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Validated Smartphone-Based Apps for Ear and Hearing Assessments: A Review


Abstract and Figures

Background An estimated 360 million people have a disabling hearing impairment globally, the vast majority of whom live in low- and middle-income countries (LMICs). Early identification through screening is important to negate the negative effects of untreated hearing impairment. Substantial barriers exist in screening for hearing impairment in LMICs, such as the requirement for skilled hearing health care professionals and prohibitively expensive specialist equipment to measure hearing. These challenges may be overcome through utilization of increasingly available smartphone app technologies for ear and hearing assessments that are easy to use by unskilled professionals. Objective Our objective was to identify and compare available apps for ear and hearing assessments and consider the incorporation of such apps into hearing screening programs Methods In July 2015, the commercial app stores Google Play and Apple App Store were searched to identify apps for ear and hearing assessments. Thereafter, six databases (EMBASE, MEDLINE, Global Health, Web of Science, CINAHL, and mHealth Evidence) were searched to assess which of the apps identified in the commercial review had been validated against gold standard measures. A comparison was made between validated apps. Results App store search queries returned 30 apps that could be used for ear and hearing assessments, the majority of which are for performing audiometry. The literature search identified 11 eligible validity studies that examined 6 different apps. uHear, an app for self-administered audiometry, was validated in the highest number of peer reviewed studies against gold standard pure tone audiometry (n=5). However, the accuracy of uHear varied across these studies. Conclusions Very few of the available apps have been validated in peer-reviewed studies. Of the apps that have been validated, further independent research is required to fully understand their accuracy at detecting ear and hearing conditions.
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Validated Smartphone-Based Apps for Ear and Hearing
Assessments: A Review
Tess Bright, BBiomedSc, MClinAud, MSc; Danuk Pallawela, BSc, MSc
London School of Hygiene & Tropical Medicine, London, United Kingdom
Corresponding Author:
Tess Bright, BBiomedSc, MClinAud, MSc
London School of Hygiene & Tropical Medicine
Keppel St
London, WC1E 7HT
United Kingdom
Phone: 44 (0)20 7636 8636
Fax: 44 (0)20 7436 5389
Background: An estimated 360 million people have a disabling hearing impairment globally, the vast majority of whom live
in low- and middle-income countries (LMICs). Early identification through screening is important to negate the negative effects
of untreated hearing impairment. Substantial barriers exist in screening for hearing impairment in LMICs, such as the requirement
for skilled hearing health care professionals and prohibitively expensive specialist equipment to measure hearing. These challenges
may be overcome through utilization of increasingly available smartphone app technologies for ear and hearing assessments that
are easy to use by unskilled professionals.
Objective: Our objective was to identify and compare available apps for ear and hearing assessments and consider the incorporation
of such apps into hearing screening programs
Methods: In July 2015, the commercial app stores Google Play and Apple App Store were searched to identify apps for ear and
hearing assessments. Thereafter, six databases (EMBASE, MEDLINE, Global Health, Web of Science, CINAHL, and mHealth
Evidence) were searched to assess which of the apps identified in the commercial review had been validated against gold standard
measures. A comparison was made between validated apps.
Results: App store search queries returned 30 apps that could be used for ear and hearing assessments, the majority of which
are for performing audiometry. The literature search identified 11 eligible validity studies that examined 6 different apps. uHear,
an app for self-administered audiometry, was validated in the highest number of peer reviewed studies against gold standard pure
tone audiometry (n=5). However, the accuracy of uHear varied across these studies.
Conclusions: Very few of the available apps have been validated in peer-reviewed studies. Of the apps that have been validated,
further independent research is required to fully understand their accuracy at detecting ear and hearing conditions.
(JMIR Rehabil Assist Technol 2016;3(2):e13) doi:10.2196/rehab.6074
hearing; testing; mobile; audiometry; smartphone; applications; app; hearing loss; hearing impairment; surveys; prevalence
In 2012, the World Health Organization (WHO) estimated that
disabling hearing impairment (DHI) affects approximately 360
million people, or 5.3% of the global population [1,2]. The
definition of DHI is a pure tone average (PTAv) of thresholds
at 500, 1000, 2000 and 4000 hertz (Hz) in the better hearing ear
of greater than 30 decibels (dB) in children, and greater than
40 dB in adults. Most people with DHI live in low- and
middle-income countries (LMICs), with the greatest burden in
the Asian Pacific, southern Asian, and sub-Saharan African
regions [3]. The estimated global prevalence of DHI is
increasing [3,4], and may be due to greater life expectancy in
many countries, resulting in: increased prevalence of age-related
hearing loss; early detection of hearing loss facilitated through
increased availability of hearing screening equipment; increasing
hearing loss due to occupational, recreational, and environmental
noise exposure; and increased and extensive use of ototoxic
medications for treating a range of medical conditions, such as
human immunodeficiency virus (HIV) [3,4].
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Hearing loss has a substantial impact on psychosocial wellbeing
and economic independence [3]. If acquired in childhood, before
speech has developed, hearing loss can impede language
development and hence limit educational attainment [3]. Hearing
loss also has high societal costs, mainly due to losses in
productivity [5]. If hearing impairment is identified early and
treatment is provided, many of these negative effects can be
avoided [6,7]. Screening for hearing impairment can be useful
for a range of age groups and patient groups, including
newborns, to detect congenital hearing impairment; school
children, to detect late-onset hearing impairment; the elderly,
to identify age-related hearing loss (presbyacusis); and those
with HIV [3,8-11]. In addition, screening for hearing impairment
in population-based surveys is important to determine its
magnitude and plan services accordingly [12]. However,
substantial challenges exist in screening for hearing impairment
(especially in LMICs) such as the need for a quiet testing
environment, prohibitively expensive specialist hearing
assessment equipment that requires regular calibration, and
skilled professionals to conduct clinical tests. In many LMICs,
there is a severe shortage of hearing health care professionals
(ie, audiologists, speech pathologists, and ear, nose, and throat
[ENT] specialists). In most of sub-Saharan Africa, services are
either nonexistent or limited to urban centers, resulting in 1
ENT per 250,000 to 7.1 million people [13]. This scarcity
contrasts with Europe, where there is 1 ENT per 10,000-30,000
people [14]. Due to these barriers, hearing impairment remains
undetected and unmanaged for a substantial number of people
in LMICs, and robust data from population-based surveys is
lacking. 2012 WHO prevalence estimates comprised of 42
population-based surveys in 29 countries [1,2,6]. In contrast,
the Rapid Assessment of Avoidable Blindness survey
methodology been used in over 200 population-based surveys
of visual impairment [33].
The gold standard for hearing screening for people >4 years of
age is Pure Tone Audiometry (PTA) [12]. For subjects <4 years
of age, objective tests such as Otoacoustic Emissions (OAE)
and Auditory Brainstem Response (ABR) testing are
recommended [12]. Understanding the probable causes of
hearing loss is vital for management and referral processes.
Causes of hearing loss are typically determined using a
comprehensive battery of tests. In hearing screening programs,
these tests include tympanometry (a test of middle ear function)
and otoscopy (visual examination of the eardrum). The
equipment and expertise required for these tests and
examinations is lacking. However, new and innovative
technologies that are low-cost, easy to use, and automated have
recently been developed and may be useful in overcoming some
of the challenges. For instance, replacing PTA (typically
conducted by an audiologist) with automated computer-based
audiometry can provide comparable results on threshold testing
[15]. Developers of smartphone apps have begun to harness this
technology to generate apps for performing self-administered
hearing screening tests. In addition, apps exist for performing
video otoscopy, whereby images of the eardrum are captured
and may be sent to an ENT specialist to diagnose and manage
ear conditions remotely. With the global rise in smartphone
penetration, apps offer a promising avenue to screen for hearing
impairment and assess the causes in a low-cost manner. A large
number of apps for measuring ear and hearing function are
thought to exist that can potentially be utilized, but their
scientific validity has not been reviewed in-depth. The aim of
this review is to identify available apps to screen for hearing
impairment, and compare the features and peer-reviewed
validation studies performed to date.
