摘要
目的根据6种步态的测试参数分别构建老人跌倒风险预警模型。方法使用数字化场地采集有跌倒史和无跌倒史老年人的6种步态参数,采用二项logistic回归分析法,建立预测老人跌倒风险的回归方程,构建跌倒预警模型。结果根据6种步态的测试参数所构建的回归方程均具有统计学意义,预测总体正确率由高到底依次为:闭眼正走(97.1%)、睁眼倒走(92.9%)、闭眼倒走(88.6%)、睁眼正走(87.1%)、睁眼上下转头(85.7%)、睁眼左右转头(82.9%)。所构建的老年人跌倒风险预警模型主要包括判定、测试、提取、计算、预警5个步骤,适合在实验室内对老年人进行步态测试与跌倒风险评估。结论 6种步态的测试参数都能够预测老年人的跌倒风险,其中闭眼正走的预测效果最好,是预测老年人跌倒风险的最佳步态。所构建的老年人跌倒风险预警模型用于预测65~75岁老年人1年内的跌倒风险,并可根据跌倒概率发出预警,对预防老年人跌倒具有积极作用。
Objective To construct an early warning model of fall risk for the elderly based on six kinds gait parameters. Methods A digital field was used to collect parameters from six kinds of gait for the elderly with or without the history of falls,and the binomial logistic regression analysis was used to establish a regression equation for predicting the fall risks in the elderly,and an early warning model was constructed. Results The regression equations constructed according to the parameters from six kinds of gait were statistically significant.The overall correct rate was predicted from high to low: walking forward with closed eyes (97. 1%),walking backward with open eyes (92. 9%),walking backward with closed eyes (88. 6%),walking forward with open eyes (87. 1%),turning head up and down with open eyes (85. 7%),turning head left and right with open eyes (82. 9%). The constructed early warning model for fall risk of the elderly mainly included five steps,namely,judgment,test,extraction,calculation and early warning,which was suitable for gait testing and evaluation of the elderly in the laboratory. Conclusions Parameters from six kinds of gait could predict the fall risk of the elderly.Among them,walking forward with closed eyes was best to predict the fall risk in the elderly. The established early warning model of fall risk for the elderly could be used to predict the fall risk of 65-75 year old people within one year,which could provide early warning based on the probability of falling,playing a positive effect on preventing falls in the elderly.
作者
游永豪
邵梦霓
胡燕杰
张阳
王广磊
朱靖靖
YOU Yonghao;Shao Mengni;HU Yanje;ZHANG Yang;WANG Guanglei;ZHU Jingjing(Department of Sports Science,Hefei Normal University,Hefei 230601,China;Department of Neurology,the Second Affiliated Hospital of Anhui Medical University,Hefei 230601,China)
出处
《医用生物力学》
EI
CAS
CSCD
北大核心
2020年第4期357-363,共7页
Journal of Medical Biomechanics
基金
2016年度安徽高校自然科学研究重点项目(KJ2016A582)
2020年度安徽高校自然科学研究重点项目(KJ2020A0129)。
