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Non‑South East Asians have 
a better running economy 
and different anthropometrics 
and biomechanics than South East 
Asians
Aurélien Patoz1,2*, Thibault Lussiana2,3,4, Bastiaan Breine2,5, Cyrille Gindre2,3, 
Laurent Mourot4 & Kim Hébert‑Losier6,7
Running biomechanics and ethnicity can influence running economy (RE), which is a critical factor 
of running performance. Our aim was to compare RE of South East Asian (SEA) and non‑South East 
Asian (non‑SEA) runners at several endurance running speeds (10–14 km/h) matched for on‑road 
racing performance and sex. Secondly, we explored anthropometric characteristics and relationships 
between RE and anthropometric and biomechanical variables. SEA were 6% less economical 
(p = 0.04) than non‑SEA. SEA were lighter and shorter than non‑SEA, and had lower body mass 
indexes and leg lengths (p ≤ 0.01). In terms of biomechanics, a higher prevalence of forefoot strikers 
in SEA than non‑SEA was seen at each speed tested (p ≤ 0.04). Furthermore, SEA had a significantly 
higher step frequency (p = 0.02), shorter contact time (p = 0.04), smaller footstrike angle (p < 0.001), 
and less knee extension at toe‑off (p = 0.03) than non‑SEA. Amongst these variables, only mass 
was positively correlated to RE for both SEA (12 km/h) and non‑SEA (all speeds); step frequency, 
negatively correlated to RE for both SEA (10 km/h) and non‑SEA (12 km/h); and contact time, 
positively correlated to RE for SEA (12 km/h). Despite the observed anthropometric and biomechanical 
differences between cohorts, these data were limited in underpinning the observed RE differences at 
a group level. This exploratory study provides preliminary indications of potential differences between 
SEA and non‑SEA runners warranting further consideration. Altogether, these findings suggest 
caution when generalizing from non‑SEA running studies to SEA runners.
Running economy (RE), which refers to steady-state oxygen consumption at a given submaximal running speed, 
is a critical factor of running  performance1. RE has been shown to differ between ethnic  groups2–5. Indeed, 
Weston, et al.2 noted greater RE in African than Caucasian distance runners though not elucidating the origin 
of these differences. Similarly, elite Kenyans were found more economical than their Caucasian  counterparts3–5. 
This difference was attributed to body dimensions, with longer legs (~ 5%), thinner and lighter calf musculature, 
as well as lower body mass and body mass index (BMI) in Kenyans than Caucasians, but not to differences in 
muscle fibre  type3–6. These findings may partially explain the success of African runners at the elite level. Indeed, 
the longer, slenderer legs of Kenyans could be advantageous when running as RE is correlated with leg  mass6. 
However, the precise mechanisms underpinning anthropometric and economy relationships are not  clear7.
OPEN
1Institute of Sport Sciences, University of Lausanne, 1015 Lausanne, Switzerland. 2Research and Development 
Department, Volodalen Swiss Sport Lab, 1860 Aigle, Switzerland. 3Research and Development Department, 
Volodalen, 39270 Chavéria, France. 4Research Unit EA3920 Prognostic Markers and Regulatory Factors of 
Cardiovascular Diseases and Exercise Performance, Health, Innovation Platform, University of Bourgogne 
Franche-Comté, 2500 Besançon, France. 5Department of Movement and Sports Sciences, Ghent University, 
9000 Ghent, Belgium. 6Division of Health, Engineering, Computing and Science, Te Huataki Waiora School of 
Health, Adams Centre for High Performance, University of Waikato, Tauranga 3116, New Zealand. 7Department 
of Sports Science, National Sports Institute of Malaysia, 57000 Kuala Lumpur, Malaysia. *email: aurelien.patoz@
unil.ch
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Research into running and ethnic differences has mostly compared Caucasian and African  runners2–5,8–13. 
These studies highlight differences in  physiological2–5,12,  anthropometrical9,14,  neuromuscular15, and running 
gait  patterns8,10,11 between ethnicities. Altogether, these results indicate caution in the generalization of results 
from one ethnic group to another.
There exists only limited inclusion of Asian cohorts in running  studies14–16 and, to the best of our knowledge, 
no study comparing their RE to another ethnic group. Nonetheless, road race participation continues to grow in 
Asia despite a decline in the number of participants since 2016 outside of  Asia17. Therefore, the relative underrep-
resentation of Asian runners in research is of concern, especially when considering their unique anthropometric 
 features18,19, autonomic responses to  exercise20, muscle–tendon unit  properties15, walking gait  characteristics21, 
and footstrike  patterns16 compared to other ethnic groups.
Although running biomechanics can influence  RE1, the relationships between select biomechanical vari-
ables and RE are unclear and even conflicting in the scientific literature. For instance, Gruber, et al.22 observed 
no difference in RE between rearfoot (RFS) and non-rearfoot (non-RFS) strike patterns, while both  RFS23 and 
non-RFS24 patterns were suggested as more economical than the other. Similarly, superior RE has been linked 
with both  long25 and  short26 ground contact times (tc), while Williams and  Cavanagh27 found no significant 
relation between RE and tc. These divergent findings might be due to differences between the cohorts examined, 
including ethnic differences.
For these reasons, our primary aim was to explore whether South East Asian (SEA) and non-South East Asian 
(non-SEA) runners demonstrate similar RE at several endurance running speeds when matched for on-road 
running performance and sex. Secondly, we aimed to explore anthropometric differences between groups and 
potential relationships between RE and anthropometric and biomechanical variables in these groups.
Materials and methods
Participants. 
An existing database of 54 runners was explored to match SEA and non-SEA runners based 
on sex and on-road running performance on 21.1  km28. The matching led to the inclusion of 34 trained run-
ners, 20 males (variable: mean ± standard deviation, age: 36 ± 6 years, mass: 68 ± 11 kg, height: 176 ± 7 cm, leg 
length: 92 ± 5 cm, BMI: 22 ± 2 kg/m2, running distance: 56 ± 20 km/week, running experience: 9 ± 7 y, and best 
half-marathon time: 93 ± 9 min) and 14 females (age: 36 ± 6 y, mass: 53 ± 6 kg, height: 162 ± 4 cm, leg length: 
84 ± 3 cm, BMI: 20 ± 2 kg/m2, running distance: 58 ± 17 km/week, running experience: 7 ± 5 years, and best half-
marathon time: 100 ± 9 min) in this study. For study inclusion, participants were required to be in good self-
reported general health with no current or recent (< 3 months) musculoskeletal injuries and to meet a certain 
level of running performance. More specifically, runners were required to have competed in a road race in the 
last year with finishing times of ≤ 50 min for 10 km, ≤ 1 h 50 min for 21.1 km or ≤ 3 h 50 min for 42.2 km. The 
ethical committee of the National Sports Institute of Malaysia approved the study protocol prior to participant 
recruitment (ISNRP: 26/2015), which was conducted in accordance with international ethical  standards29 and 
adhered to the latest Declaration of Helsinki of the World Medical Association.
Runners were classified in two ethnic groups based on their nationality: SEA and non-SEA, which led to a 
total of 17 participants per group. SEA runners were from China (n = 12), Malaysia (n = 14), and Indonesia (n = 1); 
while non-SEA runners were from England (n = 7), Sweden (n = 2), Australia, Brazil, Canada, Denmark, France, 
Norway, Poland, and Scotland (n = 1 each). All non-SEA runners identified as “white”.
Experimental procedure. 
