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SYSTEMATIC REVIEW
The Effect of Footwear on Running Performance and Running
Economy in Distance Runners
Joel T. Fuller •Clint R. Bellenger •Dominic Thewlis •
Margarita D. Tsiros •Jonathan D. Buckley
Springer International Publishing Switzerland 2014
Abstract
Background The effect of footwear on running economy
has been investigated in numerous studies. However, no
systematic review and meta-analysis has synthesised the
available literature and the effect of footwear on running
performance is not known.
Objective The aim of this systematic review and meta-
analysis was to investigate the effect of footwear on run-
ning performance and running economy in distance run-
ners, by reviewing controlled trials that compare different
footwear conditions or compare footwear with barefoot.
Methods The Web of Science, Scopus, MEDLINE,
CENTRAL (Cochrane Central Register of Controlled Tri-
als), EMBASE, AMED (Allied and Complementary
Medicine), CINAHL and SPORTDiscus databases were
searched from inception up until April 2014. Included
articles reported on controlled trials that examined the
effects of footwear or footwear characteristics (including
shoe mass, cushioning, motion control, longitudinal bend-
ing stiffness, midsole viscoelasticity, drop height and
comfort) on running performance or running economy and
were published in a peer-reviewed journal.
Results Of the 1,044 records retrieved, 19 studies were
included in the systematic review and 14 studies were
included in the meta-analysis. No studies were identified
that reported effects on running performance. Individual
studies reported significant, but trivial, beneficial effects on
running economy for comfortable and stiff-soled shoes
[standardised mean difference (SMD) \0.12; P\0.05), a
significant small beneficial effect on running economy for
cushioned shoes (SMD =0.37; P\0.05) and a significant
moderate beneficial effect on running economy for training
in minimalist shoes (SMD =0.79; P\0.05). Meta-ana-
lysis found significant small beneficial effects on running
economy for light shoes and barefoot compared with heavy
shoes (SMD \0.34; P\0.01) and for minimalist shoes
compared with conventional shoes (SMD =0.29;
P\0.01). A significant positive association between shoe
mass and metabolic cost of running was identified
(P\0.01). Footwear with a combined shoe mass less than
440 g per pair had no detrimental effect on running
economy.
Conclusions Certain models of footwear and footwear
characteristics can improve running economy. Future
research in footwear performance should include measures
of running performance.
Key Points
Running shoes with greater shoe cushioning, greater
longitudinal shoe stiffness and greater shoe comfort
were associated with improved running economy.
Running in light shoes or running barefoot reduced
metabolic cost compared with running in heavy
shoes but there was no difference in metabolic cost
between running in light shoes and running barefoot.
No studies have investigated the effect of footwear
on running performance measured using a time-trial
or time-to-exhaustion test.
J. T. Fuller (&)C. R. Bellenger D. Thewlis
M. D. Tsiros J. D. Buckley
Sansom Institute for Health Research, University of South
Australia, GPO Box 2471, Adelaide, SA 5001, Australia
e-mail: joel.fuller@mymail.unisa.edu.au
123
Sports Med
DOI 10.1007/s40279-014-0283-6
1 Introduction
Selection of appropriate footwear (or lack of footwear,
i.e. barefoot) is often advocated as an essential require-
ment for distance running [1] and as a means for
improving running performance [2]. Performance
enhancement is also a primary motivating reason that
runners try new footwear [3]. However, a systematic
search conducted in 2007 found no studies that had
investigated the effect of footwear on running perfor-
mance [4] and there is a lack of consensus amongst the
literature on what should be considered appropriate
footwear for distance running [4–6]. Despite the lack of
research investigating the effects of footwear on running
performance, several studies have investigated the effect
of footwear on running economy, a surrogate measure of
running performance [5,7]. Running economy is deter-
mined from the oxygen demand at a given velocity of
submaximal running and is a good predictor of distance
running performance [8].
Several different footwear characteristics such as shoe
mass, cushioning, motion control, longitudinal bending
stiffness, midsole viscoelasticity, drop height and comfort
have been proposed to influence running economy and in
turn influence running performance [2,6,9–14]. Shoe mass
has been shown to be important for determining running
economy, with additional shoe mass predictably increasing
metabolic cost at a given workload [9]. The effect of shoe
cushioning on running economy is less clear [13,15].
