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

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 ch...

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Main Authors: Hidalgo, B., Nielens, H., Gilliaux, M., Hall, Toby, Detrembleur, C.
Format: Journal Article
Published: Foundation for Rehabilitation Information 2014
Online Access:http://hdl.handle.net/20.500.11937/23297
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author Hidalgo, B.
Nielens, H.
Gilliaux, M.
Hall, Toby
Detrembleur, C.
author_facet Hidalgo, B.
Nielens, H.
Gilliaux, M.
Hall, Toby
Detrembleur, C.
author_sort Hidalgo, B.
building Curtin Institutional Repository
collection Online Access
description 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.
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spelling curtin-20.500.11937-232972017-09-13T13:57:03Z Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study Hidalgo, B. Nielens, H. Gilliaux, M. Hall, Toby Detrembleur, C. 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/23297 10.2340/16501977-1836 Foundation for Rehabilitation Information fulltext
spellingShingle Hidalgo, B.
Nielens, H.
Gilliaux, M.
Hall, Toby
Detrembleur, C.
Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study
title Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study
title_full Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study
title_fullStr Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study
title_full_unstemmed Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study
title_short Use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: A validation study
title_sort use of kinematic algorithms to distinguish people with chronic non-specific low back pain from asymptomatic subjects: a validation study
url http://hdl.handle.net/20.500.11937/23297