Purpose and Background. 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. Methods and Results. 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
