Energy Cost of Run-Walk Pacing 1
Run-Walk Marathon Pacing:
The Energy Cost of Frequent Walk Breaks
William P. Nolana and Andrew R. Moorea
aDepartment of Kinesiology, Augusta University, 3109 Wrightsboro Road, Augusta, GA
Corresponding Author: William P. Nolan. Primary (preferred) email: firstname.lastname@example.org
Secondary email: email@example.com Telephone: 1-706-267-5531.
This article has been accepted for publication in International Journal of Performance
Analysis in Sport, published by Taylor & Francis.
Energy Cost of Run-Walk Pacing 2
Despite the widespread adoption of run-walk pacing in the marathon as an energy conserving
strategy, the law of inertia stipulates that a runner must use more energy while frequently
changing speeds than while running at a constant pace. This study examined the energy cost of
the run-walk method. Thirty recreational runners (16 males and 14 females) each ran and walked
at fixed, self-selected paces on a level treadmill under three conditions in a randomised and
counterbalanced order: 6 min continuous running, 6 min continuous walking, and 12 min of
alternating 2-min bouts of running and walking. Energy expenditure per kilometre and metres
traversed per litre of absolute oxygen were assessed via indirect calorimetry. Ratings of
perceived exertion were taken at 2-min intervals. Compared to continuous running, continuous
walking required 8.90 fewer kilocalories per kilometres (P = 0.001). However, when alternating
gaits, athletes required 3.98 kilocalories more to traverse one kilometre than when running
continuously (P = 0.01). There was no difference in the distance traversed per litre of oxygen,
but continuous running was faster. When runners in this study alternated gaits, they paid a 6%
energy tax while benefiting from only a very slight reduction in average perceived exertion.
Keywords: Energy metabolism, running, walking, pacing strategy, endurance training
Energy Cost of Run-Walk Pacing 3
Do regular bouts of intermittent walking during a marathon conserve energy compared with
continuous running? The practice of walking periodically during the marathon dates to the first
modern Olympiad. In recent decades, several coaches and exercise physiologists (Noakes, 2003;
Osler, 1978) have sometimes recommended the practice of taking “walk breaks” as an energy
conserving strategy. Among proponents of taking walk breaks, former Olympian Jeff Galloway
(1984, 2001, 2016) is best known to contemporary runners as the chief advocate of his
trademarked Run-Walk-Run® method. With this pacing strategy, short bouts of running and
walking, ranging from seconds to minutes, are alternated regularly for the entire duration of the
race. Despite the widespread popularity of the run-walk strategy, there are no data on its
presumed energy-sparing effect.
Across a range of submaximal paces, trained runners expend approximately one
kilocalorie per kilogram of body mass per kilometre (Margaria et al., 1963; Mayhew, 1977). In
humans, walking consumes less energy for a given distance than running (Hall et al., 2002;
Loftin et al., 2010). Therefore, it is tempting to assume traversing that distance with a mixture of
walking and running should yield an “energy discount” requiring less energy than continuous
running. However, we present three arguments below suggesting that alternating gaits should
expend more energy than that predicted by averaging the running and walking components.
Our primary argument against the energy discount assumption concerns the law of
inertia. Like all other objects, a marathoner in motion tends to remain in motion at a consistent
pace and in a straight line unless acted upon by a force. Because force is required to change
inertia, either speeding up (positive acceleration) or slowing down (negative acceleration) is
costly to energy reserves (Floyd, 2012). The angular movement of the lower extremities around
Energy Cost of Run-Walk Pacing 4
the hip, knee, and ankle joints are also subject to the law of inertia. Even when the linear inertia
of a runner is held constant, there is an energy cost to gait transition (Montiero et al., 2017;
Usherwood & Bertram, 2003). Thus, slowing down for a walk break might be better described as
an energy-dissipating “walk brake.” Whether the additional energy cost is large enough to be of
any practical significance is the primary focus of this study.
Our second argument questioning the energy discount assumption behind run-walk
pacing concerns the increased allostatic load imposed on the heart. When pace is repeatedly
varied, the cardiovascular system must continually adapt to changing energy demands.
Increasing heart rate variability is widely thought to promote cardiovascular fitness (Lehrer &
Vaschillo, 2008). However, consistent pacing seems to be the optimum strategy for expending
limited energy stores at the elite level (Diaz et al., 2018). When a walk break is initiated, energy
demand drops suddenly, but heart rate decelerates slowly. Therefore, the heart, which is also a
muscle, must burn excess fuel until a new equilibrium is established.
