Using a structured format, we will perform needs assessment for children and adults with cerebral palsy (CP) using patient, caregiver, and provider input and will additionally identify what existing measures can address identified needs. Next, based on guidance and feedback from clinicians, we will validate a “motion capture in your pocket” automated motion analysis battery using computer vision for precise motor assessment in persons with CP.
Using activity and heart rate data from wearable devices (e.g., Fitbit) along with clinical data, we will identify distinct activity-heart rate patterns with clinical relevance to inform more precise prescription of physical activity in adolescents with concussion.
We will use mixture modeling on comprehensive data in the EHR to identify patient subgroups of rehabilitation readiness at acute hospital discharge and of motor recovery during outpatient rehabilitation. We will then elucidate how the rehabilitation received moderates the transition from rehabilitation readiness to recovery trajectories. Finally, we will explore how clinician engagement with data presented in the EHR informs clinical care.