Author: Ray

Awardee Organization(s): University of Southern California
Principal Investigator(s): Maja Matarić, PhD
Official Project Title: An Accessible Machine Learning-Based ADRD Screening Tool for Families and Caregivers
AITC Partner: PennAITech
Website(s): www.robotics.usc.edu/~maja

The goal of this project is to develop an app-based screening system capable of detecting early signs of Alzheimer’s Disease (AD) using data captured during sessions of standard clinical AD diagnostics. Approximately 50 million people worldwide are diagnosed with dementia. As of 2021, an estimated 6.2 million Americans, one in nine people 65 and older, are living with AD.The majority of affected people do not obtain early screening toward a timely dementia diagnosis. Consequently, there is a large and rapidly growing need for lowcost, non-invasive, and accessible tools for dementia screening toward alerting families and caregivers and encouraging them to pursue medical evaluation. The proposed app is intended for family members and caregivers and will be designed to be easy to use and encourage regular screening. The goal is for the proposed app to enable convenient early flagging of AD for the general public.

Awardee Organization(s): AutoTune Me LLC
Principal Investigator(s): Kendra Ray, PhD, MPH, MT-BC
Official Project Title: A Music-Based Mobile App to Combat Neuropsychiatric Symptoms in People Living with ADRD
AITC Partner: PennAITech
Website(s): www.autotuneme.org

The purpose of this pilot project is to develop a mobile application “TuneMind” that detects pulse and sedentary movements of homebound individuals with Alzheimer’s disease and related dementias (ADRD) and triggers auto-play of personalized songs in a wearable device. By collecting quantitative and qualitative data from the app and users, we will test the acceptability and feasibility of the app. This app will be an important tool to include in daily caregiving in a home setting by extending established benefits of music therapy for people with ADRD. The aims are as follows:
Aim 1. Develop TuneMind. TuneMind will be developed to respond to changes in the pattern of heart rate and sedentary behaviors of people with ADRD. An algorithm will be created to (1) find changes of the heart rate to trigger the application to play music; (2) adaptively improve learning when music is needed, e.g., time of day; (3) predict the music dose required to better control heart rate and movement.
Aim 2. Test the usability of the app. A total of 10 stakeholders will be asked to test TuneMind for two weeks. Based on a survey and focus groups, improvements will be made to the app.
Aim 3. Determine the performance and usability of the app for use by people with ADRD and their family caregivers at home. Twenty dyads will be recruited to test TuneMind for two weeks. We will collect physiological measures based on the app’s demonstration to auto-play music according to pulse and/or sedentary states.

Awardee Organization(s): Koda Health
Principal Investigator(s): Desh Mohan, MD
Official Project Title: Patient-Surrogate Alignment in Digital Advance Care Planning
AITC Partner: PennAITech
Website(s): www.kodahealthcare.com

Advance Care Planning (ACP) encompasses educating patients on potential future healthcare decisions, clarifying patients’ values and healthcare preferences, and sharing decisions with family and care providers. Most individuals lacking decision-making capacity near end-of-life will need a surrogate decision maker (SDM) to make or voice decisions for them. However, less than 25% of SDMs are engaged in the ACP process or aware of patient preferences, and often feel unprepared for decision-making. Koda is a machine learning based ACP platform which offers dynamic educational content, decision guidance, and advanced directive documentation for patients and SDMs. Aims of this study are to: 1) determine motivators of patient-SDM alignment among Koda users and 2) develop a machine learning algorithm to perform SDM persona identification.
Participants will be 50 patient-SDM dyads. Eligible patients will be 50+ years of age, without dementia or blindness. SDMs will be 18+ years old. All participants should have an email address and ability to read and speak English. Following informed consent, patients will complete the Koda ACP platform and a survey about SDMs’ values and experiences. SDMs will complete a self-survey and perceived alignment surveys before and after reviewing the patient’s completed ACP. Survey data will be used to train theSDM persona detection algorithm. Dyads will also be invited to complete qualitative interviews, for further exploration of patient-SDM experiences.This pilot project aims to better understand patientSDM alignments and to develop an algorithm for identification of SDM personas, with the ultimate goal of facilitating increasingly high-quality ACP and goal-concordant care.