The potential impact of advanced footwear
technology on the recent evolution of elite
sprint performances
Joel Mason1, Dominik Niedziela2, Jean-Benoit Morin3, Andreas Groll2
and Astrid Zech1
1Department of Human Movement Science and Exercise Physiology, Friedrich Schiller University
Jena, Jena, Germany
2 Department of Statistics, TU Dortmund University, Dortmund, Germany
3 Inter-University Laboratory of Human Movement Biology, University Jean Monnet Saint-
Etienne, Saint-Etienne, France
ABSTRACT
Background: Elite track and ﬁeld sprint performances have reached a point of
stability as we near the limits of human physiology, and further signiﬁcant
improvements may require technological intervention. Following the widely reported
performance beneﬁts of new advanced footwear technology (AFT) in road-running
events, similar innovations have since been applied to sprint spikes in hope of
providing similar performance enhancing beneﬁts. However, it is not yet clear based
on current evidence whether there have been subsequent improvements in sprint
performance. Therefore, the aims of this study were to establish if there have been
recent year-to-year improvements in the times of the annual top 100 and top 20
athletes in the men’s and women’s sprint events, and to establish if there is an
association between the extensive use of AFT and potential recent improvements in
sprint performances.
Methods: For the years 2016–19 and 2021–2022, the season best performances of the
top 100 athletes in each sprint event were extracted from the World Athletics Top
lists. Independent t-tests with Holm corrections were performed using the season’s
best performance of the top 100 and top 20 athletes in each year to identify signiﬁcant
differences between years for each sprint discipline. Following the classiﬁcation of
shoes worn by the top 20 athletes in each event during their annual best race (AFT or
non-AFT), separate linear mixed-model regressions were performed to determine
the inﬂuence of AFT on performance times.
Results: For the top 100 and top 20 athletes, there were no signiﬁcant differences
year-to-year in any sprint event prior to the release of AFT (2016–2019). There were
signiﬁcant differences between AFT years (2021 or 2022) and pre-AFT years
(2016–2019) in eight out of 10 events. These differences ranged from a 0.40%
improvement (men’s 100 m) to a 1.52% improvement (women’s 400 m hurdles).
In the second analysis, multiple linear mixed model regressions revealed that the use
of AFT was associated with improved performance in six out of ten events, including
the men’s and women’s 100 m, women’s 200 m, men’s 110 m hurdles, women’s 100
m hurdles and women’s 400 m hurdles (estimate range: −0.037 – 0.521, p = <0.001 –
0.021). Across both analyses, improvements were more pronounced in women’s
sprint events than men’s sprint events.
How to cite this article Mason J, Niedziela D, Morin J-B, Groll A, Zech A. 2023. The potential impact of advanced footwear technology on
the recent evolution of elite sprint performances. PeerJ 11:e16433 DOI 10.7717/peerj.16433
Submitted 7 July 2023
Accepted 18 October 2023
Published 27 November 2023
Corresponding author
Joel Mason, joel.mason@uni-jena.de
Academic editor
Ross Miller
Additional Information and
Declarations can be found on
page 15
DOI 10.7717/peerj.16433
Copyright
2023 Mason et al.
Distributed under
Creative Commons CC-BY 4.0
Conclusion: Following a period of stability, there were signiﬁcant improvements in
most sprint events which may be partly explained by advances in footwear
technology. These improvements appear to be mediated by event, sex and potentially
level of athlete.
Subjects Kinesiology, Biomechanics, Sports Medicine
Keywords Superspikes, Track and ﬁeld, Athletics, Innovation, Biomechanics, Supershoes, Running
INTRODUCTION
Track and ﬁeld sprint events are among the most prominent and revered disciplines in the
sporting world. The evolution of sprint performances over time reﬂects advancements in
physiology and training methods, as well as technological innovation such as the
introduction of synthetic tracks in the 1960s. Despite temporary regressions resulting from
the implementation of automated timing and compulsory random drug testing, the 20th
century was largely characterised by steady progress in elite sprint performances (Haake,
Foster & James, 2014; Lippi et al., 2008). Following a century of progress, sprint times have
now somewhat plateaued since the 1990s across most elite sprint disciplines as
performances have approached their asymptotic limits (Berthelot et al., 2010, 2015; Weiss
et al., 2016; Ganse & Degens, 2021). This plateau is particularly prominent in the women’s
events. One model incorporating performances from 1896–2008 indicates that no
meaningful progression has occurred in four out of ﬁve women’s sprint events since 1994
(Berthelot et al., 2010), which may be partially explained by the introduction of routine
performance enhancing drug testing (Haake, Foster & James, 2014). Similar performance
stagnations have been observed across ﬁeld events and long-distance running events for
both sexes (Berthelot et al., 2010; Haake, James & Foster, 2015), adding credence to the
wider notion that we are approaching the limits of human physiology (Berthelot et al.,
2008; Nevill & Whyte, 2005; Haugen, Tønnessen & Seiler, 2015).
