙
VO2
−1, ventilatory equivalent for oxygen; V
T
, tidal volume.
*Statistical significance.
Figure 4 —(A) Mean %˙
VO2max, (B) total
˙
VO2, (C) mean
˙
VE, (D) mean f
R
, (E) mean V
T
, (F)
˙
VCO2, (G) [La], (H) RPE, and (I) mean cadence. Data are
displayed per work interval as mean (SD) for high-intensity interval-training sessions with varied- (triangles) and constant-intensity work intervals
(squares). ƒ
R
indicates breathing frequency; [La], blood lactate concentration; RPE, rating of perceived exertion;
˙
VCO2, mean carbon dioxide output;
˙
VE, mean minute ventilation; %˙
VO2max, mean oxygen uptake as a percentage of maximal;
˙
VO2, total oxygen uptake; V
T
, tidal volume. *Different from
previous work interval (all P≤.03). †Different from work intervals 3, 4, 5, and 6 (all P≤.02). ‡Main effect of work-interval mode (all P≤.01). §Main
effect of work-interval number (all P<.001).
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Interestingly, our results suggest they play a role in the observed
changes in total
˙
VO2. Compared with constant-intensity work
intervals, varied intensity produced higher
˙
VE and
˙
VE ·
˙
VO2
−1,
implying a greater mechanical work of the pulmonary system and
an increased oxygen cost of hyperpnoea.
17,18,32
Indeed, the multi-
level analysis used in this study predicted that for each liter of
increase in
˙
VE,
˙
VO2is increased by 4.7 mL. This is nevertheless
higher than the cost of exercise hyperpnoea reported by Aaron
et al
32
as 2.9 mL of oxygen per liter of
˙
VE, or more recently by
Dominelli et al
18
as 2.4 mL·L
–1
. Altogether the model intercept of
239.6 mL, results suggest mechanisms other than an increased
˙
VE
may account to a greater extent for the observed changes in aerobic
cost of HIIT. It is therefore not surprising that only a moderate
correlation between Δ
˙
VE and Δ
˙
VO2(r= .36) was found in the
present study.
The fact that we did not find differences in ƒ
R
or V
T
between
varied-intensity and constant-intensity work intervals, alongside the
differences in
˙
VE, has some practical and mechanistic implications.
Practically, ƒ
R
has been considered a marker of physical effort,
20
reinforcing the sense of equivalence in strain levels between both
types of HIIT. Mechanistically, a higher
˙
VE with no significant
changes in either ƒ
R
or V
T
indicates that both contributed to the
increases in
˙
VE, although in small magnitudes or with interindivid-
ual differences, challenging the hypothesis of a distinct mechanistic
control of ƒ
R
and V
T
during exercise.
20
Indeed, it has been previ-
ously suggested that during high-intensity exercise central com-
mand regulates
˙
VE preferentially through changes in ƒ
R
,
20
which
our data do not support. Instead, Tipton et al
33
have proposed
˙
VE is
regulated by a complex integration of mechanical and physiological
factors, making it difficult to completely associate ƒ
R
and V
T
with a
particular type of reflex. Therefore, the higher
˙
VE in the varied-
intensity compared with the constant-intensity work intervals is
likely the result of a tightly coupled interaction between the
increases in ƒ
R
and V
T
that manifest during this type of exercise.
Additional mechanistic insight can be gained from a close
inspection of Figure 3.Repeatedsurgesat100%MAP,asopposed
to a single surge at the start of each work interval, seem required
to produce the observed differences in time at >90%˙
VO2max.
Not only the oxygen cost of hyperpnoea, but also the oxygen
cost of muscle contraction, may have been greater during the
varied-intensity compared with the constant-intensity work inter-
vals. Higher exercise intensities have been shown to elicit a more
uniform activation of the quadriceps femoris muscles
34
and their
motor units.
34,35
Thus, it is reasonable to assume some high-thresh-
old fibers were only recruited at 100%MAP. The low efficiency
and high fatigability of these fibers may have contributed to an
increased whole-body
˙
VO2and time at >90%˙
VO2max.
13
Besides,
we cannot discard the
˙
VO2kinetics hypothesis as proposed by other
authors.
6,9,11,12
If the initial 30-second surges of the varied-intensity
work intervals did not directly affect time at >90%˙
VO2max, faster
˙
VO2kinetics apparently contributed to a higher %˙
VO2max sus-
tained and total
˙
VO2. Future studies should use breath by breath
ergospirometry and leg electromyography to provide evidence for
these hypotheses.
Practical Applications
Well-trained cyclists looking for alternative strategies to optimize
training stimulus are advised to try the varied-intensity work
intervals as outlined here. Whether performance adaptations will
be superior to constant-intensity work intervals remain to be
established by a longitudinal study; but similar ƒ
R
, HR, [La], RPE,
and training load metrics suggest that it is unlikely that negative
training outcomes occur.
Conclusions
In comparison with a HIIT session with constant-intensity work
intervals, well-trained cyclists sustain higher fractions of
˙
VO2max
when power output is repeatedly varied within the work intervals.
This effect is partially mediated by an increased oxygen cost of
hyperpnoea.
Acknowledgments
A.H.B. is a CNPq (Conselho Nacional de Desenvolvimento Científico e
Tecnolo´gico—Brazil) scholarship holder (200700/2015-4). The authors
thank Joar Hansen for his technical support and Tomas Urianstad, Vemund
Lien, and Ingvild Berlandstveit for their assistance with data collection. The
authors also thank the participants for their enthusiasm to complete this study.
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