Article

Altered Running Economy Directly Translates to Altered Distance-Running Performance

Abstract and Figures

Purpose: Our goal was to quantify if small (1 - 3%) changes in running economy quantitatively affect distance-running performance. Based on the linear relationship between metabolic rate and running velocity and on earlier observations that added shoe mass increases metabolic rate by ~1% per 100 grams per shoe, we hypothesized that adding 100 and 300 grams per shoe would slow 3,000m time-trial performance by 1% and 3%, respectively. Methods: 18 male, sub-20 minute 5km runners completed treadmill testing, and three 3,000m time-trials wearing control shoes and identical shoes with 100 and 300 grams of discreetly added mass. We measured rates of oxygen consumption and carbon dioxide production and calculated metabolic rates for the treadmill tests and we recorded overall running time for the time-trials. Results: Adding mass to the shoes significantly increased metabolicrate at 3.5 m·s by 1.11% per 100grams per shoe (95% CI: 0.88-1.35%). While wearing the control shoes, participants ran the 3,000m time-trial in 626.1 ± 55.6s. Times averaged 0.65 ± 1.36% and 2.37 ± 2.09% slower for the +100g and +300g shoes respectively (p<0.001). Based on a linear fit of all the data, 3,000m time increased 0.78% per added 100 grams per shoe (95% CI: 0.52-1.04%). Conclusion: Adding shoe mass predictably degrades running economy and slows 3,000m time-trial performance proportionally. Our data demonstrate that laboratory-based running economy measurements can accurately predict changes in distance running race performance due to shoe modifications.
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Altered Running Economy Directly Translates
to Altered Distance-Running Performance
WOUTER HOOGKAMER
1
, SHALAYA KIPP
1
, BARRY A. SPIERING
2
, and RODGER KRAM
1
1
Locomotion Lab, Department of Integrative Physiology, University of Colorado Boulder, CO; and
2
Nike Explore Team,
Sport Research Lab, NIKE Inc., Beaverton, OR
ABSTRACT
HOOGKAMER, W., S. KIPP, B. A. SPIERING, and R. KRAM. Altered Running Economy Directly Translates to Altered Distance-
Running Performance. Med. Sci. Sports Exerc., Vol. 48, No. 11, pp. 2175–2180, 2016. Purpose: Our goal was to quantify if small (1%–3%)
changes in running economy quantitatively affect distance-running performance. Based on the linear relationship between metabolic rate and
running velocity and on earlier observations that added shoe mass increases metabolic rate by ~1% per 100 g per shoe, we hypothesized that
adding 100and 300 g per shoe would slow3000-m time-trial performance by 1%and 3%, respectively. Methods: Eighteen male sub-20-min
5-km runners completed treadmill testing, and three 3000-m time trials wearing control shoes and identical shoes with 100 and 300 g of
discreetly added mass. We measured rates of oxygen consumption and carbon dioxide production and calculated metabolic rates for the
treadmill tests, and we recorded overall running time for the time trials. Results: Adding mass to the shoes significantly increased metabolic
rate at 3.5 mIs
j1
by 1.11% per 100 g per shoe (95% confidence interval = 0.88%–1.35%). While wearing the control shoes, participants ran
the 3000-m time trial in 626.1 T55.6 s. Times averaged 0.65% T1.36% and 2.37% T2.09% slower for the +100-g and +300-g shoes,
respectively (PG0.001). On the basis of a linear fit of all the data, 3000-m time increased 0.78% per added 100 g per shoe (95% confidence
interval = 0.52%–1.04%). Conclusion: Adding shoe mass predictably degrades running economy and slows 3000-m time-trial performance
proportionally. Our data demonstrate that laboratory-based running economy measurements can accurately predict changes in distance-
running race performance due to shoe modifications. Key Words: RUNNING SHOES, SHOE MASS, FOOTWEAR, BIOMECHANICS,
ENERGETIC COST
Physiologists generally agree that distance-running per-
formance can be predicted by three parameters: the
maximal rate of oxygen consumption (V
˙O
2max
), the
blood lactate threshold, and running economy (RE) (14). RE
is traditionally defined as the mass-specific submaximal rate
of oxygen consumption (mL O
2
Ikg
j1
Imin
j1
) at a defined
submaximal running velocity (4). Submaximal oxygen uptake
(V
˙O
2submax
) and metabolic rate increase with running velocity
(15). Thus, an improvement in RE (lower rate of oxygen
consumption at a given velocity) theoretically would allow an
athlete to run at a faster velocity for the same physiological
effort and thus improve performance (4). However, no study
to date has demonstrated a direct link between altered RE
and altered distance-running performance (10). If a link can
be established, then laboratory-based measures of RE could
be used to predict improvements in performance, without
actually measuring performance. The primary objective of
the present study was to quantify how changes in RE affect
distance-running performance.
