%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 19
%N 6
%P e206
%T Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression
%A Lutz,Wolfgang
%A Arndt,Alice
%A Rubel,Julian
%A Berger,Thomas
%A Schröder,Johanna
%A Späth,Christina
%A Meyer,Björn
%A Greiner,Wolfgang
%A Gräfe,Viola
%A Hautzinger,Martin
%A Fuhr,Kristina
%A Rose,Matthias
%A Nolte,Sandra
%A Löwe,Bernd
%A Hohagen,Fritz
%A Klein,Jan Philipp
%A Moritz,Steffen
%+ Department of Psychology, University of Trier, Am Wissenschaftspark 25-27, Trier,, Germany, 49 651 201 2883, wolfgang.lutz@uni-trier.de
%K patterns of early change
%K depression
%K web interventions
%K psychotherapy research
%D 2017
%7 09.06.2017
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 28600278
%R 10.2196/jmir.7367
%U http://www.jmir.org/2017/6/e206/
%U https://doi.org/10.2196/jmir.7367
%U http://www.ncbi.nlm.nih.gov/pubmed/28600278