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Effects of Different Uphill Interval-Training Programs on Running Economy and Performance

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Purpose: Runners use uphill running as a movement-specific form of resistance training to enhance performance. However, the optimal parameters for prescribing intervals are unknown. The authors adopted a dose-response design to investigate the effects of various uphill interval-training programs on physiological and performance measures. Methods: Twenty well-trained runners performed an incremental treadmill test to determine aerobic and biomechanical measures, a series of jumps on a force plate to determine neuromuscular measures, and a 5-km time trial. Runners were then randomly assigned to 1 of 5 uphill interval-training programs. After 6 wk all tests were repeated. To identify the optimal training program for each measure, each runner's percentage change was modeled as a quadratic function of the rank order of the intensity of training. Uncertainty in the optimal training and in the corresponding effect on the given measure was estimated as 90% confidence limits using bootstrapping. Results: There was no clear optimum for time-trial performance, and the mean improvement over all intensities was 2.0% (confidence limits ±0.6%). The highest intensity was clearly optimal for running economy (improvement of 2.4% ± 1.4%) and for all neuromuscular measures, whereas other aerobic measures were optimal near the middle intensity. There were no consistent optima for biomechanical measures. Conclusions: These findings support anecdotal reports for incorporating uphill interval training in the training programs of distance runners to improve physiological parameters relevant to running performance. Until more data are obtained, runners can assume that any form of high-intensity uphill interval training will benefit 5-km time-trial performance.
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ORIGINAL INVESTIGATION
International Journal of Sports Physiology and Performance, 2013, 8, 639-647
© 2013 Human Kinetics, Inc.
Effects of Different Uphill Interval-Training Programs
on Running Economy and Performance
Kyle R. Barnes, Will G. Hopkins, Michael R. McGuigan, and Andrew E. Kilding
Purpose: Runners use uphill running as a movement-specic form of resistance training to enhance performance.
However, the optimal parameters for prescribing intervals are unknown. The authors adopted a dose-response
design to investigate the effects of various uphill interval-training programs on physiological and performance
measures. Methods: Twenty well-trained runners performed an incremental treadmill test to determine aerobic
and biomechanical measures, a series of jumps on a force plate to determine neuromuscular measures, and a
5-km time trial. Runners were then randomly assigned to 1 of 5 uphill interval-training programs. After 6 wk
all tests were repeated. To identify the optimal training program for each measure, each runner’s percentage
change was modeled as a quadratic function of the rank order of the intensity of training. Uncertainty in the
optimal training and in the corresponding effect on the given measure was estimated as 90% condence limits
using bootstrapping. Results: There was no clear optimum for time-trial performance, and the mean improve-
ment over all intensities was 2.0% (condence limits ±0.6%). The highest intensity was clearly optimal for
running economy (improvement of 2.4% ± 1.4%) and for all neuromuscular measures, whereas other aerobic
measures were optimal near the middle intensity. There were no consistent optima for biomechanical mea-
sures. Conclusions: These ndings support anecdotal reports for incorporating uphill interval training in the
training programs of distance runners to improve physiological parameters relevant to running performance.
Until more data are obtained, runners can assume that any form of high-intensity uphill interval training will
benet 5-km time-trial performance.
Keywords: endurance training, resistance training, oxygen consumption, neuromuscular characteristics
The authors are with the Sports Performance Research Inst
New Zealand, Auckland University of Technology, Auckland,
New Zealand.
Differences in submaximal oxygen uptake exist
between athletes running at the same speeds, and these
disparities in “running economy” are a major factor
explaining differences in running performance of
endurance athletes.1–3 Various strategies such as altitude
exposure,4 training in the heat,5 dynamic stretching,6 and
high-intensity interval training1,2,7 have been proposed as
methods to improve running economy via their effect on
1 or more of the metabolic, cardiorespiratory, neuromus-
cular, and musculoskeletal systems. Most recent research
has focused on the effects of supplementing endurance
training with different forms of heavy-resistance or
plyometric training to further improve running economy
and running performance.7–15 While coaches often use
various forms of movement-specic resistance training
in periodized training programs for distance runners,
only anecdotal reports5,16,17 and 2 research investiga-
tions18,19 exist concerning the physiological responses to
and potential improvements in performance from such
training. Ferley et al18 compared effects of uphill interval
training and control (level-grade) interval training on
various measures of performance in well-trained distance
runners. Although performance in both groups improved
substantially, the only signicant difference favored con-
trol training. Houston and Thomson19 used a combination
of uphill gradients and durations in addition to traditional
resistance training in each training session. Despite no
changes in VO2max, they found signicant improvements
in a time-to-exhaustion test, as well as increased distance
run in 60- and 90-second timed runs. The authors did not
report running economy in either study.
In view of the uncertainty about the physiological
effects of uphill training and other movement-specic
forms of resistance training on distance-running perfor-
mance, there is a clear need for more research in this area
to identify optimal training.7 The conventional approach
to investigating an optimal treatment is to perform a
repeated-measures crossover study, with each subject
receiving all treatments. However, this approach is often
impractical in training studies, because the long-lasting
effects of training prevent subjects from receiving more
than 1 type of training. To address this problem, Stepto et
al20 reported a novel and potentially more powerful dose-
response design, in which individual cyclists received only
a single form of training and the optimal training “dose”
was identied by modeling the effect of training as a poly-
nomial function of the rank-ordered training intensity. In
640 Barnes et al
the current study, we adopted the same modeling approach
in an attempt to determine the uphill interval-training pro-
tocol most effective for running economy and performance
in well-trained distance runners.
Methods
Design
We adopted a pre–post parallel-groups design with
measures conducted before and after a 6-week interven-
tion period. Subjects reported to the laboratory at least 2
hours postprandial and having avoided strenuous exercise
in the 24 hours preceding all test sessions. Before the
intervention, subjects performed baseline measures of
the dependent variables on 2 occasions separated by 3
days. The rst testing session included an incremental
treadmill test to determine aerobic and biomechanical
characteristics, followed by a series of jumps to determine
muscle-power characteristics. The second testing session
took place 3 days later and involved a 5-km outdoor time
trial. Four days after completing the nal training session,
each runner repeated the same set of tests in the same
order as preintervention testing.
Subjects
Twenty distance runners (mean ± SD; age 21 ± 4 y, body
mass 65 ± 8 kg, height 178 ± 9 cm) with an average 5-km-
race personal-best time of 16.5 ± 1.2 min, average weekly
training volume of 95 ± 25 km/wk, and training history of
6.3 ± 2.9 years were randomly assigned to 1 of 5 uphill
interval programs. The intervention training-adherence
rate for participants was 100%. As running volume was
not manipulated in the current study, subjects in all groups
continued with their normal running over the course of
the study, with the addition of the intervention substitut-
ing some of their normal running for interval training.
