Data Analysis
Descriptive statistics were used to summarize patient characteristics per treatment group. A linear mixed-effects model with group, time and group by time interaction as fixed effects was used to study the evolution of adherence in the three groups. A random intercept per subject was added to account for the correlation between observations coming from the same individual. Time was considered categorical to be able to capture a non-linear evolution over time. Post hoc pairwise testing with Bonferroni-Holm correction for multiple testing was performed. As the adherence rates were not normally distributed, with a peak at 0% and 100%, an additional sensitivity analysis using ordinal logistic generalized estimating equations (GEE) with the 4 categories defined by Wall et al. (high, moderate, low, negligible practice) was performed.We hypothesized that there would be a significant difference in adherence between the three groups, with the highest levels of adherence found in the therapist group, followed by the app group and that the lowest adherence rates would be found in the paper group. Attitudes towards exercises were analyzed by means of descriptive statistics. A linear mixed-effects model was also used to examine the effect of age and patients’ experiences towards mobile phones and apps on adherence in the app group.Data was assumed to be missing at random and this missingness was ignored in the analyses. In the linear mixed effects model all information on the available time points is incorporated. Since only 11.4% of data was missing, we did not perform a sensitivity analysis using multiple imputation, as described in our statistical analysis plan [A 