Validity of hearing screening using hearTest smartphone-based audiometry: performance evaluation of different response modes

Corona, Ana Paula; Ferrite, Sílvia; Bright, Tess; Polack, Sarah

doi:10.1080/14992027.2020.1731767

Cited by 18 publications

(21 citation statements)

References 42 publications

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“…The remaining study did not report the age of the study population [ 45 ]. In all, 15 studies operated audiometry through an iPhone (Apple Inc) operating system–based app [ 10 , 22 , 29 , 36 , 37 , 39 , 40 , 45 - 48 , 50 , 52 , 54 , 55 ], whereas the remaining 10 used an Android (Google LLC) operating system–based audiometry app [ 21 , 23 , 38 , 41 - 44 , 49 , 51 , 53 ]. A total of 15 studies used headphones for testing [ 10 , 21 , 23 , 38 , 41 - 47 , 49 , 50 , 52 , 54 ], 9 studies used earphones for the examination [ 22 , 29 , 36 , 37 , 39 , 40 , 48 , 53 , 55 ], and 1 study did not mention the equipment used [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of the 25 studies, 21 were prospective [10,21,22,29,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], 1 was retrospective [53], and the remaining 3 studies did not report the study design [23,54,55]. In all, 20 studies used PTA as the index test [10,21,22,29,[36][37][38][39][40][41][42][44][45][46][48][49][50][51][52]54], whereas the remaining 5 studies applied a speech recognition test (SRT) as the index test [23,43,47,53,55]. A total of 4 studies ...…”

Section: Study Characteristicsmentioning

confidence: 99%

“…In all, 20 studies used PTA as the index test [10,21,22,29,[36][37][38][39][40][41][42][44][45][46][48][49][50][51][52]54], whereas the remaining 5 studies applied a speech recognition test (SRT) as the index test [23,43,47,53,55]. A total of 4 studies enrolled elderly participants [10,36,37,39], whereas 7 studies included children [21][22][23]38,41,49,55], and 13 studies enrolled adult participants [29,40,[42][43][44]46,[48][49][50][51][52][53][54]. The remaining study did not report the age of the study population…”

Section: Study Characteristicsmentioning

confidence: 99%

“…A total of 15 studies used headphones for testing [10,21,23,38,41-47,49,50,52,54], 9 studies used earphones for the examination [22,29,36,37,39,40,48,53,55], and 1 study did not mention the equipment used[51]. In all, 6 studies conducted the examination in a soundproof booth[10,[49][50][51]53,55], 18 studies did not use a soundproof booth to conduct the examination[21][22][23]29,[36][37][38][39][40][41][42][43][44][45][46][47]52,54]…”

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confidence: 99%

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Diagnostic Accuracy of Smartphone-Based Audiometry for Hearing Loss Detection: Meta-analysis

Chen¹,

Lin²,

Wang³

et al. 2021

JMIR Mhealth Uhealth

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Background Hearing loss is one of the most common disabilities worldwide and affects both individual and public health. Pure tone audiometry (PTA) is the gold standard for hearing assessment, but it is often not available in many settings, given its high cost and demand for human resources. Smartphone-based audiometry may be equally effective and can improve access to adequate hearing evaluations. Objective The aim of this systematic review is to synthesize the current evidence of the role of smartphone-based audiometry in hearing assessments and further explore the factors that influence its diagnostic accuracy. Methods Five databases—PubMed, Embase, Cochrane Library, Web of Science, and Scopus—were queried to identify original studies that examined the diagnostic accuracy of hearing loss measurement using smartphone-based devices with conventional PTA as a reference test. A bivariate random-effects meta-analysis was performed to estimate the pooled sensitivity and specificity. The factors associated with diagnostic accuracy were identified using a bivariate meta-regression model. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results In all, 25 studies with a total of 4470 patients were included in the meta-analysis. The overall sensitivity, specificity, and area under the receiver operating characteristic curve for smartphone-based audiometry were 89% (95% CI 83%-93%), 93% (95% CI 87%-97%), and 0.96 (95% CI 0.93-0.97), respectively; the corresponding values for the smartphone-based speech recognition test were 91% (95% CI 86%-94%), 88% (95% CI 75%-94%), and 0.93 (95% CI 0.90-0.95), respectively. Meta-regression analysis revealed that patient age, equipment used, and the presence of soundproof booths were significantly related to diagnostic accuracy. Conclusions We have presented comprehensive evidence regarding the effectiveness of smartphone-based tests in diagnosing hearing loss. Smartphone-based audiometry may serve as an accurate and accessible approach to hearing evaluations, especially in settings where conventional PTA is unavailable.

