Depression is one of the most prevalent mental health problems and measuring depressive symptoms becomes increasingly important in science as well as medical practice. Computer Adaptive Tests (CAT) based on the Item Response Theory (IRT) promise to enhance measurement precision and reduce respondent's burden. Our aim was to develop a CAT application to measure depressive symptoms. Three thousand two hundred seventy psychosomatic patients answered an overall of 11 mental health questionnaires at the University Clinic in Berlin. Three independent reviewers rated 144 items out of these questionnaires as indicative of depressive symptoms. All items underwent six empirical steps to analyze unidimensionality, local independence and item discrimination. Finally 64 items could be used to calculate item parameters applying a Generalized Partial Credit Model (GPCM). CAT scores were estimated using an 'expected a posteriori' algorithm (EAP). Two simulation experiments showed that for theta values within the range of 2SD around the mean (98% of the cases), the latent trait can be estimated out of approximately six items with a predefined standard error of [Symbol: see text] 0.32 (reliability rho [Symbol: see text] 0.90). The CAT-scores correlated high with scores of all depression items (r = 0.95), with the Beck Depression Inventory (r = 0.79) and with a CES-D 8 item short form (r = 0.76). We conclude that the Depression-CAT measures depressive symptoms with high precision and low respondent burden.

Background: Mild to moderate depressive symptoms are common but often remain unrecognized and treated inadequately. We hypothesized that an Internet intervention in addition to usual care is superior to care as usual alone (CAU) in the treatment of mild to moderate depressive symptoms in adults. Methods: This trial was controlled, randomized and assessor-blinded. Participants with mild to moderate depressive symptoms (Patient Health Questionnaire, PHQ-9, score 5-14) were recruited from clinical and non-clinical settings and randomized to either CAU or a 12-week Internet intervention (Deprexis) adjunctive to usual care. Outcomes were assessed at baseline, 3 months (post-assessment) and 6 months (follow-up). The primary outcome measure was self-rated depression severity (PHQ-9). The main analysis was based on the intention-to-treat principle and used linear mixed models. Results: A total of 1,013 participants were randomized. Changes in PHQ-9 from baseline differed significantly between groups (t₈₂₅ = 6.12, p < 0.001 for the main effect of group). The post-assessment between-group effect size in favour of the intervention was d = 0.39 (95% CI: 0.13-0.64). It was stable at follow-up, with d = 0.32 (95% CI: 0.06-0.69). The rate of participants experiencing at least minimally clinically important PHQ-9 change at the post-assessment was higher in the intervention group (35.6 vs. 20.2%) with a number needed to treat of 7 (95% CI: 5-10). Conclusions: The Internet intervention examined in this trial was superior to CAU alone in reducing mild to moderate depressive symptoms. The magnitude of the effect is clinically important and has public health implications.

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