# Improving sleep and circadian functioning, daytime functioning, and well-being for midlife and older adults by improving patient memory for a transdiagnostic sleep and circadian treatment

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2024 · $613,411

## Abstract

Abstract
Progress toward promoting health and well-being as we age must include the identification of novel targets that
are safe, powerful, inexpensive, and deployable. Our focus is on one such target—patient memory for the
contents of treatment—because: (1) patient memory for treatment is poor, (2) poor memory for treatment is
associated poorer adherence and poorer outcome, (3) memory support strategies can improve memory for
treatment and (4) improved memory for treatment improves outcome. In this application, we propose to test a
new, streamlined, and potent approach to engaging this novel target: the Memory Support Intervention (MSI).
The MSI aims to improve patient memory for treatment. It was distilled from the basic, non-clinical research in
cognitive science and education and is comprised of four powerful memory promoting strategies that are
proactively, strategically, and intensively integrated into treatment-as-usual. Importantly, the MSI does not add
to session length, or the number of sessions needed. The aim of this proposal is to conduct a confirmatory
efficacy trial to test whether the MSI improves outcomes for midlife and older adults. As a “platform” for the
next step in investigating this approach, we focus on sleep and circadian problems and the Transdiagnostic
Intervention for Sleep and Circadian Dysfunction (TranS-C). TranS-C is a worthy platform on which to test
the MSI because (1) sleep and circadian functioning, including and beyond insomnia, is highly prevalent
among midlife and older adults, (2) poor sleep and circadian functioning has a wide range of serious negative
consequences, including on memory and (3) TranS-C addresses a range of the most common sleep and
circadian problems experienced by midlife and older adults. Promising pilot data suggest that memory for
TranS-C may be poorer among midlife and older adults, relative to younger adults, and that adding memory
support has potential to improve treatment adherence and treatment outcome for this age group. Over 5 years,
we will recruit adults who are 50 years and older and who are experiencing sleep and circadian problems (N =
178, including 20% for attrition). The sample will be randomly allocated to TranS-C plus the MSI (“TranS-
C+MSI”) vs. TranS-C alone, and all will receive eight 50-minute, weekly, individual sessions. Assessments will
be conducted at baseline, post-treatment, and at 6- and 12-month follow-up. The sample will be recruited from
two large community-based organizations that serve midlife and older adults who are low-income and
experiencing mobility impairments. The intervention will be delivered via live telehealth to improve accessibility.
We will compare the effects of TranS-C+MSI vs. TranS-C alone to determine if the MSI improves sleep and
circadian functioning, daytime functioning, and well-being (Aim 1). We will determine if patient memory for
treatment (the target) mediates the relationship between treatment condition and outcome (Aim 2). W...

## Key facts

- **NIH application ID:** 10896407
- **Project number:** 5R01AG082651-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Allison G Harvey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $613,411
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896407

## Citation

> US National Institutes of Health, RePORTER application 10896407, Improving sleep and circadian functioning, daytime functioning, and well-being for midlife and older adults by improving patient memory for a transdiagnostic sleep and circadian treatment (5R01AG082651-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10896407. Licensed CC0.

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