## 2 Materials and Methods
Tracking clusters were bilaterally affixed to each thigh, shank, and heel of the shoe. Following marker placement, a static standing calibration trial was recorded, and anatomic markers were removed. Identical marker sets were used for both HFgait and cycling trials; however, the cycling conditions included additional markers to define the crank arms and pedals. During all exercise testing, three-dimensional marker trajectories were recorded using an 8-camera motion capture system (240 Hz, Qualisys, Goteborg, Sweden) for ten seconds. Participants performed all cycling conditions on a Monark cycle ergometer (828e, Monark, Vansbro, Sweden). Raw three-dimensional marker trajectories were exported into Visual 3D for analysis (Version 6, C-Motion, Inc., Germantown, MD, USA). Data were low-pass filtered using a recursive zero-lag fourth-order Butterworth filter with a cutoff frequency of 10 Hz [17]. Angular computations were completed using a Cardan rotational sequence (X-Y-Z). Ankle dorsiflexion and inversion, knee extension and adduction, and hip flexion and adduction defined positive joint rotations. For HFgait trials, heel strike and toe-off of each step defined the stance and swing phases of gait and were determined using kinematic determinations of each event in Visual 3D [18]. All stance phases for HFgait trials and all pedal cycles for cycling trials were averaged together for each participant, and were compared between conditions across an entire stride (stance and swing phases) and cycling trials were compared across an entire revolution of the pedal. Maximum, minimum and range of motion joint angles were determined for each participant in Visual 3D by averaging the maximum, minimum, and range of motion joint angles across gait and pedal cycles recorded. Average kinematic time-series curves were computed for each participant by calculating the average time-series across all gait and pedal cycles recorded. Each time-series was then time-normalized to 101 data points (0-100%). This allowed the comparison in the time domain between HFgait and cycling patterns of different lengths. Because both exercise modalities are cyclical movements, phase offset between both curves can maximize differences between the two patterns that are not meaningful from a kinematic perspective. Therefore, a phase offset reduction algorithm (POR) using cross correlationswas used to identify the optimal phase shift (lag) of the cycling pattern that could result in the strongest correlation with HFgait. The offset reduction analysis was performed in MATLAB (2024a, MathWorks Inc., Natick, MA, USA) using the HFgait curve as the parent curve, and adjusting the cycling curve with the optimal phase shift. A continuous analysis of the time-normalized joint angle curves was performed with and without POR. Without POR, to spatially align the start of each phase between HFgait and cycling in a similar position (i.e., the foot oriented near the bottom of each cyclical motion), the pedal cycle was defined from bottom dead center to the next bottom dead center. HFgait cycles were defined from the heel strike to the next heel strike. For the discrete analysis, paired sample t-tests were performed to identify differences between discrete variables. Cohen's d was used to calculate effect sizes. One-dimensional statistical parametric mapping (SPM-1D) paired sample t-tests were used to identify differences between cycling and HFgait for each joint motion during the cycle. SPM was used to compare HFgait to cycling with and without the POR in the cycling signal. We tested the null hypothesis that there are no differences in 1D biomechanical data between the different exercise modalities by statistically examining the entire times series using SPM. All SPM analyses were conducted in MATLAB using the open-source software package spm1D M.0.4.10 (www.spm1d.org). A significance level of 0.05 was used for all statistical testing.