Influence of Step-Width Manipulation on Running Biomechanics

Summary

The present protocol outlines an experimental setup designed to investigate the influence of step width manipulation on running biomechanics using a motion capture system. The objective is to expand relevant datasets and examine the effects of varying step widths on the kinematic chain of the human lower limb.

Abstract

Step width is a critical factor influencing lower limb biomechanics during running, significantly affecting stability, performance, and injury risk. Understanding these effects is essential for optimizing running performance and minimizing injury risk. This study evaluated the effects of varying step widths on lower limb biomechanics at different running speeds. Thirteen healthy Chinese males (aged 20-24) participated in the study, running at speeds of 3.0 m/s and 3.7 m/s using six distinct step widths: the preferred step width and five variations (reductions of 13% and 6.5%, and increases of 6.5%, 13%, and 25%, based on leg length). Data were collected using a motion capture system and force plates and analyzed through repeated measures ANOVA and correlation tests. The results indicated that wider step widths significantly reduced peak knee abduction moments and hip adduction angles, whereas narrower step widths increased knee joint loading. These findings have important implications for clinicians and runners, suggesting that careful step width selection can help reduce injury risk and enhance running efficiency. This study contributes a new dataset that lays the foundation for future research into the relationship between step width and running biomechanics and serves as a reference for training and rehabilitation practices.

Introduction

Environmental factors, including spatial and temporal elements, directly influence human daily movement. Individuals may adopt different postures and movement patterns while running and walking in various environmental conditions. It is well-established that altering running techniques can impact body biomechanics, with step width being closely associated with stability and balance during human running^1,2. Step width is defined as the mediolateral distance between the midfoot and the initial ground contact of each foot, representing a variable in the frontal plane³. During walking and running, short-term variations in step width can influence lower extremity biomechanics across three planes^3,4,5.

Numerous studies have demonstrated that step width significantly affects the biomechanics of lower limb joints, kinematics, and kinetics during running. A wider step width reduces hip adduction angles, knee abduction moments, and rearfoot eversion angles, contributing to enhanced stability and potentially lowering injury risk^6,7. Conversely, a narrower step width increases knee internal rotation and hip adduction angles, potentially elevating joint loads. Specifically, a narrow step width has been associated with increased variations in knee internal rotation and peak knee abduction torque compared to a normal step width^6,7. Additionally, wider step widths have been shown to reduce tibial loading, thereby decreasing stress on the tibia during running⁸. These findings underscore the critical role of step width in influencing running biomechanics, highlighting its importance in optimizing performance and minimizing injury risk.

Studies have further demonstrated that walking and running speeds influence the biomechanical parameters of the lower limbs^9,10,11,12. However, the effects of step width change on biomechanics at varying exercise speeds remain unclear, and limited scientific data regarding human movement under different speed and step width conditions are available. Therefore, this study aims to investigate the impact of step width changes on lower limb biomechanics at different speeds, focusing on key parameters such as hip adduction angle and knee abduction moment.

To address this, a dataset was established comprising 13 healthy male participants aged 20-24 years, including C3D files and ready-to-use kinematic data. Participants were instructed to run at speeds of 3.0 m/s and 3.7 m/s using six distinct step widths. The selection of these step widths and speeds was informed by existing research findings and the current state of open-source datasets on gait biomechanics in the literature^{13,14,15,16,17}. This study aims to examine the acute effects of step width changes on the lower limb kinetic chain while expanding the dataset to provide valuable insights into the relationship between step width and lower limb biomechanics.

Protocol

The study received approval from the Ethics Committee of the Research Institute at Ningbo University (Approval Number: ty2022001). All participants provided written informed consent after being briefed on the purpose, requirements, and procedures of the experiment. Details of the consumables, equipment, and software used are listed in the Table of Materials.

