The hearScreen™ application is intellectual property owned, patented and trademarked by the University of Pretoria. The product is being developed for commercialization and more information is available at www.hearscreen.co.za. ABSTRACT ObjectivesThe study aimed to determine the validity of a smartphone hearing screening technology (hearScreen TM ) compared to conventional screening audiometry in terms of (1) sensitivity and specificity, (2) referral rate and (3) test time. Design1070 school-aged children in grade 1 to 3 (8 ±1.1 average years) were recruited from five public schools. Children were screened twice, once using conventional audiometry and once with the smartphone hearing screening. Screening was conducted in a counterbalanced sequence, alternating initial screen between conventional or smartphone hearing screening. 3 ResultsNo statistically significant difference in performance between techniques was noted, with smartphone screening demonstrating equivalent sensitivity (75.0%) and specificity (98.5%) to conventional screening audiometry. Whilst referral rates were lower with the smartphone screening (3.2 vs. 4.6%) it was not significantly different (p>0.01). Smartphone screening (hearScreen™) was 12.3% faster than conventional screening. ConclusionSmartphone hearing screening using the hearScreen™ application is accurate and time-efficient. Utilising commercially available, off-the-shelf, hardware provides an inexpensive solution that laypersons with limited training can operate since tests and interpretations are automated.

Smartphone-based threshold audiometry with automated testing has the potential to provide affordable access to audiometry in underserved contexts.To validate the threshold version (hearTest) of the validated hearScreen™ smartphone-based application using inexpensive smartphones (Android operating system) and calibrated supra-aural headphones.A repeated measures within-participant study design was employed to compare air-conduction thresholds (0.5–8 kHz) obtained through automated smartphone audiometry to thresholds obtained through conventional audiometry.A total of 95 participants were included in the study. Of these, 30 were adults, who had known bilateral hearing losses of varying degrees (mean age = 59 yr, standard deviation [SD] = 21.8; 56.7% female), and 65 were adolescents (mean age = 16.5 yr, SD = 1.2; 70.8% female), of which 61 had normal hearing and the remaining 4 had mild hearing losses.Threshold comparisons were made between the two test procedures. The Wilcoxon signed-ranked test was used for comparison of threshold correspondence between manual and smartphone thresholds and the paired samples t test was used to compare test time.Within the adult sample, 94.4% of thresholds obtained through smartphone and conventional audiometry corresponded within 10 dB or less. There was no significant difference between smartphone (6.75-min average, SD = 1.5) and conventional audiometry test duration (6.65-min average, SD = 2.5). Within the adolescent sample, 84.7% of thresholds obtained at 0.5, 2, and 4 kHz with hearTest and conventional audiometry corresponded within ≤5 dB. At 1 kHz, 79.3% of the thresholds differed by ≤10 dB. There was a significant difference (p < 0.01) between smartphone (7.09 min, SD = 1.2) and conventional audiometry test duration (3.23 min, SD = 0.6).The hearTest application with calibrated supra-aural headphones provides a cost-effective option to determine valid air-conduction hearing thresholds.

Decentralized detection and monitoring of hearing loss can be supported by new mobile health technologies using automated testing that can be facilitated by minimally trained persons. These may prove particularly useful in an infectious disease (ID) clinic setting where the risk of hearing loss is high.To evaluate the clinical utility of mobile and automated audiometry hearing health technology in an ID clinic setting.Smartphone-automated pure-tone audiometry (PTA) (hearTest™) and speech-in-noise testing (SA English digits-in-noise [DIN] test) were compared with manual audiometry (2, 4, and 8 kHz). Smartphone-automated PTA and the DIN test were repeated to determine the test–retest reliability.Two hundred subjects (73% female and 27% male) were enrolled. Fifty participants were retested with the smartphone applications. Participants ranged from an age of 18 to 55 years with a mean age of 44.4 (8.7 standard deviation).Threshold comparisons were made between smartphone audiometry testing and manual audiometry. Smartphone-automated PTA, manual audiometry, and test–retest measures were compared (Wilcoxon signed ranked test). Spearman rank correlation test was used to determine the relationship between the smartphone applications and manual audiometry, as well as for test–retest reliability.Within all participants, 88.2% of thresholds corresponded within 10 dB or less between smartphone audiometry and manual audiometry. There was a significant difference (p < 0.05) between the right ear at 4 and 8 kHz and in the left ear at 2 and 4 kHz between smartphone and manual audiometry, respectively. No significant difference was noted (p < 0.05) between test and retest measures of smartphone technology.Smartphone audiometry with calibrated headphones provides reliable results in an ID clinic setting and can be used as a baseline and monitoring tool at ID clinics.

Background The rising prevalence of hearing loss is a global health concern. Professional hearing services are largely absent within low- and middle-income countries where appropriate skills are lacking. Task-shifting to community healthcare workers (CHWs) supported by mHealth technologies is an important strategy to address the problem. Objective To evaluate the feasibility of a community-based rehabilitation model providing hearing aids to adults in low-income communities using CHWs supported by mHealth technologies. Method Between September 2020 and October 2021, hearing aid assessments and fittings were implemented for adults aged 18 and above in two low-income communities in the Western Cape, South Africa, using trained CHWs. A quantitative approach with illustrative open-ended questions was utilised to measure and analyse hearing aid outcomes. Data were collected through initial face-to-face interviews, telephone interviews, and face-to-face visits post-fitting. Responses to open-ended questions were analysed using inductive thematic analysis. The International Outcome Inventory – Hearing Aids questionnaire determined standardised hearing aid outcomes. Results Of the 152 adults in the community who self-reported hearing difficulties, 148 were successfully tested by CHWs during home visits. Most had normal hearing (39.9%), 24.3% had bilateral sensorineural hearing loss, 20.9% had suspected conductive hearing loss, and 14.9% had unilateral hearing loss, of which 5.4% had suspected conductive loss. Forty adults met the inclusion criteria to be fitted with hearing aids. Nineteen of these were fitted bilaterally. Positive hearing aid outcomes and minimal device handling challenges were reported 45 days post-fitting and were maintained at six months. The majority (73.7%) of participants fitted were still making use of their hearing aids at the six-month follow-up. Conclusions Implementing a hearing healthcare service-delivery model facilitated by CHWs in low-income communities is feasible. mHealth technologies used by CHWs can support scalable service-delivery models with the potential for improved access and affordability in low-income settings.

Objective: Access to hearing care is challenging in low-and middle-income countries, where the burden of hearing loss is greatest. This study investigated a community-based hearing screening program using smartphone testing by community care workers (CCWs) in vulnerable populations infected or affected by HIV. Experiences of CCWs were also surveyed. Design: The study comprised two phases. Phase one employed a cross-sectional research design to describe the community-based program. Phase two was a survey design to describe CCW's knowledge and experiences. Study Sample: Fifteen trained CCWs administered hearing screenings on 511 participants during home-based visits using a validated smartphone application (hearScreen™) during phase one. Diagnostic follow-up assessments included evaluation using the smartphone test (hearTest™), otoscopy and tympanometry. Phase two surveyed the 15 CCW screening experiences. Results:Referral rates for adults and children were 5.0% and 4.2% respectively. 75.0% of referred participants returned for follow-up diagnostic assessments, 33.3% were diagnosed with hearing loss and referred for further intervention. All 15 CCWs agreed that communities needed hearing services and only 6.6% did not want to continue providing hearing screening. Conclusion:Trained CCWs can decentralize hearing services to vulnerable communities using smartphone screening incorporating automated testing and measures of quality control.