A search was conducted to find apps for ear and hearing
assessments, using the most popular commercial app stores by
market share: Google Play (Android apps) and the Apple App
Store (iPhone/iPad apps) [16]. Next, a review of peer-reviewed
literature was conducted to determine whether any of the
identified apps had been validated against gold standard
Google Play and Apple App Store Search
A search was conducted on Google Play and Apple App Store
in July 2015. The main types of apps searched were those that
could perform audiometry, tympanometry, OAEs, ABR testing,
and otoscopy. These tests were chosen, as they can be used for
assessment of ear and hearing function in a range of settings,
including screening programs and population-based surveys
[12]. A range of search terms were used, including
clinically-recognized terms such as audiometry and layman’s
terms such as hearing test. Table 1 provides a list of all search
terms used.
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Table 1. Search terms used in Google Play and Apple App Store.
Search terms usedConcept
hearing exam
hearing check
hearing loss
hearing problem
hearing test
pure tone audiometry
ear nose and throat
ear test
middle ear
middle ear test
otoacoustic emissionsOtoacoustic Emissions
Inclusion and Exclusion Criteria
App titles were initially screened for relevance to the
measurement of auditory function or ear examination. Apps
were excluded based on their title if it was clear that the app
was not applicable. For example, in a search of hearing test,
apps such as Phone, Dog Hearing Test, and Motorola Gallery
were excluded based on title. Those with relevant (eg, Hearing
Test) or ambiguous titles (eg, iCare Health Monitor) went
through a second screening, in which they were reviewed in
more detail using the descriptions in the app store and on the
app’s website. Apps were included if they were
self-administered or professionally administered tests of ear or
hearing function. Apps were excluded if they did not focus on
ear examination or audiological testing; they were not in
English; they were included in the category of games,
entertainment, or music; or they were intended for educational
Literature Review of Smartphone Apps
Information Sources
Once the app store review was complete, a literature review
was conducted in July 2015 to assess app validity testing. 6
databases were searched for peer-reviewed studies related to
apps of ear and hearing function: PubMed/MEDLINE,
EMBASE, Global Health, Web of Science, CINHAL, and
mHealth Evidence. Comprehensive search terms for smartphone
apps and auditory function were identified through MeSH and
previous systematic reviews on similar topics. The names of
identified apps from the commercial review were also included
(see Multimedia Appendix 1). Developers of apps that were
validated in peer-reviewed literature were contacted if specific
information about the app was not available online.
Study Eligibility Criteria
Articles published between June 2007 and July 2015 were
included in the search to align with the time-period during which
apps have been available [17]. Any primary study identified in
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the app stores’ review that compared an app to gold standard
methods was considered for inclusion. Studies that measured
outcomes that allowed judgement of the app’s performance
were included. These outcomes included: sensitivity, specificity,
negative and positive predictive values, difference in pure-tone
thresholds, and kappa diagnostic agreement. No restrictions
were placed on study location, or types of participants included
in the studies. Studies were excluded if they were not in the
English language, or the study was not peer-reviewed. This
review focused on the validity of apps available for download
from commercial app stores. If the article did not specify the
name of the app, or if the app being studied was not previously
identified in the app stores’ review, the author was contacted
for further information about the app and its availability. The
article was included if the author could provide the app’s name
and the app was available for purchase, either on Google Play,
the Apple App Store, or elsewhere.
Study Selection
Articles were screened by two reviewers (TB and DP) first by
titles, then abstract, and finally by full paper to determine
Data Extraction
Data was extracted from eligible studies for the following study
1. Methods, including study design, comparison being made
(ie, index test [app] and reference test [gold standard]), single
or multiple smartphone devices used, headphone/transducer
type, calibration methods, and test frequencies.
2. Participants, including age, sex, and sample size.
3. Study location, including country and setting.
4. Publication details, including year, journal, and declaration
of conflicts of interest.
5. Outcomes, including type of outcome, definitions (eg,
definition of hearing loss).
6. Results, including relevant measure of validity.
All data was extracted by one reviewer (TB), and checked by
the second reviewer (DP) to ensure accuracy.
Methodological Quality of Studies
Methodological quality for each study was assessed using the
Quality Assessment for Diagnostic Accuracy Studies
(QUADAS-2) tool [18,19]. This tool assesses the following 4
1. Patient selection: assessment of study design, sampling
method, and selection criteria.
2. Index test (app): assessment of chosen test (app), testing
method, and interpretation.
3. Reference standard: assessment of choice of reference
standard and interpretation.
4. Flow and timing: assessment of time interval between index
and reference tests, proportion of sample receiving reference
standard, and proportion of participants included in the analysis.
Each domain was assessed in terms of risk of bias, and the first
three domains were assessed in terms of concerns regarding
applicability to the review question. Risk of bias and concerns
regarding applicability were scored as low, high, or unclear
using a series of signalling questions. If each signaling question
had an answer of, “yes,” the domain was rated as having a low
risk of bias or low concern of applicability. If any signaling
question was answered, “no,” the domain was scored as high
risk of bias or high concern of applicability. If any domain
provided inadequate information to make a judgement, the
domain was scored as, “unclear.” Each paper was reviewed
independently for quality by two reviewers (TB and DP).
Synthesis of Results
Results from the literature review were synthesized using a
narrative approach, rather than a meta-analysis, due to the
heterogeneity of included studies.
Google Play and Apple App Store Review
Over 1000 apps were reviewed in the searches of Google Play
and the Apple App Store, 30 of which met the inclusion criteria
(Figure 1). Of these, 17% (5/30) were Android (Google) apps,
70% (21/30) were iOS (Apple) apps, and a further 13% (4/30)
were compatible with both Android and iOS. Considering the
function of the apps, audiometry apps formed the majority, with
26 of the 30 (87%) functioning as either self-administered
automated PTA or professionally administered PTA. The
remaining apps (4/30, 13%) were designed for performing
otoscopy and required a separate specula phone attachment. No
apps for tympanometry, OAEs, or ABR were identified. Details
of the identified apps can be found in Multimedia Appendix 2.
Literature Review of Smartphone Apps
Search Results
The literature review yielded 534 results: 182 in EMBASE, 157
in MEDLINE, 153 in Web of Science, 21 in CINAHL, 13 in
Global Health, and 8 in mHealth Evidence. After removing
duplicates across search engines, and screening titles and
abstracts for relevant articles, 22 studies remained. Full text
article screening resulted in 7 eligible studies. Three studies
were excluded, as the app under study was not specified.
Attempts were made to contact the authors of these papers for
further information; however, this was not successful. Four
additional studies were identified from reference lists of included
articles, resulting in the inclusion of 11 studies overall (Figure
2). One further article was identified through app website
review; however, the full text could not be located and therefore
this article was excluded.
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Figure 1. Flow diagram for apps found in app stores. Numbers are approximate due to limitations with the search platform (a=exact number of hits
not provided and thus manual counting conducted).
Figure 2. Flowchart of study selection process.
