Chronic pain is a prevalent, disabling problem affecting as many as 50% of men and 75% of women Veterans. Cognitive Behavioral Therapy (CBT) is the current gold standard treatment for chronic pain. However, while some individuals do respond to CBT, many individuals do not obtain meaningful benefit. As a result, the average response to CBT treatment in groups of individuals with chronic pain is only modest. To address the need for effective treatments, the investigators have developed and adapted Complementary and Integrative Health (CIH) interventions such as Mindfulness-Based Cognitive Therapy (MBCT) and Hypnotic Cognitive Therapy (HYP-CT) for chronic pain management. Research shows these treatments are beneficial alternatives to CBT. However, as with CBT, response to these treatments varies, and the investigators' preliminary data suggests outcome variability is explained by a number of baseline patient factors. Research is now needed to advance knowledge regarding the pre-treatment patient factors (i.e., predictive markers) that moderate treatment outcome (i.e., patient factors that interact with treatment condition to predict outcome). The findings from this research will provide an empirical basis for developing patient-treatment matching algorithms to prospectively match a given individual to the evidence-based treatment most likely to be beneficial for them. The investigators have initiated a program of research to identify the factors that predict response to psychosocial pain treatments, including HYP-CT, MBCT, and CBT. Preliminary findings suggest that predictive markers such as brain activity (e.g., alpha and beta power, as measured by EEG), and the traits of mindfulness, hypnotizability, and catastrophizing, will predict who benefits most from different treatments. For example, post hoc analyses show that those who are "well-matched" to HYP-CT, based on the identified baseline moderators, achieve twice the amount of pain reduction with treatment, compared to those who are not well- matched. To confirm these findings, prospective research is now needed. The findings from this study will provide a foundation upon which to develop an assessment battery to identify critical values on which to base algorithms for a priori matching of individual patients to different treatments. This has the potential to substantially boost the typically modest average effect sizes that are achieved when using a more traditional "one size fits all" approach.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Change in Average Daily Pain Intensity
Timeframe: Assessed online up to 4 times within a 7-day period at pre-tx, 2-week, 4-week, 6-week, post-tx (8-week), and at 3- and 6-month follow-ups.