Training logs for all subjects were monitored before and
during the training. It was a requirement of the study that
participants had not undertaken any structured interval
training or resistance training in the previous 6 weeks. The
study was approved by the institutional ethics committee
and all participants provided informed written consent.
Treadmill Testing
All running tests were performed in a temperature-
controlled laboratory (19–21°C, 65% relative humidity)
on a motorized treadmill (PowerJog, Birmingham, UK)
set at a 1.0% gradient.3 After a standardized warm-up,
subjects completed an incremental test to determine run-
ning economy, involving repeated, progressively faster
(increments of 1.0 km/h) 4-min stages at 4 to 6 xed
running speeds ranging from 12 to 18 km/h until they
were unable to sustain steady-state VO2. A 90-second
recovery period occurred between stages for blood lactate
sampling (Lactate Pro, Arkray, Japan) for later determi-
nation of lactate threshold (D-max method).21 Expired
gases were measured continuously using a metabolic cart
(ParvoMedics TrueOne 2400, Salt Lake City, UT, USA)
for determination of VO2, VCO2, VE, and RER. Heart rate
was determined every 1 s (Polar RS800sd, Polar Electro,
Finland). Running economy was dened as the mean VO2
determined during the last minute of each running speed.
Approximately 90 seconds after completion of the nal
submaximal running stage, VO2max was determined during
an incremental test to volitional exhaustion. Subjects
commenced running at 1.0 km/h (1.0% gradient) below
the nal submaximal speed for 1 minute. Thereafter,
treadmill gradient was increased by 1% each minute until
volitional exhaustion. The highest VO2 over a 30-second
period during the test was considered VO2max. Changes in
endurance performance were indicated by the peak run-
ning speed reached at the end of the incremental treadmill
test. Because we used increases in gradient (rather than
speed) in the latter part of the treadmill test, we calculated
speed on the at as S = ST + (ST × 0.045) × i, where S =
peak speed in km/h, ST = treadmill speed in km/h, and i =
treadmill inclination in percent.22
Neuromuscular Measures
on a Force Plate
After a 30-minute passive recovery period, subjects
performed a countermovement jump and squat jump as
previously described by McGuigan et al23 and a 5-jump
plyometric test described by Saunders et al12 on an Accu-
Power force plate (Advanced Mechanical Technology
Inc, Watertown, MA) to determine neuromuscular char-
acteristics. Each jumping test was performed twice. The
following parameters were determined for each type of
jump: peak force, time to peak force, peak power, maxi-
mum rate of force development, displacement, eccentric
utilization ratio, and stiffness. Eccentric utilization ratio
was calculated as the peak power ratio between perfor-
mances on the countermovement jump compared with
the squat jump.23 Stiffness was estimated by dividing the
peak force by the vertical displacement measured during
the 5-jump test.24
Running Performance
Three days after laboratory-based tests, subjects com-
pleted a 5-km self-paced time trial on a 400-m outdoor
tartan track. After each subject’s typical self-chosen
precompetition warm-up (recorded and repeated postint-
ervention), he or she was instructed to run the distance
“as fast as possible.
Training Interventions
Subjects performed 2 uphill interval-training sessions/
wk over a 6-week period while maintaining their normal
running training outside of the weekly interval-training
sessions. Specic details of the work:rest ratios, intensity,
and uphill gradient of the different training interventions
are presented in Table 1. The work:rest ratios were not
Effects of Uphill Interval Training in Runners 641
consistent with standard interval-training practice7 but
were designed to accommodate the practicalities of uphill
interval training, when runners have to return to the bottom
of a hill to start another repetition. The outcomes are
therefore more likely to reect what athletes should expect
when they add uphill running to their training program.
Statistical Analysis
We performed simulations to determine the sample
size that would give an acceptable condence interval
for optimal performance predicted with a quadratic
dose-response model. In these simulations, the training
protocol was a variable that ranged from 1 for the highest
intensity and shortest duration through 5 for the lowest
intensity and longest duration. Data were generated that
had no real polynomial effects, because data without
effects need the largest sample sizes to dene the mag-
nitude of the effects with acceptable precision. With 20
subjects, an error of measurement for an individual’s
running economy of 2%, and a quadratic model, the 90%
condence interval was acceptable.
All performance and other outcome measures were
analyzed as percentage changes via the transformation
log[(post measurement)/(pre measurement)].25 The
transformed data were modeled as a quadratic function
of the rank-ordered intensity of the training protocols
to determine the optimal training dose and the value of
the change in the outcome measure at this dose.26 The
standard error of the estimate from the model divided
by the square root of 2 provided an estimate of the error
of measurement for the outcome measure under the
conditions of the experiment (after adjustment for the
dose-response relationship). Condence intervals for
the measures derived from the quadratic model were
generated by bootstrapping using a customized Excel
spreadsheet.27 For the value of the change in the outcome
measure at the optimal dose, bootstrapping also provided
estimates of the probabilities that the true change was
greater or less than the smallest important benecial and
harmful change.
To make conclusions about the true effects of train-
ing on performance and other outcome measures, we
used the clinical form of magnitude-based inference:
Unclear effects were those with the possibility (>25%
chance) of benet but an unacceptable risk of harm (odds
ratio of benet to harm <67).28 All other effects were
clear and reported with a qualitative probability for the
true magnitude using the following scale: 25% to 74%,
possibly; 75% to 94%, likely; 95%, very likely. This
approach to inference requires an estimate for smallest
important change in each outcome measure. The small-
est enhancement of performance that has a substantial
effect on an athlete’s chance of improvement is 0.3 of the
typical within-athlete variation of performance between
competitions.25 The variability of performance of high-
level competitive distance runners (3–10 km) is 1.1%29;
consequently, a smallest important change of 0.3% was
used for measures of performance.
