Consumer-grade wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis
Gupta, A. S.; Patel, S.; Premasiri, A.; Vieira, F.
Show abstract
ALS causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in fine motor, gross motor, bulbar, and respiratory function. Promising drug development efforts have accelerated in ALS, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of consumer-grade wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with ALS over a several year period. We utilized an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. The approach produced interpretable and highly reliable scores that progressed faster than the gold standard ALS Functional Rating Scale-Revised (-0.70 SD/year versus -0.48 SD/year), supporting its use as a sensitive, ecologically valid, and scalable measure for ALS trials and clinical care.
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