Current advances in digital cognitive assessment for preclinical Alzheimer's disease

Abstract

There is a pressing need to capture and track subtle cognitive change at the preclinical stage of Alzheimer's disease (AD) rapidly, cost-effectively, and with high sensitivity. Concurrently, the landscape of digital cognitive assessment is rapidly evolving as technology advances, older adult tech-adoption increases, and external events (i.e., COVID-19) necessitate remote digital assessment. Here, we provide a snapshot review of the current state of digital cognitive assessment for preclinical AD including different device platforms/assessment approaches, levels of validation, and implementation challenges. We focus on articles, grants, and recent conference proceedings specifically querying the relationship between digital cognitive assessments and established biomarkers for preclinical AD (e.g., amyloid beta and tau) in clinically normal (CN) individuals. Several digital assessments were identified across platforms (e.g., digital pens, smartphones). Digital assessments varied by intended setting (e.g., remote vs. in-clinic), level of supervision (e.g., self vs. supervised), and device origin (personal vs. study-provided). At least 11 publications characterize digital cognitive assessment against AD biomarkers among CN. First available data demonstrate promising validity of this approach against both conventional assessment methods (moderate to large effect sizes) and relevant biomarkers (predominantly weak to moderate effect sizes). We discuss levels of validation and issues relating to usability, data quality, data protection, and attrition. While still in its infancy, digital cognitive assessment, especially when administered remotely, will undoubtedly play a major future role in screening for and tracking preclinical AD.

Keywords: clinical assessment; clinical trials; cognition; computerized assessment; digital cognitive biomarkers; home‐based assessment; preclinical Alzheimer's disease; smartphone‐based assessment.

FIGURE 1

A, Cogstate One Back tests. Copyright© 2020 Cogstate. All rights reserved. Used with Cogstate's permission. B, CANTAB Spatial Span and Paired Associates Learning. Copyright Cambridge Cognition. All rights reserved. C, NIH‐Toolbox Pattern Comparison Processing Speed Test Age 7+ v2.1. Used with permission NIH Toolbox, © 2020 National Institutes of Health and Northwestern University

FIGURE 2

A, Ambulatory Research in Cognition (ARC) Symbols Test, Grids Test, and Prices Test. Used with permission from J. Hassenstab. B, neotiv Objects‐in‐Rooms Recall test. Used with permission from neotiv GmbH. C, Boston Remote Assessment for Neurocognitive Health (BRANCH). Used with permission from K. V. Papp

FIGURE 3

A, Sea Hero Quest Wayfinding and Path integration. Used with permission from M. Hornberger. B, Digital Maze Test from survey perspective and landmarks from a first‐person perspective. Used with permission from D. Head. C, Data and analysis process for digital Clock Drawing Test (dCDT), from data collection, the artificial intelligence (AI) analysis steps, and the machine learning (ML) analysis and reporting. Used with permission from Digital Cognition Technologies

FIGURE 4

Overview of cognitive tests and their platforms. BRANCH, Boston Remote Assessment for Neurocognitive Health; ORCA‐LLT, Online Repeatable Cognitive Assessment‐Language Learning Test; NIH‐TB, National Institutes of Health Toolbox; CANTAB, Cambridge Neuropsychological Test Automated Battery; ARC, Ambulatory Research in Cognition; M2C2, Monitoring of Cognitive Change; dCDT, digital Clock Drawing Test. *Is available for use through a web browser