To analyze potential mechanisms underlying the
effect of training on performance, changes in performance
were plotted against changes in physiological and other
measures and the scatterplots inspected for any linear
trend. A clear linear trend in the graph would have
allowed for estimation of the smallest important change
in the mechanism variable as the change that tracked the
smallest important change in performance. However,
there were no such clear linear relationships, presum-
ably because random error of measurement masked
any relationship between real individual differences in
changes in performance and the mechanism variable. A
different approach to estimating smallest changes was
therefore adopted. The enhancement in performance
turned out to be practically constant across the range
of training intensities (~2%). Therefore, to estimate the
smallest important change in each mechanism variable,
Table 1 Details and Progression of the Five 6-Week Uphill Interval-Training Programs
(2 Interval-Training Sessions/Wk)
Group 1 (n = 3) Group 2 (n = 5) Group 3 (n = 5) Group 4 (n = 4) Group 5 (n = 3)
Gradient 18% 15% 10% 7% 4%
%HRmax 100% 100% 98–100% 93–97% 88–92%
Velocity at VO2max 120% 110% 100% 90% 80%
Work:rest ratio 1:6 1:3 1:2 1:1.5 1:1
Progression
week 1 12 × 8 s 8 × 30 s 5 × 2 min 4 × 4 min 2 × 10 min
week 2 16 × 10 s 10 × 35 s 5 × 2.5 min 4 × 5 min 2 × 15 min
week 3 20 × 10 s 12 × 40 s 7 × 2 min 5 × 4.5 min 1 × 20 + 1 × 15 min
week 4 20 × 12 s 12 × 45 s 7 × 2.5 min 5 × 5 min 2 × 20 min
week 5 24 × 10 s 16 × 40 s 9 × 2 min 6 × 5 min 3 × 15 min
week 6 24 × 12 s 16 × 45 s 9 × 2.5 min 7 × 5 min 2 × 25 min
642 Barnes et al
we assumed that the tracking of changes in the means of
the mechanism and performance variables reected the
underlying relationship in the individual change scores.
The smallest important change in the mechanism variable
was therefore 0.3% × (Δ mechanism)/(Δ performance),
where Δ is the change in the mean; for example, the
smallest important change in VO2max was calculated as
0.3% × (4.1/2.0) = 0.62%.
Results
Figure 1 shows the percentage change and quadratic
trends for identifying optimal training intensity with
bootstrapped condence limits for performance and
selected other measures for the individual subjects in
the rank-order intensity of each group after the uphill
interval-training intervention. Table 2 shows baseline
values of outcome measures and statistics from the boot-
strap analyses for inferences about the optimal intensity
and duration of interval training and about the effects on
the outcome measures at the optimum. A well-dened
outcome for the effect of dose of training on outcome
measures present in Table 2 was shown if the proportion
of successful bootstrap simulations (bootstrap success
rate) was 90% and there was a reasonable condence
interval associated with the dose (group) or the condence
interval was limited to 1 of the dose extremes (ie, 1, 1 or
5, 5). Errors of measurement derived from the modeling
are also shown in Table 2 and allow assessment of the
precision of the measures in comparison with those in
reliability studies (see Discussion).
Data for the 5-km time trial showed a weak quadratic
trend (Figure 1). The modeling predicted an optimum
near the middle of the range of training intensity and
duration (group = 2.3, as shown in Table 2) and a likely
benecial effect on performance (–2.0%). However, the
bootstrap success rate (57%) represents inconsistency in
the curvature of the bootstrapped quadratics; that is, only
57% predicted a minimum in performance time, and the
resulting condence interval for the optimal treatment
extended to both extremes of the treatment range (1–5).
Changes in peak speed showed similar results.
There was a strong trend toward groups 3 and 4
having the optimal training parameters to improve all
aerobic measures besides running economy (Figure 1,
Table 2). There were well-dened outcomes for the effects
on aerobic measures obtained during the incremental
treadmill test (bootstrap success rate 90%), indicating
consistency in predicting a maximum at the turning point.
Most of the aerobic measures had reasonably narrow con-
dence limits for the training intensity (ie, group), and the
2 running-economy measures had an optimum precisely
dened at the highest intensity (group 1; Table 2). The
effects at the optima were also clear. Improvements in
all aerobic measures except running economy were made
across groups 2 through 5, with the optima occurring
near the middle of this range, whereas group 1 showed
a negative effect in most aerobic measures. However,
the reverse phenomena occurred for running economy,
where the effects only showed improvements in group 1
(Figure 1).
Improvements in biomechanical measures (Figure
1—stride rate, Table 2) favored groups 1 to 3 (Table 1).
Bootstrap success rate was variable from measure to mea-
sure. Accordingly, the condence limits for the training
intensity and effects reect this with narrow condence
limits around measures with well-dened outcomes
(bootstrap success rate 90%) and wide confidence
limits around those without well-dened outcomes (low
bootstrap success rate, Table 2). All improvements in
muscle-power measures favored group 1 (Table 2), and
the changes across all groups were similar to that of
countermovement-jump peak force shown in Figure 1.
There was a high bootstrap success rate for the eccen-
tric utilization ratio, stiffness, peak force, time to peak
force, and maximum rate of force development of all 3
jumps (85%), and a low rate in the peak-power mea-
surements of all 3 jumps (36%). Where condence
limits were narrow for the optimal-training group, so
were the condence limits for the effects at the optima.
Inferences about the effects on performance and other
outcome measures showed likely or very likely benet
at the predicted optima.
Discussion
In the current study we used a novel design and analy-
sis approach, previously adopted by Stepto et al,20 to
determine the effects of different types of uphill interval-
training programs on running economy and performance
in trained distance runners. A major nding was that no
specic uphill-training approach was associated with
greater gains in 5-km time-trial performance, but cur-
vilinear relationships existed between a continuum of
hill-training approaches on several performance-related
physiological variables including running economy
(Figure 1). Running performance improved across the
range of training intensities without a strong curvilinear
relationship between uphill-training characteristics and
a subsequent change in 5-km time-trial performance or
peak running speed. The 2% improvement in running
performance was similar to other studies demonstrating
concurrent improvements in running economy and per-
formance while employing various modes of resistance
training.10,13 Ferley et al18 also demonstrated an ~2%
improvement in estimated time-trial performance30 after
6 weeks of uphill interval training similar to group 2
training in the current study.
The error of measurement derived from the boot-
strap analysis for 5-km time-trial performance was
1.2%, which is comparable to other studies employ-
ing true reliability studies with well-trained distance
643
Figure 1 — Percentage change in performance and selected physiological measures after the 5 uphill interval-training programs.
Black dots represent individual changes in runners. Solid curved line represents mean from quadratic modeling, and dashed curved
lines are the associated condence limits generated from bootstrapping. × = the predicted group optimum; CMJ = countermove-
ment jump.
