title: A multiple regression normalization approach to evaluation of gait in total knee arthroplasty patients.
abstract: BACKGROUND
  Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients.
  
  
  METHODS
  Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed.
  
  
  RESULTS
  Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05).
  
  
  CONCLUSIONS
  Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee.
authors.affiliation: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC, Australia.
authors.forename: Ferdous
authors.initials: F
authors.lastname: Wahid
authors.affiliation: College of Sport and Exercise Science, VIC University, Melbourne, VIC, Australia.
authors.forename: Rezaul
authors.initials: R
authors.lastname: Begg
authors.affiliation: School of Allied Health, La Trobe University, Melbourne, VIC, Australia.
authors.forename: Jodie A
authors.initials: JA
authors.lastname: McClelland
authors.affiliation: School of Allied Health, La Trobe University, Melbourne, VIC, Australia.
authors.forename: Kate E
authors.initials: KE
authors.lastname: Webster
authors.affiliation: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC, Australia.
authors.forename: Saman
authors.initials: S
authors.lastname: Halgamuge
authors.affiliation: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC, Australia. Electronic address: dackland@unimelb.edu.au.
authors.forename: David C
authors.initials: DC
authors.lastname: Ackland
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019645:Arthroplasty, Replacement, Knee / Q000379:methods* / Q000534:rehabilitation; D015331:Cohort Studies; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D007719:Knee Joint / Q000502:physiology; D008297:Male; D008875:Middle Aged; D016059:Range of Motion, Articular / Q000502:physiology; D012044:Regression Analysis; D013314:Stress, Mechanical; D016138:Walking / Q000502:physiology
keywords: Biomechanics; Joint replacement; Regression model; Walking