PennAITech Awardees

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/

HOME

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.

Awardee Organization(s): University of Washington
Principal Investigator(s): Clara Berridge, PhD, MSW
Official Project Title: Talking Tech With Dementia Care Dyads: Improving a Self-Administered Tool to Support Informed Decision
AITC Partner: PennAITech
Website(s): https://socialwork.uw.edu/faculty/professors/clara-berridge

AITC Consortium; Basic Information; PennAITech Awardees
Abstract: The proposed project is to enhance the Let’s Talk Tech (LTT) intervention that is delivered as a web application. LTT is the first of its kind self-administered tool to help families meaningfully engage people living with mild dementia in digital technology use planning to enable optimal use to support dementia caregiving at home. Let’s Talk Tech is an education and communication intervention that supports decision making and planning for technology use. It includes the following components: education accessible to people living with mild AD/ADRD and care partners about multiple technologies, facilitation of dyadic communication, and documentation of the person living with dementia’s preferences. LTT has demonstrated in a pilot promising preliminary feasibility and efficacy on targeted measures for informed shared decision making about technologies. This project will implement what was learned from the pilot study about ways to further expand its reach, relevance, and sharing with the entire care network. Aim 1 is to enhance Let’s Talk Tech to achieve wider relevance and equitable access with 4 new features. Aim 2 is to implement EHR integration and patient-controlled sharing of LTT’s preference summaries, and Aim 3 is to assess clinician acceptability of viewing dyad’s LTT preferences in a test instance the EHR. This will expand the intervention’s reach and functionality, employ standards to promote interoperable sharing of documented preferences, and further test and iteratively improve LTT.