Lindh-Rengifo Magnus, Jonasson Stina B, Ullén Susann, Stomrud Erik, Palmqvist Sebastian, Mattsson-Carlgren Niklas, Hansson Oskar, Nilsson Maria H
Department of Health Sciences, Faculty of Medicine, Lund University, Lund, Sweden.
Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Gait Posture. 2022 Mar;93:83-89. doi: 10.1016/j.gaitpost.2022.01.012. Epub 2022 Jan 17.
Several objective gait parameters are associated with cognitive impairment, but there is limited knowledge of gait models in people with mild cognitive impairment (MCI).
How can 18 objective gait characteristics be used to define different components of gait in people with MCI (with suspected incipient neurocognitive disorder) and cognitively unimpaired people (CU), respectively?
Spatiotemporal gait data were collected by using an electronic walkway (GAITRite®), i.e. assessments in comfortable gait speed. Using cross-sectional gait data, two principal component analyses (PCA) were performed (varimax rotation) to define different components of gait in people with MCI (n = 114) and CU (n = 219), respectively, from the BioFINDER-2 study.
Both PCAs produced four components, here called Variability, Pace/Stability, Rhythm and Asymmetry. Total variance explained was 81.0% (MCI) versus 80.3% (CU). The Variability component explained the largest amount of variance (about 25%) in both groups. The highest loading gait parameter was the same for both groups in three out of four components, i.e. step velocity variability (Variability), mean step length (Pace/Stability) and mean step time (Rhythm). In the asymmetry component, stance time asymmetry (MCI) and swing time asymmetry (CU) loaded the highest.
The gait components seem similar in people with and without MCI, although there were some differences. This study may aid the identification of gait variables that represent different components of gait. Gait parameters such as step velocity variability, mean step length, mean step time as well as swing and stance time asymmetry could serve as interesting core variables of different gait components in future research in people with MCI (with suspected incipient neurocognitive disorder) and CU. However, the selection of gait variables depends on the purpose. It needs to be noted that assessment of variability measures requires more advanced technology than is usually used in the clinic.
几个客观的步态参数与认知障碍相关，但对于轻度认知障碍（MCI）患者的步态模型了解有限。
如何利用18个客观步态特征分别定义MCI（疑似早期神经认知障碍）患者和认知未受损人群（CU）的不同步态成分？
使用电子步道（GAITRite®）收集时空步态数据，即在舒适步态速度下进行评估。利用BioFINDER - 2研究中的横断面步态数据，分别对MCI患者（n = 114）和CU患者（n = 219）进行了两次主成分分析（PCA）（方差最大化旋转），以定义不同的步态成分。
两次PCA均产生了四个成分，这里称为变异性、步速/稳定性、节律和不对称性。解释的总方差分别为81.0%（MCI）和80.3%（CU）。变异性成分在两组中解释的方差量最大（约25%）。在四个成分中的三个成分中，两组的最高负荷步态参数相同，即步速变异性（变异性）、平均步长（步速速/稳定性）和平均步时（节律）。在不对称性成分中，站立时间不对称性（MCI）和摆动时间不对称性（CU）的负荷最高。
尽管存在一些差异，但MCI患者和非MCI患者的步态成分似乎相似。本研究可能有助于识别代表不同步态成分的步态变量。步态参数，如步速变异性、平均步长、平均步时以及摆动和站立时间不对称性，可能成为未来MCI（疑似早期神经认知障碍）患者和CU患者研究中不同步态成分的有趣核心变量。然而，步态变量的选择取决于目的。需要注意的是，变异性测量评估需要比临床通常使用的技术更先进的技术。
