Running for Exercise Mitigates Age-Related
Deterioration of Walking Economy
Justus D. Ortega1*, Owen N. Beck1,2, Jaclyn M. Roby2, Aria L. Turney1, Rodger Kram2
1 Department of Kinesiology & Recreation Administration, Humboldt State University, Arcata, California, United States of America, 2 Department of Integrative Physiology,
University of Colorado, Boulder, Colorado, United States of America
Abstract
Introduction: Impaired walking performance is a key predictor of morbidity among older adults. A distinctive characteristic
of impaired walking performance among older adults is a greater metabolic cost (worse economy) compared to young
adults. However, older adults who consistently run have been shown to retain a similar running economy as young runners.
Unfortunately, those running studies did not measure the metabolic cost of walking. Thus, it is unclear if running exercise
can prevent the deterioration of walking economy.
Purpose: To determine if and how regular walking vs. running exercise affects the economy of locomotion in older adults.
Methods: 15 older adults (6963 years) who walk $30 min, 3x/week for exercise, ‘‘walkers’’ and 15 older adults (6965 years)
who run $30 min, 3x/week, ‘‘runners’’ walked on a force-instrumented treadmill at three speeds (0.75, 1.25, and 1.75 m/s).
We determined walking economy using expired gas analysis and walking mechanics via ground reaction forces during the
last 2 minutes of each 5 minute trial. We compared walking economy between the two groups and to non-aerobically
trained young and older adults from a prior study.
Results: Older runners had a 7–10% better walking economy than older walkers over the range of speeds tested (p = .016)
and had walking economy similar to young sedentary adults over a similar range of speeds (p = .237). We found no
substantial biomechanical differences between older walkers and runners. In contrast to older runners, older walkers had
similar walking economy as older sedentary adults (p = .461) and ,26% worse walking economy than young adults
(p,.0001).
Conclusion: Running mitigates the age-related deterioration of walking economy whereas walking for exercise appears to
have minimal effect on the age-related deterioration in walking economy.
Citation: Ortega JD, Beck ON, Roby JM, Turney AL, Kram R (2014) Running for Exercise Mitigates Age-Related Deterioration of Walking Economy. PLoS ONE 9(11):
e113471. doi:10.1371/journal.pone.0113471
Editor: Yuri P. Ivanenko, Scientific Institute Foundation Santa Lucia, Italy
Received September 8, 2014; Accepted October 23, 2014; Published November 20, 2014
Copyright:  2014 Ortega et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper.
Funding: Support was provided by the California State University Program for Education and Research in Biotechnology New Investigator [Grant #: HM531]
(http://www.calstate.edu/csuperb/grants/). This funder had no role in study design, data collection and analysis, decision to publish, or preparation of the
manuscript. Support was also provided by the National Institutes of Health Clinical and Translational Science Award [Grant #: UL1 TR000154] (http://www.ncats.
nih.gov/research/cts/ctsa/funding/funding.html). This funder provided the facility where the data was collected.
Competing Interests: The authors have declared that no competing interests exist.
* Email: Justus.Ortega@humboldt.edu
Introduction
Walking performance typically deteriorates with advanced age
[1], and impaired walking performance is a key predictor of
morbidity among older adults [2]. A distinctive characteristic of
impaired walking performance among older adults is a 15–20%
greater metabolic cost for walking (worse economy) compared to
young adults [3–5]. Several factors are known to determine the
metabolic cost of walking in humans across all ages. These major
biomechanical factors include the costs associated with: supporting
body weight, performing mechanical work, leg swing and balance
[5–8]. Studies investigating age-related biomechanical determi-
nants of walking cost have found that older adults have a similar
cost of balance and perform a similar amount, or even less,
external mechanical work during walking as young adults [5,9,10].
Despite these similarities, other studies suggest that a decrease in
muscular efficiency and an increase in antagonist leg muscle co-
activation, contribute to the greater cost of walking in both healthy
sedentary and active older adults [3,5,7,10,11]. Yet, no study has
found a sole mechanical determinant that accounts for the 15–
20% greater metabolic cost of walking in older adults. Therefore,
interventions for improving walking economy in older age have
been elusive.
