School of Kinesiology, University of the Fraser Valley, Chilliwack, British Columbia, Canada.
Department of Physical Therapy & Athletic Training, Boston University, Boston, MA; Section of Rheumatology, Boston University School of Medicine, Boston, MA.
Arch Phys Med Rehabil. 2021 Oct;102(10):1910-1917. doi: 10.1016/j.apmr.2021.03.038. Epub 2021 Jul 3.
To determine associations between knee moment features linked to osteoarthritis (OA) progression, gait muscle activation patterns, and strength.
Cross-sectional secondary analysis.
Gait laboratory.
Convenience sample of 54 patients with moderate, medial knee OA (N=54).
None.
Knee moments and quadriceps and hamstrings activation were examined during walking. Knee extensor and flexor strength were measured. Waveform patterns were extracted using principal component analysis. Each measured waveform was scored against principal components (PCs) that captured overall magnitude (PC1) and early to midstance difference (PC2) features, with higher PC2 scores interpreted as greater moment differential and more prolonged muscle activity. Correlations were calculated between moment PC scores and muscle PC and strength scores. Regression analyses determined moment PC score variance explained by muscle PC scores and strength.
All correlations for knee adduction moment difference feature (KAMPC2) and prolonged muscle activity (PC2) were significant (r=-0.40 to -0.54). Knee flexion moment difference feature (KFMPC2) was significantly correlated with all quadriceps and medial hamstrings PC2 scores (r=-0.47 to -0.61) and medial hamstrings magnitude feature (PC1) (r=-0.52). KAMPC2 was significantly correlated with knee flexor strength (r=0.43), and KFMPC2 was significantly correlated with knee extensor (r=0.60) and flexor (r=0.55) strength. Regression models including muscle PC2 scores and knee flexor strength explained 46% of KAMPC2 variance, whereas muscle PC2 scores and knee extensor strength explained 59% of KFMPC2 variance.
Muscle activation patterns and strength explained significant variance in moment difference features, highest for the knee flexion moment. This supports that exercises such as neuromuscular training, focused on appropriate muscle activation patterns, and strengthening have the potential to alter dynamic loading gait patterns associated with knee OA clinical progression.
确定与骨关节炎(OA)进展相关的膝关节力矩特征与步态肌肉激活模式和力量之间的关联。
横断面二次分析。
步态实验室。
54 例中度内侧膝关节 OA 患者的便利样本(N=54)。
无。
行走过程中检测膝关节力矩和股四头肌及腘绳肌激活情况。测量膝关节伸肌和屈肌力量。使用主成分分析提取波形模式。根据捕获整体幅度(PC1)和中足差异(PC2)特征的主成分(PC)对每个测量的波形进行评分,较高的 PC2 评分表示更大的力矩差和更长的肌肉活动。计算力矩 PC 评分与肌肉 PC 和力量评分之间的相关性。回归分析确定由肌肉 PC 评分和力量解释的力矩 PC 评分的方差。
膝关节内收力矩差特征(KAMPC2)和延长肌肉活动(PC2)的所有相关性均有统计学意义(r=-0.40 至-0.54)。膝关节屈曲力矩差特征(KFMPC2)与所有股四头肌和内侧腘绳肌 PC2 评分(r=-0.47 至-0.61)和内侧腘绳肌幅度特征(PC1)(r=-0.52)均有显著相关性。KAMPC2 与膝关节屈肌力量(r=0.43)显著相关,KFMPC2 与膝关节伸肌(r=0.60)和屈肌(r=0.55)力量显著相关。包括肌肉 PC2 评分和膝关节屈肌力量的回归模型解释了 KAMPC2 方差的 46%,而肌肉 PC2 评分和膝关节伸肌力量解释了 KFMPC2 方差的 59%。
肌肉激活模式和力量解释了力矩差特征的显著差异,膝关节屈曲力矩最大。这表明,神经肌肉训练等锻炼方法侧重于适当的肌肉激活模式和力量增强,有可能改变与膝关节 OA 临床进展相关的动态负重步态模式。