Vol.:(0123456789)
Sports Medicine (2023) 53:1255–1271 
https://doi.org/10.1007/s40279-023-01816-1
ORIGINAL RESEARCH ARTICLE
Variability in Running Economy of Kenyan World‑Class and European 
Amateur Male Runners with Advanced Footwear Running Technology: 
Experimental and Meta‑analysis Results
Melanie Knopp1,3  · Borja Muñiz‑Pardos2  · Henning Wackerhage3  · Martin Schönfelder3  · Fergus Guppy4  · 
Yannis Pitsiladis5  · Daniel Ruiz1 
Accepted: 26 January 2023 / Published online: 2 March 2023 
© The Author(s) 2023
Abstract
Background Advanced footwear technology improves average running economy compared with racing flats in sub-elite 
athletes. However, not all athletes benefit as performance changes vary from a 10% drawback to a 14% improvement. The 
main beneficiaries from such technologies, world-class athletes, have only been analyzed using race times.
Objective The aim of this study was to measure running economy on a laboratory treadmill in advanced footwear technol-
ogy compared to a traditional racing flat in world-class Kenyan (mean half-marathon time: 59:30 min:s) versus European 
amateur runners.
Methods Seven world-class Kenyan and seven amateur European male runners completed a maximal oxygen uptake assess-
ment and submaximal steady-state running economy trials in three different models of advanced footwear technology and a 
racing flat. To confirm our results and better understand the overall effect of new technology in running shoes, we conducted 
a systematic search and meta-analysis.
Results Laboratory results revealed large variability in both world-class Kenyan road runners, which ranged from a 11.3% 
drawback to a 11.4% benefit, and amateur Europeans, which ranged from a 9.7% benefit to a 1.1% drawback in running 
economy of advanced footwear technology compared to a flat. The post-hoc meta-analysis revealed an overall significant 
medium benefit of advanced footwear technology on running economy compared with traditional flats.
Conclusions Variability of advanced footwear technology performance appears in both world-class and amateur runners, 
suggesting further testing should examine such variability to ensure validity of results and explain the cause as a more per-
sonalized approach to shoe selection might be necessary for optimal benefit.
Key Points 
Running economy of world-class Kenyan and amateur 
European runners with next-generation long-distance 
running shoes that contain advanced footwear technol-
ogy varies greatly, with a range from a 11.4% benefit to a 
11.3% detriment.
Meta-analysis results reveal an overall statistically sig-
nificant medium benefit of advanced footwear technol-
ogy on running economy when compared with tradi-
tional racing flats and confirmed the variability we report 
when examining the performance benefits of advanced 
footwear technology.
Our results suggest a more personalized approach to new 
footwear technology.
 * Melanie Knopp 
 
Melanie.Knopp@adidas.com
1 
adidas Innovation, adidas AG, Herzogenaurach, Germany
2 
GENUD Research Group, Faculty of Health and Sport 
Sciences, University of Zaragoza, Saragossa, Spain
3 
Department of Sport and Health Sciences, Technical 
University of Munich, Munich, Germany
4 
Institute of Life and Earth Sciences, Heriot Watt University, 
Edinburgh, UK
5 
School of Sport and Health Sciences, University of Brighton, 
Eastbourne, UK
1256
 
M. Knopp et al.
1 Introduction
Kenyan elite runners win many international track and road 
distance races, which has stimulated research into the causes 
of this success [1–6]. When examining the geographical dis-
tribution of the top 20 running performances for male and 
female athletes in both middle- and long-distance events 
(800 m, 1500 m, 3000 m, 5000 m, 10,000 m, 5 km, 10 km, 
half-marathon, and marathon) in the past 5 years (since the 
last Olympic cycle: 5 August, 2016 to 29 August, 2021), 
41.6% have been achieved by Kenyan athletes [7]. Such 
running performances depend on three main physiological 
factors: (1) an athletes’ maximal oxygen uptake ( ̇VO2max), 
(2) their fractional utilization of ̇VO2max or the ability of 
an athlete to sustain a high percentage of their ̇VO2max for 
long periods of time, and (3) their running economy [8–11]. 
Previous research examining the uniqueness specifically of 
Kenyan or other elite East African runners has suggested 
that of these, it is running economy that is particularly 
unique in this population [6, 10, 12]. Various studies have 
further attributed this especially to the anthropometric char-
acteristics of East Africans with smaller body size, thinner 
lower legs, and a greater Achilles tendon moment arm with 
a shorter forefoot length [1, 10, 12–14].
Running economy can be defined as the ability to move 
efficiently in terms of energy demand while running at a 
specified submaximal velocity and can be measured as the 
rate of oxygen uptake per kilogram body weight and min-
ute ( ̇VO2 in mL  O2/kg/min) at that speed [10, 11, 15, 16]. 
Previous work has reported that among elite runners with 
similar ̇VO2max levels, running economy can account for 
65.4% of the variation observed in a 10-km race perfor-
mance [17]. Running economy is affected by many factors 
including anthropometric, biomechanical, metabolic, neu-
romuscular, and cardiorespiratory efficiency [11]. One ele-
ment that has gained interest in recent years is an athlete’s 
mechanical efficiency being affected by different footwear 
characteristics such as weight, cushioning, and longitudinal 
bending stiffness, all of which are included in recent tech-
nological advances in long-distance running shoes [18–21]. 
Previously published work has attributed the improvements 
of performance of such advanced footwear technology to 
various mechanisms [20, 22]. The advances in shoe tech-
nology themselves have been designed to maximize run-
ning economy while minimizing energy loss and consist of 
a curved stiff element component and a high midsole stack 
height made of a compliant, resilient, and lightweight foam 
(Fig. 1). The curved rigid element increases the longitudinal 
bending stiffness of the shoe and thereby creates a mecha-
nism with a teeter-totter effect on the running mechanics, 
which occurs when a runner’s center of pressure overcomes 
the bending point of the curved structure and causes the 
reaction force to act on the heel perpendicular to the stiff ele-
ment providing leverage during push-off [20, 23]. The high 
midsole stack height enhances this mechanism and allows 
for a more curved plate to be inserted into the midsole [20]. 
The compliant, resilient, lightweight foam material for the 
midsole ensures that the shoe weight remains light while 
still having a soft foam with a high-energy return as these 
have all been suggested to also affect performance [18–20].
The impact of advanced footwear technology on running 
events is reflected in the progression of world records, with 
every male and female world record starting from 5 km to 
the marathon broken by athletes wearing different versions 
of these shoes since their release [24]. Previous research 
completed on such footwear technology in the field quanti-
fies this impact on performance, with data from the Strava 
fitness app on more than a million marathon and half-mar-
athons revealing that shoes containing this new technology 
could improve race performance in sub-elite athletes, as 
individuals ran 4–5% faster in advanced footwear technology 
than runners wearing an average racing flat [25]. Similarly, 
Rodrigo-Carranza et al. showed that in a sub-cohort of top-
100 men’s marathon performances from 2015 to 2019 that 
completed races in both advanced footwear technology and 
traditional flats, 29 of 40 athletes (72.50%) improved their 
Fig. 1  Schematic of different 
long-distance running shoes, 
including A a traditional racing 
flat, which is classically low 
to the floor with relatively thin 
soles with the focus here being 
to keep the shoes lightweight, 
and B advanced footwear 
technology, which consists of 
a curved stiff element in the 
forefoot of the shoe, as well as a 
high midsole stack height made 
up of a resilient, compliant, and 
lightweight foam
1257
Variability in Advanced Footwear Technology
performance with this type of footwear [26]. This is also 
supported by various laboratory-based running economy 
studies comparing advanced footwear technology to tradi-
tional racing flats in sub-elite athletes, suggesting that the 
design of these shoes reduces the energy cost of running on 
average by about 2.7–4.4%, thereby benefiting overall run-
ning performance [15, 27–30].
While previous studies have compared the running econ-
omy of non-elite runners wearing different shoe technologies 
in relatively controlled laboratory settings [15, 27–30], no 
study has examined the variability in running economy of 
the main beneficiaries (i.e., world-class athletes). Knowing 
this, the primary aim of this study was to answer the research 
question: how does the variability in physiological response 
in terms of running economy on a laboratory treadmill in 
advanced footwear technology compare to a traditional rac-
ing flat in world-class Kenyan distance runners (half-mar-
athon mean time: 59:30 min:s) versus European amateur 
runners? Based on the obtained results, we decided to sys-
tematically search the literature for similar relevant studies 
and conducted a post-hoc meta-analysis to confirm the found 
range of variability, and better understand the overall effect 
of advanced footwear technology.
2  Materials and Methods
2.1  Participants
Fifteen subjects volunteered to participate in this study 
and were classified as either world class or amateur. Run-
ners with current or recent injuries that prevented them 
from training were excluded, as well as those uncomforta-
ble with running on a treadmill. Shoe size was also part of 
the inclusion criteria because of shoe cost considerations. 
One participant dropped out as he struggled to run on a 
treadmill, meaning 14 participants were finally included 
for analysis in this study.
The world-class cohort comprised seven male 
world-class Kenyan runners (mean ± standard devia-
tion, age: 22.7 ± 3.2 years, height: 1.7 ± 0.05 m, mass: 
59.9 ± 4.8  kg, body mass index: 19.7 ± 0.6  kg/m2, 
̇VO2peak: 75.9 ± 3.5 mL/kg/min) (Table 1) [31]. These 
runners were recruited through sponsorship deals with 
collaborating companies and were all professional road 
racing athletes who had an official mean personal record 
for the half-marathon of 59:30 ± 0:48 min:s, and a 10-km 
personal best of 27:33 ± 0:41 min:s. The amateur cohort 
consisted of seven well-trained male amateur European 
runners, who at the time of measurement were training 
daily, (mean ± standard deviation, age: 28.1 ± 4.2 years, 
height: 1.8 ± 0.03 m, mass: 72.1 ± 7.0 kg, body mass 
index: 21.9 ± 1.8 kg/m2, ̇VO2peak: 62.3 ± 5.1 mL/kg/min) 
and volunteered to take part in this research (Table 1). 
All participants gave written informed consent to being a 
part of this study after they understood the experimental 
procedures, potential injury risks, and possible benefits.
2.2  Shoes
Throughout the experimental protocol, analyzed shoe con-
ditions included a commercially available traditional rac-
ing shoe (FLAT) used by the subjects regularly for their 
own training, as well as three different commercially avail-
able models of AdvFootTech (1–3) that differed in their 
geometry and weight (Table 2). As all athletes were the 
same shoe size, everyone tested in UK 8.5 (US 9/EU 42 
2/3).
2.3  Experimental Protocol
This study comprised two laboratory visits occurring on 
separate days, with a 24-h pause for recovery, at the adidas 
Sports Science Research Laboratory in Herzogenaurach, 
Germany located close to sea level at an altitude of 300 m 
(Fig. 2). During the first session, we collected ̇VO2peak 
and baseline measurements. In the subsequent session, we 
measured running economy in different footwear conditions 
at either 75% (world class) or 70% (amateur) of the corre-
sponding velocity to the measured ̇VO2peak, (v ̇VO2peak) 
[32]. We chose the 75/70% of v ̇VO2peak as this was a sub-
maximal speed related to speeds these subjects would use 
when running at a marathon pace.
To ensure consistency and avoid any confounding effects 
of circadian rhythm [33], we tested participants at the same 
time of day and encouraged them to match their diet, sleep, 
and training patterns prior to each session. Furthermore, 
to ensure the athletes felt comfortable being in a foreign 
Table 1  Participant descriptive and physiological characteristics for 
each of the measured cohorts
Data shown are mean ± standard deviation
̇VO2peak maximal oxygen uptake, v ̇VO2peak velocity at ̇VO2peak, 
Student’s t test
*Significance (p < 0.05)
Variable
World class
Amateur
p-value
n = 7
n = 7
Age (years)
22.7 ± 3.2
28.1 ± 4.2
0.020*
Height (cm)
174.3 ± 4.9
181.4 ± 2.6
0.008*
Weight (kg)
59.9 ± 4.8
72.1 ± 7.0
0.003*
̇VO2peak (mL/kg/min)
75.9 ± 3.5
62.3 ± 5.1
< 0.001*
̇VO2peak (L/min)
4.53 ± 0.43
4.49 ± 0.48
0.870
v ̇VO2peak (km/h)
22.3 ± 0.6
18.8 ± 1.2
 < 0.001*
1258
 