Recent studies by Thomas et al. [12] and Malatesta et al. [13]
show that vigorous walking interval training effectively reduces the
metabolic cost of walking in older adults by as much as 20%. Yet,
the mechanisms for the decreases were not elucidated. Conversely,
a generalized year-long training program that included resistance,
aerobic and balance exercises had no effect on post-training
walking economy in older adults [14]. The different effects of these
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exercise interventions, high intensity aerobic versus generalized
exercise with only a moderate aerobic component, suggest higher
intensity aerobic activities may mitigate the typical age-related
decrease in walking economy, and consequently, preserve mobility
into older age.
In contrast, running economy does not exhibit the same age-
related trend as walking economy. Two studies have reported that
adults (45–61 years) who consistently participated in running
exercise retain a similar metabolic economy of running as young
runners (23–27 years) [15,16]. Although these results seem to
support the hypothesis that vigorous aerobic exercise mitigates the
decline in locomotion economy, i.e. metabolic cost of running and
walking, it is also possible that a decline in running economy does
not occur until late into the 6th decade of life, as observed with
walking economy [17]. Perhaps the subjects in these studies
[15,16] were not ‘‘old’’ enough to exhibit declines in locomotion
economy. Another possible explanation is that running economy,
unlike walking economy, is simply not affected by age. However,
since these running studies did not measure walking economy, it
remains unclear if regular participation in running exercise
mitigates the typical age-related deterioration of walking economy.
Our purpose was to determine if and how regular participation
in walking or running exercise affects the metabolic cost and
biomechanics of walking in older adults. We hypothesized that
older runners would consume less metabolic energy for walking
than older walkers. Further, we also investigated whether the two
groups demonstrate different walking biomechanics. We measured
metabolic rates, ground reaction forces and spatio-temporal stride
variables of two groups, older walkers and older runners, while
they walked on a dual-belt, force-sensing treadmill at three speeds.
Methods
Subjects
Thirty healthy older adults (15 males and 15 female) who either
walk (4 Male, 11 Female) or run (10 Male, 5 Female) regularly for
exercise volunteered. Table 1 summarizes the anthropometric
characteristics of the subjects. We recruited subjects with a
minimum age of 65 years, which is in accordance with prior
studies reporting age-related impairments of walking performance
become most apparent at this age [3,18–20]. All subjects were free
of neurological, orthopedic and cardiovascular disorders. Walkers
self-reported walking for exercise three or more times per week for
at least 30 minutes per bout and for at least six months prior to the
study. Runners self-reported running for exercise three or more
times per week for at least 30 minutes per bout and for at least six
months prior to the study. The experiment was performed in
accordance with the ethical standards of the 1964 Declaration of
Helsinki and was approved by the Humboldt State University and
University of Colorado Institutional Review Boards. All subjects
gave written informed consent prior to participation in the study.
Protocol
Subjects completed three sessions. In the first session, subjects
underwent a physician’s examination to determine neurological,
orthopedic and cardiovascular health, a body composition test
(DXA) to determine percent body fat and lean tissue mass and a
VO2 max treadmill test to determine maximal aerobic capacity. In
the second session, at least five days following the first session, we
measured standing metabolic rate and familiarized the subjects to
treadmill walking. For the treadmill familiarization, subjects
walked on a dual-belt, force-instrumented treadmill (FIT, Bertec
Corporation, Columbus, OH, USA) at three speeds (0.75, 1.25
and 1.75 m/s) for at least 7 minutes at each speed. These speeds
correspond to 1.67, 2.80, 3.91 MPH. Thus, subjects completed a
minimum of 21 minutes total of walking familiarization. This
familiarization period is over double the recommended minimum
treadmill habituation time of 10 minutes [21,22]. In the third
session, at least two days following familiarization, we measured
each subject’s metabolic rate during quiet standing and while
walking on the treadmill at three speeds (0.75, 1.25 and 1.75 m/s)
in random order. All trials were five minutes in duration with at
least five minutes of rest between trials. Throughout each trial, we
measured the rates of oxygen consumption (VO2) and carbon
dioxide production (VCO2) in order to determine metabolic rate.
