The literature on relapse includes numerous methodological inconsistencies, with wide variation in the definition of relapse, assessment methodologies, and models of relapse-related factors. Traditional methodologies for collecting data on relapse included: 1) retrospective reviews in which participants are asked to recall instances of relapse and the factors preceding them; 2) prospective reports, in which information about potential antecedents is collected at baseline or periodically and then examined for association with a detected relapse; and 3) near real-time reports, in which participants are asked or electronically prompted to report on factors near the actual time of relapse. The lack of research using behavioral observation of daily life is mainly because collecting this data has been almost impossible. However, near real-time reports are optimal because relapse vulnerability factors such as self-efficacy, drug cues, anxiety, stress, drug craving, and social support can change over a period of a few minutes. In the context of these challenges, this project will real-time reports as a tool to detect and predict relapse in patients attending substance use treatment programs and in patients who are in recovery. Public health research and practice are just beginning to taken advantage of emerging changes in communication media by using methods and tools that analyze social media language and data generated from smartphones and wearable devices. This project will adapt advanced data analytic techniques to examine the digital footprints left by individuals in substance use treatment and in those who are experiencing long-term recovery. We will use natural language processing and machine learning techniques to build models that predict future relapse and long-term recovery. Passive measurement will be used to capture behavioral data at a finer level of detail than is typically achieved using conventional methods. The majority of relapse prevention approaches utilize only a fraction of the available information about a participant typically gathered through surveys and interviews. Even when relapse risk is measured repeatedly over time, relapse vulnerability is typically based on the last available measurement. However, this approach discards valuable information on the dynamically changing nature of relapse vulnerability factors and does not use information from other patients in recovery to improve predictions. This project will result in the dynamic, real-time predictions of relapse vulnerability linked to rapid changes in relapse risk. Our long-term goal in this lab is to develop an automated, continuous system for monitoring digital sources (social media language, smartphone phone sensor data, data from wearable devices) to forecast daily relapse vulnerability scores. We will then develop a relapse vulnerability feedback tool to be used by addiction treatment providers, people in treatment, and people in recovery. This will enable the use of novel approaches to clinical research and practice by developing applications that automatically intervene when a patient is at risk.