Each participant completed one experimental laboratory session. After provid-
ing written informed consent, the right leg length of participants was measured (from anterior superior iliac 
spine to medial malleolus in supine). Participants then ran 5 min at 9 km/h on a treadmill (h/p/cosmos mer-
cury®, h/p/cosmos sports & medical gmbh, Nussdorf-Traunstein, Germany) as a warm-up. Participants then 
completed 3 × 4-min runs at 10, 12, and 14 km/h (with 2-min recovery periods between runs) on the tread-
mill, during which time RE was assessed. Retro-reflective markers were subsequently positioned on individuals 
(described in Data Collection section) to assess running kinematics. For each participant, a 1-s static calibration 
trial was recorded, which was followed by 3 × 30-s runs at 10, 12, and 14 km/h (with 1-min recovery periods 
between each runs) to collect three-dimensional (3D) kinematic data in the last 10-s segment of these runs 
(30 ± 2 running steps), resulting in at least 25 steps being  analysed30. RE and biomechanics were assessed sepa-
rately given laboratory constraints and interference with data quality (e.g., presence of testing equipment that 
occluded markers). All participants were familiar with running on a treadmill as part of their usual training 
programs and wore their habitual running shoes during testing.
Data collection. 
Gas exchange was measured using TrueOne 2400 (ParvoMedics, Sandy, UT, USA) during 
the 3 × 4-min runs. Prior to the experiment, the gas analyzer was calibrated using ambient air  (O2: 20.93% and 
 CO2: 0.03%) and a gas mixture of known concentration  (O2: 16.00% and  CO2: 4.001%). Volume calibration was 
performed at different flow rates with a 3 L calibration syringe (5530 series, Hans Rudolph, Shawnee, KS, USA). 
Oxygen consumption ( ˙VO2 ), carbon dioxide production ( ˙VCO2 ), and respiratory exchange ratio (RER) values 
were averaged over the last minute of each 4-min run. Steady state was confirmed through visual inspection of 
the ˙VO2 and ˙VCO2 curves for all running trials. RER had to remain below unity during the trials for data to be 
included in the analysis, otherwise the corresponding data were excluded as deemed to not represent a submaxi-
mal effort. No trial was excluded on this basis. RE was expressed as the oxygen cost per mass to the power of 
0.75 per kilometer (ml/kg0.75/km) to minimize the influence of body mass per se on ˙VO2 during  running31. RE 
expressed in ml/kg/km was also computed for reference and is provided as supplementary materials. A higher 
RE value indicates a less economical runner.
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3D kinematic data were collected at 200 Hz using seven infrared Oqus cameras (five Oqus 300+, one Oqus 
310+, and one Oqus 311+) and Qualisys Track Manager software version 2.1.1 build 2902 together with the Pro-
ject Automation Framework Running package version 4.4 (Qualisys AB, Göteborg, Sweden). A virtual laboratory 
coordinate system was generated such that the x–y–z axes denoted the medio-lateral (pointing towards the right 
side of the body), posterior-anterior, and inferior-superior directions, respectively. Thirty-five retro-reflective 
markers (Fig. 1) of 12 mm in diameter were used for static calibration and running trials, and were affixed to 
the skin and shoes of individuals over anatomical landmarks using double-sided tape following standard guide-
lines from the Project Automation Framework Running  package32. The 3D marker data were exported in .c3d 
format and processed in Visual3D Professional software version 5.02.25 (C-Motion Inc., Germantown, MD, 
USA). More explicitly, the 3D marker data were interpolated using a third-order polynomial least-square fit 
algorithm, allowing a maximum of 20 frames for gap filling, and subsequently low-pass filtered at 20 Hz using 
a fourth-order Butterworth filter.
Biomechanical variables. 
From the marker set, a full-body biomechanical model with six degrees of free-
dom at each joint and 15 rigid segments was constructed. The model included the head, upper arms, lower 
arms, hands, thorax, pelvis, thighs, shanks, and feet. Segments were assigned inertial properties and centre 
of mass (COM) locations based on their  shape33 and attributed relative mass based on standard regression 
 equations34. Kinematic variables were calculated using rigid-body analysis and whole-body COM location was 
calculated from the parameters of all 15 segments. Ankle ( θankle ) and knee ( θknee ) joint angles were defined 
as the orientation of the distal segment relative to the proximal  one35. Angles were computed using an x–y–z 
Cardan  sequence36,37 equivalent to the joint coordinate  system36,38, leading to rotations with functional and ana-
tomical meaning (flexion–extension, abduction–adduction, and internal–external rotation). Noteworthy, only 
the flexion–extension Cardan angle was considered for analysis due to possible errors linked with kinematic 
 crosstalk39–41. Joint angles were calculated at footstrike and toe-off events. Footstrike angle (FSA) was calculated 
following the procedure described in Altman and  Davis42. FSA was normalized by taking the angle of the foot 
at footstrike and subtracting the angle of the foot during standing trial. The mean FSA was used to categorise 
footstrike patterns of runners in two categories: RFS when the FSA was greater than 8°, and non-RFS when 8° 
or  less42. Among all running trials, 5% and 7% were borderline (within 1°) RFS and non-RFS, respectively. These 
borderline footstrike patterns were only present in SEA runners. Visual inspection confirmed the footstrike pat-
tern classifications were correct.
Running events were derived from the trajectories of the 3D marker data using similar procedures to those 
previously reported 43,44. All events were verified to ensure correct identification and were manually adjusted 
when required.
Swing time (ts) and tc were defined as the time from toe-off to footstrike and from footstrike to toe-off of the 
same foot, respectively. Flight time (tf) was defined as the time from toe-off to footstrike of the contralateral foot. 
Step frequency (SF) was calculated as SF =
1
tc+tf  , and step length (SL) as SL = s/SF , where s represents running 
speed. In addition to raw units, SL was expressed as a percentage of participant’s leg length. The spring-mass 
characteristics of the lower limb were estimated using a sine-wave model following the procedure defined by 
Morin, et al.45. More explicitly, leg stiffness (kleg) was calculated as [Eq. (1)]
Figure 1.  Retro-reflective markers (N = 35) placed on anatomical landmarks of participants for biomechanical 
data collection. R and L at the start of the acronyms denote right and left, respectively.
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where Fz,max represents the maximal vertical force and was estimated using Fz,max = mg π
2
 tf
tc + 1

 , L is the 
maximal leg length deformation, i.e., the leg spring compression and given by L =

z2
COM,FS + s2t2
b − zCOM,MS , 
where s defines running speed, tb denotes the braking time, i.e., the time from footstrike to mid-stance, and 
 zCOM,FS and  zCOM,MS are the COM heights at footstrike and mid-stance, respectively. For all biomechanical meas-
ures, the values extracted from the 10-s data collection for each participant were averaged for subsequent statisti-
cal analyses.
Statistical analysis. 
Descriptive statistics are presented using mean ± standard deviation (SD). Data nor-
mality and homogeneity of variances were verified using Kolmogorov–Smirnov and Levene’s test, respectively. 
Participant characteristics between SEA and non-SEA runners were compared using unpaired two-sided Welch’s 
t-tests when homogeneity of variance assumptions were violated and unpaired two-sided Student’s t-tests oth-
erwise. The effect of group (SEA, non-SEA) and running speed on RE and biomechanical variables was evalu-
ated using a linear mixed effects model fitted by restricted maximum likelihood. The within-subject nature was 
controlled for by including random effects for participants (individual differences in the intercept of the model). 
