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.

This study has a single overall aim: to identify patient predictive markers that determine who benefits most from two CIH treatments (hypnotic cognitive therapy [HYP-CT] and Mindfulness-Based Cognitive Therapy [MBCT]) and the current gold standard non-pharmacological treatment (CBT) vs. usual care (UC). This aim will be addressed in the context of a clinical trial in which participants with chronic pain will be randomized to one of the three active chronic pain treatments or UC. Based on findings from prior research, the investigators hypothesize that five primary predictive markers assessed at pre-treatment (alpha power, beta power, hypnotizability, nonreactivity [a mindfulness domain], and catastrophizing) will modify subsequent treatment-related changes in pain intensity following MBCT, HYP-CT, and CBT, relative to usual care.

The primary clinical endpoint is reduction (change) in average pain intensity from pre- to post-treatment; the post-treatment assessment point is the primary endpoint. Average pain intensity will be measured using a composite of up to 4 separate pain ratings assessed within a 1-week period at each study assessment window. Although there are up to four ratings, each from a different day in a 7-day window, that are used to compute post-treatment average pain severity score, these variables will be used to compute a single score representing average pain during the post-treatment period; therefore, there is only a single primary post-treatment end point.

The primary predictive markers are EEG-assessed alpha and beta power, researcher-measured hypnotizability, and self-reported-pain catastrophizing and non-reactivity mindfulness, all of which will be assessed at pre-treatment.

The five primary study hypotheses are as follows:

Hypotheses 1a, 1b, and 1c: Baseline alpha power will predict pre- to post-treatment change in pain intensity, such that, relative to UC, more alpha power predicts greater change (reduction) in pain intensity in response to HYP-CT (1a), and lower levels of alpha power predict greater pain intensity change (reduction) in response to both CBT (1b) and MBCT (1c).

Hypothesis 2: Baseline beta power will predict pre- to post-treatment change in pain intensity, such that, relative to UC, more beta power predicts greater change (reduction) in pain intensity in response to CBT.

Hypotheses 3a and 3b: Baseline hypnotizability will predict pre- to post-treatment change in pain intensity, such that, relative to UC, more hypnotizability predicts greater change (reduction) in pain intensity in response to HYP-CT (3a), and predicts less change (reduction) in pain intensity in response to CBT (3b), relative to UC.

Hypothesis 4a and 4b: Baseline catastrophizing and the nonreactivity domain of mindfulness will predict pre- to post-treatment change in pain intensity, such that lower levels of baseline catastrophizing (4a) and greater levels of nonreactivity (4b) will predict greater pain intensity change (reduction) in response to MBCT, relative to UC.

Hypothesis 5: Baseline mindfulness will predict pre- to post-treatment change in pain intensity, such that higher levels of baseline mindfulness will predict greater pain reduction in response to MBCT.

Secondary study objectives include:

• Understand the zero order associations between predictive markers and pre- to post-treatment pain reductions for participants in each treatment condition separately.
• Understand the predictive role of the primary predictive markers on longer-term outcomes (i.e., 3 and 6-months post-treatment follow-ups) for the four treatment conditions.
• Understand any differences in clinical outcomes between the three active treatments, relative to usual care.
• Determine if there are any effects of "dose" of treatment on clinical outcomes.

Tertiary/exploratory study objectives include:

• Identify additional potential predictive markers of treatment-related improvements in pain.
• Identify potential predictive markers of treatment effects on secondary outcomes.
• Develop initial treatment matching algorithm.

Inclusion Criteria:

• ≥ 18 years old.
• Having chronic pain, operationalized as average pain intensity in the last week rated as ≥ 3 on a 0-10 Numerical Rating Scale (NRS) and having pain on most days for 3 months or more.
• Able to read, speak, and understand English.
• Willingness to be randomized to condition and use videoconferencing with audio and video enabled.
• Access to a private place with adequate internet reception to support participation in videoconferencing treatment sessions.
• Not currently participating in another clinical trial or interventional study for chronic pain and willing to refrain from participation in any other clinical trial or interventional study for chronic pain during active participation in this study.
• Willing, able, and committed to participate in an in-person EEG assessment.
• Able to use a smart phone, tablet, or computer independently to access email and webpages or have someone available in their home who can help them with initial session set-up and then leave for the treatment sessions.

Exclusion Criteria:

The exclusion criteria for Veteran participants will be assessed via self-report and verified by VA medical records chart review. Eligibility for non-Veteran participants will be assessed by self-report (no medical records chart review). An individual who meets any of the following criteria at the time of screening will be excluded from participation in this study and will not be enrolled:

• Active suicidal ideation/intent indicating significant risk.
• Unstable medical or psychiatric condition (e.g., mania, psychotic symptoms) that would interfere with study participation.
• Behavioral issues noted in the record or observed during the screening process that would interfere with appropriate or safe videoconferencing treatment session participation or study procedures.
• Alcohol abuse (operationalized as scoring 15 or more if male or 13 or more if female on the Alcohol Use Disorders Identification Test), or any illicit drugs, all of which may impact EEG measures.
• Severe cognitive impairment defined as two or more errors on the Six-Item Screener.
• Having an EEG confounder (e.g., congenital or acquired skull defects, missing sections or holes in the skull, or plates, screws, or other implants within the skull or brain) that would interfere with reliable EEG data collection.
• Active cancer treatment or primary pain is due to cancer.

Final data sets underlying publications resulting from the proposed research will be shared outside UW and VA in electronic format through a de-identified, anonymized dataset. Such sharing will take place under a written agreement that adheres to any applicable Informed Consent provisions and prohibits the recipient from identifying or re-identifying any individual whose data are included in the dataset. The data will be de-identified to remove any variables from which it would be possible to identify any individual participants. Requests for data will be considered on a case-by-case basis through a formal application and review process. The minimal amount of data will be provided to address the specific request. Only limited, fully de-identified data will be shared.

The data used for the analyses for any papers published will become available to interested researchers by request after that article is published. Those data will continue to be available for at least five years following the publication of the article.

The investigators will make the data and associated documentation available to users under a written agreement that adheres to any applicable Informed Consent provisions and prohibits the recipient from identifying or re-identifying any individual whose data are included in the dataset. The recipient will have sufficient knowledge, training, and resources to adequately design and conduct the studies replicating the original work, or can otherwise determine the validity of results by reviewing the data provided. Requests for data will be considered on a case by case basis through a formal application and review process. The minimal amount of data will be provided to address the specific request. Only limited, fully de-identified data will be shared.