In order to further substantially improve human performances, exogenous measures to
overcome the limits of our physiology may be required, including artiﬁcial conditions and
new technologies (Marck et al., 2017). For road running events, the recent introduction of
advanced footwear technology (AFT, Frederick, 2022) has marked a new era in
long-distance running performance, headlined by new world records in every distance
from 5-km to the marathon for both men and women. AFT’s combination of “lightweight
resilient midsole foams with rigid moderators and pronounced rocker proﬁles in the sole”
(Frederick, 2022) has been demonstrated to improve the metabolic cost of running
compared to conventional marathon shoes (Hoogkamer et al., 2018). Analyses of the
annual top 100 times worldwide across all road-running distances following the
introduction of AFT conﬁrm the paradigm shift, indicating that road-racing times have
improved by 1–3% since their release (Senefeld et al., 2021; Rodrigo-Carranza et al., 2021;
Bermon et al., 2021; Rodrigo-Carranza et al., 2022). Subsequent to this resounding success,
similar innovative upgrades have since been introduced in track spikes for both sprint and
middle-distance disciplines, with the ultimate ambition of inducing similar
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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performance-enhancing effects. So-called superspikes use an analogous approach of a
plated midsole (often carbon ﬁbre or nylon) combined with a thick midsole of foam (or
pods of air), which is a clear departure from preceding sprint spike designs emphasising
slim midsoles to minimise weight. Carbon ﬁbre plates are not a recent introduction to
sprint spikes, and there is evidence of how this longitudinal bending stiffness may
inﬂuence both acceleration and maximal velocity (Stefanyshyn & Fusco, 2004; Smith et al.,
2016; Willwacher et al., 2016). However, there is no publicly available evidence
demonstrating how changes in midsole material and midsole thickness may inﬂuence
sprinting when paired with the increased longitudinal bending stiffness provided by a
plated sole. Therefore, precisely how this new generation of spikes interacts with the
biomechanical and metabolic determinants of sprinting to potentially augment
performance remains unclear (Healey et al., 2022). Further, how these potential beneﬁts
may vary according to sex and ability level, both factors suggested to mediate the
performance enhancing effects of AFT on long-distance running performance (Knopp
et al., 2023; Senefeld et al., 2021; Bermon et al., 2021), is also unknown.
Although high-quality evidence for the mechanisms and associated performance-
enhancing effects is currently lacking, AFT sprint spikes have been widely adopted by both
recreational and elite sprinters, and there are preliminary indications of a potential shift in
elite sprint performances. Since the introduction of AFT to sprinting in 2020, there have
been world records set in the men’s and women’s 400 m hurdles, women’s indoor 400 m
and world junior records in the men’s 100 and 200 m. Further, although only 50% of gold
medals in throwing events at the Tokyo Olympics exceeded the performance from the Rio
Olympics ﬁve years earlier, 90% of sprinting gold medals exceeded the performances from
Rio. Additionally, there is plausible theory underlying an AFT-induced improvement in
sprint times (Healey et al., 2022). However despite these factors, there has yet to be a
systematic appraisal of the inﬂuence of AFT on elite sprint performances, with only a
pre-print available which provides no link between AFT and performance improvements
(Willwacher et al., 2023). Therefore, the primary aims of this study were (1) to establish if
there have been recent year-to-year improvements in the annual top 100 and top 20
athletes of men’s and women’s sprint events, and (2) to establish if there is an association
between the introduction of AFT and the potential recent improvements in sprint
performances in each event. We hypothesised that recent improvements in sprint times
will be at least partially be explained by the use of AFT.
MATERIALS AND METHODS
All procedures adhered to the Declaration of Helsinki and were approved by the ethics
committee of the Friedrich Schiller University Jena (approval number: FSV 23/057).
Due to all analysis involving data available in the public domain, informed consent was not
required.
Database search and data selection
For the years 2016–19 and 2021–2022, the season best performances of the top 100 athletes
in each sprint event were extracted from the World Athletics Top lists (World Athletics,
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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2023; accessed January 2023), including the men’s and women’s 100, 200, 400, 400 m
hurdles, women’s 100 m hurdles and men’s 110 m hurdles. Only one performance per
athlete was recorded, and only wind-legal times recorded electronically in an outdoor
competition were included. The year 2020 was also excluded due to signiﬁcant
pandemic-induced interruptions to training and competition opportunities, including the
postponement of the 2020 Olympic Games. We selected 2016 as a cut-off point to capture
the most recent evolution in sprint performances, in line with time periods used by
previous studies characterising the inﬂuence of AFT on road-racing times (Rodrigo-
Carranza et al., 2021, 2022). Data from 2010 onwards is included as a supplementary ﬁle
(Supplementary File 2), which, alongside of the results of Willwacher et al. (2023), indicates
that altering the time period of our study has no bearing on our analysis and subsequent
ﬁndings.
Definition and identification of AFT
For the top 20 performers in each event in the years 2021 and 2022, two investigators
independently identiﬁed the shoes worn in each athlete’s season best race in order to
classify the footwear worn as either AFT or non-AFT. As for Bermon et al. (2021), the
identiﬁcation of the footwear of the top 100 athletes was not feasible due to limited
availability of information. Identiﬁcation of spikes used in each race was completed
through media content, including race footage or photos from athlete and event social
media, YouTube, or other ofﬁcial event photography services available online.
Any disagreement was resolved by consensus with the remaining authors. Previous studies
have used the same method to identify AFT use in elite road-race athletes (Senefeld et al.,
2021, Rodrigo-Carranza et al., 2021; Bermon et al., 2021).
AFT was deﬁned as per Healey et al. (2022), whereby a superspike incorporates “a
combination of lightweight, compliant and resilient foams (and/or air pods) with a stiff
(nylon, PEBA, carbon-ﬁber) plate”. Therefore, spikes which contained only a stiff plate or
only a thick midsole of innovative foam without the presence of the other were not
classiﬁed as AFT. Eligibility of models was assessed through manufacturer details of shoe
composition available online.
Data analysis and statistics
Multiple one-sided independent t-tests with Holm correction (Holm, 1979) were
performed using the season’s best performance of the top 100 athletes in each year to
identify signiﬁcant differences between the years 2016, 2017, 2018, 2019, 2021 and 2022 in
each event.
