Literature DB >> 28635499

Hearing aid fine-tuning based on Dutch descriptions.

Thijs Thielemans1,2,3, Donné Pans2, Michelene Chenault3,4, Lucien Anteunis3.   

Abstract

OBJECTIVE: The aim of this study was to derive an independent fitting assistant based on expert consensus. Two questions were asked: (1) what (Dutch) terms do hearing impaired listeners use nowadays to describe their specific hearing aid fitting problems? (2) What is the expert consensus on how to resolve these complaints by adjusting hearing aid parameters?
DESIGN: Hearing aid dispensers provided descriptors that impaired listeners use to describe their reactions to specific hearing aid fitting problems. Hearing aid fitting experts were asked "How would you adjust the hearing aid if its user reports that the aid sounds…?" with the blank filled with each of the 40 most frequently mentioned descriptors. STUDY SAMPLE: 112 hearing aid dispensers and 15 hearing aid experts. The expert solution with the highest weight value was considered the best solution for that descriptor. Principal component analysis (PCA) was performed to identify a factor structure in fitting problems.
RESULTS: Nine fitting problems could be identified resulting in an expert-based, hearing aid manufacturer independent, fine-tuning fitting assistant for clinical use.
CONCLUSIONS: The construction of an expert-based, hearing aid manufacturer independent, fine-tuning fitting assistant to be used as an additional tool in the iterative fitting process is feasible.

Entities:  

Keywords:  Fine-tuning; assistive technology; fitting assistant; hearing aids; hearing aids satisfaction; instrumentation

Mesh:

Year:  2017        PMID: 28635499     DOI: 10.1080/14992027.2017.1288302

Source DB:  PubMed          Journal:  Int J Audiol        ISSN: 1499-2027            Impact factor:   2.117


  3 in total

1.  Consistency of Hearing Aid Setting Preference in Simulated Real-World Environments: Implications for Trainable Hearing Aids.

Authors:  Els Walravens; Gitte Keidser; Louise Hickson
Journal:  Trends Hear       Date:  2020 Jan-Dec       Impact factor: 3.293

2.  Discrimination of Gain Increments in Speech.

Authors:  Benjamin Caswell-Midwinter; William M Whitmer
Journal:  Trends Hear       Date:  2019 Jan-Dec       Impact factor: 3.293

3.  Discrimination of Gain Increments in Speech-Shaped Noises.

Authors:  Benjamin Caswell-Midwinter; William M Whitmer
Journal:  Trends Hear       Date:  2019 Jan-Dec       Impact factor: 3.293

  3 in total

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