One way to predictably induce a change in RE is to add
mass to the shoes (10). Frederick et al. (9) demonstrated
that adding 100 g of mass per shoe degrades RE (increases
V
˙O
2submax
) by approximately 1% over a range of running
speeds. More recently, Franz et al. (8) confirmed those classic
findings using modern, very lightweight racing flats.
In the present study, we imposed small degradations in
RE by adding mass to running shoes and evaluated the cor-
responding effects on 3000-m time-trial performance. On the
basis of the linear relationship between V
˙O
2submax
and run-
ning velocity combined with Frederick_s 1% rule for added
shoe mass, we hypothesized that adding 100 and 300 g per
shoe would slow 3000-m time-trial performance by 1% and
3%, respectively.
METHODS
Participants. Eighteen males (age = 24.2 T3.3 yr, mass =
64.2 T6.3 kg, height = 176.1 T7.0cm)whoworemen_s
shoe sizes 9–11 and had recently run a sub-20-min 5-km
race participated in this study (V
˙O
2max
=63.8T6.7 mL
O
2
Ikg
j1
Imin
j1
, range = 53.5–72.0 mL O
2
Ikg
j1
Imin
j1
).
Participants gave written informed consent that followed
Address for correspondence: Wouter Hoogkamer, Ph.D., Department of
Integrative Physiology, University of Colorado Boulder, 354 UCB, Boulder,
CO 80309-0354; E-mail: wouter.hoogkamer@colorado.edu.
Submitted for publication February 2016.
Accepted for publication June 2016.
0195-9131/16/4811-2175/0
MEDICINE & SCIENCE IN SPORTS & EXERCISE
Ò
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DOI: 10.1249/MSS.0000000000001012
2175
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the guidelines of the institutional review board of the
University of Colorado Boulder.
Experimental setup. All participants wore Nike Zoom
Streak 5 racing flats in men_s size 9, 10, or 11. For each of
the sizes, we used three nearly identical looking pairs of
shoes (Fig. 1). The control (no added mass) shoe masses
ranged from 202 g (size 9) to 225 g (size 11) per shoe. For the
second pair in each size, we discreetly added 100 g of lead
beads per shoe, which were distributed within the tongue of
the shoe. For the third pair, we added 100 g within each tongue
and 200 g of lead beads in medial and lateral side pockets,
which were inconspicuously sewn inside the shoe uppers in
the midfoot area.
The study comprised five visits. Participants kept a 24-h
dietary, sleep, and training log before each visit. We strongly
encouraged the participants to match their diet, sleep, and
training pattern for all time trials and laboratory measure-
ments. If compliance was not met, we postponed the testing.
Upon arrival for the first visit, we deceived the participants
as to the actual study purpose. We told the participants that
our objective was to establish a predictive equation for per-
formance based on V
˙O
2max
and RE, validated with a series of
weekly time trials. This deception was needed to ensure par-
ticipants were not biased into thinking that they should be
running faster or slower based on the shoes they were wearing.
During each of the five visits, an experimenter deliberately
helped the participant put on the shoes, such that the partici-
pants never handled the shoes. Slade et al. (20) found that
participants are able to perceive small differences in mass
when they handle different shoes manually, but not when
they are only worn on their feet. To make the deception
plausible, we created two small, lightweight (4.6 g) faux
accelerometers and lightly wrapped them with thin, self-
adhering elastic bandage material (VetWrap; 3M Inc., St.
Paul, MN) atop of the participant_s fourth metatarsal of
each foot. To ensure that participants did not manually
handle the shoes, we told them that because of the fragile
nature of the accelerometers, we needed to place their socks
and shoes back on their feet. We then allowed the partici-
pants to tie their shoes.
