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The purpose of this research is (1) to identify disease specific walking-related digital biomarkers of disease severity, and (2) monitor longitudinal changes in natural environments, for extended periods of time, in DMD and SMA.
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Spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are genetic disorders that often result in progressive weakness and impaired function. Our recent findings suggest that novel machine-learning (ML)-based abstraction models may map noisy signals from foot-worn sensors (namely, instrumented insoles developed by the project team) into accurate and clinically relevant spatiotemporal and kinetic gait parameters. These gait parameters derived from instrumented insoles may serve as functional biomarkers to detect changes in real world function. All participants will be observed and measured while wearing the instrumented insoles in the lab and in real-life environments.
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106 participants in 3 patient groups
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Jacqueline Montes, PT, EdD; Cara Kanner, DPT
Data sourced from clinicaltrials.gov
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