M. Knopp et al.
environment and understood all that was asked of them, their 
coach as well as manager traveled with them and helped with 
testing. This favored a clearer communication between the 
research team and the athletes.
2.3.1  Visit 1
In this preliminary visit, we collected physiological baseline 
and anthropometric measurements. Throughout the whole 
experiment, all treadmill sessions were conducted in the 
same standardized laboratory chamber (mean ± standard 
deviation, temperature: 25.5 ± 1.1 °C, humidity: 60.2 ± 8.8%, 
pressure: 980.7 ± 4.9 mBar) on a HP Cosmos motorized 
treadmill (Venus 200/75; h/p/cosmos sports and medical 
GmbH, Nussdorf-Traunstein, Germany) set at a 1% gradient 
to mimic the energetic cost of running outdoors [34]. Given 
that some runners were not accustomed to treadmill running 
or using a ̇VO2peak protocol, we familiarized subjects dur-
ing a 15-min session on the treadmill with increasing speeds. 
Once they felt comfortable running on a treadmill, we fitted 
each athlete with a heart rate monitor (Polar H7; Polar Elec-
tro Oy, Kempele, Finland) and face mask (7450 Series V2 
Mask; Hans Rudolph, Inc., Shawnee, KS, USA), connected 
to the MetaMax 3B portable cardiopulmonary gas exchange 
measuring device (CORTEX Biophysik GmbH, Leipzig, 
Germany). We then collected respiratory parameters from 
the subjects using an automated breath-by-breath method, 
via the measurement and evaluation software, MetaSoft Stu-
dio (CORTEX Biophysik GmbH, Leipzig, Germany). Before 
each testing session, we calibrated this system according to 
the manufacturer’s instructions [35, 36].
To assess maximal aerobic capacity, athletes completed 
a ̇VO2peak ramp test using an incremental speed protocol 
with a continuous 1% incline. For this, athletes ran in the 
new pairs of the traditional racing FLAT test condition. For 
the world-class athletes, this test started at 10 km/h for 2 min 
and increased progressively at 1 km/h/min until volitional 
exhaustion. Amateurs completed the same protocol starting 
at 8 km/h. During this test, we verbally encouraged all ath-
letes to ensure a maximal output was reached.
Table 2  Descriptive 
characteristics of the 
AdvFootTech and FLAT
NShoe characteristics based on size UK 8.5/US 9
Energy return classification: low: < 70%; medium: 70–80%; high: > 80%
AdvFootTech advanced footwear technology, FLAT traditional racing flat
Shoe label
Mass (g)
Forefoot stack 
height (mm)
Rearfoot stack 
height (mm)
Heel-to-toe 
drop (mm)
Energy 
return (%)
Stiff element?
AdvFootTech 1
225
31.5
39
8.5
High
Yes
AdvFootTech 2
210
29.5
39.5
10
High
Yes
AdvFootTech 3
196
31
39.5
8.5
High
Yes
FLAT
197
19
24
5
Low
No
Fig. 2  Illustration of the methods protocol of the present study. A 
For visit 1, we collected baseline information of the subjects, which 
included conducting a maximal oxygen uptake ( ̇VO2peak) assess-
ment. B On the second day of testing, we then assessed the run-
ning economy of both traditional racing flat (FLAT) and different 
advanced footwear technology (AdvFootTech) models. v ̇VO2peak 
velocity at ̇VO2peak 
1259
Variability in Advanced Footwear Technology
Upon completion, two experienced exercise physiolo-
gists detected and agreed upon ventilatory thresholds and ̇V
O2peak values. For all cardiorespiratory data, we cleaned the 
breath-by-breath raw data by removing outlying data points 
that were more than two standard deviations away from the 
mean of a seven-breath window. After these outliers were 
removed, data were smoothed further by taking a moving 
seven-breath average. The ̇VO2max value was recorded as 
the highest cleaned and smoothed value during the test. As 
we did not repeat a verification test to confirm these val-
ues, the highest recorded ̇VO2 value will be defined as a 
‘ ̇VO2peak’ [37]. The measured v ̇VO2peak (km/h) was also 
recorded and used to prescribe the running speed for the run-
ning economy tests during visit 2. Ventilatory threshold data 
as well as previously recorded personal bests of each athlete 
were used to ensure the selected speeds were sufficient in 
obtaining testing data that are relevant to racing and would 
not be affected by fatigue.
2.3.2  Visit 2
During visit 2, we assessed running economy for each of 
the different shoes at 75% of v ̇VO2peak (17.0 ± 0.4 km/h) 
for world-class athletes and 70% (13.1 ± 1.0 km/h) for ama-
teur athletes. When subjects arrived, they first completed a 
6-min standardized warm-up in the FLAT. This was then 
followed by a 12-min break during which we prepared the 
equipment for the test that consisted of 6-min bouts with a 
12-min rest between bouts. Before each new treadmill trial, 
athletes changed their shoes for the next bout. The last 30 s 
of this break were recorded on the treadmill to obtain rest-
ing values.
From the recorded measurements, we calculated run-
ning economy, oxygen cost of transport, and energetic cost 
using the Péronnet and Masicotte equation expressed in 
mL/kg/min, mL/kg/km, and W/kg, respectively, from the 
̇VO2 data during the 60-s period from minute 4 to 5 of 
each test [38].
2.4  Data and Statistical Analysis
All data analysis and statistical tests were performed 
using RStudio [39]. Statistical analyses of the data were 
performed using the R package ‘stats’ (version 4.0.0) in 
RStudio [39, 40] using the traditional level of signifi-
cance (p < 0.05). Power and sample size calculations were 
performed using the R package ‘pwr’ (version 1.3-0) in 
RStudio also using the traditional level of significance 
(p < 0.05), 80% power, and four different groups for the 
four different shoes. We conducted a Student’s t test on 
the descriptive characteristics to analyze population dif-
ferences between the measured world-class and amateur 
athletes. Additionally, an analysis of variance test with 
repeated measures and a Bonferroni post-hoc correction 
were conducted on the steady-state physiological data [41, 
42].
2.5  Systematic Review and Meta‑analysis
To confirm the found range of variability with the previ-
ously published literature, and better understand the overall 
effect of advanced footwear technology, we conducted a sys-
tematic electronic search of relevant studies and a related 
meta-analysis.
 For this retrospective systematic literature search, Sco-
pus, SPORT-Discus, PubMed, Web of Science, and Foot-
wear Science databases were searched using the terms 
“Racing Shoes” and “Running Shoes + Running Economy” 
through 21 November, 2021. Inclusion criteria for this 
review were studies that (1) examined the running perfor-
mance effect of different versions of advanced footwear tech-
nology for road running compared to a traditional racing flat 
control condition; and (2) measured the running economy 
(mL/kg/min) of this comparison. Additional secondary out-
come measures including oxygen cost of transport (mL/kg/
km) and energetic cost (W/kg) were also analyzed to pro-
vide a bigger picture of the effects of such new technology 
on running performance. These results were then pooled 
using Hedge’s g for a standardized effect size [43] and the 
inverse heterogeneity (IVhet) model using the Epigear Meta 
XL software (version 5.3) [44]. We further analyzed out-
comes of the meta-analysis using a z-score for significance, 
Cochran’s Q statistic for heterogeneity, and I-squared for 
inconsistency [45] and assessed the risk of bias using the 
Cochrane Risk of Bias Instrument for RCTs (RoB 2) [46].
3  Results
3.1  Running Economy
From the available dataset (n = 14), for running economy 
there was a significant difference between shoe types 
in the amateur athletes (F(3) = 8.308, p = 0.001) where 
running economy in the advanced footwear technology 
was significantly lower than in the FLAT. Compared to 
the FLAT shoe, amateur athletes saw running economy 
improved by 3.5 ± 3.7% (pBonferroni = 0.042) with AdvFoot-
Tech 1, 4.6 ± 2.7% (pBonferroni = 0.005) with AdvFootTech 
2, and 5.0 ± 3.4% (pBonferroni = 0.002) with AdvFootTech 3 
(Fig. 3B, Table 3), with no significant differences between 
the three advanced footwear technology conditions.
Both the world-class and amateur athletes showed a 
large inter-individual variability with individual trials 
1260
 