Experimental protocol. During visit 1, participants
completed a set of laboratory treadmill tests. This visit was
in part to decoy the participants because we told them that
our objective was to establish a predictive equation for per-
formance based on V
˙O
2max
and RE. To establish their RE,
participants wore the control shoes and ran at velocities of
2.5, 3.0, 3.5, and 4.0 mIs
j1
on a classic Quinton 18–60
treadmill, which has a rigid bed. We used a handheld digital
tachometer (Shimpo DT-107A; Electromatic Equipment
Inc., Cedarhurst, NY) to measure the treadmill speeds. Trials
lasted 5 min, and participants took a 5-min break in between
trials. We measured submaximal rates of oxygen consump-
tion and carbon dioxide production during the trials using an
open-circuit expired-gas analysis system (True One 2400;
Parvo Medics, Salt Lake City, UT).
After a 10-min break, participants completed a V
˙O
2max
test. On the basis of each participant_s most recent 5-km
time, we set the treadmill speed between 3.5 and 4.5 mIs
j1
and increased the incline by 1% each minute until exhaus-
tion (5). During the V
˙O
2max
test, we measured the rates of
oxygen consumption and carbon dioxide production as
well as heart rate (Polar Wearlink Nike+ Transmitter; Polar
Electro Inc., Lake Success, NY). Together, these measure-
ments allowed us to determine that V
˙O
2max
was reached.
We used the maximum heart rate data to determine whether
participants reached 90% of maximum heart rate during the
second half of their time trials. This was our criterion for
determining if participants gave an ‘‘honest effort’’ during
the time trials.
During visits 2, 3, and 4, participants completed solo 3000-m
time trials on the University of Colorado_s unbanked 200-m
indoor track facility. The time trials took place once per
week, for three successive weeks, on the same day of the
week and the same time of day. During the time trials,
participants wore each of the three pairs of shoes (control,
+100 g, or +300 g), in counterbalanced randomized order.
We instructed the participants to run the time trial as fast as
possible, and we awarded a $30 monetary incentive per time
trial when they met the 90% of maximum heart rate honest
effort criterion. We did not allow the participants to wear a
watch during the time trials but they were able to see a lap
counter. We recorded 200-m lap split times but did not pro-
vide them to the runners. One experimenter read from a
standardized cheering script for all participants during the
time trials, and another experimenter recorded lap split times.
We recorded the total times for each 3000-m time trial as the
primary outcome measures and revealed them to the partici-
pants only at the end of their fifth visit.
During visit 5, participants ran on the treadmill at 3.5 mIs
j1
in the control shoes, the +100-g and +300-g shoes (5 min
each). After a 5-min standing trial and warming up,
FIGURE 1—The control shoe (left) and +300 g shoe (right) were nearly
identical in appearance.
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participants completed four trials. They ran with the con-
trol shoes during the first and fourth trials and wore the
+100-g or +300-g shoes during the second and third trials,
in counterbalanced randomized order. During the 5-min
break between trials, an experimenter took off the partici-
pants_shoes, took the shoes outside the room, returned
about a minute later, and put the appropriate shoes on the
participants_feet.
On visits 1 and 5, during each trial, we measured sub-
maximal rates of oxygen consumption and carbon dioxide
production. Traditionally, RE is defined as V
˙O
2submax
at a
specific running velocity (4), but as shown by Fletcher et al.
(7), expressing RE in terms of metabolic rate is a more
appropriate measure because it accounts for changes in sub-
strate utilization. Therefore, we expressed RE in terms of
metabolic rate (WIkg
j1
). We calculated metabolic rate based
on the rates of oxygen consumption and carbon dioxide pro-
duction over the last 2 min of each trial, using the Brockway
equation (1). For visit 5, we averaged the metabolic data
for the two baseline trials (control shoes at 3.5 mIs
j1
). One
participant_s metabolic data for the two baseline trials differed
by more than 10%, likely because of an equipment malfunc-
tion. Thus, we excluded his data for visit 5 from the analyses.
