Gait and balance metrics comparison among different fall risk groups and principal component analysis for fall prediction in older people.

IF 6 2区 医学 Q1 GERIATRICS & GERONTOLOGY
Lulu Yin, Hyeri Nam, Yaru Wei, Tianyi Feng, Feifei Li, Yushan Wang, Yu Zhang, Lin Wang
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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.

不同跌倒风险组的步态和平衡指标比较及老年人跌倒预测的主成分分析。
背景:跌倒是老年人发病和死亡的主要原因,通常与步态和平衡障碍有关。目的:比较社区居住老年人跌倒风险水平的步态和平衡指标,并确定预测跌倒风险的主要成分。设计:回顾性队列研究。设置:普通社区。受试者:使用STEADI工具将300名老年人分为低、中、高跌倒风险组。方法:比较各组患者的步态和平衡指标。主成分分析(PCA)降维,二元逻辑回归评估成分的预测价值。结果:高危人群节奏较慢,步长较短,步宽较宽,步态变异性较大,压力中心(CoP)和质量中心(CoM)摇摆增加。PCA识别出4个步态成分和7个平衡成分,分别解释了71.62%和75.88%的方差。Logistic回归结果显示:步态主成分(PC)2(不稳定性)(OR = 2.545, P)。结论:步态不稳定性、节奏控制和姿势摇摆增加是跌倒风险的关键预测因素。整合步态和平衡指标增强跌倒风险分层,支持临床决策。
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来源期刊
Age and ageing
Age and ageing 医学-老年医学
CiteScore
9.20
自引率
6.00%
发文量
796
审稿时长
4-8 weeks
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.
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