The IFCP distribution changes were due to intraindividual alterations in IFCP at higher speeds. That is, 45% of the runners made one or even two "transitions" toward a more anterior IFCP (and 3% shows some other type of transition between initial foot contact styles as speed increases). However, 52% of the runners remained with the same IFCP.

This study assessed kinematic differences between different foot strike patterns and their relationship with peak vertical instantaneous loading rate (VILR) of the ground reaction force (GRF). Fifty-two runners ran at 3.2 m · s while we recorded GRF and lower limb kinematics and determined foot strike pattern: Typical or Atypical rearfoot strike (RFS), midfoot strike (MFS) of forefoot strike (FFS). Typical RFS had longer contact times and a lower leg stiffness than Atypical RFS and MFS. Typical RFS showed a dorsiflexed ankle (7.2 ± 3.5°) and positive foot angle (20.4 ± 4.8°) at initial contact while MFS showed a plantar flexed ankle (-10.4 ± 6.3°) and more horizontal foot (1.6 ± 3.1°). Atypical RFS showed a plantar flexed ankle (-3.1 ± 4.4°) and a small foot angle (7.0 ± 5.1°) at initial contact and had the highest VILR. For the RFS (Typical and Atypical RFS), foot angle at initial contact showed the highest correlation with VILR (r = -0.68). The observed higher VILR in Atypical RFS could be related to both ankle and foot kinematics and global running style that indicate a limited use of known kinematic impact absorbing "strategies" such as initial ankle dorsiflexion in MFS or initial ankle plantar flexion in Typical RFS.

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.

Purpose. We aimed to determine the effect of speed-induced changes in foot contact patterns on the vertical instantaneous loading rate (VILR). We hypothesized that transition runners, i.e. runners that shift towards a mid-(MF) or forefoot contact pattern (FF) when running speed increases, show smaller increases in VILR than non-transition runners, i.e. runners that remain with a rearfoot contact pattern (RF). Methods. Fifty-two male and female runners ran overground at 3.2, 4.1, 5.1 and 6.2 m s −1 . Ground reaction forces, lower limb sagittal plane knee and ankle kinematics and plantar pressures were recorded. Multi-level linear regression models were used to assess differences between transition and non-transition runners. Results. Non-transition runners experienced larger speed-induced increases in VILR (48.6 ± 2.6 BW s −1 per m s −1 ) than transition runners (-1.4 ± 7.6 BW s −1 per m s −1 ). Transition runners showed higher VILRs and a more flat foot touch down at the same pre-transition speed than non-transition runners. Conclusion. When running speed increases, some runners transition towards more anterior foot contact patterns. This reduces or even eliminates the speed-induced increase in VILR. This result is especially the case for those RF runners who already have relatively high VILRs and flat foot positioning at slower 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.

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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