# Optimizing a closed-loop digital meditation intervention for remediating cognitive decline and reducing stress in older adults

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $1,362,781

## Abstract

Abstract
Deficits in cognitive control are at the core of much cognitive decline experienced by many older adults, often
leading to functional decline and eventually dementia. The rapidly growing segment of the population facing
such cognitive decline has the potential to negatively impact society broadly and it has been estimated that
maintaining or improving cognition in older adults (OA) could potentially prevent or delay the onset of an
estimated 10 million new cases of Alzheimer’s disease and other dementias. Given the lack of success in
discovering effective pharmacological or preventative therapies to prevent dementias, developing targeted
interventions to remediate cognitive deficits is vital. To this end, we developed a novel closed-loop, digital
meditation intervention (MediTrain) that was designed to improve regulation of focused attention in healthy OA.
In a mechanistic RCT, we recently showed that MediTrain lead to broad improvements in cognitive control, with
the greatest gains seen in a subgroup of OA with cognitive deficits (i.e., MCI-like). In addition, this intervention
led to reduced stress reactivity and improvements in cellular markers of aging. A goal of this proposal will be to
extend the scope of our intervention by conducting a mobile RCT (mRCT) in a large sample, recruited nationally,
who will complete the study entirely on mobile devices, providing the statistical power to perform planned
moderator and subgroup analyses to understand the sources of variability in treatment response. Another
important question that emerged from our initial RCT of MediTrain in OA was: what is the minimal and/or optimal
dose of the intervention required to achieve the benefits we observed? Thus, this proposed research will tackle
two specific aims: First, we will conduct in a large, mRCT of MediTrain in OA at varying doses of treatment to
determine the minimum effective dose required for cognitive improvement and stress reduction. Second, we will
examine the moderating effect of cognitive decline on treatment effects. We will also include an exploratory aim
to examine the impact of potential genetic (Alzheimer’s polygenic hazard scores), physiological (cardiovascular
risk), and social (race/ethnicity) moderators on the treatment effects. To accomplish these aims, we will conduct
a large-scale mRCT of MediTrain deployed on mobile devices in a diverse, nation-wide sample of OA (N = 3240),
who will complete the study entirely on mobile devices. This large, national cohort will provide the sample size
necessary to examine individual and subgroup differences in treatment response across a diverse swath of the
general population. All participants will complete baseline, immediate follow-up, and 6-month follow-up
assessments of cognitive and functional outcomes. We anticipate that this unique methodological approach and
experimental design will significantly advance the development of treatment programs directed at the broad
range of cognitive ab...

## Key facts

- **NIH application ID:** 10424030
- **Project number:** 1R01AG076668-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** ADAM H GAZZALEY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,362,781
- **Award type:** 1
- **Project period:** 2022-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424030

## Citation

> US National Institutes of Health, RePORTER application 10424030, Optimizing a closed-loop digital meditation intervention for remediating cognitive decline and reducing stress in older adults (1R01AG076668-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10424030. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*