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A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk

BACKGROUND: Network analysis provides a new method for conceptualizing interconnections among psychological and behavioral constructs. OBJECTIVE: We used network analysis to investigate the complex associations between depressive symptoms and patient activation dimensions among patients at elevated...

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Autores principales: Lee, Chiyoung, Wolever, Ruth Q., Yang, Qing, Vorderstrasse, Allison, Min, Se Hee, Hu, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988674/
https://www.ncbi.nlm.nih.gov/pubmed/35399615
http://dx.doi.org/10.1177/2164957X221086257
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author Lee, Chiyoung
Wolever, Ruth Q.
Yang, Qing
Vorderstrasse, Allison
Min, Se Hee
Hu, Xiao
author_facet Lee, Chiyoung
Wolever, Ruth Q.
Yang, Qing
Vorderstrasse, Allison
Min, Se Hee
Hu, Xiao
author_sort Lee, Chiyoung
collection PubMed
description BACKGROUND: Network analysis provides a new method for conceptualizing interconnections among psychological and behavioral constructs. OBJECTIVE: We used network analysis to investigate the complex associations between depressive symptoms and patient activation dimensions among patients at elevated risk of cardiovascular disease. METHODS: This secondary analysis included 200 patients seen in primary care clinics. Depressive symptoms were assessed using the 21-item Beck Depression Inventory. Patient activation was measured using the 13-item Patient Activation Measure. Glasso networks were constructed to identify symptoms/traits that bridge depressive symptoms and patient activation and those that are central within the network. RESULTS: “Self-dislike” and “confidence to maintain lifestyle changes during times of stress” were identified as important bridge pathways. In addition, depressive symptoms such as “punishment feelings,” “loss of satisfaction,” “self-dislike,” and “loss of interest in people” were central in the depressive symptom–patient activation network, meaning that they were most strongly connected to all other symptoms. CONCLUSIONS: Bridge pathways identified in the network may be reasonable targets for clinical intervention aimed at disrupting the association between depressive symptoms and patient activation. Further research is warranted to assess whether targeting interventions to these central symptoms may help resolve other symptoms within the network.
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spelling pubmed-89886742022-04-08 A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk Lee, Chiyoung Wolever, Ruth Q. Yang, Qing Vorderstrasse, Allison Min, Se Hee Hu, Xiao Glob Adv Health Med Original Article BACKGROUND: Network analysis provides a new method for conceptualizing interconnections among psychological and behavioral constructs. OBJECTIVE: We used network analysis to investigate the complex associations between depressive symptoms and patient activation dimensions among patients at elevated risk of cardiovascular disease. METHODS: This secondary analysis included 200 patients seen in primary care clinics. Depressive symptoms were assessed using the 21-item Beck Depression Inventory. Patient activation was measured using the 13-item Patient Activation Measure. Glasso networks were constructed to identify symptoms/traits that bridge depressive symptoms and patient activation and those that are central within the network. RESULTS: “Self-dislike” and “confidence to maintain lifestyle changes during times of stress” were identified as important bridge pathways. In addition, depressive symptoms such as “punishment feelings,” “loss of satisfaction,” “self-dislike,” and “loss of interest in people” were central in the depressive symptom–patient activation network, meaning that they were most strongly connected to all other symptoms. CONCLUSIONS: Bridge pathways identified in the network may be reasonable targets for clinical intervention aimed at disrupting the association between depressive symptoms and patient activation. Further research is warranted to assess whether targeting interventions to these central symptoms may help resolve other symptoms within the network. SAGE Publications 2022-04-04 /pmc/articles/PMC8988674/ /pubmed/35399615 http://dx.doi.org/10.1177/2164957X221086257 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Lee, Chiyoung
Wolever, Ruth Q.
Yang, Qing
Vorderstrasse, Allison
Min, Se Hee
Hu, Xiao
A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk
title A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk
title_full A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk
title_fullStr A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk
title_full_unstemmed A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk
title_short A Network Analysis of the Association Between Depressive Symptoms and Patient Activation Among Those With Elevated Cardiovascular Risk
title_sort network analysis of the association between depressive symptoms and patient activation among those with elevated cardiovascular risk
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988674/
https://www.ncbi.nlm.nih.gov/pubmed/35399615
http://dx.doi.org/10.1177/2164957X221086257
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