Mind to move: Differences in running biomechanics between sensing and intuition shod runners.

Delving into the complexities of embodied cognition unveils the intertwined influence of mind, body, and environment. The connection of physical activity with cognition sparks a hypothesis linking motion and personality traits. Hence, this study explored whether personality traits could be linked to biomechanical variables characterizing running forms. To do so, 80 runners completed three randomized 50-m running-trials at 3.3, 4.2, and 5m/s during which their running biomechanics [ground contact time (tc), flight time (tf), duty factor (DF), step frequency (SF), leg stiffness (kleg), maximal vertical ground reaction force (Fmax), and maximal leg compression of the spring during stance (ΔL)] was evaluated. In addition, participants' personality traits were assessed through the Myers-Briggs Type Indicator (MBTI) test. The MBTI classifies personality traits into one of two possible categories along four axes: extraversion-introversion; sensing-intuition; thinking-feeling; and judging-perceiving. This exploratory study offers compelling evidence that personality traits, specifically sensing and intuition, are associated with distinct running biomechanics. Individuals classified as sensing demonstrated a more grounded running style characterized by prolonged tc, shorter tf, higher DF, and greater ΔL compared to intuition individuals (p≤0.02). Conversely, intuition runners exhibited a more dynamic and elastic running style with a shorter tc and higher kleg than their sensing counterparts (p≤0.02). Post-hoc tests revealed a significant difference in tc between intuition and sensing runners at all speeds (p≤0.02). According to the definition of each category provided by the MBTI, sensing individuals tend to focus on concrete facts and physical realities while intuition individuals emphasize abstract concepts and patterns of information. These results suggest that runners with sensing and intuition personality traits differ in their ability to use their lower limb structures as springs. Intuition runners appeared to rely more in the stretch-shortening cycle to energetically optimize their running style while sensing runners seemed to optimize running economy by promoting more forward progression than vertical oscillations. This study underscores the intriguing interplay between personality traits of individuals and their preferred movement patterns.

Concurrent endurance training with either plyometric or dynamic body-weight training both improve running economy with minimal or no changes in running biomechanics.

We compared the effects of two 8-week concurrent strength and endurance trainings (CSETs) on running economy (RE) and running biomechanics, and we explored whether the effects on running biomechanics were mediated by responder status [high vs low responder based on -2.6% change in RE]. Thirty-one male recreational runners were randomly assigned to a standard endurance running training combined with either plyometric (CSET-PLY) or dynamic body-weight (CSET-DYN) training. RE and running biomechanics [contact (tc) and flight (tf) time, step frequency (SF), duty factor (DF), and leg stiffness (kleg)] were measured pre- and post-intervention. RE significantly improved following CSET (RE = -2.1 ± 3.9%; p = 0.005) and no changes in tc, DF, SF, and kleg (p ≥ 0.10) but a shorter tf (p ≥ 0.03) from pre- to post-intervention were seen. The prevalence of high responders was 42% (RE = -5.7 ± 2.4%). Among high responders, there were no changes in running biomechanics except participants following CSET-DYN who increased their SF (+3%). These results indicate that improvements in RE obtained through CSET-PLY and CSET-DYN involve minimal to no changes in running biomechanics and that there was not a training modality, which was better than the other. More detailed biomechanical assessments involving kinematics, kinetics, and electromyography could shed light on the underlying mechanisms of RE improvement.

Comparison of different machine learning models to enhance sacral acceleration-based estimations of running stride temporal variables and peak vertical ground reaction force.