M. Knopp et al.
showing a ± 11.4% variation in performance (Fig. 3). 
When examining the individual advanced footwear tech-
nology conditions for the world-class population, the 
inter-individual range in overall performance changes of 
all included subjects vary by 14.6% on average for the dif-
ferent shoes. A similar pattern is also seen in the amateur 
population where values here range from a 9.7% benefit 
to a 1.1% drawback for advanced footwear technology 
when compared to the flat for a narrower inter-individual 
total range of 10.8% (Fig. 3B). For this population, the 
individual advanced footwear technology range in perfor-
mance changes was narrower than that of the world-class 
population for an average of a 9.5% difference between 
the maximum and minimum percent change per shoe. 
Via a time and running economy interaction analysis, we 
ensured the shoe order did not have a significant effect 
on the described results (world-class: p = 0.61; amateur: 
p = 0.67).
In Table 3, we present the results for running economy, 
oxygen consumption, and percentage change in running 
economy in the advanced footwear technology models 
compared to a traditional running flat for both the world-
class and amateur cohorts. Here, we compare the different 
shoes among cohorts, stratifying the data according to the 
amateur or world-class athlete results, as well as global 
effects comparing all tested subjects.
3.2  Systematic Review Study Characteristics
From the initial search that resulted in 929 studies, 30 were 
selected for a full-text analysis after excluding by duplicates, 
title, and abstract, and five studies were finally included after 
fulfilling the inclusion criteria (Fig. 4). All examined stud-
ies were randomized crossover trials investigating a range 
of recreational to highly trained runners with a combined 
average measured ̇VO2peak of 67.1 ± 8.2 mL/kg/min. All 
studies examined a steady-state running analysis on a tread-
mill with different advanced footwear technology shoes 
compared to traditional racing flats, with Hébert-Losier et al. 
also including participants’ own shoes and spray painting 
the others to blind participants to model details [27]. Of the 
five studies, Barnes and Kilding was the only experiment 
to also include a female cohort [15]. Examined footwear 
conditions of the studies included in the meta-analysis are 
described in Table 4, please note data of shoe conditions 
irrelevant for this study, such as track spikes, were excluded 
in the meta-analysis [15]. When repeated conditions were 
used for the meta-analysis comparison, the corresponding 
conditions were divided by the number of repeated com-
parisons to ensure no double counting of effects. The test-
ing was conducted at a variety of different speeds either 
between 14 and 18 km/h or in the case of Hébert-Losier 
et al., at different speeds relative to ̇VO2peak [27]. Hereby, 
we decided to subgroup the analysis based on the speed at 
which physiological variables were measured according to 
the protocols. We included four different speed categoriza-
tions starting with a very low speed that included 60% of v ̇V
O2peak where the speed was 11.0 ± 0.6 km/h; the low speed 
category included those conditions measured at 14 km/h for 
both men and women or 70% of v ̇VO2peak with a speed 
of 12.9 ± 0.7 km/h; the medium-speed category included 
16 km/h for men, 15 km/h for women, and 80% of v ̇VO2peak 
with a speed of 14.7 ± 0.8 km/h; finally, the high-speed cat-
egory included 18 km/h for men, and 16 km/h for women.
Considering the risk of bias assessment of the included 
studies, all studies had some concerns for the category 
of bias arising from period and carryover effects, given 
the unknown effect of the physiological starting point 
between the trials and what carryover or how long a car-
ryover might be with regard to running in advanced foot-
wear technology. The overall risk of bias across all stud-
ies was of some concern owing to the similarities in the 
protocol of the study and the period and carryover effects.
Fig. 3  Percentage change in 
steady-state running economy 
oxygen consumption (mL/kg/
min) relative to a traditional 
running flat (FLAT) in different 
shoe conditions for both A 
world-class and B amateur pop-
ulations. These shoes include a 
FLAT on the far left as well as 
three different advanced foot-
wear technology (AdvFootTech) 
conditions. Here, a negative 
percentage change indicates less 
oxygen consumption at a given 
speed and therefore a better run-
ning economy
1261
Variability in Advanced Footwear Technology
Table 3  Steady-state physiological results for each of the different AdvFootTech and FLAT models separated between the world-class and amateur cohorts as well as statistical findings of the 
whole combined sample
AdvFootTech advanced footwear technology, ANOVA analysis of variance, FLAT traditional racing flat, SD standard deviation
*Significant difference (p < 0.05)
† Shoes with value significantly different to the FLAT
Variable
World class (mean ± SD)
Among 
world-class 
subjects
Amateur (mean ± SD)
Among ama-
teur subjects
Combined sample
n = 7
n = 7
Main effect 
shoes within 
subjects
Main popula-
tion effect 
between 
subjects
Interaction 
effect within 
subjects
FLAT
AdvFoot-
Tech 1
AdvFootTech 2
AdvFootTech 3
Repeated-
measures 
ANOVA
FLAT
AdvFootTech 1
AdvFootTech 2
AdvFootTech 3
Repeated- 
measures 
ANOVA
Running 
economy 
(mL  O2/kg/
min)
54.5 ± 2.0
54.9 ± 1.6
54.7 ± 2.8
53.5 ± 3.1
F = 0.743
p = 0.541
47.7 ± 2.6
46.1 ± 3.2†
pBonf = 0.043
45.5 ± 2.1†
pBonf = 0.004
45.3 ± 1.9†
pBonf = 0.002
F = 8.308
p = 0.001*
F = 3.360
p = 0.030*
F = 46.608
p < 0.001*
F = 1.741
p = 0.177
Oxygen cost 
of transport 
(mL  O2/kg/
km)
192.3 ± 8.1
193.8 ± 6.6
192.9 ± 11.8
188.7 ± 9.1
F = 0.875
p = 0.474
220.2 ± 12.3
212.3 ± 5.0†
pBonf = 0.047
209.9 ± 8.8†
pBonf = 0.006
208.9 ± 10.4†
pBonf = 0.003
F = 7.511
p = 0.002*
F = 4.245
p = 0.012*
F = 20.757
p < 0.001*
F = 2.478
p = 0.077
Energetic cost 
(W/kg)
19.4 ± 0.7
19.6 ± 0.6
19.5 ± 1.0
19.0 ± 1.3
F = 0.836
p = 0.493
16.9 ± 0.9
16.2 ± 1.2†
pBonf = 0.018
16.0 ± 0.8†
pBonf = 0.002
15.9 ± 0.7†
pBonf = < 0.001
F = 10.007
p < .001*
F = 3.572
p = 0.024*
F = 47.887
p < 0.001*
F = 1.886
p = 0.150
Respiratory 
exchange 
ratio
0.92 ± 0.02
0.93 ± 0.02
0.93 ± 0.02
0.90 ± 0.05 F = 1.001
p = 0.416
0.91 ± 0.03
0.88 ± 0.02†
pBonf = 0.029
0.88 ± 0.03†
pBonf = 0.016
0.88 ± 0.03†
pBonf = 0.005
F = 6.518
p = 0.004*
F = 2.741
p = 0.058
F = 4.935
p = 0.048*
F = 1.663
p = 0.193
Heart rate 
(bpm)
158.4 ± 8.8
157.7 ± 8.5
157.3 ± 10.1
155.6 ± 11.2 F = 0.919
p = 0.453
160.3 ± 5.9
157.2 ± 7.2
160.1 ± 6.5
158.8 ± 7.5
F = 1.527
p = 0.242
F = 1.542
p = 0.221
F = 0.278
p = 0.609
F = 1.072
p = 0.373
% Change 
in running 
economy to 
traditional 
running 
FLAT
0.0 ± 0.0
0.8 ± 5.0
0.3 ± 3.9
− 1.9 ± 5.6
F = 0.74
p = 0.543
0.0 ± 0.0
− 3.5 ± 3.7†
pBonf = 0.042
− 4.6 ± 2.7†
pBonf = 0.005
− 5.0 ± 3.4†
pBonf = 0.002
F = 7.969
p = 0.001*
F = 3.579
p = 0.023*
F = 4.170
p = 0.066
F = 2.039
p = 0.126
1262
 
