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Record W4403807026 · doi:10.2196/60476

Safe Listening Beliefs, Attitudes, and Practices Among Gamers and Esports Participants: International Web-Based Survey

2024· article· en· W4403807026 on OpenAlexvenueno aff
Nicola Diviani, Shelly Chadha, Peter Mulas, Sara Rubinelli

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsPreprintActive listeningPsychologyInternet privacyComputer scienceWorld Wide WebCommunication

Abstract

fetched live from OpenAlex

BACKGROUND: The global rise of video gaming and esports has raised significant concerns about hearing loss due to loud sound exposure. While these activities provide entertainment and have applications in health care, the auditory health risks and behavioral factors influencing listening habits among gamers remain underexplored. Research is needed to develop tailored interventions that address the unique barriers, attitudes, and beliefs of gamers and esports participants, promoting safer listening practices and minimizing auditory health risks. OBJECTIVE: This study aimed to explore listening behaviors, attitudes, and awareness regarding hearing health risks among video gamers and esports participants. The findings are intended to guide the design and implementation of technological features that encourage safer listening practices, in alignment with the World Health Organization's Safe Listening initiative. METHODS: An open web-based survey was conducted from September 2022 to January 2023, targeting video gamers and esports enthusiasts. Participants were recruited via World Health Organization social media platforms and outreach to stakeholders. The survey assessed gaming behaviors, listening habits, awareness about hearing health, beliefs, readiness to change listening behaviors, and communication preferences. Data were analyzed using descriptive statistics and multinomial logistic regression. RESULTS: A total of 488 responses were collected, with 67.2% (n=328) of participants identifying as male, and 56.4% (n=275) having a college degree or higher. Of the respondents, 90.8% (n=443) were actively engaged in video gaming, while 54.9% (n=268) viewed esports, and 13.9% (n=68) participated in esports events. Notably, 24.8% (n=110) of gamers, 18.3% (n=49) of esports viewers, and 37.1% (n=23) of esports players reported using high or very high volume settings. Despite around half of the participants experiencing symptoms indicative of hearing damage (eg, ringing in the ears), only 34.3% (n=152) of gamers, 35.8% (n=92) of esports players, and 39.7% (n=27) of esports viewers reported taking sound breaks every hour. The study identified a balanced distribution across readiness-to-change stages, with 30.3% (n=148) in the precontemplation stage, 35.3% (n=173) in the contemplation stage, and 34.2% (n=167) in the action stage. Factors such as perceived susceptibility to hearing loss, perceived benefits of preventive action, and self-efficacy significantly influenced readiness to change. Communication preferences indicated that 51% (n=249) of participants were interested in receiving more information on hearing health, with health care professionals and governmental agencies being the most trusted sources. CONCLUSIONS: The findings highlight an urgent need for interventions to promote safe listening practices among gamers, emphasizing a gap between awareness and preventive action. The integration of safe listening features into video games and esports platforms, along with targeted communication strategies, can enhance auditory health awareness and protective behaviors. Future research should evaluate the effectiveness of these interventions to ensure comprehensive auditory health protection in the digital entertainment sector.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.098
GPT teacher head0.478
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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