What research has found about predictors of treatment response in anxiety disorders, why prediction is harder than expected, and what personalized treatment efforts have achieved so far.
What effect sizes mean in anxiety research, why statistical significance alone is misleading, and how to read Cohen's d, Hedges' g and odds ratios.
How research distinguishes worry from rumination, whether the distinction holds up empirically, and what transdiagnostic models of repetitive negative thinking propose.
What attention bias modification is, the theory behind it, why early results were promising, and why meta-analyses found smaller and less reliable effects than expected.
What anxiety studies actually measure: how questionnaires like the GAD-7, clinical interviews, body signals and lab tasks perform, and what each misses.
What anxiety sensitivity is, how it differs from trait anxiety, what the Anxiety Sensitivity Index measures, and what research shows about its role in panic and other conditions.
What interoceptive sensitivity is, how it is measured, whether heightened body awareness really drives anxiety, and why the heartbeat detection task may not measure what we think.
What safety behaviors are, how they maintain anxiety according to cognitive models, what experimental and clinical evidence shows, and where the research is less settled.
Why anxiety and depression co-occur so often, how comorbidity affects trial results and clinical decisions, and what structural models suggest about the overlap.
Why waitlist-controlled anxiety trials overestimate treatment effects, what nocebo and expectation confounds they introduce, and what better comparisons look like.