Accurate forward prediction of maladaptive behaviors and emotional states involved in various forms of psychopathology offers an intriguing avenue for prevention and intervention science. To date, however, such prediction has been limited by a heterogeneity in factors determining individuals' behaviors and emotions. To address this limitation, the proposed project adopts an idiographic (i.e. person specific) approach while focusing on two exemplar targets that constitute transdiagnostic components in various psychological disorders – namely, behavioral avoidance and maladaptive emotional states. Specifically, the project aims to (a) develop person-specific models for predicting the two targets; and (b) use the models to construct a person-specific just-in-time adaptive intervention (JITAI) system, a novel, accessible, and highly promising means for affecting change. To do so, the project will utilize ecological momentary assessment (EMA) and harness three methodological innovations: (1) an adaptive assessment and personalized item-selection tool which will reduce participant burden; (2) a scalable personalized system that provides accurate forward prediction using brief psychosocial questions and automatically-collected timing/location data; (3) a personalized JITAI app that will inform participants in real time regarding increased likelihood of impending targets, and prompt appropriate interventions based on individual predictors. The project can significantly promote understanding of transdiagnostic maladaptive processes and lead to efficient precision interventions. It builds on the fellow's expertise in EMA and affect research and will develop his statistical and analytic tools for exploring ideographic time-series data, his skills in translating such data into tailored interventions, and his ability to shift back and forth between basic and applied science. The team assembled for this work is ideal in terms of both methodological and theoretical expertise.