Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials

Mathias Harrer; David Daniel Ebert; Paula Kuper; Sarah Paganini; Sandra Schlicker; Yannik Terhorst; Benedikt Reuter; Lasse B Sander; Harald Baumeister

doi:10.1016/j.invent.2023.100634

Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials

Internet Interv. 2023 Jun 7:33:100634. doi: 10.1016/j.invent.2023.100634. eCollection 2023 Sep.

Authors

Mathias Harrer^{1

2}, David Daniel Ebert¹, Paula Kuper^{1

3}, Sarah Paganini^{4

5}, Sandra Schlicker², Yannik Terhorst⁶, Benedikt Reuter^{3

7}, Lasse B Sander⁸, Harald Baumeister⁶

Affiliations

¹ Psychology & Digital Mental Health Care, Technical University Munich, Munich, Germany.
² Clinical Psychology and Psychotherapy, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany.
³ Clinical Psychology, Institute for Psychology, Humboldt University Berlin, Berlin, Germany.
⁴ Department of Sport Psychology, Institute of Sports and Sport Science, University of Freiburg, Freiburg, Germany.
⁵ Department of Rehabilitation Psychology and Psychotherapy, Institute of Psychology, Albert-Ludwigs-University Freiburg, Freiburg, Germany.
⁶ Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, Germany.
⁷ Department Humanmedizin, Medical School Berlin, Berlin, Germany.
⁸ Medical Psychology and Medical Sociology, Faculty of Medicine, Albert-Ludwigs University Freiburg, Freiburg, Germany.

Abstract

Background: Depression is highly prevalent among individuals with chronic back pain. Internet-based interventions can be effective in treating and preventing depression in this patient group, but it is unclear who benefits most from this intervention format.

Method: In an analysis of two randomized trials (N = 504), we explored ways to predict heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain. Univariate treatment-moderator interactions were explored in a first step. Multilevel model-based recursive partitioning was then applied to develop a decision tree model predicting individualized treatment benefits.

Results: The average effect on depressive symptoms was d = -0.43 (95 % CI: -0.68 to -0.17; 9 weeks; PHQ-9). Using univariate models, only back pain medication intake was detected as an effect moderator, predicting higher effects. More complex interactions were found using recursive partitioning, resulting in a final decision tree with six terminal nodes. The model explained a large amount of variation (bootstrap-bias-corrected R² = 45 %), with predicted subgroup-conditional effects ranging from d_i = 0.24 to -1.31. External validation in a pilot trial among patients on sick leave (N = 76; R² = 33 %) pointed to the transportability of the model.

Conclusions: The studied intervention is effective in reducing depressive symptoms, but not among all chronic back pain patients. Predictions of the multivariate tree learning model suggest a pattern in which patients with moderate depression and relatively low pain self-efficacy benefit most, while no benefits arise when patients' self-efficacy is already high. If corroborated in further studies, the developed tree algorithm could serve as a practical decision-making tool.

Keywords: Back pain; Depression; Internet-delivered CBT; Machine learning.