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Elite Sprinting: Are Athletes Individually Step-Frequency or Step-Length Reliant?


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The aim of this study was to investigate the step characteristics among the very best 100-m sprinters in the world to understand whether the elite athletes are individually more reliant on step frequency (SF) or step length (SL). A total of 52 male elite-level 100-m races were recorded from publicly available television broadcasts, with 11 analyzed athletes performing in 10 or more races. For each run of each athlete, the average SF and SL over the whole 100-m distance was analyzed. To determine any SF or SL reliance for an individual athlete, the 90% confidence interval (CI) for the difference between the SF-time versus SL-time relationships was derived using a criterion nonparametric bootstrapping technique. Athletes performed these races with various combinations of SF and SL reliance. Athlete A10 yielded the highest positive CI difference (SL reliance), with a value of 1.05 (CI = 0.50-1.53). The largest negative difference (SF reliance) occurred for athlete A11 as -0.60, with the CI range of -1.20 to 0.03. Previous studies have generally identified only one of these variables to be the main reason for faster running velocities. However, this study showed that there is a large variation of performance patterns among the elite athletes and, overall, SF or SL reliance is a highly individual occurrence. It is proposed that athletes should take this reliance into account in their training, with SF-reliant athletes needing to keep their neural system ready for fast leg turnover and SL-reliant athletes requiring more concentration on maintaining strength levels.
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Elite Sprinting: Are Athletes Individually
Step-Frequency or Step-Length Reliant?
Sport and Exercise Science, University of Bath, Bath, UNITED KINGDOM;
Cardiff School of Sport, University of
Wales Institute Cardiff, Cardiff, UNITED KINGDOM; and
Health and Social Care Institute, Teesside University,
Middlesbrough, UNITED KINGDOM
SALO, A. I. T., I. N. BEZODIS, A. M. BATTERHAM, and D. G. KERWIN. Elite Sprinting: Are Athletes Individually Step-Frequency
or Step-Length Reliant? Med. Sci. Sports Exerc., Vol. 43, No. 6, pp. 1055–1062, 2011. Purpose: The aim of this study was to investigate
the step characteristics among the very best 100-m sprinters in the world to understand whether the elite athletes are individually more
reliant on step frequency (SF) or step length (SL). Methods: A total of 52 male elite-level 100-m races were recorded from publicly
available television broadcasts, with 11 analyzed athletes performing in 10 or more races. For each run of each athlete, the average SF
and SL over the whole 100-m distance was analyzed. To determine any SF or SL reliance for an individual athlete, the 90% confidence
interval (CI) for the difference between the SF–time versus SL–time relationships was derived using a criterion nonparametric boot-
strapping technique. Results: Athletes performed these races with various combinations of SF and SL reliance. Athlete A10 yielded the
highest positive CI difference (SL reliance), with a value of 1.05 (CI = 0.50–1.53). The largest negative difference (SF reliance) occurred
for athlete A11 as j0.60, with the CI range of j1.20 to 0.03. Conclusions: Previous studies have generally identified only one of these
variables to be the main reason for faster running velocities. However, this study showed that there is a large variation of performance
patterns among the elite athletes and, overall, SF or SL reliance is a highly individual occurrence. It is proposed that athletes should
take this reliance into account in their training, with SF-reliant athletes needing to keep their neural system ready for fast leg turnover
and SL-reliant athletes requiring more concentration on maintaining strength levels. Key Words: ATHLETICS, BIOMECHANICS,
An athlete’s running velocity is the product of step
frequency (SF) and step length (SL)—a step being
from one foot contact to the next contact of the
contralateral foot. The term stride is also used in the litera-
ture, which is equal to two consecutive steps. Although the
equation of velocity equals SF multiplied by SL is very
straightforward and simple in theory, athletes face problems
in practice because the relationship between SF and SL is
generally an inverse relationship at maximum effort. Thus,
an increase in one parameter could typically lead to a de-
crease in the other. This is due to the negative interaction
apparent in the production of these variables (11). Conse-
quently, this relationship has attracted attention in the bio-
mechanics literature.
Luhtanen and Komi (16) were among the first to com-
prehensively analyze the relationship between SF and SL
and presented the development of SF and SL in track ath-
letes when running velocity was increased from jogging at
3.9 mIs
to sprinting at 9.3 mIs
. However, this study is
not directly relevant to elite sprint athletes, who always need
to run at very high individual velocities in competition. In a
study of 28 sprint-related sportsmen (background, e.g., in
athletics, soccer, touch rugby), Hunter et al. (11) found that
at the group-level SL was significantly related to running
velocity, whereas SF was not. However, at the individual
level, the subjects performed with a significantly higher SF
in their fastest trial in comparison with their third fastest
trial. SL did not reveal significant differences in the indi-
vidual analysis (11). The authors offered a potential expla-
nation for these differences between individual and group
analysis by stating that SF may be the more important factor
in the short term, whereas longer steps may require the de-
velopment of strength and power during a longer period.
Hunter et al. (11) also offered further detailed explanations
of the technique issues that were behind the aforementioned
negative interaction between SL and SF. The sprinting ve-
locities, however, ranged from 7.44 to 8.80 mIs
(11) and
were measured only 16 m into the sprint. Thus, whereas the
article provides general information about step character-
istics and can be helpful to developing athletes, it is not fully
Address for correspondence: Aki I.T. Salo, Ph.D., Sport and Exercise
Science, University of Bath, Bath, BA2 7AY, United Kingdom; E-mail:
Submitted for publication April 2010.
Accepted for publication October 2010.
Copyright Ó2011 by the American College of Sports Medicine
DOI: 10.1249/MSS.0b013e318201f6f8
Copyright © 2011 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
applicable to elite sprinters, whose running velocities are
much higher.
To fully explore how elite athletes could fine-tune their
performances, it would be necessary to understand how they
perform in competition. Mann and Herman (17) analyzed
the first-, second-, and eighth-place finishers in the 1984
Olympic men’s 200-m final and highlighted the fact that
the major difference between the three athletes (especially
those in first and second) was SF. Interestingly, all three
athletes increased velocity, SF, and SL between the non-
fatigued (125-m mark) and fatigued (180-m mark) phases of
the sprint.
Ae et al. (1) analyzed the final of the men’s 100 m from
the 1991 World Championships in Athletics. One of the key
points highlighted by Ae et al. (1) in their conclusions was
that the gold medalist generally exhibited a shorter SL and
higher SF than the silver medalist, although this was not
consistent throughout the whole race. A similar type of
analysis over each 10 m was performed by Gajer et al. (8)
from the semifinals and final of the men’s 100 m at the 1996
French Championships. The six fastest (10.18 T0.05 s) and
six slowest athletes (10.52 T0.08 s) were divided into sep-
arate groups. SL was consistently higher in the faster group
and was significantly higher in 7 of 10 sections. On the other
hand, SF was higher in the slower group in all but the last
10-m section, although it was significantly higher in only 1
of 10 sections. The authors (8) drew the conclusion from
their results that SL was the more important factor at the
highest level. Recent competition analysis from the World
Championships in Helsinki 2005 (13) provided a similar
trend to that of Gajer et al. (8). Eighteen male sprinters from
the 100-m heats were divided into faster and slower groups
(nine athletes in each group; high-performance group =
10.12–10.32 s, lower performance group = 10.40–10.90 s).
In the full-stride phase (around 60 m), the longest SL was
significantly longer (PG0.003) by 0.12 T0.03 m for the
faster group than the slower group, whereas there were no
significant differences in SF.
Gajer et al. (8) also reanalyzed the data of Ae et al. (1) by
splitting the eight finalists into two groups: the first- to
fourth-place and fifth- to eighth-place finishers. The four
fastest athletes had a higher average SL in 9 of 10 intervals,
whereas the four slowest athletes had a higher average SF
in 7 of the intervals. This was presented by Gajer et al. (8) as
further evidence to support their own conclusions. Thus, at
the group level the finding was opposite to the conclusion of
Ae et al. (1) regarding the first- and second-place finishers.
