MassAITC Cohort: Year 1
Project Abstract: The aim of this project is to develop and validate algorithms to capture measures of real-world walking behavior in patients with Alzheimer’s disease (AD).
Preserving functional independence is a major goal of older adults with AD. Wearable sensors enable remote, passive, and continuous assessments of how patients are functioning at home. Digital measures of walking behavior in AD therefore have the potential to advance AD research and care by creating a better connection between patients, caregivers and clinical evidence used to drive drug development. Wide adoption will require evidence demonstrating that these novel measures are meaningful, accurately quantifiable, and clinically relevant.
This project will address a fundamental measurement problem in accurately assessing walking behavior in older adults with AD and will provide critical validation evidence to support regulatory decision making.
Twenty older adults (≥65 years of age) with (n=10) and without (n=10) mild AD will participate in this study. Participants will complete a motion capture laboratory visit and perform a variety of walking tasks while wearing inertial sensors on their wrist, thigh, and ankle. Machine learning algorithms will be developed to derive measures of walking behavior using raw sensor data as inputs, labeled with truth data obtained via motion capture system. Participants will have the option to wear the sensors at home for 14 days to demonstrate feasibility of characterizing real-world walking behaviors in patients with and without AD.