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通过肌肉力量测量识别老年跌倒者。

Identification of elderly fallers by muscle strength measures.

作者信息

Pijnappels Mirjam, van der Burg Petra J C E, Reeves Neil D, van Dieën Jaap H

机构信息

Research Institute MOVE, Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT, Amsterdam, The Netherlands.

出版信息

Eur J Appl Physiol. 2008 Mar;102(5):585-92. doi: 10.1007/s00421-007-0613-6. Epub 2007 Dec 11.

Abstract

For efficient prevention of falls among older adults, individuals at a high risk of falling need to be identified. In this study, we searched for muscle strength measures that best identified those individuals who would fall after a gait perturbation and those who recovered their balance. Seventeen healthy older adults performed a range of muscle strength tests. We measured maximum and rate of development of ankle plantar flexion moment, knee extension moment and whole leg push-off force, as well as maximum jump height and hand grip strength. Subsequently, their capacity to regain balance after tripping over an obstacle was determined experimentally. Seven of the participants were classified as fallers based on the tripping outcome. Maximum isometric push-off force in a leg press apparatus was the best measure to identify the fallers, as cross-validation of a discriminant model with this variable resulted in the best classification (86% sensitivity and 90% specificity). Jump height and hand grip strength were strongly correlated to leg press force (r = 0.82 and 0.59, respectively) and can also be used to identify fallers, although with slightly lower specificity. These results indicate that whole leg extension strength is associated with the ability to prevent a fall after a gait perturbation and might be used to identify the elderly at risk of falling.

摘要

为有效预防老年人跌倒,需要识别出跌倒高风险个体。在本研究中,我们寻找了能最佳识别那些在步态扰动后会跌倒以及能恢复平衡的个体的肌肉力量测量指标。17名健康老年人进行了一系列肌肉力量测试。我们测量了踝关节跖屈力矩、膝关节伸展力矩和全腿蹬地力的最大值及发展速率,以及最大跳跃高度和握力。随后,通过实验确定了他们在被障碍物绊倒后恢复平衡的能力。根据绊倒结果,7名参与者被归类为跌倒者。腿部推举器械中的最大等长蹬地力是识别跌倒者的最佳指标,因为使用该变量的判别模型交叉验证得出了最佳分类结果(灵敏度为86%,特异度为90%)。跳跃高度和握力与腿部推举力密切相关(分别为r = 0.82和0.59),也可用于识别跌倒者,不过特异度略低。这些结果表明,全腿伸展力量与步态扰动后预防跌倒的能力相关,可能用于识别有跌倒风险的老年人。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5553/2226001/c0a0bfe3f47b/421_2007_613_Fig1_HTML.jpg

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