Computer-based Auditory Training in New Adult Cochlear Implant Recipients: A Review of Training Outcomes in More than 100 Patients in the Outpatient Setting
Year: 2025
Type: review
Abstract: Objective: To review research on the effectiveness of computer-based auditory training (CBAT) in new adult cochlear implant (CI) recipients. Methods: Studies by the primary author on auditory training in new adult CI recipients were reviewed, focusing on a database of auditory training data in >100 patients followed over their first-year post-activation. Results: CBAT, as used in a standard outpatient setting, affords benefits compared to other commonly employed exercises/programs. In new adult CI users, CBAT was associated with greater improvement in CNC words, AzBio sentences, and Cochlear Implant Quality of Life-35 (CIQOL-35) Profile scores at 3 months post-activation compared to CI recipients who did not train or used other resources. By 12 months, patients showed similar improvement in speech recognition scores, regardless of training modality; however, patients who used CBAT continued to show significantly greater improvement in CIQOL global and all domain scores compared to CBAT nonusers. This benefit was noted regardless of whether CBAT was started before or after 3 months post-activation. No demographic/lifestyle factors, audiological measures, or pre-CI CIQOL/CIQOL-Expectations scores were associated with CBAT use. Conclusion: CBAT during the first-year post-activation may contribute to early gains in CI speech recognition and to persistent improvements in functional abilities, as measured via the CIQOL-35 profile, in new implant recipients. Consideration of specific recommendations for the use of CBAT resources may be valuable, given the minimal risk, ease of access, and affordability of these programs.
Source: Seminars in Hearing
Institutions: Mayo Clinic, Medical University of South Carolina
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4414340772
