title: Relationship between knee adduction moment patterns extracted using principal component analysis and discrete measures with different amplitude normalizations: Implications for knee osteoarthritis progression studies.
abstract: BACKGROUND
  Knee adduction moment discrete features (peaks and impulses) are commonly reported in knee osteoarthritis gait studies, but they do not necessarily capture loading patterns. Principal component analysis extracts dynamic patterns, but can be difficult to interpret. This methodological study determined relationships between external knee adduction moment discrete measures and principal component analysis features, and examined whether amplitude-normalization methods influenced differences in those with knee osteoarthritis who progressed to surgery versus those that did not.
  
  
  METHODS
  54 knee osteoarthritis patients had three-dimensional biomechanical measures assessed during walking. Knee adduction moments were calculated and non-normalized and amplitude-normalized waveforms using two common methods were calculated. Patterns were extracted using principal component analysis. Knee adduction moment peak and impulse were calculated. Correlation coefficients were determined between two knee adduction moment patterns extracted and peak and impulse. T-tests evaluated between-group differences.
  
  
  RESULTS
  An overall magnitude pattern was correlated with peak (r=0.88-0.90, p<0.05) and impulse (r=0.93, p<0.05). A pattern capturing a difference between early and mid/late -stance knee adduction moment was significantly correlated with peak (r=0.27-0.40, p<0.05), but explained minimal variance. Between-group peak differences were only affected by amplitude-normalization method.
  
  
  CONCLUSIONS
  Findings suggest that the overall magnitude knee adduction moment principal pattern does not provide unique information from peak and impulse measures. However, low correlations and minimal variance explained between the pattern capturing ability to unload the joint during mid-stance and the two discrete measures, suggests that this pattern captured a unique waveform feature.
authors.affiliation: School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada; Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: GHatfield@dal.ca.
authors.forename: Gillian L
authors.initials: GL
authors.lastname: Hatfield
authors.affiliation: School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada; Department of Surgery, Division of Orthopaedics, Dalhousie University, Halifax, NS, Canada. Electronic address: Wstanish@dal.ca.
authors.forename: William D
authors.initials: WD
authors.lastname: Stanish
authors.affiliation: School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada; School of Physiotherapy, Dalhousie University, Halifax, NS, Canada. Electronic address: clk@dal.ca.
authors.forename: Cheryl L
authors.initials: CL
authors.lastname: Hubley-Kozey
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D001696:Biomechanical Phenomena / Q000502:physiology; D018450:Disease Progression; D005260:Female; D005684:Gait / Q000502:physiology; D006801:Humans; D007719:Knee Joint / Q000503:physiopathology*; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D020370:Osteoarthritis, Knee / Q000503:physiopathology*; D025341:Principal Component Analysis; D016138:Walking / Q000502:physiology
keywords: Biomechanics; Gait; Knee adduction moment; Knee osteoarthritis; Principal component analysis