Chronic musculoskeletal pain (CP) is a major public health concern. Psychosocial treatments have been shown to be efficacious when compared to largely inert control conditions, but they are characterized by modest effects on primary outcomes. One strategy to boost efficacy is to increase our understanding of treatment mediators. Studies of mediators that directly compare different treatments with each other are needed to determine which mediators are treatment-specific, which are shared across treatments, and which contribute the most to clinical outcomes. Another strategy is to identify the patient characteristics that moderate treatment responses. Research is needed that is guided by theoretical models and that tests moderators across multiple treatments. Identifying subgroups of patients more likely to respond to one or another treatment can advance precision medicine by informing a priori patient-treatment matches that can optimize treatment effects. To accomplish these goals, the authors will conduct a randomized clinical trial to compare the mediators and moderators of the clinical effects of Cognitive-Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT), and Emotional Awareness and Expression Therapy (EAET) on adults with chronic spinal (axial) pain. Following baseline assessment of outcome variables as well as potential mediators and moderators, 460 participants will be randomized to CBT, ACT, EAET, or treatment-as-usual control (TAU). The three treatments will be conducted as individual therapy provided weekly for 8 weeks via telehealth. The researchers will conduct weekly assessments of both potential mediators and outcomes, as well as post-treatment and 6-month follow-up assessments. The goal of the study is to identify the most powerful treatment mechanisms - specific and shared -- and reveal for whom the mediator-outcome pathways are strongest.This project can increase the effects of our psychosocial chronic pain treatments by identifying the most effective treatment mechanisms and by informing patient-treatment matches that can optimize treatment effects.