1. Experimental preparation

1. Equipment preparation
   1. Position eight infrared cameras in appropriate locations during equipment calibration and check for any reflections in the experimental area (e.g., reflective markers on participants' clothing). Remove or cover the reflections, and ensure sufficient lighting.
   2. Insert the paired encryption dongle into the PC's parallel port. Start the motion capture cameras, tracking software, force platform amplifier, and ADC.
   3. Open the tracking software and wait for the eight cameras to initialize. Confirm the process is complete when the camera lights turn from red to green.
   4. Switch to Camera mode, and expand the System Resources panel on the left. Select all eight cameras.
   5. Adjust the settings in the left panel under Properties. Set the flash intensity to 0.95-1, gain to 1x, and grayscale mode to Auto. Under Centroid Fitting, set the threshold to 0.2-0.4, minimum circularity ratio to 0.5, and maximum blob height to 50.
   6. Position the T-frame with markers at the center of the motion capture area. Re-select all eight cameras from the toolbar on the left.
   7. Perform calibration in the Tools panel on the right. Select the 5 marker Wand & T-Frame calibration object from the T-frame list.
   8. Click on the Stop button under the Mask Camera option in the System Preparation Tools panel. After the screen turns blue, click on the button again to revert it to the Stop state.
   9. Click on the Start button under the Calibrate Cameras option. Move the T-frame back and forth within the capture range, ensuring the swinging height matches the camera's focal height. Stop when the blue lights on the cameras stop flashing.
   10. Switch the view to 3D Perspective, and place the T-frame back at the center of the motion capture area. Ensure alignment with the force platform boundaries.
   11. Click on the Start button under the Set Volume Origin option in the right panel.
2. Pressure platform preparation
   1. Position two embedded force plates in the center of the motion capture area and synchronize them at 1000 Hz.
   2. Mark six step widths on the force platform using different colored tapes to represent each condition, including the preferred step width and five variations (13% and 6.5% reductions, and 25%, 13%, and 6.5% increases in leg length). Connect the platform to the PC for data collection.
      NOTE: Ensure that the system records the foot's anterior-posterior (Y-axis) coordinates upon contact, with yellow tape marking the preferred step width and other colors indicating variations.
3. Timing system preparation
   1. Place a single-beam electronic timing gate on a tripod to record the participants' running speed as they pass over the force plates.
      NOTE: Ensure the distance between the electronic timing gates is 3 m (as shown in Figure 1).

2. Participants preparation

1. Obtain written informed consent from participants, briefly explain the study's purpose and procedures, and encourage questions. Ensure all participants are right-leg dominant to minimize variability.
   NOTE: The present study involves 13 healthy Chinese males, with an average age of 22.7 years, weight of 70.9 kg, and height of 1.75 m. Ensure no lower limb injuries or foot deformities in the past six months. This sample selection reduces the influence of age and sex on biomechanical parameters, ensuring statistical validity and reproducibility. The sample size is sufficient for analyzing the effects of step width on biomechanics.
2. Instruct participants to wear tight-fitting athletic pants and standardized experimental shoes to minimize gait interference. Remove excess clothing.
3. Collect and record anthropometric data: height, weight, leg length, shoulder width, and hip width.
   NOTE: Measure leg length from the anterior superior iliac spine to the lateral malleolus. Measure shoulder width and hip width as the direct distances between respective anatomical landmarks.
4. Keep participants' skin dry and attach reflective markers to anatomical landmarks using double-sided tape.
   NOTE: Use 38 reflective markers with specific positions and label names detailed in Table 1. Ensure precise placement and secure attachment of the markers. For areas with high motion or uneven surfaces, reinforce the markers with muscle tape or skin-friendly adhesive. After placing the markers, take photographs from the front, back, and sides for documentation.
5. Instruct participants to jog for 5 min as a warm-up before starting the static and dynamic recordings.

3. Static calibration

1. Launch the tracking software and create a new database by selecting New Database from the toolbar. In the Data Management section, choose New Patient Classification, then New Patient, and finally, click on New Session to set up a participant information database.
2. Click on Go Live in the left toolbar, then use the Split horizontal button in the View interface. Select graphics to view trajectory counts.
3. Click on the Subject Preparation in the toolbar. Instruct the participant to stand with feet shoulder-width apart, one foot on the force platform, arms parallel to the shoulders, and looking straight ahead. Click on Start to begin data collection, and hold the position for 10 s. Click on Stop to complete the static capture.
   NOTE: Switch to "3D Perspective" mode to ensure the camera captures at least one frame showing the 38 reflective markers on the participant's body.

