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Is the rearfoot pattern the most frequently foot strike pattern among
Physical Therapy in Sport 16 (2015) 29e33
Contents lists available at ScienceDirect
Physical Therapy in Sport
journal homepage: www.elsevier.com/ptsp
Original research
Is the rearfoot pattern the most frequently foot strike pattern among
recreational shod distance runners?
Matheus Oliveira de Almeida a, b, *, Bruno Tirotti Saragiotto a, b, Tiê Parma Yamato a, b,
Alexandre Dias Lopes a, b
a
b
Masters Program in Physiotherapy, Universidade Cidade de São Paulo (UNICID), Rua Cesário Galeno 448, Tatuapé, CEP 03071-000, São Paulo, SP, Brazil
São Paulo Running Injury Group (SPRunIG), São Paulo, Brazil
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 10 June 2013
Received in revised form
5 February 2014
Accepted 13 February 2014
Objective: To determine the distribution of the foot strike patterns among recreational shod runners and
to compare the personal and training characteristics between runners with different foot strike patterns.
Design: Cross-sectional study.
Setting: Areas of running practice in São Paulo, Brazil.
Participants: 514 recreational shod runners older than 18 years and free of injury.
Outcomes measures: Foot strike patterns were evaluated with a high-speed camera (250 Hz) and photocells to assess the running speed of participants. Personal and training characteristics were collected
through a questionnaire.
Results: The inter-rater reliability of the visual foot strike pattern classification method was 96.7% and
intra-rater reliability was 98.9%. 95.1% (n ¼ 489) of the participants were rearfoot strikers, 4.1% (n ¼ 21)
were midfoot strikers, and four runners (0.8%) were forefoot strikers. There were no significant differences between strike patterns for personal and training characteristics.
Conclusion: This is the first study to demonstrate that almost all recreational shod runners were rearfoot
strikers. The visual method of evaluation seems to be a reliable and feasible option to classify foot strike
pattern.
Ó 2014 Elsevier Ltd. All rights reserved.
Keywords:
Running
Jogging
Biomechanics
Sports
1. Introduction
Foot strike patterns during running have been the subject of
investigation for the past three decades (Cavanagh & Lafortune,
1980; Kerr, Beauchamp, Fikhkr, & Neil, 1983); however comparisons between studies are difficult because different classification
criteria exist (Lieberman, 2012). Conventionally, foot strike patterns
are defined by the part of the foot that first contacts the running
surface and this is typically divided into three categories: (1)
rearfoot strike, when the runner lands with the heel; (2) midfoot
strike, when the runner simultaneously lands with the heel and ball
of the foot; and (3) forefoot strike, when the ball of the foot lands
first (Fellin, Rose, Royer, & Davis, 2010; Lieberman et al., 2010).
Several factors can influence the adoption of foot strike patterns
during running. The use of running shoes with cushioning on the
* Corresponding author. Universidade Cidade de São Paulo, Rua Cesário Galeno
448, Tatuapé, São Paulo, SP, CEP 03071-000, Brazil. Tel.: þ55 11 982737246.
E-mail address: [email protected] (M.O. de Almeida).
http://dx.doi.org/10.1016/j.ptsp.2014.02.005
1466-853X/Ó 2014 Elsevier Ltd. All rights reserved.
posterior portion of the shoe may induce a rearfoot pattern
(Lieberman et al., 2010; Squadrone & Gallozzi, 2009), while running
speed and instructions from coaching staff could also influence the
pattern adopted by runners (Giandolini et al., 2013; Nilsson &
Thorstensson, 1989).
The adoption of certain foot strike strategies has received special
attention because how the foot lands during running can lead to
different kinematic and kinetic characteristics of the lower extremities (Goss & Gross, 2012b). The rearfoot pattern is characterized by a rapid, high impact peak in the ground reaction force after
the initial contact with the ground (Cavanagh & Lafortune, 1980;
Laughton, McClay Davis, & Hamill, 2003; Lieberman et al., 2010),
generating high vertical loading rates (Arendse, Noakes, Azevedo,
Romanov, Schwellnus, & Fletcher, 2004; Shih, Lin, & Shiang, 2013;
Williams, McClay, & Manal, 2000). On the other hand, forefoot
and midfoot patterns do not demonstrate a marked impact peak,
and the vertical forces are transmitted to the lower extremities in a
more spread out way, attenuating the overload that occurs when
the foot collides with the ground through eccentric control of the
triceps surae (Cavanagh & Lafortune, 1980; Lieberman et al., 2010).
