Encouraging Hearing Loss Prevention in Music Listeners Using Personalized Technology: Questionnaire Study
Bibliographic record
Abstract
Background Noise-induced hearing loss (NIHL) affects millions of people despite being almost completely preventable. For recreational music listening through personal listening equipment (such as earbuds), it seems that listeners do not yet have a way to accurately assess their risk of developing hearing loss and prevent it accordingly. Objective The aim of this study is to analyze the perceived utility of a hypothetical device that encourages NIHL prevention based on listeners’ exposure to noise and to determine the most effective methods of such encouragement. Here, we describe 3 different potential NIHL risk notification method types, as follows: auditory, external visual, and visual. Methods An open, web-based survey was created on Google Forms, and the link was posted to Amazon’s Mechanical Turk as well as music-related Reddit communities. The survey was designed to gauge each respondent’s self-assessed NIHL awareness, willingness to lower their audio if reminded, and NIHL risk notification type preference. The likelihood of a specific notification type to encourage NIHL prevention among its users was based on the average of each user’s responses to 2 survey questions. Data collection started on July 13, 2020, and ended on July 17, 2020. Results Of the 116 respondents, 92 (79.3%) reported having prior awareness about NIHL; however, 60 (51.7%) described doing nothing to prevent it despite 96 (82.8%) feeling a moderate, high, or extreme risk of developing NIHL. Of those who already prevented NIHL, 96% (53.5/56) described using estimates to guide their prevention instead of using data. A Kruskal-Wallis test corrected for ties showed that despite the visual NIHL risk notification type being selected by the highest number of participants (84/116, 72.4%), the auditory type had a significantly higher (H1=6.848; P=.03) average percentage likelihood of encouraging NIHL prevention (62%, SD 24%) among the 40 respondents who chose it, with a median likelihood of 56% (95% CI 50%-75%). The visual type’s average likelihood was 50% (SD 28.1%), with a median of 50% (95% CI 37.5%-56.3%). Regardless of the NIHL risk notification type, 69% (80/116) of respondents were not opposed to using NIHL risk notifications and lowering their audio volume accordingly. Conclusions The hypothetical device detailed here was thought to be useful because most respondents (82.8%, 96/116) felt an extreme to moderate risk of developing NIHL and such a device could provide accurate data to those who currently use estimates to prevent NIHL, and most respondents were willing to act on NIHL risk notifications. The most effective NIHL risk notification type seemed to be the auditory type, but many aspects of this study need further research to determine which implementation method should reach the public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".