Data Analysis
REGRESSION
All data analysis was conducted using RStudio version 4.1.2 (Posit). In the IG, the sample size was determined based on the capacity of the neurological rehabilitation clinic to conduct assessments, in addition to providing routine care to inpatients. The capacity was limited to 66 patients undergoing the assessment. To ensure that propensity score matching (PSM) could be applied, if necessary, to increase the comparability between groups without reducing the sample size further, the target sample size of the ACG was set to twice the size of the IG. For the CompG, as many participants as possible were recruited.For the first hypothesis, the IG and ACG were combined to reach a sufficient sample size and were compared with the CompG using hierarchical-linear models (HLMs). The logistic regression to compare groups was significant for age. As there was no randomization, PSM was used to increase the groups’ comparability. Individuals were matched applying a 1:1 ratio using the To test the second hypothesis, the IG and the ACG were compared over time regarding their symptom severity from T1 to T5, in addition to work ability and social participation (both measured at T2, T4, and T5) using HLMs in R. Before testing this hypothesis, a randomization check was conducted using logistic regression analysis based on the To evaluate the third and final hypothesis, 2 linear regression analyses were conducted to test whether a reduction in symptoms over time (T1-T4) predicted better work ability and social participation, respectively, at follow-up, controlled for age, sex, the BMI, and previous health conditions. To operationalize symptom reduction, a difference variable was calculated (mean symptom score at T4 – mean symptom score at T1).