Research Project
10329058
ABSTRACT Cognitive frailty (CF), the combined presence of physical frailty and cognitive impairment, is a strong and independent predictor of cognitive decline over time. The International Association of Gerontology and Geriatrics and the International Academy on Nutrition and Aging have recommended the use of cognitive frailty assessment to track the progression of mild cognitive impairment (MCI) towards dementia, Alzheimer's disease (AD), and loss of independence. However, there is no practical tool for assessment of CF during telehealth visits. This is especially important as telehealth is growing in popularity amongst older adults and increasingly accepted by healthcare payers, and as the ongoing COVID-19 pandemic has significantly accelerated these trends. Therefore, there is an unmet need for a software-based solution that enables remote assessment of CF during telehealth visit and can be integrated into existing telehealth systems. In Phase I, we successfully designed a prototype of a novel CF measurement tool, called Tele-CF, that uses deep learning-based image processing to remotely measure CF. The Tele-CF algorithms extracts kinematic features of the forearm motion from a video of 20-second elbow flexion and extension, and quantifies weakness, slowness, rigidity, and exhaustion (which are phenotypes of frailty) to generate a frailty index (FI). FI ranges from zero to one, with higher values indicating progressively greater severity of frailty. When applied under dual-task conditions (e.g., while simultaneously performing a working memory task), Tele-CF can also screen cognitive impairment. In Phase I, we demonstrated the feasibility and proof of concept validity of Tele-CF for use in older adult population (n=29, age: 78.6±6.5), including 18 subjects with MCI or mild dementia, by comparing the results against validated tools (a sensor-based tool for in-clinic frailty assessment, dual-task gait, and a clinical cognitive scale, MMSE). After successfully achieving all milestones of Phase I, we are proposing to complete the development of Tele-CF and carry out a clinical study to demonstrate the ability of Tele-CF for remote tracking of CF over a 12-month period and to predict decline in CF at 12 months. To achieve the clinical aim of the study, we will recruit 100 adults (age 60+) with clinically confirmed MCI or mild dementia including 50 subjects without physical frailty and 50 subjects with physical frailty. All subjects will be assessed in clinic using conventional cognitive-motor assessment tools, at baseline, 6 months, and 12 months, and also remotely starting from the baseline every two months using Tele-CF. Tele-CF provides an easy-to-use desktop application for remote assessment of CF for clinicians that works with existing telehealth platforms. Tele-CF desktop application will enable clinicians to identify, document, and track CF in older patients. In addition, Tele-CF will have broader applications in collection and analysis of video biomarkers in telehealth and clinical trials.