It seems, however, that the results are very dependent on
the grouping. The grouping used by Gajer et al. (8) for the
data from Ae et al. (1) meant that the groups were equal in
number, each containing four athletes. When the finishing
times for the eight athletes were examined, a different
method of grouping could be justified. The first six finishers
all completed the race in times in a close range of 9.86–
9.96 s. The last two finishers were considerably slower,
finishing with times of 10.12 and 10.14 s. New calculations
reveal the opposite trend to that presented by Gajer et al. (8).
With the modified groupings based on the absolute level of
performance, the six fastest athletes recorded a higher SF in
9 of the 10 intervals, whereas the slowest two athletes had a
higher SL in 7 of the intervals. This change occurred be-
cause the fifth- and sixth-place athletes typically displayed
short SL and high SF values when compared with the other
six athletes. This example shows that an average group-
based analysis can actually mask important issues at the
individual level. Because of this problem, Dixon and Kerwin
(6) called for a multiple single-subject approach in studies
where important individual differences that are not visible
in general trends of a group analysis may be present. This
might be even more important for individual elite athletes
because any improvement in their performance may give
them an advantage over the competitors. Thus, when elite
sprinters try to improve their performance by seeking to cut
hundredths of a second from their race time, it is very im-
portant to understand the individual performance and step
characteristics issues rather than analyze them at the group
level. Recently, in track-and-field biomechanics, there have
been single-subject analyses published in sprinting (3) and
sprint hurdling (22).
It is clear from the results presented on elite athletes in a
competition situation that there is no consensus of opinion
over which factor, SF or SL, is the more important at this
level of competition. These are important findings, none-
theless, because they give a good insight into the perfor-
mance of the very best athletes in a competitive situation,
something that a laboratory- or training-based study is not
capable of doing. There is, however, a lack of consideration
for the possibility that individual athletes may adopt dif-
fering strategies from one another, about optimizing SF
and SL. Further insight could be realized if the same elite
athletes were analyzed over several runs. Such analysis is
clearly missing from the current biomechanics literature.
Thus, the aim of this study was to investigate the step
characteristics among the very best 100-m sprinters in the
world to understand whether the elite athletes are individu-
ally more reliant on SF or SL.
A total of 52 male elite-level 100-m races were recorded
from publicly available television broadcasts. The com-
petitions included several Olympic, World, and European
Championships; International Association of Athletics Fed-
erations (IAAF) Grand Prix series competitions; European
Cups; and some National Championships. The summary of
competitions is in Table 1. Data were collected from semi-
finals and finals of the major championships and heats and
finals of individual Grand Prix series competitions. Offi-
cial race times were recorded from the IAAF Web site (12).
A similar approach of analyzing publicly available data
from sport competitions for research purposes has been
carried out by Stewart and Hopkins (23), who analyzed the
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consistency of performance between swimming strokes, race
distances, and two competitions across 221 swimmers. In
the current study, athletes’ individual races were analyzed
if the athlete ran fully through the finish line. Thus, indi-
vidual races in which an athlete clearly eased off before the
finish line (e.g., in some heats or semifinals), sustained an
injury, or in any way was deemed not to perform normally,
were disregarded from the analysis. Consequently, the
worst individual race time analyzed was 10.39 s. It is clear
that not every analyzed athlete was involved in each com-
petition. All athletes who performed in 10 or more races
were taken for this analysis yielding a total of 11 athletes.
The number of races per athlete is listed in Table 2. Of
these 11 athletes, 9 ran under 10.00 s at least once in these
For each run of each athlete, the average SL and SF over
the whole 100-m distance were analyzed as follows. The
total number of steps taken in the race by each of the athletes
of interest was counted by viewing the race in slow motion
on a normal television and using a video player (AG-7550;
Panasonic, Osaka, Japan), which yielded 50 video fields per
second. Because the athlete did not necessarily complete a
step exactly at 100 m, the displacement of the last step (S
was defined. This was the overall displacement from the
start line to the toe of the ground foot in the step closest to
the finish line (either side). The displacement estimation was
based on using the track markings, the length of the foot
(approximately 0.3 m), and the expected values for SL.
Because the first step out from the starting blocks does not
cover as much ground as all subsequent steps and it clearly
takes the longest time, this step was disregarded from
the calculations. To do that, a pilot test was set up. Four
national-level athletes (who provided informed consent) were
videotaped with a high-speed video camera (Motionscope
500C; Redlake Imaging Corp., Alameda, CA) at 250 Hz to
estimate the length of the first step both as a distance and time
(from the start signal to the instance of the first contact). On
the basis of these four athletes’ performances over 16 runs
(4 each), a distance of 0.55 m and a time of 0.52 s were sub-
tracted from the calculations. Average SL throughout the race
was therefore calculated as follows:
¼ðSLS 0:55 mÞ=ðnS1Þ;
where SL
is average step length, S
is displacement of
the last step, and n
is number of full steps. The total number
of steps that were taken over the exact 100 m (n
then calculated as follows (this provides the last step as a
nS100 ¼nSþ½ð100 m SLS Þ=SL
From this, the average SF for the race was calculated as
¼ðnS100 1Þ=ðtr0:52 sÞ
where SF
is the average step frequency and t
is the official
race time.
Statistical analysis. All athletes were analyzed in-
dividually. SF, SL, and race time data were natural log-
transformed before analysis to normalize distributions and
stabilize variance. To determine any SF or SL reliance for
an individual athlete, the 90% confidence interval (CI)
for the difference between the SF–time versus SL–time
relationships was derived using a criterion nonparametric
bootstrapping technique (7) (Resampling Stats 4.0.7; Re-
sampling Stats, Inc., Arlington, VA). Briefly, for each set
of nraces for each individual athlete, 10,000 resamples
with replacement (of ncases) of the race time, SF and SL
variables were taken (maintaining case correspondence).
On each bootstrap resample, the SF–time and SL–time
correlations (Pearson r) were derived, and the difference
between these correlations was calculated and stored (SF
minus SL). The 90% CI (10) for the difference between the
SF and SL correlations was obtained using a simple per-
centile method, from the 5th and 95th percentiles of the
distribution of 10,000 differences. The threshold for a
practically important difference between SF and SL cor-
relations (in either direction) was set at a value of 0.1—a
‘‘small’’ effect size for the correlation coefficient (4). An
athlete was declared SF reliant if the lower limit of the 90%
CI was at or beyond the threshold of j0.1, with the upper
limit G+0.1 (precluding SL reliance). Conversely, an athlete
was declared SL reliant if the frequencyjlength correla-
tion difference was positive (favoring length), with the 90%
CI precluding frequency reliance (ej0.1). An effect was
deemed ‘‘unclear’’ if the 90% CI simultaneously extended
into regions, suggesting both SF and SL reliance; the athlete
could be SF reliant or SL reliant, or there could be a trivial
difference favoring neither step characteristic. In addition,
to investigate whether the elite athletes were more reliant on
TABLE 2. Number of races with the mean official race time (SD) for each individual
athlete in those races.
Athlete No. of Races Mean Time (s) SD (s)
A1 21 10.02 0.12
A2 27 10.08 0.09
A3 23 10.05 0.12
A4 20 10.12 0.12
A5 15 10.17 0.10
A6 15 10.12 0.13
A7 17 10.08 0.08
A8 16 10.16 0.08
A9 10 10.12 0.06
A10 14 10.17 0.12
A11 11 10.18 0.14
Mean 17 10.12
TABLE 1. Summary of analyzed competitions.
