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

Author:

Thielecke Janika123ORCID,Kuper Paula14ORCID,Ebert David2ORCID,Cuijpers Pim56ORCID,Smit Filip5678ORCID,Riper Heleen569ORCID,Lehr Dirk10ORCID,Buntrock Claudia4ORCID

Affiliation:

1. Professorship of Psychology and Digital Mental Health Care, Department of Sports and Health Sciences Technical University of Munich Munich Germany

2. Department of Clinical Psychology and Psychotherapy, Institute of Psychology Friedrich‐Alexander ‐University Erlangen‐Nürnberg Erlangen Germany

3. The Netherlands Organization for Applied Scientific Research (TNO) Leiden The Netherlands

4. Institute of Social Medicine and Health Systems Research, Faculty of Medicine Otto‐von‐Guericke University Magdeburg Magdeburg Germany

5. Department of Clinical, Neuro and Developmental Psychology VU University Amsterdam The Netherlands

6. Amsterdam Public Health Amsterdam University Medical Centers Amsterdam The Netherlands

7. Department of Mental Health and Prevention Trimbos Institute, Netherlands Institute of Mental Health and Addiction Utrecht The Netherlands

8. Department of Epidemiology and Biostatistics University Medical Center Amsterdam msterdam The Netherlands

9. Department of Psychiatry VU University Medical Center Amsterdam The Netherlands

10. Department of Health Psychology and Applied Biological Psychology Leuphana University Luneburg Lüneburg Germany

Abstract

AbstractBackgroundEvidence 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.MethodsA 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.ResultsSmall 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.ConclusionMore thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention.Patient or Public ContributionThis 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 RegistrationOriginal studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.

Publisher

Wiley

Subject

Public Health, Environmental and Occupational Health

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