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Evaluating Low-Gain Hearing Aids with Speech-Based and Conventional Programming in Blast-Exposed Veterans with Hearing Difficulties
Led by VA Office of Research and Development · Updated on 2026-04-14
80
Participants Needed
3
Research Sites
N/A
Total Duration
AI-Summary
What this Trial Is About
This research aims to evaluate low-gain hearing aids programmed using either conventional audiogram-based methods or a new speech-based audiometry SBA approach in blast-exposed Veterans experiencing functional hearing difficulties FHDs despite normal or near-normal hearing. Blast injury has been linked to increased risk of auditory problems, and this study focuses on improving hearing aid fitting to better address speech recognition and listening challenges in this population. Participants will be randomly assigned to receive hearing aids programmed with either the SBA method or standard audiogram-based settings. Each group is further divided into subgroups where some use their hearing aids daily over six weeks, while others use them only for outcome testing. The study includes prescreening, preparatory visits for fitting and verification, a baseline period with assessments including fMRI and speech-in-noise tests, a five-week treatment period with weekly monitoring, and a follow-up period with repeated assessments. Throughout the study, participants will undergo a series of evaluations including audiological exams, questionnaires on hearing difficulties and cognitive function, speech recognition tests, and brain imaging during speech tasks. Hearing aid performance and usage are closely monitored with specialized equipment and data logging. Primary outcomes include changes in speech-in-noise test scores and subjective hearing aid benefit measured before and after the six-week intervention. Safety and hearing aid function are regularly checked during weekly visits, with study completion followed by transition back to routine care.
CONDITIONS
Brief Title
Comparing Hearing Aid Fitting Methods in Blast-exposed Veterans
Research Team
J
Jonathan Venezia, PhD
A
Akai R McKinley, BA
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