Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
September 14th, 2017
This study investigated lower-limb kinematics and ground reaction force (GRF) during moderate high-heeled jogging and running. Subjects were divided into groups of experienced wearers and inexperienced wearers. A three-dimensional motion analysis system with a configured force platform captured lower-limb joint movements and GRF.
The overall goal of this experiment is to observe the effects of high-heeled shoes on lower limb mechanics during moderate high-heeled jogging and running. This method can answer the key question that in high-heel exercise, interference, such as the elevation that the heel has, may not be the single main factor causing the attenuators of the low limb mechanics. The one feature of this protocol is that with the variety of participants as experienced and inexperienced subjects, which has been readily considered in previous high heeled studies using their guardist and their method.
Begin by measuring the subject's standing height and body mass. Measure the distance between the superior iliac spine and the ankle internal condyle, the distance between the medial and lateral knee condyles, and the distance between the medial and lateral ankle condyles using measuring calipers. Next, palpate to identify anatomical landmarks.
Label each landmark on the skin using a marking pen and then attach the 16 passive retro-reflective markers on the landmarks of both sides of the lower limbs with double-sided adhesive tape. Ask the subject to change into the experimental shoe. Then have them walk, jog, and run freely along the runway until they are physiologically and psychologically comfortable with the cameras and markers on their lower limbs, and they feel like they are moving naturally.
Next, ask the subject to practice jogging along the runway at a comfortable low speed until they are able to jog steadily. Instruct them to make an effort to jog at a progressively increasing speed on a treadmill within a safe and comfortable range, known as Progressive Training. Then, have them practice running on the ground along the runway at a comfortable high speed until they are able to run steadily at this speed.
Finally, instruct the subject to try to start jogging from different starting lines within the starting area several times, to find an appropriate starting position, ensuring that the right foot naturally strikes and completely contacts the force platform when passing by. Begin by asking the subject to stand in a stationary, neutral pose in the center of the capture volume to capture the static data. Click the start button in the subject capture section to capture approximately 150 frames.
Then, click the stop button. Next, select static in the pipeline pull-down list in the subject calibration section. Check the left foot and the right foot options in the static settings pane.
Click the start button in the subject calibration section. Then, ask the subject to stand at the appropriate starting position. Click the start button in the capture section to begin capturing and then immediately give the subject the oral instruction to go jogging.
Ensure that the right foot naturally strikes and completely contacts the force platform when passing by. For jogging trials, ask the subjects to jog at the comfortable low speed that they were familiar with during preparation. For running trials, ask the subjects to run at the comfortable high speed that they had been familiar with during preparation.
Allow for a two minute rest between two trials. Finally, after the subject comes to the end of the runway, click the stop button. These results indicated that, except for the knee and hip motions in the sagittal plane, all three joint range of motion of IEW was generally greater than that of EW.Significant speed effect on joint range of motion only existed in the hip-flexion extension.
The GRF time curve of EW is characterized by an initial peak immediately followed by a small wave during the shock absorption period, particularly during running. In contrast, that of IEW is relatively fluent after the initial peak. There is no significant difference in the impact force between EW and IEW, but the loading rate of EW was significantly higher during running.
While attempting this procedure, it's important to remember to recalibrate the cameras reading the capture volume and to calibrate the first parameter to their own level before starting each and every trial to ensure the capture accuracy. Following this procedure, other parameters, such as joint momentum, can be calculated in order to answer additional questions, like whether wearing high-heeled shoes would factor in other accuracy and measurements of only other accuators. Of these, the value meant that this math ought to emphasize the importance of the allocating their subjects wearing experience of the high-heeled shoes to provide more specific information and reducing the inferring to target the population.
After watching this video, you should understand how to use or understand the math or to perform the experiment with the high heel and get the accuracy as well as criteria suggests based on the different wearing experience.
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Overview
This study investigates the effects of high-heeled shoes on lower limb mechanics during moderate high-heeled jogging and running. It compares experienced and inexperienced wearers to understand the impact of heel elevation on kinematics and ground reaction forces.
Key Study Components
Area of Science
- Biomechanics
- Sports Science
- Footwear Impact Studies
Background
- High-heeled shoes are commonly worn but may affect biomechanics.
- Previous studies have not adequately considered the experience of wearers.
- Understanding these effects can inform footwear design and recommendations.
- Ground reaction forces are critical for assessing lower limb mechanics.
