Improved Speech Recognition in Adults With Conductive or Mixed Hearing Loss Using a Direct-to-Consumer Bone-Conduction Device: A Multiple Methods Intervention Study
Bibliographic record
Abstract
Background: Hearing loss affects 20% of the global population, including 250 million experiencing chronic suppurative otitis media, which can present challenges for conventional hearing aids due to ear discharge. Although assistive technology for hearing is available in high-income settings, provision is poor in low-income settings due to high costs and low availability of audiology services, reaching approximately 3% of those who could benefit from it. Objective: This study aimed to evaluate the performance of a low-cost self-fitted direct-to-consumer bone-conduction headset for individuals with conductive or mixed hearing loss. Methods: We conducted a multiple methods study to test the efficacy and acceptability of this device using a purposive sample. Participants with a range of conductive and mixed hearing loss underwent speech-in-quiet speech audiometry with and without the device and took part in feedback interviews exploring their subjective impressions of the device. Results: In 33 participants, the device improved speech recognition in those with bone conduction thresholds <50 dB by a median of 11%, with larger air-bone gap associated with larger improvement. Participants rated the device positively on weight, style, and ease of use. Conclusions: This multiple methods study assessed the acceptability and efficacy of a low cost self-fitted bone-conduction device in adults. We found the device provides hearing benefit for those with conductive or mixed hearing loss (with bone conduction thresholds <50dB HL). Those with significant conductive hearing loss were measured to have their speech perception significantly improved. Participants had a mixed response to device aesthetics. Further studies should seek to establish if this type of device has effectiveness in real-world trials and which individuals are most likely to benefit. This low cost device could provide hearing benefits to millions of people without access to other devices. Product designers and clinical researchers should explore device optimization. Given the economic impacts of hearing loss across the globe, this style of self-fitted device could represent a paradigm shift in future assistive technology for hearing loss, in both high and low resource settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".