The fixed effects included group and running speed (both categorical variables). Cohen’s d effect size was calcu-
lated when a significant group effect was  observed46, and classified as small, moderate, and large when d values 
were larger than 0.2, 0.5, and 0.8,  respectively46. Footstrike distribution between SEA and non-SEA runners were 
compared at all running speeds using Fisher exact tests given that some of the expected frequencies were less 
than five.
A correlation matrix between anthropometric characteristics (mass and height, leg length, BMI, and ratio of 
leg length over height) was generated to identify unrelated anthropometric characteristics. Pearson correlation 
coefficients (r) between RE and the identified independent anthropometric variables were computed using RE 
values at the three running speeds separately, as well as with and without subgrouping of participants based on 
ethnicity. Similarly, Pearson correlation coefficients (r) between RE and biomechanical variables were computed 
at the three running speeds separately, as well as with and without subgrouping of participants based on ethnicity. 
Correlations were considered very high, high, moderate, low, and negligible when absolute r values were between 
0.90–1.00, 0.70–0.89, 0.50–0.69, 0.30–0.49, and 0.00–0.29,  respectively47. Given the number of correlations and 
exploratory nature of these analyses, only significant correlations reaching the moderate threshold were deemed 
meaningful. Statistical analyses were performed using Jamovi (version 1.2.17, Computer Software, retrieved from 
https:// www. jamovi. org) and R (version 3.5.0, The R Foundation for Statistical Computing, Vienna, Austria) 
with a level of significance set at p ≤ 0.05.
Results
Participant characteristics. 
Non-SEA runners were significantly heavier and taller, had a larger BMI and 
longer legs, had footwear with a larger heel-to-toe drop, and were more experienced than SEA runners (p ≤ 0.02; 
Table 1). Otherwise, demographic and footwear characteristics of non-SEA and SEA runners were similar (see 
Table 1).
Running economy. 
SEA runners were significantly less economical (6%) than non-SEA runners (average 
across speeds: 522.6 ± 47.4 vs 492.4 ± 42.2 ml/kg0.75/km), with a moderate main effect of group on RE (p = 0.04, 
(1)
kleg = Fz,max
L
Table 1.  Participant and footwear characteristics for South East Asian (SEA) and non-South East Asian (non-
SEA) runners. Significant differences (p ≤ 0.05) identified by Student’s or Welch’s t-tests are reported in bold. M 
male, F female, BMI body mass index, and NA not applicable.
Characteristics
SEA
Non-SEA
p
Sex
M = 10; F = 7
M = 10; F = 7
NA
Age (y)
34 ± 4
38 ± 7
0.08
Mass (kg)
56 ± 9
68 ± 12
0.002
Height (cm)
167 ± 8
175 ± 9
0.01
Leg length (cm)
86 ± 4
91 ± 6
0.01
BMI (kg/m2)
20 ± 2
22 ± 2
0.004
Leg length over height (%)
52 ± 1
52 ± 1
0.54
Running distance (km/week)
60 ± 19
54 ± 18
0.32
Running experience (y)
6 ± 3
11 ± 7
0.02
Running performance on 21.1 km (min)
96 ± 9
96 ± 10
0.81
Shoe mass (g)
231 ± 32
215 ± 39
0.22
Shoe stack height (mm)
25 ± 3
25 ± 3
0.83
Shoe heel-to-toe drop (mm)
8 ± 3
6 ± 3
0.01
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d = 0.67; Fig. 2). There was no significant main effect of speed (p = 0.27) or group x speed interaction effect 
(p = 0.89) on RE. Larger differences were seen between SEA and non-SEA runners when expressing RE in ml/
kg/km instead of ml/kg0.75/km (see section S1 of supplementary materials).
Biomechanical characteristics. 
There was a significant main effect of group on SF, SL, and tc (p ≤ 0.04; 
Table 2), with SEA having a higher SF (moderate effect; d = 0.75), smaller SL (small effect; d = 0.36), and shorter 
tc (moderate effect; d = 0.67) than non-SEA runners. There was no group effect on normalized SL, tf and kleg 
(p ≥ 0.23; Table 2). A significant speed effect was observed for all temporal variables (p ≤ 0.01; Table 2). SF, SL, and 
tf increased with increasing speed, whereas tc and kleg decreased with increasing speed. None of these variables 
demonstrated a group x speed interaction (p ≥ 0.32; Table 2).
There was a significant group effect on θankle at footstrike and θknee at toe-off (p ≤ 0.03; Table 3), with SEA 
having less ankle dorsiflexion than non-SEA at footstrike (large effect; d = 1.20) and less knee extension at toe-off 
(moderate effect; d = 0.75). A significant speed effect was observed for θankle and θknee at toe-off (p ≤ 0.02; Table 3), 
with greater flexion at footstrike and extension at toe-off with increasing speed. None of these variables showed 
a group x speed interaction except θankle at footstrike (p = 0.007; Table 3), with SEA decreasing dorsiflexion with 
increasing speed while non-SEA increased dorsiflexion with increasing speed.
Footstrike angle and pattern. 
SEA had a significantly lower FSA than non-SEA runners (large effect; 
d = 1.67), as depicted by the group effect on FSA (p < 0.001; Table  4). A speed effect was observed on FSA 
(p < 0.001; Table 4), indicating an increase of FSA with increasing running speed, while no significant group x 
speed interaction effect was noted (p = 0.13; Table 4). Footstrike distribution between SEA and non-SEA runners 
differed significantly at all speeds, with non-SEA being more commonly RFS (p ≤ 0.04; Table 4).
Figure 2.  Running Economy (RE) of South East Asian (SEA) and non-South East Asian (non-SEA) runners at 
several endurance running speeds. Linear mixed effects modelling identified a significant group effect (p ≤ 0.05).
Table 2.  Step frequency (SF), step length (SL), contact time (tc), flight time (tf), and spring-mass 
characteristics of the lower limb as given by leg stiffness (kleg) for South East Asian (SEA) and non-South East 
Asian (non-SEA) runners at endurance running speeds. Significant differences (p ≤ 0.05) identified by linear 
mixed effects modelling are indicated in bold. SL was expressed as a percentage of participant’s leg length in 
addition to raw units. a Step length normalized to leg length.
Running speed (km/h)
Group
SF (steps/min)
SL (cm)
SL (%)a
tc (ms)
tf (ms)
kleg (kN/m)
10
SEA
176 ± 9
95 ± 5
110 ± 7
268 ± 24
78 + 21
12.3 ± 2.5
Non-SEA
168 ± 9
100 ± 5
110 ± 6
287 ± 31
84 + 23
13.5 ± 2.8
12
SEA
181 ± 10
111 ± 6
128 ± 8
237 ± 22
96 ± 21
12.3 ± 2.4
Non-SEA
173 ± 10
116 ± 7
127 ± 6
253 ± 23
98 ± 25
13.5 ± 3.0
14
SEA
187 ± 11
125 ± 7
145 ± 9
215 ± 20
107 ± 19
12.0 ± 2.2
Non-SEA
179 ± 11
131 ± 8
144 ± 7
231 ± 21
107 ± 23
12.8 ± 2.7
Group effect
0.02
0.03
0.78
0.04
0.67
0.23
Running speed effect
 < 0.001
 < 0.001
 < 0.001
 < 0.001
 < 0.001
0.009
Interaction effect
0.93
0.48
0.68
0.81
0.44
0.32
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Relationship between RE and anthropometric characteristics. 