644
Table 2 Outcome Measures at Baseline and Statistics From the Bootstrap Analyses for Inferences
About the Effects at the Predicted Group Optimum
Baseline values
(mean ± SD)
Error
a
(%)
Bootstrap
success
rate (%)
Predicted Optimal Group
and Corresponding Effect (90%CL)
Group
b
Effect (%)
Running performance
5-km performance time 17.0 ± 1.3 min 1.2 57 2.3 (1, 5) –2.0 (–2.5, –1.3)*
peak speed 21.4 ± 1.9 km/h 1.2 54 3.1 (1, 5) 2.0 (1.8, 3.7)**
Aerobic measures
VO2max 63.9 ± 5.9 mL · kg–1 · min–1 3.0 96 3.6 (2.9, 4.6) 4.1 (2.2, 6.6)**
vVO2max 18.7 ± 1.5 km/h 1.7 98 3.4 (2.7, 4.1) 2.0 (1.0, 3.4)**
lactate threshold velocity 15.9 ± 1.6 km/h 1.4 91 3.4 (2.2, 5) 2.9 (2.3, 4.2)**
VO2submax @ 14 km/h 53.7 ± 3.0 mL · kg–1 · min–1 1.5 90 1 (1, 1) –2.4 (–3.9, –1.0)**
VO2submax @ 14 km/h 201 ± 11 mL · kg–1 · min–1 1.5 90 1 (1, 1) –2.4 (–3.9, –1.0)**
% of VO2max @ 14 km/h 84.4 ± 7.6%VO2max 3.0 96 3.5 (1.9, 4.4) –3.2 (–6.2, –1.9)*
Biomechanical measures
stride rate 87.8 ± 4.5 strides/min 1.0 93 1 (1, 1) 2.1 (0.9, 2.7)**
stride length 3.03 ± 0.21 m 1.7 64 2.3 (1, 5) 0.4 (0.0, 2.3)*
contact time 0.22 ± 0.02 s 3.0 88 3.1 (1, 5) –5.2 (–8.0, –3.9)**
ight time 0.12 ± 0.02 s 6.1 91 3.4 (2.8, 5) 10 (6, 18)**
Neuromuscular measures
eccentric utilization ratio 1.03 ± 0.06 2.2 98 1 (1, 1) 12 (8, 16)**
stiffness 11.0 ± 2.5 kN/m 3.5 85 1 (1, 1.8) 25 (8, 39)**
Countermovement jump
peak force 63 ± 19 N/kg 7.7 100 1 (1, 1) 15 (9, 24)**
time to peak force 1.82 ± 0.47 s 13 92 1 (1, 3.2) 7.2 (–2, 29)*
peak power 42.6 ± 6.3 W/kg 7.5 36 1 (1, 5) 2.6 (–4.1, 8.8)*
maximum RFD 101 ± 50 kN/s 20 100 1 (1, 5) 29 (6, 52)**
Squat jump
peak force 58 ± 14 N/kg 7.7 100 1 (1, 1) 12 (7, 22)**
time to peak force 2.04 ± 0.69 s 12 92 1 (1, 1) –7.5 (–16, 4)*
peak power 43.9 ± 6.0 W/kg 7.9 12 1 (1, 5) –3.9 (–11, 2)?
maximum RFD 94 ± 34 kN/s 12 99 1 (1, 1) 19 (13, 29)**
5-jump test
peak force 64.5 ± 5.4 N/kg 7.7 96 1 (1, 1) 8.4 (–1, 13)**
time to peak force 2.75 ± 0.71 s 15 99 1 (1, 1) –22 (–33, 0)**
peak power 69 ± 13 W/kg 7.6 29 1 (1, 5) 4.5 (–2, 10)*
maximum RFD 105 ± 47 kN/s 20 97 1 (1, 3.6) 21 (–5, 48)*
Abbreviations: CL, condence limits; VO2max, maximal aerobic capacity; vVO2max, velocity at VO2max; RFD, rate of force development.
a Error of measurement derived from the bootstrap analysis (which adjusts for any quadratic effect of group and thereby provides an estimate of error
approximating the typical error in a 6-wk reliability study without an intervention). b Group range = 1–5 (Table 1). For example, 2.3 indicates that
the optimal training fell between groups 2 and 3 and therefore had an intensity of 100–110% (of vVO2max), a duration of 30–120 s, and a gradient
of 10–15%.
*Likely benecial. **Very likely benecial. ?Unclear.
Effects of Uphill Interval Training in Runners 645
runners,29,31,32 suggesting to us that there is limited
evidence for individual responses to training. The
correlations between percentage changes in 5-km
performance and percentage changes in each of the
aerobic, biomechanical, neuromuscular, and peak-
speed measures were unclear. A lack of clear correla-
tions provides additional support for no individual
responses to explain. However, because every partici-
pant demonstrated some sort of improvement in 5-km
time-trial performance it can be suggested that running
performance can be enhanced as a result of changes in
a variety of mechanistic variables caused by varying
the uphill-running loading parameters.
With regard to effect of uphill interval training on
improvements on selected aerobic, neuromuscular, and
biomechanical measures, the various 6-week uphill
interval-training programs resulted in curvilinear trends,
often with an identied optimum (Figure 1). There was
a well-dened outcome for the effect of dose of training
on all aerobic measures and most biomechanical and
neuromuscular measures. A larger sample size would
be needed to establish clear optima for the other out-
comes. Except for improvements in running economy,
our model predicted optimal enhancements after work
bouts associated with an intensity between groups 3 and
4 training (Table 1). The enhancements observed for
aerobic measures (Table 2) besides running economy
are perhaps unsurprising, since the intensity of these
work bouts occurred at or near VO2max, which is in
accord with the principle of specicity. It is highly likely
these changes were a result of the additional uphill
interval training because all subjects were undertaking
similar running training outside of the current study (95
± 25 km/wk). In contrast, the 2 studies utilizing uphill
interval training reported no change19 or a decrement
in VO2max.18
We observed that training at the highest intensi-
ties (group 1 and 2) was associated with the greatest
improvements in running economy and neuromuscular
characteristics, as well as increased stride rate. Ours is
the rst study to demonstrate that a regimen of high-
intensity uphill interval training improves running
economy. The magnitude of the improvement (2.4%) is
consistent with previous studies reporting positive effects
of traditional resistance training or plyometric training
on running economy in runners with a wide range of
ability,8–15 as well as anecdotal reports of the benets
of uphill sprinting.5,16,17 The observed improvement in
running economy was accompanied by similar reduction
in VO2max and consequently an increase in %VO2max in
group 1 (Figure 1). This is not surprising given that the
training imposed on athletes in group 1 (Table 1) would
be unlikely to augment VO2max in any way. It is known
a positive relationship exists between maximal and sub-
maximal VO2, indicating that athletes with higher aerobic
demands of running (ie, poorer running economy) tend
to have higher VO2max values, which may also explain
the positive shift in running economy and negative shift
in VO2max.33–36 The theoretical underpinnings of this
observation have yet to be fully elucidated but may
relate to various neuromuscular and/or biomechanical
characteristics. It should be noted that regardless of the
changes in running economy, VO2max, and %VO2max,
group 1 training still resulted in an ~2% improvement
in 5-km run performance (Figure 1).
