This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
The goal of this observational study is to develop and validate a biomarker for lumbar myofascial pain (MP) based on ultrasound obtained measurements of the lumbar muscles and fascia. The investigators will use advanced machine learning approaches and validation in a randomized controlled trial. The main questions it aims to answer are: * Will the deep learning-based marker reliably identify subjects from the 4 different groups: healthy, MP without trigger points, MP with latent trigger points, and MP with active trigger points? * Will the deep learning-based marker accurately classify/predict the severity of MP in subjects with cLBP? Participants in the healthy group will be asked to do the following tasks: * Consent/Enrollment * Measure Height/Weight * Complete Questionnaires on REDCap * Participate in Ultrasound Imaging Experiment Sessions Participants in the chronic low back pain group will be asked to do the following tasks: * Consent/Enrollment * Complete Questionnaires on REDCap * Measure Height/Weight * Undergo a Standardized Clinical Exam * Participate in Ultrasound Imaging Experiment Sessions
Development and Validation of a Noninvasive Multimodal Ultrasound-Based Imaging Biomarker for Myofascial Pain
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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Sponsor: University of Pittsburgh
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.