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Defining and predicting patterns of early response in a web-based intervention for depression

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posted on 2017-06-01, 00:00 authored by Wolfgang Lutz, Alice Arndt, Julian Rubel, Thomas Berger, Johanna Schröder, Christina Späth, Björn Meyer, Wolfgang Greiner, Viola Gräfe, Martin Hautzinger, Kristina Fuhr, Matthias Rose, Sandra Nolte, Bernd Löwe, Fritz Hohagen, Jan Philipp Klein, Steffen Moritz
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.

History

Journal

Journal of medical internet research

Volume

19

Issue

6

Article number

e206

Pagination

1 - 16

Publisher

JMIR Publiations

Location

Toronto, Ont.

eISSN

1438-8871

Language

eng

Publication classification

C Journal article; C1.1 Refereed article in a scholarly journal

Copyright notice

2017, The Authors