Increased shoe cushioning does not always reduce meta-
bolic cost [13] and running barefoot or in minimalist shoes
with no cushioning has been shown to be more economical
than some cushioned shoes [2,6]. Indeed, running barefoot
or in minimalist shoes that have a flat shoe sole with no
cushioning can cause runners to make acute, short-term
changes in running gait from a rearfoot strike to a forefoot
strike, increase cadence and reduce vertical oscillation of
the centre of mass, which can contribute to improved
running economy [2,6].
A review of the current available literature concerning
the effects of footwear on distance running performance
and running economy is important given the increasing
amount of research being published in the area and the
need for synthesis of this information to provide direction
for ongoing research. There is also an increasing desire on
the part of athletes to understand the effects of different
footwear [3] and a systematic review could help determine
the optimal footwear for distance running. As a result, the
aim of this review was to investigate the effect of different
types of footwear (heavy, light or minimalist) and footwear
characteristics (shoe mass, cushioning, motion control,
longitudinal bending stiffness, midsole viscoelasticity, drop
height and comfort) on running performance and running
economy in distance runners, by reviewing controlled trials
that compare different footwear conditions or compare
footwear with barefoot.
2 Methods
This review followed the PRISMA statement for improved
reporting of systematic reviews [16].
2.1 Information Sources
A literature search was conducted on 5 April 2014. The
following databases were searched: Web of Science, Sco-
pus, MEDLINE, EMBASE, AMED (Allied and Comple-
mentary Medicine), CINAHL (Cumulative Index to
Nursing and Allied Health), SPORTDiscus and CENTRAL
(Cochrane Central Register of Controlled Trials). Dat-
abases were searched from inception up until April 2014.
Searches were supplemented by forward citation searching
and hand searching the reference lists of eligible studies.
2.2 Search Strategy
In each database the title, abstract and keyword search
fields were searched using the following search strategy:
run* AND shoe* OR footwear OR shod AND per-
formance OR race* OR racing OR marathon* OR
time OR distance OR speed OR endurance OR
economy OR efficiency OR oxygen OR VO
2
NOT
orthotic OR pain OR injury
Where possible, limits were placed on searches
according to publication type so that only controlled trials,
which provide the highest quality of scientific evidence,
were included. Additionally, searches were limited to
human participant and English language only publications.
Eligibility criteria are shown in Table 1.
2.3 Study Selection
Eligibility and risk of bias assessment were performed
independently by two reviewers (JTF and CRB) with dis-
agreement settled by consensus. All records were examined
by title and abstract in order to exclude obviously irrelevant
records. Full-text articles for the remaining records were
retrieved and assessed for eligibility. Data including the
publication details, study design, participant characteris-
tics, randomisation, allocation, blinding, testing proce-
dures, description of intervention and results of any
analysis of running performance or running economy out-
comes were extracted from all eligible studies. If insuffi-
cient information was reported (e.g. shoe mass not
J. T. Fuller et al.
123
reported) authors were contacted to seek clarification or
additional information about the included studies.
2.4 Risk of Bias Assessment
This review used the Cochrane Collaboration’s tool for
assessing risk of bias in controlled trials [17]. Additionally,
as all studies identified by the search were of crossover
design, the appraisal of bias also considered the appropri-
ateness of using a crossover design and whether appropriate
statistical analysis had been performed on the paired data.
2.5 Statistical Considerations
No studies concerning the effect of footwear on distance
running performance were identified. As a result, statistical
analysis was confined to the effect of footwear on running
economy. For each running economy study outcome,
standardised mean differences (SMDs) were calculated.
Mean differences were standardised using the pooled
between-subject standard deviation for the two footwear
conditions being compared. Effects were quantified as
trivial (\0.2), small (0.2–0.6), moderate (0.61–1.2), large
(1.21–2.0) and very large ([2.0) [18].
To investigate the effect of shoe mass on running econ-
omy, the SMD for all studies comparing shod (heavy, light or
minimalist shoe) and barefoot conditions were plotted
against the respective shoe mass, calculated as the combined
mass of both shoes per pair. Studies that controlled for shoe
mass in the comparison between shod and barefoot were not
included because they had controlled for the shoe mass that
was the focus of this analysis. The association between
running economy and shoe mass was explored using bivar-
iate correlation analysis and linear regression.
Meta-analysis was undertaken for studies that compared
running economy between heavy shoes, light shoes and
barefoot without controlling for shoe mass across condi-
tions. It was not possible to control for other footwear
characteristics (e.g. cushioning, motion control, longitudi-
nal bending stiffness, etc.) when comparing between heavy
and light shoes. As a result, statistical analysis considered
only the average effect of a heavy or light shoe. Meta-
analysis also compared running economy between mini-
malist and conventional running shoes in studies that
controlled for difference in shoe mass and between soft and
hard cushioned heavy running shoes. Statistical signifi-
cance was set at P\0.05.