Machine learning (ML) was used to predict contact (tc) and flight (tf) time, duty factor (DF) and peak vertical force (Fv,max) from IMU-based estimations. One hundred runners ran on an instrumented treadmill (9-13 km/h) while wearing a sacral-mounted IMU. Linear regression (LR), support vector regression and two-layer neural-network were trained (80 participants) using IMU-based estimations, running speed, stride frequency and body mass. Predictions (remaining 20 participants) were compared to gold standard (kinetic data collected using the force plate) by calculating the mean absolute percentage error (MAPE). MAPEs of Fv,max did not significantly differ among its estimation and predictions (P = 0.37), while prediction MAPEs for tc, tf and DF were significantly smaller than corresponding estimation MAPEs (P ≤ 0.003). There were no significant differences among prediction MAPEs obtained from the three ML models (P ≥ 0.80). Errors of the ML models were equal to or smaller than (≤32%) the smallest real difference for the four variables, while errors of the estimations were not (15-45%), indicating that ML models were sufficiently accurate to detect a clinically important difference. The simplest ML model (LR) should be used to improve the accuracy of the IMU-based estimations. These improvements may be beneficial when monitoring running-related injury risk factors in real-world settings.

Accurate estimation of peak vertical ground reaction force using the duty factor in level treadmill running.

This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimate the peak vertical ground reaction force ( F v , max ) and (2) to compare the estimated F v , max to force plate gold standard (GSM). One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force (1000 Hz) and kinematic (200 Hz) data were acquired with an instrumented treadmill and an optoelectronic system, respectively, to assess force-plate and kinematic based DFs. SMM linearly relates F v , max to the inverse of DF because DF was analytically associated with the inverse of the average vertical force during ground contact time and the latter was very highly correlated to F v , max . No systematic bias and a 4% root mean square error (RMSE) were reported between GSM and SMM using force-plate based DF values when considering all running speeds together. Using kinematic based DF values, SMM reported a systematic but small bias (0.05BW) and a 5% RMSE when considering all running speeds together. These findings support the use of SMM to estimate F v , max during level treadmill runs at endurance speeds if underlying DF values are accurately measured.

Using statistical parametric mapping to assess the association of duty factor and step frequency on running kinetic.

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.

PIMP Your Stride: Preferred Running Form to Guide Individualized Injury Rehabilitation.

Despite the wealth of research on injury prevention and biomechanical risk factors for running related injuries, their incidence remains high. It was suggested that injury prevention and reconditioning strategies should consider spontaneous running forms in a more holistic view and not only the injury location or specific biomechanical patterns. Therefore, we propose an approach using the preferred running form assessed through the Volodalen® method to guide injury prevention, rehabilitation, and retraining exercise prescription. This approach follows three steps encapsulated by the PIMP acronym. The first step (P) refers to the preferred running form assessment. The second step (I) is the identification of inefficiency in the vertical load management. The third step (MP) refers to the movement plan individualization. The answers to these three questions are guidelines to create individualized exercise pathways based on our clinical experience, biomechanical data, strength conditioning knowledge, and empirical findings in uninjured and injured runners. Nevertheless, we acknowledge that further scientific justifications with appropriate clinical trials and mechanistic research are required to substantiate the approach.

Examination of running pattern consistency across speeds.

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 (tc) and flight (tf) 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-tf, and low to high between DF-tc, SF-tc, and SF-tf. 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.

Duty factor and foot-strike pattern do not represent similar running pattern at the individual level.

Runners were classified using their duty factor (DF) and using their foot-strike pattern (FSP; rearfoot, midfoot, or forefoot strikers), determined from their foot-strike angle (FSA). High and low DF runners showed different FSPs but DF was assumed to not only reflect what happens at initial contact with the ground (more global than FSP/FSA). Hence, FSP and DF groups should not necessarily be constituted by the same runners. However, the relation between FSP and DF groups has never been investigated, leading to the aim of this study. One hundred runners ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body kinematics (200 Hz) were acquired by an instrumented treadmill and optoelectronic system and were used to classify runners according to their FSA and DF. Weak correlations were obtained between FSA and DF values and a sensitivity of 50% was reported between FSP and DF groups, i.e., only one in two runners was attributed to the DF group supposedly corresponding to the FSP group. Therefore, 'local' FSP/FSA and DF do not represent similar running pattern information when investigated at the individual level and DF should be preferred to FSP/FSA when evaluating the global running pattern of a runner.