High positive correlations were 
identified between mass and height (r ≥ 0.83; p < 0.001), mass and leg length (r ≥ 0.74; p < 0.001), and mass and 
BMI (r ≥ 0.84; p < 0.001), while the correlation between mass and ratio of leg length over height was negligible 
and not significant (r ≤ 0.17; p ≥ 0.35). Hence, relationships between RE and mass and ratio of leg length over 
height were further explored (Table 5). For SEA runners, a high positive correlation was observed between RE 
and mass at 12 km/h (r = 0.69, p < 0.001; Table 5), while high positive correlations were observed between RE and 
mass for non-SEA runners at all speeds (r ≥ 0.65, p ≤ 0.005; Table 5). For runners combined, the strongest cor-
relations were low. Table 6 presents all correlations, including the low and negligible ones. Relationships between 
RE expressed in ml/kg/km and anthropometric characteristics are provided in section S1 of supplementary 
materials.
Table 3.  Flexion–extension angle of the lower limb for South East Asian (SEA) and non-South East Asian 
(non-SEA) runners at endurance running speeds. Significant differences (p ≤ 0.05) identified by linear mixed 
effects modelling are indicated in bold. θankle : ankle joint angle, θknee : knee joint angle, FS: footstrike, and TO: 
toe-off.
Running speed (km/h)
Group
θankle(°)
θknee(°)
FS
TO
FS
TO
10
SEA
9 ± 5
 − 12 ± 8
17 ± 2
27 ± 4
Non-SEA
14 ± 6
 − 9 ± 3
18 ± 3
24 ± 7
12
SEA
8 ± 5
 − 14 ± 8
17 ± 3
24 ± 4
Non-SEA
15 ± 6
 − 11 ± 3
18 ± 4
21 ± 5
14
SEA
8 ± 6
 − 14 ± 9
18 ± 3
24 ± 4
Non-SEA
15 ± 6
 − 11 ± 4
18 ± 4
20 ± 4
Group effect
0.001
0.18
0.57
0.03
Running speed effect
0.31
0.02
0.65
 < 0.001
Interaction effect
0.007
0.95
0.09
0.65
Table 4.  Footstrike angle (FSA) and footstrike distribution [rearfoot strike (RFS) for FSA > 8° and non-
rearfoot strike (non-RFS)  otherwise42] for South East Asian (SEA) and non-South East Asian (non-SEA) 
runners at endurance running speeds. Significant differences (p ≤ 0.05) identified by linear mixed effects 
modelling and by Fisher exact tests are indicated in bold.
Running speed (km/h)
Group
FSA (°)
RFS—non-RFS
p
10
SEA
6 ± 4
4–13
 < 0.001
Non-SEA
13 ± 5
16–1
12
SEA
7 ± 4
6–11
 < 0.001
Non-SEA
15 ± 5
16–1
14
SEA
9 ± 4
10–7
0.04
Non-SEA
17 ± 6
16–1
Group effect
 < 0.001
NA
Running speed effect
 < 0.001
NA
Interaction effect
0.13
NA
Table 5.  Pearson correlation coefficients between running economy and anthropometric characteristics (mass 
and ratio of leg length over height), together with their corresponding p-values underneath for South East 
Asian (SEA), non-South East Asian (non-SEA), as well as all runners pooled together (ALL). Note. Only the 
relationships between running economy and mass and ratio of leg length over height were considered because 
mass was highly and significantly correlated to height, leg length, and body mass index. Statistical significances 
(p ≤ 0.05) gray shaded boxes denote correlation coefficients above an absolute value of 0.5 (moderate).
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Relationships between RE and biomechanics. 
For SEA runners, a high positive correlation was seen 
between RE and tc at 12 km/h (|r|≥ 0.70, p ≤ 0.002; Table 6). SF and θankle at footstrike at 10 km/h were moderately 
and negatively correlated to RE, whereas SL (10 km/h) was moderately and positively correlated to RE (|r|≥ 0.50, 
p ≤ 0.04; Table 6).
For non-SEA runners, a moderate and negative correlation was observed between RE and SF at 12 km/h 
(|r|≥ 0.51, p ≤ 0.04; Table 6). Besides, moderate positive correlations between RE and SL (12 km/h) and kleg 
(10 km/h) were identified (|r|≥ 0.51, p ≤ 0.04; Table 6).
For runners combined, the strongest correlations were low. Table 6 presents all correlations, including the low 
and negligible ones. Relationships between RE expressed in ml/kg/km and biomechanics are given in section S1 
of supplementary materials.
Discussion
Differences in RE were observed between SEA and non-SEA runners despite being matched for recent (< 1 year) 
road running performance and sex. SEA runners were less economical than non-SEA runners at endurance 
running speeds. Anthropometric differences were observed between groups, depicting that SEA were lighter 
and shorter than non-SEA runners, and had a lower BMI and shorter legs. Differences in running biomechan-
ics between cohorts were also observed, but correlations between anthropometric and biomechanical variables 
and RE measures at a group-level were of small magnitudes at best, and provided limited explanations of the 
underlying differences in RE.
Non-SEA were 6% more economical than SEA runners at endurance running speeds (Fig. 2). The lower 
RE in SEA than non-SEA runners could in part be due to anthropometric differences. We observed that SEA 
were lighter and shorter than non-SEA runners, and had a lower BMI and shorter legs (Table 1). Mass was 
significantly related to RE in both ethnic groups, with more economical runners having lower body mass. Mass 
was highly related to RE in SEA runners at 12 km/h and non-SEA at all speeds, but correlations became low 
or non-significant when pooling all runners together (Table 5). Previous studies showing that elite Caucasian 
runners were less economical than Kenyans attributed RE differences to longer legs (~ 5%), thinner and lighter 
calf musculature, and lower mass and BMI of Kenyan than Caucasian  runners3–6. Indeed, RE being correlated 
with leg mass, Kenyan runners could benefit from their long, slender  legs6. In our case, the ratio of leg length 
over height was not related to RE (Table 5) and was similar between SEA and non-SEA, indicating similar lower 
limb proportions in these two groups (Table 1). In fact, due to both smaller mass and shorter legs (Table 1), SEA 
might have had a proportionally similar leg mass than non-SEA runners.
Table 6.  Pearson correlation coefficients between running economy and biomechanical variables [step 
frequency (SF), step length (SL), contact time  (tc), flight time  (tf), spring-mass characteristics of the lower limb 
as given by leg stiffness  (kleg), footstrike angle (FSA), and flexion–extension ankle ( θankle ) and knee ( θknee ) joint 
angle at footstrike (FS) and toe-off (TO)], together with their corresponding p-values underneath for South 
East Asian (SEA), non-South East Asian (non-SEA), as well as all runners pooled together (ALL). Statistical 
significances (p ≤ 0.05) are indicated in bold. Gray shaded boxes denote correlation coefficients above an 
absolute value of 0.5 (moderate). SL was expressed as a percentage of participant’s leg length in addition to raw 
units. a Step length normalized to leg length.
