Kinematic and Coordination Variability in Individuals With Acute and Chronic Patellofemoral Pain

Inter-joint coordination variability is associated with pain severity and joint loading in persons with knee osteoarthritis

As the lower extremity is a linked-joint system, the contribution of movements at the hip and ankle, in addition to the knee, to gait patterns should be considered for persons with knee osteoarthritis (OA). However, the relationships of joint coordination variability to OA symptoms, particularly knee pain, and joint loading is unknown. The purpose of this study was to determine the relationship of joint coordination variability to knee pain severity and joint loading in persons with knee OA. Thirty-four participants with knee OA underwent gait analysis. Vector coding was used to assess coordination variability during the early, mid, and late stance phase. Hip-knee coupling angle variability (CAV) during midstance was associated with Knee Injury and Osteoarthritis Outcome Score (KOOS) pain (r = -0.50, p = 0.002) and Visual Analog Scale pain (r = 0.36, p = 0.04). Knee-ankle CAV during midstance was associated with KOOS pain (r = -0.34, p = 0.05). Hip-knee CAV during early and midstance were associated with knee flexion moment (KFM) impulses (r = -0.46, p = 0.01). Knee-ankle CAV during early and midstance were associated with peak KFM (r = -0.51, p < 0.01; r = -0.70, p < 0.01). Moreover, knee-ankle CAV during early, mid, and late stance phase were associated with KFM impulses (r = -0.53, p < 0.01; r = -0.70, p < 0.01; r = -0.54, p < 0.01). These findings suggest that joint coordination variability may be a factor that influences pain and knee joint loading in persons with knee OA. Statement of Clinical Significance: Movement coordination of the hip, knee, and ankle should be considered in the clinical management and future research related to knee OA.

Measurement error associated with gait cycle selection in treadmill running at various speeds

A common approach in the biomechanical analysis of running technique is to average data from several gait cycles to compute a 'representative mean.' However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to 'global' means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset (n = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5 m s^-1, 3.5 m s^-1 and 4.5 m s^-1). 'Ground truth' values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of n (range = 5-30) consecutive gait cycles. We also compared representative means calculated from n (range = 5-15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the 'ground truth' mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of 'error' were observed between the 2.5 m s^-1 and 3.5 m s^-1 speeds at comparable gait cycle numbers -where the maximum errors were < 1.5 degrees even with a small number of gait cycles (i.e., 5-10). At the 4.5 m s^-1 speed, maximum errors typically exceeded 2-4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e., 25-30) was required to achieve low errors (i.e., 1-2 degrees) at the 4.5 m s^-1 speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e., < 1.5 degrees) and consistent across the different number of gait cycles at the 2.5 m s^-1 and 3.5 m s^-1 speeds, and consistent but larger (i.e., up to 2-4 degrees) at the 4.5 m s^-1 speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential 'error.' Analysing a small sample (i.e., 5-10 cycles) will typically result in minimal 'error' (i.e., < 2 degrees), particularly at lower speeds (i.e., 2.5 m s^-1 and 3.5 m s^-1). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential 'error' when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential 'error' introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.