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Table 2. Characteristics of apps validated in peer-reviewed literature.
Additional featuresTransducer type
and model
testing out-
put (deci-
Test fre-
Cost (US $)a
App functionApp and op-
erating sys-
Noise monitoring,
data storage with
user identification,
and questionnaire
to evaluate the im-
pact of hearing loss
Air conduction
(AC), standard ap-
ple headphones;
bone conduction
(BC), not mea-
Calibrated with stan-
dard Apple head-
phones using refer-
ence equivalent
threshold sound pres-
sure levels for TDH39
headphones (ISO389-
900.25, 0.5,
1, 2, 4, 6
audiometry app
uHear, iOS
Noise monitoring,
masking (auto cal-
culated), and data
AC, TDH-39 or
EAR 3A insert
headphones; BC,
B-71 bone transduc-
Calibrated with audio-
metric transducers us-
ing American Nation-
al Standards Institute
S3.6-2004 standards
90-1150.25, 0.5,
1, 2, 4, 6,
$2000c, standard
version $3100c, pro-
fessional version
Self- or tester-ad-
ministered au-
diometry app
Ability to export
results as a photo-
graph to photos
app, and integrated
with Print, Mail,
and WhatsApp
AC, Apple head-
phones; BC, not
Calibrated for most
models of iPhone/iPad
using Apple head-
phones (standards not
750.5, 1, 2,
3, 4, 8
tered audiometry
AudCal, iOS
Noise monitoring,
data capturing and
sharing, and loca-
tion-based referral
AC, Sennheiser
HD202 head-
phones; BC, not
Calibrated with
nonaudiometric head-
phones according to
standards (within 0.1
decibel accuracy)
401, 2, 4
tered screening
audiometry app
(ie, pass/fail re-
Data storage, auto-
mated masking
noise, and amplifi-
cation device
AC, commercially
available earbuds
(eg, standard Ap-
ple headphones);
BC, not measured
Calibrated with Ap-
ple’s earbuds (stan-
dards not specified)
90-1000.25, 0.5,
0.75, 1,
1.5, 2, 3,
4, 6, 8
audiometry app
Port for pneumatic
N/AN/AN/ANot appli-
$79efor iPhone
case, otoscope attach-
ment, 4 reusable
Otoscopy app
with separate at-
aSubject to change.
bPrice excludes cost of device and transducers.
cPrice includes transducers, software, and first year’s calibration. Price excludes the price of the iPad.
dPrice includes device, transducers, and calibration.
ePrice excludes cost of device.
Results of Included Studies
Of the 30 apps found in the review of the app stores, 5 appeared
in validation studies in the peer-reviewed literature. These apps
were uHear, shoeBOX audiometry, EarTrumpet, CellScope,
and AudCal. One study was identified in the literature that
validated an Android hearing screening app, hearScreen, that
is not yet commercially available on Google Play. Thus, 6
previously validated apps were identified in the review. Of these
apps, the function of 4 was self- or tester-administered PTA
(uHear, shoeBOX audiometry, AudCal, and EarTrumpet), one
performed screening audiometry (hearScreen; pass/fail result),
and one functioned as video otoscope (CellScope).
Table 2 provides a summary of the validated apps and their
specific characteristics, including function, costs, test
frequencies, maximum output, calibration method, recommended
transducers, and administration method.
Overview of Study Characteristics
The 11 selected studies are summarized in Multimedia Appendix
3by study setting, study design, participants/sample and sample
size, index (app) and reference test (gold standard), transducers
and devices used, test administration method (eg, self- or
tester-administered), outcome measures, calibration method,
and results. Studies were performed in Canada (n=3) [20-22],
Spain (n=1) [23], Israel (n=2) [24,25], USA (n=2) [26,27], and
South Africa (n=3) [28-30]. The sample size of the included
studies ranged from 25 to 110 participants. Participants in the
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included studies came from a range of age groups: adults (>18
years; n=4) [21,23,24,27], the elderly (>65 years; n=1) [25],
children (<18 years; n=5) [20,26,28,30], and both children and
adults (15-80 years; n=1) [29].
All included studies used a within-subjects’ study design. Ten
of the 11 studies focused on comparing audiometry apps to
conventional PTA [20-25,27-30], while the remaining study
compared the diagnosis made with an otoscope app to traditional
otoscopy [26].
Of the 10 studies validating audiometry apps, the majority
carried out testing with the app in a quiet room (ambient noise
levels 40-50 A-weighted decibels [dBA]; n=7) [21,24,25,27-30].
The remaining studies were performed only in a soundproof
room (ambient noise <40 dBA; n=3) [20,22,23]. Three studies
performed testing in multiple environments to determine the
effect of ambient noise on test accuracy [21,27,29]. In terms of
outcome measures, most studies (6/10, 60%) performed
sensitivity and specificity analyses with defined pass/fail dB
cut-offs [20-22,24,25,29]. The remaining studies (4/10, 40%)
used alternative outcome measures, including the mean
difference in thresholds between the app and conventional PTA
[23,27,28,30]. Validation of audiometry apps in all 10 studies
focused on the comparison of air conduction (AC) thresholds
only, as opposed to including bone conduction (BC) threshold
as well. In the single study validating the otoscopy app, Cohen’s
kappa agreement was used to determine diagnostic agreement
with traditional otoscopy [26].
Summary of Main Results
Audiometry Apps
Of all the apps reviewed in the literature, uHear has been
validated in the most studies, none of which declared a conflict
of interest (n=5). Results from 3 of the 5 studies on uHear
suggest that when screening for moderate or worse DHI (PTAv
>40 decibels Hearing Level [dBHL]) in adults, a high sensitivity
(ranging from 98.2-100%) was achieved; however, specificity
was variable (ranging from 60.0-82.1%) if tests were conducted
in environments with ambient noise floor at 40-50 dBA (quiet
room) [21,25,29]. Ambient noise levels had significant impacts
on the accuracy of uHear [21,29]. Sensitivity remained high in
all test settings; however, specificity decreased in a waiting
room setting (ambient noise >50 dBA) and increased when
conducted in a soundproof room (ambient noise <40 dBA) [29].
Two studies concluded that uHear cannot accurately determine
the precise level of hearing impairment as compared to
conventional PTA, suggesting that the app could be used for
screening, but not diagnostic purposes [21,25].
Two validity studies compared shoeBOX audiometry to standard
pediatric audiometry, both of which declared a conflict of
interest [20,22]. Sensitivity in these studies ranged from
91.2-93.3% and specificity ranged from 57.8-94.5%, depending
on transducers used and test environment [20]. Individual
validity studies were identified for EarTrumpet, AudCal, and
hearScreen, each declaring a conflict of interest. Hearing
thresholds obtained with EarTrumpet and AudCal were found
to be within 10 dBHL of conventional PTA, on average [23,27].
hearScreen, a screening app that gives a pass/refer result, was
found to have comparable referral rates to conventional
screening audiometry [30].
Otoscopy Apps
Only one study focused on validating an otoscopy app. This
study compared the diagnosis obtained using traditional
otoscopy to that obtained using the iPhone otoscope, CellScope
(n=54) [26]. This study found high levels of agreement between
the two diagnostic methods. Refer to Multimedia Appendix 3
for further details of the study results.
Table 3. Summary of quality review of included studies (assessed using the QUADAS-2 tool) where 1 represents low risk of bias/low concern of
applicability, 2 represents unclear/inadequate information to make judgement, and 3 represents high risk of bias/high concern of applicability.
