Network Analysis of Depression and Anxiety Symptoms in Chinese Rheumatoid Arthritis Patients

Overview

Journal PeerJ

Specialties Biology
Environmental Health
General Medicine

Date 2023 Nov 13

PMID 37953775

Authors

Lijuan Zhang

Weiyi Zhu

Beiwen Wu

Affiliations

Soon will be listed here.

Abstract

Background: Rheumatoid arthritis (RA) patients are susceptible to comorbid anxiety and depression. From the network model perspective, comorbidity is due to direct interactions between depression and anxiety symptoms. The objective of this study was to assess the network structure of depression and anxiety symptoms in Chinese RA patients and identify the central and bridge symptoms as well as how depression and anxiety symptoms are related to quality of life (QoL) in the network.

Methods: A total of 402 Chinese RA patients were included in this study. Depression and anxiety symptoms were measured by the Hospital Anxiety and Depression Scale (HADS). R software was used to estimate the network. Specifically, we computed the predictability, expected influence (EI) and bridge expected influence (BEI) for each symptom and showed a flow network of "QoL".

Results: Our network revealed that the strongest edge was D2 "See the bad side of things" and D3 "Not feeling cheerful" across the whole network. For centrality indices, D3 "Not feeling cheerful" and D6 "Feeling down" had the highest EI values in the network, while A4 "Trouble relaxing" and D6 "Feeling down" had the highest BEI values of their respective community. As to "QoL", the strongest direct edge related to it was A1 "Nervousness".

Conclusions: "Feeling down" and "Not feeling cheerful" emerged as the strongest central symptoms, while "Trouble relaxing" and "Feeling down" were bridge symptoms in the anxiety-depression network of RA patients. Intervention on depression and anxiety symptoms in nurses should prioritize these symptoms.

Citing Articles

Depressive, anxiety, and sleep disturbance symptoms in patients with obstructive sleep apnea: a network analysis perspective.

Luo X, Li S, Wu Q, Xu Y, Fang R, Cheng Y BMC Psychiatry. 2025; 25(1):77.

PMID: 39875912 PMC: 11773896. DOI: 10.1186/s12888-025-06532-w.

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