Gait and neuromuscular pattern changes are associated with differences in knee osteoarthritis severi - PowerPoint PPT Presentation

1 / 30
About This Presentation
Title:

Gait and neuromuscular pattern changes are associated with differences in knee osteoarthritis severi

Description:

... data associated with knee osteoarthritis an application of principal ... alterations during walking in persons with moderate knee osteoarthritis. ... – PowerPoint PPT presentation

Number of Views:218
Avg rating:3.0/5.0
Slides: 31
Provided by: jw57
Category:

less

Transcript and Presenter's Notes

Title: Gait and neuromuscular pattern changes are associated with differences in knee osteoarthritis severi


1
Gait and neuromuscular pattern changes are
associated with differences in knee
osteoarthritis severity levels
  • Janie L. Astephen, Kevin J Deluzio, Graham E.
    Caldwell, Micheal J. Dunbar, Cheryl L.
    Hubley-Kozey

2
Measuring Knee OA Progression
  • Gait Analysis
  • Biomechanical response of the musculoskeletal
    system to OA
  • EMG analysis
  • Concomitant changes in neuromuscular response
  • Issues with Research
  • Lack of studies examining neuromuscular function
    joint dynamics simultaneously
  • Examining joint dynamics in all of the joints of
    the lower extremities
  • Poor treatment and characterization of disease
    severity
  • Longitudinal studies ideal
  • Very difficult

3
Purpose
  • Explore the association between biomechanical
    changes and knee OA severity
  • Understand the changing role of biomechanical
    factors in the progression of knee OA and severe
    knee OA subjects prior to total joint replacement
  • Hypothesized (based on previous literature)
  • Changes in joint moments that represent loading
    patterns would be most important in
    discriminating moderate knee OA gait patterns
    from asymptomatic
  • Kinematic variables most important in defining
    the severe OA group

4
Single group OA design
  • Difficult to determine if the biomechanical
    changes are
  • Involved in the development of the disease
  • In response to the degenerative changes in the
    joint
  • Are a compensatory mechanism in response to these
    degenerative changes or other related factors
  • Joint pain
  • Few studies
  • Different levels of OA radiographic severity
  • Short-term longitudinal progression
  • Frontal plane kinetics of knee and hip only

5
Classification of OA Severity
  • Classification of OA is difficult
  • Clinical severity should reflect
  • Symptomatic markers
  • Radiographic markers
  • Clinical severity criteria used to distinguish
    between moderate and severe OA levels
  • Total joint replacement
  • Resulted in good seperation in terms of
  • Demographics
  • WOMAC health outcome measures

6
Methods
  • 60 asymptomatic subjects
  • Agegt35 years, walk a city block, jog 5 m, walk
    upstairs in a reciprocal manner, no history of
    knee pain or surgery on either limb
  • 60 moderate knee OA subjects
  • Clinical assessment
  • Radiographs
  • Physical exams
  • Not candidates for total knee replacement
  • 61 severe knee OA subjects
  • Total knee replacement surgery immediately after
    gait testing

7
Subject Group Characteristics
8
Gait Analysis
  • 3D kinematics and external ground reaction forces
  • Optotrak 3020 motion capture system
  • AMTI force platform
  • Three-marker triads of light emitting diodes
  • Pelvis, thigh, leg and foot segments
  • Individual markers during static trial
  • Greater trochanter, lateral epicondyle, lateral
    malleollus and shoulder
  • 8 virtual markers
  • Joint angles and net external joint moments were
    normalized to body mass

9
EMG Analysis
  • 7 muscles
  • Vastus lateralis (VL), vastus medialis (VM),
    rectus femoris (RF), biceps femoris (lateral
    hamstrings, LH), semimembranosus (medial
    hamstrings MH), lateral gastrocnemius (LG), and
    medial gastrocnemius (MG)
  • MVICs used to scale EMG amplitudes during gait
    trials

10
Statistical Analysis
  • Multivariate analysis used to identify the most
    important combinations of biomechanical factors
    that distinguished between the 3 severity groups
  • Principal component analysis (gait and EMG
    waveforms)
  • Multivariate statistical technique that has been
    shown to be an effective tool in the reduction
    and interpretation of gait waveform data (Deluzio
    Astephen, 2007)
  • Gait
  • 12 gait measures total
  • 3 joints (hip, knee, ankle) by 4 measures
  • Flexion/extension angle
  • Net external flexion/extension moments
  • Ab/adduction moments
  • Internal/external rotation moments
  • EMG
  • 3 muscle sets
  • Quadriceps (VL, VM, RF)
  • Hamstrings (LH, MH)
  • Gastrocnemius (LG, MG)

11
What is PCA
  • Principal component analysis (PCA) is 1 type of
    waveform pattern analysis (pattern recognition)
  • Gait analysis results in highly complex
    multidimensional, correlated data (Deluzio
    Astephen, 2007)
  • PCA tries to reduce this complex data to a
    smaller set of numbers that can be analyzed
    statistically or quantitatively