M. Knopp et al.
3.3  Meta‑analysis Primary Outcome Measure: 
Running Economy
The meta-analysis of running economy (mL/kg/min) in all 
five examined studies comparing different advanced foot-
wear technology to racing flat conditions revealed a statisti-
cally significant benefit of advanced footwear technology on 
running economy measures with an overall medium effect of 
− 0.58 [mean (95% confidence interval); g = − 0.58 (− 0.75, 
− 0.42), Z = − 6.86 (p < 0.001)], where a negative value 
indicates improved efficiency when running (Fig. 5). When 
sub-grouped by speed, the analysis showed a small effect 
[g = − 0.29 (− 0.87, 0.31)] at very low speeds, a medium 
effect [g = − 0.58 (− 0.90, − 0.26)] at low speeds, a medium 
effect [g = − 0.54 (− 0.79, − 0.28)] at medium speeds, and 
a large effect [g = − 0.92 (− 1.31, − 0.52)] at high speeds. 
Incorporating the data presented in this study, results are 
Fig. 4  Flow chart showing 
study selection. Adapted from 
the PRISMA flow diagram [60]
Table 4  Descriptive characteristics of shoe products included in the meta-analysis
Shoe characteristics based on size UK 8.5/US 9 and obtained from original journal articles used in the meta-analysis or measurements conducted 
from RunningWarehouse.com. FLAT 6 varies (mean ± standard deviation) as it is a combination of the participants own footwear and includes 
sizes varying from US 8.5 to 12. Missing information (n/a) is because of the confidentiality of midsole material or missing information in the 
examined studies
AdvFootTech advanced footwear technology, EVA ethylene–vinyl acetate, FLAT traditional racing flat, n/a not available, PEBA polyether block 
amide, TPU thermoplastic polyurethane
Shoe label
Mass (g)
Forefoot stack 
height (mm)
Rearfoot stack 
height (mm)
Heel-to-toe 
drop (mm)
Midsole material
Stiff element?
AdvFootTech 1
225
31.5
39
8.5
n/a
Yes
AdvFootTech 2
210
29.5
39.5
10
n/a
Yes
AdvFootTech 3
196
31
39.5
8.5
n/a
Yes
AdvFootTech 4 [15, 27–29]
195
21
31
10
PEBA
Yes
AdvFootTech 5 [30]
196
32
40
8
PEBA
Yes
AdvFootTech 6 [30]
210
27
35
8
n/a
Yes
AdvFootTech 7 [30]
207
24
34
10
TPU
Yes
AdvFootTech 8 [30]
213
30
35
5
EVA
Yes
AdvFootTech 9 [30]
207
33
38
5
n/a
Yes
AdvFootTech 10 [30]
213
31
39
8
PEBA
Yes
AdvFootTech 11 [30]
210
36
40
4
PEBA
Yes
FLAT
197
19
24
5
TPU
No
FLAT 2 [28, 29]
181
15
23
8
EVA
No
FLAT 3 [28]
221
13
23
10
TPU
No
FLAT 4 [15, 29]
224
13
23
10
TPU
No
FLAT 5 [27]
130
13
13
1
TPU
No
FLAT 6 [27]
313 ± 44
n/a
26.0 ± 7.9
9.4 ± 6.7
Varies
No
FLAT 7 [30]
210
21
30
9
EVA
No
1263
Variability in Advanced Footwear Technology
showing an overall medium effect [g = − 0.39 (− 1.01, 0.23)]. 
When this sub-analysis is further distributed by population, 
the world-class subgroup showed a small effect [g = − 0.02 
(− 0.88, 0.85)], and the amateur subgroup showed a large 
effect [g = − 0.80 (− 1.70, 0.10)]. In this analysis, no statisti-
cally significant heterogeneity, as assessed via Q, was found 
(Q = 14.42, p = 1.00) and inconsistency, as assessed using I2 
as an extension of Q, was very low (I2 = 0%) [45].
3.4  Meta‑analysis Secondary Outcome Measures: 
Oxygen Cost of Transport and Energetic Cost
The meta-analysis of oxygen cost of transport (mL/kg/km) 
of the three studies that included this data revealed a statis-
tically significant benefit of advanced footwear technology 
on the oxygen cost of transport measures [mean (95% CI); 
g = − 0.67 (− 0.87, − 0.47), Z = − 6.60 (p = < 0.001), Fig. 6]. 
Considering the subgroup analysis by speed, a medium 
effect [g = − 0.58 (− 0.96, − 0.20)] was found at low speeds, 
a medium effect [g = − 0.62 (− 0.95, − 0.30)] at medium 
speeds, and a large effect [g = − 0.92 (− 1.31, − 0.52)] at high 
speeds. Incorporating the data presented in this study, an 
overall medium effect [g = − 0.47 (− 1.10, 0.16)] was found. 
Here as well, no statistically significant heterogeneity was 
found (Q = 14.03, p = 0.99) and inconsistency was very low 
(I2 = 0%) among the examined studies [45].
Finally, the meta-analysis of energetic cost (W/kg) of 
the four studies showed a statistically significant benefit of 
advanced footwear technology on energetic cost measures 
[mean (95% CI); g = − 0.54 (− 0.71, − 0.37), Z = − 6.28 
(p = < 0.001), Fig. 7]. Further examination of the subgroup 
speed analysis shows a small effect [g = − 0.27 (− 0.86, 
0.31)] at very low speeds, a medium effect [g = − 0.53 
(− 0.85, − 0.21)] at low speeds, a medium effect [g = − 0.55 
(− 0.82, − 0.27)] at medium speeds, and a large effect 
[g = − 0.69 (− 1.07, − 0.31)] at high speeds. Analysis of the 
present study shows an overall medium effect [g = − 0.41 
(− 1.04, 0.21)]. Again, here, no statistically significant het-
erogeneity was found (Q = 8.44, p = 1.00) and inconsistency 
was very low (I2 = 0%) between the subgroups [45].
4  Discussion
In this study, we aimed to assess the variability in running 
economy in advanced footwear technology compared to a 
traditional racing flat on a treadmill in world-class Kenyan 
versus European amateur runners at speeds proportional to 
a marathon pace. Our laboratory results revealed ± 11.4% 
variability of the running economy of different advanced 
footwear technology running shoes in world-class Kenyan 
road runners, while for amateur Europeans, results range 
from a 9.7% benefit to a 1.1% drawback. The post-hoc 
meta-analysis revealed an overall statistically significant 
medium benefit of advanced footwear technology on run-
ning economy when compared with traditional flats.
4.1  Running Economy and Running Performance 
Inter‑Individual Variability
The running economy of the measured advanced footwear 
technology compared to a traditional racing flat of all tested 
subjects revealed a large inter-subject variability with overall 
values that ranged from an 11.4% benefit to an 11.3% draw-
back (Fig. 3). To compare this variation of running economy 
to other studies, we conducted a systematic literature search. 
Interestingly, this revealed similar variability in the found 
research considering the obtained confidence intervals in 
the conducted meta-analysis (Figs. 5, 6, 7). Hoogkamer et al. 
examined for the first time advanced footwear technology 
versus previously established marathon racing flats, all mass 
neutralized, in high-caliber athletes at three distinct speeds. 
The results found a range of 1.97–6.26% benefit in energetic 
cost (W/kg) of the new advanced footwear technology versus 
flats [28]. A similar study conducted by Barnes and Kild-
ing showed a 1.72–7.15% running economy benefit (mL/
kg/min) in highly trained runners in favor of the advanced 
footwear technology with only trivial-to-small differences 
between the tested men and women [15]. On average, this 
study found a 4.2% running economy benefit of advanced 
footwear technology versus the flat, which decreased to 
2.9% when these conditions were weight matched, indicat-
ing the effect weight might have on such testing [15]. In an 
additional study, Hunter et al. found a response range of a 
0.0–6.4% improvement in running economy (mL/kg/min) 
for advanced footwear technology and further suggested that 
different runners may require individualized shoe stiffnesses 
to enhance performance [29]. Hébert-Losier et al. examined 
both running economy and performance during a 3-km time 
trial and found a variability in running economy (mL/kg/
min) of a worsening by a 10.3–13.3% improvement across 
conditions in recreational runners, and a time trial variability 
of a worsening by a 4.7–9.3% improvement [27]. To com-
pare seven different models of advanced footwear technol-
ogy, Joubert et al. conducted running economy tests (mL/
kg/min) with trained distance runners and found that when 
all advanced footwear technology shoes are combined, the 
responses, as calculated from presented mean and stand-
ard deviations as well as described values, ranged from a 
1% disadvantage to a 5.3% advantage [30]. An additional 
group of research studies also conducted a similar analysis 
by examining race performance measures instead of physi-
ological data obtained in a laboratory. Considering these as 
well, Guinness et al. examined marathon race performance 
results from hundreds of elite marathoners who switched 
to advanced footwear technology and found that 74.5% of 
1264
 
