A comparison of pain level and entropy changes following core stability exercise intervention

As reported in our previous studies, the complexity of physiologic time series is a sensitive measure of muscle fatigability. This study compared the differences between 2 different analyses following 4 weeks of core stability exercises (CSE) in subjects with and without chronic low back pain (LBP). We examined whether the observed Shannon (information) entropy, as compared with median frequency (MF), was able to differentiate fatigability of the thoracic and lumbar parts of the erector spinae (ES) muscles following the intervention.

In total, 32 subjects participated in this study. There were 13 subjects in the CSE intervention group (average age 50.4 ± 9.1 years) and 19 subjects in the control group (average age 46.6 ± 9.1 years). The CSE group performed the specific exercise intervention, but the control group was asked to maintain their current activity and/or exercise levels. The endurance of the back muscles was determined by using a modified version of the isometric fatigue test as originally introduced by Sorensen.

Pain level decreased significantly for all subjects (F=25.29, p=0.001), but there was no difference between groups (F=0.42, p=0.52). The MF was not different between groups following treatment (F=0.81, p=0.37). Although there was no entropy level changes following treatment (F=0.01, p=0.93), the interactions between muscles and groups following treatment were significant (F=7.25, p=0.01). The entropy level decreased in both thoracic ES muscles following intervention in the exercise group, while remaining the same in the control group.

Although the change in pain level was not different between groups, the Shannon entropy measure more sensitively differentiated the exercise intervention than did MF. In addition, the results also suggested that complexity is related to muscle fatigue, which corresponds to the values of entropy between groups. Further studies are needed to investigate the effectiveness of nonlinear time series of EMG data for fatigability.