Leveraging Physical and Online Digital Infrastructures to Infer College Students' Social Isolation and its Relationship to Risk of Suicide

Suicide is a serious mental health concern that affects millions of individuals worldwide. From 1999 through 2017, the age-adjusted suicide rate increased 33% from 10.5 to 14.0 per 100,000. These challenges are pronounced in college campuses, which have been disproportionately affected by the growing incidents of suicide. In spite of the urgency of this public health problem, there exists a lack of real-time information on suicidal risk to guide prevention or intervention efforts. One approach to this public health issue is to improve understanding of college students’ social context and its relationship to mental health – a person’s social network can provide instrumental, informational, or emotional assistance, which are known to boost self-esteem and self-efficacy, at the same time buffer against the negative effects. However current understanding the relationship between offline/online social isolation and risk of suicidal ideation/thoughts, has largely focused on population-level assessments.

Researchers further note the limited power of existing risk factor studies due to a reliance on fixed time point sampling of cross-sectional data. Working with the Office of Information Technology and Student Health Services at Georgia Tech and Emory University, for a cohort of 100 consented students seeking psychiatric treatment on campus for mood and/or anxiety disorders and who have been at risk of suicide, this interdisciplinary team comprising machine learning, ubiquitous computing, and clinical researchers at Georgia Tech and Emory University/Grady Health System will leverage anonymized campus WiFi logs and de-identified Facebook data to automatically and passively assess students’ social context, interactions, and importantly isolation in the physical world the online world respectively. By employing state of the art and innovative machine learning and forecasting approaches, the team will examine if depleted social interaction and unexpected changes in social context, thus measured, predicts students’ exacerbated risk in the future.

This preliminary one-year study will provide rich data toward seeking future extramural (NIH) funding focusing on developing timely, evidence-based clinical and service interventions and treatment paradigms that support college students affected by poor mental health, particularly suicidal ideation/behaviors. Notably, because of our topical focus and the approach adopted (passive/social media data; machine learning), the work fits squarely with and seeks to advance IPRCE priorities of the Center for Disease Control and Prevention’s National Center for Injury Prevention and Control (NCIPC) around suicide prevention in particular, and self-directed violence in general.

1. Computational extraction of digital phenotypes of offline and online social interaction from physical and digital infrastructure data volunteered by college student patients at risk of suicide.
2. Machine learning modeling and prediction of associations between the digital phenotypes of offline and online social interaction and suicidal risk exacerbation in college student patients.
3. (Emory only): Explore the risk and protective factors associated with suicidality among university students, focusing on social isolation, intrapersonal factors and social determinants of health. 
4. (Emory only): Describe students’ experience of surviving the loss of a close friend/fellow student, family member, or professional colleague to suicide.