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

Wolfgang Lutz,Christina Späth,Viola Gräfe,Sandra Nolte,Steffen Moritz,Björn Meyer,Julian Rubel,Thomas Berger,Alice Arndt,Kristina Fuhr,Wolfgang Greiner,Jan Philipp Klein,Fritz Hohagen,Matthias Rose,Martin Hautzinger,Bernd Löwe,Johanna Schröder

doi:10.2196/jmir.7367

Abstract

BackgroundWeb-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.ObjectiveThe aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects.MethodsWe 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.ResultsOverall, 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).ConclusionsThese 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.

Highlights

Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective addition to face-to-face psychotherapy or pharmacological treatment
This study examined patterns of early change during the first 4 weeks of a 12-week cognitive behavioral therapy (CBT)-oriented Web-based intervention for depression by applying a piecewise growth mixture modeling (PGMM) analysis
Hope and positive expectations have an impact on early response; we don’t yet know much about specific personal characteristics such as self-efficacy

Summary

Introduction

Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective addition to face-to-face psychotherapy or pharmacological treatment. Such interventions may be appropriate for individuals who have difficulty accessing psychological treatment or do not want to utilize face-to-face treatment [1,2,3,4,5]. 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. 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

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Conclusion