Community-based hearing screening for young children using an mHealth service-delivery model.

BACKGROUND: Hearing loss is one of the most common developmental disorders identifiable at birth with its prevalence increasing throughout school years. However, early detection programs are mostly unavailable in low- and middle-income countries (LMICs) where more than 80% of children with hearing loss reside. OBJECTIVE: This study investigated the feasibility of a smartphone-based hearing screening program for preschool children operated by community healthcare workers (CHWs) in community-based early childhood development (ECD) centers. METHOD: Five CHWs were trained to map ECD centers and conduct smartphone-based hearing screenings within a poor community in South Africa over a 12-month period. The hearScreenTM smartphone application employed automated test protocols operating on low-cost smartphones. A cloud-based data management and referral function allowed for remote monitoring for surveillance and follow up. RESULTS: 6424 children (3-6 years) were screened for hearing loss with an overall referral rate of 24.9%. Only 39.4% of these children attended their follow-up appointment at a local clinic, of whom 40.5% referred on their second screening. Logistic regression analysis indicated that age, gender and environmental noise levels (1 kHz) had a significant effect on referral rates (p < 0.05). The quality index reflecting test operator test quality increased during the first few months of testing. CONCLUSION: Smartphone-based hearing screening can be used by CHWs to detect unidentified children affected by hearing loss within ECD centers. Active noise monitoring, quality indices of test operators and cloud-based data management and referral features of the hearScreenTM application allows for the asynchronous management of hearing screenings and follow-ups.

Smartphone hearing screening with integrated quality control and data management.

OBJECTIVE: To determine if a smartphone application could be used as a calibrated screening audiometer with real-time noise monitoring for school screening using automated test sequences. DESIGN: The investigation comprised three studies. Study 1 evaluated calibration accuracy across four Samsung S5301 smartphones (Android v4.0.4) using commercial Sennheiser HD202 headphones. Study 2 involved referencing smartphone microphone sensitivity to narrowband noise intensity as measured in octave bands by a sound-level meter between 30 and 75 dB SPL (5 dB increments). Study 3 compared screening outcomes of smartphone based and conventional hearing screening. STUDY SAMPLE: Study 2: 15 normal-hearing subjects (age range, 18-22 years; all female). Study 3: 162 children (324 ears) aged 5 to 7 years. RESULTS: Smartphone calibration at 20, 30, and 40 dB was within 1 dB of recommended reference equivalent thresholds levels. Microphone calibration for noise monitoring had maximum variability across phones of 0.9, 0.6, and 2.9 dB at 1, 2, and 4 kHz, respectively, from reference intensities (30 to 75 dB SPL). Screening outcomes demonstrated no significant difference between smartphone and conventional audiometry with an overall referral rate of 4.3% and 3.7%, respectively. CONCLUSIONS: The newly developed smartphone application can be accurately calibrated for audiometry with valid real-time noise monitoring, and clinical results are comparable to conventional screening.

Validity of automated threshold audiometry: a systematic review and meta-analysis.

OBJECTIVES: A systematic literature review and meta-analysis on the validity (test-retest reliability and accuracy) of automated threshold audiometry compared with the gold standard of manual threshold audiometry was conducted. DESIGN: A systematic literature review was completed in peer-reviewed databases on automated compared with manual threshold audiometry. Subsequently a meta-analysis was conducted on the validity of automated audiometry. METHODS: A multifaceted approach, covering several databases and using different search strategies was used to ensure comprehensive coverage and to cross-check search findings. Databases included: MEDLINE, Scopus, and PubMed; a secondary search strategy was the review of references from identified reports. Reports including within-subject comparisons of manual and automated threshold audiometry were selected according to inclusion/exclusion criteria before data were extracted. For the meta-analysis weighted mean differences (and standard deviations) on test-retest reliability for automated compared with manual audiometry were determined to assess the validity of automated threshold audiometry. RESULTS: In total, 29 reports on automated audiometry (method of limits and the method of adjustment techniques) met the inclusion criteria and were included in this review. Most reports included data on adult populations using air conduction testing with limited data on children, bone conduction testing and the effects of hearing status on automated audiometry. Meta-analysis test-retest reliability for automated audiometry was within typical test-retest variability for manual audiometry. Accuracy results on the meta-analysis indicated overall average differences between manual and automated air conduction audiometry (0.4 dB, 6.1 SD) to be comparable with test-retest differences for manual (1.3 dB, 6.1 SD) and automated (0.3 dB, 6.9 SD) audiometry. No significant differences (p > 0.01; summarized data analysis of variance) were seen in any of the comparisons between test-retest reliability of manual and automated audiometry compared with differences between manual and automated audiometry. CONCLUSION: Automated audiometry provides an accurate measure of hearing threshold, but validation data are still limited for (1) automated bone conduction audiometry; (2) automated audiometry in children and difficult-to-test populations and; (3) different types and degrees of hearing loss.

Validity of Automated Threshold Audiometry: A Systematic Review and Meta-Analysis.

OBJECTIVES: A systematic literature review and meta-analysis on the validity (test-retest reliability and accuracy) of automated threshold audiometry compared with the gold standard of manual threshold audiometry was conducted. DESIGN: A systematic literature review was completed in peer-reviewed databases on automated compared with manual threshold audiometry. Subsequently a meta-analysis was conducted on the validity of automated audiometry. A multifaceted approach, covering several databases and using different search strategies was used to ensure comprehensive coverage and to cross-check search findings. Databases included: MEDLINE, SCOPUS, and PubMed with a secondary search strategy reviewing references from identified reports. Reports including within-subject comparisons of manual and automated threshold audiometry were selected according to inclusion/exclusion criteria before data were extracted. For the meta-analysis weighted mean differences (and standard deviations) on test-retest reliability for automated compared with manual audiometry were determined to assess the validity of automated threshold audiometry. RESULTS: In total, 29 reports on automated audiometry (method of limits and the method of adjustment techniques) met the inclusion criteria and were included in this review. Most reports included data on adult populations using air conduction testing with limited data on children, bone conduction testing, and the effects of hearing status on automated audiometry. Meta-analysis test-retest reliability for automated audiometry was within typical test-retest variability for manual audiometry. Accuracy results on the meta-analysis indicated overall average differences between manual and automated air conduction audiometry (0.4 dB; 6.1 SD) to be comparable with test-retest differences for manual (1.3 dB; 6.1 SD) and automated (0.3 dB; 6.9 SD) audiometry. Nosignificant differences (p > 0.01; summarized data analysis of variance) were seen in any of the comparisons between test-retest reliability of manual and automated audiometry compared with differences between manual and automated audiometry. CONCLUSIONS: Automated audiometry provides an accurate measure of hearing threshold, but validation data are still limited for (a) automated bone conduction audiometry; (b) automated audiometry in children and difficult-to-test populations; and (c) different types and degrees of hearing loss.