PROJECT SUMMARY Chronic pain is a widespread concern, affecting 12% of young adults globally (ages 18-29). These individuals face significant burden, including the risk of chronic pain continuation from childhood and elevated rates of depression, anxiety, and sleep disturbances. While psychological treatments have shown efficacy in adults and adolescents, their effectiveness in young adults remains uncertain. An important challenge is how to develop and maximize psychological treatments for young adults, who experience significant barriers to accessing treatment. We are at a crucial point in the development of effective care for young adults with chronic pain, with a priority being to address the lack of availability of treatments developed specifically for this population. Mobile Health (mHealth) interventions (smartphone apps), and in particular those that leverage just-in-time adaptive mechanisms (JITAI) are a promising solution for improving access to and use of psychological strategies for pain management, particularly in young adults. JITAI leverage real-time, real-world data (i.e., smartphone surveys and passive sensing) to deliver personalized support. However, the lack of real-time data on contextual vulnerabilities that predict pain in young adults is a significant gap in knowledge impeding development of personalized mobile interventions. Our proposed K23 research aims to fill this knowledge gap, leveraging ecological momentary assessment (EMA) to develop and test a mobile "just-in-time" adaptive intervention (JITAI) for young adults with chronic pain. Using advanced methodologies, we will (1) conduct a study using ecological momentary assessment to identify contextual vulnerabilities that predict exacerbations in pain in young adults to inform JITAI targets; (2) use findings from the EMA study and input from young adult patient partners to develop a mobile app JITAI prototype including tailored messages suggesting brief or more effortful psychological strategie; and (3) pilot the JITAI using a 28-day micro-randomized trial (MRT) to assess different message types' impact on engagement in the strategies. The expected outcomes of this research include preliminary data on dynamic pain predictors (EMA) and proof-of-concept methodologies (micro- randomized JITAI) for a large-scale MRT for Optimization. Furthermore, I will be well-positioned to launch my career, leveraging cutting-edge digital approaches for personalized, scalable mobile health intervention for chronic pain. The study and training activities included in this proposal will advance my knowledge and skills in multilevel longitudinal methods, designing JITAIs, utilizing micro-randomized trials to optimize JITAIs, young adult pain management and engagement, and conducting clinical trials. Leveraging advancements in theory, digital methodologies, and human-centered design holds promise in creating a new generation personalized mobile interventions that can reduce the burden of pai...