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Tele-CF: A practical platform for remote monitoring of cognitive frailty

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R44AG061951-02
Agency Tracking Number: R44AG061951
Amount: $2,500,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: NIA
Solicitation Number: PAS19-316
Timeline
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-09-01
Award End Date (Contract End Date): 2023-08-31
Small Business Information
165 PLEASANT ST APT 302
Cambridge, MA 02139-4654
United States
DUNS: 802270988
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 ASHKAN VAZIRI
 (888) 589-6213
 ashkan.vaziri@biosensics.com
Business Contact
 NEDA MOVAGHAR
Phone: (888) 589-6213
Email: neda.movaghar@biosensics.com
Research Institution
N/A
Abstract

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.

* Information listed above is at the time of submission. *

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