30
M.O. de Almeida et al. / Physical Therapy in Sport 16 (2015) 29e33
It is believed that high vertical loading rates transmitted to the
lower limbs during running may contribute to the high incidence of
specifically running-related injuries (Pohl, Hamill, & Davis, 2009;
Zadpoor & Nikooyan, 2011). Because of that, rearfoot strikers may
be more susceptible to injuries characterized by overloads in the
lower extremities, such as tibial stress fractures, plantar fasciitis,
and anterior knee pain (Davis, Bowser, & Mullineaux, 2010; Pohl
et al., 2009; Zadpoor & Nikooyan, 2011). In contrast, forefoot
strikers, due to the increased eccentric activity of the triceps surae
and high demand at the foot and ankle (Williams, Green, &
Wurzinger, 2012; Williams et al., 2000), may be more at risk of
developing Achilles tendinopathy and calf muscle injuries. Unfortunately, investigation of injury rates comparison between foot
strike patterns is scarce in the literature. Only two retrospective
studies examined and found significantly higher rates of injury in
rearfoot strikers (Daoud, Geissler, Wang, Saretsky, Daoud, &
Lieberman, 2012; Goss & Gross, 2012a).
Some studies (Hasegawa, Yamauchi, & Kraemer, 2007; Kerr
et al., 1983; Larson et al., 2011) evaluated the distribution of the
foot strike patterns among shod runners at different levels (elite,
sub-elite and recreational) and found that the rearfoot pattern is
the most common. However, important differences in the distribution of patterns were found, principally when elite runners are
compared with recreational runners, suggesting that runners at
different levels may demonstrate different foot strike patterns.
Furthermore, all these studies were conducted during a competitive race setting and foot strike patterns were determined through
the analysis of only a single stride. These factors may limit their
findings of foot strike pattern distribution only to the conditions
cited above. The previous studies (Hasegawa et al., 2007; Kerr et al.,
1983; Larson et al., 2011) also did not account for running speed
during data acquisition or other personal characteristics that may
influence strike patterns.
While the available literature highlights the possible effects of
running pattern on injury risk, the knowledge of foot strike pattern
distribution among recreational runners is an important first step
for clinicians and researchers that deal with running injuries.
Therefore, the main objective of this study was to determine the
distribution of the foot strike patterns among recreational shod
runners not in race conditions. We also aimed to compare the
personal and training characteristics between runners with
different foot strike patterns.
2. Methods
A convenience sample of 514 runners was used in this crosssectional study. The participants were recruited in areas of high
runner traffic (such as public parks) in São Paulo, Brazil. Participants were eligible for inclusion if they were recreational runners
at least 18 years old and free of musculoskeletal injury at the time of
data acquisition. Participants were evaluated in shod conditions
(conventional running shoes).
Hereafter, the participants were instructed to run two 25 m laps
on the track and run back to the start. A high-speed digital camera
(Casio EXFX1) recording at a frequency of 250 Hz was located
perpendicular to the runway at 12.5 m from the starting point. The
camera was positioned on a tripod 15 cm above the floor and 2 m
away from the runway (Fig. 1). The participants were instructed to
run at a comfortable speed that was measured by a photocell timing
mode (TC-Timing System). All data acquisition was done by three
researchers.
2.2. Image analysis
The videos with foot strike images were captured and subsequently analyzed visually in a subjective way using a video analysis
program (KinoveaÔ 0.8.15). Each video was evaluated independently by two observers. In instances where there was disagreement among the observers, a third one was used. The foot strike
pattern for each participant was analyzed from the lateral view of
the foot and evaluated four times; two times on each foot. When
the same runner demonstrated different patterns, the most
frequently chosen pattern was used in the analysis. Foot strike
patterns were classified in three categories (Lieberman et al., 2010):
(1) rearfoot, when the heel is the first region to contact the ground
(Fig. 2a); (2) midfoot, when the heel and ball of the foot simultaneously contact the ground (Fig. 2b); and (3) forefoot, when the ball
of the foot contacts the ground prior to the heel (Fig. 2c).
2.3. Statistical analysis
Descriptive analyses were used to evaluate the relationship
between foot strike patterns and the participants’ characteristics.
