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BESTEST MINI-BEST BRIEF-BEST AND BBS: ABILITY TO IDENTIFY FALLS STATUS INSTITUTIONALIZED ELDERLY

机译:最好的最小的简短的和BBS的:能够识别跌倒状态并且已经过制度化

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摘要

Objectives: To verify the ability of the Balance Evaluation Systems Test (BESTest), Mini-BESTest, Brief-BESTest and Berg Balance Scale (BBS) to identify institutionalized elderly falling. Methods: This is a retrospective cohort study. Institutionalized elderly (n=39; aged 62–90 years; mean=78.26 [7.3] years) were recruited from a philanthropic nursing home of Sao Paulo City (Brazil). All participants were tested with BESTest, Mini-BESTest, Brief-BESTest, and BBS and the history of falls from a year ago was collected by the medical records. For the analysis, a falls status indicator was used and the sample was dichotomized in “Non-Faller” (participants who had no fall in the last year) and “Faller” (participants who had a fall or more in the last year). The sensitivity and specificity between the total score of the BESTest, Mini-BESTest, Brief-BESTest, and BBS was calculated based on the fall hazard indicator. The cut-off points were determined using the Receiver Operating Characteristic (ROC) curves. The statistical significance of each analysis was verified by the area under the ROC curve (AUC) and by their respective 95% confidence intervals (95% CI). Results: All balance tests were able to identify falls status (AUC=0.63; 0.70; 0.78 and 0.75, BESTest, Mini-BESTest, Brief-BESTest, and BBS, respectively), the Brief-BESTest (sensitivity=94%, specificity=61%) and the BBS (sensitivity=94%, specificity=56%) had the higher ability. Conclusions: All balance tests are valuable to identify fall status in institutionalized elderly. The Brief-BESTest presented slightly higher ability to identify falls status in our sample.
机译:目的:验证平衡评估系统测试(BESTest),Mini-BESTest,Brief-BESTest和Berg平衡量表(BBS)的能力,以识别机构化的老年人跌倒。方法:这是一项回顾性队列研究。从圣保罗市(巴西)的一家慈善养老院招募了制度化的老年人(n = 39; 62-90岁;平均= 78.26 [7.3]岁)。所有参与者均接受BESTEST,Mini-BESTest,Brief-BESTest和BBS的测试,并从病历中收集了一年前的跌倒史。为了进行分析,使用了跌倒状态指示器,并将样本分为“非跌倒”(去年没有跌倒的参与者)和“跌倒”(去年跌倒或以上的参与者)两部分。基于跌倒危险指标,计算出Bestest,Mini-BESTest,Brief-BESTest和BBS的总分之间的敏感性和特异性。临界点是使用接收器工作特性(ROC)曲线确定的。通过ROC曲线下的面积(AUC)和它们各自的95%置信区间(95%CI)验证了每次分析的统计显着性。结果:所有平衡测试均能够识别跌倒状态(AUC = 0.63; 0.70; 0.78和0.75,Bestest,Mini-BESTest,Brief-BESTest和BBS),Best-BESTest(灵敏度= 94%,特异性= 61%)和BBS(敏感性= 94%,特异性= 56%)具有更高的能力。结论:所有平衡测试对于确定住院老人的跌倒状态均很有价值。 Brief-BESTest在我们的样本中识别跌倒状态的能力略高。

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