Evidence map›Paper›PMID 41013923›Full record

ArticleAddiction (Abingdon, England)2026

Red Box, Green Box: Psychometric evaluation of a self-report behavioral frequency measurement approach for behavioral addictions research.

Matthew W R Stevens, Marcela Radünz, Christina Galanis, Blake Quinney, Ian Zajac, Joël Billieux, Paul H Delfabbro, Daniel L King

Abstract read
In one paragraph

Article in Addiction (Abingdon, England), 2026. 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

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

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

8 authors.

Matthew W R StevensCollege of Education, Psychology and Social Work, Flinders University, Adelaide, Australia.ORCID 0000-0002-8797-9244
Marcela RadünzCollege of Education, Psychology and Social Work, Flinders University, Adelaide, Australia.ORCID 0000-0002-2858-0313
Christina GalanisCollege of Education, Psychology and Social Work, Flinders University, Adelaide, Australia.ORCID 0000-0002-9612-4192
Blake QuinneySchool of Psychology, University of Adelaide, Adelaide, Australia.ORCID 0000-0002-2899-2510
Ian ZajacCollege of Education, Psychology and Social Work, Flinders University, Adelaide, Australia.ORCID 0000-0002-7786-3993
Joël BillieuxInstitute of Psychology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0002-7388-6194
Paul H DelfabbroSchool of Psychology, University of Adelaide, Adelaide, Australia.ORCID 0000-0002-0466-5611
Daniel L KingCollege of Education, Psychology and Social Work, Flinders University, Adelaide, Australia.ORCID 0000-0002-1762-2581

Funding

National Health and Medical Research Council 2033280
6 · The paper itself

Abstract

aimsThe behavioral addictions field lacks clinically useful behavior frequency measures. This study evaluated the psychometric performance of the new 'Red Box, Green Box' method for measuring gaming behavior with a focus on its utility for gaming disorder (GD) screening. DESIGN, SETTING AND

participantsA prospective, cross-sectional survey study was conducted using an online crowdsourcing platform. Participants were 1149 male gamers aged 18-35 years from Australia, Canada, United States, United Kingdom and Asia, reporting ≥12 hours of weekly gaming. MEASUREMENTS: Gaming time was measured using a conventional weekly hours item, Red Box hours (gaming instead of fulfilling responsibilities) and Green Box hours (gaming during free or leisure time). GD was assessed by the Internet Gaming Disorder Test (IGDT-10), with International Classification of Diseases, 11th Revision (ICD-11) and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) scoring approaches. Psychological distress [Depression, Anxiety and Stress Scale - 21 Items (DASS-21)] and impulsivity [Barratt Impulsiveness Scale-short form (BIS-15)] were measured.

findingsGamers with GD reported higher Red Box hours [mean (M) = 21.1, standard deviation (SD) = 11.3] than those without (M = 8.7, SD = 8.4; P < 0.001), and greater Red Box proportion (41.9% vs. 26.8%; P < 0.001). Red Box hours demonstrated superior diagnostic accuracy for GD [area under the curve (AUC) = 0.86, sensitivity = 0.94, specificity = 0.63] and Internet gaming disorder (IGD) (AUC = 0.76, sensitivity = 0.88, specificity = 0.56), outperforming comparative measures. A Red Box response of ≥ 9.5 hours had a 94% likelihood of indicating ICD-11 GD.

conclusionsThe 'Red Box, Green Box' method appears to effectively identify International Classification of Diseases, 11th Revision, gaming disorder risk among males. Red Box hours demonstrated greater classification validity than the conventional weekly hours approach. This method provides a simple tool for epidemiological research, routine screening (e.g. outpatient consultation) and clinical assessment and treatment planning. Further validation in clinical populations and longitudinal studies is needed.

Indexed as

Behavior, AddictiveInternet Addiction DisorderSelf ReportVideo GamesAdolescentAdultAustraliaCanadaCross-Sectional StudiesHumansImpulsive BehaviorMaleProspective StudiesPsychometricsReproducibility of ResultsUnited Kingdomaddictionbehaviorgaming disorderICD‐11measurementtool

Identifiers

PMID41013923
PMCPMC12779596

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.