Towards Context-Aware Sensing and Personalized Interventions for Mental Health

In today's digital age, ubiquitous smart devices, such as smartphones and wearables, are revolutionizing healthcare by transitioning from a ‘one-size-fits-all’ model into personalized interventions. These devices, which capture a wide range of physiological and behavioral data, offer valuable insights into human bio-behavior and provide scalable methods to promote positive behaviors. My research focuses designing an innovative ecosystem for context-aware just-in-time adaptive interventions (JITAIs) that deliver personalized, context-sensitive support to individuals with elevated social anxiety. By utilizing mobile sensing technologies, my study explores how these devices can detect anxiety-relevant contexts, such as social interactions, evaluative threats, and momentary anxiety states. The contextual information, combined with individual user factors, is then used to tailor digital interventions, offering timely and personalized support to improve health outcomes for those experiencing social anxiety. From controlled lab studies to real-world applications, my research aims to develop ethical, context-sensitive, and personalized AI-mediated interventions. These interventions provide support precisely when and where it is needed most, effectively promoting individuals' well-being outcomes.