Abstract
A significant barrier to developing disease-modifying therapies for spinocerebellar ataxias (SCAs) and multiple system atrophy of the cerebellar type (MSA-C) is the scarcity of tools to measure disease progression sensitively in clinical trials. Wearable sensors worn continuously during natural behaviour at home have the potential to produce ecologically valid and precise measures of motor function by leveraging frequent and numerous high-resolution samples of behaviour. Here we test whether movement building block characteristics (i.e. submovements), obtained from the wrist and ankle during natural behaviour at home, can capture disease progression sensitively in SCAs and MSA-C, as recently shown in amyotrophic lateral sclerosis and ataxia telangiectasia. Remotely collected cross-sectional (n = 76) and longitudinal (n = 27) data were analysed from individuals with ataxia (SCAs 1, 2, 3 and 6, MSA-C) and controls. Machine learning models were trained to produce composite outcome measures based on submovement properties. Two models were trained on data from individuals with ataxia to estimate ataxia rating scale scores. Two additional models, previously trained entirely on longitudinal amyotrophic lateral sclerosis data to optimize sensitivity to change, were also evaluated. All composite outcomes from both wrist and ankle sensor data had moderate to strong correlations with ataxia rating scales and self-reported function, showed differences between ataxia and control groups with high effect size, and had high within-week reliability. The composite outcomes trained on longitudinal amyotrophic lateral sclerosis data most strongly captured disease progression over time. These data demonstrate that outcome measures based on accelerometers worn at home can capture the ataxia phenotype accurately and measure disease progression sensitively. This assessment approach is scalable and can be used in clinical or research settings with relatively low individual burden.