Our third argument against the energy discount assumption relates to the continual
disruption of the harmonies which develop amongst physiological systems during prolonged
rhythmic running. With continuous running, complex harmonies are established amongst the
musculoskeletal, pulmonary, and cardiovascular systems. Without conscious effort, for example,
breathing becomes entrained with a runner’s footfalls through the process of respiratory-
locomotor synchronisation (Bonsignore et al., 1998; Coates & Kowalchik, 2013; Hill et al.,
1988; Kirby et al., 1989). Breathing in turn can become harmonised with the cardiovascular
system through the phenomenon of respiratory sinus arrhythmia (Hayano et al., 1996). When the
alveoli fill with air during inhalation, the heart’s inter-beat interval decreases slightly to capture
more available oxygen, and then increases slightly as the alveoli empty during exhalation.
Energy Cost of Run-Walk Pacing 5
Synchronisation has also been observed between the cardiovascular and musculoskeletal
systems. For example, Constantini et al. (2018) found that when a runner’s foot strikes coincide
with the diastolic phase of the cardiac cycle, there is increased tissue perfusion. Based on all
these complex natural harmonies, it seems reasonable to speculate that frequently switching gaits
and paces will degrade the metabolic efficiencies that they likely provide.
The physics and the physiology of taking walk breaks become more problematic as the
frequency of gait transition increases. Consider the following thought experiment. Suppose that
an athlete runs the first half of a marathon and walks the second half. Given that continuous
walking is more efficient than continuous running, the athlete would indeed save energy over the
course of the race at the cost of a slower finish time. What if the runner switched gaits every ten
kilometres, or every five kilometres, or perhaps every kilometre? The law of inertia would
extract a tiny cost for each of these gait transitions, but perhaps not enough to override the
reduction in energy cost. Now suppose that gait transitions occur every few minutes or even
more frequently than every minute. At some point the law of inertia may begin to impose a larger
net energy cost. This is especially true for transitions occurring say every 15 seconds or so. As
Usherwood and Bertram (2003) observed, there is a 75% energy spike for several steps
immediately following a gait transition. At an extreme frequency of gait transition, virtually all
steps become high-cost transition steps.
The aim of the present study was to test the assumption that the run-walk pacing strategy
reduces the energy cost of traversing a given distance. Specifically, what effect does intermittent
running and walking have on energy expenditure per kilometre when compared with continuous
running? Secondarily, what effect does run-walk pacing have on oxygen consumption as indexed
Energy Cost of Run-Walk Pacing 6
by the distance traversed per litre of absolute oxygen? Since these research questions cannot be
adequately addressed within the context of an actual marathon, a laboratory approach was used.
2. Materials and methods
2.1. Study participants
Thirty healthy adult distance runners with racing experience (16 males, 14 females) participated
in the study. Athletes were recruited from the running community in Augusta, Georgia via
advertisements distributed in conjunction with local road races. Any runner with one or more
“Yes” responses on a 7-item version of the Physical Activity Readiness Questionnaire (PAR-Q)
was excluded from the study (Warburton et al., 2011). To avoid introducing extraneous variables
related to gait and body mass, pregnant female runners were also excluded. Runners were
instructed to follow their normal pre-run diet on the day of testing. The demographic and
anthropometric characteristics of the participants are summarised in Table 1.
2.2. Study design
This study was approved by the Institutional Review Board at Augusta University. Each athlete
signed a written informed consent before participating. We employed a within-participants
design in which each runner served as his/her own control. Thus, each participant provided a
sample of continuous running, a sample of continuous walking, and a sample of intermittent run-
walk pacing where running and walking were alternated. To control for possible sequence effects
such as cardiac drift and shifts in energy substrates, five participants were randomly assigned to
each of the six possible sequences of the three experimental conditions.
Prior to testing, runners were given an 8 to 10-min warm-up period, during which they were
familiarised with the Precor treadmill, and their preferred walking and running paces were
Energy Cost of Run-Walk Pacing 7
determined. These preferred running and walking paces were used during testing to replicate
actual performance as closely as possible. Beginning at a speed of 0.89 m∙s-1 (18:43 min∙km-1
pace), the speed of the treadmill was increased by 0.16 km∙h-1 every 3-5 s until the runner
reached a pace that he or she would choose for a “long, slow training run.” The participant’s
spontaneous gait transition speed from walking to running was noted. Next, the participant ran 4
min at the self-selected pace. If the runner’s heart rate exceeded 80% of heart rate reserve, or if
the runner showed visible signs of over-exertion (i.e., “huffing and puffing”) the pace was
adjusted downward to a more appropriate setting. Finally, the speed of the treadmill was
gradually reduced until the runner reached a walking pace that would be preferred for “taking a
walk break during a long run.” The runner’s spontaneous gait transition speed from running to
walking was also noted.
