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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Performance of an Adapted Version of the Timed Up-and-Go Test in People with Cognitive Impairments
Laura Mumic de Melo1, Juliana Hotta Ansai2, Paulo Giusti Rossi1
1Postgraduate Program of Physiotherapy, Physiotherapy Department, Federal University of São Carlos , São Carlos , São Paulo , Brazil .
Abstract:
In clinical practice, older people with cognitive impairment may have difficulties to understand the instructions of the Timed Up-and-Go (TUGT) test and present a bad performance. The purpose of this study was to identify differences in the TUGT performance, in an adapted version, between older adults with preserved cognition (PC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to identify the association between the adapted TUGT performance and cognition among groups. A cross-sectional study was conducted with 118 community-dwelling older adults divided in three groups: PC (n = 40), MCI (n = 40) and AD (n = 38). The evaluation was composed by the adapted TUGT and cognitive assessment (Addenbrooke's Cognitive Examination and Frontal Assessment Battery). Only the cadence of TUGT presented significant difference between groups, specifically between AD versus MCI and PC groups. The main correlations were found between time of TUGT with fluency domain and global cognitive function, especially in the AD Group. The findings contribute to the understanding of how cognition interferes on functional mobility in older people with MCI and AD. The adapted TUGT is easy to perform in clinical practice and can be useful when assessing mobility in people with cognitive impairment.
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