Evidence map›Paper›PMID 39088282›Full record

ArticleJournal of behavioral addictions2024

The Gaming Disorder Identification Test (GADIT) - A screening tool for Gaming Disorder based on ICD-11.

Gary C K Chan, John B Saunders, Daniel Stjepanović, Caitlin McClure-Thomas, Jason Connor, Leanne Hides, Andrew Wood, Daniel King, Kristiana Siste, Jiang Long and 1 more

Abstract read
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In one paragraph

Article in Journal of behavioral addictions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

11 authors.

Gary C K Chan1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.ORCID 0000-0002-7569-1948
John B Saunders1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.
Daniel Stjepanović1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.
Caitlin McClure-Thomas1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.
Jason Connor1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.
Leanne Hides1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.
Andrew Wood3School of Applied Psychology, Griffith University, Australia.
Daniel King4College of Education, Psychology and Social Work, Flinders University, Australia.
Kristiana Siste5Department of Psychiatry, Faculty of Medicine, Universitas Indonesia, Indonesia.
Jiang Long7Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Janni K Leung1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.ORCID 0000-0001-5816-2959

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment. Method: We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder. Results: Confirmatory factor analyses of the GADIT showed good model fit (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets. Conclusion: The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.

Indexed as

International Classification of DiseasesInternet Addiction DisorderPsychometricsVideo GamesAdolescentAdultAustraliaCanadaFactor Analysis, StatisticalFemaleHumansMaleMiddle AgedPsychiatric Status Rating ScalesReproducibility of ResultsYoung Adultgaming disorderpsychometricsreliabilityscale developmentscreening toolvalidity

Identifiers

PMID39088282
PMCPMC11457026

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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.