步态
物理医学与康复
步态参数对能量消耗的影响
正交旋转
认知
主成分分析
节奏
步态分析
心理学
医学
数学
统计
发展心理学
神经科学
心理测量学
克朗巴赫阿尔法
作者
Magnus Lindh-Rengifo,Stina B. Jonasson,Susann Ullén,Erik Stomrud,Sebastian Palmqvist,Niklas Mattsson,Oskar Hansson,Maria H Nilsson
标识
DOI:10.1016/j.gaitpost.2022.01.012
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI