Title

Discriminating Healthy Controls and Two Clinical Sub-Groups of Chronic Low Back Pain Patients Using Trunk Muscle Activation and Lumbo-Sacral Kinematics of Postures and Movements - A Statistical Classification Model

Document Type

Journal Article

Faculty

Computing, Health and Science

School

Exercise, Biomedical and Health Science, Centre for Exercise and Sports Science Research

RAS ID

8191

Comments

This article was originally published as: Dankaerts, W., O'Sullivan, P., Burnett, A. F., Straker, L., Davey, P., & Gupta, R. (2009). Discriminating Healthy Controls and Two Clinical Sub-Groups of Chronic Low Back Pain Patients Using Trunk Muscle Activation and Lumbo-Sacral Kinematics of Postures and Movements - A Statistical Classification Model. Spine, 34(15), 1610-1618. Original article available here

Abstract

Study Design. Statistical Classification Model for nonspecific chronic low back pain (NS-CLBP) patients and controls based on parameters of motor control. Objective. Develop a Statistical Classification Model to discriminate between 2 subgroups of NS-CLBP (Flexion Pattern [FP] and Active Extension Pattern [AEP]) and a control group using biomechanical variables quantifying parameters of motor control. Summary of Background Data. It has been well documented that many CLBP patients have motor control impairments of their lumbar spine. O’Sullivan proposed a mechanism-based classification system for NS-CLBP with motor control impairments based on a comprehensive subjective and physical examination to establish the relationship between pain provocation and spinal motor control. For the FP and AEP s, 2 groups defined by O’Sullivan and under investigation is this study, the motor control impairment is considered to be the mechanism maintaining their CLBP. No previous studies have used a Statistical Model with measurements of motor control impairment to subclassify NS-CLBP patients. Methods. Thirty-three NS-CLBP patients (20 FP and 13 AEP) and 34 asymptomatic subjects had synchronized lumbosacral kinematics and trunk muscle activation recorded during commonly reported aggravating postures and movements. Biomechanical variables were quantified and a Statistical Classification Model was developed. Results. The Statistical Model used 5 kinematic and 2 electromyography variables. The model correctly classified 96.4% of cases. Conclusion. Selected biomechanical variables were predictors for subgroup membership and were able to discriminate the 3 subgroups. This study adds further support toward the validation of the proposed classification system.

DOI

10.1097/BRS.0b013e3181aa6175

 

Link to publisher version (DOI)

10.1097/BRS.0b013e3181aa6175