{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:58:23Z","timestamp":1777395503136,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2021,6,29]]},"abstract":"<jats:p>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\u200a=\u200a1,594) completed the DSST. Total time to draw each symbol was divided into \u2018writing\u2019 and non-writing or \u2018thinking\u2019 time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90\u200as test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores. Results: Clustering revealed four \u2018thinking\u2019 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 \u2018writing\u2019 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 \u2018writing\u2019 and \u2018thinking\u2019 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.<\/jats:p>","DOI":"10.3233\/jad-201119","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T05:37:50Z","timestamp":1625031470000},"page":"17-32","source":"Crossref","is-referenced-by-count":14,"title":["Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior"],"prefix":"10.1177","volume":"82","author":[{"given":"Stacy L.","family":"Andersen","sequence":"first","affiliation":[{"name":"Geriatrics Section, Department of Medicine, Boston University School of Medicine, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"on behalf of the Long Life Family Study","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Sweigart","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nancy W.","family":"Glynn","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary K.","family":"Wojczynski","sequence":"additional","affiliation":[{"name":"Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bharat","family":"Thyagarajan","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, University of Minnesota School of Medicine, Minneapolis, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Mengel-From","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Epidemiology, Biostatistics and Biodemography Unit, University of Southern Denmark, Odense, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Thielke","sequence":"additional","affiliation":[{"name":"Geriatric Research, Education, and Clinical Center, Puget Sound VA Medical Center, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas T.","family":"Perls","sequence":"additional","affiliation":[{"name":"Geriatrics Section, Department of Medicine, Boston University School of Medicine, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David J.","family":"Libon","sequence":"additional","affiliation":[{"name":"New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Stratford, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rhoda","family":"Au","sequence":"additional","affiliation":[{"name":"Department of Anatomy and Neurobiology and Neurology, Boston University School of Medicine, Boston, MA, USA"},{"name":"Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanie","family":"Cosentino","sequence":"additional","affiliation":[{"name":"Cognitive Neuroscience Division of the Department of Neurology, Taub Institute for Research on Alzheimer\u2019s Disease and the Aging Brain, Columbia University, New York, NY, USA"},{"name":"Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paola","family":"Sebastiani","sequence":"additional","affiliation":[{"name":"Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-201119_ref1","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/S1474-4422(13)70044-9","article-title":"Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer\u2019s disease: A prospective cohort study","volume":"12","author":"Villemagne","year":"2013","journal-title":"Lancet Neurol"},{"key":"10.3233\/JAD-201119_ref2","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1001\/jama.2015.4668","article-title":"Prevalence of cerebral amyloid pathology in persons without dementia: A meta-analysis","volume":"313","author":"Jansen","year":"2015","journal-title":"JAMA"},{"key":"10.3233\/JAD-201119_ref3","unstructured":"Ashendorf L , Swenson R , Libon DJ (2013) The Boston process approach to neuropsychological assessment: A practitioner\u2019s guide, Oxford University Press, Oxford."},{"key":"10.3233\/JAD-201119_ref4","unstructured":"Kaplan E (1988) A process approach to neuropsychological assessment In Clinical neuropsychology and brain function: Research, measurement, and practice, Boll T, Bryant BK, eds. 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