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Network analysis of somatic symptoms in Chinese patients with depressive disorder
INTRODUCTION: Network theory conceptualizes somatic symptoms as a network of individual symptoms that are interconnected and influenced by each other. In this conceptualization, the network's central symptoms have the strongest effect on other symptoms. Clinical symptoms of patients with depres...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040552/ https://www.ncbi.nlm.nih.gov/pubmed/36992877 http://dx.doi.org/10.3389/fpubh.2023.1079873 |
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author | Li, Yang Jia, Shoumei Cao, Baohua Chen, Li Shi, Zhongying Zhang, Hao |
author_facet | Li, Yang Jia, Shoumei Cao, Baohua Chen, Li Shi, Zhongying Zhang, Hao |
author_sort | Li, Yang |
collection | PubMed |
description | INTRODUCTION: Network theory conceptualizes somatic symptoms as a network of individual symptoms that are interconnected and influenced by each other. In this conceptualization, the network's central symptoms have the strongest effect on other symptoms. Clinical symptoms of patients with depressive disorders are largely determined by their sociocultural context. To our knowledge, no previous study has investigated the network structure of somatic symptoms among Chinese patients with depressive disorders. The aim of this study was to characterize the somatic symptoms network structure in patients with depressive disorders in Shanghai, China. METHOD: A total of 177 participants were recruited between October 2018 and June 2019. The Chinese version of the Patient Health Questionnaire-15 was used to assess somatic symptoms. In order to quantify the somatic symptom network structure, indicators of “closeness,” “strength,” and “betweenness” were employed as identifiers for network-central symptoms. RESULT: The symptoms of “feeling your heart pound or race,” “shortness of breath,” and “back pain” had the highest centrality values, indicating that these symptoms were central to the somatic symptom networks. Feeling tired or mentally ill had the strongest positive correlation with insomnia or other sleep problems (r = 0.419), followed by chest pain and breathlessness (r = 0.334), back pain, and limb or joint pain (r = 0.318). DISCUSSION: Psychological and neurobiological research that offers insights into somatic symptoms may focus on these central symptoms as targets for treatment and future research. |
format | Online Article Text |
id | pubmed-10040552 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100405522023-03-28 Network analysis of somatic symptoms in Chinese patients with depressive disorder Li, Yang Jia, Shoumei Cao, Baohua Chen, Li Shi, Zhongying Zhang, Hao Front Public Health Public Health INTRODUCTION: Network theory conceptualizes somatic symptoms as a network of individual symptoms that are interconnected and influenced by each other. In this conceptualization, the network's central symptoms have the strongest effect on other symptoms. Clinical symptoms of patients with depressive disorders are largely determined by their sociocultural context. To our knowledge, no previous study has investigated the network structure of somatic symptoms among Chinese patients with depressive disorders. The aim of this study was to characterize the somatic symptoms network structure in patients with depressive disorders in Shanghai, China. METHOD: A total of 177 participants were recruited between October 2018 and June 2019. The Chinese version of the Patient Health Questionnaire-15 was used to assess somatic symptoms. In order to quantify the somatic symptom network structure, indicators of “closeness,” “strength,” and “betweenness” were employed as identifiers for network-central symptoms. RESULT: The symptoms of “feeling your heart pound or race,” “shortness of breath,” and “back pain” had the highest centrality values, indicating that these symptoms were central to the somatic symptom networks. Feeling tired or mentally ill had the strongest positive correlation with insomnia or other sleep problems (r = 0.419), followed by chest pain and breathlessness (r = 0.334), back pain, and limb or joint pain (r = 0.318). DISCUSSION: Psychological and neurobiological research that offers insights into somatic symptoms may focus on these central symptoms as targets for treatment and future research. Frontiers Media S.A. 2023-03-13 /pmc/articles/PMC10040552/ /pubmed/36992877 http://dx.doi.org/10.3389/fpubh.2023.1079873 Text en Copyright © 2023 Li, Jia, Cao, Chen, Shi and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Li, Yang Jia, Shoumei Cao, Baohua Chen, Li Shi, Zhongying Zhang, Hao Network analysis of somatic symptoms in Chinese patients with depressive disorder |
title | Network analysis of somatic symptoms in Chinese patients with depressive disorder |
title_full | Network analysis of somatic symptoms in Chinese patients with depressive disorder |
title_fullStr | Network analysis of somatic symptoms in Chinese patients with depressive disorder |
title_full_unstemmed | Network analysis of somatic symptoms in Chinese patients with depressive disorder |
title_short | Network analysis of somatic symptoms in Chinese patients with depressive disorder |
title_sort | network analysis of somatic symptoms in chinese patients with depressive disorder |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040552/ https://www.ncbi.nlm.nih.gov/pubmed/36992877 http://dx.doi.org/10.3389/fpubh.2023.1079873 |
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