# Digital Monitoring of Impulsivity as a Proximal Risk Factor for Suicidal Outcomes

> **NIH NIH K23** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $195,625

## Abstract

Background: Suicide is a leading cause of death, but progress in suicide prevention has been slowed by
critical gaps in knowledge about predictors of imminent risk. Impulsivity is an ideal candidate for a proximal risk
factor: it is a known transdiagnostic distal risk factor, it fluctuates over time within individuals, and it is a
modifiable target for intervention. Existing suicide research, however, has not examined multiple components
of real-time, state impulsivity over high-risk periods — a necessary step to test (a) whether impulsivity reduces
ability to resist suicidal urges in real time, (b) which components of this multi-faceted construct are associated
with suicide risk and when, and (c) whether patterns differ for individuals or subgroups. Research: We propose
a fine-grained, intensive longitudinal investigation of associations between components of impulsivity and
suicidal urges in two samples at high risk for suicide. Aim 1 will involve secondary data analysis of a digital
monitoring study of individuals presenting to an emergency department with suicidal thoughts to analyze real-
time associations between impulsivity, suicidal urges, and ability to resist suicidal urges. We will test whether
state impulsivity is predictive beyond the effect of trait impulsivity. In Aim 2, we will conduct a digital monitoring
study of 140 individuals hospitalized for suicidal thoughts to assess multiple components of state impulsivity
using self-report, mobile tasks, and passive phone data, and we will test specific associations with suicidal
urges and ability to resist them in real time. In Aim 3, we will compare group-level, subgroup-level, and
personalized models of these data using a combination of inferential statistics (network modeling) and
predictive analytics (machine learning). This work will allow us to dramatically improve understanding of a key
transdiagnostic process, laying the groundwork for development of detection and intervention strategies
targeted at specific elements of impulsivity at an optimal timescale. Candidate’s Career Development, Goals,
and Environment: This proposal’s research aims and the candidate’s career development will be supported
by the many resources available at Massachusetts General Hospital/Harvard Medical School as well as formal
training and mentorship in (T1) digital monitoring of patients at high risk for suicide, (T2) advanced multivariate
longitudinal data analysis, (T3) digital phenotyping, and (T4) preparing for an intervention-focused R01
submission. The mentorship team includes Mentor Dr. Jordan Smoller, leading expert in precision psychiatry
and predictive analytics; Co-Mentors Dr. Matthew Nock, leader in the study of suicide; and Dr. Evan Kleiman,
expert in real-time monitoring and digital phenotyping of suicidal states; and Consultants Dr. Aidan Wright,
expert in multilevel and personalized statistical modeling; Dr. JP Onnela, leader in digital phenotyping and
statistical network science; and Dr...

## Key facts

- **NIH application ID:** 10805477
- **Project number:** 5K23MH132766-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Rebecca Gwen Fortgang
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $195,625
- **Award type:** 5
- **Project period:** 2023-03-15 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10805477

## Citation

> US National Institutes of Health, RePORTER application 10805477, Digital Monitoring of Impulsivity as a Proximal Risk Factor for Suicidal Outcomes (5K23MH132766-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10805477. Licensed CC0.

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