Chronic pain is a major health problem typically associated with mental disorders such as depression or anxiety and is still difficult to treat despite enormous efforts in the biomedical community. This project proposes a radical shift from the symptomatic focus on chronic pain and predefined diagnostic categories to a mechanistic analysis and treatment of psychobiological factors that shape the transition to chronic pain and its associated mental diorders in a transdiagnostic approach. The proposal aims to identify transdiagnostic mechanisms of the development of chronic pain across three major diagnostic subtypes of pain (primary musculoskeletal pain, neuropathic pain, primary headache) that are usually analyzed and treated in isolation and conducts a proof of principle study of mechanism-based modular digital and virtual reality-based interventions. Work package (WP) 1 will use meta-analyses to determine common and specific transdiagnostic mechanisms of chronicity across these three diverse pain disorders, WP 2 will analyze longitudinal data from existing databases on pain development, using machine learning and knowledge graph approaches. WP 3 will develop an assessment battery for core psychobiological mechanisms and identify predictors of chronicity as well as comorbidity with mental disorder across diagnostic groups using advanced unsupervised learning algorithms. WP 4 will devise easy to apply real life digital and virtual reality interventions that target core psychobiological processes. WP 5 will contain a proof of principle study that contrasts standard cognitive-behavioral pain management with the mechanism-based intervention modules. They will be applied in a digital format and contain virtual reality-based interventions. Statistical analyses will include linear mixed models, an n=1 design as well as a microrandomized trial. We believe that this transdiagnostic mechanistic approach will make a major contribution to the prevention of chronic pain.