Mechanisms of change in digital interventions for depression: A systematic review and meta-analysis of six mediator domains

Florian Angerer; Vera Mennel; Simon Grund; Axel Mayer; Rebekka Büscher; Lasse B Sander; Pim Cuijpers; Yannik Terhorst; Harald Baumeister; Matthias Domhardt

doi:10.1016/j.jad.2024.09.055

Mechanisms of change in digital interventions for depression: A systematic review and meta-analysis of six mediator domains

J Affect Disord. 2024 Sep 14:368:615-632. doi: 10.1016/j.jad.2024.09.055. Online ahead of print.

Authors

Affiliations

¹ Department of Clinical Psychology and Psychotherapy, Ulm University, Germany.
² Psychology with focus on Quantitative Methods, Universität Hamburg, Germany.
³ Department of Psychological Methods and Evaluation, Bielefeld University, Germany.
⁴ Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Germany; Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, the Netherlands.
⁵ Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Germany.
⁶ Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, the Netherlands.
⁷ Department of Clinical Psychology and Psychotherapy, Ulm University, Germany. Electronic address: matthias.domhardt@uni-ulm.de.

PMID: 39284530
DOI: 10.1016/j.jad.2024.09.055

Abstract

Background: While the efficacy of digital interventions for the treatment of depression is well established, comprehensive knowledge on how therapeutic changes come about is still limited. This systematic review aimed to provide an overview of research on change mechanisms in digital interventions for depression and meta-analytically evaluate indirect effects of potential mediators.

Methods: The databases CENTRAL, Embase, MEDLINE, and PsycINFO were systematically searched for randomized controlled trials investigating mediators of digital interventions for adults with depression. Two reviewers independently screened studies for inclusion, assessed study quality and categorized potential mediators. Indirect effects were synthesized with a two-stage structural equation modeling approach (TSSEM).

Results: Overall, 25 trials (8110 participants) investigating 84 potential mediators were identified, of which attentional (8 %), self-related (6 %), biophysiological (6 %), affective (5 %), socio-cultural (2 %) and motivational (1 %) variables were the scope of this study. TSSEM revealed significant mediation effects for combined self-related variables (ab = -0.098; 95 %-CI: [-0.150, -0.051]), combined biophysiological variables (ab = -0.073; 95 %-CI: [-0.119, -0.025]) and mindfulness (ab = -0.042; 95 %-CI: [-0.080, -0.015]). Meta-analytical evaluations of the other three domains were not feasible.

Limitations: Methodological shortcomings of the included studies, the considerable heterogeneity and the small number of investigated variables within domains limit the generalizability of the results.

Conclusion: The findings further the understanding of potential change mechanisms in digital interventions for depression and highlight recommendations for future process research, such as the consideration of temporal precedence and experimental manipulation of potential mediators, as well as the application of network approaches.

Keywords: Depression; Digital intervention; Mechanism of change; Mediator; Process research; Psychotherapy.

Publication types

Review