Final Semifinal
Olympic Games 1 2
World and European Championships and Commonwealth
IAAF Golden League 10 2
IAAF Grand Prix 11 7
European Cup 2
National Championships 2 3
Semifinal column contains Golden League and Grand Prix heats because these
competitions do not have separate semifinals.
Copyright © 2011 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
SF or SL or whether height influenced this reliance, three
further Pearson correlations were carried out: the point dif-
ference between the SF–time versus SL–time correlation
values from above was further correlated with the individual
mean race times as well as with the athletes’ personal best
times and heights, both of which were obtained from the
athletes’ biographical information on the IAAF Web pages
(12). These data were not natural log-transformed because
the point difference yielded negative values that cannot be
log-transformed. The 90% CI values were calculated, and
the threshold for a practically important difference was set
at 0.1 as above.
Table 3 provides the correlation coefficients for each
athlete between the independent variables and the race time.
The correlation values between SF and race time varied
between 0.16 and j0.79. Contrary to SF, all athletes yielded
a negative correlation between SL and race time. The range
of correlation values for SL varied from j0.16 to j0.89.
Figure 1 provides the difference between correlations
for SF–time and SL–time, together with its 90% CI. Athlete
A10 yielded the highest positive difference with a value of
1.05 (with the CI ranging from 0.50 to 1.53). The largest
negative difference occurred for athlete A11 as –0.60 with
the CI ranging from –1.20 to 0.03. The area of T0.1 to in-
dicate the smallest practically worthwhile difference be-
tween correlations is also shown in Figure 1.
Owing to the large variation shown in r-difference values,
three athletes’ data are specifically shown in Figure 2 to
illustrate the athletes’ times as a function of SL and SF. On
the basis of data in Figure 1, athlete A10 (Fig. 2, A and B)
had the largest SL reliance, athlete A4 (Fig. 2, C and D) did
not yield any reliance either on SF or on SL, and athlete A11
(Fig. 2, E and F) was the only athlete who was clearly SF
reliant. The minimum, maximum, and mean of average SL
and SF values for each athlete are presented in Table 4,
showing that the lowest range for the average SL was 0.06
m, whereas the largest range was 0.14 m. The respective
values for the average SF range were 0.07 and 0.30 Hz.
The SF–SL reliance (as correlation difference) did not
show a meaningful relationship with the athletes’ mean race
time (CI for r,j0.27 to 0.71), personal best times (j0.63 to
0.40), or height (j0.13 to 0.77).
This study was designed to increase our understanding of
the SF and SL characteristics of elite athletes in major com-
petitions. The main results showed that these characteristics
vary considerably between the athletes. Previous studies
have generally identified only one of these variables to be
the main reason for faster running velocities, and the results
have given a somewhat confusing picture. Kuitunen et al.
(15) showed that SF was the dominant factor when running
velocity increased from 70% to 100%. Higher SF also
seemed to be the major difference between three Olympic
200-m finalists (17). On the other hand, Gajer et al. (8)
found that better 100-m sprinters in their study had longer
SL than slower athletes, and Hunter et al. (11) showed that
the SL was significantly related to running velocity at the
group level (whereas SF was not). The results of Hunter
et al. (11), however, showed that, within individuals, SF was
higher in the fastest trials. None of these studies, however,
have looked at elite athletes across different races to deter-
mine how an individual athlete performs. From an elite
athlete’s point of view, the group-level data do not provide
appropriate information to improve individual performance.
For example, by executing an average performance of
100-m Olympic finalists, the athlete would not win the race.
In fact, often, if an athlete were to achieve the average per-
formance of all the finalists, that would not be sufficient to
even place that athlete on the podium. Thus, it is important
to look at each elite athlete individually. To the best of our
knowledge, this is the first study that has looked at step
TABLE 3. Correlation values for natural log-transformed SF and SL versus time.
Athlete SF vs Time Correlation SL vs Time Correlation
A1 j0.27 j0.38
A2 j0.57 j0.16
A3 j0.54 j0.31
A4 j0.39 j0.36
A5 0.11 j0.69
A6 j0.61 j0.65
A7 j0.12 j0.49
A8 j0.37 j0.58
A9 0.07 j0.80
A10 0.16 j0.89
A11 j0.79 j0.19
FIGURE 1—The rdifference (diamonds) with 90% CI (bars) for each
athlete A1 to A11. The area of T0.1 from zero in the middle demon-
strates the trivial (nonreliant) effect.
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characteristics of elite athletes individually and longitudi-
nally across multiple competitive races. In addition, a novel
aspect of the current study is the use of a criterion boot-
strapping method, together with a criterion for practical
significance, to elucidate the within-athlete differences be-
tween SF and SL.
Average SF multiplied by average SL provides the aver-
age running velocity, which, in turn, has an inverse rela-
tionship with the race time. This means that both SF and SL
are inversely linked with the race time and strongly related
to each other. This collinearity between SF and SL makes it
impossible to properly separate the independent influence of
these predictors on race time, if both are entered together as
predictors in a multiple regression model. Therefore, a novel
approach was sought to understand any reliance on partic-
ular step characteristics by athletes. Consequently, it was
decided that the most appropriate approach was to adopt
a bootstrapping technique to calculate the 90% CI for the
difference between SF–time versus SL–time correlations to
inform how practically meaningful this effect was. The in-
terpretation of the results in Figure 1 follows the recom-
mendations by Batterham and Hopkins (2). The effect is
FIGURE 2—Three athletes’ race times as a function of SF and SL: athlete A10 (A and B) showed SL reliance, athlete A4 (C and D) did not yield either
reliance, and athlete A11 (E and F) was SF-reliant. Please note that yaxes have been inverted because quicker times demonstrate improved perfor-
mance. Points on the figures with trend lines are from the original data; rvalues are from the log-transformed data (Table 3). Because of inverted
yaxes, the signs of rvalues do not match the visual impression.
Copyright © 2011 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
considered reliant if the 90% CI is fully on either the SF or
SL side or if one end reaches only to the area of a trivial
effect in the middle. If the CI extends to include both fre-
quency and length reliance, then the effect is considered
Overall, the results in Figure 1 revealed that there is a
large variation of performance patterns among the elite ath-
letes. There were clearly athletes at the highest elite level
of 100-m sprinting who were SL reliant (athletes A10, A9,
and A5), whereas only athlete A11 was clearly SF reliant.
All other athletes did not have clear reliance on either side,
although there were trends implying that, for example, ath-
lete A7 was most likely to be SL reliant and athlete A2 was
most likely to be SF reliant. When looking at the results in
further detail, athlete A10 yielded a 90% CI (0.50–1.53),
which did not even cross over the T0.1 trivial effect region
(Fig. 1). Thus, athlete A10 performed best when he was able
to produce long steps (within his own range; Fig. 2B). Such
reliance of SL meant that if the athlete was not able to pro-
duce long steps, he could not compensate the performance
enough with high SF to produce fast 100-m times. On the
contrary, athlete A11 performed his best times when he was
capable of producing high step frequencies (within his own
range; Fig. 2E). The 90% CI (j1.20 to 0.03) crosses over
into the trivial effect region from the SF reliance but does
not reach to a SL-reliant effect (Fig. 1). This meant that, if
the athlete could not produce high step frequencies (e.g., if
the nervous system was not ready to fire quickly enough to
have a fast turnover of the steps), the SL had not compen-
sated the running velocity enough. The athletes whose 90%
CI reached over all three different zones in Figure 1 were
such that they produced the best times sometimes with
slightly higher SL (and lower SF) and sometimes with
slightly higher SF (and lower SL; see an example of athlete
A4 in Fig. 2, C and D).
