{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Du M"],"funding":["NIA NIH HHS","National Institute on Aging"],"pagination":["e12292"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8902814"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_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 additi"],"journal":["Alzheimer's & dementia (Amsterdam, Netherlands)"],"pubmed_title":["Digitally generated Trail Making Test data: Analysis using hidden Markov modeling."],"pmcid":["PMC8902814"],"funding_grant_id":["5U01AG023744","5U01AG023755","5U19AG063893","5U01AG023746","K01 AG057798","5U01AG023712","K01AG057798","5U01AG023749"],"pubmed_authors":["Andersen SL","Sebastiani P","Cosentino S","Du M","Boudreau RM","Perls TT"],"additional_accession":[]},"is_claimable":false,"name":"Digitally generated Trail Making Test data: Analysis using hidden Markov modeling.","description":"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 additi","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-21T16:50:36.806Z","creation":"2025-04-21T16:50:36.806Z"},"accession":"S-EPMC8902814","cross_references":{"pubmed":["35280964"],"doi":["10.1002/dad2.12292"]}}