Funded Projects

Chronic low back pain (cLBP) is recurrent and often nonresponsive to conservative treatments. Biomechanists, physical therapists, and surgeons each utilize a variety of tools and techniques to assess and interpret qualitative movement changes to understand potential mechanical and neurological sources of low back pain and as critical elements in their treatment paradigm. However, objectively characterizing and communicating this information is currently impossible, since clinically feasible (i.e., cost-effective, objective, and accurate) tools and quantitative benchmarks do not exist. This research addresses the challenge to improve cLBP outcomes through the use of unique, inexpensive, screen-printable, elastomer-based, nanocomposite, piezoresponsive sensors, which will be integrated into a SPInal Nanosensor Environment (SPINE) sense system to measure lumbar kinematics and provide an objective, quantitative platform for diagnosis, monitoring, and follow-up assessment of cLBP.

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.

A primary factor contributing to acute or recurrent back injury is overexertion via excessive peak and cumulative forces on the back and the primary factors involved in the progression of acute low back injury to chronic low back pain (cLBP) include maladaptive motor control strategies, muscle hyperactivity, reduced movement variability, and the development of fear cognitions. This project will focus on the development of robotic apparel with integrated biofeedback components that can reduce exertion; encourage safe, varied movement strategies; and promote recovery. Robotic apparel will be capable of providing supportive forces to the back and hip joints through adaptive control algorithms that respond to dynamic movements and becoming fully transparent when assistance is no longer needed. This technology can be used to prevent cLBP caused by overexertion and provide a new tool to physical therapists and the clinical community to enhance rehabilitation programs.

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.

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.

This project will examine the association between end plate pathology and chronic low back pain (cLBP) and improve patient selection by developing and translating new imaging tools, technologies, and/or methods (iTTM) that provide accurate, noninvasive measures of end plate pathologies. A search for clinically relevant biomarkers of end plate pathology will focus on novel imaging measures of end plate bone marrow lesion (BML) severity with IDEAL MRI and cartilage endplate (CEP) fibrosis/damage with UTE MRI, assess interactions with paraspinal muscles, and identify metrics that associate with pain, disability, and degeneration. The research will refine imaging and post-processing methodologies by leveraging and expanding existing cross-sectional cohorts and then deploy and validate the new end plate iTTM to other BACPAC sites to test the most promising metrics’ clinical utility. These studies will provide validated iTTM that are useful for addressing the end plates pathology’s role in cLBP, identifying sub-phenotypes, discovering pain mechanisms, uncovering treatment targets, and selecting patients.

The UCSF Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back Pain (UCSF REACH) is an interdisciplinary consortium of basic and clinical scientists dedicated to understanding and clarifying the biopsychosocial mechanisms of chronic low back pain (cLBP). The goal of REACH is to define cLBP phenotypes and pain mechanisms that can lead to effective, personalized treatments for patients across the population. UCSF REACH has six cores that will support a single research project that is focused on the challenge of developing validated and adoptable tools that enable comprehensive yet routine clinical assessment and treatment of cLBP patients. Overall, the object of REACH is to make optimum use of all available resources to catalyze discovery and translation of novel diagnostics and therapeutics that improve outcomes of cLBP patients.

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.

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.

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.

Multiple factors, including inflammation contribute to chronic low back pain. Inflammation is mediated by numerous genes. The study aims to determine how variations in the genes encoding key inflammatory mediators impact the response of patients with chronic low back pain to physical therapy treatment. Gene variations that are known to be linked to inflammation and pain will be tested against their possible association on physical therapy treatment outcomes, to inform clinical decisions on optimal care. This study will support training in clinical research on pain within the context of the HEAL BACPAC Mechanistic Research Center. It will provide resources for a research project relevant to the parent grant and the career development of an individual in the field of pain research. The ability to identify a set of genetic variations and classify patients according to treatment response might enable use of DNA testing as a screening tool for targeted treatments for patients with CLBP.