# DETECT-AD:  Digital Evaluations and Technologies Enabling Clinical Translation for Alzheimer's Disease

> **NIH NIH R56** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $1,474,986

## Abstract

Project Summary
This study (DETECT-AD or Digital Evaluations and Technologies Enabling Clinical Translation for AD)
objective is to fundamentally
asymptomatic
improve assessment of meaningful cognitive function in early pre- or
Alzheimer's Disease (AD) using largely passive home-based digital assessment methods.To
achieve this goal the approach will be tested in a prospective 36-month pre-clinical AD trial simulation using a
current, sharable, technology agnostic, home-based assessment platform, continuously generating passive
and interactive sensor-derived digital biomarkers (DBs) and metrics of everyday cognition and function (digital
indicators of active life status or DIALS). The platform also allows for remote capture of conventional clinical
assessments. Participants with defined amyloid status (Aβ-high vs. Aβ-low based on relative amyloid PET
burdens) will be enrolled. Aβ-high participants will progress as if they were receiving placebo; those with lower
Aβ burdens will have less progression, simulating effective treatment. Participants will be asked
to take a daily multivitamin as a study `drug' to mimic trial conditions. The primary outcome will be change in
the DBs and composite DIALS from four key domains: mobility (gait speeds), cognition (computer usage),
sleep (sleep times), socialization (time out of home). Specific Aims are to: 1) Determine rates of progression of
DBs (of mobility, cognition, sleep and social engagement) individually and as an aggregate metric (the DIALS)
in FDA stage 1-3 AD (Aβ-high vs. Aβ-low) participants, by establishing early base rates (first months of
monitoring) of progression, and then, longitudinal change; 2) Establish the utility of these DBs compared to
conventional measures (CDR-SoB, ADCS-PACC) used in trials; and 3) Investigate Exploratory Aims
examining several high-value features of these DBs for application in trials and related studies including: 3.1)
Correlate DBs and DIALS with conventional imaging and blood-based biomarkers of inflammation,
neurodegeneration, vascular risk or injury, and nutritional health; 3.2) Determine the change over time of
embedded “cognitive clocks” (time to complete regular weekly online report queries and monthly cognitive
tests); 3.3) Establish adverse event fluctuations over time (mood; illness; pain; ER, doctor, hospital visits;
injury; non-study medication changes) via weekly remote assessment; and 3.4) Assess the study partner's
DBs change relative to the person with AD (mobility, sleep, social engagement).
study
thus
Successful completion of this
will provide foundational validated DB and DIALS data improving treatment response readout sensitivity;
advancing AD clinical trial capability and capacity.The intent is to not only validate a single app or device,
but to advance ecologically valid multi-domain assessment, as well as an entire trials-environment specific,
DB-facilitated protocol that could be adapted and shared for use by any clinical trial going forwar...

## Key facts

- **NIH application ID:** 10459706
- **Project number:** 1R56AG074321-01
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** JEFFREY A KAYE
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,474,986
- **Award type:** 1
- **Project period:** 2021-09-15 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10459706

## Citation

> US National Institutes of Health, RePORTER application 10459706, DETECT-AD:  Digital Evaluations and Technologies Enabling Clinical Translation for Alzheimer's Disease (1R56AG074321-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10459706. Licensed CC0.

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