To verify the normal distribution assumption of our data for the t-test, visual analyses
with kernel density estimations were completed. A Levene’s-Test was also conducted to
test for unequal variances within the events (Levene, 1960). As the normality assumption
appears to be somewhat critical in some events, particularly because the underlying top 20
or top 100 performance variables are cut off at the upper tails, we additionally performed
Wilcoxon–Mann–Whitney tests in order to validate the ﬁndings from our t-test analysis.
This approach tested for the null hypothesis that it is equally likely that a value chosen at
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random from one year is greater or less than a value chosen at random from another year’s
population), and the ﬁndings can be found in Supplementary File 3. Identical analysis was
performed using the season best performances of solely the top 20 athletes each year to
provide the basis for the regression analyses assessing the impact of AFT on performances.
Following the identiﬁcation and classiﬁcation of shoes worn by the top 20 athletes in
each event during their season best race, separate linear mixed-model regressions were
performed for each event to determine the inﬂuence of AFT on performance times. Use of
AFT (or not) and year were used as ﬁxed effects, and participant ID used as random effect
predictors. For the linear mixed models, the normality assumption is also implicitly
relevant. However, on the basis of a careful goodness-of-ﬁt analysis using residual and QQ-
plots, we found no mentionable violations here for all ﬁtted models.
All data analysis and visualisations were completed in R (R Core Team, 2023). The t-test
analyses were performed using the pairwise.t.test function from the base R package. For the
mixed effects regression analyses, the packages lme4 and lmerTest were applied. For the
visualisations, the packages dplyr and ggplot2 were employed. Signiﬁcance for all analyses
was set at p < 0.05, and Cohen’s d to calculate effect size, with values of <0.5, 0.5–0.79 and
>0.80 considered small, medium and large respectively (Cohen, 1992).
RESULTS
Comparison of the annual top 100 sprint performances in each sprint
event
Table 1 and Figs. 1–3 demonstrate the changes in the season best performances of the top
100 men and women in each sprint event between the years 2016–2022. For the pre-AFT
period (2016–2019), no meaningful changes and no signiﬁcant improvements were
detected via t-test comparisons with Holm correction in the top 100 times in all sprint
events for both sexes (Table 1). The sprint times of the AFT era years (2021 or 2022) were
signiﬁcantly faster compared to sprint times from the pre-AFT era in seven sprint events
(women’s 100, 200, 400, 100 m hurdles and men’s 100, 200 and 110 m hurdles), with
signiﬁcant improvements ranging from 0.40% (men’s 100 m) to 0.90% (women’s 100 m)
(Table 2). For the women’s 100 m, women’s 400 m and men’s 110 m hurdles, the year 2022
was signiﬁcantly faster than all pre-AFT years.
Figures 1–3 displays the raw data together with boxplots and kernel density estimates
for all ten sprint events. In most events, the distributions of the year 2021 (magenta) and
2022 (blue) are clearly shifted down in comparison to earlier years, indicating an
improvement in times.
Comparison of the annual top 20 sprint performances in each sprint
event
Table 3 demonstrates the changes in the season best performances of the top 20 men and
women in each sprint event between the years 2016–2022. For the pre-AFT period
(2016–2019), no meaningful changes and no signiﬁcant improvements were detected via
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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Figure 1 Season best performances of the top 100 athletes from the years 2016–2022 in the men’s and women’s 100 and 200 m.
Full-size
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DOI: 10.7717/peerj.16433/ﬁg-1
Table 1 Annual best performances of the top 100 athletes in each sprint event. Times listed as seconds (mean ± SD).
Pre-AFT period
AFT period
Event (m)
2016
2017
2018
2019
2021
2022
W100
11.13 ± 0.14#
11.13 ± 0.14#
11.13 ± 0.11#
11.14 ± 0.12*#
11.09 ± 0.14
11.04 ± 0.13
M100
10.04 ± 0.08
10.07 ± 0.08#
10.06 ± 0.08#
10.06 ± 0.08#
10.04 ± 0.10
10.02 ± 0.08
W200
22.68 ± 0.27
22.73 ± 0.28*
22.69 ± 0.26*
22.77 ± 0.30*#
22.63 ± 0.35
22.57 ± 0.33
M200
20.25 ± 0.19
20.29 ± 0.16#
20.25 ± 0.20
20.26 ± 0.21#
20.25 ± 0.20
20.18 ± 0.22
W400
51.41 ± 0.68*#
51.46 ± 0.68*#
51.32 ± 0.72*#
51.39 ± 0.73*#
50.99 ± 0.75
51.07 ± 0.65
M400
45.18 ± 0.50
45.13 ± 0.46
45.10 ± 0.47
45.17 ± 0.53
45.15 ± 0.46
45.08 ± 0.43
W100H
12.87 ± 0.16
12.91 ± 0.19*#
12.89 ± 0.19*
12.88 ± 0.20*
12.83 ± 0.18
12.80 ± 0.22
M110H
13.43 ± 0.16#
13.44 ± 0.17*#
13.48 ± 0.16*#
13.46 ± 0.16*#
13.38 ± 0.17
13.36 ± 0.16
W400H
55.69 ± 0.91
55.78 ± 1.07
55.94 ± 1.00
55.90 ± 1.05
55.62 ± 1.19
55.55 ± 1.16
M400H
49.23 ± 0.53
49.22 ± 0.51
49.20 ± 0.59
49.25 ± 0.59
49.14 ± 0.80
49.07 ± 0.70
Notes:
* Signiﬁcantly slower than 2021 (via t-test comparison).
# Signiﬁcantly slower than 2022 (via t-test comparison).
AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles; SD, standard deviation.
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t-test comparisons in the top 20 times in all sprint events for both sexes (Tables 3 and 4).
