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Audiometric Considerations for Hearing Aid Fitting
(and Success)
Ruth A. Bentler
Introduction
first ASA standard providing specifications for audiometers, masking noise, and even shock hazard, appeared in
1951 (ASA 1951), some 30 years after the first prototype
of a modern, vacuum-tube audiometer was developed.
The Hughson-Westlake approach to establishing threshold values, first published in 1944, continues to be the
psychophysical approach of choice since Carhart and
Jerger (1959) argued that all audiologists should follow
the same methodology so that test results would not be
influenced by differences in test procedures.
Outcome measures, on the other hand, continue to
multiply. Aided speech perception tests and self-report
inventories are the tools most often used to evaluate the
success of hearing aid fittings. Self-report outcome
measures used to quantify the success from hearing aid
fittings cover many domains, including handicap (activity limitation), disability (participation restriction), device use, satisfaction, and quality of life, along with economic considerations such as cost-benefit and cost-utility. For the purposes of this paper, those studies relating
audiometric correlates to hearing aid success in any of
the domains will be considered. One should consider,
however, that the bandwidth, distortion and overall fidelity of earlier technology may have precluded meaningful outcomes (Bentler and Duve 2000).
In 1982, Tom Giolas did a “critical review of the evidence” (in evidence based practice [EBP] terminology)
to explore the relationship between audiometric variables (pure tone thresholds and speech discrimination
scores) and self-reported hearing handicap. His goal
and that of other contemporary clinician-researchers of
the day was to attempt to explain the variance in hearing
handicap using audiometric measures. The correlations
between pure tone thresholds and self-report data
ranged from .37 to .73; the correlations between speech
recognition and self-reported handicap ranged from
In the current era of audiologic management, much
of the focus has been placed on hearing aid technologies
and fitting schemes. With rapid advances in component
miniaturization and digital processing algorithms, there
is a tacit assumption that all hearing loss can be managed. Yet, any clinical audiologist will attest to the fact
that success for one individual is no guarantee of success for another, even when audiometrically similar.
Even the earliest of audiological efforts suggested there
was little relationship between the audiogram and
speech perception ability, pre- or post-fitting (Hirsch
1952). The earliest “fitters” viewed the audiometric profile as useful only as a guide to how powerful a hearing
aid should be, not as a guide to the best frequency characteristics (Davis 1947). With the current programmability across many frequency bands, and the multiple approaches to assessing outcomes of amplification fittings,
one would expect the relationship between the audiometric data and the hearing aid effectiveness data to be
more discernable.
How Do Thresholds Relate to Success?
There has been little updating over the past 70 years
of the threshold-taking procedures that go on in audiology clinics of the world. The early investigators/clinicians eventually concurred on what constitutes normal
audiometric thresholds (e.g., refer to Stevens 1951). The
Contact Information: Ruth A. Bentler, PhD, Professor, Department
of Speech Pathology and Audiology, University of Iowa, 250 Hawkins
Drive, Iowa City, Iowa 52242, 319.335.8723, FAX: 319.335.8851,
[email protected].
89
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Hearing Care for Adults
-.12 to -.73. Attributing these weak, but widely variable,
correlations to the various inventories used and the varied subject populations studied, such effort only reinforced Davis (1947) and Hirsch’s (1952) early contentions of the poor relationship between objective and
subjective measures of hearing and hearing aid success
that would be further interrogated over the following 5060 years. Although threshold measurement had
reached some stage of standardization by that time, outcome mea-sures using self-report were only beginning
to provide psychometric data to support the reliability
and validity of their usage. Several of those inventories
are still in use today and will be discussed here.
In 1983, two studies were published addressing the
relationship between thresholds and hearing aid success. Weinstein and Ventry (1983) studied the correlation of pure tone threshold average (PTA) to scores on
the Hearing Handicap Inventory for the Elderly (HHIE),
a commonly used outcome measure today, and noted a
.61 correlation over 100 non-institutionalized older adults
(mean age 75.7). Two years later Brainerd and Frankel
(1985) reported a correlation of .35 between PTA and the
Social Hearing Handicap Index (SHHI)1, probable cause
for that inventory’s demise from our repertoire. The
SHHI was one of two inventories studied by these investigators; neither provided useful predictive power leading these investigators to assert, “…the relationships between audiometric (data) and self-report measures are
weak and appear unlikely to be assessing the same issue
(p. 92).” Similarly, only a moderate correlation (.54) between PTA and the Revised Hearing Performance Inventory2 was reported by Hawes and Niswander (1985). By
1992, Mulrow, Tuley and Agular reported that less than
11% of the variance in hearing aid success (defined as improvement in HHIE scores) could be accounted for by
functional gain in the high frequencies and gain in
Speech Reception Thresholds (SRTs), both directly related to threshold values. Of interest in that study of
39 adults (mean age of 52) with noise induced hearing
loss, the authors concluded that baseline handicap (as
measured with the HHIE), in combination with age,
education, and number of medications, was more predictive of hearing aid success with or benefit from hearing
aids than routine audiometric data.