Non-South East Asians have a better running economy and different anthropometrics and biomechanics than South East Asians.

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.

A Single Sacral-Mounted Inertial Measurement Unit to Estimate Peak Vertical Ground Reaction Force, Contact Time, and Flight Time in Running.

Peak vertical ground reaction force (Fz,max), contact time (tc), and flight time (tf) are key variables of running biomechanics. The gold standard method (GSM) to measure these variables is a force plate. However, a force plate is not always at hand and not very portable overground. In such situation, the vertical acceleration signal recorded by an inertial measurement unit (IMU) might be used to estimate Fz,max, tc, and tf. Hence, the first purpose of this study was to propose a method that used data recorded by a single sacral-mounted IMU (IMU method: IMUM) to estimate Fz,max. The second aim of this study was to estimate tc and tf using the same IMU data. The vertical acceleration threshold of an already existing IMUM was modified to detect foot-strike and toe-off events instead of effective foot-strike and toe-off events. Thus, tc and tf estimations were obtained instead of effective contact and flight time estimations. One hundred runners ran at 9, 11, and 13 km/h. IMU data (208 Hz) and force data (200 Hz) were acquired by a sacral-mounted IMU and an instrumented treadmill, respectively. The errors obtained when comparing Fz,max, tc, and tf estimated using the IMUM to Fz,max, tc, and tf measured using the GSM were comparable to the errors obtained using previously published methods. In fact, a root mean square error (RMSE) of 0.15 BW (6%) was obtained for Fz,max while a RMSE of 20 ms was reported for both tc and tf (8% and 18%, respectively). Moreover, even though small systematic biases of 0.07 BW for Fz,max and 13 ms for tc and tf were reported, the RMSEs were smaller than the smallest real differences [Fz,max: 0.28 BW (11%), tc: 32.0 ms (13%), and tf: 32.0 ms (30%)], indicating no clinically important difference between the GSM and IMUM. Therefore, these results support the use of the IMUM to estimate Fz,max, tc, and tf for level treadmill runs at low running speeds, especially because an IMU has the advantage to be low-cost and portable and therefore seems very practical for coaches and healthcare professionals.

There Is No Global Running Pattern More Economic Than Another at Endurance Running Speeds.

PURPOSE: The subjective Volodalen® score (V®score) and the objective duty factor metric can both assess global running patterns. The authors aimed to investigate the relation between running economy (RE) at endurance running speeds and the global running pattern quantified using both subjective and objective measures. METHODS: RE and 3-dimensional whole-body kinematics were acquired by indirect calorimetry and an optoelectronic system, respectively, for 52 trained runners during treadmill runs at 10, 12, and 14 km/h. RESULTS: Correlations between RE and V®score and RE and duty factor were negligible and nonsignificant across speeds tested (P ≥ .20), except for a low and significant correlation between RE and V®score at 10 km/h. CONCLUSIONS: These findings suggest there is no global running pattern more economic than another at endurance running speeds. Therefore, there is no advantage of choosing, favoring, or prescribing one specific global running pattern along a continuum based on V®score or duty factor metrics, and coaches should not try to modify the spontaneous running pattern of runners at endurance running speed to improve RE.

A Multivariate Polynomial Regression to Reconstruct Ground Contact and Flight Times Based on a Sine Wave Model for Vertical Ground Reaction Force and Measured Effective Timings.