Random-effects meta-analysis was performed in Review
Manager (RevMan) software (version 5.2, Cochrane Col-
laboration, Oxford, UK) using the inverse-variance
method. Where not reported, the standard error of mean
difference and correlations between treatment outcomes
were estimated from Pvalues using the equivalent T-sta-
tistic or F-statistic. When this was not possible, standard
error of mean difference was estimated according to the
methods described by Elbourne et al. [19], using the lowest
correlation estimate among other studies. Presence of sta-
tistical heterogeneity was determined using the I
2
and
Cochran’s Q statistics [20].
3 Results
After removal of duplicates, the initial search identified
634 records. An additional six records were identified
through hand searching of the reference lists of articles
identified in the electronic search. A summary of the
search, including number of studies suitable for meta-
analysis and reasons for exclusion, is shown in Fig. 1.All
19 studies included in the review were of crossover design
and are summarised in Table 2[2,5–7,9–15,21–28].
3.1 Reasons for Exclusion
Five studies were excluded for not using a study sample of
distance runners only [29–33], one study was excluded for
Table 1 Eligibility criteria
Criterion Description
Type of participant Healthy adult distance runners (aged [18 years). Eligible studies had to describe participants as runners
Type of intervention Running shoe
Type of comparison A comparative running shoe condition or barefoot but not running shoe plus orthotic
Type of outcome measure
Running performance Race time, time-trial or time-to-exhaustion test for distances C1,500 m or respective running speeds
Running economy Measured using steady-state oxygen consumption or energy expenditure calculated using indirect calorimetry
Type of study Controlled studies
Publication status Peer-reviewed journal publication
Publication date Publication date did not form part of the eligibility criteria
Language of publication English language publication
Effect of Footwear on Running Performance and Running Economy
123
only comparing running shoe with running shoe plus
orthotic [34], two studies were excluded for comparing
running shoe with military boots [35] or spring boots [36],
three studies were excluded for using short-distance run-
ning tests (18, 20 and 60 m) that were not considered
representative of distance running performance [37–39],
two studies were excluded for measuring running economy
while running on an underwater treadmill [40,41] and one
study was excluded for not reporting running performance
or economy data [42].
3.2 Risk of Bias
All eligible studies used a crossover design and produced
paired data. Only one study [13] did not report use of
appropriate pairwise analysis. Thomson et al. [13] reported
use of a two-sample ttest for analysis of data obtained
from repeated measures on the same participants and was
at a high risk of detection bias. No studies reported suffi-
cient information regarding randomisation or allocation
concealment. No studies provided information regarding
blinding of participants, personnel or outcome assessors,
and it is unclear what influence lack of blinding would have
had on running performance and running economy out-
comes. Four studies [9,23,24,26] did not provide suffi-
cient information regarding the number of participants
assessed and analysed and were at an unclear risk of
attrition bias. Two studies [11,21] excluded participants
from analysis and were at a high risk of detection bias.
Nigg et al. [11] excluded one participant from analysis due
to unreliable oxygen consumption measurement and
Burkett et al. [21] excluded two participants from analysis
without providing a reason. It is unclear what effect
selective reporting may have had on the results of this
review.
3.3 Participants
In all, 243 distance runners had running economy com-
pared between differing footwear conditions. Eleven
studies [6,9–11,13,15,21,22,25–27] included male
participants only, two studies [2,23] included female
participants only, three studies [5,7,28] included both
male and female participants, and three studies [12,14,24]
did not report participant sex. Of the seven studies that
considered barefoot running economy, three studies [22,
27,28] included only participants who were experienced
barefoot runners, one study [2] excluded participants with
barefoot running experience and in three studies [7,9,21]
participant experience with barefoot running was unclear.