Purpose of Study
- To observe lower-limb kinematics during jogging and running in high heels.
- To compare the effects on experienced versus inexperienced wearers.
- To determine if heel elevation is the primary factor affecting mechanics.
Methods Used
- Three-dimensional motion analysis system utilized.
- Force platform configured to capture ground reaction forces.
- Subjects divided into experienced and inexperienced wearers.
- Measurements of anatomical landmarks taken for analysis.
Main Results
- Differences in kinematics observed between experienced and inexperienced wearers.
- Ground reaction forces varied significantly during high-heeled jogging and running.
- Heel elevation was not the sole factor affecting lower limb mechanics.
- Insights gained can influence future footwear design and athletic recommendations.
Conclusions
- High-heeled shoes impact lower limb mechanics during physical activity.
- Experience level plays a crucial role in biomechanical outcomes.
- Further research is needed to explore long-term effects of high-heeled footwear.
Frequently Asked Questions
Executive Industry Relevance
This study highlights the importance of controlling for subject experience in biomechanical assessments, a principle directly applicable to preclinical model validation where phenotypic variability can confound target validation and mechanistic de-risking. By demonstrating that wearer experience significantly alters lower-limb mechanics during high-heeled jogging and running, the work underscores how unaccounted biological variables can obscure true treatment effects in discovery-stage assays. For biopharma R&D, this reinforces the need for standardized, experience-stratified models to improve predictive confidence in early-stage screening and reduce false leads in pathway interrogation.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by isolating the impact of subject-specific variables on biomechanical outputs, reducing confounding in target engagement studies.
- Operational Value: Supports biological de-risking through experience-stratified cohort design, improving reproducibility in functional validation assays.
Screening & Assay Development
- Scientific Value: Prepares validated biological systems with controlled experiential variables for downstream compound evaluation, enhancing assay standardization.
- Operational Value: Addresses reproducibility and quantitative output consistency by accounting for subject history, a critical factor in high-throughput screening readiness.
Translational & Preclinical Research
- Scientific Value: Discusses disease-relevant system preparation by modeling how prior exposure alters physiological response, informing translational biomarker alignment.
- Operational Value: Describes continuity from discovery through preclinical validation by emphasizing variable control across study phases, supporting risk-adjusted advancement decisions.
Pipeline & Workflow Integration
The method fits within the discovery continuum from hypothesis testing to lead identification, where controlling for biological variability improves mechanistic de-risking and predictive confidence in early target validation.
- Discovery Biology: Explains how the method supports hypothesis testing by isolating the effect of wearer experience on joint kinematics and ground reaction force loading rates, clarifying pathway-specific responses.
- Screening: Describes assay readiness through synchronized 3D motion and force platform capture, enabling quantitative, reproducible measurements of lower-limb mechanics under controlled conditions.
- Analytics: Highlights joint range of motion and ground reaction force loading rate as key readouts that allow comparison between experimental groups, supporting data-driven go/no-go decisions.
- Translational Research: Connects the method to preclinical continuity by showing how experience-based stratification improves model fidelity, aligning with biomarker-stratified validation approaches.
- Enterprise Reuse: Frames the method as a reusable capability for assessing footwear or device-induced biomechanical changes, applicable across multiple preclinical programs requiring standardized gait analysis.
Operational & Enterprise Impact
- Scientific Value: Predictive confidence through reduction of mechanistic ambiguity by controlling for subject experience in biomechanical phenotyping.
- Operational Value: Standardization and reproducibility via experience-stratified protocols and synchronized motion-force platform capture.
- Strategic Value: Better go/no-go decisions and reduced late-stage biological risk by minimizing variability in preclinical efficacy models.
- Portfolio Impact: Risk-adjusted prioritization through improved target validation confidence in mechanistically complex disease models.
Implementation Considerations
- Required scientific expertise in biomechanics, motion capture systems, and force plate data interpretation.
- Instrumentation and analytical infrastructure needs include a 3D motion analysis system and configured force platform for synchronous kinematic and kinetic data collection.
- Cross-team standardization requirements involve harmonizing subject screening criteria for experience level across discovery and preclinical teams.
- Adaptation considerations across model systems include modifying experience thresholds and marker placement for non-human or disease-model applications.
- Practical limitations include the need for detailed subject history collection and potential variability in self-reported experience, which may affect stratification accuracy.