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Section: Resultsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

mentioning

confidence: 99%

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Diagnostic Accuracy of Smartphone-Based Audiometry for Hearing Loss Detection: Meta-analysis

Chen¹,

Lin²,

Wang³

et al. 2021

JMIR Mhealth Uhealth

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“…An automated portable diagnostic audiometer using improved passive attenuation and real-time environmental noise monitoring making audiometry possible in unconventional settings Swanepoel et al, 2014 37,39,[72][73][74][75][76][77]…”

Section: Kuduwavementioning

confidence: 99%

Automated and machine learning approaches in diagnostic hearing assessment: a scoping review

Pragt¹,

Wasmann²,

Eikelboom³

et al. 2021

Preprint

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Hearing loss affects one in five people worldwide and is estimated to affect one in four by 2050. Treatment relies on accurate diagnosis of hearing loss, but this first step is out of reach for more than 80% of those affected. Increasingly automated digital approaches are being developed for self-administered hearing assessment without professionals’ direct involvement. This scoping review provides an overview of automated approaches, based on 56 reports from 2012 until June 2021, adding to the 29 published prior to 2012. Twenty-seven automated approaches were identified. An increasing number of approaches report similar accuracy as manual hearing assessments. Machine learning approaches are more efficient and personal digital devices make assessments more affordable and accessible. Validity can be enhanced by quality surveillance including noise monitoring and detecting inconclusive results. Employed within identified limitations, automated hearing assessments can support task-shifting, self-care, and clinical care pathways.

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Association of Polygenic Risk Scores for Hearing Difficulty in Older Adults With Hearing Loss in Mid-Childhood and Midlife

Wang

Lange

Sung

et al. 2023

JAMA Otolaryngol Head Neck Surg

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ImportanceAlthough more than 200 genes have been associated with monogenic congenital hearing loss, the polygenic contribution to hearing decline across the life course remains largely unknown.ObjectiveTo examine the association of polygenic risk scores (PRSs) for self-reported hearing difficulty among adults (40-69 years) with measured hearing and speech reception abilities in mid-childhood and early midlife.Design, Setting, and ParticipantsThis was a population-based cross-sectional study nested within the Longitudinal Study of Australian Children that included 1608 children and 1642 adults. Pure tone audiometry, speech reception threshold against noise, and genetic data were evaluated. Linear and logistic regressions of PRSs were conducted for hearing outcomes. Study analysis was performed from March 1 to 31, 2022.Main Outcomes and MeasuresGenotypes were generated from saliva or blood using global single-nucleotide polymorphisms array and PRSs derived from published genome-wide association studies of self-reported hearing difficulty (PRS1) and hearing aid use (PRS2). Hearing outcomes were continuous using the high Fletcher index (mean hearing threshold, 1, 2, and 4 kHz) and speech reception threshold (SRT); and dichotomized for bilateral hearing loss of more than 15 dB HL and abnormal SRT.ResultsIncluded in the study were 1608 children (mean [SD] age, 11.5 [0.5] years; 812 [50.5%] male children; 1365 [84.9%] European and 243[15.1%] non-European) and 1642 adults (mean [SD] age, 43.7 [5.1] years; 1442 [87.8%] female adults; 1430 [87.1%] European and 212 [12.9%] non-European individuals). In adults, both PRS1 and PRS2 were associated with hearing thresholds. For each SD increment in PRS1 and PRS2, hearing thresholds were 0.4 (95% CI, 0-0.8) decibel hearing level (dB HL) and 0.9 (95% CI, 0.5-1.2) dB HL higher on the high Fletcher index, respectively. Each SD increment in PRS increased the odds of adult hearing loss of more than 15 dB HL by 10% to 30% (OR for PRS1, 1.1; 95% CI, 1.0-1.3; OR for PRS2, 1.3; 95% CI, 1.1-1.5). Similar but attenuated patterns were noted in children (OR for PRS1, 1.1; 95% CI, 0.8-1.2; OR for PRS2, 1.2; 95% CI, 1.0-1.5). Both PRSs showed minimal evidence of associations with speech reception thresholds or abnormal SRT in children or adults.Conclusions and RelevanceThis population-based cross-sectional study of PRSs for self-reported hearing difficulty among adults found an association with hearing ability in mid-childhood. This adds to the evidence that age-related hearing loss begins as early as the first decade of life and that polygenic inheritance may play a role together with other environmental risk factors.

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Validity of hearing screening using hearTest smartphone-based audiometry: performance evaluation of different response modes

Cited by 18 publications

References 42 publications

Diagnostic Accuracy of Smartphone-Based Audiometry for Hearing Loss Detection: Meta-analysis

Diagnostic Accuracy of Smartphone-Based Audiometry for Hearing Loss Detection: Meta-analysis

Automated and machine learning approaches in diagnostic hearing assessment: a scoping review

Association of Polygenic Risk Scores for Hearing Difficulty in Older Adults With Hearing Loss in Mid-Childhood and Midlife

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