Statistical analysis
We compared the sample before and after shut-down due to covid-19 as well as those who dropped out compared to those who did not using independent sample t-tests for continuous measures that were normally distributed, a Kruskal-Wallis test for skewed continuous variables, and a chi-square test for proportions.To investigate the effect of the intervention, we fit a linear mixed effects model for each outcome measure of interest (FGA, DHI, VVAS, ABC, TUG, FSST) on group (C.S.I. or Traditional), time (pre-, post-intervention) and their interaction. The models also included random intercepts for each participant to account for the inherent correlation between each participant’s performances across the two timepoints. We used sum coding for the categorical predictor variables (group and time) in order to obtain estimates for average differences. Therefore, the coefficient for the factor of time can be interpreted as the average changes in time across both groups. The interaction term between the two variables can be interpreted as any differences observed in one group but not in the other.Because we did not observe any significant differences in the baseline outcome measures between those who participated in the entire study and those who dropped out (see Appendices 