A lower duty factor (DF) reflects a greater relative contribution of leg swing versus ground contact time during the running step. Increasing time on the ground has been reported in the scientific literature to both increase and decrease the energy cost (EC) of running, with DF reported to be highly variable in runners. As increasing running speed aligns running kinematics more closely with spring-mass model behaviours and re-use of elastic energy, we compared the centre of mass (COM) displacement and EC between runners with a low (DF low ) and high (DF high ) duty factor at typical endurance running speeds. Forty well-trained runners were divided in two groups based on their mean DF measured across a range of speeds. EC was measured from 4 min treadmill runs at 10, 12 and 14 km h −1 using indirect calorimetry. Temporal characteristics and COM displacement data of the running step were recorded from 30 s treadmill runs at 10, 12, 14, 16 and 18 km h −1 . Across speeds, DF low exhibited more symmetrical patterns between braking and propulsion phases in terms of time and vertical COM displacement than DF high . DF high limited global vertical COM displacements in favour of horizontal progression during ground contact. Despite these running kinematics differences, no significant difference in EC was observed between groups. Therefore, both DF strategies seem energetically efficient at endurance running speeds.

The aim was to identify the differences in lower limb kinematics used by high (DFhigh) and low (DFlow) duty factor (DF) runners, particularly their sagittal plane (hip, knee, and ankle) joint angles and pelvis and foot segment angles during stance. Fifty-nine runners were divided in two DF groups based on their mean DF measured across a range of speeds. Temporal characteristics and whole-body three-dimensional kinematics of the running step were recorded from treadmill runs at 8, 10, 12, 14, 16, and 18 km/h. Across speeds, DFhigh runners, which limit vertical displacement of the COM and promote forward propulsion, exhibited more lower limb flexion than DFlow during the ground contact time and were rearfoot strikers. On the contrary, DFlow runners used a more extended lower limb than DFhigh due to a stiffer leg and were midfoot and forefoot strikers. Therefore, two different lower limb kinematic mechanisms are involved in running and the one of an individual is reflected by the DF.

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.

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.

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.

Machine learning (ML) was used to predict contact (t c ) and ight (t f ) time, duty factor (DF) and peak vertical force (F v;max ) from IMUbased 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 neuralnetwork 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 F v;max did not signi cantly di er among its estimation and predictions (P = 0.37), while prediction MAPEs for t c , t f and DF were signi cantly smaller than corresponding estimation MAPEs (P ≤ 0.003). There were no signi cant di erences 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 di erence for the four variables, while errors of the estimations were not (15-45%), indicating that ML models were su ciently accurate to detect a clinically important di erence. The simplest ML model (LR) should be used to improve the accuracy of the IMU-based estimations. These improvements may be bene cial when monitoring runningrelated injury risk factors in real-world settings.

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