4. Dynamic trials

1. Inform participants to prepare for the experiment and enter the experimental state.
2. Software operations
   1. Click on Go Live in the left Resources panel and Capture in the right Tools panel. Once participants are ready, start the trial by clicking on Start and end it by clicking on Stop after completion.
      NOTE: Label different trials with varying step widths in the "Trial Name" section.
3. Conduct the preferred step width experiment, capturing motion at three different speeds.
   1. Instruct participants to walk naturally along a straight pathway with two force platforms, placing the left foot on platform A (Kistler) and the right foot on platform B (AMTI).
   2. Consider the trial successful if the time to complete one run (passing over the force platforms) falls between 0.95-1.05 s at 3.0 m/s or between 0.76-0.86 s at 3.7 m/s. Repeat the trial if the timing does not meet these criteria to minimize the influence of speed fluctuations on running biomechanics. The primary objective is to investigate the influence of speed.
      NOTE: These time ranges were calculated based on predefined running speeds (3.0 m/s and 3.7 m/s) and the known distance between the electronic timing gates. This ensures participants maintain a consistent running speed throughout the trial and accurately reflect the experimental conditions.
   3. Instruct participants to run at a speed of 3.7 m/s on a straight pathway with two force platforms.
      NOTE: If the time passing over the force platform is between 0.76-0.86 s, consider the trial successful; otherwise, retry. Ensure participants consistently place their left foot on platform A and right foot on platform B. Prior studies have shown significant biomechanical differences at various speeds^9,10,11,12. Instruct participants to practice multiple times before formal trials to ensure a natural gait. Monitor the trials closely, ensuring correct execution and that markers stay in place. Each condition requires at least five successful trials.
4. Measuring the preferred step width and setting different step widths
   1. Based on the definition of step width and previous research, measure the participants' preferred step width in three movement modes^8,18,19,20. Using the motion capture system, ensure the Y-axis in the lab coordinate system represents the mediolateral (side-to-side) direction.
      1. Record the Y-axis coordinates of the left and right heel markers during initial ground contact. Calculate the preferred step width as the difference in the Y-axis coordinates of the two heels, providing a direct measure of the lateral distance between the feet.
   2. Mark the force platforms with different colored tapes corresponding to five step width conditions (step width reduced or increased by 13%, 6.5%, or 25% of leg length). The yellow tape indicates the preferred step width.
      NOTE: Previous studies have demonstrated significant biomechanical differences when step width changes by 13% relative to leg length^5,8,18,19. Based on this finding, introduce additional step width conditions.
   3. Instruct the participants to walk straight while focusing ahead. Ensure the left foot steps on the yellow tape on platform A and the right foot steps on a different colored tape on platform B. Randomly assign specific tape colors to alter step width.
      NOTE: Collect at least five successful trials for each step width condition.
   4. Instruct participants to run at speeds of 3.0 m/s and 3.7 m/s with the five different step widths.
      NOTE: Ensure participants follow instructions correctly and monitor for marker detachment. Collect at least five successful trials for each condition, and ensure adequate rest time after each test.

5. Data processing

1. After the experiment, manually process all collected data using compatible software. Sequentially connect the data according to the marker points set in Table 1.
2. Check each static or dynamic trajectory for any missing or interrupted markers on the participants. Use the point connection/fill tool in the software to manually reconnect the missing markers, referencing nearby markers for accuracy.
3. Analyze ground reaction forces (GRFs) during dynamic activities by setting a vertical threshold of 20 N to detect foot contact and toe-off, defining the stance phase of each gait cycle.
   NOTE: GRF data from heel contact to toe-off defines one gait cycle and is saved as raw C3D files.
4. Export the processed data in C3D, MOT, TRC, and CSV formats. Ensure each participant completes approximately 55 trials across static, walking, and running conditions with six different step widths. Use the data to compare step widths and limb characteristics.
   NOTE: Despite strict protocol adherence, some participants may have incomplete data due to marker trajectory disappearance in the equipment system.

6. Statistical analysis

1. Analyze the data using statistical software (e.g., SPSS, Python), focusing on the effects of step width and running speed on lower limb biomechanics.
2. Use repeated measures ANOVA to analyze biomechanical parameters, applying Bonferroni correction for post-hoc tests if necessary.
3. 6.3. Use non-parametric methods like the Friedman test when normality and sphericity assumptions are not met. Set all significance levels at p < 0.05.

Results

After the experiment and data processing, the processed marker trajectory and simulated ground reaction force (GRF) data were saved in C3D files, as outlined in Table 1. The C3D.zip folder contains the raw dataset obtained from the motion capture experiment. All data records are listed in Table 2. The dataset is organized into folders, each representing one of the 13 participants. Within these subfolders, the experimental files are named NNN_CV_TT.XXX, where NNN denotes the part...

Discussion

The impact of step width on human running is a multifaceted and significant issue. Step width refers to the lateral distance between the center of the heel and the ground upon initial contact of each foot³. Changing step width may affect stability, balance, biomechanics, and energy expenditure during running. Earlier research suggests that an increase in step width may reduce the hip adduction angle, knee abduction moment, and peak impulse. Conversely, narrowing the step width could potentially in...

Materials

Name	Company	Catalog Number	Comments
AMTI Force Plate	AMTI	OR6-7	Watertown, MA, USA
Colored Tape for Floor Marking	Generic	-	Used to mark different step widths on force plates, purchased online
Kistler Force Plate	Kistler	9260AA	Winterthur, Switzerland
MATLAB	MathWorks	Version R2021b	Data processing and modeling
Reflective Markers	Vicon Metrics Ltd.	-	Used for marking participant body points
Single-Beam Electronic Timing Gate	Brower Timing System	-	Draper, UT, USA, Used to record running speed
Standard Experimental Shoes	Designated Brand	-	Standardized shoes to minimize gait interference
Vicon Motion Capture System	Vicon Metrics Ltd.	Vicon T-Series	Oxford, UK, Used for motion capture
Vicon Nexus Software	Vicon Metrics Ltd.	Version 1.8.5A	Data collection and analysis