Gait parameters have been investigated as an additional tool for differential diagnosis in neurocognitive disorders, especially among healthy elderly (HE), those with mild cognitive impairment (MCI), and Alzheimer's disease (AD) patients. A videogrammetry system could be used as a low-cost and clinically practical equipment to capture and analyze gait in older adults. The aim of this study was to select the better gait parameter to differentiate these groups among different motor test conditions with videogrammetry analyses. Different motor conditions were used in three specific assessments: 10-meter walk test (10mWT), timed up and go test (TUGT), and treadmill walk test (TWT). These tasks were compared among HE (n=17), MCI (n=23), and AD (n=23) groups. One-way ANOVA, Kruskal-Wallis, and Bonferroni post-hoc tests were used to compare variables among groups. Then, an effect size (ES) and a linear regression analysis were calculated. The gait parameters showed significant differences among groups in all conditions, but not in TWT. Controlled by confounding variables, the gait velocity in 10mWT at usual speed, and TUGT in dual-task condition, predicts 39% and 53% of the difference among diagnoses, respectively. Finally, these results suggest that a low-cost and practical video analysis could be able to differentiate HE, those with MCI, and AD patients in clinical assessments.

中文翻译：

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影像测量的步态分析可以区分健康的老年人，轻度的认知障碍和阿尔茨海默氏病：一项横断面研究。

已经对步态参数进行了研究，作为在神经认知障碍中进行鉴别诊断的另一工具，尤其是在健康的老年人（HE），轻度认知障碍（MCI）和阿尔茨海默氏病（AD）患者中。影像测量系统可以用作低成本和临床实用的设备，以捕获和分析老年人的步态。这项研究的目的是选择更好的步态参数，以通过视频测量分析在不同的运动测试条件之间区分这些人群。在三个特定的评估中使用了不同的运动条件：10米步行测试（10mWT），定时跑测试（TUGT）和跑步机步行测试（TWT）。在HE（n = 17），MCI（n = 23）和AD（n = 23）组之间比较了这些任务。单向方差分析，Kruskal-Wallis，和Bonferroni事后检验用于比较各组之间的变量。然后，计算了效应量（ES）和线性回归分析。步态参数显示在所有条件下各组之间的显着差异，但在TWT中没有。受混杂变量控制，正常速度下10mWT的步态速度和双任务条件下的TUGT分别预测诊断之间差异的39％和53％。最后，这些结果表明，低成本，实用的视频分析可以在临床评估中区分HE，MCI和AD患者。在正常速度下10mWT的步态速度和双任务条件下的TUGT分别预测诊断之间差异的39％和53％。最后，这些结果表明，低成本，实用的视频分析可以在临床评估中区分HE，MCI和AD患者。在正常速度下10mWT的步态速度和双任务条件下的TUGT分别预测诊断之间差异的39％和53％。最后，这些结果表明，低成本，实用的视频分析可以在临床评估中区分HE，MCI和AD患者。