Literature DB >> 29189432

The South African English Smartphone Digits-in-Noise Hearing Test: Effect of Age, Hearing Loss, and Speaking Competence.

Jenni-Marí Potgieter1, De Wet Swanepoel, Hermanus Carel Myburgh2, Cas Smits3.   

Abstract

OBJECTIVES: This study determined the effect of hearing loss and English-speaking competency on the South African English digits-in-noise hearing test to evaluate its suitability for use across native (N) and non-native (NN) speakers.
DESIGN: A prospective cross-sectional cohort study of N and NN English adults with and without sensorineural hearing loss compared pure-tone air conduction thresholds to the speech reception threshold (SRT) recorded with the smartphone digits-in-noise hearing test. A rating scale was used for NN English listeners' self-reported competence in speaking English. This study consisted of 454 adult listeners (164 male, 290 female; range 16 to 90 years), of whom 337 listeners had a best ear four-frequency pure-tone average (4FPTA; 0.5, 1, 2, and 4 kHz) of ≤25 dB HL.
RESULTS: A linear regression model identified three predictors of the digits-in-noise SRT, namely, 4FPTA, age, and self-reported English-speaking competence. The NN group with poor self-reported English-speaking competence (≤5/10) performed significantly (p < 0.01) poorer than the N and NN (≥6/10) groups on the digits-in-noise test. Screening characteristics of the test improved with separate cutoff values depending on English-speaking competence for the N and NN groups (≥6/10) and NN group alone (≤5/10). Logistic regression models, which include age in the analysis, showed a further improvement in sensitivity and specificity for both groups (area under the receiver operating characteristic curve, 0.962 and 0.903, respectively).
CONCLUSIONS: Self-reported English-speaking competence had a significant influence on the SRT obtained with the smartphone digits- in-noise test. A logistic regression approach considering SRT, self-reported English-speaking competence, and age as predictors of best ear 4FPTA >25 dB HL showed that the test can be used as an accurate hearing screening tool for N and NN English speakers. The smartphone digits-in-noise test, therefore, allows testing in a multilingual population familiar with English digits using dynamic cutoff values that can be chosen according to self-reported English-speaking competence and age.

Entities:  

Mesh:

Year:  2018        PMID: 29189432     DOI: 10.1097/AUD.0000000000000522

Source DB:  PubMed          Journal:  Ear Hear        ISSN: 0196-0202            Impact factor:   3.570


  15 in total

1.  Remote self-report and speech-in-noise measures predict clinical audiometric thresholds.

Authors:  Lina Motlagh Zadeh; Veronica Brennan; De Wet Swanepoel; Li Lin; David R Moore
Journal:  medRxiv       Date:  2022-07-07

2.  Diotic and Antiphasic Digits-in-noise Testing as a Hearing Screening and Triage Tool to Classify Type of Hearing Loss.

Authors:  Karina C De Sousa; Cas Smits; David R Moore; Hermanus C Myburgh; De Wet Swanepoel
Journal:  Ear Hear       Date:  2022 May/Jun       Impact factor: 3.562

3.  Characteristics and Help-Seeking Behavior of People Failing a Smart Device Self-Test for Hearing.

Authors:  Danielle Schönborn; Faheema Mahomed Asmail; Karina C De Sousa; Ariane Laplante-Lévesque; David R Moore; Cas Smits; De Wet Swanepoel
Journal:  Am J Audiol       Date:  2020-06-08       Impact factor: 1.493

4.  Assessment of noise pollution and its effects on human health in industrial hub of Pakistan.

Authors:  Zia Ur Rahman Farooqi; Muhammad Sabir; Junaid Latif; Zubair Aslam; Hamaad Raza Ahmad; Iftikhar Ahmad; Muhammad Imran; Predrag Ilić
Journal:  Environ Sci Pollut Res Int       Date:  2019-12-13       Impact factor: 4.223

5.  Innovation in the Context of Audiology and in the Context of the Internet.

Authors:  Lynne E Bernstein; Jana Besser; David W Maidment; De Wet Swanepoel
Journal:  Am J Audiol       Date:  2018-11-19       Impact factor: 1.493

6.  A Smartphone National Hearing Test: Performance and Characteristics of Users.

Authors:  Karina C De Sousa; De Wet Swanepoel; David R Moore; Cas Smits
Journal:  Am J Audiol       Date:  2018-11-19       Impact factor: 1.493

Review 7.  Smartphone-Based Applications to Detect Hearing Loss: A Review of Current Technology.

Authors:  Alexandria L Irace; Rahul K Sharma; Nicholas S Reed; Justin S Golub
Journal:  J Am Geriatr Soc       Date:  2020-12-29       Impact factor: 5.562

8.  Improved Sensitivity of Digits-in-Noise Test to High-Frequency Hearing Loss.

Authors:  Lina Motlagh Zadeh; Noah H Silbert; De Wet Swanepoel; David R Moore
Journal:  Ear Hear       Date:  2021 May/Jun       Impact factor: 3.562

9.  Evaluating a smartphone digits-in-noise test as part of the audiometric test battery.

Authors:  Jenni-Mari Potgieter; De Wet Swanepoel; Cas Smits
Journal:  S Afr J Commun Disord       Date:  2018-05-21

10.  FreeHear: A New Sound-Field Speech-in-Babble Hearing Assessment Tool.

Authors:  David R Moore; Helen Whiston; Melanie Lough; Antonia Marsden; Harvey Dillon; Kevin J Munro; Michael A Stone
Journal:  Trends Hear       Date:  2019 Jan-Dec       Impact factor: 3.293

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