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

Lutz, Wolfgang, Arndt, Alice, Rubel, Julian, Berger, Thomas, Schröder, Johanna, Späth, Christina, Meyer, Bjorn, Greiner, Wolfgang, Gräfe, Viola, Hautzinger, Martin, Fuhr, Kristina, Rose, Matthias, Nolte, Sandra, Löwe, Bernd, Hohagen, Fritz, Klein, Jan Philipp and Moritz, Steffen 2017, Defining and predicting patterns of early response in a web-based intervention for depression, Journal of medical internet research, vol. 19, no. 6, pp. 1-16, doi: 10.2196/jmir.7367.

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Title Defining and predicting patterns of early response in a web-based intervention for depression
Author(s) Lutz, Wolfgang
Arndt, Alice
Rubel, Julian
Berger, Thomas
Schröder, Johanna
Späth, Christina
Meyer, Bjorn
Greiner, Wolfgang
Gräfe, Viola
Hautzinger, Martin
Fuhr, Kristina
Rose, Matthias
Nolte, SandraORCID iD for Nolte, Sandra
Löwe, Bernd
Hohagen, Fritz
Klein, Jan Philipp
Moritz, Steffen
Journal name Journal of medical internet research
Volume number 19
Issue number 6
Article ID e206
Start page 1
End page 16
Total pages 16
Publisher JMIR Publiations
Place of publication Toronto, Ont.
Publication date 2017-06
ISSN 1438-8871
Keyword(s) depression
patterns of early change
psychotherapy research
web interventions
Middle Aged
Young Adult
Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
Summary 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.
Language eng
DOI 10.2196/jmir.7367
Field of Research 111799 Public Health and Health Services not elsewhere classified
08 Information And Computing Sciences
11 Medical And Health Sciences
17 Psychology And Cognitive Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
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Document type: Journal Article
Collections: Faculty of Health
School of Health and Social Development
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