Author: Ray

Awardee Organization(s): University of Michigan
Principal Investigator(s): Robin Brewer, PhD
Official Project Title: Conversational Care Technologies
AITC Partner: PennAITech
Website(s): www.umich.edu, www.si.umich.edu

This project focuses on designing and developing conversational care technologies for older adults and their caregivers. In prior work, we surveyed informal caregivers and older adult care receivers to understand their care routines. Survey findings showed how nearly 20% of care partners used voice assistants in their homes, signaling an opportunity to extend research on older adults’ conversational technology use to include care partners. Next, we conducted a diary study and interviews with caregivers and care receivers to investigate gaps in care interactions and conversations. We found that care receivers experienced more communication frustrations than caregivers and that older adult caregivers wanted more opportunities to influence their care routines.
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.

Awardee Organization(s): University of Minnesota
Principal Investigator(s): Robin Austin, PhD, DNP, DC, RN-BC
Official Project Title: Designing Usable Technologies for Older Adults via Data-Driven Whole-Person User Personas
AITC Partner: PennAITech
Website(s): www.nursing.umn.edu

The long-term goal of this research led by Dr. Robin Austin at the University of Minnesota and her team is to improve health outcomes by combining whole-person patient-generated health data with EHR data, to inform clinical conversations, predict patient trajectories, and identify appropriate interventions. This research, Designing Usable Technologies for Older Adults via Data-Driven Whole-Person User Personas, will create a set of data-driven user personas based on data from 6 studies where 783 adults 65+ years old independently completed a comprehensive health assessment using the MyStrengths+MyHealth (MSMH) mobile app developed by Dr. Robin’s team. MSMH assesses 42 strength/problem areas (e.g., Income, Spirituality, Nutrition) divided into four categories (e.g., My Living, My Mind and Networks, My Body, My Self- Care). Individuals can specify any of 335 challenges (e.g., Hard to concentrate) and any of 4 needs related to each strength/problem area (e.g., Check-ins, Hands-on Care, Info/Guidance, Care Coordination). We will use machine learning approaches, clustering analysis and association rule learning, which are frequently used to develop user personas. These whole-person user personas will account for a 360 degree view of the person, meaning the environments in which individuals live, their psychosocial and physical health needs, and their strengths. This research will inform person-centered technology design and develop a better understanding of the types of older adults who may use AI-based technologies.

Awardee Organization(s): University of Colorado | Kaiser Permanente
Principal Investigator(s): Jennifer Portz, PhD
Official Project Title: Leveraging Patient Portals to Support Caregivers
AITC Partner: PennAITech
Website(s): www.medschool.cuanschutz.edu/general-internal-medicine, www.kpco-ihr.org

Caregivers for people living with dementia (PLWD) make up a diverse group of individuals and can include family, friends, and paid direct care workers. Nearly 30% of caregivers in the United States report caring for a PLWD. While providing care to a loved one can be rewarding, some caregivers may feel caregiver burden and become self-neglectful (e.g., eating poorly, poor exercise habits), which have the potential to lead to poor health outcomes among caregivers. Caregiver-specific interventions are beneficial for improving mental health, confidence in caregiving, and self-care. However, caregivers often experience barriers to such interventions such as time and access. The objective of this pilot is to develop a data-framework for finding caregivers from the electronic medical record (EMR) who can benefit from caregiver and dyadic interventions. PLWD and their caregivers often receive health care services within the same healthcare system and their data hosted within the same EMR. Our previous work found that health outcomes, such as hospitalization, for caregiver-PLWD dyads living in the same household are linked. However, our current model is limited to caregivers living in the same household, often spouses, who share health insurance. Natural language processing can fill this gap by analyzing unstructured EMR data to find patterns among caregivers that will allow us to further identify non-traditional caregivers (e.g., friends, neighbors) and caregivers outside the home (e.g., adult children, extended family members). By automating the process of caregiver identification through the EMR, interventions can be more easily delivered to engage and support the caregiver.

Awardee Organization(s): Cogwear LLC
Principal Investigator(s): David Yonce, MS, MBA
Official Project Title: Physiological Detection and Monitoring of Alzheimer’s Disease
AITC Partner: PennAITech
Website(s): www.cogweartech.com

Cogwear has developed a wireless dry-sensor EEG wearable that can easily collect clinical-grade brainwave data anywhere, anytime with comfort and no limits on mobility. Initially applied to behavioral health, here we propose to extend our platform to early detection and trending of dementia and Alzheimer’s Disease (AD) based upon brain physiology. With the ability to sense EEG from the frontal and temporal lobes, the parts of the brain that regulate short and long-term memory, planning, and executive functions, our system can detect EEG changes implicated in dementia and AD: slowing, reduced complexity, decrease in synchronization, and neuromodulatory deficits.
Our project will focus on two components: migrating our wearable to a soft goods form factor with downsized electronics more appropriate for in-clinic and home use and begin to develop the EEG signal processing and applications to quantitatively detect and trend brain processes associated with dementia and AD. Deliverables will include an advanced prototype and pilot testing of algorithms with humans in a small sample of healthy and AD/ADRD patients.
Our expectation is that these algorithms will ultimately show efficacy to detect presymptomatic brain changes, allowing intervention by caregivers to prepare patients and families. Further, because subtle EEG shifts can be indicative of changing disease states, we can provide quantitative trending of AD based upon brain physiology, providing new methods to titrate pharmaceuticals and evaluate disease treatments. Through earlier detection and enhanced monitoring, our goal is to better support patients and their families by enabling more years of high-functioning and independent living.

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.