Statistics. We present all results as mean TSD values in
the text and figures. We used repeated-measures ANOVA
and linear least-squares regression analysis to evaluate
and quantify the effects of shoe mass on metabolic rate and
on 3000-m running time. To evaluate the effect of shoe
mass on pacing strategy, we performed a two-way repeated-
measures ANOVA (lap number shoe mass) on lap split
time. Furthermore, we used a repeated-measures ANOVA
to evaluate the effect of time trial order on 3000-m running
time. We performed linear regression analysis to assess
potential correlations between changes in metabolic rate
and in 3000-m running time versus factors such as body
mass and 3000-m running time in the control shoes. To
evaluate the effects of individual time trial order on 3000-m
running time, we used Spearman regression analyses. In
addition, we calculated reliability measures for metabolic
rate, following Saunders et al. (17). We calculated the typ-
ical error as the standard deviation of relative change be-
tween the two baseline trials (control shoes at 3.5 mIs
j1
),
divided by ¾2. The so-called smallest worthwhile change
for metabolic rate was calculated as 0.2 times the between-
participant standard deviation. We used a traditional level of
significance (>= 0.05) and performed all analyses with
MATLAB (The MathWorks, Inc., Natick, MA).
RESULTS
As expected, both submaximal V
˙O
2
and gross meta-
bolic rate increased linearly with running velocity (V
˙O
2
[mL
O
2
Ikg
j1
Imin
j1
] = 10.61 velocity [mIs
j1
] + 2.28; r
2
=
0.779; metabolic rate [WIkg
j1
] = 3.69 velocity [mIs
j1
]+
0.47; r
2
= 0.774). On the basis of the regression, a 1% in-
crease in running velocity increased metabolic rate by 0.97%,
i.e., almost exactly in direct proportion.
The metabolic rate for running at 3.5 mIs
j1
wearing the
control shoes was 13.19 T1.26 (WIkg
j1
). Adding mass to
the shoes significantly increased metabolic rate (PG0.001;
Fig. 2). For the +100-g shoes, the metabolic rate was 0.61% T
1.74% higher (13.27 T1.15 WIkg
j1
), and for the +300-g
shoes, the metabolic rate was 3.51% T1.18% higher (13.66 T
1.36 WIkg
j1
). A linear fit through the data predicts a meta-
bolic rate increase of 1.11% per added 100 g of shoe mass at
3.5 mIs
j1
(95% confidence interval [CI] = 0.88%–1.35%).
While wearing the control shoes, participants ran the 3000-m
time trial in 626.1 T55.6 s (10:26.1 T0:55.6, range = 9:02.4–
12:04.5; Table 1). Adding mass to the shoes significantly in-
creased total running time (PG0.001; Fig. 3). For the +100-g
FIGURE 2—Adding mass to the shoes significantly increased metabolic
rate (PG0.001). Linear least-squares regression equation for all data
points: % increase in metabolic rate = 0.0111 addedmassingrams;
r
2
= 0.47. Error bars indicate T1SEM.
TABLE 1. Individual performance times for the 3000-m time trials in the control shoes
and percent changes in performance time compared with the control shoe for both the
+100-g shoes and the +300-g shoes.
Participant
Time Control Shoes
(s)
$Time +100-g Shoes
(%)
$Time +300-g Shoes
(%)
1 568.6 3.08 8.48
2 724.5 1.09 4.13
3 687.8 0.23 3.43
4 646.5 0.82 2.58
5 672.9 j0.71 1.10
6 560.1 1.64 1.11
7 663.7 j1.36 2.17
8 551.0 j0.44 2.69
9 551.0 1.74 0.91
10 652.8 1.59 1.99
11 597.0 j1.19 1.54
12 655.1 1.05 3.76
13 633.3 0.39 1.37
14 681.3 j1.81 j1.13
15 570.5 0.37 j0.60
16 542.4 2.29 3.61
17 616.2 0.83 3.89
18 690.3 3.01 2.51
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shoes, times averaged 0.65% T1.36% slower (10:30.0 T
0:55.0), and for the +300-g shoes, times were 2.37% T2.09%
slower (10:40.9 T0:58.1). On the basis of a linear fit of all
the data, time increased 0.78% per added 100 g of shoe mass
(95% CI = 0.52%–1.04%). All participants exceeded the 90%
of maximum heart rate ‘‘honest effort’’ criterion during all three
time trials.
Pacing strategy was not affected by shoe mass (lap number–
shoe mass interaction effect: P= 0.10; Fig. 4). During all three
time trials, the participants went out fast in the first lap, and
then they gradually slowed down, before ‘‘kicking’’ during their
last lap(s). Furthermore, we observed no significant effect of
time-trial order on 3000-m time: the participants ran their first
(630.6 T51.5 s), second (632.6 T56.7 s), and third time trials
(633.6 T61.5 s) equally fast (P=0.73).
