Characterizing The Dynamics Of Acute Suicidal Affective Disturbance
Principal Investigator: 

April Smith

Miami University

This project examines symptoms of acute suicidal affective disturbance (ASAD) among at-risk Service members. In so doing, we will identify at both the group and individual levels how ASAD symptoms maintain and exacerbate one another across time, thereby providing critical information about the most relevant treatment targets

Many studies have been dedicated to unveiling suicide risk factors, yet accurate suicide prediction is no better than chance.  To advance prediction, methodologies must shift from studying single risk factors in isolation and over multi-year intervals to assessing multiple risk factors simultaneously over very short prediction windows.  Several disparate symptoms are often present in the hours or days preceding suicidal behavior.  These include drastic increases in suicidal intent, alienation from self or others, hopelessness about alienation, and over arousal.  Joiner and colleagues propose that this cluster, termed acute suicidal affective disturbance (ASAD), should be studied as a suicide disorder, rendering ASAD research a novel endeavor that addresses methodological gaps in suicidology.

Network analysis is an innovative methodology to study ASAD. Network analysis identifies how mental disorder symptoms maintain and exacerbate one another over time. The symptoms that have the greatest influence on other symptoms (i.e., central symptoms) are key intervention targets - treating central symptoms decreases input to other symptoms, thereby reducing psychopathology. The current study will use between-subjects and intra-individual network analysis to identify central ASAD symptoms within a military sample.

The purpose of this research study is to learn about the specific experiences and symptoms that precede and maintain suicidal thoughts and suicidal behavior in the short term (i.e., over the course of days and weeks).  

The study will: 1) Determine if individual differences exist in intra-individual temporal and contemporaneous networks; 2) Identify central ASAD symptoms at a group level through the estimation of between-subjects, temporal, and contemporaneous networks; and, 3) Determine whether receiving individualized network information improves outcomes at 1-month and 3-month follow-up.

Results of Smith’s project will identify central ASAD symptoms among the overall sample and among individual participants. Identifying central symptoms guides clinical decision making both in general and when working with individual patients.  That is, considering ASAD central symptoms to be primary treatment targets has potential to disrupt the entire symptom network. In ASAD, a disrupted symptom network may translate to reduced suicidal thoughts and the prevention of suicidal behavior.