When looking at the individual SL and SF values within
athletes and across the races, the three examples in Figure 2
provided a very similar range of values. SL range was 0.08
m for A11, 0.11 m for A10, and 0.13 m for A4. The re-
spective SF ranges were 0.22, 0.11, and 0.30 Hz. Athlete A4
had the largest range in both SL and SF from all athletes
(Table 4). As the range of values on SF and SL were quite
similar for all athletes, it reinforced that SF or SL reliance
occurred within the normal range of that variable in indi-
vidual athletes, and it was not due to some clear outliers in
occasional runs.
The average within-athlete SF in this study ranged from
4.43 to 5.19 Hz, whereas the average SL ranged from 2.01 to
2.34 m (Table 4). It is clear that the average SL over the full
100 m in this study were less than those found in the max-
imum velocity phase in the literature because data in the
current study also contain steps at the start of the run, which
are shorter than later in the run. Ae et al. (1) reported SL
from 2.29 up to 2.71 m for the World Championships’ fi-
nalists in the maximum velocity phase. SL values reported
by Gajer et al. (8) fell within the range provided by Ae et al.
(1). SF values in the current study match more closely to
those at maximum velocity because SF does not alter largely
during the race. This is due to the fact that when early con-
tact phases are generally longer, the flight phases are shorter.
This ratio gradually changes; however, the total step time
(and thus frequency) does not drastically change, as visible
in the data of the first four steps out of the blocks in a study
by Salo et al. (21). Step frequencies in the maximum ve-
locity phase provided by Ae et al. (1) and Gajer et al. (8)
generally matched the range seen in the current study.
At the group level, SF–SL reliance did not yield mean-
ingful relationships with athletes’ mean race times (CI for
r,j0.27 to 0.71) or the personal best times (j0.63 to 0.40).
This means that, for example, SL-reliant athletes were not
any faster than SF-reliant athletes. Thus, it is possible to
reach the absolute top level of sprinting in the world (run
under 10.00 s) with widely varying patterns of SF and SL
reliance. The results also showed an unclear (trivial) effect
(i.e., there was no relationship) between the height of the
athletes and SF–SL reliance (CI for r,j0.13 to 0.77). This
means that taller athletes within this group were not SL re-
liant (against the general perception) or that shorter athletes
were not SF reliant and vice versa. Overall, these three re-
sults support the idea that either SF or SL reliance is a highly
individual occurrence.
The wind has been shown to influence the finishing time
in sprinting. For example, the theoretical calculation by
Ward-Smith (24) showed that a 2-mIs
following wind
improves a 100-m result at the elite level (10.00-s runner) by
0.10 s, whereas the same head wind would slow the runner
down by 0.13 s. However, the situation in the current study
was different from that of an individual race because data
were collected during a long period and across numerous
races. It is clear that athletes train and target some major
competitions, and thus, they potentially run faster in these
races regardless of the wind speed in comparison with races
perhaps earlier in a season. There were no clear trends that
the faster times were set with better wind conditions. In
addition, regardless of the wind, the race time was per-
formed with that specific SF and SL combination found in
the analysis, and it is this specific SF–SL pattern (reliance)
that is the interest in the current study.
TABLE 4. Minimum (Min), maximum (Max), and mean of average SL and SF values for
each individual athlete.
SL (m) SF (Hz)
Athlete Min Max Mean Min Max Mean
A1 2.14 2.27 2.20 4.62 4.91 4.75
A2 2.18 2.28 2.23 4.50 4.77 4.66
A3 2.01 2.11 2.07 4.91 5.19 5.05
A4 2.07 2.21 2.14 4.64 4.94 4.83
A5 2.12 2.23 2.17 4.66 4.82 4.74
A6 2.20 2.28 2.24 4.54 4.73 4.63
A7 2.13 2.24 2.18 4.67 4.86 4.76
A8 2.28 2.34 2.30 4.43 4.54 4.47
A9 2.28 2.34 2.31 4.44 4.51 4.48
A10 2.13 2.24 2.19 4.65 4.76 4.71
A11 2.14 2.22 2.18 4.62 4.84 4.73
Mean 2.15 2.25 2.20 4.61 4.81 4.71
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Because the SF or SL reliance varied considerably between
the athletes, it is proposed here that this should be taken
into account in their training, especially in the preparations
for the most important competitions. The effect of different
types of training on athletes’ performance is difficult to prove
owing to two factors: first, there is an inherent problem in
getting elite athletes to participate in training studies (14),
and second, it is practically impossible to isolate the training
influence of one specific type of exercise or mode of exer-
cise. However, some indirect conclusions can be drawn from
the literature and theory of specificity in training.
On the basis of animal research, Heglund and Taylor (9)
concluded that the increased stride length in various animals
primarily required higher average muscle force production
pointing toward the association between muscle strength and
stride (step) length. Studying humans’ sprinting perfor-
mance, Weyand et al. (25) concluded that the faster running
speeds were achieved by greater vertical ground reaction
forces rather than more rapid leg movements. The higher
average force production during the contact (i.e., strength)
resulted in considerably higher stride lengths. The regression
analysis showed that a 1.8-fold increase in top running speed
was achieved with 1.69 times longer strides (and with an
average vertical force production that was 0.5 times body
weight larger). It is acknowledged that, in the same study,
higher stride frequency was also associated with increased
force production. This was because a higher vertical force
production allowed athletes to produce the required impulse
in a shorter contact time. However, the regression analysis
showed only a 1.16-times increase in stride frequency across
the same range of top speeds as above. On the other hand,
Mero and Komi (18) found that only well-trained athletes
(as opposed to less-trained athletes) were able to increase
SF when towed to supramaximal velocities. Ross et al. (20)
concluded that this ability to increase SF may have been
caused by neural adaptations of training. Furthermore,
Heglund and Taylor (9) stated that higher stride frequencies
in animals require faster production of cross-bridges owing
to faster force generation demands pointing toward the as-
sociation between SF and neural conditioning.
Hunter et al. (11) hypothesized, based on their results and
the literature, that developing longer SL requires long-term
development of strength and power, especially to increase
horizontal ground reaction impulse. Cronin et al. (5) studied
how two types of resistive training (sled towing and
weighted vest) acutely influenced step characteristics over
the first 30 m in comparison with unresisted sprinting. Be-
cause relative strength due to additional weights was re-
duced, the decrease in performance was mainly due to lower
SL with only small decreases in SF. Moir et al. (19) had a
slightly different approach when the authors studied the in-
fluence of 8 wk of resistance training on step characteristics.
Although these were analyzed only over the first three steps
after the start, and thus may not be fully applicable to the
current article, the results gave indications of how such
training affects these step variables. Increased maximum and
explosive strength was associated with increased SL and
reduced SF over these first three steps (19).
Thus, overall, it is reasonable to conclude that SL is re-
lated more to increased force production, and SF is associ-
ated with faster force production during the contact and
quick leg turnover requiring neural adaptations. Higher SF
requires cross bridges within the muscles to be built at high
rates, and thus, these need a high rate of neural activation.
Consequently, it is proposed that the SF-reliant athletes are
required to concentrate on neural activation in their final
preparations for the major races and have a nervous system
ready such that they can produce the quick turnover of the
legs. On the other hand, the SL-reliant athletes need to keep
their strength levels up throughout the season and have the
required flexibility in the hip area to produce long steps.
Naturally, athletes cannot totally forget the nonreliant vari-
able because any disproportionate reductions in one variable
cannot be generally compensated for by the other variable.
The athletes who cross over the T0.1 trivial effect region
should perhaps focus equally on SL and SF in their training.
It is also good to remember that the current study was based
on the average step variables over the whole 100 m, whereas
SL, especially, varies throughout the race. It is estimated that
the accuracy of our measurements is about 0.01 m for the SL
and 0.06 Hz for the SF. The final caution is that this article
was able to provide only results based on how people have
performed not how an ideal performance could be created.
Thus, to further understand how SF and SL influence each
other and interact to produce the velocity of the individual
athlete, these same variables should be analyzed individually
at the maximum velocity phase and longitudinally in train-
ing. Some questions about the SF, SL, and velocity rela-
tionships could probably be best answered by adopting a
modeling approach.
