AI Article Synopsis

  • - Amyotrophic lateral sclerosis (ALS) leads to the degeneration of motor neurons, causing muscle weakness and motor function impairment, with current drug development limited by the lack of precise outcome measurements.
  • - The study explores using wearable sensors on the limbs of 376 ALS patients to objectively quantify motor function and disease progression by analyzing accelerometer data for submovements.
  • - The results show that this new method provides a faster progression scoring system than the traditional rating scale, suggesting it could reduce the number of participants needed for clinical trials and improve ALS care.

Article Abstract

Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with amyotrophic lateral sclerosis. We use 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. We show that this approach produces scores that progress faster than the gold standard Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (-0.86 ± 0.70 SD/year versus -0.73 ± 0.74 SD/year), resulting in smaller clinical trial sample size estimates (N = 76 versus N = 121). This method offers an ecologically valid and scalable measure for potential use in amyotrophic lateral sclerosis trials and clinical care.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442344PMC
http://dx.doi.org/10.1038/s41467-023-40917-3DOI Listing

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