Advances in health care have been dramatic since the beginning of the millennium. As a result, people are living longer with age-related diseases, and the number of older individuals unable to live independently is rising rapidly. Mobile computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to automate analysis of functional health in a person's everyday settings. This project focuses on evaluating the performance and commercial viability of technologies that will meet some of the needs that this coming age wave introduces by automating assessment of a person's functional performance. The primary objective of this Phase I STTR application is to evaluate the feasibility of assessing an individual's cognitive and mobility-based health using behavior patterns as sensed by a smart watch. Achieving this objective will provide a foundation for the Phase II application goal of using multiple information sources to automatically-generate activity scores and functional health measures from sensor data. Building on our prior collaborative work, our approach creates a profile of a person's routine behavior through automated real-time recognition of complex activities from mobile sensor data (Aim 1). We will evaluate the use of machine learning techniques to map behavior features onto cognitive and mobility health scores provided through app and in-person neuropsychological assessment (Aim 2). Finally, we will evaluate an interactive visual tool for displaying behavior patterns to provide individuals and their caregivers with insights on their routines and relationship with their health status (Aim 3). This work has important health care implications as functional impairment has been associated with negative outcomes including increased health care utilization, falls, and conversion to dementia. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, it is imperative to commercialize technologies that increase functional independence while improving quality of life for both individuals and their caregivers.
We propose to evaluate the performance and commercial viability of an application that can predict an individual's cognitive and mobility-based health based on patterns that are sensed by a smart watch, and effectively and efficiently present this information to the caregiver. This work will lay the foundation for a commercial tool to analyze behavior routines and automate assessment of functional health. This research is relevant to public health because these technologies can extend the functional independence of our aging society through technology-assisted health self-management, reduce caregiver burden, and improve quality of life.