Advancing Real-Time Suicide Risk Detection Through the Digital Phenotyping Smartphone Application Screenomics

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT Suicide is a leading public health problem, accounting for over 45,000 deaths in 2017 alone 1. With suicide rates continuing to rise 2, and the prediction of suicidal thoughts and behaviors (STBs) remaining stagnant 3, there is a need to shift the focus from identifying who is at risk to when individuals are at risk for suicide. Studies utilizing ecological momentary assessment to collect data at several intervals per day have demonstrated that suicidal ideation and STB risk factors change rapidly across the course of the day 4; yet, there is a need to improve the granularity of assessment to improve identification of real-time risk elevation. To enable reliable detection of STBs within a relatively short window of time (e.g., minutes) will require technologically innovative methodologies that can continuously capture the dynamic nature of suicide risk. We propose the use of a novel form of digital phenotyping, termed Screenomics 5-6, that captures screenshots from participant’s phones every five seconds. These data can then be utilized to indirectly identify STBs in real- time (via generated and viewed text), as well as prospectively predict STBs via individual engagement in produced and consumed social interactions (via application usage, text messages, and social media text), which have knowns links to STBs 7. Among 80 individuals with past-month STBs, two primary aims will be investigated. Aim 1 is to demonstrate that text collected through smartphone use (i.e., web browser, text messages) can serve as an accurate proxy for the direct assessment of STBs. Aim 2 will identify prospective, short-term STB risk associated with produced and consumed social interactions not demonstrated via direct assessment. The research team (Co-PIs: Ammerman, Jacobucci; Co-I: Jiang; Consultants: Kleiman, Ram, Robinson, Reeves, Bourgeois, Liu) has access to world-class expertise, with extensive experience in EMA data collection in high-risk samples, machine learning for predicting suicide, collecting and modeling continuous data streams, including screenshot data, and ethical and privacy practices unique to technological innovations. To meaningfully reduce suicide rates, a more nuanced understanding of STBs and associated risk factors in real-time is required. Screenomics provides near continuous monitoring, allowing for a closer approximation of the true associations between risk factors and STBs. Indeed, there is a need to identify near-term risk factors prior to STB occurrences to successfully deliver an intervention and prevent STBs. These findings will lay the groundwork necessary for utilizing passive data in STB detection and intervention. Given the grave personal and societal cost of suicide, this work has important public health implications.