Normality of continuous data (age, weight, body mass index,
weekly distance, years of practice, average speed and Hshoes) was
evaluated using curve symmetry analysis. Data with normal distribution were described in mean and standard deviation. Median
and interquartile ranges were used for non-normally distributed
data.
A KruskaleWallis test was conducted to compare the personal
and training characteristics among rearfoot, midfoot and forefoot
patterns. For all analyses, p < 0.05 was assumed to be statistically
significant. Intra and inter-rater reliabilities of the evaluation of the
foot strike pattern from participants were evaluated using the
agreement percentage. All analyses were performed in SPSS 17.0.
2.4. Sample size
The sample size of this study was estimated based on a pilot
study (Uribe, Almeida, Hespanhol Junior, & Lopes, in press) that
2.1. Data collection
The 514 shod runners completed a questionnaire that contained
demographic information (gender, age, weight and height) and
running routine (predominant type of surface, special insoles,
weekly distance, coach instruction and years of experience). Participants were also asked to identify any previous musculoskeletal
injuries related to running in the last 12 months. The injury definition adopted was: “any musculoskeletal running related injury
that was severe enough to prevent running practice during the last
12 months”.
Fig. 1. Schematic representation of the area for image acquisition.
M.O. de Almeida et al. / Physical Therapy in Sport 16 (2015) 29e33
31
Fig. 2. Foot strike patterns during running (2a e Rearfoot pattern; 2b e Midfoot pattern; 2c e Forefoot pattern).
determined that 95% of participants would be rearfoot strikers.
Statistical precision was assumed to be 2.5% with a significance
level of 0.01, which revealed a need for 506 runners. In this study,
514 runners were included to account for potential withdrawal or
technical problems with data collection and video analysis.
3. Results
Our sample was comprised of mostly males (68.9%, n ¼ 354),
with a mean age of 41.7 (SD ¼ 12.2) years and a weekly running
distance of 24.8 km (SD ¼ 20.7). The average speed from our sample
during data collection was 12.2 km/h (SD ¼ 2.4). All the runners’
characteristics are detailed in Table 1.
The inter-rater reliability of the visual foot strike pattern evaluation method was 96.7%, while the intra-rater reliability was
98.9%. None of the participants demonstrated an equal number of
patterns (e.g., rearfoot twice and midfoot twice). 489 (95.1%) of the
runners were rearfoot strikers, 21 (4.1%) were midfoot strikers, and
only four runners (0.8%) were forefoot strikers (Table 2). The rate of
the midfoot pattern was higher in male runners (5.1%, n ¼ 18) than
in female runners (1.9%, n ¼ 3). There was no significant difference
for the average speed when the characteristics between foot strike
patterns were compared. The average speed was 12.2 km/h
(SD ¼ 2.3) in rearfoot strikers, 12.9 km/h (SD ¼ 3.3) in midfoot
strikers and 12.9 km/h (SD ¼ 8.8) in forefoot strikers. There were no
statistical differences among the three patterns for the other personal and training characteristics (Table 3).
4. Discussion
This is the first study to evaluate foot strike patterns among
recreational shod runners not in race conditions. We found that
Table 1
Characteristics of the recreational runners included in the study.
Gender
Male
Female
Age (years)
Weigth (kg)
Heigth (m)
BMI (kg/cm2)
Average Speed
(km/h)
Km/week
Time of practice
(years)*
Previous injury
Yes
No
68.9% (354)
31.1% (160)
41.7 (12.2)
71.6 (12.7)
1.71 (0.09)
24.4 (3.0)
12.2 (2.4)
24.8 (20.7)
5 (9)
Insoles
Yes
No
Coach instruction
Yes
No
Surface
Hard
14% (72)
86% (442)
47.1% (242)
52.9% (272)
83.3% (428)
Sand
Grass
6.4% (33)
0.6% (3)
Treadmill
9.7% (50)
nearly all of these recreational runners used a rearfoot pattern
(95.1%) and only four runners (0.8%) were forefoot strikers. There
were more male midfoot strikers compared with female runners.
No significant differences were noted between rearfoot, midfoot
and forefoot patterns for personal and training characteristics.
