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Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia.

Nergis C KhanVineet PandeyKrzysztof Z GajosAnoopum S Gupta
Published in: Cerebellum (London, England) (2021)
With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen's d = 1.48) and fewer high intensity movements (p < 0.001; Cohen's d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen's d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71-0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67-0.92; p < 0.01), demonstrated high reliability (r: 0.86-0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group.Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.
Keyphrases
  • high intensity
  • blood pressure
  • randomized controlled trial
  • healthcare
  • risk factors
  • physical activity
  • resistance training
  • health information
  • machine learning
  • risk assessment
  • study protocol
  • big data