This study analyzed SL and SF of world elite male 100-m
sprinters over multiple competitions. Because group-level
analysis could mask personal differences, this study con-
centrated on analyzing each athlete separately. Individually,
some athletes’ performances were more reliant on SL, one
athlete was clearly SF reliant and some athletes used com-
binations that showed a reliance on neither. It is proposed
that athletes should take this reliance into account in their
training, with SF-reliant athletes needing to keep their neural
system ready for fast leg turnover and SL-reliant athletes
requiring more concentration on maintaining strength levels.
This study has been partly funded by UK Athletics Ltd. and the
Leverhulme Trust, United Kingdom.
The authors thank the BBC Sports Library and its staff for access
to broadcast archives, and the Research Institute for Olympic
Sports, Finland, and their staff are thanked for their assistance in
the pilot test for the ‘‘first-step’’ evaluations.
The authors declare that they have no conflict of interest and that
the results of the present study do not constitute endorsement by
the American College of Sports Medicine.
Copyright © 2011 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
1. Ae M, Ito A, Suzuki M. The men’s 100 metres. N Stud Athlet.
2. Batterham AM, Hopkins WG. Making meaningful inferences
about magnitudes. Int J Sports Physiol Perform. 2006;1(1):50–7.
3. Bezodis IN, Kerwin DG, Salo AIT. Lower-limb mechanics during
the support phase of maximum-velocity sprint running. Med Sci
Sports Exerc. 2008;40(4):707–15.
4. Cohen J. Statistical Power Analysis for the Behavioral Sciences.
2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates; 1988. p. 567.
5. Cronin J, Hansen K, Kawamori N, McNair P. Effects of weighted
vests and sled towing on sprint kinematics. Sports Biomech.
6. Dixon SJ, Kerwin DG. Variations in Achilles tendon loading with
heel lift intervention in heel-toe runners. J Appl Biomech. 2002;
7. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New
York (NY): Chapman & Hall; 1993. p. 436.
8. Gajer B, The
´paut-Mathieu C, Lehe
´naff D. Evolution of stride and
amplitude during course of the 100 m event in athletics. N Stud
Athlet. 1999;14(1):43–50.
9. Heglund NC, Taylor CR. Speed, stride frequency and energy-cost
per stride—how do they change with body size and gait. J Exp
Biol. 1988;138:301–18.
10. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive
statistics for studies in sports medicine and exercise science. Med
Sci Sports Exerc. 2009;41(1):3–12.
11. Hunter JP, Marshall RN, McNair PJ. Interaction of step length and
step rate during sprint running. Med Sci Sports Exerc. 2004;36(2):
12. International Association of Athletics Federations Web site [Internet].
Monaco: IAAF; [cited 2009 Sep 16]. Available from: http://www.
13. Ito A, Ishikawa M, Isolehto J, Komi PV. Changes in the step
width, step length, and step frequency of the world’s top sprinters
during the 100 metres. N Stud Athlet. 2006;21(3):35–9.
14. Kearney JT. Sport performance enhancement: design and analysis
of research. Med Sci Sports Exerc. 1999;31(5):755–6.
15. Kuitunen S, Komi PV, Kyro¨la¨inen H. Knee and ankle joint
stiffness in sprint running. Med Sci Sports Exerc. 2002;34(1):
16. Luhtanen P, Komi PV. Mechanical factors influencing running
speed. In: Asmussen E, JLrgensen K, editors. Biomechanics VI-B—
International Series on Biomechanics. Baltimore (MD): University
Park Press; 1978. p. 23–9.
17. Mann R, Herman J. Kinematic analysis of Olympic sprint perfor-
mance: men’s 200 meters. Int J Sport Biomech. 1985;1:151–62.
18. Mero A, Komi PV. Effects of supramaximal velocity on biome-
chanical variables in sprinting. Int J Sport Biomech. 1985;1(3):
19. Moir G, Sanders R, Button C, Glaister M. The effect of periodized
resistance training on accelerative sprint performance. Sports
Biomech. 2007;6(3):285–300.
20. Ross A, Leveritt M, Riek S. Neural influences on sprint running—
training adaptations and acute responses. Sports Med. 2001;31(6):
21. Salo AIT, Kera¨nen T, Viitasalo JT. Force production in the first
four steps of sprint running. In: Wang Q, editor. Proceedings
of XXIII International Symposium on Biomechanics in Sports.
Beijing (China): The China Institute of Sport Science; 2005.
p. 313–7.
22. Salo AIT, Scarborough S. Changes in technique within a sprint
hurdle run. Sports Biomech. 2006;5(2):155–66.
23. Stewart AM, Hopkins WG. Consistency of swimming perfor-
mance within and between competitions. Med Sci Sports Exerc.
24. Ward-Smith AJ. New insights into the effect of wind assistance on
sprinting performance. J Sports Sci. 1999;17(4):325–34.
25. Weyand PG, Sternlight DB, Bellizzi MJ, Wright S. Faster top
running speeds are achieved with greater ground forces not more
rapid leg movements. J Appl Physiol. 2000;89(5):1991–9.
http://www.acsm-msse.org1062 Official Journal of the American College of Sports Medicine
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... If whole-body acceleration strategies are stable (i.e., reliable) at the intra-individual level, it would be possible to longitudinally monitor these acceleration strategies for individuals. Since optimum technique can be considered as the motions yielding maximum performance for a given individual under the constraints applied to them (Hatze, 1973), this may provide a way to determine the technical variables of interest that individuals are reliant on for better acceleration performance, building on similar, previous work conducted on elite to world class level (Tiers 4 to 5; McKay et al., 2022) sprinters and their performance during 100 m races (Salo et al., 2011). This information could then be used to apply individual-specific training interventions aimed at changing the acceleration strategies of rugby backs to enhance their sprint performance during the initial steps. ...
... While this is yet to be proven, some research exists to suggest that the technical features athletes exhibit for better sprinting performance may differ at the inter-individual level. For example, Salo et al. (2011) observed that elite to world class (Tiers 4 and 5) sprinters were typically individually reliant on the production of either higher step length or higher step rate (when averaged over multiple 100 m races) for better 100 m race performance. Although the same findings do not necessarily translate to rugby backs during the initial acceleration, a similar approach which seeks to determine the favourable techniques which are associated with the sprint performance of rugby backs at the intraindividual may provide direction for the individualisation of sprint training interventions of practitioners tasked with enhancing the acceleration performance of rugby backs. ...
... Since a decrease in either step rate or step length, without a proportional increase in the other, will compromise step velocity, and that different combinations of each variable can be utilised to achieve similar sprint performance, it is logical that there is no strong consensus on whether one variable is more important to initial acceleration performance. In track sprinting over the course of multiple 100 m sprints, elite sprinters' competition performances were shown to be individually reliant on step rate or step length, whilst some were shown to have no reliance on either (Salo et al., 2011). Although these findings may not translate to the initial steps of sprinting in rugby backs, they provide a potential explanation for the lack of consistency observed between the relationships of spatiotemporal variables with sprint performance during the initial steps. ...
