@Article{info:doi/10.2196/jmir.7367,
author="Lutz, Wolfgang
and Arndt, Alice
and Rubel, Julian
and Berger, Thomas
and Schr{\"o}der, Johanna
and Sp{\"a}th, Christina
and Meyer, Bj{\"o}rn
and Greiner, Wolfgang
and Gr{\"a}fe, Viola
and Hautzinger, Martin
and Fuhr, Kristina
and Rose, Matthias
and Nolte, Sandra
and L{\"o}we, Bernd
and Hohagen, Fritz
and Klein, Jan Philipp
and Moritz, Steffen",
title="Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression",
journal="J Med Internet Res",
year="2017",
month="Jun",
day="09",
volume="19",
number="6",
pages="e206",
keywords="patterns of early change; depression; web interventions; psychotherapy research",
abstract="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. ",
issn="1438-8871",
doi="10.2196/jmir.7367",
url="http://www.jmir.org/2017/6/e206/",
url="https://doi.org/10.2196/jmir.7367",
url="http://www.ncbi.nlm.nih.gov/pubmed/28600278"
}