Assessing upper limb function in multiple sclerosis using an engineered glove

The importance of upper limb function in multiple sclerosis (MS) is increasingly recognized, especially for the evaluation of patients with progressive MS with reduced mobility. Two sensor‐engineered gloves, able to measure quantitatively the timing of finger opposition movements, were previously used to assess upper limb disability in MS. The aims of the present study were: (1) to confirm the association between glove‐derived variables and standard measures of MS disability in a larger cohort; (2) to assess the correlation with quantitative magnetic resonance imaging (MRI) and quality of life (QoL) measures; and (3) to determine if the glove‐derived variables offer advantages over the standard measure for assessing upper limb function in MS, namely, the Nine‐Hole Peg Test (9HPT).

[1]  M. Bove,et al.  Subclinical motor impairment assessed with an engineered glove correlates with magnetic resonance imaging tissue damage in radiologically isolated syndrome , 2018, European journal of neurology.

[2]  M. Bove,et al.  An engineered glove for investigating the neural correlates of finger movements using functional magnetic resonance imaging , 2015, Front. Hum. Neurosci..

[3]  M. Bove,et al.  Functional connectivity in the resting‐state motor networks influences the kinematic processes during motor sequence learning , 2015, The European journal of neuroscience.

[4]  Jeffrey A. Cohen,et al.  Defining the clinical course of multiple sclerosis: the 2013 revisions. , 2014, Neurology.

[5]  Laura Bonzano,et al.  Upper limb motor rehabilitation impacts white matter microstructure in multiple sclerosis , 2014, NeuroImage.

[6]  P. Feys,et al.  Assessing upper limb function in multiple sclerosis , 2014, Multiple sclerosis.

[7]  Maria Pia Sormani,et al.  Quantitative Assessment of Finger Motor Impairment in Multiple Sclerosis , 2013, PloS one.

[8]  M. Battaglini,et al.  Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.

[9]  L. von Koch,et al.  The relationship between walking, manual dexterity, cognition and activity/participation in persons with multiple sclerosis , 2012, Multiple sclerosis.

[10]  R. Motl,et al.  Upper and Lower Extremity Motor Function and Cognitive Impairment in Multiple Sclerosis , 2011, Journal of the International Neuropsychological Society.

[11]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[12]  X. Montalban,et al.  Responsiveness and predictive value of EDSS and MSFC in primary progressive MS , 2008, Neurology.

[13]  Alessandra Solari,et al.  The multiple sclerosis functional composite: different practice effects in the three test components , 2005, Journal of the Neurological Sciences.

[14]  G. W. Ellison,et al.  A health-related quality of life measure for multiple sclerosis , 1995, Quality of Life Research.

[15]  D. Bourdette,et al.  Inter‐ and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke Expanded Disability Status Scale (EDSS) , 1992, Neurology.

[16]  R L Hewer,et al.  Arm function after stroke. An evaluation of grip strength as a measure of recovery and a prognostic indicator. , 1989, Journal of neurology, neurosurgery, and psychiatry.

[17]  J. Kurtzke.  Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[18]  C. P. Hughes,et al.  A New Clinical Scale for the Staging of Dementia , 1982, British Journal of Psychiatry.

[19]  M. Sormani,et al.  EDSS progression assessment heterogeneity in MS according to geographical areas , 2018 .