Evidence map›Paper›PMID 39576292›Full record

ArticleJournal of behavioral addictions2024

User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution.

Alexandre Infanti, Alessandro Giardina, Josip Razum, Daniel L King, Stephanie Baggio, Jeffrey G Snodgrass, Matthew Vowels, Adriano Schimmenti, Orsolya Király, Hans-Juergen Rumpf and 2 more

Abstract read
PubMed Full text at PMC Publisher
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

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

12 authors.

Alexandre Infanti1Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.ORCID 0000-0002-7023-0712
Alessandro Giardina2Institute of Psychology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-6313-4290
Josip Razum3Ivo Pilar Institute of Social Sciences, Zagreb, Croatia.ORCID 0000-0002-2633-3271
Daniel L King4College of Education, Psychology & Social Work, Flinders University, Australia.ORCID 0000-0002-1762-2581
Stephanie Baggio5Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland.ORCID 0000-0002-5347-5937
Jeffrey G Snodgrass7Department of Anthropology and Geography, Colorado State University, Fort Collins, CO 80523-1787, USA.ORCID 0000-0003-2725-8063
Matthew Vowels2Institute of Psychology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0002-8811-1156
Adriano Schimmenti8Department of Human and Social Sciences, UKE - Kore University of Enna, Cittadella Universitaria, Enna, Italy.ORCID 0000-0001-5181-2648
Orsolya Király9Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary.ORCID 0000-0001-9981-4212
Hans-Juergen Rumpf11University of Lübeck, Department of Psychiatry and Psychotherapy, Lübeck, Germany.ORCID 0000-0001-6848-920X
Claus Vögele1Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.ORCID 0000-0001-8086-0788
Joël Billieux2Institute of Psychology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0002-7388-6194

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In their study, Stavropoulos et al. (2023) capitalized on supervised machine learning and a longitudinal design and reported that the User-Avatar Bond could be accurately employed to detect Gaming Disorder (GD) risk in a community sample of gamers. The authors suggested that the User-Avatar Bond is a "digital phenotype" that could be used as a diagnostic indicator for GD risk. In this commentary, our objectives are twofold: (1) to underscore the conceptual challenges of employing User-Avatar Bond for conceptualizing and diagnosing GD risk, and (2) to expound upon what we perceive as a misguided application of supervised machine learning techniques by the authors from a methodological standpoint.

Indexed as

Internet Addiction DisorderAvatarHumansSupervised Machine LearningUser-Computer InterfaceVideo Gamesclassificationdiagnosisgaming disordermachine learninguser-avatar bond

Identifiers

PMID39576292
PMCPMC11737422

What OpenQuestion holds

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Registered trials

None linked

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.