3.4 Footwear
A variety of footwear conditions and footwear character-
istics were compared (Table 2). Five studies [2,9,22,27,
28] compared a light shoe with barefoot, five studies [2,7,
9,21,27] compared a heavy shoe with barefoot, eight
studies [2,5,6,9,23,25–27] compared a heavy shoe with
a light shoe, five studies [2,5,6,25,27] compared mini-
malist shoes with conventional shoes, four studies [13,15,
24,26] assessed the effect of sole cushioning, one study
[14] compared a heavy cushioned shoe with a motion
Fig. 1 Literature search flow chart. nnumber of studies
J. T. Fuller et al.
123
Table 2 Summary of included studies
Study Year nWashout period Running economy Study comparisons SMD (A–B)
Unit of measure Speed Footwear condition A Footwear condition B
Moore et al. [2] 2014 F: 15 10 min mLkg
-1
min
-1
2.78 ms
-1
Barefoot Light minimalist shoe -0.33*
a
Barefoot Heavy shoe -0.52*
b
Light minimalist shoe Heavy shoe -0.26*
a
Tung et al. [28] 2014 M: 10
F: 2
3 min mLkg
-1
min
-1
Wkg
-1
3.35 ms
-1
Barefoot Lightweight shoe -0.06
Lussiana et al. [25] 2013 M: 14 5 min mLkg
-1
m
-1
2.78 ms
-1
Light minimalist shoe Heavy shoe -0.33*
Sinclair et al. [26] 2013 M: 12 HR \110 bpm mLkg
-1
min
-1
4.0 ms
-1
Light hard shoe
c
Heavy soft shoe -0.01
Franz et al. [22] 2012 M: 12 4 min Wkg
-1
3.35 ms
-1
Barefoot Light shoe 0.28
Barefoot
c
Light shoe 0.45*
Perl et al. [5] 2012 M: 13
F: 2
Same day mLkg
-1
m
-1
3.0 ms
-1
Light minimalist shoe
c
Heavy shoe -0.34*
Warne and Warrington [6] 2012 M: 15 24 h mLkg
-1
min
-1
3.06 ms
-1
Light minimalist shoe Heavy shoe -0.12
3.61 ms
-1
Light minimalist shoe Heavy shoe -0.79*
d
Hanson et al. [7] 2011 M: 5
F: 5
HR \110 bpm mLkg
-1
min
-1
70 % vVO
2max
Barefoot Heavy shoe -0.09
Barefoot Heavy shoe -0.29
e
Luo et al. [10] 2009 M: 10 3 min mLkg
-1
min
-1
0.23 ms
-1
[vaT
Most comfortable heavy shoe
c
Least comfortable heavy shoe
c
-0.09*
f
Rubin et al. [14] 2009 14 2–7 days mLkg
-1
min
-1
65 % vVO
2max
Heavy cushioned shoe Heavy motion control shoe -0.30
Squadrone and Gallozzi [27] 2009 M: 8 4 min mLkg
-1
min
-1
3.33 ms
-1
Barefoot Light minimalist shoe 0.35
Barefoot Heavy shoe -0.3
Light minimalist shoe Heavy shoe -0.65*
Divert et al. [9] 2008 M: 12 2 min mLkg
-1
min
-1
3.61 ms
-1
Barefoot Light shoe 0.03
Barefoot Heavy shoe -0.54
Light shoe Heavy shoe -0.56*
Barefoot
c
Light shoe 0.07
Barefoot
c
Heavy shoe -0.21
Roy and Stefanyshyn [12] 2006 13 5 min mLkg
-1
min
-1
0.22 ms
-1
\vLT
Heavy stiff (38 Nmm) shoe Heavy control (18 Nmm) shoe -0.12*
Heavy stiffest (45 Nmm) shoe Heavy control (18 Nmm) shoe -0.03
Heavy stiffest (45 Nmm) shoe Heavy stiff (38 Nmm) shoe 0.09
Hardin et al. [24] 2004 12 HR \120 bpm mLkg
-1
min
-1
3.40 ms
-1
[vaT
Soft shoe
g
Hard shoe
g
0.12
h
Nigg et al. [11] 2003 M: 18 3 min mLkg
-1
min
-1
‘‘Slightly above’’ vaT Heavy viscous shoe Heavy elastic shoe 0.0
Thomson et al. [13] 1999 M: 14 Separate days mLkg
-1
min
-1
3.14 ms
-1
4.02 ms
-1
Heavy soft shoe Heavy hard shoe -0.04
Hamill et al. [23] 1988 F: 8 Unclear mLkg
-1
min
-1
90 % vVO
2max
Lightweight shoe Heavy shoe -0.12
Effect of Footwear on Running Performance and Running Economy
123
control shoe, one study [10] assessed the effect of shoe
comfort, one study [12] assessed the effect of longitudinal
bending stiffness and one study [11] assessed the effect of
shoe sole viscoelasticity.