The fact that the greatest improvements in neuro-
muscular measures also occurred with the highest inten-
sity of training (Table 2) may support the aforementioned
premise that the enhancement of running economy was
due to a range of mechanisms relating to recruitment and
coordination of muscle bers and efciency of muscle
power development, as well as better use of the muscle-
tendon units’ stored elastic energy. An indirect measure of
this storage and return of muscular energy is the eccentric
utilization ratio, in which we found 12% improvements
in group 1 training (Table 2). Another key function of the
active skeletal musculature during running is to regulate
the stiffness of the muscle-tendon apparatus to maximize
the exploitation of elastic energy, which improves running
economy.24 Like other neuromuscular characteristics,
leg stiffness measured in this study showed the greatest
improvements at the highest training intensity (Table 2).
The error of measurement for neuromuscular measures
in Table 2 adds some uncertainty to the true relationship
between training dose and effect but is not unreasonable,
given that the measured error is population specic and
is still comparable to other reliability studies.37 The
improvements in neuromuscular measures are also in
agreement with a number of other studies using various
forms of explosive resistance training or plyometric type
of activities such as hopping, jumping, and bounding as
ways to directly or indirectly potentiate neuromuscular
adaptations.10–13,15
Finally, another plausible explanation for improved
running economy after high-intensity uphill interval train-
ing is training-induced alteration in stride rate, which was
also greatest at the highest intensity of training (Figure 1,
Table 2). Paavolainen et al10 observed similar changes in
stride characteristics in response to 9 weeks of explosive
strength training in well-trained endurance runners along
with concurrent ~8% improvement in running economy.
The changes in biomechanical measures may themselves
be explained at least partly by changes in neuromuscular
characteristics.
Practical Applications
and Conclusion
Our ndings provide support for incorporating uphill
interval running in the training programs of distance
runners to improve various physiological, biomechanical,
646 Barnes et al
and neuromuscular parameters relevant to running per-
formance. Different uphill-training approaches appear
to induce specic physiological and mechanical adapta-
tions, which suggests that hill training should be carefully
matched to the strengths and weaknesses of the athlete,
the underlying demands of the event, and the training or
competitive focus. Until more data are obtained, runners
can assume that performance enhancements can be made
as a result of changes in a variety of mechanistic variables
caused by varying the uphill-running loading param-
eters, since every participant demonstrated some sort of
improvement in 5-km time-trial performance. Further
studies are required to establish whether improvements
derived from uphill interval training can be established
through variations in the frequency, duration, volume,
and periodization of training.
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... Stiffness contributes to the rebound of energy during the stretch-shortening cycle (SSC) (Butler et al., 2003). Although Watsford et al. (2010) have associated increased stiffness with injury in athletics, recent evidence suggests a relationship between increased lower extremity stiffness and enhanced athletic performance, specifically in distance running (Barnes et al., 2013(Barnes et al., , 2015Spurrs et al., 2003). Barnes et al. (2015) reported that warming up with a weighted vest resulted in an increased running velocity as well as improved running economy. ...
... Spurrs et al. (2003) also reported an increase in leg stiffness and improved 3-km running times when male distance runners completed a 6-week plyometric training program. Similar changes in leg stiffness were reported when runners followed a 6-week hill interval program (Barnes et al., 2013). During a nine week plyometric intervention, Saunders et al. (2006) attributed improvements in running economy of highly trained runners to increased muscletendon stiffness. ...
... Many of the previously mentioned studies suggest that increased stiffness is associated with improved running economy (Barnes et al., 2013(Barnes et al., , 2015Saunders et al., 2006;Spurrs et al., 2003). Running economy is a factor closely related with running performance (Conley and Krahenbuhl, 1980;Conley et al., 1984). ...
Article
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Previous evidence has suggested that there is a relationship between leg stiffness and improved running performance. The purpose of this investigation was to determine how leg stiffness of runners was influenced in the 24 and 48 hour period following a cross country race. Twenty-two collegiate cross-country runners (13 males, 9 females, 19.5 ± 1.4 yr) were recruited and participated in the study. Leg stiffness was assessed 24 hours before and after a race as well as 48 hours post-race. Three jumping protocols were conducted: 1) a static jump, 2) a countermovement jump, and 3) a vertical hopping test. Two embedded force plates (1000 Hz) were utilized to measure ground reaction forces for each test and a metronome was utilized to maintain hopping frequency (2.2 Hz). A significant main effect was found for a static jump, a countermovement jump and leg stiffness. Leg stiffness was significantly reduced 24 hours post-race (pre-race 36.84 kN·m ⁻¹ , 24h post 33.11 kN·m ⁻¹ , p < 0.05), but not 48 hours post-race (36.30 kN·m ⁻¹ ). No significant differences were found in post-hoc analysis for the squat jump, countermovement jump height and the eccentric utilization ratio. Following a cross-country race, leg stiffness significantly declined in a group of collegiate runners in the immediate 24 hours post-race, but returned to baseline 48 hours post-race. Sport scientists and running coaches may be able to monitor leg stiffness as a metric to properly prescribe training regiments.
... However, despite the scientific evidence supporting the use of endurance training to reduce the oxygen cost, the effects of the training method used (interval vs. continuous) are still a matter of debate in the literature. On the one hand, several studies investigating the effects of interval training at intensities between 93-120% of the speed at VO 2max and continuous training at the onset of blood lactate accumulation speed have reported similar running economy improvements of around 1-7% [21][22][23][24][25]. On the other hand, previous research using similar training strategies found no significant improvements at all [26,27], while some authors suggest that the endurance training modality used exerts a trivial effect on the oxygen cost [19,28]. ...
... There are contradictory findings regarding INT training programs and its effects on the oxygen cost. Some researchers have reported no running economy improvements after an INT intervention [25,47], whereas others have found the opposite [48]. Others, such as Billat et al. [23] have reported oxygen cost improvements when adding high intensity training to baseline running, although this effect seems to be lost when that high intensity training is performed too often. ...
