Background: Coupling digital technology with traditional neuropsychological test performance allows collection of high-precision metrics that can clarify and/or define underlying constructs related to brain and cognition. Objective: To identify graphomotor and information processing trajectories using a digitally administered version of the Digit Symbol Substitution Test (DSST). Methods: A subset of Long Life Family Study participants (n = 1,594) completed the DSST. Total time to draw each symbol was divided into 'writing' and non-writing or 'thinking' time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90 s test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores. Results: Clustering revealed four 'thinking' time trajectories, with two clusters showing significant changes within the test. Participants in these clusters obtained lower episodic memory scores but were similar in other health and functional characteristics. Clustering of 'writing' time also revealed four performance trajectories where one cluster of participants showed progressively slower writing time. These participants had weaker grip strength, slower gait speed, and greater perceived physical fatigability, but no differences in cognitive test scores. Conclusion: Digital data identified previously unrecognized patterns of 'writing' and 'thinking' time that cannot be detected without digital technology. These patterns of performance were differentially associated with measures of cognitive and physical function and may constitute specific neurocognitive biomarkers signaling the presence of subtle to mild dysfunction. Such information could inform the selection and timing of in-depth neuropsychological assessments and help target interventions.
|Original language||English (US)|
|Number of pages||16|
|Journal||Journal of Alzheimer's Disease|
|State||Published - 2021|
Bibliographical noteFunding Information:
This work was supported by the National Institute on Aging (K01AG057798 to S.L.A., U01AG023749 to S.C., U01AG023755 to T.T.P., U01AG023712, U01AG023744, U01AG023746, U19AG063893); the National Institute of General Medical Sciences Interdisciplinary Training Grant for Biostatisticians (T32 GM74905) to B.S.; the Boston University School of Medicine Department of Medicine Career Investment Award to S.L.A.; and the Marty and Paulette Samowitz Foundation to T.T.P. Additionally, the Claude D. Pepper Older Americans Independence Center, Research Registry and Developmental Pilot Grant (NIH P30 AG024827), and the Intramural Research Program, National Institute on Aging supported N.W.G. to develop the Pittsburgh Fatigability Scale.
This work was supported by the National Institute on Aging (K01AG057798 to S.L.A., U01AG023749 to S.C., U01AG023755 to T.T.P., U01AG023712, U01AG023744, U01AG023746, U19AG063893); the National Institute of General Medical Sciences Interdisciplinary Training Grant for Biostatisticians The supplementary material is available in the electronic version of this article: https://dx.doi.org/ 10.3233/JAD-201119.
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- bayesian approach
- boston process approach
- digit symbol substitution test
- executive function
- graphomotor performance
- neuropsychological tests