1
Social Hearing Handicap Scale, from Ewertsen and
Birk-Nielsen 1973.
2
Revised Hearing Performance Inventory, from Lamb,
Owens and Shubert 1983.
Perhaps the best correlations of the era came from
the work of Coren and Hakistan (1992). The investigators
developed a Hearing Screening Inventory that produced
a high correlation to PTA (.82) but was never implemented as an outcome measure for hearing aid success
or benefit, in part, perhaps, because it was developed for
group survey administration and not for outcomes use.
Satisfaction with hearing aids can certainly be construed as success. In their exhaustive review of studies
relating degree of hearing loss to satisfaction measures, Wong, Hickson and McPherson (2003) noted that
9 out of 14 related studies found no such relationship.
They suggest that such findings are related, in part, to
the homogeneity of the subjects within a given study.
Studies reporting on subjects with a wider range of
hearing loss (e.g., Dillon, Birtles and Lovegrove 1999;
Kochkin 2000; Gatehouse, Naylor and Eberling 2006)
have reported moderate correlations between degree
of loss and reported satisfaction.
In another large-scale study relating pre-fitting
measures to satisfaction with amplification, HosfordDunn and Halpern (2001) correlated patient-related information (including gender, age, years of use, and
pure-tone average) to global satisfaction measures obtained using the Satisfaction with Amplification in Daily
Life (SADL; Cox and Alexander 1999). None of those
variables alone or in combination with each other provided predictive insight; the highest significant correlation coefficient was for age (r = -.18), indicating older
subjects tended to report less overall satisfaction than
younger subjects. Most recently, an attempt was made
by Ricketts and Mueller (2000) to use high frequency
average threshold and audiometric slope of hearing to
predict success with directional microphone schemes
implemented in modern hearing aids. Directional benefit was defined as the difference score based on Hearing
in Noise Test (HINT; Nilsson, Soli and Sullivan 1994)
scores obtained in omni- and directional modes. High
frequency average loss was calculated as the average of
thresholds at 2000, 3000 and 4000 Hz. No significant relationship emerged. Audiometric slope was calculated
as the difference between thresholds measured at 2000,
3000, and 4000 Hz divided by 2.5 octaves (or the
separation between 500 and 3000 Hz). Although the
slopes across their 80 subjects varied considerably
(from 18 dB/octave to -2.7 dB/octave), no significant
correlation to directional benefit was found.
Perhaps the strongest evidence to support the relationship of hearing levels to hearing aid success can be
inferred from Purdy and Jerram (1998). If hearing aid
Audiometric Considerations for Hearing Aid Fitting (and Success)
use time is an acceptable outcome domain, these investigators found a significant correlation within their
102 subjects between hours of use and degree of hearing
loss (r = .42, p < .001) and between hours of use and overall satisfaction (r = .57, p < .001). The authors conclude
that subjects who were more satisfied tended to be
those with greater hearing loss and, as a result, wore
their hearing aids for more hours per day. These results
are in contrast to other investigations involving use time
and self-reported success (e.g., Crowley and Nabalek
1996; Cox 1997).
Recently, candidature for different signal processing schemes has been studied and audiometric correlates of preference and performance determined. Gatehouse et al. (2006) found that greater listening comfort
as well as reported and measured speech intelligibility
with linear hearing aids was associated with a flatter audiogram and wider dynamic range; higher listening
comfort, higher satisfaction and reported intelligibility
with nonlinear processors was associated with a more
sloping audiogram and a more restricted dynamic
range.
What about Speech Measures?
In 1952, Ira Hirsch noted, “So far as the relationship
between the audiogram and discrimination loss is concerned, …here lies one of the greatest points of ignorance in contemporary audiology” (p. 149). If the pure
tone information does not predict hearing aid success
(and bears no relationship to the speech measures),
maybe pre-fitting speech measures will offer some insight. The data suggest otherwise.
