Literature DB >> 32955951

Bipedal hopping as a new measure to detect subtle sensorimotor impairment in people with multiple sclerosis.

Megan C Kirkland1, Katie P Wadden1, Michelle Ploughman1.   

Abstract

BACKGROUND: Bipedal hopping has the potential to detect subtle multiple sclerosis (MS)-related impairments, especially among patients who "pass" typical movement tests. In this narrative review, we outline the biomechanics of bipedal hopping and propose its usefulness as a novel outcome measure for people with MS having mild disability.
METHODS: We summarize articles that (1) examined the biomechanics of jumping or hopping and (2) tested the validity and/or reliability of hopping tests. We consolidated consistencies and gaps in research and opportunities for future development of the bipedal hop test.
RESULTS: Bipedal hopping requires immense power, coordination, balance, and ability to reduce co-contraction; movement components typically affected by MS. These impairments can be measured and differentiated by examining specific variables, such as hop length (power), symmetry (coordination), center of pressure (balance), and coefficient of variability (co-contraction/spasticity). Bipedal hopping challenges these aspects of movement and exposes sensorimotor impairments that may not have been apparent during walking.
CONCLUSIONS: Testing of bipedal hopping on an instrumented walkway may detect and monitor sensorimotor control in people with MS who do not currently present with clinical deficits. Early measurement is imperative for precise rehabilitation prescription to slow disability progression prior to onset of measurable gait impairment.Implications for rehabilitationJumping and hopping tests detect lower limb and balance impairments in children, athletes, and older adults.Bipedal hop test measures multiple domains: power, coordination, balance, and muscle timing.Bipedal hop test may expose subtle sensorimotor impairments in people with multiple sclerosis.Multiple variables measured can discern type of sensorimotor impairment to direct personalized rehabilitation programs.

Entities:  

Keywords:  Lower extremity; assessment; jump; movement; multiple sclerosis; outcome measure; rehabilitation

Mesh:

Year:  2020        PMID: 32955951     DOI: 10.1080/09638288.2020.1820585

Source DB:  PubMed          Journal:  Disabil Rehabil        ISSN: 0963-8288            Impact factor:   3.033


  1 in total

1.  Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis.

Authors:  Wenting Hu; Owen Combden; Xianta Jiang; Syamala Buragadda; Caitlin J Newell; Maria C Williams; Amber L Critch; Michelle Ploughman
Journal:  Front Artif Intell       Date:  2022-09-29
  1 in total

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