3.5 Study Outcomes
No studies provided information concerning the effect of
footwear on running performance. All eligible studies
provided information concerning the effect of footwear on
running economy. All studies expressed oxygen uptake
(VO
2
) relative to body mass. Fifteen studies [2,6,7,9–14,
21,23,24,26–28] expressed VO
2
relative to time, three
studies [5,15,25] expressed VO
2
relative to distance and
two studies [22,28] converted VO
2
to caloric expenditure.
The unit of measure chosen to assess running economy did
not appear to affect study findings (Table 2).
The washout period between assessments in different
footwear conditions ranged from 2 min to 7 days
(Table 2). The length of the washout period was unclear in
two studies [13,23]. All studies, except one [7], assessed
running economy during submaximal running bouts on a
treadmill. Hanson et al. [7] compared running economy
between barefoot and heavy shoe conditions during sub-
maximal running bouts on a treadmill and overground. The
authors reported an SMD in running economy for barefoot
compared with heavy shoes during overground running
that was three times that reported for treadmill running
(Table 2). This suggests that footwear might affect running
economy differently for treadmill compared with over-
ground running. However, the overground running results
reported by Hanson et al. [7] have been challenged in the
literature, with concerns about the presence of systematic
error in the experimental procedures used to assess over-
ground running economy [43,44]. Due to the potential
difference in running economy outcomes tested on tread-
mill and overground, only study outcomes assessed on a
treadmill were included in the meta-analysis.
SMD in running economy ranged from 0 to 0.79
(Table 2). Two studies [6,27] reported SMDs for light
minimalist shoe compared with heavy shoe that were of
moderate effect (0.65–0.79). Two other studies reported
SMDs for light shoe [9] and barefoot [2,9] compared with
heavy shoe that were close to moderate effect (0.52–0.56).
The remaining studies reported SMDs that were of trivial
to small effect.
3.6 Regression Analysis
There was a strong correlation between the combined mass
of a shoe pair and change in VO
2
relative to running
barefoot (R=0.85, P\0.01) [7,9,21,22,27,28]. The
metabolic cost of running increased linearly with
Table 2 continued
Study Year nWashout period Running economy Study comparisons SMD (A–B)
Unit of measure Speed Footwear condition A Footwear condition B
Frederick et al. [15] 1986 M: 10 Same day mLkg
-1
km
-1
3.65–4.55 ms
-1
Soft shoe
g
Hard shoe
g
-0.37*
Burkett et al. [21] 1985 M: 19 5 min mLkg
-1
min
-1
3.35 ms
-1
Barefoot Heavy shoe
i
-0.18
bpm beats per minute, Ffemale, HR heart rate, Mmale, nsample size, SMD standardised mean difference, vaT velocity at aerobic threshold, vLT velocity at lactate threshold, vVO
2max
velocity at maximal
oxygen consumption
*P\0.05
a
Adjusted for shoe mass and stride length
b
Adjusted for shoe mass
c
External mass added to footwear conditions to balance mass difference between conditions
d
Comparison after 4-week familiarisation in lightweight shoe
e
Oxygen uptake measured during overground running
f
Calculated relative to pooled standard deviation across all studies because information not available
g
Shoe mass not reported but the mass of the compared shoes reported to be within 0–31 g
h
Effect size estimated based on graphical data
i
Mass of heavy shoe estimated from the body mass-normalised shoe mass reported by authors
J. T. Fuller et al.
123
increasing shoe mass (Fig. 2). The linear relationship pre-
dicted that there would be no difference in VO
2
between
shod and barefoot running for footwear with a combined
shoe mass of 440 g per pair (Fig. 2). Extrapolation of this
linear relationship to the hypothetical situation where shoe
mass was zero indicated that shoe characteristics other than
shoe mass had a theoretical combined small beneficial
effect on running economy (SMD =0.58; Fig. 2).
3.7 Meta-Analysis
A summary of within-study comparisons and methods used
to calculate the individual study standard error of mean
difference is shown in Table 3. Results of meta-analysis
are shown in Fig. 3. Shoe conditions were grouped into two
categories based on shoe mass:
•Light shoe (combined shoe mass per pair [0to
B440 g);
•Heavy shoe (combined shoe mass per pair [440 g).
