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

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.

Early response to psychological therapy as a predictor of depression and anxiety treatment outcomes: A systematic review and meta‐analysis

“…Measures of psychological distress included the Outcomes Questionnaire (OQ‐45), and the Clinical Outcomes in Routine Evaluation (CORE). Only two studies used general psychological distress measures rather than disorder‐specific measures to assess symptom change (Lutz et al, ; Rubel et al, ), while two used a combination of both (Lutz, Stulz & Köck, ; Schibbye et al, ).…”

“…Previous authors have suggested that the early response effect could be explained by a trend of improvement that began prior to the start of treatment, which might be indicative of regression to the mean (Beckham, ). Two of the reviewed studies did in fact observe that early pretreatment gains are associated with trajectories of improvement, however both studies still found significant effects of early response during treatment after controlling for pretreatment gains (Delgadillo et al, ; Lutz et al, ). On this basis, the effect of early response cannot be entirely explained by regression to the mean.…”

“…Other sources of heterogeneity such as differences in the types of treatments and study design were examined by removing systematically different studies from the meta-analysis and examining the influence on pooled effect sizes and indices of heterogeneity. The small number of studies (<20) that were included in meta-analysis and the imbalance in the number of studies per each of the above categories of heterogeneity precluded the use of categorical moderator analysis, according to conventional guidelines (Lutz et al, 2017;Rubel et al, 2015), while two used a combination of both (Lutz, Stulz & Köck, 2009;Schibbye et al, 2014).…”

“…Some studies have found that early symptomatic response is associated with better posttreatment outcomes (e.g., Crits‐Christoph et al, ; Grilo, Masheb, & Wilson, ; Lutz et al, ; Stauffer et al, ; van Calker et al, ). However, the magnitude of this association varies considerably across studies and clinical samples.…”

Impact and change of attitudes toward Internet interventions within a randomized controlled trial on individuals with depression symptoms

“…These processes might be independent of usage duration. Behavioral pathways might also be important, though, as there is evidence that depressed individuals with more negative attitudes are more likely to discontinue the use of Internet interventions (Lutz et al, 2017). Regardless of the still unresolved question of how attitudes affect outcome among users of Internet interventions, the APOI might be used as a screening tool to identify individuals who may be well suited for these interventions, or to identify those who require support or for whom other interventions might be preferable.…”