PROJECT SUMMARY/ABSTRACT The objective of this K23 career development award is to support Dr. Emily Paolillo in acquiring the expertise to become an independent clinical researcher focused on using passive digital health methodologies and machine learning analytics to improve detection and monitoring of neurobehavioral change in Alzheimer's disease and related dementias (ADRD). Through the support of this K23 and the enriched multidisciplinary training environment at the UCSF Memory and Aging Center, Dr. Paolillo aims to accomplish targeted goals within the following training domains: 1) passive digital phenotyping in ADRD; 2) big data/ machine learning analytics; and 3) scientific leadership. Dr. Paolillo will translate the knowledge, skills, and findings from this K23 into an R01 to integrate multimodal digital health tools for comprehensive, person-specific assessment of realworld neurobehavioral change. To achieve these goals, Dr. Paolillo has assembled an accomplished mentorship team with specific project-relevant expertise, including: biobehavioral correlates of dementia prevention and wearable actigraphy (Primary Mentor Dr. Kaitlin Casaletto ); neurobehavioral phenotyping of age-related cognitive decline (Co-Primary Mentor Dr. Joel Kramer); smartphone-based cognitive assessment (Co-Mentor Dr. Adam Staffaroni); in-home passive sensor technology (Co-Mentor Dr. Jeffrey Kaye); machine learning and health data science (Co-Mentor Dr. Stathis Gennatas); longitudinal data analysis in dementia (CoMentor Dr. John Karnak) and neurobehavioral assessment of frontotemporal dementia (Scientific Advisor Dr. Howard Rosen). The overarching goal of the proposed study is to examine the utility of a cutting-edge digital health approach, namely passive smartphone monitoring, to detect neurobehavioral impairment in neurodegenerative disease. Smartphone-based digital assessment tools have potential to overcome accessibility limitations of traditional in-person neuropsychological evaluations, as they are remote and utilize ubiquitous mobile technology. Examining digital assessment methods strongly aligns with Actions 2.B.1 ("Identify and disseminate appropriate assessment tools") and 2.B.2 ("Support technology to advance mobile monitoring of cognitive changes") of the 2022 National Plan to Address Alzheimer's Disease. Passively collected digital metrics based on naturalistic smartphone interactions represent a feasible, accessible, lowburden tool for remote monitoring and early detection of neurobehavioral change. The aims of the proposed study are to determine the utility of passive smartphone metrics to measure and monitor clinical outcomes in frontotemporal dementia (FTD) and Alzheimer's disease (AD) by testing associations with gold-standard measures of cognition, neurodegeneration, and functional impairment cross-sectionally and longitudinally. This study will be among the first to examine passive smartphone monitoring in FTD and AD, which if successful, woul...