Christopher A. Bailey, Alexandre Mir-Orefice, Julie Nantel, Ryan B. Graham
{"title":"年轻人步态运动波动表型的分类。","authors":"Christopher A. Bailey, Alexandre Mir-Orefice, Julie Nantel, Ryan B. Graham","doi":"10.1016/j.jbiomech.2025.112523","DOIUrl":null,"url":null,"abstract":"<div><div>Stride-to-stride fluctuations are natural in gait. These fluctuations are marked by inter-individual variability, suggesting that different fluctuation strategies (i.e., phenotypes) may exist. This study investigates the presence of gait fluctuation phenotypes. Whole-body kinematics were measured from young, healthy males and females (N = 51) while walking on a treadmill at their preferred speed. Motor fluctuation metrics (i.e., magnitude of variability, local dynamic stability, and regularity) were measured for 32 joint angles across the upper and lower body. These metrics were reduced to principal components (PCs) via principal component analysis and then grouped into clusters using the <em>k</em>-means method. One-way ANOVAs were conducted to test for cluster differences in motor fluctuation PCs. Three PCs were extracted, explaining 39.7 % of all 96 motor fluctuation metrics. Higher PC1 scores represent more fluctuation across all joints, higher PC2 scores represent greater upper limb fluctuations with fewer fluctuations in the lower limb, and PC3 scores represent less regularity in fluctuations. PC scores best grouped into four clusters in 54.0 % of iterations. Clusters 1–4 each had a significantly different PC1 score (p < 0.022), and Cluster 3 had a higher PC2 score than all other clusters (p < 0.022). Motor fluctuations in treadmill gait of young adults were characterised by four gait fluctuation phenotypes, interpreted as repeaters, replacers, moderate fluctuators, and mixed fluctuators (i.e. more upper limb but fewer lower limb fluctuations); extending the repeaters vs replacers hypothesis. The identified phenotypes add a new perspective that may help clarify the link between motor fluctuations and gait instability.</div></div>","PeriodicalId":15168,"journal":{"name":"Journal of biomechanics","volume":"180 ","pages":"Article 112523"},"PeriodicalIF":2.4000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of young adult motor fluctuation phenotypes in gait\",\"authors\":\"Christopher A. Bailey, Alexandre Mir-Orefice, Julie Nantel, Ryan B. Graham\",\"doi\":\"10.1016/j.jbiomech.2025.112523\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Stride-to-stride fluctuations are natural in gait. These fluctuations are marked by inter-individual variability, suggesting that different fluctuation strategies (i.e., phenotypes) may exist. This study investigates the presence of gait fluctuation phenotypes. Whole-body kinematics were measured from young, healthy males and females (N = 51) while walking on a treadmill at their preferred speed. Motor fluctuation metrics (i.e., magnitude of variability, local dynamic stability, and regularity) were measured for 32 joint angles across the upper and lower body. These metrics were reduced to principal components (PCs) via principal component analysis and then grouped into clusters using the <em>k</em>-means method. One-way ANOVAs were conducted to test for cluster differences in motor fluctuation PCs. Three PCs were extracted, explaining 39.7 % of all 96 motor fluctuation metrics. Higher PC1 scores represent more fluctuation across all joints, higher PC2 scores represent greater upper limb fluctuations with fewer fluctuations in the lower limb, and PC3 scores represent less regularity in fluctuations. PC scores best grouped into four clusters in 54.0 % of iterations. Clusters 1–4 each had a significantly different PC1 score (p < 0.022), and Cluster 3 had a higher PC2 score than all other clusters (p < 0.022). Motor fluctuations in treadmill gait of young adults were characterised by four gait fluctuation phenotypes, interpreted as repeaters, replacers, moderate fluctuators, and mixed fluctuators (i.e. more upper limb but fewer lower limb fluctuations); extending the repeaters vs replacers hypothesis. The identified phenotypes add a new perspective that may help clarify the link between motor fluctuations and gait instability.</div></div>\",\"PeriodicalId\":15168,\"journal\":{\"name\":\"Journal of biomechanics\",\"volume\":\"180 \",\"pages\":\"Article 112523\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of biomechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S002192902500034X\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biomechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S002192902500034X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
Classification of young adult motor fluctuation phenotypes in gait
Stride-to-stride fluctuations are natural in gait. These fluctuations are marked by inter-individual variability, suggesting that different fluctuation strategies (i.e., phenotypes) may exist. This study investigates the presence of gait fluctuation phenotypes. Whole-body kinematics were measured from young, healthy males and females (N = 51) while walking on a treadmill at their preferred speed. Motor fluctuation metrics (i.e., magnitude of variability, local dynamic stability, and regularity) were measured for 32 joint angles across the upper and lower body. These metrics were reduced to principal components (PCs) via principal component analysis and then grouped into clusters using the k-means method. One-way ANOVAs were conducted to test for cluster differences in motor fluctuation PCs. Three PCs were extracted, explaining 39.7 % of all 96 motor fluctuation metrics. Higher PC1 scores represent more fluctuation across all joints, higher PC2 scores represent greater upper limb fluctuations with fewer fluctuations in the lower limb, and PC3 scores represent less regularity in fluctuations. PC scores best grouped into four clusters in 54.0 % of iterations. Clusters 1–4 each had a significantly different PC1 score (p < 0.022), and Cluster 3 had a higher PC2 score than all other clusters (p < 0.022). Motor fluctuations in treadmill gait of young adults were characterised by four gait fluctuation phenotypes, interpreted as repeaters, replacers, moderate fluctuators, and mixed fluctuators (i.e. more upper limb but fewer lower limb fluctuations); extending the repeaters vs replacers hypothesis. The identified phenotypes add a new perspective that may help clarify the link between motor fluctuations and gait instability.
期刊介绍:
The Journal of Biomechanics publishes reports of original and substantial findings using the principles of mechanics to explore biological problems. Analytical, as well as experimental papers may be submitted, and the journal accepts original articles, surveys and perspective articles (usually by Editorial invitation only), book reviews and letters to the Editor. The criteria for acceptance of manuscripts include excellence, novelty, significance, clarity, conciseness and interest to the readership.
Papers published in the journal may cover a wide range of topics in biomechanics, including, but not limited to:
-Fundamental Topics - Biomechanics of the musculoskeletal, cardiovascular, and respiratory systems, mechanics of hard and soft tissues, biofluid mechanics, mechanics of prostheses and implant-tissue interfaces, mechanics of cells.
-Cardiovascular and Respiratory Biomechanics - Mechanics of blood-flow, air-flow, mechanics of the soft tissues, flow-tissue or flow-prosthesis interactions.
-Cell Biomechanics - Biomechanic analyses of cells, membranes and sub-cellular structures; the relationship of the mechanical environment to cell and tissue response.
-Dental Biomechanics - Design and analysis of dental tissues and prostheses, mechanics of chewing.
-Functional Tissue Engineering - The role of biomechanical factors in engineered tissue replacements and regenerative medicine.
-Injury Biomechanics - Mechanics of impact and trauma, dynamics of man-machine interaction.
-Molecular Biomechanics - Mechanical analyses of biomolecules.
-Orthopedic Biomechanics - Mechanics of fracture and fracture fixation, mechanics of implants and implant fixation, mechanics of bones and joints, wear of natural and artificial joints.
-Rehabilitation Biomechanics - Analyses of gait, mechanics of prosthetics and orthotics.
-Sports Biomechanics - Mechanical analyses of sports performance.