Evidence map›Paper›PMID 27499227›Full record

ArticleJournal of behavioral addictions2016

Predictors and patterns of problematic Internet game use using a decision tree model.

Mi Jung Rho, Jo-Eun Jeong, Ji-Won Chun, Hyun Cho, Dong Jin Jung, In Young Choi, Dai-Jin Kim

Abstract read
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In one paragraph

Article in Journal of behavioral addictions, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Prediction of Problematic Smartphone Use: A Machine Learning Approach.International journal of environmental research and public health · 2021
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Risk Factors for Internet Gaming Disorder: Psychological Factors and Internet Gaming Characteristics.International journal of environmental research and public health · 2017
    Article
  13. Article
  14. 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

7 authors.

Mi Jung Rho1 Department of Medical Informatics, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
Jo-Eun Jeong3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
Ji-Won Chun3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
Hyun Cho3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
Dong Jin Jung3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
In Young Choi1 Department of Medical Informatics, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
Dai-Jin Kim3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims Problematic Internet game use is an important social issue that increases social expenditures for both individuals and nations. This study identified predictors and patterns of problematic Internet game use. Methods Data were collected from online surveys between November 26 and December 26, 2014. We identified 3,881 Internet game users from a total of 5,003 respondents. A total of 511 participants were assigned to the problematic Internet game user group according to the Diagnostic and Statistical Manual of Mental Disorders Internet gaming disorder criteria. From the remaining 3,370 participants, we used propensity score matching to develop a normal comparison group of 511 participants. In all, 1,022 participants were analyzed using the chi-square automatic interaction detector (CHAID) algorithm. Results According to the CHAID algorithm, six important predictors were found: gaming costs (50%), average weekday gaming time (23%), offline Internet gaming community meeting attendance (13%), average weekend and holiday gaming time (7%), marital status (4%), and self-perceptions of addiction to Internet game use (3%). In addition, three patterns out of six classification rules were explored: cost-consuming, socializing, and solitary gamers. Conclusion This study provides direction for future work on the screening of problematic Internet game use in adults.

Indexed as

InternetAdultAlgorithmsBehavior, AddictiveDecision TreesFemaleHealth Knowledge, Attitudes, PracticeHumansMaleMarital StatusMiddle AgedPrognosisRepublic of KoreaSelf ConceptSocial BehaviorTime Factorschi-square automatic interaction detectordecision tree analysispatternpredictorsproblematic Internet game use

Identifiers

PMID27499227
PMCPMC5264417

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LicenceCC BY-NC
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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.