Evidence map›Paper›PMID 35945974›Full record

ArticleComputers in human behavior2023

Network analysis of internet addiction and depression among Chinese college students during the COVID-19 pandemic: A longitudinal study.

Yue Zhao, Diyang Qu, Shiyun Chen, Xinli Chi

Open access · greenAbstract read
In one paragraph

Article in Computers in human behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
19.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 111 citations in OpenAlex.

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  5. Comparison of depressive symptoms between heterosexual and sexual minority university students: network analysis.Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS · 2026
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  8. [Relationship between adolescent internet addiction and depressive symptoms: A longitudinal study based on a cross-lagged panel network models].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Yue ZhaoSchool of Psychology, Shenzhen University, Shenzhen, Guangdong, 518061, China.
Diyang QuVanke School of Public Health, Tsinghua University, Beijing, 100091, China.
Shiyun ChenUniversity College London Institute of Education, London, WC1H0AL, United Kingdom.
Xinli ChiSchool of Psychology, Shenzhen University, Shenzhen, Guangdong, 518061, China.
Shenzhen University · CNTsinghua University · CNUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There has been growing evidence of comorbidity between internet addiction and depression in youth during the COVID-19 period. According to the network theory, this may arise from the interplay of symptoms shared by these two mental disorders. Therefore, we examined this underlying process by measuring the changes in the central and bridge symptoms of the co-occurrence networks across time. Methods: A total of 852 Chinese college students were recruited during two waves (T1: August 2020; T2: November 2020), and reported their internet addiction symptoms and depressive symptoms. Network analysis was utilized for the statistical analysis. Results: The internet addiction symptoms "escape" and "irritable," and depression symptoms "energy" and "guilty" were the central symptoms for both waves. At the same time, "guilty" and "escape" were identified as bridge symptoms. Notably, the correlation between "anhedonia" and "withdrawal" significantly increased, and that between "guilty" and "escape" significantly decreased over time. Conclusions: This study provides novel insights into the central features of internet addiction and depression during the two stages. Interestingly, "guilty" and "escape," two functions of the defense mechanism, are identified as bridge symptoms. These two symptoms are suggested to activate the negative feedback loop and further contribute to the comorbidity between internet addiction and depression. Thus, targeting interventions on these internalized symptoms may contribute to alleviating the level of comorbidity among college students.

Indexed as

Bridge symptomsCentral symptomsDepressionInternet addictionLongitudinal dataNetwork analysis

Identifiers

PMID35945974
PMCPMC9352366
OpenAlexW4289833682

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.