Evidence map›Paper›PMID 42487803›Full record

ArticleFrontiers in public health2026

Network analysis of comorbid internet addiction, anxiety, and depression symptoms among Chinese junior high school students.

Ting Yu, Hong-Lei Wang, Xiao-Peng Deng, Juan Sheng, Bi-Ting Liu, Xiang-Zhan Chen, Xian-Ming Yuan, Yi-Xin Jin, Yuan Wang, Xin-Yi Pang and 3 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ting Yu *Mental Health Center of Yangtze University, Jingzhou, Hubei, China.
Hong-Lei Wang *Mental Health Center of Yangtze University, Jingzhou, Hubei, China.
Xiao-Peng DengMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Juan ShengMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Bi-Ting LiuMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Xiang-Zhan ChenMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Xian-Ming YuanMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Yi-Xin JinMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Yuan WangMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Xin-Yi PangMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Xin-Feng ZhangMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Song QinMental Health Center of Yangtze University, Jingzhou, Hubei, China.
Zhe-Ming TuMental Health Center of Yangtze University, Jingzhou, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Internet addiction (IA) frequently co-occurs with anxiety and depression during adolescence. However, traditional latent variable models based on total scores often mask specific symptom-level interactions. This study utilized network analysis to elucidate the fine-grained associations between IA, anxiety, and depression symptoms among Chinese adolescents and to explore potential gender differences in these network structures. Methods: A cross-sectional study was conducted among 10,491 junior high school students (mean age = 13.88 years; 51.98% male) in Jingzhou City, China. Participants completed the Internet Addiction Test (IAT), the Generalized Anxiety Disorder-7 (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9). A Gaussian Graphical Model (GGM) was estimated to visualize the symptom network. Centrality indices and bridge strength were calculated to identify key symptoms. The Network Comparison Test (NCT) was employed to assess gender differences in global strength and network structure. Results: The network analysis revealed a stable structure driven primarily by symptoms reflecting escapism and social withdrawal. Specifically, "Preferring online interaction to real-world intimacy" (IAT19) and "Using the internet to block disturbing thoughts" (IAT10) exhibited the highest bridge strength, directly connecting to core depressive symptoms such as anhedonia (PHQ1) and depressed mood (PHQ2). The NCT indicated that while the global strength of the network was invariant across genders ( Conclusion: Escapism and the displacement of offline social interactions are critical mechanisms maintaining the comorbidity of IA and emotional distress. Interventions should prioritize targeting these bridge symptoms-specifically by improving emotion regulation strategies and enhancing real-world social connections-to disrupt the feedback loop between addiction and internalizing problems. The structural differences observed suggest that gender-sensitive approaches may be required for effective intervention.

Indexed as

AnxietyDepressionInternet Addiction DisorderStudentsAdolescentChinaComorbidityCross-Sectional StudiesEast Asian PeopleFemaleHumansInternetMaleSurveys and Questionnairesanxietydepressioninternet addictionnetwork analysisyouth students

Identifiers

PMID42487803
PMCPMC13388152

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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.