The sprint times of the AFT era years (2021 or 2022) were signiﬁcantly faster compared to
sprint times from the pre-AFT era in eight sprint events (women’s 100, 200, 400, 100 m
Figure 2 Season best performances of the top 100 athletes from the years 2016–2022 in the men’s and women’s hurdles.
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Figure 3 Season best performances of the top 100 athletes from the years 2016–2022 in the men’s and women’s 400 m.
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DOI: 10.7717/peerj.16433/ﬁg-3
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Table 2 Overview of signiﬁcant year-to-year differences in the annual top 100 sprint performances.
Event and comparison
Performances (s)
Δ
p-value
Effect size
Women’s 100 m
2019–2021
11.14 ± 0.12 vs 11.09 ± 0.14
0.45%
0.027
−0.387
2016–2022
11.13 ± 0.14 vs 11.04 ± 0.13
0.81%
<0.001
−0.662
2017–2022
11.13 ± 0.14 vs 11.04 ± 0.13
0.81%
<0.001
−0.710
2018–2022
11.13 ± 0.11 vs 11.04 ± 0.13
0.81%
<0.001
−0.661
2019–2022
11.14 ± 0.12 vs 11.04 ± 0.13
0.90%
<0.001
−0.760
2021–2022
11.09 ± 0.14 vs 11.04 ± 0.13
0.45%
0.033
−0.373
Men’s 100 m
2017–2022
10.07 ± 0.08 vs 10.02 ± 0.08
0.50%
<0.001
−0.553
2018–2022
10.06 ± 0.08 vs 10.02 ± 0.08
0.40%
0.034
−0.391
2019–2022
10.06 ± 0.08 vs 10.02 ± 0.08
0.40%
0.012
−0.430
Women’s 200 m
2019–2021
22.77 ± 0.30 vs 22.63 ± 0.35
0.62%
0.005
−0.470
2017–2022
22.73 ± 0.28 vs 22.57 ± 0.33
0.71%
0.002
0.517
2018–2022
22.69 ± 0.26 vs 22.57 ± 0.33
0.53%
0.028
−0.395
2019–2022
22.77 ± 0.30 vs 22.57 ± 0.33
0.88%
<0.001
−0.666
Men’s 200 m
2017–2022
20.29 ± 0.16 vs 20.18 ± 0.22
0.54%
0.002
−0.525
2019–2022
20.26 ± 0.21 vs 20.18 ± 0.22
0.40%
0.045
−0.384
Women’s 400 m
2016–2021
51.41 ± 0.68 vs 50.99 ± 0.75
0.82%
<0.001
−0.589
2017–2021
51.46 ± 0.68 vs 50.99 ± 0.75
0.92%
<0.001
−0.654
2018–2021
51.32 ± 0.72 vs 50.99 ± 0.75
0.65%
0.004
−0.462
2019–2021
51.39 ± 0.73 vs 50.99 ± 0.75
0.78%
<0.001
−0.553
2016–2022
51.41 ± 0.68 vs 51.07 ± 0.65
0.67%
0.003
−0.476
2017–2022
51.46 ± 0.68 vs 51.07 ± 0.65
0.76%
0.001
−0.541
2018–2022
51.32 ± 0.72 vs 51.07 ± 0.65
0.49%
0.046
−0.349
2019–2022
51.39 ± 0.73 vs 51.07 ± 0.65
0.62%
0.006
−0.440
Women’s 100 m H
2017–2021
12.91 ± 0.19 vs 12.83 ± 0.18
0.62%
0.028
−0.397
2017–2022
12.91 ± 0.19 vs 12.80 ± 0.22
0.86%
0.001
−0.541
2018–2022
12.89 ± 0.19 vs 12.80 ± 0.22
0.70%
0.001
−0.455
2019–2022
12.88 ± 0.20 vs 12.80 ± 0.22
0.62%
0.027
−0.401
Men’s 110 m H
2017–2021
13.44 ± 0.17 vs 13.38 ± 0.17
0.45%
0.043
−0.358
2018–2021
13.48 ± 0.16 vs 13.38 ± 0.17
0.74%
<0.001
−0.570
2019–2021
13.46 ± 0.16 vs 13.38 ± 0.17
0.59%
<0.001
−0.436
2016–2022
13.43 ± 0.16 vs 13.36 ± 0.16
0.52%
0.014
−0.414
2017–2022
13.44 ± 0.17 vs 13.36 ± 0.16
0.60%
0.003
−0.486
2018–2022
13.48 ± 0.16 vs 13.36 ± 0.16
0.89%
<0.001
−0.698
2019–2022
13.46 ± 0.16 vs 13.36 ± 0.16
0.75%
<0.001
−0.564
Note:
M, metres; H, hurdles; Δ, percentage change.
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Table 3 Annual best performances of the top 20 athletes in each sprint event, reported in seconds (mean ± SD).
