Presentation Type
Poster
Discipline Track
Biomedical Science
Abstract Type
Research/Clinical
Abstract
Background: Adequate gait function is pivotal for many activities of daily living and high quality of life. Following many neurodegenerative diseases, such as Parkinson’s Disease, gait abnormalities can manifest and range from reduced stride length, inability to turn, foot drop or shuffling. To track and monitor such changes in gait, gait analysis techniques are gaining clinical popularity and have the ability to gather a range of data in a short duration. Gait analysis techniques go beyond simple visual observation and include instrumental gait analysis and weight distribution of the gait cycle. Here, we sought to optimize the gait analysis system located at the Institute of Neuroscience at UTRGV School of Medicine. Specifically, numerous studies have indicated that factors such as BMI, foot size, foot covering and leg length can influence metrics collected with a gait analysis system. Here, we sought to identify the influence of such variables on clinical assessments with the gait analysis system.
Methods: A single-session study was conducted wherein healthy controls were recruited to undergo gait analysis in three conditions: bare foot, socks and with shoes. We used the ProtoKinetics Zeno Walkway Gait Analysis system for the study. The study received IRB approval and has enrolled 5 participants to date. After enrollment, we collected each subject’s height, weight and foot size (Brannock device). Gait belts were worn by all subjects as a safety precaution for fall risk during the study. Subjects were then instructed to walk normally on the gait mat, making several passes back and forth (one ambulation period is across the mat and back, participants performed at least 7 ambulation periods for optimal data receiving). Subjects were asked to complete the gait passes with shoes, socks and barefoot.
Results/Discussion: We observed that ambulation foot velocity varied depending on foot covering. For example, mean foot velocity barefoot was faster (111.40 cm/s) compared to wearing shoes (107.69 cm/s). In addition, we observed changes in participant cadence between conditions. Mean cadence barefoot was slower (106.1 steps/min) compared to with shoes (108.8 steps/min).
Conclusion: Our preliminary findings suggest that foot coverings play a significant role in gait analysis metrics. While subjects velocity was faster moving their feet when they were barefoot, their mean cadence was slower. We believe this result might be instinctual and related to added comfort that is provided when wearing shoes. We anticipate our findings will help research groups establish standard operating procedures for clinical deployment of gait analysis in clinical populations.
Academic/Professional Position
Undergraduate
Mentor/PI Department
Neuroscience
Recommended Citation
Medellin, Gerardo; Bolado, Katherine S.; Salinas, Daniel; and Potter-Baker, Kelsey, "Optimization of Gait Analysis System for Clinical Applications" (2024). Research Symposium. 55.
https://scholarworks.utrgv.edu/somrs/2024/posters/55
Included in
Optimization of Gait Analysis System for Clinical Applications
Background: Adequate gait function is pivotal for many activities of daily living and high quality of life. Following many neurodegenerative diseases, such as Parkinson’s Disease, gait abnormalities can manifest and range from reduced stride length, inability to turn, foot drop or shuffling. To track and monitor such changes in gait, gait analysis techniques are gaining clinical popularity and have the ability to gather a range of data in a short duration. Gait analysis techniques go beyond simple visual observation and include instrumental gait analysis and weight distribution of the gait cycle. Here, we sought to optimize the gait analysis system located at the Institute of Neuroscience at UTRGV School of Medicine. Specifically, numerous studies have indicated that factors such as BMI, foot size, foot covering and leg length can influence metrics collected with a gait analysis system. Here, we sought to identify the influence of such variables on clinical assessments with the gait analysis system.
Methods: A single-session study was conducted wherein healthy controls were recruited to undergo gait analysis in three conditions: bare foot, socks and with shoes. We used the ProtoKinetics Zeno Walkway Gait Analysis system for the study. The study received IRB approval and has enrolled 5 participants to date. After enrollment, we collected each subject’s height, weight and foot size (Brannock device). Gait belts were worn by all subjects as a safety precaution for fall risk during the study. Subjects were then instructed to walk normally on the gait mat, making several passes back and forth (one ambulation period is across the mat and back, participants performed at least 7 ambulation periods for optimal data receiving). Subjects were asked to complete the gait passes with shoes, socks and barefoot.
Results/Discussion: We observed that ambulation foot velocity varied depending on foot covering. For example, mean foot velocity barefoot was faster (111.40 cm/s) compared to wearing shoes (107.69 cm/s). In addition, we observed changes in participant cadence between conditions. Mean cadence barefoot was slower (106.1 steps/min) compared to with shoes (108.8 steps/min).
Conclusion: Our preliminary findings suggest that foot coverings play a significant role in gait analysis metrics. While subjects velocity was faster moving their feet when they were barefoot, their mean cadence was slower. We believe this result might be instinctual and related to added comfort that is provided when wearing shoes. We anticipate our findings will help research groups establish standard operating procedures for clinical deployment of gait analysis in clinical populations.