TY - JOUR
T1 - Anxiety and depressive symptoms in college students during the late stage of the COVID-19 outbreak
T2 - a network approach
AU - Bai, Wei
AU - Cai, Hong
AU - Liu, Shou
AU - Chen, Xu
AU - Sha, Sha
AU - Cheung, Teris
AU - Lin, Jessie Jingxia
AU - Cui, Xiling
AU - Ng, Chee H.
AU - Xiang, Yu Tao
N1 - Funding Information:
The study was supported by the National Science and Technology Major Project for investigational new drug (2018ZX09201-014), the Beijing Municipal Science & Technology Commission (No. Z181100001518005), the University of Macau (MYRG2019-00066-FHS), and Beijing Municipal Administration of Hospitals Incubating Program (PX2018063).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms “Nervousness” and “Uncontrollable worry”. “Fatigue” has the highest node strength in the anxiety and depression network, followed by “Excessive worry”, “Trouble relaxing”, and “Uncontrollable worry”. “Motor” showed the highest bridge strength, followed by “Feeling afraid” and “Restlessness”. The whole network was robust in both stability and accuracy tests. Central symptoms “Fatigue”, “Excessive worry”, “Trouble relaxing” and “Uncontrollable worry”, and critical bridge symptoms “Motor”, “Feeling afraid” and “Restlessness” were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.
AB - Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms “Nervousness” and “Uncontrollable worry”. “Fatigue” has the highest node strength in the anxiety and depression network, followed by “Excessive worry”, “Trouble relaxing”, and “Uncontrollable worry”. “Motor” showed the highest bridge strength, followed by “Feeling afraid” and “Restlessness”. The whole network was robust in both stability and accuracy tests. Central symptoms “Fatigue”, “Excessive worry”, “Trouble relaxing” and “Uncontrollable worry”, and critical bridge symptoms “Motor”, “Feeling afraid” and “Restlessness” were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.
UR - http://www.scopus.com/inward/record.url?scp=85121522049&partnerID=8YFLogxK
U2 - 10.1038/s41398-021-01738-4
DO - 10.1038/s41398-021-01738-4
M3 - Journal article
C2 - 34921138
AN - SCOPUS:85121522049
VL - 11
JO - Translational Psychiatry
JF - Translational Psychiatry
SN - 2158-3188
IS - 1
M1 - 638
ER -