Pre-AFT period
AFT period
Event (m)
2016
2017
2018
2019
2021
2022
W100
10.90 ± 0.11
10.92 ± 0.08*
10.96 ± 0.05*#
10.96 ± 0.11*#
10.86 ± 0.12
10.84 ± 0.08
M100
9.92 ± 0.04
9.95 ± 0.03*#
9.93 ± 0.04#
9.94 ± 0.04*#
9.89 ± 0.06
9.90 ± 0.05
W200
22.25 ± 0.21*#
22.29 ± 0.25*#
22.29 ± 0.18*#
22.28 ± 0.23*#
22.02 ± 0.23
22.02 ± 0.20
M200
19.94 ± 0.12
20.02 ± 0.12#
19.91 ± 12
19.92 ± 0.15
19.94 ± 0.18
19.83 ± 0.18
W400
50.27 ± 0.47#
50.27 ± 0.25#
50.15 ± 0.52
50.30 ± 0.84#
49.74 ± 0.48
50.00 ± 0.43
M400
44.38 ± 0.45
44.37 ± 0.37
44.35 ± 0.30
44.29 ± 0.37
44.39 ± 0.33
44.36 ± 0.32
W100H
12.61 ± 0.14#
12.59 ± 0.11#
12.60 ± 0.13#
12.57 ± 0.12#
12.53 ± 0.10
12.44 ± 0.13
M110H
13.17 ± 0.09
13.17 ± 0.12
13.23 ± 0.10*#
13.20 ± 0.11#
13.12 ± 0.10
13.10 ± 0.10
W400H
54.21 ± 0.52
53.98 ± 0.70
54.47 ± 0.69#
54.25 ± 0.88
53.76 ± 1.12
53.65 ± 0.90
M400H
48.48 ± 0.36
48.37 ± 0.23
48.25 ± 0.56
48.32 ± 0.59
47.89 ± 0.84
47.93 ± 0.67
Notes:
* Signiﬁcantly slower than 2021 (via t-test comparison).
# Signiﬁcantly slower than 2022 (via t-test comparison).
AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles; SD, standard deviation.
Table 4 Overview of signiﬁcant year-to-year differences in the annual top 20 sprint performances
(according to t-test comparison).
Event and comparison
Performances (s)
Δ
p-value
Effect size
Women’s 100 m
2018–2021
10.96 ± 0.05 vs 10.86 ± 0.12
0.92%
0.013
−0.920
2019–2021
10.96 ± 0.11 vs 10.86 ± 0.12
0.92%
0.013
−0.924
2017–2022
10.92 ± 0.08 vs 10.84 ± 0.08
0.74%
0.046
−0.781
2018–2022
10.96 ± 0.05 vs 10.84 ± 0.08
1.10%
0.001
−1.143
2019–2022
10.96 ± 0.11 vs 10.84 ± 0.08
1.10%
0.001
−1.148
Men’s 100 m
2017–2021
9.95 ± 0.03 vs 9.89 ± 0.06
0.60%
0.005
−1.059
2019–2021
9.94 ± 0.04 vs 9.89 ± 0.06
0.50%
0.039
−0.842
2017–2022
9.95 ± 0.03 vs 9.90 ± 0.05
0.50%
0.035
−0.860
Women’s 200 m
2016–2021
22.25 ± 0.21 vs 22.02 ± 0.23
1.04%
0.006
−0.917
2017–2021
22.29 ± 0.25 vs 22.02 ± 0.23
1.22%
0.001
−1.073
2018–2021
22.29 ± 0.18 vs 22.02 ± 0.23
1.29%
0.001
−1.065
2019–2021
22.28 ± 0.23 vs 22.02 ± 0.23
1.17%
0.001
−1.063
2016–2022
22.25 ± 0.21 vs 22.02 ± 0.20
1.04%
0.004
−0.949
2017–2022
22.29 ± 0.25 vs 22.02 ± 0.20
1.22%
0.001
−1.106
2018–2022
22.29 ± 0.18 vs 22.02 ± 0.20
1.29%
0.001
−1.098
2019–2022
22.28 ± 0.23 vs 22.02 ± 0.20
1.17%
0.001
−1.096
Men’s 200 m
2017–2022
20.02 ± 0.12 vs 19.83 ± 0.18
0.95%
<0.001
−1.255
Women’s 400 m
2016−2021
50.27 ± 0.47 vs 49.74 ± 0.48
1.06%
0.020
−0.925
2017−2021
50.27 ± 0.25 vs 49.74 ± 0.48
1.06%
0.020
−0.921
(Continued)
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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hurdles and 400 m hurdles and men’s 100, 200 and 110 m hurdles), with signiﬁcant
improvements ranging from 0.50% (men’s 100 m) to 1.52 % (women’s 400 m hurdles)
(Table 4). For the women’s 200 m and women’s 100 m hurdles, the year 2022 was
signiﬁcantly faster than all pre-AFT years.
The influence of AFT on recent sprint performances
A total of 97.75% of shoes worn by the top 20 athletes of 2021 and 2022 in their season best
performance were able to be identiﬁed via media content (Table 5).
According to the mixed effects models, the use of AFT signiﬁcantly improved
performance in six out of ten events, including the men’s and women’s 100 m, women’s
Table 4 (continued)
Event and comparison
Performances (s)
Δ
p-value
Effect size
2019−2021
50.30 ± 0.84 vs 49.74 ± 0.48
1.12%
0.014
−0.967
Women’s 100 m H
2016−2022
12.61 ± 0.14 vs 12.44 ± 0.13
1.36%
<0.001
−1.279
2017−2022
12.59 ± 0.11 vs 12.44 ± 0.13
1.20%
0.001
−1.159
2018−2022
12.60 ± 0.13 vs 12.44 ± 0.13
1.28%
<0.001
−1.216
2019−2022
12.57 ± 0.12 vs 12.44 ± 0.13
1.04%
0.004
−1.010
Men’s 110 m H
2018−2021
13.23 ± 0.10 vs 13.12 ± 0.10
0.83%
0.007
−1.009
2018−2022
13.23 ± 0.10 vs 13.10 ± 0.10
0.98%
0.002
−1.112
2019–2022
13.20 ± 0.11 vs 13.10 ± 0.10
0.76%
0.026
−0.870
Women’s 400 m H
2018–2022
54.47 ± 0.69 vs 53.65 ± 0.90
1.52%
0.015
−0.960
Note:
M, metres; H, hurdles; Δ, percentage change.
Table 5 Number of top 20 athletes wearing AFT, non-AFT and unidentiﬁable spikes in the years
2021 and 2022 for each sprint event.