Applicability concernsRisk of biasStudy authors (year)
Index TestPatient Selec-
Flow and
Index TestPatient
1131113Abu-Ghanem et al (2015) [25]
1111113Khoza-Shangase et al (2013) [28]
1113111Peer et al (2015) [29]
1111112Szudek et al (2012) [21]
3331133Handzel et al (2013) [24]
1111111Foulad et al (2013) [27]
1113111Yeung et al (2013) [20]
1113131Yeung et al (2015) [22]
1111111Larrosa et al (2015) [23]
1111113Swanepoel et al (2014) [30]
1111133Richards et al (2015) [26]
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Methodological Quality of Included Studies
Of the 11 peer-reviewed studies included in this review, 2
achieved a rating of low risk of bias and low concern of
applicability in all domains [23,27]. The main source of bias in
the included studies was selection bias. Results of the quality
assessment are summarized in Table 3 and detailed in
Multimedia Appendix 4.
Screening for hearing impairment is not feasible for many
LMICs, mainly due to the dearth of skilled professionals
available to conduct the required tests and high costs of specialist
equipment. However, the increasing availability of apps provides
an opportunity to integrate their use into screening for ear and
hearing conditions in a cost effective and mobile way. This
paper provides a comprehensive summary of the currently
available apps for ear and hearing assessments (up to July 2015)
and provides a summary of those that have been validated
against gold standard measures.
Thirty commercially available apps meeting the inclusion criteria
were identified on Google Play and the Apple App Store. Of
these, only 5 had undergone validation, as per the peer-reviewed
literature (Table 2). One additional peer-reviewed validation
study referred to an Android app that is not yet available
commercially. The vast majority of apps identified in the initial
commercial review have not been validated against a gold
standard measure in peer-reviewed literature. Most of the
available apps were designed to perform audiometry (26/30,
87%) with a small proportion for otoscopy (4/30, 13%). No
apps were identified for conducting OAEs, ABR, or
The literature review identified 11 peer-reviewed validation
studies. Studies were quite heterogeneous, with variation in the
cut-off level for performing sensitivity/specificity analyses,
patient population, units of analysis (results of each ear
separately or individual), and exclusion/inclusion criteria for
participants, thus making direct comparisons across apps
difficult. The quality of included studies was variable, with only
2 studies achieving a low risk of bias and low concerns about
applicability in all domains (Table 3). Five peer reviewed studies
were identified on uHear; however, the accuracy results varied
considerably across these studies (Multimedia Appendix 3)
[21,24,25,28,29]. A specificity as low as 60%, found by
Abu-Ghanem et al in a quiet room setting, would result in a
high rate of false positives in a screening program, and thus an
unnecessary rate of referrals for diagnostic assessments, which
would increase the burden on already strained health services
[25]. The small sample sizes and the limited variability in degree
and types of hearing loss included in the studies on uHear may
limit generalizability based on the studies reviewed. Individual
peer-reviewed validation studies were identified for AudCal,
hearScreen, EarTrumpet, and CellScope [23,27,30]. Although
the results of these studies appear to be promising, there is
limited evidence to allow robust conclusions to be drawn.
Several studies demonstrated that the testing environment had
a significant impact on the accuracy of results [21,27,29]. This
finding is important, as ambient noise levels in screening
environments are a substantial challenge and can often exceed
the recommended minimum of 40 dBA [7]. Studies of
audiometry apps focused on comparison with AC thresholds
only, reinforcing the fact that these apps function as screening
(rather than diagnostic) tools. BC testing is important for
differentiating between conductive and sensorineural hearing
loss; however, shoeBOX audiometry that runs on an iPad device
is currently the only app compatible with BC transducers. Thus,
the validity of BC testing from smartphone devices warrants
further investigation. The range of frequencies that are tested
in the current audiometry apps does not typically extend to 8000
Hz, thus screening for certain conditions such as ototoxicity
and noise-induced hearing loss would not be possible with
current app technology.
Most studies conducted tests using a single device and
transducer; however, in reality there may be significant
variability in results obtained with different transducer/device
combinations due to issues with calibration. Annual calibration
of audiometric devices is a key consideration to ensure test
accuracy. Of 10 audiometry studies, only half performed
calibration as part of their study [20,22,23,27,30]. This finding
may be due to the fact that no standardized calibration procedure
currently exists for performing tests on smartphone devices
coupled with nonaudiometric headphones [30]. Several recent
studies have investigated calibration methods; however, further
research evidence is necessary [31,32]. Some authors suggested
that poor sound attenuation provided by commercially available
earbuds might have resulted in the poor accuracy of results
found in nonsoundproof environments. Accuracy may improve
if headphones with greater attenuation of ambient noise are
utilized. However, the cost of these types of headphones can be
prohibitive and calibration is still an important issue.
Audiometric headphones adhering to International Organization
for Standardization calibration standards (ISO389-9:2009) are
vastly more expensive than commercially available headphones.
Nonaudiometric supraaural headphones may assist in providing
some attenuation from ambient noise. Swanepoel et al
determined that Sennheiser HD202 headphones coupled to a
smartphone hearing screening device can be calibrated to a
professional standard using TDH-39 Reference Equivalent
Threshold in Sound Pressure Levels as a reference [30]. Thus,
it seems possible to use lower-cost transducers whilst ensuring
test accuracy. The expertise required to professionally calibrate
audiometric devices is often nonexistent in low resource settings,
and equipment can remain out of calibration for lengthy periods.
Hence, ongoing calibration is an additional challenge for
performing accurate screening of hearing loss using apps.
Although the cost of the apps themselves are low (indeed many
are free; Multimedia Appendix 2) additional costs are incurred
for the device, headphones, and regular calibration. Android
devices are often much less expensive than Apple products and
more widely available in LMICs; however, the vast majority of
available apps identified in this review were designed for Apple
devices. Some of the apps identified in the literature search
(shoeBOX audiometry, and hearScreen) are sold as a package
including headphones, calibration for the first year, and the
device (hearScreen). Although these apps appear to be
JMIR Rehabil Assist Technol 2016 | vol. 3 | iss. 2 | e13 | p.8
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higher-cost, these features allow for a level of quality control
that is not currently available for apps that can be downloaded
from app stores and used on various device/transducer
Strengths and Limitations
This review has several strengths. Comprehensive search terms
were identified and applied across multiple electronic databases
to reduce publication bias. A clear approach to searching,
screening, reviewing, and extracting data was performed
independently by two reviewers. Citation bias was minimized
by reviewing references of included studies. Thus, the search
strategy of peer-reviewed literature is not likely to be a
significant limitation.
The search of app stores was conducted using a range of search
terms and the most commonly used commercial app stores were
searched; however, this search had several limitations. First,
unlike searches of academic databases, app store searches do
not allow complex search functions such as Boolean operators
or the searching of phrases such as, “hearing test.” Second,
search engines did not provide the total number of hits for each
search. Therefore, an estimation had to be made of the total
number of apps reviewed (>1000). In addition, app store
categories may not always reflect the true nature of the app,
implying that some relevant apps (ie, those in the category of
games) may have been missed. Furthermore, the range of search
terms used may not have been fully exhaustive. For instance,
alternative screening tools for hearing loss, such as self-reported
questionnaires, were not included in the search. Finally, if time
and resources permitted, each app would have been downloaded
and tested to assess eligibility. However, this was not feasible
within the scope of this study. Thus, assessment of the apps’
eligibility proved difficult in some instances if limited or vague
information about the app was provided on the app stores. Given
these limitations, the search of the app stores may not have been
fully exhaustive, despite the range of search terms utilized and
the predefined eligibility criteria.
