Evidence map›Paper›PMID 39576295›Full record

ArticleJournal of behavioral addictions2024

Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity.

Vasileios Stavropoulos, Maria Prokofieva, Daniel Zarate, Michelle Colder Carras, Rabindra Ratan, Rachel Kowert, Bruno Schivinski, Tyrone L Burleigh, Dylan Poulus, Leila Karimi and 6 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 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. Article
  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

16 authors.

Vasileios Stavropoulos1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0001-6964-4662
Maria Prokofieva2Victoria University, Australia.ORCID 0000-0003-1974-3827
Daniel Zarate1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0002-1508-8637
Michelle Colder Carras3Bloomberg School of Public Health, John Hopkins University, Baltimore, USA.ORCID 0000-0003-0750-524X
Rabindra Ratan4Michigan State University, USA.ORCID 0000-0001-7611-8046
Rachel Kowert5Psychology Research Institute, Faculty of Social Studies, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-9522-9945
Bruno Schivinski7School of Media and Communication, RMIT University, Australia.ORCID 0000-0002-4095-1922
Tyrone L Burleigh1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0002-3405-140X
Dylan Poulus8Physical Activity, Sport and Exercise Research Theme, Faculty of Health, Southern Cross University, Gold Coast, Australia.ORCID 0000-0003-4502-6821
Leila Karimi1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0003-2364-504X
Angela Gorman-Alesi10Catholic Care Victoria, Australia.
Taylor Brown1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0001-9814-3832
Rapson Gomez1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0001-7637-1551
Kaiden Hein1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.ORCID 0000-0002-1758-9208
Nalin Arachchilage11School of Computing, RMIT University, Australia.ORCID 0000-0002-0059-0376
Mark D Griffiths12International Gaming Research Unit, Psychology Dept, Nottingham Trent University, UK.ORCID 0000-0001-8880-6524

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In response to our study, the commentary by Infanti et al. (2024) raised critical points regarding (i) the conceptualization and utility of the user-avatar bond in addressing gaming disorder (GD) risk, and (ii) the optimization of supervised machine learning techniques applied to assess GD risk. To advance the scientific dialogue and progress in these areas, the present paper aims to: (i) enhance the clarity and understanding of the concepts of the avatar, the user-avatar bond, and the digital phenotype concerning gaming disorder (GD) within the broader field of behavioral addictions, and (ii) comparatively assess how the user-avatar bond (UAB) may predict GD risk, by both removing data augmentation before the data split and by implementing alternative data imbalance treatment approaches in programming.

Indexed as

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

Identifiers

PMID39576295
PMCPMC11737409

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.