12
How does PCA work?
  • Create Average waveform for all trials
  • Find the shape that accounts for as much of the
    power as possible in the signals.
  • 1st principal component
  • scaling factor is adjusted for each trial
    individually
  • basic shape is not adjustable

13
1st PCA
  • 1st principal component represents the best basic
    shape or waveform of all the trials
  • Once 1st PC is found find scale factor (amount of
    waveform accounted for) for each trial
  • Scale factor could even be negative
  • PC scores are the contribution of each PC to the
    individuals waveform
  • Find scale factors or PC scores for all groups
  • Compare these values using Anovas
  • Statistical signifigance shows a difference in
    the size or amount of difference from the mean
    waveform between groups

14
2nd PC and so on
  • Subtract 1st PC score from each trial
  • Residual waveform
  • What was not accounted for by the 1st analysis
  • Repeat steps with residual waveforms
  • Not the actual collected data
  • 2nd PC
  • Waveform that accounts for most of the individual
    differences or residual waveforms
  • Continue analyzing PCs until desired variance of
    the original data is accounted for
  • 3 PCs to account for 80

15
Analysis of PCs
  • Group differences of gait PC scores
  • One-way analysis of variance (ANOVA)
  • Group differences of EMG PC scores
  • Two-factor (muscle set, subject group) one-way
    analysis of variance (ANOVA)
  • 45 ANOVas performed to compare the 45 PC scores
    between the 3 groups
  • 3 PCs for the 12 kinematic/kinetic gait variables
  • 3 PCs for the 3 EMG groups

16
Discriminant Analysis
  • Used to determine optimal boundaries between
    groups
  • Performed on all combinations of subject groups
    with PC scores
  • Determine significant pair-wise differences
  • Not all PCs found to be significant
  • Not used to determine group separation

17
ANOVA results
  • 16 significant PC differences
  • Asymptomatic and Moderate OA
  • 28 significant PC differences
  • Moderate OA and Severe OA
  • 33 significant PC differences
  • Asymptomatic and Severe OA

18
Descriminant Variables
  • Asymptomatic-Moderate OA
  • 5 variables
  • Moderate-Severe OA
  • 5 variables
  • Asymptomatic-Severe OA
  • 12 variables

19
Results Asymptomatic vs. Moderate OA
20
Not Actual Measured Data!!
21
Results Moderate OA vs. Severe OA
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
Discussion
  • Biomechanical pattern changes used in the
    descriminant model between successive severity
    groups varied
  • Asymptomatic vs. Moderate OA
  • Hip/knee joint kinetics pattern changes
  • Moderate OA vs. Severe OA
  • Knee kinematic hip/ankle kinetic pattern
    changes
  • Suggests changing role of biomechanical factors
    in the progression of OA at different stages of
    severity

26
Conclusions
  • Knee kinetic changes used in asymptomatic-
    moderate OA model but not moderate-severe model
  • May suggest that early intervention focusing on
    changing loading of knee joint may be effective
  • No kinematic changes at any joint for
    asymptomatic-moderate model
  • Mid-stance hip adduction moments (increased in
    moderate) found to be more important than peak
    values
  • Suggesting importance of more prolonged frontal
    plane loading of hip joint

27
Conclusions
  • Medial gastroc
  • Continued activation (severe OA)
  • May be a stiffness response to pain or laxity
  • Moderate-Severe model (Transverse Plane)
  • Severe had altered patterns of hip ankle
    internal/external rotation moments
  • Not previously identified
  • Asymptomatic-Moderate model (Transverse Plane)
  • Moderate had higher late stance knee internal
    rotation moment
  • Reduced hip external rotation moment in early and
    late stance
  • May place new loads on articular cartiage that
    was conditioned for other loads

28
Conclusions
  • Found changes in knee adduction moments with all
    OA individuals
  • may be important to characterize OA but not to
    separate severity levels
  • Misclassification between groups groups
  • adjacent groups 20
  • Asymptomatic-severe 6.6adsf

29
Questions?
  • Thanks

30
References
  • Astephen, JL. Deluzio, KJ, Caldwell GE, Dunbar
    MJ, Hubley-Kozey CL. 2008. Gait and
    neuromuscular pattern changes are associated with
    differences in knee osteoarthritis severity
    levels. Jopurnakl of Biomechanics 41 868-876
  • Andriacchi TP, Mundermann A, Smith RL, Alexander
    EJ, Dyrby CO, Koo S. 2004 A framework for the in
    vivo pathomechanics of oesteoarthritis at the
    knee. Annals of Biomedical Engineering. 32(3)
    447-457
  • Deluzio KJ, Astephen JL. 2007. Biomechanical
    features of gait waveform data associated with
    knee osteoarthritis an application of principal
    componenet analysis. Gait Posture 25 86-93
  • Hubley-Kozey et al. 2006. Neuromuscular
    alterations during walking in persons with
    moderate knee osteoarthritis. Jouranl of
    Electromyographys and Kinesiology 16365-378
  • Thanks to Dr. Bill Rose
Write a Comment
User Comments (0)
About PowerShow.com