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Participants wore their own running shoes during testing similar to previous research exploring differences 
in running mechanics between ethnic  groups10. Given that differences in footwear characteristics can underpin 
differences in running  biomechanics48 and  RE49, using a standardised shoe might have led to different study 
outcomes. Noteworthy, however, is that there were no significant difference in shoe mass or stack height between 
groups, with the 2 mm difference in heel-to-toe drop between groups likely having limited biomechanical or 
performance  implications50. Recreational runners are more comfortable wearing their own  shoes51, and show 
individual responses to novel  footwear51,52 and cushioning  properties53. A recent meta-analysis indicates rec-
reational runners demonstrate improved RE when wearing more comfortable  shoes54, supporting the appropri-
ateness of participants wearing their own footwear for this investigation. Nevertheless, it is possible that other 
footwear characteristics not assessed as part of this study differed between groups, such as midsole cushioning 
and/or the longitudinal bending  stiffness50, and contributed to the biomechanical and RE differences observed.
Among all correlations between biomechanical variables and RE, only SF and SL were significantly related to 
RE in both ethnic groups. The SF and SL variables were moderately related to RE in SEA runners at 10 km/h and 
non-SEA at 12 km/h, but correlations became low and non-significant when all runners were pooled together 
(Table 6). Noteworthy, correlations between SL and RE were smaller and became non-significant when normal-
ized to leg length. In addition, tc was highly and positively related to RE for SEA runners at 12 km/h. The identi-
fied correlations between SF (and SL) and RE and between tc and RE suggest that individuals with higher SF 
(and shorter SL) and smaller tc (for SEA runners) are more economical. However, SEA had intrinsically higher 
SF (and shorter SL) and shorter tc, but worse RE than non-SEA runners (Table 2); therefore, contradicting the 
observed correlations. Based on the cost-of-generating-force hypothesis, one requires less metabolic energy with 
increased tc and longer leg  lengths55–57, both observed in non-SEA (Table 1). The longer tc in non-SEA suggests 
that muscles had more time to shorten and produce the necessary forces to move the body than SEA runners. 
Based on the force–velocity relationship, if a muscle is shortening slower but only a given force is necessary (i.e., 
running on a treadmill), it could be speculated that the activation levels of the muscles were lower to reach the 
target force. These theories might partially explain the reduced metabolic cost in non-SEA than SEA runners, 
i.e., a longer tc, lower SF, and longer leg lengths are more economical.
Nevertheless, studies indicate that increasing SF above self-selected ones in novice (156 ± 6 steps/min, 
9.6 km/h) and trained (169 ± 11 steps/min, 12.6 km/h) runners acutely improves RE (+ 2%)58, as does undertak-
ing a 10-day training programme to increase SF (from 166 ± 4 to 180 ± 1 steps/min, 12.3 km/h)59. At 12 km/h, 
mean SF values were 173 (range: 151 to 185) in non-SEA and 181 (range: 159 to 200) in SEA. Further increasing 
SF in runners with an intrinsically high SF might not be energetically optimal, but has yet to be examined. An 
extremely high SF might be suboptimal at endurance speeds given the greater mechanical power associated with 
increased frequency of reciprocal movements, which may require a greater reliance on less economical type II 
muscle  fibers60. Indeed, Kaneko et al.60 suggested that SF and RE could be related through muscle fiber recruit-
ment. Besides, given the shorter stature of our SEA vs non-SEA runners, their higher SF aligns with findings of 
moderate correlations between leg lengths and SF (r = −0.53, p < 0.001; 12 km/h), in agreement with previous 
literature (r = −0.45, p < 0.001) 61, whereby individuals with shorter legs tend to adopt higher SF.
Alongside their higher SF and smaller SL, SEA had shorter tc, smaller FSA (more forefoot strike pattern), 
and smaller θankle at footstrike than non-SEA runners (Tables 2, 3, 4). Previous studies observed that running 
at a higher SF led to smaller tc
62 and  FSA63, which is consistent with our findings. In addition, the prevalence 
of RFS was shown to be lower in Asian than North American recreational  runners16, aligning with the findings 
of the present study. A smaller tc might be associated with smaller braking and propulsion phases. Although 
short braking phases are considered important for economical  running64, SEA runners were less economical. 
Braking forces were not recorded herein due to unavailability of instrumented treadmills. Shorter braking times 
does not necessarily equate minimising braking forces, which is important in the context of  RE65. Moreover, 
it could be that the orientation of the ground reaction forces in SEA runners was suboptimal. Indeed, Moore, 
et al.66 observed that a better alignment of the leg axis during propulsion and resultant ground reaction force 
improved RE, mainly via a more horizontal application of the ground reaction force. This idea is supported by 
our data, which show less extension of θknee at toe-off (Table 3), and thus potentially less horizontal propulsion 
for SEA than non-SEA runners. Nevertheless, θknee at toe-off was not correlated to RE (Table 6). Though SF, SL 
and tc significantly differed between groups, no difference in kleg was identified (Table 2), contradicting previous 
findings that kleg relates to the aforementioned  variables67–69. These studies were all within-subject comparisons 
rather than between-subject ones; hence, at an individual level, the relationship might still hold within SEA and 
non-SEA participants. The lack of difference in kleg between groups despite differences in SF, SL, and tc potentially 
relates to the body mass difference between groups that is counterbalancing the spatiotemporal differences in the 
biomechanical variables [see Eq. (1)]. These biomechanical data were not clearly able to explain the variances in 
RE between groups, and support that RE improvements in various groups might need individualized training 
and considerations. A similar conclusion was made by Santos-Concejero, et al.10 when assessing RE differences 
between Eritrean and European runners. Moreover, these divergent findings overall suggest there is no unique 
or ideal running pattern that is the most economical amongst  runners1. The running pattern of an individual 
results from a complex interaction between several biomechanical factors 70 that are interconnected and interact 
in a global and dynamic  manner71 to optimize RE.
A few limitations to the present study exist. Although the effect size was moderate (d = 0.67), the between-
group difference in RE units was rather small (mean difference = 30.1 ml/kg0.75/km; p = 0.04). In addition, the 
within-group variability in RE and biomechanical variables at a given running speed were relatively small. There-
fore, observed correlations between RE and biomechanical variables might have been greater in more heterogene-
ous groups. Given the exploratory nature of this investigation, several variables were compared, leading to a high 
likelihood of finding a spurious difference or correlation. Nonetheless, our research provides preliminary indica-
tions of potential differences between SEA and non-SEA runners warranting further consideration. Moreover, 
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an underpinning factor to the differences in RE might be the running experience given that experienced runners 
self-optimize their running patterns better than novice  runners1. Non-SEA runners were more experienced (years 
running) than SEA runners (Table 1), but all runners trained regularly and had a minimum of 2 years running 
experience, indicating they were all "experienced" and not "novice" runners. Nonetheless, a gradual improvement 
in RE (+ 15%) over an 11-year time span has been reported for a former world record holder in the women’s 
 marathon72. Therefore, an effect due to running experience cannot be ruled out. Besides, several morphological 
factors which were not measured in this study might have partly explained differences in RE between SEA and 
non-SEA  runners18,19,73–78 (more details are provided in section S2 of supplementary materials). Furthermore, 
although all SEA runners identified as “white”, the numerous nationalities of the non-SEA group potentially 
increased the heterogeneity of our cohort and influenced our results. Lastly, RE and biomechanics were collected 
within the same experimental session, but the two were not collected simultaneously (as common in running 
 research79). Although possible that participants altered their runs, research indicates that metabolic equipment 
does not affect sagittal plane running kinematics and are comparable to running without metabolic  testing80.
Conclusion
SEA and non-SEA runners were different in terms of RE, with SEA runners being less economical than non-SEA 
runners at endurance running speeds. Differences in anthropometric characteristics and running biomechanics 
between cohorts were also observed, but explained differences in RE to a limited extent. Other factors, which 
could be related to ethnicity, might be underpinning such differences. Unfortunately, these factors were not 
measured in this study. Nonetheless, caution must be taken when generalizing from non-SEA running studies 
to SEA runners.