Event (m)
2021
2022
Non-AFT
AFT
Unidentiﬁed
Non-AFT
AFT
Unidentiﬁed
W100
9
11
0
1
18
1
M100
8
12
0
2
18
0
W200
10
9
1
1
19
0
M200
13
7
0
3
16
1
W400
8
11
1
0
19
1
M400
11
9
0
3
17
0
W100H
15
5
0
1
17
2
M110H
15
4
1
4
16
0
W400H
9
11
0
0
20
0
M400H
14
6
0
3
16
1
Note:
AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles.
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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200 m, men’s 110 m hurdles, women’s 100 m hurdles and women’s 400 m hurdles
(Table 6).
DISCUSSION
We sought to identify whether there have been recent changes in the annual top sprint
performances, and to subsequently evaluate the inﬂuence of AFT on elite sprint times.
Our key ﬁndings include that: (1) following a plateau in performances in all sprint events
between 2016–2019, statistically signiﬁcant and speciﬁc improvements were identiﬁed in
most sprint disciplines which coincided with widespread adoption of AFT in 2021 and
particularly 2022, (2) these signiﬁcant improvements ranged from 0.40–1.52%, and were
typically more pronounced in women’s events than men’s events, (3) the use of AFT may
partially explain these recent improvements in sprint times, with a signiﬁcant relationship
identiﬁed in six out of ten events.
This study provides the ﬁrst peer-reviewed evidence suggesting that performances in
some sprint events have signiﬁcantly improved following a period of stability, and that this
improvement has been at least partially driven by the widespread adoption of AFT.
Although the changes in performance were less substantial, less consistent and less
unanimous as the AFT-induced performance improvements in road-running events with
longer distances (Rodrigo-Carranza et al., 2022, 2021; Bermon et al., 2021), our results
provide initial evidence that along with the technological innovation there is meaningful
advancement in sprint performances. This ﬁnding is also in line with a recent pre-print
using a similar approach to characterise improvements in sprint times between 2010–2022
(Willwacher et al., 2023).
A key cornerstone of our ﬁndings is that between 2016–2019, there were no signiﬁcant
differences in the season best performances of the top 100 or top 20 athletes in any of the
Table 6 The estimated regression effect of AFT usage on performance times in each sprint event according to linear mixed effects models.
Fixed effects
Random effects
Use of AFT
Year
Athlete
Residual
Event (m)
Estimate
Error
p-value
Estimate
Error
p-value
Variance
SD
Variance
SD
W100
−0.106
0.027
<0.001*
0.004
0.006
0.493
0.004
0.060
0.005
0.072
M100
−0.053
0.016
0.001*
0.001
0.003
0.698
0.001
0.030
0.002
0.043
W200
−0.149
0.064
0.021*
−0.031
0.013
0.022*
0.015
0.123
0.032
0.179
M200
−0.037
0.045
0.411
−0.014
0.009
0.100
0.006
0.078
0.016
0.127
W400
−0.084
0.171
0.623
−0.067
0.035
0.057
0.111
0.333
0.173
0.416
M400
−0.190
0.104
0.070
0.030
0.020
0.139
0.027
0.165
0.093
0.305
W100H
−0.093
0.034
0.008*
0.017
0.007
0.014*
0.004
0.064
0.009
0.097
M110H
−0.087
0.030
0.005*
−0.003
0.005
0.621
0.004
0.060
0.007
0.086
W400H
−0.521
0.216
0.018*
−0.020
0.045
0.589
0.285
0.534
0.303
0.551
M400H
−0.085
0.155
0.586
−0.081
0.030
0.007*
0.128
0.358
0.170
0.413
Notes:
* Statistical signiﬁcance (p = < 0.05).
AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles; SD, standard deviation.
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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sprint events, supporting the notion that performances had reached a plateau as we likely
near the limits of human physiology (Berthelot et al., 2008; Nevill & Whyte, 2005; Haugen,
Tønnessen & Seiler, 2015). This adds substantial weight to our ﬁnding that AFT is likely a
factor explaining recent performance improvements. For example, in the annual top 100
performances in the women’s 100 m prior to the release of AFT (2016–2019), the average
performance was stable between 11.13–11.14 s, with an average year-to-year variation of
less than 0.1%, which underlines the signiﬁcance of the 0.90% improvement in 2022
compared to 2019.
There are a number of candidate mechanisms which potentially accrue and interact to
underpin the performance beneﬁts of AFT observed in the current study. Given that
carbon ﬁbre plates in isolation have existed in sprint spikes for an extended period of time,
the presence of a stiff plate alone is likely insufﬁcient to explain the signiﬁcant
improvements in sprint times, and instead it is more likely that innovative midsole
materials and geometry are the key drivers alongside longitudinal bending stiffness.
For example, new foams such as polyether block amide demonstrate far superior energy
restitution than traditional midsoles made of ethylene–vinyl acetate (Hoogkamer et al.,
2018). In addition to the composition of the midsole, the increased thickness/height of the
midsole (and its spatial distribution beneath the foot) in the new generation of spikes
compared to traditionally minimal racing spikes potentially provides several advantages.
An increase in the midsole thickness, which is capped at 20 mm by World Athletics
regulations (World Athletics, 2021), may create more beneﬁcial lever arms, potentially
creating favourable shifts in ratio of force during acceleration towards horizontal reaction
ground force orientation, which is a central determinant of sprint performance (Morin,
Edouard & Samozino, 2011; Rabita et al., 2015). Changes in both shank position and
dorsiﬂexion range of movement, both of which may be achieved via a higher midsole stack
height, have been recently linked with better ratio of force during acceleration (King et al.,
2022).
