# Digital Monitoring of Agitation for Short-Term Suicide Risk Prediction

> **NIH NIH K23** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $198,877

## Abstract

Suicide is a prevalent and burdensome public health problem that warrants immediate attention. As the tenth
leading cause of death in the United States, suicide claims the lives of more than 44,000 Americans each year.
There is an urgent need to identify objective and clinically informative markers of imminent risk for suicidal
behavior. Agitation, defined in DSM-5 as excessive motor activity associated with a feeling of inner tension, is
listed as a warning sign for suicide by leading organizations and in widely used risk assessment protocols. Yet,
prior research on the association between agitation and suicide has key methodological limitations (including
related to the operationalization of agitation), which has resulted in minimal empirical evidence to support
agitation as a proximal risk factor for suicide. Addressing this gap in knowledge has the potential for significant
impact, including informing both the clinical assessment of suicide risk and the development of just-in-time
interventions for detecting and responding to acute suicide risk. This project will overcome the limitations of
prior suicide risk factor research by assessing multiple behavioral (motor activity and vocal features [e.g.,
volume, speaking rate, pitch]) and subjective components of agitation and suicidal thoughts and behaviors in a
sample at elevated risk for suicide over a short, high-risk period. We will test the hypotheses that (1) objectively
measured real-time indicators of agitation correlate with both momentary subjective ratings and validated, gold
standard measures of agitation, and (2) both subjective and objective indicators of agitation improve prediction
of short-term increases in suicide ideation, plan, and attempt above and beyond other distal and proximal risk
factors. We propose to collect high-resolution self-report (e.g., ecological momentary assessment) and passive
(e.g., accelerometer) data on agitation using smartphones and wearable sensors from psychiatric inpatients
admitted for suicide ideation or attempt during inpatient treatment and the four weeks after discharge. Multi-
level modeling and machine learning approaches will be implemented to examine (1) associations between
objective and subjective real-time indicators of agitation and validated measures of agitation, and (2) the
degree to which real-time indicators of agitation predict momentary fluctuations in suicidal ideation and suicide
plan and attempt above and beyond other distal and proximal risk factors. The scientific aims of this study map
onto the candidate’s training in three primary areas: (1) digital monitoring of high-risk patients, (2) advanced
longitudinal multivariate data analysis, and (3) identification of behavioral and vocal biomarkers. The
candidate’s training plan includes mentorship from Dr. Matthew Nock (primary mentor), Dr. Jordan Smoller (co-
mentor), Dr. Maurizio Fava (co-mentor), and Drs. Rosalind Picard, Evan Kleiman, and Thomas Quatieri
(consultants), as wel...

## Key facts

- **NIH application ID:** 9981035
- **Project number:** 5K23MH120436-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Kate H. Bentley
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $198,877
- **Award type:** 5
- **Project period:** 2019-07-19 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9981035

## Citation

> US National Institutes of Health, RePORTER application 9981035, Digital Monitoring of Agitation for Short-Term Suicide Risk Prediction (5K23MH120436-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9981035. Licensed CC0.

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