NIH NOSI: Digital Technology for Early Detection and Monitoring of Alzheimer’s Disease (AD) and AD-Related Dementias (ADRD)

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Notice Number: NOT-AG-24-043

Purpose

This Notice of Special Interest (NOSI) encourages research on the use of digital technology for early detection and monitoring of cognitive and functional decline in persons with Alzheimer’s disease (AD) and AD-related dementias (ADRD). This NOSI is a reissue of NOT-AG-21-048.

Objectives

The goal of this NOSI is to facilitate research on the use of digital signals and data as digital phenotyping that may flag or signal early changes within individuals at risk of AD/ADRD before cognitive symptoms are evidenced by current cognitive assessment and/or brain imaging biomarkers. Promising areas of research include, but are not limited to, the following:

  1. Developing and optimizing sensors (bioengineering and design), repurposing existing sensors, or intergrating multiple sensing technologies (active and passive) for daily activity measurement in older adults (e.g. physical activity/sleep patterns, gait, behavioral, and life-space monitoring).
  2. Developing new or refining existing sensors for physiological and psychological measurements, such as speech spectral analysis, blood pressure monitoring, heart rate variability, emotion regulation, and sleep pattern monitoring.
  3. Validating digital markers in all stages of AD/ADRD, including prodromal, mild cognitive impairment (MCI), and dementia to enhance sensitivity and specificity for early detection of AD/ADRD.
  4. Improving the accessibility of digital devices for use by individuals from diverse socioeconomic and geographical backgrounds (e.g., improving digital devices for health disparities research).
  5. Developing and validating cognitive screening instruments or assessments and translating them into systems (e.g., electronic health records) for assisting with meaningful care recommendations for individuals living with cognitive impairment.
  6. Developing machine learning approaches for mining data from multiple sources, such as wearable devices or home sensors and clinical data, and validating them for predicting cognitive decline.

The anticipated outcomes should be centered on cost-effective, user-friendly solutions that can be readily used, or adapted, for persons living in remote, urban, and peri-urban communities. The anticipated activities performed during the award should lead to collaborations among multidisciplinary teams (e.g., software engineers, bioengineers, physicians, behavioral scientists, psychophysiologists, neuroscientists, and clinicians) that have substantial potential for the early identification of individuals who are at high risk for AD/ADRD, and subsequently inform prevention and disease monitoring efforts.

For more information, please see the announcement website.