M. Knopp et al.
g
1
0
-1
-2
-3
Study or Subgroup  
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 18, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 18, Men
 
Present Study, AdvFootTech 3 vs FLAT, 70%, Amateur Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Men
 
High Speed subgroup  
Present Study, AdvFootTech 2 vs FLAT, 70%, Amateur Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Women
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 14, Men
 
Joubert et al., 2021 - AdvFootTech 11 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 16, Men
 
Joubert et al., 2021 - AdvFootTech 5 vs FLAT 7, 16, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 18, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 14, Men
 
Joubert et al., 2021 - AdvFootTech 9 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 14, Women
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 16, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 18, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 14, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 16, Women
 
Very Low Speed  
Q=0.36, p=0.55, I2=0%
Low Speed  
Q=1.33, p=0.99, I2=0%
Medium Speed  
Q=2.76, p=1.00, I2=0%
High Speed  
Q=3.68, p=0.60, I2=0%
Present Study  
Q=2.04, p=0.84, I2=0%
Overall  
Q=14.42, p=1.00, I2=0%
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 14, Men
 
Low Speed subgroup  
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 15, Women
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 6, 70%, Men
 
Medium Speed subgroup  
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 6, 80%, Men
 
Hunter et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 14, Women
 
Present Study, AdvFootTech 1 vs FLAT, 70%, Amateur Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 6, 60%, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 15, Women
 
Joubert et al., 2021 - AdvFootTech 10 vs FLAT 7, 16, Men
 
Present Study subgroup  
Joubert et al., 2021 - AdvFootTech 7 vs FLAT 7, 16, Men
 
Hunter et al., 2019 - AdvFootTech 4 vs FLAT 2, 16, Men
 
Present Study, AdvFootTech 3 vs FLAT, 75%, World-Class Men
 
Very Low Speed subgroup  
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 5, 80%, Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 5, 70%, Men
 
Joubert et al., 2021 - AdvFootTech 6 vs FLAT 7, 16, Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 5, 60%, Men
 
