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Home-based wearable sensors have the potential to track changes in disease state, by measuring submovements — the building blocks that make up a movement. In this study, the authors captured submovement characteristics by using wrist and ankle sensors worn for 1 week by 51 patients with spinocerebellar ataxia (SCA 1, 2, 3, and 6), or multiple system atrophy (MSA-C) and 25 healthy controls for a cross-sectional analysis. Of the participants, 27 patients and 8 controls also underwent longitudinal follow-up within 1 year. The investigators used two models developed through machine learning to estimate ataxia rating scale scores and two amyotrophic lateral sclerosis (ALS) models developed to optimize the sensitivity to change.
The sensor data co…