TY - JOUR AU - Lutz, Wolfgang AU - Arndt, Alice AU - Rubel, Julian AU - Berger, Thomas AU - Schröder, Johanna AU - Späth, Christina AU - Meyer, Björn AU - Greiner, Wolfgang AU - Gräfe, Viola AU - Hautzinger, Martin AU - Fuhr, Kristina AU - Rose, Matthias AU - Nolte, Sandra AU - Löwe, Bernd AU - Hohagen, Fritz AU - Klein, Jan Philipp AU - Moritz, Steffen PY - 2017 DA - 2017/06/09 TI - Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression JO - J Med Internet Res SP - e206 VL - 19 IS - 6 KW - patterns of early change KW - depression KW - web interventions KW - psychotherapy research AB - Background: Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment. Objective: The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects. Methods: We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression. Results: Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04). Conclusions: These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources. SN - 1438-8871 UR - http://www.jmir.org/2017/6/e206/ UR - https://doi.org/10.2196/jmir.7367 UR - http://www.ncbi.nlm.nih.gov/pubmed/28600278 DO - 10.2196/jmir.7367 ID - info:doi/10.2196/jmir.7367 ER -