Joubert et al., 2021 - AdvFootTech 8 vs FLAT 7, 16, Men
 
Present Study, AdvFootTech 2 vs FLAT, 75%, World-Class Men
 
Present Study, AdvFootTech 1 vs FLAT, 75%, World-Class Men
 
    g (95% CI)          % Weight
  -1.88  ( -3.08, -0.68)      1.9
  -1.27  ( -2.35, -0.18)      2.3
  -1.08  ( -2.69,  0.54)      1.1
  -1.01  ( -2.06,  0.04)      2.5
  -0.92  ( -1.31, -0.52)     17.6
  -0.89  ( -2.46,  0.68)      1.1
  -0.86  ( -1.89,  0.17)      2.6
  -0.82  ( -1.85,  0.20)      2.6
  -0.79  ( -2.43,  0.85)      1.0
  -0.76  ( -1.78,  0.26)      2.6
  -0.76  ( -2.40,  0.87)      1.0
  -0.74  ( -1.57,  0.09)      4.0
  -0.72  ( -1.55,  0.11)      4.0
  -0.71  ( -2.34,  0.92)      1.0
  -0.71  ( -1.73,  0.30)      2.7
  -0.68  ( -1.50,  0.14)      4.0
  -0.68  ( -1.50,  0.15)      4.1
  -0.67  ( -1.49,  0.16)      4.1
  -0.64  ( -1.46,  0.18)      4.1
  -0.63  ( -1.64,  0.38)      2.7
  -0.58  ( -0.75, -0.42)    100.0
  -0.58  ( -1.59,  0.42)      2.7
  -0.58  ( -0.90, -0.26)     26.3
  -0.56  ( -1.56,  0.44)      2.7
  -0.54  ( -1.40,  0.33)      3.7
  -0.54  ( -0.79, -0.28)     41.0
  -0.53  ( -1.49,  0.43)      3.0
  -0.52  ( -1.31,  0.27)      4.4
  -0.48  ( -1.48,  0.52)      2.8
  -0.47  ( -1.98,  1.04)      1.2
  -0.47  ( -1.30,  0.37)      3.9
  -0.40  ( -1.39,  0.59)      2.8
  -0.40  ( -2.01,  1.21)      1.1
  -0.39  ( -1.01,  0.23)      7.0
  -0.37  ( -1.97,  1.24)      1.1
  -0.36  ( -1.15,  0.42)      4.5
  -0.30  ( -1.83,  1.22)      1.2
  -0.29  ( -0.87,  0.30)      8.0
  -0.21  ( -1.16,  0.73)      3.1
  -0.18  ( -1.03,  0.67)      3.8
  -0.16  ( -1.76,  1.45)      1.1
  -0.11  ( -0.93,  0.71)      4.0
  -0.02  ( -1.62,  1.58)      1.1
   0.04  ( -1.44,  1.52)      1.3
   0.20  ( -1.29,  1.68)      1.2
1265
Variability in Advanced Footwear Technology
the men ran faster with an estimate of a 1.4–2.8% improve-
ment in performance, while 71.4% of the women ran faster 
with an estimate of a 0.6–2.2% performance improvement 
[47]. Similarly, Senefeld et al. further examined performance 
and racing shoes in elite racers in four major marathons and 
found that in a subgroup of athletes with subsequent race 
performance of a flat then advanced footwear technology, 
the between-race change in performance for female athletes 
had a 95% confidence interval range from a 6.9% hindrance 
to a 13.8% advantage and a 5.4% hindrance to an 11.4% 
advantage in male athletes, suggesting that observed find-
ings in a laboratory setting translate to real improvements in 
racing conditions [48]. Finally, Bermon et al. analyzed sea-
sonal best times throughout the years to determine the effect 
of switching to advanced footwear technology, and found 
that in half-marathon and marathon races of a subgroup of 
athletes who competed in the same event with and without 
these shoes, all athletes (except male half-marathon runners) 
significantly improved their performance times with calcula-
tions on presented data showing that on average the female 
athletes showed a greater benefit of 1.9% faster in both races 
when compared with a 0.8% better performance found in 
the male athletes [49]. Overall, comparable to the present 
study, the variability in previously published data range from 
a 13.8% benefit to a 10.3% drawback in an overall change 
in performance of advanced footwear technology versus 
traditional racing flats as measured both in the laboratory 
with steady-state running physiology tests, and in the field 
examining race times.
Additional results from the five studies included after a 
retrospective systematic review and meta-analysis revealed 
that advanced footwear technology had an overall significant 
medium effect of − 0.58 when compared with a flat in terms 
of running economy, oxygen cost of transport, and energetic 
cost, even when accounting for the large individual vari-
ability found in these individual studies [15, 27–30]. Inter-
estingly, as revealed via the subgroup analysis, the effect 
changed with the speed sub-groups where very low speeds 
showed a small effect and high speeds showed a greater 
effect, aligning with what has previously been shown in the 
literature [50]. This suggests that mechanisms involved in 
the advanced footwear technology might be proportional to 
the other biomechanical aspects such as changes in stride or 
gait cycle that alter with speed, with the mechanism reduc-
ing the energy required for running bouts proportionally 
higher when running at higher speeds [51].
Despite the findings of the meta-analysis, it remains 
important to consider the great inter-individual differences 
in the response to footwear conditions with individuals in 
the presented study as well as subjects in previous research 
showing significant inter-individual differences. Such results 
suggest possible methodological limitations of measuring 
the performance of running shoes (e.g., laboratory-based 
studies, insufficient familiarization protocols), as well as 
the importance of an individualized approach for athletes 
considering different biomechanical or anthropometrics that 
could be contributing to optimize their response to advanced 
footwear technology.
4.2  Intra‑Individual Running Economy Differences 
in Shoe Conditions
When examining the individual cases, some subjects showed 
meaningful effects depending on the specific advanced 
footwear technology shoe being tested, and others were not 
always trending the same way among all advanced footwear 
technology models. For example, given the results here, one 
of the world-class Kenyan runners showed a range from an 
11.4% to a 0.2% benefit in the different advanced footwear 
technology models (Fig. 3A). For the aforementioned ath-
lete, comparing personal best half-marathon times, this 
individual did indeed improve a sub-1-h half-marathon time 
by over 1:20 (min:s) in a shoe where this athlete was more 
economical during testing [52]. However, for another world-
class subject who exhibited a running economy range of 
a 2.5% benefit to a 6.6% drawback for different advanced 
footwear technology, comparing marathon seasonal best 
times, this athlete was able to set a new personal record 
by reducing 2 min off a time already under 2:10 (h:min) in 
shoes that they, according to our test, should have performed 
worse in. This further affirms possible limitations of testing 
shoe performance in this way, particularly with a world-class 
Kenyan running population where further confounders such 
as a lack of familiarization to treadmill running and testing 
conditions might be playing a role.
4.3  Populations Running Economy Differences
When examining in our study the differences in variability 
ranges between the world-class (an 11.4% benefit to a 11.3% 
drawback) and the amateur (a 9.7% benefit to a 1.1% draw-
back) populations, further exploration into the data revealed 
possible explanations. As we did not measure the running 
economy of all participants at the same speed, we are unable 
to conclude how the running efficiency of these two popula-
tions compared as a baseline in the same traditional racing 
flat. However, previously published research established 
that East Africans have a running economy advantage when 
compared with their Spanish counterparts [12]. Therefore, 
Fig. 5  Forest plot displaying running economy (mL/kg/min) com-
parisons between advanced footwear technology (AdvFootTech) and 
traditional racing flats (FLAT) sub-categorized into different speeds. 
Study labels consist of the study name, the examined AdvFootTech 
versus FLAT condition where + indicates conditions that are weight 
matched, the speed either in km/h or as a % of peak, and the exam-
ined population. CI confidence interval
◂
1266
 
M. Knopp et al.
g
1
0
-1
-2
-3
Study or Subgroup  
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 18, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 18, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 14, Men
 
Present Study, AdvFootTech 1 vs FLAT, 70%, Amateur Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Men
 
Present Study, AdvFootTech 2 vs FLAT, 70%, Amateur Men
 
Present Study, AdvFootTech 3 vs FLAT, 70%, Amateur Men
 
High Speed subgroup  
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Women
 
Joubert et al., 2021 - AdvFootTech 11 vs FLAT 7, 16, Men
 
Joubert et al., 2021 - AdvFootTech 5 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 16, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 18, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 14, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 14, Women
 
Joubert et al., 2021 - AdvFootTech 9 vs FLAT 7, 16, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 16, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 18, Men
 
Low Speed  
Q=4.01, p=0.55, I2=0%
Medium Speed  
Q=1.79, p=1.00, I2=0%
High Speed  
Q=3.67, p=0.60, I2=0%
Present Study  
Q=2.42, p=0.79, I2=0%
Overall  
Q=14.03, p=0.99, I2=0%
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 15, Women
 
Medium Speed subgroup  
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 16, Women
 
Low Speed subgroup  
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 14, Women
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 15, Women
 
Present Study subgroup  
Joubert et al., 2021 - AdvFootTech 10 vs FLAT 7, 16, Men
 
Joubert et al., 2021 - AdvFootTech 7 vs FLAT 7, 16, Men
 
Present Study, AdvFootTech 3 vs FLAT, 75%, World-Class Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 14, Men
 
Joubert et al., 2021 - AdvFootTech 6 vs FLAT 7, 16, Men
 
Joubert et al., 2021 - AdvFootTech 8 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 14, Men
 
