Physical mechanisms underlying back pain impairment are poorly understood. Measuring movement features linked to back pain should help understand its causes and decide on best management. Previous kinematic studies have pointed to diverse features distinguishing back pain sufferers. However, the complexity of 3D kinematics means that it is difficult to choose, a priori, which variables or variable combinations are most important. This study set out to obtain a rich set of kinematic data from spinal regions and lower extremities during typical movement tasks, and analyse all of these variables simultaneously to obtain globally important distinguishing features. To this end, a novel distance metric between pairs of motion sequences was used to construct distance matrices. Analyses were carried out directly on these distance matrices. 20 controls (age: 28 ± 7.6, 10 female) and 20 chronic LBP subjects (age: 41 ± 10.7, 4 female) were recruited. Kinematic data were obtained whilst subjects stood from sitting (‘STS’), picking up (‘Picking’) and lowering (‘Lowering’) a 5kg box, and walking (right (‘WalkRight’) and left sides (‘WalkLeft’)). For each task, permutation tests for group differences were carried out, based on the pseudo-F statistic calculated from the distance matrices. A similar approach was used to identify local differences at time points and joints. Group mean motion sequences were compared using a custom OpenSim model. Significant differences were obtained for STS (pseudo-F=2.8, p=0.017), WalkRight (pseudo-F=3.27, p=0.008) and WalkLeft (pseudo-F=3.39, p=0.005).Purpose and Background
Methods and Results
Low back pain (LBP) is the top leading global cause of years lived with disability. In order to examine LBP, researchers have typically viewed the spine in isolation. Clinically, it is imperative that the lower limbs are also considered. The aim of this study was to design a holistic and reliable multi-segmental kinematic model of the spine and lower limbs. The spine was modelled according to easily identifiable anatomical landmarks, including upper thoracic (T1-T6), lower thoracic (T7-T12) and lumbar (L1-L5) segments. Pelvis, thigh, shank and foot segments were included. A 10-camera 3D motion capture system was used to track retro-reflective markers, which were used to define each segment of 10 healthy participants as they walked 3 times at a comfortable speed over a 6km walkway. The relative peak angles between each segment were calculated using the Joint Coordinate System convention and Intraclass Correlation Coefficients (ICCs) were used to determine intra-rater and inter-rater reliability (between an experienced clinician and biomechanical scientist).Introduction
Method
