# Multi-site, double-blind, randomized-controlled, efficacy trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction for depression symptoms in older adults with high suicide risk

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $2,809,928

## Abstract

ABSTRACT: Older adults with moderate-to-severe depression symptoms (i.e., PHQ-9 scores ≥ 10) plus active
suicidal ideation (SI) and/or a history of attempt are at high risk for suicidal behaviors and death. Data suggest
that sleep-wake rhythm disruption could provide a modifiable target mechanism to improve depression
treatment outcomes. Controlled pilot data from a sample of older adults with serious mental illness show,
compared with treatment as usual (TAU), a behavioral approach called the Transdiagnostic Intervention for
Sleep and Circadian Dysfunction (TranS-C) may improve sleep-wake rhythm stability and lead to more
sustained depression responses six-months later. We propose to confirm TranS-C’s target engagement (Aim
1) and efficacy for depression (Aim 2A) in older adults with depression symptoms plus high suicide risk. The
primary efficacy outcome is clinically significant: depression symptom response rates six-months post-
treatment (≥50% reductions in pre-treatment non-sleep GRID Hamilton Depression Rating Scale scores). Key
secondary outcomes include SI rates six-months post-treatment (i.e., active SI with a method defined as
Columbia Suicide Severity Rating Scale ideation scores ≥ 3) and the incidence of a suicidal behavior
composite (i.e., escalating planning/attempt/suicide-related hospitalization) over six-months. Pilot data support
the hypothesized target engagement and efficacy, but as is typical of psychiatric treatments, we anticipate
treatment response variability. Exploratory Aim 3 is therefore to develop an algorithm indicating for whom
TranS-C is efficacious. To accomplish these aims, we will conduct a three-site, double-blind, randomized
controlled trial (n=420) testing 8-weeks of TranS-C+TAU versus a contact-time matched active listening control
plus TAU (AL+TAU). Eligibility criteria include being 55+ years old, having PHQ-9 scores ≥ 10, Scale for
Suicide Ideation scores ≥ 3 or a past suicide attempt, and elevated sleep disturbances/impairment (PROMIS)
despite TAU with at least the minimum effective depression pharmacotherapy dose. The main target
engagement measure is actigraphy inter-daily stability post-treatment, an objective rhythm measure, which
correlated with depression symptom reductions six-months after TranS-C in our pilot. Therapists will be
centrally-trained and carefully monitored for fidelity. Assessments pre-, post-, and 6-months post-treatment
include diagnostic interviews, self-report measures, and a week of actigraphy/sleep diary. Weekly during the
treatment phase, and monthly six-months thereafter, participants will receive calls from blinded assessors
charting treatment effects and actively monitoring for safety. Participants will also wear wrist actigraphy in the
treatment phase to objectively track changes in sleep-wake patterns. Analyses seek to confirm if TranS-C has
efficacy for sustained depression responses; and to evaluate mediation via the target/alternative mechanisms.
Moderator analyses w...

## Key facts

- **NIH application ID:** 10950450
- **Project number:** 1R01MH137185-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Stephen F Smagula
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,809,928
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10950450

## Citation

> US National Institutes of Health, RePORTER application 10950450, Multi-site, double-blind, randomized-controlled, efficacy trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction for depression symptoms in older adults with high suicide risk (1R01MH137185-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10950450. Licensed CC0.

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