Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study.

Abstract

Objective: To determine whether kinematic algorithms can distinguish subjects with chronic non-specific low back pain from asymptomatic subjects and subjects simulating low back pain, during trunk motion tasks. Design: Comparative cohort study. Subjects: A total of 90 subjects composed 3 groups; 45 chronic non-specific low back pain patients in the CLBP group; 45 asymptomatic controls people in the asymptomatic controls group. 20/45 subjects from the asymptomatic controls group composed the CLBP simulators group as well. Method: During performance of 7 standardized trunk motion tasks 6 spinal segments from the kinematic spine model were recorded by 8 infrared cameras. Two logit scores, for range of motion and speed, were used to investigate differences between the groups. Group allocation based on logit scores was also calculated, allowing the assessment of sensitivity and specificity of the algorithms. Results: For the 90 subjects (pooled data), the logit scores for range of motion and speed demonstrated highly significant differences between groups (p < 0.001). The logit score means and standard deviation (SD) values in the asymptomatic group (n = 45) and chronic non-specific low back pain group (n = 45), respectively, were -1.6 (SD 2.6) and 2.8 (SD 2.8) for range of motion and -2.6 (SD 2.5) and 1.2 (SD 1.9) for speed. The sensitivity and specificity (n = 90) for logit score for range of motion were 0.80/0.82 and for logit score for speed were 0.80/0.87, respectively. Conclusion: These results support the validity of using 2 movement algorithms, range of motion and speed, to discriminate asymptomatic subjects from those with low back pain. However, people simulating low back pain cannot be distinguished from those with real low back pain using this method.

Short-Term Effects of Mulligan Mobilization With Movement on Pain, Disability, and Kinematic Spinal Movements in Patients With Nonspecific Low Back Pain: A Randomized Placebo-Controlled Trial

“…Estimation of the required sample size was calculated on the basis of the minimal detectable change 95% of the primary outcome measure (KA-R and KA-S) 20 with a desired power of 0.80 and an α level of .05; we have obtained an estimation of the sample size in each group from 16 patients. Similarity of baseline measures between groups (T0) was assessed using a Student t test.…”

“…19,20 The final results were 2 KA, 1 KA-R and 1 KA-S according to the following equations (see 19,20 for more information):…”

“…The test procedure and recording conditions have been described previously. 19,20 Briefly, trunk movements were assessed in a sitting position, trunk flexion, left and right rotation, and combined movement of trunk flexion associated with left and right rotation of the pelvis (Fig 3). Each trunk movement was performed and recorded 10 times.…”

“…19,20 Moreover, it has been proposed that future clinical studies should target their interventions on a more homogeneous subgroup of patients with LBP to improve clinical outcomes as well as effect sizes (ES) for outcome measures. 10 Based on these findings, we used the kinematic spine model to assess whether lumbar SNAGs were able to improve the kinematic features of trunk movement in a targeted group of subjects with LBP.…”

Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions

“…This multidimensional aspect of pain requires that measurement of pain should include not only subjective ratings, but also psychological and neurophysiological events related to pain perception. Accordingly, research on cognitive pain has been conducted using several approaches and tools such as questionnaires (Pagé et al, 2015;Traeger et al, 2016), Quantitative Sensory Tests (QST) (Cruz-Almeida and Fillingim, 2014;Cámara et al, 2016), genetic factors (Diatchenko et al, 2005;Ablin and Buskila, 2015;Ultsch et al, 2016), patterns of physical activity (Hidalgo et al, 2012(Hidalgo et al, , 2014Paraschiv-Ionescu et al, 2012), EEG (Pinheiro et al, 2016), neuroimaging (Davis et al, 2012;Schmidt-Wilcke, 2015), and more recently functional near-infrared spectroscopy (fNIRS) (Lopez-Martinez et al, 2018.…”