NEW FRONTIERS IN DIGITAL METHODS OF ASSESSING COGNITIVE PERFORMANCE. (11th November 2018)

• Record Type: Journal Article
• Authors: Andersen, S L; Cohen, P
• Is Part Of: Innovation in aging. Volume 2(2018)Supplement 1
• Page Start: 71
• Page End: 71
• Publication Date: 2018-11-11
• Subjects: Aging -- Periodicals; Gerontology -- Periodicals; 612.67
• DOI: 10.1093/geroni/igy023.267
• Languages: English
• ISSNs: 2399-5300

Abstract

Abstract: Neuropathologies underlying AD and other dementias are known to begin years before they are clinically evident and thus it is likely there are cognitive changes that are undetected by traditional neuropsychological test scores. For example, a participant with subtle brain changes may correctly answer a test question but at a much slower rate or with more errors and self-corrections than someone without underlying brain pathology. Typical scoring would classify the individual as unimpaired whereas accounting for how the person completed the task would detect cognitive impairment. Therefore the process approach, or looking at behaviors during the completion of a task, is a more sensitive method of detecting cognitive impairment. Similar to the use of wearable biosensors for tracking health metrics, new applications of digital technologies allow for measurements of the behavioral process of cognitive task completion. This symposium addresses several innovations in the collection and extraction of high-precision digital metrics enable the analysis of cognitive processes in ways that have not previously been possible. We will describe how digital data collection has been implemented with a digital pen and digital voice recorder in a study of healthy aging families and how smartphone applications and wearables collect continuous data among individuals with neurological disorders. We will then describe methods of extracting digital metrics from digital pen image files to disassociate total task completion time into writing time and thinking time. Finally, we will show how digital cognitive metrics can be used to divide test performance into components reflecting distinct cognitive processes.