What’s in an App? Scoping Review and Quality Assessment of Clinically Available Hearing-Aid-Connected Apps
Bibliographic record
Abstract
Background/Objectives: Mobile health (mHealth) tools, such as smartphone apps, support person-centred care for persons with hearing loss engaging in the hearing aid management process. Hearing-aid-connected apps are increasingly common in audiological care, making it important to evaluate their availability and quality for clinicians, developers, and end-users. This scoping review aimed to identify, summarize, and synthesize information on clinically available hearing-aid-connected apps and evaluate their quality. Methods: A search of the Apple App Store (Canada) was conducted in August 2024 to identify current hearing-aid-connected apps that support hearing aid management. Metadata and features were extracted, and app quality was assessed using the Mobile Application Rating Scale (MARS). Quality was assessed across four objective domains (engagement, functionality, aesthetics, and information) and one subjective domain. Results: Apps had varying levels of metadata detail, including updates, compatibility, and target populations. All apps included common hearing aid controls (e.g., volume adjustment, microphone directionality), while more specialized features (tinnitus management, health tracking, remote clinician support) varied. High-performing apps scored significantly higher in engagement, functionality, aesthetics, and subjective quality, and all apps scored low in information quality, particularly for evidence and credibility. Conclusions: Findings highlight the need for transparent and informative metadata reporting and patient-centred design to improve clinical awareness, usability, and uptake of hearing-aid-connected apps.
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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 | 0.093 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.039 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".