Effective contact ( t c e ) and flight ( t f e ) times, instead of ground contact ( t c ) and flight ( t f ) times, are usually collected outside the laboratory using inertial sensors. Unfortunately, t c e and t f e cannot be related to t c and t f because the exact shape of vertical ground reaction force is unknown. However, using a sine wave approximation for vertical force, t c e and t c as well as t f e and t f could be related. Indeed, under this approximation, a transcendental equation was obtained and solved numerically over a t c e  x   t f e grid. Then, a multivariate polynomial regression was applied to the numerical outcome. In order to reach a root-mean-square error of 0.5 ms, the final model was given by an eighth-order polynomial. As a direct application, this model was applied to experimentally measured t c e values. Then, reconstructed t c (using the model) was compared to corresponding experimental ground truth. A systematic bias of 35 ms was depicted, demonstrating that ground truth t c values were larger than reconstructed ones. Nonetheless, error in the reconstruction of t c from t c e was coming from the sine wave approximation, while the polynomial regression did not introduce further error. The presented model could be added to algorithms within sports watches to provide robust estimations of t c and t f in real time, which would allow coaches and practitioners to better evaluate running performance and to prevent running-related injuries.

A novel kinematic detection of foot-strike and toe-off events during noninstrumented treadmill running to estimate contact time.

Contact time (tc) relies upon the accuracy of foot-strike and toe-off events, for which ground reaction force (GRF) is the gold standard. However, force plates are not always available, e.g., when running on a noninstrumented treadmill. In this situation, a kinematic algorithm (KA) - an algorithm based on motion capture data - might be used if it performs equally for all foot-strike angles across speeds. The purpose of this study was to propose a novel KA, using a combination of heel and toe kinematics (three markers per foot), to detect foot-strike and toe-off and compare it to GRF at different speeds and across foot-strike angles. One hundred runners ran at 9 km/h, 11 km/h, and 13 km/h. Force data and whole-body kinematic data were acquired by an instrumented treadmill and optoelectronic system. Foot-strike and toe-off showed small systematic biases between GRF and KA at all speeds (≤5 ms), except toe-off at 11 km/h (no bias). The root mean square error (RMSE) was ≤9 ms and was mostly constant across foot-strike angles for toe-off (7.4 ms) but not for foot-strike (4.1-11.1 ms). Small systematic biases (≤8 ms) and significant differences (P ≤ 0.01) were reported for tc at all speeds, and the RMSE was ≤14 ms (≤5%). The RMSE for tc increased with increasing foot-strike angle (3.5-5.4%). Nonetheless, this novel KA computed smaller errors than existing methods for foot-strike, toe-off, and tc. Therefore, this study supports the use of this novel KA to accurately estimate foot-strike, toe-off, and tc from kinematic data obtained during noninstrumented treadmill running independent of the foot-strike angle.

Estimating effective contact and flight times using a sacral-mounted inertial measurement unit.

Effective ground contact (tce) and flight (tfe) times were proven to be more appropriate to decipher the landing-take-off asymmetry of running than usual ground contact (tc) and flight (tf) times. To measure these effective timings, force plate is the gold standard method (GSM), though not very portable overground. In such situation, alternatives could be to use portable tools such as inertial measurement unit (IMU). Therefore, the purpose of this study was to propose a method that uses the vertical acceleration recorded using a sacral-mounted IMU to estimate tce and tfe and to compare these estimations to those from GSM. Besides, tce and tfe were used to evaluate the landing-take-off asymmetry, which was further compared to GSM. One hundred runners ran at 9, 11, and 13 km/h. Force data (200 Hz) and IMU data (208 Hz) were acquired by an instrumented treadmill and a sacral-mounted IMU, respectively. The comparison between GSM and IMU method depicted root mean square error ≤22 ms (≤14%) for tce and tfe along with small systematic biases (≤20 ms) for each tested speed. These errors are similar to previously published methods that estimated usual tc and tf. The systematic biases on tce and tfe were subtracted before calculating the landing-take-off asymmetry, which permitted to correctly evaluate it at a group level. Therefore, the findings of this study support the use of this method based on vertical acceleration recorded using a sacral-mounted IMU to estimate tce and tfe for level treadmill runs and to evaluate the landing-take-off asymmetry but only after subtraction of systematic biases and at a group level.

Both a single sacral marker and the whole-body center of mass accurately estimate peak vertical ground reaction force in running.