In addition to the limitations in the app store search, given the
rapid pace of app development and lengthy publication process,
it might have been appropriate to broaden the search to include
grey literature (eg, reports, conference papers). However, given
the lack of peer review of grey literature sources, the decided
methodology was justified. Finally, the review is based on an
electronic search, which was completed in July 2015, and as
such the review may not be entirely up-to-date.
Future Research
This review has identified a need for further research, as many
of the commercially available apps have not been validated
against gold standard measures. Furthermore, many of the
validated apps were not studied independently. Thus, further
independent validation studies are needed for each available
app for ear and hearing assessments. Studies providing a
comparison of the accuracy between available audiometry apps
would also be useful. The utility of telemedicine techniques,
such as video otoscopy using otoscopy apps such as CellScope,
could be investigated in field studies. These techniques would
involve an offsite ENT, negating the need for such a specialist
to be present with the patient, to help deal with the substantial
human resource shortage. This additional evidence would assist
in making a clear evidence-based decision about which of the
apps, if any, could be recommended to be used for screening
of ear and hearing conditions.
Most studies in this review focused on populations in high
income countries, in which the need for validated smartphone
apps still exists; however, we focused on screening for hearing
impairment in low-resource settings. This discrepancy highlights
the need for further research evidence for populations with DHI
living in LMICs, where the greatest burden exists [2]. Finally,
it is important to regularly update this review and monitor further
app developments, especially for suitable apps to test pediatric
populations and those who cannot perform PTA.
There are a number of apps available for ear and hearing
assessments; however, very few have been validated in
peer-reviewed literature. Of the apps that have been validated,
further independent research is required to fully understand their
accuracy for detecting ear and hearing conditions. Given the
results of this review, audiometry apps cannot be recommended
to replace gold standard PTA conducted by an audiologist.
However, despite the limited evidence obtained in this review,
the portability, accessibility, self-administration, and low-cost
nature of ear and hearing apps still offer an exciting opportunity
to overcome the key barriers to screening for ear and hearing
conditions in LMICs.
We thank Dr Hannah Kuper, Islay MacTaggart, and Dr Silvia Ferrite for providing thoughtful feedback during the preparation
of the manuscript.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Search strategy for EMBASE.
[PDF File (Adobe PDF File), 28KB - rehab_v3i2e13_app1.pdf ]
JMIR Rehabil Assist Technol 2016 | vol. 3 | iss. 2 | e13 | p.9
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Multimedia Appendix 2
Summary of apps identified on Google Play and AppStore reviews.
[PDF File (Adobe PDF File), 48KB - rehab_v3i2e13_app2.pdf ]
Multimedia Appendix 3
Summary of selected peer-reviewed studies included in the review.
[PDF File (Adobe PDF File), 78KB - rehab_v3i2e13_app3.pdf ]
Multimedia Appendix 4
Risk of bias of included studies.
[PDF File (Adobe PDF File), 32KB - rehab_v3i2e13_app4.pdf ]
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ABR: Auditory Brainstem Response
AC: air conduction
BC: bone conduction
dB: decibel
dBA: A-weighted decibels
dBHL: decibels Hearing Level
ENT: ear, nose, and throat
HIV: human immunodeficiency virus
Hz: Hertz
LMIC: low- and middle-income country
OAE: Otoacoustic Emissions
PTA: Pure Tone Audiometry
PTAv: Pure Tone Average
QUADAS-2: Quality Assessment for Diagnostic Accuracy Studies
WHO: World Health Organization
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Edited by G Eysenbach; submitted 23.06.16; peer-reviewed by A Paglialonga, O Handzel, F Mahomed-Asmail, S Moodie; comments
to author 23.08.16; revised version received 15.09.16; accepted 29.10.16; published 23.12.16
Please cite as:
Bright T, Pallawela D
Validated Smartphone-Based Apps for Ear and Hearing Assessments: A Review
JMIR Rehabil Assist Technol 2016;3(2):e13
©Tess Bright, Danuk Pallawela. Originally published in JMIR Rehabilitation and Assistive Technology (,
23.12.2016. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in JMIR Rehabilitation and Assistive Technology, is properly cited. The complete
bibliographic information, a link to the original publication on, as well as this copyright and license information
must be included.
JMIR Rehabil Assist Technol 2016 | vol. 3 | iss. 2 | e13 | p.12
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... Many hearing-related smartphone apps have been developed and are commercially available in app stores. Bright and Pallawela (2016) conducted one of the earliest reviews identifying English smartphone hearing screening apps, which were evaluated in peer-reviewed publications. They also assessed the methodological quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2; Whiting et al. 2011). ...
... We identified 187 remote hearing assessment tools from app stores and online platforms (or 167 tools if we treat apps working on multiple platforms as a single app) with considerable variability in functionality and quality. This number of tools is considerably greater than those identified in the reviews by Bright and Pallawela (2016) and Irace et al. (2021). This increase could be attributed to both the passage of time and because this review applied wider inclusion criteria and involved non-English and web-based tools. ...
Full-text available
Objective: Remote hearing screening and assessment may improve access to, and uptake of, hearing care. This review, the most comprehensive to date, aimed to (i) identify and assess functionality of remote hearing assessment tools on smartphones and online platforms, (ii) determine if assessed tools were also evaluated in peer-reviewed publications and (iii) report accuracy of existing validation data. Design: Protocol was registered in INPLASY and reported according to PRISMA-Extension for Scoping Reviews. Study sample: In total, 187 remote hearing assessment tools (using tones, speech, self-report or a combination) and 101 validation studies met the inclusion criteria. Quality, functionality, bias and applicability of each app were assessed by at least two authors. Results: Assessed tools showed considerable variability in functionality. Twenty-two (12%) tools were peer-reviewed and 14 had acceptable functionality. The validation results and their quality varied greatly, largely depending on the category of the tool. Conclusion: The accuracy and reliability of most tools are unknown. Tone-producing tools provide approximate hearing thresholds but have calibration and background noise issues. Speech and self-report tools are less affected by these issues but mostly do not provide an estimated pure tone audiogram. Predicting audiograms using filtered language-independent materials could be a universal solution.
... The most common platform and avenue used for automated audiometry by the smartphone-or tablet-based applications in the review was iOS. Among the 18 mHealth applications reviewed, the uHear™ iOS-based smartphone application was published on most frequently since 2000, a finding in agreement with that of Bright & Pallawela (2016). The hearScreen™ smartphone application on the other hand was the first application to be developed on the Android platform. ...
... Given the decreasing costs and increased use of internet-enabled smartphones in Africa (Malila, Mutsvangwa & Douglas, 2019) and other developing regions, the mobile solutions reviewed offer an opportunity for more users to be reached compared to conventional audiometers or computer- The mHealth applications also provide initial screening services that may be followed up by conventional measures where the latter do exist. Hearing testing with smartphone-and tablet-based applications presents several challenges including calibration (Corry, Sanders & Searchfield, 2017;Louw et al., 2017;Na et al., 2014) and evaluation (Bright & Pallawela, 2016). Further development of the applications and advances in technology are likely to address these challenges, increasing the utility of the applications. ...
