STUDY DESIGN: This study observed the trunk angular motion features of healthy subjects and those experiencing chronic low back disorders as they flexed and extended their trunks in five symmetric and asymmetric planes of motion. Trunk angular position, velocity, and acceleration were evaluated during several cycles of motion. OBJECTIVE: The trunk angular motion features of the low back disorder group were normalized relative to the healthy subjects and used to 1) evaluate the repeatability and reliability of trunk motion as a measure of trunk musculoskeletal status, 2) quantify the extent of the disorder, 3) determine the extent to which trunk motion measures might be used as quantifiable means to help classify low back disorders. SUMMARY OF BACKGROUND DATA: Given the magnitude of the low back disorder problem, it is problematic that there are few quantitative methods for objectively documenting the extent of a disorder. Impairment ratings of low back disorders can vary by as much as 70% using current systems. Diagnoses and classification schemes are rarely based upon quantitative indicators and we are unable to easily assess and diagnose low back disorders. It is important to quantitatively evaluate low back disorders so that proper treatment can be administered and the risk of exacerbating the problem can be minimized. METHODS: Three-hundred-thirty-nine men and women between 20 and 70 years old who had not experienced significant back pain were recruited as the healthy subjects in this study. One hundred-seventy-one patients with various chronic low back disorders also were recruited and compared with the healthy group of subjects. All subjects wore a triaxial goniometer on their trunks that documented the angular position, velocity, and acceleration of the trunk as the subjects flexed and extended their trunks in each of five planes of motion. Trunk motion features first were normalized for subject gender and age. Several two-stage eight-variable models that account for trunk motion interactions were developed to classify the 510 healthy and low back injured subjects into one of 10 anatomic and symptom-based low back disorder classification categories. RESULTS: Using conservative cross-validation measures, it was found that the stage one eight-variable model could correctly classify more than 94% of the subjects as either healthy or having a low back disorder. One of the stage two eight-variable models was able to reasonably classify the patients with low back disorders into one of 10 low back disorder classification groups. CONCLUSION: The motion-related parameters may relate to biomechanical or learned sensitivities to spinal loading. This study suggests that higher-order trunk motion characteristics hold great promise as a quantitative indicator of the trunk's musculoskeletal status and may be used as a measure of the extent of a disorder and as a measure of rehabilitative progress. Furthermore, once the interactive nature of these trunk motion characteristics is considered, the model could help diagnose low back disorders. However, independent data sets are needed to validate these findings.
STUDY DESIGN: This study observed the trunk angular motion features of healthy subjects and those experiencing chronic low back disorders as they flexed and extended their trunks in five symmetric and asymmetric planes of motion. Trunk angular position, velocity, and acceleration were evaluated during several cycles of motion. OBJECTIVE: The trunk angular motion features of the low back disorder group were normalized relative to the healthy subjects and used to 1) evaluate the repeatability and reliability of trunk motion as a measure of trunk musculoskeletal status, 2) quantify the extent of the disorder, 3) determine the extent to which trunk motion measures might be used as quantifiable means to help classify low back disorders. SUMMARY OF BACKGROUND DATA: Given the magnitude of the low back disorder problem, it is problematic that there are few quantitative methods for objectively documenting the extent of a disorder. Impairment ratings of low back disorders can vary by as much as 70% using current systems. Diagnoses and classification schemes are rarely based upon quantitative indicators and we are unable to easily assess and diagnose low back disorders. It is important to quantitatively evaluate low back disorders so that proper treatment can be administered and the risk of exacerbating the problem can be minimized. METHODS: Three-hundred-thirty-nine men and women between 20 and 70 years old who had not experienced significant back pain were recruited as the healthy subjects in this study. One hundred-seventy-one patients with various chronic low back disorders also were recruited and compared with the healthy group of subjects. All subjects wore a triaxial goniometer on their trunks that documented the angular position, velocity, and acceleration of the trunk as the subjects flexed and extended their trunks in each of five planes of motion. Trunk motion features first were normalized for subject gender and age. Several two-stage eight-variable models that account for trunk motion interactions were developed to classify the 510 healthy and low back injured subjects into one of 10 anatomic and symptom-based low back disorder classification categories. RESULTS: Using conservative cross-validation measures, it was found that the stage one eight-variable model could correctly classify more than 94% of the subjects as either healthy or having a low back disorder. One of the stage two eight-variable models was able to reasonably classify the patients with low back disorders into one of 10 low back disorder classification groups. CONCLUSION: The motion-related parameters may relate to biomechanical or learned sensitivities to spinal loading. This study suggests that higher-order trunk motion characteristics hold great promise as a quantitative indicator of the trunk's musculoskeletal status and may be used as a measure of the extent of a disorder and as a measure of rehabilitative progress. Furthermore, once the interactive nature of these trunk motion characteristics is considered, the model could help diagnose low back disorders. However, independent data sets are needed to validate these findings.