## 2 Materials and Methods
### Processing  
Dynamic trials were digitised using Qualisys Track Manager (Qualisys Medical AB, Gothenburg, Sweden) in order to identify anatomical and tracking markers, and then exported as C3D files to Visual 3D (C-Motion, Germantown, MD, USA). All data were linearly normalized to 100% of the stance phase. GRF data and marker trajectories were smoothed with cutoff frequencies of 50 Hz and 12 Hz, respectively, using a low-pass Butterworth 4 th-order zero-lag filter. Lower extremity kinematics were quantified using an XYZ Cardan sequence of rotations (where X is flexion-extension, Y is ab/adduction, and is Z is internal-external rotation). Tibial internal rotation kinematics were also calculated as described by [PERSON] et al. [37]. Finally, in order to quantify contralateral pelvic drop, the coronal plane angle of the pelvic segment was quantified with respect to the laboratory. The following three-dimensional kinematic measures were extracted for statistical analysis: (1) angle at footstrike; (2) peak angle; and (3) range of motion (representing the angular displacement from footstrike to peak angle). Running speed was also quantified within Visual 3D, using the linear velocity of the model centre of mass in the anterior direction. In accordance with the protocol of [PERSON], [38], an impulse-momentum modelling approach was utilized to calculate effective mass as a percentage of bodyweight (% BW), which was quantified in accordance with the following equation:  
Effective mass = vertical GRF integral/(\(\Delta\) foot vertical velocity + gravity * \(\Delta\) time)  
The impact peak was defined in the maximal and traditional running shoes as the first peak in vertical GRF. The time to impact peak (\(\Delta\) time) was quantified as the duration from footstrike to impact peak. For runners who lacked an impact peak, in accordance with [PERSON] et al. [39], we calculated the relative position of the impact peak as a percentage of the stance phase, based on a mean of the other runners, and positioned the impact peak here in those who did not exhibit a disenable peak in the vertical GRF-time curve. The vertical GRF integral during the period of the impact peak was calculated using a trapezoidal function. The change in foot vertical velocity (\(\Delta\) foot vertical velocity) was determined as the change in vertical foot velocity between the instances of footstrike and the impact peak [40]. The velocity of the foot was quantified using the centre of mass of the foot segment in the vertical direction, within Visual 3D [39]. The loading rate, expressed as bodyweight per second (BW/s), was also extracted by obtaining the peak increase in vertical GRF between adjacent data points using the first derivative function within Visual 3D. Furthermore, the GRF impulse expressed as bodyweight per millisecond (BW\(\cdot\)ms) during the stance phase in all three anatomical directions was calculated using a trapezoidal function. The strike index (%) was calculated as the position of the centre of pressure location at footstrike, relative to the total length of the foot [41] (Figure 2a-c). A strike index of 0-33.3% denotes a rearfoot, 33.4-66.6% a midfoot, and 66.7-100% a forefoot strike pattern. Finally, vertical limb stiffness during running was quantified using a mathematical spring-mass model [42]. Vertical limb stiffness expressed as bodyweight per metre (BW/m) was calculated from the ratio of the peak vertical GRF normalized to bodyweight (BW) to the maximum vertical compression of the leg spring, which was calculated as the change in limb length from footstrike to minimum length during the stance phase [43]. Limb length (m) was quantified as the vertical height of the proximal end of the thigh segment within Visual 3D. Following this, data during the stance phase were exported from Visual 3D into OpenSim 3.3 software (Simtk.org). A validated musculoskeletal model was used to process the biomechanical data, which were scaled to account for the anthropometrics of each runner.