Evidence map›Paper›PMID 40072966›Full record

ArticlePloS one2025

Analysis and prediction of addiction among virtual reality users.

Jing He, Shuman Yu, Jingzhao Zhang

Abstract read
In one paragraph

Article in PloS one, 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. Review
  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

3 authors.

Jing HeKey Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, China.ORCID https://orcid.org/0009-0007-8408-2435
Shuman YuSchool of Science, Engineering, and Medicine, The University of Warwick, Coventry, United Kingdom.
Jingzhao ZhangInstitute for Advanced Studies in Humanities and Social Science, Beihang University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo understand the addiction situation and influencing factors of virtual reality users, and provide reference basis for timely and effective prevention and intervention of user addiction.

methodsBased on a questionnaire survey, univariate analysis, multivariate analysis, and model prediction were conducted on the data of 1164 participants in VR related Facebook groups and Reddit subedits.

resultsThe single factor analysis results show that the user's own attributes, usage duration, perception level, and application types of virtual reality devices can significantly affect the degree of addiction; The results of multivariate analysis showed that the age of users, the number of days used per week, the number of hours used per day, and the perceived level of the device can significantly affect the probability of addiction. In addition, this study used decision tree algorithm to predict adolescent virtual reality device addiction, with a prediction accuracy of 0.957.

conclusionThe addiction of virtual reality users is related to multiple factors such as gender, age, usage time, application type, and perception level. When developing VR applications and content, consideration should be given to balancing user immersion and healthy use, and reasonable control of usage time is also an effective means to prevent VR addiction.

Indexed as

Technology AddictionVirtual RealityAdolescentAdolescent BehaviorAdultDecision TreesFactor Analysis, StatisticalFemaleHumansLinear ModelsMaleSurveys and QuestionnairesTime FactorsYoung Adult

Identifiers

PMID40072966
PMCPMC11902025

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.