Statistical analyses
stroke, spasticity
STROKE, SECONDARY
The sample size was based on preliminary data showing an average elbow-flexor TSRT change of 10° in stroke patients with spasticity who underwent a 2-week UL intervention, compared to 2° in a control group (unpublished data). Considering an α-level of 5% and a 95% power (effect size = 2.23) to detect differences using a mixed-design ANOVA (G*Power 3.1.1), the minimal sample size was 13 patients/group. For the initial analysis, the mixed model analysis included one between-subjects factor – Group with three levels (personalized training + a-tDCS, non-personalized training + a-tDCS and personalized training + sham-tDCS), and one within-subject factor – Time with three levels (Pre, Post and Follow-up for raw data; Post–Pre and Follow-up-Pre). One-way ANOVAs were used to evaluate change scores, noting confidence intervals (CIs). Between-group differences in proportions were analyzed with Chi-Square tests. Initial For the secondary statistical analysis, subjects were divided according to 2 intervention factors: 1) Restricted-range, and 2) Non-restricted range. Normal distributions were verified for each measure and group using Shapiro–Wilk normality tests and normal Q-Q plots. When normality criteria were unmet, data were normalized using reciprocal log or logit transformation functions.Descriptive statistics highlighted the main demographic and clinical participant characteristics. Statistical analysis was performed using Linear Mixed Models (LMM), in which the linear predictor contains random and fixed effects. The fixed effects for all measures were: training range or “Range” (Restricted, Non-restricted) and “Time” (Pre, Post, Follow-up), and their interaction. In addition, kinematic and stochastic measures for each joint included a fixed effect of “Target” (NC, FC, IL, CL) and the interaction of Target with the other fixed factors. The random effect was “Subject”. Each measure was first analyzed using the appropriate full model, and then backward elimination was used to remove non-significant factors (i.e., factors with a low contribution to the covariance matrix), where 