Osteopathy has been shown to be effective in the management of chronic low back pain. Guidelines recommend biopsychosocial care for chronic, complex musculoskeletal conditions, including non-specific low back pain but there is a lack of evidence comparing standard osteopathic care, which has traditionally been based on dated and disputed biomechanical theories of dysfunction, with more contemporary biopsychosocial approaches. A multiple baseline single case experimental design trial with 11 UK osteopaths and 60 patients is currently assessing effectiveness of osteopathic treatment for patients with non-specific low back pain of more than 12 weeks’ duration. Patients are randomised to early, middle, or late treatment start dates to increase the validity of inferences about the effects of treatment. Osteopaths have participated in one course on the study protocol and processes pre-participation and will take an e-learning course on the biopsychosocial management of patients with low back pain after the first patient recruitment stage. Statistical analysis will assess the degree and rate of change between baseline, intervention and follow-up periods, and whether differences in effect are observed after the osteopaths have completed the biopsychosocial patient management training course. Primary outcomes will be the Numeric Pain Rating and Patient Specific Function Scales, measured daily at baseline and for 6 weeks during the intervention stage, and weekly or fortnightly during a 12-week follow-up period.Background
Methods and results
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
Identifying features in nonspecific low back pain (NSLBP) subjects that distinguish them from controls, or for elucidating subgroups, has proved elusive. Yet these would be helpful to monitor progress, improve management, and understand the nature of the condition. Previous work using quantitative videofluoroscopy (QF) has indicated that the distribution of motion between lumbar intervertebral joints is more uneven in those with a history of NSLBP. However, there maybe other features of these complex motion patterns yet to be revealed. A multivariate analysis was therefore carried out to explore other possible differences. Intervertebral motion data of L2/3 to L4/5, from a previously published study was used. This examined 40 patients with NSLBP and 40 healthy controls, matched for gender, age and body mass index, who underwent passive recumbent QF in the coronal and sagittal planes. For each motion direction, principal components analysis was carried out and salient dimensions selected. Using a lower dimensional principal components (PC) representation, groups were compared using Hoteling's T test. Linear and quadratic discriminant analysis (LDA and QDA) was carried out using PC representations to examine group differences. The features most clearly distinguishing groups from the LDA was examined graphically. An analysis of the sensitivity of the results to the number of PC dimensions was carried out. The performance of the LDA and QDA classifiers were examined using leave-one-out cross-validation.Purpose and background
Methods and results