The shoe mass of 440 g was selected as the demarcation
between heavy and light shoes based on the results of the
linear regression analysis, which predicted that footwear
Table 3 Available data and results for 18 outcomes across 14 studies included in meta-analysis
Study Year nInformation available Footwear comparison SMD
(A–B)
SE
(A–B)
Correlation
used
Condition A Condition B
Moore et al. [2] 2014 15 Treatment-specific summaries, Pvalues
(ttest)
Minimalist Conventional -0.26 0.09 0.94
Tung et al. [28] 2014 12 Treatment-specific summaries, Pvalues
(ttest)
Barefoot Light -0.06 0.09 0.97
Lussiana et al. [25] 2013 14 Treatment-specific summaries,
correlation assumed
Light Heavy -0.33 0.19 0.77
Franz et al. [22] 2012 12 Treatment-specific summaries, Pvalues
(ttest)
Barefoot Light 0.28 0.17 0.84
Perl et al. [5] 2012 15 Individual-specific data Minimalist Conventional -0.34 0.11 0.91
Warne and
Warrington [6]
2012 15 Treatment-specific summaries, 95 % CI
provided by authors
Light Heavy -0.12 0.17 0.81
Hanson et al. [7] 2011 10 Treatment-specific summaries,
correlation assumed
Barefoot Heavy -0.09 0.21 0.77
Squadrone and
Gallozzi [27]
2009 8 Treatment-specific summaries,
correlation assumed
Barefoot Heavy -0.30 0.24 0.77
Barefoot Light 0.35 0.24 0.77
Light Heavy -0.65 0.24 0.77
Divert et al. [9] 2008 12 Treatment-specific summaries,
correlation assumed
Barefoot Heavy -0.54 0.21 0.77
Barefoot Light 0.03 0.20 0.77
Light Heavy -0.56 0.21 0.77
Hardin et al. [24] 2004 12 Treatment-specific summaries,
correlation assumed
Soft Hard 0.12 0.20 0.77
Thomson et al. [13] 1999 14 Treatment-specific summaries, 95 % CI
provided by authors
Soft Hard -0.04 0.18 0.77
Hamill et al. [23] 1988 8 Treatment-specific summaries,
correlation assumed
Light Heavy -0.12 0.24 0.77
Frederick et al. [15] 1986 10 Individual-specific data Soft Hard -0.37 0.09 0.96
Burkett et al. [21] 1985 19 Treatment-specific summaries, F-statistic
(repeated measures ANOVA)
Barefoot Heavy -0.18 0.11 0.90
ANOVA analysis of variance, CI confidence interval, nsample size, SE standard error of mean difference, SMD standardised mean difference
Fig. 2 Change in oxygen uptake for shod running in shoes of
different mass compared with barefoot. Shoe mass values are the
combined mass of a shoe pair [7,9,21,22,27,28]. SMD standardised
mean difference, VO
2
oxygen uptake
Effect of Footwear on Running Performance and Running Economy
123
with a combined shoe mass greater than 440 g per pair
would increase the metabolic cost of running (Fig. 2).
Light shoes ([0toB440 g per pair) and barefoot sig-
nificantly reduced the metabolic cost of running compared
with heavy shoes ([440 g per pair) (P\0.01), but there
was no significant difference between light shoes ([0to
B440 g per pair) and barefoot (P=0.34). When shoe
mass was controlled for, minimalist shoes were signifi-
cantly more economical for running than conventional
running shoes (P\0.01). There was no significant
Fig. 3 Results of meta-analysis. CI confidence interval, nnumber of participants, SE standard error, SMD standardised mean difference
J. T. Fuller et al.
123
difference between running economy in soft shoes and
running economy in hard shoes of the same shoe mass
(P=0.40). However, this result was significantly affected
by statistical heterogeneity (P=0.04, I
2
=69 %).
4 Discussion
The effect of footwear on running performance is an area
of increasing interest, with almost half of the 19 studies
identified by the search published in the last 3 years
(Table 2). However, despite the increased interest, no
studies identified by the search determined the effect of
footwear on time or distance measures of running perfor-
mance. Instead, all studies identified used running econ-
omy measured at submaximal running speeds as a measure
of running performance. This choice has logical validity
given that running performance is dependent to some
extent on running economy [8] and running economy is
likely to be affected by footwear. Indeed, several authors
have previously observed a strong association between
running economy and running performance [45,46].
However, a strong association between running economy
and running performance has not always been found [47,
48] and time or distance measures should be considered the
reference standard for assessment of running performance.
All studies included in this review used crossover
designs and only one study [6] included a longer-term
follow-up of 4 weeks. To the authors’ knowledge, there
have been no studies investigating what the appropriate
washout period for footwear intervention studies is and it is
unclear whether the washout periods of several minutes to
several days used by studies included in this review were
appropriate (Table 2). Additionally, all but one study [6]
focused on only the acute, short-term effects of footwear on
running economy. Focusing on acute effects ignores
potential long-term effects on running economy that may
be associated with running in certain footwear over time.
