Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression
JOURNAL OF MEDICAL INTERNET RESEARCH. Bd. 19. H. 6. 2017
Erscheinungsjahr: 2017
Publikationstyp: Zeitschriftenaufsatz
Doi/URN: 10.2196/jmir.7367
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Inhaltszusammenfassung
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 depre...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 (N409): 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. » weiterlesen» einklappen
