Funded Projects

Pain in the muscles and surrounding connective tissue (myofascial pain) is a significant health concern affecting hundreds of millions of Americans. Understanding and managing myofascial pain has been limited due to a lack of tools to help clinicians diagnose and treat this disorder. While past efforts to understand myofascial pain have focused on impairments in how connective tissues connect to other tissues in the body, this project will use a new imaging technique to study myofascial tissue physical properties, including how they move in the body and their structural stiffness. This research will identify an imaging biomarker to be used in a randomized controlled clinical trial to predict patient responses to a myofascial pain treatment.

Pain in muscles and surrounding connective tissue (myofascial pain) is a significant health concern affecting hundreds of millions of Americans. There is no objective way to identify and measure myofascial pain. This project will address this unmet challenge by developing a robust approach to identify imaging biomarker(s) that can distinguish different states of myofascial pain. The research will then examine the ability of identified biomarker(s) to predict patient responses to a myofascial pain treatment in a randomized controlled clinical trial.

Pain in the muscles and surrounding connective tissues (myofascial pain) can affect many regions of the body and is a key component of chronic low back pain. Patients with chronic low back pain have a range of musculoskeletal problems perpetuating their pain. There is a significant clinical need to identify the components of myofascial pain in people with chronic low back pain. Advances in ultrasound technology have allowed researchers to identify several differences in muscle and connective tissues related to myofascial pain. This project will develop and validate an ultrasound-based biomarker signature for myofascial pain in the low back. This research will also refine the biomarker signature using advanced machine learning approaches, toward future testing in in a randomized controlled clinical trial.

Many barriers exist in primary care offices where socioeconomically disadvantaged patients are most often treated. This project seeks to address chronic pain disparities that affect racially diverse, socioeconomically disadvantaged individuals. The study aims to optimize multimodal pain management in primary care clinics for low-income populations. This study includes two group-based models: integrative group medical visits and group acupuncture. These two interventions will be compared to typical treatment to measure both pain interference and social isolation. National experts and patient stakeholders will refine and optimize the design of the study with English- or Spanish-speaking patients with chronic pain in two primary care clinics for low-income populations.

Both pain experience and treatment response are determined by a variety of factors, including race and ethnicity. Inequities in access to healthcare and pain treatment affect patients from minority populations, such as Hispanic/Latino populations of all age groups. This study will develop and test an online intervention—Web-based Tailored Intervention for Preparation of Parents and Children for Outpatient Surgery (L-WebTIPS)—tailored for Latino families of children having outpatient surgeries. The intervention aims to lower child and family anxiety before surgery as well as to reduce post-surgical pain by enhancing parent self-efficacy and behavioral pain coping strategies. After an exploratory phase to assess usability and acceptability of the intervention, the study will evaluate the impact of L-WebTIPS on child pre-surgery anxiety and post-surgery pain as well assess other child and parent outcomes.

There is a need to improve access to treatments and address the stigma, bias, and mistrust that harm and isolate people with chronic pain, especially those from ethnic and racial minority populations. The Integrating Nonpharmacologic Strategies for Pain with Inclusion, Respect, and Equity (INSPIRE) Chronic Pain (CP) intervention blends cognitive-behavioral therapy, physical therapy, mindfulness, and pain education, and is delivered by a trilingual mobile app and supported by a telehealth pain coach who coordinates with doctors. The coach will collect and summarize patient reports on pain, depression, anxiety, substance use, and social factors, and share them with healthcare providers. In this project, researchers will create the digital tool and coaching protocol, develop educational and implementation strategies for healthcare providers, and conduct a pilot test. They will then perform a randomized clinical trial to compare INSPIRE to current treatment, analyze its effects, and evaluate outcomes.

Cancer remains a leading cause of death among Hispanic/Latino populations in the United States. Compared with non-Hispanic Whites, Hispanic/Latino cancer patients are more likely to experience poor quality of life and inadequate cancer-related care, including less effective pain relief and poor patient‒provider communication. Additionally, Hispanic/Latino populations often have inadequate access to pain treatment, due to both social disparities and language barriers. However, most behavioral and psychosocial oncology research continues to focus on non-Hispanic Whites, and empirically validated and effective treatment interventions, particularly psychosocial interventions, are often not available in Spanish. This project will generate a Spanish-language version of the painTRAINER internet-based coping skills training program that is both linguistically and culturally sensitive and will evaluate its feasibility and acceptability in Hispanic/Latino patients with persistent cancer-related pain.

Mindfulness has been shown to be effective in treating chronic low back pain, but it has not been embedded into routine clinical care. The OPTIMUM study (Optimizing Pain Treatment In Medical settings Using Mindfulness) will address barriers to delivering mindfulness in primary care and determine the effectiveness in this setting. This project extends the stakeholder engagement efforts of the OPTIMUM study by increasing the size and responsibilities of the Community Advisory Board, adding focus groups for participants in both study arms, and collecting stories from study nonparticipants about their experience seeking care for chronic low back pain and their views on participating in research. This expanded effort will optimize recruitment of a diverse and underrepresented sample, maximize retention, and prepare for future implementation and dissemination.

Participants in many clinical trials do not represent the U.S. population. Racial/ethnic minorities are often underrepresented, as are people with lower socioeconomic status and lower education levels, who face additional barriers, such as lack of transportation or childcare. Thus, both recruitment and retention of such populations is challenging, particularly for complementary and integrative health trials. This project proposes to enhance diversity, inclusion, and retention of participants in an ongoing study by creating a patient and caregiver Diversity, Recruitment, and Retention Advisory Board as well as adding a recruitment and retention specialist to coordinate the advisory board and implement necessary activities. The project will also provide evidence-based recruitment tools and will conduct structured interviews with patients who choose not to participate in a study as well as those who are at risk of dropping out to enhance understanding of the barriers and factors contributing to trial recruitment, loss to follow-up, and successful completion.

This award funds EPPIC-NET’s first phase 2 clinical trial, testing the novel oral drug CNTX-6970 in patients with moderate to severe knee osteoarthritis pain. It will include 150 participants at EPPIC-Net sites across the United States. Preclinical studies of CNTX-6970, which binds effectively and dose-proportionally to C-C chemokine type 2 (CCR2) receptors, have demonstrated potent analgesia in multiple pain models, with no emergent safety issues. CNTX-6970 has effects both at an affected joint, as well as on neural signaling. Participants will be randomized to receive CNTX-6970, placebo, or a third pain medication and will be followed for 24 weeks.

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.

The NIH Back Pain Consortium (BACPAC) Research Program brings together leading centers with expertise in studying and treating chronic low back pain to advance understanding of the mechanisms that underlie the condition and to identify novel treatment strategies. BACPAC is undertaking a multisite precision medicine clinical trial taking into account patient-specific information to understand which patients with chronic low back pain respond best to various nonopioid, evidence-based treatments. The trial seeks to enroll a racially, ethnically, and socioeconomically diverse patient population to ensure that the results are applicable to all Americans with chronic low back pain. This project aims to develop comprehensive recruitment and retention plans for study sites that can recruit from historically underrepresented populations in clinical research (e.g., Black and Hispanic populations) and to provide dedicated financial resources to engage patients from these populations using tailored, culturally appropriate strategies.

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.