Measuring walking, University of Salford

1. Data quality assurance
Richard Baker
Professor of Clinical Gait Analysis
Blog: wwRichard.net
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2. Gait analysis is based on measurement …
… if we can’t make good measurements
there is no point us being here.
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3. 3
14 chapters on how to make measurements.
1 chapter on what to do with them.

4. Measuring walking
• Both a science and an art
We need to
• understand the science
• practice the art
Need training in both and there is very little
available (www.CMAster.eu)
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5. Quality assurance
• Staff training and education
• Vigilance for errors in data

6. Before the analysis
Staff training
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7. Normative datasets
For too long we have used normative datasets
as an excuse for doing things differently.
Normative data should be compared between
centres to show we are doing the same things
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8. Normative datasets
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Differences in average traces suggest systematic differences in how
markers are applied
Differences in standard deviations suggest one lab has more
repeatable practices than the other.

9. Repeatability studies
Measurement science can be quite simple.
All we need to know is the standard error of
measurement (SEM - Standard deviation of
repeat measurements made on the same
subject).
Two measurements need to differ by 3xSEM
for there to be evidence of difference.
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10. Other repeatability measure
• Never use a repeatability measure you
don’t understand.
• Never use a repeatability measure that is
not expressed in the original units of
measurement.
• Never trust someone else’s definition of
“acceptable repeatability (particularly a
psychologist)
• “For many clinical measurements ICC should exceed 0.9 to ensure
reasonable validity” (Portney and Watkins, 2009)
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11. Repeatability studies
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SEM>5° “concerning”
measurement
variability may mis-lead
interpretation.
2°<SEM<5° “reasonable” need to
consider measurement
variability in
interpretation.
SEM<2° “acceptable” don’t
need to consider
measurement
variability explicitly in
interpretation
McGinley, J. L., Baker, R., Wolfe, R., & Morris, M. E. (2009). The reliability of three-dimensional kinematic gait measurements: a
systematic review. Gait and Posture, 29(3), 360-369.

12. Physical examination
Fosang et al. Dev Med Child Neurol, 2003 McDowell et al. Gait & Posture, 2000

13. Repeatability studies
Gait analysis measures can be more
repeatable than physical exam measures …
… but may not be in your laboratory
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14. Repeatability studies
Require one or more analyst to make repeat
measurements on same person.
If repeat testing of single analyst space
measurements out.
If comparison of multiple analysts have them
close together.
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15. Informal repeatability study
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Measurements from three therapists (different colours) each
measuring the same person on two different days

16. Formal repeatability study
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17. Formal repeatability study
• Considerable undertaking
• Extremely difficult on children with cerebral
palsy
• Considerable uncertainty in SEM
estimates
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18. Quality assurance
• Protocols written by team making measurements
– Process more important than result
• Regular review
• Repeatability studies
• Critical self-appraisal
– by individuals
– within teams
– within community (peer review)
• Open and honest culture
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19. During and after the analysis
Vigilance for errors
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20. Vigilance for errors
• Check data before the patient leaves
• Requires processed data to be available
before then (preferably before markers
removed)
• Keep assessments short and focussed so
that both patient and analyst are prepared
to repeat tests if necessary.
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21. Is the data likely to be
representative for the patient?
• General health
• Pain
• Fatigue
• Behaviour
• No way of telling this from data

22. Pst
Dwn
Agreement with data from other sources –
Clinical exam
0
Hip Flexion
70
Flex
deg
Ext
-20
Knee Flexion
75
Flx
-30
Hip 30
Add
deg
Abd
-30
Knee 30
Var
Bilateral hip flexion contracture

23. Flex
Add
Int
Agreement with data from other sources –
deg
Video.
deg
Ext
-20
Knee Flexion
75
Flx
deg
Ext
-15
Dorsiflexion
30
Dor
Gait data may help explain the video data but it should not contradict it
deg
Abd
-30
Knee Adduction
30
Var
deg
Val
-30
Ankle Rotation
30
Int
deg
deg
Ext
-30
Knee 30
Int
deg
Ext
-30
Foot 30
Int
deg

24. Agreement with data from other sources –
Video.

25. Smooth data
Be very suspicious of jerky data
If one kinetic graph is wrong you should be highly suspicious of all of them even
if artefact is less obvious.

26. Smooth data
Gait data is almost always smooth (it has
been filtered to be so)

27. Consistent data
• I can’t see all the detail
• Should you be
interpreting detail you
can’t see?

28. Consistent data
• Be particularly careful if traces fall into
groups.
• If this occurs in kinetics but not in
kinematics then check force plates
Picture from J Stebbins
with permission

29. Swing phase ankle moments

30. Learn consequences of marker
placement error
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31. Hip rotation offsets
5° offsets of KAD

32. Consequences of marker
placement error
• Play!
• Place markers erroneously on a colleague
and predict changes in gait graphs.
• If you can’t then you shouldn’t be placing
markers on patients at all.
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33. Professional competencies
• Excellent data quality can only be provided by
excellent gait analysts
• Requires combination of biomechanical and
clinical competencies
• In many centres these are provided by different
people

34. Professional competencies
• Gait analysis requires:
– Patient (and parent) management skills
– Physical examination skills
– Biomechanical measurement skills
– Biomechanical analysis skills
• Recruit staff with some of these skills
• Train them in the others
• Longer term training
• Assessed competencies

35. Thanks for listening
Richard Baker
Professor of Clinical Gait Analysis
Blog: wwRichard.net
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