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解决步态变异指数的局限性,提高其适用性:增强型步态变异指数(EGVI)。

Addressing limitations of the Gait Variability Index to enhance its applicability: The enhanced GVI (EGVI).

机构信息

Gait and Balance Academy, ProtoKinetics, Havertown, Pennsylvania, United States of America.

Laboratory « Performance, Santé, Métrologie, Société (PSMS) », UFR STAPS de Reims, Reims, France.

出版信息

PLoS One. 2018 Jun 1;13(6):e0198267. doi: 10.1371/journal.pone.0198267. eCollection 2018.

Abstract

Prior research has established the Gait Variability Index (GVI) as a composite measure of gait variability, based on spatiotemporal parameters, that is associated with functional outcomes. However, under certain circumstances the magnitude and directional specificity of the GVI is adversely affected by shortcomings in the calculation method. Here we present an enhanced gait variability index (EGVI) that addresses those shortcomings and improves the utility of the measure. The EGVI was further enhanced by removing some input spatiotemporal variables that captured overlapping/redundant information. The EGVI was used to reanalyze data from four previously published studies that used the original GVI. After removing data affected by the GVI's prior shortcomings, the association between EGVI and GVI values was stronger for the pooled dataset (r2 = 0.95) and for the individual studies (r2 = 0.88-0.98). The EGVI also revealed stronger associations between the index value and functional outcomes for some studies. The EGVI successfully addresses shortcomings in the GVI calculation that affected magnitude and directional specificity of the index. We have confirmed the validity of prior published work that used the original GVI, while also demonstrating even stronger results when these prior data were re-analyzed with the EGVI. We recommend that future research should use the EGVI as a composite measure of gait variability.

摘要

先前的研究已经确立了步态变异指数(GVI),作为一种基于时空参数的步态变异性综合衡量指标,与功能结果相关。然而,在某些情况下,由于计算方法的缺陷,GVI 的幅度和方向特异性会受到不利影响。在这里,我们提出了一种改进的步态变异指数(EGVI),可以解决这些缺陷,提高该测量指标的实用性。通过去除一些捕捉重叠/冗余信息的时空变量,进一步增强了 EGVI。使用 EGVI 重新分析了之前使用原始 GVI 的四项已发表研究的数据。在去除受 GVI 先前缺陷影响的数据后,对于汇总数据集(r2 = 0.95)和个别研究(r2 = 0.88-0.98),EGVI 与 GVI 值之间的关联更强。EGVI 还揭示了该指数值与某些研究中功能结果之间更强的关联。EGVI 成功解决了影响指数幅度和方向特异性的 GVI 计算中的缺陷。我们已经证实了先前使用原始 GVI 的已发表工作的有效性,同时还展示了当使用 EGVI 重新分析这些先前的数据时,结果更加强劲。我们建议未来的研究应将 EGVI 作为步态变异性的综合衡量指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/394d/5983480/516adb47158c/pone.0198267.g001.jpg

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