The value of headphone accommodations in Apple Airpods Pro for managing speech-in-noise hearing difficulties of individuals with normal audiograms
Bibliographic record
Abstract
To investigate the extent to which Headphone Accommodations in Apple AirPods Pro attend to the hearing needs of individuals with normal audiograms who experience hearing difficulties in noisy environments. Single-arm interventional study using acoustic measures, speech-in-noise laboratory testing, and real-world measures via questionnaires and ecological momentary assessment. Seventeen normal-hearing individuals (9 female, 21–59 years) with self-reported hearing-in-noise difficulties. Acoustic measures showed that, relative to unaided, AirPods Pro provided a SNR advantage of +5.4 dB. Speech intelligibility performance in laboratory testing increased 11.8% with AirPods Pro, relative to unaided. On average, participants trialling AirPods Pro in real-world noisy venues reported that their overall hearing experience was a bit better than without them. Five participants (29%) reported that they would continue using AirPods Pro in the future. The most relevant barriers that would discourage their future use were limited hearing benefit, discomfort, and stigma. Occasional use of AirPods Pro may help some individuals with normal audiograms ameliorate their speech-in-noise hearing difficulties. The identified barriers may inspire the development of new technological solutions aimed at providing an optimal management strategy for the hearing difficulties of this segment of the population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".