Chronic MSK pain is marked by a complex biologic response accompanied by physiological perturbance in cognition, sleep, and energy levels (fatigue), and is associated with impairments in physical and emotional function. Moreover, the chronic pain experience is not stable over time with intra- and inter-daily fluctuations and the presence of pain flares contributing to unpredictability, uncertainty, and ultimately greater impairment. Current gold standard self-report assessment is burdensome and falls short of providing comprehensive, reliable measures of the pain experience, typically reflecting single point-of-care assessment with inherent recall bias. A potential solution lies in the ubiquitous consumer adoption of wearable devices that provide a window into human health. Through artificial intelligence (AI) and machine learning (ML) several ground-breaking digital biosignatures of human health have been developed. This proposal overcomes the limitations of self-report by combining the precise physiological, sleep, and physical activity measures via wearable devices with AI/ML to develop and validate a monitoring digital biosignature of the individual pain experience in youth with MSK pain. We are well positioned to execute UG3/UH3 aims with: (1) a highly skilled team with scientific expertise in digital technology, AI/ML, digital endpoint development, and clinical trials, clinical expertise in chronic pain in youth, and lived experience expertise from patients, caregivers, and pain advocacy groups; (2) a centralized and standardized digital data collection, processing, and storage system, the scalable and secure My Personal Health Dashboard (MyPHD), and (3) preliminary data to support our digital biosignature development capability. For UG3 phase we will enroll 500 youth (ages 14-24) with chronic MSK pain, capturing continuous physiological (heart, respiratory), sleep, and physical (activity level, mobility, gait) activity metrics via wearables with repeated intra-daily gold standard self-report of pain experience (pain interference, pain intensity, fatigue, mood, stress, pain flares). We will Incorporate user feedback on wearable use and quality of life relevance of data captured, develop a digital biosignature of the pain experience, and prepare for the UH3 phase through outreach and collaboration with (a) individuals with lived experience, (b) individuals who experience health disparities, and (c) FDA to ensure relevance, acceptability, and recruitment of underrepresented youth coupled with scalability of the algorithm for clinical use. For UH3 phase we will enroll 400 diverse youth with chronic MSK pain capturing wearable and self-report of pain experience metrics for clinical validation of the pain experience digital biosignature and accuracy of an opportunity for enhanced wellness alert system. The successful development and validation of digital endpoints are crucial for the evolution of pain management. These endpoints can advance therapeutic development by providing robust, objective measures to monitor treatment response. Our studies, supported by this RFA, will be fundamental in seeking regulatory approval for the commercialization of the associated software or for disseminating open-source analysis packages for future clinical trials.

The project aims to transform the management of chronic musculoskeletal pain in youth by harnessing wearable technology and advanced analytics. It is predicated on developing a real-time digital biosignature through machine learning algorithms, which will provide an objective, comprehensive assessment of the pain experience beyond self-reporting. This endeavor not only promises to enhance patient outcomes through personalized interventions but also stands to significantly reduce the economic and emotional burden of chronic pain by enabling timely and effective treatment strategies.