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, Björn; Greiner, Wolfgang; Gräfe, Viola; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Nolte, Sandra; Löwe, Bernd; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen (2017). Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression. Journal of medical internet research, 19(6), e206. Centre of Global eHealth Innovation 10.2196/jmir.7367

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

07 Faculty of Human Sciences > Institute of Psychology > Clinical Psychology and Psychotherapy
07 Faculty of Human Sciences > Institute of Psychology

UniBE Contributor:

Berger, Thomas

Subjects:

100 Philosophy > 150 Psychology
600 Technology > 610 Medicine & health

ISSN:

1439-4456

Publisher:

Centre of Global eHealth Innovation

Language:

English

Submitter:

Salome Irina Rahel Bötschi

Date Deposited:

24 Apr 2018 17:57

Last Modified:

07 Jun 2018 10:03

Publisher DOI:

10.2196/jmir.7367

PubMed ID:

28600278

BORIS DOI:

10.7892/boris.113554

URI:

https://boris.unibe.ch/id/eprint/113554

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