Funded Projects

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Project # Project Title Research Focus Area Research Program Administering IC Institution(s) Investigator(s) Location(s) Year Awarded
1UM1DA049394-01
HEALing Communities Study Data Coordinating Center Translation of Research to Practice for the Treatment of Opioid Addiction HEALing Communities Study NIDA RTI International WILLIAMS, RICK L Research Triangle, NC 2019
NOFO Title: HEALing Communities Study: Developing and Testing an Integrated Approach to Address the Opioid Crisis (Data Coordinating Center) (UM1- Clinical Trials Not Allowed)
NOFO Number: RFA-DA-19-017
Summary:

Although there are effective prevention and treatment programs and services to address opioid misuse, opioid use disorder (OUD), and overdose, gaps remain between those needing and those receiving prevention and treatment, in part because of a need to better understand how to make these programs and services most effective at a local level. The National Institutes of Health (NIH) and the Substance Abuse and Mental Health Services Administration (SAMHSA) launched the HEALing Communities Study to generate evidence about how tools for preventing and treating opioid misuse and OUD are most effective at the local level. This multisite implementation research study will test the impact of an integrated set of evidence-based practices across health care, behavioral health, justice, and other community-based settings. The goal of the study is to reduce opioid-related overdose deaths by 40 percent over three years. As the Data Coordinating Center, RTI will provide coordination and facilitate communications to unite the HEALing Communities Study research centers into a cohesive research cooperative.
3U19AR076725-01S3
HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes Clinical Research in Pain Management Back Pain Consortium Research Program NIAMS UNIVERSITY OF PITTSBURGH AT PITTSBURGH SOWA, GWENDOLYN A Pittsburgh, PA 2021
NOFO Title: Notice of Special Interest to Encourage Eligible NIH HEAL Initiative Awardees to Apply for PA-20-222: Research Supplements to Promote Diversity in Health-Related Research (Admin Supp - Clinical Trial Not Allowed)
NOFO Number: NOT-NS-20-107
Summary:

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.
3U19AR076725-01S2
HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes Clinical Research in Pain Management Back Pain Consortium Research Program NIAMS UNIVERSITY OF PITTSBURGH AT PITTSBURGH SOWA, GWENDOLYN A Pittsburgh, PA 2021
NOFO Title: Notice of Special Interest to Encourage Eligible NIH HEAL Initiative Awardees to Apply for Administrative Supplements to Support Career Enhancement Related to Clinical Research on Pain (Admin Supp – Clinical Trial Not Allowed)
NOFO Number: NOT-NS-21-048
Summary:

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.
3U19AR076725-01S1
HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes Clinical Research in Pain Management Back Pain Consortium Research Program NIAMS UNIVERSITY OF PITTSBURGH AT PITTSBURGH SOWA, GWENDOLYN A Pittsburgh, PA 2020
NOFO Title: Notice of Special Interest to Encourage Eligible NIH HEAL Initiative Awardees to Apply for Administrative Supplements to Promote Training in Clinical Research on Pain (Admin Supp ? Clinical Trial Not Allowed)
NOFO Number: NOT-NS-20-044
Summary:

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.
1U19AR076725-01
HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes Clinical Research in Pain Management Back Pain Consortium Research Program NIAMS UNIVERSITY OF PITTSBURGH AT PITTSBURGH SOWA, GWENDOLYN A (contact); VO, NAM V Pittsburgh, PA 2019
NOFO Title: HEAL Initiative: Back Pain Consortium (BACPAC) Research Program: Mechanistic Research Centers (U19 Clinical Trial Optional)
NOFO Number: RFA-AR-19-026
Summary:

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.