Data availability
The dataset supporting this article is available on request to the corresponding author.
Received: 8 August 2021; Accepted: 21 March 2022
References
 1. Moore, I. S. Is there an economical tunning technique? A review of modifiable biomechanical factors affecting running economy. 
Sports Med. 46, 793–807. https:// doi. org/ 10. 1007/ s40279- 016- 0474-4 (2016).
 2. Weston, A. R., Mbambo, Z. & Myburgh, K. H. Running economy of African and Caucasian distance runners. Med. Sci. Sports 
Exerc. 32, 1130–1134. https:// doi. org/ 10. 1097/ 00005 768- 20000 6000- 00015 (2000).
 3. Larsen, H. B. & Sheel, A. W. The Kenyan runners. Scand. J. Med. Sci. Sports 25(Suppl 4), 110–118. https:// doi. org/ 10. 1111/ sms. 
12573 (2015).
 4. Larsen, H. B., Christensen, D. L., Nolan, T. & Søndergaard, H. Body dimensions, exercise capacity and physical activity level of 
adolescent Nandi boys in western Kenya. Ann. Hum. Biol. 31, 159–173. https:// doi. org/ 10. 1080/ 03014 46041 00016 63416 (2004).
 5. Saltin, B. et al. Aerobic exercise capacity at sea level and at altitude in Kenyan boys, junior and senior runners compared with 
Scandinavian runners. Scand. J. Med. Sci. Sports 5, 209–221. https:// doi. org/ 10. 1111/j. 1600- 0838. 1995. tb000 37.x (1995).
 6. Larsen, H. B. Kenyan dominance in distance running. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 136, 161–170, doi:https:// 
doi. org/ 10. 1016/ s1095- 6433(03) 00227-7 (2003).
 7. Lucia, A. et al. Physiological characteristics of the best Eritrean runners-exceptional running economy. Appl. Physiol. Nutr. Metab. 
31, 530–540. https:// doi. org/ 10. 1139/ h06- 029 (2006).
 8. Lieberman, D. E. et al. Foot strike patterns and collision forces in habitually barefoot versus shod runners. Nature 463, 531–535. 
https:// doi. org/ 10. 1038/ natur e08723 (2010).
 9. Marino, F. E., Lambert, M. I. & Noakes, T. D. Superior performance of African runners in warm humid but not in cool environ-
mental conditions. J. Appl. Physiol. 96, 124–130. https:// doi. org/ 10. 1152/ jappl physi ol. 00582. 2003 (2004).
 10. Santos-Concejero, J. et al. Gait-cycle characteristics and running economy in elite Eritrean and European runners. Int. J. Sports 
Physiol. Perform. 10, 381–387. https:// doi. org/ 10. 1123/ ijspp. 2014- 0179 (2015).
 11. Santos-Concejero, J. et al. Differences in ground contact time explain the less efficient running economy in north African runners. 
Biol. Sport 30, 181–187. https:// doi. org/ 10. 5604/ 20831 862. 10591 70 (2013).
 12. Tam, E. et al. Energetics of running in top-level marathon runners from Kenya. Eur. J. Appl. Physiol. 112, 3797–3806. https:// doi. 
org/ 10. 1007/ s00421- 012- 2357-1 (2012).
 13. Wishnizer, R. R., Inbar, O., Klinman, E. & Fink, G. Physiological differences between Ethiopian and Caucasian distance runners 
and their effects on 10 km running performance. Adv Phys Educ 3, 9. https:// doi. org/ 10. 4236/ ape. 2013. 33023 (2013).
 14. Shu, Y. et al. Foot morphological difference between habitually shod and unshod runners. PLoS ONE 10, e0131385. https:// doi. 
org/ 10. 1371/ journ al. pone. 01313 85 (2015).
 15. Sano, K. et al. Can measures of muscle–tendon interaction improve our understanding of the superiority of Kenyan endurance 
runners?. Eur. J. Appl. Physiol. 115, 849–859. https:// doi. org/ 10. 1007/ s00421- 014- 3067-7 (2015).
 16. Patoz, A., Lussiana, T., Gindre, C. & Hébert-Losier, K. Recognition of foot strike pattern in Asian recreational runners. Sports 7, 
147. https:// doi. org/ 10. 3390/ sport s7060 147 (2019).
 17. Andersen, J. J. & International Association of Athletics Federations. The State of Running 2019, (2020).
 18. Hawes, M. R. et al. Ethnic differences in forefoot shape and the determination of shoe comfort. Ergonomics 37, 187–196. https:// 
doi. org/ 10. 1080/ 00140 13940 89636 37 (1994).
 19. Zárate-Kalfópulos, B., Romero-Vargas, S., Otero-Cámara, E., Correa, V. C. & Reyes-Sánchez, A. Differences in pelvic parameters 
among Mexican, Caucasian, and Asian populations. J. Neurosurg. Spine 16, 516–519. https:// doi. org/ 10. 3171/ 2012.2. Spine 11755 
(2012).
 20. Sun, P. et al. Autonomic recovery is delayed in Chinese compared with Caucasian following treadmill exercise. PLoS ONE 11, 
e0147104. https:// doi. org/ 10. 1371/ journ al. pone. 01471 04 (2016).
 21. Chen, W.-L., O’Connor, J. J. & Radin, E. L. A comparison of the gaits of Chinese and Caucasian women with particular reference 
to their heelstrike transients. Clin. Biomech. 18, 207–213. https:// doi. org/ 10. 1016/ S0268- 0033(02) 00187-0 (2003).
 22. Gruber, A. H., Umberger, B. R., Braun, B. & Hamill, J. Economy and rate of carbohydrate oxidation during running with rearfoot 
and forefoot strike patterns. J. Appl. Physiol. 115, 194–201. https:// doi. org/ 10. 1152/ jappl physi ol. 01437. 2012 (2013).
 23. Ogueta-Alday, A. N. A., RodrÍGuez-Marroyo, J. A. & GarcÍA-LÓPez, J. Rearfoot striking runners are more economical than midfoot 
strikers. Med. Sci. Sports Exerc. 46 (2014).
10
Vol:.(1234567890)
Scientific Reports |         (2022) 12:6291  | 
https://doi.org/10.1038/s41598-022-10030-4
www.nature.com/scientificreports/
 24. Di Michele, R. & Merni, F. The concurrent effects of strike pattern and ground-contact time on running economy. J. Sci. Med. Sport 
17, 414–418. https:// doi. org/ 10. 1016/j. jsams. 2013. 05. 012 (2014).
 25. Støren, Ø., Helgerud, J. & Hoff, J. Running stride peak forces inversely determine running economy in elite runners. J. Strength 
Cond. Res. 25 (2011).
 26. Paavolainen, L. M., Nummela, A. T. & Rusko, H. K. Neuromuscular characteristics and muscle power as determinants of 5-km 
running performance. Med. Sci. Sports Exerc. 31, 124–130. https:// doi. org/ 10. 1097/ 00005 768- 19990 1000- 00020 (1999).
 27. Williams, K. R. & Cavanagh, P. R. Relationship between distance running mechanics, running economy, and performance. J. Appl. 
Physiol. 63, 1236–1245. https:// doi. org/ 10. 1152/ jappl. 1987. 63.3. 1236 (1987).
