Suicide is the second leading cause of death among adolescents. In addition to deaths, 16% of adolescents report seriously considering suicide each year, and 8% make one or more attempts. Despite these alarming statistics, little is known about factors that confer imminent risk for suicide. Thus, developing effective methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical. Currently, our most robust predictors of STBs are demographic or clinical indicators that have relatively weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has identified a number of promising candidates, including rapid escalation of: (a) emotional distress, (b) social dysfunction (i.e., bullying, rejection), and (c) sleep disturbance. Yet, prior studies are limited in two critical ways. First, they rely almost entirely on self-report. Second, most studies have not focused on assessment of these risk factors using intensive longitudinal assessment techniques that are able to capture the dynamics of changes in risk states. These are fundamental limitations. While suicidal ideation may precede an attempt by years, socio-emotional changes preceding a suicide attempt often occurs within the time span of minutes to hours. This study will capitalize on recent developments in real-time monitoring methods that harness adolescents' naturalistic use of smartphone technology. Specifically, we now have the capacity to use: (a) smartphone technology to conduct intensive longitudinal assessments monitoring putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. The project will include high-risk adolescents (n = 200) aged 13-18 years recruited from outpatient and inpatient clinics: (a) recent suicide attempters with current ideation (n = 70), (b) current suicide ideators with no attempt history (n = 70), and (c) a psychiatric control group with no STB history (n = 60). Effortless Assessment of Risk States (EARS) will be used to continuously measure variables relevant to key risk domains?emotional distress, social dysfunction, and sleep disturbance?through passive monitoring of participants' smartphone use. First, we will test between-group differences in risk factors during an initial 2-week period, and determine the extent to which risk factors derived from mobile phones improves discrimination over self-reported indicators. Second, we will use statistical techniques to test whether the risk factors improve short-term prediction of STBs (e.g., suicide attempts, hospitalization) during the 6-month follow-up period above and beyond clinical assessments. Third, computational machine learning techniques?based on a priori and learned features?will develop predictive models that utilize the full range of intensive longitudinal data collected by the active and passive monitoring methods to predict group membership and STB outcomes. Ultimately, by leveraging smartphone technology, we aim to improve the short-term STB prediction and provide clinicians and patients with reliable, scalable and actionable tools that will reduce the needless loss of life.
Suicide is the second leading cause of death among adolescents, and despite this pressing public health crisis, little is known about factors that confer imminent risk for suicide. However, recent advancements in mobile technologies afford the capacity to monitor known risk factors?including emotional distress, social dysfunction, and sleep disturbance?which has the potential to revolutionize our insight and clinical management of short- term risk for suicidal thoughts and behaviors. Therefore, the present study will leverage adolescents' naturalistic use of smartphone technology, along with advanced signal processing and computational modeling approaches, to identify promising short-term predictors of suicide among high-risk adolescents, which ultimately, may reduce needless loss of life.