This possibility has not been thoroughly tested, but it
would seem reasonable to expect that, over time, learning
and training effects would occur in response to running in
footwear. Indeed, a study measuring running economy in a
novel shoe condition with and without a 4-week familiar-
isation found significant differences between the effect of
the novel footwear when tested with and without the fa-
miliarisation [6]. Knowledge of these possible learning and
training effects would be valuable information for runners
who spend extensive time running in different footwear.
A variety of different footwear characteristics were
investigated across the studies included in this review
(Table 2). The effect of motion control characteristics [14],
midsole longitudinal bending stiffness [12], heel visco-
elasticity [11] and shoe comfort [10] was trivial to small
(SMD =0.0–0.30) and each was only investigated by an
individual study so could not be pooled for meta-analysis.
Despite the small effects observed for these characteristics,
improvements in running economy were significant for
shoes with stiff midsole components (38–45 Nmm) [12]
and comfortable shoes [10]. These significant effects were
of a magnitude that was classified as trivial
(SMD =0.08–0.12) [10,12]. However, even these small
effects on running economy may be important for high-
performance athletes for whom relatively small improve-
ments in performance can have large effects on the out-
come of major competitive events [49].
The largest individual study effect sizes were reported
by studies investigating the effect of light shoes, minimalist
shoes or barefoot on running economy [2,6,9,27]. This
suggests that shoe mass is a critical consideration for
designing and selecting shoes for use in distance running
competition. The importance of shoe mass in determining
running economy is intuitive. If one were to consider the
simple inertial differences between a heavy and light shoe
that must be accelerated with and against gravity with each
step taken, it is logical that the reduced muscular effort will
lead to improved running economy. Indeed, a positive
association between shoe mass and the oxygen cost of
running has been previously reported [22] and our linear
regression model found a similar positive association
(Fig. 2). However, interestingly, our model suggested that
the detrimental effect on running economy for shoe mass
compared with barefoot was only evident for shoes
weighing greater than 440 g per pair and shoes weighing
less than this would have a beneficial effect on running
economy.
When using 440 g as the demarcation between light and
heavy shoes, meta-analysis found light shoes and barefoot
to be significantly more economical than heavy shoes
(SMD =0.24–0.34), but found no difference between light
shoes and barefoot. The reason that the mass of a light shoe
(\440 g per pair) does not have detrimental effects on
running economy relative to barefoot, and may even
improve running economy, remains untested. It would
seem likely that, for footwear weighing less than 440 g per
pair, any disadvantage due to having to repeatedly accel-
erate and decelerate the shoe against gravity might be
balanced by the beneficial effects on running economy
derived from the shoe cushioning [15], stiffness [12] and
comfort [10]. Indeed, our linear regression model predicted
that if shoe mass could be zero, then the combined effect of
other shoe characteristics would have close to a moderate
beneficial effect on running economy (Fig. 2).
Although still considered a light shoe, minimalist shoes
differ from conventional running shoes in regards to drop
height, sole thickness and toe box structure. Two studies [2,
5] compared the effect of these differences on running
Effect of Footwear on Running Performance and Running Economy
123
economy by controlling for the effect of shoe mass. Meta-
analysis of these two studies found a significant small
improvement (SMD =0.29) in running economy for
minimalist shoes compared with conventional running
shoes (Fig. 3). It has been suggested that the flat, thin-soled
minimalist shoes cause runners to increase cadence and
adopt a forefoot strike, which in turn improves running
economy [6]. However, both studies included in the meta-
analysis of minimalist shoes found significant improve-
ments in running economy for minimalist shoes compared
with conventional running shoes even when controlling for
changes in foot strike and cadence [2,5]. As a result, there
must be further reason for the observed difference in run-
ning economy. Perl et al. [5] suggested that flat, thin-soled
shoes increase the storage and release of elastic energy in
the Achilles tendon and longitudinal arch of the foot.
However, to date, this hypothesis has not been thoroughly
tested. Nonetheless, when controlling for shoe mass, run-
ning in minimalist shoes has a beneficial effect on running
economy.
