Elderly fall risk prediction using static posturography.
Elderly fall risk prediction using static posturography.
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DOI：
10.1371/journal.pone.0172398
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
McIlroy WE
中科院分区：
文献类型：
--
作者：
Howcroft J;Lemaire ED;Kofman J;McIlroy WE
维持和控制姿势平衡对于日常生活活动是重要的，不良的姿势平衡预示着未来的福尔斯。本研究调查了老年人的睁眼和闭眼站立姿势描记术，以确定差异并确定前瞻性跌倒者、单次跌倒者、多次跌倒者和非跌倒者分类的适当结局测量截止评分。100名老年人（75.5 ± 6.7岁）安静地站着，睁着眼睛，然后闭上眼睛，同时收集Wii平衡板数据。计算前后（AP）和内外（ML）压力中心（CoP）运动范围; AP和ML CoP均方根距均值（RMS）距离;以及AP、ML和向量和幅度（VSM）CoP速度。计算所有参数的Romberg商（RQ）。参与者报告了6个月的跌倒史和6个月后的评估跌倒发生率。组为回顾性跌倒者（24例）、前瞻性所有跌倒者（42例）、前瞻性跌倒者（22例单次跌倒，6例多次跌倒）和前瞻性非跌倒者（47例）。非坠落者RQ AP范围和RQ AP RMS与前瞻性所有坠落者、坠落者和单个坠落者不同。非跌倒者的闭眼AP速度、闭眼VSM速度、RQ AP速度和RQ VSM速度与多次跌倒者不同。RQ计算与老年人跌倒风险评估特别相关。临床截断评分、ROC曲线和判别函数的截断评分在临床上适用于多胎分类，并且比单胎分类提供更好的准确性。RQ AP范围与截止值1.64可用于筛选老年人谁可能跌倒一次。具有判别函数的多瀑布前瞻性分类（-1.481 +0.146 x闭眼AP速度-0.114 x闭眼矢量和幅度速度-2.027 x RQ AP速度+2.877 x RQ矢量和幅度速度）和截止分数0.541实现了84.9%的准确度并且作为具有多次福尔斯风险的老年人的筛选工具是可行的。
Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity—0.114 x Eyes Closed Vector Sum Magnitude Velocity—2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older people at risk of multiple falls.