Cohort 3

Awardee Organization(s): Foresite Healthcare
Principal Investigator(s): Nicholas Kalaitzandonakes, PhD
Official Project Title: AI/ML Analyses of Mobility Changes Among Elderly Using Continuous Gait Data
AITC Partner: PennAITech
Website(s): https://www.foresitehealthcare.com/

Novel pharmacological and non-pharmacological interventions for Alzheimer’s Disease (AD) and Alzheimer’s Disease and Related Dementias (ADRD) (e.g., physical therapy, occupational therapy, exercise, etc.) can slow the disease progression, but timely diagnosis is necessary for such interventions to be effective. Yet, early diagnosis of the disease remains difficult. Various biomarkers and specialized brain scans are accurate and effective in diagnosing the disease early, but they are expensive, invasive, and difficult to execute in practice.
In previous studies, gait (e.g., walking speed) and motion characteristics (e.g., cadence, stride time and variability, step length, step width, sacrum mediolateral range of motion) have been found to strongly associate with the onset of AD/ADRD and to, often, precede cognitive decline and the presence of other dementia symptoms. As such, it may be possible to use gait and mobility features as diagnostics for AD/ADRD.
In this project, we will identify and develop gait- and motion-related predictive biomarkers for AD/ADRD. For this purpose, we will analyze multiyear gait and motion data from more than 5,000 older adults in assisted living (AL) and memory care (MC) communities around the US. Residents in MC units are all professionally diagnosed with AD/ADRD.
The identified biomarkers will be used as digital diagnostics for early, easy, and inexpensive identification of AD/ADRD, including through passive monitoring of populations in communities with care management and those aging in place (e.g., via passive, physiological, sensors and wearables).

Awardee Organization(s): Beth Israel Deaconess Medical Center
Principal Investigator(s): Chun Lim, MD, PhD
Official Project Title: Mobile Technology as a Cognitive Biomarker of Alzheimer’s Disease
AITC Partner: PennAITech
Website(s):
https://www.bidmc.org/

The Cognitive Neurology Unit

https://bidmcneurology.org/

Alzheimer’s disease’s hallmark is insidious memory loss often accompanied by a lack of awareness of the deficit. Its diagnosis requires evidence of cognitive impairment and remains reliant on clinical assessments, primarily traditional pen and paper cognitive tasks, which, with its many limitations results in only one-half of patients ever diagnosed by physicians. Thus, a simple, inexpensive, and at-home method to capture more of these patients earlier in their disease process could facilitate earlier therapy and planning.
We propose to modernize the clinical diagnosis of Alzheimer’s disease by taking advantage of smartphones to collect multiple streams of behavioral information including active data such as reaction/response time to cognitive tasks and games as well as data captured passively on the smartphone such as movement, location, and typing speed. Using advanced analytical tools, we propose to develop a new smartphone-based app for use in the home environment that detect signs and symptoms of early cognitive impairment and to continuously monitor for progression by capturing passive, real-world information, and active data.