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Intrarelationships between suboptimal health status and anxiety symptoms: A network analysis.
Liu, Yangyu; Ge, Pu; Zhang, Xiaoming; Wu, Yunchou; Sun, Zhaocai; Bai, Qian; Jing, Shanshan; Zuo, Huali; Wang, Pingping; Cong, Jinyu; Li, Xiang; Liu, Kunmeng; Wu, Yibo; Wei, Benzheng.
Afiliação
  • Liu Y; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
  • Ge P; School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100105, China.
  • Zhang X; Department of Cardiovascular Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
  • Wu Y; School of Psychology, Southwest University, Chongqing 400715, China.
  • Sun Z; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
  • Bai Q; School of Management, Beijing University of Chinese Medicine, Beijing 100105, China.
  • Jing S; College of Health Sciences, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China.
  • Zuo H; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.
  • Wang P; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
  • Cong J; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
  • Li X; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
  • Liu K; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
  • Wu Y; School of Public Health, Peking University, Haidian District, Beijing 100191, China. Electronic address: bjmuwuyibo@outlook.com.
  • Wei B; Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China; Qingdao Key Laboratory of Artificial Intelligence Technolog
J Affect Disord ; 354: 679-687, 2024 Jun 01.
Article em En | MEDLINE | ID: mdl-38527530
ABSTRACT

BACKGROUND:

Suboptimal health status is a global public health concern of worldwide academic interest, which is an intermediate health status between health and illness. The purpose of the survey is to investigate the relationship between anxiety statuses and suboptimal health status and to identify the central symptoms and bridge symptoms.

METHODS:

This study recruited 26,010 participants aged <60 from a cross-sectional study in China in 2022. General Anxiety Disorder-7 (GAD-7) and suboptimal health status short form (SHSQ-9) were used to quantify the levels of anxiety and suboptimal health symptoms, respectively. The network analysis method by the R program was used to judge the central and bridge symptoms. The Network Comparison Test (NCT) was used to investigate the network differences by gender, place of residence, and age in the population.

RESULTS:

In this survey, the prevalence of anxiety symptoms, SHS, and comorbidities was 50.7 %, 54.8 %, and 38.5 %, respectively. "Decreased responsiveness", "Shortness of breath", "Uncontrollable worry" were the nodes with the highest expected influence. "Irritable", "Exhausted" were the two symptom nodes with the highest expected bridge influence in the network. There were significant differences in network structure among different subgroup networks.

LIMITATIONS:

Unable to study the causal relationship and dynamic changes among variables. Anxiety and sub-health were self-rated and may be limited by memory bias.

CONCLUSIONS:

Interventions targeting central symptoms and bridge nodes may be expected to improve suboptimal health status and anxiety in Chinese residents. Researchers can build symptom networks for different populations to capture symptom relationships.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ansiedade / Transtornos de Ansiedade Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ansiedade / Transtornos de Ansiedade Idioma: En Ano de publicação: 2024 Tipo de documento: Article