Full-text available
Hearing impairment is a chronic condition for which limited screening and diagnostic services are available in low-and-middle income countries (LMICs). In addition to the conventional medical devices existing to screen for the condition, several smartphone- and tablet-based applications have been introduced as mobile health (mHealth) solutions. This study was aimed at reviewing the set of mobile health tools available for screening for hearing loss in both developed countries and LMICs. Furthermore, to consider the suitability of the screening tools identified in the first objective for use in developing countries. The research approach adopted for this study was that of a state-of-the-art review. Relevant literature on mobile technology solutions to assess hearing loss were identified in electronic databases and reviewed. The mHealth solutions were reviewed with a focus on: countries of origin and evaluation; devices, software platforms and hardware considerations; hearing loss characteristics of recruited populations; features of the tests conducted and of the testing environment; reference methods to which the mobile application was compared; application performance; feedback from users; and cost. Eighteen available smartphone- and tablet-based applications for hearing loss screening were reviewed. Studies on these applications included participants from a variety of ages and, with and without hearing loss. A variety of testing environments were used. Studies on the applications found 11 of them to have acceptable functionality for use in screening for hearing loss. These 11 applications are also potentially suitable for use in LMICs, although they have some limitations. While these applications are not able to replace the conventional audiometer, they have potential as a first point of access for referral to conventional audiometry, and to help increase access to hearing loss tests in resource-constrained health systems.
... Screening methods that make it easier for populations to access information related to hearing health and reduce the demands for assistance in health care units have been the foundation for several pieces of research in both the academic environment and tech field. These methods can be used to identify hearing loss, reduce expenses, and facilitate monitoring and early detection (9) . ...
Full-text available
Purpose: Verify how demographic and socioeconomic variables on the in-noise speech recognition threshold (SRT) from the digits-in-noise test (DIN) in Brazilian Portuguese influence normal-hearing subjects. Methods: Cross-sectional, prospective study. The convenience sample had 151 normal-hearing subjects between 12 and 79 years (mean=34.66) who underwent pure tone audiometry and digits-in-noise test with white noise using a sequence of three numbers in diotic stimulus (in-phase) on the same day. The DIN was performed using a Motorola Z3 Play smartphone with internet access and in-ear headphones. In-noise digit speech recognition threshold (SRT) was analyzed for gender, age, educational levels, and socioeconomic status. We used the non-parametric version of the Kruskal-Wallis and Mann-Whitney U tests to compare independent samples adopting a significance level of 5%. Results: The mean SRT was -8.47 dBNA (SD -3.89) with a median of -9.6 dBNA. The SRT was proportionally inverse to educational levels and socioeconomic status and more negative (better) with lower age groups. Gender did not influence the DIN SRT. Conclusion: Age, educational levels, and socioeconomic status influenced the DIN threshold. These variables must be considered when analyzing DIN performance in Brazilian Portuguese in normal-hearing subjects.
... Recently, a number of automated air-conducted pure-tone threshold and word recognition testing programs, or apps, have been developed and validated for smartphones. [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Though useful, air-conducted pure-tone thresholds and word recognition do not provide a complete assessment of hearing; in particular, they do not allow for the determination of whether there is a conductive component to the hearing loss. ...
... However, a cost-effective approach for large-scale hearing screenings is still lacking, which is particularly concerning given that the number of older individuals is increasing, and there is a shortage of hearing and geriatric healthcare professionals [19,20]. Some studies have utilized telemedicine methods, such as telephone-based [21], computer-based [22,23], internet-based, or smartphone-based applications [24][25][26], to screen for hearing impairment in elderly individuals. Moreover, questionnaires can provide inexpensive, efficient, and truly self-perceived measurements of hearing loss. ...
Full-text available
Key points: Question: Can the traditional Chinese version of the hearing handicap inventory for elderly screening (HHIE-S) checklist screen for age-related hearing loss (ARHL) in elderly individuals? Findings: In this cross-sectional study of 1696 Taiwanese patients who underwent annual government-funded geriatric health checkups, the Chinese version of the HHIE-S had a sensitivity of 76.9% and a specificity of 79.8% with a cutoff score greater than 6 for identifying patients with disabled hearing loss (defined as a PTA > 40 dB). Meaning: The traditional Chinese version of the HHIE-S is an effective test to detect ARHL and can improve the feasibility of large-scale hearing screening among elderly individuals. Purpose: The traditional Chinese version of the hearing handicap inventory for elderly screening (TC-HHIE-S) was translated from English and is intended for use with people whose native language is traditional Chinese, but its effectiveness and diagnostic performance are still unclear. The purpose of this study was to evaluate the validity and reliability of the traditional Chinese version of the HHIE-S for screening for age-related hearing loss (ARHL). Methods: A total of 1696 elderly people underwent the government's annual geriatric medical examination at community hospitals. In this cross-sectional study, we recorded average conducted pure-tone averages (PTA) (0.5 kHz, 1 kHz, 2 kHz, 4 kHz), age, sex, and HHIE-S data. Receiver operating characteristic (ROC) curve analysis was used to identify the best critical point for detecting hearing impairment, and the validity of the structure was verified by the agreement between the TC-HHIE-S and PTA results. Results: The HHIE-S scores were correlated with the better-ear pure-tone threshold averages (PTAs) at 0.5-4 kHz (correlation coefficient r = 0.45). The internal consistency of the total HHIE-S score was excellent (Cronbach's alpha = 0.901), and the test-retest reliability was also excellent (Spearman's correlation coefficient = 0.60, intraclass correlation coefficient = 0.75). In detecting disabled hearing loss (i.e., PTA at 0.5-4 kHz > 40 dB), the HHIE-S cutoff score of > 6 had a sensitivity of 76.9% and a specificity of 79.8%. Conclusions: The traditional Chinese version of the HHIE-S is a valid, reliable, and efficient tool for large-scale screening for ARHL.
Introduction: This study aimed to assess the extent to which a single item of self-reported hearing difficulties is associated with future risk of falling among community-dwelling older adults. Methods: We used data from two Australian population-based cohorts: three waves from the PATH Through Life study (PATH; n = 2,048, 51% men, age 66.5 ± 1.5 SD years) and three waves from the Concord Health and Ageing in Men Project (CHAMP; n = 1,448, 100% men with mean age 77.3 ± 5.3 SD years). Hearing difficulties were recorded on a four-point ordinal scale in PATH and on a dichotomous scale in CHAMP. The number of falls in the past 12 months was reported at each wave in both studies. In CHAMP, incident falls were also ascertained by triannual telephone call cycles for up to four years. Multivariable-adjusted random intercept negative binomial regression models were used to estimate the association between self-reported hearing difficulties and number of falls reported at the following wave or 4-monthly follow-ups. Results: In PATH, self-reported hearing difficulties were associated with a higher rate of falls at follow-up (incidence rate ratio = 1.15, 95% CI = 1.03-1.27 per a one-level increase in self-reported hearing difficulties), after adjusting for sociodemographic characteristics, health behaviours, physical functioning, balance, mental health, medical conditions, and medications. There were no significant associations between hearing difficulties and the rate of falls based on either repeated survey or 4-monthly follow-ups in CHAMP. Conclusion: Though we find mixed results, findings from PATH data indicate an ordinal measure of self-reported hearing loss may be predictive of falls incidence in young-old adults. However, the null findings in the male-only CHAMP preclude firm conclusions of a link between hearing loss and falls risk.
