Research
Current and Prior Research Projects:
Advancing Real-Time Suicide Risk Detection Through the Digital Phenotyping Smartphone Application Screenomics
Funding Sources: National Institute of Mental Health
Description: Studies utilizing ecological momentary assessment to collect data at several intervals per day have demonstrated that suicidal ideation and suicide risk factors change rapidly across the course of the day; yet, there is a need to improve the granularity of assessment to improve identification of real-time risk elevation. To improve reliable detection of suicide risk within a relatively short window of time (e.g., minutes) we propose the use of a novel form of digital phenotyping, termed Screenomics, that captures screenshots from participant’s phones every five seconds. These data will be utilized to indirectly identify suicidal thoughts and behaviors in real-time (via generated and viewed text), as well as prospectively predict suicie risk via individual engagement in produced and consumed social interactions (via application usage, text messages, and social media text), which have knowns links to suicidal thoughts and behaviors.
EMERGE: Ecological Momentary Evaluation of Responses to Gain/Loss and Emotions
Funding Sources: National Institute of Mental Health
Description: Most research on suicide risk factors has identified static, dispositional factors. These data inform us about groups at actuarial risk, but do very little to predict with useful precision when people will have suicidal thoughts or act on suicide thoughts. Thus, there is an urgent need to identify proximal risk factors for suicide risk. Two of the leading factors distally associated with increased suicide risk are dysfunctional emotional responses5 and decision-making deficits. Suicidal individuals have poorer reinforcement learning, demonstrating impaired ability to learn and modify behavior in response to reward and punishment. Individuals at high risk for suicide also show difficulties learning from punishment and reward. In this study we will examine both emotional and cognitive processes associated with suicidal thoughts and behaviors using ecological momentary assessment methods.
Understanding the Impact of COVID-19 on Problematic Substance Use through the Quality of Sobriety Framework
Funding Sources: Institute for Precision Health Award (Formerly Advanced Diagnostics & Therapeutics Discovery Fund)
Description: Substance use is a public health concern with widespread impairment, which has been increasing since the inception of the COVID-19 pandemic and is notable suicide risk factor. COVID-19-related adversities may disproportionately influence those with substance use disorder by negatively impacting numerous spheres of daily functioning important for sobriety (i.e., social, vocational). This study collects daily level data on substance use cognitions (i.e., cravings) and behaviors (i.e., problematic use), in addition to subjective and objective data across several domains of functioning outlined by the Quality of Sobriety framework. Findings will shed light on whether substance use cognitions and behaviors are both directly and indirectly impacted by the current COVID-19 circumstance and, if so, to what extent each domain of functioning is most relevant.
Identifying Momentary Risk Factors in the Co-Occurrence of Substance Use Disorders and Self-Injurious Thoughts and Behaviors
Funding Sources: Institute for Precision Health Award (Formerly Advanced Diagnostics & Therapeutics Discovery Fund)
Description: Substance use is a public health concern with widespread impairment. Moreover, the significant overlap of substance use disorders and self-injurious thoughts and behaviors heighten the potential for physical harm, including death, among these individuals. Prior research highlights the potential role of negative mood states as a factors contributing this co-occurrent, however, very little research has directly investigated these relationships. The overarching goal of this project is to improve our understanding of the co-occurrence of SUD and SITB through a nuanced evaluation of key risk factors via multiple modalities. More specifically, we examine emotion regulation and chronic interpersonal stress, in addition to the momentary experiences of elevated negative mood states and interpersonal contexts, as drivers in the joint occurrence of substance use and self-injurious thoughts and behaviors.
Using Integrative Data Mining to Improve the Prediction of Suicide: An Initial Application
Funding Sources: Institute for Precision Health Award (Formerly Advanced Diagnostics & Therapeutics Discovery Fund)
Description: In this project we utilize data integration to combine multiple datasets to examine the complex relationship between risk factors and suicidal ideation, plans, and attempts through machine learning techniques. It was one of the first projects to combine the use of data integration to create a large-scale database (allowing for sufficient predictive power to identify small effects and interactions) in service of improving suicide prediction.