We calculated the average VO2 and VCO2 for the last two
minutes of each trial. We also measured ground reaction forces
(GRFs) from the force-instrumented treadmill for 1 minute during
the last 2.5 minutes of each trial to determine kinetics and spatio-
temporal stride variables.
Metabolic Power Consumption
We measured VO2 and VCO2 using an open-circuit expired gas
analysis system (TrueOne 2400, ParvoMedic, Sandy, UT, USA).
We calculated average gross metabolic power per kilogram body
mass (W/kg) [23] using the average VO2 (mlO2/min) and VCO2
(mlCO2/min) for the last two minutes of each trial, when VO2 and
respiratory exchange ratio reached steady state ensuring that each
subject was working sub-maximally and oxidative metabolism was
the main metabolic pathway. We then divided gross metabolic
power by speed to calculate gross metabolic cost of transport
(CoT) (J/kg/m) for walking.
Ground Reaction Forces and Spatio-temporal Stride
Variables
For each walking trial, we collected the ground reaction forces
(vertical and horizontal components) of each leg from the force-
sensing treadmill at 2000 Hz for a 1 minute period during the last
2.5 minutes of each trial. A custom MATLAB script (Math Work
Inc., Natick, Mass) was then used to process all force data. The
Figure 1. Mean (SE) gross metabolic power as a function of
walking speed in older walkers (m) and older runners (X)
walkers (m). Lines represent least square regression for older walkers
(y = 2.709x2–3.539x+4.523, r2 = 0.86) and older runners (y = 2.382x2–
3.189x+4.233, r2 = 0.89). Symbols shown on vertical axis represent
standing metabolic rate of both groups. Asterisks (*) indicate significant
differences between older runners and walkers (p,0.05).
doi:10.1371/journal.pone.0113471.g001
Running Mitigates Age-Related Decline of Walking Economy
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GRF data were filtered with a 4th order zero-lag low pass
Butterworth filter with a cutoff frequency of 30 Hz. For each trial,
we calculated vertical and horizontal peak GRFs across all 10
strides. Using the filtered GRF data, we determined gait cycle
events and spatio-temporal stride variables (stride frequency,
stance time, and duty factor as percent of the gait cycle) for 10
strides of each trial (10 steps per each leg).
Statistical Analyses
We used a repeated-measures ANOVA (p,.05) to determine
statistical differences due to exercise group (walkers vs. runners)
and walking speed, as well as, the exercise group-walking speed
interaction. When a significant main effect of exercise group was
found, we performed independent-samples t-tests with Bonferroni
correction to determine at which speed(s) the differences occurred.
To determine if difference in metabolic cost and GRF was related
to sex differences in our runner and walker groups, we examined
differences in metabolic cost, ground reaction forces and spatio-
temporal stride variables due to sex among each group and
analyzed difference in metabolic cost, GRFs and spatio-temproal
stride variables using sex as a covariate. We found no effect of sex
on any dependent variable and differences between runners and
walkers were not affected by sex. We performed all statistical
analyses using SPSS 21.0 (SPSS, Inc.) software. In addition to our
comparison between older walkers and runners, we used a mixed-
model repeated-measures ANOVA (p,.05) to make further post-
hoc comparisons of gross metabolic cost in walkers and runners
collected in the present study to data for young and older
sedentary adults previously collected in our lab at similar speeds
[5]. To make these comparison between exercise/age group (old
walkers, old runners, old sedentary and young sedentary) using a
linear mixed model, walking speed squared (m/s)2 was used as the
repeated measure.
Results
In support of our hypothesis, older runners consumed 7–10%
less metabolic energy for walking than older walkers across the
range of speeds tested (Fig. 1; p = .016). Gross metabolic power
consumption increased significantly across the range of walking
speeds tested in both older runners and walkers, (p,.0001).
Compared to walking at the slowest speed of 0.75 m/s, gross
metabolic power increased by 95% to walk at 1.75 m/s in older
walkers but only 86% in older runners (speed X group interaction,
p = .009). Mass-specific standing metabolic rates were similar
between older runners and walkers (p = .250; Table 1).