When shoe mass and running gait were not controlled
for between minimalist shoes and conventional running
shoes, the beneficial effect of minimalist shoes on running
economy increased (SMD =0.12–0.79) [6,25,27]
(Table 2). This larger beneficial effect could be partly
explained by the reduction in shoe mass associated with
minimalist shoes compared with conventional shoes but
could also be due to changes in foot strike and cadence that
have previously been associated with running in a mini-
malist shoe [6,27,50]. It is thought that the heightened
somatosensory feedback associated with running in mini-
malist shoes, which lack cushioning, prompts runners to
increase cadence and land with a more anterior foot strike
[2]. Indeed, Squadrone and Gallozzi [27] and Warne and
Warrington [6] observed SMD improvements in running
economy of 0.65 and 0.79 for the minimalist shoe when
runners also adopted a forefoot strike [6,27] and increased
cadence [6]. The participants were either experienced
barefoot runners [27] or were given 4 weeks to familiarise
themselves with the minimalist shoes [6]. Given the size of
these effects, future research should further explore the
long-term effects of running in minimalist shoes on run-
ning economy and biomechanics.
Although running in minimalist shoes may be associated
with large improvements in running economy [6,27],
running in these shoes may have negative effects on injury
risk [51] and this effect on injury should not be ignored.
Running in minimalist shoes is associated with increased
peak tibial acceleration [26], which is known to be sig-
nificantly greater in runners who have sustained a recent
tibial stress fracture than in healthy controls [52]. Addi-
tionally, changing from a rearfoot strike to a forefoot strike
is associated with increased ankle joint contact forces and
increased plantar flexor muscle forces [53]. These unac-
customed high forces could increase the risk of injury until
the associated muscular and articular tissue has had time to
adapt [53]. The long-term safety of minimalist shoes
should be investigated before they are advocated as a
means for runners to improve running economy.
Meta-analysis was also possible for studies that com-
pared running economy between soft- and hard-soled shoes
(Table 3)[13,15,24]. Meta-analysis found no significant
difference in running economy between soft- and hard-
soled shoes of similar mass (Fig. 3). However, this result
was significantly affected by statistical heterogeneity and
should be interpreted with caution (Fig. 3). It is likely that
the heterogeneity in results was due to differences in the
extent of cushioning provided by each of the shoe condi-
tions considered across the different studies. Tung et al. [28]
showed that while 10 mm of surface cushioning signifi-
cantly improved barefoot running economy on a treadmill,
20 mm of surface cushioning had no significant effect. The
authors hypothesised that there may be an optimum amount
of surface cushioning for each individual, which minimises
the metabolic cost of running [28]. Future research should
investigate this hypothesis to determine if it is possible to
predict the amount of shoe cushioning needed to optimise
running economy based on the characteristics of a runner
(e.g. body size, body composition, etc.).
In addition to the aforementioned limitations associated
with use of short washout periods between testing in dif-
ferent shoe conditions and lack of long-term follow-up, two
additional limitations should be considered when inter-
preting the findings of this review. Firstly, all study find-
ings were based on running economy measurements at
submaximal running speeds and assume that the response
to footwear will be the same at the faster speeds that may
be used in competition. Secondly, all results were based on
running economy assessed on a treadmill and different
levels of treadmill cushioning between laboratories may
have influenced the findings [28]. Additionally, the single
study [7] that compared overground running with treadmill
running found significant differences in economy which
favoured overground running. Although the accuracy of
this finding has been debated in the literature [43,44],
overground running has the greatest external validity for
investigating the effect of footwear on running perfor-
mance and future research should further explore the
validity and reliability of overground running economy
assessment.
5 Conclusion
This review found trivial and small effects on running
economy, such that greater longitudinal shoe stiffness,
J. T. Fuller et al.
123
greater shoe cushioning and greater shoe comfort were
associated with improved running economy. Light shoes
and barefoot also had a small effect on running economy
when compared with heavy shoes. Shoe mass was posi-
tively associated with metabolic cost of running. However,
for footwear with a combined shoe mass of less than 440 g
per pair, there was no detrimental effect on running
economy. When controlling for differences in shoe mass,
foot strike and cadence, minimalist shoes had a small
beneficial effect on running economy compared with con-
ventional running shoes. This beneficial effect appeared to
increase further in response to gait adaptations resulting
from training in minimalist shoes. However, further
research is required to confirm this finding and any long-
term beneficial effects on running economy associated with
running in these shoes must be considered against the
potential to affect injury risk. Future research in footwear
performance should include time or distance measures of
running performance and include a long-term follow-up.
Acknowledgments No sources of funding were used to assist in the
preparation of this review. Dr. Dominic Thewlis has been a recipient
of funding from ASICS Oceania (ASICS Oceania Pty Ltd, Eastern
Creek, NSW, Australia) to undertake separate research. All other
authors declare no potential conflicts of interest and have no financial
relationships with any organisations that might have an interest in the
submitted work.
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