Full-text available
Biomechanics and motor control of early acceleration: Enhancing the initial sprint performance of professional rugby union backs Sprint acceleration is an important performance feature in many sports. For professional rugby union backs, short distance sprints are frequently carried out in training and competition, but how technique and strength-based characteristics contribute to their acceleration performance during these initial steps is not currently well understood. A series of investigations were therefore undertaken to, firstly, advance the understanding of this area and, secondly, to apply this information by prescribing individual-specific interventions to enhance initial acceleration performance. Three initial investigations sought to determine how technical features and strength-based qualities of professional rugby union backs related to their sprint performance (quantified as normalised average horizontal external power) during the initial steps. Findings from these investigations highlighted that focussing on the contribution of discrete technical variables to acceleration performance in isolation is an overly reductionist approach which overlooks how complex systems achieve high sprint performance. Findings also highlighted how important information on individuals can be lost using group-based study designs, since different inter-athlete strategies were adopted to achieve similar performance outcomes. In the fourth investigation, four subgroups of participants were identified, using cluster analysis, based on their whole-body kinematic strategies. At the intra-individual level, the variables which portrayed their individual strategies remained stable (CV: 1.9% to 6.7%) across multiple separate occasions. This characterisation of whole-body strategies was used to develop a novel and rigorous approach to longitudinally assess the efficacy of technical-based acceleration interventions. Demonstrating the application of this approach in the final investigation, several individual-specific interventions were prescribed to professional rugby union backs based on within-individual relationships of their technique strategies and strength-based capabilities with acceleration performance. Changes in within-individual technique and acceleration performance were measured at multiple time points across an 18-week intervention period where meaningful enhancements in acceleration were observed. This demonstrated that individual-specific technical interventions were effective in manipulating aspects of acceleration technique and performance. The outcome of these investigations provides a novel approach for practitioners working to individualise sprint-based practices.
... Despite these biomechanical differences, the running economy of runners within these two DF groups are similar (Lussiana et al., 2019), suggesting two energetically equivalent strategies at endurance running speeds. On the other hand, SF can reveal individual muscle recruitment patterns of runners and strategies to increase running speed (Dorn et al., 2012) or achieve top-end running speeds (Salo et al., 2011). Even in subgroups of individuals with similar sprint velocities, a range of SF and step length combinations are present (Hunter et al., 2004). ...
... Worth noting is the large interindividual variations in temporal variables (DF, t c , t f , and SF) reported at absolute running speeds (Lussiana et al., 2019;Ogueta-Alday et al., 2014) and the large interindividual variations in the individual strategies adopted to adapt to changes in running speeds (Forrester & Townend, 2015;Hébert-Losier et al., 2015;Salo et al., 2011). For instance, a curve-clustering approach on the footstrike angle of runners across speeds revealed three subgroups: those that maintained a rearfoot strike pattern, those that maintained a forefoot or midfoot strike pattern, and those that transitioned from a rearfoot to a less rearfoot strike pattern with increasing speed (Forrester & Townend, 2015). ...
... speeds. This agrees with previous observations that individuals adapt to running speeds differently (Forrester & Townend, 2015;Hébert-Losier et al., 2015;Salo et al., 2011), which might be linked to differences in anthropometric characteristics, age, and running training (van Oeveren et al., 2019). Performing a more detailed analysis that incorporates clustering approaches might reveal subgroups that respond similarly to changes in running speeds. ...
Full-text available
Duty factor (DF) and step frequency (SF) are key running pattern determinants. However, running patterns may change with speed if DF and SF changes are inconsistent across speeds. We examined whether the relative positioning of runners was consistent: 1) across five running speeds (10-18 km/h) for four temporal variables [DF, SF, and their subcomponents: contact (t c) and flight (t f) time]; and 2) across these four temporal variables at these five speeds. Three-dimensional whole-body kinematics were acquired from 52 runners , and deviations from the median for each variable (normalised to minimum-maximum values) were extracted. Across speeds for all variables, correlations on the relative positioning of individuals were high to very high for 2-4 km/h speed differences, and moderate to high for 6-8 km/h differences. Across variables for all speeds, correlations were low between DF-SF, very high between DF-t f , and low to high between DF-t c , SF-t c , and SF-t f. Hence, the consistency in running patterns decreased as speed differences increased, suggesting that running patterns be assessed using a range of speeds. Consistency in running patterns at a given speed was low between DF and SF, corroborating suggestions that using both variables can encapsulate the full running pattern spectrum. ARTICLE HISTORY
... Step kinematics were analyzed according to the methodology by Salo et al. [29] and independently verified by authors (DH and RVT) using video analysis software (Kinovea v0.9.5) [30] to determine average step length and step frequency across all 100-meter performances accessible on video across the season (Athlete 1, n = 6, Athlete 2, n = 8). ...
... Contradictions to these findings have also been presented [13] identifying a clear association between step frequency (group mean: 4.85 Hz) and 100-meter performance (10.16 ± 0.16 s), with lower step frequency noted in specific training blocks (4.34 Hz). It has previously been suggested that step length is more related to increased force production, whereas step frequency is associated with higher rates of force production during ground contact and leg turnover requiring greater neural adaptations [29,50], which may also be a reflection of training load and training content during the COMP phase. It could therefore be concluded, that limiting the volume of speed endurance and strength endurance leading into important competitions has maximized mechanical characteristics and step kinematics necessary to drive 100-meter performance outcomes. ...
Full-text available
Objective: This case study aimed to explore changes to sprint force-velocity characteristics across a periodized training year (45 weeks) and the influence on sprint kinematics and performance in national level 100-meter athletes. Force-velocity characteristics have been shown to differentiate between performance levels in sprint athletes, yet limited information exists describing how characteristics change across a season and impact sprint performance, therefore warranting further research. Methods: Two male national level 100-meter athletes (Athlete 1: 22 years, 1.83 m, 81.1 kg, 100 m time: 10.47 s; Athlete 2: 19 years, 1.82 cm, 75.3 kg, 100 m time: 10.81 s) completed 12 and 11 force-velocity assessments, respectively, using electronic timing gates. Sprint mechanical characteristics were derived from 30-meter maximal sprint efforts using split times (i.e., 0–10 m, 0–20 m, 0–30 m) whereas step kinematics were established from 100-meter competition performance using video analysis. Results: Between the preparation (PREP) and competition (COMP) phase, Athlete 1 showed significantly large within-athlete effects for relative maximal power (PMAX), theoretical maximal velocity (v0), maximum ratio of force (RFMAX), maximal velocity (VMAX), and split time from 0 to 20 m and 0 to 30 m (−1.70 ≤ ES ≥ 1.92, p ≤ 0.05). Athlete 2 reported significant differences with large effects for relative maximal force (F0) and RFMAX only (ES: ≤ −1.46, p ≤ 0.04). In the PREP phase, both athletes reported almost perfect correlations between F0, PMAX and 0–20 m (r = −0.99, p ≤ 0.01), however in the COMP phase, the relationships between mechanical characteristics and split times were more individual. Competition performance in the 100-meter sprint (10.64 ± 0.24 s) showed a greater reliance on step length (r ≥ −0.72, p ≤ 0.001) than step frequency to achieve faster performances. The minimal detectable change (%) across mechanical variables ranged from 1.3 to 10.0% while spatio-temporal variables were much lower, from 0.94 to 1.48%, with Athlete 1 showing a higher ‘true change’ in performance across the season compared to Athlete 2. Conclusions: The estimated sprint force-velocity data collected across a training year may provide insight to practitioners about the underpinning mechanical characteristics which affect sprint performance during specific phases of training, plus how a periodized training design may enhance sprint force-velocity characteristics and performance outcomes.
... However, it is not clear which of the two criteria is decisive for maximum sprinting velocity. Salo et al. [116] reported that in elite sport it strongly depends on the individual athlete whether he achieves a high pace by a high step frequency, a large step length or a combination of both criteria. If one now considers sprinting movements with RSPs, it is evident that step length and step frequency must play a decisive role here as well: Weyand and Bundle [131] have observed higher step frequencies for bilateral transtibial amputee sprinting, which they attribute to short swing times due to the light carbon fiber prostheses. ...