After the warm-up, the runner rested for 5 min while a face mask connected to a
calibrated metabolic cart (Parvo Medics TrueOne® 2400) was donned and adjusted. Each runner
then experienced all three experimental conditions in a randomised and counterbalanced order: 6
min of continuous running (R), 6 min of continuous walking (W), and a 12-min run-walk (RW)
segment during which bouts of running and walking were alternated every 2 min, beginning with
a period of running. The run-walk ratio we employed—2 min of running to 2 min of walking—
was well within the parameters of Galloway’s (1984, 2001, 2016) evolving recommendations for
the frequency of walk breaks. The distance traversed while running and walking depended on the
participant’s preferred paces determined during the warm-up. At the mean running speed of 2.61
m∙s-1 (6:23 min∙km-1 pace) and the mean walking speed of 1.71 m∙s-1 (9.75 min∙km-1 pace), the
average participant ran 1.88 km and walked 1.23 km during the study. Between experimental
Energy Cost of Run-Walk Pacing 8
conditions, there were 5-min rest periods during which participants were permitted to remove the
face mask connected to the metabolic cart and to sip water ad libitum.
2.4. Dependent measures
Absolute oxygen uptake ( O2) and respiratory exchange ratio (RER) were recorded continuously
by the metabolic cart. In addition to respiratory gases, heart rate (HR) was also recorded
continuously using a wireless Polar telemetry system. These physiological measures were
subsequently averaged over 2-min intervals.
Measures of the energy cost of locomotion and oxygen consumption were computed from
the data. The primary measure of interest was energy expenditure over distance expressed in
kcal∙km-1. A secondary measure considered was the distance traversed on the treadmill per litre
of absolute oxygen consumption (m∙LO2-1). This latter measure, which essentially represents “gas
mileage,” approximates the inverse of the former, but does not account for the relative
proportions of fat and carbohydrate being metabolised.
At odd-numbered minutes throughout each experimental condition, the participant was
asked to report subjective exertion using the Borg Rating of Perceived Exertion (RPE) Scale
(Borg, 1998). This scale ranges from a rating of 6 (no exertion) to 20 (maximum exertion) and
corresponds to estimated heart rate when a zero is added to the rating.
2.5. Statistical analysis
For each of the six dependent variables described above, data were analysed using separate
repeated measures one-way ANOVAs with three levels of the independent variable condition (R,
W, RW). Bonferroni adjusted post hoc comparisons were performed for any statistically
significant (P < 0.05) ANOVA results.
Energy Cost of Run-Walk Pacing 9
Data for all variables were screened for outliers, which were operationally defined as data points
with group standardised values > ±3.0. Two such outliers were identified within the energy
efficiency variable m∙LO2-1. The statistical analysis for this variable was performed with the
outliers included and again with them removed. Removing these points from the analysis did not
change the results of the ANOVA or post hoc tests. Therefore, the results presented in this paper
are those with all data points included.
Normality of the data in each condition for each variable was assessed using the Shapiro
Wilk test of normality. The data in all groups of all variables met the assumption of normality,
except for each group of the energy efficiency variable m∙LO2-1 (P < 0.05). The ANOVA test is
robust to violations of normality; therefore, no adjustments were made to the data (Blanca et al.,
2017). Mauchley’s test of sphericity was used to test each variable for violations to sphericity.
All the physiological variables ( O2, RER, and HR), and the one psychological variable (RPE)
violated this assumption (P < 0.05). The Greenhouse-Geisser correction was applied to the
ANOVA results for these data.
Table 2 presents the mean values for the three experimental conditions with respect to the
two energy efficiency variables (kcal∙km-1 and m∙LO2-1), the three underlying physiological
variables ( O2, RER, and HR), and the one psychological variable (RPE). As noted in Table 2,
ANOVA results were significant (P < 0.05) for all the dependent variables analysed. Bonferroni
adjusted post hoc test results with 95% confidence intervals for the group differences are
presented in Table 3.
