Individual Aided Speech-Recognition Performance and Predictions of Benefit for Listeners With Impaired Hearing Employing FADE.
Marc R Schädler, David Hülsmeier, Anna Warzybok and 1 others
PMID 32924797WHAT IT FOUND
Simulating hearing aid benefit is more accurate when tone-in-noise tests are used to measure distortion, not just hearing loss.
This method predicted individual speech recognition in noise better than standard audiograms, though it remains a laboratory model not yet ready for clinical fitting.
Key findings
01Predicting speech recognition benefit was most accurate when the model included individual distortion data from tone-in-noise tests, reducing average error to 3.4 dB.
02Standard clinical audiograms alone produced larger prediction errors, with an average error of 7.1 dB for hearing aid benefit.
03Hearing aid benefit varied widely by individual, ranging from a 15 dB decrease to a 30 dB improvement in speech recognition scores.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
The study used a computer simulation and laboratory conditions, not real-world field tests. The sample size was small (18 listeners), and only 11 had complete data for the most accurate prediction method. The model did not account for binaural hearing or cognitive factors, which affect real-world listening. The hearing aid settings were simulated using a master hearing aid, not actual commercial devices fitted to patients.
Declared interests
Funded by the Deutsche Forschungsgemeinschaft (German Research Foundation). No commercial conflicts of interest reported.
The easy way to misread this
Do not interpret the reduced prediction error as evidence that tone-in-noise testing is currently a valid clinical tool for fitting hearing aids. The study validated a computer model's accuracy against laboratory data, not the clinical utility of the test itself for real-world patient management.