Present Study, AdvFootTech 2 vs FLAT, 75%, World-Class Men
 
Present Study, AdvFootTech 1 vs FLAT, 75%, World-Class Men
 
    g (95% CI)          % Weight
  -1.86  ( -3.06, -0.66)      2.8
  -1.27  ( -2.36, -0.18)      3.4
  -1.17  ( -2.04, -0.30)      5.2
  -1.02  ( -2.62,  0.58)      1.5
  -1.01  ( -2.05,  0.04)      3.6
  -0.97  ( -2.56,  0.62)      1.6
  -0.94  ( -2.52,  0.65)      1.6
  -0.92  ( -1.31, -0.52)     25.4
  -0.86  ( -1.90,  0.17)      3.7
  -0.78  ( -2.42,  0.85)      1.5
  -0.75  ( -2.39,  0.88)      1.5
  -0.75  ( -1.77,  0.27)      3.8
  -0.73  ( -1.56,  0.10)      5.8
  -0.72  ( -1.54,  0.11)      5.8
  -0.72  ( -1.73,  0.30)      3.8
  -0.71  ( -2.34,  0.92)      1.5
  -0.68  ( -1.51,  0.14)      5.8
  -0.68  ( -1.50,  0.15)      5.8
  -0.67  ( -0.87, -0.47)    100.0
  -0.66  ( -1.48,  0.16)      5.9
  -0.64  ( -1.65,  0.37)      3.9
  -0.62  ( -0.95, -0.30)     37.6
  -0.62  ( -1.62,  0.39)      3.9
  -0.58  ( -0.96, -0.20)     27.1
  -0.48  ( -1.48,  0.52)      4.0
  -0.48  ( -1.47,  0.52)      4.0
  -0.47  ( -1.10,  0.16)     10.0
  -0.40  ( -2.01,  1.21)      1.5
  -0.36  ( -1.97,  1.25)      1.5
  -0.35  ( -1.89,  1.18)      1.7
  -0.23  ( -1.22,  0.75)      4.1
  -0.16  ( -1.76,  1.45)      1.5
  -0.02  ( -1.62,  1.58)      1.5
   0.04  ( -0.94,  1.02)      4.1
   0.05  ( -1.43,  1.53)      1.8
   0.19  ( -1.29,  1.68)      1.8
Fig. 6  Forest plot displaying oxygen cost of transport (mL/kg/km) 
comparisons between advanced footwear technology (AdvFootTech) 
and traditional racing flats (FLAT) sub-categorized into different 
speeds. Study labels consist of the study name, the examined Adv-
FootTech versus FLAT condition where + indicates conditions that 
are weight matched, the speed either in km/h or as a % of peak, and 
the examined population. CI confidence interval
1267
Variability in Advanced Footwear Technology
one consideration could be that our world-class cohort was 
already more economical when running in the traditional 
racing flat and therefore would not benefit as much when 
compared to the amateur European population.
Additionally, regarding the methodology, certain dif-
ferences between the two populations are also apparent. 
First, while the relative effort between populations might 
be comparable, the speed at which they attained such effort 
differed with the average submaximal velocity for the 
world-class runners being 17.1 ± 0.4 km/h compared with 
13.1 ± 1.0 km/h of the amateurs. These differences could 
be affecting the percentage benefits of advanced footwear 
technology in regard to running economy [53]. Moreover, 
even with a brief warm-up and familiarization session, some 
world-class runners were not used to running on a treadmill, 
which as Colino et al. has suggested, changes the mechanics 
compared with overground running [54, 55]. Furthermore, 
of note, at the point of testing, the world-class population 
had already been training in a version of the advanced foot-
wear technology and were therefore familiar with the high-
stack height and the feel of running with this technology. In 
contrast, the amateurs were not regularly running in such 
shoes outside of the present study. Previous research con-
ducted has suggested injury risks and possible biomechani-
cal changes when transitioning to novel footwear (e.g., mini-
malist shoes) too quickly, recommending a longer adaptation 
period [56–58]. Both considerations could have biased the 
results of the present study.
4.4  Limitations
Several limitations to this study must also be acknowledged. 
First, we acknowledge the present study is underpowered. As 
no previous study had been conducted examining a world-
class cohort, we had to do power and sample size calcu-
lations post-hoc. To start with the amateur cohort, using 
the smallest found effect size of 0.47 for running economy, 
sample size calculations revealed that 14 participants should 
be considered for such an analysis, consistent with the 14 
total participants we had recruited at the start of the experi-
ment. Using this same effect size for the amateur cohort, 
calculations revealed a power of 46.2%. When considering 
each cohort separately, as with most other studies examin-
ing sub-elite populations, we were able to see differences 
in advanced footwear technology for the amateurs. For the 
world-class cohort, the effect sizes for running economy 
of advanced footwear technology shoes compared to the 
flat varied from 0.04 to − 0.30. Considering this range in 
effect size, the power calculation here revealed a 5.2% up 
to a 20.4%. As this signifies our study as being underpow-
ered, we also calculated the necessary sample size that 
would be needed for the world-class cohort to achieve the 
desired power of 80%. Based on which effect size, results 
here revealed 32–1705 participants would be needed, which 
is a challenge to maintain the high level required in such a 
large group of participants. This is a common issue that stud-
ies using world-class athletes are often underpowered given 
the singularity and inaccessibility to this sample, resulting 
rather in case studies or studies with a limited sample size 
[59]. With the world-class athletes, we must also consider 
the margin of the examined population, where even a mini-
mal improvement in efficiency can reduce the finishing time 
over the duration of a marathon and could be the difference 
between a podium place or not. Furthermore, the results 
reflect that we must consider the large inter-subject vari-
ability and therefore the individuality of the athletes. The 
question remains of how to detect the marginal changes in 
an elite population. To further examine this, future studies 
should also consider examining the test–retest reliability of 
steady-state running economy laboratory assessments con-
ducted on world-class athletes.
Additional limitations must also be considered owing to 
the athletes’ schedules and availability. More time would 
have also allowed us to repeat testing measures with the 
athletes, which would have ensured further reliability of the 
testing. An additional limitation was that no female athletes 
were tested within the scope of this study as we only had 
access to male athletes. Previous results considering both 
sexes range from only trivial to small differences in labora-
tory testing to significant differences in performance finish-
ing times for female athletes [15, 48, 49]. Furthermore, it 
is important to note that because the intention was to test 
with shoes readily available on the market, it was impossible 
to blind the participants as to the shoe they were testing. 
As mentioned, because some athletes were already familiar 
with and training in versions of these shoes, athletes may 
have had pre-established opinions that could have influenced 
the results and the placebo effect cannot be excluded [29]. 
It must be noted, however, that related research comparing 
the running economy of different shoes where subjects were 
blinded to the shoes that were painted in black still revealed 
similar results [27].
Limitations related to the systematic review and meta-
analysis include methodological and characterization varia-
tions. For example, some studies manipulated the shoe con-
ditions in terms of weight matching or spray painting for 
blinding. Additionally, the ambiguity in subject definition 
related to the caliber of runners makes it difficult to place 
the results according to populations. Finally, with respect to 
the described shoe conditions, the specific model or version 
of a shoe within a franchise was not always clearly labeled, 
thus we had to make an informed categorization based on 
the information available.
1268
 
M. Knopp et al.
g
1.8
0.9
0
-0.9
-1.8
-2.7
Study or Subgroup  
Present Study, AdvFootTech 3 vs FLAT, 70%, Amateur Men
 
Present Study, AdvFootTech 2 vs FLAT, 70%, Amateur Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 14, Men
 
Joubert et al., 2021 - AdvFootTech 11 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 16, Women
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 18, Men
 
Joubert et al., 2021 - AdvFootTech 5 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 16, Women
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 14, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 18, Men
 
Joubert et al., 2021 - AdvFootTech 9 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 16, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 2, 16, Men
 
High Speed subgroup  
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 14, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 18, Men
 
Hoogkamer et al., 2018 - AdvFootTech 4+ vs FLAT 3, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 14, Women
 
