Data analysis
pain
REGRESSION
Normality of continuous variables was visually inspected by Q–Q plots, box plots and histograms and checked with the Kolmogorov–Smirnov test. Baseline demographic variables and clinical symptoms were compared between the groups (experimental vs. control; good vs. poor outcome in the experimental group) using independent sample t-tests or Mann–Whitney U tests for continuous variables and chi-square tests for dichotomous variables.Changes over time in pain intensity (VAS) and physical tests, and differences between groups were assessed using linear mixed models with fixed factor (time) and covariate (group) and interaction (time*group) with an intention-to-treat approach. Data for the inflammatory markers were Ln-transformed as these were not normally distributed. The inflammatory indices were calculated on the Ln-transformed and z-score levels (based on the control group or poor outcome group) of the inflammatory markersDemographic and clinical characteristics.Values are presented as mean (SD) for normal distributed continuous data, as median with the interquartile range (25th–75th percentiles) for non-normal distributed continuous data and as percentages for categorical data. Sig. represents the p-value between the experimental intervention and control intervention or between those in the experimental group with a good outcome versus those with a poor outcome. Finally, linear mixed models were used to further test whether the changes in pain scores in the experimental group were associated with the change in immediate neuroimmune responses. Linear regression analyses with an intention-to-treat approach were used to compare serum cortisol levels and differences in phenotypic analysis of peripheral white blood cells between the experimental group and control group, and between people with a good and poor outcome in the experimental group. Different models were tested: 