Validating Digital Health Tools for Assessing Upper Extremity Activity in Cervical Spinal Cord Injury: A Multimodal Analysis - Regaining upper extremity function is crucial for individuals with cervical spinal cord injury (CSCI), as it significantly impacts their quality of life and independence. Traditional clinical outcome assessments (COAs) for evaluating upper extremity function in individuals with tetraplegia after CSCI provide standardized measurements and snapshots of function. However, they often fall short of capturing real-world upper extremity activity and patients’ perspectives on their health and function. Wearable sensors and SCI-focused online communities offer novel data sources to investigate real-world upper extremity activity and patient perspectives on upper extremity function, overall function, and health after CSCI. However, analytic techniques using these sources of digital data require validation against COAs and priorities from prior patient-centric work. The primary goal of this dissertation is to validate emerging digital health technologies, including sensor-based metrics from wearables and text analytics of online communities, for assessing upper extremity function and patient-centered outcomes in individuals with CSCI. This multimodal approach will involve (1) analyzing associations between sensor-based outcomes of upper extremity activity and established COAs (performance-based, clinician-reported, and patient-reported outcomes) in individuals with CSCI and (2) using text mining to identify outcomes and experiences of individuals with CSCI from online forums for comparison with outcomes identified from prior patient-centered research. By leveraging sensor-derived metrics, COAs, and text mining outcomes, this research has the potential to (1) validate sensor-based metrics as proxies for COAs, enabling continuous monitoring of upper extremity recovery, and (2) reveal underrepresented patient priorities, leading to improved patient-reported outcome measures grounded in the lived experiences of individuals with CSCI. This innovative approach, combining expertise in outcomes assessment, wearable sensor technology, and text analytics, has the potential to extend assessment capabilities, reveal new insights into function and health, and inform more comprehensive, patient-centered rehabilitation practices, ultimately optimizing upper extremity function, overall function, and health outcomes for individuals with CSCI.
