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

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...

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Autores principales: Harrer, Mathias, Ebert, David Daniel, Kuper, Paula, Paganini, Sarah, Schlicker, Sandra, Terhorst, Yannik, Reuter, Benedikt, Sander, Lasse B., Baumeister, Harald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457531/
https://www.ncbi.nlm.nih.gov/pubmed/37635949
http://dx.doi.org/10.1016/j.invent.2023.100634
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author Harrer, Mathias
Ebert, David Daniel
Kuper, Paula
Paganini, Sarah
Schlicker, Sandra
Terhorst, Yannik
Reuter, Benedikt
Sander, Lasse B.
Baumeister, Harald
author_facet Harrer, Mathias
Ebert, David Daniel
Kuper, Paula
Paganini, Sarah
Schlicker, Sandra
Terhorst, Yannik
Reuter, Benedikt
Sander, Lasse B.
Baumeister, Harald
author_sort Harrer, Mathias
collection PubMed
description 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 R(2) = 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; R(2) = 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.
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spelling pubmed-104575312023-08-27 Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()() Harrer, Mathias Ebert, David Daniel Kuper, Paula Paganini, Sarah Schlicker, Sandra Terhorst, Yannik Reuter, Benedikt Sander, Lasse B. Baumeister, Harald Internet Interv Full length Article 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 R(2) = 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; R(2) = 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. Elsevier 2023-06-07 /pmc/articles/PMC10457531/ /pubmed/37635949 http://dx.doi.org/10.1016/j.invent.2023.100634 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Full length Article
Harrer, Mathias
Ebert, David Daniel
Kuper, Paula
Paganini, Sarah
Schlicker, Sandra
Terhorst, Yannik
Reuter, Benedikt
Sander, Lasse B.
Baumeister, Harald
Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()()
title Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()()
title_full Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()()
title_fullStr Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()()
title_full_unstemmed Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()()
title_short Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials()()()
title_sort predicting heterogeneous treatment effects of an internet-based depression intervention for patients with chronic back pain: secondary analysis of two randomized controlled trials()()()
topic Full length Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457531/
https://www.ncbi.nlm.nih.gov/pubmed/37635949
http://dx.doi.org/10.1016/j.invent.2023.100634
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