BACKGROUND: While running, the human body absorbs repetitive shocks with every step. These shocks can be quantified by the peak vertical ground reaction force (Fv,max). To measure so, using a force plate is the gold standard method (GSM), but not always at hand. In this case, a motion capture system might be an alternative if it accurately estimates Fv,max. RESEARCH QUESTION: The purpose of this study was to estimate Fv,max based on motion capture data and validate the obtained estimates with force plate-based measures. METHODS: One hundred and fifteen runners participated at this study and ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body kinematics (200 Hz) were acquired with an instrumented treadmill and an optoelectronic system, respectively. The vertical ground reaction force was reconstructed from either the whole-body center of mass (COM-M) or sacral marker (SACR-M) accelerations, calculated as the second derivative of their respective positions, and further low-pass filtered using several cutoff frequencies (2-20 Hz) and a fourth-order Butterworth filter. RESULTS: The most accurate estimations of Fv,max were obtained using 5 and 4 Hz cutoff frequencies for the filtering of COM and sacral marker accelerations, respectively. GSM, COM-M, and SACR-M were not significantly different at 11 km/h but were at 9 and 13 km/h. The comparison between GSM and COM-M or SACR-M for each speed depicted root mean square error (RMSE) smaller or equal to 0.17BW (≤6.5 %) and no systematic bias at 11 km/h but small systematic biases at 9 and 13 km/h (≤0.09 BW). COM-M gave systematic biases three times smaller than SACR-M and two times smaller RMSE. SIGNIFICANCE: The findings of this study support the use of either COM-M or SACR-M using data filtered at 5 and 4 Hz, respectively, to estimate Fv,max during level treadmill runs at endurance speeds.

Different plantar flexors neuromuscular and mechanical characteristics depending on the preferred running form.

Two main types of endurance runners have been identified: aerial runners (AER), who have a larger flight time, and terrestrial runners (TER), who have a longer ground contact time. The purpose of this study was to assess the neuromuscular characteristics of plantar flexors between AER and TER runners. Twenty-four well-trained runners participated in the experiment. They were classified either in a TER or AER group according to the Volodalen® scale. Plantar flexors' maximal rate of force development (RFD) and maximal voluntary contraction force (MVC) were assessed. Percutaneous electrical stimulation was delivered to the posterior tibial nerve to evoke maximal M-waves and H-reflexes of the triceps surae muscles. These responses, as well as voluntary activation, muscle potentiation, and V-waves, were recorded by superimposing stimulations to MVCs. RFD was significantly higher in AER than in TER, while MVC remained unchanged. This was accompanied by higher myoelectrical activity recorded in the soleus muscle. While M-waves and other parameters remained unchanged, maximal H-reflex was significantly higher in AER than in TER, still in soleus only. The present study raised the possibility of different plantar flexors' neuromuscular characteristics according to running profile. These differences seemed to be focused on the soleus rather than on the gastrocnemii.

Does Characterizing Global Running Pattern Help to Prescribe Individualized Strength Training in Recreational Runners?

This study aimed to determine if concurrent endurance and strength training that matches the global running pattern would be more effective in increasing running economy (RE) than non-matched training. The global running pattern of 37 recreational runners was determined using the Volodalen® method as being aerial (AER) or terrestrial (TER). Strength training consisted of endurance running training and either plyometric (PLY) or dynamic weight training (DWT). Runners were randomly assigned to a matched (n = 18; DWT for TER, PLY for AER) or non-matched (n = 19; DWT for AER, PLY for TER) 8 weeks concurrent training program. RE, maximal oxygen uptake V̇O2max) and peak treadmill speed at V̇O2max (PTS) were measured before and after the training intervention. None of the tested performance related variables depicted a significant group effect or interaction effect between training and grouping (p ≥ 0.436). However, a significant increase in RE, V̇O2max, and PTS (p ≤ 0.003) was found after the training intervention. No difference in number of responders between matched and non-matched groups was observed for any of the performance related variables (p ≥ 0.248). In recreational runners, prescribing PLT or DWT according to the global running pattern of individuals, in addition to endurance training, did not lead to greater improvements in RE.

Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter.

Equations predicting stride frequency (SF) and duty factor (DF) solely based on running speed have been proposed. However, for a given speed, kinematics vary depending on the global running pattern (GRP), i.e., the overall individual movement while running, which depends on the vertical oscillation of the head, antero-posterior motion of the elbows, vertical pelvis position at ground contact, antero-posterior foot position at ground contact, and strike pattern. Hence, we first verified the validity of the aforementioned equations while accounting for GRP. Kinematics during three 50-m runs on a track (n = 20) were used with curve fitting and linear mixed effects models. The percentage of explained variance was increased by ≥133% for DF when taking into account GRP. GRP was negatively related to DF (p = 0.004) but not to SF (p = 0.08), invalidating DF equation. Second, we assessed which parameters among anthropometric characteristics, sex, training volume, and GRP could relate to SF and DF in addition to speed, using kinematic data during five 30-s runs on a treadmill (n = 54). SF and DF linearly increased and quadratically decreased with speed (p < 0.001), respectively. However, on an individual level, SF was best described using a second-order polynomial equation. SF and DF showed a non-negligible percentage of variance explained by random effects (≥28%). Age and height were positively and negatively related to SF (p ≤ 0.05), respectively, while GRP was negatively related to DF (p < 0.001), making them key parameters to estimate SF and DF, respectively, in addition to speed.

Duty Factor Is a Viable Measure to Classify Spontaneous Running Forms.

Runners were classified using two different methods based on their spontaneous running form: (1) subjectively using the V®score from the Volodalen® scale, leading to terrestrial and aerial groups; and (2) objectively using the duty factor (DF), leading to high (DFhigh) and low (DFlow) DF groups. This study aimed to compare these two classification schemes. Eighty-nine runners were divided in two groups using the V®score (VOL groups) and were also ranked according to their DF. They ran on a treadmill at 12 km·h-1 with simultaneous recording of running kinematics, using a three-dimensional motion capture system. DF was computed from data as the ratio of ground contact time to stride time. The agreement (95% confidence interval) between VOL and DF groups was 79.8% (69.9%, 87.6%), with relatively high sensitivity (81.6% (68.0%, 91.2%)) and specificity (77.5% (61.6%, 89.2%)). Our results suggest that the DF and V®score reflect similar constructs and lead to similar subgroupings of spontaneous running form (aerial runners if DF < 27.6% and terrestrial runners if DF > 28.8% at 12 km·h-1). These results suggest that DF could be a useful objective measure to monitor real-time changes in spontaneous running form using wearable technology. As a forward-looking statement, spontaneous changes in running form during racing or training could assist in identifying fatigue or changes in environmental conditions, allowing for a better understanding of runners.

Recognition of Foot Strike Pattern in Asian Recreational Runners.

Close to 90% of recreational runners rearfoot strike in a long-distance road race. This prevalence has been obtained from North American cohorts of runners. The prevalence of rearfoot strikers has not been extensively examined in an Asian population of recreational runners. Therefore, the aim of this study was to determine the prevalence of rearfoot, midfoot, and forefoot strikers during a long-distance road race in Asian recreational runners and compare this prevalence to reported values in the scientific literature. To do so, we classified the foot strike pattern of 950 recreational runners at the 10 km mark of the Singapore marathon (77% Asian field). We observed 71.1%, 16.6%, 1.7%, and 10.6% of rearfoot, midfoot, forefoot, and asymmetric strikers, respectively. Chi-squared tests revealed significant differences between our foot strike pattern distribution and those reported from North American cohorts (P < 0.001). Our foot strike pattern distribution was similar to one reported from elite half-marathon runners racing in Japan (Fisher exact test, P = 0.168). We conclude that the prevalence of rearfoot strikers is lower in Asian than North American recreational runners. Running research should consider and report ethnicity of participants given that ethnicity can potentially explain biomechanical differences in running patterns.