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Identifying and Quantifying Neurological Disability via Smartphone.

Alexandra K Boukhvalova1, Emily Kowalczyk1,2, Thomas Harris1

  • 1Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States.

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Summary
This summary is machine-generated.

Smartphone tests can accurately measure neurological functions in multiple sclerosis (MS) patients, differentiating them from healthy volunteers. These digital tools offer richer, domain-specific insights than traditional tests, aiding disease management and drug development.

Keywords:
diagnosticsmedical technologymultiple sclerosisneurological examinationneurologyoutcomesprecision medicinesmartphone app

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Area of Science:

  • Digital health
  • Neurology
  • Mobile sensing

Background:

  • Smartphone sensors enable patient-autonomous neurological assessments for disease monitoring and drug development.
  • Current neurological examinations can be time-consuming and may not capture subtle, domain-specific deficits.
  • Digital tools offer potential for more granular and frequent neurological assessments.

Purpose of the Study:

  • To test the hypothesis that aggregated data from two smartphone tests of fine finger movements can identify domain-specific neurological deficits.
  • To correlate smartphone-derived outcomes with established neurological examination components in multiple sclerosis (MS) patients.
  • To compare the performance of smartphone tests with traditional clinical assessments.

Main Methods:

  • Developed an Android-based MS test suite including Finger tapping and Balloon popping tests.
  • Assessed 76 MS patients and 19 healthy volunteers (HV) using smartphone tests and traditional neurological examinations (e.g., 9-hole peg test).
  • Analyzed cross-sectional and longitudinal data to evaluate primary and secondary outcome measures.

Main Results:

  • Smartphone tests differentiated MS patients from HV with performance comparable to the 9-hole peg test.
  • Secondary outcomes derived from smartphone tests correlated significantly with neurological examination findings, identifying specific deficits (e.g., cerebellar dysfunction, motor fatigue, reaction time).
  • Longitudinal data showed low intra-individual variance, indicating reliability.

Conclusions:

  • Smartphone-based tests provide rich, reliable, and domain-specific neurological measurements efficiently.
  • These digital tools hold promise for comprehensive neurological assessment, potentially recreating the entire neurological examination.
  • Smartphone applications can advance neurological disease identification, management, and therapeutic development.