The performances of sprinters and long jumpers with below the knee amputation (BKA) have improved continuously since the development of prostheses specifically for athletic movements. In the last years, a number of athletes with BKA have attempted to compete in non-amputee competitions. Due to the specific shape and material properties of the running-specific prosthesis (RSP), concerns exist that it may give athletes an advantage over non-amputee athletes. In this work, we investigate and compare sprinting and long jump movements of athletes with and without unilateral BKA using accurate computer models. In this context, the aim of the work is to describe similarities and differences between the athletes’ movements and to show that the employed model- and optimization-based computations are useful for this purpose. We created subject-specific multi-body models for five different athletes (four non-amputee athletes, one athlete with unilateral BKA) in order to be able to investigate the different movements. Depending on the research question, the models vary in the number of degrees of freedom (DOFs), from 16 DOFs for a two-dimensional model in the sagittal plane to 31 DOFs for a three-dimensional model. For the athlete with BKA, we created a three-segment model of the RSP with one rotational DOF in the sagittal plane. The respective motion is described by a sequence of several phases, which differ by the type of ground contact. Each of these phases is described by its own set of ordinary differential equations (ODEs) or differential algebraic equations (DAEs). We use multi-phase optimal control problems (OCPs) with discontinuities to generate sprint and long jump motions. Three different formulations of OCPs are adopted in this work. (1) We formulate a least squares OCP to reconstruct the dynamics of sprint and long jump motion capture recordings of the individual athletes. (2) For the generation of realistic motions, which can be used for prediction, we formulate a synthesis OCP; this optimizes an objective function consisting of a weighted combination of chosen optimization criteria. (3) Last, in the study of sprint movements, we use an inverse optimal control problem (IOCP): this consists of an inner loop, in which a synthesis OCP is solved, and an outer loop, which adjusts the weights of the individual optimization criteria such that the distance between the inner loop solution and a reference movement becomes minimal. We have successfully applied these three optimization problem formulations to the computation of two sprint steps of three athletes without and one athlete with unilateral transtibial amputation. Here, the movements of the non-amputee athletes differ from that of the amputee athlete in a large number of variables. In particular, the athletes use different actuation strategies for running with and without a RSP. We have observed lower torques in the amputee athlete in the leg affected by the amputation than in the non-amputee control group. In contrast, significantly larger torques occurred in the joints of the upper extremity in the amputee athlete. Furthermore, the comparison has shown that the asymmetry created by the RSP is reflected throughout the body and affects the entire movement. Using the OCPs for motion reconstruction (1) and synthesis (2), we have successfully computed the last three steps of the approach and the jump of a long jump for an athlete without and an athlete with unilateral amputation. In the reconstructed solutions, the amputee athlete achieves a greater jump distance compared to the non-amputee athlete, despite a slower approach velocity, because his take-off is more efficient. In the synthesis solutions, on the other hand, the non-amputee athlete achieves the greater jump distance because he generates a greater vertical force during the take-off and achieves a better ratio of gain of vertical to loss of horizontal velocity. Finally, we have presented our idea of a simulator tool to compare the amputee athlete with himself without amputation and have demonstrated it using the sprint and long jump movements. For this purpose, we have kept the model of the athlete with unilateral transtibial amputation from the previous studies and have created a non-amputee version of the same model by mirroring the biological leg. We have selected one objective function each for sprinting and for long jump and have solved the OCP for motion synthesis (2) for both model versions. Using the differences to the solutions based on the models of two real athletes, we have highlighted the importance of the simulator tool in the evaluation of advantages and disadvantages due to the use of the RSP.
... In relation to SF, this variable can reveal individual strategies to increase running speed (Dorn et al., 2012) or achieve top-end running speeds (Salo et al., 2011). Indeed, the consistency in SF was shown to decrease as speed differences increased (tested running speeds: 10-18 km/h) (Patoz et al., 2022) and each runner was shown to self-optimize his step length over SF ratio (Hunter et al., 2017;van Oeveren et al., 2021). ...
Full-text available
Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determinants. Hence, this study aimed to investigate the association of DF and SF on 1) the vertical and fore-aft ground reaction force signals using statistical parametric mapping; 2) the force related variables (peaks, loading rates, impulses); and 3) the spring-mass characteristics of the lower limb, assessed by computing the force-length relationship and leg stiffness, for treadmill runs at several endurance running speeds. One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body three-dimensional kinematics (200 Hz) were acquired by an instrumented treadmill and optoelectronic system, respectively. Both lower DF and SF led to larger vertical and fore-aft ground reaction force fluctuations, but to a lower extent for SF than for DF. Besides, the linearity of the force-length relationship during the leg compression decreased with increasing DF or with decreasing SF but did not change during the leg decompression. These findings showed that the lower the DF and the higher the SF, the more the runner relies on the optimization of the spring-mass model, whereas the higher the DF and the lower the SF, the more the runner promotes forward propulsion.
... Jahrhunderts wurde die Schrittfrequenz als wichtigster Indikator für eine gute Sprintgeschwindigkeit eingeschätzt und eine hohe Körpergröße als Nachteil für diesen Prädiktor gesehen . Nach den Erfolgen von Bolt zeigte sich jedoch, dass eine Kombination aus Schrittfrequenz und Schrittweite, welche wiederum von der Körpergröße bedingt wird, die 100m-Sprint-Zeiten erklärt (Salo et al., 2011) und der Fokus auf beiden Prädiktoren liegen muss. ...
Die bisherige Forschungslage zur Validität von Talentidentifikationsstudien im Sport ist sehr gering. Es gibt weder ein einheitliches Konzept zur Validierung von Talentstudien noch gesicherte Befunde zur Validität der Talentprognosen. Ziel der vorliegenden Studie war es daher, einen Leitfaden für eine zielgerichtet Bewertung der Validität von Talentidentifikationen zu erstellen und anhand dieser die Validität einer Talentidentifikationskampagne, dem Fuldaer Bewegungs-Check, zu überprüfen. Insgesamt werden dafür in der vorliegenden Arbeit sieben Schritte vorgestellt, die eine ganzheitliche Bewertung der Validität der Talentidentifikation ermöglichen sollen. Neben der Validitätsbestimmung der Prädiktoren, der Stichprobe, des angesetzten Kriteriums, der Selektionsquote und der verwendeten Methoden (z.B. Neuronale Netze oder Diskriminanzanalysen), werden hierbei auch die Prognosegüte (Sensitivität, Spezifität, etc.) und ihre Nützlichkeit (RIOC, F1-Score, Youden-Index, AUC, etc.) betrachtet. Die Gesamtvalidität des Fuldaer Bewegungs-Check kann im Vergleich zu anderen Studienergebnissen abschließend mit gut bewertet werden. Das vorliegende Konzept ist ein erster Ansatz die wissenschaftliche Auswertung der Validität von Talentstudien zu vereinfachen und Studienergebnisse vergleichbar zu machen. Die Ergebnisse der verwendeten Talentkampagne sind vielversprechend und lassen darauf hindeuten, dass eine Identifikation von Leistungssportlern teilweise bereits 10 Jahre im Voraus geschehen kann. Zukünftige Studien sind jedoch notwendig, um die Befunde zu verhärten und auf größere Stichproben auszuweiten.
... As the main parameters that are decisive in the result performance of running at 100m, most authors state latent time (depends on neuromuscular activity), running speed (on the section), stride length and stride frequency. The inverse relationship between frequency and stride length has been confirmed by a 67 Health, sport, rehabilitation Здоров'я, спорт, реабілітація Здоровье, спорт, реабилитация 8(3) number of authors, depending on the body height and active weight of runners [25][26][27][28]. The length of the steps depends on the height and weight of the sprinter, and the frequency depends on the speed of neuromuscular regulation, ie the development of the CNS. ...