Objectives: To evaluate a tablet-based hearing screening game in primary school aged children. To examine the prevalence of middle/outer ear pathology, hearing loss and spatial processing disorder in primary school aged children. Design: The automated hearing test Sound Scouts was used as a screening tool, which measures hearing abnormalities through tests of speech-in-quiet/noise and tone-in-noise. Children who failed the screenings underwent follow up testing with pure tone audiometry, tympanometry, otoscopy, and the Listening in Spatialised Noise-Sentences test. Results of each test were compared to measure efficacy. Study sample: 1256 children aged 4-13 years from 8 primary schools. Results: 111 children (8.84%) presented with evidence of middle/outer ear pathologies. Eight children (0.64%) had hearing loss in at least one ear. Thirty-two children (2.55%) were diagnosed with spatial processing disorder. False positive rate was 5.93%, indicating that a relatively small proportion of the children who failed the screenings were subsequently shown to have normal auditory function. Conclusions: A game based program testing sound detection and binaural speech processing can be effective in detecting undiagnosed hearing deficits, in large format school-based hearing screenings. Prevalence of hearing abnormalities in Victorian primary school aged children were established, highlighting the value of school hearing screening programs.
Introduction . The article discusses methods of screening for hearing impairments in patients of the older age group in the frame-work of primary health care. Aim of the study . Conduct a comparative analysis of the effectiveness of different hearing assessment protocols in primary health care. Materials and methods . Сlinical and demographic data were collected in 585 elderly and senile patients (mean age 76.43 ± 9.83), tonal threshold audiometry was performed in the frequency range from 250 Hz to 12000 Hz, hearing was studied using the web application “Automated primary hearing assessment” (patent No. 2019664671) and analyzed the self-assessment of hearing with the HHIE questionnaire. Results . Most of the patients were elderly people (57.44%) with a high percentage of concomitant diseases (up to 89.23%). When interviewing a geriatrician about a complaint of hearing loss, a sensitivity of 91.5% and a specificity of 82.2% for detecting mild hearing loss, a sensitivity of 95.5% and a specificity of 71.8% for screening for moderate to severe hearing loss were obtained. When assessing the total score of the HHIE questionnaire (>17 points) and moderate hearing loss, the sensitivity was 84.7% and the specificity was 88.7%. The sensitivity index of the web application «Automated primary hearing assessment» for detecting moderate hearing impairment was 90.6% for the left ear and 88.5% for the right ear, and specificity – 88.5% for the left ear and 97.5% for the right ear. Discussion . Raising awareness of hearing problems through the introduction of feasible methods of assessing hearing function should lead to an increase in the number of older adults receiving adequate hearing rehabilitation. Conclusions . The authors conclude that it is important to take preliminary account of data on the sensitivity and specificity of assessment protocols for detecting hearing impairments of varying severity at the stage of examination of an older patient by a geriatrician and an otorhinolaryngologist.
Persistent underutilization of cochlear implants (CIs) in the United States is in part a reflection of a lack of hearing health knowledge and the complexities of care delivery in the treatment of sensorineural hearing loss. An evaluation of the patient experience through the CI health care delivery process systematically exposes barriers that must be overcome to undergo treatment for moderate-to-severe hearing loss. This review analyzes patient-facing obstacles including diagnosis of hearing loss, CI candidate identification and referral to surgeon, CI evaluation and candidacy criteria interpretation, and lastly CI surgery and rehabilitation. Pervasive throughout the process are several themes which demand attention in addressing inequities in hearing health disparities in the United States.
Objective To offer pragmatic, evidence-informed advice on administering corticosteroids in otolaryngology during the coronavirus disease 2019 (COVID-19) pandemic, considering therapeutic efficacy, potential adverse effects, susceptibility to COVID-19, and potential effects on efficacy of vaccination against SARS-CoV-2, which causes COVID-19. Data Sources PubMed, Cochrane Library, EMBASE, CINAHL, and guideline databases. Review Methods Guideline search strategies, supplemented by database searches on sudden sensorineural hearing loss (SSNHL), idiopathic facial nerve paralysis (Bell’s palsy), sinonasal polyposis, laryngotracheal disorders, head and neck oncology, and pediatric otolaryngology, prioritizing systematic reviews, randomized controlled trials, and COVID-19–specific findings. Conclusions Systemic corticosteroids (SCSs) reduce long-term morbidity in individuals with SSNHL and Bell’s palsy, reduce acute laryngotracheal edema, and have benefit in perioperative management for some procedures. Topical or locally injected corticosteroids are preferable for most other otolaryngologic indications. SCSs have not shown long-term benefit for sinonasal disorders. SCSs are not a contraindication to vaccination with COVID-19 vaccines approved by the US Food and Drug Administration. The Centers for Disease Control and Prevention noted that these vaccines are safe for immunocompromised patients. Implications for Practice SCS use for SSNHL, Bell’s palsy, laryngotracheal edema, and perioperative care should follow prepandemic standards. Local or topical corticosteroids are preferable for most other otolaryngologic indications. Whether SCSs attenuate response to vaccination against COVID-19 or increase susceptibility to SARS-CoV-2 infection is unknown. Immunosuppression may lower vaccine efficacy, so immunocompromised patients should adhere to recommended infection control practices. COVID-19 vaccination with Pfizer-BioNTech, Moderna, or Johnson & Johnson vaccines is safe for immunocompromised patients.
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Introduction: With improved access to antiretroviral therapy (ART), HIV infection is becoming a chronic illness. Preliminary data suggest that HIV-infected children have a higher risk of disabilities, including hearing impairment, although data are sparse. This study aimed to estimate the prevalence and types of hearing loss in HIV-infected children in Lilongwe, Malawi. Methods: This was a cross-sectional survey of 380 HIV-infected children aged 4-14 years attending ART clinic in Lilongwe between December 2013-March 2014. Data was collected through pediatric quality of life and sociodemographic questionnaires, electronic medical record review, and detailed audiologic testing. Hearing loss was defined as >20 decibels hearing level (dBHL) in either ear. Predictors of hearing loss were explored by regression analysis generating age- and sex-adjusted odds ratios. Children with significant hearing loss were fitted with hearing aids. Results: Of 380 patients, 24% had hearing loss: 82% conductive, 14% sensorineural, and 4% mixed. Twenty-one patients (23% of those with hearing loss) were referred for hearing aid fitting. There was a higher prevalence of hearing loss in children with history of frequent ear infections (OR 7.4, 4.2-13.0) and ear drainage (OR 6.4, 3.6-11.6). Hearing loss was linked to history of WHO Stage 3 (OR 2.4, 1.2-4.5) or Stage 4 (OR 6.4, 2.7-15.2) and history of malnutrition (OR 2.1, 1.3-3.5), but not to duration of ART or CD4. Only 40% of caregivers accurately perceived their child's hearing loss. Children with hearing impairment were less likely to attend school and had poorer emotional (p = 0.02) and school functioning (p = 0.04). Conclusions: There is an urgent need for improved screening tools, identification and treatment of hearing problems in HIV-infected children, as hearing loss was common in this group and affected school functioning and quality of life. Clear strategies were identified for prevention and treatment, since most hearing loss was conductive in nature, likely due to frequent ear infections, and many children with hearing loss qualified for hearing aids. Screening strategies need to be developed and tested since caregivers were not reliable at identifying hearing loss, and often mis-identified children with normal hearing as having hearing loss. Children with frequent ear infections, ear drainage, TB, severe HIV disease, or low BMI should receive more frequent ear assessments and hearing evaluations.
