Evidence map›Paper›PMID 39915799›Full record

ArticleBMC psychiatry2025

Classifying problematic gaming using a latent profile approach based on personality traits in Chinese young adolescent.

Jingyue Hao, Zhenjiang Liao, Qiuping Huang, Xinxin Chen, Shuhong Lin, Yongyan Shi, Yifan Li, Yin Tang, Xuhao Wang, Hongxian Shen

Abstract read
In one paragraph

Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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

10 authors.

Jingyue Hao *Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Zhenjiang Liao *Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Qiuping HuangDepartment of Applied Psychology, School of Humanities and Management, Hunan University of Chinese Medicine, Changsha, China.
Xinxin ChenDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Shuhong LinDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Yongyan ShiDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Yifan LiDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Yin TangDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Xuhao WangDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Hongxian ShenDepartment of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China. shenhx2018@csu.edu.cn.

Funding

National Natural Science Foundation of China 81971249National Natural Science Foundation of Hunan Province 2023JJ40476
6 · The paper itself

Abstract

backgroundInternet gaming has gained widespread popularity in China, yet the classification of problematic gaming subtypes based on personality traits remains limited. This study aimed to employ latent profile analysis (LPA) to identify distinct groups of online gamers and compare key variables across these groups.

methodsAn online survey was conducted within 5593 internet gaming users, including a demographic questionnaire, queries on internet gaming usage, the Video Gaming Dependency Scale, and the Chinese Big Five Inventory-brief version. LPA was applied to identify distinct user groups, followed by an examination of associations between profile membership and auxiliary variables.

resultsLPA identified three gamer categories for problematic gaming: "high-risk" (64.78%), "medium-risk" (3.22%), and "low-risk" (32%). High-risk gamers allocated more time and financial resources to gaming for escapism and leisure purposes. The medium-risk group sought enjoyable experiences, exhibiting traits that were intermediate between the high- and low-risk groups. High-risk gamers demonstrated elevated levels of neuroticism, accompanied by lower scores in other Big Five personality traits. In contrast, medium-risk gamers scored low across all Big Five dimensions, while low-risk gamers achieved higher scores in all traits except neuroticism. Notably, the low-risk group reported forming the fewest new online friendships, despite sharing similar social motivations with the other groups.

conclusionTraits such as low neuroticism and high conscientiousness serve as protective factors against gaming addiction, while being unmarried or an only child provides additional safeguards. Conversely, increased time and financial investment in gaming activities are associated with a heightened risk of addiction. These findings are crucial for identifying high-risk gamers and informing the development of targeted interventions.

Indexed as

Behavior, AddictiveInternet Addiction DisorderPersonalityVideo GamesAdolescentChinaEast Asian PeopleFemaleHumansInternetMaleSurveys and QuestionnairesChinese adolescent gamersInternet gaming use characteristicsLatent profile analysisPersonality traits (big five model)Problematic gaming

Identifiers

PMID39915799
PMCPMC11800559

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.