Gait analysis with videogrammetry can differentiate healthy elderly, mild cognitive impairment, and Alzheimer's disease: A cross-sectional study.

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.