Thursday, April 3, 2025
4/3/2025
Improving momentary suicide risk identification through adaptive time sampling - PROJECT SUMMARY/ABSTRACT Suicide rates have risen sharply over the past 20 years1. There is a need to more precisely identify proximal risk indicators for the development of near-term suicide risk in order to effectively intervene. Studies utilizing ecological momentary assessment (EMA) to collect data at several intervals per day have demonstrated that suicidal ideation (SI) and proximal risk factors change rapidly across the course of the day2. However, prior EMA studies examining SI dynamics implement stable assessments, with intervals of several hours between SI assessments across the duration of a study period3 for all participants. This one-size-fits-all approach to SI assessment fails to capture the nuanced within-person variability of the timescale of the development of acute suicide risk. In turn, we lack even a basic understanding of within-person variability in the time varying relationship between SI and its proximal risk factors. The proposed study aims to address the limitations of current assessment approaches in proximal suicide risk research through the development of a personalized, adaptive time sampling system. The specific objectives of the proposed research are to: (1) develop a novel, adaptive time assessment system that more efficiently and accurately identifies when an individual is at highest risk for SI; and (2) advance the understanding of SI and its theoretically-informed proximal risk factors at finer timescales. Data collected according to varied timing schedules in the first phase will be used to train an algorithm that generates predictions of suicide risk, predictions that will be adaptively use to determine assessment timing during the second phase of data collection. Aim 1 is to develop the adaptive time assessment system, followed by assessing the predictive accuracy of the adaptive sampling system (Aim 2) and identifying variations in person-specific effects of the relationship between SI and theoretically-informed risk factors (Aim 3). The research team (PIs: Ammerman, Jacobucci; Co-I: Cheng; Consultants: Burke) has access to world-class expertise, with extensive experience in EMA data collection in high-risk samples, machine learning for suicide prediction, longitudinal data analysis, collecting and modeling continuous data streams, and the development of adaptive assessment platforms. To meaningfully reduce suicide rates, a more nuanced understanding of suicidal thoughts and associated risk factors is required. Our adaptive assessment platform will more efficiently assess suicidal thoughts and risk factors, allowing for a closer approximation of the true associations. Indeed, there is a need to identify near-term risk factors prior to suicidal thought occurrences to successfully deliver an intervention and prevent suicidal outcomes. These findings will support the successful implementation of just-in-time adaptive interventions through increased precision of suicide risk detection and targeted intervention timing. Given the grave personal and societal cost of suicide, this work has important public health implications.
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