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Purpose The present study aimed to develop and standardize a screening tool for elderly people who wish to check for themselves their level of hearing loss. Methods The Self-assessment for Hearing Screening of the Elderly (SHSE) consisted of 20 questions based on the characteristics of presbycusis using a five-point scale: seven questions covered general issues related to sensorineural hearing loss, seven covered hearing difficulty under distracting listening conditions, two covered hearing difficulty with fast-rated speech, and four covered the working memory function during communication. To standardize SHSE, 83 elderly participants took part in the study: 25 with normal hearing, and 22, 23, and 13 with mild, moderate, and moderate-to-severe sensorineural hearing loss, respectively, according to their hearing sensitivity. All were retested 3 weeks later using the same questionnaire to confirm its reliability. In addition, validity was assessed using various hearing tests such as a sentence test with background noise, a time-compressed speech test, and a digit span test. Results SHSE and its subcategories showed good internal consistency. SHSE and its subcategories demonstrated high test–retest reliability. A high correlation was observed between the total scores and pure-tone thresholds, which indicated gradually increased SHSE scores of 42.24%, 55.27%, 66.61%, and 78.15% for normal hearing, mild, moderate, and moderate-to-severe groups, respectively. With regard to construct validity, SHSE showed a high negative correlation with speech perception scores in noise and a moderate negative correlation with scores of time-compressed speech perception. However, there was no statistical correlation between digit span results and either the SHSE total or its subcategories. A confirmatory factor analysis supported three factors in SHSE. Conclusion We found that the developed SHSE had valuable internal consistency, test–retest reliability, and convergent and construct validity. These results suggest that SHSE is a reliable and valid measure to represent the degree of hearing loss in the elderly.
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The application described in this study appears to be accurate and valid, thus allowing calculation of a hearing handicap and assessment of the pure-tone air conduction threshold with iPhone/iPad devices. To develop and evaluate a newly developed professional, computer-based hearing handicap calculator and a manual hearing sensitivity assessment test for the iPhone and iPad (AudCal). Multi-center prospective non-randomized validation study. One hundred and ten consecutive adult participants underwent two hearing evaluations, a standard audiometry and a pure-tone air conduction test using AudCal with an iOS device. The hearing handicap calculation accuracy was evaluated comparing AudCal vs a web-based calculator. Hearing loss was found in 83 and 84 out of 220 standard audiometries and AudCal hearing tests (Cohen's Kappa = 0.89). The mean difference between AudCal and standard audiogram thresholds was -0.21 ± 6.38 dB HL. Excellent reliability and concordance between standard audiometry and the application's hearing loss assessment test were obtained (Cronbach's alpha = 0.96; intra-class correlation coefficient = 0.93). AudCal vs a web-based calculator were perfectly correlated (Pearson's r = 1).
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Developing countries have the world's highest prevalence of hearing loss, and hearing screening programmes are scarce. Mobile devices such as smartphones have potential for audiometric testing. To evaluate the uHear app using an Apple iPhone as a possible hearing screening tool in the developing world, and to determine accuracy of certain hearing thresholds that could prove useful in early detection of hearing loss for high-risk populations in resource-poor communities. This was a quasi-experimental study design. Participants recruited from the Otolaryngology Clinic, Groote Schuur Hospital, Cape Town, South Africa, completed a uHear test in three settings--waiting room (WR), quiet roon (QR) and soundproof room (SR). Thresholds were compared with formal audiograms. Twenty-five patients were tested (50 ears). The uHear test detected moderate or worse hearing loss (pure-tone average (PTA) > 40 dB accurately with a sensitivity of 100% in all three environments. Specificity was 88% (SR), 73% (QR) and 68% (WR). Its was highly accurate in detecting high-frequency hearing loss (2 000, 4 000, 6 000 Hz) in the QR and SR with 'good' and 'very good' kappa values, showing statistical significance (p < 0.05). It was moderately accurate in low-frequency hearing loss (250, 500, 1 000 Hz) in the SR, and poor in the QR and WR. Using the iPhone, uHear is a feasible screening test to rule out significant hearing loss (PTA > 40 dB). It is highly sensitive for detecting threshold changes at high frequencies, making it reasonably well suited to detect presbycusis and ototoxic hearing loss from HIV, tuberculosis therapy and chemotherapy. Portability and ease of use make it appropriate to use in developing world communities that lack screening programmes.
Objective: Evaluation of the Sennheiser HD 202 II supra-aural headphones as an alternative headphone to enable more affordable hearing screening. Design: Study 1 measured the equivalent threshold sound pressure levels (ETSPL) of the Sennheiser HD 202 II. Study 2 evaluated the attenuation of the headphones. Study 3 determined headphone characteristics by analyzing the total harmonic distortion (THD), frequency response and force of the headband. Study sample: Twenty-five participants were included in study 1 and 15 in study 2 with ages ranging between 18 and 25. No participants were involved in study 3. Results: The Sennheiser HD 202 II ETSPLs (250-16000 Hz) showed no significant effects on ETSPL for ear laterality, gender or age. Attenuation was not significantly different (p > 0.01) to TDH 39 except at 8000 Hz (p < 0.01). Maximum permissible ambient noise levels (MPANL) were specified accordingly. The force of the headband was 3.1N. THD measurements showed that between 500 and 8000 Hz intensities of 90 dB HL and higher can be reached without THD >3%. Conclusion: Sennheiser HD 202 II supra-aural headphones can be used as an affordable headphone for screening audiometry provided reported MPANLs, maximum intensities and ETSPL values are employed.
Presbycusis or age related hearing loss can be defined as a progressive, bilateral and symmetrical sensorineural hearing loss due to age related degeneration of inner ear structures. It can be considered a multifactorial complex disorder with environmental and genetic factors. The molecular, electrophysiological and histological damage at different levels of the inner ear cause a progressive hearing loss, which usually affects the high frequencies of hearing. The resulting poor speech recognition has a negative impact on cognitive, emotional and social function in older adults. Recent investigations revealed an association between hearing impairment and social isolation, anxiety, depression and cognitive decline in elderly. These findings emphasize the importance of diagnosis and treating hearing loss in the elderly population. Hearing aids are the most commonly used devices for treating presbycusis. The technical progress of implantable hearing devices allows an effective hearing rehabilitation even in elderly with severe hearing loss. However, most people with hearing impairments are not treated adequately. © Georg Thieme Verlag KG Stuttgart · New York.
In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
Background and objective: The timely diagnosis and treatment of acquired hearing loss in the pediatric population has significant implications for a child's development. Audiological assessment in children, however, carries both technological and logistical challenges. Typically, specialized methods (such as play audiometry) are required to maintain the child's attention and can be resource intensive. These challenges were previously addressed by a novel, calibrated, interactive play audiometer for Apple(®) iOS(®) called "ShoeBOX Audiometry". This device has potential applications for deployment in environments where traditional clinical audiometry is either unavailable or impractical. The objective of this study was to assess the screening capability of the tablet audiometer in an uncontrolled environment using consumer ear-bud headphones. Methods: Consecutive patients presenting to the Audiology Clinic at the Children's Hospital of Eastern Ontario (ages 4 and older) were recruited. Participants' hearing was evaluted using the tablet audiometer calibrated to Apple(®) In-Ear headphones. The warble tone thresholds obtained were compared to gold standard measurements taken with a traditional clinical audiometer inside a soundbooth. Results: 80 patients were enrolled. The majority of participants were capable of completing an audiologic assessment using the tablet computer. Due to ambient noise levels outside a soundbooth, thresholds obtained at 500Hz were not consistent with traditional audiometry. Excluding 500Hz threholds, the tablet audiometer demonstrated strong negative predictive value (89.7%) as well as strong sensitivity (91.2%) for hearing loss. Conclusion: Thresholds obtained in an uncontrolled setting are not reflective of diagnostic thresholds due to the uncalibrated nature of the headphones and variability of the setting without a booth. Nevertheless, the tablet audiometer proved to be both a valid and sensitive instrument for unsupervised screening of warble-tone thresholds in children.