Real-World Effectiveness of Wearable Augmented Reality Device for Patients With Hearing Loss: Prospective Study
Bibliographic record
Abstract
BACKGROUND: Hearing loss limits communication and social activity, and hearing aids (HAs) are an efficient rehabilitative option for improving oral communication and speech comprehension, as well as the psychosocial comfort of people with hearing loss. To overcome this problem, over-the-counter amplification devices including personal sound amplification products and wearable augmented reality devices (WARDs) have been introduced. OBJECTIVE: This study aimed to evaluate the clinical effectiveness of WARDs for patients with mild to moderate hearing loss. METHODS: A total of 40 patients (18 men and 22 women) with mild to moderate hearing loss were enrolled prospectively in this study. All participants were instructed to wear a WARD, Galaxy Buds Pro (Samsung Electronics), at least 4 hours a day for 2 weeks, for amplifying ambient sounds. Questionnaires including the Korean version of the abbreviated profile of hearing aid benefit (K-APHAB) and the Korean adaptation of the international outcome inventory for hearing aids (K-IOI-HA) were used to assess personal satisfaction in all participants. Audiologic tests, including sound field audiometry, sound field word recognition score (WRS), and the Korean version of hearing in noise test (K-HINT), were administered to 14 of 40 patients. The tests were performed under two conditions: unaided and aided with WARDs. RESULTS: The mean age of the participants was 55.4 (SD 10.7) years. After 2 weeks of the field trial, participants demonstrated a benefit of WARDs on the K-APHAB. Scores of 3 subscales of ease of communication, reverberation, and background noise were improved significantly (P<.001). However, scores regarding aversiveness were worse under the aided condition (P<.001). K-IOI-HA findings indicated high user satisfaction after the 2-week field trial. On audiologic evaluation, the K-HINT did not show significant differences between unaided and aided conditions (P=.97). However, the hearing threshold on sound field audiometry (P=.001) and the WRS (P=.002) showed significant improvements under the aided condition. CONCLUSIONS: WARDs can be beneficial for patients with mild to moderate hearing loss as a cost-effective alternative to conventional hearing aids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".