Targeted Realtime Assessment of Chronic Pain (TRAC-Pain) in Youth

Up to 5% of adolescents (~3.5 million in the US alone) suffer from high-impact chronic musculoskeletal (MSK) pain, affecting quality of life, school attendance, mood, and family function, and posing a significant economic burden of $19.5 billion annually in the US. A substantial proportion of these youths continue to suffer from pain into adulthood. Chronic MSK pain is characterized by a complex biological response, including physiological disturbances in cognition, sleep, and energy levels (fatigue), and is associated with impairments in both physical and emotional function. The chronic pain experience fluctuates over time with intra- and inter-daily variations and the occurrence of pain flares, contributing to unpredictability, uncertainty, and greater impairment.

Current gold standard self-report assessments are burdensome and fail to provide comprehensive, reliable measures of the pain experience due to inherent recall bias. A potential solution lies in the widespread adoption of digital health technologies, particularly wearable devices, which offer continuous monitoring of physiological, sleep, and physical activity data. This approach can yield unprecedented insights into individual health, informing diagnosis, prevention, monitoring, and treatment.

Through artificial intelligence (AI) and machine learning (ML), several groundbreaking digital biosignatures of human health have been developed by the research team, including those for glucose variability, preterm birth, panic attacks, fall risk, and surgical recovery. This real-time, personalized approach not only empowers patients but also enables healthcare providers to make more informed decisions, optimizing treatment strategies and improving outcomes. Despite these advancements, less than half of adolescents with chronic MSK pain who undergo pain treatment experience meaningful improvement.

The scientific rationale of this proposal is to overcome the limitations of self-report by integrating precise physiological, sleep, and physical activity measures from wearable devices with AI/ML to develop and validate a monitoring digital biosignature of the individual pain experience in youth with MSK pain. This biosignature will monitor the pain experience, track its progression, assess responses to interventions, and evaluate impacts on quality of life.

The research team is well positioned to execute these aims with: (1) a diverse, highly skilled team with expertise in digital technology, AI/ML, digital endpoint development, and clinical trials, alongside clinical expertise in chronic pain in youth and lived experience from patients, caregivers, and pain advocacy groups; (2) a secure, scalable, centralized, and standardized digital data collection, processing, and storage system; and (3) cutting-edge preliminary data supporting the capability to develop this digital health biosignature.