Pen-and-Paper and Smartphone Self-Reported Cognitive Concerns to Predict Longitudinal Alzheimer's Biomarkers in a Diverse, Non-Demented Older Adult Population

Project Summary This project aims to evaluate the utility of self-reported subjective cognitive concerns (SCC) as an early screening tool for Alzheimer’s Disease (AD) by assessing their relationship with longitudinal AD biomarkers. SCC, which reflect individuals’ self-reported cognition, may serve as a cost-effective and accessible tool for detecting early signs of neurodegenerative disorders. However, the optimal method for assessing SCC remains unclear, with both traditional pen-and- paper assessments and smartphone-based Ecological Momentary Assessment (EMA) offering distinct advantages. Plasma biomarkers, valuable indicators of AD pathology, provide a non- invasive and cost-effective means to track AD risk. This project will leverage both SCC and plasma biomarkers to explore their combined utility in predicting AD risk. We will: 1) evaluate the longitudinal relationship between baseline conventional pen-and-paper measures of SCC and subsequent plasma AD biomarkers, 2) examine whether baseline smartphone-based EMA SCC measures—focusing on both the number of reported concerns and day-to-day variability— predict subsequent plasma AD biomarkers, and 3) apply machine learning to identify the joint and independent information captured by pen-and-paper and EMA SCC assessments, developing integrated scores based on data from both modalities to improve prediction of plasma AD biomarkers. By integrating machine learning techniques, this study aims to enhance the accuracy of SCC assessments, providing a more sensitive tool for predicting AD biomarkers. This research will be conducted within the Einstein Aging Study, a diverse cohort of older adults from the Bronx, allowing us to examine how SCC assessments perform across different racial and ethnic groups.