Lulu Yin, Hyeri Nam, Yaru Wei, Tianyi Feng, Feifei Li, Yushan Wang, Yu Zhang, Lin Wang
{"title":"Gait and balance metrics comparison among different fall risk groups and principal component analysis for fall prediction in older people.","authors":"Lulu Yin, Hyeri Nam, Yaru Wei, Tianyi Feng, Feifei Li, Yushan Wang, Yu Zhang, Lin Wang","doi":"10.1093/ageing/afaf076","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Falls are a leading cause of morbidity and mortality among older adults, often linked to gait and balance impairments.</p><p><strong>Objective: </strong>To compare gait and balance metrics across fall risk levels in community-dwelling older adults and identify principal components predictive of fall risk.</p><p><strong>Design: </strong>Retrospective cohort study.</p><p><strong>Setting: </strong>General community.</p><p><strong>Subjects: </strong>Three hundred older adults were stratified into low, moderate and high fall risk groups using the STEADI toolkit.</p><p><strong>Methods: </strong>Gait and balance metrics were compared across groups. Principal component analysis (PCA) reduced dimensionality, and binary logistic regression assessed the predictive value of components.</p><p><strong>Results: </strong>High-risk individuals showed slower cadence, shorter step length, wider step width, greater gait variability and increased centre of pressure (CoP) and centre of mass (CoM) sway. PCA identified four gait and seven balance components, explaining 71.62% and 75.88% of variance, respectively. Logistic regression revealed Gait_principal component (PC)2 (instability) (OR = 2.545, P < .001), Gait_PC3 (rhythm control) (OR = 1.659, P = .006), Balance_PC1 (CoP sway during single-leg stance) (OR = 1.628, P = .007), Balance_PC2 (CoM sway velocity variability) (OR = 1.450, P = .032) and Balance_PC4 (CoP sway during double-leg stance, eyes closed) (OR = 1.616, P = .004) as significant predictors. The model achieved 77.2% accuracy, with a sensitivity of 73.1% and a specificity of 79.4%.</p><p><strong>Conclusions: </strong>Gait instability, rhythm control and increased postural sway are key predictors of fall risk. Integrating gait and balance metrics enhances fall risk stratification, supporting clinical decision-making.</p>","PeriodicalId":7682,"journal":{"name":"Age and ageing","volume":"54 4","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Age and ageing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/ageing/afaf076","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Falls are a leading cause of morbidity and mortality among older adults, often linked to gait and balance impairments.
Objective: To compare gait and balance metrics across fall risk levels in community-dwelling older adults and identify principal components predictive of fall risk.
Design: Retrospective cohort study.
Setting: General community.
Subjects: Three hundred older adults were stratified into low, moderate and high fall risk groups using the STEADI toolkit.
Methods: Gait and balance metrics were compared across groups. Principal component analysis (PCA) reduced dimensionality, and binary logistic regression assessed the predictive value of components.
Results: High-risk individuals showed slower cadence, shorter step length, wider step width, greater gait variability and increased centre of pressure (CoP) and centre of mass (CoM) sway. PCA identified four gait and seven balance components, explaining 71.62% and 75.88% of variance, respectively. Logistic regression revealed Gait_principal component (PC)2 (instability) (OR = 2.545, P < .001), Gait_PC3 (rhythm control) (OR = 1.659, P = .006), Balance_PC1 (CoP sway during single-leg stance) (OR = 1.628, P = .007), Balance_PC2 (CoM sway velocity variability) (OR = 1.450, P = .032) and Balance_PC4 (CoP sway during double-leg stance, eyes closed) (OR = 1.616, P = .004) as significant predictors. The model achieved 77.2% accuracy, with a sensitivity of 73.1% and a specificity of 79.4%.
Conclusions: Gait instability, rhythm control and increased postural sway are key predictors of fall risk. Integrating gait and balance metrics enhances fall risk stratification, supporting clinical decision-making.
期刊介绍:
Age and Ageing is an international journal publishing refereed original articles and commissioned reviews on geriatric medicine and gerontology. Its range includes research on ageing and clinical, epidemiological, and psychological aspects of later life.