As was the case for PTA and hearing aid success reported earlier, Weinstein and Ventry (1983) also reported on speech perception ability as it related to handicap reduction (HHIE). They found a weak correlation
( r = -.42) for their elderly subjects. Contrarily, Hawes and
Niswander (1985) reported a higher correlation of word
recognition to the Revised Hearing Performance Inventory (R-HPI) (r = -.67) than they had reported for pure
tones. Bentler, Anderson, Niebuhr and Getta (1993a, b)
found weak correlations between various measures of
speech perception and self-report measures of hearing
aid satisfaction and disability, among others, for a group
of 65 aided adults followed over a one-year interval. In
fact, no significant correlation could be found between
traditional unaided speech measures and aided satisfaction in a critical review of the evidence undertaken by
Killion and Gudmundsen (2005).
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Occasionally, speech testing is used as a pre-test as
well as an outcome measure. It should be recognized
that almost any relationship of unaided word recognition ability to aided word recognition ability can be explained based on audibility. That is, if the testing is done
before and after a careful fitting of amplification, one
would assume the increased audibility would improve
the measured performance. If the testing is accomplished in a difficult noise background (e.g., signal-tonoise ratios (SNRs) poorer than +5) that relationship
may no longer hold, due to the reduced ability of the impaired auditory system to sort the speech from the
noise. In fact, speech perception testing that is undertaken to determine the SNR for 50% performance may
offer the greatest insight into the auditory integrity of
the individual. Tests such as the Hearing in Noise Test
(HINT; Nilsson et al. 1994) or the Speech in Noise (SIN)
test (Fikret-Pasa 1993; Bentler 2000) are not based
strictly on the audibility of the intended signal, but
rather the integrity of the auditory filters to separate the
intended signal from the background noise. At least one
investigation has shown this test paradigm to provide
predictive insight. Walden and Walden (2004) suggested that the QuikSIN (2001), a modified version of
the SIN, was the best predictor of hearing aid success in
daily life. In that study, a large number of predictive
measures were included in a regression analysis, including age, PTA, audibility index (both aided and unaided),
recorded NU-6 scores, QuickSIN (both aided and unaided) hearing aid experience, and current use time. Although the unaided audibility index bore a positive correlation to the aided audibility index (r = .80 at p < .01),
none of the other audiometric measures held the same
strong relationship. As for correlations between the predictive measures and the two outcome measures (IOIHA3 and HAUS4), only hours of use (r = .47 to IOI-HA)
and QuickSIN scores (r = -.34 to -.40 for both IOI-HA and
HAUS) provided significant results.
Other Audiometric Considerations
When considering audiometric correlates to hearing
aid “success”, the matter of dead regions needs to be addressed. Brian Moore describes these “regions of the
cochlea with no (or very few) functioning inner hair cells
3
4
International Outcomes Inventory for Hearing Aids,
from Cox et al. 2000.
Hearing Aid Usefulness Scale, from Walden and Walden 2004.
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Hearing Care for Adults
and/or neurons” as an old phenomenon (p. 46; Troland
1929), with significant consequence relative to hearing
aid fitting (Moore 2001, 2004). Assuming we can actually
identify those regions with considerable accuracy (a feat
of controversy), it seems obvious that providing gain to
areas void of transmission cells will not likely provide
success to the hearing aid user. In fact, Preminger, Carpenter and Ziegler (2005) found that approximately onethird of those subjects with thresholds between 50 and
80 dB HL (re: ANSI 1996) tested positive for dead regions, using the Threshold Equalizing Noise (TEN) test
(Moore 2001). Those subjects exhibited poorer sentence understanding in noise, and poorer self-perceived
benefit while listening in backgrounds of noise or reverberation compared to those subjects who did not test
positive for the condition. Using the same TEN test, data
from Vestergaard (2003) support the notion that providing audibility within the dead region does not lead to concomitant increases in speech intelligibility.
It has been suggested that measures of loudness
(comfort or discomfort) may also improve hearing aid
success. In an evidence-based review of the literature,
Mueller and Bentler (2005) asked the question: Are the
clinical measures of LDL for adults predictive of aided acceptance and satisfaction of loudness for high inputs in the
real world? Although over 200 articles addressed the
topic, only three met the pre-defined criteria for inclusion. As the authors note, the evidence is weak due to
the limited number of studies and the poor statistical
power in the study, but does support the use of loudness
discomfort to increase user acceptance and success.
Earlier work by Ricketts (1996) did not indicate that amplification outcomes (e.g., speech perception) would be
enhanced by laboratory measures of loudness growth
or preference (another type of audiometric testing).