The participants in this experiment were a broad sample of veteran distance runners ranging in
age from 23 to 70. Our sample of runners skewed somewhat older and slower than the average
Energy Cost of Run-Walk Pacing 10
marathon finisher. However, many aging runners gravitate to Galloway’s low volume, low
intensity marathon program with its run-walk philosophy (Galloway, 2006). Marathoners who
employ run-walk pacing use different walk break ratios, with faster runners taking less frequent
walk breaks. The ratio used in this study is typical of that used by beginners or slower runners. In
addition, allowing our runners to use their customary training paces further enhanced the external
validity of the study.
To our knowledge, this study was the first to test the assumption that regular bouts of
intermittent walking conserve energy during prolonged distance running. The results of our
experiment did not support the implicit energy discount assumption behind run-walk pacing. The
energy required to walk one kilometre was 8.90 kcal less than that required to run the same
distance. However, when gaits were alternated every two minutes, participants required an
additional 3.98 kcal to traverse one kilometre than when running continuously. As a practical
matter, this energy tax extrapolates to an additional 168 kcal over the 42.2 km marathon distance,
or enough energy to propel a typical runner about 2 km.
Our participants traversed 12.31 meters farther per litre of oxygen consumed while
walking than while running. This advantage over continuous running disappeared, however,
when participants walked intermittently. Although we found no difference between continuous
and intermittent running on our secondary measure of efficiency (m∙LO2-1), this null finding also
leads us to reject the energy discount hypothesis.
Inspection of Table 2 shows that the underlying physiological variables recorded by the
metabolic cart ( O2, RER, and HR) all increased in a stepwise fashion as participants progressed
from walking to intermittent running to continuous running. Mean RER varied in a narrow band
(0.82 to 0.89) reflecting the predominantly aerobic nature of the activity. The rate of absolute
Energy Cost of Run-Walk Pacing 11
oxygen consumption was lower for the RW condition than for the R condition. However, this
would not translate to more economical locomotion when performance is considered.
Specifically, intermittent running consumed less oxygen per unit of time than continuous running
but not per unit of distance. Using the same amount of oxygen over a given distance, continuous
running was faster, and therefore more efficient.
Our data showed that there was only a very small reduction in average perceived exertion
for the run-walk sample compared with the continuous running sample. Subjects rated exertion
under both conditions as “light.” Galloway (2016) explicitly states that his intermittent style of
running is a cognitive strategy that allows recreational marathoners, especially beginners, to
better manage the distress of prolonged running by “erasing fatigue.” However, the run-walk
method fosters a “dissociative” coping style (Masters & Ogles, 1998) whereby avoidance of
distress is desired and is negatively reinforced by repeated reductions in mild discomfort. On the
positive side, this contingency could potentially reduce the risk of injury or overtraining.
However, it imposes a built-in governor limiting potential performance. Improved performance
typically occurs with an “associative” style of coping (Masters & Ogles, 1998) where the athlete
is attentive to and accepting of pain and discomfort. In fact, successful runners tend to use
distress as a cue to increase effort (Morgan & Pollack, 1977). By repeating this process, runners
gradually learn to tolerate more discomfort and perhaps nudge their brain’s “central governor”
(Noakes, 2003; Noakes et al., 2005) a little closer to the body’s physiological red line without
Like other studies conducted on a treadmill, this experiment was noteworthy for the
absence of wind resistance and did not account for the energy cost of drag. Davies (1980)
computed the energy cost of overcoming air resistance as 2% for competitive marathoners
Energy Cost of Run-Walk Pacing 12
running at 5 m∙s-1. Running at only half that speed, our slower recreational runners would have
experienced only one-fourth the drag. Had we conducted this experiment on a track in a calm
wind, the mean wind speed differential of 0.9 m s‧-1 (1.75 knots) would have been negligible.
Consider also that a marathoner resuming running outdoors after a walk break must accelerate
against exponentially increasing wind resistance.
The use of a well-controlled laboratory environment to precisely evaluate the effects of
different locomotion patterns may limit the generalizability of the results to actual racing
environments. However, given the exigencies of the COVID-19 pandemic, more and more
running competition is likely to occur remotely on treadmills in laboratory-like settings.
Therefore, the conditions under which this study was conducted would be directly relevant to
such virtual competition.
Most of the purported benefits of the run-walk pacing strategy are based on athlete
testimonials and are not yet grounded in sport and exercise science. For example, Galloway
(2001, pg. 138) estimates that the ratio of running and walking used in this study yields a 25%
energy discount relative to continuous running, yet no evidence is provided to support this claim.
