# Capturing the Structure and Dynamics of Suicidal Thinking

> **NIH NIH F31** · HARVARD UNIVERSITY · 2022 · $32,855

## Abstract

Project Summary/Abstract
Suicide is a devastating public health problem. Over 40,000 people die by suicide each year in the United
States and it is the fourth leading contributor to years of life lost. The suicide rate has not changed over time,
prediction of suicide has not improved over time, and the efficacy of interventions has not changed over time.
In order to improve the understanding, prediction, and prevention of suicide, there is an urgent need for precise
conceptualizing, operationalizing, and describing of suicidal phenomena. Suicidal thoughts are an antecedent
of suicidal behavior and a central part of the pathway to suicide. Many features of suicidal thoughts, such as
their duration, are largely unknown. Preliminary descriptive work has used smartphones to observe how
suicidal thoughts unfold in daily life and consistently found that suicidal thoughts seem to ebb and flow within-
people over time. In light of the accumulating evidence of the variability of suicidal thinking, theorists are
beginning to argue that suicide should be viewed through the lens of dynamical systems. Included in these
theories is the notion that there are multiple discrete states of suicide risk that people move through over time.
Despite the powerful theoretical and clinical implications of suicidal states, no empirical work has tested and
validated suicidal states. The proposed project aims to address this major gap in suicide research by
combining computational modeling and dynamic real-time data to capture suicidal states. Aim 1 of the project
is to identify the number of within-person suicidal states. To achieve this aim, Hidden Markov Models, a form of
computational model that identifies hidden discrete states in dynamic data, will be applied to multiple real-time
measures of suicidal thinking from an ongoing NIMH-funded intensive longitudinal study of suicidal thoughts
and behaviors (N = 300). Aim 2 is to capture the duration of suicidal states. The temporal dynamics of suicidal
states will be operationalized as when participants transition between states and on average how long
participants stay in a given state. Aim 3 is to test which suicidal states are predictive of near-term risk of
suicidal behavior. An existing event-time prediction model framework will be applied to generate interpretable
and precise event-time predictions of suicide attempts for each type of suicidal state. The proposed study’s
greatest potential impacts are to provide foundational information on suicidal thinking and to generate suicidal
states that could be used in future Just-in-Time-Adaptive Interventions for suicide prevention. The proposed
study also promotes a program of research that includes two major NIMH priorities of suicide prevention and
computational psychiatry. If successful the proposed project would advance the understanding of suicidal
thinking which could one day improve the prediction and prevention of suicidal behavior.

## Key facts

- **NIH application ID:** 10536436
- **Project number:** 1F31MH130055-01A1
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Daniel Coppersmith
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $32,855
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10536436

## Citation

> US National Institutes of Health, RePORTER application 10536436, Capturing the Structure and Dynamics of Suicidal Thinking (1F31MH130055-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10536436. Licensed CC0.

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