 28. Lussiana, T., Gindre, C., Mourot, L. & Hébert-Losier, K. Do subjective assessments of running patterns reflect objective parameters?. 
Eur. J. Sport Sci. 17, 847–857. https:// doi. org/ 10. 1080/ 17461 391. 2017. 13250 72 (2017).
 29. Harriss, D. J., Macsween, A. & Atkinson, G. Standards for ethics in sport and exercise science research: 2018 update. Int. J. Sports 
Med. 38, 1126–1131. https:// doi. org/ 10. 1055/s- 0043- 124001 (2017).
 30. Oliveira, A. S. & Pirscoveanu, C. I. Implications of sample size and acquired number of steps to investigate running biomechanics. 
Sci. Rep. 11, 3083. https:// doi. org/ 10. 1038/ s41598- 021- 82876-z (2021).
 31. Svedenhag, J. & Sjödin, B. Body-mass-modified running economy and step length in elite male middle- and long-distance runners. 
Int. J. Sports Med. 15, 305–310. https:// doi. org/ 10. 1055/s- 2007- 10210 65 (1994).
 32. Tranberg, R., Saari, T., Zügner, R. & Kärrholm, J. Simultaneous measurements of knee motion using an optical tracking system 
and radiostereometric analysis (RSA). Acta Orthop. 82, 171–176. https:// doi. org/ 10. 3109/ 17453 674. 2011. 570675 (2011).
 33. Hanavan, E. A mathematical model of the human body. AMRL-TR. Aerospace Med Res Lab 1, 1–149 (1964).
 34. Dempster, W. T. Space requirements of the seated operator: geometrical, kinematic, and mechanical aspects of the body with special 
reference to the limbs. (Wright Air Development Center, 1955).
 35. Woltring, H. Representation and calculation of 3-D joint movement. Hum. Mov. Sci. 10, 603–616. https:// doi. org/ 10. 1016/ 0167- 
9457(91) 90048-3 (1991).
 36. Cole, G. K., Nigg, B. M., Ronsky, J. L. & Yeadon, M. R. Application of the joint coordinate system to three-dimensional joint atti-
tude and movement representation: a standardization proposal. J. Biomech. Eng. 115, 344–349. https:// doi. org/ 10. 1115/1. 28954 
96 (1993).
 37. Davis, R. B., Õunpuu, S., Tyburski, D. & Gage, J. R. A gait analysis data collection and reduction technique. Hum. Mov. Sci. 10, 
575–587. https:// doi. org/ 10. 1016/ 0167- 9457(91) 90046-Z (1991).
 38. Grood, E. S. & Suntay, W. J. A joint coordinate system for the clinical description of three-dimensional motions: application to the 
knee. J. Biomech. Eng. 105, 136–144. https:// doi. org/ 10. 1115/1. 31383 97 (1983).
 39. Blankevoort, L., Huiskes, R. & de Lange, A. The envelope of passive knee joint motion. J. Biomech. 21, 705–720. https:// doi. org/ 
10. 1016/ 0021- 9290(88) 90280-1 (1988).
 40. Kadaba, M. P., Ramakrishnan, H. K. & Wootten, M. E. Measurement of lower extremity kinematics during level walking. J. Orthop. 
Res. 8, 383–392. https:// doi. org/ 10. 1002/ jor. 11000 80310 (1990).
 41. Piazza, S. J. & Cavanagh, P. R. Measurement of the screw-home motion of the knee is sensitive to errors in axis alignment. J. 
Biomech. 33, 1029–1034. https:// doi. org/ 10. 1016/ s0021- 9290(00) 00056-7 (2000).
 42. Altman, A. R. & Davis, I. S. A kinematic method for footstrike pattern detection in barefoot and shod runners. Gait Posture 35, 
298–300. https:// doi. org/ 10. 1016/j. gaitp ost. 2011. 09. 104 (2012).
 43. Lussiana, T., Patoz, A., Gindre, C., Mourot, L. & Hébert-Losier, K. The implications of time on the ground on running economy: 
less is not always better. J. Exp. Biol. 222, jeb192047, doi:https:// doi. org/ 10. 1242/ jeb. 192047 (2019).
 44. Maiwald, C., Sterzing, T., Mayer, T. A. & Milani, T. L. Detecting foot-to-ground contact from kinematic data in running. Footwear 
Sci. 1, 111–118. https:// doi. org/ 10. 1080/ 19424 28090 31339 38 (2009).
 45. Morin, J.-B., Dalleau, G., Kyröläinen, H., Jeannin, T. & Belli, A. A simple method for measuring stiffness during running. J. Appl. 
Biomech. 21, 167–180. https:// doi. org/ 10. 1123/ jab. 21.2. 167 (2005).
 46. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. (Routledge, 1988).
 47. Hinkle, D. E., Wiersma, W. & Jurs, S. G. Applied Statistics for the Behavioral Sciences. 768 (Houghton Mifflin (p. 109), 2002).
 48. Sinclair, J., Fau-Goodwin, J., Richards, J. & Shore, H. The influence of minimalist and maximalist footwear on the kinetics and 
kinematics of running. Footwear Sci. 8, 33–39. https:// doi. org/ 10. 1080/ 19424 280. 2016. 11420 03 (2016).
 49. Fuller, J. T., Bellenger, C. R., Thewlis, D., Tsiros, M. D. & Buckley, J. D. The effect of footwear on running performance and running 
economy in distance runners. Sports Med. 45, 411–422. https:// doi. org/ 10. 1007/ s40279- 014- 0283-6 (2015).
 50. Sun, X., Lam, W.-K., Zhang, X., Wang, J. & Fu, W. Systematic review of the role of footwear constructions in running biomechanics: 
implications for running-related injury and performance. J. Sports Sci. Med. 19, 20–37 (2020).
 51. Hébert-Losier, K. et al. Metabolic and performance responses of male runners wearing 3 types of footwear: Nike Vaporfly 4%, 
Saucony Endorphin racing flats, and their own shoes. J. Sport Health Sci. https:// doi. org/ 10. 1016/j. jshs. 2020. 11. 012 (2020).
 52. Tam, N., Tucker, R. & Astephen Wilson, J. L. Individual Responses to a barefoot running program: insight into risk of injury. Am. 
J. Sports Med. 44, 777–784, doi:https:// doi. org/ 10. 1177/ 03635 46515 620584 (2016).
 53. Tung, K. D., Franz, J. R. & Kram, R. A test of the metabolic cost of cushioning hypothesis during unshod and shod running. Med. 
Sci. Sports Exerc. 46, 324–329. https:// doi. org/ 10. 1249/ MSS. 0b013 e3182 a63b81 (2014).
 54. Van Alsenoy, K., van der Linden, M. L., Girard, O. & Santos, D. Increased footwear comfort is associated with improved running 
economy - a systematic review and meta-analysis. Eur. J. Sport Sci., 1–13, doi:https:// doi. org/ 10. 1080/ 17461 391. 2021. 19986 42 
(2021).
 55. Roberts, T. J., Kram, R., Weyand, P. G. & Taylor, C. R. Energetics of bipedal running. I. Metabolic cost of generating force. J. Exp. 
Biol. 201, 2745–2751, doi:https:// doi. org/ 10. 1242/ jeb. 201. 19. 2745 (1998).
 56. Roberts, T. J., Chen, M. S. & Taylor, C. R. Energetics of bipedal running. II. Limb design and running mechanics. J. Exp. Biol. 201, 
2753–2762, doi:https:// doi. org/ 10. 1242/ jeb. 201. 19. 2753 (1998).
