Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests

Sunghoon I. Lee, Alex Huang, Bobak Mortazavi, Charles Li, Haydn A. Hoffman, Jordan Garst, Derek S. Lu, Ruth Getachew, Marie Espinal, Mehrdad Razaghy, Nima Ghalehsari, Brian H. Paak, Amir A. Ghavam, Marwa Afridi, Arsha Ostowari, Hassan Ghasemzadeh, Daniel C. Lu, Majid Sarrafzadeh

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Cervical spondylotic myelopathy (CSM) is a chronic spinal disorder in the neck region. Its prevalence is growing rapidly in developed nations, creating a need for an objective assessment tool. This article introduces a system for quantifying hand motor function using a handgrip device and target tracking test. In those with CSM, hand motor impairment often interferes with essential daily activities. The analytic method applied machine learning techniques to investigate the efficacy of the system in (1) detecting the presence of impairments in hand motor function, (2) estimating the perceived motor deficits of patients with CSM using the Oswestry Disability Index (ODI), and (3) detecting changes in physical condition after surgery, all of which were performed while ensuring test-retest reliability. The results based on a pilot data set collected from 30 patients with CSM and 30 nondisabled control subjects produced a c-statistic of 0.89 for the detection of impairments, Pearson r of 0.76 with p < 0.001 for the estimation of ODI, and a c-statistic of 0.82 for responsiveness. These results validate the use of the presented system as a means to provide objective and accurate assessment of hand motor function impairment and surgical outcomes.

Original languageEnglish (US)
Pages (from-to)1007-1022
Number of pages16
JournalJournal of Rehabilitation Research and Development
Volume53
Issue number6
DOIs
StatePublished - 2016
Externally publishedYes

Keywords

  • Cervical spondylotic myelopathy
  • Classifier
  • Hand impairment
  • Hand movement
  • Machine learning
  • Motor deficit
  • Patient monitoring
  • Quantification
  • Spinal cord disorder
  • Tracking test

ASJC Scopus subject areas

  • Rehabilitation

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