Further, an increase in midsole thickness may result in between a 1–3% increase in
overall limb length and enhance stride length, the consequences of which are increasingly
studied in the context of athletes with transtibial amputations. Although the topic is
currently keenly debated (Taboga et al., 2020; Beck, Taboga & Grabowski, 2022; Zhang-Lea
et al., 2023; Weyand et al., 2022.), there is evidence of an association between longer leg
length and faster maximal velocity (Weyand et al., 2022). In the world’s best transtibial
amputation 400 m runner, reducing limb length by 5 cm produced a substantial drop in
maximal treadmill velocity from 11.4 to 10.9 m/s (Weyand et al., 2022), leading to
substantial projected and actual reductions in race performance. Although reduced leg
length in amputee athletes resulting in slower speed does not guarantee that increasing leg
length results in higher speed in able-bodied athletes, there is also evidence from
non-amputee athletes that longer leg lengths may be particularly beneﬁcial for longer
sprinting (i.e., 400 m) (Weyand & Davis, 2005; Tomita et al., 2020). These factors,
combined with improvements in running economy (Hoogkamer et al., 2018), which are
increasingly valuable in distances over 100 m, potentially explain some of the performance
enhancing effects of AFT observed in this study. It should be noted that these mechanisms
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remain primarily speculative at this stage, based on studies which do not directly
investigate midsole thickness. Importantly, an increase in midsole thickness alone is not
sufﬁcient to improve running economy over longer distances (Barrons, Wannop &
Stefanyshyn, 2023), indicating that if midsole thickness is indeed involved in performance
enhancements, then it likely acts in concert with other components of the footwear,
including the longitudinal bending stiffness. A key example which further demonstrates
the uncertainty of the mechanisms is that AFT potentially also creates a less beneﬁcial lever
arm, considering that for a constant hip torque, a longer effective leg length will result in a
smaller propulsive force. Therefore, future studies should seek to clarify the precise
mechanisms through which AFT ultimately contributes to enhanced sprint performance.
This study also demonstrated via two unique methods that women’s sprint events have
undergone more substantial and more widespread recent improvements than male events,
and that AFT had a greater impact on women’s performances than men’s performances.
This is consistent with road-running research indicating that women beneﬁted more from
AFT than men (Bermon et al., 2021), including the ﬁndings that AFT improved marathon
ﬁnishing time by 0.8% for males and by 1.6% for females in a subsample of marathon
ﬁnishers (Senefeld et al., 2021). Importantly, this ﬁnding may provide further insight into
the potential mechanisms which may underpin the AFT-induced improvements in
sprinting performance in some events. Firstly, the overall stature discrepancy between elite
male and female sprinters is approximately 6% (Weyand & Davis, 2005), meaning that a
similar absolute increase in midsole thickness (e.g., the maximum allowed 20 mm) affords
a greater relative increase in leg length for female sprinters than male sprinters. Given the
previously described relationship between leg length and maximal velocity (Weyand et al.,
2022) and the relationship between stride length and sprinting performance, this may
partially explain our observation that female sprinters generally beneﬁt more from AFT
than males. Similarly, the geometry of AFT may also inﬂuence the sex-speciﬁc results
observed in this study. Although World Athletics rules stipulate that a marginally thicker
sole beyond the 20 mm regulation is permitted in the case of larger shoe sizes (World
Athletics, 2021), we understand that the 20 mm stack height is not scaled proportionately
according to shoe size, and the 20 mm midsole stack is kept relatively consistent across
shoe sizes. Theoretically, this creates a more advantageous lever for those with smaller shoe
sizes than for those sprinters with larger shoe sizes, due to unique midsole thickness/foot
length ratios. Given that continued horizontal force application to the ground at high
velocities is a key discriminator of sprint performance between males and females
(Slawinski et al., 2017), this potential creation of more advantageous levers via smaller shoe
sizes may help to explain why female sprinters appear to accrue greater beneﬁts from AFT
than male sprinters. Finally, differences in body mass may interact with energy restitution
and conformity of both the rigid plate and the midsole foam. Combined, these factors may
help to explain the sex-speciﬁc improvements in performance achieved via AFT.
Similarly, but more tentatively, we observed that performance improvements were
generally more pronounced in the top 20 compared to the top 100 athletes. While this may
be partly explained by statistical factors related to sample size, it may also suggest that AFT
preferentially beneﬁts sprinters with certain characteristics, such as technique or speciﬁc
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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strength. Indeed, there is evidence indicating that optimal longitudinal bending stiffness of
sprint spikes is speciﬁc to the individual (Stefanyshyn & Fusco, 2004), and may be mediated
such as toe ﬂexor strength, plantar ﬂexor strength, rebound jump performance and body
mass (Willwacher et al., 2016; Nagahara, Kanehisa & Fukunaga, 2017). Similar
performance-level dependency has been reported with the AFT-induced enhancement of
distance running performance, whereby large variations in the magnitude of performance
enhancement have been observed which are partially mediated by the standard of the
athlete (Knopp et al., 2023). However, given that our top 20 and top 100 cohorts have some
overlap, this result should be interpreted with caution, and future studies are needed to
more clearly elucidate the potential performance-level speciﬁc improvements associated
with the use of AFT.
Although we provide initial insight into the recent improvements in some sprint events
and the potential performance-enhancing effects of AFT on sprint times, the consistency
of our results warrants further discussion. For comparison, studies investigating the
inﬂuence of AFT on annual long-distance road-race times in elite athletes have reported a
universal beneﬁt for all events assessed across both sexes (Rodrigo-Carranza et al., 2022,
2021; Bermon et al., 2021). Contrarily, we observed that recent improvements (regardless
of AFT inﬂuence) were not consistent across all events or across all years, and that AFT
was not a signiﬁcant predictor of performance in four of the ten events analysed.