Following from the metabolic rate data, the older runners had
an average of 7–10% lower gross metabolic cost of transport
compared to the older walkers. Older walkers and runners
exhibited similar U-shape relations between gross CoT and
walking speed (Fig. 2). Between the three speeds, gross CoT was
significantly lower at the intermediate speed of 1.25 m/s as
compared to the faster and slower walking speeds in both the older
walkers
(3.4960.09
J/kg/m,
p,.0001)
and
older
runners
(3.1860.08 J/kg/m, p,.0001). Although there were a greater
number of male runners in the study, our statistical analysis
showed that the difference in metabolic cost between runners and
walkers was not due to sex or any other anthropometric variable.
Despite the substantial differences in walking economy, older
walkers and runners exhibited nearly identical spatio-temporal
stride variables and kinetics across the range of speeds (Table 2).
Among spatio-temporal gait characteristics, we found no signifi-
cant differences between older walkers and older runners in
Table 1. Subject characteristics (Mean 6SD) with statistics for older walkers and older runners.
Older Walkers (n = 15; 4M, 11 F)
Older Runners (n = 15; 10M, 5 F)
Age, years
68.963.0
68.964.7
Height, m
1.6160.09
1.7060.09*
Leg length, m
0.8360.06
0.8860.06
Body mass, kg
61.7611.0
66.56 13.0
Lean tissue mass, kg
39.267.1
48.669.2*
Body fat, % body mass
31.569.6
23.466.0*
VO2 Max, mlO2/kg/min
27.763.6
37.365.3*
Standing metabolic rate, W/kg
1.3460.21
1.2660.14
0.75 m/s, gross metabolic power, W/kg
3.3960.33
3.1860.31*
1.25 m/s, gross metabolic power, W/kg
4.3360.56
3.9760.40*
1.75 m/s, gross metabolic power, W/kg
6.3360.71
5.9560.52*
Asterisk indicates the only significant group difference (p,.05).
doi:10.1371/journal.pone.0113471.t001
Figure 2. Mean (SE) gross metabolic cost of transport as a
function of speed in older walkers (m) and older runners (X).
Asterisks (*) indicate significant differences between older walkers and
runners (p,.05).
doi:10.1371/journal.pone.0113471.g002
Running Mitigates Age-Related Decline of Walking Economy
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regards to stride time, stride frequency (p = .879), single leg stance
time (p = .126) or duty factor (p = .126). However, older runners
walked with slightly (6%) shorter strides in relation to their leg
length compared to older walkers (p = .033). This difference
remained nearly constant across the range of speeds. With regards
to ground reaction forces, older walkers and runners exhibited
similar first (p = .838) and second (p = .282) peak vertical ground
reaction force (Figure 3). Additionally, peak anterior-posterior
braking (p = .182) and propulsive (p = .056) ground reaction forces
were similar for both exercise groups.
We also compared gross metabolic cost of walking for older
walkers and older runners to data from young and older sedentary
adults collected in our lab from a prior study over a similar range
of speeds [5]. The speeds used in these two studies were slightly
different. Thus, in order to statistically make this comparison using
a linear mixed model repeated measures ANOVA, we determined
gross metabolic power as a function of speed squared (Fig. 4). The
results of this analysis showed that across the range of speeds, older
walkers consume metabolic energy at a similar rate as sedentary
older adults (p = .461) and 14–22% faster than young sedentary
adults (p,.0001). In contrast, older runners consume metabolic
energy at a slower rate compared to older sedentary adults
(p = .016). However, our most striking finding was that older
runners consumed metabolic energy at a similar rate as young
sedentary adults across the range of walking speeds (p = .237).
Discussion and Conclusions
In this study, we distinguished the effects of regular walking vs.
running exercise on the metabolic cost and biomechanics of
walking in older adults. In support of our hypothesis, older runners
consumed less metabolic energy for walking than older walkers.
Although the older runners consumed less metabolic energy for
walking than the older walkers, the two groups had almost
identical walking biomechanics.
Given that there were virtually no differences in walking
biomechanics between the older walkers and runners, other factors
Table 2. Spatio-temporal stride variables and ground reaction force data (Mean 6SD) with statistics for older walkers and older
runners.