In this study, we found a greater number of rearfoot strikers
compared to previous studies. Larson et al. (2011), Kerr et al. (1983)
and Hasegawa et al. (2007) found, respectively, that 88%, 80% and
75% of their participants were rearfoot strikers. These differences
may be attributed to the subject sample and lower self-selected
running speed. While we evaluated recreational runners who ran
at an average of 12.2 km/h, Hasegawa et al. (2007) evaluated elite
marathoners who ran between 17.7 and 19.6 km/h. Similarly, Kerr
et al. (1983) used a sample of competitive runners who ran between 12.4 and 19.9 km/h. In the study by Larson et al. (2011),
participants ran at a slower average pace of 11 km/h, which may
explain the higher rate of rearfoot strikers. This was similar to our
findings. The higher percentage of rearfoot strikers in our study and
in Larson et al. (2011) confirms that runners at different levels use
different foot strike patterns.
It is also possible that different methodologies of image acquisition contributed to a lower incidence of rearfoot strikers in the
previous studies. It has been shown that the accuracy of foot strike
classification is directly related to the image acquisition frequency
(Fellin et al., 2010). Previous studies that used video acquisition to
determine strike pattern used cameras with video capture rates of
60 Hz (Kerr et al., 1983) and 120 Hz (Hasegawa et al., 2007). When
image acquisition frequency is low, the exact moment of initial foot
contact may not be discernible. Thus, the foot strike pattern may be
erroneously classified; in particular, those who may have been
rearfoot strikers may be identified as midfoot strikers. The method
used to classify foot strike pattern in this study demonstrated an
inter-rater reliability and intra-rater reliability of 96.7% and 98.9%,
respectively. So, it is possible to conclude that the subjective visual
method of foot strike pattern classification is a reliable method,
besides being a cheap and feasible way in comparison to methods
used in other studies (Altman & Davis, 2012).
There was a higher percentage of female rearfoot strikers runners compared to male runners. Similarly, there was a higher percentage of male midfoot strikers than female midfoot strikers,
which is consistent with the results from Hasegawa et al. (2007).
Despite this difference in foot strike patterns between genders,
Table 2
Foot strike patterns of recreational runners.
33.1% (170)
66.9% (344)
Continuous data with normal distribution are expressed in mean and standard
deviation. Continuous data with non-normal distribution are expressed in median
and interquartile range (Time of practice*). Categorical data are expressed in percentage and number of runners.
Rearfoot
Midfoot
Forefoot
Total
n ¼ 514
Males
n ¼ 354
Females
n ¼ 160
95.1 (489)
4.1 (21)
0.8 (4)
94.1 (333)
5.1 (18)
0.8 (3)
97.5 (156)
1.9 (3)
0.6 (1)
All data were expressed in percentage and numbers of runners.
32
M.O. de Almeida et al. / Physical Therapy in Sport 16 (2015) 29e33
Table 3
Comparison of the recreational runners’ characteristics between foot strike patterns.
Gender
Male
Female
Age (years)
Weigth (kg)
Heigth (m)
BMI (Kg/cm2)
Average speed (km/h)
Distance (km/week)
Experience (years)a
Previous injury
Yes
No
Insoles
Yes
No
Coach instruction
Yes
No
Surface
Hard
Sand
Grass
Treadmill
Rearfoot
n ¼ 489
Midfoot
n ¼ 21
Forefoot
n¼4
68.1% (333)
31.9% (156)
41.7 (12)
71.6 (12.7)
1.70 (0.1)
24.4 (2.3)
12.2 (2.3)
24.4 (20.7)
5 (9)
85.6% (18)
14.4% (3)
40.9 (14.9)
73.4 (13.9)
1.74 (0.1)
24 (3)
12.9 (3.3)
28.4 (17.9)
5 (9)
75% (3)
25% (1)
39.5 (48)a
63.5 (17.1)a
1.70 (0.1)a
21.4 (3.5)a
12.9 (8.8)a
37.5 (66)a
15 (36)
33.1% (162)
66.9% (327)
28.6% (6)
71.4% (15)
50% (2)
50% (2)
14.3% (70)
85.7% (419)
0% (0)
100% (21)
50% (2)
50% (2)
46.4% (227)
53.6% (262)
57.1% (12)
42.9% (9)
75% (3)
25% (1)
83.2% (407)
6.3% (31)
0.4% (2)
10% (49)
85.6% (18)
4.8% (1)
4.8% (1)
4.8% (1)
75% (3)
25% (1)
0% (0)
0% (0)
p
0.22
0.90
0.32
0.26
0.10
0.52
0.09
0.26
0.70
0.06
0.33
0.90
Continuous data with normal distribution are expressed in mean and standard
deviation.