Combined, our data suggests that although AFT in sprint spikes inﬂuences performance in
some events, they do not discriminate sprint performance to the same extent as AFT
discriminates road-racing performance. Some of this inconsistency may be explained by
differences in the adoption of AFT in different events and different years. For example, in
2021 only 42.5% of the top 20 athletes (across all events) wore AFT, whereas in 2022, 88.5%
of the top 20 athletes utilised AFT. However, our mixed model analyses revealed that other
factors are likely also involved in recent sprint time improvements. Changes in factors such
as athlete characteristics like age (Elmenshawy, Machin & Tanaka, 2015) and stature
(Marck et al., 2017), weather conditions, career trajectories, changes in training methods
and injury status, sex-based and event-based differences in proximity to physiological
limits, and increased globalisation are all candidate mediators of performance changes.
The COVID-19 pandemic also provided a unique set of circumstances which conceivably
inﬂuenced the observed performance increases. For example, athletes were afforded the
opportunity to train for a prolonged period of time without reducing load as they typically
would to peak for major competitions. It is also noteworthy that there was a 46% reduction
in drug testing worldwide in 2020 (World Anti-Doping Authority, 2021), allowing athletes
more opportunity to enhance their performances exogenously (Negro, Di Trana &
Marinelli, 2022; Lima et al., 2021). Given the history and prevalence of performance
enhancing drugs in track and ﬁeld (Faiss et al., 2020; Berthelot et al., 2015), this is a
plausible explanation for some improvement.
There are also limitations of our study which must be considered when interpreting the
current results. This is perhaps most practically demonstrated by the unexpected ﬁnding of
no signiﬁcant improvements in the men’s 400 m hurdles, despite nine of the top 10 times
in history being run since the introduction of AFT in 2020. This highlights the limitations
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
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of our initial statistical approach in dealing with outliers, such as Karsten Warholm’s 2021
world record (which he ran without AFT), likely due to large standard deviations.
The authors do not propose that no meaningful change has occurred in this event, but
rather concede the limitations of the initial statistical approach. Interestingly, our mixed
model analysis detected that year was a signiﬁcant predictor of men’s 400 m hurdles
performance. Further, another weakness of this study was the failure to account for more
deterministic factors in the mixed model analysis, such as age, training content and
context, environmental conditions and athlete nationality. Importantly, annual changes in
competition opportunity have been demonstrated to inﬂuence annual performances
(Haake, Foster & James, 2014). In this speciﬁc case, the absence of a major global
championship in 2018 may inﬂuence the results. Finally, the dataset is limited by size, with
only two years of data where athletes had the opportunity to wear AFT. This may limit the
interpretation of our mixed model results.
CONCLUSION
This is the ﬁrst evidence indicating that sprint times have become signiﬁcantly faster in
some events in the last two years, and that these improvements may be partially driven by
technological innovation with sprint footwear design, which aligns with our hypothesis.
Further, these improvements appear to be mediated by event, sex and potentially the level
of athlete.
Future studies should seek to identify the precise mechanisms through which AFT may
improve sprint performance in both sexes independently, and to elucidate the athlete
characteristics which may moderate these performance enhancing effects, such as athlete
stature, foot-length/midsole thickness ratio, sprinting mechanics and speciﬁc strength
characteristics. Additional analysis of recent performance trends in events which do not
have superspikes available (for example, shot put and discus) would also provide insight
into whether recent track performance improvements have been driven by technology or
by more general sport-wide improvements in training methodology and competition
opportunities, for example. Further, given recent commentary on the potentially enhanced
risk of injury with AFT (Tenforde et al., 2023), the long-term ramiﬁcations of repeated
exposure to AFT in sprint spikes should be investigated, especially in youth and developing
athletes.
ACKNOWLEDGEMENTS
The authors wish to sincerely thank Sean Whipp (Whipp Sports) for his assistance with
identifying the spikes of athletes.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this research. We received support for the APC from
the German Research Foundation Project No. 512648189 and the Open Access Publication
Fund of the Thueringer Universitaets-und Landesbibliothek Jena. The funders had no role
Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433
15/19
in study design, data collection and analysis, decision to publish, or preparation of the
manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
APC from the German Research Foundation: 512648189.
Competing Interests
Astrid Zech is an Academic Editor for PeerJ.
Author Contributions
 Joel Mason conceived and designed the experiments, performed the experiments,
analyzed the data, prepared ﬁgures and/or tables, authored or reviewed drafts of the
article, and approved the ﬁnal draft.
 Dominik Niedziela performed the experiments, analyzed the data, prepared ﬁgures
and/or tables, authored or reviewed drafts of the article, and approved the ﬁnal draft.
 Jean-Benoit Morin analyzed the data, authored or reviewed drafts of the article,
interpreted the data, and approved the ﬁnal draft.
 Andreas Groll performed the experiments, analyzed the data, prepared ﬁgures and/or
tables, authored or reviewed drafts of the article, and approved the ﬁnal draft.
 Astrid Zech conceived and designed the experiments, authored or reviewed drafts of the
article, and approved the ﬁnal draft.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
The Ethics Commission of the Friedrich Schiller University Jena granted ethical
approval to complete this study: FSV 23/057.
Data Availability
The following information was supplied regarding data availability:
The raw data used for analysis are available in the Supplemental Files.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.16433#supplemental-information.
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The potential impact of advanced footwear technology on the recent evolution of elite sprint performances.
Mason, Joel,Niedziela, Dominik,Morin, Jean-Benoit,Groll, Andreas,Zech, Astrid