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Background Oxygen cost of running is largely influenced by endurance training strategies, including interval and continuous training. However, which training method better reduces the oxygen cost remains unknown. Objective This study aimed to systematically review the scientific literature and performs a meta-analysis to address the effects of different endurance training modalities on the oxygen cost of running. Methods A literature search on 3 databases (MEDLINE, SPORTDiscus and Web of Science) was conducted on February 28, 2019. After analysing 8028 resultant articles, studies were included if they met the following inclusion criteria: (a) studies were randomised controlled trials, (b) studies included trained runners without previous injuries (c) interventions lasted at least 6 weeks, with participants allocated to Interval (INT) or Continuous (CON) groups, and (d) oxygen cost was assessed pre- and post-training intervention. Six studies (seven trials) met the inclusion criteria and were included in the meta-analysis. This resulted in 295 participants (n = 200 INT; n = 95 CON training method). Standardised mean difference with 95% confidence intervals (CI) between INT and CON conditions and effect sizes were calculated. To assess the potential effects of moderator variables (such as, age, VO2max of participants, number of weeks of intervention) on main outcome (oxygen cost of running), subgroup analyses were performed. Results Comparing changes from pre- to post-intervention, oxygen cost improved to a greater extent in CON when compared to INT interventions (0.28 [95% CI 0.01, 0.54], Z = 2.05, p = 0.04, I² = 30%). Oxygen cost improvements were larger in participants with higher VO2max (≥ 52.3 ml kg⁻¹ min⁻¹) (0.39 [95% CI 0.06, 0.72], Z = 2.34, p = 0.02), and in programs greater or equal to 8 weeks (0.35 [95% CI 0.03, 0.67], Z = 2.13, p = 0.03). When the total volume per week of INT was ≥ 23.2 min, there was a significant improvement favorable to CON (0.34 [95% CI 0.01, 0.61], Z = 2.02, p = 0.04). Conclusion Continuous training seems, overall, a better strategy than interval training to reduce the oxygen cost in recreational endurance runners. However, oxygen cost reductions are influenced by several variables including the duration of the program, runners’ aerobic capacity, the intervals duration and the volume of interval training per week. Practitioners and coaches should construct training programs that include both endurance training methods shown to be effective in reducing the oxygen cost of running.
... As HIT incorporates a broad spectrum of intensities, performing exercise across this range is an effective strategy to induce a differential adaptive response (Barnes et al., 2013;Rønnestad et al., 2015). Exercise bout duration represents a key programming variable because of the inverse relationship between duration and intensity (i.e., shorter intervals typically involve higher intensity exercise). ...
... Shorter (30 s) compared with long duration cycle-based intervals (300 s) have been demonstrated to result in a higher training intensity (363 ± 32 W vs. 324 ± 42 W) and lead to significant increases in VO 2max (8.7 ± 5.0%) and PPO (8.5 ± 5.2%) (Rønnestad et al., 2015). Furthermore, following uphill runningbased HIT, improvements in aerobic fitness and performance variables were optimal around the middle intensity (100% velocity at VO 2max ; 10% gradient; 1:2 work:rest ratio) with increases in neuromuscular measures (e.g., peak power, maximum rate of force development) greatest at the highest intensity (Barnes et al., 2013). Repeated-sprint training (RST), typically defined as a series of short sprints (3-7 s in duration), separated by recovery periods of less than 60 s (Buchheit and Laursen, 2013a), is another HIT derivative at the highest end of the intensity spectrum. ...
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High-intensity interval training (HIT) is an effective approach for improving a range of physiological markers associated with physical fitness. A considerable body of work has demonstrated substantial improvements in cardiorespiratory fitness following short-term training programmes, while emerging evidence suggests that HIT can positively impact aspects of neuromuscular fitness. Given the detrimental consequences of prolonged exposure to microgravity on both of these physiological systems, and the potential for HIT to impact multiple components of fitness simultaneously, HIT is an appealing exercise countermeasure during human spaceflight. As such, the primary aim of this mini review is to synthesize current terrestrial knowledge relating to the effectiveness of HIT for inducing improvements in cardiorespiratory and neuromuscular fitness. As exercise-induced fitness changes are typically influenced by the specific exercise protocol employed, we will consider the effect of manipulating programming variables, including exercise volume and intensity, when prescribing HIT. In addition, as the maintenance of HIT-induced fitness gains and the choice of exercise mode are important considerations for effective training prescription, these issues are also discussed. We conclude by evaluating the potential integration of HIT into future human spaceflight operations as a strategy to counteract the effects of microgravity.
... Hill training is another strategy that enhances RE. Only one study examined the effects of six-week hill training on RE in distance runners and showed its improvement by 2.4% ± 1.4% [5]. ...
... The control did not show significant improvement in any of the selected variables. The past studies on selected anaerobic performance reveals of Barnes et al (2013) opined that improved running economy after high-intensity uphill interval training is training-induced alteration in stride rate, which was also greatest at the highest intensity of training. ...
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The purpose of the study was to find out the impact of hill resistance training on anaerobic performance namely maximum power, minimum power, and average power and fatigue index among male Handball players. To achieve the purpose of the study twenty four male handball players have been randomly selected from various colleges in the state of Tamil Nadu, India. The age of subjects were ranged from 18 to 25 years. The subjects had past experience of at least three years in handball and only who those represented their respective college teams were taken as subjects. A series of anaerobic performance tests was carried out on each participant. Maximum power, minimum power, average power and fatigue index assessed by running based anaerobic sprint test. The subjects were randomly assigned into two groups of twelve each, such as experimental and control groups. The experimental group participated in the hill resistance training for 3 days a week, one session per day and for 8 weeks each session lasted 45 minutes. The control group maintained their daily routine activities and no special training was given. The subjects of the two groups were tested on selected variables prior and immediately after the training period.
... The underlying assumption is that an improvement in RE will also lead to an improvement in P (Barnes & Kilding, 2015b). Research findings suggest that various training interventions improve RE, including strength (Paavolainen, Hakkinen, Hamalainen, Nummela, & Rusko, 1999;Storen, Helgerud, Stoa, & Hoff, 2008), plyometric (Berryman, Maurel, & Bosquet, 2010;Pellegrino, Ruby, & Dumke, 2016), hill training (Barnes, Hopkins, Mcguigan, & Kilding, 2013), and combinations of strength training and running intensity variation (Chtara et al., 2005;Sedano, Marin, Cuadrado, & Redondo, 2013;Skovgaard et al., 2014). Exposure to variable extrinsic factors such as time spent at natural or artificial altitude (Levine & Straygundersen, 1997;Saunders, Telford, Pyne, Hahn, & Gore, 2009), compliant running surfaces (Kerdok, Biewener, Mcmahon, Weyand, & Herr, 2002), lighter footwear (Warne & Warrington, 2014), environmental temperatures and humidity (Sandsund et al., 2012) have all been shown to produce improvements in RE. ...
