Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students

Jinqi Ding,1,* Yue Wu,1,* Hanxiaoran Li,1 Shengsheng Wang,1 Jia Cai,2 Hong Cheng,3 Sugai Liang1 1Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 2Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Hangzhou City University, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sugai Liang, Email [email protected]: Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed to investigate whether the severity of depressive symptoms in college students affects the multivariate relationships among anhedonia, smartphone addiction, and mediating factors.Methods: A survey was conducted among 1347 Chinese college students (587 female) to assess depressive symptoms, anhedonia, addictive behaviors, anxiety, and insomnia. The participants were categorized the non-depressive symptom (NDS) and depressive symptom (DS) groups based on a cut-off score of 5 on the 9-item Patient Health Questionnaire-9. Network analysis was performed to investigate the symptom-to-symptom influences of symptoms in these two groups.Results: The network of the DS group was more densely connected than that of the NDS group. Social anticipatory anhedonia was a central factor for DS, while withdraw/escape (one factor of smartphone addiction) was a central factor for NDS. The DS group exhibited greater strength between the PHQ9 score and social anticipatory anhedonia, as well as between the PHQ9 score and alcohol misuse score, compared to the NDS group. On the other hand, the NDS group had higher strength between anxiety and feeling lost, as well as between anxiety and withdraw/escape, compared to the DS group.Conclusion: The findings suggest that there is a close relationship between social anhedonia, smartphone addiction, and alcohol consumption in the DS group. Addressing on ameliorating social anhedonia and smartphone addiction may be effective in preventing and managing depression in college students.Keywords: depressive symptoms, anhedonia, smartphone addiction, network analysis, college students

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-10-01
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students

SHILAP Revista de lepidopterología
Magnetic confinement fusion research
article

Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students

article en

Abstract

Jinqi Ding,1,* Yue Wu,1,* Hanxiaoran Li,1 Shengsheng Wang,1 Jia Cai,2 Hong Cheng,3 Sugai Liang1 1Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 2Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Hangzhou City University, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sugai Liang, Email [email protected]: Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed to investigate whether the severity of depressive symptoms in college students affects the multivariate relationships among anhedonia, smartphone addiction, and mediating factors.Methods: A survey was conducted among 1347 Chinese college students (587 female) to assess depressive symptoms, anhedonia, addictive behaviors, anxiety, and insomnia. The participants were categorized the non-depressive symptom (NDS) and depressive symptom (DS) groups based on a cut-off score of 5 on the 9-item Patient Health Questionnaire-9. Network analysis was performed to investigate the symptom-to-symptom influences of symptoms in these two groups.Results: The network of the DS group was more densely connected than that of the NDS group. Social anticipatory anhedonia was a central factor for DS, while withdraw/escape (one factor of smartphone addiction) was a central factor for NDS. The DS group exhibited greater strength between the PHQ9 score and social anticipatory anhedonia, as well as between the PHQ9 score and alcohol misuse score, compared to the NDS group. On the other hand, the NDS group had higher strength between anxiety and feeling lost, as well as between anxiety and withdraw/escape, compared to the DS group.Conclusion: The findings suggest that there is a close relationship between social anhedonia, smartphone addiction, and alcohol consumption in the DS group. Addressing on ameliorating social anhedonia and smartphone addiction may be effective in preventing and managing depression in college students.Keywords: depressive symptoms, anhedonia, smartphone addiction, network analysis, college students

SHILAP Revista de lepidopterología
Openalex Percentile: Top 60%
Magnetic confinement fusion research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students · SHILAP Revista de lepidopterología (2024) | TGRS Research Map | TGRS