Digitally generated Trail Making Test data: Analysis using hidden Markov modeling. Issue 1 (8th March 2022)

• Record Type: Journal Article
• Authors: Du, Mengtian; Andersen, Stacy L.; Cosentino, Stephanie; Boudreau, Robert M.; Perls, Thomas T.; Sebastiani, Paola
• Is Part Of: Alzheimer's & dementia. Volume 14:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-08
• Subjects: aging -- Bayesian hidden Markov models -- cognitive function -- digital biomarker -- Trail Making Test; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1002/dad2.12292
• Languages: English
• ISSNs: 2352-8729

Abstract

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.