Secondary analyses
phobic, depression, anxiety, pain
ADVERSE EFFECTS, SECONDARY, ADVERSE EFFECT
As secondary analyses, we will evaluate treatment effects (from baseline to post-treatment, as described in hypothesis 2a and from post-treatment to follow-up, as described in hypothesis 2c) on quality of life, as measured by the WHO-QOL-BREF, interpersonal pleasure, as measured by the ACIPS, further SAD-related symptoms, as measured by the SPIN and the CGI, social phobic cognitions, as measured by the SPK, symptoms of depression, as measured by the BDI-FS and the QIDS-C, general psychological distress, as measured by the BSI, and limitations in everyday life, as measured by the PDI. We will also assess adverse effects measured by the self-developed adverse effect scale. Moreover, we will compare posttreatment LSAS scores with scores at follow-up, as stated in hypothesis 1c, using the linear mixed model described earlier.To control for moderating effects on decreases in social anxiety symptoms, as indicated in hypothesis 3, we include the scores of the individual learning style, as measured by Kolb’s LSI and experienced pain in social situations, as measured by the SPQ-5, as predictors in the model and specify three-way interactions with treatment conditions and time for all these factors. We will conduct a similar analysis to test whether social approach behavior measured by the SPWSS has a mediating effect on social anxiety symptoms, as we hypothesized in hypothesis 4. We will test all interactions by comparing the full to a reduced model without the respective interaction term via likelihood ratio tests (LRTs) [To evaluate effects between two conditions at a given time, e.g., between the APP and TG-APP conditions at post-treatment, as indicated in hypothesis 1b and 2b, we will calculate the statistical significance of pairwise differences in LSAS based on the linear mixed model using the R package “emmeans” [We will fit all measures as outcome variables in linear mixed models including the same predictors as in the primary analysis and add the average weekly time participants spent using the app and the number of behavioral experiments conducted as additional explanatory predictors.Besides that, we will analyze and compare responder rates between the different treatment conditions at post-treatment using a chi-squared test. Response to treatment will be defined as a 29% score decrease of the clinician-rated LSAS from baseline to post-treatment according to Glischinski et al. [