On the contrary, results presented here refute this claim and contribute to the current
understanding of how run-walk strategies can impact physiology and performance. Investigators
who have studied other aspects of the run-walk method such as its effect on cardiac stress
(Hottenrott et al., 2016) and muscular stress (Stanton et al., 2000) have reported no decrease in
these variables. Several key questions regarding the run-walk strategy remain unexplored: Can
regular walk breaks delay the onset of “hitting the wall” in the marathon? Can the use of regular
walk breaks during training reduce overuse injuries? What effect does the run-walk strategy have
on race performance? These issues should be explored in future research.
Energy Cost of Run-Walk Pacing 13
Using small, tightly controlled samples of running and walking, this study demonstrated that the
physics and physiology behind the run-walk method are problematic. Alternating bouts of
running and walking did not yield an energy discount per kilometre when compared to
continuous running. In fact, given the run-to-walk ratio employed in this study, participants who
alternated gaits paid a 6% energy tax while benefiting from only a very slight reduction in
average perceived exertion. While the run-walk strategy might potentially promote
cardiovascular fitness and reduce risk of injury, it is not as efficient as running without walk
breaks. The psychology behind taking walk breaks (i.e., avoidance of distress) is also
problematic if optimum performance is desired.
The authors thank the runners who participated in this study. They volunteered without
compensation and participated purely in the interest of advancing running science.
This research did not receive any funding from agencies in the public, commercial, or not-for-
profit sectors. The authors declare no conflicts of interest regarding the content of this study.
William P. Nolan https://orcid.org/0000-0001-8413-2462
Andrew R. Moore https://orcid.org/0000-0002-7021-3883
Energy Cost of Run-Walk Pacing 14
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Energy Cost of Run-Walk Pacing 17
Measure Males (n = 16) Females (n = 14) Combined
Age and Body Composition
Age (yr) 49.5 ± 13.5 47.2 ± 11.4 48.4 ± 12.4
Height (cm) 177.9 ± 6.7 165.7 ± 7.8 172.2 ± 9.4
Weight (kg) 80.5 ± 8.8 63.4 ± 8.8 72.5 ± 12.3
25.5 ± 3.2 23.0 ± 1.9 24.3 ± 2.9
Running 2.76 ± 0.38 2.45 ± 0.29 2.61 ± 0.37
Walking 1.70 ± 0.21 1.73 ± 0.10 1.71 ± 0.17
Walking to Running 1.85 ± 0.19 1.89 ± 0.14 1.87 ± 0.17
Running to Walking 1.89 ± 0.16 1.95 ± 0.12 1.92 ± 0.14
Values are reported as means ± SD.
Energy Cost of Run-Walk Pacing 18
Descriptive statistics and ANOVA results for the three experimental conditions
Variable Run Walk Run-Walk P$
Energy Efficiency Variables
61.9 ± 16.0 53.0 ± 13.8 65.9 ± 17.4 <0.001a0.638
% 84.4 ± 32.9 96.7 ± 27.9 84.1 ± 35.0 <0.001b0.271
' 2.03 ± 0.60 1.14 ± 0.31 1.71 ± 0.48 <0.001a0.818
RER 0.89 ± 0.04 0.82 ± 0.05 0.86 ± 0.05 <0.001a0.607
HR 150 ± 13.3 113 ± 14.5 133 ± 12.9 <0.001a0.921
RPE 11.6 ± 1.5 8.8 ± 1.7 10.2 ± 1.5 <0.001a0.751
a Indicates significant differences between all three conditions
b Indicates significant differences between W and both R and RW
Values are reported as means ± SD.
Energy Cost of Run-Walk Pacing 19
Bonferroni adjusted post hoc comparisons with confidence intervals of differences
Run minus Walk Run-Walk minus Walk Run minus Run-Walk
95% CI P95% CI P95% CI P
Energy Efficiency Variables
5.70 to 11.98 <0.001 9.25 to 16.40 <0.001 0.81 to 7.16 <0.001
% -19.78 to -4.48 0.001 -21.90 to -3.35 0.005 -6.34 to 6.98 1.000
' 0.71 to 1.07 <0.001 0.45 to 0.69 <0.001 0.21 to 0.43 <0.001
RER 0.045 to 0.093 <0.001 0.029 to 0.065 <0.001 0.008 to 0.035 0.001
HR 32.35 to 41.75 <0.001 17.46 to 23.16 <0.001 13.49 to 20.00 <0.001
RPE 2.11 to 3.47 <0.001 0.93 to 1.77 <0.001 0.96 to 1.92 <0.001