Screening system for assessing suitability of cognitive behavioral therapy for chronic low back pain

Figures

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  Fig. 1. Flow diagram for the study. The study flow, inclusion criteria, exclusion criteria, and evaluation items of the research collaborators are shown.

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  Fig. 2. Specs for the Muse2. The specifications of the Muse2 electroencephalograph used in the study, such as the electrode placement and sample rate.

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  Fig. 3. Assessment of cognitive abilities using the WAIS-IV and AQ. Results of two-way ANOVAs of WAIS and AQ results by group are shown. The significance level was set at 0.05. AQ, autism-spectrum quotient; WAIS-IV, Wechsler Adult Intelligence Scale-IV.

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  Fig. 4. Comparison of MFSE between groups. Results of MFSE with repeated measures ANOVA for each group are shown. The significance level was set at 0.05. The scale factor corresponds to the target EEG frequency (frequency: 200 [Hz]/τ), with low scale factors reflecting the complexity of high-frequency bands and high scale factors reflecting that of low-frequency bands. ANOVA, one-way analysis of variance; EEG, electroencephalogram; MFSE, multiscale fuzzy sample entropy.

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  Fig. 5. Forest Plot of factors decreasing the effectiveness of CBT treatment. Factors influencing the effectiveness of CBT are illustrated based on the results of the logistic regression analysis. The odds ratios and P values of items with significant effects are shown in red. CBT, cognitive behavioral therapy.

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  Fig. 6. MFSE ROC curve and cutoff value. Cut-off values for MFSE scores predicting suitability for CBT are shown. AUC is an index of discriminatory ability, and a value higher than 0.8 is considered highly accurate. AUC, area under the curve; CBT, cognitive behavioral therapy; MFSE, multiscale fuzzy sample entropy; ROC, receiver operating characteristic.