... The underlying assumption is that an improvement in RE will also lead to an improvement in P (Barnes & Kilding, 2015b). Research findings suggest that various training interventions improve RE, including strength (Paavolainen, Hakkinen, Hamalainen, Nummela, & Rusko, 1999;Storen, Helgerud, Stoa, & Hoff, 2008), plyometric (Berryman, Maurel, & Bosquet, 2010;Pellegrino, Ruby, & Dumke, 2016), hill training (Barnes, Hopkins, Mcguigan, & Kilding, 2013), and combinations of strength training and running intensity variation (Chtara et al., 2005;Sedano, Marin, Cuadrado, & Redondo, 2013;Skovgaard et al., 2014). Exposure to variable extrinsic factors such as time spent at natural or artificial altitude (Levine & Straygundersen, 1997;Saunders, Telford, Pyne, Hahn, & Gore, 2009), compliant running surfaces (Kerdok, Biewener, Mcmahon, Weyand, & Herr, 2002), lighter footwear (Warne & Warrington, 2014), environmental temperatures and humidity (Sandsund et al., 2012) have all been shown to produce improvements in RE. ...
Article
Improvements in running economy (RE) are thought to lead to improvements in running performance (P). Multiple interventions have been designed with the aim of improving RE in middle and long-distance runners. The aim of this study was to assess the effect of interventions of at least 2-weeks’ duration on RE and P and to determine whether there is a relationship between changes in RE (ΔRE) and changes in running performance (ΔP). A database search was carried out in Web of Science, Scopus and SPORTDiscus. In accordance with a PRISMA checklist 10 studies reporting 12 comparisons between interventions and controls were included in the review. There was no correlation between percentage ΔRE and percentage ΔP (r = 0.46, P = 0.936, 12 comparisons). There was a low risk of reporting bias but an unclear risk of bias for other items. Meta-analyses found no statistically significant differences between interventions and controls for RE (SMD (95% CI) = −0.37 (−1.43, 0.69), 204 participants, p = 0.49) or for P (SMD (95% CI) = −0.65 (−26.02, 24.72, 204 participants, p = 0.99). There is a need for studies of greater statistical power, methodological quality, duration and homogeneity of intervention and population. Standardised measures of performance and greater control over non-intervention training are also required.
... Based only on our RE data, we have no reason to suggest that athletes need to prioritize the inclusion of uphill-and downhill-specific training so as to optimize their overall running performance. Uphill-specific running training improves LRE (Barnes et al. 2013), but we are not aware of any study showing improvements in URE in response to specific uphill running training. Moreover, RE is not the only factor affecting performance. ...
Article
Full-text available
Purpose Exercise economy is not solely an intrinsic physiological trait because economy in one mode of exercise (e.g., running) does not strongly correlate with economy in another mode (e.g. cycling). Economy also reflects the skill of an individual in a particular mode of exercise. Arguably, level, uphill and downhill running constitute biomechanically different modes of exercise. Thus, we tested the hypothesis that level running economy (LRE), uphill running economy (URE) and downhill running economy (DRE) would not be strongly inter-correlated. Methods We measured the oxygen uptakes of 19 male trained runners during three different treadmill running speed and grade conditions: 238 m/min, 0%; 167 m/min, + 7.5%; 291 m/min, − 5%. Mean oxygen uptakes were 46.8 (SD 3.9), 48.0 (3.4) and 46.9 (3.7) ml/kg/min for level, uphill and downhill running, respectively, indicating that the three conditions were of similar aerobic intensity. Results We reject our hypothesis based on the strong correlations of r = 0.909, r = 0.901 and r = 0.830, respectively, between LRE vs. URE, LRE vs. DRE and URE vs. DRE. Conclusion Economical runners on level surfaces are also economical on uphill and downhill grades. Inter-individual differences in running economy reflect differences in both intrinsic physiology and skill. Individuals who have experience with level, uphill and downhill running appear to be equally skilled in all three modes.
... Recently, there has been a focus on the effectiveness of sport-specific resistance training strategies, allowing runners to achieve the benefits of resistance training during running itself. Such strategies include, but are not limited to: hill (Barnes, Hopkins, McGuigan, & Kilding, 2013), hypergravity (Groppo, Eastlack, Mahar, Hargens, & Pedowitz, 2005), sled (Cross, Brughelli, Samozino, Brown, & Morin, 2017) and sand (Yiǧit & Tuncel, 1998) running. A related strategy is wearable resistance training; a type of resistance involving the application of load directly to the body via a suit or vest-like garment worn during sporting movements (Barnes, Hopkins, McGuigan, & Kilding, 2015;Cross, Brughelli, & Cronin, 2014;Hrysomallis, 2012;Macadam, Cronin, & Simperingham, 2017;Puthoff, Darter, Nielsen, & Yack, 2006). ...
Article
Full-text available
Wearable resistance training involves added load attached directly to the body during sporting movements. The effects of load position during running are not yet fully established. Therefore, the purpose of this research was to determine spatio-temporal and kinetic characteristics during submaximal running using upper, lower and whole-body wearable resistance (1-10% body mass (BM)). Twelve trained male runners completed eight 2-min tread-mill running bouts at 3.9 m/s with and without wearable resistance. The first and last bouts were unloaded, while the middle 6 were randomised wearable resistance conditions: upper body (UB) 5% BM, lower body (LB) 1%, 3%, 5% BM and whole body (WB) 5%, 10% BM. Wearable resistance of 1-10% BM resulted in a significant increase in heart rate (5.40-8.84%), but minimal impact on spatio-temporal variables. Loads of 5% BM and greater caused changes in vertical stiffness, vertical and horizontal force, and impulse. Functional and effective propulsive force (2.95%, 2.88%) and impulse (3.40%, 3.38%) were significantly (p < 0.05) greater with LB5% than UB5%. Wearable resistance may be used to increase muscular kinetics during running without negatively impacting spatio-temporal variables. The application of these findings will vary depending on athlete goals. Future longitudinal studies are required to validate training contentions.
