Remote and in-clinic digital cognitive screening tools outperform the MoCA to distinguish cerebral amyloid status among cognitively healthy older adults

Louisa I. Thompson, Zachary J. Kunicki, Sheina Emrani, Jennifer Strenger, Alyssa N. De Vito, Karysa J. Britton, Catherine Dion, Karra D. Harrington, Nelson Roque, Stephen Salloway, Martin J. Sliwinski, Stephen Correia, Richard N. Jones

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

INTRODUCTION: We evaluated the accuracy of remote and in-person digital tests to distinguish between older adults with and without AD pathological change and used the Montreal Cognitive Assessment (MoCA) as a comparison test. METHODS: Participants were 69 cognitively normal older adults with known beta-amyloid (Aβ) PET status. Participants completed smartphone-based assessments 3×/day for 8 days, followed by TabCAT tasks, DCTclock™, and MoCA at an in-person study visit. We calculated the area under the curve (AUC) to compare task accuracies to distinguish Aβ status. RESULTS: Average performance on the episodic memory (Prices) smartphone task showed the highest accuracy (AUC = 0.77) to distinguish Aβ status. On in-person measures, accuracy to distinguish Aβ status was greatest for the TabCAT Favorites task (AUC = 0.76), relative to the DCTclockTM (AUC = 0.73) and MoCA (AUC = 0.74). DISCUSSION: Although further validation is needed, our results suggest that several digital assessments may be suitable for more widespread cognitive screening application.

Original languageEnglish (US)
Article numbere12500
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume15
Issue number4
DOIs
StatePublished - Oct 1 2023

All Science Journal Classification (ASJC) codes

  • Clinical Neurology
  • Psychiatry and Mental health

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