Similar results have been reported by Kiessling (2001)
and Smeds et al. (2006).
Relative to predicting bilateral success with amplification, Mueller, Grimes and Jerome (1981) reported that the best predictor of binaural superiority
over unilateral fitting of hearing aids was the slope of
the performance-intensity function. In that investigation, both phonetically-balanced (PB) word lists and
Synthetic Sentence Test (SSI) lists were presented to
24 older adults (mean age 69.4). The unilateral versus
bilateral performance differences were unremarkable; yet, the slope of the PI function for PB words
was a significant predictor of bilateral performance.
With the current concern over binaural interference
in the older adult, it seems logical that speech materi-
als be tested in both unilateral and bilateral modes in
order to better counsel those individuals for whom
the unilaterally fit hearing aid will provide the better
choice for communication in backgrounds of noise
(Walden and Walden 2004; Sobiesiak and Bentler under review).
Understanding Those Factors Useful
in Predicting Success
Some of the most comprehensive undertakings
relative to understanding the relationship between audiometric variables and hearing aid outcomes are attributable to Humes and his colleagues (Humes 1999,
2001; Humes, Wilson, Barlow and Garner 2002a;
Humes, Wilson, Barlow, Garner and Amos 2002b;
Humes, Wilson and Humes 2003). In the absence of
any published large-scale studies aimed at modeling
hearing aid “success’ and identifying those factors
that influence this success (e.g., Gatehouse, see Chapter 8 in this Proceeding), Humes (2003) has undertaken several factor-analyses efforts in an attempt to
determine the number and nature of the dimensions
of successful hearing aid outcome. Using a principalcomponents factor analysis, he examined the results
of three studies of hearing aid outcomes: the
NIDCD/VA multi-site study of 338 elderly users of single-channel, linear, and in-the-ear hearing aids; an Indiana University-based study of 53 elderly users of
two-channel, wide dynamic range in-the-canal hearing
aids; and, an Indiana University-based study of 173
elderly wearers of single-channel, linear, output compression in-the-ear hearing aids. Factor analysis revealed that three to five principle components capture
the individual differences in outcome measure. For
the Indiana-based studies (n = 53 and n = 173) the
three factors included speech discrimination scores,
subjective benefit and satisfaction and hearing aid usage, and accounted for two-thirds of the total variance
in the outcome measures. For the VA/NIDCD study,
the five orthogonal components, or factors, included
speech perception in quiet, speech perception in
noise, subjective benefit, satisfaction, and hearing aid
usage. For this analysis, aided speech perception in
quiet accounted for 40% of the variance found in outcome measures, subjective benefit accounted for
22.8%, hearing aid usage accounted for 15.1%, aided
speech perception in noise accounted for 6.7% and satisfaction accounted for 6% of the variance. The authors
summarize their findings as:
Audiometric Considerations for Hearing Aid Fitting (and Success)
1) Aided speech perception was best predicted by degree of hearing loss, cognitive performance and age;
2) Hearing aid use was best predicted by previous hearing aid use;
3) Hearing aid satisfaction was best predicted by aided
sound quality measures.
In a recent and related study, Humes and his
colleagues (Humes et al. 2003) sought to identify factors influencing the decision-making relative to amplification. Using three groups matched for age, degree/configuration of hearing loss, and gender proportion, all had significant hearing loss and were advised by an audiologist to pursue the use of hearing
aids. One group (n = 26) opted not to pursue amplification (non-adherent group); one group (n = 24) opted
to purchase the hearing aids but soon rejected them
(reject-HA group); and, one group (n = 26) pursued
hearing aids and were still using them at six months
(adherent group). The group rejecting the hearing aid
recommendation was not different from the other two
groups in terms of any audiometric variable. For the
two groups who tried the hearing aids, the only audiometric-related variable that distinguished the subjects
was the higher pre-fitting loudness discomfort level
(LDL) measures; finger dexterity was the other distinguishing predictor of hearing aid success. One might
postulate that those with the better dexterity could
more easily/quickly manage the volume control on
the linear hearing aids worn by all subjects (adherents
and hearing aid-reject groups).
Summar y
It appears that the more traditional audiometric information such as thresholds, including degree and configuration of hearing loss, speech perception, and loudness ratings are of limited use in predicting hearing aid
success, even when broadly defining success. It is clear
that the greater the hearing loss, the more the use time
of the amplification scheme. It is also clear from this
review that other variables or factors account for the
positive self-reported outcomes.
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Audiometric Considerations for Hearing Aid Fitting (and Success)
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