Funded Projects

Therapeutic virtual reality (VR) has emerged as a promising and evidence-based treatment modality for musculoskeletal pain, including chronic low back pain (cLBP). Users of VR wear a pair of goggles with a close-proximity stereoscopic screen that creates a sensation of being transported into lifelike, three-dimensional worlds. By stimulating the visual cortex while engaging other senses, VR modulates the user’s processing of nociceptive stimuli. Functional magnetic resonance imaging (fMRI) of the brain reveals that VR has similar effects on the sensory and insular cortex as opioids, and head-to-head trials show that VR achieves similar or greater analgesia as hydromorphone. Since there are few data regarding long-term efficacy and safety of VR in cLBP, this study will measure patient-reported outcomes, biometric outcomes, and opioid use in nonspecific cLBP patients under various experimental conditions using VR therapy.

The University of California, San Francisco, as part of the Back Pain Consortium (BACPAC) Research Program, has established a Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back Pain (REACH). The main goal of REACH is to define different subtypes (phenotypes) of chronic low back pain as well as to identify underlying pain mechanisms that can lead to effective, personalized treatments for patients across all population subgroups. To achieve this goal, REACH is, or will be, participating in several clinical trials, and it is imperative that the patients participating in these trials reflect the diversity of the U.S. population. Therefore, this project seeks to adapt methods that have successfully improved minority participation in other settings as well as to develop and deploy digital strategies that can promote recruitment and engagement of patients from marginalized populations.

Because pain is a multidimensional phenomenon with physical and psychosocial components, a pain management approach relying solely on analgesics is unlikely to be efficacious. Nonpharmacologic therapies for co-occurring chronic pain and opioid use in hemodialysis patients should target and alter cognitive-affective circuits that govern responses elicited by pain, stress, mood disorders, and opioid-related cues. These domains are directly addressed through the behavioral therapy program known as MORE (Mindfulness-Oriented Recovery Enhancement)—a multipronged mindfulness-oriented individualized group therapy that integrates mindfulness training, cognitive reappraisal, and enhancement of natural reward processing. The specific aims are 1) to determine the impact of MORE on chronic pain and opioid use in hemodialysis patients and 2) to determine predictors of chronic pain, opioid use, and response to MORE.

There is little evidence available to guide clinical care for patients on long-term opioid therapy for whom risks exceed benefits. Given valid fears about both pain and withdrawal during decreased dosing (tapering), these individuals face challenges, including concerns about being abandoned by providers and uncertainty about the need to discontinue opioids. As such, these patients are hesitant to enroll voluntarily in opioid discontinuation research, further deepening the clinical evidence gap. This project will evaluate three effective and scalable interventions for individuals on long-term opioid therapy: (1) low-dose transdermal buprenorphine (without) of opioid withdrawal, (2) a brief cognitive behavioral intervention for pain, and (3) noninvasive brain stimulation.

The MGH EPPIC-Net hub will utilize two well-established collaborative entities in both patient care and clinical research at the Massachusetts General Hospital (MGH): 1) MGH Division of Pain Medicine and 2) MGH Center for Translational Pain Research. This hub-spoke network at MGH will include four core spokes consisting of both academic centers and community health care organizations, as well as over a dozen spokes that can be recruited as needed based on special requirements of phase II trials and research studies. The responsibilities of this hub-spoke network at MGH include a) coordinating phase II trials/clinical biomarker validation studies; b) recruiting well-phenotyped subjects in a timely manner; c) collecting clinical data and targeted outcome data tailored to meet the needs of each clinical trial/study; and d) maintaining communications within and outside the hub, including the NIH EPPIC-Net.

To enhance availability of cognitive behavioral therapy for chronic pain (CBT-CP), this study will 1) refine strategies to identify and recruit patients, finalize intervention procedures, and ensure data infrastructure and quality; 2) determine the effectiveness of online and telephonic CBT-CP on patients and pain severity and secondary outcomes, including depression, sleep, quality of life, and pain-related health care utilization, from the electronic health record; and 3) assess the cost and incremental cost-effectiveness of online and telephonic CBT-CP compared with usual care. Eligible participants will be randomized to one of two painTRAINER interventions or usual care. Interventions will be eight weekly 45-minute sessions of the online program or telehealth-style phone coaching by trained behavioral health specialists. Self-reported pain severity and secondary outcomes will be assessed at baseline and at three, six, and 12 months.

Despite the significance of spine disorders, there are few reliable methods to determine appropriate patient care and evaluate intervention effectiveness. The research and tool development take the critical next step in the clinical translation of faster, quantitative magnetic resonance imaging (MR) of patients with lower back pain. The multidisciplinary Technology Research Site (Tech Site) of BACPAC will develop Phase IV (i.e., technology optimization) technologies and/or methods (TTMs) to leverage two key technical advancements: development of machine learning-based, faster MR acquisition methods and machine learning for image segmentation and extraction of objective disease related features from images. The team will develop, validate, and deploy end-to-end deep learning-based technologies (TTMs) for accelerated image reconstruction, tissue segmentation, and detection of spinal degeneration to facilitate automated, robust assessment of structure-function relationships between spine characteristics, neurocognitive pain response, and patient-reported outcomes.

Despite the pervasive use of opioid analgesics in the dialysis population and the substantial risks they engender, their efficacy is limited in treating common chronic pain conditions. Most patients receiving long-term opioid therapy continue to experience severe pain and functional limitations. To simultaneously address problems related to chronic pain and opioid use in the U.S. hemodialysis population, this study will evaluate tailored patient-centered interventions to manage pain and reduce opioid use. Patients will be assigned randomly to one of three groups over a 12-month study period: 1) pain care management (PCM) versus 2) PCM plus an online pain self-management program (PSM) that incorporates cognitive behavioral training and is delivered during dialysis sessions, versus 3) control with medication review and education. The study will also evaluate the effectiveness of offering buprenorphine by randomly assigning participants in both active treatment arms to being offered buprenorphine rotation versus continued standard opioid taper support without the option of buprenorphine.

Exacerbation of health disparities has emerged during the COVID 19 pandemic and highlighted the recognition that minority underrepresentation in clinical research may contribute to racial disparities in health outcomes. In clinical trials related to pain, disparities in trial patient inclusion are documented by white patients often being overrepresented. Mitigating these disparities is an area in which an early-career pain investigator training and contributions may have lasting benefits. The pandemic also drove rapid expansion of telehealth for pain management without knowledge of how social and demographic factors affect utilization patterns of this care delivery model. This supplement supports research to examine the extent to which disparities exist in access to and outcomes of telehealth in socially marginalized pain patients. Findings will be applied to enrich the diversity in clinical trial populations for phase 2 safety trials performed in the HEAL EPPIC Network.

Current diagnostics and treatments of chronic low back pain (cLBP) rely primarily on subjective metrics and do not target all the multidimensional biopsychosocial mechanisms. This multidisciplinary effort will develop and validate a digital health platform and provide meaningful data-driven metrics that enable an integrated approach to clinical evaluation and treatment of cLBP. This platform will facilitate the use of quantitative spinal motion metrics (function), patient-reported outcomes, and patient preference information to enable deep patient phenotyping and inform clinical decision making on personalized treatments in order to improve outcomes. This effort will involve software and hardware development to enable data collection, analysis, and visualization in clinical settings. The outcome of this project will be a digital health platform with data to support regulatory submission for clinical use. At the end of this effort, the researchers will have a validated tool for integration in clinical research studies supported by the BACPAC Consortium.