Funded Projects

The University of Michigan (UM) will lead a Mechanistic Research Center (MRC) as part of the broader BACPAC initiative that will take patients with chronic low back pain (cLBP) and use a patient-centric, SMART design study to follow these individuals longitudinally as they try several different evidence-based therapies while mechanistic studies are overlaid to draw crucial inferences about what treatments will work in what patient endotypes. Interventional Response Phenotyping describes the need in any precision medicine initiative to phenotype participants based on what therapies they do and do not respond to so that one can later link mechanistically distinct disease endophenotypes with those who preferentially respond to therapies targeting those mechanisms.

Chronic overlapping pain conditions represent up to half of all chronic pain cases and can be more debilitating than other forms of chronic pain. These conditions include but are not limited to the following: temporomandibular disorders, fibromyalgia, irritable bowel syndrome, vulvodynia, interstitial cystitis/painful bladder syndrome, painful endometriosis, chronic tension type headache, migraine headache, chronic low back pain, and chronic fatigue syndrome. Common neurobiological mechanisms have been suspected to account for the overlap between these conditions, but until recently it has been difficult to efficiently classify each condition within individual patients. A digital classification tool for clinicians has been developed for this purpose, but access to the tool remains limited. Here we propose converting this chronic overlapping pain conditions classification tool into a common web-based application format.

Back pain, including low back and neck pain, is one of the most prevalent and disabling pain disorders. Treatment requires ongoing self-management, but most healthcare systems do not support self-care and instead focus on costly, provider-dependent therapies that remain inaccessible to many Black and Hispanic Americans and individuals with less education and income. This project will address these health disparities by developing a personalized self-management treatment program that includes pain education, mindfulness, cognitive behavioral therapy, and exercise – and make it available in community settings.

There are a dearth of integrated treatments that simultaneously address the fundamental causes of chronic pain and opioid misuse/opioid use disorder and that focus on well-being among individuals with chronic pain and opioid misuse/disorder. This research will study how to improve the lives of patients with chronic pain and opioid misuse/disorder via tailored interventions that explicitly target increasing quality of life and engagement in valued activities, the cultural centering of interventions to meet the needs of diverse patient populations and reducing stigma of chronic pain and opioid misuse/disorder. Specific research projects will i) test the effectiveness, mechanisms, and implementation of an integrated psychosocial treatment for chronic pain and opioid use disorder among individuals receiving buprenorphine from outpatient treatment clinics, and ii) will use community-based participatory research methods to develop a culturally centered strategy for screening and brief intervention of chronic pain and opioid use disorder among American Indian/Alaska Native patients in primary care settings. This research will shed light on a difficult problem and improve health and wellbeing with a focus on diverse and underserved populations.

A significant gap exists in understanding of the barriers blocking access to specialized care for children of color who experience headaches, as well as to understand and appreciate the impact of undertreatment on a child’s functional ability and quality of life. Long-term, this research aims to understand these barriers to care and test interventions to remedy disparities. As the first step, this project's primary objective is to identify socioeconomic and clinical factors that lead children experiencing headache to seek care in an emergency department in lieu of outpatient neurology care. The results of this research will help to inform efforts to reduce the negative effects of emergency department overuse in this population and guide them to potentially more appropriate outpatient care.

The University of Pittsburgh Low Back Pain: Biological, Biomechanical, and Behavioral Phenotypes (LB3P) Mechanistic Research Center (MRC) will to perform in-depth phenotyping of patients with chronic low back pain (cLBP), using a multimodal approach to characterize patients and provide insight into the phenotypes associated with experience of cLBP to direct targeted and improved treatments. The LB3P MRC will be formed of three Research Cores, three support cores, and one research project. This approach will leverage and integrate distinctive resources at the University of Pittsburgh laboratories to deliver quantified biomechanical, biological, and behavioral characteristics; functional assessments; and patient-reported outcomes, coupled with advanced data analytics using a novel Network Phenotyping Strategy (NPS). By eliminating isolated and disconnected approaches to treatment and focusing on personalized patient-centric approaches, this approach will yield improved outcomes and patient satisfaction.

Identifying optimal chronic low back pain treatments on a patient-specific basis is an important and unresolved challenge. Tailoring interventions according to patient movement characteristics is one option. This research is characterizing patients based on spinal motion during functional tasks and daily activities and will use artificial intelligence to objectively characterize motions of the spine during both clinical assessments and day-to-day life. During clinical assessments, participants will be asked to perform functional tasks while wearing motion sensors. Data collected from the sensors will be used to identify tasks of interest, such as activities of daily living and aberrant/painful motions. An artificial intelligence approach will then interpret data collected continuously during assessment in patients’ homes over a 7-day testing period. Ultimately, this data could be used to help clinicians tailor treatments that are responsive to a patient’s real-world functional impairments.

