Real-Time Monitoring to Understand, Predict, and Prevent Suicidal Thoughts and Behaviors

Abstract

Suicide is one of the most devastating aspects of human nature and has perplexed scholars for centuries. One reason suicide has remained so perplexing is how difficult it is to measure suicidal thoughts and behaviors (STBs). New technologies, such as smartphones, offer tools to measure STBs as they unfold in real-time. The real-time measurement of STBs provides an opportunity to advance the understanding, prediction, and prevention of STBs. This dissertation grapples with core issues in this emerging field of real-time monitoring of suicidal thoughts and behaviors. Across three papers, this dissertation addresses issues including safety, prediction, and intervention. In Study 1, I used an experimental design to test the effect of frequent assessment of suicidal thinking on the severity of suicidal thinking. Across multiple analyses, I found no evidence to support the notion that repeated assessment of suicidal thoughts is iatrogenic. In Study 2, I used a novel-form of statistical modeling that generates a statistical model for each individual to test the generality of risk factors for suicidal thinking. In a large clinical sample, I found heterogeneity in the pathways through which people experience suicidal thoughts. In Study 3, I adapted an evidence-based Barrier Reduction Intervention that aims to increase the use of crisis resources for deployment in real-time monitoring. I then tested the feasibility, acceptability, and usability of this tool in a real-time monitoring study of STBs. There was high engagement with the tool, but zero participants reported using crisis services after engaging with the tool. These three studies highlight both the promise and challenge of using real-time monitoring technologies for understanding, predicting, and prevention.