Older Walkers (n = 15)
Older Runners (n = 15)
Speed 0.75 m/s
Stride Time, sec
1.2660.11
1.1960.08
Stance Time, % of stride
6562
6661
Swing Time, % of stride
3562
3461
Stride Frequency, Hz
0.8060.07
0.8460.06
Stride Length, Leg Length
1.1460.08
1.0260.10*
First Peak VGRF, BW%
10463
10463
Second Peak VGRF, BW%
10163
10062
Braking HGRF, BW%
2861
2861
Propulsive HGRF, BW%
1162
1061
Speed 1.25 m/s
Stride Time, sec
1.0460.07
1.0560.06
Stance Time, % of stride
6362
6462
Swing Time, % of stride
3762
3762
Stride Frequency, Hz
0.9760.07
0.9560.06
Stride Length, Leg Length
1.5760.08
1.4960.10*
First Peak VGRF, BW%
11065
10864
Second Peak VGRF, BW%
10665
10563
Braking HGRF, BW%
21762
21662
Propulsive HGRF, BW%
1960.02
1762
Speed 1.75 m/s
Stride Time, sec
0.9260.05
0.9360.04
Stance Time, % of stride
616 1
6362
Swing Time, % of stride
3961
3862
Stride Frequency, Hz
1.0960.06
1.0860.05
Stride Length, Leg Length
1.8860.10
1.8360.10
First Peak VGRF, BW%
134612
12964
Second Peak VGRF, BW%
11069
11967
Braking HGRF, BW%
22862
22665
Propulsive HGRF, BW%
2663
2563
Peak vertical ground reaction forces (VGRF) and horizontal ground reaction forces (HGRF) are represented as % body weight (BW). Asterisk indicates significant group
difference (p,.05).
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must underlie the lower cost of walking observed for the older
runners. One factor may be muscle co-activation. Older adults,
both sedentary and active walkers, use 30–50% greater co-
activation of antagonist leg muscles compared to young adults
[6,10,24]. It has been suggested that older adults may use greater
co-activation to increase joint stiffness and the stabilization of the
body, thus reducing the risk of walking related falls [25]. Yet,
increased
co-activation
has
been
associated
with
increased
metabolic cost of walking in older adults [6,10]. It is possible that
older runners are able to maintain a lower metabolic cost of
walking compared to older walkers because they use less
antagonist leg muscle activation. Some research shows that older
adults who participated in a lower limb strength training program
reduce leg muscle co-activation by 5–10% [26]. Perhaps, by
regularly running three or more times per week for 30 minutes per
bout, older runners are able to maintain or even increase leg
muscle strength and reduce co-activation. However, a decrease in
co-activation associated with running that is similar in magnitude
to the decrease observed after strength training is likely not
sufficient to explain the 7–10% difference in metabolic cost of
walking. It is also possible that other neuromuscular factors such as
widening of EMG/motoneuronal bursts [27] may also help to
explain the difference in metabolic cost between older runners and
walkers.
Better muscular efficiency may also help explain why older
runners have a lower metabolic cost of walking than older walkers.
Aging has been associated with reduced muscular efficiency
[10,28]. More specifically, mitochondrial dysfunction associated
with the uncoupling of oxidative phosphorylation (reduced ATP
synthesis per O2 uptake) effectively reduces muscular efficiency
and increased the metabolic cost of muscle activation [28].
Interestingly, recent evidence suggests that aerobic exercise
training may ameliorate mitochondrial uncoupling and improve
muscular efficiency in older adults [29].
Perhaps studies of cycling efficiency in older adults can provide
insight. In contrast to the effects of running we have observed, the
muscular efficiency of cycling declines with age despite regular
cycling exercise [30]. More recently, Brisswalter et al. [31]
measured the cycling efficiency of active triathletes (who regularly
swim, bike, and run for exercise) across age-groups and found a
decline in cycling efficiency past the 5th decade. These data suggest
that older cyclist and triathletes are unable to maintain muscular
efficiency with age. However, Peiffer et al. [32] found no
difference in cycling efficiency between their youngest age group
(3963 years) and their oldest (6564 years). Intriguingly, their
oldest training group cycled 58 km more per week (359 km per
week) than the youngest group. Possibly the greater quantity of
aerobic cycling exercise mitigated the decrease in muscular
efficiency with age.