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The first edition of Exercise Physiology: Human Bioenergetics and Its Applications was a departure in terms of focus on human bioenergetics in describing muscle performance in terms of energy transduction at cellular levels. Our approach came out of the then (early 1970s) burgeoning field of exercise biochemistry and use of various techniques such as electron microscopy and mitochondrial respirometry. In the second and third editions we utilized findings of human metabolism based on use of isotope tracers to study metabolism. That technique resulted in articulation of the Crossover Concept and proving of the Lactate Shuttle hypothesis in human subjects. In the fourth edition of Exercise Physiology this theme has been retained, but the approach has become increasingly mechanistic due to many developments, including the use of molecular and cellular biology and isotope tracer technology in the field. Now, in the fifth edition we continue in traditions of the first four editions, but adjust and revise as the ever-increasing appreciation of exercise physiology increases, as reflected in the Exercise is Medicine, and publication of the 2018 Physical Activity guidelines and the application of exercise physiology to develop countermeasures for space travel.
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In brief: American mile record holder Steve Scott was tested for maximal aerobic capacity and running economy on three occasions over nine months, a period that included off-season, preseason, the indoor season, and the early part of the outdoor season. The laboratory results demonstrated that Scott's training raised his maximal aerobic capacity approximately 8% and improved his running economy 5%. In comparing Scott's data with that of former American record holder Jim Ryun, it appears that Scott's better economy, which allowed him to perform at a lower percentage of his maximal aerobic capacity, was the essential difference between the two runners.
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The purpose of this study was to evaluate the relationship between training methods of NCAA Division I runners and 10,000-m performance. Fourteen qualifying teams of the National Collegiate Athletic Association Division I national cross-country meet and 16 randomly chosen, nonqualifying teams participated in the study. A survey was used to evaluate the training methods of the respective teams throughout the training season. The results of the study indicated that the use of speed work, fartlek, mileage, and running twice a day during the transition phase of training were associated with a slower team performance. Interval training and fartleks during the competition phase were related to a slower team performance. Intervals and tempo training during the peaking period were related to a better performance. The multiple regression equation revealed that hill training during the transition phase was related to a faster team time. The transition phase of training appears to be related to success at the end of the season.
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Most endurance athletes use high-intensity training to prepare for competitions. In this review we consider the effects of high-intensity interval and resistance training on endurance performance and related physiological measures of competitive endurance athletes. METHODS. There were 22 relevant training studies. We classified training as intervals (supramaximal, maximal, submaximal) and resistance (including explosive, plyometrics, and weights). We converted all effects on performance into percent changes in mean power and included effects on physiological measures that impact endurance performance. FINDINGS. All but one study was performed in non-competitive phases of the athletes' programs, when there was otherwise little or no high-intensity training. Endurance performance of the shortest durations was enhanced most by supramaximal intervals (~4%) and explosive sport-specific resistance training (4-8%). Endurance performance of the longest durations was enhanced most by intervals of maximal and supramaximal intensities (~6%), but resistance training had smaller effects (~2%). Interval training achieved its effects through improvements of maximum oxygen consumption, anaerobic threshold, and economy, whereas resistance training had benefits mainly on economy. Effects of some forms of high-intensity training on performance or physiology were unclear. CONCLUSIONS. Addition of explosive resistance and high-intensity interval training to a generally low-intensity training program will produce substantial gains in performance. More research is needed to clarify the effects of the various forms of high-intensity training on endurance performance, to determine whether prescribing specific forms of resistance training can improve specific deficits of an endurance athlete's physiology, and to determine the effects of combining the various forms in periodized programs. KEYWORDS: aerobic, anaerobic threshold, economy, plyometrics, resistance, strength. Reprint pdf · Reprint doc · Commentaries by Foster and Saunders and Pyne.
Conference Paper
Purpose: The purpose of this study was to assess research aimed at measuring performance enhancements that affect success of individual elite athletes in competitive events. Analysis: Simulations show that the smallest worthwhile enhancement of performance for an athlete in an international event is 0.7-0.4 of the typical within-athlete random variation in performance between events. Using change in performance in events as the outcome measure in a crossover study, researchers could delimit such enhancements with a sample of 16-65 athletes, or with 65-260 in a fully controlled study. Sample size for a study using a valid laboratory or field test is proportional to the square of the within-athlete variation in performance in the test relative to the event; estimates of these variations are therefore crucial and should be determined by repeated-measures analysis of data from reliability studies for the test and event. Enhancements in test and event may differ when factors that affect performance differ between test and event; overall effects of these factors can be determined with a validity study that combines reliability data for test and event. A test should be used only if it is valid, more reliable than the event, allows estimation of performance enhancement in the event, and if the subjects replicate their usual training and dietary practices for the study; otherwise the event itself provides the only dependable estimate of performance enhancement. Publication of enhancement as a percent change with confidence limits along with an analysis for individual differences will make the study more applicable to athletes. Outcomes can be generalized only to athletes with abilities and practices represented in the study. Conclusion: estimates of enhancement of performance in laboratory or field tests in most previous studies may not apply to elite athletes in competitive events.
Article
Ferley, DD, Osborn, RW, and Vukovich, MD. The effects of uphill vs. level-grade high-intensity interval training on V ̇ O 2 max, V max , V LT , and T max in well-trained distance runners. J Strength Cond Res 27(6): 1549–1559, 2013—Uphill running represents a frequently used and often prescribed training tactic in the development of competitive distance runners but remains largely uninvestigated and unsubstantiated as a training modality. The purpose of this investigation included documenting the effects of uphill interval training compared with level-grade interval training on maximal oxygen consumption (V ̇ O 2 max), the running speed associated with V ̇ O 2 max (V max), the running speed associated with lactate threshold (V LT), and the duration for which V max can be sustained (T max) in well-trained distance runners. Thirty-two well-trained distance runners (age, 27.4 6 3.8 years; body mass, 64.8 6 8.9 kg; height, 173.6 6 6.4 cm; and V ̇ O 2 max, 60.9 6 8.5 ml$min 21 $kg 21) received assignment to an uphill interval training group (G Hill = 12), level-grade interval training group (G Flat = 12), or control group (G Con = 8). G Hill and G Flat completed 12 interval and 12 continuous running sessions over 6 weeks, whereas G Con maintained their normal training routine. Pre-and posttest measures of V ̇ O 2 max, V max , V LT , and T max were used to assess performance. A 3 3 2 repeated measures analysis of variance was performed for each dependent variable and revealed a significant difference in T max in both G Hill and G Flat (p , 0.05). With regard to running performance, the results indicate that both uphill and level-grade interval training can induce significant improvements in a run-to-exhaustion test in well-trained runners at the speed associated with V ̇ O 2 max but that traditional level-grade training produces greater gains.