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Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

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 interve...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482926/
https://www.ncbi.nlm.nih.gov/pubmed/28600278
http://dx.doi.org/10.2196/jmir.7367
Descripción
Sumario: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.