The chronic low back pain (cLBP) subgroup with comorbid depression or anxiety disorders, known as high negative affect (NA), needs better non-opioid, comprehensive pain treatment options. Data shows that the combination of antidepressants (AD) and fear avoidance physical therapy is more efficacious at improving pain, function, depression, and anxiety in cLBP patients with high NA than each treatment alone or a control condition. Research also finds that an enhanced fear avoidance rehabilitation protocol (EFAR; fear avoidance-based physical therapy, pain education, and motivational messaging) further improves outcomes. To address the unmet needs of cLBP patients with high NA, this study will test in a randomized trial whether the combination of AD and EFAR is more effective than each treatment alone at relieving pain, improving function, combating depression, and preventing opioid misuse. This multimodal combination approach of pharmacotherapy and behavioral therapy is novel to the field and has the potential to shift current treatment paradigms.

The goal of the Tailored Retention and Engagement for Equitable Treatment of Opioid use disorder (OUD) and Pain (TREETOP) research program is to develop effective, equitable, and sustainable interventions for chronic pain and opioid misuse/disorder that improve engagement in medication for opioid misuse/disorder treatment and retention in office-based addiction treatment. TREETOP will prioritize disproportionately affected rural and Black communities. The Engagement research project will investigate whether pain self-management can improve pain and engage primary care patients to seek medication treatment for opioid misuse/disorder. The Retention project will investigate whether pain self-management and/or flexibly dosed buprenorphine/naloxone can improve pain and retention in treatment among patients who have already begun care in office-based addiction treatment programs. With engagement from stakeholders and representatives with varied perspectives and lived experiences, this research will advance the science of sustainably and equitably managing chronic pain and opioid misuse/disorder, prioritizing disproportionately affected communities.

Medications have proven to be effective for treating opioid use disorder (OUD). Increasing accessibility to buprenorphine provides an opportunity for many with OUD to benefit from its proven effectiveness. Adherence to medication-based treatments however is low, in part because of the stigma associated with use of this and other effective drugs and as such, leads to inadequate treatment and poor outcomes. This study aims to understand the effects of stigma on patient engagement, retention, and outcomes of buprenorphine treatment. Knowledge drawn from the HIV Stigma Theory and tools developed to reduce HIV associated stigma will be used to assess OUD stigma and to develop interventions to reduce it in the context of buprenorphine treatment. The study findings may provide resources to address stigma and thus maximize treatment adherence among those affected by OUD.

Identifying optimal chronic low back pain treatments on a patient-specific basis is an important and unresolved challenge. Tailoring interventions according to patient movement characteristics is one option. This research is characterizing patients based on spinal motion during functional tasks and daily activities and will use artificial intelligence to objectively characterize motions of the spine during both clinical assessments and day-to-day life. During clinical assessments, participants will be asked to perform functional tasks while wearing motion sensors. Data collected from the sensors will be used to identify tasks of interest, such as activities of daily living and aberrant/painful motions. An artificial intelligence approach will then interpret data collected continuously during assessment in patients’ homes over a 7-day testing period. Ultimately, this data could be used to help clinicians tailor treatments that are responsive to a patient’s real-world functional impairments.

The NIH’s HEAL Initiative aims to support collaboration between clinical research experts in academia and industry to accelerate the development of highly efficacious, nonaddictive analgesics for well-defined chronic pain syndromes. The University of Rochester (UR), and its leadership for the UR Hub and Spokes within Early Phase Pain Investigation Clinical Network (EPPIC-Net), will recruit subjects with a broad range of pain conditions, with a focus on leveraging clinical trial infrastructure to support patient recruitment and retention, timely and accurate data entry, and regulatory documentation, as well as recruit additional Spoke sites through a national network of analgesic researchers.

Improving pain treatment for is a major goal of the NIH HEAL Initiative. This award supports an early career physician toward achieving a future in clinical pain research and in conducting phase II clinical trials focused on pain. Research activities will provide this individual with the skills needed to serve as a primary investigator for future clinical trials in chronic pain and will help to answer a key question that could improve the design of analgesic clinical trials for neurogenic intermittent claudication, a distinct form of chronic low back pain for which no available treatment exists.