Key Dates

Related Announcements

• December 30, 2024 - Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R21 Clinical Trial Optional). See NOFO PAR-25-331.
• December 30, 2024 - Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R01 Clinical Trial Optional). See NOFO PAR-25-332. 
• January 6, 2022 - Notice of Special Interest (NOSI): Digital Technology for Early Detection and Monitoring of Alzheimer’s Disease and Related Dementias. See Notice NOT-AG-21-048.

Issued by

National Institute on Aging (NIA)

National Library of Medicine (NLM)

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.

Background

Early diagnosis and monitoring of AD/ADRD is an unmet need and a major goal in the Department of Health and Human Services' National Plan to Address Alzheimer’s Disease. There is evidence that the functional, psychological, pathological, and physiological changes underlying AD/ADRD may emerge many years prior to the clinical manifestation of cognitive symptoms, prompting this call to identify earlier indicators of disease risk and subclinical disease burden.

Many current biomarkers for early detection of prodromal AD/ADRD (e.g., PET/MRI imaging) are costly and invasive. Digital technology—the branch of scientific or engineering knowledge that deals with the creation and practical use of digital or computerized devices, methods, and systems—and data produced by digital devices offer novel capabilities with great potential for early detection of cognitive and associated functional change. Digital signals can provide high frequency, and/or intensive time-series data produced by a single individual, which capture health-related aspects of daily life. Examples of digital signals range from simple measures of physical activity (e.g., gait and geospatial location, sleep duration and quality, heart rate, pain, and, speech/voice) to more complex behaviors such as driving. Digital phenotyping is derived via signal processing (of high-dimensional and often noisy digital signals) and can be used to inform disease prediction and management at both the individual and population level.

This NOSI is based on expert discussions from the National Institutes of Health (NIH)'s AD and ADRD research summits and NIA workshops, including Applying Digital Technology for Early Diagnosis and Monitoring of Alzheimer’s Disease and Related Dementias (2019) and Cost-Effective Early Detection of Cognitive Decline (2017).

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 a variety of 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. 

Application and Submission Information

This notice applies to due dates on or after March 11, 2025 and subsequent receipt dates through November 17, 2027.

Submit applications for this initiative using one of the following notice of funding opportunity (NOFO) or any reissues of these announcements through the expiration date of this notice.

• PAR-25-331 - Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R21 Clinical Trial Optional)
• PAR-25-332 - Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R01 Clinical Trial Optional)

All instructions in the How to Apply - Application Guide and the notice of funding opportunity used for submission must be followed, with the following additions:

• For funding consideration, applicants must include “NOT-AG-24-043” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.

Inquiries

Please direct all inquiries to the contacts in Section VII of the listed notice of funding opportunity with the following additions/substitutions:

Please direct all inquiries to:

Yuan Luo, Ph.D.
Division of Neuroscience
National Institutes on Aging (NIA)
Telephone: 301-496-9350
Email: yuan.luo@nih.gov

Dinesh John, Ph.D.,
Division of Behavioral and Social Research
National Institutes on Aging (NIA)
Telephone: (301) 496-3136
Email: dinesh.john@nih.gov 

Lyndon Joseph, Ph.D.
Division of Geriatrics and Clinical Gerontology
National Institutes on Aging (NIA)
Telephone: 301-496- 6761
Email: josephlj@mail.nih.gov

Yanli Wang, PhD
National Library of Medicine (NLM)
Telephone: 301-435-4372
Email:  yanli.wang@nih.gov