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Purpose. Athletic sprint runs are cyclical movements of maximum intensity. Speed, reaction time, agility and explosiveness are of special importance in sprinters. The main goal of the research is to determine the influence of Body height (BH) and Body weight (BW) with the best achieved results of in sprint disciplines (60m,100m,200m). Material and methods. In study included 40 competitors, top male sprinters (BH=180,45±6,88cm; BW=78,83±7,69kg). Their achieved best results in sprint disciplines were analyzed (60m, 100m, 200m). Pearson correlation coefficient was used to determine the relationship between body height and body weight and the results of sprint disciplines. Also a univariate model of regression analysis was applied and the relevant coefficients were calculated. The level of acceptance of statistical significance was set to p<0.05. Results. The simple regression analysis did not show a statistically significant influence of body height and body weight on the result of sprint running. Low correlations (BH vs. 100m = -0.306), (BW vs. 100m = -0.226) and (BH vs. 200m = -0.221) and insignificant correlations with an inverse relationship between results and anthropometric measures are mainly evident. Conclusion. Body height and body weight did not have a statistically significant effect on the results of the 60m sprint, while their influence is evident in the 100m, and especially in the 200m (but without statistical significance). This influence on the result of running 100 and 200m is a consequence of the exceptional motor-functional abilities of the sprinter to show greater force in the last phase of the rebound. Otherwise in the sprint, the rear rebound phase is much more important than the front rebound phase. A long step with the body weight (muscle) of the sprinter produces a higher rebound force, which with a big frequency of steps and good tecnique guarantees a good result.
... Several factors influence sprint performance in youth, including the motions of the body (i.e., kinematics) (Hunter, Marshall, & McNair, 2005;Meyers et al., 2016;Salo et al., 2011), the forces that produce, arrest or modify the motions of the body (i.e., kinetics) (Meylan et al., 2014;Read et al., 2016;Rumpf et al., 2015) and the measurements and proportions of the body (i.e., anthropometry) (Lloyd et al., 2016b;Meyers et al., 2016; 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Speed 1 Speed 2 (Girls) Speed 2 (Boys) Meyers et al., 2017b). Since the anthropometrical factors (PHV, PWV, body composition) have already been discussed in the previous sections, this section will specifically review the kinetics and kinematic associated with sprinting speed in young boys and girls across maturation. ...
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The use of spontaneous painful disease in companion pet animals has been highlighted as one of the changes that could be made to help improve translation of basic science to new therapeutics, acting as a bridge between preclinical and clinical studies, with the goal of accelerating the approval of new therapeutics. This review focuses on the utility of companion pet dogs for translational research by reviewing what outcome measures can be measured, and importantly, the relevance of these outcome measures to human translational research. It also details the practical considerations involved in incorporating companion dogs into human therapeutic development.
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Performance in sprint exercise is determined by the ability to accelerate, the magnitude of maximal velocity and the ability to maintain velocity against the onset of fatigue. These factors are strongly influenced by metabolic and anthropometric components. Improved temporal sequencing of muscle activation and/or improved fast twitch fibre recruitment may contribute to superior sprint performance. Speed of impulse transmission along the motor axon may also have implications on sprint performance. Nerve conduction velocity (NCV) has been shown to increase in response to a period of sprint training. However, it is difficult to determine if increased NCV is likely to contribute to improved sprint performance. An increase in motoneuron excitability, as measured by the Hoffman reflex (H-reflex), has been reported to produce a more powerful muscular contraction, hence maximising motoneuron excitability would be expected to benefit sprint performance. Motoneuron excitability can be raised acutely by an appropriate stimulus with obvious implications for sprint performance. However, at rest H-reflex has been reported to be lower in athletes trained for explosive events compared with endurance-trained athletes. This may be caused by the relatively high, fast twitch fibre percentage and the consequent high activation thresholds of such motor units in power-trained populations. In contrast, stretch reflexes appear to be enhanced in sprint athletes possibly because of increased muscle spindle sensitivity as a result of sprint training. With muscle in a contracted state, however, there is evidence to suggest greater reflex potentiation among both sprint and resistance-trained populations compared with controls. Again this may be indicative of the predominant types of motor units in these populations, but may also mean an enhanced reflex contribution to force production during running in sprint-trained athletes. Fatigue of neural origin both during and following sprint exercise has implications with respect to optimising training frequency and volume. Research suggests athletes are unable to maintain maximal firing frequencies for the full duration of, for example, a 100m sprint. Fatigue after a single training session may also have a neural manifestation with some athletes unable to voluntarily fully activate muscle or experiencing stretch reflex inhibition after heavy training. This may occur in conjunction with muscle damage. Research investigating the neural influences on sprint performance is limited. Further longitudinal research is necessary to improve our understanding of neural factors that contribute to training-induced improvements in sprint performance.
This study investigated the influence of heel lift interventions on the loading of the Achilles tendon for heel-toe runners. It was hypothesized that the peak Achilles tendon force and peak rate of loading would be reduced by the increase in heel lift, and that the peak Achilles tendon force would occur significantly later in stance. Achilles tendon forces were determined by calculating sagittal-plane ankle joint moments using inverse-dynamics techniques and dividing these moments by Achilles tendon moment arm lengths. Methods for estimating Achilles tendon moment arm length using skin markers were justified via MRI data for one participant. Seven participants underwent running trials under three heel lift conditions: zero, 7.5-mm, and 15-mm heel lift. Average magnitude and occurrence time of peak Achilles tendon force and peak rate of loading were determined for each condition over the 7 participants. Despite group reductions in peak Achilles tendon force and peak rate of loading for the increased heel lift conditions, statistical analysis (ANOVA) revealed no significant differences for these variables, p > 0.05. Individual participant observations highlighted varied responses to heel lift; both increases and decreases in peak Achilles tendon force were observed. For the group data, the time of peak impact force occurred significantly later in the 15-mm heel lift condition than in the zero heel lift, p < 0.05. It is suggested that the success of increased heel lift in treating Achilles tendon injury may be due to a later occurrence of peak Achilles tendon force in response to this intervention, reducing Achilles tendon average rate of loading. In addition, the individuality of Achilles tendon peak force changes with heel lift intervention highlights the need for individual participant analysis.
The effects of running at supramaximal velocity on biomechanical variables were studied in 13 male and 9 female sprinters. Cinematographical analysis was employed to investigate the biomechanics of the running technique. In supramaximal running the velocity increased by 8.5%, stride rate by 1.7%, and stride length by 6.8% over that of the normal maximal running. The elite male sprinters increased their stride rate significantly but did not increase their stride length. The major biomechanical differences between supramaximal and maximal running occurred during the contact phase. In supramaximal running the inclination of the ground shank at the beginning of eccentric phase was more "braking" and the angle of the ground knee was greater. During the ground contact phase, the maximal horizontal velocity of the swinging thigh was faster. The duration of the contact phase was shorter and the flight phase was longer in the supramaximal run as compared to the maximal run. It was concluded that in supramaximal effort it is possible to run at a higher stride rate than in maximal running. Data suggest that supramaximal sprinting can be beneficial in preparing for competition and as an additional stimulus for the neuromuscular system during training. This may result in adaptation of the neuromuscular system to a higher performance level.
Selected kinematic variables in the performance of the Gold and Silver medalists and the eighth-place finisher in the men's 200-meter sprint final at the 1984 Summer Olympic Games were investigated. Cinematographic records were obtained for all track running events at the Games, with the 200-meter performers singled out for initial analysis. In this race, sagittal view filming records (100 fps) were collected at the middle (125-meter mark) and end (180-meter mark) of the performance. Computer-generated analysis variables included both direct performance variables (body velocity, stride rate, etc.) and upper and lower body kinematics (upper arm position, lower leg velocity, etc.) that have previously been utilized in the analysis of elite athlete sprinters. The difference in place finish was related to the performance variables body horizontal velocity (direct), stride rate (direct), and support time (indirect). The critical body kinematics variables related to success included upper leg angle at takeoff (indirect), upper leg velocity during support (direct), lower leg velocity at touchdown (direct), foot to body touchdown distance (indirect), and relative foot velocity at touchdown.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.