Funded Projects

Chronic musculoskeletal pain is common and often severe enough to be disabling. Some treatments such as cognitive behavioral therapies or analgesics may relieve pain for some, but not all patients. Combining effective therapies and providing support to ensure that patients are motivated to adhere to their treatment may prove to be more beneficial to patients than prescribing a drug or recommending a single non-pharmacological treatment. This study aims to evaluate a combination of complementary treatments and Registered Nurse (RN) support to motivate patients to use and maintain combined therapies. Some patients will receive phone-based motivational interviews with an RN to enhance their adherence to pain coping skills learned through web-based cognitive behavioral therapy in combination with duloxetine, a pain-relieving drug. Others will receive both treatments but will not receive support from an RN. The study aims to determine whether motivational nursing support enhances adherence to newly learned pain coping skills, and results in improved pain relief and physical function.

This research is intended to create multidisciplinary team science collaborations to develop effective interventions, best models of care for delivery of services, and sustainable implementation strategies for access to quality care for complex patients with chronic pain and opioid use disorder or opioid misuse. To allow comparison and analysis of data created in nine unique clinical trials funded across four centers, common data elements (CDEs) were selected to assess all aspects of a patient’s condition and experience. The purpose of this project is to make the IMPOWR CDE data more FAIR (Findable, Accessible, Interoperable, Reusable) by building a tool that will automate the mapping/conversion of HEAL-related data to the Observational Medical Outcomes Partnership data model that allows for systematic analysis of data from different databases. Upon completion, this tool would be shared with the HEAL research community as a new resource to enable broader harmonization and secondary data analysis.

Decreasing opioid dosing faster than advised by clinical recommendations often leaves chronic pain unaddressed and may increase the risk of overdose and suicide compared to continuing long-term opioid treatment. Clinical and research communities are uncertain about how to assess and manage long-term opioid therapy, despite having diagnostic and treatment frameworks for chronic pain and opioid use disorder. Because of this undefined space, health policy, institutions, and practitioners lack clear advice on long-term opioid prescribing in chronic pain. The goal of the MRC is to provide infrastructure support for the network; create a risk-benefit decision tool to assist providers in determining when opioids should be continued as prescribed, tapered, or tapered/discontinued; and develop and validate a clinical definition for this population (name, identifying associated symptoms/behaviors, and generating a screening tool). This project will leverage big data analytics in administrative datasets, natural language processing approaches in electronic health records, and cohort modeling techniques to accomplish these key responsibilities. These efforts will complement the qualitative data collection approaches in the Becker Resource Center.