DISCUSSION
Overall, we accept our hypothesis that time-trial perfor-
mance would slow when 100 and 300 g were added per
shoe. The main effect for mass was significant, and the
overall slope indicated a time increase of 0.78% per 100 g.
We confirmed that metabolic rate increases linearly and
proportionally with running velocity over the range of 2.5 to
4.0 mIs
j1
. This linear relation between metabolic rate and
running velocity is consistent with many previous studies
(3,6,12,15). We also confirmed that metabolic rate increases
when 100 and 300 g were added per shoe. The main effect
for mass was significant and the overall slope indicated a
1.11% increase in metabolic rate per 100 g at 3.5 mIs
j1
.
Our observation that metabolic rate increases linearly with
added shoe mass is consistent with earlier findings (8,9).
Frederick et al. (9) observed an increase in V
˙O
2submax
of
1.2% per 100 g added mass per shoe at a running velocity of
3.83 mIs
j1
. Similarly, Franz et al. (8) observed a metabolic
rate increase of 1.16% per 100 g added mass per shoe at a
running velocity of 3.35 mIs
j1
.
Can we directly translate these changes in RE to distance-
running performance? On the basis of the observed linear
relationship between metabolic rate and running velocity
and the observed 1.11% increase in metabolic rate per 100 g
added shoe mass, one might predict that 3000-m time-trial
performance would have slowed by 1.11% per 100 g added
shoe mass. However, we observed that overall race time
increased by only 0.78% per added 100 g of shoe mass.
Expecting the degradation in performance at race pace to be
directly proportional to the degradation in RE measured at a
slower submaximal pace assumes that the effect of adding
shoe mass on metabolic rate is independent of running ve-
locity. We only evaluated the effect of adding shoe mass at
one speed (3.5 mIs
j1
), so our data cannot confirm nor reject
this assumption. However, in their seminal paper, Frederick
FIGURE 3—Adding mass to the shoes significantly slowed 3000-m
performance time (PG0.001). Linear least-squares regression equation
for all data points: % change in time = 0.0078 added mass in grams;
r
2
= 0.19. Error bars indicate T1 SEM.
FIGURE 4—Pacing strategy was not affected by shoe mass (lap number–shoe mass interaction effect: P= 0.10). Relative differences from the average
lap time, per lap for the time trials in the control (dotted line), +100-g (dashed line), and +300-g (solid line) shoes. Error bars indicate T1 SEM and are
slightly offset horizontally for clarity.
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et al. (9) evaluated the effect of adding mass on V
˙O
2submax
for a range of speeds, and their data suggest that the effect of
adding shoe mass on V
˙O
2submax
is dependent on running
velocity. Because it was a maximal effort, we could not
measure the effects of shoe mass on RE at the average 3000-m
time-trial pace of our participants (4.79 mIs
j1
). Fortunately,
Frederick et al. (9) studied a higher caliber group of runners
and were thus able to measure V
˙O
2submax
up to 4.88 mIs
j1
,
just slightly faster than the time-trial pace of our participants.
Frederick et al. reported that at 4.88 mIs
j1
,V
˙O
2submax
in-
creased by 0.8% per 100 g mass added to each shoe, which is
strikingly close to the 0.78% decrease in performance per 100 g
massaddedtoeachshoethatweobservedinourparticipants
running on average at 4.79 mIs
j1
. Taken together, these
observations indicate that altered RE directly translates to
altered distance-running performance.
Overall, we observed substantial interindividual variabil-
ity in the responses to added shoe mass. For the change in
metabolic rate, the 95% CI ranged from 0.88% to 1.35% per
100 g added mass. For the change in time-trial performance,
the 95% CI ranged from 0.52% to 1.04% per 100 g added
mass. As discussed above, the data of Frederick et al. (9)
suggest that the effect of adding shoe mass on V
˙O
2submax
is
dependent on running velocity. As such, one could expect
that part of the interindividual variability in running perfor-
mance might be related to differences in the individual run-
ning speed of the participants. We did a regression analysis
on the individual change in time-trial performance per 100 g
added shoe mass versus the individual race time in the control
shoes but did not find a significant correlation (P=0.76).
