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| Funder | NATIONAL INSTITUTE ON AGING |
|---|---|
| Recipient Organization | Wellsaid.Ai, Llc |
| Country | United States |
| Start Date | May 15, 2022 |
| End Date | Apr 30, 2024 |
| Duration | 716 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10772862 |
PROJECT SUMMARY: Mild Cognitive Impairment (MCI), as well as Alzheimer’s Dementia and Related Dementias (ADRD) remain woefully under diagnosed. This is despite the availability of numerous scientifically validated screening instruments and recent financial incentives to physicians. This “evidence-practice” gap is at least in large part
because available screening techniques are cumbersome to administer, expensive or too time consuming for regular use in daily clinical care. Additionally, current instruments often require trained docent administration, and/or caregiver confirmation, and typically are administered in the artificial clinical setting rather than
monitored in the setting where impairment affects daily living, introducing potential recall bias. Conversely, attempts to digitize assessments typically are not form and function optimized for the target demographic, requiring additional computer literacy and skills or extensive user technology training, and limiting user
acceptance. We have developed and demonstrated the potential utility of conversational agent technology (CAT) delivered on smart speakers and smart displays for serially monitoring the cognitive and daily living status of older adults in the home. Preliminary data on 760 older adults (age 60-95) suggests the ability to
deploy cognitive screening tools enabled by voice and screen administration, without the potential limitations of unfamiliar technology interfaces or dependency on a clinical administrator, all in the comfort of the subject’s living environment. A companion smart phone app allows caregiver assessment where needed. Following
literature review and expert panel input, we have determined that cognitive assessment tests from the well- validated NIH Toolbox Cognitive Battery (NIHTCB) are the best target for adaptation and potential broad scale clinical and commercial use. The present study has two objectives: 1) Create an in-home conversational agent
administered comprehensive cognitive assessment platform utilizing assessment tests from the NIHTCB; and 2) Define the psychometric properties of CAT adapted cognitive assessments and compare them to standard assessments. In our Stage 1A study, the specific aim is proving feasibility and usability in a cohort of 30
patients recruited from our current commercial user base. Upon satisfactory evidence of technology viability, we will undertake a Stage 1B study with the specific aim of defining the psychometric properties of the adapted cognitive assessment instruments in 100 patient-caregiver dyads. CAT administered assessments will be
evaluated for concurrent criterion validity compared to current standard assessments. We hypothesize Intraclass Correlation Coefficient (ICC) >0.9 compared to current standard assessments. Intra-rater reliability will be assessed with test-retest methods and we hypothesize r >0.8. Results from this study will inform a
Phase II project assessing the clinical utility of longitudinal assessments and machine learning algorithms as predictors of the trajectory of cognitive decline. 1
Wellsaid.Ai, Llc
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