Utilizing mobile behavioral data with machine learning to monitor, diagnose, and track AD/ADRD progression

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.

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Wearable sensors capturing digital autonomic biomarkers to detect empathy loss for frontotemporal dementia (bvFTD)

Awardee Organization(s): University of Pennsylvania Frontotemporal Degeneration Center
Principal Investigator(s): Emma Rhodes, PhD
Official Project Title: Feasibility of Digital Monitoring to Detect Autonomic Markers of Empathy Loss in bvFTD
AITC Partner: PennAITech
Website(s): www.med.upenn.edu/ftd
Loss of empathy is a core symptom of behavioral variant frontotemporal dementia (bvFTD) that negatively impacts daily functioning and is highly distressing to families and caregivers. Scientific research has struggled to understand the specific causes of empathy loss in bvFTD. A relatively unexplored but promising avenue of scientific inquiry is the role of autonomic nervous system (ANS) arousal in empathy loss in bvFTD. The ANS is comprised of two complementary subsystems, the sympathetic and parasympathetic nervous systems, which operate together to regulate an individual’s level of physiologic arousal in response to emotional cues from the environment. Patients with bvFTD show abnormalities in autonomic arousal that are linked to symptoms of social dysfunction, including loss of empathy, but this line of research has been hindered by reliance on traditional methods of measuring autonomic arousal, namely hard-wired EKG and skin conductance sensors, which restrict the movement of the patient and are sensitive to motion effects. Recent advances in wearable smartwatch technology allow for precise, unobtrusive measurement of autonomic arousal with built-in motion sensors that more accurately capture key arousal variables, such as respiratory sinus arrythmia and skin conductance. Use of smartwatch technology will advance our understanding of physiologic mechanisms of empathy loss in bvFTD and other neuropsychiatric symptoms in ADRD and identify potential treatment targets. The overarching goal of this project is to test the feasibility of using a smartwatch to capture abnormalities in autonomic arousal in bvFTD and validate digital markers of autonomic abnormalities against behavioral measures of empathy loss.
In this project, we will use these findings to develop in-home conversational technologies that use prompts to structure care conversations between older adults and their caregivers.
We contribute a nuanced dyadic perspective to care relationships as most care research focuses solely on caregiver perspectives. We also extend conversational technology research beyond information seeking to include more social uses by developing conversational technology applications with mainstream voice technologies (e.g., Amazon Alexa) to support improved care relationships, social and emotional well-being, and quality of life.

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Web-app tool to support technology use decision making for individuals with AD/ADRD and their caregivers

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.

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