Experienced Adult Cochlear Implant Users Show Improved Speech Recognition When Target Fitting Parameters Are Applied

Abstract

Objectives: The aim of the present study was to investigate whether prediction models built by de Graaff et al. (2020) can be used to improve speech recognition in experienced adult postlingual implanted Cochlear CI users. de Graaff et al. (2020) found relationships between elevated aided thresholds and a not optimal electrical dynamic range (<50 CL or >60 CL), and poorer speech recognition in quiet and in noise. The primary hypothesis of the present study was that speech recognition improves both in quiet and in noise when the sound processor is refitted to match targets derived from the prediction models from de Graaff et al. (2020). A second hypothesis was that subjectively, most of the CI users would find the new setting too loud because of an increase in C levels, and therefore, prefer the old settings. Design: A within-participant repeated measures design with 18 adult Cochlear CI users was used. T- and C-levels were changed to “optimized settings,” as predicted by the model of de Graaff et al. (2020). Aided thresholds, speech recognition in quiet, and speech recognition in noise were measured with the old settings and after a 4-week acclimatization period with the optimized settings. Subjective benefit was measured using the Device Oriented Subjective Outcome Scale questionnaire. Results: The mean electrical dynamic range changed from 41.1 (SD = 6.6) CL to 48.6 (SD = 3.0) CL. No significant change in aided thresholds was measured. Speech recognition improved for 16 out of 18 participants and remained stable for 2 participants. Average speech recognition scores in quiet significantly improved by 4.9% (SD = 3.8%). No significant change for speech recognition in noise was found. A significant improvement in subjective benefit was found for one of the Device Oriented Subjective Outcome subscales (speech cues) between the old and optimized settings. All participants chose to keep the optimized settings at the end of the study. Conclusions: We were able to improve speech recognition in quiet by optimizing the electrical dynamic range of experienced adult CI users, according to the prediction models built by de Graaff et al. (2020). There was no significant change in aided thresholds nor in speech recognition in noise. The findings of the present study suggest that improved performance for speech recognition in quiet in adult Cochlear CI users can be achieved by setting the dynamic range as close as possible to values between 50 and 60 CL when the volume level is at 10.