# Facilitating effective coping to reduce suicide risk following ED discharge: A micro-randomized trial to develop an adaptive text-based intervention

> **NIH NIH R34** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $234,000

## Abstract

Project Summary/Abstract
Suicide is the 10th leading cause of death in the United States and has increased by 35% in the last two
decades. Emergency Departments (EDs), often serving as the only clinical contact for individuals at risk for
suicide, are a critical point for initiating suicide prevention interventions. Limited availability of psychiatric beds
and challenges with linking to, or sustaining, post-ED care exacerbate the already heightened risk for suicidal
behavior among discharged individuals. Best-practice guidelines for EDs recommend providing individuals at
elevated suicide risk with brief interventions that include safety planning—emphasizing coping strategies to
mitigate suicidal crises—as well as post-discharge contacts, however busy EDs often lack adequate resources
to offer these interventions consistently or with fidelity. New approaches that can provide accessible,
personalized, and resource-efficient continuity of care are urgently needed to prevent suicidal behavior during
the high-risk post-ED period. Leveraging accessible technologies, we propose to develop and pilot an ED-
initiated intervention package that incorporates an electronic safety plan (ESP) and adaptive text-based
support to facilitate effective post-discharge coping and safety plan use, and ultimately reduce suicidal
behavior in high-risk adults. Adults presenting to an ED for suicide-related concerns (N=120) will be
randomized to ESP (n=40) or ESP + text-based support program (n=80) delivered for a month after discharge.
The ESP + text-based support condition will include an embedded micro-randomized trial (MRT), with twice-
daily randomizations over the month-long intervention, to optimize the frequency, timing, and content of
messages and to inform the design of a just-in-time adaptive intervention (JITAI) for suicide prevention. Follow-
ups will occur at 1 and 3 months, as well as twice-daily for the first month after discharge. The specific aims
are to: (1) Develop and refine ESP and text-based support with stakeholder input; (2) Demonstrate feasibility,
acceptability, and explore initial impact on mechanisms (coping self-efficacy, motivation for safety plan use)
and distal outcomes (e.g., suicidal ideation severity), assessed at 1 and 3 months, for ESP with text-based
support compared to ESP alone; and (3) Conduct a pilot MRT to inform the optimization of adaptive text-based
support (JITAI). Specifically, through the series of micro-randomizations, we will explore if provision of (a) any
coping message, (b) a specific type of coping message (untailored vs. ESP-tailored; with or without dynamic
personalized feedback based on daily-level functioning), and (c) message timing (e.g., morning vs. evening)
influence daily-level proximal mechanisms (e.g., safety plan use, coping behavior, self-efficacy) and distal
outcomes (suicidal ideation). With potential for high public health impact, this proposal addresses a critical
need for effective and scalable conti...

## Key facts

- **NIH application ID:** 10808431
- **Project number:** 1R34MH133057-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Ewa Karina Czyz
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $234,000
- **Award type:** 1
- **Project period:** 2024-04-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10808431

## Citation

> US National Institutes of Health, RePORTER application 10808431, Facilitating effective coping to reduce suicide risk following ED discharge: A micro-randomized trial to develop an adaptive text-based intervention (1R34MH133057-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10808431. Licensed CC0.

---

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