Development of real-world motor outcome measures in ataxia-telangiectasia

Abstract Objective, sensitive, and clinically meaningful assessments of disease severity are needed in ataxia- telangiectasia (A-T) and other neurological disorders to support drug development efforts, facilitate clinical care, and provide caregivers and patients with real-time information about disease status. This need has become even more critical as the number of disease-modifying therapies under evaluation rapidly expands. Daily fluctuation in symptoms, superimposed changes related to child development, and variable participation in task-based assessments pose challenges to precisely measuring disease severity. Passive and continuous (i.e., task-free) collection of movement data at-home using consumer-grade wearable sensors, combined with motor control theory-inspired signal processing and machine learning methods, provides an opportunity to produce reliable, interpretable, and meaningful motor measures that can sensitively capture disease change. We have recently made substantial preliminary progress in creating algorithms to extract and characterize movement building blocks called “submovements” from a single wrist or ankle sensor worn continuously at home. These sensor-based features show strong potential as novel disease measures: they demonstrate high reliability, correlate with clinician assessments of motor severity and caregiver-reported motor function, and show potential to sensitively quantify disease progression. By passively measuring everyday activity, the information obtained can be more ecologically valid and comprehensive than task- specific measurements and is applicable in young children as well in older, non-ambulatory individuals. We propose to further develop and expand this technology over a wide age range and in a longitudinal cohort of A- T and age- and gender-matched control participants to establish reliability, age relationships, and sensitivity to disease change. Additional clinical, caregiver-reported, and molecular data will be obtained alongside wrist and ankle sensor data to increase the interpretability and clinical meaningfulness of the measures and move toward clinical trial-ready outcome measures. The overall goal of this project is to develop accessible technologies that produce highly sensitive, meaningful, and ecologically-valid measures of motor function for use in clinical trials and clinical practice in A- T as well as other pediatric and adult neurodegenerative disorders.