Categorical data are expressed in percentage and number of runners.
a
Continuous data with non-normal distribution are expressed in median and
interquartile range.
researchers who evaluated biomechanical variables found no significant differences between genders for kinematic variables at the
ankle, foot joint or the knee joint during the foot’s initial contact
during running (Chumanov, Wall-Scheffler, & Heiderscheit, 2008;
Ferber, Davis, & Williams, 2003). However, the influence of foot
strike pattern was not specifically evaluated in those previous
studies.
Interestingly, we found no significant differences in personal or
training characteristics among the different foot strike patterns.
Despite the low number of runners with midfoot and forefoot
patterns that limited the comparative analyses, we expected to find
significant differences between patterns, in particular, related to
running speed and previous injuries. We believed that speed would
be slower in rearfoot strikers as compared to midfoot and forefoot
runners, as found in previous studies (Hasegawa et al., 2007; Kerr
et al., 1983), but the difference was not significant. We think a
threshold of speed may exist to differentiate rearfoot strikers who
are normally slower than midfoot and forefoot runners.
Regarding previous injuries, 33% of the participants reported
previous injuries in the last 12 months, but the difference was also
not significant among the different foot strike patterns. Although
the possible effects of foot strike patterns on injury development
has been not established in the literature, this rate of previous
injury may be attributed to the fact that almost all of the participants in our study were rearfoot strikers. Because a rearfoot pattern
is characterized by increased vertical loading rates, as compared to
forefoot and midfoot patterns (Arendse et al., 2004; Shih et al.,
2013; Williams et al., 2000), rearfoot strikers may be more susceptible to developing running injuries that are commonly related
to overuse in the lower extremities (Hreljac, 2005).
Despite a large sample size and high frequency of image
acquisition, this study does have some limitations. Firstly, despite
the subjective visual method of foot strike pattern classification
being a reliable and feasible method, it has limited ability to
accurately classify runners with the midfoot pattern (Altman &
Davis, 2012). Maybe the use of automatic foot strike detection algorithms could be an alternative method to avoid bias on foot strike
classification (De Witt, 2010; Hreljac & Marshall, 2000). Secondly,
the time at which runners participated in this study was not
standardized and thus occurred either before or after a recreational
run. It is possible that fatigue may have affected the running
pattern in these individuals, although the effect of fatigue on strike
pattern preference has not been evaluated. Thirdly, we did not ask
the participants about the length of time that they had been free of
injury. We also did not attempt to measure the typical training pace
of the participants which would have allowed us to confirm if the
speed collected during data acquisition was truly representative of
the participants’ typical pace.
Another limitation of this study was that we did not evaluate the
stride length and step frequency of participants, although there is
no consensus in the literature as to whether there are significant
differences among the different foot strike patterns regarding these
aspects (Ardigo, Lafortuna, Minetti, Mognoni, & Saibene, 1995;
Arendse et al., 2004; Squadrone & Gallozzi, 2009). Lastly, our
study lacks additional comparative analysis between foot strike
patterns due to the low number of runners from the midfoot and
forefoot groups, preventing any inference from such analysis.
The results of the present study are important because they
demonstrated for the first time that the great majority of recreational shod runners, when evaluated not in race conditions, are
rearfoot strikers. The knowledge that the rearfoot pattern, the most
common, is important for clinicians who that should be aware
about the biomechanical characteristics of this pattern and its implications on running practice. Future studies should elucidate the
possible relationship between foot strike pattern and injuries in
recreational runners through studies with longitudinal design.
Conclusions
This is the first study done not in race conditions to show that
almost all recreational runners are rearfoot strikers. There were no
significant differences in personal or training characteristics between the three foot strike patterns. The visual method of evaluation seems to be a reliable and feasible option to classify foot strike
pattern.
Conflict of Interest
None declared.
Ethical Approval
This study was approved in April 2011 by the Ethics Committee
of the University of the City of São Paulo.
Funding
None declared.
Acknowledgments
The authors acknowledge Raphael Pereira, Luiz Hespanhol Júnior and Thais Lyrio for their help with this paper.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.ptsp.2014.02.005
M.O. de Almeida et al. / Physical Therapy in Sport 16 (2015) 29e33
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