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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10457531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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