# Developing Personalized Predictive Models of Aggression

> **NIH NIH K01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $179,431

## Abstract

PROJECT SUMMARY/ABSTRACT
Aggressive behavior is a transdiagnostic indicator of both youth and adult psychiatric disorders and a
significant public health concern due to the direct harms to victims and its broader economic impact.
Nonetheless, prediction of aggressive behavior is challenging due to significant variability in how, why, and
when people act aggressively. This heterogeneity impedes efforts to establish etiological factors, identify
biological substrates, and develop uniformly effective treatments. Though theories of aggression emphasize
that it is a context-dependent, dynamic interpersonal behavior, research rarely attempts to study aggression in
the contexts where it normally occurs and is most consequential (i.e., daily life). The current project seeks to
improve on past research by studying the transdiagnostic mechanisms of aggression using novel analytic and
measurement methodology necessary for pursuing a personalized medicine approach in aggressive behavior
research and prevention. This project will use real-time data capture in conjunction with state-of-the-art analytic
methods to deconstruct the heterogeneous behavioral phenotypes that relate to aggression. To achieve this,
we will use relevant passively-sensed and self-reported data via smartphones from a sample of young adults
(age=18-30; N=150) diagnosed with mental and behavioral disorders and at elevated risk for aggression. Data
will be collected over the course of a 3-week ambulatory assessment protocol. We will apply machine learning
methods capable of uncovering and modeling the complex dynamic processes observed in aggression at the
level of each individual (i.e., personalized models) to prospectively predict aggressive urges and behavior. The
results will pave the way for scalable just-in-time adaptive interventions tailored to an individual’s specific
antecedents of aggression. The proposed study will contribute to NIMH Strategic Priorities 3.2 by 1) focusing
on personalized models that can accommodate the complex topography of aggression and its antecedents and
2) applying innovative computational approaches (i.e., machine learning) to multiple streams of data (passively
sensed, self-report) to identify potential just-in-time intervention targets for aggressive individuals. The
comprehensive research and training plan detailed in this proposal will allow this candidate to address the
primary research questions of the proposal and develop the expertise necessary to be an independent
scientist. Specifically, this candidate will receive training in 1) personalized models of psychopathology and
aggression; 2) methods for carrying out EMA-based studies and modeling intensive longitudinal data; and 3)
collecting, processing, and predictive modeling with passive sensor data. This candidate has assembled a
team of expert mentors (Wright, Jacobson) and consultants who possess the expertise to supervise the project
and provide the training necessary to support the candidate...

## Key facts

- **NIH application ID:** 10808109
- **Project number:** 5K01MH130746-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Colin Vize
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $179,431
- **Award type:** 5
- **Project period:** 2023-03-15 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10808109

## Citation

> US National Institutes of Health, RePORTER application 10808109, Developing Personalized Predictive Models of Aggression (5K01MH130746-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10808109. Licensed CC0.

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