Statistics
restless, ±, SD, pain, NRS
Normally distributed questionnaire data, self‐reported pain, and sleep interference NRS values were analyzed with a mixed model analysis of variance (ANOVA) using a between‐subjects factor “treatment” and a within‐subject factor “time.” Correlation of different self‐reported parameters was tested with Pearson's product‐moment correlation.In the evaluation of Oura data at baseline and after the intervention, we only included those recorded periods that comprised more than one night of Oura data in each patient. Across the patients, Oura data were collected during 5.1 ± 0.9 (mean ± SD) and 4.9 ± 1.2 nights in the pretreatment phase and during 4.4 ± 0.9 and 4.1 ± 1.3 nights in the posttreatment phase from the rTMS and sham groups. There were no differences in the number of nights between the two groups (independent samples We tested, with univariate statistical tests and in a multivariate machine learning framework, whether the rTMS‐treatment had induced changes in the sleep patterns between the pre‐ and posttreatment phases. Univariate testing was conducted by comparing the effect of rTMS and sham stimulation on the four Oura variables (change between the pre‐ and posttreatment phase in the median values across nights) using nonparametric Mann–Whitney Significant linear correlation was detected between “awake” and “restless” (In the LDA, we first trained the model across all patients using the differences between individual pre‐ and posttreatment nights in the Oura variables. Subsequently, the model was tested using Oura data from different individual nights than those used for the training data to determine how many patients the model could correctly classify to the right group. These phases were repeated 10,000 times by randomly sampling the individual nights used for the training and testing data. For each round, the mean classification accuracy across the 17 patients was stored, and the mean classification value across the 10,000 randomizations was taken as the total classification accuracy for each of the 7 models. The significance of the classification accuracy was evaluated by using permutation testing. This procedure followed the computation of the classification accuracy, but before the 10,000 random samplings of individual nights for the training and testing data, the labels of the rTMS and sham groups were permuted 1000 times. Here, we kept the sizes of the two groups the same as for the original data (10 patients in the rTMS and 7 in the sham group). The mean classification scores were stored for each permutation round, and the 95% confidence levels were computed from these distributions for each of the seven models. The Finally, to evaluate association of changes in pain with sleep parameters, we tested the correlation of change in each OURA parameter with the percentage of pain reduction (calculated as (NRS