Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA.
Arizona Center on Aging (ACOA), Department of Medicine, University of Arizona, College of Medicine, Tucson, AZ, USA.
Sci Rep. 2019 Jul 29;9(1):10911. doi: 10.1038/s41598-019-46925-y.
The purpose of the current study was to develop an objective tool based on dual-task performance for screening early-stage Alzheimer's disease (AD) and mild cognitive impairment (MCI of the Alzheimer's type). Dual-task involved a simultaneous execution of a sensor-based upper-extremity function (UEF) motor task (normal or rapid speed) and a cognitive task of counting numbers backward (by ones or threes). Motor function speed and variability were recorded and compared between cognitive groups using ANOVAs, adjusted for age, gender, and body mass index. Cognitive indexes were developed using multivariable ordinal logistic models to predict the cognitive status using UEF parameters. Ninety-one participants were recruited; 35 cognitive normal (CN, age = 83.8 ± 6.9), 34 MCI (age = 83.9 ± 6.6), and 22 AD (age = 84.1 ± 6.1). Flexion number and sensor-based motion variability parameters, within the normal pace elbow flexion, showed significant between-group differences (maximum effect size of 1.10 for CN versus MCI and 1.39 for CN versus AD, p < 0.0001). Using these parameters, the cognitive status (both MCI and AD) was predicted with a receiver operating characteristic area under curve of 0.83 (sensitivity = 0.82 and specificity = 0.72). Findings suggest that measures of motor function speed and accuracy within a more practical upper-extremity test (instead of walking) may provide enough complexity for cognitive impairment assessment.
本研究旨在开发一种基于双重任务表现的客观工具，用于筛查早期阿尔茨海默病（AD）和轻度认知障碍（阿尔茨海默病型 MCI）。双重任务涉及同时执行基于传感器的上肢功能（UEF）运动任务（正常或快速速度）和倒数数字的认知任务（逐个或三个三个地）。使用方差分析比较认知组之间的运动功能速度和变异性，并根据年龄、性别和体重指数进行调整。使用多变量有序逻辑模型开发认知指标，使用 UEF 参数预测认知状态。共招募了 91 名参与者；35 名认知正常（CN，年龄=83.8±6.9）、34 名 MCI（年龄=83.9±6.6）和 22 名 AD（年龄=84.1±6.1）。在正常速度肘部弯曲时，弯曲次数和基于传感器的运动变异性参数显示出显著的组间差异（CN 与 MCI 之间的最大效应大小为 1.10，CN 与 AD 之间为 1.39，p<0.0001）。使用这些参数，认知状态（MCI 和 AD）的预测接收者操作特征曲线下面积为 0.83（灵敏度=0.82，特异性=0.72）。研究结果表明，在更实际的上肢测试（而不是步行）中，运动功能速度和准确性的测量可能为认知障碍评估提供足够的复杂性。
