Hatfield Gillian L, Stanish William D, Hubley-Kozey Cheryl L
School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada; Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada; Department of Surgery, Division of Orthopaedics, Dalhousie University, Halifax, NS, Canada.
Clin Biomech (Bristol). 2015 Dec;30(10):1146-52. doi: 10.1016/j.clinbiomech.2015.08.011. Epub 2015 Aug 28.
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.
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.
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.
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.
膝关节内收力矩离散特征(峰值和冲量)在膝骨关节炎步态研究中经常被报道,但它们不一定能捕捉到负荷模式。主成分分析提取动态模式,但可能难以解释。这项方法学研究确定了膝关节外展力矩离散测量与主成分分析特征之间的关系,并研究了幅度归一化方法是否会影响进展为手术的膝骨关节炎患者与未进展患者之间的差异。
54名膝骨关节炎患者在行走过程中进行了三维生物力学测量。计算膝关节内收力矩,并使用两种常用方法计算未归一化和幅度归一化波形。使用主成分分析提取模式。计算膝关节内收力矩峰值和冲量。确定提取的两种膝关节内收力矩模式与峰值和冲量之间的相关系数。t检验评估组间差异。
总体大小模式与峰值(r=0.88-0.90,p<0.05)和冲量(r=0.93,p<0.05)相关。捕捉早期和中期/后期站立膝关节内收力矩差异的模式与峰值显著相关(r=0.27-0.40,p<0.05)但解释的方差最小。组间峰值差异仅受幅度归一化方法影响。
研究结果表明,膝关节内收力矩主模式的总体大小并不能从峰值和冲量测量中提供独特信息。然而,在站立中期卸载关节的模式捕捉能力与两种离散测量之间的低相关性和最小方差解释表明,这种模式捕捉到了独特的波形特征。