Article: Digitally generated Trail Making Test data: Analysis using hidden Markov modeling.
Alzheimer's & dementia (Amsterdam, Netherlands)
2022 Volume 14, Issue 1, Page(s) e12292
Abstract: The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L ... ...
Abstract | The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT-A and -B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT-A and -B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT-B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance. |
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Language | English |
Publishing date | 2022-03-08 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2832898-X |
ISSN | 2352-8729 |
ISSN | 2352-8729 |
DOI | 10.1002/dad2.12292 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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