Knowledge and Expectations of Hearing Aid Apps Among Smartphone Users and Hearing Professionals: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: Despite the increasing prevalence of hearing loss, the cost and psychological barriers to the use of hearing aids may prevent their use in individuals with hearing loss. Patients with hearing loss can benefit from smartphone-based hearing aid apps (SHAAs), which are smartphone apps that use a mobile device as a sound amplifier. OBJECTIVE: The aim of this study is to determine how ear, nose, and throat outpatients perceive SHAAs, analyze the factors that affect their perceptions, and estimate the costs of an annual subscription to an app through a self-administered questionnaire survey of smartphone users and hearing specialists. METHODS: This study used a cross-sectional, multicenter survey of both ear, nose, and throat outpatients and hearing specialists. The questionnaire was designed to collect personal information about the respondents and their responses to 18 questions concerning SHAAs in five domains: knowledge, needs, cost, expectations, and information. Perception questions were rated on a scale of 1 (strongly disagree) to 5 (strongly agree). Questions about the expected cost of SHAAs were included in the questionnaire distributed to hearing experts. RESULTS: Among the 219 smartphone users and 42 hearing specialists, only 8 (3.7%) respondents recognized SHAAs, whereas 18% (47/261) of respondents reported considering the use of an assistive device to improve their hearing capacity. The average perception score was 2.81 (SD 1.22). Among the factors that shaped perceptions of SHAAs, the needs category received the lowest scores (2.02, SD 1.42), whereas the cost category received the highest scores (3.29, SD 1.14). Age was correlated with the information domain (P<.001), and an increased level of hearing impairment resulted in significantly higher points in the needs category (P<.001). Patients expected the cost of an annual app subscription to an SHAA to be approximately US $86, and the predicted cost was associated with economic status (P=.02) and was higher than the prices expected by hearing specialists (P<.001). CONCLUSIONS: Outpatients expected SHAAs to cost more than hearing specialists. However, the perception of the SHAA was relatively low. In this regard, enhanced awareness is required to popularize SHAAs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".