Detecting differences between asymptomatic and osteoarthritic gait is influenced by changing the knee adduction moment model

Newell, Robyn; Hubley‐Kozey, Cheryl L.; Stanish, William D.; Deluzio, Kevin J.

doi:10.1016/j.gaitpost.2007.06.004

Cited by 21 publications

(11 citation statements)

References 24 publications

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“…The average Incidence tibia was registered to the average Control tibia using a Procrustes analysis without scaling (MATLAB R2008b, The Mathworks, Inc., Natick, MA). The resulting transformation was applied to the average Incidence femur so that the average Incidence knee was registered to the average Control knee in a coordinate system defined by the average Control tibia (Grood and Suntay, 1983; Kadaba et al, 1989; Newell et al, 2008). Differences in joint space of the average Control and Incidence knees were investigated by determining vectors between corresponding surface vertices and cumulatively evaluating changes in the tibial and femoral articular surfaces.…”

Section: Methodsmentioning

confidence: 99%

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Bredbenner

Eliason

Potter

et al. 2010

Journal of Biomechanics

129

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We hypothesize that variability in knee subchondral bone surface geometry will differentiate between patients at risk and those not at risk for developing osteoarthritis (OA) and suggest that statistical shape modeling (SSM) methods form the basis for developing a diagnostic tool for predicting the onset of OA. Using a subset of clinical knee MRI data from the osteoarthritis initiative (OAI), the objectives of this study were to (1) utilize SSM to compactly and efficiently describe variability in knee subchondral bone surface geometry and (2) determine the efficacy of SSM and rigid body transformations to distinguish between patients who are not expected to develop osteoarthritis (i.e. Control group) and those with clinical risk factors for OA (i.e. Incidence group). Quantitative differences in femur and tibia surface geometry were demonstrated between groups, although differences in knee joint alignment measures were not statistically significant, suggesting that variability in individual bone geometry may play a greater role in determining joint space geometry and mechanics. SSM provides a means of explicitly describing complete articular surface geometry and allows the complex spatial variation in joint surface geometry and joint congruence between healthy subjects and those with clinical risk of developing or existing signs of OA to be statistically demonstrated.

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Section: Methodsmentioning

confidence: 99%

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Bredbenner

Eliason

Potter

et al. 2010

Journal of Biomechanics

129

View full text Add to dashboard Cite

show abstract

“…The choice of a coordinate system (2D, 3D tibial or 3D floating axis) changed the overall magnitude and shape of the knee adduction moment waveform calculated from the same healthy and knee osteoarthritis datasets. In fact, differences in the peak knee adduction moment between the osteoarthritis and control groups were not detected when the 3D floating axis coordinate system was used [16 ].…”

Section: Abnormal Knee Loading In Osteoarthritismentioning

confidence: 96%

“…The authors [15] speculated that the extremely high mean body mass index in the osteoarthritis sample may explain the finding. The discrepancy may also reflect the inconsistencies between laboratories in biomechanical models used to analyze gait, which are not always specified in publications [16 ]. The choice of a coordinate system (2D, 3D tibial or 3D floating axis) changed the overall magnitude and shape of the knee adduction moment waveform calculated from the same healthy and knee osteoarthritis datasets.…”

Section: Abnormal Knee Loading In Osteoarthritismentioning

confidence: 99%

Abnormal and cumulative loading in knee osteoarthritis

Maly¹

2008

Current Opinion in Rheumatology

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Knee osteoarthritis pathomechanics involves an interaction between abnormal and excessive and/or repetitive loading. Mechanics of lower extremity joints and muscle activation patterns influence the knee loading environment. Future work could integrate measures of abnormal loading with assessments of the total exposure to loading during physical activity to better link biomechanics with clinical outcomes in knee osteoarthritis.

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“…foot progression angle) and the methods utilized to calculate the moment (e.g. coordinate system) [10,11]. Variable protocols for changing speed introduce intrasubject and intersubject variability, which might explain the inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

The effect of gait speed on the knee adduction moment depends on waveform summary measures

Robbins

Maly

2009

Gait & Posture

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Detecting differences between asymptomatic and osteoarthritic gait is influenced by changing the knee adduction moment model

Cited by 21 publications

References 24 publications

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Abnormal and cumulative loading in knee osteoarthritis

The effect of gait speed on the knee adduction moment depends on waveform summary measures

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