Mohammadi Hedieh, Maghsoudpour Adel, Noroozian Maryam, Mohammadian Fatemeh
Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Cognitive Neurology, Dementia, and Neuropsychiatry Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Front Aging Neurosci. 2025 Feb 19;17:1533573. doi: 10.3389/fnagi.2025.1533573. eCollection 2025.
While gait analysis is well-documented, turn performance-which is a more complex task and involves multiple brain regions-has been less explored. This study aims to assess the diagnostic potential of turn dynamics as a novel tool for detecting cognitive decline.
We recruited 75 participants, including 26 neurotypical (NT) older adults, 25 with amnestic mild cognitive impairment (aMCI), and 24 with mild Alzheimer's disease (AD). Participants completed a dual-task walk and turn (DTWT) test using a dual Kinect setup while counting backwards by ones. Key measures analyzed included spatial-temporal parameters of gait and turn dynamics. Statistical analyses including analyses of variance and linear regression were performed to identify key features as well as to assess their correlation with cognitive performance.
Gait speed and stride time significantly differentiated among groups in DTWT conditions. More notably, turn dynamics, particularly segmental peak speeds and step length, displayed stronger discriminatory power with more significant -values compared to gait features. Linear regression analysis indicated that turn dynamics had stronger correlations with executive function and working memory, suggesting a more pronounced relationship between cognitive performance and turn features than gait variables.
In contrast to straight walk metrics, this study shows that DTWT turn dynamics are more sensitive to detect cognitive impairment. Consequently, incorporating turning movements into gait analysis techniques could enhance diagnostic protocols in clinical settings, offering a valuable tool for monitoring the progression of conditions associated with cognitive aging.
虽然步态分析已有充分记录，但转身表现——这是一项更复杂的任务，涉及多个脑区——却较少被研究。本研究旨在评估转身动力学作为检测认知衰退的一种新工具的诊断潜力。
我们招募了75名参与者，包括26名神经正常（NT）的老年人、25名遗忘型轻度认知障碍（aMCI）患者和24名轻度阿尔茨海默病（AD）患者。参与者在使用双Kinect设备进行双任务行走和转身（DTWT）测试的同时逐个数数倒着数。分析的关键指标包括步态和转身动力学的时空参数。进行了包括方差分析和线性回归在内的统计分析，以确定关键特征并评估它们与认知表现的相关性。
在DTWT条件下，步态速度和步幅时间在各组之间有显著差异。更值得注意的是，与步态特征相比，转身动力学，特别是节段峰值速度和步长，显示出更强的辨别力，具有更显著的p值。线性回归分析表明，转身动力学与执行功能和工作记忆有更强的相关性，表明认知表现与转身特征之间的关系比步态变量更明显。
与直线行走指标相比，本研究表明DTWT转身动力学对检测认知障碍更敏感。因此，将转身动作纳入步态分析技术可以增强临床环境中的诊断方案，为监测与认知衰老相关疾病的进展提供一个有价值的工具。
