Advancing real-time suicide risk assessment and intervention in daily life
The ASSIST Lab develops technology-enabled approaches to understand, detect, and intervene on suicide risk as it unfolds in everyday life. We use ecological momentary assessment, passive sensing, screenomics, computational modeling, and adaptive intervention designs to build scalable tools for suicide prevention.
Pillar 1: Detecting risk in daily life
We use ecological momentary assessment, screenomics, passive sensing, computerized adaptive testing, and adaptive sampling to better understand when suicide risk changes in daily life.
Pillar 2: Understanding mechanisms
We study emotional, social, cognitive, and behavioral processes that contribute to self-injurious thoughts and behaviors, including emotion regulation, social connection, disclosure, reward/loss learning, and decision-making.
Pillar 3: Building scalable interventions
We develop, adapt, and evaluate scalable interventions that can extend suicide prevention beyond traditional clinical encounters. This includes screener tools, just-in-time adaptive interventions, peer-to-peer support apps, brief digital interventions, and adaptations of existing evidence-based interventions for delivery in daily life, outpatient care, and community settings.