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Clinical use of the gaze stabilization test for screening falling risk in community-dwelling older adults

机译:凝视稳定测试在筛查社区居民老年人跌倒风险中的临床应用

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Objective: This study examined the clinical use of the computerized gaze stabilization test (GST) as a screener for falls. Design: Cross-sectional, descriptive. Setting: Tertiary medical center. Subjects: Fifteen older community-dwelling adults with a history of falls and 15 controls without a history of falls were recruited for participation in the study. Main Outcome Measures: Participants performed GST with yaw plane head movements. The GST velocity was measured and compared with the dynamic gait index (DGI). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) identified GST velocity cut points for identification of fallers based on history of falls and as compared with DGI score. Results: Our results suggested that GST can discriminate between individuals at risk for falls versus those not at risk. ROC analysis identified an AUC of 0.92 (≤100.5 degrees per second criterion value) for GST based on history of falls and an AUC of 0.85 (≤100.5 degrees per second criterion value) based on DGI for classifying falling risk. When GST and DGI scores were combined, the protocol identified an AUC of 1.0 (100% sensitivity, 100% specificity) for identifying falling risk. Conclusion: There were significant head movement velocity differences from participants classified by history of falls and the DGI. Therefore, GST may serve as a potential falling risk assessment measure for older individuals with a history of falls. It is recommended that GST be used in a combined protocol with DGI to accurately identify individuals with falling risk rather than used in isolation.
机译:目的:本研究检查了计算机凝视稳定测试(GST)作为跌倒检查器的临床应用。设计:横断面,描述性的。地点:第三级医疗中心。受试者:招募了15名有跌倒史的老年人和15名无跌倒史的对照组。主要观察指标:参加者进行GST时偏航头平面运动。测量GST速度并将其与动态步态指数(DGI)进行比较。接收器的工作特性(ROC)曲线和ROC曲线下的面积(AUC)确定了GST速度切入点,用于根据跌倒的历史并与DGI得分进行比较来识别跌倒者。结果:我们的结果表明,商品及服务税可以区分有跌倒危险的人和没有跌倒危险的人。 ROC分析基于跌倒的历史记录确定了GST的AUC为0.92(≤100.5度/秒的标准值)和基于DGI的跌倒风险将AUC的AUC为0.85(≤100.5度/秒的标准值)。当将GST和DGI评分合并时,该方案将AUC值确定为1.0(灵敏度为100%,特异性为100%),以识别跌倒风险。结论:根据跌倒史和DGI分类,参与者的头部运动速度存在显着差异。因此,商品及服务税可以作为具有跌倒历史的老年人的潜在跌倒风险评估手段。建议将GST与DGI结合使用,以准确识别风险下降的个体,而不是孤立地使用。

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