Alternatively, the intensity of exercise may hold the key to
maintaining or improving muscular efficiency. Two prior studies
have found that 6–7 weeks of vigorous aerobic exercise (fast
walking) that elicits a heart rate close to the ventilatory threshold
can improve walking economy by 8–20% [12,13]. More vigorous
aerobic exercise such as walking uphill, fast walking or running
may be required to elicit improvement in walking economy.
Clinicians and others who work with older adults to improve their
fitness may need to prescribe more vigorous, more prolonged and/
or more frequent aerobic exercise to prevent the decline in walking
performance. To test this hypothesis and help guide clinicians, a
future study should investigate the effects of different intensity
aerobic exercises on muscular efficiency and more specifically, the
economy of walking.
Limitations
One limitation of the current study is the cross-sectional design.
It is possible that older runners may not be economical walkers
because of the effect of running exercise but rather they run
because they are more economical in their locomotion. To better
address this issue, a future study might quantify the longitudinal
effects of a running training program. One such study conducted
by Trappe et al. [16] on the longitudinal effect of running exercise
on running economy spanned 22 years. In that study, Trappe et al.
[16] showed that running economy did not decline in older adults
who maintained their health and fitness over the 22 year period,
whereas runners who became unfit had worse running economy.
Although these results suggest that running may help to prevent a
decline in running economy, Trappe et al. [16] did not measure
walking economy.
Figure 3. Average individual leg vertical (A) and horizontal (B)
ground reaction force for older walkers (dashed lines) and
older runners (solid lines) at the intermediate walking speed of
1.25 m/s.
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Another potential limitation of the current study is the different
numbers of male and female participants in each group. Although
the
sex
difference
may
have
influenced
the
difference
in
anthropometrics between runners and walker, our results showed
no main effect of sex on walking economy (p = .211) and no sex
difference in walking economy among older runners (p = .131) or
older walkers (p = .331). Based on post-hoc power analysis, it is
clear that we did not have sufficient statistical power to detect sex
differences that might exist but that would require ,300 subjects.
However, when treated as covariates, sex and anthropometrics did
not statistically account for the difference in walking economy
between runners and walkers. Thus, while it would have been
preferable to have a larger sample size with more similar sex and
anthropometric matched cohorts, it would not have changed our
overall conclusion.
Future Studies
Based on the results of this study and others, future studies of the
effect of age and exercise on walking economy are warranted.
Although the average age of our runners and walkers was 69 years,
a future study might look to see if running exercise continues to
prevent or slow the decline in walking economy in even older
runners (over the age of 80 years). It seems plausible that at some
age that exercise may not be able to sufficiently offset the normal
decline in muscular efficiency and walking economy associated
with aging. It is also not known whether there is an intensity
threshold of aerobic exercise that is needed to prevent the decline
in walking economy. Thus, it would be beneficial for future studies
to investigate the relative effect of exercises with different levels of
aerobic intensity on walking economy.
Conclusions
In conclusion, older runners mitigate the age-related deteriora-
tion of walking economy. However, older walkers are unable to
forestall the decline of walking economy as they require the same
metabolic consumption as sedentary older adults. The difference
in walking economy between older runners and older walkers
remains unexplained due to no substantial differences found in
either the kinetic or spatio-temporal data between the groups.
Other factors such as decreased muscle co-activation and/or
increased muscular efficiency may contribute to the superior
walking economy exhibited by the older runners.
Author Contributions
Conceived and designed the experiments: JO OB JR RK. Performed the
experiments: JO OB JR AT RK. Analyzed the data: JO OB JR AT RK.
Contributed reagents/materials/analysis tools: JO RK. Wrote the paper:
JO OB JR AT RK.
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Figure 4. Gross metabolic power as a function of speed2 in
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and young sedentary adults (#). Lines denote least square
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y = 1.12x+2.42, r2 = 0.88; young sedentary: y = 1.01x+2.27, r2 = 0.87).
Symbols on vertical axis represent standing metabolic rate of each
group.
doi:10.1371/journal.pone.0113471.g004
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Running for exercise mitigates age-related deterioration of walking economy.
Ortega, Justus D,Beck, Owen N,Roby, Jaclyn M,Turney, Aria L,Kram, Rodger