Medium Speed subgroup  
Very Low Speed  
Q=0.43, p=0.51, I2=0%
Low Speed  
Q=1.46, p=0.98, I2=0%
Medium Speed  
Q=2.41, p=1.00, I2=0%
High Speed  
Q=0.25, p=1.00, I2=0%
Present Study  
Q=2.33, p=0.80, I2=0%
Overall  
Q=8.44, p=1.00, I2=0%
Present Study, AdvFootTech 1 vs FLAT, 70%, Amateur Men
 
Low Speed subgroup  
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 6, 80%, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 14, Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 6, 70%, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 18, Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 6, 60%, Men
 
Barnes et al., 2019 - AdvFootTech 4 vs FLAT 4, 15, Women
 
Present Study subgroup  
Joubert et al., 2021 - AdvFootTech 10 vs FLAT 7, 16, Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 15, Women
 
Joubert et al., 2021 - AdvFootTech 7 vs FLAT 7, 16, Men
 
Present Study, AdvFootTech 3 vs FLAT, 75%, World-Class Men
 
Barnes et al., 2019 - AdvFootTech 4+ vs FLAT 4, 14, Women
 
Very Low Speed subgroup  
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 5, 80%, Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 5, 70%, Men
 
Joubert et al., 2021 - AdvFootTech 6 vs FLAT 7, 16, Men
 
Hebert-Losier et al., 2020 - AdvFootTech 4 vs FLAT 5, 60%, Men
 
Joubert et al., 2021 - AdvFootTech 8 vs FLAT 7, 16, Men
 
Present Study, AdvFootTech 2 vs FLAT, 75%, World-Class Men
 
Present Study, AdvFootTech 1 vs FLAT, 75%, World-Class Men
 
    g (95% CI)          % Weight
  -1.14  ( -2.77,  0.49)      1.1
  -0.94  ( -2.53,  0.64)      1.1
  -0.88  ( -1.91,  0.15)      2.7
  -0.80  ( -1.83,  0.22)      2.7
  -0.80  ( -2.44,  0.84)      1.1
  -0.78  ( -1.80,  0.24)      2.7
  -0.76  ( -1.59,  0.07)      4.1
  -0.75  ( -2.39,  0.88)      1.1
  -0.73  ( -1.75,  0.28)      2.8
  -0.73  ( -1.55,  0.10)      4.2
  -0.70  ( -1.53,  0.12)      4.2
  -0.70  ( -2.33,  0.93)      1.1
  -0.69  ( -1.70,  0.32)      2.8
  -0.69  ( -1.37, -0.02)      6.2
  -0.69  ( -1.07, -0.31)     19.4
  -0.67  ( -1.49,  0.16)      4.2
  -0.66  ( -1.67,  0.35)      2.8
  -0.65  ( -1.32,  0.02)      6.3
  -0.55  ( -1.55,  0.45)      2.8
  -0.55  ( -0.82, -0.27)     37.6
  -0.54  ( -0.71, -0.37)    100.0
  -0.53  ( -2.05,  0.98)      1.2
  -0.53  ( -0.85, -0.21)     27.4
  -0.51  ( -1.47,  0.45)      3.1
  -0.51  ( -1.51,  0.49)      2.9
  -0.48  ( -1.34,  0.38)      3.8
  -0.47  ( -1.47,  0.52)      2.9
  -0.47  ( -1.31,  0.36)      4.1
  -0.46  ( -1.45,  0.54)      2.9
  -0.41  ( -1.04,  0.21)      7.2
  -0.39  ( -2.00,  1.22)      1.1
  -0.34  ( -1.33,  0.65)      2.9
  -0.34  ( -1.95,  1.27)      1.1
  -0.33  ( -1.86,  1.20)      1.2
  -0.33  ( -1.32,  0.66)      2.9
  -0.27  ( -0.86,  0.31)      8.3
  -0.19  ( -1.14,  0.75)      3.2
  -0.17  ( -1.02,  0.68)      3.9
  -0.15  ( -1.76,  1.45)      1.1
  -0.08  ( -0.90,  0.75)      4.2
  -0.02  ( -1.62,  1.58)      1.1
   0.07  ( -1.41,  1.55)      1.3
   0.22  ( -1.27,  1.71)      1.3
1269
Variability in Advanced Footwear Technology
5  Conclusions
Next-generation long-distance running shoes that contain 
advanced footwear technology result in large inter- and intra-
subject variability when measured for changes in running 
economy in both world-class Kenyan and amateur Euro-
pean runners with overall values that range from an 11.3% 
hindrance to an 11.4% benefit. Similar variability was also 
found in the literature as measured both in the laboratory 
and with real race performance. Additionally, meta-analy-
sis results reveal an overall significant medium benefit of 
advanced footwear technology on running economy when 
compared with traditional flats. Such results have impor-
tant indications. First of all, while testing the performance 
of shoes with running economy tests has become standard 
practice, further research should consider other methods that 
ensure ecological validity, which could include repeated 
economy tests or field-based tests. Furthermore, perfor-
mance testing should be standardized to get a better com-
parison between studies. This is particularly important for 
the world-class athletes where additional constraints could 
be affecting their results as well as the acknowledgment that 
they may already have a better running economy. Second, 
this study acknowledges that a more personalized approach 
is necessary and that, when confirmed with additional test-
ing, the inter- as well as intra-subject variability should be 
considered by stakeholders involved in elite sport. First, 
among others, it could affect athletes and coaches regarding 
their shoe selection; sport associations should acknowledge 
the importance of individualization in sport; shoe manufac-
turers should consider this when implementing new technol-
ogy; and governing bodies should consider what impact this 
might have on the sport, with regard to which magnitude of 
effect is acceptable and fair.
Acknowledgements This study was conducted at the adidas Sports Sci-
ence Research Laboratory in Herzogenaurach, Germany. The authors 
acknowledge the runners for their voluntary participation in this study 
and the coaches for allowing the athletes to participate. Additional 
thanks go to members of the adidas research team including Harry 
Miles, Julian Fritz, Alejandro Alcañiz, Mario Fleiter, Tobias Luckfiel, 
and Heiko Schlarb and athlete servicing team including Fabian Sch-
weizer who all supported this research.
Declarations 
Funding This study was supported by adidas AG. Open Access fund-
ing enabled and organized by Projekt DEAL.
Conflict of interest MK and DR are both employees of adidas AG. YP 
is the founding member of the Sub2 marathon project (http:// www. 
sub2h rs. com). BM-P, FG, HW, and MS have no conflicts of interest 
that are directly relevant to the content of this article.
Ethics approval This experiment was submitted to the Technical Uni-
versity of Munich Ethics Committee, who advised that formal approval 
was not required. This study was conducted in accordance with the 
ethical standards of the Declaration of Helsinki.
Consent to participate All participants gave written informed consent 
to being a part of this study after they were informed of and under-
stood the experimental procedures, potential injury risks, and possible 
benefits.
Consent for publication Not applicable.
Data availability Considering the inherent characteristics of this 
research, the participants of this study did not agree to publicly share 
the obtained individual data.
Code availability Not applicable.
Author contributions MK and DR conceived and designed the 
research. MK and DR performed and supported the experiments with 
the help of additional colleagues. MK, DR, BM-P, HW, MS, FG, and 
YP analyzed the data. MK and FG conducted the statistical analysis. 
MK, DR, BM-P, HW, MS, and YP interpreted the results of the experi-
ment. MK drafted the manuscript. DR, BM-P, HW, MS, FG, and YP 
edited and revised the manuscript.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long 
as you give appropriate credit to the original author(s) and the source, 
provide a link to the Creative Commons licence, and indicate if changes 
were made. The images or other third party material in this article are 
included in the article's Creative Commons licence, unless indicated 
otherwise in a credit line to the material. If material is not included in 
the article's Creative Commons licence and your intended use is not 
permitted by statutory regulation or exceeds the permitted use, you will 
need to obtain permission directly from the copyright holder. To view a 
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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Variability in Running Economy of Kenyan World-Class and European Amateur Male Runners with Advanced Footwear Running Technology: Experimental and Meta-analysis Results.
Knopp, Melanie,Muñiz-Pardos, Borja,Wackerhage, Henning,Schönfelder, Martin,Guppy, Fergus,Pitsiladis, Yannis,Ruiz, Daniel