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Randomized Controlled Trial
. 2024 Feb;27(1):e13951.
doi: 10.1111/hex.13951.

Does outcome expectancy predict outcomes in online depression prevention? Secondary analysis of randomised-controlled trials

Janika Thielecke  1   2   3 , Paula Kuper  1   4 , David Ebert  2 , Pim Cuijpers  5   6 , Filip Smit  5   6   7   8 , Heleen Riper  5   6   9 , Dirk Lehr  10 , Claudia Buntrock  4
Affiliations
Randomized Controlled Trial

Does outcome expectancy predict outcomes in online depression prevention? Secondary analysis of randomised-controlled trials

Janika Thielecke et al. Health Expect. 2024 Feb.

Abstract

Background: Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention.

Methods: A secondary data analysis was conducted using data from two randomised-controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close-to-symptom-free status postintervention (6-7 weeks) and at follow-up (3-6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression.

Results: Small effects were observed at follow-up for depressive symptoms (β = -.39, 95% confidence interval [CI]: [-0.75, -0.03], p = .032, padjusted = .130) and close-to-symptom-free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, padjusted = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity.

Conclusion: More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention.

Patient or public contribution: This secondary analysis did not involve patients, service users, care-givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults.

Clinical trial registration: Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.

Keywords: CBT; depression; expectancy; online intervention; prediction; prevention; secondary analyses.

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Conflict of interest statement

David Ebert is a stakeholder of the GET.ON Institute/HelloBetter, which aims to implement scientific findings related to digital health interventions into routine care. David Ebert has served as a consultant to/on the scientific advisory boards of Sanofi, Novartis, Minddistrict, Lantern, Schoen Kliniken, Ideamed and German health insurance companies (BARMER, Techniker Krankenkasse) and a number of federal chambers for psychotherapy. All other authors report no conflicts of interest.

Figures

Figure 1
Figure 1
Participant flow in the original study and inclusion in the secondary analyses. PREV‐DEP I and PREV‐DEP II are the study acronyms used for the original studies.,
See this image and copyright information in PMC

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