The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms.

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model.

To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived "good enough" dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.

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 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.

Effects of a Psychological Internet Intervention in the Treatment of Mild to Moderate Depressive Symptoms: Results of the EVIDENT Study, a Randomized Controlled Trial.

BACKGROUND: Mild to moderate depressive symptoms are common but often remain unrecognized and treated inadequately. We hypothesized that an Internet intervention in addition to usual care is superior to care as usual alone (CAU) in the treatment of mild to moderate depressive symptoms in adults. METHODS: This trial was controlled, randomized and assessor-blinded. Participants with mild to moderate depressive symptoms (Patient Health Questionnaire, PHQ-9, score 5-14) were recruited from clinical and non-clinical settings and randomized to either CAU or a 12-week Internet intervention (Deprexis) adjunctive to usual care. Outcomes were assessed at baseline, 3 months (post-assessment) and 6 months (follow-up). The primary outcome measure was self-rated depression severity (PHQ-9). The main analysis was based on the intention-to-treat principle and used linear mixed models. RESULTS: A total of 1,013 participants were randomized. Changes in PHQ-9 from baseline differed signixFB01;cantly between groups (t825 = 6.12, p < 0.001 for the main effect of group). The post-assessment between-group effect size in favour of the intervention was d = 0.39 (95% CI: 0.13-0.64). It was stable at follow-up, with d = 0.32 (95% CI: 0.06-0.69). The rate of participants experiencing at least minimally clinically important PHQ-9 change at the post-assessment was higher in the intervention group (35.6 vs. 20.2%) with a number needed to treat of 7 (95% CI: 5-10). CONCLUSIONS: The Internet intervention examined in this trial was superior to CAU alone in reducing mild to moderate depressive symptoms. The magnitude of the effect is clinically important and has public health implications.

Patterns of change and their relationship to outcome and follow-up in group and individual psychotherapy for depression.

OBJECTIVE: The study explored the presence of different patterns of change in a sample of patients who received cognitive therapy for depression sequentially in two different formats: group and individual. Our hypothesis was that patients' baseline characteristics (e.g., symptom severity, self-esteem) would discriminate patterns of response to group and individual therapy. METHOD: 108 adults who met criteria for depression and completed the treatments included in a randomized controlled trial (RCT) were assessed with the Beck Depression Inventory-II (BDI-II), the Clinical Outcome in Routine Evaluation-Short Form B (CORE-SFB), the Global Assessment of Functioning (GAF), and the repertory grid technique. Growth mixture modeling was carried out to identify the patterns of change. Mixed linear models and repeated measures analysis of variance were performed to compare patients' characteristics in each pattern. Multinomial logistic regression was used to compute predictive models for the patterns from patients' characteristics. Finally, hierarchical linear regression was used to establish the power of each pattern to predict treatment outcome. RESULTS: A 3-class solution was obtained: group therapy improvers, individual therapy improvers, and nonimprovers. Group therapy improvers started therapy with less severe levels of depression and psychological distress, higher functioning and self-esteem, lower perceived social isolation, and lower dilemmatic construction of the self than the other groups. Individual therapy improvers and nonimprovers presented similar characteristics at baseline. However, a significant proportion of nonimprovers presented a concurrent diagnosis of fibromyalgia. CONCLUSION: The greater the impairment that patients present at baseline, the more likely they are to benefit from individual therapy after group therapy. A diagnosis of fibromyalgia can be considered a risk factor for therapy failure in the treatment of depression. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Outpatient and self-referred participants: Adherence to treatment components and outcome in an internet intervention targeting anxiety disorders.

OBJECTIVE: While adherence is an important factor influencing the effectiveness of internet interventions, many studies operationalize adherence only by the number of sessions and do not report adherence to specific treatment components. The goal of this study was to investigate adherence to treatment components as well as outcome in outpatients and self-referred participants who participated in an internet intervention targeting anxiety. METHOD: Outpatients (N = 50) were compared to self-referred (N = 37) participants and a matched outpatient waitlist sample (based on nearest neighbor matching): Using t-test and χ2 tests adherence to treatment components based on the number of completed exercises was compared between participant groups. A 2 × 2 repeated measures ANOVA was used to compare pre-to post symptom change between participant groups. Primary measures included the Generalized Anxiety Disorder Scale-7 (GAD-7) and the Mini Social Phobia Inventory (Mini-SPIN). Using nonparametric bootstrap analyses number of sessions and adherence to treatment components were investigated as potential mediators of the relationship between participant group and outcome. Finally, predictors of adherence to treatment components in outpatient participants were investigated using LASSO and logistic regression. RESULTS: Self-referred participants were more adherent than outpatient participants, however the groups did not differ significantly in outcome. Outpatient participants who adhered to relaxation showed greater improvement during the waiting period than the matched outpatient waitlist sample. The effect of participant group on outcome was mediated via adherence to exposure and number of sessions. CONCLUSIONS: In internet interventions adherence to treatment components differs between participant groups and has a mediating effect on treatment outcome. Therefore, it should be fostered, especially when participants are not self-referred. In line with these findings more studies should investigate relevant participant characteristics in more depth.