Theoretically, the interindividual variability in performance
changes to added shoe mass could be related to the order in
which the individual participants ran their time trials. How-
ever,regressionanalysesshowednosignificantcorrela-
tions between changes in performance and time-trial order
(P90.2). An alternative possible explanation for the
interindividual variability is the fact thatthe absolute differences
in shoe mass (+100 g and +300 g, respectively) were identical for
all participants and not adjustedfortheirbodymass.Onecan
imagine that adding 300 g mass to the shoe of a 60-kg runner
might have a larger relative effect than adding mass to the
shoe of an 80-kg runner. However, we did not observe
significant correlations between changes in metabolic rate or
in performance and body mass (all P90.2). Future studies
could further address the large interindividual variability in the
responses to added shoe mass, using kinetic and kinematic
analyses of running with shoes of different masses.
During the time trials, only 1 of our 18 participants noticed
that some of the shoes had added mass. This occurred just be-
fore that participant_s third time trial, when he was wearing the
+300-g shoes. That participant has wide feet, and because of the
bulk of the shoes, he noticed that the shoelaces of the +300-g
shoes were shorter than for previous time trials. He subsequently
felt the added lead pockets with his hands. As such, 17 of the 18
participants did not perceive a difference between the shoe
masses during the time trials that were separated by a week.
Intriguingly, the pacing data of the time trials (Fig. 4)
suggest that, in general, the participants unconsciously per-
ceived differences in running effort related to the mass dif-
ferences between the shoes and adjusted their effort level
and pacing accordingly. That is, they unconsciously ran
slower when they wore the heavier shoes. The similar pacing
patterns for all shoe masses suggest that the varied effort was
unconsciously sensed very quickly, within the first 200-m
lap (Fig. 4). These observations are in line with recent find-
ings on the selection of energetically optimal step frequencies
during walking and running (18,19). Snyder et al. (19) eval-
uated how quickly runners refind their optimal step fre-
quency after a period of running while matching a metronome
set to nonoptimal frequencies. They observed that responses
were dominated by a fast process with a response time of ~1.5 s
followed by a slower, fine-tuning process with a response
time of ~34 s. Using robotic exoskeletons, Selinger et al. (18)
shifted people_s energetically optimal walking step frequency
and showed that people rapidly sense the change and alter
their gait to minimize metabolic rate.
Recently, Fuller et al. (11) similarly addressed how changes
in RE affect distance-running performance. They compared
V
˙O
2submax
and performance during a self-paced 5-km tread-
mill running trial between conventional running shoes and
very lightweight racing flats. At submaximal speeds, par-
ticipants used 0.7%–2.5% less oxygen running in the racing
flats as compared with the conventional shoes. With the racing
flats, they ran ~1.7% faster during the self-paced 5-km tread-
mill running trial. Although that study provided new insight,
an important limitation was that it was impossible to blind
the participants as to the shoe conditions during the treadmill
time trials. As Fuller et al. noted in their discussion, runners
expect superior race performance when wearing racing flats.
Mohr et al. (16) showed that there can be dramatic placebo
effects for athletic shoes, although they measured vertical
jump performance rather than distance-running performance.
Limitations. Although we made every effort to mini-
mize differences in sleep, training, and dietary status be-
tween the different time trials, we could not absolutely
control for everything. We studied good but not elite run-
ners, and thus directly extrapolating our results to elite run-
ners should be conducted with caution. Furthermore, as
mentioned, we could not measure the effects of shoe mass
on RE at the 3000-m time-trial pace of our participants be-
cause this distance is normally run at an intensity close to or
above V
˙O
2max
(2), invalidating RE measurements at that
pace. Another limitation is that although we are ultimately
interested in the effects of improved RE on performance,
we could only study the effect of adding mass and thus
RE degradation.
Implications. The current men_s world best for a mar-
athon (42.195 km) is 2:02:57 (5.72 mIs
j1
) and was run
wearing ~230-g shoes. Assuming that our results for running
3000 m at 4.79 mIs
j1
directly transfer to running 42 km at
5.72 mIs
j1
, a reduction of shoe mass by 100 g per shoe
would reduce the marathon record by ~0.78% or 57.5 s.