 57. Kram, R. & Taylor, C. R. Energetics of running: a new perspective. Nature 346, 265–267. https:// doi. org/ 10. 1038/ 34626 5a0 (1990).
 58. de Ruiter, C. J., Verdijk, P. W. L., Werker, W., Zuidema, M. J. & de Haan, A. Stride frequency in relation to oxygen consumption in 
experienced and novice runners. Eur. J. Sport Sci. 14, 251–258. https:// doi. org/ 10. 1080/ 17461 391. 2013. 783627 (2014).
 59. Quinn, T. J., Dempsey, S. L., LaRoche, D. P., Mackenzie, A. M. & Cook, S. B. Step frequency training improves running economy 
in well-trained female runners. J. Strength Cond. Res. https:// doi. org/ 10. 1519/ jsc. 00000 00000 003206 (2019).
 60. Kaneko, M., Matsumoto, M., Ito, A. & Fuchimoto, T. Optimum step frequency in constant speed running. (Human Kinetics, 1987).
 61. Tenforde, A. S., Borgstrom, H. E., Outerleys, J. & Davis, I. S. Is cadence related to leg length and load rate?. J. Orthop. Sports Phys. 
Ther. 49, 280–283. https:// doi. org/ 10. 2519/ jospt. 2019. 8420 (2019).
 62. Adams, D., Pozzi, F., Willy, R. W., Carrol, A. & Zeni, J. Altering cadence or vertical oscillation during running: effects on running 
related injury factors. Int. J. Sports Phys. Ther. 13, 633–642 (2018).
 63. Allen, D. J., Heisler, H., Mooney, J. & Kring, R. The effect of step rate manipulation on foot strike pattern of long distance runners. 
Int. J. Sports Phys. Ther. 11, 54–63 (2016).
 64. Nummela, A., Keränen, T. & Mikkelsson, L. Factors related to top running speed and economy. Int. J. Sports Med. 28, 655–661 
(2007).
11
Vol.:(0123456789)
Scientific Reports |         (2022) 12:6291  | 
https://doi.org/10.1038/s41598-022-10030-4
www.nature.com/scientificreports/
 65. Lieberman, D. E., Warrener, A. G., Wang, J. & Castillo, E. R. Effects of stride frequency and foot position at landing on braking 
force, hip torque, impact peak force and the metabolic cost of running in humans. J. Exp. Biol. 218, 3406–3414. https:// doi. org/ 
10. 1242/ jeb. 125500 (2015).
 66. Moore, I. S., Jones, A. M. & Dixon, S. J. Reduced oxygen cost of running is related to alignment of the resultant GRF and leg axis 
vector: a pilot study. Scand. J. Med. Sci. Sports 26, 809–815. https:// doi. org/ 10. 1111/ sms. 12514 (2016).
 67. Farley, C. T. & González, O. Leg stiffness and stride frequency in human running. J. Biomech. 29, 181–186. https:// doi. org/ 10. 1016/ 
0021- 9290(95) 00029-1 (1996).
 68. Morin, J. B., Samozino, P., Zameziati, K. & Belli, A. Effects of altered stride frequency and contact time on leg-spring behavior in 
human running. J. Biomech. 40, 3341–3348. https:// doi. org/ 10. 1016/j. jbiom ech. 2007. 05. 001 (2007).
 69. Monte, A., Muollo, V., Nardello, F. & Zamparo, P. Sprint running: how changes in step frequency affect running mechanics and 
leg spring behaviour at maximal speed. J. Sports Sci. 35, 339–345. https:// doi. org/ 10. 1080/ 02640 414. 2016. 11643 36 (2017).
 70. Saunders, P. U., Pyne, D. B., Telford, R. D. & Hawley, J. A. Factors affecting running economy in trained distance runners. Sports 
Med. 34, 465–485. https:// doi. org/ 10. 2165/ 00007 256- 20043 4070- 00005 (2004).
 71. Dickinson, M. H. et al. How animals move: an integrative view. Science 288, 100. https:// doi. org/ 10. 1126/ scien ce. 288. 5463. 100 
(2000).
 72. Jones, A. M. The physiology of the world record holder for the women’s marathon. Int. J. Sports Sci. Coach. 1, 101–116. https:// doi. 
org/ 10. 1260/ 17479 54067 77641 258 (2006).
 73. Yue, B. et al. Differences of knee anthropometry between Chinese and white men and women. J. Arthroplasty 26, 124–130. https:// 
doi. org/ 10. 1016/j. arth. 2009. 11. 020 (2011).
 74. Scholz, M. N., Bobbert, M. F., van Soest, A. J., Clark, J. R. & van Heerden, J. Running biomechanics: shorter heels, better economy. 
J. Exp. Biol. 211, 3266. https:// doi. org/ 10. 1242/ jeb. 018812 (2008).
 75. Hunter, G. R. et al. Tendon length and joint flexibility are related to running economy. Med. Sci. Sports Exerc. 43, 1492–1499. 
https:// doi. org/ 10. 1249/ MSS. 0b013 e3182 10464a (2011).
 76. Ueno, H. et al. Relationship between Achilles tendon length and running performance in well-trained male endurance runners. 
Scand. J. Med. Sci. Sports 28, 446–451. https:// doi. org/ 10. 1111/ sms. 12940 (2018).
 77. Kunimasa, Y. et al. Specific muscle-tendon architecture in elite Kenyan distance runners. Scand. J. Med. Sci. Sports 24, e269-274. 
https:// doi. org/ 10. 1111/ sms. 12161 (2014).
 78. Mooses, M. et al. Dissociation between running economy and running performance in elite Kenyan distance runners. J. Sports 
Sci. 33, 136–144. https:// doi. org/ 10. 1080/ 02640 414. 2014. 926384 (2015).
 79. Hunter, I. et al. Running economy, mechanics, and marathon racing shoes. J. Sports Sci. 37, 2367–2373. https:// doi. org/ 10. 1080/ 
02640 414. 2019. 16338 37 (2019).
 80. Sloan, R. S., Wight, J. T., Hooper, D. R., Garman, J. E. J. & Pujalte, G. G. A. Metabolic testing does not alter distance running lower 
body sagittal kinematics. Gait Posture 76, 403–408. https:// doi. org/ 10. 1016/j. gaitp ost. 2020. 01. 001 (2020).
Acknowledgements
The authors thank Mr. Chris Tee Chow Li for assistance during the data collection process. The authors also 
thank the participants for their time and participation.
Author contributions
Conceptualization: T.L., C.G., and K.H.-L.; Methodology: T.L., L.M., C.G., and K.H.-L.; Investigation: T.L. and 
K.H.-L.; Formal analysis: T.L., A.P., K.H.-L., and B.B.; Writing—original draft preparation: A.P. and B.B.; Writ-
ing—review and editing: A.P., B.B., T.L., L.M., C.G., and K.H.-L.; Supervision: L.M. and K.H.-L; Project admin-
istration: L.M.; Funding acquisition: T.L., L.M., and K.H.-L.
Funding
This study was financially supported by the University of Bourgogne Franche-Comté (France) and the National 
Sports Institute of Malaysia.
Competing interests 
The authors declare no competing interests.
Additional information
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 
10. 1038/ s41598- 022- 10030-4.
Correspondence and requests for materials should be addressed to A.P.
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© The Author(s) 2022

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