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The Effects of Leg Length Discrepancy on Stability and Kinematics-Kinetics Deviations: A Systematic Review.

Nurul Azira AzizanKhairul Salleh BasaruddinAhmad Faizal Salleh
Published in: Applied bionics and biomechanics (2018)
Various studies have examined body posture stability, including postural sway and associated biomechanical parameters, to assess the severity effects of leg length discrepancy (LLD). However, various viewpoints have been articulated on the results of these studies because of certain drawbacks in the comprehensive analysis of the effect of variations in LLD magnitude. Therefore, this systematic review was performed to help focus on the current findings to help identify which biomechanical parameters are most relevant, commonly used, and able to distinguish and/or have specific clinical relevance to the effect of variations in LLD magnitude during static (standing) and dynamic (walking) conditions. Several electronic databases containing studies from the year 1983 to 2016 (Scopus, ScienceDirect, PubMed, PMC, and ProQuest) were obtained in our literature search. The search process yielded 22 published articles that fulfilled our criteria. We found most of the published data that we analyzed to be inconsistent, and very little data was obtained on the correlation between LLD severity and changes in body posture stability during standing and walking. However, the results of the present review study are in line with previous observational studies, which describe asymmetry in the lower limbs corresponding to biomechanical parameters such as gait kinematics, kinetics, and other parameters described during static (standing) postural balance. In future investigations, we believe that it might be useful to use and exploit other balance-related factors that may potentially influence body posture stability.
Keyphrases
  • systematic review
  • meta analyses
  • case control
  • big data
  • electronic health record
  • finite element
  • finite element analysis
  • lower limb
  • machine learning
  • artificial intelligence
  • deep learning