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Extending this line of reasoning, one might expect that
running barefoot (as opposed to wearing 230-g racing flats)
could facilitate a marathon time of ~2:00:45, just through the
elimination of the shoe mass. However, barefoot and shod
running are different in more aspects than mass, and previ-
ous studies in our laboratory (8,21) have demonstrated the
interactions and trade-offs between shoe mass and shoe
cushioning properties in terms of RE. For example, Tung
et al. (21) showed that running barefoot on a rigid surface is
equally economical as running in lightweight, cushioned
shoes on the same surface. However, when Tung et al.
eliminated shoe mass but conserved cushioning by having
participants run barefoot on a surface cushioned with 10 mm
of common midsole foam, RE was enhanced. Thus, to opti-
mize RE, shoe mass should not be minimized by eliminating
cushioning. Further, significant muscle damage occurs over a
marathon (13). A shoe that combines low mass with minimal
cushioning might provide energy savings when evaluated in
short-term treadmill testing, but not be optimal for marathon
running because extra cushioning might mitigate muscle dam-
age and thus be beneficial in the long run.
While our observations indicate that altered RE directly
translates to altered distance-running performance, it is
important to note that changes in RE observed for a specific
individual need to be considered in relation to the reliability
of RE measurements. The typical error in RE in our study
was 0.84%, and the ‘‘smallest worthwhile change’ was 1.91%
for our sample. When evaluating changes in RE for an indi-
vidual runner, for a single observation, only changes in RE
of 2.0% or more can be considered to be ‘‘real’’ and ‘‘worth-
while’’ (17) and not simply related to measurement error and
typical variation. However, real effects smaller than 2% can
be identified with repeated measurements on an individual
and/or with adequate sample sizes as we have shown in the
present study.
CONCLUSION
We confirmed that adding shoe mass degrades RE, and
we showed, for the first time, that adding shoe mass slows
3000-m time-trial performance proportionally (0.78% per
100 g per shoe). Our data demonstrate that laboratory-
based RE measurements can accurately predict changes in
distance-running race performance due to shoe modifications.
This study was supported by Nike Inc. We thank Maddie Alm for
assistance during the data collections.
Barry Spiering is an employee of Nike Inc., and Rodger
Kram is a paid consultant to Nike Inc. The results of the present
study do not constitute endorsement by the American College of
Sports Medicine.
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http://www.acsm-msse.org2180 Official Journal of the American College of Sports Medicine
APPLIED SCIENCES
Copyright © 2016 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
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People prefer to move in ways that minimize their energetic cost [1-9]. For example, people tend to walk at a speed that minimizes energy use per unit distance [5-8] and, for that speed, they select a step frequency that makes walking less costly [3, 4, 6, 10-12]. Although aspects of this preference appear to be established over both evolutionary [9, 13-15] and developmental [16] timescales, it remains unclear whether people can also optimize energetic cost in real time. Here we show that during walking, people readily adapt established motor programs to minimize energy use. To accomplish this, we used robotic exoskeletons to shift people's energetically optimal step frequency to frequencies higher and lower than normally preferred. In response, we found that subjects adapted their step frequency to converge on the new energetic optima within minutes and in response to relatively small savings in cost (<5%). When transiently perturbed from their new optimal gait, subjects relied on an updated prediction to rapidly re-converge within seconds. Our collective findings indicate that energetic cost is not just an outcome of movement, but also plays a central role in continuously shaping it.
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Running shoes are often marketed based on mass. A total of 50 young adult males participated across two separate experiments to determine how well they could perceive the relative masses of five different running shoes using hands versus feet. For the foot portion, subjects were blindly fitted with the shoes and asked to rank their masses individually using visual analogue scales (VAS) and verbal rankings. For the hand portion, two different methods were used, one presenting all shoes simultaneously and the other presenting the shoes individually. Verbal accuracy and VAS scores correlated across subjects for the hand and foot, but accuracy in mass perception by the feet was 30% compared to 92% or 63% by the hand (depending on the method). These results indicate the foot perceives mass poorly compared to the hand, and that consumers' perception of shoe mass may come more from handling shoes versus wearing them.
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The relationship between VO2 and velocity of running (running economy) has been rather casually dealt with until very recently, and there still remains considerable disagreement as to the importance of this variable. Various factors which have been shown, or appear, to affect running economy include environment (temperature, altitude, running surface), fatigue, age, weight, state of fitness, and inherent differences. That differences between individuals and within individuals can and do exist seems clear; the questions which need to be addressed in future research are: (1) What type of training is most effective in bringing about changes in running economy? and (2) How much change in economy can be expected with optimum training? Furthermore, it is suggested that running economy be investigated as an entity, so that changes that may take place with time or training can be more accurately related to their cause. (C)1985The American College of Sports Medicine