Abstract
Objective: Remote hearing screening and assessment may improve access to, and uptake of, hearing care. This review, the most comprehensive to date, aimed to: (i) identify and assess functionality of remote hearing assessment tools on smartphones and online platforms, (ii) determine if assessed tools were also evaluated in peer-reviewed publications; and (iii) report accuracy of existing validation data.
Design: Protocol was registered in INPLASY and reported according to PRISMA-Extension for Scoping Reviews.
Study Sample: 187 remote hearing assessment tools (using tones, speech, self-report or a combination) and 101 validation studies met the inclusion criteria. Quality, functionality, bias and applicability of each app were assessed by at least two authors.
Results: Assessed tools showed considerable variability in functionality. Twenty-two (12%) tools were peer-reviewed and only 14 had acceptable functionality. The validation results and their quality varied greatly, largely depending on the category of the tool.
Conclusion: Tone-producing tools provide approximate hearing thresholds but have calibration and background noise issues. Speech and self-report tools are less affected by these issues, but mostly do not provide an estimated pure tone audiogram. Predicting audiograms using filtered language-independent materials could be a universal solution.
Design: Protocol was registered in INPLASY and reported according to PRISMA-Extension for Scoping Reviews.
Study Sample: 187 remote hearing assessment tools (using tones, speech, self-report or a combination) and 101 validation studies met the inclusion criteria. Quality, functionality, bias and applicability of each app were assessed by at least two authors.
Results: Assessed tools showed considerable variability in functionality. Twenty-two (12%) tools were peer-reviewed and only 14 had acceptable functionality. The validation results and their quality varied greatly, largely depending on the category of the tool.
Conclusion: Tone-producing tools provide approximate hearing thresholds but have calibration and background noise issues. Speech and self-report tools are less affected by these issues, but mostly do not provide an estimated pure tone audiogram. Predicting audiograms using filtered language-independent materials could be a universal solution.
| Original language | English |
|---|---|
| Journal | International Journal of Audiology |
| Publication status | Accepted/In press - 5 May 2022 |
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Manchester Centre for Audiology and Deafness (ManCAD)
Munro, K., Millman, R., Lamb, W., Dawes, P., Plack, C., Stone, M., Kluk-De Kort, K., Moore, D., Morton, C., Prendergast, G., Couth, S., Schlittenlacher, J., Chilton, H., Visram, A., Dillon, H., Guest, H., Heinrich, A., Jackson, I., Littlejohn, J., Jones, L., Lough, M., Morgan, R., Perugia, E., Roughley, A., Whiston, H., Wright, C., Saunders, G., Kelly, C., Cross, H., Loughran, M. & Hoseinabadi, R.
Project: Research